@article {pmid41849117, year = {2026}, author = {Luo, Z and She, Q and Jin, T and Li, Y and Xi, X}, title = {Adaptive graph convolution domain adaptation network for cross-subject EEG emotion recognition.}, journal = {Medical & biological engineering & computing}, volume = {}, number = {}, pages = {}, pmid = {41849117}, issn = {1741-0444}, support = {No. 62371172//National Natural Science Foundation of China/ ; No. 62371178//National Natural Science Foundation of China/ ; }, abstract = {Electroencephalogram (EEG)-based emotion recognition is essential for the advancement of affective brain-computer interface (aBCI) system. However, in cross-subject scenarios, the dynamic nature and subject-specific characteristics of EEG signals significantly hinder knowledge transfer, thereby leading to reduced model performance on previously unseen target domain. To overcome these limitations, we design an innovative domain adaptation model, the adaptive graph convolution domain adaptation network (AGCDAN), to capture the dynamic spatial information of EEG signals and reduce inter-domain distribution discrepancies by aligning both marginal and conditional distributions through domain adaptation. Specifically, adaptive graphs are firstly constructed based on differential entropy features extracted from EEG signals to extract dynamic frequency-spatial representations. A multi-branch neural network is then employed to extract customized feature representations tailored to each source domain and the target domain individually. Subsequently, discriminator-free adversarial learning is employed to align marginal distributions, and introduces subdomain metric learning guided by label information to achieve conditional distribution alignment. Finally, domain-specific classifiers, combined with a decision fusion strategy, produce the final emotion predictions. We evaluate AGCDAN on three datasets (SEED, SEED-IV, DEAP) under a multi-source domain adaptation setting for cross-subject emotion recognition. Achieving recognition accuracies of 89.68%, 68.61%, and 68.13%, respectively, demonstrating superior performance over current state-of-the-art (SOTA) domain adaptations techniques in cross-subject emotion recognition, showcasing its strong capability in modeling dynamic emotional states and reducing negative transfer effects.}, }
@article {pmid42277539, year = {2026}, author = {Xu, R and Huang, X and Chen, Z and Mohamed, HA and He, X and Lu, F and Ou, Y and Li, G and Zhang, K}, title = {Application Mechanisms and Clinical Prospects of Brain-Computer Interface Technology in Radiation-Induced Brain Injury.}, journal = {Cellular and molecular neurobiology}, volume = {}, number = {}, pages = {}, doi = {10.1007/s10571-026-01758-y}, pmid = {42277539}, issn = {1573-6830}, support = {2024A1515011451//Natural Science Foundation of Guangdong Province/ ; 2023-CCA-TCM-033//Li Xin Traditional Chinese Medicine Research and Innovation Fund/ ; BYPDF2411213//Wohua Research Fund/ ; 82271395//the National Natural Science Foundation of China/ ; 0011/2025/RIA1//the Science and Technology Development Fund of Macau/ ; 2023A1515030073//the Guangdong Basic and Applied Basic Research Foundation/ ; 2025A04J4740//the Guangzhou Science and Technology Plan Project/ ; KY0120220133 and DFJHBF202111//the Special Project of Dengfeng Program of Guangdong Provincial People's Hospital/ ; KY012026190//Excellence Initiative Project of the National Natural Science Foundation of China/ ; }, abstract = {Radiation-induced brain injury (RIBI) is a common complication of radiotherapy that leads to neurological symptoms, significantly impairing patients' daily functioning and long-term rehabilitation. Consequently, the development of effective therapeutic strategies has received considerable attention. As an emerging approach in the management of neurological disorders, brain-computer interface (BCI) technology shows substantial potential for both the assessment and treatment of RIBI. This review synthesizes evidence retrieved from electronic databases and examines RIBI from the perspectives of its mechanisms and clinical manifestations. Current findings suggest that BCI technology holds promise for several applications in RIBI, including early diagnosis, mitigation of neuroinflammation, alleviation of associated symptoms, and prediction and management of complications. The implementation of BCI is likely to play a significant role in early assessment and treatment processes for RIBI. Furthermore, with ongoing technological advancements, the development of next-generation BCI is expected to enable more targeted treatment that address additional pathological mechanisms of RIBI, thereby progressively improving the quality of life for affected patients.}, }
@article {pmid42280831, year = {2026}, author = {Zini, S and Bidone, F and Napoletano, P}, title = {A Robust Multi-Branch CNN-LSTM Architecture for Cross-Subject Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {11}, pages = {}, pmid = {42280831}, issn = {1424-8220}, support = {PNC0000003//AdvaNced Technologies for Human-centrEd Medicine (ANTHEM)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Long Short Term Memory ; Convolutional Neural Networks ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Movement/physiology ; Algorithms ; *Imagination/physiology ; }, abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) aim to convert electroencephalographic (EEG) activity into reliable device commands across users and recording setups. However, low signal-to-noise ratio and strong inter-subject variability still limit true "plug-and-play" deployment without lengthy calibration. To address these challenges, we propose a multi-branch convolutional long short-term memory (CNN-LSTM) architecture that jointly performs multi-scale temporal feature extraction and within-trial sequence modeling. The model employs four parallel 1D convolutional branches with distinct kernel sizes, each followed by an LSTM module and late fusion, combined with group normalization and supervision over sequences of sub-windows within each trial. We evaluate the approach on the EEG Motor Movement/Imagery (EEGMMI) dataset from PhysioNet under strictly subject-independent conditions, and on the ISLab-MI Dataset, a 32-channel wearable-EEG collection designed to assess cross-setup robustness. On EEGMMI, the network achieves up to 82.63% accuracy for binary left/right MI and 74.10% for a four-class task using 4 s trials under 5-fold cross-validation, outperforming an EEGNet-style baseline by 1-10% depending on class count and window length. Under a leave-one-subject-out protocol, the model attains 74.9% mean accuracy for a three-class MI task. Zero-shot transfer to ISLab-MI yields 64.60% and 63.02% accuracy in three- and four-class settings, respectively, while brief subject-specific fine-tuning using only 20% of each session improves performance to 81.38% and 73.48%. These findings show that combining multi-scale convolutional feature extraction with explicit sequence modeling and robust normalization yields accurate, data-efficient, and portable MI decoders suitable for practical BCI applications.}, }
@article {pmid42280857, year = {2026}, author = {Shen, X and Zhong, H and Gu, Y and Han, R}, title = {DO-PI-EATCNet: Efficient-Attention- and Dream-Optimization-Based Channel Selection for EEG Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {11}, pages = {}, pmid = {42280857}, issn = {1424-8220}, support = {Grant No. BK20251914//Jiangsu Province Science and Technology Department/ ; Grant No. JC2023072//the Nantong Natural Science Foundation/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Algorithms ; *Attention/physiology ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; }, abstract = {Existing deep-learning-based motor imagery (MI) electroencephalogram (EEG) decoding methods face challenges in generalizing across sessions and providing channel-level physiological interpretability. These limitations hinder the practical application of MI-EEG systems. Accordingly, DO-PI-EATCNet (Dream-Optimization-Enhanced, Physics-Inspired, Efficient-Attention Temporal Channel Network) is proposed to improve generalization and interpretability in MI-EEG classification. Unlike models that simply combine multiple components, DO-PI-EATCNet assigns distinct roles to feature representation, temporal channel modeling, temporal regularization, and channel compactness. Latent-Projected Attention (LPA) enhances spatiotemporal discriminability by aligning attention in a low-dimensional latent space, and Temporal Channel Cascaded Collaborative Attention (TCCA) refines dependencies between time and channels. Fractional-Order Difference Temporal Consistency Loss (FD-TCL) is introduced as a neurodynamics-inspired temporal regularizer to reduce high-frequency fluctuations in prediction sequences and improve within-subject cross-session prediction stability. The Multi-Population Dream Optimization Algorithm (MPDOA) is used for channel selection to obtain a compact EEG channel subset and reduce computational load, although it introduces a slight accuracy decrease compared with the uncompressed full model. Under a within-subject cross-session protocol on the BCI Competition IV-2a four-class MI dataset, the final compact model achieves an average accuracy of 84.4% and Cohen's κ of 0.790, outperforming the reimplemented baselines. Compared with the uncompressed LPA-TCCA-FD-TCL variant, MPDOA slightly decreases accuracy from 84.9% to 84.4%, but reduces EEG channels from 22 to about 15 and decreases MACs by 27%. Scalp topographies and selected-channel visualizations provide qualitative support for channel-level anatomical plausibility, as the selected electrodes are mainly located over expected sensorimotor-related regions, while t-SNE offers a descriptive visualization of the learned feature distributions.}, }
@article {pmid42280920, year = {2026}, author = {Zhang, Y and Gong, X and Yuan, X}, title = {MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {11}, pages = {}, pmid = {42280920}, issn = {1424-8220}, support = {62171152//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Algorithms ; Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; }, abstract = {Hybrid brain-computer interfaces (BCIs) have attracted growing research attention because they combine the millisecond-level temporal resolution of electroencephalography (EEG) with the spatially informative hemodynamic responses of functional near-infrared spectroscopy (fNIRS). However, most existing deep fusion methods rely on static late-fusion strategies, which tend to underexploit latent cross-modal dependencies and are vulnerable to modality-specific signal degradation. To address these limitations, we propose MGFNet, a multi-granularity fusion network for hybrid BCI decoding. MGFNet contains three components: (1) intra-modal encoders that learn modality-specific spatiotemporal representations from EEG, oxygenated hemoglobin (HbO), and deoxygenated hemoglobin (HbR) signals; (2) cross-modal interaction encoders that temporally align paired modalities and use dilated convolutions to capture long-range EEG-fNIRS dependencies; and (3) a Coupling-Guided Sparse Component Routing (CGSCR) module that estimates sample-specific cross-modal coupling and performs adaptive discrete routing. We further introduce a deep supervision strategy to stabilize optimization and improve branch-level discriminability. Under a within-subject held-out evaluation protocol on a public benchmark dataset, MGFNet achieved classification accuracies of 99.40% on the n-back task and 99.03% on the word generation (WG) task, outperforming representative comparison methods evaluated under a matched protocol. Ablation studies further confirmed the contributions of the intra-modal encoders, the cross-modal interaction encoders, and the CGSCR module. Under controlled EEG corruption with additive white Gaussian noise at -10 dB, MGFNet outperformed a static-fusion variant by 9.23 percentage points on the n-back task and 6.31 percentage points on the WG task. These results support the effectiveness of MGFNet in the present offline within-subject setting and indicate improved robustness under controlled single-modality degradation.}, }
@article {pmid42282632, year = {2026}, author = {Yoon, S and Avansino, DT and Madugula, S and Levin, AD and Fan, C and Abramovich Krasa, B and Singh, A and Vo, C and Hahn, NV and Card, NS and Fogg, Z and Wairagkar, M and Nason-Tomaszewski, SR and Jacques, BG and Bechefsky, PH and Iacobacci, C and Deo, DR and Hochberg, LR and Brandman, DM and Stavisky, SD and Au Yong, N and Pandarinath, C and Henderson, JM and Willett, FR}, title = {Neural decoding of speech using deep neural ensembles.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.06.02.729705}, pmid = {42282632}, issn = {2692-8205}, abstract = {Speech brain-computer interfaces (BCIs) can restore rapid communication to people with paralysis, but decoding errors still limit performance. In recent brain-to-text decoding competitions, deep ensemble methods, which combine predictions from multiple independently trained decoders, have delivered striking accuracy improvements and account for the largest gains over baseline approaches. However, these methods have not previously been tested in real-time, require substantial computational resources, and their performance under various clinically relevant constraints remains poorly understood. Here, we present the first closed-loop test of deep ensembles in a participant with bilateral intracortical microelectrode arrays, demonstrating a reduction in word error rate from 33.7% to 26.0% on a large-vocabulary task. Using additional data from three participants, we then assess how these gains depend on baseline error rate, training dataset size, and ensemble size, including the resource-accuracy tradeoffs most relevant for real-world deployment. Finally, we introduce a computationally efficient pseudoensembling approach based on test-time augmentation that improves decoding accuracy while requiring only a single base decoder, greatly reducing the computational burden of ensembling. Together, these results show that the benefits of deep ensembling can be realized in real time and under practical resource constraints, bringing speech BCIs closer to broader clinical adoption.}, }
@article {pmid42283210, year = {2026}, author = {Ding, W and Li, L and Liu, H and Liu, Y and Wang, J and Wang, Z and Tao, L}, title = {BOOI-Defined Obstruction Stratifies Early Outcomes After ThuLEP in Men With Detrusor Underactivity: A Retrospective Complete-Case Cohort Study.}, journal = {Lower urinary tract symptoms}, volume = {18}, number = {4}, pages = {e70073}, pmid = {42283210}, issn = {1757-5672}, support = {2022cg29//Wuhu Municipal Science and Technology Bureau/ ; }, mesh = {Humans ; Male ; *Urinary Bladder Neck Obstruction/surgery/etiology/physiopathology ; Retrospective Studies ; *Prostatic Hyperplasia/surgery/complications ; *Urinary Bladder, Underactive/complications/surgery/physiopathology ; Aged ; Treatment Outcome ; *Transurethral Resection of Prostate/methods ; Middle Aged ; Thulium/therapeutic use ; *Laser Therapy/methods ; }, abstract = {BACKGROUND: The benefit of transurethral outlet surgery in men with benign prostatic enlargement/benign prostatic obstruction (BPE/BPO) and detrusor underactivity (DU) remains uncertain, particularly when bladder outlet obstruction (BOO) is not demonstrated by pressure-flow testing.<br><br>OBJECTIVE: To evaluate early outcomes after transurethral thulium laser enucleation of the prostate (ThuLEP) in men with DU and BPE/BPO-related voiding dysfunction, and to examine whether BOOI-defined obstruction status stratifies early postoperative improvement.<br><br>METHODS: We retrospectively reviewed 189 ThuLEP records from a single centre and constructed a 100-patient complete-case cohort with interpretable preoperative pressure-flow studies and 3-month follow-up. DU was defined as bladder contractility index (BCI) <&#x2009;100. BOO status was classified using bladder outlet obstruction index (BOOI) as definite BOO, equivocal BOO or no BOO. Functional, symptom, catheter-removal, anatomical and safety outcomes were compared descriptively across BOO strata.<br><br>RESULTS: Ninety-one patients (91.0%) had DU: 56 had definite BOO, 25 had equivocal BOO and 10 had no BOO. In the DU cohort, mean Qmax increased from 3.88&#x2009;&#xb1;&#x2009;1.76&#x2009;mL/s at baseline to 9.98&#x2009;&#xb1;&#x2009;3.68&#x2009;mL/s at 1&#x2009;month and 11.03&#x2009;&#xb1;&#x2009;4.02&#x2009;mL/s at 3&#x2009;months (both p&#x2009;<&#x2009;0.001). Mean PVR decreased from 267.8&#x2009;&#xb1;&#x2009;167.4 to 142.6&#x2009;&#xb1;&#x2009;115.3&#x2009;mL and 124.7&#x2009;&#xb1;&#x2009;103.4&#x2009;mL, respectively (both p&#x2009;<&#x2009;0.001). IPSS and QoL also improved. Because enucleated specimen weight was unavailable, postoperative prostate volume was analyzed as an anatomical surrogate; mean 3-month postoperative prostate volume was 18.8&#x2009;&#xb1;&#x2009;4.3&#x2009;mL, corresponding to an absolute volume reduction of 38.0&#x2009;&#xb1;&#x2009;14.2&#x2009;mL and a percentage reduction of 65.4%&#x2009;&#xb1;&#x2009;6.3% in the DU cohort. Catheter-removal success was 83.9% in definite BOO, 68.0% in equivocal BOO and 40.0% in no BOO patients (descriptive p&#x2009;=&#x2009;0.009).<br><br>CONCLUSIONS: Preoperative BOOI-based stratification may help counsel men with DU regarding expected early functional improvement after ThuLEP. BOOI-defined no-BOO patients in this cohort were clinically selected after counseling, and the findings should be interpreted as exploratory because of the retrospective design, complete-case selection, short follow-up, and the small no-BOO subgroup.}, }
@article {pmid42284173, year = {2026}, author = {Meunier, A and Zak, MR and Munz, L and Garkot, S and Eder, M and Xu, J and Grosse-Wentrup, M}, title = {A Conversational Brain-Artificial Intelligence Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3703091}, pmid = {42284173}, issn = {1558-0210}, abstract = {We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-level intentions, while a pre-trained AI agent determines low-level details. This approach enlarges the target audience of BCIs to individuals with cognitive impairments, a population often excluded from the benefits of conventional BCIs. We present the general concept of BAIs and illustrate the potential of this new approach with a Conversational BAI based on electroencephalography (EEG), termed EEGChat. In particular, we show in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to be able to generate language. Our work thus demonstrates the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies.}, }
@article {pmid42285527, year = {2026}, author = {Chen, P and Ma, M and Liu, X and Ju, Y and Zhang, Y and Liao, M and Liu, S and Ming, D}, title = {Neural abnormalities in cognitive subprocesses of emotional conflict control in bipolar II disorder: Evidence from ERPs and brain functional networks.}, journal = {Journal of affective disorders}, volume = {}, number = {}, pages = {122124}, doi = {10.1016/j.jad.2026.122124}, pmid = {42285527}, issn = {1573-2517}, abstract = {Patients with bipolar disorder (BD) exhibit deficits in emotional conflict control. These abnormalities may be related to alterations in distinct cognitive subprocesses involved in emotional conflict processing; however, the specific stages affected remain unclear. Given the temporal and stage-dependent nature of emotional conflict control, examining specific processing stages may clarify the mechanisms underlying these deficits in BD. Therefore, this study combined a face-word emotional Stroop task with EEG, integrating event-related potentials (ERPs) and brain functional network analyses to characterize the cognitive subprocesses involved in emotional conflict control in bipolar II disorder (BD-II). BD-II patients showed significant abnormalities in early cognitive stages, including emotional stimulus perception and conflict monitoring (p < 0.05). These abnormalities were mainly reflected by reduced N200 amplitudes, right temporal region (T8)-centered network changes, and alterations in both global topology and frontal network organization. Machine learning analysis further suggested that these abnormal electrophysiological features may contain information relevant to distinguishing BD-II patients from healthy controls (HC), yielding an accuracy of 83.3% on the held-out test set. In summary, this study suggests that emotional conflict control deficits in BD-II are mainly reflected in early-stage electrophysiological abnormalities, with ERP amplitude changes and T8-centered right temporal network alterations representing the core findings. These findings provide candidate EEG features for further investigation of emotional conflict control abnormalities in BD-II, but require validation in larger independent samples.}, }
@article {pmid42285811, year = {2026}, author = {Cleary, HL and Bernard, MD and Davenport, DL and Bernard, AC}, title = {Diagnostic value of serum troponin in stable patients at risk for blunt cardiac injury.}, journal = {Injury}, volume = {}, number = {}, pages = {113448}, doi = {10.1016/j.injury.2026.113448}, pmid = {42285811}, issn = {1879-0267}, abstract = {INTRODUCTION: There are no gold standard criteria for diagnosing blunt cardiac injury (BCI). While the combination of electrocardiogram (ECG) and serum troponin is considered the most sensitive screening method for those being considered for discharge, little evidence exists regarding the diagnostic value and clinical utility of initial and serial serum troponin levels in stable patients who are being admitted with a possible BCI. Therefore, this study seeks to determine if troponin level is an independent predictor of adverse cardiac events in stable, admitted patients at risk for BCI.<br><br>METHODS: This was a five-year retrospective study using the trauma database at a University Level I Trauma Center. The study population included adult trauma patients presenting with a physician diagnosis of BCI or a sternal fracture who met prespecified stability criteria (SBP &#x2265; 90&#xa0;mmHg, HR < 110bpm, shock index < 1, GCS &#x2265; 14). The data collected included all troponin values and ECG interpretations as well as echocardiogram interpretations, when performed. A patient was classified as having an adverse cardiac event if they were diagnosed with a new arrhythmia requiring treatment, had cardiac surgery, or suffered cardiac-related mortality. Sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratios were calculated to analyze the diagnostic performance of the different BCI screening modalities. Diagnostic tests were compared using the Exact Mcnemar's test and the exact binomial test.<br><br>RESULTS: 350 patients met inclusion criteria. There were 12 adverse cardiac events; each were new arrhythmias requiring treatment with one patient also requiring synchronized cardioversion. Only one adverse cardiac event occurred in a patient with a normal ECG and troponin (n&#xa0;=&#xa0;160). No patients with a normal ECG and abnormal troponin had an adverse cardiac event (n&#xa0;=&#xa0;83). Patients with an abnormal ECG were more likely to have an adverse cardiac event (p&#xa0;<&#xa0;0.001).<br><br>DISCUSSION: In stable patients in this cohort, troponin level did not predict adverse cardiac events. Therefore, admitted patients at risk for BCI who meet stability criteria might be safely observed without measurement of serum troponin.}, }
@article {pmid42286462, year = {2026}, author = {Dai, W and Jahangir, M and Li, T and Guo, WJ}, title = {Early-life stress and adolescent circadian dysrhythmia drives unique behavioral and microbial profiles in rats.}, journal = {BMC microbiology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12866-026-05287-y}, pmid = {42286462}, issn = {1471-2180}, support = {82171487//National Natural Science Foundation of China/ ; 2024C03006//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang/ ; TD2024003//Leading innovation and entrepreneurship team of Hangzhou/ ; }, abstract = {OBJECTIVES: Early life adversity and circadian disruptions are known to impact neurodevelopment and physiology. This study investigated the effects of maternal separation (MS), adolescent circadian dysrhythmia, and their combination (double hit) on anxiety-like behavior and gut microbiota composition in rats.<br><br>METHODS: Rats were divided into four groups: CL (control group: normal early-life conditions with a standard light/dark cycle during adolescence), MS&#x2009;+&#x2009;N (maternal separation (MS) with a standard light/dark cycle (N=normal)) during adolescence), N&#x2009;+&#x2009;ALD (normal early-life conditions (N) with an altered light/dark cycle (ALD) during adolescence), and MS&#x2009;+&#x2009;ALD (combined exposure: MS with an altered light/dark cycle (ALD) during adolescence). Anxiety-like behavior and locomotor activity were assessed using the Open Field Test. Gut microbial diversity and taxonomic composition were analysed to identify microbial shifts across groups.<br><br>RESULTS: Behavioral analysis indicated that the combined stress group (MSLD) spent significantly (p&#x2009;<&#x2009;0.05) more time in the center of the arena compared to the CL, MS&#x2009;+&#x2009;N, and N&#x2009;+ ALD groups, suggesting a compromise in risk assessment ability due to dual stress exposure. Microbiome profiling revealed that while a core microbiome was conserved, each stressor generated a unique taxonomic signature. The N&#x2009;+&#x2009;ALD group appeared as the most distinct outlier, characterized by the lowest number of unique features and a specific enrichment of the viral species of phylum Uroviricota. Conversely, the MS&#x2009;+&#x2009;ALD group was distinguished by an enrichment of Bacteroidota species, including Muribaculum intestinale and Phocaeicola vulgatus, while the MS&#x2009;+&#x2009;N group showed enrichment in Bacteroides acidifaciens. Mycobiome analysis showed that early-life stress was the primary driver of fungal restructuring, distinguishing maternal separation groups by the loss of Neocallimastix species and the competitive expansion of Piromyces finnis. While adolescent circadian disruption alone largely preserved the baseline mycobiome, the cumulative dual-hit stress (MS&#x2009;+&#x2009;ALD) generated a distinct dysbiotic profile evident by the unique proliferation of Anaeromyces robustus.<br><br>CONCLUSIONS: In conclusion, the developmental timing of stress exposure drives distinct dysbiotic shifts. Specifically, adolescent circadian disruption selectively targets the virome, whereas early-life stress causes shift in the microbiome which endures a long-term foundation for adolescent psychiatric vulnerability. Notably, the cumulative effect of early life and adolescence stressors results in a unique microbial and behavioral profile, highlighting that the specific developmental window of exposure is a decisive factor in gut-brain axis dysfunction.}, }
@article {pmid42287979, year = {2026}, author = {Jin, Z and Li, D and Zhou, Q and Wang, Z}, title = {NEURAL-VOX: NEURal auditory language decoding for voice and text reconstruction.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {204}, number = {}, pages = {109221}, doi = {10.1016/j.neunet.2026.109221}, pmid = {42287979}, issn = {1879-2782}, abstract = {Neural decoding of perceived linguistic content from non-invasive brain recordings remains a profound scientific challenge with transformative implications for assistive technologies. Existing approaches often struggle to generate intermediate representations, such as mel spectrograms or phonemes, and seldom integrate multi-modal information to enhance text decoding. This study presents a framework for decoding non-invasive brain activity into text, phoneme sequences, and mel-spectrogram-based acoustic representations, termed NEURAL-VOX. Leveraging a three-stage training strategy, NEURAL-VOX not only improves the accuracy of brain-to-text decoding, but also enables text generation to benefit from joint optimization with speech synthesis. By incorporating multi-scale frequency-domain analysis, our model more effectively captures the hierarchical structure of language processing in neural activity. Experiments across multiple datasets demonstrate that NEURAL-VOX achieves substantial gains over existing methods. The learned phoneme representations encode rich linguistic information and further strengthen text decoding, while model interpretability analysis reveals strong alignment with neurobiological patterns.}, }
@article {pmid42288545, year = {2026}, author = {Nechchad, M and Elkari, B and Midaoui, IE and Cadi, SAE and M'Hifed, Z and Ourabah, L and Workneh, AD and Chaibi, Y and Irshad, SM and El-Barbary, ZMS and Yessef, M}, title = {EEG blink and gaze control using random forest classification for accessible assistive robotic navigation in real world conditions.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-56416-6}, pmid = {42288545}, issn = {2045-2322}, abstract = {This study presents an EEG-based Brain-Computer Interface for intuitive robotic navigation driven by ocular activity. A real multi-subject dataset collected from 15 participants was used to extract blink- and gaze related EEG features for five control commands. Eight supervised classifiers were evaluated under stratified 5-fold cross-validation: Random Forest, multilayer perceptron, support vector machine, k-nearest neighbors, RUSBoost, Naive Bayes, decision tree, and linear discriminant analysis. Among them, Random Forest achieved the best overall performance, reaching 98.74 ± 1.19% accuracy, 0.9874 Macro-F1, and 0.9993 macro-AUC, demonstrating excellent robustness and class separability across the five ocular classes. The decoded commands were transmitted wirelessly to an embedded Raspberry Pi platform, where they were converted into safe motor actions for wheelchair type robot navigation. Real world experiments in indoor environments confirmed stable motion control, reliable command execution, and successful obstacle avoidance without physical interaction from the user. These findings support the feasibility of a low-cost, portable, and non-invasive BCI solution based on realistic multi-subject EEG data for assistive mobility applications. Although the full control loop operated online, the end-to-end system latency was not quantitatively benchmarked in the present study and remains an important limitation to be addressed in future work. Future developments will focus on expanding the command set, improving robustness under more variable conditions, and validating the system in broader assistive mobility scenarios.}, }
@article {pmid42292038, year = {2026}, author = {Wang, NN and Cao, F and Xu, D}, title = {Hydrogels in Neurological Disorders: Emerging Diagnostic and Therapeutic Applications.}, journal = {International journal of nanomedicine}, volume = {21}, number = {}, pages = {618081}, pmid = {42292038}, issn = {1178-2013}, mesh = {*Hydrogels/chemistry/therapeutic use ; Humans ; *Nervous System Diseases/diagnosis/therapy/drug therapy ; Animals ; Drug Delivery Systems/methods ; Brain-Computer Interfaces ; Electroencephalography/methods ; Blood-Brain Barrier ; }, abstract = {The clinical management of neurological disorders remains a major challenge worldwide, constrained by fundamental limitations in both diagnosis and therapy. Electroencephalography (EEG), the cornerstone of neurological assessment, is limited by low spatial resolution and inconsistent signal quality. Therapeutically, the blood-brain barrier (BBB) restricts drug delivery to the brain, resulting in subtherapeutic intracerebral concentrations. These convergent diagnostic and delivery bottlenecks underscore an urgent imperative for innovative materials and technologies. Hydrogels, characterized by biomimetic three-dimensional (3D) architectures, have emerged as a versatile material platform to bridge this gap. From a diagnostic perspective, hydrogels-based electrodes exhibit exceptional biocompatibility and low interfacial impedance, enabling high-fidelity EEG acquisition while minimizing insult to sensitive neural and skin tissues. From a therapeutic perspective, their 3D architecture provides versatile scaffolds for therapeutic agents, supporting high loading efficiency and programmable release profiles for neurological interventions. In this review, we first outline the physicochemical properties and fabrication techniques of hydrogels. We then discuss their applications, with particular emphasis on neural bio-electrodes, brain-computer interfaces (BCIs), drug delivery, and neuro-bioengineering. Finally, we examine the challenges impeding the clinical translation of hydrogels and outline prospective mitigation strategies. The integration of these functionalities is anticipated to advance closed-loop therapeutic systems for the precise management of complex neurological disorders.}, }
@article {pmid42292814, year = {2026}, author = {Wang, Z and Zhao, W and Chen, X and Zhao, S and Li, X and Yang, Q and Zong, F and Zhang, H}, title = {Activation of GLP-1R ameliorates alcohol withdrawal induced anxiety-like behavior by regulating neuronal mitochondrial quality control.}, journal = {Frontiers in pharmacology}, volume = {17}, number = {}, pages = {1820128}, pmid = {42292814}, issn = {1663-9812}, abstract = {INTRODUCTION: Alcohol use disorder (AUD) is a specific psychological state induced by repeated heavy drinking, and withdrawal symptoms such as anxiety are closely related to relapse after withdrawal. While neuronal damage caused by alcohol is considered a significant precipitating factor for withdrawal-induced anxiety, the underlying molecular mechanisms remain unclear.<br><br>METHODS: In this study, we established a mouse model of alcohol withdrawal through 3 months of chronic ethanol exposure (CEE) followed by withdrawal. Mice were treated with semaglutide (0.03 mg/kg) via intraperitoneal injection and subjected to behavioral, biochemical, and morphological analyses.<br><br>RESULTS: Our results demonstrate that the glucagon-like peptide-1 receptor (GLP-1R) agonist semaglutide alleviates anxiety-like behaviors in CEE withdrawal mice and reverses the downregulation of GLP-1R and its downstream effector CREB in the mitochondria of prefrontal cortex (PFC) neurons. Enhancing the GLP-1R/CREB pathway regulates mitochondrial quality control, including fission, fusion, and mitophagy, to maintain mitochondrial function and ameliorate synaptic impairment.<br><br>DISCUSSION: These findings suggest that activation of GLP-1R ameliorates alcohol withdrawal-induced anxiety-like behaviors by regulating neuronal mitochondrial function, providing a potential therapeutic target for AUD.}, }
@article {pmid42294101, year = {2026}, author = {Sümer-Arpak, E and Saini, R and Chakladar, DD and Varun, SK and Simistira Liwicki, F}, title = {The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1838064}, pmid = {42294101}, issn = {1662-5161}, abstract = {Inner speech (IS), or imagined speech without overt articulation, is a promising target for brain-computer interfaces (BCIs) aimed at restoring communication in individuals with severe speech impairments, such as locked-in syndrome. Foundation models (FMs), typically trained using self-supervised learning (SSL) on large-scale datasets, offer new opportunities for learning transferable and robust representations from neural signals. This mini review provides an overview of FM-based approaches for IS decoding using non-invasive neuroimaging modalities, including functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and functional near-infrared spectroscopy, highlighting architectural trends, pretraining strategies, and model adaptation techniques. We discuss how recent models move beyond task-specific classification toward scalable representation learning and semantic-level decoding. Despite these advances, several challenges remain, including the weak, noisy, and non-stationary nature of neural signals, variability in data acquisition, and limitations in dataset scale, standardization, computational resources, interpretability, and evaluation metrics. Ethical and privacy considerations are also critical. Overall, FMs provide a promising paradigm for non-invasive IS decoding, addressing neurophysiological, methodological, and ethical challenges is essential for developing scalable and reliable BCI systems.}, }
@article {pmid42035123, year = {2026}, author = {Xi, L and Liu, Q and Li, H and Li, W and He, D and Yao, L and Yang, X}, title = {Comparative efficacy of motor imagery augmented with central non-invasive brain stimulation versus peripheral electrical stimulation for upper extremity rehabilitation post-stroke: a systematic review and network meta-analysis.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {23}, number = {1}, pages = {}, pmid = {42035123}, issn = {1743-0003}, support = {zx2019-04-02//Rehabilitation Clinical Medical Centre of Yunnan Province/ ; 2019IC034//Jiajie Expert Workstation of Yunnan Province/ ; 202203AC100007-6//Study on a New Model of Comprehensive Intervention in Rehabilitation and Psychology of "Brain and Heart together"/ ; 202305AF150032//Science and Technology Talent and Platform Program (Academician and Expert Workstation)/ ; 2022YFC2009700//Research and Development of Integrated Chinese and Western Medicine Rehabilitation Technology and Multi-modal Monitoring System for movement Disorders/ ; 2018YFC2002301//National Key Research and Development Program of China/ ; 2024XKTDTS18//the Neurorehabilitation Team of Kunming Medical University/ ; FWCY-ZNT2024011//Systematic Development and Industrial Applications of Balneotherapy in Stroke Rehabilitation: A Translational Research Framework/ ; 202402AA310058//Application and Innovative Research of Balneotherapy in Chronic Disease Management/ ; 2024J0383//Scientific Research Fund project of Education Department of Yunnan Province/ ; 2023BS01//Doctoral research project of the Second Affiliated Hospital of Kunming Medical University/ ; 2025KFZD006//Investigating the Impact of iTBS on the HPA Axis in Stroke Patients via Stimulation of Different Brain Regions Using Resting-State EEG/ ; 82560452//National Natural Science Foundation of China/ ; 202501AY070001-185//Investigating the Modulation of Negative Emotions by the Deep Cerebellar Nuclei-Hippocampal Neural Circuit Using Transcranial Magnetic Stimulation/ ; }, abstract = {BACKGROUND: Upper limb dysfunction is a common and debilitating consequence of stroke, severely affecting patients’ activities of daily living and quality of life. Motor imagery (MI) has emerged as a promising rehabilitation technique, and its combination with various forms of non-invasive stimulation, both central (e.g., repetitive transcranial magnetic stimulation, rTMS; transcranial direct current stimulation, tDCS) and peripheral (e.g., functional electrical stimulation, FES), has been increasingly investigated. While previous meta-analyses have confirmed the general benefit of combined interventions, the relative efficacy of different MI-based combination strategies remains unclear. This systematic review and network meta-analysis aimed to directly and indirectly compare the effectiveness of MI augmented with different non-invasive central or peripheral stimulation modalities for upper extremity recovery post-stroke.<br><br>METHODS: We registered the study on PROSPERO (CRD420251131264) and followed the PRISMA guidelines. Randomized controlled trials (RCTs) were searched in PubMed, Cochrane Library, EMBASE, Scopus, CNKI, and Wanfang databases from inception until August 4, 2025. The included RCTs involved adult stroke patients with upper limb dysfunction receiving MI combined with any non-invasive stimulation. The primary outcome was the change in upper limb motor function measured by the Fugl-Meyer Assessment (FMA or FMA-UE). A frequentist network meta-analysis was performed using random-effects models. Risk of bias was assessed using the Cochrane RoB 2 tool. Subgroup, sensitivity, and meta-regression analyses were conducted to explore heterogeneity.<br><br>RESULTS: Seventeen RCTs involving 846 participants were included in the systematic review, with 13 studies forming the network for meta-analysis, comparing 9 intervention strategies. Network meta-analysis for the FMA outcome showed that MI combined with low-frequency rTMS (MI-LF-rTMS) showed a statistically significant difference compared to conventional rehabilitation alone (Standardized Mean Difference, SMD&#x2009;=&#x2009;1.755, 95% CI 0.631 to 2.879, p&#x2009;=&#x2009;0.002). No other intervention, including MI-tDCS, MI-FES, or any single therapy, showed a statistically significant difference compared to conventional rehabilitation. MI-LF-rTMS also showed a statistically significant difference in upper limb functional activity (Action Research Arm Test). Subgroup analyses indicated that the statistically significant difference for MI-LF-rTMS was also observed across intervention durations&#x2009;&#x2264;&#x2009;4 weeks, disease stages&#x2009;&#x2264;&#x2009;3 months post-stroke, and in protocols not using brain-computer interface technology. Meta-regression identified that the use of a brain-computer interface, publication year, and patient mean age were significant sources of heterogeneity.<br><br>CONCLUSION: Among the intervention strategies evaluated in this network meta-analysis, motor imagery combined with low-frequency repetitive transcranial magnetic stimulation (MI-LF-rTMS) showed a statistically significant difference compared to conventional rehabilitation. This regimen integrates central neuromodulation with cognitive training and may be a clinically feasible option, particularly for patients in the early phase after stroke. Future research should focus on parameter optimization, mechanistic exploration, and validation in larger, more diverse populations.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-026-02002-w.}, }
@article {pmid42277056, year = {2026}, author = {Wang, Y and Lin, Z and Wu, D and Yang, Y and Zhang, J}, title = {Associations of DTI-ALPS index and choroid plexus volume with clinical severity across the Parkinson's disease spectrum.}, journal = {NPJ Parkinson's disease}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41531-026-01432-6}, pmid = {42277056}, issn = {2373-8057}, support = {82571418//National Natural Science Foundation of China/ ; 32530027//National Natural Science Foundation of China/ ; 2024C03098//Key Research and Development Program of Zhejiang Province/ ; }, abstract = {Impaired clearance of pathogenic proteins may contribute to Parkinson's disease (PD) progression, but the clinical relevance of brain clearance-associated processes in humans remains incompletely understood. Using cross-sectional data from 1,861 participants (704 PD, 997 prodromal, 160 healthy controls) in Parkinson Progression Marker Initiative, we investigated whether the magnetic resonance imaging (MRI)-based indirect markers linked to brain clearance-related processes, diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index and normalized choroid plexus volume (NCPV), are associated with disease stage and motor/non-motor manifestations across the PD spectrum. The ALPS index declined with age, was lower in males, and showed a stepwise reduction from controls to prodromal individuals and PD patients. Clinically, lower ALPS index correlated with greater motor severity in prodromal and PD groups, with stronger associations at advanced stages. Lower ALPS index also correlated with rapid eye movement sleep behavior disorder, cognitive impairment, and depressive symptoms. NCPV showed a complementary trend, demonstrating positive associations with clinical severity measures and a significant negative correlation with the ALPS index. Together, these findings suggest that MRI markers linked to brain clearance processes are associated with clinical progression across the PD continuum and may provide imaging biomarkers relevant to disease staging and pathophysiology.}, }
@article {pmid42272373, year = {2025}, author = {Aabideen, M and Ashokan, A and Nivargi, SM and Ramzan, M and Boddu, D and Aboobacker, F and Odat, A and Taher, M and Mohamed, R and Cingapagu, R and Al Shamsi, HO and Aabideen, Z}, title = {Single Centre Experience of Treating Children with Cancer in the United Arab Emirates.}, journal = {The Gulf journal of oncology}, volume = {1}, number = {49}, pages = {80-84}, pmid = {42272373}, issn = {2521-3881}, mesh = {Humans ; United Arab Emirates ; Male ; Child, Preschool ; Retrospective Studies ; Child ; *Neoplasms/therapy/mortality/pathology ; Female ; Infant ; Adolescent ; }, abstract = {INTRODUCTION: Cancer is a leading cause of death in children. Advancements in medical sciences have significantly improved childhood cancer outcome. However, the pattern of malignancy and outcomes of childhood cancer in the UAE have not been published in the literature in the recent period. Therefore, we aim to investigate this in a leading cancer institute in Abu Dhabi, United Arab Emirates (UAE).<br><br>METHODOLOGY: This is a retrospective study. We collected data including diagnosis; age at diagnosis; treatments used e.g., chemotherapy, radiation, surgery, immunotherapy and bone marrow transplant (BMT) and outcomes. Overall survival (OS) and Event-Free Survival (EFS) were estimated using the Kaplan Meier method.<br><br>RESULTS: There are 82 children with cancer. The male-tofemale ratio is 1.2. Most patients (45%) were diagnosed between one to five years of age. The most common malignancies are B cell Acute Lymphoblastic Leukaemia (32, 39%), brain tumours (12, 15%); Neuroblastoma (9, 10%), Hodgkin Lymphoma (8, 9%) and Wilms Tumour (5, 6%); Acute Myeloid Leukaemia (3, 4%); Non-Hodgkin Lymphoma (3, 4%); Ewing Sarcoma (3, 4%); T ALL (2, 2%); Osteosarcoma (2, 2%); Angiosarcoma (1, 1%); Synovial Sarcoma (1, 1%). 28 (34%) of patients had completed all treatment at Burjeel Cancer Institute (BCI); 15 (18%) had completed their treatment at another centre and attending follow-up at BCI; and 5 (6%) commenced their treatment at BCI and were transferred to another centre. 34 (41%) but currently still undergoing treatment at BCI. The abandonment rate is 0%. Overall survival is 94% and event-free survival is 90%.<br><br>DISCUSSION: The rapid progression of UAE cancer care over the past four decades has contributed massively to our favourable survival outcomes. Radiation therapy, bone marrow stem cell transplantation, strict medication regulation and monitoring by the UAE Department of Health have been established in recent years to further enhance treatment for cancer patients in the UAE.<br><br>CONCLUSION: The results of our study are comparable to the international standard. More studies involving multiple centres in the UAE are needed to ascertain the exact pattern of paediatric malignancy and outcomes in the UAE.}, }
@article {pmid42274110, year = {2026}, author = {Chen, HL and Zou, S and Zheng, LL}, title = {Brain-Computer Interface Applications in Craniofacial Nerve Functional Reconstruction: A Narrative Review.}, journal = {The Journal of craniofacial surgery}, volume = {}, number = {}, pages = {}, doi = {10.1097/SCS.0000000000013038}, pmid = {42274110}, issn = {1536-3732}, abstract = {BACKGROUND: Brain-computer interface (BCI) technology is increasingly relevant to craniofacial nerve functional reconstruction because it can decode cortical motor intent and convert it into physical or digital output when peripheral motor pathways are impaired. Facial nerve palsy, dysphagia, and oromandibular motor dysfunction remain difficult to treat when conventional nerve repair, muscle transfer, or electrical stimulation cannot restore coordinated and natural movement.<br><br>METHODS: This narrative review synthesized peer-reviewed literature on BCI-related craniofacial functional reconstruction. A targeted search of PubMed, Embase, Web of Science, and Google Scholar was performed, covering English-language articles published from 2014 to April 12, 2026. Eligible core articles addressed BCI-based facial motor restoration, swallowing or oromandibular BCI paradigms, speech or orofacial neuroprosthetics, neural interface integration in craniofacial surgery, flexible facial bioelectronic sensing, or functional electrical stimulation systems with direct relevance to craniofacial nerve recovery. Background literature was cited separately to contextualize disease burden, conventional reconstruction, dysphagia, outcome assessment, calibration, and neuroethical issues.<br><br>RESULTS: Twenty-two core articles were included in the final thematic synthesis and organized into 3 domains: facial expression motor reconstruction, oromandibular and swallowing rehabilitation, and neural interface integration in craniofacial surgery. EEG-based facial-expression decoding has shown promising accuracy under controlled laboratory conditions, speech neuroprosthetics provide potentially transferable frameworks for orofacial motor decoding that remain unproven in facial palsy or dysphagia rehabilitation, swallowing motor-imagery studies support physiological feasibility for dysphagia-oriented BCI, and flexible facial biosensors may support future closed-loop systems.<br><br>CONCLUSIONS: BCI technology should be regarded as a potential complement to conventional craniofacial reconstruction rather than a replacement for established surgical techniques. Current evidence supports technical feasibility, but clinical translation will require naturalistic decoding, durable interfaces, faster patient-specific calibration, meaningful outcome measures, and early attention to ethical issues.}, }
@article {pmid42274641, year = {2026}, author = {Shahbaz, H and Sherman, AB and Shaikh, FA and Elsawwah, JK and Charles, EJ and Curran, T and Nemeth, ZH}, title = {Risk factors and outcomes of blunt cardiac injury in adult motor vehicle collision patients.}, journal = {Traffic injury prevention}, volume = {}, number = {}, pages = {1-5}, doi = {10.1080/15389588.2026.2674238}, pmid = {42274641}, issn = {1538-957X}, abstract = {OBJECTIVES: Motor vehicle protective equipment, such as seatbelts and airbags, has improved occupant safety. However, while seatbelts reduce facial and abdominal injuries, they may not significantly prevent head, neck, or thoracic trauma. Limited data exist on blunt cardiac injury (BCI). This study evaluated patterns of BCI, associated thoracic injuries, and hospital outcomes in adult trauma patients following motor vehicle collisions (MVCs).<br><br>METHODS: We analyzed the 2023 American College of Surgeons Trauma Quality Improvement Program (ACS-TQIP) database for adult MVC occupants. Abbreviated Injury Scale codes 4208xx.x, 4404xx.x, 4410xx.x, 4412xx.x, 4413xx.x, and 4416xx.x identified patients with BCI. Those without BCI formed the reference cohort. A 1:1 propensity score match (PSM) on Injury Severity Score (ISS) was performed using RStudio to balance collision severity.<br><br>RESULTS: In the overall cohort, the incidence of BCI was 1.2% (1,914/161,446). After PSM, 1,914 patients remained in each cohort with a mean ISS of 22.7. Both seatbelt plus airbag use and airbag use alone were independently associated with increased odds of BCI. BCI was strongly associated with thoracic injuries, including sternum fracture (odds ratio [OR] 3.492; 95% CI 2.95-4.14), hemothorax (OR 2.928; 95% CI 2.29-3.75), thoracic aortic injury (OR 1.773; 95% CI 1.29-2.44), and pulmonary contusion (OR 1.382; 95% CI 1.18-1.62). In multivariable analysis with BCI as the outcome, mortality (OR 2.325; 95% CI 1.93-2.79) and cardiac arrest (OR 1.827; 95% CI 1.29-2.59) were independently associated with BCI.<br><br>CONCLUSION: Protective equipment use correlates with BCI and thoracic trauma. In MVC patients using seatbelts and airbags, concomitant chest injuries should heighten suspicion for BCI and prompt further evaluation.}, }
@article {pmid42275342, year = {2026}, author = {Abdollahpour, N and Artan, NS}, title = {EC-Transformer: Connectivity-Informed Embeddings and Adaptive Gating for fNIRS.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3702505}, pmid = {42275342}, issn = {2168-2208}, abstract = {Functional Near-Infrared Spectroscopy (fNIRS) provides a non-invasive modality for monitoring brain activity, yet jointly modeling temporal dynamics and inter-regional interactions remains challenging for accurate brain-computer interface (BCI) decoding. This study proposes an Effective Connectivity Transformer (EC-Transformer), which integrates connectivity-informed representations into transformer-based modeling of fNIRS signals. The architecture combines a time- wise embedding that captures temporal dynamics using positional encoding and bidirectional LSTMs with a connectivity-based embedding that encodes low-frequency directed dependency patterns. An adaptive gating mechanism dynamically fuses these representations during classification. The model was evaluated using leave-one-subject-out validation on two public fNIRS datasets involving mental arithmetic and motor execution tasks, achieving accuracies of $76.83 \pm 2.4$ and $76.03 \pm 2.00$, respectively. The proposed framework demonstrates competitive performance relative to existing transformer-based approaches while maintaining substantially lower model complexity (approximately 0.7 M parameters compared to 1.7M-3.5 M in prior models). Ablation and control analyses further suggest that EC-based embeddings provide connectivity-informed representations that complement temporal modeling while maintaining competitive decoding performance. Interpretability analyses revealed task-related connectivity patterns broadly consistent with distributed cognitive and motor-related networks. Overall, the findings suggest that incorporating connectivity-informed representations can provide physiologically structured complementary information for transformer-based fNIRS decoding while maintaining competitive performance and computational efficiency.}, }
@article {pmid42275895, year = {2026}, author = {Chen, W and Daly, I and Chen, Y and Li, J and Wu, X and Zhao, R and Wang, X and Cichocki, A and Jin, J}, title = {EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {204}, number = {}, pages = {109197}, doi = {10.1016/j.neunet.2026.109197}, pmid = {42275895}, issn = {1879-2782}, abstract = {Motor imagery is a non-invasive process that operates independently of external stimuli, and can be used to establish a direct connection between the brain and external devices solely through the imagination of a specific movement. Nonetheless, the complexity and variability of neural patterns pose substantial challenges, as accurately decoding motor imagery from electroencephalography signals remains a significant obstacle. This paper introduces an enhanced dynamic spatiotemporal -frequency attention convolutional neural network (EDSF-Net) for the precise decoding of motor imagery. EDSF-Net employs a refined spatiotemporal attention mechanism, grounded in enhanced dynamic convolution (EDConv), to emphasize localized spatial features alongside high and low-frequency temporal characteristics. Subsequently, EDConv is utilized for global spatial feature extraction. Following this, group convolutions formed by EDConv are implemented to fuse the extracted features effectively. Ultimately, a synchronized channel-frequency attention mechanism is employed to capture critical channel and frequency domain information, facilitating the model's focus on features most pertinent to the task throughout the learning process. We conducted a comprehensive evaluation of the performance of EDSF-Net on two public datasets, BCI Competition IV 2a and OpenBMI. In the hold-out session experiments, EDSF-Net achieved decoding accuracies of 84.26% and 75.14%, respectively. In the leave-one-subject-out experiments, EDSF-Net attained decoding accuracies of 66.78% and 82.24%, respectively. These results show that EDSF-Net has robust generalization capabilities, affirming its efficacy in addressing complex pattern recognition tasks, with significant potential for diverse applications.}, }
@article {pmid42275898, year = {2026}, author = {Cheng, C and Zhang, J and Cheng, Y and Jia, Z and He, W}, title = {MS-STGAN: A dual-branch multi-scale spatio-temporal generative adversarial framework for incomplete EEG-based emotion recognition.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {204}, number = {}, pages = {109203}, doi = {10.1016/j.neunet.2026.109203}, pmid = {42275898}, issn = {1879-2782}, abstract = {Electroencephalography (EEG) enables high-resolution emotion recognition but often suffers from incomplete data in real-world scenarios due to sensor failures or preprocessing errors. To this end, we propose a Multi-Scale Spatio-Temporal Generative Adversarial Network (MS-STGAN). Specifically, we first apply random masking to the EEG channels data to simulate missing data conditions in practical environments. Then, we design a spatio-temporal dual-branch generator to reconstruct complete representations: the spatial branch employs graph convolutional networks (GCNs) to capture robust inter-regional dependencies, while the temporal branch leverages the BiMamba state space model to encode the dynamic evolution of emotions. To further enhance feature learning, multi-scale 2D convolution and deconvolution layers are incorporated before and after both branches, enabling the extraction of diverse spatio-temporal features. Additionally, we introduce a generative adversarial framework, where the generator restores informative features from incomplete inputs and the discriminator enforces the authenticity of reconstructed data. Finally, a fusion module integrates the outputs of both branches for downstream classification. Extensive experiments on the DEAP and SEED-IV datasets validate the effectiveness of each component and demonstrate that MS-STGAN achieves superior performance and strong generalization ability.}, }
@article {pmid42276068, year = {2026}, author = {Kistler, W and Fakhreddine, R and Rodriguez, GR and Hayward, M and Buch, ER and Bestmann, S and Cohen, LG}, title = {Early skill learning is shaped by the offline emergence of expert synergies.}, journal = {Current biology : CB}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cub.2026.05.032}, pmid = {42276068}, issn = {1879-0445}, abstract = {Everyday skilled actions depend on the formation of coordinated motor synergies that integrate multiple digits into stable, low-dimensional control units. Although initial practice of a new skill leads to rapid performance improvements, it is unclear whether the underlying movement kinematics reorganize on a similar timescale or in a way that directly relates to these gains. It also remains uncertain whether such reorganization occurs mainly during active practice or instead during brief rest breaks. Here, we tracked the temporal evolution of multi-digit synergy formation during early learning of a naturalistic keypress skill. Initial practice rapidly sculpted the motor repertoire toward higher-order, temporally compressed, and overlapping multi-digit synergies. These synergies emerged after only minutes of practice and continued to be expressed throughout the full training session. Notably, they were primarily shaped across brief rest breaks and robustly predicted individual skill proficiency. Across learning, distinct synergy subtypes emerged that differed in their heuristic prevalence. Rarely expressed synergies reflected transient novice patterns, synergies expressed at intermediate levels could index exploratory and trial-initiation strategies, and highly expressed synergies emerged later to dominate performance, reflecting the consolidation and expansion of skilled motor control. Together, these findings indicate that skilled performance is supported by the early formation of a compact repertoire of expert multi-digit synergies that emerge preferentially across rest periods and predict subsequent skill gains. They further raise the hypothesis that explicitly training such expert synergies alongside task goals could enhance learning in domains such as the arts, sports, and neurorehabilitation.}, }
@article {pmid42182433, year = {2026}, author = {Cattabriga, M and Alamri, AH and Hobbs, TG and Emonds, AMX and Sobinov, AR and Gaunt, RA and Greenspon, CM and Valle, G}, title = {Spatiotemporal encoding of touch signals in the human somatosensory and motor cortices.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {42182433}, issn = {2692-8205}, abstract = {The sense of touch is fundamental for dexterous manipulation, object interaction, and body awareness. It is primarily processed in the somatosensory cortex (SC), yet our understanding of how tactile information is encoded at the level of neural populations and single neurons in humans remains limited. It is unclear how natural tactile signals are represented in SC and how they may be influenced by visual inputs, as well as how closely sensory and motor cortices interact during passive touch. Here, we investigated the neural basis of touch in the human SC using chronically implanted microelectrode arrays in three participants. By delivering controlled mechanical stimuli, we characterized neural responses to natural touch and mapped detailed somatotopic receptive fields (the patch of skin that elicits neural responses when stimulated) in humans, including multidigit representations. Surprisingly, we also found strong, clearly somatotopic activation in the motor cortex (MC) during passive touch, even in the absence of movement, highlighting a tight and functionally relevant sensorimotor coupling. We further examined how vision shapes tactile processing by comparing neural activity during actual touch with and without vision, and during observation of touch on another person's hand. While touch to the participants' hands elicited robust, event-locked, and somatotopically organized responses in the SC, observation of tactile actions alone did not produce significant activation, suggesting limited vicarious encoding at this level. These findings provide a detailed characterization of human touch processing at the level of neuronal populations and give insights for the design of microstimulation strategies of the SC for the restoration of touch.}, }
@article {pmid42246522, year = {2026}, author = {Rodionova, KN and Vigovskaya, EA and Novosad, YA and Shabunin, AS and Vissarionov, SV}, title = {[Materials and technologies in neural interfaces: optimization ways for chronic implantation].}, journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova}, volume = {126}, number = {5}, pages = {21-28}, doi = {10.17116/jnevro202612605121}, pmid = {42246522}, issn = {1997-7298}, mesh = {Humans ; *Brain-Computer Interfaces ; *Brain/physiology ; }, abstract = {A neural interface is a set of tools that enable information exchange between the brain and an external device. Such systems are widely used in biomedicine, including the recovery of nervous system functions. This review summarizes the operating principles of neurointerfaces, reviews the materials used in their design, and presents examples of this technology's use in medicine, including chronic implantation.}, }
@article {pmid42262704, year = {2026}, author = {Luo, N and Yang, Z and Song, M and Di, S and Chu, C and Shi, W and Yue, W and Zhang, Y and Yan, H and Zhang, X and Zhang, D and Sui, J and Calhoun, V and Jiang, T}, title = {Measuring the Impacts of Urbanicity and Different Exposome Factors on Human Brain through Exposure Network Mapping.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {42262704}, issn = {1995-8218}, abstract = {While urbanicity increases the risk of mental health issues, its effects on brain networks are heterogeneous and underexplored in relation to different exposome factors. Using a coordinate network mapping strategy termed exposure network mapping (ENM) across eight datasets, this study first consolidated heterogeneous findings of urbanicity to a significant, replicable network involving the middle frontal gyrus, orbital gyrus, and anterior cingulate gyrus. Afterwards, among the other factors examined (air pollution, noise, income, stress, green space), only stress converged into a distinct common network, highlighting the orbital gyrus, caudate, anterior/middle cingulate gyrus, hippocampus, and middle frontal gyrus. This ENM-stress map exhibited the highest correlation with both the ENM-urbanicity map (r = 0.77) and a transdiagnostic map (r = 0.72). In addition, sleep-related coordinates also formed a consistent network, involving the middle cingulate gyrus, orbital gyrus, caudate, and putamen, which correlated strongly with urbanicity (r = 0.75), stress (r = 0.80), and the transdiagnostic pattern (r = 0.55). Collectively, this study highlights the potential risks of urbanicity and stress, as well as the protective role of sleep on brain networks, which may offer new insights for preventing mental health issues in urban environments.}, }
@article {pmid42262958, year = {2026}, author = {Mrachacz-Kersting, N and Pasluosta, C and Meyer, B and Nascimento, OFD and Stieglitz, T and Farina, D}, title = {Cortical Activity Associated with Phantom Leg Movements.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3701504}, pmid = {42262958}, issn = {1558-0210}, abstract = {We tested the feasibility for amputees to control artificial limbs using non-invasive electroencephalography (EEG). Thirteen participants engaged in attempts of isometric ankle plantar-flexions using their phantom or intact limb at slow or ballistic speeds. EEG data were analyzed for movement-related cortical potentials (MRCPs), the slow negative potentials related to the planning and execution of movements. We focused on temporal profiles and single-trial classification at electrode location Cz where MRCPs are most prominent. Distinctly different MRCP morphologies were observed for both movement speeds and phantom versus intact limbs. Crucially, time since amputation correlated significantly with classification errors for distinguishing tasks performed with the intact limb from those of the phantom limb (R = 0.36, p =0.004) and movement speed during trials of only the phantom limb (R = -0.33, p = 0.01). Here we show the persistent capacity of amputees to plan and attempt to execute limb motions at varying speeds using their phantom limb. This has implications for understanding neural adaptations over extended post-amputation periods and for the practical implementation of the MRCP in the design of brain-computer interfaces to control prosthetic devices using single-electrode EEG recordings.}, }
@article {pmid42263493, year = {2026}, author = {Offenberg, EC and Berezutskaya, J and Müller, L and Freudenburg, ZV and Ramsey, NF and Vansteensel, MJ}, title = {Optimal positioning and size of high-density electrocorticography grids for speech brain-computer interfaces.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {190}, number = {}, pages = {2111940}, doi = {10.1016/j.clinph.2026.2111940}, pmid = {42263493}, issn = {1872-8952}, abstract = {OBJECTIVE: Speech-based brain-computer interfaces (BCIs) can offer an intuitive communication method for those who have lost the ability to speak due to paralysis. Significant progress has been made in classifying individual words from high numbers of electrocorticographic (ECoG) electrodes on the sensorimotor cortex (SMC). As implantations of larger grids with more ECoG electrodes are associated with higher surgical risk, we investigated whether confined electrode configurations can match the classification accuracy of larger grids.<br><br>METHODS: We analyzed data from eight able-bodied participants with high-density ECoG grids (64 to 128 electrodes) performing a 12-word repetition task in Dutch.<br><br>RESULTS: Word pronunciation elicited high frequency band activity in two SMC foci: one ventral, one dorsal. Smaller, rectangular configurations with surface areas of 325&#xa0;mm[2] to 561&#xa0;mm[2] (32 electrodes) could achieve similar word classification accuracies as larger grids: 76&#xa0;&#xb1;&#xa0;16% versus 75&#xa0;&#xb1;&#xa0;17% across participants, respectively (practical chance level 16.7%). The best configurations were oriented vertically and centered on the central sulcus.<br><br>CONCLUSION: These findings indicate that a 32-electrode ECoG grid placed optimally can be sufficient for achieving high word classification accuracy on a closed set of words.<br><br>SIGNIFICANCE: These findings support the targeted placement of small ECoG grids, reducing surgical demands on end users and justifying energy- and complexity-efficient designs of fully implantable BCI devices for individuals with severe paralysis.}, }
@article {pmid42264810, year = {2026}, author = {Haroon, N and Jabbar, H and Jeong, TT and Khan, US and Rashid, N and Naseer, N}, title = {Investigating cognitive fatigue recovery through mechanical massage and binaural beats: An AI-driven fNIRS study.}, journal = {Journal of bodywork and movement therapies}, volume = {47}, number = {}, pages = {305-331}, doi = {10.1016/j.jbmt.2026.04.004}, pmid = {42264810}, issn = {1532-9283}, abstract = {Cognitive fatigue is a state of reduced mental performance resulting from prolonged periods of cognitive activity. It is characterized by a sense of tiredness that reduces decision-making abilities. To date, there remains a significant gap in classifying cognitive fatigue under the influence of mechanical massage via massage chair and binaural beats brain massage aided by functional Near-Infrared Spectroscopy. Our aim is to explore the impact of mechanical and binaural brain massage on cognitive fatigue recovery whilst carrying out an extensive comparative analysis of the efficacy of the existing Deep Learning (DL) models alongside conventional Machine Learning (ML) models. The experimental paradigm is consisted of two treatments: Treatment A (Control (General Rest) Group) and B (Experimental Group). Real-time data acquisition of 10 test subjects before and after both treatments is being done. Following a meticulous features extraction protocol, a comprehensive set of 8 DL and 8 ML models is utilized, and their performance is evaluated through a comparative analysis. The categorical results unequivocally demonstrate that Temporal Convolutional Network achieves superior performance by outperforming other DL models, boasting a remarkable accuracy of 97% and 96.52% for Treatment A and B, respectively. Likewise, Support Vector Machine with Radial Basis Function overtakes other ML models by yielding 91.00% and 87.50% accuracy for Treatment A and B, respectively. Upon evaluation of models' performance in Brain-Computer Interface application, it's been concluded that mechanical massage along with binaural beats significantly helps to relieve mental fatigue, enhance working memory, and mental vigilance.}, }
@article {pmid42265105, year = {2026}, author = {Gracia, DI and Iáñez, E and Ortiz, M and Azorín, JM}, title = {An Electrospinography Database of Gait-Related Tasks and Motor Imagery Exercises.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-026-07592-7}, pmid = {42265105}, issn = {2052-4463}, abstract = {This study presents a dataset of electrospinography (ESG) signals recorded from the human spinal cord during gait-related activities and motor imagery tasks. The dataset was acquired as part of a broader initiative to develop a spinal-machine interface (SMI) for closed-loop control of lower limb exoskeletons. ESG signals were collected using high-density surface electromyography (HD-sEMG) electrodes from fourteen able-bodied participants performing baseline trials (2), movement execution tasks (12) and motor imagery tasks conducted both in static conditions (5) and during movement (5). The dataset encompasses multiple electrode configurations targeting the brachial and lumbar plexuses, as well as surrounding musculature, across three experimental protocols. A total of 10 sessions were recorded for Experiment 1 (one 64-electrode matrix), 10 sessions for Experiment 2 (two 32-electrode matrices) and 5 sessions for Experiment 3 (two-32 electrode matrices). Preprocessing techniques were applied to mitigate cardiac and motion artifacts. The data provides a valuable and pionneering resource for advancing neurorehabilitation research, allowing the refining of exoskeleton control strategies and improving artifact removal methods.}, }
@article {pmid42265352, year = {2026}, author = {Busch, EL and Fincke, EC and Lajoie, G and Krishnaswamy, S and Turk-Browne, NB}, title = {Human learning of noninvasive brain-computer interfaces via manifold geometry.}, journal = {Nature neuroscience}, volume = {}, number = {}, pages = {}, pmid = {42265352}, issn = {1546-1726}, support = {1839308//National Science Foundation (NSF)/ ; 2139841//National Science Foundation (NSF)/ ; 2047856//National Science Foundation (NSF)/ ; R01MH069456//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; R01GM130847//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R01GM135929//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; FG-2021-15883//Alfred P. Sloan Foundation/ ; }, abstract = {Brain-computer interfaces (BCIs) promise to restore and enhance human capabilities. Yet, their adoption has been limited by slow and inconsistent learning across users. We show that BCI learning is accelerated by leveraging the naturally occurring geometry, or intrinsic manifold, of brain activity, extracted using data diffusion. Participants were trained with real-time functional magnetic resonance imaging to control an avatar in a video game by self-modulating activity in brain regions supporting spatial navigation. We perturbed the mapping between brain activity and avatar movement to test how neural manifolds constrain human BCI learning. When new mappings relied on directions of significant variance on the intrinsic manifold, participants successfully gained control by realigning brain activity along these directions. When new mappings did not follow the intrinsic manifold, participants could not learn to control the avatar. These findings show how manifold geometry in higher-order brain regions guides human learning of complex cognitive tasks, identifying a principle for improving future neurotechnologies.}, }
@article {pmid42265834, year = {2026}, author = {Ling, Y and Sun, P and Guo, T and Luo, B}, title = {"Digital eye tracking and plasma biomarkers: Distinguishing functional cognitive impairment from Alzheimer's disease biology".}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {22}, number = {6}, pages = {e71574}, doi = {10.1002/alz.71574}, pmid = {42265834}, issn = {1552-5279}, support = {2022C03064//the key Research and Development Program of Zhejiang/ ; 2025ZFJH01//the Fundamental Research for the Central Universities/ ; 2022KY067//Medical and Health Science and Technology Project of Zhejiang Province/ ; 82422027//National Natural Science Foundation of China/ ; U24A20340//National Natural Science Foundation of China/ ; }, }
@article {pmid42266219, year = {2026}, author = {Gu, T and Li, J and Chen, T and Pan, Y and Yu, Q and Sha, J}, title = {Effect of biofeedback electrical stimulation combined with HoLEP on surgical outcomes in patients with benign prostatic hyperplasia complicated with detrusor underactivity: a retrospective cohort study.}, journal = {Frontiers in surgery}, volume = {13}, number = {}, pages = {1847062}, pmid = {42266219}, issn = {2296-875X}, abstract = {OBJECTIVE: To investigate the clinical efficacy and safety of biofeedback electrical stimulation combined with holmium laser enucleation of the prostate (HoLEP) in the treatment of patients with benign prostatic hyperplasia (BPH) complicated by detrusor underactivity (DUA).<br><br>METHODS: A retrospective analysis was conducted on 100 patients with BPH and DUA who had surgical indications and were treated in the Department of Urology of our hospital from January 2023 to June 2025. Patients were divided into an intervention group (n&#x2009;=&#x2009;51) and a control group (n&#x2009;=&#x2009;49) according to the treatment modality they received. Patients in the intervention group underwent HoLEP followed by biofeedback electrical stimulation therapy (three times per week for a total of 10 sessions), whereas those in the control group received HoLEP alone. The International Prostate Symptom Score (IPSS), Quality of Life score (QOL), maximum urinary flow rate (Qmax), bladder contractility index (BCI), bladder outlet obstruction index (BOOI), maximum detrusor pressure (Pdetmax), post-void residual volume (PVR), voiding efficiency (VE), and postoperative complications were compared between the two groups before surgery and at 3 months postoperatively.<br><br>RESULTS: Baseline characteristics were comparable between the two groups (P&#x2009;>&#x2009;0.05). At 3 months postoperatively, the intervention group showed significantly higher Qmax (14.38&#x2009;&#xb1;&#x2009;1.47&#x2005;mL/s vs. 10.01&#x2009;&#xb1;&#x2009;0.85&#x2005;mL/s, P&#x2009;<&#x2009;0.001) and BCI (111.68&#x2009;&#xb1;&#x2009;10.15 vs. 93.96&#x2009;&#xb1;&#x2009;8.42, P&#x2009;<&#x2009;0.001), significantly lower IPSS (10.8&#x2009;&#xb1;&#x2009;1.9 vs. 18.6&#x2009;&#xb1;&#x2009;2.1, P&#x2009;<&#x2009;0.001) and QOL scores (2.1&#x2009;&#xb1;&#x2009;0.8 vs. 3.0&#x2009;&#xb1;&#x2009;0.6, P&#x2009;<&#x2009;0.001), significantly lower PVR (21.8&#x2009;&#xb1;&#x2009;5.8&#x2005;mL vs. 40.2&#x2009;&#xb1;&#x2009;7.5&#x2005;mL, P&#x2009;<&#x2009;0.001), and significantly higher VE (77.8&#x2009;&#xb1;&#x2009;6.2% vs. 61.9&#x2009;&#xb1;&#x2009;5.8%, P&#x2009;<&#x2009;0.001) compared with the control group. The proportion of patients achieving Qmax &#x2265;15&#x2005;mL/s at 3 months postoperatively was 39.2% in the intervention group vs. 20.8% in the control group (P&#x2009;=&#x2009;0.022). At 90 days postoperatively, the incidence rates of urinary tract infection (13.7% vs. 28.6%, P&#x2009;=&#x2009;0.047), urinary incontinence (9.8% vs. 24.5%, P&#x2009;=&#x2009;0.039), and indwelling catheter reinsertion (2.0% vs. 12.2%, P&#x2009;=&#x2009;0.037) were significantly lower in the intervention group than in the control group. No significant differences were observed in the incidence of postoperative bleeding or urethral stricture between the two groups (P&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: Biofeedback electrical stimulation combined with HoLEP significantly improves voiding function, clinical symptoms, and quality of life in patients with BPH and DUA, enhances bladder contractility, and reduces the risk of postoperative complications, offering clear clinical benefits and a favorable safety profile, warranting broader clinical adoption.}, }
@article {pmid42266278, year = {2026}, author = {Achanccaray, D and Clodic, A and Roy, RN}, title = {Error-related potentials detection to enhance human-robot collaboration: a mini review.}, journal = {Frontiers in neuroergonomics}, volume = {7}, number = {}, pages = {1769098}, pmid = {42266278}, issn = {2673-6195}, abstract = {Error-related potentials (ErrPs) have been studied to evaluate wrong decisions or actions in several contexts. An ErrP is an electrical potential on the scalp generated by the perception of errors and occurs unwittingly. In human-robot collaboration (HRC), ErrP detection can be used to trigger a feedback or an action to adapt the system to the user. This contributes to the improvement of HRC, taking into account user performance. However, to our knowledge, the detection of ErrPs in HRC has not been widely explored, resulting in only a few studies. This systematic review will present work on ErrP-based interfaces related to adaptation, control, and neuroergonomics for HRC. Thirteen articles were included after the exclusion criteria of the review stages. The average accuracy of ErrP detection was between 54 and 87.2%. In most cases, the authors simulated the occurrence of unexpected behavior of the robot. The robot mistakes occurred randomly between 20 and 35% of the total trials. Some works focused on the robot learning process and adaptation between humans and robots. The mental model and the robot behavior policy were updated based on the decoded ErrPs during collaborative interactions. Control-related works have included ErrPs detection/features as input inside the control loop or algorithm. Other studies assessed the influence of mental workload variability in the adaptation process, given that a high mental workload affects the cognitive processes needed to perceive errors. Thus, ErrPs present advantages for enhancing HRC, and this review opens the way to further developments in the robotic domain.}, }
@article {pmid42269199, year = {2026}, author = {Wei, J and Ye, H and Shao, B and Ma, L and Ding, Q and Zhou, Z and Cao, X and Zhong, J and He, H}, title = {A spatiotemporal dependency-aware lightweight CNN-ViT network for 3D MRF with a balanced acceleration strategy.}, journal = {Medical image analysis}, volume = {113}, number = {}, pages = {104147}, doi = {10.1016/j.media.2026.104147}, pmid = {42269199}, issn = {1361-8423}, abstract = {The push for rapid MRI acquisition aims to enhance clinical efficiency and diagnostic consistency by shortening scan times. 3D Magnetic Resonance Fingerprinting (MRF) has emerged as a promising technique for fast, multi-parametric quantitative imaging. However, its accuracy and relatively long acquisition time remain a limiting factor for clinical adoption. Accelerating MRF while preserving quantitative accuracy constitutes a crucial research objective. Deep learning approaches have recently been applied to accelerate MRF parameter quantification, but existing methods still exhibit notable limitations in both acceleration scheme design and the ability to model the complex contextual information embedded in MRF data. To address these limitations, we propose a lightweight spatiotemporal attention enhanced network (LiST-UNet) that integrates convolutional neural networks with lightweight Vision Transformer components to model long-range spatiotemporal dependencies in 3D MRF. A precursor-successor network is included to model interrelationships among tissue parameters, improving T2 quantification accuracy, while a balanced k-space and temporal-frame acceleration strategy significantly reduces errors compared with single-dimension undersampling schemes. Experimental results demonstrate that the proposed method enables whole-brain MRF imaging in approximately 1.25 min, achieving an eightfold acceleration over conventional 10-minute acquisitions with superior quantification accuracy and image quality compared to previously proposed deep learning methods. This work combines architectural improvements in MRF reconstruction with an acceleration strategy, supporting the future clinical translation of 3D MRF.}, }
@article {pmid42270932, year = {2026}, author = {Shen, JJ and Yang, YJ and Tang, YY and Yan, S and Ying, MD and Chen, WH and Xu, LL}, title = {m[6]A-modified Mid1 promotes sevoflurane-induced cognitive impairment in neonatal mice by ubiquitin-mediated degradation of Syngap1.}, journal = {Experimental &amp; molecular medicine}, volume = {}, number = {}, pages = {}, pmid = {42270932}, issn = {2092-6413}, abstract = {Investigating the cognitive effects of sevoflurane exposure during early development is essential due to its potential long-term neurodevelopmental impacts. This investigation systematically explored the molecular basis of sevoflurane-induced cognitive impairment, with emphasis on m[6]A RNA modifications and ubiquitin-dependent proteostasis involving Mid1 and Syngap1. Using integrated approaches, including methylated RNA immunoprecipitation sequencing (MeRIP-seq), transcriptomic profiling, neurobehavioural testing and molecular analyses, 2091 m[6]A methylation sites were identified that were differentially regulated. Mechanistically, Mid1 was found to orchestrate Syngap1 degradation via the ubiquitin-proteasome pathway, establishing a direct link between protein stability control and cognitive outcomes. Behavioural phenotyping demonstrated that Mid1 suppression ameliorated learning and memory deficits in sevoflurane-exposed mice, which was corroborated by improved neuronal viability and attenuated apoptotic signalling in biochemical assays. Epigenetic regulation studies further revealed that the m[6]A eraser ALKBH5 and the reader YTHDF2 collaboratively modulate Mid1 mRNA stability, thereby contributing to neuropathological progression. Pathway analysis uncovered Mid1-Syngap1 axis-mediated dysregulation of MAPK signalling cascades, proposing this network as a potential therapeutic target. Collectively, the present findings delineated a novel m[6]A-ubiquitin regulatory circuit centred on Mid1 that drives sevoflurane-associated cognitive dysfunction, offering mechanistic insights for the development of neuroprotective interventions against anaesthesia-related neurotoxicity in paediatric and other at-risk populations.}, }
@article {pmid42270979, year = {2026}, author = {Wang, J and Yang, C and Chang, S and Jiao, D and Lin, J and Yang, X and Cai, W and Ma, D and Ding, ZJ and Huang, J and Huang, J and Fan, M and Hu, M and Wang, Y and Xu, H and Su, N and Guo, J}, title = {Cryo-EM structures of Drosophila OR67d-Orco complexes reveal insect pheromone sensing mechanism.}, journal = {Cell research}, volume = {}, number = {}, pages = {}, pmid = {42270979}, issn = {1748-7838}, support = {32371204//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32421001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371300//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2024T170801//China Postdoctoral Science Foundation/ ; }, abstract = {Pheromones mediate intraspecific communication to regulate the physiology and behavior of animals, particularly insects. The detection of pheromones is initiated by the binding of pheromone molecules, e.g., 11-cis-vaccenyl acetate (cVA) in Drosophila, to specific receptor proteins in chemosensory neurons, but the underlying molecular mechanisms remain unclear. Here, we report structures of Drosophila pheromone receptor OR67d-Orco complexes in apo closed, pheromone-bound open, and synthetic agonist VUAA1-bound open conformations. OR67d and Orco assemble into a hetero-tetrameric channel with a 1:3 stoichiometry. In OR67d, the inverted L-shaped cVA or its analog binds into a deep and bent hydrophobic pocket, inducing both local and global conformational changes that lead to an asymmetrical opening of the channel gate. By comparison, VUAA1 binds to Orco instead of OR67d to cause a similar asymmetrical opening. Together, our studies reveal the structural basis for pheromone activation of hetero-tetrameric pheromone receptors.}, }
@article {pmid42271499, year = {2026}, author = {Shi, Z and Yuan, Z and Gao, L and Hu, Y and Ni, G and Liao, W and Xie, Y and He, J and Xiao, D and Chen, X and Wang, Z}, title = {Mixed reality assisted target localization for transcranial magnetic stimulation navigation: a feasibility study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-02045-z}, pmid = {42271499}, issn = {1743-0003}, support = {3502Z20254ZD1008//Xiamen Municipal Guiding Project for Medical and Health Services/ ; }, abstract = {BACKGROUND: Transcranial magnetic stimulation (TMS), as a non-invasive neurostimulation technique, modulates neural activity by applying electromagnetic fields to specific areas of the brain. It is clinically used for several approved indications, including major depressive disorder, obsessive‑compulsive disorder, and migraine with aura, and is under active investigation for other neurological and psychiatric conditions. Accurate stimulation targeting is crucial for the effectiveness of TMS. Existing targeting methods, such as generic brain localization caps and the international 10-20 electroencephalogram (EEG) system, generally provide only rough localization, leading to significant targeting errors. In recent years, significant progress has been made in the application of mixed reality (MR) technology in medicine, particularly in surgical navigation, offering new ideas and possibilities for developing a simple, low-cost, and efficient TMS navigation system.<br><br>OBJECTIVE: This study proposes, for the first time, a portable MR navigation system for non-invasive neural modulation target localization. The aim is to evaluate its localization accuracy and operational efficiency in TMS through preclinical validation. This system seeks to provide a simple and high-precision localization solution for other non-invasive technologies, with the goal of improving localization accuracy and simplifying the operational workflow in clinical applications.<br><br>METHODS: The system is based on Microsoft HoloLens 2 and features three specifically designed interaction tools. Five different types of simulation head models were selected, and ten target points were set on each head model. CT scanning was used to obtain imaging data for each head model. Three researchers used the system to perform target localization and repeated the verification process by adjusting the head model posture (from standing to lying) to assess localization accuracy and efficiency.<br><br>RESULTS: The validation conducted by the three researchers showed the following results: In the standing position of the simulated head model, the measurement errors were 2.4 (IQR: 1.4-2.7) mm, 2.3 (IQR: 1.7-2.7) mm, and 2.6 (IQR: 1.9-3.0) mm, respectively. In the lying position of the simulated head model, the measurement errors were 1.9 (IQR: 1.6-2.4) mm, 2.0 (IQR: 1.4-3.0) mm, and 2.5 (IQR: 1.9-2.9) mm, respectively. There was a significant difference between researchers (p&#x2009;<&#x2009;0.05), but no significant difference within the same researcher (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: The TMS-Guide, based on mixed reality technology, is a portable and simple navigation solution that provides higher localization accuracy than traditional manual targeting. It shows promising potential for broader applications in non-invasive neural modulation and brain-computer interface fields.}, }
@article {pmid42271590, year = {2026}, author = {Imura, T}, title = {[Communication Support for Neurological Disorders].}, journal = {Brain and nerve = Shinkei kenkyu no shinpo}, volume = {78}, number = {6}, pages = {701-705}, doi = {10.11477/mf.188160960780060701}, pmid = {42271590}, issn = {1881-6096}, abstract = {Communication support for patients with neurological disorders extends beyond high-technology communication aids. It also includes non-aided and low-technology methods and requires flexible selection and the combined use of these methods depending on the situation. Gaining experience with various communication strategies in a stepwise manner from an early stage enables the smoother introduction of advanced communication devices when necessary. Effective support must be tailored to the disease stage, as communication abilities and needs change over time. In this context, collaboration among multiple professionals is essential. Such interprofessional collaboration enables appropriate assessment, timely intervention, and continuity of care across disease stages. A team-based, continuous support system benefits patients, and caregivers and professionals involved in their care. By sharing knowledge, skills, and responsibilities within a support team, the burden on individual supporters can be reduced, and the quality and consistency of communication support can be enhanced. Looking to the future, further development of emerging technologies such as eye-gaze input systems, personalized speech synthesis, and brain-machine interfaces is highly anticipated. However, careful consideration of their characteristics, limitations, and potential risks is necessary to ensure their safe and effective use in clinical practice.}, }
@article {pmid42256857, year = {2026}, author = {Evetović, N and Rosipal, R and Polyanskaya, A and Rošťáková, Z and Dvornák, K and Vankó, M and Korečko, &#x160; and Trejo, LJ}, title = {EEG-based monitoring of mental fatigue during virtual-reality motor imagery tasks.}, journal = {Frontiers in behavioral neuroscience}, volume = {20}, number = {}, pages = {1810723}, pmid = {42256857}, issn = {1662-5153}, abstract = {Prolonged motor-imagery training in immersive virtual-reality environments can induce mental fatigue, reducing engagement and potentially limiting the effectiveness of neurorehabilitation. This study investigated neural markers of mental fatigue by recording electroencephalography (EEG) from healthy participants during extended motor-imagery and control sessions in a head-mounted display setup. Multidimensional analysis was applied to extract spectral, spatial, and temporal features while using a novel deflation step for removing task-related motor components to isolate fatigue-specific activity. Evidence of mental fatigue was consistently seen in parieto-occipital alpha-band modulation, with increases in alpha power corresponding to subjective reports and EEG-based measures of mental fatigue. The derived models were robust to common EEG artifacts and demonstrated consistent fatigue estimation across tasks and sessions. These findings suggest that individualized neural markers can enable real-time monitoring of fatigue (with an accuracy of 83.49 ± 6.34%), allowing adaptive adjustments of task difficulty or pacing in brain-computer interface systems. This work advances understanding of the neurophysiological dynamics of mental fatigue during immersive motor-imagery tasks and provides a foundation for designing more effective, personalized neurorehabilitation protocols.}, }
@article {pmid42258675, year = {2026}, author = {Wang, Z and He, X and Wang, H and Wu, D}, title = {CKD: Contrastive Knowledge Distillation for Cross-Dataset EEG Classification.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3701548}, pmid = {42258675}, issn = {1558-2531}, abstract = {OBJECTIVE: Cross-dataset transfer in electroencephalography (EEG)-based brain-computer interfaces (BCIs) remains challenging due to substantial distribution shifts across datasets, including differences in subjects, acquisition devices, and recording protocols. This study aims to improve cross-dataset EEG decoding by enhancing knowledge transfer beyond conventional output-level distillation.<br><br>METHODS: We propose a contrastive knowledge distillation (CKD) framework for cross-dataset EEG classification. CKD follows a two-stage transfer strategy, consisting of cross-dataset teacher pretraining and cross-subject online adaptation, and jointly exploits logit-level distillation and feature-level contrastive alignment. In this way, the student model is encouraged to inherit both the predictive behavior and representation structure of the teacher.<br><br>RESULTS: Experiments on five motor imagery EEG datasets showed that CKD consistently outperformed twelve conventional training, representative knowledge distillation, and domain adaptation baselines under both single-source and multi-source transfer settings. Additional visualizations and quantitative analyses further confirmed that CKD improves teacher-student alignment in terms of feature geometry and distribution consistency, and can be further enhanced by explicit domain adaptation.<br><br>CONCLUSION: The proposed CKD provides an effective solution for cross-dataset EEG decoding by jointly improving predictive knowledge transfer and latent feature alignment under severe dataset shifts.<br><br>SIGNIFICANCE: This work improves the robustness and generalizability of EEG decoding across heterogeneous datasets, which is important for practical BCI deployment under real-world acquisition conditions.}, }
@article {pmid42259065, year = {2026}, author = {Motegi, M and Chikamatsu, K}, title = {Management of conductive and mixed hearing loss intolerant to air-conduction hearing aids: A stepwise algorithm and narrative review from a Japanese perspective.}, journal = {Auris, nasus, larynx}, volume = {53}, number = {4}, pages = {567-578}, doi = {10.1016/j.anl.2026.05.001}, pmid = {42259065}, issn = {1879-1476}, abstract = {OBJECTIVE: Conductive and mixed hearing loss in the complicated ear, including chronically inflamed ears with recurrent otorrhea, postoperative cavities, tympanic membrane lateralization, canal stenosis/atresia, and congenital malformations, remains a frequent, consequential problem in cases where conventional air-conduction hearing aids (ACHAs) are unusable or provide insufficient functional benefits. The expansion of the therapeutic landscape from non-implantable (e.g., cartilage conduction hearing aids [CCHA] and adhesive bone-conduction systems) to implantable (e.g., bone-conduction implants [BCIs] and active middle ear implants [Vibrant Soundbridge[®︎,] VSB]) options has not only increased opportunities for personalized rehabilitation but also created a practical "paradox of choice." Japan provides a distinctive clinical context because major implantable auditory devices are reimbursed under defined indications, whereas access to non-implantable options frequently depends on out-of-pocket purchases and/or subsidy programs.<br><br>METHOD: This Japan-based narrative review synthesized peer-reviewed evidence and integrated the domestic indication framework to propose a pragmatic, stepwise device-selection algorithm for complicated ears with conductive or mixed hearing loss. Step 1 comprises a Gatekeeper Trial using a non-surgical option (e.g., CCHA/adhesive systems or headband/soft band stimulation) to confirm real-world benefits, identify coupling-related limitations, and provide counseling. Step 2 categorizes the cochlear reserve into zones A, B, or C based on bone-conduction thresholds to align the device output capacity with the inner-ear reserve; this step also incorporates Japan-aligned indications and a high-frequency "B-C border" flag (e.g., >65&#xa0;dB HL at high frequencies) that can shift the balance between BCI and VSB. Step 3 applies clinically decisive modifiers: ear status and infection-control strategy, imaging-based surgical feasibility, high-frequency listening demands, and patient priorities, such as cosmesis, skin tolerance, maintenance burden, and MRI considerations.<br><br>RESULTS: Asymmetric hearing loss is managed as a dedicated differentiator. When appropriate, BCI-mediated transcranial stimulation can add useful contralateral cochlear access to improve speech perception in relevant spatial noise configurations, whereas counseling emphasizes situation-dependent benefits and limited binaural restoration.<br><br>CONCLUSION: Finally, we introduce the concept of Device Readiness Surgery, reframing otologic surgery as a staged effort to achieve a safe, dry, and stable ear that enables ACHA use whenever realistic; when ACHA remains ineffective, the ear is optimized for the selected device. This review provides a clinically oriented roadmap to improve the consistency of counseling and device selection in complicated ears and highlights the priorities for prospective validation and comparative-effectiveness research.}, }
@article {pmid42259101, year = {2026}, author = {Ghafoori, S and Cetera, A and Rabiee, A and Farhadi, MH and Singh, R and Furmanek, M and Shahriari, Y and Abiri, R}, title = {Cross-frequency bispectral EEG analysis of reach-to-grasp planning and execution.}, journal = {Computers in biology and medicine}, volume = {213}, number = {}, pages = {111791}, doi = {10.1016/j.compbiomed.2026.111791}, pmid = {42259101}, issn = {1879-0534}, abstract = {Neural motor control of reach-to-grasp emerges from complex, nonlinear interactions across multiple brain cortices. However, most electroencephalography (EEG)-based motor analysis has largely relied on linear and second-order spectral measures. Here, we investigate whether higher-order cross-frequency dynamics encode meaningful distinctions between motor planning and execution during natural reach-to-grasp movements. Using a cue-based experimental paradigm, EEG was recorded during precision and power plan-to-grasp tasks, enabling stage-resolved analysis of grasp planning and execution-related neural activity. Cross-frequency bispectral analysis was applied to compute complex bicoherence matrices across canonical frequency band pairs, from which magnitude- and phase-based features were extracted. Classification, permutation-based feature selection, and within-subject statistical testing revealed that execution is associated with stronger nonlinear coupling than planning, with dominant contributions from β- and γ-driven interactions. In contrast, decoding of precision versus power grasps showed similar performance across stages, indicating that grasp-type representations emerge during planning and persist into execution. Exploratory single-feature analyses further identified focal, stage-dependent modulation of nonlinear coupling in central motor regions. Informative bispectral features reflected coordinated activity across prefrontal, central, and occipital areas, while feature redundancy enabled dimensionality reduction without loss of performance. Compared with the conventional analytical methods as baselines, bispectral features provided consistent advantages for grasp-type discrimination and multiclass classification, highlighting the value of nonlinear cross-frequency analysis. In summary, our results extend bispectral analysis to clinically relevant grasping tasks and highlight nonlinear cross-frequency coupling as an informative marker of motor stages offering a foundation for future BCI and neuroprosthetic research.}, }
@article {pmid42259478, year = {2026}, author = {Chen, Y and Li, Z and Wang, Y and Li, Y and Zheng, J and Yang, H and Liu, M and Cukur, T and Fan, Q and Li, Z and Lu, J and Tian, Q}, title = {MicroKAN: Mapping Human Brain Microstructure Using Diffusion MRI and Adaptive Nonlinear Modeling.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {122032}, doi = {10.1016/j.neuroimage.2026.122032}, pmid = {42259478}, issn = {1095-9572}, abstract = {Diffusion magnetic resonance imaging (dMRI) provides powerful insights into brain microstructure, but conventional microstructural modeling methods require long acquisition times for covering sufficient diffusion directions and are computationally intensive. While deep learning has shown promise in reducing the direction requirement and accelerating the modeling, traditional architectures such as CNNs often struggle to capture the highly nonlinear relationships between multi-shell diffusion signals and microstructural properties. We present MicroKAN, a novel framework built upon Kolmogorov-Arnold Networks with adaptive spline-based activations, specifically designed to represent complex biophysical models with enhanced flexibility and efficiency. MicroKAN supports both supervised and self-supervised paradigms: the supervised variant learns mappings from data to reference metrics, while the self-supervised variant estimates model parameters directly by reconstructing signals through the forward diffusion process, eliminating the need for ground-truth labels. Evaluated on diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) across multiple datasets, MicroKAN substantially accelerates acquisition and improves the fidelity of microstructural parameter estimation. Beyond supervised training, its self-supervised formulation shows strong robustness to distribution shifts, enabling reliable performance even without annotations. Furthermore, transfer learning with minimal labeled data preserves high accuracy, underscoring the framework's adaptability to diverse scenarios. These advances establish MicroKAN as a versatile and efficient tool for dMRI analysis, offering new opportunities to accelerate neuroscience research and expand the clinical utility of microstructural imaging. Our source code is available at https://github.com/JustlfC03/MicroKAN.}, }
@article {pmid42259992, year = {2026}, author = {Fan, Y and Gao, L and Lin, Z and Zhang, T and Bai, X and Chen, L}, title = {The effect of brain-computer interface training on cognitive function in stroke patients: a systematic review and meta-analysis.}, journal = {Journal of neurology}, volume = {273}, number = {7}, pages = {}, pmid = {42259992}, issn = {1432-1459}, support = {82471345//National Natural Science Foundation of China/ ; 2024-LCYJ-MS-17//Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University/ ; NDYGN2025005//Aid project ofJiangsu Ningai Medical Development &amp;Medical Aid Foundation/ ; }, abstract = {OBJECTIVE: This study aims to assess the therapeutic impact of BCI-based interventions on global and domain-specific cognitive functions (attention, memory, and executive function), and activities of daily living in stroke survivors. Furthermore, we seek to identify the potential moderating effects of feedback modes and BCI paradigms on the overall rehabilitative efficacy.<br><br>METHODS: A systematic search of PubMed, Embase, Web of Science, the Cochrane Library, and CNKI databases was conducted to identify eligible randomized-controlled trials (RCTs). Meta-analyses were performed by pooling standardized mean differences (SMDs) to synthesize effect sizes. To explore sources of heterogeneity and the effects of potential moderators, subgroup analyses were conducted according to outcome measures, stroke phase, BCI paradigm, and feedback type.<br><br>RESULTS: Twelve studies were included. The meta-analysis demonstrated that BCI training significantly improved global cognitive function (SMD&#x2009;=&#x2009;0.62, P&#x2009;<&#x2009;0.00001), attention, and executive function, alongside enhanced activities of daily living performance. However, no significant improvement was observed in memory function. Subgroup analyses revealed that superior and more robust effects were associated with subacute patients, active BCI paradigms, and multimodal feedback (visual&#x2009;+&#x2009;auditory&#x2009;+&#x2009;proprioceptive).<br><br>CONCLUSION: BCI training is an effective intervention for post-stroke cognitive recovery. Early initiation of therapy and the integration of multimodal feedback appear to be critical factors for maximizing therapeutic outcomes.}, }
@article {pmid42260169, year = {2026}, author = {Su, Z and Gan, KB and Sim, KS}, title = {Decoding Upper-Limb Motor Imagery from EEG Signals: A Systematic Review of Methods and Applications.}, journal = {Annals of biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {42260169}, issn = {1573-9686}, support = {FRGS/1/2024/TK07/UKM/02/14//Ministry of Higher Education, Malaysia/ ; }, abstract = {Brain-computer interfaces (BCIs) have emerged as a promising technology with significant potential across various domains in recent years, including healthcare, industry, and entertainment. Among the many BCI paradigms, motor imagery (MI) based on electroencephalography (EEG) is one of the most commonly used and has been widely applied in medical settings. However, due to the inherently low signal-to-noise ratio and non-stationary nature of EEG signals, current decoding accuracy remains suboptimal-particularly in the classification of movements involving the same limb, where finer motion distinctions and higher decoding precision are urgently needed. This review summarizes the research on upper-limb MI-EEG classification and applications over the past 5 years and analyzes the relevant data extracted from the literature. The objective is to provide a comprehensive overview of the current state of research on decoding hand motor imagery from MI-EEG signals and to examine the challenges encountered in practical applications. We systematically investigate state-of-the-art methods, compare their performance and underlying assumptions, and discuss emerging trends and open challenges. Furthermore, we explore how these decoding methods can be translated into real-world applications, highlighting their potential as well as their limitations. The aim of this work is to provide valuable insights and guidance for researchers and developers in the field of EEG-based BCIs.}, }
@article {pmid42256276, year = {2026}, author = {Yu, M and Guo, T and Han, S and Xue, N and Yang, W and Huang, J and Chen, H and He, C and Ding, J and Xia, L}, title = {Integrating metacognitive mechanisms optimizes EEG generative models via hierarchical regularization.}, journal = {iScience}, volume = {29}, number = {6}, pages = {115785}, pmid = {42256276}, issn = {2589-0042}, abstract = {Obtaining sufficient electroencephalography (EEG) signals for training deep neural networks (DNNs) in brain-computer interfaces (BCIs) is challenging due to individual differences in neural activity, which require large per-participant data to map signals to actions, while factors like movement artifacts often limit data collection. Existing advances mainly leverage generative models with various regularizers to produce sufficient EEG signals. However, selecting appropriate regularizers remains challenging. Inspired by metacognition, the human cognitive process that monitors and regulates learning and decision-making, we propose a metacognitive regulation module including three regularizers that explicitly capture EEG temporal dynamics and functional resolution, thereby improving both the diversity and similarity of generated data. Through extensive theoretical and empirical validation on two datasets, we demonstrate that our module: (1) significantly improves generative models for generating highly complex, realistic EEG activity; (2) improves generalization across different generative models; and (3) endows DNN models with enhanced human-like decision-making and adaptation capabilities.}, }
@article {pmid42256671, year = {2026}, author = {Enériz, D and Antolín, D and Medrano, N and Calvo, B}, title = {Reproducible testing for embedded BCIs: a demultiplexing PCB and acquisition system for EEG signal emulation.}, journal = {HardwareX}, volume = {26}, number = {}, pages = {e00800}, pmid = {42256671}, issn = {2468-0672}, abstract = {Validating machine learning models for Brain-Computer Interfaces (BCIs) on resource-constrained edge devices is challenging, as traditional methods rely on costly EEG equipment or simulations that fail to capture real-world electronic characteristics. To bridge this gap, we introduce the DEEGMUX, a low-cost, open-source hardware system for high-fidelity, hardware-in-the-loop (HIL) testing of EEG classification algorithms. The system comprises an EEG Demultiplexer Board that converts a multiplexed EEG signal into 8 parallel channels, and an EEG Acquisition and Processing Board featuring an ADS1299 24-bit ADC interfaced with an Arduino Nano 33 BLE. This setup enables the use of real EEG datasets, such as the PhysioNet Motor Imagery dataset, to generate precisely timed electronic signals. Characterization demonstrated high signal fidelity, with a Mean Squared Error of 1.7·10[-10] V[2] and a Signal-to-Noise Ratio of 16 dB relative to the original digital data. Furthermore, an EEGNet motor imagery classifier evaluated on hardware-acquired signals showed a negligible accuracy difference of (-0.3 ± 5)% compared to evaluation on the original data, confirming that the emulation chain preserves classification-relevant features. The DEEGMUX provides a scalable, reproducible, and affordable platform for rigorously testing edge-deployed CNN models against realistic electronic inputs, accelerating the transition from simulation to robust real-world BCI deployment.}, }
@article {pmid42239131, year = {2026}, author = {Yang, L and Zhang, J and Wang, J and Huang, HH and Han, H and Razansky, D and ,  and Rominger, A and Lu, J and Ni, R}, title = {Divergent scalp-to-region distance alteration patterns in autism spectrum disorders, Parkinson's disease and Alzheimer's disease.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.05.14.725296}, pmid = {42239131}, issn = {2692-8205}, abstract = {UNLABELLED: Brain stimulation is increasingly recognized as an effective and important therapeutic intervention for many brain diseases. Distance between the scalp and other brain regions is a pivotal variable for neurostimulation planning and the development of new techniques, but alterations in the distance between the scalp and other regions in brain diseases are largely unknown. In this study, we developed an automatic pipeline to calculate scalp-to-region distance (SRD) values from T1 MR images and applied it to a total of 1382 participants, including patients with autism spectrum disorder (ASD), Parkinson's disease (PD), Alzheimer's disease (AD), and cognitively normal controls (CNs). Cloud points were uniformly sampled on the automatically extracted scalp surface and cortex surface, on which the point-wise distance maps were generated. The brain was then coregistered with the BCI-DNI atlas, and SRD value for each brain region was extracted. Analysis of covariance (ANCOVA) was performed for SRD in each brain region, with age and sex as covariates. Compared with CNs, ASD patients showed widespread SRD decreases across the brain with prominent involvement of the frontal lobe, especially the orbitofrontal cortex and adjacent regions. In contrast, in AD patients, significantly increased SRD values were observed in various regions of the frontal gyrus. No significant SRD alteration was found in PD patients after correction. The automatic SRD calculation pipeline and the different patterns of SRD alterations in these diseases might be helpful for future neurostimulation planning in clinical practice.<br><br>HIGHLIGHTS: Automatic pipeline enables scalp-to-region distance (SRD) measurement, facilitates brain stimulation planning.ASD patients show widespread SRD decreases, especially in the orbitofrontal cortex and adjacent regions.AD patients present increased SRD in the frontal gyrus and decreased SRD in the parahippocampal gyrus.}, }
@article {pmid42239466, year = {2026}, author = {Welton, TA and Currie, T and Fontaine, A and Caldwell, J and Weir, RF and Restrepo, D and Gibson, EA}, title = {Multi-site temporal control of optogenetic stimulation enhances firing frequencies in peripheral nerves.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.05.15.724667}, pmid = {42239466}, issn = {2692-8205}, abstract = {We find that multi-site temporal control of optogenetic photostimulation in peripheral nerves can enhance firing rates by overcoming the intrinsic limitation of opsin photophysics. The benefits of multi-site optogenetic stimulation were demonstrated with three approaches: (1) in silico modeling, (2) ex vivo in the sciatic nerve, and (3) in vivo in the vagus nerve. An in silico model of multi-site optogenetic stimulation was developed in two Hodgkin and Huxley type neuron models, that supported our hypothesis. The ex vivo sciatic nerve showed an increase in firing frequency that is physiologically relevant for functional control. The technique was then applied in vivo for optogenetic vagus nerve stimulation resulting in significant changes in heart rate compared with standard methods of single-site stimulation. Improving the control of optogenetically induced neural firing will have broad impacts for future developments in optical nerve interfaces and brain-machine interfaces.}, }
@article {pmid42243238, year = {2026}, author = {Geng, X and Xiong, Z and Yu, P and Dai, T and Wu, S and Wang, H}, title = {ZMW-RSVP: a time-frequency prior-guided normalization-free RSVP-BCI decoding model.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-56317-8}, pmid = {42243238}, issn = {2045-2322}, support = {No. 20240404059ZP//Jilin Province Science and Technology (S&amp;T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&amp;T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&amp;T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&amp;T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&amp;T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&amp;T)/ ; }, abstract = {Rapid Serial Visual Presentation (RSVP)-based brain-computer interfaces (BCIs) are valuable for target detection and rehabilitation because they require no motor involvement and can elicit reliable event-related potentials (ERP). However, single-trial EEG decoding remains challenging due to low signal-to-noise ratio and substantial inter-subject variability. To address the limitations of conventional approaches that rely on handcrafted features and to further explore internal feature transformation in single-trial ERP decoding, this paper proposes an RSVP-BCI decoding model, termed ZMW-RSVP, by extending a time-frequency Transformer framework with oscillatory gated attention and a normalization-free dynamic activation network. The proposed model incorporates neuroscience-inspired priors through a multi-band oscillatory gating mechanism, with an emphasis on theta-related activity associated with RSVP/P300 processing, and further guides attention allocation via a time-window bias. In addition, the model replaces internal LayerNorm modules with Dynamic Tanh, which provides an element-wise internal transformation without explicit mean-variance normalization and is evaluated as a task-related alternative for ERP-related discriminative feature modeling. Experiments on the Tsinghua RSVP dataset and the NeuBCI Target Retrieval RSVP-EEG dataset demonstrate that ZMW-RSVP achieves improved classification performance under the evaluated cross-subject settings, validating the effectiveness of the proposed approach for single-trial RSVP decoding.}, }
@article {pmid42243338, year = {2026}, author = {Ga, YJ and Yeh, JY}, title = {Eradication of Mycoplasma contamination in HeLa cells using neomycin resistance gene introduction and aminoglycoside G418 (Geneticin) treatment.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-55513-w}, pmid = {42243338}, issn = {2045-2322}, support = {RS-2025-02307583//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry/ ; }, abstract = {Mycoplasma contamination remains a persistent problem in continuous cell culture, compromising cellular physiology, altering gene expression, and potentially leading to erroneous experimental conclusions. Mycoplasmas can profoundly affect cultured cells, underscoring the need for efficient eradication strategies. Here, we developed a simple and effective method to eliminate Mycoplasma from HeLa cell lines. A neomycin resistance gene was introduced into Mycoplasma-contaminated cells, conferring resistance to aminoglycoside-induced cytotoxicity. Subsequently, a high concentration of the neomycin analogue G418 (Geneticin), combined with single-cell cloning, was applied to achieve complete removal of the Mycoplasma contamination. Mycoplasma presence and clearance were confirmed by PCR targeting the 16 S rRNA gene and immunofluorescence using a Mycoplasma-specific monoclonal antibody. This genetic-antibiotic combination proved technically simple and highly effective for long-term Mycoplasma eradication in continuous cell lines. To investigate the functional impact of Mycoplasma contamination, we compared protein expression following transient transfection with GFP and FAM-labeled reporters in Mycoplasma-eradicated versus Mycoplasma-contaminated cells. Fluorescence analysis revealed a marked increase in transfection efficiency and reporter expression in Mycoplasma-eradicated cells. Our findings provide an effective strategy for eliminating Mycoplasma contamination in cell line cultures, ensuring the reliability of cell-based research and the accuracy of experimental data.}, }
@article {pmid42245765, year = {2026}, author = {Shivakumar, D and Gupta, CN and Hazra, B}, title = {Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.}, journal = {Frontiers in neuroinformatics}, volume = {20}, number = {}, pages = {1823408}, pmid = {42245765}, issn = {1662-5196}, abstract = {Understanding the temporal organization of brain activity requires methods that capture scale-free dynamics while accounting for the high-dimensional, spatially correlated nature of the electroencephalogram (EEG) data. We propose a novel framework that integrates nonlinear manifold learning (Isometric mapping) with detrended fluctuation analysis (DFA) to quantify long-range temporal correlations (LRTC) in the alpha-band of EEG signals. We applied this framework to two music related EEG datasets, as music is known to evoke different emotions and synchronize brain activity. The first dataset was obtained during live Indian classical music (ICM) listening that included two ragas, Yaman and Puriya Dhanashree. EEG was recorded from 13 healthy volunteers (24 channels, sampled at 500 Hz). The second dataset is the Music BCI dataset (006-2015), which includes Jazz and Synth-pop musical clips, with EEG collected from 11 subjects (64 channels, downsampled to 200 Hz). The EEG data from both datasets were preprocessed, band-limited to 8-13 Hz, and segmented into non-overlapping 2-s windows. Alpha-band power was extracted from each channel to form the feature matrix used for embedding. For the ICM dataset, Isometric mapping (Isomap) produced a low-dimensional representation (d = 3), which we analyzed using two approaches: (i) a norm-based approach and (ii) a mean-based approach. For comparison, an equivalent PCA-based pipeline (d = 5) was implemented. The Isomap mean-based DFA yielded consistent scaling exponents (α) in the range of 0.66-0.70, with higher goodness-of-fit (R [2]) and narrower bootstrap confidence intervals than the norm-based approach. PCA produced similar trends but required more dimensions. Paired t-tests showed that the Isomap mean-based approach detected music-related changes more sensitively than PCA (Yaman p = 0.02; Puriya Dhanashree p = 0.008). Comparable results were also observed for the second Music BCI dataset, where Isomap achieved a compact representation with d = 5, compared to d = 8 for PCA. In this dataset as well, the mean-based DFA yielded α values in the range of 0.62-0.65 and higher goodness-of-fit. Overall, the results suggest that combining nonlinear manifold embeddings with mean-based DFA provides a compact and robust framework for characterizing scale-free temporal structure in EEG data.}, }
@article {pmid42246084, year = {2026}, author = {Fischer, T and Middell, E and Moradi, S and von Lühmann, A}, title = {fNIRS Single-trial decoding improves systematically with higher optode density, model-based noise regression, and image reconstruction.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae782f}, pmid = {42246084}, issn = {1741-2552}, abstract = {Advances in high-density diffuse optical tomography (HD-DOT) promise to overcome long-standing performance limitations in classification of sparse functional Near-Infrared Spectroscopy (fNIRS) signals, but their combined impact on single-trial brain decoding and generalization remains largely unquantified. Here, we systematically evaluate how probe density, physiology removal via short-separation (SS) regression within a general linear model (GLM), and image-space feature representations aligned with brain parcellation schemes shape single-trial decoding accuracy. To enable a structured investigation and validation via realistic ground truth data, we introduce a flexible, easy-to-use framework that allows users to augment their own channel space resting-state fNIRS data with configurable synthetic hemodynamic response functions (HRFs) on target areas of the brain, using a state-of-the art diffuse optical forward model. Using three high-density fNIRS datasets -a whole-head resting-state recording augmented with synthetic HRFs and two motor ball-squeezing datasets -we derive sparse-to-HD optode subsets, integrate GLM-based SS regression into cross-validation, and compare channel-space and parcel-space features derived from HD-DOT image reconstructions. High-density configurations consistently and significantly improve classification accuracy and robustness to focal activations. SS correction yields systematic gains of 4 percentage points in within-subject decoding and more than 10 percentage points in cross-dataset transfer. Parcel-space features outperform channel-space features at matched dimensionality, enabling robust leave-one-subject-out decoding (mean accuracy 79%) and cross-dataset generalization across different probe layouts (72% with SS correction). All methodology is implemented and available in the open-source Cedalion framework. Together, these results demonstrate that HD-DOT, GLM-based SS regression, and parcel-space representations jointly enable significantly more accurate, robust, and probe-independent fNIRS classification pipelines.}, }
@article {pmid42246470, year = {2026}, author = {Grill, WM and Chestek, CA and Wang, Y and Aberra, AS and Destexhe, A and Chan, RHM and Lu, BL and Hanein, Y and Robinson, JT and Whitmire, C and Song, AW and Farina, D and Ferris, DP and Green, R and Goding, J and Asplund, M}, title = {A Roadmap to Navigate the Future of Neural Engineering.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae78c2}, pmid = {42246470}, issn = {1741-2552}, abstract = {A group of leaders in neural engineering collaborated to develop a roadmap to navigate the future of neural engineering. We covered a range of themes, including brain machine interfaces, neural modelling, artificial intelligence and machine learning, neural interfaces, neural imaging, augmented rehabilitation, and neuromaterials. For each topic we reviewed the current status, identified current and future challenges, and speculated on the emerging and necessary advances in science and technology to meet these challenges. Neural engineering will continue to yield the approaches and insights that advance the diagnosis and treatment of nervous system disorders, as well as provide new understanding of neural function. .}, }
@article {pmid42250530, year = {2026}, author = {C, S and C, S and S, IJ}, title = {Efficient FPGA accelerator for low-power high-speed BCI motor imagery classification using novel deep learning.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {203}, number = {}, pages = {109105}, doi = {10.1016/j.neunet.2026.109105}, pmid = {42250530}, issn = {1879-2782}, abstract = {A brain-computer interface (BCI) is an advanced technology that enables direct communication between the human brain and external systems, eliminating the need for intermediaries. Electroencephalography (EEG) is a commonly used signal for developing BCIs. However, EEG signals have challenges such as a poor signal-to-noise ratio, high dimensionality, nonlinearity, and instability. This necessitates the development of an automated system using deep learning (DL) models for motor imagery (MI) classification. Many researchers have worked on MI classification and developed various algorithms; however, several issues remain unsolved, including achieving high accuracy for EEG data across all groups and unseen data, effective feature extraction, and deploying DL models on edge devices with low power consumption and high-speed MI classification. To address these challenges, a novel DL model, Few-Shot Learning (FSL)-Dual Attention-based SqueezeNet, is designed and tested. FSL enables learning MI classification with a small amount of data and improves accuracy on unseen data. SqueezeNet, combined with a Dual Attention Mechanism (DAM), effectively extracts important temporal and frequency features from EEG signals with low computational cost. The proposed network is evaluated on the BCI Competition IV 2a dataset under intra-session, cross-subject, and inter-session settings. It achieves accuracies of 0.9704, 0.8702, and 0.9568, respectively, outperforming well-known DL models such as EfficientNet (0.9426 in intra-session). Further comparisons with existing methods demonstrate competitive and consistent performance across different evaluation protocols. For generalizability analysis, the proposed network is tested on other public datasets. The proposed network achieves an accuracy of over 98% across all datasets, proving its effectiveness for MI classification using EEG signals. Next, a hardware accelerator is designed to deploy the proposed network on edge devices. The hardware is optimized for fast MI detection and low power consumption by employing a dual-core DPU and a dual-buffer scheme. The proposed accelerator's performance and hardware utilization are analyzed. The hardware design consumes only 12.14 W, which is 4.8 and 6.3 times lower than CPU and GPU power consumption, respectively. It performs MI classification in just 5.01 ms, significantly faster than CPU and GPU inference times. The proposed DL network and hardware accelerator demonstrate that the framework is well-suited for real-time deployment in MI classification tasks.}, }
@article {pmid42250823, year = {2026}, author = {Márton, A and Benke, J and Markus, M and Hoheisel, W and Bartsch, J and Thommes, M}, title = {Promising advancements to the blue dye ingress test - Quantification of blister integrity by leakage rate.}, journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V}, volume = {}, number = {}, pages = {115140}, doi = {10.1016/j.ejpb.2026.115140}, pmid = {42250823}, issn = {1873-3441}, abstract = {The most common packaging type for solid dosage forms is the blister package. The critical quality attribute of blisters is the integrity, which is required to be tested. Hereby it is crucial to develop methodologies representing an improvement compared to the current standard, the blue dye ingress test, regarding sensitivity limits and quantification. In this study, two analytical methods (optical emission spectroscopy and a helium mass spectrometry, which rely on a similar principle), were characterized. For the latter a sample preparation procedure was also developed for filling the blister packages with helium tracer gas. Leaky blister packages were prepared via laser drilling, and the leakage rate was measured. Quantification within the experimental space was found to be feasible using optical emission spectroscopy, and partially feasible using helium mass spectrometry. Furthermore, the repeatability was examined and the measurement results were verified with physical and empirical models describing the molecular flow. In conclusion, the two characterized methods represent promising competition to the established standard test due to quantification. Additionally, the procedures can serve as a sensitive reference method for development as well as production.}, }
@article {pmid42253789, year = {2026}, author = {Heskebeck, F and Bernhardsson, B and Bergeling, C}, title = {Rotation-based metric on the Riemannian manifold of SPD matrices with applications to source data selection for brain-computer interface transfer learning.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1824613}, pmid = {42253789}, issn = {1662-5161}, abstract = {This paper introduces the pole ratio metric and presents a sphere-based view of symmetric positive-definite matrix rotations on the Riemannian manifold of symmetric positive-definite matrices equipped with the affine-invariant Riemannian metric. The pole ratio quantifies whether data from different users lie on this Riemannian manifold in a way that enables effective transfer learning. The sphere-based view provides insight into the rotational step of transfer learning using the Riemannian Procrustes analysis method and highlights the limitations of rotation. For effective transfer learning, selecting appropriate source data is essential for good performance. The pole ratio is shown to be an effective metric for selecting source data. The main contribution of the paper is the insight into the limitations of rotations on a Riemannian manifold; the usefulness of the pole ratio as a source selection metric is a natural extension of this insight. This paper focuses on Brain-Computer Interfaces (BCIs), but the sphere-based view of rotations of symmetric positive-definite matrix data and the pole ratio are applicable to any field that models two-class data using symmetric positive-definite matrices.}, }
@article {pmid42253794, year = {2026}, author = {Melby, SR and Asok Kumar, JN and Bigus, ER and Kellis, S}, title = {Clinical evaluation of communication brain computer interfaces in amyotrophic lateral sclerosis: a landscape analysis.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1771146}, pmid = {42253794}, issn = {1662-5161}, abstract = {INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that leads to severe motor impairment, including loss of communication ability, and ultimately death. Communication brain computer interfaces (cBCIs) have the potential to restore communication without reliance on motor function, thereby improving quality of life, independence, and palliative care. However, standardized methods to evaluate cBCI efficacy necessary for clinical implementation are not yet established.<br><br>METHODS: We conducted a systematic literature review, semi structured interviews with key opinion leaders (KOLs), and a clinical assessment review panel to (1) identify clinical outcome assessments (COAs) relevant to cBCIs in ALS, (2) obtain expert feedback, and (3) synthesize the current clinical and scientific landscape.<br><br>RESULTS: A total of 21 COAs were identified as potentially relevant and may serve as a foundation for cBCI specific measures. However, no existing COA was found to comprehensively capture the clinical benefit or functional impact of cBCIs in ALS.<br><br>DISCUSSION: Current COAs are insufficient to evaluate cBCIs in ALS, highlighting a critical gap. Development of cBCI specific outcome measures is needed to support clinical validation, regulatory evaluation, and adoption.}, }
@article {pmid42253796, year = {2026}, author = {Kojima, S and Kanoh, S}, title = {The ASME-speller: 30-class auditory brain-computer interface speller using stream segregation and the QWERTY layout.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1807535}, pmid = {42253796}, issn = {1662-5161}, abstract = {INTRODUCTION: This study presents the ASME-speller, a novel 30-class auditory brain-computer interface (BCI) speller system that combines auditory stream segregation with the familiar QWERTY keyboard layout to facilitate intuitive and visionfree communication.<br><br>METHODS: In the ASME-speller, three distinct auditory streams are presented simultaneously, each corresponding to a row on the QWERTY keyboard. The low-, middle-, and high-frequency streams represent the bottom, middle, and top rows, respectively. Within each stream, alphabet letters and selected symbols are repeatedly presented as spoken voice stimuli. Users are instructed to focus exclusively on the stream corresponding to the row containing the target letter and to selectively attend to that letter within the stream. By leveraging the QWERTY layout and auditory stream segregation, the proposed approach enables users to restrict their attentional focus to a subset of letters by directing selective attention to auditory streams, while the mapping between QWERTY rows and stream pitch facilitates intuitive letter selection. We conducted online experiments with ten healthy participants to evaluate system performance.<br><br>RESULTS: The ASME-speller achieved an average classification accuracy of 0.76 and an average information transfer rate (ITR) of 2.16 bits/min. Excluding one participant whose EEG data contained excessive artifacts, these values improved to 0.84 and 2.40 bits/min, respectively. Post-hoc analyses further examined the effects of preprocessing parameters, classification pipelines, and early stopping strategies. Among four pipelines tested, a linear discriminant analysis (LDA) combined with dynamic stopping demonstrated the most robust performance across participants (accuracy of 0.80 and ITR of 4.76 bits/min). For the best participant, a deep learning model (EEGNet4,2) with dynamic stopping achieved accuracy of 1.0 with ITR of 14.44 bits/min.<br><br>DISCUSSION: Compared to previous auditory BCI spellers, the ASME-speller demonstrates performance comparable to existing systems, while offering advantages in terms of simplicity, requiring only standard headphones and no visual support. These findings demonstrate the feasibility of the ASME-speller and pave the way toward practical auditory BCI applications for communication.}, }
@article {pmid42236468, year = {2026}, author = {Wu, M and Di, Y and Kuang, S and Hu, J and Zhang, J and Wang, K and Zhang, J and Chen, C and Zhou, J and Li, T and Luo, B and Ding, N}, title = {Neural Response to Familiar Names Predicts Outcome of Comatose ICU Patients: A Prospective Observational Cohort Study.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-73878-4}, pmid = {42236468}, issn = {2041-1723}, support = {LQN26H090007//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {Predicting the outcome of comatose patients in the intensive care unit (ICU) can inform decision making but remains challenging. Recent studies suggest that task-state electroencephalography (EEG) can detect covert cognition and facilitate patient prognosis. This study aimed to predict the outcome of comatose patients, by assessing covert processing of familiar names using a state-of-the-art EEG frequency tagging approach. Eighty-nine comatose patients following acute brain injury were recruited from five ICUs. Patients were presented with a rapid stream of familiar names and acoustically matched but unintelligible control sounds. EEG responses tracking the familiar names and control sounds were extracted in the frequency domain and utilised to predict the outcome of each patient, which was assessed at 1, 3, and 6 months post-injury using the Glasgow Outcome Scale-Extended (GOSE). Name-tracking EEG responses positively correlated with GOSE scores. A machine learning model integrating EEG responses and clinical characteristics achieved AUCs of 0.86, 0.88, and 0.86 in the test set, and 0.91, 0.90, and 0.85 in the external validation set, for predicting outcomes at 1, 3, and 6 months, respectively. These findings underscore that EEG assessment of residual processing of familiar names relates to patient outcomes and has the potential to predict outcome of comatose ICU patients.}, }
@article {pmid42236749, year = {2026}, author = {Hazen, M and Cushing, SL and Gordon, KA}, title = {Impacts of hearing history, etiology, vestibular and balance function, and socioeconomic marginalization on developmental outcomes in children with cochlear implants.}, journal = {Scientific reports}, volume = {16}, number = {1}, pages = {}, pmid = {42236749}, issn = {2045-2322}, mesh = {Humans ; Female ; *Cochlear Implants ; Child, Preschool ; Male ; Child ; Adolescent ; *Postural Balance/physiology ; Memory, Short-Term ; *Vestibule, Labyrinth/physiopathology ; Socioeconomic Disparities in Health ; *Hearing Loss/etiology/physiopathology ; }, abstract = {Children with bilateral cochlear implants (BCIs) remain vulnerable to developmental challenges relative to typically developing peers (TD), even when access to sound through acoustic or amplified hearing is achieved within the first year of life. This study evaluated contributions of hearing history, etiology, vestibular/balance function, and socioeconomic marginalization to language, working memory, and academic outcomes. Ninety-six children aged 4.65-17.85 years participated: 66 with BCIs (mean [SD] age, 11.54 [3.57]) and 30 typically developing (TD) peers (mean [SD], 11.69 [2.68]). Standardized assessments included the CELF, Dot Matrix, Corsi Block, Digit Span, and WIAT-III subtests. Regression and principal component analysis (PCA) identified predictors of developmental outcomes. Children with BCIs scored significantly lower than TD peers in language (p = 0.003), visuospatial working memory (p = 0.001), math (p < 0.001), and word reading (p = 0.048). PCA identified four components: hearing loss history, auditory experience/resources, social marginalization, and vestibular/balance function. Only auditory experience predicted developmental outcomes across domains (p's < 0.05). Vestibular and balance function were impaired in the BCI group (p < 0.001) but did not predict language, working memory, or academic scores. Deficits were most pronounced in children with congenital cytomegalovirus, cochleovestibular anomalies, and genetic hearing loss. Results emphasize the importance of sustained early auditory access together with etiology-informed, multidisciplinary support to optimize developmental outcomes in children with BCIs.}, }
@article {pmid42233289, year = {2026}, author = {Aksen, DE and Potenza, MN and Meda, SA and Pearlson, GD and Lichenstein, SDD}, title = {Behavioral Inhibition Network Predicts Alcohol Use in Men and Stress in Women.}, journal = {Journal of studies on alcohol and drugs}, volume = {}, number = {}, pages = {}, doi = {10.15288/jsad.25-00235}, pmid = {42233289}, issn = {1938-4114}, abstract = {OBJECTIVE: Impulsivity, a complex construct linked to addictions, is often inconsistently assessed and conceptualized, making it difficult to effectively target in addiction treatment. The current study aimed to identify neural substrates underlying distinct impulsivity domains and explore their relationships with alcohol use and stress in both women and men.<br><br>METHOD: We utilized a whole-brain machine learning strategy, connectome-based predictive modeling (CPM), to investigate brain networks linked to four composite impulsivity-related domains previously identified in the NIAAA-funded Brain and Alcohol Research in College Students dataset: impulsive action, approach/appetitive motivation, impulsivity/compulsivity, and behavioral inhibition/punishment sensitivity (BIPS). CPM (5-fold cross-validation, 100 repeats, and permutation testing) was applied using Monetary Incentive Delay Task fMRI data from 287 undergraduates. Identified networks were examined in relation to alcohol use and stress across sexes.<br><br>RESULTS: The CPM model predicting BIPS was significant (r = 0.24, p = .001). Higher BIPS was associated with increased connectivity between default mode, motor/sensory, and cerebellar networks, and decreased connectivity among medial frontal, frontoparietal, default mode, and motor/sensory networks. BIPS network strength differed by sex (t(285) = 8.26, p < .001), with negative associations with alcohol use (p < .05) in men and positive associations with stressful life events (p < .05) in women.<br><br>CONCLUSIONS: Identifying a neuromarker of BIPS in young adults may inform targeted interventions for impulsive behaviors, considering sex differences. Future research should explore whether neuromodulation or other interventions targeting this network could mitigate problem drinking in men and stress-related concerns in women.}, }
@article {pmid42234314, year = {2026}, author = {Kumar, A and Kuo, SH}, title = {Investigating Cerebello-Cortical Networks With EEG: Advances and Future Challenges.}, journal = {Cerebellum (London, England)}, volume = {25}, number = {4}, pages = {}, pmid = {42234314}, issn = {1473-4230}, support = {#R01 NS136686/NS/NINDS NIH HHS/United States ; }, abstract = {The cerebellum is widely recognized for its contributions to motor, cognitive, and affective processes through dynamic cerebello-cortical networks. Recent studies using cerebellar-cortical electroencephalography (EEG), a technique that enables noninvasive, millisecond-resolution recordings of cerebellar and cortical activity, have revealed disease-specific spectral and network alterations in patients with movement and neurodegenerative disorders, including ataxia, essential tremor (ET), Parkinson's disease (PD), dystonia, as well as in healthy individuals. Synchronous cerebellar-cortical EEG reliably detects these signals and captures network dynamics, providing mechanistic insights into cerebellar-specific functions and interactions that may inform the development of brain-computer interfaces, targeted neuromodulation, and future applications in neurological disorders.}, }
@article {pmid42235550, year = {2026}, author = {Quattrociocchi, I and Caracci, V and Rotondo, E and Colamarino, E and Pichiorri, F and Riccio, A and Cincotti, F and Toppi, J and Astolfi, L}, title = {Improving P300 morphology through single-trial latency realignment: a comparative study of template-matching approaches.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae7766}, pmid = {42235550}, issn = {1741-2552}, abstract = {Trial-to-trial latency variability - well known as latency jitter - is a major source of distortion in event-related potential (ERP) analysis, particularly for late cognitive components such as the P300. Several template-matching algorithms have been proposed to estimate single-trial latency and improve ERP reconstruction, but direct comparisons across different methodological approaches remain limited. This study provides a structured evaluation of three representative algorithms: the Woody Filter (WF), operating in the time domain; the Adaptive Wavelet Filter (CWT-AWF), extending template matching to the time-frequency domain; and ReSync, a decomposition-based method that combines signal decomposition with time-restricted realignment. Approach.The algorithms were evaluated using surrogate EEG-like data with controlled amplitude ratios (reported as SNR) and known latency jitter, and real EEG recordings from healthy participants performing an auditory oddball task. Performance was assessed in terms of latency-estimation accuracy, latency variability, ERP morphology, and waveform quality. Results. Across simulated conditions, ReSync achieved significantly lower latency-estimation errors and reduced variability compared to WF and CWT-AWF, demonstrating robustness even at low SNR levels. Importantly, this advantage persisted when all methods were constrained within the same temporal window, indicating that performance gains are not solely attributable to time restriction. In real EEG data, all algorithms enhanced P300 morphology relative to non-aligned averages, but ReSync yielded the most consistent improvements, including the lowest latency jitter and stable latency distributions within a range consistent with previous findings. Complementary SNR analysis further indicated improved waveform quality when interpreted jointly with latency-based metrics. ReSync also remained stable across both single-channel and multi-channel realignment strategies. Significance. These findings highlight the advantage of combining decomposition and targeted realignment for mitigating ERP latency jitter. ReSync provides a reliable and morphology-preserving framework for single-trial ERP analysis, with potential applications in cognitive neuroscience, brain-computer interfaces, and clinical contexts. .}, }
@article {pmid41963527, year = {2026}, author = {Gayathri, T and Manjula, G and Kenchannavar, HH and Sudha, D and Jankatti, SK and Kaur, R and Venkateswarlu, B}, title = {QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.}, journal = {Scientific reports}, volume = {16}, number = {1}, pages = {}, pmid = {41963527}, issn = {2045-2322}, abstract = {Electroencephalography (EEG) is a non-invasive, high-temporal-resolution method for diagnosing and monitoring neurological disorders. Deep learning has recently substantially enhanced the state of the art for automated EEG analysis. However, many of the currently applied paradigms are still challenged by limited generalisation across datasets, vulnerability to noise or preprocessing changes, and the absence of interpretable decision rules. Additionally, many deep learning models operate like black boxes, which limits their use in clinical settings where interpretability and trust are key. Although the potential of quantum-inspired learning has recently been demonstrated through improved feature separability in high-dimensional signal spaces, its scope of applicability does not yet extend to deep temporal modelling and explainable artificial intelligence applications. We address these limitations by introducing QuantumNeuroXAI, a quantum-inspired deep learning framework implemented on classical hardware that leverages structured feature encoding inspired by quantum neural networks to provide inherent explainability for EEG-based diagnosis of neurological disorders. This framework hybridises quantum-inspired feature encoding with a deep-learning architecture that blends temporal convolutional and attention-based recurrent modelling to capture local and long-range patterns of dependencies in EEG signals. The framework incorporates a multi-level explainability module relevant at the signal, model, and quantum-representation levels, allowing predictions to be interpreted in a clinically meaningful and transparent fashion. We conduct extensive experiments on three publicly available EEG datasets (TUH EEG, CHB-MIT, and BCI Competition IV-2a) to evaluate the proposed framework. These quantitative results show that QuantumNeuroXAI achieves statistically significant and large effect sizes, with macro-F1 improvements of up to 5.2% over classical machine learning, deep learning, and hybrid baseline models. Additional robustness and scalability analyses further validate stable performance against dataset shift and across various preprocessing configurations. In summary, QuantumNeuroXAI is an interpretable and reproducible solution for EEG-based neurological analysis, demonstrating promise for clinical decision support and future scalability to multimodal brain signal applications. It is important to note that the proposed framework does not rely on quantum hardware and is fully implemented using classical computational resources. The implementation of the proposed framework is publicly available at: https://github.com/venkateshwarlu-bondu/QuantumNeuroXAI.}, }
@article {pmid41968126, year = {2026}, author = {Kuroda, N and Sato, Y and Harada, S and Teraoka, R and Teramoto, W}, title = {Bayesian causal inference reveals declined proprioception, increased integration bias underlie older adults' stronger visual bias in hand position perception.}, journal = {Scientific reports}, volume = {16}, number = {1}, pages = {}, pmid = {41968126}, issn = {2045-2322}, support = {23K18980//Japan Society for the Promotion of Science/ ; 20H05801 and 23H00076//Japan Society for the Promotion of Science/ ; }, abstract = {UNLABELLED: Self-localization is fundamental to bodily self-consciousness across the lifespan. Humans estimate body-part position by integrating afferent signals such as vision and proprioception. Rubber and mirror hand illusions highlight the dominant role of vision in hand position perception. Although older adults rely more heavily on visual information, the computational mechanisms underlying age-related increases in visual bias remain unclear. Here, we examined age-related changes in visuo-proprioceptive integration using a Bayesian causal inference (BCI) model. Two experiments introduced spatial discrepancies between visual and proprioceptive hand positions to manipulate the likelihood of integration. Participants reached toward a target after the visual hand disappeared, allowing the BCI model to estimate sensory reliabilities and the prior probability of a common cause ([Formula: see text]). Decision-making strategies were also compared within the BCI framework. Older adults exhibited reduced proprioceptive reliability and a higher [Formula: see text], indicating a stronger tendency to infer a shared source for visual and proprioceptive signals. No age-related differences were observed in decision-making strategy. These findings suggest that age-related visual bias reflects changes not only in sensory reliability but also in causal inference during multisensory integration.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1038/s41598-026-45797-3.}, }
@article {pmid42232076, year = {2026}, author = {Mannino, C and Sorrentino, P and Chavez, M and Corsi, MC}, title = {Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {4}, number = {}, pages = {}, pmid = {42232076}, issn = {2837-6056}, abstract = {Motor imagery-based Brain-Computer Interfaces (BCIs) restore control in persons with motor impairments, but up to 30% of users struggle, a phenomenon known as "BCI inefficiency". This study tackles a key limitation of current protocol: the use of fixed-length sessions training paradigms that ignore individual learning variability. We propose a novel approach based on neuronal avalanches, spatiotemporal cascades of brain activities, as biomarkers to characterize and predict user-specific learning. From electroencephalography data across four sessions in 20 subjects, we characterized avalanches by their length and their spatiotemporal size. These features showed significant training and task effects and were found to correlate to BCI performance across sessions. We further assessed their ability to predict BCI success through longitudinal models, achieving up to 91% accuracy, improved by spatial filtering on selected brain regions. These findings demonstrate the utility of neuronal avalanche dynamics as robust biomarkers for BCI training, supporting the development of personalized protocols aimed at mitigating BCI illiteracy.}, }
@article {pmid42232464, year = {2026}, author = {Hess, RM and Lavadi, RS and Agarwal, N and Levy, EI}, title = {The role of ambulatory surgical centers in current neurosurgical practice.}, journal = {Surgical neurology international}, volume = {17}, number = {}, pages = {275}, pmid = {42232464}, issn = {2229-5097}, abstract = {BACKGROUND: The recent focus on improving quality and reducing cost within the US healthcare system has increased care being performed in the outpatient setting. The impact on neurosurgeons' practice patterns has not yet been fully elucidated. In addition, how this transition may affect neurosurgery resident training is unclear. To better understand these issues, we surveyed neurosurgeons.<br><br>METHODS: A 13-question survey was sent to Council of State Neurosurgical Societies email subscribers. The survey focused on training or practice level, location, practice setting, ambulatory surgical center (ASC) utilization, and types of procedures performed at ASCs. Responses were tabulated. Statistical analysis was performed.<br><br>RESULTS: Among 11,091 subscribers, 101 responses (0.9%) were recorded. Most of the respondents (57.4%) utilized an ASC in their practice. The commonly performed procedures were microdiscectomy (98.1%), hemilaminectomy (94.2%), battery changes (87.5%), single-level anterior cervical discectomy and fusion (84.6%), single-level lumbar or thoracic laminectomy (80.8%), and peripheral nerve decompression (66.7%). Cranial procedures were seldom performed. Other device-related procedures were common and included vagal nerve stimulation (32.5%), spinal cord stimulation (67.5%), baclofen pump placement (25%), and baclofen pump replacement (27.5%). Only 17.1% of respondents who worked in academia taught residents in an ASC.<br><br>CONCLUSION: According to our survey results, most neurosurgeons have incorporated ASCs into their practices in some capacity and most frequently for simple spine procedures, device-related procedures, and peripheral nerve decompression. The limited resident involvement in procedures in the ASC setting, even among attending academic neurosurgeons, suggests an increased need for ASC incorporation in residency training.}, }
@article {pmid42232652, year = {2026}, author = {Gao, Z and Zhu, Z and Wang, S and Wu, Y and Song, Z and Guo, Y and Wang, H and Mao, YJ}, title = {Effects of motor imagery brain-computer interface task on quantitative EEG features in patients with prolonged disorders of consciousness.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1815881}, pmid = {42232652}, issn = {1662-4548}, abstract = {OBJECTIVE: To analyze quantitative electroencephalographic (EEG) characteristics during Motor Imagery Brain-Computer Interface (MI-BCI) task in patients with prolonged disorders of consciousness (pDoC).<br><br>METHODS: Forty-three patients with pDoC due to various brain injuries were enrolled. Based on modified Coma Recovery Scale-Revised (CRS-R) assessments, the patients were divided into 19 in the unresponsive wakefulness syndrome (UWS) group and 24 in the minimally conscious state (MCS) group. All patients underwent 5 min of resting-state (RS) EEG followed by 5 min of MI-BCI task. Relative power, DTABR, and average brain engagement (BE) during MI-BCI were analyzed across resting and MI-BCI states using Fast Fourier Transform (FFT) spectra.<br><br>RESULTS: Mixed-design ANOVA showed significant main effects of condition and group across all EEG frequency bands, indicating clear differences between the RS and MI-BCI conditions and between UWS and MCS patients. Significant group &#xd7; condition interactions were found in the delta, beta, and gamma bands, as well as in DTABR. Simple effects analysis showed that delta power was higher in RS than in MI-BCI in both groups, with UWS consistently exhibiting higher delta power than MCS under both conditions. In contrast, beta and gamma power were higher in MI-BCI than in RS in both groups. For beta power, UWS was higher than MCS under RS, whereas MCS was higher than UWS under MI-BCI, showing a reversal of the interaction pattern. For gamma power, MCS showed higher values than UWS under both conditions, with a larger between-group difference during MI-BCI. DTABR was significantly higher in RS than in MI-BCI in both groups; however, MCS exhibited higher DTABR than UWS under RS, whereas the opposite pattern was observed under MI-BCI. In addition, during MI-BCI tasks, the MCS group showed greater average BE than the UWS group.<br><br>CONCLUSION: MI-BCI shows potential as a diagnostic or assessment tool for evaluating the level of consciousness in patients with pDoC.}, }
@article {pmid42232896, year = {2026}, author = {Alsolai, H and Khan, S and Mahendran, RK and Panwar, A and Alabduallah, BI and Alhayan, F}, title = {Rehab-DRLX: explainable neurorehabilitation prognosis using deep reinforcement learning and transformer-based models.}, journal = {Frontiers in computational neuroscience}, volume = {20}, number = {}, pages = {1808274}, pmid = {42232896}, issn = {1662-5188}, abstract = {Neurorehabilitation poses a crucial problem in clinical recovery tasks, particularly for individuals with poor motor functions and neurological impairments, and problems in activities of daily living (ADL). To resolve this, we design a novel model, Rehab-DRLX, with a hybrid deep learning (HDL) framework that combines deep reinforcement learning (DRL) with an explainable transformer model to provide interpretable, accurate prognostic results. The propounded model is designed to effectively process the multimodal data inputs, which include clinical records, sensor-entrenched motion data, and neuroimaging, along with time-dependent recovery patterns from its reinforced representation learning (RRL) module. The RRL module employs a convolutional neural network (CNN) within the DRL agent, which performs spatiotemporal feature encoding and dynamically recovers a policy from its reward-guided learning method. To ensure interpretability, the explainable prognosis transformer (XPT) is utilized, which contains clinical contextual positional encoding and a hierarchical attention mechanism to enable transparent and reliable decision-making. This duality in the Rehab-DRLX architecture enables effective forecasting of the recovery outcomes, including functional independence probability, with both interpretability and accuracy, addressing the drawbacks of conventional black box prognosis tools. The experimental results of Rehab-DRLX show the noteworthy improvements in metrics such as accuracy (94.6%), F1-score (0.93), root mean square (RMSE) (0.082), and mean absolute error (MAE) (0.061) compared to existing studies. The ablation studies reveal the significant contribution of every architectural component and its overall performance. The results show the practical viability of Rehab-DRLX, which not only improves decision-making but also builds clinical trust through explainable insights.}, }
@article {pmid42225660, year = {2026}, author = {Sarkar, S and Nathan, K and Kilicarslan, A and Eguren, D and Grossman, R and Contreras-Vidal, JL}, title = {EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy Individuals.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-026-07476-w}, pmid = {42225660}, issn = {2052-4463}, abstract = {Brain-machine interfaces (BMIs) translate brain signals into motor commands for assistive devices. Despite significant advances, the long-term effects of BMI training on neural adaptation, classifier stability, and individual variability remain poorly understood. We present a multimodal, longitudinal dataset collected from seven healthy participants over nine sessions spanning 15 to 81 days. The dataset includes high-density electroencephalography (EEG), electrooculography (EOG), inertial measurement unit (IMU) data, and exoskeleton state information during BMI control. During the open-loop training phase, participants performed kinesthetic motor imagery (KI) while a remotely controlled exoskeleton executed walking and stopping commands. After the open loop training phase, the system transitioned to closed loop BMI control. For closed-loop control, lower delta band EEG signals were classified using Local Fisher Discriminant Analysis and a Gaussian Mixture Model. The classifier was continuously updated using open-loop data from Sessions#1-5, after which its parameters were fixed. The dataset also includes post-experiment MRI scans from five participants performing KI while viewing themselves walking in the exoskeleton. This report outlines the experimental setup, data collection, and preliminary validation, providing a resource for future BMI research.}, }
@article {pmid42226205, year = {2026}, author = {Gosden, J and Ascione, G and Wolf, S and Turek, JW and George, I}, title = {Alpha-gal xenoantigens in bioprosthetic valve recipients: clinical implications for bioprosthesis longevity.}, journal = {Journal of cardiothoracic surgery}, volume = {}, number = {}, pages = {}, doi = {10.1186/s13019-026-04270-y}, pmid = {42226205}, issn = {1749-8090}, abstract = {BACKGROUND: Structural valve degeneration (SVD) is a key limitation of bioprosthetic heart valves (BHVs). The underlying mechanisms for this degeneration and pathophysiology remains only partially defined. Emerging evidence implicates a xenogeneic carbohydrate epitope, galactose-α-1,3-galactose (Alpha-gal), as a potential driver of immune-mediated valve deterioration. This review explores the current knowledge on alpha-gal (AG) sensitization and evidence linking it to SVD and the potential clinical implications.<br><br>METHODS: A literature search was conducted using Embase, PubMed and Scopus, using variants of the following keywords, such as "alpha-gal", "bioprosthetic valve", and "degeneration". Studies included reported human subject findings and focused on BHVs. Only original works were permitted, published between January 2014 and December 2025.<br><br>RESULTS: Six studies met the inclusion criteria. Case reports demonstrated heterogenous clinical outcomes with, rapid SVD observed in some alpha-gal sensitized patients, while other patients showed tolerance to bioprosthetic implantation in the perioperative and short-term period. The only study with longitudinal follow-up demonstrated that anti-AG IgG responses were associated with increased SVD and calcification. Another study found no perioperative adverse valvular outcomes, although follow-up was limited to in-hospital assessment. Overall, his manuscript identifies that AG sensitization may contribute to SVD in certain patients, however, its broader significance remains uncertain.<br><br>CONCLUSIONS: Immune recognition of AG may contribute to SVD based on the limited available evidence. Larger prospective investigations are required to clarify a causal relationship and to assist in guiding potential preventative strategies. Recognition of this mechanism may ultimately inform management of valve replacement and bioprosthesis selection plans.}, }
@article {pmid42226947, year = {2026}, author = {Delavari, F and Santaniello, S}, title = {Lateralization in scalp EEG brain connectivity during hand motor imagery can improve task classification for brain-computer interfaces.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {103}, pmid = {42226947}, issn = {1871-4080}, abstract = {This study evaluates brain connectivity reorganization during motor imagery (MI) tasks and assesses the predictive value of EEG-based functional connectivity measures for MI classification compared to µ-band (8-13 Hz) power spectrum of selected EEG channels, which are commonly used in MI decoders. We analyzed left- and right-hand MI EEG data from the BCI Competition IV 2a (BCI-IV-2a) and PhysioNet Motor Imagery (PHYS-MI) datasets. Phase Locking Value (PLV), cross-correlation (CC), weighted Phase Lag Index (wPLI), and Granger causality (GC) were evaluated as connectivity measures, and their decoding performance was compared against µ-band power features using Random Forest classifiers. Feature importance and graph-theoretical metrics were also used to examine node relevance, edge contributions, and global network topology across MI conditions. We found that PLV yields the most reliable MI decoding performance across both datasets, with accuracy comparable to power (65.3 ± 11.0% vs. 61.3 ± 11.0% and 58.4 ± 9.9% vs. 58.6 ± 15.7%, mean ± std. dev. across subjects for BCI-IV-2a and PHYS-MI, respectively). Moderate correlation (R [2] = 0.62 and 0.40 for BCI-IV-2a and PHYS-MI, respectively) was found between the mean difference in PageRank centrality of the nodes of the PLV-based network in left- vs. right-hand MI and the Gini importance score of the single-channel power values. Also, while the PLV-based network topology remained stable over time, a small set of connections (7.8 ± 4.5% and 3.1 ± 2.5% of edges) lateralized to the hemisphere contralateral to the movement altered considerably and enhanced classification accuracy by 6.7 ± 5.6% and 16.3 ± 7.5% across subjects. These findings suggest that MI primarily modulates a limited number of task-specific functional connections. Rather than replacing established power-based approaches, connectivity measures provide complementary, network-level insight into how MI-related information is organized, which may inform interpretable feature selection and the design of future brain-computer interface models.}, }
@article {pmid42227949, year = {2026}, author = {Zhang, T and Ngetich, RK and Zhang, J and Jin, Z and Li, L}, title = {Erratum to: The role of emotion in economic decision making: behavioral and neurophysiological evidence from the Wheel of Fortune Gambling Task.}, journal = {Reviews in the neurosciences}, volume = {}, number = {}, pages = {}, doi = {10.1515/revneuro-2026-0101}, pmid = {42227949}, issn = {2191-0200}, }
@article {pmid42227984, year = {2026}, author = {Shen, Y and You, C and Zhang, Y and Ji, N and Zhao, X and Zhang, P and Huang, S and Kang, H and Liu, X and Peng, Y and Sun, C and Yan, B and Zhang, Y and Zhu, S and Zhu, W and Lei, T and Tang, Z and Ding, M and Hu, F and Shu, K}, title = {Assessment of the utility of optically pumped magnetometer magnetoencephalography in preoperative localization of refractory epilepsy: A prospective study.}, journal = {Epilepsia}, volume = {}, number = {}, pages = {}, doi = {10.1002/epi.70273}, pmid = {42227984}, issn = {1528-1167}, support = {SCZ2024008//Jian Dao (JD) Major Program of Hubei Province/ ; 2022YFC2403905//National Key Research and Development Program of China/ ; 2023YFC2510001//National Key Research and Development Program of China/ ; }, abstract = {OBJECTIVE: Precise localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery success. Optically pumped magnetometer magnetoencephalography (OPM-MEG) is a promising noninvasive technique requiring rigorous clinical validation.<br><br>METHODS: In this prospective diagnostic study, 68 patients with refractory epilepsy underwent 90-min interictal OPM-MEG. Dipoles were fitted to interictal epileptiform discharges for localization. The primary objective was to evaluate the spatial concordance between OPM-MEG and the EZ defined by intracranial electroencephalography (iEEG; stereo-EEG or electrocorticography), assessed at the sublobar level using Gwet AC1. The secondary objective was to evaluate the diagnostic value of OPM-MEG for surgical outcome. This analysis included 51 patients who underwent curative intervention (resection or thermocoagulation). The reference standard was a composite of the treated brain region and seizure freedom (International League Against Epilepsy [ILAE] class 1 or Engel class I) at &#x2265;12-month follow-up, from which sensitivity, specificity, and diagnostic odds ratio (OR) were calculated.<br><br>RESULTS: OPM-MEG showed almost perfect agreement with iEEG-based EZ localization overall (AC1&#x2009;=&#x2009;.885, concordance rate&#x2009;=&#x2009;90.0%), with substantial agreement in temporal (80.1%, AC1&#x2009;=&#x2009;.723) and almost perfect agreement in extratemporal regions (92.0%, AC1&#x2009;=&#x2009;.926). The Euclidean centroid distance between OPM-MEG and iEEG localizations was significantly shorter in concordant versus discordant cases. In the assessment of diagnostic value, OPM-MEG demonstrated a sensitivity of 85.7% and specificity of 65.2% (OR&#x2009;=&#x2009;11.25) under ILAE criteria, and a sensitivity of 73.0% and specificity of 64.3% (OR&#x2009;=&#x2009;4.86) under Engel criteria.<br><br>SIGNIFICANCE: OPM-MEG demonstrates high concordance with iEEG for EZ localization and provides robust diagnostic value for predicting postoperative seizure freedom, supporting its utility in the presurgical evaluation of refractory epilepsy.}, }
@article {pmid42229420, year = {2026}, author = {Zhou, Q and Dong, B and Gao, P and Wei, J and Xiao, J and Wang, W and Liang, P and Lin, D and Lu, J and Zuo, XN and He, H}, title = {AmygdalaGo-BOLT for boundary-aware segmentation of the human amygdala.}, journal = {Cell reports methods}, volume = {}, number = {}, pages = {101473}, doi = {10.1016/j.crmeth.2026.101473}, pmid = {42229420}, issn = {2667-2375}, abstract = {Tracing the boundaries of the amygdala from brain images remains a major challenge in human neuroscience. Although large-scale neuroimaging studies increasingly collect thousands of scans to investigate structural development in children and adolescents, reliable segmentation of the amygdala is difficult due to its small size and complex morphology-particularly in pediatric populations. To address this, we developed AmygdalaGo-BOLT, a boundary-aware deep learning model specifically designed for amygdala segmentation. The model was trained and validated on 1,086 manually labeled pediatric MRI scans, with independent datasets used to assess generalizability. It integrates multiscale feature extraction, spatial priors, and self-attention mechanisms within a compact encoder-decoder architecture to enhance boundary detection. Across imaging centers and age groups, AmygdalaGo-BOLT demonstrates strong agreement with expert manual annotations, while substantially improving efficiency and accuracy relative to existing tools. This enables robust and scalable analysis of amygdala morphology in population neuroscience studies where manual tracing is impractical.}, }
@article {pmid42229507, year = {2026}, author = {Swanson, E and Dohle, E and Bashford, L and Horsfall, HL and Jovanovic, L and Muirhead, W and Brannigan, JFM}, title = {Recalibration of implantable brain-computer interfaces to enable long-term independent use - a systematic review.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae7694}, pmid = {42229507}, issn = {1741-2552}, abstract = {Implantable brain-computer interfaces (iBCIs) decode neural signals to generate command signals for effector devices to restore lost functions, such as movement or speech. However, maintaining device performance over time requires recalibration of decoding algorithms due to inherent instability in neural signals. Objective: To systematically review recalibration procedures in iBCIs for patients with motor impairments, focusing on the clinical implications of recalibration requirements and strategies which can enable long-term, independent use. Approach: A systematic search was conducted across EMBASE, MEDLINE, and CINAHL databases to identify studies involving recalibration of iBCIs. Data on recalibration frequency, duration, staff requirements, and location were extracted and analysed. Main Results: Recalibration practices varied widely amongst studies and were typically performed according to predetermined study protocols, rather than practical need following deteriorating device performance. Common practices include manual recalibration requiring a specialist research team, semi-automatic recalibration which could be performed by a non-specialist caregiver, and automatic recalibration methods whereby patients did not require assistance. Devices utilising electrocorticography (ECoG) recording arrays generally required less frequent recalibration compared to those using microelectrode arrays (MEAs). Extended independent use was more frequently reported with ECoG-based iBCIs. Significance: Reducing recalibration frequency or complexity can improve patient autonomy, which is crucial for enhancing long-term independent iBCI use in home and clinical settings. ECoG iBCIs typically have a low recalibration burden due to inherent signal stability. Conversely, MEA iBCIs typically involve a higher recalibration burden, though recent studies have reduced this by incorporating spectral data and continuously updating models. Despite this progress, recalibration procedures are often not fully defined in iBCI studies, and where they are, they usually relate to the study protocol rather than the clinically meaningful recalibration requirement due to worsening device performance. Future studies should continue to develop user-friendly recalibration procedures and outline the clinically relevant recalibration requirements where possible.}, }
@article {pmid42230569, year = {2026}, author = {Hao, ZJ and Wu, QH and Li, YL and Guo, ZM and Li, ZW and Wang, G and Meng, M and Yuan, SL and Wufuer, Y and Zhang, MH and Chen, J and Yang, T and Chen, MX and Zhu, J and Qi-Hang, W and Li, Q and Yu, SH and Lu, M and Xiong, HY and Feng, YR and Dong, MQ and Xu, JH and Xu, JL and Chen, L and Yang, HT and Miao, JK and Zhu, H and Yang, B and Zhao, HY and Shi, XM and Bian, S and Li, TY and Hu, RG}, title = {Anti-asthma drug montelukast induces autistic behaviors via disrupting neuronal retinoic acid signaling.}, journal = {Signal transduction and targeted therapy}, volume = {11}, number = {1}, pages = {}, pmid = {42230569}, issn = {2059-3635}, mesh = {Cyclopropanes/adverse effects ; Humans ; Animals ; *Quinolines/adverse effects/administration &amp; dosage ; *Tretinoin/metabolism ; Signal Transduction/drug effects ; Rats ; Sulfides/adverse effects ; *Acetates/adverse effects/administration &amp; dosage ; Neurons/pathology/metabolism/drug effects ; *Anti-Asthmatic Agents/adverse effects ; *Retinoic Acid Receptor alpha/genetics/metabolism ; Female ; *Autism Spectrum Disorder/chemically induced/genetics/pathology/metabolism ; *Autistic Disorder/chemically induced/genetics/pathology ; }, abstract = {Autism spectrum disorders (ASD) affect approximately 1.0% of children worldwide with still increasing global prevalence. The fact that genetic factors contribute to less than 50% of ASD suggests some critical yet enigmatic roles of non-genetic factors in ASD etiology. Here, we reported that montelukast (MTK), a cysteinyl leukotriene receptor antagonist and one of the most commonly prescribed anti-asthma drugs, potently disrupted neuronal retinoic acid (RA) signaling and altered synaptic plasticity of the primary neurons from rat pre-frontal cortex (PFC). Prenatal or early postnatal exposure to MTK induced autistic-like behaviors in wild-type rats, which could be significantly alleviated by supplementing all-trans retinoic acid (atRA). MTK altered neuronal RA signaling and forebrain patterning in brain organoids derived from human embryonic stem cells through antagonizing RA in RA signaling. Meanwhile, molecular docking followed by biochemical validation strongly indicated that MTK could physically interact with RA receptors (RARs), e.g. RA receptor α (RARA). Furthermore, multi-center survey with a large Chinese ASD cohort suggested that MTK administration during early childhood might indeed increase the risk of ASD in children. Altogether, our findings have not only established MTK use as a yet unrecognized risk factor for human ASD, but highlighted the key importance of safer use of medicines to prevent ASD.}, }
@article {pmid42230793, year = {2026}, author = {Sun, S and Li, J and Wang, S and Li, J and Ren, J and Bao, Z and Sun, L and Ma, X and Zheng, F and Ma, S and Sun, L and Wang, M and Yu, Y and Ma, M and Wang, Q and Chen, Z and Ma, H and Wang, X and Wu, Z and Zhang, H and Yan, K and Yang, Y and Zhang, Y and Zhang, S and Lei, J and Teng, ZQ and Liu, CM and Bai, G and Wang, YJ and Li, J and Wang, X and Zhao, G and Jiang, T and Belmonte, JCI and Qu, J and Zhang, W and Liu, GH}, title = {Author Correction: CHIT1-positive microglia drive motor neuron ageing in the primate spinal cord.}, journal = {Nature}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41586-026-10728-9}, pmid = {42230793}, issn = {1476-4687}, }
@article {pmid42231717, year = {2026}, author = {Saeed, S and Sang, R and Zhixin, L and Wang, H and Xu, L and Zhang, X and Hu, S}, title = {Circadian rhythms in major depressive disorder: mechanistic insights and therapeutic frontiers.}, journal = {Annals of medicine}, volume = {58}, number = {1}, pages = {2671594}, doi = {10.1080/07853890.2026.2671594}, pmid = {42231717}, issn = {1365-2060}, mesh = {Humans ; *Major Depressive Disorder/therapy/physiopathology/genetics ; *Circadian Rhythm/physiology/genetics ; Hypothalamo-Hypophyseal System/physiopathology/metabolism ; Pituitary-Adrenal System/physiopathology ; Melatonin/metabolism ; Phototherapy/methods ; Chronotherapy/methods ; Sleep/physiology ; CLOCK Proteins/genetics ; }, abstract = {BACKGROUND: Major Depressive Disorder (MDD) has emerged as a leading cause of disability worldwide, affecting over 264 million people. Recent evidence reveals that disruption of circadian rhythms may be fundamental to MDD pathophysiology, opening new avenues for therapeutic intervention.<br><br>METHODS: This review synthesizes current understanding of the intricate relationship between circadian system disruption and MDD, highlighting molecular mechanisms and clinical implications. We examine evidence from genetic studies, clinical observations, and therapeutic trials.<br><br>RESULTS: Patients with MDD exhibit profound alterations in circadian-regulated processes, including sleep-wake cycles, diurnal mood patterns, and metabolic functions. Genetic studies have identified variants in core clock genes, particularly CLOCK, TIMELESS, and CRY1, that correlate with both circadian disruption and MDD susceptibility. These genetic insights, combined with evidence of dysregulated hypothalamus-pituitary-adrenal axis function and abnormal melatonin signaling, suggest that circadian dysfunction may be causal in MDD pathogenesis rather than merely symptomatic.<br><br>CONCLUSIONS: Emerging chronotherapeutic approaches, such as light therapy, sleep interventions, and targeted pharmacology, show significant potential for improving depressive symptoms. Personalized circadian-based treatments, guided by genetic and molecular markers, could transform MDD care. Advancing our understanding of the circadian-depression connection offers a promising path to revolutionizing treatment strategies.}, }
@article {pmid42232026, year = {2026}, author = {Zhu, Y and Yu, X and Yin, C and Liu, X and Wu, Z and Zhang, D and Lee, HJ and Tian, L}, title = {Mg[2+]-Dependent Remodeling of Biomolecular Condensates' Microenvironments for Tunable Molecular Uptake and Altered Biochemical Dynamics.}, journal = {Chem &amp; bio engineering}, volume = {3}, number = {5}, pages = {535-545}, pmid = {42232026}, issn = {2836-967X}, abstract = {Biomolecular condensates have emerged as a transformative paradigm in biomedical and materials sciences due to their unique capacity for molecular sequestration and dynamic adaptability. Precise modulation of their microenvironmental properties enables versatile applications including protocell engineering, targeted therapeutics, and smart bioreactor systems. Here, we demonstrate that multivalent ions, exemplified by magnesium ions (Mg[2+]), exert concentration-dependent regulation of condensate physicochemical properties and biological functions. Using a model system composed of cationic arginine decamer (R10) and anionic polyglutamate (PolyE), we systematically show that Mg[2+] concentration gradients influence the size distribution, surface charge, viscosity, and internal polarity. Critically, we establish links between ion-induced microenvironmental changes and functional outcomes: (i) dsDNA structural stability and ssDNA hybridization kinetics are altered in an ion-dependent manner; (ii) guest molecule enrichment capacity shows selective tuning; and (iii) alkaline phosphatase (ALP) catalytic efficiency exhibits nonlinear dose-response relationships. These findings offer mechanistic insights into cellular ion homeostasis and provide design principles for ion-responsive synthetic condensates with programmable functionality. Our work bridges fundamental biophysical principles with translational applications in smart biomaterials and precision medicine.}, }
@article {pmid42094556, year = {2026}, author = {Abramovich Krasa, B and Kunz, EM and Kamdar, F and Avansino, D and Hahn, N and Singh, A and Card, NS and Wairagkar, M and Iacobacci, C and Hochberg, LR and Brandman, DM and Stavisky, SD and Henderson, JM and Willett, FR and Druckmann, S}, title = {Premotor cortex uses a compositional neural geometry to plan words.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {42094556}, issn = {2692-8205}, abstract = {Speech requires precise serial ordering of words and phonemes into novel combinations. To accomplish this, the brain is believed to flexibly prepare utterances before producing them, even allowing pronunciation of never-before spoken words. To discover how neural populations achieve this, intracortical activity from premotor cortex was recorded while two speech neuroprosthesis pilot clinical trial participants attempted to speak factorially-balanced phoneme sequences. During preparation, activity encoded not only the next-phoneme, but multiple upcoming phoneme positions spanning whole words. We found that word-level plans were formed by compositionally combining phoneme representations, a mechanism that may enable efficient planning of novel sequences. When utterances contained more than one word, premotor cortex activity was largely limited to the first word, suggesting that articulatory planning is segmented by higher-order features. Together, these results reveal a compositional, hierarchically-segemented planning geometry, potentially a universal neural strategy for sequence organization across higher levels of language.}, }
@article {pmid42217986, year = {2026}, author = {Alcala, I and Desailly, E and Arcizet, F and Marazova, K and Gauvain, G and Picaud, S}, title = {Vision restoration: From prostheses to genetic-based brain-machine interfaces.}, journal = {Handbook of clinical neurology}, volume = {218}, number = {}, pages = {387-400}, doi = {10.1016/B978-0-443-22212-2.00017-3}, pmid = {42217986}, issn = {0072-9752}, mesh = {Humans ; *Brain-Computer Interfaces ; Animals ; *Visual Prosthesis ; *Optogenetics/methods ; *Genetic Therapy/methods ; *Vision Disorders/therapy ; }, abstract = {Visual restoration is the major challenge for brain-machine interfaces because vision requires perception of images containing a high number of pixels that have to be presented at a high refresh rate. Classically, visual prostheses were made of electrode arrays with electrode numbers varying from very few to more than thousands. They demonstrated the feasibility of restoring useful vision either at the retinal level in diseases with photoreceptor degeneration or at the cortical level following optic nerve atrophy. Patients can find contrasted objects on a table and read letters or even words. However, they cannot recognize faces. The revolution of biotechnology and gene therapy is offering novel strategies to stimulate neuronal circuits without direct contact to the tissue as with electrodes. Optogenetic therapy is rendering neurons sensitive to light, thanks to a microbial opsin while sonogenetic therapy generates neurons sensitive to ultrasound waves. While optogenetic therapy has already been validated in patients recovering some vision following photoreceptor degeneration, sonogenetic therapy has only been evaluated in rodents at the cortical level. These novel brain-machine interfaces offer novel perspectives for restoring vision in blind patients, but their applications may easily extend to other handicaps or neurologic diseases.}, }
@article {pmid42218423, year = {2026}, author = {Qiao, MX and Wei, W and Zhou, M and Li, ML and Zhang, YM and Li, XJ and Deng, W and Guo, WJ and Wang, Q and Yu, H and Li, T}, title = {Habenular structural-functional dysconnectivity in bipolar disorder: evidence from multimodal imaging and transcriptomic integration.}, journal = {BMC psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12888-026-08216-5}, pmid = {42218423}, issn = {1471-244X}, support = {82101598//National Natural Science Foundation of China/ ; 82230046//National Natural Science Foundation of China/ ; 2025HZGF10//Construction Fund of Key Medical Disciplines of Hangzhou/ ; CXTD202501053//Zhejiang Clinovation Pride/ ; STI2030-2021ZD0200404//China Brain Project/ ; }, abstract = {BACKGROUND: Bipolar disorder (BD) is a highly heritable condition characterized by recurrent shifts between manic and depressive states. Here we investigated the potential involvement of the habenula because it plays a central role in negative affect and behavioral regulation.<br><br>METHODS: We investigated bilateral habenular volume and seed-based resting-state functional connectivity in a discovery cohort (78 BD, 102 controls) and an independent replication cohort (72 BD, 85 controls). Associations among habenular features, clinical symptoms, and molecular correlates were examined by integrating pathway-specific polygenic risk scores and brain-wide gene expression data from the Allen Human Brain Atlas.<br><br>RESULTS: Across both cohorts, BD was associated with reduced bilateral habenular volume and increased rs-FC between the habenula and right precentral gyrus. Habenular volume correlated positively with severity of mania symptoms and negatively with severity of symptoms of anxiety and somatization. Polygenic risk scores linked the altered volume to dopaminergic pathways and altered connectivity to serotonergic pathways, while transcriptomic data linked the altered connectivity to changes in expression of synaptic membrane structures, transporter complexes, and other proteins involved in synaptic transmission.<br><br>CONCLUSIONS: Structural, functional and transcriptomic data identify the habenula as a critical neural hub in BD and therefore important for understanding pathogenesis and clinical manifestations.<br><br>CLINICAL TRIAL NUMBER: Not applicable.}, }
@article {pmid42219941, year = {2026}, author = {Wang, D and Huang, K and Zhou, X and Ye, X and Chen, J and Xu, H and Dong, G and Yuan, T and Chen, X and Zhou, H and Potenza, MN and Fu, J}, title = {Food Addiction Risk Accelerates Fat Accumulation in Youth: Potential Protective Roles of Left Insula and Mindful Eating.}, journal = {Obesity (Silver Spring, Md.)}, volume = {}, number = {}, pages = {}, doi = {10.1002/oby.70225}, pmid = {42219941}, issn = {1930-739X}, support = {2023C03047//Key Research and Development Program of Zhejiang Province/ ; 2021YFC2701900//National Key Research and Development Program of China/ ; 82370863&#xff1b;82501790//National Natural Science Foundation of China/ ; BMI2400013//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; }, abstract = {OBJECTIVE: Food addiction (FA) is implicated in obesity, yet the potential moderating role of mindful eating and the underlying neural mechanisms in youth remain unclear.<br><br>METHODS: This study integrated a multicenter cross-sectional survey, a longitudinal study with 6- and 12-month follow-ups, and an independent magnetic resonance imaging (MRI) sample. FA, eating motives, mindful eating, BMI&#xa0;z-score, fat content, and visceral fat level were assessed. Analyses utilized structural equation modeling, latent growth modeling, and voxel-based morphometry.<br><br>RESULTS: Among 2071 screened, 1601 youth (55.5% boys; mean age&#x2009;=&#x2009;12.69&#x2009;&#xb1;&#x2009;3.04&#x2009;years) completed the baseline survey, with 880 and 564 completing the 6- and 12-month follow-ups, respectively. FA mediated the relationship between eating motives and weight status, and mindful eating moderated this pathway (p&#x2009;<&#x2009;0.05). Longitudinally, baseline FA predicted accelerated accumulation of fat content and visceral fat level, but not BMI z-score (p&#x2009;>&#x2009;0.05). The independent 75-MRI sample revealed that left insula gray-matter volume was negatively associated with FA but positively associated with mindful eating.<br><br>CONCLUSIONS: FA may link eating motives to fat accumulation in youth, particularly abdominal fat; mindful eating may be protective, with left insula structure and left insula-striatum connectivity as possible neural correlates.}, }
@article {pmid42220611, year = {2026}, author = {Xiao, X and Ma, C and Wang, Y and Zhu, J and Chen, J and Li, S and Wang, Y and Gong, W and Si, K}, title = {SynClear: A one-step synchronous clearing and labeling strategy for multiscale 3D brain mapping.}, journal = {Materials today. Bio}, volume = {38}, number = {}, pages = {103261}, pmid = {42220611}, issn = {2590-0064}, abstract = {High-resolution three-dimensional imaging is essential for resolving the multiscale organization of biological tissues. However, conventional workflows treat tissue clearing and molecular labeling as separate steps, leading to a kinetic mismatch between reagent transport and probe binding that limits imaging depth, labeling uniformity, and throughput. Here, we introduce SynClear, a one-step strategy that synchronizes nuclear labeling with tissue clearing by embedding fluorescent probes within a chemically engineered clearing medium. This integrated formulation enables rapid and uniform labeling across millimeter-scale samples while preserving endogenous fluorescence and remaining compatible with multiplexed immunostaining. We demonstrate the general applicability of SynClear across diverse tissue types, including mouse brain, peripheral organs, and post-mortem human cortex. In mouse brain sections, SynClear supports accurate 3D atlas registration and quantitative mapping of cytoarchitecture. In glioblastoma models, it resolves pathological features across scales, from tumor boundaries to immune microenvironments. In human cortex, it enables laminar-resolved structural analysis and neuronal subtype mapping. By coupling labeling and clearing within a single chemical framework, SynClear provides a robust and scalable platform for volumetric tissue imaging, with potential applications in both basic neuroscience and translational pathology.}, }
@article {pmid42222354, year = {2026}, author = {Zheng, ZW and Liu, H and Guo, LY and Fan, LN and Liu, C and Li, FR and Zhou, R and Qu, L and Wang, RM and Xu, WQ and Yang, GM and Dong, B and Dong, Y and Wu, ZY}, title = {Prevalence of pre-existing neutralizing antibodies to AAV5 and AAV8 in patients with Wilson's disease.}, journal = {Molecular therapy. Advances}, volume = {34}, number = {2}, pages = {201755}, pmid = {42222354}, issn = {3117-387X}, abstract = {Wilson's disease (WD) is an autosomal recessive copper metabolism disorder. AAV-based gene therapy is promising but hindered by pre-existing neutralizing antibodies (NAbs), with no region-specific data on AAV5 and AAV8 NAbs in WD patients. This study aimed to address this gap. We investigated AAV5 and AAV8 NAb seroprevalence and dynamics in a cohort of Chinese WD patients via a cell-based transduction inhibition assay. Results showed that seroprevalence of AAV8 (58.52%) was higher than that of AAV5 (44.89%), with AAV8 NT50 titers 4.6-fold higher (p < 0.001). Seroprevalence increased with age, and AAV5 and AAV8 NAbs were strongly correlated (r = 0.848, p < 0.001) with no AAV5-only positivity. Longitudinal data revealed stable serostatus (3.8% seroconversion, no seroreversion) and no significant associations with other clinical parameters. The p.I1148T variant of ATP7B correlated with higher NAb titers. These findings provide epidemiological insights into pre-existing immunity to AAV vectors in WD patients and may help inform vector selection considerations for future gene therapy studies. Early intervention and personalized strategies may improve therapeutic accessibility. This study provides critical data for AAV-ATP7B trial design in Chinese WD patients.}, }
@article {pmid42223364, year = {2026}, author = {Bose, A and Gupta, P and Vemuri, K and Das, D and Ghosh, D and Kumar Dana, S and Saha, S and Hens, C}, title = {Ordinal pattern of brain electrical activity as a marker of stroke-induced alterations in motor imagery task.}, journal = {Chaos (Woodbury, N.Y.)}, volume = {36}, number = {6}, pages = {}, doi = {10.1063/5.0314904}, pmid = {42223364}, issn = {1089-7682}, mesh = {Humans ; *Stroke/physiopathology ; *Electroencephalography/methods ; Female ; Male ; *Brain/physiopathology ; *Imagination/physiology ; Middle Aged ; Entropy ; Aged ; Adult ; }, abstract = {While the multidimensional features of electroencephalographic (EEG) signals have proven to be a valuable source of information, the development of a comprehensive diagnostic tool remains elusive due to variability of responses as observed within the subjects and epochs. We investigate whether ordinal-pattern-based complexity measures of EEG signals can capture stroke-related alterations in motor imagery (MI) tasks. EEG recordings from 36 stroke patients (acute and minor) and 36 healthy controls were analyzed using permutation entropy (PE), a robust symbolic measure of temporal irregularity. Stroke patients perform left- and right-hand MI tasks, while controls are recorded only under eye-open MI and eye-closed resting conditions. Results show that resting-state EEG from healthy participants exhibits low PE values, reflecting structured and regular dynamics, whereas eye-open MI EEG from the same cohort produces high PE values consistent with near-maximally complex, information-rich neural dynamics. Stroke patients demonstrate intermediate PE values during MI tasks, suggesting altered but partially preserved physiological complexity. These findings indicate that entropy-based measures can distinguish between healthy and stroke-related neural dynamics, providing potential biomarkers for tailoring brain-computer interface (BCI) driven rehabilitation strategies.}, }
@article {pmid42223450, year = {2026}, author = {Meyer, LM and Zamani, M}, title = {But do we need high bandwidth? Applications and scaling challenges of invasive brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {23}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ae6dfd}, pmid = {42223450}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/trends/standards ; Humans ; *Electroencephalography/methods ; }, abstract = {Invasive brain-computer interfaces (iBCIs) have expanded from single to thousands of channels, primarily driven by the goal to restore autonomy and social participation for people with severe neurological impairment. This article evaluates whether this increase in bandwidth (here, the aggregate neural data stream) aligns with clinical benefit or yields diminishing returns against rising challenges. The application landscape reveals that performance typically improves with rising channel count. However, the performance curve also depends on other factors such as task complexity, the evaluation metric, spatial redundancy, and decoder capacity. For today's clinical goals (reliable communication and functional motor restoration), moderate bandwidth already suffices when coupled with model-based priors, structured output spaces, and shared-control architectures; next-horizon goals, e.g. unconstrained natural speech, embodied dexterity, and cognitive restoration, however, require abundant sampling but remain constrained by biological, technical, and ethical hurdles, with the engineering trilemma of bandwidth, power, and latency as the primary bottleneck for fully implantable systems. Solving this requires a shift towards low-power on-implant processing to handle increasing neural datastreams. Looking forward, the field is increasingly orienting toward solutions that balance risk and resolution. Large-scale micro-electrocorticography (µECoG) arrays represent such an approach and complement intracortical strategies, aiming to resolve the long-standing trade-off between invasiveness and bandwidth in clinically viable iBCIs.}, }
@article {pmid42223595, year = {2026}, author = {Klei, DS and Benders, KEM and Leenen, LPH and van Wessem, KJP}, title = {Epidemiology and outcomes of traumatic sternal fractures and associated blunt cardiac injury: a nationwide cohort study in the Netherlands.}, journal = {European journal of trauma and emergency surgery : official publication of the European Trauma Society}, volume = {52}, number = {1}, pages = {}, pmid = {42223595}, issn = {1863-9941}, mesh = {Humans ; Male ; Netherlands/epidemiology ; *Sternum/injuries ; Middle Aged ; *Wounds, Nonpenetrating/epidemiology ; Retrospective Studies ; *Fractures, Bone/epidemiology ; *Heart Injuries/epidemiology ; Female ; Aged ; Registries ; Hospital Mortality ; Adult ; Incidence ; Injury Severity Score ; Accidents, Traffic/statistics &amp; numerical data ; }, abstract = {PURPOSE: Comprehensive data on epidemiology, trauma mechanisms, associated injuries, and outcomes of traumatic sternal fractures are scarce. This study analysed nationwide data to improve diagnosis and management within the Dutch healthcare system.<br><br>METHODS: This nationwide retrospective cohort study using the Dutch National Trauma Registry included adult patients admitted with traumatic sternal fractures between 2015 and 2023. Patients with prehospital cardiopulmonary resuscitation or penetrating trauma were excluded. Incidence, patient characteristics, trauma mechanisms, associated injuries, and in-hospital outcomes were analysed. Subgroup analyses evaluated patients with concomitant blunt cardiac injury (BCI).<br><br>RESULTS: Of 568,399 adult trauma admissions, 4,765 patients (0.84%) sustained traumatic sternal fractures. Median age was 62 years; 60% were male. Motor vehicle accidents (48%) and falls (28%) were the leading mechanisms. 35% were severely injured (ISS&#x2009;&#x2265;&#x2009;16). Associated injuries included rib fractures (51%), spinal fractures (36%), and lung contusions (18%). Critical care unit admission was 40%, with median mechanical ventilation duration of 4 days; median hospital stay was 5 days. In-hospital mortality was 5.7%, and 30-day mortality 6.0%. BCI occurred in 9.5% of patients and was associated with a higher number of injuries and increased injury severity, emergency interventions, and critical care admission, but not higher mortality.<br><br>CONCLUSION: Traumatic sternal fractures are uncommon, but the incidence in The Netherlands is gradually rising. Sternal fractures frequently occur with severe multisystem injuries. Patients with BCI showed greater injury severity and resource needs. Future research should focus on criteria and clinical significance of BCI, and sternal fracture-specific outcomes and treatment strategies in large patient cohorts.}, }
@article {pmid42204016, year = {2026}, author = {Chen, X and Qiu, Y and Fu, Y and Shen, M and Chen, H}, title = {Item-specific source misattribution drives short-term source amnesia.}, journal = {Psychonomic bulletin &amp; review}, volume = {33}, number = {5}, pages = {}, pmid = {42204016}, issn = {1531-5320}, support = {32171046//National Natural Science Foundation of China/ ; 32200844//National Natural Science Foundation of China/ ; 32441105//National Natural Science Foundation of China/ ; 32471093//National Natural Science Foundation of China/ ; 2022ZD0210800//Science and Technology Innovation 2030-"Brain Science and Brain-like Research" Major Project/ ; 226-2024-00118//Fundamental Research Funds for the Central Universities/ ; 226-2024-00207//Fundamental Research Funds for the Central Universities/ ; 226-2025-00127//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Memory, Short-Term/physiology ; *Amnesia/physiopathology ; *Mental Recall/physiology ; Adult ; Young Adult ; Female ; Male ; }, abstract = {Source amnesia refers to the failure to remember the source format of information despite remembering the content itself. While well-documented in long-term memory, recent studies have revealed that source amnesia can also occur in short-term or working memory. Across four experiments, the present study aimed to investigate why short-term source amnesia arises, focusing on whether it results from source misattribution between items or item-specific interference caused by repeated exposure to the same content in different formats. We found that source misattribution persisted even for a single item presented per trial, suggesting that item-source misbinding between simultaneously presented items is not necessary for source-amnesia effect. Source misattribution was significantly reduced when the test item was novel or had consistently appeared in a single format across trials, but reliably emerged when the same item had been presented in different formats. These findings suggest that short-term source amnesia reflects item-specific source misattribution, driven by the coexistence of conflicting source traces for the same content. We propose that the task-irrelevant source information for target stimuli is stored in an intermediate representational state-activated long-term memory-which maintains weak bindings to its content but lacks robust contextual indexing.}, }
@article {pmid42205683, year = {2026}, author = {Jha, N and Liu, C and Rogers, A and Lozano, A}, title = {Payers, Proof, and Public Trust: Lessons From Deep Brain Stimulation for Scaling Brain-Computer Interfaces.}, journal = {Mayo Clinic proceedings. Digital health}, volume = {4}, number = {2}, pages = {100366}, pmid = {42205683}, issn = {2949-7612}, }
@article {pmid42206960, year = {2026}, author = {Lee, HK and Kim, HB and Park, SU and Joo, J and Min, J and Lee, G and Kang, J and Jeong, H and Yoo, JY and Won, SM}, title = {Full-Stack Architectures for Intelligent Brain-Computer Interfaces.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e75838}, doi = {10.1002/advs.75838}, pmid = {42206960}, issn = {2198-3844}, support = {RS-2024-00427006//Korea Planning &amp; Evaluation Institute of Industrial Technology/ ; IITP-2025-RS-2020-II201821//National Research Foundation of Korea (NRF) grant funded by the Korea government/ ; RS-2025-02303342//National Research Foundation of Korea (NRF) grant funded by the Korea government/ ; RS-2024-00406152//National Research Foundation of Korea (NRF) grant funded by the Korea government/ ; RS-2024-00406674//Basic Research Laboratory Project from the National Research Foundation/ ; RS-2024-00418086//Korea Institute for Advancement of Technology/ ; RS-2024-00435693//Korea Institute for Advancement of Technology/ ; }, abstract = {Brain-computer interfaces (BCIs) have made consistent advances in supporting motor and communication functions; nevertheless, their adoption in everyday environments remains constrained by enduring challenges, including chronic instability at the electrode-tissue interface, motion-induced artifacts, inter-user variability, and strict power and bandwidth limitations. To address these issues, recent work has increasingly focused on system-level innovations encompassing electrode design, wireless communication strategies, and neural decoding algorithms. At the interface level, enhancements in electrochemical performance and mechanical compliance improve long-term electrode-tissue coupling and help maintain signal integrity during naturalistic movement. For signal acquisition and transmission, miniaturized front-end electronics and energy-efficient telemetry architectures enable higher channel counts while minimizing power consumption and optimizing bandwidth utilization. In parallel, decoding approaches have evolved from static, feature-based pipelines toward adaptive machine-learning and deep-learning methods that are more resilient to nonstationary neural signals and capable of supporting low-latency, closed-loop operation. This review consolidates findings from contemporary preclinical and human studies to provide a comprehensive perspective on system-level engineering strategies for practical BCI technologies, emphasizing neural interface architecture and system-design approaches that enhance signal stability and real-world usability, while also identifying emerging design paradigms that may facilitate next-generation BCIs with improved scalability and broader practical impact.}, }
@article {pmid42212280, year = {2026}, author = {Yu, H and Wang, J and Li, Q and Xu, P and Xu, S and Chen, C and Lu, J and Li, F and Yao, D and Xu, P and Hou, J and Ma, X and Yi, C}, title = {Dynamic central-peripheral balance in brain-muscle interactions reveals motor impairment in post-stroke hemiplegia: an exploratory study.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {102}, pmid = {42212280}, issn = {1871-4080}, abstract = {Hemiplegia following stroke is characterized by disrupted neuromuscular interactions, yet the central-peripheral dynamics remain unclear. This study investigated dynamic causal interactions between electroencephalography (EEG) and electromyography (EMG) using the adaptive directed transfer function (ADTF) during a thumb-pressing task in hemiplegic patients and explored the central-peripheral balance between central motor commands and peripheral sensory feedback. Results suggested that patients with better motor functions may exhibit a dynamic transition from relatively balanced bidirectional interactions to centrally dominated descending control and back to balance. Patients with more severe hemiplegia exhibited pronounced descending control impairment and ascending feedback enhancement, particularly on the affected side. The difference between the out-degrees of central-peripheral pathways during the motor preparatory phase served as a potential predictor of motor function, as assessed by the Barthel Index. This finding provides exploratory evidence for the imbalance between peripheral-to-central and central-to-peripheral coupling as a potential neural biomarker for functional recovery, tentatively supporting the development of more targeted and personalized rehabilitation strategies.}, }
@article {pmid42213304, year = {2026}, author = {Shi, B and Li, J and Shao, B and Song, C and Bai, G and Lan, T and Gao, T and Li, Y and Song, T and Sun, B}, title = {Stiffness-Switchable Conductive Nanocomposites with Temperature-Invariant Conductivity for Long-Term Brain-Computer Interfaces on Hair-Covered Scalp.}, journal = {Small (Weinheim an der Bergstrasse, Germany)}, volume = {}, number = {}, pages = {e73966}, doi = {10.1002/smll.73966}, pmid = {42213304}, issn = {1613-6829}, support = {22408248//National Natural Science Foundation of China/ ; 62274116//National Natural Science Foundation of China/ ; 62474120//National Natural Science Foundation of China/ ; ZZ2506//Jiangsu Provincial Key Laboratory of Carbon-Based Functional Materials and Devices for High-Tech Research/ ; //111 Program/ ; //Collaborative Innovation Center of Suzhou Nano Science and Technology/ ; 0004/2025/RDP//Macau SAR/ ; }, abstract = {Reliable neural recording on densely hair-covered scalp remains challenging due to the incompatibility between efficient hair penetration, conformal skin contact, and low-impedance electrical interfacing. Here, we report a claw-shaped dry electrode that integrates thermoresponsive phase-transition networks for reversible stiffness switching, with temperature-invariant conductivity enabled by crystallization-induced confinement, achieving comfortable, low-impedance neural interfacing on densely hair-covered scalps. The electrode comprises a bottlebrush polymer/multi-walled carbon nanotubes (MWCNTs) composite, in which crystallizable alkyl side chains act as switching units, enabling rigid hair penetration at ambient conditions and compliant, adhesive scalp interfacing at skin temperature. Importantly, side-chain crystallization imposes spatial confinement on MWCNTs, enabling efficient percolated networks with an ultralow percolation threshold (0.47 wt.%) and high electrical conductivity (1.8 S m[-1]). Meanwhile, strong MWCNTs-polymer interfacial interactions provide multipoint anchoring that helps preserve conductive pathway continuity across phase transitions, achieving low electrode-scalp impedance (∼38 kΩ) upon softening and conformal contact. Validated by steady-state visual evoked potential measurements, this electrode enables high-fidelity and frequency-resolved neural signal acquisition and maintains stable operation for over 100 days, supporting a fully wearable brain-computer interface with real-time drone control.}, }
@article {pmid42214321, year = {2026}, author = {Uengsawapak, B and Kongwudhikunakorn, S and Kiatthaveephong, S and Polpakdee, W and Chaisaen, R and Chuenchit, C and Manoonpong, P and Bhakdisongkhram, G and Wilaiprasitporn, T}, title = {EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in Motor Imagery and execution.}, journal = {GigaScience}, volume = {}, number = {}, pages = {}, doi = {10.1093/gigascience/giag065}, pmid = {42214321}, issn = {2047-217X}, abstract = {This study presents the first publicly accessible electroencephalography (EEG) dataset explicitly targeting sit-to-stand and stand-to-sit transitions during both motor execution (ME) and motor imagery (MI) tasks. Twenty-two healthy participants performed sitting and standing transitions under well-controlled experimental conditions while 60-channel EEG, electrooculography (EOG), and electromyography (EMG) signals were synchronously recorded. The dataset enables the exploration of neural activation patterns associated with lower-limb movements and supports the development of EEG-based brain-computer interface (BCI) algorithms for mobility assistance and rehabilitation. To validate the dataset, benchmark classification was conducted on three baseline deep learning methods-CTNet, EEGNet, and TCANet. Given the high inter-subject variability inherent to EEG, leave-one-subject-out cross-validation (LOSOCV) is used to ensure no subject bias during evaluation. Results demonstrated consistent decoding performance with mean accuracies of approximately 81% for ME and 73% for MI, indicating the reliability and usability of the dataset. Additionally, analyses of movement-related cortical potentials (MRCPs) and event-related desynchronization/synchronization (ERD/ERS) patterns revealed distinct neural signatures across the transition phases. This dataset provides a comprehensive foundation for studying lower-limb motor control, neural dynamics, and the advancement of MI-based BCIs for rehabilitation and assistive technologies.}, }
@article {pmid42215489, year = {2026}, author = {Liu, L and Ferrante, O and Ghafari, T and Hetenyi, D and Yang, S and Hirschhorn, R and Gorska-Klimowska, U and Sripad, P and Taheriyan, F and Brown, T and Das, D and Kahraman, K and Bonacchi, N and Pitts, M and Mudrik, L and Jensen, O and Luo, H and Melloni, L}, title = {An open multi-center MEG-EEG dataset for studying conscious visual perception.}, journal = {Scientific data}, volume = {13}, number = {1}, pages = {}, pmid = {42215489}, issn = {2052-4463}, support = {TWCF0389,TWCF0486//Templeton World Charity Foundation (Templeton World Charity Foundation, Inc.)/ ; grant number 227420//Wellcome Trust (Wellcome)/ ; NIHR203316//DH | National Institute for Health Research (NIHR)/ ; }, mesh = {Humans ; Female ; *Magnetoencephalography ; *Visual Perception ; *Electroencephalography ; *Consciousness ; Young Adult ; Male ; Adult ; Magnetic Resonance Imaging ; }, abstract = {Here, we present a large-scale, multi-center dataset of combined magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, along with eye-tracking data and high-resolution structural MRI (T1); complementing with iEEG and fMRI datasets that are shared in accompanying data papers. The data was obtained through an adversarial collaboration between advocates of two neuroscientific theories of consciousness: the Global Neuronal Workspace Theory and the Integrated Information Theory. The dataset includes recordings from 100 individuals (mean age 22.79 ± 3.59 years, 54 female, all right-handed) across two research centers (UK and China), using a standardized data collection protocol. During the experiment, participants were asked to perform a non-speeded Go/No-Go target detection task, during which they were exposed to visual stimuli from four distinct categories (faces, objects, letters, false fonts) presented at different orientations (front, left, right view), and for varying durations (0.5, 1.0, 1.5 s), under different task conditions. The quality of the data was assessed and organized according to the Brain Imaging Data Structure (BIDS). It is accompanied by extensive metadata to enhance reusability.}, }
@article {pmid42202176, year = {2026}, author = {Zhao, R and Daly, I and He, X and Xu, R and Wang, C and Wang, X and Cichocki, A and Jin, J}, title = {Breaking the Depth Barrier in Motor Imagery Classification via a Residual Depthwise-Separable Network.}, journal = {IEEE transactions on cybernetics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TCYB.2026.3690707}, pmid = {42202176}, issn = {2168-2275}, abstract = {Lightweight networks that include depthwise-separable convolution are widely used in motor imagery (MI) electroencephalogram (EEG) decoding of brain-computer interface (BCI). Many established MI classification networks are relatively shallow, preventing them from benefiting from the hierarchical feature extraction capabilities of deeper structures. Due to suboptimal residual connection structures, the mismatched residual baseline layer design, and the poor compatibility between data preprocessing and residual modules, the deepening of networks cannot be effectively combined with residual structures. This creates a depth barrier that hinders further performance improvements. To address these challenges, we propose a novel method, residual depthwise-separable deep neural network (ResDSNet), built upon an unraveled view-path analysis of residual connection structures. The analysis reveals that the residual mechanism achieves optimal performance when the layer distribution across different paths approximates a binomial distribution. Furthermore, we design a residual depthwise-separable convolution module and a tailored data-preprocessing module that effectively integrate with the residual structure, filtering noise and retaining MI task features. We evaluate ResDSNet on three publicly available datasets, including the BCI Competition IV Dataset IIa, the BCI Competition IV Dataset IIb, and the PhysioNet dataset, which collectively contain EEG signals recorded from 127 human subjects. ResDSNet achieves accuracies of 79.36%, 84.95%, and 64.13%, outperforming state-of-the-art methods by 3.16%, 1.59%, and 8.40% with statistical significance. Experimental results indicate that ResDSNet fully unlocks the hierarchical representation capabilities of deep networks for MI-EEG decoding, achieving robust performance and demonstrating substantial potential to overcome the inherent challenges in BCIs.}, }
@article {pmid42202831, year = {2026}, author = {Gorenshtein, A and Omar, M and Barash, Y and Nadkarni, G and Klang, E}, title = {Large Language Models Integrated into Brain-Computer Interfaces for Communication and Control: A Systematic Review.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae737b}, pmid = {42202831}, issn = {2057-1976}, abstract = {Large language models (LLMs) are starting to be coupled with brain-computer interfaces (BCIs) for assistive communication, but the resulting systems differ widely in where the model sits in the pipeline and in what they actually measure. We performed a systematic review, prepared according to PRISMA, of eleven studies that combine an LLM with a BCI for communication or control. The included work covers P300, SSVEP, cVEP, passive affective and auditory paradigms, and five integration patterns: autocomplete, post-edit correction, intent expansion, dynamic interface generation and affective support. For each study we extracted the hardware and decoding pipeline, the LLM and prompting strategy, latency reporting and outcomes; we used scenario-appropriate metrics rather than a single common metric. Risk of bias was judged with an adapted ROBINS-I framework that stratified studies into online, offline-simulation and system-proposal categories. In the copy-spelling scenario, two studies that measured keystroke savings directly reported values above 50%, with one study exceeding 60% in a multi-turn condition; on an intent-based ALS message-bank task, one online study reached 42 characters per minute with a semantic accuracy of 88%. None of the eleven studies enrolled motor-impaired patients, seven of eleven relied on remote OpenAI endpoints, and reporting of end-to-end latency and failure modes was sparse. We propose a five-category taxonomy of BCI/LLM integration, separate findings that are supported from those that are still speculative, and give a checklist of metrics that should be reported by future studies. The taxonomy and the reporting checklist are the main contributions; clinical benefit for the target population remains to be shown.}, }
@article {pmid42150595, year = {2026}, author = {Li, X and Zheng, C and Tian, Y}, title = {Distinct electrophysiological profiles of bacterial mechanosensitive channels for sonogenetic actuator selection.}, journal = {Journal of neural engineering}, volume = {23}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ae6f83}, pmid = {42150595}, issn = {1741-2552}, mesh = {Animals ; Rats ; *Ion Channels/physiology/genetics ; *Mechanotransduction, Cellular/physiology ; Local Field Potential Measurement ; *Primary Visual Cortex/physiology ; *Escherichia coli Proteins/genetics/physiology ; Male ; *Ultrasonic Waves ; }, abstract = {Objective.Sonogenetics combines ultrasound stimulation with genetically encoded mechanosensitive (MS) ion channels for cell-targeted neuromodulation. Actuator choice, however, remains largely empirical becausein vivoelectrophysiological response signatures are rarely compared under matched conditions. Here, we conducted an exploratoryin vivobenchmarking of three bacterial MS channels (MscL-G22S, MscL-G22N, and MscS) during transcranial ultrasound stimulation in anesthetized rat primary visual cortex (V1).Approach.local field potentials (LFPs) were recorded via a microelectrode array from V1 expressing AAV-delivered channels during graded ultrasound stimulation (1 MHz;Ispta100-400 mW cm[-2]). We quantified baseline activity, ultrasound-evoked potentials (UEPs), trial-to-trial response distributions, and frequency-band power dynamics.Main results.Channel identity shaped both baseline and ultrasound-evoked cortical activity. MscS increased baseline LFP total power (∼2.5 dB vs control,P= 0.0035), whereas MscL-G22S shifted baseline band composition (reduced theta, enhanced gamma). MscL-G22S showed the lowest detectable UEP threshold, producing a detectable N1 at 100 mW cm[-2]and an intensity-dependent N1 increase up to ∼2-fold at 400 mW cm[-2](P< 0.0001). Latency depended on both channel and intensity: MscL-G22N responded faster at low intensity, while MscL-G22S accelerated at higher intensities. MscL expression narrowed trial-to-trial response distributions (bimodal to unimodal). Spectrally, MscL-G22N enhanced theta power, whereas MscL-G22S recruited beta-gamma oscillations at high intensity.Significance.Under matched stimulation and expression conditions, bacterial MscL produced distinct network-level response profiles spanning UEP threshold, response timing, trial-to-trial consistency, and oscillatory modulation. These exploratory benchmarks provide quantitative reference data for comparing sonogenetic actuators and may inform actuator selection for closed-loop neuromodulation.}, }
@article {pmid42197993, year = {2026}, author = {Hu, X and Kang, M and Liu, Y and Shi, T and Shi, X and Fu, Y and Gong, A}, title = {Design and Experimental Investigation of a Multi-Level Heartbeat Sound Feedback-Based Neurofeedback System: Neural Mechanisms.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {10}, pages = {}, pmid = {42197993}, issn = {1424-8220}, support = {62006246, 82172058, 62376112, 81771926, 61763022//National Natural Science Foundation of China/ ; 2023M734315//China Postdoctoral Science Foundation/ ; 959202413100//the Xi'an Science and Technology Association Youth Talent Support Program/ ; }, mesh = {*Neurofeedback/physiology/methods ; Humans ; Brain-Computer Interfaces ; Electroencephalography/methods ; *Heart Rate/physiology ; Male ; Brain/physiology ; }, abstract = {Auditory neurofeedback training (NFT) based on brain-computer interfaces (BCIs) has recently entered the precision motor domain as a task-embedded neural state regulation paradigm. Compared to traditional standalone NFT approaches (e.g., relaxation or attention training designed to enhance general cognitive abilities), task-embedded paradigms integrate feedback directly into the motor task execution process. However, this design inevitably creates a dual-task scenario, and the effects of such a scenario on neural activity and behavioral performance have received limited systematic investigation in the existing literature. This study designed and implemented a closed-loop BCI system employing five-level heartbeat sound feedback and used this system as a research platform to examine the immediate neural mechanism changes and potential dual-task interference effects induced by single-session auditory NFT in moderately skilled shooters. The system maps real-time EEG features onto graded auditory signals varying in playback rate and volume intensity, incorporating a dynamic threshold adjustment mechanism. Twenty-two moderately skilled shooters completed three within-subject conditions (no-sound baseline, SMR enhancement, and theta suppression) in a single session with 32-channel EEG and behavioral data recorded simultaneously. Analyses employed whole-brain cluster-based permutation tests, cross-frequency coupling analysis, and functional connectivity analysis. Cluster-based permutation tests revealed that theta feedback induced a significant frontal 4-7 Hz suppression cluster (cluster p = 0.004), whereas SMR feedback did not produce significant 12-15 Hz enhancement at the group level. Theta feedback elicited cross-frequency spillover as follows: sensorimotor SMR power decreased significantly in theta responders (d = -0.69), with frontal theta and sensorimotor SMR changes positively correlated (r = 0.67, p < 0.001). Functional connectivity analysis using debiased weighted phase lag index (dwPLI) further demonstrated significant theta-band network reorganization (cluster p = 0.034). At the neural level, clear modulation effects were observed, but shooting ring values did not improve significantly under feedback conditions, and aiming time was significantly prolonged-a behavioral pattern consistent with potential dual-task interference from task-embedded auditory feedback. Single-session auditory NFT can act on the prefrontal cognitive control network and induce cross-frequency network reorganization, but the feedback channel itself constitutes a parallel task that may limit the short-term transfer of induced neural states to behavioral performance. This study examined the neural mechanisms of task-embedded auditory NFT and reported the dual-task costs that have been less characterized in prior "task + feedback" research, providing design considerations and preliminary mechanistic evidence for future development of auditory NFT in precision motor skill training.}, }
@article {pmid42198020, year = {2026}, author = {Zhang, H and Siok, WT and Wang, N}, title = {Imagined Speech Brain-Computer Interface: A Task-Oriented Review of Neural Decoding.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {10}, pages = {}, pmid = {42198020}, issn = {1424-8220}, support = {P0053210, P0053738, P0048377, P0056428, P0058097//Hong Kong Polytechnic University/ ; C5033-24G//Research Grants Council Collaborative Research Fund/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Speech/physiology ; *Brain/physiology ; Electroencephalography ; *Imagination/physiology ; }, abstract = {Imagined speech decoding has attracted growing interest in brain-computer interface (BCI) research, as it may enable language-related information to be recovered from non-overt neural activity. Current studies in this area are often treated as a single, unified research problem, despite substantial differences in decoding target, output constraints, and system output forms. This review examines recent imagined speech decoding research from a task-oriented perspective, with a focus on how different neural decoding tasks are defined, constrained by their output spaces, and expressed through different output pathways. The included studies are organized into four main task levels: semantic/intent, phoneme/syllable, word, and sentence/language decoding. They are further compared along two auxiliary dimensions: output-space property and output pathway, with particular attention to closed-set and open-vocabulary settings. The review shows that current studies span markedly different linguistic granularities and communication objectives, from low-bandwidth intent recognition to text or speech reconstruction. Finally, it concludes that imagined speech should not be treated as a single homogeneous decoding problem, and that a task-oriented framework provides a clearer basis for comparing heterogeneous studies and guiding future communication-oriented BCI research.}, }
@article {pmid42198082, year = {2026}, author = {Suffian, M and Ieracitano, C and Mammone, N and Pascarella, A and Ferlazzo, E and Morabito, FC}, title = {An EEG-Based Edge-AI Framework for Alzheimer's and Creutzfeldt-Jakob Disease Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {10}, pages = {}, pmid = {42198082}, issn = {1424-8220}, mesh = {Humans ; *Creutzfeldt-Jakob Syndrome/diagnosis/classification/physiopathology ; *Alzheimer Disease/diagnosis/classification/physiopathology ; *Electroencephalography/methods ; *Artificial Intelligence ; Convolutional Neural Networks ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Neural Networks, Computer ; Female ; }, abstract = {Electroencephalography (EEG) has emerged as a promising non-invasive tool for the diagnosis of neurodegenerative disorders, and artificial intelligence (AI) has shown significant potential in this domain, as demonstrated by recent studies. However, strong inter-subject variability remains a major challenge, limiting the ability of AI-based models to learn disease-specific features that generalize across individuals, thereby hindering the development of clinically deployable subject-independent systems. In this work, we propose a cross-subject, AI-based EEG classification framework to distinguish between Alzheimer's disease (AD), Creutzfeldt-Jakob disease (CJD), and healthy control subjects using clinical EEG data collected from a local hospital. A lightweight hybrid deep learning model is developed, combining a two-layer one-dimensional convolutional neural network with a two-layer Transformer encoder to capture both local temporal patterns and long-range dependencies in EEG signals. The proposed model achieves an average classification accuracy of 97%, representing a 3% improvement over a baseline model evaluated on a cohort of 36 subjects. To assess deployment feasibility in real-time clinical settings, the trained model is implemented and evaluated on an edge-AI platform (NVIDIA Jetson AGX Orin), demonstrating energy efficiency for the inference with a compact model footprint. These results indicate that the proposed approach provides an accurate, efficient, and practically deployable solution for subject-independent EEG-based classification of neurological disorders.}, }
@article {pmid42202025, year = {2026}, author = {Won, C and Cho, YU and Kweon, S and Cho, S and Kwon, C and Kim, HW and Lee, JY and Park, SH and Han, S and Kim, YT and Jang, J and Jekal, J and Kim, JG and Jang, KI and Xu, S and Gao, W and Cho, IJ and Yu, KJ and Lee, T}, title = {Structurally engineered ultrasoft PEDOT:PSS fiber microelectrodes with enhanced electrochemical performance for neural interfaces.}, journal = {Science advances}, volume = {12}, number = {22}, pages = {eaee2754}, pmid = {42202025}, issn = {2375-2548}, mesh = {Animals ; Microelectrodes ; *Polystyrenes/chemistry ; Mice ; *Neurons/physiology ; *Polymers/chemistry ; *Bridged Bicyclo Compounds, Heterocyclic/chemistry ; Hippocampus/physiology ; *Electrochemical Techniques ; Thiophenes ; }, abstract = {Stable and reliable neural interfacing is essential for the diagnosis and treatment of chronic neurological disorders. Flexible neural probes are particularly important for this purpose, as they minimize tissue damage and inflammatory responses while maintaining stable electrode-tissue coupling; however, achieving both high electrical performance and tissue-like mechanics remains challenging. Here, we present a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) fiber microelectrode (PFME), an all-organic neural probe capable of recording single-neuron activities with potential for long-term interfacing. The PFME is entirely composed of organic components and fabricated without thermal processing. In addition, the posttreatment process enables to selectively remove PSS binder networks while promoting PEDOT chain alignment to optimize mechanical compliance and electrochemical performance. In vivo, the PFME enables stable single-unit recordings from the mouse hippocampus. Histological analysis after 1 week of implantation reveals minimal glial activation comparable to that elicited by a conventional probe. This structurally engineered PFME establishes a pathway to achieve minimally invasive neural interfacing platforms for chronic applications.}, }
@article {pmid42195527, year = {2026}, author = {Wu, Q and Gong, Y and Liu, X}, title = {Bridging the Gap: Integrated High-Density Microelectrode Arrays for Cellular, Organoid, and Clinical Electrophysiology.}, journal = {Micromachines}, volume = {17}, number = {5}, pages = {}, pmid = {42195527}, issn = {2072-666X}, abstract = {High-density microelectrode arrays (HDMEAs) have become increasingly important tools in neuroscience and biomedical engineering because of their high spatial and temporal resolution for recording and modulating electrical activity across diverse biological systems. Initially developed for in vitro studies of cultured cells, HDMEAs are now being applied to increasingly complex models, including organoids, animal systems, and even human neural systems. These advancements enable a deeper investigation of cellular interactions, network dynamics, and disease mechanisms, as well as providing novel therapeutic and diagnostic tools for neurological disorders. This review explores the evolution of HDMEAs, emphasizing recent innovations in their design, fabrication, and functionalization. We discuss their applications across cellular models, organoid systems, animal studies, and human electrophysiology, and highlight current challenges such as biocompatibility, long-term stability, scalability, and translational deployment. Finally, we outline future directions for advancing HDMEA technologies in both research and clinical settings.}, }
@article {pmid42196223, year = {2026}, author = {Ma, S and Li, Y and Fei, T}, title = {CRISPR Screening in Hepatocellular Carcinoma: From Tumor Progression to Immune Evasion and Therapeutic Resistance.}, journal = {International journal of molecular sciences}, volume = {27}, number = {10}, pages = {}, pmid = {42196223}, issn = {1422-0067}, support = {2023A1515140084//Guangdong Basic and Applied Basic Research Foundation/ ; 32470673//National Natural Science Foundation of China/ ; B16009//the 111 Project/ ; 2022JH13/10200026//the Construction Project of Liaoning Provincial Key Laboratory, China/ ; }, mesh = {Humans ; *Carcinoma, Hepatocellular/genetics/therapy/immunology/pathology ; *Liver Neoplasms/genetics/immunology/therapy/pathology ; *Drug Resistance, Neoplasm/genetics ; *CRISPR-Cas Systems ; Animals ; Disease Progression ; *Tumor Escape/genetics ; *Clustered Regularly Interspaced Short Palindromic Repeats ; *Immune Evasion/genetics ; }, abstract = {Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and a leading cause of cancer-related mortality worldwide. Despite advances in targeted therapies and immunotherapies, clinical outcomes remain poor owing to profound molecular heterogeneity, intrinsic therapeutic resistance, and complex immune evasion mechanisms. Although genomic profiling has identified recurrent alterations in HCC, large-scale functional validation of candidate drivers and vulnerabilities remains challenging. CRISPR (clustered regularly interspaced short palindromic repeats)-based screening technologies have transformed this landscape by enabling systematic interrogation of gene function in physiologically relevant contexts. In this review, we summarize recent studies that have applied CRISPR screening approaches in HCC research. These efforts have uncovered multilayered dependency programs that govern ferroptosis resistance, metabolic reprogramming, epigenetic regulation, tumor suppressor networks, immune evasion, and resistance to targeted therapies. We also discuss the major limitations of current studies, including model bias, incomplete representation of HCC heterogeneity, and technical constraints intrinsic to pooled screening. Overall, integration of CRISPR screening with patient-derived models, single-cell readouts, and precision editing technologies is expected to accelerate mechanistic discovery and biomarker-guided therapeutic prioritization for HCC.}, }
@article {pmid42185513, year = {2026}, author = {He, M and Sha, L and Tang, G and Pang, J and Jin, L and Fu, Y and Huang, S and Wang, W and Wen, S and Yao, Y and Wei, P and Chen, L}, title = {Towards generalizable seizure monitoring: EpiVLM for cross-environment detection and classification.}, journal = {NPJ digital medicine}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41746-026-02810-3}, pmid = {42185513}, issn = {2398-6352}, support = {JCYJ20220818100213029//Shenzhen Science and Technology Innovation Committee/ ; ZYYC23011//1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University/ ; 2025NSFTD0027//the Supported by Sichuan Science and Technology Program/ ; STI2030-Major Projects+2021ZD0204300//the Ministry of Science and Technology of the People's Republic of China grant/ ; }, abstract = {The translation of automated seizure detection from controlled clinical units to real-world settings is hindered by heterogeneous recording conditions and limited expert monitoring. We introduce EpiVLM, a multimodal vision-language system that combines clinically structured prompts with video reasoning for cross-environment seizure monitoring. Evaluated on a robust and diverse dataset of 232 video recordings from 127 patients, totaling 11,666 expert-annotated segments from two tertiary centers, unconstrained home recordings, and an independent public dataset, EpiVLM recognized five major semiologies with accuracy 0.795-0.947 and sensitivity 0.842-0.957. With prompts and decision thresholds fixed a priori, performance remained consistent across diverse real-world acquisition conditions without site-specific recalibration. In external validation sets, EpiVLM sustained strong recognition while maintaining low video-level false detections (0.47-2.45%) and timely detection (mean onset-to-detection delay <6 s). Compared with standard video deep-learning baselines, EpiVLM achieved superior overall performance. These results support scalable seizure recognition from routine video and motivate prospective evaluation for remote outcome monitoring.}, }
@article {pmid42185690, year = {2026}, author = {Zhang, J and Zhang, H and Yang, Y}, title = {Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification.}, journal = {Brain informatics}, volume = {}, number = {}, pages = {}, doi = {10.1186/s40708-026-00308-y}, pmid = {42185690}, issn = {2198-4018}, support = {21A13022003//Humanity and Social Science Foundation of the Ministry of Education of China/ ; LMS26F020033//Natural Science Foundation of Zhejiang Province/ ; }, abstract = {Cross-subject motor imagery classification remains challenging due to EEG data scarcity and inter-subject variability. This study proposes a novel framework integrating generative data augmentation with domain adaptation. First, we employ a diffusion probabilistic model to generate high-fidelity synthetic EEG samples, effectively enriching the training data. Subsequently, we propose the AMSC-DANN architecture, which synergizes an Adaptive Multi-Scale Convolution (AMSC) module for extracting multi-granular features with a Domain Adversarial Neural Network (DANN). This combination enables the model to learn discriminative temporal-spectral representations while simultaneously aligning feature distributions across different subjects. Extensive experiments on BCI Competition IV datasets 2a and 2b demonstrate that our proposed framework outperforms state-of-the-art baselines, validating its effectiveness in enhancing cross-subject generalization.}, }
@article {pmid42185714, year = {2026}, author = {Lim, Z and Nguyen, HL and Zeng, Y and Qu, Q and Le, Y and Sun, M and Wang, X and Zhu, H and Qian, Y and Saeed, S and Wang, H and Rong, D and Wang, Y and Zhang, X and Hu, S}, title = {Correction to: Life Cycle and Circadian Rhythms in Central Resident Immunity and Neuropsychiatric Pathology.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1007/s12264-026-01638-x}, pmid = {42185714}, issn = {1995-8218}, }
@article {pmid42186045, year = {2026}, author = {Belfrouh, S and Salmam, FZ and Errattahi, R and Hanine, M and Obidallah, WJ and Alhulayyil, H}, title = {Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.}, journal = {BMC medical informatics and decision making}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12911-026-03552-8}, pmid = {42186045}, issn = {1472-6947}, abstract = {The loss of communication constitutes a critical challenge for people living with paralysis. Brain-computer interfaces (BCIs) paired with artificial intelligence (AI) provide an opportunity to restore this ability. This systematic review examined the use of AI to decode speech from brain signals through both invasive and non-invasive neural interfaces. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we reviewed 115 studies published between 2019 and 2025 to extract data on acquisition protocols, signal preprocessing, and AI architectures. The quality of each study was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The results indicated that the invasive approach achieved a higher median multiclass classification accuracy than the non-invasive method (77.7%, interquartile range (IQR): 61.4-93.2% vs. 73.0%, IQR: 49.3-89.2%; N = 82 studies with comparable multiclass metrics), computed from the best-performing model per study across heterogeneous task types, vocabulary sizes, and predominantly subject-dependent evaluation paradigms (84.3% of studies). However, this narrow gap in raw accuracy (4.7% points) should not be interpreted as a direct cross-modality performance ranking, as it obscures substantial differences in task complexity (invasive studies typically decoded larger vocabularies and continuous speech), evaluation paradigm, and participant population. Additionally, the hybrid convolutional neural network/recurrent neural network (CNN/RNN) architecture and transformers outperformed traditional classifier models. Nevertheless, the quality assessments showed significant limitations; notably, 62.6% of the studies evaluated had a high risk of selection bias due to patient characteristics, and only six studies (5.2%) validated results in paralyzed individuals-all relying on invasive modalities. Among these, classification accuracy ranged from 47.1% to 90.0%, while word error rates for continuous speech decoding ranged from 25.6% to 58.8%, demonstrating feasibility but with substantial variability across paralyzed cohorts. No non-invasive study has demonstrated functional speech decoding in paralyzed populations. This validation gap represents the most urgent translational priority in this field. We proposed a decision framework to address accuracy, cost constraints, and clinical applicability.}, }
@article {pmid42188271, year = {2026}, author = {Zhang, Y and Liu, Y}, title = {User Needs and Preferences for Multimodal Interaction in Social Robots for Later-Life Support: An Exploratory Survey and Conceptual Five-Layer Architecture.}, journal = {Journal of Intelligence}, volume = {14}, number = {5}, pages = {}, pmid = {42188271}, issn = {2079-3200}, abstract = {Social robots hold promise for enhancing later-life support, but user needs and preferences for multimodal interaction modalities remain underexplored. This study explores awareness, willingness, perceived barriers, and modality-function associations across multiple interaction modalities among middle-aged and older adults, and proposes a conceptual five-layer architecture for design guidance. A questionnaire survey with 199 Chinese respondents (aged 45-64: 89.4%, 65+: 10.6%) examined perceptions of voice, visual, gestural, affective, sEMG, and brain-computer interface interactions. Voice and visual modalities were the most preferred; gesture and affective interactions were moderately accepted; awareness of sEMG was high but may reflect confusion with other sensor technologies; and BCI awareness and willingness were low. Based on survey findings and the literature, a conceptual five-layer architecture is presented to inform future social-robot design. The sample predominantly comprised middle-aged participants, so findings reflect prospective later-life users rather than the broader older-adult population. This study offers user-centered insights into multimodal social-robot interaction and provides design implications for future development rather than evaluating emotional-health interventions.}, }
@article {pmid42189404, year = {2026}, author = {He, E and Chen, K and Liu, S and Chen, H and Xiao, Y and Chen, R and Tu, P and Pan, G and Lin, P}, title = {Advances in neuroprostheses: interfaces, materials, and applications.}, journal = {Nano convergence}, volume = {13}, number = {1}, pages = {}, pmid = {42189404}, issn = {2196-5404}, support = {62301483//Natural Science Foundation of China/ ; 62574180//Natural Science Foundation of China/ ; LZ26F010001//Zhejiang Provincial Natural Science Foundation of China/ ; 2026C01008//Pioneer R&amp;D Program of Zhejiang/ ; }, abstract = {Neuroprostheses have become a pivotal technology for restoring sensory, motor, and cognitive functions, offering transformative therapeutic strategies for neurological disorders by bridging or bypassing damaged neural pathways through electronic systems. However, achieving long-term stability and high-fidelity interaction between biological and electronic systems remains a significant challenge due to the mismatch at the neural interface. This review examines the critical role of nanotechnology in building high performance neuroprostheses across six key classes: motor, visual, tactile, language, memory and olfactory. A system architecture of the neuroprostheses is proposed that highlights two critical interfaces, namely, "neural-electronic" and "environment-electronic" interfaces. We survey recent advances in materials and devices that shape better neural electrodes and novel sensors, and discuss the potential utilization of neuromorphic computing for efficient edge processing in neuroprostheses. This review aims to outline future trajectories toward high-throughput bidirectional interaction, biomimetic encoding, and adaptive closed-loop systems, aspiring to achieve seamless integration between electronic systems and biological neural circuitry.}, }
@article {pmid42190715, year = {2026}, author = {Ju, S and Ming, G and Dong, G and Gao, X and Wang, Y}, title = {A color-coded SSVEP-based brain-computer interface.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae7309}, pmid = {42190715}, issn = {1741-2552}, abstract = {OBJECTIVE: Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) predominantly employ frequency, phase, or spatial coding. This study proposes a color-dimension SSVEP encoding scheme and evaluates its feasibility and elicited response characteristics.<br><br>APPROACH: Seven isoluminant colors were paired to form 21 combinations, and four stimulation paradigms (sliding checkerboard, reversing checkerboard, flickering checkerboard, and solid-color flicker) were used to investigate the modulatory effects of color on SSVEP. Offline simulations and an online four-target SSVEP-BCI were conducted for validation purposes.<br><br>MAIN RESULTS: Under identical frequencies and initial phases, different color combinations produced separable SSVEP patterns in amplitude, topography, and phase, enabling reliable classification. At 10 Hz, the online four-target system with solid-color flicker achieved an average information transfer rate (ITR) of 80 &#xb1; 0 bits per minute (bits/min).<br><br>SIGNIFICANCE: The proposed approach introduces an additional encoding dimension for SSVEP-BCI, expanding stimulus design options and supporting broader applications.}, }
@article {pmid42190717, year = {2026}, author = {Yu, C and Dong, X and Zhang, Y and Wan, X and Zhou, Y and Zheng, Y and Liu, H and Li, P and Cui, Z and Wan, C and Li, Y}, title = {Development and feasibility of a motor imagery-based brain-computer interface-controlled closed-loop functional electrical stimulation system for swallowing rehabilitation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae730b}, pmid = {42190717}, issn = {1741-2552}, abstract = {OBJECTIVE: Conventional swallowing functional electrical stimulation (FES) is usually delivered in open loop or triggered by peripheral signals, which may not align precisely with voluntary swallowing intention. We developed a motor imagery-based brain-computer interface (MI-BCI)-controlled closed-loop swallowing FES system for post-stroke dysphagia (PSD) and investigated its neurophysiological basis, decoding performance, and short-term feasibility.<br><br>APPROACH: Two experiments were conducted. In Experiment 1, swallowing motor imagery (SMI)-related electroencephalography (EEG) features were identified in healthy controls (HC, n = 15), patients with PSD (n = 15), and post-stroke patients without dysphagia (PSND, n = 15). A threshold-based decoder based on Fp1 spectral power ratios was then validated in an independent HC cohort (n = 10). In Experiment 2, 10 patients with PSD received 10 sessions of MI-BCI-controlled closed-loop swallowing FES over 2 weeks, and feasibility, usability, and safety were assessed.<br><br>MAIN RESULTS: During SMI, Fp1 spectral power ratios decreased relative to rest. The &#x3b4;/&#x3b1; ratio decreased significantly in all three groups, whereas the &#x3b4;/(&#x3b1; + &#x3b2;) ratio and the (&#x3b4; + &#x3b8;)/(&#x3b1; + &#x3b2;) ratio decreased significantly in HC and PSND and showed the same downward trend in PSD. Patients with PSD also showed higher &#x3b8;-band power at T3 than HC and PSND (P = 0.0382). The decoder achieved a mean classification accuracy of 71.5% in the independent validation cohort. In Experiment 2, adherence was 100%, with 29.8 &#xb1; 6.2 successful closed-loop triggers per session, a mean System Usability Scale score of 72.8 &#xb1; 4.2, and no serious adverse events.<br><br>SIGNIFICANCE: These findings support the technical feasibility of the proposed system, indicate acceptable short-term usability, and show no major safety concerns during the intervention period. &#xD;Trial registration: ChiCTR2400079388.}, }
@article {pmid42193456, year = {2026}, author = {Aktepe, OH and Ulasli, T and Butun, O and Yalcin, S}, title = {Elevated B12/CRP Index as a Simple Prognostic Indicator in Patients with Metastatic Renal Cell Carcinoma Treated with First-Line Targeted Therapy.}, journal = {Biomedicines}, volume = {14}, number = {5}, pages = {}, doi = {10.3390/biomedicines14051131}, pmid = {42193456}, issn = {2227-9059}, abstract = {Background/Objectives: The vitamin B12 (VB12)/C-reactive protein (CRP) index (BCI), a clinically derived index calculated as serum VB12 multiplied by CRP, has shown prognostic value in several cancers. However, its association with survival outcomes in metastatic renal cell carcinoma (mRCC) remains unclear. Therefore, the aim of the present study was to evaluate the prognostic significance of BCI in patients with mRCC treated with targeted therapy. Methods: The BCI was calculated as serum VB12 concentration (pg/mL) × serum CRP concentration (mg/L). The patients were categorized into two BCI prognostic subgroups, high BCI (BCI > 40,000) and low BCI (≤40,000). Survival differences between prognostic subgroups were measured using the Kaplan-Meier method with a log-rank test. Univariate and multivariable analyses were used to determine the association between the selected variables and survival outcomes. Results: We included 213 patients with mRCC, with a median follow-up time of 76 months. The median progression-free survival (PFS) and overall survival (OS) were 10.9 months and 47.7 months, respectively. Patients with high BCI had poorer PFS and OS times than those with low BCI (7.8 months vs. 12.6 months, p = 0.002 for PFS; 22.6 months vs. 68 months, p < 0.001 for OS, respectively). After adjusting for potential confounders, high BCI remained independently associated with poorer PFS and OS (hazard ratio [HR]: 2.40, 95% confidence interval [CI] 1.35-4.26, p = 0.003 for PFS; HR 2.01, 95% CI 1.40-2.88, p < 0.001 for OS). Conclusions: BCI appears to be a promising prognostic biomarker in patients with mRCC treated with first-line targeted therapy. However, its applicability to immune checkpoint inhibitor-based or combination regimens requires prospective validation.}, }
@article {pmid42193552, year = {2026}, author = {Wang, L and Huang, Y and Liu, Y and Zhou, J}, title = {When Scarcity Meets Sustainability: Consumer Preferences for Recycled Products.}, journal = {Behavioral sciences (Basel, Switzerland)}, volume = {16}, number = {5}, pages = {}, doi = {10.3390/bs16050673}, pmid = {42193552}, issn = {2076-328X}, support = {STI 2030 Major Projects 2021ZD0200409//Ministry of Science and Technology/ ; Grant No. 72371226//National Natural Science Foundation of China/ ; }, abstract = {The widespread disposal of waste has led to severe environmental challenges, making the reuse of materials critical for sustainable development. Recycled products, which transform waste into valuable items, are gaining increasing attention from consumers. This research examines how perceived resource scarcity shapes consumer preferences for recycled products and the psychological mechanisms underlying this effect. Across four studies, we induced perceptions of scarcity using two distinct approaches and found that consumers experiencing resource scarcity exhibit higher purchase intentions for recycled products compared with those who do not. This effect is mediated by holistic thinking, which allows consumers to integrate information about a product's past and present identities, enhancing appreciation for transformation and reuse. Moreover, perceived product contamination moderates this relationship. When contamination concerns are low, scarcity strengthens preference for recycled products, whereas high contamination perceptions weaken or eliminate this effect. These findings extend understanding of how resource scarcity influences sustainable consumption, highlight the cognitive processes driving recycled product demand, and provide practical guidance for policymakers and businesses promoting environmentally responsible consumption.}, }
@article {pmid42194293, year = {2026}, author = {Huang, CJ and Cao, CF and Shyu, KK and Lee, TM and Lee, PL}, title = {Continual-Learning-Enhanced CNN-Transformer Framework for Real-Time Motor-Imagery BCI in Virtual Environments.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {13}, number = {5}, pages = {}, doi = {10.3390/bioengineering13050536}, pmid = {42194293}, issn = {2306-5354}, abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) provide an intuitive pathway for neural interaction and rehabilitation, yet their practical deployment remains constrained by long calibration requirements, substantial inter-subject variability, and the non-stationary nature of EEG signals. These challenges are amplified when using dry-electrode EEG, which offers superior convenience for real-world systems but produces noisier and less stable recordings than traditional wet electrodes. As a result, online or real-time four-class MI detection-especially with dry electrodes-has been explored only in a limited number of studies, underscoring an important gap in the field and the need for adaptive, intelligent models capable of coping with continuous signal drift. In this study, we propose a real-time MI-BCI framework that integrates immersive action observation (AO) in virtual reality with a continual learning strategy to manage the evolving nature of dry-EEG features. A CNN-Transformer hybrid model is first initialized through AO-enhanced pre-training and subsequently refined via online continual adaptation during user interaction. This continual learning mechanism enables the classifier to incrementally assimilate new MI patterns while preserving previously acquired knowledge, thereby mitigating the performance degradation that typically arises in extended MI-BCI sessions. Experimental results across four motor classes demonstrate improved decoding accuracy and strengthened sensorimotor activation over time, confirming the system's capacity for user-specific and session-to-session adaptation. By addressing the rarely studied combination of dry electrodes, online four-class MI decoding, and continual learning, the proposed approach enhances MI-BCI robustness, reduces calibration burden, and supports sustainable long-term deployment in intelligent neurotechnology applications.}, }
@article {pmid42194318, year = {2026}, author = {Gravunder, A and Studnicki, A and Kline, J and Behboodi, A and Bulea, TC and Damiano, DL}, title = {Novel Time-Series Forecasting Method to Enhance Accuracy of Real-Time EEG Detection for BCI-Based Neurofeedback Motor Training in Individuals with Cerebral Palsy and Other Neurological Disorders.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {13}, number = {5}, pages = {}, doi = {10.3390/bioengineering13050561}, pmid = {42194318}, issn = {2306-5354}, support = {13-CC-0110//National Institutes of Health Clinical Center/ ; }, abstract = {Real-time detection of motor intent using electroencephalography (EEG) with high accuracy remains a technical challenge for neurorehabilitation. Brain-computer interface-based neurofeedback training (BCI-NFT) paradigms need to detect pre-movement EEG to activate robotics or electrical stimulation nearly simultaneously with movement to promote neuroplasticity. We present a novel detection method commonly used in time-series forecasting (e.g., stock market trends), identifying crosses in fast (short) and slow (long) moving average windows to identify negative deflections in slow movement-related cortical potentials (MRCPs) or event-related desynchronization (ERD) within -400-+100 ms of movement onset. We recorded EEG data from the Cz electrode during our cued ankle dorsiflexion BCI-NFT paradigm in four adult participants, two neurotypical and two with cerebral palsy. Simulated real-time offline analyses demonstrated an 85.9% mean true positive rate and 14.1% false positive rate of detecting motor intent at a mean -182 ms from movement onset. We further evaluated whether the detection indicated a MRCP and/or ERD, with MRCP detected in 70-80% of trials in three participants, but high ERD detection (87%) instead in the other. Preliminary results indicate that this approach offers a straightforward, accurate, and well-timed method for real-time EEG detection during neurofeedback training and as a control signal for brain-computer interfaces.}, }
@article {pmid42194335, year = {2026}, author = {Fan, K and Gu, Q and Ruan, Y}, title = {EEG-ShuffleFormer: A Multi-View Hybrid Network Integrating Time-Frequency and Raw Signal Representations for Few-Channel Motor Imagery EEG Classification.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {13}, number = {5}, pages = {}, doi = {10.3390/bioengineering13050578}, pmid = {42194335}, issn = {2306-5354}, abstract = {Electroencephalogram (EEG) signals hold significant research value in brain function decoding, disease diagnosis, and brain-computer interfaces (BCIs). Few-channel EEG recording devices feature superior portability, simple operation, and facilitated real-time monitoring implementation. However, few-channel motor imagery (MI) EEG signals inherently suffer from data scarcity and limited spatial discriminative information, which pose critical challenges, including insufficient feature extraction and poor robustness in classification tasks. To address these issues, this paper presents EEG-ShuffleFormer, a hybrid network that integrates two complementary views of EEG signals: time-frequency representations obtained via continuous wavelet transform and the original raw signal representations. A lightweight ShuffleNet backbone extracts local features, followed by a Transformer encoder that models long-range temporal dependencies. Evaluated on the BCI Competition IV Dataset 2b, the proposed method achieves an average classification accuracy of 82.23%, with a substantial improvement on challenging subjects compared to the closest baseline method. Compared with existing methods, the proposed multi-view fusion strategy raises the performance floor while maintaining high accuracy on typical subjects, demonstrating its potential to enhance robustness for different subjects in few-channel scenarios.}, }
@article {pmid42195385, year = {2026}, author = {Bastidas-Benalcazar, N and Calero-Apunte, JA and Almeida-Galarraga, D and Navas-Boada, P and Alvarado-Cando, O and Tirado-Espín, A and Villalba-Meneses, F and Carvajal Mora, H and Orozco Garzón, N}, title = {The Neuro-Cardiac Symbiotic Engine: A Multimodal Fusion Architecture for Cognitive State Decoding via High-Performance Computing.}, journal = {Life (Basel, Switzerland)}, volume = {16}, number = {5}, pages = {}, doi = {10.3390/life16050830}, pmid = {42195385}, issn = {2075-1729}, support = {577.A.XVII.25//Universidad de Las Am&#xe9;ricas/ ; 41991//Universidad San Francisco de Quito/ ; }, abstract = {Robust decoding of latent cognitive states from non-stationary physiological time series is a challenging high-dimensional signal processing problem. Traditional unimodal frameworks based only on electroencephalography often show covariate shift and weak cross-task generalization. This study presents the Neuro-Cardiac Symbiotic Engine, a multimodal fusion architecture that combines high-frequency cortical EEG dynamics with low-frequency autonomic regulation derived from heart rate variability within a unified discriminative feature space. The pipeline integrates spectral decomposition and autonomic quadratic descriptors through a memory-optimized high-performance computing workflow on the CEDIA supercomputer. To reduce domain discrepancy between memory and piloting tasks, we design a few-shot calibration strategy based on affine manifold alignment and probabilistic ensemble inference. Validation on 29 subjects reaches a mean classification accuracy of 99.13 percent, far above the zero-shot baseline near 38 percent. Topological analysis also indicates phase-space contraction under high workload, where fused vagal and frontal-parietal biomarkers concentrate system dynamics into a low-entropy attractor. The results establish a mathematically grounded framework for passive brain-computer interfaces and show that orthogonal neuro-visceral integration is critical for reliable cognitive state estimation.}, }
@article {pmid42184469, year = {2026}, author = {Zhang, Y and Li, T and Jiang, J and Li, H and Sun, R and Li, Y and Chen, W}, title = {Altered temporal organization of neural response dynamics during attention processing differentiates ADHD subtypes in children.}, journal = {NeuroImage. Clinical}, volume = {50}, number = {}, pages = {104011}, doi = {10.1016/j.nicl.2026.104011}, pmid = {42184469}, issn = {2213-1582}, abstract = {BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) shows marked heterogeneity, and conventional event-related potential (ERP) measures have limited sensitivity to subtype differences. This study examined whether alterations in the temporal organization of neural responses during attentional processing differentiate ADHD subtypes.<br><br>METHODS: Children with predominantly inattentive ADHD (ADHD-I), combined-type ADHD (ADHD-C), and typically developing (TD) controls completed an auditory oddball task during electroencephalography. Neural responses were analyzed using time-resolved scalp topographies, low-dimensional neural trajectory analysis, and data-driven neural state modeling. Associations with clinical symptoms were examined.<br><br>RESULTS: Both ADHD subtypes showed altered temporal alignment of neural responses relative to TD children, particularly during target processing. Neural trajectories exhibited reduced differentiation between standard and target stimuli, with ADHD-I showing reduced trajectory separation and ADHD-C showing exaggerated but inefficient state excursions. Data-driven analyses further revealed subtype-specific alterations in neural state stability and transitions, which showed exploratory associations with attentional and behavioral impairment.<br><br>CONCLUSIONS: ADHD is characterized by disrupted temporal organization of neural responses that is not captured by conventional ERP measures. Subtype-specific neural dynamics provide a mechanistic account of ADHD heterogeneity.}, }
@article {pmid42184818, year = {2026}, author = {Hennesy, TB and Zander, DA and Kryzer, TJ and Honce, JM and Carlson, ML and Nassiri, AM}, title = {Magnetic Resonance Imaging Artifact Associated With the Oticon Medical Sentio Ti Transcutaneous Bone Conduction Hearing Implant.}, journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology}, volume = {}, number = {}, pages = {}, doi = {10.1097/MAO.0000000000004949}, pmid = {42184818}, issn = {1537-4505}, abstract = {OBJECTIVE: To evaluate magnetic resonance (MR) imaging artifact and image distortion associated with the Oticon Medical Sentio Ti bone conduction implant (BCI) and identify optimized imaging techniques.<br><br>STUDY DESIGN: Cadaveric study.<br><br>INTERVENTION: One cadaveric head specimen was unilaterally implanted with Sentio Ti BCI according to the manufacturer's instructions.<br><br>MAIN OUTCOME MEASURES: Imaging was performed with a Siemens 1.5 Tesla MR machine on XA60 software before and after implantation. Imaging was performed with both standard and metal mitigation techniques. Image scoring (diagnostic vs. nondiagnostic) and qualitative assessment of anatomic subsites were performed by 2 experienced neuroradiologists.<br><br>RESULTS: Image distortion and artifact were noted in all postimplant sequences. For all sequences, imaging of the ipsilateral middle ear, mastoid, and internal auditory canal (IAC) was nondiagnostic. The axial T1 turbo spin echo high bandwidth sequence had the best artifact reduction; however, the ipsilateral temporal bone remained nondiagnostic. Notably, nonecho planar diffusion-weighted imaging (non-EPI DWI) was nondiagnostic for both the ipsilateral temporal bone and the contralateral IAC and middle ear.<br><br>CONCLUSIONS: After implantation of the Sentio Ti BCI, imaging of the ipsilateral temporal bone is rendered nondiagnostic on all MR sequences due to artifact despite the use of metal mitigation techniques. Importantly, the non-EPI DWI HASTE sequence, which is used for cholesteatoma surveillance, is nondiagnostic for all ipsilateral and most contralateral temporal bone subsites, making cholesteatoma surveillance challenging with an implant in place. This finding is critical for clinical decision-making, as rehabilitation of conductive hearing loss in the setting of chronic otitis media is among the most common indications for use of a BCI.}, }
@article {pmid42185119, year = {2026}, author = {Ding, Y and Kosnoff, J and He, B}, title = {A holistic perspective on noninvasive brain-computer interfaces.}, journal = {Trends in neurosciences}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tins.2026.04.009}, pmid = {42185119}, issn = {1878-108X}, abstract = {Brain-computer interfaces (BCIs) decode neural activity to enable direct communication with external devices. This process consists of three modules: signal acquisition, signal processing, and output translation. While invasive BCIs have demonstrated sophisticated and intuitive capabilities, their reliance on surgical implantation limits widespread use. Noninvasive BCIs, in contrast, are more broadly applicable but have traditionally been constrained by low spatial resolution and suboptimal signal quality. Emerging methodological advances are beginning to overcome these limitations. In this review, we examine recent progress in noninvasive BCIs, focusing on neuromodulation-paired BCIs for signal enhancement, deep neural network-based signal processing approaches, and expanded applications through robotic integration. Together, these parallel developments are driving the emergence of more robust, intuitive, and adaptive BCI systems for human use.}, }
@article {pmid42185310, year = {2026}, author = {Jian, Y and Jin, S and Liu, P and Zheng, X and Hong, X and Han, Y and Semyanov, A and Duan, S and Tong, X}, title = {GABA signaling in NG2 glia mediates empathy-like behavior under observational social defeat.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-73488-0}, pmid = {42185310}, issn = {2041-1723}, abstract = {Empathy, ranging from emotional contagion to consolation, is central to social cognition. While neural mechanisms of observed pain are well studied, how witnessing trauma affects empathy-related behaviors remains unclear. Using an observational social defeat (OSD) model, we find that OSD-exposed mice display enhanced allogrooming toward defeated conspecifics, indicating increased consolation behavior. Whole-brain cFos mapping and fiber photometry reveal selective activation of medial amygdala (MeA) GABAergic neurons during empathic allogrooming. NG2 glia modulate this behavior via GABA signaling; their specific ablation in the MeA reduces inhibitory synaptic transmission, disinhibiting neighboring GABAergic neurons and increasing allogrooming. Single-cell RNA analysis reveals that GABA signaling originates from Gad1-expressing NG2 glia. Genetic knockout of Gad1 in NG2 glia recapitulates the phenotype. This mechanism requires elevated corticosterone induced by social defeat. Our findings highlight the role of NG2 glia-GABA neuron interactions in promoting prosocial empathy and suggest targeting GABA signaling in NG2 glia as a potential therapeutic strategy for vicarious trauma.}, }
@article {pmid42185473, year = {2026}, author = {Choi, D and Yip, C and Choi, A and Park, J}, title = {Trust-gated synthetic EEG augmentation reduces performance drops when generalizing to new patients.}, journal = {NPJ digital medicine}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41746-026-02778-0}, pmid = {42185473}, issn = {2398-6352}, support = {2025 NSERC Discovery Grant Bridge Fund//University of Calgary/ ; }, abstract = {Synthetic augmentation can silently harm subject-disjoint EEG generalization. We propose trust-gated augmentation (TGA), a control layer that scores synthetic windows using a teacher trained on real data to ensure label consistency and confidence; only samples above a confidence quantile q are eligible. A fail-closed selector injects synthetic data only if the validation AUROC exceeds the real-only AUROC by a margin; otherwise, it reverts to real-only. In PainMunich chronic-pain EEG (n = 189; 101 chronic pain/88 controls) at 5% subject scarcity, ungated augmentation harmed 56% of paired runs (ΔAUROC < - 0.01), whereas TGA at q = 0.99 reduced harm to 24% with comparable mean AUROC. In BCI IV-2a motor imagery (n = 9) at 25% scarcity, strict gating improved AUROC (0.679 vs. 0.627) and reduced harm (0.16 vs. 0.44). A covariance-manifold audit showed synthetic windows were strongly off-manifold (mean distance ratio 2.39 × 10[4]), motivating explicit governance.}, }
@article {pmid42167136, year = {2026}, author = {Härmä, V and Palsola, M and Kuusipalo, A and Lindh, E and Melin, M and Nohynek, H}, title = {Lessons from the 2024 avian influenza vaccination campaign in Finland: a qualitative inquiry.}, journal = {Vaccine}, volume = {86}, number = {}, pages = {128736}, doi = {10.1016/j.vaccine.2026.128736}, pmid = {42167136}, issn = {1873-2518}, abstract = {Highly pathogenicity avian influenza H5N1 (HPAI H5N1) viruses cause a continuous threat to wild avian populations. During recent years, spillover to both wild and domestic mammals has occurred with an increasing frequency. As a consequence of the recent developments in the epidemiological situation, the human-animal interface with the risk of human exposure to HPAI H5 has expanded. In 2024, Finland became a global forerunner to offer H5 vaccine to occupational risk groups, specifically fur and poultry workers, following an extensive HPAI H5N1 outbreak in 2023 in fur-farmed minks and foxes. Despite targeted efforts to reach the people at increased risk, only 8,6% of the target population received the first dose and 7,5% completed both doses. To seek a better understanding of the barriers behind low vaccine uptake a Behavioural and Cultural (BCI) insight approach was chosen. A rapid qualitative study was conducted in late 2024 (n = 17), utilising semi-structured interviews with health authorities, industry stakeholders, and risk group representatives in the Ostrobothnia region in Finland. Barriers were identified across three dimensions: (1) logistical failures, including poor timing and difficulties in reaching target groups (2) divergent risk perceptions, where economic livelihood overshadowed personal health risks; and (3) political distrust, stemming from perceived stigmatization by national health authorities. The results will provide vital information for future pre-pandemic communication and implementation strategies and helps to identify key stakeholders and target groups.}, }
@article {pmid42167252, year = {2026}, author = {Feng, X and Le, T and Liu, B and Zhao, H and Su, Y and Li, K and Shao, Y and Liu, J and Li, Z and Deng, G and Cao, K and Zhu, Z and Chen, J and Zeng, L and Han, Y and Yang, H and Yu, YQ and Duan, S and Sun, L}, title = {Slow-wave sleep engages brainstem circuitry to prevent stress-induced anxiety.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2026.04.041}, pmid = {42167252}, issn = {1097-4199}, abstract = {The beneficial effects of sleep on anxiety are established, but the mechanisms remain unclear. We identify a GABAergic circuit from the parafacial zone (PZ) to the lateral parabrachial nucleus (LPB) neurons that project to the oval bed nucleus of the stria terminalis (ovBNST) as a node for slow-wave sleep (SWS)-mediated anxiolysis. Optogenetic activation of PZ GABAergic neurons following social defeat stress induces time-locked SWS and prevents anxiety. Multi-region Ca[2+] recording reveals suppressed activity in LPB and ovBNST during natural and PZ-initiated SWS. The LPB-ovBNST pathway is required to drive wakefulness and anxiety, whereas the LPB-basal forebrain pathway promotes arousal without affecting anxiety. PZ neurons inhibit LPB calcitonin gene-related peptide (CGRP)-expressing neurons, which promote wakefulness and anxiety via ovBNST. This effect specifically requires LPB input to ovBNST corticotropin-releasing hormone (Crh) neurons. Thus, we define a PZ[Vgat]-LPB[CGRP]-ovBNST[Crh] circuit essential for sleep-related anxiolysis, providing a potential therapeutic target for anxiety disorders.}, }
@article {pmid42172342, year = {2026}, author = {Zheng, L and Pan, L and Fu, X and Wang 王思羽, S and Wu, Y and Xiao, H and Yang, J and Wang 王思雨, S and Yang, L and Wu, X and Pan, F and Yang, H and Chen, G and Wang, H}, title = {The posteroventral part of the medial amygdala nucleus glutamatergic neurons encodes conspecifics' individual identity in rodents.}, journal = {Science advances}, volume = {12}, number = {21}, pages = {eady9830}, pmid = {42172342}, issn = {2375-2548}, mesh = {Animals ; Mice ; *Neurons/metabolism/physiology ; Olfactory Bulb/physiology ; Odorants ; Male ; Social Behavior ; *Corticomedial Nuclear Complex/physiology/metabolism/cytology ; Female ; Vesicular Glutamate Transport Protein 2/metabolism ; *Amygdala/physiology ; Smell/physiology ; Behavior, Animal ; Optogenetics ; }, abstract = {The medial amygdala (MeA) processes social olfactory cues, but its precise neural mechanisms remain unclear. We identified the posteroventral MeA (MeApv) as critical for individual conspecific odor discrimination in mice. Exposure to conspecifics or their odors markedly elevates calcium signals and c-Fos expression in MeApv VGluT2-positive neurons. Optogenetic silencing of these neurons or activating Gad2-positive neurons disrupts odor-driven social behaviors, including identity recognition, odor discrimination, and sex discrimination. Social information is directly relayed from the accessory olfactory bulb (AOB) to the MeApv, and acute AOB-MeApv pathway disruption impairs social discrimination. A distinct MeApv VGluT2-positive neuron population encodes individual-specific cues, as revealed by microendoscopic calcium imaging at a single-cell resolution. Selective silencing of these neurons induces deficits in odor-guided social interactions with related conspecifics, confirming the MeApv as a central hub for social information encoding. These findings establish the MeApv's dual necessity and sufficiency in translating olfactory signals into social behavioral responses.}, }
@article {pmid42172365, year = {2026}, author = {Cramer, SC and Stein, J and Richards, LG and Hayward, KS}, title = {Advances in Stroke 2026: Recovery and Rehabilitation.}, journal = {Stroke}, volume = {57}, number = {6}, pages = {1792-1795}, doi = {10.1161/STROKEAHA.125.054526}, pmid = {42172365}, issn = {1524-4628}, }
@article {pmid42173506, year = {2026}, author = {Gong, C and Song, Z and He, Z and Liu, H and Han, R and Li, S and Gao, J and Wei, Y and Wen, D and Xue, T and Xu, Z}, title = {Interfacial Polarization Engineering in MXene-Polymer Nanofibers for High-Output Triboelectric Nanogenerators.}, journal = {Langmuir : the ACS journal of surfaces and colloids}, volume = {}, number = {}, pages = {}, doi = {10.1021/acs.langmuir.6c01705}, pmid = {42173506}, issn = {1520-5827}, abstract = {In mechanical energy harvesting and sensing, triboelectric nanogenerators (TENGs) have garnered significant attention for effectively extracting energy from low-frequency, irregular motion and directly transducing it into sensing signals. However, poly(vinylidene fluoride)-based (PVDF-based) TENGs frequently exhibit limitations, including low power density, low output current, and high matched load resistance during operation. Herein, we report a TENG based on MXene, hydroxypropyl methylcellulose (HPMC), and PVDF-HFP composite nanofibers membrane (MHPm) as the negative tribo-layer and Al foil as the counter tribo-layer/electrode for low-frequency mechanical energy harvesting and real-time, ultrasensitive respiratory monitoring. HPMC acts as a synergistic regulator that promotes interfacial interactions among MXene, HPMC, and PVDF-HFP, reduces the coherent stacking scale of MXenes, and facilitates the maintenance of a discrete conductor-polymer-conductor structure, thereby strengthening interfacial polarization and electrical output performance. Under the drive of 70 N and 6 Hz, this device achieves a peak-to-peak (p-p) open-circuit voltage of 1.02 kV, a p-p short-circuit current density of 0.133 A·m[-2], and a peak power density of 27.40 W·m[-2] at a matched load resistance of 20 MΩ, while maintaining a stable current output over 15,000 contact-separation cycles. Moreover, the electrical outputs also provide well-differentiated breathing waveforms, enabling direct self-powered signal acquisition and supporting integrated wearable functionality.}, }
@article {pmid42173881, year = {2026}, author = {Zheng, JL and Zheng, YX and Chen, K and Wang, S and Liang, JW and Wang, SX and Yang, L and Shi, Y}, title = {Cryo-EM structures of ALECT2 filaments from human renal biopsies.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-73602-2}, pmid = {42173881}, issn = {2041-1723}, support = {2025YFC3409700//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 82371415, 82170724//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2024SSYS0018//Science and Technology Department of Zhejiang Province/ ; 2023-PT310-01//Chinese Academy of Medical Sciences (CAMS)/ ; 2025M782772//China Postdoctoral Science Foundation/ ; }, abstract = {Leukocyte chemotactic factor 2 is a recently identified amyloidogenic protein, whose abnormal aggregation defines a systemic amyloidosis termed ALECT2 amyloidosis. Due to the lack of reliable biomarkers, diagnosis relies primarily on histological demonstration and typing of amyloid deposits in renal biopsies. However, immunohistochemical detection of ALECT2 is often inconsistent, leading to diagnostic uncertainty. The underlying basis remains poorly understood, reflecting our limited knowledge of ALECT2 deposits. Here, using cryo-electron microscopy (cryo-EM), we determined the structures of ALECT2 filaments from renal biopsies of five living patients. Unlike filaments assembled from recombinant proteins in vitro, all 133 residues of mature LECT2 are incorporated into the filament cores, with native disulfide linkages preserved. The filaments consistently adopt the shared six-layered folds in all five patients, indicating a common mechanism of amyloidogenesis. Because all residues are incorporated into the fibril core, epitope accessibility is limited. This can explain variability in immunohistochemical detection and thus highlights the need for conformation-specific antibodies and antibody-independent detection strategies for improving diagnostic accuracy. This biopsy-based workflow not only expands the availability of patient-derived tissue for cryo-EM studies but also demonstrates the potential of cryo-EM as a tool for precise diagnosis of systemic amyloidosis.}, }
@article {pmid42174242, year = {2026}, author = {Fu, TM and Liu, G and Milkie, DE and Ruan, X and Görlitz, F and Shi, Y and Ferro, V and Divekar, NS and Wang, W and York, HM and Kilic, V and Mueller, M and Liang, Y and Daugird, TA and Gacha-Garay, MJ and Larkin, KA and Adikes, RC and Harrison, N and Shirazinejad, C and Williams, S and Nourse, JL and Sheu, SH and Gao, L and Li, T and Mondal, C and Achour, K and Hercule, W and Stabley, DR and Emmerich, K and Dong, P and Drubin, DG and Liu, ZJ and Mumm, JS and Koyama, M and Killilea, AN and Bravo-Cordero, JJ and Keene, CD and Luo, L and Kirchhausen, T and Pathak, MM and Arumugam, S and Nuñez, JK and Gao, R and Matus, DQ and Martin, BL and Swinburne, IA and Betzig, E and Legant, WR and Upadhyayula, S}, title = {A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms.}, journal = {Nature methods}, volume = {}, number = {}, pages = {}, pmid = {42174242}, issn = {1548-7105}, support = {LDRD 7647437 and 7721359//DOE | LDRD | Lawrence Berkeley National Laboratory (Berkeley Lab)/ ; 1DP2GM136653//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; 1DP2GM136653//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; F32GM133131//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R35GM118149//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; DP2AT010376//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R01NS109810//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; T32 CA078207/CA/NCI NIH HHS/United States ; T32EY7143&#x2011;22//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R35GM118149//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R01OD020376//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R01CA244780//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R01&#x2011;DC005982//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; DP2AT010376//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R01NS109810//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R01GM121597//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R35GM150290//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; 1R01DC021710//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; 1DP2GM136653//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; P30&#x2011;CA196521//U.S. Department of Health &amp; Human Services | NIH | National Cancer Institute (NCI)/ ; GM130386//U.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; }, abstract = {Understanding biological systems requires observing features and processes across vast spatial and temporal scales, spanning nanometers to centimeters and milliseconds to days, often using multiple imaging modalities within complex native microenvironments. Yet, achieving this comprehensive view is challenging because microscopes optimized for specific tasks typically lack versatility due to inherent optical and sample handling tradeoffs, and frequently suffer performance degradation from sample-induced optical aberrations in multicellular contexts. Here, we present Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC), a reconfigurable microscope that integrates multiple advanced imaging techniques including light-sheet, label-free, super-resolution and multiphoton, all equipped with adaptive optics. MOSAIC enables noninvasive imaging of subcellular dynamics in both cultured cells and live multicellular organisms, nanoscale mapping of molecular architectures across millimeter-scale expanded tissues and structural/functional neural imaging within live mice. MOSAIC facilitates correlative studies across biological scales within the same specimen, providing an integrated platform for broad biological investigation.}, }
@article {pmid42174338, year = {2026}, author = {Pan, Y and Porteous, F and Rosenbaum, D and Fallgatter, A and Ehlis, AC and Koole, SL and Dikker, S}, title = {Inter-brain coupling tracks emotional co-regulation.}, journal = {Cognitive, affective &amp; behavioral neuroscience}, volume = {}, number = {}, pages = {}, pmid = {42174338}, issn = {1531-135X}, abstract = {When we have a negative emotional experience, we often recount this experience to others. Such emotional sharing plays a key role in building and maintaining interpersonal relationships, and is a vital component of most psychotherapies. Yet, while research shows the importance of social relationships for brain health, the neural underpinnings of emotional sharing remain largely unknown. Here, we asked whether successful processing and regulation of negative emotions can be linked to shared brain responses between dyads during and after emotional sharing. Participants watched videos eliciting either negative or neutral emotions, after which they shared their feelings about these videos with a friend. We simultaneously recorded the brain activity of both friends using functional near-infrared spectroscopy (fNIRS) and compared inter-brain coupling within sharer-listener dyads before, during, and after sharing sessions. We found that shifts in inter-brain coupling were associated with changes in mood. Specifically, an increase in inter-brain coupling after recounting a video that elicited negative emotions was associated with reduced anger and dejection in listeners and increased vigor in sharers. These findings suggest that inter-brain coupling facilitates the co-regulation of negative emotions, and thereby maintaining a healthy homeostatic balance. This knowledge holds potential relevance for informing psychotherapeutic interventions.}, }
@article {pmid42179096, year = {2026}, author = {Hari, K and Anand, A and Naveed, A and P, V}, title = {Genetic algorithm-optimized machine learning approaches for EEG-based silent speech decoding.}, journal = {Journal of medical engineering &amp; technology}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/03091902.2026.2676658}, pmid = {42179096}, issn = {1464-522X}, abstract = {The phases of human communication consist of speech perception, production, and imagination. The objective of this work is to understand and analyse the changes that occur in the neural signals during the hearing phase by examining electroencephalogram (EEG) patterns of the subject for different sentences. We propose optimising the decoding process using Genetic Algorithms (GA). Six different experiments are performed on Dataset 3 of coSpeech EEG Database. Both handcrafted features and CNN-based features are used for classification. GA is used for two purposes - channel selection as well as feature selection. Two classifiers - decision trees and SVMs are used for sentence classification. A benchmark accuracy of 41.92% is obtained using the proposed methods. Accuracy improves in the alpha, beta and gamma frequency sub-bands (41.79%, 40.92%, 40.27% respectively). Channel selection using GA reduces the computational load significantly (∼ 90%) while producing comparable results (34.37%, 33.20%, 32.93% in the alpha, beta and gamma sub-bands). This work highlights that EEG is a viable, non-invasive way to decode speech from subjects, which would help people with speech disorders communicate in a better way without exertion. Silent speech decoding has applications in assisting speech-impaired individuals, ensuring private communication, and enhancing human-computer interaction.}, }
@article {pmid42179723, year = {2026}, author = {Choudhary, PK and Choudhary, S and Saha, S and Rajpoot, YS and Ciocan, VC and Nicolae-Lucian, V and Gorgan, CM and Șufaru, C}, title = {Brain-computer interfaces and neural synchronization in esports: a systematic review of effects on reaction time, decision-making, and cognitive performance.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1774230}, pmid = {42179723}, issn = {1662-5161}, abstract = {BACKGROUND: The rapid expansion of esports has intensified interest in the cognitive and neurophysiological mechanisms underlying elite performance, particularly reaction time (RT), decision-making (DM), and neural efficiency. Advances in brain-computer interfaces (BCIs) offer targeted neural modulation that may enhance these abilities through improved neural synchronization. To systematically review evidence on the effects of BCI-based neural synchronization, including motor imagery (MI) BCIs, visual evoked potential (VEP/c-VEP) systems, neural entrainment, and dual-brain coupling, on RT, DM, and related cognitive outcomes in esports athletes and competitive gamers.<br><br>METHODS: Following PRISMA 2020 guidelines, comprehensive searches were conducted across PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, ScienceDirect, and Google Scholar. Studies examining BCI-induced neural modulation and its cognitive or performance effects in esports players or experienced gamers were included. Eighteen studies met the criteria, comprising controlled trials, pre-post interventions, cross-sectional neurophysiology studies, comparative behavioural analyses, and supporting systematic reviews. Due to methodological heterogeneity, results were synthesised narratively. Although the review follows PRISMA 2020 guidelines for systematic study identification and selection, the synthesis adopts a structured integrative narrative approach due to substantial heterogeneity in study designs, BCI modalities, and outcome measures.<br><br>RESULTS: Across studies, BCI-mediated neural synchronization produced consistent improvements in RT, DM accuracy, cortical oscillatory stability, and neural connectivity. MI-BCI and gamified systems enhanced MI accuracy, user engagement, and cognitive load regulation. VEP-based BCIs accelerated perceptual processing by improving signal reliability and reducing latency. Dual-brain coupling improved coordinated decision behaviour. Additional evidence indicates that experienced gamers display superior working memory, attentional control, and visuomotor coordination compared with non-gamers. However, variability in study design, small samples, and moderate risk of bias limit the strength of causal inference.<br><br>DISCUSSION: BCI-based neural synchronization shows promise as a tool for enhancing neurocognitive performance in esports athletes. Future studies should prioritize standardized training protocols, multimodal neural-measurement methods, and longitudinal designs to determine long-term effectiveness and real-world applicability.}, }
@article {pmid42181257, year = {2026}, author = {Xia, Y and Jin, S and Zhou, W and Huang, R and Huang, S}, title = {A comparative study of five telerehabilitation therapies for improving core symptoms in stroke patients: A network meta-analysis (2,833 patients).}, journal = {iScience}, volume = {29}, number = {6}, pages = {115774}, pmid = {42181257}, issn = {2589-0042}, abstract = {This network meta-analysis demonstrates that virtual reality therapy exhibits significant advantages in specific functional domains of remote rehabilitation: Remote virtual reality technology demonstrated the most pronounced effects in improving gait (SUCRA = 92.4%, standardized mean difference [SMD] = -1.27) and upper limb functional recovery (SUCRA = 71.3%, SMD = -0.64), while remote brain-computer interfaces showed the most significant effects in fine motor control. SMD = -1.27) and upper limb functional recovery (SUCRA = 71.3%, SMD = -0.64), while remote brain-computer interfaces showed the greatest effect in fine motor control (SUCRA = 87.6%, SMD = -1.20). Regarding quality of life improvement, exoskeleton training yielded the best results (SUCRA = 62.4%, SMD = 0.05). The findings of this study provide evidence-based support for developing personalized telerehabilitation protocols tailored to specific rehabilitation goals in clinical practice. This approach facilitates a shift in the telerehabilitation field from empirical selection to precision-targeted intervention strategies.}, }
@article {pmid42182055, year = {2026}, author = {Feng, C and Zhang, E and Jia, Y and Zhu, Z and Zhu, J and Wu, D and Xu, K}, title = {Distributed cortico-subcortical networks enable robust speech state detection from sparse intracranial recordings.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1816455}, pmid = {42182055}, issn = {1662-4548}, abstract = {INTRODUCTION: Accurate and reliable detection of speech state transitions is a prerequisite for practical speech brain-computer interfaces (BCIs). While cortical language areas have been extensively studied, it remains unclear whether speech onset information is exclusively localized to these regions or distributed across a broader cortico-subcortical network. Here, we investigated the feasibility of decoding speech state transitions using sparse stereo-electroencephalography (SEEG) recordings that sample both cortical and subcortical structures.<br><br>METHODS: Four Mandarin-speaking epilepsy patients undergoing clinical SEEG monitoring performed a sentence-reading task. Neural signals were segmented and labeled as rest or speech based on acoustic onset. A convolutional neural network was trained to classify speech states using broadband or high-gamma features derived from different anatomical channel subsets. We further evaluated continuous decoding performance, model robustness to channel dropout, and the specific contributions of different brain regions.<br><br>RESULTS: Speech state decoding accuracy exceeded chance level (50%) in all participants, with peak single-participant accuracies surpassing 90%. Models integrating both cortical and subcortical signals generally outperformed those restricted to a single anatomical domain. Notably, broadband signals yielded higher classification accuracy than high-gamma features. In continuous decoding simulations, performance remained above chance, although reduced relative to discretized evaluation. Crucially, decoding accuracy was robust to random channel reduction (up to 50%) and remained above 70% even after excluding classical speech-related cortical regions. Contribution analyses indicated participant-specific patterns of model sensitivity, with relatively higher contributions observed in frontal regions and the thalamus in multiple participants.<br><br>DISCUSSION: These findings support the hypothesis that speech state information is represented in a distributed cortico-subcortical network rather than being confined to canonical language areas. The robustness of decoding performance despite channel reduction and regional exclusion suggests that sparsely sampled SEEG data can effectively drive speech detection modules. This study demonstrates the feasibility of utilizing deep brain recordings for speech BCIs, offering a pathway toward more stable and generalized implantable systems. Moreover, such autonomous speech state detection may also serve as an ethical safeguard, ensuring that neural language decoding is activated only during intended communicative acts.}, }
@article {pmid42182066, year = {2026}, author = {Feng, C and Ni, H and Zhu, Z and Jiang, H and Zheng, Z and Ming, W and Wang, S and Xu, K and Zhu, J}, title = {Dataset of chronic intracranial EEG of epilepsy patients via responsive neurostimulation system.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1815732}, pmid = {42182066}, issn = {1662-4548}, }
@article {pmid42183960, year = {2026}, author = {Chen, LW and Lian, YH and Dong, XL and Luo, KL and Zhan, LQ and Xie, LL and Sun, QK and Lin, W and Gan, SR and Cheng, XP and Ni, J and Chen, XY}, title = {Intermittent Theta-Burst Stimulation (iTBS) Improves Motor Coordination and Modulates Neuroinflammation and Autophagy in SCA3/MJD Mice.}, journal = {Cerebellum (London, England)}, volume = {25}, number = {4}, pages = {}, pmid = {42183960}, issn = {1473-4230}, support = {2023QH1088//Startup Fund for Scientific Research of Fujian Medical University/ ; 82402952//National Natural Science Foundation of China/ ; YXRQN-CXY2025//the Excellence Talent Program of The First Affiliated Hospital of Fujian Medical University/ ; }, mesh = {Animals ; *Machado-Joseph Disease/therapy/physiopathology/pathology/genetics ; *Autophagy/physiology ; Mice ; Mice, Transgenic ; Ataxin-3/genetics/metabolism ; *Transcranial Magnetic Stimulation/methods ; *Neuroinflammatory Diseases/therapy/pathology/physiopathology ; Cerebellum/pathology/metabolism ; *Psychomotor Performance/physiology ; Disease Models, Animal ; }, abstract = {Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is an autosomal dominant neurodegenerative disorder characterized by misfolded ataxin-3 aggregation and neuronal intranuclear inclusions. Its primary symptom is progressive ataxia, progressively restricting daily living activities. While repetitive transcranial magnetic stimulation (rTMS) may alleviate symptoms, the effects and mechanisms of specific rTMS paradigms, particularly intermittent and continuous theta burst stimulation (iTBS/cTBS), remain unclear in SCA3. This study therefore aimed to investigate the impacts of iTBS and cTBS on motor coordination, cerebellar neuroinflammation, and autophagy in SCA3 transgenic mice. Thirty 14-week-old SCA3 transgenic mice were randomly divided into sham, cTBS, and iTBS groups. Cerebellar stimulation was delivered at 30% maximum output (600 pulses/session, once daily, 5 days/week for 2 weeks). Motor coordination was assessed via rotarod and CatWalk gait analysis. Pathological changes were evaluated by measuring ataxin-3 protein and ubiquitin-positive inclusions. Cerebellar neuroinflammation was analyzed using Iba-1, CD206, and a cytokine array, while autophagy was assessed via Beclin-1 and LC3B expression. iTBS significantly improved motor coordination in SCA3 mice, reducing rotarod falls (vs. sham P < 0.001, vs. cTBS P < 0.05) and improving gait symmetry (vs. sham P < 0.05) and regularity index (vs. sham P < 0.01, vs. cTBS P < 0.01). It also alleviated cerebellar pathology, lowering ataxin-3 expression (vs. sham P < 0.01, vs. cTBS P < 0.01) and ubiquitin-positive inclusions (vs. sham P < 0.01, vs. cTBS P < 0.05). While both iTBS and cTBS increased Iba-1-positive cells (P < 0.05 and P < 0.05, respectively, vs. sham), only iTBS raised CD206-positive cells (vs. sham P < 0.05) and downregulated pro-inflammatory cytokines. Furthermore, iTBS activated autophagy, enhancing Beclin-1 (vs. sham P < 0.05) and LC3B expression (vs. sham P < 0.0001, vs. cTBS P < 0.001). iTBS improved motor coordination and alleviated core cerebellar pathology in SCA3 mice. This effect may be mediated through the downregulation of cerebellar neuroinflammation and the activation of autophagy. Furthermore, the therapeutic efficacy of iTBS was superior to that of cTBS across multiple dimensions, demonstrating distinct paradigm specificity.}, }
@article {pmid42184169, year = {2026}, author = {Mathon, B and Mokhtari, K and Galanaud, D and Carpentier, A}, title = {Development and preclinical evaluation of a hybrid stereoelectroencephalographic-laser depth electrode for magnetic resonance imaging-guided interstitial thermal therapy in drug-resistant epilepsy.}, journal = {Epilepsia}, volume = {}, number = {}, pages = {}, doi = {10.1002/epi.70313}, pmid = {42184169}, issn = {1528-1167}, abstract = {OBJECTIVE: This study was undertaken to design and validate a hybrid depth electrode combining stereoelectroencephalographic (sEEG) recording and magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) under real-time magnetic resonance thermometry, to streamline the transition from invasive localization to focal ablation in patients with drug-resistant focal epilepsy.<br><br>METHODS: We engineered a magnetic resonance imaging (MRI)-compatible depth probe that integrated intracerebral EEG contacts and a central optical fiber for laser delivery. The contact materials and geometry were optimized to reduce susceptibility artifacts and preserve the proton resonance frequency (PRF) thermometry. Preclinical testing included MRI artifact screening in phantoms, thermal performance testing, PRF thermometry validation against temperature sensors in phantoms and ovine brain, artifact quantification versus clinical depth electrodes, electrophysiologic signal quality assessment before/after thermal stress, and in&#xa0;vivo canine feasibility with serial MRI and histology. MRI compatibility was confirmed for a next generation contact variant.<br><br>RESULTS: The optimized contact design produced small MRI artifacts and preserved PRF thermometry outside an approximately 2-mm pericontact exclusion zone. Thermal testing showed localized heating with rapid postlaser decay, modulation by coolant flow, and performance comparable to that of clinical LITT applicators. In dipole-phantom testing, baseline electrophysiological recordings from the new hybrid electrode were comparable to clinical depth-electrode controls, whereas a previously heated hybrid electrode showed increased noise under low-amplitude conditions. In&#xa0;vivo, MRgLITT produced sharply demarcated lesions that scaled with the delivered energy without hemorrhage, edema, midline shift, or device damage. Histological examination revealed coagulative necrosis with a narrow perilesional zone and no carbonization at the contacts.<br><br>SIGNIFICANCE: This patented hybrid sEEG-laser electrode supports a "diagnose-model-treat-verify" strategy along a single stereotactic trajectory, enabling sEEG confirmation followed by MRgLITT without a second stereotactic implantation in selected patients. These data support progression to first-in-human evaluation and integration into epilepsy surgery workflows, particularly for MRI-negative focal epilepsies, where minimally invasive strategies are favored.}, }
@article {pmid42184181, year = {2026}, author = {An, Y and Tong, Y and Wang, W and Su, SW}, title = {Enhancing Brain Signal Generation Through A Hybrid Approach Integrating Reinforcement Learning And Diffusion Models.}, journal = {IEEE transactions on medical imaging}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TMI.2026.3696676}, pmid = {42184181}, issn = {1558-254X}, abstract = {Developing a reliable EEG-based Brain Computer Interface (BCI) system typically requires large and diverse training datasets, but collecting sufficient data remains challenging due to subject fatigue and interindividual variability. To address these limitations, this study proposes a reinforcement learning-enhanced EEG diffusion (RLED) framework for adaptive data augmentation in endogenous EEG tasks, with a focus on motor imagery and emotion recognition. The framework integrates a reinforcement learning mechanism to dynamically regulate the diffusion training process and achieve a flexible balance among temporal, spectral, and class-related features. Experiments on four datasets demonstrate that the proposed method generates high-quality synthetic EEG signals and consistently improves classification performance. These findings show that the proposed RLED framework may serve as a promising tool for EEG data augmentation and generalization in practical BCI applications.}, }
@article {pmid42184467, year = {2026}, author = {Lu, J and Wang, D and Kong, D and Meng, K and Zhang, R and Wan, H and Chen, M}, title = {Identifying the seizure onset zone with phase-amplitude coupling.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {203}, number = {}, pages = {109151}, doi = {10.1016/j.neunet.2026.109151}, pmid = {42184467}, issn = {1879-2782}, abstract = {Accurate identification of the seizure onset zone (SOZ) is critical for the diagnosis and treatment of drug-resistant epilepsy (DRE). In recent years, although phase-amplitude coupling (PAC) has played an important role in epilepsy-related studies, few investigations have focused on applying PAC methods to SOZ identification. To this end, leveraging the capability of PAC to characterize neural interactions within the brain, this study computes the modulation index (MI) from clinical electrocorticography (ECoG) recordings of DRE patients. Subsequently, a statistical analysis of temporally evolving distributions of MI values across multiple frequency bands is conducted to analyze the differences in MI distribution features between SOZ and non-seizure onset zone (NSOZ) regions. Finally, distribution features of MI values are integrated with machine learning techniques to systematically evaluate the influence of different frequency bands and time windows on SOZ identification performance. The results demonstrate that MI distribution features can achieve accurate SOZ identification, with classification accuracy reaching 90.69%, indicating their potential as biomarkers for SOZ identification.}, }
@article {pmid42163088, year = {2026}, author = {Shah, HA and Khan, A}, title = {Modeling and classifying neuronal activity with a fusion of mathematical and machine learning techniques.}, journal = {BMC bioinformatics}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12859-026-06487-z}, pmid = {42163088}, issn = {1471-2105}, abstract = {Predicting neuron spike patterns is crucial because spikes are the brain's fundamental language, revealing how information is encoded and transmitted. Such prediction also supports disease diagnosis, brain-machine interfaces, and the control of robotic arms, wheelchairs, and neuromorphic AI design. Yet, simulation techniques often suffer from limited biological realism, numerical instability, and poor generalization across diverse neuronal activity types. These limitations are further compounded by the scarcity of high-quality, labeled datasets that capture the full spectrum of neuronal dynamics, restricting the training and evaluation of machine learning models. To address these challenges, we proposed SpikeNet, a hybrid framework that integrates the Izhikevich neuron model with the Runge-Kutta fourth-order (RK4) algorithm to generate synthetic voltage signals that are both biologically plausible and computationally precise. These signals are then used to train a Bidirectional Long Short-Term Memory (Bi-LSTM) network, which effectively captures long-range temporal dependencies in spike trains. SpikeNet combines accurate simulations with advanced sequence modeling to improve spike pattern classification, providing a scalable solution for reliable data generation and prediction. The proposed model was evaluated on multiple datasets, including single-spike data, multi-label spike data, and the Allen dataset, and demonstrated strong performance across all evaluation metrics.}, }
@article {pmid42164752, year = {2026}, author = {Bushnell, BD and Boes, N and Cil, A and Farmer, K and Gilot, G and Kassam, H and Khan, A and Miniaci, A and Port, J and Sanchez-Sotelo, J and Schultzel, M and Steinmann, S and Suri, M and Weinstein, D and Wright, M}, title = {Augmentation for rotator cuff repair - clinical use patterns and limited patient access: the American Shoulder and Elbow Surgeons bio-advocacy work group survey.}, journal = {JSES reviews, reports, and techniques}, volume = {6}, number = {3}, pages = {100748}, pmid = {42164752}, issn = {2666-6391}, abstract = {BACKGROUND: Over the last decade, treatment algorithms of rotator cuff pathology have increasingly included various forms of augmentation of rotator cuff repair (RCR). This study aimed to quantify real-world clinical patterns for RCR augmentation and provide consensus statements for clinical practice and payor consideration. It was our hypothesis that augmentation would be popular amongst surgeons, especially for reduction in retear rates, and that a high percentage of respondents would also identify restrictions to access.<br><br>MATERIAL AND METHODS: The American Shoulder and Elbow Surgeons Advocacy Committee distributed a 12-question digital survey to all current members of American Shoulder and Elbow Surgeons. The survey evaluated current surgical techniques and augmentation usage, limitations on augmentation access, target patients for augmentation selection, and desired clinical outcomes. Questions were analyzed as either frequency of response or as a rank average with 95% confidence intervals.<br><br>RESULTS: The survey was sent to 1,210 surgeons, and 103 surgeons participated in the survey (8.5% response rate). The survey revealed the following: (1) use of RCR augmentation is reported by 76.2% and 85.1% of surgeons for partial-thickness tears (PTT) and full-thickness tears (FTT), respectively. However, 74.5% of surgeons indicate that they have limited or variable access to augmentation options. (2) A bioinductive collagen implant (BCI) is the most preferred form of augmentation for PTT (52.5% of respondents), while both the BCI (45.5%) and human dermal allograft augmentation (45%) are most preferred for FTT.(3) The decision to use augmentation is largely based on positive clinical outcomes (9.4/10) and a defined target patient population (8.4/10), with the most critical outcome being a lower retear rate for both PTT (7/10) and FTT (8/10). (4) For PTT, patient comorbidities (7/10) are of greatest concern and are the most impactful criteria for the decision to use augmentation (6/10). For FTT, poor tendon quality (8.6/10) and increasing tear size (2.9-9.1/10) are of greatest concern, with tear size indicated as the most impactful criteria for selecting augmentation (7.6/10).<br><br>CONCLUSION: This expert-opinion survey confirmed the growing popularity of RCR augmentation and the significant limitations in access faced by surgeons and their patients. BCI and human dermal allograft were the most popular augmentation options. Surgeons identify multiple factors as important to decision-making for implant use, including positive clinical outcomes, low retear rates, defined patient populations, patient comorbidities, poor tendon quality, and tear size. Research in this area continues to expand, but additional work on payor approval remains to ensure appropriate access to this technology.}, }
@article {pmid42166269, year = {2026}, author = {Rao, Z and Lu, Z and Xiao, J and Li, K and Zhu, M and Guan, Z and Chen, Y and Cao, H and Li, Y}, title = {Calibration-Free Online Detection in Wearable Motor Imagery Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3695774}, pmid = {42166269}, issn = {1558-0210}, abstract = {Motor imagery brain-computer interfaces (MI-BCIs) remain challenging for practical use due to their reliance on multi-channel EEG devices and long calibration. To address these limitations, we proposed a wearable system for calibration-free online decoding using a lightweight, few-channel EEG headband, enabling portability, ease of use, and rapid setup. Specifically, we first built a large-scale wearable MI-EEG dataset from 100 healthy subjects to train a subject-independent model. We then developed a CNN-based temporal convolutional network (CTCNet) for online MI detection, which reduced computational complexity while maintaining high decoding performance. Furthermore, we introduced a supervised self-training (SST) strategy that leverages labeled online data and progressively fine-tunes a pre-trained subject-independent model, enabling calibration-free BCI operation without offline calibration. Four online experiments were conducted, involving 25 healthy subjects (Experiments II-IV) and 10 stroke patients (Experiment V). With the SST strategy, the accuracy of the subject-independent model improved from 69 % initially to 81 % after the first update and further increased to 86% after the second update, surpassing the subject-specific model (80%). Stroke patients exhibited a similar improvement trend. Moreover, simulated experiments confirmed the superiority of the subject-independent model compared to training from scratch. These findings demonstrate the effectiveness of the wearable MI-BCI system based on SST and CTCNet for online MI detection and highlight its substantial potential for motor recovery in stroke patients.}, }
@article {pmid42166618, year = {2026}, author = {Wagner, A and Eisenkolb, VM and Utzschmid, A and Gempt, J and Jacob, SN and Meyer, B}, title = {Chronic Implantation of Planar Microelectrode Arrays as a Brain-Computer Interface: A Technical Note and Operational Workflow.}, journal = {Operative neurosurgery (Hagerstown, Md.)}, volume = {}, number = {}, pages = {}, doi = {10.1227/ons.0000000000002067}, pmid = {42166618}, issn = {2332-4260}, support = {16ME0540//Bundesministerium f&#xfc;r Bildung und Forschung/ ; }, abstract = {BACKGROUND AND OBJECTIVES: Chronic implantation of brain-computer interface facilitates stable, high-fidelity neuronal recordings over extended periods of time. Planar microelectrode arrays [Utah arrays (UAs)] are commonly used for intracortical signal acquisition. Here, we describe the surgical workflow for chronic implantation of multiple UAs in 2 patients and report safety and signal-quality outcomes.<br><br>METHODS: Two patients (MB, MM) underwent chronic UA implantation within a translational research program. Preoperative planning included magnetic resonance imaging and navigated transcranial magnetic stimulation mapping for localization of functional targets. MB presented with aphasia after a left hemisphere media territory stroke 6 years before implantation and received 4 UAs in speech-related areas. MM presented with tetraparesis after a high level spinal cord injury and received 4 UAs in areas related to grasping functionality, totaling 256 intracortical electrodes for each patient.<br><br>RESULTS: The duration of the chronic implantation has currently amounted to 41 months for MB and 4 months for MM. Optimal signal quality has been recorded in MB in 3 of 4 UAs and in MM in all UAs. After 15 months, MB suffered from wound breakdown, necessitating surgical debridement and intravenous antibiotic treatment. Unimpaired signal acquisition resumed after the wound had healed, and no further complications from UA implantation were recorded otherwise.<br><br>CONCLUSION: Chronic implantation of UAs across distinct cortical areas is safe. A standardized workflow-combining imaging-based functional navigated transcranial magnetic stimulation mapping, intraoperative neuronavigation, and structured postoperative surveillance-supports reliable, long-term intracortical signal acquisition.}, }
@article {pmid42162083, year = {2026}, author = {Chaturvedi, S and Ahirwal, MK}, title = {SHAP analysis of an improved EEG-based mental workload classification framework: utilizing data augmentation and explainable AI.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-52330-z}, pmid = {42162083}, issn = {2045-2322}, abstract = {Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research area in brain-computer interface (BCI). Since the EEG signals fluctuate a lot across sessions and individuals, there is a need for a robust classification model that generalizes well for real-world applications. In this work, we used the publicly available dataset "An EEG dataset for cross-session mental workload estimation: passive BCI competition of the Neuroergonomics Conference 2021", and the standard EEGNet model to classify the MWL into three classes (Low, Med, and High). To improve the performance of the model, a synthetic minority oversampling technique (SMOTE) was used by creating synthetic EEG samples, and key hyperparameters (F1, F2, and D) of EEGNet were systematically varied to identify the optimal configuration. Furthermore, Shapley Additive Explanations (SHAP) analysis was performed to identify the most influential EEG channels for model prediction. The proposed approach achieves the highest accuracy of 80.5% and 82.7% without and with SMOTE, respectively. The comparative analysis showed that applying SMOTE resulted in an average performance improvement of approximately 3%. A Wilcoxon signed-rank test confirmed that this improvement was statistically significant (p < 0.05). Finally, the SHAP analysis revealed that the most informative EEG channels were located over the parieto-occipital and temporal regions, which is consistent with established neurophysiological evidence related to MWL processing. The proposed framework improves both performance and explainability in EEG-based MWL classification, representing a systematic integration of SMOTE and SHAP analysis.}, }
@article {pmid42162276, year = {2026}, author = {Liu, W and Wu, SA and Zhang, BX and Guo, SH and Li, L and Sun, W and Xiong, X and Nan, J and Wu, J and Zeng, L and Li, P and Cai, ZY and Ye, HF and Zhang, S and Nie, S and Li, B and Wu, D and Cheng, P and Qi, X and Fang, D and Chen, W and Zhang, Y and Chen, Q and Yang, ZH and Han, J and Mo, W}, title = {Tau aggregates cause reactivation of transposable DNA elements, leading to Z-RNA-ZBP1-mediated neuronal death.}, journal = {Nature neuroscience}, volume = {}, number = {}, pages = {}, pmid = {42162276}, issn = {1546-1726}, abstract = {Once tau aggregates are formed, their neurotoxicity significantly contributes to neuronal death and cognitive decline in tauopathies, with Alzheimer's disease being the most well-known example. Despite its central pathogenic role, however, effective therapeutic strategies targeting the neurotoxicity of tau remain poor. Here we demonstrate the pathogenic role of neuronal cell death in tau-related neurodegeneration (PS19 mouse model). Tau-expressing neurons undergo cell death through Z-DNA-binding protein 1 (ZBP1) activation triggered by endogenous Z-RNAs. These Z-RNAs are derived from reactivated transposable elements that are typically silenced within heterochromatin. Tau aggregates show a strong affinity for H3K9me3-modified chromatin, effectively sequestering these epigenetic marks from heterochromatin protein 1 (HP1), thereby disrupting the condensation of constitutive heterochromatin. Clinically, an inverse correlation between ZBP1 expression levels in excitatory neurons and cognitive performance in individuals with Alzheimer's disease was observed. Importantly, Zbp1 haploinsufficiency significantly ameliorated cognitive deficits in aged (24-month-old) tau-transgenic mice, highlighting the therapeutic potential of ZBP1 inhibition to combat neurodegeneration in tauopathies.}, }
@article {pmid42154699, year = {2026}, author = {Fu, R and Fang, Y and Xu, F and Hua, C and Hua, C}, title = {SAND: Spectral-Attention Neural Decoding of Hand Kinematics from Low-Frequency EEG Dynamics.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3694935}, pmid = {42154699}, issn = {1558-2531}, abstract = {Brain-Computer Interface (BCI) technology, integrating neuroscience and artificial intelligence, has been widely applied in neural rehabilitation. However, hand kinematics decoding via electroencephalography (EEG) is constrained by limited precision and cross-subject adaptability. This study proposes the Spectral-Attention Neural Decoder (SAND) - a hybrid framework synergizing spectral decomposition and adaptive deep learning for robust 2D/3D trajectory reconstruction. Systematic analysis of the WAY EEG Grasp-and-Lift dataset revealed that hand movement information is primarily encoded in low-frequency EEG bands. Therefore, a dual-branch continuous decoding architecture was developed: (1) a frequency-domain pathway for noise-resistant spectral embedding, and (2) a temporal-attention pathway utilizing transformer networks to capture dynamic neural modulations. Five-fold cross-validation results demonstrate that SAND achieves state-of-the-art performance in hand-trajectory decoding. The Pearson correlation coefficients for the x, y, and z axes reach 0.9595 ± 0.0148, 0.9534 ± 0.0151, and 0.9293 ± 0.0250, respectively, representing an improvement of 0.07-0.13 over baselines. To assess cross-task generalization, we validate SAND on a self-collected dataset, where it attains average correlation coefficients of 0.90 (x-axis) and 0.96 (y-axis) in 2D trajectory reconstruction. The temporal alignment with ground-truth kinematic recordings was validated by remarkable performance in dynamic time warping analysis. These results confirm SAND as an effective solution for precise hand motion decoding advances non-invasive BCI applications.}, }
@article {pmid42154722, year = {2026}, author = {Hu, C and Liang, S and Li, R and Ong, ST and Xu, H and Zhang, J}, title = {EEGDTF: Time-Frequency Disentangled Diffusion for High-Fidelity EEG Signal Generation.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3694047}, pmid = {42154722}, issn = {2168-2208}, abstract = {EEG signal generation is hindered by challenges such as complex time-frequency structures, the lack of explicit spectral modeling, limited data availability, and limited generalization across subjects and tasks. To address these issues, we propose EEGDTF, a diffusion-based generative framework for synthesizing high-fidelity EEG signals with improved time-frequency modeling. EEGDTF first employs a multi-scale residual encoder to enhance temporal representation learning and training stability. It further introduces a dual-branch encoder-decoder architecture for time-frequency disentanglement: the frequency branch models both periodic and aperiodic components via power spectral parameterization, while the temporal branch captures waveform continuity and long-range dependencies. A frequency-guided cross-attention mechanism integrates both branches effectively. The model is optimized through a joint waveform and spectral loss, enabling stable clean-signal estimation during reverse sampling. Experiments on four benchmark datasets demonstrate that EEGDTF achieves state-of-the-art performance in both time and frequency domains, particularly under cross-subject conditions. These results underscore the model's robustness and generalizability, positioning EEGDTF as a reliable tool for EEG data augmentation and BCI-related applications.}, }
@article {pmid42155274, year = {2026}, author = {Houshmand, MH and Pishgoo, B}, title = {Real-time emotion recognition based on EEG signals using a hybrid batch-stream architecture.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {202}, number = {}, pages = {109072}, doi = {10.1016/j.neunet.2026.109072}, pmid = {42155274}, issn = {1879-2782}, abstract = {In recent years, emotion recognition using brain-computer interface (BCI) systems has gained substantial attention. Existing models are typically implemented in either offline (batch) or online (streaming) modes. While batch processing approaches generally achieve higher classification accuracy, they are limited by slow processing speed. In contrast, stream processing approaches offer real-time performance but often compromise accuracy. To address this trade-off, we propose a hybrid batch-streaming framework that integrates the strengths of both paradigms while alleviating their individual limitations. The architecture, features a probabilistic intelligent switching mechanism that estimates the reliability of the streaming module based on its historical performance. This reliability measure dynamically determines the probability of selecting outputs from either the batch or streaming unit. The proposed framework is evaluated on three benchmark datasets (DEAP, AMIGOS, and SEED) achieving classification accuracies of 85%, 94%, and 74%, respectively. Also, experiments were conducted to investigate the performance of the switch mechanism and performance of system components against concept drift. Experimental results demonstrate that our method effectively balances classification accuracy and computational efficiency. It is expected that in the future, such hybrid ideas are widely used in feedback - based systems.}, }
@article {pmid42155486, year = {2026}, author = {Wimmer, M and Elsayed, N and Thomas, BH and Müller-Putz, GR and Veas, EE}, title = {An online brain-computer interface for detecting incongruity in augmented reality applications.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae6ff1}, pmid = {42155486}, issn = {1741-2552}, abstract = {OBJECTIVE: Augmented reality can provide digital information about physical entities presented within its real-world context. However, this information might disagree with the user's expectations due to factual errors in the data or cognitive biases. Such incongruity can impair user experience and undermine trust in the AR system. To address this issue, we propose detecting inconsistencies between physical objects and digital information through hybrid braincomputer interfaces.<br><br>APPROACH: We conducted two complementary experiments. First, we implemented a strategy that integrates eye-tracking and brain signals for incongruity detection in an offline study. Subsequently, we assessed our approach in an online study in which participants received immediate feedback on the classification.<br><br>MAIN RESULTS: The grand average event-related potentials revealed consistent electroencephalographic responses to incongruent augmentations, specifically a centroparietal N400 effect, across both experiments. We could further distinguish between congruent and incongruent information with an average balanced accuracy of 70 % in the online study.<br><br>SIGNIFICANCE: These findings demonstrate the feasibility of detecting incongruity online, allowing for autonomous system adaptation, like presenting information in a more accessible format or providing contextual support.}, }
@article {pmid42155589, year = {2026}, author = {Esaian, S and Smith, BA and Oh, J and Espinoza, JC and Vidmar, AP and Goran, MI}, title = {Carbohydrate composition of infant formula and glycemic regulation in early infancy using continuous glucose monitoring: cross-sectional evidence of altered glucose patterns with corn syrup solid-based formulas.}, journal = {The American journal of clinical nutrition}, volume = {}, number = {}, pages = {101325}, doi = {10.1016/j.ajcnut.2026.101325}, pmid = {42155589}, issn = {1938-3207}, abstract = {BACKGROUND: Infant formulas vary widely in carbohydrate composition, yet associations between exposure to nonlactose carbohydrates and glycemic patterns in early infancy remain poorly characterized.<br><br>OBJECTIVES: We assessed associations between infant feeding strategy and continuous glucose monitor (CGM)-derived measures of glycemic variability in a cross-sectional observational cohort of infants at 6 mo of age.<br><br>METHODS: Forty-five infants (28.0 &#xb1; 1.2 wk; 47% female) wore CGMs recording interstitial glucose every 15 min for 3 to 8 d. Feeding strategy was categorized as exclusive human milk, formula containing lactose or corn syrup solids (CSS), or mixed human milk/lactose-based formula. Twenty-eight CGM-derived metrics were computed using the R package iglu. Group differences were tested using Freedman-Lane analysis of covariance with permutation-based post hoc tests; effect sizes (&#x3b7;[2]p) and 95% bootstrap confidence intervals (BCI) were reported for all key comparisons. Exploratory hierarchical clustering (Ward's D2) examined glycemic variability subgroups independent of feeding strategy.<br><br>RESULTS: Approximately 46% of CGM-derived metrics differed significantly across feeding strategies, all reflecting contrasts between CSS-based formula and other groups; no metrics differed among human milk, lactose-based formula, or mixed feeding. Compared with human milk, CSS-fed infants were associated with greater glycemic variability and large effect sizes (though the study was powered only to detect large effects), including greater time in hyperglycemia (&#x3b7;[2] = 0.21; 95% BCI = -2.59,2.49), glycemic risk assessment diabetes equation (&#x3b7;[2] = 0.31; 95% BCI = -0.25,0.24), J index (&#x3b7;[2] = 0.24; 95% BCI = -1.07,1.08), and mean amplitude of glycemic excursions (&#x3b7;[2] = 0.40; 95% BCI = -6.14,6.03). Exploratory clustering identified 4 glycemic variability subgroups. One subgroup exhibited broadly elevated glucose variability and included &#x223c;36% of CSS-fed infants, with no representation from other feeding strategies.<br><br>CONCLUSIONS: Infant feeding strategy was associated with differences in CGM-derived glycemic variability at 6 mo, driven by greater glucose variability among CSS-fed infants. Human milk and lactose-based formula feeding did not differ. Exploratory analyses identified a subgroup with pronounced glycemic variability that included a subset of CSS-fed infants, highlighting interindividual variability in glycemic response.}, }
@article {pmid42158453, year = {2026}, author = {Li, Y and Yi, R and Hu, Z}, title = {Brain-computer interface technology for motor rehabilitation in severe stroke: a narrative review.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {14}, number = {}, pages = {1822784}, pmid = {42158453}, issn = {2296-4185}, abstract = {This review examines the application of brain-computer interface (BCI) technology for motor rehabilitation in patients with severe stroke-a population often excluded from conventional therapies due to minimal movement. BCIs establish electronic links between the brain and external devices, enabling motor intention recognition without muscular activity. By pairing neural activation with sensory feedback, these systems promote neuroplasticity and strengthen adaptive motor pathways. Compared with standard therapies, preliminary evidence suggests BCI interventions may facilitate additional motor recovery, though current effect size estimates are limited by small sample sizes, high study heterogeneity, and inherent performance biases. Effective modalities include motor imagery with functional electrical stimulation, robotic-assisted training in virtual environments, and multimodal systems. Despite promising results, challenges persist regarding signal reliability, protocol optimization, patient selection, and cost. Emerging research focuses on integrating artificial intelligence, adaptive closed-loop systems, and portable platforms to enhance clinical feasibility. Interdisciplinary collaboration may help transition BCI technology from experimental use to routine rehabilitation, improving outcomes for severely impaired stroke survivors.}, }
@article {pmid42158584, year = {2026}, author = {Chaiyanan, C and Phukhachee, T and Iramina, K and Kaewkamnerdpong, B}, title = {Toward practical BCIs: a BMNABC-based feature selection and sensor optimization framework for implicit learning detection from multimodal EEG-fNIRS data.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1778884}, pmid = {42158584}, issn = {1662-5161}, abstract = {Implicit learning is a fundamental cognitive process whose identification is critical for understanding human cognition and developing innovative training methodologies. We propose a generalizable feature selection and sensor optimization framework using simultaneous EEG and fNIRS to identify these events. Our approach leverages a two-stage optimization process driven by a binary multi-neighbor artificial bee colony (BMNABC) algorithm. The BMNABC uses the model's classification accuracy to guide the heuristic search for the most discriminative feature subset. First, the framework prioritizes optimal features from high-dimensional, multimodal data using a normalized weighted sum (NWS) metric. Second, it implements a recursive backward elimination mechanism to reduce the number of sensors for practical brain-computer interface (BCIs) applications. Our results demonstrate that the BMNABC framework successfully identifies a superior feature set, leading to a significant improvement in classification accuracy over using either modality alone. Critically, the selected features provided neurophysiological validation, isolating key biomarkers in the prefrontal cortex. We also show that a sparse yet highly effective sensor configuration can be achieved, maintaining high performance with up to 66% fewer sensors. This work not only provides a data-driven method for detecting implicit learning but also advances the design of more efficient and user-friendly BCI systems.}, }
@article {pmid42160746, year = {2026}, author = {Chen, H and Wang, J and Lai, S and Peng, G and Zong, G and Yuan, C and Luo, B}, title = {Smoking Cessation, Weight Change, and Risk of Dementia: A Prospective Cohort Study.}, journal = {Neurology}, volume = {106}, number = {12}, pages = {e218123}, doi = {10.1212/WNL.0000000000218123}, pmid = {42160746}, issn = {1526-632X}, mesh = {Humans ; Female ; *Dementia/epidemiology ; *Smoking Cessation ; Male ; *Weight Gain/physiology ; Prospective Studies ; Aged ; Middle Aged ; Risk Factors ; Cohort Studies ; }, abstract = {BACKGROUND AND OBJECTIVES: Smoking cessation is universally prioritized for the prevention of cardiovascular disease and cancer, but its impact on dementia risk remains uncertain. We aimed to evaluate the associations of smoking cessation and postcessation weight gain with long-term risk of dementia and cognitive trajectories.<br><br>METHODS: We conducted a prospective cohort study using data from the US Health and Retirement Study (1995-2020). A total of 32,802 dementia-free adults (mean age 60.5 years [SD 10.7]; 57.1% female) were included. Smoking status and body weight were assessed biennially through structured interviews. The primary outcome was incident dementia identified using the Langa-Weir algorithm, and the secondary outcome was cognitive function measured on a 27-point scale.<br><br>RESULTS: Over 25 years of follow-up (median 9.9 years, interquartile range 4.4-16.4 years), 5,868 dementia cases were documented. Compared with current smokers, individuals who quit during follow-up had a lower dementia risk after quitting (hazard ratio 0.84, 95% CI 0.73-0.95), similar to those who had quit before baseline (0.79, 0.72-0.87) and to never smokers (0.75, 0.69-0.83). The benefits of cessation were largely limited to participants with no or modest 2-year postcessation weight gain (&#x2264;5 kg). By contrast, the association of quitting accompanied by >10-kg weight gain was not statistically significant (1.33, 0.87-1.82). Restricted cubic spline analysis showed decreasing dementia risk with longer time since quitting, and the risk approached that of never smokers and plateaued at around 7 years after cessation. Cognitive trajectory analyses showed that quitting was associated with long-term slower cognitive decline (slope difference 0.19 points per decade, 95% CI 0.00-0.38) but no transient cognitive change (0.57; 95% CI -0.69 to 1.83), especially among those with minor weight gain (slope difference 0.23 per decade, 95% CI 0.03-0.43).<br><br>DISCUSSION: Smoking cessation was associated with a sustained lower dementia risk and slower cognitive decline, comparable to never smokers and those without short-term risk increase. However, postcessation weight gain may attenuate these advantages, highlighting the need for weight management in cessation programs. These findings should be interpreted cautiously, given the potential residual confounding and measurement error.}, }
@article {pmid42161119, year = {2026}, author = {Pei, X and Sun, M and Chen, R and Wu, M and Wang, X and Xu, D and Li, R and Luo, S and Jin, Y and Tang, YN and Lu, Y and Wang, Q and Ma, L and Hu, B and Mei, Y and Xiao, X and Wei, D and Liu, Y and Song, E}, title = {Dual-frequency-channel integrated bioelectronics for in-sensor decoupling high-dimension neurophysiologic signals.}, journal = {Biosensors &amp; bioelectronics}, volume = {309}, number = {}, pages = {118784}, doi = {10.1016/j.bios.2026.118784}, pmid = {42161119}, issn = {1873-4235}, abstract = {Accurate electrophysiological mapping of biological signals with high spatial and temporal resolution has always been an important requirement to elucidate physiological functions. Herein, we develop a photolithographic organic electrochemical transistor (OECT) matrix with two frequency-dependent channels, which can spatiotemporally map electroneurographic and neurotransmitter signals. The active material can be patterned photolithographically, forming a nanoscale interpenetrating network. The porous structure facilitates fast ion transport, establishing a high-frequency channel to monitor electroneurographic signals; meanwhile enzymatic reaction of glutamate on the surface creates a low-frequency channel to detect neurotransmitter signals, due to the relatively slow diffusion and doping processes. A low detection limit down to 900 zM for glutamate is achieved. During the test, the horseshoe network structure of the OECT array gives the device the ability of conformal contact on the surface of the cerebral cortex, avoiding the motion artifact noise, and the signal-to-noise ratio (SNR) can reach ∼40 dB. The dual-frequency channels efficiently decouple electroneurographic and neurotransmitter signals to avoid signal interference. Finally, the photolithographic matrix images dual-mode neurophysiological patterns in the cerebral cortex of mice, and can dynamically colocalize epileptic focus with high resolution for precise neurosurgical intervention.}, }
@article {pmid42161915, year = {2026}, author = {Lu, C and Jiang, L and Jia, Q and Jiao, P and Li, S and Sun, S and Luo, J and Wang, M and Cai, X and Wu, Y}, title = {Analysis and dynamic modeling of firing synchronization in electrically interconnected dual-compartment neuronal networks.}, journal = {Microsystems &amp; nanoengineering}, volume = {12}, number = {1}, pages = {}, doi = {10.1038/s41378-026-01309-x}, pmid = {42161915}, issn = {2055-7434}, support = {62121003, T2293730, T2293731,62333020, 62171434, 62471291//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {In vitro cultured neuronal networks offer controllable experimental models for investigating neuronal information processing mechanisms and network plasticity. However, research into synchronization and functional connectivity transitions following physical electrical interconnection between isolated compartments remains elusive. This study presents a microsystem that includes a compartmentalized microchamber neuron chip (CMNC) with programmable electrical interconnection and multichannel electrophysiological recording capabilities. The microsystem is utilized to establish artificial electrical interconnection between dual-compartment neuronal networks (DCNNs). We quantitatively evaluated network functional connectivity throughout control, interconnection, and post-disconnection phases, focusing on three key dimensions: spike timing synchrony, firing activity correlation and phase coherence. The experimental data showed that the electrical interconnection had sustained effects on firing synchrony and phase coherence across the DCNNs. After disconnection, synchrony decreased but remained significantly higher than control levels, suggesting a plastic response of the neuronal networks to the electrical coupling. To bridge experimental observations with mechanistic insights, we developed an Electrical-Interconnection Wilson-Cowan Model (EI-WCM), which quantitatively links physical coupling parameters (K) to network-level integration dynamics. The integrated microsystem and dynamical model presented here provide a stable, controllable platform and approach for studying functional connectivity, synergetic interactions and plasticity of neuronal networks, demonstrating significant potential for applications in brain-computer interfaces and neuronal information processing.}, }
@article {pmid42151503, year = {2026}, author = {Xu, JN and Li, JT and Xu, RX and Wang, YF and Gao, HW and He, HT and Guo, H and Liang, Y and Zhu, YD and Li, XW and Yang, JM and Li, XM and Chen, YH and Gao, TM}, title = {The multilevel exploration test, a novel paradigm to measure exploratory behavior in depression animal models and the involvement of the PL-ZI circuit.}, journal = {Acta pharmacologica Sinica}, volume = {}, number = {}, pages = {}, pmid = {42151503}, issn = {1745-7254}, abstract = {Diminished drive is one of the core symptoms of major depressive disorder (MDD) diagnosis, yet its underlying neural mechanisms remain elusive, primarily due to a lack of appropriate animal models. We developed a novel Multilevel Exploration Test (MET) apparatus to evaluate exploratory behavior, which is captured as a dynamic, stage-dependent process involving "search", "attend/investigate", and "approach" phases. We employed fiber photometry to measure real-time dopamine dynamics in the nucleus accumbens. We further combined cFos staining and neural circuit tracing to identify relevant brain regions and circuits, and employed chemogenetics to selectively modulate prelimbic cortex (PL) inputs to zona incerta (ZI). The MET tests were conducted across five depression models, with ketamine administration to evaluate rescue effects. Machine learning algorithms were utilized to analyze MET data and predict individual emotional states (normal, anxiety-like, depression-like). Here, we developed a novel paradigm to assess exploratory behavior, which demonstrates etiological validity, face validity and predictive validity. Depressed mice exhibited reduced motivation for exploration in this paradigm, while stimulation of the PL-ZI circuit not only restored exploratory deficits but also alleviated other depression-like behaviors in these mice. Furthermore, we established a machine learning-based model to predict individual animals' emotional states by integrating data from the new paradigm, achieving a prediction accuracy of over 92%. The MET provides a functional, high-throughput paradigm for dissecting motivation-related pathology. It facilitates the assessment of depressive-like behaviors, enables the prediction of emotional states, and supports the discovery of novel targets for antidepressant development.}, }
@article {pmid42152642, year = {2026}, author = {Begum, A and Sultana, A and Bin Heyat, MB and Rahman, K and Akhtar, F and Al-Huda, Z and Banu, T and Khaleeq, K and Bian, S and Sawan, M}, title = {Efficacy of Pimpinella anisum L. in Menopausal Women with Psychological Symptoms: A Randomized Controlled Study Integrated with Machine Learning Analysis.}, journal = {Current pharmaceutical design}, volume = {}, number = {}, pages = {}, doi = {10.2174/0113816128433736260209142133}, pmid = {42152642}, issn = {1873-4286}, abstract = {INTRODUCTION: Menopausal women commonly experience psychological symptoms. These symptoms reduce their quality of life. Pimpinella anisum (anise) is an Unani remedy traditionally used for such problems. This study aimed to test the effects of anise on psychological and menopausal symptoms, along with appraising machine learning models in classifying treatment response between the anise and control groups.<br><br>METHODS: A total of 60 menopausal women received either 4 g of anise per day or a matched placebo, administered in two divided doses over an eight-week period. Primary outcomes included the Depression, Anxiety, and Stress Scale 21 (DASS-21) in menopausal women. Secondary outcomes included overall Modified Kuppermen Index (MKI), Vaginal Health Index (VHI), and treatment satisfaction (MS-TSQ). Machine learning classifiers, including Gradient Boosting (GB), AdaBoost (AB), K-Nearest Neighbours (KNN), Naive Bayes (NB), and Random Forest (RF), were utilised. Safety was monitored weekly through interviews. Hepatic and renal function were evaluated at baseline and after 12 weeks.<br><br>RESULTS: Baseline variables were similar between the two groups. Anise significantly reduced the DASS-21 scores compared to placebo at 8 weeks (p < 0.0001). At 8 weeks, participants receiving anise demonstrated significant improvements across multiple measures. DASS&#x2011;21 scores declined markedly compared with placebo (p < 0.0001), with more than 80% reporting no symptoms of depression, anxiety, or stress. MKI and VHI scores also improved significantly in the anise group (p < 0.0001), while the control group showed no notable change. Satisfaction ratings on the MS&#x2011;TSQ were high among anise recipients but low in the placebo arm. No adverse effects were observed. In addition, the KNN model achieved outstanding performance, correctly classifying group membership with 99.20% accuracy.<br><br>DISCUSSION: Anise demonstrated significant benefits in reducing psychological and menopausal symptoms, with no adverse effects reported, supporting its potential as a safe non&#x2011;hormonal therapy. The strong performance of the KNN model additionally exemplifies how machine learning can improve menopausal research by precisely distinguishing treatment responses. Upcoming studies with larger and more varied populations will be important to endorse these findings and to discover long&#x2011;term outcomes.<br><br>CONCLUSION: This research indicates that anise is a safe and effective alternative for relieving psychological symptoms in menopausal women. The KNN model reliably classified treatment outcomes, signifying that the integration of anise treatment with AI&#x2011;based assessment methods could enrich research on menopause care.}, }
@article {pmid42153184, year = {2026}, author = {Wang, M and Gong, Z and Li, Y and Li, Y and Zheng, Y}, title = {Robust decoding for MI-EEG: a hybrid transformer network using multi-perspective collaborative attention and dynamic hyperbolic tangent.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {93}, pmid = {42153184}, issn = {1871-4080}, abstract = {Deep learning methods have been extensively applied in brain-computer interface (BCI) systems based on motor imagery (MI) for decoding electroencephalogram (EEG) signals. However, most existing hybrid architectures often struggle to effectively eliminate redundant noise in multi-channel signals and lack adaptability to the inherent non-stationarity and distribution drift of EEG signals. This work proposes a novel end-to-end hybrid attention Transformer network (HATNet) for EEG classification. HATNet first employs a convolutional neural network to extract local spatio-temporal features. To overcome the limitations of existing models, it fuses a Collaborative Attention Mechanism for Lightweight Channels, which dynamically recalibrates feature channels through multidimensional pooling strategies, including entropy pooling, to achieve precise spatial noise suppression. Addressing the non-stationary nature of EEG signals, an innovative Dynamic Hyperbolic Tangent module drives the Transformer encoding layer, adapting in real-time to data distribution drifts and significantly enhancing the model's ability to capture individual variations. Furthermore, cross-layer residual fusion pathways deeply integrate global contextual features with raw local spatio-temporal features. To ensure clear scope definition, experiments explicitly distinguish between primary MI tasks and auxiliary motor execution (ME) tasks. HATNet's performance was evaluated on three primary MI benchmark datasets, namely BCIC-IV-2a, BCIC-IV-2b, and the large-scale OpenBMI, as well as one auxiliary ME dataset, HGD. Experimental results demonstrate that HATNet achieves state-of-the-art performance across all analyses. In subject-dependent evaluations, average accuracy rates reached 81.25%, 86.65%, and 69.57% on the three primary MI datasets respectively, and 96.20% on the auxiliary ME dataset. Furthermore, in subject-independent evaluations, it achieved 60.88%, 80.79%, and 76.28% on the MI datasets respectively, alongside 73.95% on the ME dataset. Through multidimensional feature selection and dynamic adaptive modeling, HATNet exhibits superiority and robustness in enhancing both MI and ME decoding performance.}, }
@article {pmid42143986, year = {2026}, author = {Sun, Y and Sha, L and Tang, Y and Fu, Y and Duan, Y and Peng, A and Chen, L and Chen, L}, title = {Impaired default mode network connectivity and deviated dorsal-ventral attention networks in catamenial epilepsy.}, journal = {Epilepsy &amp; behavior : E&amp;B}, volume = {182}, number = {}, pages = {111101}, doi = {10.1016/j.yebeh.2026.111101}, pmid = {42143986}, issn = {1525-5069}, abstract = {OBJECTIVES: This study investigated alterations in resting-state brain networks in catamenial epilepsy (CE) and their associations with serum sex hormone levels.<br><br>METHODS: First, we constructedfunctional networks based on resting-state fMRI datato compute nodal attributes and identify brain regions exhibiting significant group differences. Subsequently, independent component analysis (ICA) identified important networks and characterized their connectivity patterns. Finally, associations between these network metrics and sex hormone levels were examined.<br><br>RESULTS: A total of45 patientswere included in the final analysis, comprising19with CE,26with non-catamenial epilepsy (NCE), and27healthy controls (HC). Nodal efficiency (Ne) differed significantly in key brain regions between patient group and HC group, including the right precentral gyrus (PreCG.R), left and right calcarine cortex (CAL.L and CAL.R), left lingual gyrus (LING.L), right superior parietal gyrus (SPG.R), right inferior parietal lobule (IPL.R) and left precuneus (PCUN.L). While connectivity between the default mode network (DMN) and sensory networks (visual/auditory) was generally weakened in epilepsy patients, CE specifically exhibited a reconfigured attention-network profile: strengthened connectivity between the dorsal attention network (DAN) and auditory network (AN), and weakened connectivity between the ventral attention network (VAN) and visual network (VN). After correction for multiple comparisons, partial correlation analysis controlling for age revealed no statistically significant correlations between sex hormones and brain network metrics.<br><br>CONCLUSION: CE patients exhibited decreased Ne in critical regions of the AN, DMN and VN, alongside predominant disruptions in DMN connectivity. These alterations may be partially compensated by increased connectivity in the DAN, giving rise to a unique network pathological pattern. The regulatory effects of sex hormones on brain networks require further confirmation in large-scale longitudinal studies.}, }
@article {pmid42144204, year = {2026}, author = {Chen, J and Liu, X and Wang, R and Li, J and Xu, Z and Yang, B and He, Q and Yang, X and Yan, H and Luo, P}, title = {Sotorasib induces intestinal epithelial injury through suppression of the cAMP/PKA/CREB signaling axis.}, journal = {Biochemical pharmacology}, volume = {}, number = {}, pages = {118078}, doi = {10.1016/j.bcp.2026.118078}, pmid = {42144204}, issn = {1873-2968}, abstract = {Sotorasib, a first-in-class KRAS G12C inhibitor, has demonstrated substantial clinical efficacy in KRAS G12C-mutant malignancies but is frequently associated with gastrointestinal adverse events, the mechanisms of which remain poorly understood. In this study, we investigated the molecular basis of sotorasib-induced intestinal toxicity using complementary in vitro and an HP-β-CD-optimized in vivo model. Sotorasib treatment disrupted intestinal epithelial homeostasis by promoting apoptosis and suppressing proliferative capacity, resulting in impaired epithelial barrier integrity. Mechanistically, sotorasib markedly suppressed intracellular cAMP signaling in intestinal epithelial cells, as evidenced by reduced protein kinase A (PKA) activity and decreased phosphorylation of the transcription factor CREB. These effects were consistently observed in IEC-6 cells and murine colonic tissues lacking the KRAS G12C mutation, indicating a KRAS-independent mechanism. Pharmacological elevation of intracellular cAMP with forskolin partially restored CREB phosphorylation, attenuated epithelial apoptosis, enhanced proliferative activity, and improved expression of barrier-associated markers. In contrast, in NCI-H358 cells, forskolin increased CREB phosphorylation but failed to rescue sotorasib-induced growth inhibition, highlighting a pronounced cell type-dependent response to sotorasib. Collectively, our findings identify suppression of the cAMP/PKA/CREB signaling axis as a key mechanism underlying sotorasib-induced intestinal epithelial injury and provide mechanistic insight into the tissue-selective gastrointestinal toxicity of KRAS G12C-targeted therapy.}, }
@article {pmid42144446, year = {2026}, author = {Liu, JY and Yang, DL and Liu, HY and Xiang, SF and Cao, J and Yang, B and He, QJ and Wang, JH and Shao, XJ and Ying, MD}, title = {Advances in targeted therapies for pediatric tumors.}, journal = {Acta pharmacologica Sinica}, volume = {}, number = {}, pages = {}, pmid = {42144446}, issn = {1745-7254}, abstract = {Pediatric tumors represent a major cause of disease-related mortality in children and exhibit biological features that differ markedly from those of adult cancers. Pediatric malignancies display unique molecular architectures, with lower mutation frequency, higher frequency of chromosomal alterations such as gene rearrangement and amplification, a distinct alteration spectrum marked by dysregulated developmental genes, as well as a characteristic pattern of differentiation blockage. These alterations often arise during developmental windows and sustain tumor dependency, providing unique drug targets for targeted therapy. This review first describes the molecular characteristics and oncogenic drivers of pediatric tumors, as well as the potential mechanisms underlying the formation of oncogenic driver events in these tumors. It subsequently systematically synthesizes recent advances in targeted therapeutic strategies for pediatric tumors, categorizing strategies by disease type and oncogenic driver events, including oncofusion-directed inhibitors, agents targeting amplified or mutated genes, differentiation-inducing approaches, antibody-based therapies, and cellular therapies. We highlight both pediatric-specific drug development and the extrapolation of adult therapies to pediatric patients, while underscoring persistent challenges in clinical translation. This work advocates for a biology-driven framework to accelerate the development of effective targeted therapies for pediatric tumors.}, }
@article {pmid42145810, year = {2026}, author = {Benachour, A and Syrov, N and Lebedev, M}, title = {Motor imagery affects both cortical and spinal circuitry: a transcranial and trans-spinal magnetic stimulation study.}, journal = {Frontiers in neural circuits}, volume = {20}, number = {}, pages = {1809125}, pmid = {42145810}, issn = {1662-5110}, mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; *Imagination/physiology ; Adult ; *Evoked Potentials, Motor/physiology ; Female ; *Motor Cortex/physiology ; *Spinal Cord/physiology ; Young Adult ; Electromyography ; Muscle, Skeletal/physiology ; Brain-Computer Interfaces ; }, abstract = {INTRODUCTION: Motor imagery (MI), the mental rehearsal of movement without physical execution, is a key technique in brain-computer interfaces (BCIs), known for eliciting cortical modulations similar to those exhibited during real movement. Beyond cortical effects, MI could also modulate spinal cord processing, which offers additional potential for neurorehabilitation in conditions like spinal cord injury (SCI) and stroke, where BCIs are used for therapy.<br><br>MATERIAL AND METHODS: To investigate the interactions of MI with both the cortex and the spinal cord, we employed both transcranial magnetic stimulation (TMS) and trans-spinal magnetic stimulation (TSMS) while recording brain and muscle activities.<br><br>RESULTS AND CONCLUSION: With proper coil orientation, TSMS elicited lateralized MEPs in ipsilateral forearm muscles at significantly shorter latencies than M1-evoked MEPs, confirming direct spinal cord activation. Importantly, right-hand kinesthetic MI selectively facilitated TSMS-evoked MEPs in the stimulated ipsilateral side only, providing direct evidence that MI modulates spinal cord excitability. Moreover, TSMS-evoked cortical responses were modulated by imagery, demonstrating that MI increases cortical processing of the ascending spinal volley. This within-group demonstration of MI affecting both cortical and spinal circuitry underscores its potential as a powerful strategy for BCI-driven neurorehabilitation, including pairing MI with spinal magnetic stimulation.}, }
@article {pmid42147125, year = {2026}, author = {Bariffi, F}, title = {Mind, machine, and the law: reimagining neurotechnology governance through disability rights.}, journal = {Journal of law and the biosciences}, volume = {13}, number = {1}, pages = {lsag011}, doi = {10.1093/jlb/lsag011}, pmid = {42147125}, issn = {2053-9711}, abstract = {The convergence of neurotechnologies and disability raises urgent questions about autonomy, mental integrity, and legal capacity for persons with disabilities. This article examines the human rights implications of emerging neurotechnologies-from brain-computer interfaces to cognitive monitoring tools-through the lens of the United Nations Convention on the Rights of Persons with Disabilities (CRPD). Drawing on historical abuses under the medical model of disability, it argues that the uncritical deployment of neurotechnologies risks replicating patterns of coercion, paternalism, and exclusion. By advancing a normative framework rooted in the CRPD and the social model of disability, the article proposes legal and ethical safeguards to protect mental privacy, ensure informed consent, and affirm supported decision-making. It calls for regulatory and design paradigms that shift from enhancement and correction to inclusion and empowerment. Ultimately, the article contends that disability rights must be at the center of neurotechnologies governance to prevent ableist harms and foster equitable innovation.}, }
@article {pmid42149465, year = {2026}, author = {Huang, Y and Zheng, J and Xu, H}, title = {An Immune-Brain Signaling Mediates Sickness-Induced Social Withdrawal.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {42149465}, issn = {1995-8218}, }
@article {pmid42150721, year = {2026}, author = {Fu, Z and Wu, Z and Wu, X and Jabban, L and Chen, L and Zhang, D}, title = {Stage-Dependent Modulation of High- and Low-Frequency Neural Activity During Motor Imagery based on Stereoelectroencephalography.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {122005}, doi = {10.1016/j.neuroimage.2026.122005}, pmid = {42150721}, issn = {1095-9572}, abstract = {Motor imagery (MI) recruits motor networks without overt movement and underpins many brain-computer interface (BCI) paradigms, yet how neural activity is organized across distinct task stages remains unclear. Using stereoelectroencephalography (sEEG) from ten epilepsy patients performing cued limb MI, we compared preparation and imagery stages and quantified trial-wise power changes in low-frequency (8-30 Hz) and high-frequency (60-115 Hz) bands across cortical and subcortical contacts. Low-frequency activity predominantly showed suppression during preparation followed by activation during imagery, consistent with ERD/ERS-like dynamics, whereas high-frequency responses were stronger, observed across a greater number of regions, and additionally showed activation-suppression sequences in a subset of contacts. These findings indicate that neural responses evolve differently across preparation and imagery, reflecting frequency- and region-specific dynamics rather than a uniform task-related response. Modulation in deep structures, including hippocampal subfields, suggests that MI can engage a distributed network beyond canonical sensorimotor areas. These results refine the temporal and spectral characterization of MI and may inform stage-aware BCI feature design and neurorehabilitation.}, }
@article {pmid42141222, year = {2026}, author = {Ye, Z and Ding, J and Cheng, C and Yu, H and Xu, F and Sun, Q and Yang, H and Hua, T and Wang, H}, title = {Visual Perception and Gamma Oscillations in Cat V1 are Dynamically Correlated in Contrast Sensitivity Functions.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {42141222}, issn = {1995-8218}, abstract = {Local field potentials (LFPs) encode visual information through power variations across multiple frequencies. However, the mechanism through which LFPs encode visual contrast sensitivity during visual perception remains unclear. Herein, we developed a method to decode visual perception levels using LFPs and found that gamma oscillations exhibited the best performance in the detection of visual contrast. Furthermore, gamma power and theta-gamma phase amplitude coupling employed different strategies to code contrast sensitivity. Subsequently, suppressing the top-down influence from area 21a lowered both behavioral and gamma power-measured contrast sensitivity across the same spatial frequencies. Model analysis revealed that gamma oscillations modulated contrast-tuning responses via a contrast gain mechanism and were involved in the external noise exclusion mechanism through a top-down influence. Our findings reveal a link between gamma oscillations and visual contrast sensitivity and demonstrate that a reduction in gamma oscillation power through the suppression of top-down influences impairs perception of visual contrast.}, }
@article {pmid42141422, year = {2026}, author = {Zeng, X and Huang, Z and Shen, H and Luo, DY and Jin, T}, title = {Retrospective evaluation of clinical performance of three measurement catheter fixation methods in urodynamic studies.}, journal = {BMC urology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12894-026-02169-3}, pmid = {42141422}, issn = {1471-2490}, abstract = {BACKGROUND: Catheter displacement during urodynamic studies remains a common challenge, potentially introducing artifacts, compromising test accuracy, and decreasing patient comfort. Despite the clinical significance of stable catheter fixation, evidence-based recommendations for optimal fixation techniques are lacking. This study seeks to address this gap by comparing the effectiveness and patient comfort associated with three commonly used catheter fixation methods during urodynamic study.<br><br>METHODS: We retrospectively collected data from non-randomized patients who underwent urodynamic studies (UDS) at West China Hospital of Sichuan University between April and June 2023. Patients were selected based on predefined inclusion and exclusion criteria and assigned to one of three catheter fixation methods. The effectiveness of the following fixation techniques was evaluated: waterproof tape fixation (Group 1: catheter secured to the skin with adhesive tape), (2) patient-manual fixation (Group 2: patient holds the catheter manually throughout the procedure), and (3) silk thread fixation (Group 3: catheter secured with silk suture tied and fixed externally).<br><br>RESULTS: A total of 168 patients were enrolled in the study, with 56 patients in each group. The median ages for Groups 1, 2, and 3 were 66 (47.25, 76), 67 (61,71), and 66 (48, 76.75) years, respectively. There were no statistically significant differences among the three groups in terms of maximum cystometric capacity (MCC), bladder compliance (BC), maximum flow rate (Qmax), detrusor pressure at Qmax (Pdet.Qmax), bladder contractility index (BCI), or bladder outlet obstruction index (BOOI) (P&#x2009;>&#x2009;0.05). The overall incidence of catheter displacement was 35.71% in Group 1, 0% in Group 2, and 14.29% in Group 3. Statistically significant differences in Comfort-B scale scores were observed between Group 1 and Group 2, and between Group 2 and Group 3 (P&#x2009;<&#x2009;0.000). Similarly, visual analogue scale (VAS) scores also showed significant differences between Group 1 and Group 2, and between Group 2 and Group 3 (P&#x2009;<&#x2009;0.000).<br><br>CONCLUSIONS: Our preliminary assessment indicated that the three catheter fixation methods did not significantly influence urodynamic parameters. Notably, patient-manual fixation achieved the lowest catheter displacement rate (0%) but was associated with the highest pain and discomfort scores. In contrast, waterproof tape and silk thread fixation offered better patient comfort but with higher displacement rates. These findings highlight a trade-off between catheter stability and patient comfort, suggesting that fixation method selection should be individualized based on patient characteristics and procedural requireme.<br><br>CLINICAL TRIAL NUMBER: Not applicable.}, }
@article {pmid42142489, year = {2026}, author = {Chen, X and Song, H and Shen, M and Chen, H and Fu, Y}, title = {Sensory reliance in visual working memory across active and passive states.}, journal = {Cognition}, volume = {274}, number = {}, pages = {106587}, doi = {10.1016/j.cognition.2026.106587}, pmid = {42142489}, issn = {1873-7838}, abstract = {Visual working memory (VWM) has been thought to operate in active and passive states, but whether these states differentially engage sensory storage remains debated. The current study aims to delve into this debate further by testing whether increasing the load of active/passive states in VWM affects detection sensitivity to an incoming visual stimulus, a psychophysiological probing method which has been verified to specifically uncover the sensory nature of working memory storage. Across Experiments 1-3, we consistently found that loading either active or passive state impaired visual detection to a similar degree, indicating comparable sensory demands for both states. In Experiment 4, we further validated the manipulation of VWM states by observing dissociative memory-driven attentional bias effect of different states. Experiment 5 showed that information released from VWM no longer impaired visual detection, further confirming the specific role of working memory storage (in either active or passive state) in interfering with concurrent sensory processing. Together, these findings suggest that both active and passive states in VWM engage sensory storage, with comparable functional consequences for ongoing sensory processing.}, }
@article {pmid42143102, year = {2026}, author = {Siam, MJN and Showrov, TA and Hossain, MS and Ayaan, NS and Bari, SMS and Tariq, F and Mahmud, AA}, title = {Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-50997-y}, pmid = {42143102}, issn = {2045-2322}, abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) enable users to control external devices using EEG signals, offering great potential in assistive and rehabilitation technologies. However, MI recognition remains challenging due to EEG's low signal-to-noise ratio (SNR), inter-subject variability, and complex spatiotemporal patterns. Existing approaches often suffer from limited accuracy, high computational cost, and poor interpretability. In response to these challenges, we present the first comprehensive benchmarking of the publicly available EEG-hand movement (EEG-HM) dataset. Our study aims to establish a standardized performance baseline, guide the selection of optimal models by jointly considering accuracy, prediction time, and explainability, and ultimately accelerate progress in MI-BCI development. We have proposed a two-stage optimization of machine learning models that employs both feature selection and hyperparameter tuning. We exploit five feature selection algorithms for selecting the best set of EEG electrodes and frequency bands, while Bayesian optimization is exploited for machine learning model optimization through hyperparameter tuning. Furthermore, to validate the neurophysiological basis of our model's decisions, we leverage explainable AI (XAI) algorithms-LIME and SHAP-quantifying the contributions of specific EEG electrodes and frequency bands to interpret its decision-making process. Through extensive simulations, the proposed two-stage optimization of the machine learning model demonstrates a superior performance in terms of accuracy, precision, and recall. This method outperforms the existing methods by 21.47% in accuracy with competitive prediction time. Its performance is further evaluated on the PhysioNet MI dataset, achieving a 4.67% accuracy improvement over state-of-the-art methods. Through LIME and SHAP, we provide the local and global explanations for no activity, left-hand, and right-hand imagery movements. Additionally, we analyze how various EEG frequency bands and electrode locations interact during the performance of different motor imagery hand movements.}, }
@article {pmid42143201, year = {2026}, author = {Osman, YBM and Elsanosi, AHM and Jing, C and Shen, Y and Kuai, H and Wang, S and Ng, MK and Lei, B and Wang, S}, title = {Diffusion models for brain imaging computing: a survey of frameworks and applications.}, journal = {Brain informatics}, volume = {}, number = {}, pages = {}, doi = {10.1186/s40708-026-00301-5}, pmid = {42143201}, issn = {2198-4018}, abstract = {Advances in brain imaging have generated unprecedented volumes of high-dimensional data, yet extracting meaningful information from complex, noisy, and incomplete brain imaging data remains a significant challenge. Diffusion models (DMs) have introduced a paradigm shift in this field, surpassing traditional generative approaches. This review systematically examines the theoretical foundations of diffusion models, and their practical applications in eight brain imaging computing tasks: registration, super-resolution, cross-modal reconstruction and synthesis, segmentation, classification, brain network analysis, brain-computer interface (BCI) signals augmentation, and BCI decoding. Additionally, we emphasize obstacles that hinder deployment in practice, including computational scalability and sampling inefficiency, limited generalization under domain shift sensitivity, as well as multimodal integration and alignment constraints, while outlining potential future directions that emphasize the convergence of diffusion models with large-scale foundation models, which hold the potential to advance scalable, reliable, and clinically embedded brain imaging solutions. Throughout this review, we aim to establish a roadmap of progress and translational hurdles to guide emerging research and accelerate collaboration spanning DMs, clinical brain imaging, and engineering disciplines.}, }
@article {pmid42131835, year = {2026}, author = {Qian, L and Shi, Y and Liu, W and Yao, Q}, title = {Muscle energy techniques for post-stroke spasticity: mechanisms and clinical applications.}, journal = {Frontiers in neurology}, volume = {17}, number = {}, pages = {1773854}, pmid = {42131835}, issn = {1664-2295}, abstract = {Spasticity is a common and disabling complication after stroke, often leading to progressive joint stiffness, restricted movement, and reduced functional independence. Current management strategies for post-stroke spasticity (PSS) are limited by inconsistent efficacy and a lack of standardized protocols. Muscle energy techniques (MET) have emerged as a promising non-invasive approach, though their mechanisms and clinical value in PSS remain poorly understood. This review summarizes available evidence on MET for PSS based on systematic searches of PubMed, Web of Science, CNKI, and WanFang up to November 2025. MET may alleviate PSS through two main routes, namely inhibiting spinal and cortical motor neuron excitability and modulating pain pathways, though the evidence for these mechanisms remains limited and comes mainly from experimental studies. Key clinical studies indicate that MET can reduce muscle tone, improve range of motion, and enhance functional outcomes, with particularly notable effects on upper limb spasticity. However, heterogeneity in treatment protocols and a shortage of high-quality trials limit the strength of current conclusions. We further discussed critical limitations, including the reliance on active patient participation, which may preclude its use in persons with stroke with significant cognitive or motor deficits. Future directions include standardizing treatment protocols and integrating MET with emerging technologies such as biofeedback and brain-computer interfaces. This review offers a mechanistic and clinical framework to support the evidence-based integration of MET into PSS rehabilitation.}, }
@article {pmid42132445, year = {2026}, author = {Mokienko, O and Zisman, M and Bobrov, P and Ustinova, K}, title = {Brain-Computer Interfaces for Gait Rehabilitation After Stroke: A Scoping Review.}, journal = {American journal of physical medicine &amp; rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1097/PHM.0000000000002943}, pmid = {42132445}, issn = {1537-7385}, abstract = {Brain-computer interfaces (BCIs) represent a promising technology for restoring lower limb motor functions and gait after stroke. The application of BCIs in this field is supported by a limited number of studies. The objective of the review was to systematically and critically evaluate the current evidence on the BCIs use for lower limb function rehabilitation in stroke patients. A systematic literature search was conducted in PubMed, Scopus, and Web of Science databases. The inclusion criteria were as follows: studies involving adult patients with post-stroke hemiparesis; implementation of non-invasive BCIs specifically targeting lower limb rehabilitation; detailed reporting of training protocols. Quality assessment was conducted using the National Institutes of Health Study Quality Assessment Tools. Twenty-two studies were included in the analysis with the following results. Electroencephalography-based BCIs integrated with functional electrical stimulation of muscles (EEG-BCI-FES) represent the most extensively investigated technology in this field, with efficacy and safety demonstrated in three randomized controlled trials (RCTs) and one non-RCT. BCI systems integrated with mechanical devices have been less studied, with evidence from two RCTs, while systems with only visual feedback have also been evaluated in two RCTs. For BCIs with exoskeletons, only technical feasibility has been demonstrated. Further research is needed to optimize training protocols and study long-term effects of using BCI for rehabilitation.}, }
@article {pmid42133616, year = {2026}, author = {Song, H and Zeng, J and Zheng, Y and Huang, H and Wang, H}, title = {Data-driven differentiation analysis of urban high-tech industries: Research on bibliometrics and large language models.}, journal = {PloS one}, volume = {21}, number = {5}, pages = {e0348590}, pmid = {42133616}, issn = {1932-6203}, mesh = {*Bibliometrics ; China ; *Language ; Humans ; Cities ; *Industry ; Artificial Intelligence ; Large Language Models ; }, abstract = {This study examines inter-city heterogeneity in China's high-tech industries from a regional innovation systems (RIS) perspective, with a particular focus on how variations in knowledge production, technological application, and actor configurations are associated with divergent urban innovation trajectories. We compile more than 39,000 publications from the Web of Science (WOS) and nearly 10,000 patent records from the national patent database for the period 2016-2025, covering four representative cities-Wuhan, Chengdu, Hangzhou, and Tianjin-and four technological domains: artificial intelligence (AI), fiber-optic communication (FOC), intelligent connected vehicles (ICV), and storage chips (SC). The study develops an integrated analytical framework combining bibliometric analysis, co-word network modeling, collaboration network mapping, and large language model (LLM)-assisted semantic interpretation. LLMs are employed primarily in keyword cleaning, terminology standardization, and topic identification, improving the consistency and interpretability of textual metadata. Visualizations generated using VOSviewer highlight pronounced inter-city differences in technological portfolios, research priorities, and collaboration structures. The results suggest distinct urban innovation configurations across the four cities. Wuhan exhibits strong positioning in FOC and SC, reflecting a combined industry-academy orientation. Hangzhou shows high frontier intensity in AI and ICV, consistent with an industry-led and digitally driven innovation profile. Chengdu demonstrates substantial academic output but comparatively weaker evidence of technological translation, while Tianjin, despite a smaller overall scale, displays notable specialization in applied domains such as brain-computer interfaces and smart port technologies. Rather than replacing quantitative analysis, LLM-assisted interpretation supports the identification and contextualization of these patterns by enhancing semantic coherence and reducing noise in large-scale textual data. Overall, the proposed framework provides a reproducible and scalable approach for examining regional technological differentiation and is applicable to comparative studies of urban innovation systems across different regions and industrial contexts.}, }
@article {pmid42136985, year = {2026}, author = {Ameer, OZ}, title = {Autonomic-vascular dysregulation in CKD-associated hypertension: a narrative review with evidence hierarchy.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1808065}, pmid = {42136985}, issn = {1662-4548}, abstract = {Hypertension and chronic kidney disease frequently coexist and mutually accelerate cardiovascular and renal injury. This narrative review prioritizes direct human autonomic phenotyping (Level 1: microneurography, HRV/BRS), human vascular correlates (Level 2: PWV, FMD), and complementary preclinical evidence (Level 3) to elucidate autonomic-vascular mechanisms. Autonomic imbalance, characterized by sympathetic overactivity and reduced parasympathetic restraint, represents a key interface between neural control and vascular pathology in this setting. This narrative review synthesizes experimental and clinical evidence on how the autonomic nervous system shapes vascular function in hypertension and CKD. We outline physiological autonomic control of vascular tone (baroreflex pathways, central networks, brain-kidney communication), characteristic autonomic alterations in hypertension (elevated MSNA, impaired HRV/BRS), and their vascular consequences (endothelial dysfunction, arterial stiffness). We emphasize CKD-specific autonomic drivers (renal afferents, uremic toxins, inflammation) and their translation to exaggerated vascular injury and adverse BP phenotypes. Finally, we discuss pharmacological/device-based strategies targeting autonomic-vascular pathways, highlighting opportunities for neuromodulation, biomarker-guided risk stratification, and individualized treatment. By integrating multidisciplinary evidence, this review frames CKD hypertension as amplified autonomic-vascular injury and positions the autonomic nervous system as a promising therapeutic target.}, }
@article {pmid42139128, year = {2026}, author = {Hong, J and Rao, P and Wang, W and Najafizadeh, L}, title = {EMBC Special Issue: ChatBCI-Assist: An Intent-Based P300 Speller with A Locally-Deployed LLM and Adaptive Stopping Strategy Enabling Record Online Spelling Performance.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3693965}, pmid = {42139128}, issn = {1558-2531}, abstract = {P300-based speller brain computer interfaces (BCIs) provide promising communication solutions for individuals with severe motor impairments such as those with amyotrophic lateral sclerosis (ALS). However, existing P300 spellers are constrained by slow typing speed and limited efficiency. Here, we present ChatBCI-Assist, an intent-based P300 speller that integrates a locally-deployed large language model (LLM), fine-tuned for the task at hand, with an adaptive stopping strategy for key selection and a graphical user interface (GUI) designed for efficient message composition, to achieve record-level online spelling performance. The LLM, trained on an ALS-specific communication corpus using low-rank adaptation (LoRA), produces context-aware, semantically coherent, and prefix-constrained word and phrase predictions in real time. The proposed GUI supports efficient, user-adaptive message composition, while the adaptive stopping strategy dynamically adjusts stimulus presentation based on each subject's classification performance. Combined with a subject specific stepwise linear discriminant analysis (SWLDA) classifier, ChatBCI-Assist enhances spelling efficiency. Results from online experiments demonstrate that ChatBCI-Assist achieves record performance, with an average information transfer rate (ITR) of 105.2 bits/min, an overall character-level mutual information rate (MIR) of 52.9 bits/min and characters per minute (CPM) of 19.7 in copy-spelling tasks, and 30.7 CPM in semantic spelling tasks. Evaluated using semantic ITR (SITR), a metric proposed to characterize semantic communication efficiency, ChatBCI-Assist achieved SITR of 147.1 bits/min. User experience evaluations further confirm reduced workload and higher usability from LLM-based semantic spelling configurations, compared to traditional copy-spelling paradigms (dictionary or LLM). This work demonstrates that integrating locally-adapted LLMs with intent driven design and subject-specific decoding optimization can substantially improve the speed, efficiency, and user experience of BCI-based communication systems.}, }
@article {pmid42140229, year = {2026}, author = {Lu, J and Meng, K and Zhou, Z and Li, L}, title = {The application of the unscented Kalman filter in epilepsy research: a review.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae6e46}, pmid = {42140229}, issn = {2057-1976}, abstract = {Epilepsy is a complex neurological disorder characterized by nonlinear dynamic interactions among multiple brain regions. The Unscented Kalman Filter (UKF), a high-precision algorithm for nonlinear state and parameter estimation, has recently gained prominence in epilepsy research as it infers latent physiological parameters from electrophysiological signals to reveal the underlying seizure mechanisms. This review provides a comprehensive overview of recent progress in applying UKF to epileptic dynamics modeling and signal analysis, focusing on three major aspects: parameter estimation and model optimization based on neural computational models, seizure detection and prediction, and closed-loop control for seizure intervention. Studies have demonstrated that UKF can robustly reconstruct neuronal dynamics under noise and nonstationary conditions, providing real-time tracking of seizure evolution and contributing to a unified framework that integrates modeling, signal interpretation, and intervention. Despite these advances, important challenges remain, including noise covariance selection, high-dimensional parameter estimation, large-scale network modeling, and limited clinical validation. Future research should focus on adaptive mechanisms, improved multi-parameter estimation, and broader validation using multimodal data and real-patient cohorts. Overall, UKF has shown considerable promise as a model-based framework for epilepsy research and, more broadly, as an interpretable engineering approach for latent neural-state estimation from noisy physiological signals, although broader clinical evidence and further methodological refinement are still required before it can be considered a clinically mature framework.}, }
@article {pmid41742719, year = {2026}, author = {Chowdhury, P and Crichton, CA and Finster, R and Whiting, GL and Bihar, E and Kireev, D}, title = {Skin Conformal Hydrogel Bioelectrodes for High-Fidelity Electrophysiology and Human-Machine Interfaces.}, journal = {Advanced healthcare materials}, volume = {15}, number = {18}, pages = {e05753}, doi = {10.1002/adhm.202505753}, pmid = {41742719}, issn = {2192-2659}, support = {24CDA1049224//American Heart Association/ ; DGE2040434//National Science Foundation Graduate Research Fellowship Program/ ; }, mesh = {Humans ; *Hydrogels/chemistry ; *Skin ; Electrodes ; Polyvinyl Alcohol/chemistry ; Polystyrenes/chemistry ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; *Electrophysiology ; }, abstract = {Bioelectric interfaces used in electrophysiology must be capable of high-quality signal capture, mechanical conformance, and real-time interactivity. This research presents a conformable, reusable, and stretchable hydrogel bioelectrode composed of inkjet-printed PEDOT:PSS with a soft polyvinyl alcohol based substrate. This results in a strong, ion-conductive matrix 100 ± 16 kPa (n = 3) modulus, 660% ± 72% (n = 3) stretchability) and stable impedance (<6.4% drift over 72 h). The hydrogel bioelectrodes maintain <15% resistance drift after 50 strain cycles. The hydrogel bioelectrodes can effectively capture six bioelectrical signals, including heart, brain, muscle, ocular, electrodermal, and sympathetic skin nerve activities with outstanding signal-to-noise (SNR) ratios (up to 70 dB). Brain's alpha activity (8-12 Hz) is clearly detected, confirming the hydrogel bioelectrode's sensitivity to low-amplitude cortical signals. Sympathetic bursts in sympathetic skin nerve activity also show a 21% increase during the Valsalva maneuver, consistent with clinical observations. The hydrogel bioelectrodes also enable real-time human-computer interaction, where a subject-calibrated algorithm converts oculography signals from both eyes into directional drone control commands.}, }
@article {pmid42127907, year = {2026}, author = {Xu, W and Li, H and Zhang, W and Bai, G and Shen, C and Zhang, K}, title = {S-acylation of TDP43 regulates its condensation in amyotrophic lateral sclerosis.}, journal = {Molecular cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.molcel.2026.04.016}, pmid = {42127907}, issn = {1097-4164}, abstract = {TDP43 inclusion bodies are widely present in the majority of patients with familial and sporadic amyotrophic lateral sclerosis (ALS). The mechanisms regulating TDP43 solubility remain incompletely understood. Here, we report that TDP43 undergoes S-acylation primarily at the Cys244 residue by the S-acyltransferase zDHHC23. This S-acylation maintains the liquid-like properties of TDP43 by reducing the aberrant interaction with poly(ADP-ribose) polymerase 1 (PARP1) and PARylated proteins, thereby countering the pathological condensation of TDP43. S-acylation-deficient TDP43 inclusions sequester the translational machinery and inhibit cytoplasmic protein translation, ultimately resulting in neurotoxicity. Importantly, TDP43 S-acylation is decreased in the familial ALS-associated TDP43 mutants as well as in SOD1-G93A mice and C9orf72-ALS induced pluripotent stem cell (iPSC)-derived neurons, suggesting the widespread involvement of TDP43 S-acylation in ALS pathogenesis. Our findings reveal an undescribed modification of TDP43 and provide deeper insight into the regulation of TDP43 pathological condensation in ALS.}, }
@article {pmid42127969, year = {2026}, author = {Jensen, N and Ly, K and Kochnev Goldstein, A and Devaud, Q and Palanker, D}, title = {Maximizing the fidelity of a photovoltaic subretinal prosthesis for human patients.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae6d77}, pmid = {42127969}, issn = {1741-2552}, abstract = {PRIMA subretinal implants provide pros thetic vision to patients blinded by age-related macular degeneration, with acuity closely matching the sampling limit of the pixel pitch: a single 100µm pixel per line of a letter corresponds to 20/420 acuity. Decreasing the pixel size in the same flat geometry is difficult due to the constrained electric field, especially consid ering a 40µm thick debris layer separating the implant from the target neurons. Here we optimize the electrode design to help overcome such limitations. Approach. An end-to-end modeling pipeline combines the retinal photovoltaic implant simulator (RPSim) based on the Xyce circuit simulator with an interface to COMSOL Multiphysics for electric field modelling. It was used to generate and characterize implants in an open-loop sampling based optimization. Implant performance was evaluated with respect to voltage drop across bipolar cells (representing the stimulation strength), pattern contrast, and neural selectivity. Main Results. The highest selectivity in stimulation of bipolar cells was achieved with arrays having active electrodes on pillars and return electrodes connected in a mesh surrounding the photovoltaic pixels in the array. Such a design, even with pixels down to 20µm, provides stimulation strength exceeding, and contrast similar to that of flat 100µm PRIMA pixels. Significance. Using a novel 3-D electrode design, the pitch of the photovoltaic array can be decreased to 20µm, while providing performance that exceeds the flat 100µm PRIMA pixels. In humans, 20µm resolution on the retina corresponds to a visual acuity of 20/80 a five times improvement compared to the current clinical device. .}, }
@article {pmid42128168, year = {2026}, author = {Dörterler, S and Şahin, E and Özdemir, D}, title = {A dynamic subject-invariant fragment mixing strategy to suppress subject variability in EEG imagined speech classification.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2026.05.004}, pmid = {42128168}, issn = {1873-7544}, abstract = {This study presents the first systematic benchmark on a recently released 31-class Arabic imagined-speech EEG dataset for which no prior computational analysis has been reported. The task is exceptionally challenging due to pronounced inter- and intra-subject variability and frequent signal degradations, yielding very low performance when existing architectures are applied directly (baseline accuracy typically within the ∼ 7-15% range). To address these limitations, we propose NeuroSilentia, an EEG-tailored spatio-temporal model that integrates channel reweighting and efficient multi-scale temporal modeling, improving accuracy to 20.45%. We then examine a wide set of subject-invariant and domain-alignment strategies including contrastive objectives, DANN, MMD, CORAL, center/prototype losses, and Riemannian geometry-based methods, showing that global alignment alone provides limited gains in this high-cardinality setting. Building on these findings, we introduce Dynamic Subject-Invariant Fragment Mixing (DSIFM), an epoch-wise fragment-level mixing strategy that disrupts subject- and session-specific shortcuts while preserving class structure. Compared with the NeuroSilentia baseline, DSIFM substantially improves generalization, and when combined with contrastive learning reaches 51.60% accuracy. Data cleaning further increases performance, achieving 60.29% accuracy in the full 31-class setting. Extensive evaluations including 10-fold cross-validation, subject/class/channel-wise analyses, confusion-matrix diagnostics, t-SNE representation studies, and reduced-class transfer experiments (16/8/4 classes) consistently confirm the robustness of the proposed approach. Overall, this work delivers the first comprehensive benchmark for 31-class Arabic imagined speech EEG decoding and establishes DSIFM as an effective strategy for mitigating subject variability in complex multi-class EEG classification.}, }
@article {pmid42129136, year = {2026}, author = {Huo, Y and Huang, W and Liu, Z and Chen, H and Bai, T and Wang, Y and Wang, K and Zhang, D and Cheng, J and Sun, Y and Ma, G and Zhao, C and Zhang, Z and Shu, N}, title = {Functional system-specific brain aging across the Alzheimer's disease continuum.}, journal = {Translational psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41398-026-04081-8}, pmid = {42129136}, issn = {2158-3188}, support = {82301608, 32271145, 81871425, 210510238//National Natural Science Foundation of China (National Science Foundation of China)/ ; L252087//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; }, abstract = {Accelerated brain aging is implicated in Alzheimer's disease (AD). However, the spatial heterogeneity of brain aging patterns across different functional systems along the AD continuum remains largely unexplored. We developed functional system-specific brain age models derived from structural magnetic resonance imaging in a healthy adult cohort (n = 22,672) and applied them to 1478 participants across the AD continuum. Using up to 6 years of retrospective longitudinal data before clinical AD conversion, we quantified predicted age differences (PADs) and their change rates, characterized heterogeneous brain aging trajectories, and examined their associations with AD biomarkers, cognitive performance, and clinical progression. Progressive mild cognitive impairment (MCI) individuals showed early PAD deviations in the default mode network and accelerated changes in attention and control networks. System-wise PAD dynamics mediated the effects of AD-related biomarkers on cognitive decline. Integrating PAD features can improve predictive accuracy of MCI-to-AD conversion (AUC = 0.95). Functional system-specific PADs can be sensitive biomarkers for early detection and monitoring of individualized AD risk.}, }
@article {pmid42130383, year = {2026}, author = {Meng, L and Bell, JM and Barbre, K and Guthrie, S and Mason, L and Massey, J and Wiegand, R and Rowe, T and Sillions Prosper, M and Woods, A and Qureshi, I and Reses, H and Stuckey, M and Kuhar, D and Lindley, M and Hernandez-Romieu, AC and Benin, A}, title = {Using a longitudinal k-means clustering method to explore nursing home factors associated with SARS-CoV-2 infection peak and resilience to a COVID-19 surge.}, journal = {Infection control and hospital epidemiology}, volume = {}, number = {}, pages = {1-7}, doi = {10.1017/ice.2026.10406}, pmid = {42130383}, issn = {1559-6834}, abstract = {OBJECTIVE: Nursing home residents have been disproportionately impacted by respiratory virus-related morbidity and mortality due to inherent vulnerability and communal living environments. This study aims to identify nursing homes with higher infection rates during a period of intense SARS-CoV-2 transmission and explore facility-level characteristics potentially associated with infection surges.<br><br>DESIGN: A longitudinal k-means clustering approach followed by exploratory regression analyses.<br><br>SETTING: U.S. Nursing homes reporting to the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN).<br><br>METHODS: A longitudinal k-means method (kmlShape) classified the facilities based on their weekly SARS-CoV-2 incidence rate epidemic curve, identifying two categories (low vs high infection peak) based on the magnitude of infection peaks. A logistic regression model with bootstrapping was developed to assess facility characteristics associated with higher SARS-CoV-2 infection surges.<br><br>RESULTS: Among 11,990 nursing homes analyzed, 9,058 were classified as having a low infection peak, while 2,932 had a high infection peak. Nursing homes that are for-profit (OR = 1.570, 95% bootstrap confidence interval [BCI] 1.441-1.807), with high staff turnover (OR = 1.292, 95% BCI 1.154-1.451), or located in areas with higher social vulnerability (OR = 1.457, 95% BCI 1.239-1.880) were more likely to experience high infection peaks. Nursing homes with higher residents' vaccination coverage (OR = .321, 95% BCI .248-.380) and located in urban areas were less likely to experience high infection peaks.<br><br>CONCLUSIONS: The facility-level characteristics associated with lower SARS-CoV-2 infection peaks may indicate resiliency and help evaluate the capacity of nursing homes to endure stressors such as respiratory viruses and other communicable illnesses.}, }
@article {pmid42127061, year = {2026}, author = {Tao, W and Jia, Z and Yang, Y and Wong, CM and Li, C and Chen, X and Chen, CLP and Jung, TP and Wan, F}, title = {Fast BCIs: Leveraging Dual-Scale Time Windows with Test-Time Adaptation to Enhance Accuracy.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3692973}, pmid = {42127061}, issn = {1558-2531}, abstract = {Brain-computer interfaces (BCIs) must deliver outputs rapidly in numerous practical applications. However, the decoding accuracy may decline significantly when the time window (TW) is too short, a challenge exacerbated by the increasing adoption of deep learning methods in BCIs. For fast yet accurate outputs, this paper introduces a dual-scale time window (DTW) strategy with test-time adaptation (TTA), wherein the short TW decoding benefits from long TW setting through the TTA mechanism. Specifically, this strategy employs two specialized networks trained on EEG data with a short TW and a long TW respectively: the Main Network (MainNet), optimized for fast recognition within the short TW, and the Auxiliary Network (AuxNet), which generates high-confidence pseudo-labels with the long TW to update the MainNet during testing. The AuxNet's assistance leads to more accurate outputs from the MainNet in the short TW. We evaluated the method across diverse paradigms, including motor imagery (MI), steady-state visually evoked potential (SSVEP), and event-related potential (ERP) tasks, covering both high and low signal-to-noise ratio (SNR) conditions. At a 0.5 s TW, DTW-TTA achieved 74.64 % accuracy and 29.69 bits/min ITR on BCI-IV 2b (MI), 80.15 % and 128.16 bits/min on GIGA (SSVEP) dataset, 91.86 % and 229.82 bits/min on Benchmark (SSVEP) dataset, and 92.13 % and 74.04 bits/min on a VR-ERP dataset, outperforming state-of-the-art baselines in all cases. These results demonstrate that DTW-TTA effectively stabilizes short TW decoding and can be seamlessly integrated into deep learning-based BCI systems across paradigms.}, }
@article {pmid42127065, year = {2026}, author = {Kim, JA and Lee, H and Hong, J and Kim, J and Xu, D and Park, K and Kim, BJ and Ji, S and Ahn, JH}, title = {Phantom Brain model Replicating Multiple ECoG Signals for Preclinical Device Testing.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3692970}, pmid = {42127065}, issn = {1558-2531}, abstract = {OBJECTIVE: Phantom brain models are essential for overcoming the limitations of animal experiments in developing medical devices, such as ECoG multichannel electrodes, which are crucial for diagnosing severe brain disorders and advancing brain-computer interface (BCI) technology. However, conventional phantom brains still use bulky electrodes, resulting in low spatial resolution and volume conduction effects. These limitations lead to aliasing between adjacent electrodes and signal interference, making them insufficient for accurately evaluating high-density ECoG electrodes.<br><br>METHODS: Here, we present a phantom model that mimics the real cerebral cortex by replicating multiple ECoG signals simultaneously. The phantom brain model, which consists of graphene electrodes to mimic small-scale ECoG and perforated structure filled with Sodium chloride (NaCl) gel chosen for its electrical properties similar to the cerebral cortex, was designed using multiple arrays to ensure no signal interference.<br><br>RESULTS: This model mimicked different epileptic seizure signals originating from distinct regions of the cerebral cortex. Using multiple ECoG electrodes, it was confirmed that the ECoG signals caused by seizures in the cortex could be successfully monitored.<br><br>CONCLUSION: This demonstrated the excellent mimicry performance of the phantom brain and proved that it can also be used to test the performance of ECoG electrodes.<br><br>SIGNIFICANCE: This approach can serve as an alternative to preclinical testing and offer great potential to examine the performance of different ECoG electrodes through a model that mimics accurately ECoG signals.}, }
@article {pmid42127612, year = {2026}, author = {Li, X and Zhang, W and Tian, S and Wang, Z and Xiao, Z}, title = {Passive acoustic monitoring captures spatiotemporal dynamics of urban zoo soundscapes.}, journal = {Journal of environmental management}, volume = {408}, number = {}, pages = {129670}, doi = {10.1016/j.jenvman.2026.129670}, pmid = {42127612}, issn = {1095-8630}, abstract = {Urban zoos are important but complex artificial ecosystems that integrate wildlife conservation with public education. Their soundscapes, including anthrophony, biophony, and geophony, show significant spatiotemporal dynamics. Persistent fluctuations could contribute to chronic stress in captive animals and human visitors, impairing welfare and conservation outcomes. Existing research focuses on static noise levels, neglecting spatiotemporal dynamics in zoo soundscapes. To address this gap, we deployed passive acoustic monitoring (PAM) with 20 acoustic recorders at Zhengzhou Zoo across seven functional zones between weekdays and weekends during summer in 2024. We analyzed six acoustic indices: Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bioacoustic Index (BIO), Normalized Difference Soundscape Index (NDSI) and A-weighted sound pressure level (SPL(dBA)). These were combined with non-metric multidimensional scaling, generalized additive models, and kernel principal component analysis. Quantitatively, we found that SPL (dBA) increased by up to 10 dB on weekends compared to weekdays, while ACI rose by approximately 20%. A very strong negative correlation was observed between ADI and AEI, indicating an inverse dynamic between acoustic diversity and evenness. Building on these results, the analysis revealed distinct diel patterns, with biophony dominating during dawn and dusk periods, while anthrophony peaked during day hours. The weekend effect was significantly identified with these elevated SPL (dBA) and ACI, alongside reduced acoustic stability. Significant acoustic divergence was observed among functional zones, with elevated SPL (dBA) and ACI in High-traffic Zone but with reduced BIO in others. The results indicate that biological rhythms, human activities, and environmental structures collectively shape zoo soundscapes, and PAM and acoustic indices can provide a robust scientific basis for acoustic-based animal welfare and visitor management in zoos.}, }
@article {pmid42127676, year = {2026}, author = {Cheng, M and Chen, X and Cheng, H and Gao, X and Ao, H and Bao, X and Song, X and Tai, Y and Jin, D and Zhang, L}, title = {An ultrasensitive CRISPR/Cas12a based electrochemical biosensor for detection of toxigenic Clostridioides difficile.}, journal = {Biosensors &amp; bioelectronics}, volume = {308}, number = {}, pages = {118779}, doi = {10.1016/j.bios.2026.118779}, pmid = {42127676}, issn = {1873-4235}, abstract = {Clostridioides difficile (C. difficile) infection (CDI) represents a formidable global healthcare challenge, necessitating the development of rapid, accurate, and cost-effective diagnostic platforms to mitigate nosocomial transmission and improve patient outcomes. Compared with the conventional methods, CRISPR/Cas systems featured by specific target reorganization by a single chain RNA, coupled with electrochemical technology enables highly sensitive detection of various biomarkers. However, their application to CDI has remained unexplored due to the lack of tailored crRNAs. Herein, we present the integration of CRISPR/Cas12a with electrochemical transduction for the direct detection of C. difficile. A novel crRNA was engineered to specifically recognize the toxin B gene (tcdB), activating the trans-cleavage activity of Cas12a upon target binding. This cascade triggers the cleavage of immobilized ssDNA reporters on the electrode surface, generating measurable amperometric signal changes. The developed biosensor demonstrates exceptional performance, achieving a detection limit of pM level for tcdB DNA within 40 min, while exhibiting high specificity against non-target pathogens and robust stability over 7 days. This work establishes a rapid and reliable CRISPR-electrochemical diagnostic platform, offering significant potential for point-of-care CDI management.}, }
@article {pmid42116129, year = {2026}, author = {Liu, Y and Wang, N and Liu, F and Li, K and Ward, MP and Grassly, NC and Tu, W and Yu, J and Li, W and Zhao, Y and Zhang, J and Dong, J and Lu, T and Liu, M and Chang, Z and Zhang, Z}, title = {Estimating the nationwide incidence of coxsackievirus A6-associated hand, foot and mouth disease in China, 2008-2022.}, journal = {Infectious diseases of poverty}, volume = {15}, number = {1}, pages = {}, pmid = {42116129}, issn = {2049-9957}, support = {24ZR1414700//Natural Science Foundation of Shanghai/ ; 82473736//National Natural Science Foundation of China/ ; 2022YFC2602900//National Key Research and Development Program of China/ ; }, mesh = {*Hand, Foot and Mouth Disease/epidemiology/virology ; Humans ; China/epidemiology ; Incidence ; Child, Preschool ; Infant ; Bayes Theorem ; Female ; Male ; *Enterovirus A, Human/isolation &amp; purification ; Infant, Newborn ; }, abstract = {BACKGROUND: Due to insufficient routine surveillance, the nationwide disease burden of hand, foot and mouth disease (HFMD) caused by coxsackievirus A6 (CVA6), an emerging serotype, in China remains unclear. This study aimed to estimate the incidence of CVA6-associated HFMD across the Chinese mainland.<br><br>METHODS: CVA6 positive data from 511 locations across the Chinese mainland during 2008-2022 were integrated from the national pathogen surveillance system and literature, and reported HFMD cases during the same period were obtained from the national infectious disease&#xa0;surveillance system. The predicted positivity rate and incidence of CVA6-associated HFMD in children under five years of age across the Chinese mainland were estimated using a Bayesian geostatistical Gaussian model based on positivity data, reported cases, and environmental, socioeconomic, demographic, and vaccination factors.<br><br>RESULTS: The model estimated that the average positivity rate of CVA6 in the Chinese mainland from 2008 to 2022 was 24.1%, with a 95% Bayesian credible interval (BCI) of 11.9-43.3%. The corresponding average annual incidence of CVA6-associated HFMD in children under five years of age was 506 (95% BCI: 272-805) per 100,000. The yearly incidence of CVA6-associated HFMD in children under five years of age peaked in 2018 (873 per 100,000; 95% BCI: 513-1309) before a subsequent decline after 2020. The incidence was highest in South China (1571 per 100,000; 95% BCI: 890-2420) and lowest in Northeast China (208 per 100,000; 95% BCI: 106-340). The estimated CVA6-associated HFMD incidence showed a consistent upward trend across different economic level groups before 2020, and tended to be higher in high-gross domestic product (GDP) per capita regions than in medium- and low-GDP regions.<br><br>CONCLUSIONS: Model-based estimates indicate a potentially high incidence of CVA6-associated HFMD on the Chinese mainland, particularly in South China, highlighting the need for enhanced surveillance of CVA6 and targeted control efforts in high-incidence regions.}, }
@article {pmid42117387, year = {2026}, author = {Maughan, J and Woods, I and O'Connor, C and Quintana-Sarti, P and Caffrey, E and Munuera, JM and Carey, T and Dervan, A and López Valdés, A and Mamad, O and Caldwell, MA and O'Brien, FJ and Coleman, JN}, title = {PolyGraph - Flexible, Biocompatible &amp; Electrically Optimized Graphene-Polymer Composites for Next-Generation Neural Interfaces.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e05076}, doi = {10.1002/adhm.202505076}, pmid = {42117387}, issn = {2192-2659}, support = {SFI/12/RC/2278_P2//Research Ireland AMBER Centre/ ; //Anatomical Society Studentship/ ; //Ministry for Digital Transformation and the Civil Service/ ; //European Union's Next Generation funds/ ; }, abstract = {Neural interfacing materials must deliver exceptional electrochemical performance, while integrating safely with the central nervous system. In this study we develop PolyGraph, a flexible, conductive, and biocompatible graphene-polycaprolactone (PCL) nanocomposite designed to strike this balance, which enables fabrication of conformable multichannel microelectrode arrays. Optimized liquid-phase exfoliation produces conductive, biocompatible PVP-stabilized graphene nanosheets, which are incorporated into PCL to form flexible, processable composites - PolyGraph. This material demonstrates bio- and immuno-compatibility with sensitive primary and iPSC-derived neuronal and glial cells. PolyGraph achieves low impedance (∼1.6 Ω cm[2] @ 1 kHz) and high charge injection capacity (11.7 mC/cm[2] for a 100 ms pulse), enhanced by NaOH surface roughening and AuPd coating. Leveraging their processability, PolyGraph composites are fabricated into flexible, individually isolated microneedle electrode arrays with biomimetic soft hyaluronic acid backings. These arrays demonstrate bidirectional neural interfacing capabilities, enabling both the delivery of controlled stimulation pulses in physiological buffer and high-resolution neuronal recording in murine brain slices, with machine learning-based event classification. Together, these advances establish PolyGraph as an optimal material platform for next-generation brain-computer interfaces and soft bioelectronic devices.}, }
@article {pmid42117762, year = {2026}, author = {Qin, Y and Dang, M and Yu, D and Chang, Q and Mugo, SM and Wang, H and Zhang, Q}, title = {Bidirectional Brain-Machine Communication and Neuromodulation by Supramolecular Hydrogel Neural Probes for Chronic Pain Management.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e73367}, doi = {10.1002/adma.73367}, pmid = {42117762}, issn = {1521-4095}, support = {22377122//National Natural Science Foundation of China/ ; SKL202402018//Jilin Province Science and Technology Development Plan/ ; 2024GZZ14//Changchun City Science and Technology Development Plan/ ; 029GJHZ2024038FN//International Partnership Program of the Chinese Academy of Sciences/ ; }, abstract = {Chronic pain continues to pose a significant therapeutic challenge due to its complex pathophysiology and the limited efficacy of conventional pharmacological treatments. Brain-machine interfaces (BMIs) have emerged as a promising strategy for recording neural activity, modulating neural circuits, and treating neurological disorders. However, the long-term performance of conventional rigid implantable probes is severely constrained by their mechanical mismatch with soft brain tissue. This mismatch provokes chronic inflammatory responses and results in gradual signal deterioration. Additionally, most existing probes lack integrated functionality for simultaneous in situ neuromodulation and neural signal recording. In this work, we developed a supramolecular hydrogel based on α-helical polypeptide cross-linkers, achieving an optimal balance of mechanical compliance, electrical conductivity, and optical transparency. When implanted in the rat prelimbic cortex, the hydrogel probe enabled stable recording of local field potentials (LFPs) for up to 16 weeks. Importantly, the probe enabled in situ neuromodulation while concurrently recording evoked LFPs, resulting in enhanced prelimbic cortical activity, increased mechanical withdrawal thresholds, and reduced cold allodynia in a chronic neuropathic pain model. These findings advance neural interface technology by enabling integrated, long-term monitoring and neuromodulation, representing a paradigm shift in the design of implantable devices for chronic pain therapy.}, }
@article {pmid42119262, year = {2026}, author = {Cheng, J and Ran, R and Fang, B}, title = {SPD-DANN: An SPD manifold unsupervised domain adaptation method for cross subject motor imagery EEG decoding.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {202}, number = {}, pages = {109076}, doi = {10.1016/j.neunet.2026.109076}, pmid = {42119262}, issn = {1879-2782}, abstract = {Electroencephalogram (EEG) signals convey abundant physiological and psychological information. Decoding these signals is fundamental for brain-computer interfaces (BCIs) and medical rehabilitation. Nevertheless, the non-stationarity and inter-individual variability of EEG signals impede current models from achieving robust cross-subject generalization without expensive subject-specific recalibration, thereby restricting their practical deployment. Unsupervised domain adaptation (UDA) aims to improve generalization by minimizing distribution discrepancies between source and target domains. Recent studies treat individual subjects as distinct domains and leverage UDA to facilitate cross-subject EEG classification, learning domain-invariant features through discrepancy minimization or adversarial training. However, these conventional methods are primarily developed in Euclidean space, which is insufficient to capture the non-linear structural characteristics inherent in EEG data. To address this limitation, we propose a deep adversarial neural network on the Symmetric Positive Definite (SPD) matrix manifold, termed SPD-DANN, which facilitates the extraction of subject-invariant features via adversarial learning. Additionally, we design an SPD domain feature alignment loss and an SPD class prototype pair loss to simultaneously promote feature alignment across subjects and enhance discriminability within the feature space. Extensive experiments on four BCI datasets demonstrate that our method outperforms several state-of-the-art UDA techniques. Furthermore, the proposed loss functions are readily adaptable to broader unsupervised and semi-supervised domain adaptation frameworks.}, }
@article {pmid42119454, year = {2026}, author = {Jiang, M and Qu, D and Luo, Q and Duan, J}, title = {The aging effect in deeper-level processing of emotion words.}, journal = {Acta psychologica}, volume = {267}, number = {}, pages = {106866}, doi = {10.1016/j.actpsy.2026.106866}, pmid = {42119454}, issn = {1873-6297}, abstract = {It remains uncertain, both theoretically and empirically, whether older adults undergo age-related decline in the capability to process emotion words. By employing a valence judgment task with two subtypes of appraisal emotion words, namely, sensory-quality appraisal emotion words and intrinsic-quality appraisal emotion words, matched with positive or negative valence, the present study undertook an investigation into this issue. It was found that aging effect did occur. Older adults exhibited a general slowing in processing speed, and they also showed a significantly stronger processing advantage towards positivity words relative to young adults.}, }
@article {pmid42119571, year = {2026}, author = {Fei, SW and Hu, YB and Chen, JL}, title = {Time-frequency feature extraction method for EEG signals utilizing fractional-order transient-extracting transform.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae6c2d}, pmid = {42119571}, issn = {2057-1976}, abstract = {In light of the challenges in capturing transient features of electroencephalographic (EEG) signals under the motor imagery (MI) paradigm, this paper proposes a Fractional-order transient-extracting transform (FOTET). Transient features refer to short-duration, non-stationary waveform segments that reflect critical neural activity during MI, and their accurate extraction is essential for effective brain-computer interface (BCI) performance. FOTET enhances transient feature extraction and time-frequency energy aggregation by introducing a fractional-order transient extracting operator and an iterative optimization process, which can efficiently capture weak transient signal features while overcoming the limitations of the traditional methods. Moreover, the method can balance the time and frequency resolution by adjusting the fractional order parameter . The experimental results, based on data from 10 healthy subjects performing four-class MI tasks, indicate that FOTET can accurately extract transient features in noisy environments, effectively distinguishing EEG signals across different classes. When combined with the DenseNet-LSTM, it achieves a classification accuracy of 96.71% when , significantly surpassing results obtained using traditional TFA methods, effectively validating the superiority of FOTET in EEG signal feature extraction.}, }
@article {pmid42120369, year = {2026}, author = {Wang, Y and Luo, J and Zhang, C and Jin, Z and Wang, M and Xu, Z and Cai, X and Chen, J}, title = {An advanced TMR sensor-based magnetrode for in vivo LFP magnetic field recording.}, journal = {Microsystems &amp; nanoengineering}, volume = {12}, number = {1}, pages = {}, pmid = {42120369}, issn = {2055-7434}, support = {62271469//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62121003//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {The detection and interpretation of brain signals are crucial for advancing brain-computer interface (BCI) technologies. Local field potential (LFP) signals, reflecting synchronized neuronal ensemble activity, offer insights into coordinated neural function. In this study, we present a miniaturized tunneling magnetoresistance (TMR)-based neural magnetrode optimized for in vivo LFP magnetic recording. The magnetrode achieves a magnetoresistance ratio (145%) and low-field sensitivity (16.59 %/mT), while maintaining low detection limits of 4.8 nT/√Hz at 1 Hz and 140 pT/√Hz at 1 kHz. Noise analysis revealed that reducing bias current and applying high-frequency AC excitation significantly suppress low-frequency 1/f noise. In vitro simulations validate LFP reconstruction capability, and in vivo experiments demonstrate a strong correlation (r = 0.857 ± 0.031, p < 0.01) between magnetic and electrical LFPs. Furthermore, in vitro durability tests in artificial cerebrospinal fluid demonstrated exceptional stability, with negligible signal drift (<0.4% variation in TMR ratio) over a 7-day period. This work establishes the TMR-based magnetrode emerges as a new potential tool for neural interface technologies, with implications real-time BCI and neuropathology research.}, }
@article {pmid42121193, year = {2026}, author = {Higgins, N and Blakely, B and Everingham, R and Gilbert, F and Griffin, S and Harris, AR and Herring, S and Ho, CWL and Hoy, K and Kiel-Chisholm, S and Koplin, J and Lawn, S and McCay, A and Phillipson, N and Richards, B and Rosenfeld, JV and Shamsi Gooshki, E and Viana, JN and Gardner, J and Carter, A}, title = {Recommendations on post-trial responsibility in implantable neural device research: a multidisciplinary consensus study.}, journal = {BMC medical ethics}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12910-026-01475-7}, pmid = {42121193}, issn = {1472-6939}, support = {Australia Research Training Program (RTP) Stipend//Department of Education, Australian Government/ ; Discovery Early Career Research Award (DE240100386)//Australian Research Council/ ; Future Fellowship (ID: FT220100509)//Australian Research Council/ ; }, abstract = {The clinical development of implantable neural devices raises complex ethical questions about post-trial responsibilities to participants. Continued support for participants who continue to use investigational implantable neural devices requires ongoing specialist care, technical expertise, access to tertiary clinical infrastructure, and substantial financial resources to pay for the device and related procedures. However, continued access may not be possible if the trial shows no benefit, if financial barriers limit commercial viability, or if safety concerns lead to suspension or early termination. Specific ethical guidance on post-trial responsibility is urgently needed. To address this challenge within the Australian innovation context, we conducted a modified Delphi study with a multidisciplinary panel of 24 experts, including representatives from industry, bioethics, law, neurosurgery, clinical psychology/neuropsychology, clinical research, neural engineering, regulation and governance, and lived experience advocacy. The process involved two workshops and a survey, guided by established RAND/UCLA methods with context-specific modifications. Drawing on prior empirical research and regulatory review, the panel developed 11 consensus recommendations for responsible post-trial practices. All recommendations achieved high levels of agreement and were rated as highly important for addressing ethical risks in the Australian environment. These are the first jurisdiction-specific recommendations of their kind, and we anticipate they will substantially enhance ethical and practical standards for post-trial responsibility in implantable neural device research in Australia and internationally.}, }
@article {pmid42121328, year = {2026}, author = {Gao, L and Zhu, L and Wang, S and Wu, K and Pang, Y and Zhang, R and Sun, Y}, title = {Network-Level Mechanisms of Sustained Recovery from Mental Fatigue Differentially Modulated by Acute Exercise and Rest.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2650038}, doi = {10.1142/S0129065726500383}, pmid = {42121328}, issn = {1793-6462}, abstract = {Mental fatigue, a prevalent yet underestimated state, impairs cognitive performance and increases the risk of errors and accidents, creating persistent challenges in occupational and clinical contexts. While rest is commonly used for recovery, its benefits are context-dependent and typically short-lived. Acute aerobic exercise has shown promise in alleviating cognitive impairments, yet the neural mechanisms distinguishing exercise and rest, as well as their recovery effects during interventions, remain unclear. In this study, a within-subject design was employed with three prolonged psychomotor vigilance task (PVT) sessions: one with mid-task exercise, one with passive rest, and one as a no-intervention control. EEG-derived functional networks were constructed and analyzed to characterize immediate, carryover, and recovery effects across task and intervention periods. Both interventions elicited immediate behavioral improvements but induced divergent frequency-specific network reorganization. During task reengagement, exercise exhibited a state of functional integration characterized by reduced [Formula: see text]-band local efficiency and sustained [Formula: see text]-band global efficiency, whereas rest displayed network segregation. Recovery analyses revealed that exercise modulated distributed [Formula: see text]-band connectivity, with discriminative frontal features reliably distinguishing recovery pathways. Despite similar behavioral outcomes between exercise and rest, acute exercise uniquely sustained network integration distinct from the topological segregation observed during rest, highlighting its potential for fatigue regulation under cognitively demanding contexts from a brain network perspective.}, }
@article {pmid42122020, year = {2026}, author = {Jeong, H and Yoon, C and Kim, J and Park, E and Kim, H and Park, S and Kim, HG and Jung, CK}, title = {HER2 Score-Aware Virtual Immunohistochemistry via Non-Contrastive Multi-Task Translation.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {16}, number = {9}, pages = {}, doi = {10.3390/diagnostics16091319}, pmid = {42122020}, issn = {2075-4418}, support = {RS-2021-KH113146//Korea Health Industry Development Institute (KHIDI)/ ; RS-2019-II191906, Artificial Intelligence Graduate School Program(POSTECH)//the Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP) grant funded by the Korea government(MSIT)/ ; 2710086164//Commercialization Promotion Agency for R&amp;D Outcomes (COMPA)/ ; RS-2025-00558651//National Research Foundation of Korea (NRF)/ ; N/A//Basic Medical Science Facilitation Program through the Catholic Medical Center of the Catholic University of Korea, funded by the Catholic Education Foundation/ ; }, abstract = {Background/Objectives: While human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) is pivotal for breast cancer management, its reliance on additional tissue processing beyond routine H&amp;E staining remains a clinical burden. Although virtual staining offers a potential solution, current methods often fail to explicitly account for HER2 score-specific expression patterns. To address this gap, we developed a score-aware framework designed for the precise generation of virtual HER2 IHC images. Methods: We introduce the non-contrastive multi-task (NCMT) framework, which integrates negative-free patch alignment, style-content constraints, and auxiliary HER2 score supervision for high-fidelity H&amp;E-to-IHC translation. For rigorous evaluation, the model was validated on the BCI dataset, utilizing an official split of 3896 training and 977 independent test images derived from 51 whole-slide images. Results: NCMT demonstrated superior virtual staining performance, achieving a Fréchet Inception Distance (FID) of 38.8, a Kernel Inception Distance (KID) of 5.6, and an average Perceptual Hash Value (PHV) of 0.439. In downstream HER2 scoring tasks, while virtual IHC images alone yielded an accuracy of 83.01%, the fusion of H&amp;E and virtual IHC further elevated performance to 97.85% accuracy and a 98.23% F1 score. These findings suggest that our framework effectively preserves diagnostic features while providing complementary information to H&amp;E-based morphological analysis. Conclusions: NCMT enables HER2 score-aware virtual IHC generation from H&amp;E and can serve as a complementary tool for HER2 assessment in digital pathology.}, }
@article {pmid42122470, year = {2026}, author = {Liu, B and Chen, G and Yin, L and Liu, J}, title = {Decoding Mandarin Action Verbs from EEG Using a Dual-LSTM Network: Towards Practical Assistive Brain-Computer Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {9}, pages = {}, doi = {10.3390/s26092749}, pmid = {42122470}, issn = {1424-8220}, support = {No. 2024RC1054//Department of Science and Technology of Hunan Province/ ; No. 2025JJ70113//Department of Science and Technology of Hunan Province/ ; No. 2025JJ70123//Department of Science and Technology of Hunan Province/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Language ; Neural Networks, Computer ; Young Adult ; Support Vector Machine ; }, abstract = {Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) offer a promising pathway for restoring communication. Decoding tonal languages like Mandarin from EEG remains challenging due to homophones and complex temporal dynamics. This study investigates the decoding of six high-frequency Mandarin action verbs-Chi (eat), He (drink), Chuan (wear), Na (take), Kan (look), and Dai (put on)-from EEG signals. We designed a visual-cue-based overt speech production experiment and collected EEG data from 30 participants during visually guided verb reading aloud. A recurrent neural network framework incorporating dual Long Short-Term Memory (LSTM) layers was implemented to model the long-range temporal dependencies in EEG patterns. The proposed model was compared against a traditional Common Spatial Pattern combined with Support Vector Machine (CSP-SVM) baseline. Our LSTM-based model achieved an average classification accuracy of 69.93% ± 3.07% for the six-class task, significantly outperforming the CSP-SVM baseline (36.53% ± 3.17%). Accuracy exceeded 75% under specific training conditions, including more than 15 training repetitions and a training-data proportion of 38%. Furthermore, the model attained this performance level utilizing approximately 38% of the available trial data for training, demonstrating data efficiency. The results indicate that the LSTM architecture can effectively capture the neural signatures associated with Mandarin verb processing, providing a foundation for developing practical EEG-based assistive communication technologies. The inference latency of the trained model, quantified as the post-training per-trial testing time, was under 2 s, supporting near-real-time applications.}, }
@article {pmid42122502, year = {2026}, author = {Pino, A and Vrailas, D and Kouroupetroglou, G}, title = {Comparison of Point-and-Click Performance Between the Brainfingers BCI and the Mouse.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {9}, pages = {}, doi = {10.3390/s26092777}, pmid = {42122502}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Male ; Adult ; Female ; Electromyography ; Electrooculography ; Young Adult ; Wearable Electronic Devices ; }, abstract = {This study quantitatively evaluates the performance of a non-invasive hybrid brain-computer interface (BCI) compared to a conventional mouse in pointing (point-and-click) tasks. A commercial wearable BCI (Brainfingers), based on electromyography (EMG) and electrooculography (EOG) signals with low-level electroencephalography (EEG) components, was assessed against a Microsoft Optical Mouse using ISO/TS 9241-411-based one-dimensional (1D) and two-dimensional (2D) target acquisition tasks. Pointer coordinates were recorded and analyzed using Fitts' law metrics. A total of 48 non-disabled participants completed the experiments. The results reveal significant performance differences between the two input devices. The BCI device exhibits substantially lower performance than the mouse across the reported Fitts' law measures. Mean throughput was 0.35 bits/s for the BCI and 6.03 bits/s for the mouse in the 1D tests and 0.43 bits/s for the BCI and 5.17 bits/s for the mouse in the 2D tests. Despite the BCI's low performance and although the present experiments involved non-disabled participants, the findings, considered alongside the prior literature on Brainfingers and non-invasive BCIs for computer access, suggest that the device may still have assistive technology value for users with severe motor impairments.}, }
@article {pmid42122581, year = {2026}, author = {Ran, G and Li, S and Jiang, Z and Zhang, H and Long, X and Lai, D}, title = {UDC-SNN: An Uncertainty-Aware Dynamic Cascading Framework with Spiking Neural Network for Balancing Performance and Energy in Multimodal Emotion Recognition.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {9}, pages = {}, doi = {10.3390/s26092859}, pmid = {42122581}, issn = {1424-8220}, support = {2025ZNSFSC0457//Sichuan Science and Technology Program/ ; }, mesh = {Humans ; *Emotions/physiology ; *Neural Networks, Computer ; Electroencephalography/methods ; Uncertainty ; Electrocardiography ; Bayes Theorem ; Algorithms ; Entropy ; }, abstract = {The aim of this study is to propose an uncertainty-aware dynamic cascading framework based on spiking neural network (UDC-SNN) for multimodal emotion recognition, particularly to address the inherent trade-off between recognition performance and energy efficiency. An asymmetric dynamic routing mechanism was proposed to enable demand-driven activation of the high-power electroencephalogram (EEG) branch, coupled with preliminary inference on a low-power electrocardiogram (ECG) branch and uncertainty quantification via Shannon entropy. Meanwhile, a parameter-free log-linear aggregation strategy was developed to transform modality-specific entropy into dynamic Bayesian weights through an exponential decay function, effectively mitigating the negative transfer effects induced by unimodal noise. The UDC-SNN was evaluated on the multimodal affective dataset DREAMER, comprising 23 subjects (170,660 segments). The averaged recognition accuracy and energy consumption across the three dimensions of valence, arousal, and dominance were 90.75% and 4.62 μJ, respectively. The obtained results suggest that the proposed framework could potentially achieve a favorable balance between high emotion recognition and low energy consumption, thereby establishing its applicability for real-time monitoring in resource-constrained scenarios.}, }
@article {pmid42122618, year = {2026}, author = {Wang, Z and Yi, L and Zhang, G and Ma, X and Tian, Y and Zhang, B and Liu, X and Tang, L}, title = {Fingerprint Recognition Based on Molecular-Scale Conductance Response via Electrochemically Gated Quantum Tunnelling.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {9}, pages = {}, doi = {10.3390/s26092896}, pmid = {42122618}, issn = {1424-8220}, support = {62127818, 22374129//National Natural Science Foundation of China/ ; 2024R0100//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; }, abstract = {Molecular-scale detection based on quantum tunnelling is promising for molecular electronics and high-sensitivity analysis, owing to its sensitivity to molecular structure and energy levels. However, conventional two-electrode tunnelling measurements suffer from overlapping conductivity of different molecules, limiting molecular discrimination in complex systems. To address this, we propose an electrochemical-gate-controlled nanoscale tunnelling strategy that expands the two-electrode system to a three-electrode configuration via a tunable gate potential, enabling the differentiation of distinct molecules at near-single-molecule sensitivity. Scanning the gate potential under constant tunnelling bias modulates the alignment between molecular orbitals and the electrode Fermi level, altering the statistical characteristics of molecular tunnelling transport. Experimental results show that target molecules induce a bimodal distribution of tunnelling current (background and molecule-correlated channels), with the second peak exhibiting distinct gate potential dependence. Comparative analysis of ascorbic acid (AA), acetylcholine (ACh), and uric acid (UA) reveals unique trajectories of characteristic peaks with gate potential, forming an electrochemical gate response fingerprint. This gate-dependent conductance trajectory provides a novel statistical dimension for molecular recognition, enabling differentiation of distinct molecules.}, }
@article {pmid42123509, year = {2026}, author = {Li, Y and Yao, Y and Xu, Z and Xiong, Y and Zhang, C and Yu, L and Gao, H and Fei, T}, title = {Genome-Wide CRISPR Screening Identifies Genetic Modulators of Amyloid Precursor Protein Processing.}, journal = {International journal of molecular sciences}, volume = {27}, number = {9}, pages = {}, doi = {10.3390/ijms27093926}, pmid = {42123509}, issn = {1422-0067}, mesh = {Humans ; *Amyloid beta-Protein Precursor/metabolism/genetics ; *CRISPR-Cas Systems ; *Alzheimer Disease/genetics/metabolism ; Amyloid beta-Peptides/metabolism/genetics ; HEK293 Cells ; }, abstract = {The proteolytic processing of the amyloid precursor protein (APP) is a core pathological event in Alzheimer's disease (AD) pathogenesis, yet the global genetic regulatory networks modulating this process have not been fully characterized. To systematically identify novel regulators of APP cleavage, we performed a genome-wide CRISPR/Cas9 knockout screen utilizing an optimized UAS-GAL4-based cellular reporter, and identified genetic modulators governing amyloidogenic and non-amyloidogenic processing. The screen uncovered distinct functional gene clusters regulating the APP, prominently involving cellular metabolism, protein modification, and vesicular trafficking. Specifically, LDHB, PIAS2, CCDC53, and TRIM61 emerged as novel functional modulators. Biochemical validation confirmed that ablating these genes significantly alters the metabolic balance between sAPPα and amyloid-β (Aβ) production. Finally, integration with human AD transcriptomic datasets demonstrated that these identified modulators undergo significant dysregulation in clinics. Together, these findings establish a reporter-based functional screening framework for APP processing and identify candidate regulatory nodes linked to metabolism, protein modification, and vesicular trafficking. These candidates provide a resource for future mechanistic investigation and validation in more disease-relevant AD models.}, }
@article {pmid42124661, year = {2026}, author = {Gontier, C and Hockeimer, W and Kunigk, NG and Canario, E and Endsley, LJ and Downey, JE and Weiss, JM and Dekleva, B and Collinger, JL}, title = {Closed-loop error damping in human BCI using pre-error motor cortex activity.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.02.25.707999}, pmid = {42124661}, issn = {2692-8205}, abstract = {Intracortical brain-computer interfaces (BCIs) are used to decode motor intent from neural population activity; their main clinical application is to restore function for individuals with motor or communication deficits. However, when trying to reconstruct movement trajectories, such as in computer cursor control, even state-of-the-art decoders fall short of able-bodied performance during online BCI control. This calls for alternative approaches to improve the usability of motor BCIs. Here, we leveraged an error signal, i.e. a neural correlate of faulty motor control that can be detected from neural activity. By detecting this error signal in parallel to performing movement decoding, it is possible to perform error modulation, i.e. real-time error detection and correction during a closed-loop motor BCI task. We analyzed data from four individuals with upper limb impairment due to cervical spinal cord injury who each used an intracortical BCI to perform a continuous cursor control task with visual feedback. A classifier was trained to detect the error signal and was used to perform online error detection during BCI control to limit ongoing errors (defined as movement of the controller away from its target) without requiring any specific action from the participants. Our contribution is three-fold. First, we show that the error signal has a pre-error component. Cortical activity was already significantly modulated before the onset of the kinematically-defined error, theoretically allowing for earlier detection. Second, we show that error modulation significantly improves performance during online BCI control of cursor kinematics. Finally, we show that the error signal can be robustly leveraged across contexts, as error modulation improves performance in more complex motor tasks (involving for instance grasp and drag actions) or other environments without task-specific calibration. Overall, our results suggest that the error signal can be robustly disentangled from motor intent in cortical activity, and that even a simple linear classifier can enable error modulation in parallel to a continuous kinematic decoder, yielding more reliable and accurate BCI control.}, }
@article {pmid42125055, year = {2026}, author = {Moaveninejad, S and Santamaría-Vázquez, E and Xu, J and Porcaro, C}, title = {Editorial: Non invasive BCI for communication.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1836774}, pmid = {42125055}, issn = {1662-5161}, }
@article {pmid42127032, year = {2026}, author = {Rao, S and Deng, G and Song, H and Chen, Q and Luo, M and Zhang, Y and Zhao, S and Pan, G and Li, T and Jiang, H}, title = {Automating multi-label crisis detection in psychological support hotlines with pre-trained models.}, journal = {PLOS digital health}, volume = {5}, number = {5}, pages = {e0001383}, doi = {10.1371/journal.pdig.0001383}, pmid = {42127032}, issn = {2767-3170}, abstract = {Psychological support hotlines provide immediate help to individuals in crisis, with operators assessing emotional states and suicide risk. However, increasing demand has led to a shortage of trained professionals, emphasizing the need for AI-driven crisis detection models. This study included 1,057 calls from the Hangzhou Hotline (2022-2023) to evaluate the effectiveness of deep learning and pre-trained models in detecting psychological crises using audio (Wave2Vec, Whisper) and transcribed text (RoBERTa, GPT). We adopted two strategies: deep learning classification with pre-trained models and Large Language Models (LLMs)-based prediction via prompt engineering (GPT-4 and DeepSeek series). The deep learning framework, employing GPT embeddings excelled in multi-label predictions compared to auditory model, achieving 80.48% [80.18%, 80.78%] F1 scores for identifying high-risk calls in prospective tests. Fusion experiments revealed that acoustic features offered negligible predictive value compared with text semantics. Notably, GPT-4o and DeepSeek-R1, utilizing few-shot learning, demonstrated performance comparable to the GPT-embedding deep learning model across multiple tasks. This suggests that their advanced Chain-of-Thought reasoning effectively mitigates data dependency gap, enabling LLMs to align with clinical domains using a few examples. Expert evaluation confirmed the clinical applicability of GPT-generated explanations. Taken together, these findings highlight the potential of LLMs in mental health crisis detection and lay the foundation for future research.}, }
@article {pmid42112212, year = {2026}, author = {Chen, Y and Hu, S}, title = {From challenges to solutions: Strengthening mental health support for university students and young researchers in China.}, journal = {General psychiatry}, volume = {39}, number = {}, pages = {e70021}, pmid = {42112212}, issn = {2517-729X}, }
@article {pmid42112453, year = {2025}, author = {Usman, M and Ashebir, S and Okey-Mbata, C and Yun, Y and Kim, S}, title = {Neuroengineering Frontiers: A Selective Review of Neural Interfaces, Brain-Machine Interactions, and Artificial Intelligence in Neurodegenerative Diseases.}, journal = {Applied sciences (Basel, Switzerland)}, volume = {15}, number = {21}, pages = {}, pmid = {42112453}, issn = {2076-3417}, support = {SC1 NS122448/NS/NINDS NIH HHS/United States ; UG3 EB036466/EB/NIBIB NIH HHS/United States ; }, abstract = {Neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), present a growing public health challenge globally. Recent advancements in neurotechnology and neuroengineering have significantly enhanced brain-computer interfaces, artificial intelligence, and organoid technologies, making them pivotal instruments for diagnosis, monitoring, disease modeling, treatment development, and rehabilitation of various diseases. Nonetheless, the majority of neural interface platforms focus on unidirectional control paradigms, neglecting the need for co-adaptive systems where both the human user and the interface continually learn and adapt. This selected review consolidates information from neuroscience, artificial intelligence, and organoid engineering to identify the conceptual underpinnings of co-adaptive and symbiotic human-machine interaction. We emphasize significant shortcomings in the advancement of long-term AI-facilitated co-adaptation, which permits individualized diagnostics and progression tracking in Alzheimer's disease and Parkinson's disease. We concentrate on incorporating deep learning for adaptive decoding, reinforcement learning for bidirectional feedback, and hybrid organoid-brain-computer interface platforms to mimic disease dynamics and expedite therapy discoveries. This study outlines the trends and limitations of the topics at hand, proposing a research framework for next-generation AI-enhanced neural interfaces targeting neurodegenerative diseases and neurological disorders that are both technologically sophisticated and clinically viable, while adhering to ethical standards.}, }
@article {pmid42114557, year = {2026}, author = {Li, X and Zeng, Y and Zhou, Y and An, S and Wang, J and Huang, Y and Feng, X and Li, W and Jia, Y and Zhang, P}, title = {Dual-VCT: A dual-branch VMD-CNN-Transformer model for local field potentials decoding.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae6bf1}, pmid = {42114557}, issn = {1741-2552}, abstract = {OBJECTIVE: Local field potential (LFP) decoding is critical for the clinical translation of intracortical brain-machine interfaces (iBMIs), yet existing decoding methods are limited by three key bottlenecks: insufficient single-scale feature utilization, inefficient multi-scale feature fusion, and poor robustness across task paradigms and chronic recording conditions.<br><br>APPROACH: To address these challenges, we propose Dual-VCT, a novel dual-branch Variational Mode Decomposition-Convolutional Neural Network-Transformer (VMD-CNN-Transformer) model for end-to-end LFP decoding. The core innovation of Dual-VCT is its symmetric time-frequency parallel architecture with independent VMD modules embedded in both branches: a temporal branch decomposes local motor potential (LMP) signals via VMD to capture motion-related instantaneous neural activity, while a frequency-domain branch leverages VMD to isolate task-relevant spectral power components, with a hierarchical fusion pipeline enabling robust cross-scale feature integration.<br><br>MAIN RESULTS: Validated in non-human primate experiments, Dual-VCT achieved a classification accuracy of 0.930&#xb1;0.023 in the 3-class spatial grasping task, and a Pearson correlation coefficient (CC) of 0.910&#xb1;0.023 in the finger point-to-point tracking task. It significantly outperformed all comparative dual-branch methods under identical experimental conditions (p < 0.05), delivered a 4% performance gain over single-feature decoding, and exhibited strong cross-task robustness and cross-day stability. Ablation experiments confirmed the core contribution of the dual-branch VMD design.<br><br>SIGNIFICANCE: This work provides a high-performance structured paradigm for LFP decoding, with a clinically oriented design that supports the long-term stability of chronic iBMI systems.}, }
@article {pmid42114561, year = {2026}, author = {Suwandjieff, P and Crell, MR and Kostoglou, K and Müller-Putz, GR}, title = {Turning motor intentions into words: An MRCP-based BCI speller for motor-impaired users enhanced by task-specific calibration.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae6bef}, pmid = {42114561}, issn = {1741-2552}, abstract = {Communication is fundamental to human interaction, yet neuromuscular disorders can severely restrict it. For individuals with advanced conditions such as Locked-in syndrome, maintaining reliable communication is crucial, and Brain-Computer Interfaces (BCIs) offer a promising approach. This study presents a movement-related cortical potential (MRCP) based speller designed for users with severe motor impairments. By detecting brain signals elicited by self-initiated movements that function as a brain click, the system enables intuitive control across various interfaces and shows improved performance when the classifier is adapted to specific applications. To identify the most robust neural signals for control, we evaluated five right-hand gestures: Fist, Pincer, Y, Pistol, and Hand-up, performed solely to evoke movement-related signals serving as the brain-click input. A two-stage training strategy was employed. First, the classifier was trained on generalized cue-based data aligned to visual cues. Then, as a new contribution, it was retrained using data recorded during real speller operation, allowing adaptation to the user's online brain-click patterns and substantially improved practical performance. Across participants, retraining increased average true positive rate from 27.3 percent (1.1 false positives per minute (FP/min), 1.1 characters per minute (CPM)) to 63.0 percent (1.0 FP/min, 2.7 CPM), highlighting the benefit of task-specific adaptation. While no statistically significant differences were observed across gestures, Hand-up consistently yielded the highest detection accuracy and was selected most frequently, suggesting its suitability as a default control signal. Compared to earlier MRCP based systems that rely on overt movement, our cue-aligned approach achieved higher true positive and lower false-positive rates when adapted to the specific interface, representing an important improvement toward more efficient communication for users with severe motor impairments. While not tested in home settings, these results point toward the possibility of a home-usable MRCP speller and highlight the value of personalized, adaptive BCI control. .}, }
@article {pmid42114745, year = {2026}, author = {Buczinski, S and Gomes, V and Vergnes, G and Poirier, MC and Owusu-Afryie, S and Berman, J and Dendukuri, N}, title = {Lung ultrasonography used as a diagnostic test for respiratory disease diagnosis in calves: systematic review and meta-analysis using a Bayesian latent-class modelling approach.}, journal = {Journal of dairy science}, volume = {}, number = {}, pages = {}, doi = {10.3168/jds.2025-28206}, pmid = {42114745}, issn = {1525-3198}, abstract = {Bovine respiratory disease complex is a common disease which commonly affects calves in the form of bronchopneumonia. There is currently no affordable perfectly accurate reference standard test and the routine diagnosis is commonly based on clinical signs assessment or other ancillary tests. Lung ultrasonography (LUS) has emerged as a practical calf-side test that can be done routinely. However, information on this diagnostic test's accuracy is limited especially because no test reaches the accuracy of a gold-standard comparator test. The objective of this systematic review and meta-analysis was to determine the accuracy of LUS while adjusting for imperfect accuracy of available reference standard (RS) tests, including clinical scoring systems, using a Bayesian latent class meta-analysis approach. A structured literature search was performed and from 875 studies screened, 26 studies reported at least one 2 × 2 table with cross classification of LUS (positive vs negative) calves and RS test (positive vs negative).The RS test included were Wisconsin Clinical Respiratory Score (WCRS, n = 16), California Clinical Respiratory Score (CaCRS, n = 4), other clinical signs combinations (n = 5), clinical score combined with bronchial lavage results (n = 1), thoracic radiograph (n = 1), expert panel diagnosis (n = 1) and necropsy (n = 1). Various LUS thresholds for test positivity were reported but 2 thresholds of maximal consolidation depth ≥ 1cm or ≥ 3cm, were most commonly reported in 12 and 8 studies, respectively. The QUADAS-2 assessment for risk of bias and applicability revealed that besides limitations associated with an imperfect RS test, it was difficult to know if LUS results were interpreted without the knowledge of RS test results. A Bayesian latent class meta-analysis accounting for the imperfect accuracy of WCRS was performed for both positivity thresholds. Eight studies were available for LUS ≥ 1cm. The median pooled sensitivity (95% BCI) and specificity were 68.4% (51.7-87.4%) and 91.2% (78.5-99.4%) respectively. Five studies were available for LUS ≥ 3cm. The median pooled sensitivity and specificity were 58.2% (38.4-80%) and 95.8% (86.7-99.4%) respectively. The LUS appeared as a moderately sensitive test with a relatively high specificity. The models revealed heterogeneity that could not be further investigated due to the low number of available studies. This is the first reported meta-analysis to determine LUS accuracy accounting for the RS test uncertainty. This study helps to identify the gaps of knowledge and reporting issues such as standardization of LUS scanning protocol, reporting maximal consolidation in a continuous way as well as providing raw data sets to continue gathering information on LUS accuracy for the diagnosis of calf respiratory disease.}, }
@article {pmid42115034, year = {2026}, author = {Cimolato, A and Sparapani, A and Raspopovic, S}, title = {Technologies in clinical neurophysiology for brain-body interfacing: IFCN handbook chapter.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {}, number = {}, pages = {2111913}, doi = {10.1016/j.clinph.2026.2111913}, pmid = {42115034}, issn = {1872-8952}, abstract = {Interfacing the brain with the body represents a central challenge in contemporary clinical neurophysiology. Neurological injuries such as stroke, spinal cord injury, and limb loss disrupt the bidirectional flow of information between central and peripheral circuits, impairing voluntary movement, sensation, and embodiment. Clinical neurophysiology provides the quantitative and methodological foundation necessary to understand and modulate this communication. By characterizing neural signals across cortical, spinal, and peripheral levels, it establishes the principles required to decode and stimulate the nervous system. Traditionally focused on diagnostic assessment through electrophysiological techniques, clinical neurophysiology provides the conceptual and technical basis for active brain-body interfacing. Within this framework, interfacing encompasses both "reading" and "writing" neural information. Decoding refers to the extraction of meaningful variables (e.g., movement intention) from neural activity. Actuation refers to the targeted delivery of electrical stimulation or mechanical assistance to modulate neural circuits or generate movement and sensation. This chapter reviews neurophysiology-based strategies across multiple neuroanatomical levels and how integrating decoding and actuation into closed-loop architectures enables the re-establishment of bidirectional information flow. By coupling neural control with functional feedback, these systems enhance control precision, support embodiment, and engage activity-dependent plasticity, providing a mechanistically grounded strategy for modulating disrupted neural communication.}, }
@article {pmid42115433, year = {2026}, author = {Choudhari, V and Nentwich, M and Johnson, S and Herrero, JL and Bickel, S and Mehta, AD and Friedman, D and Flinker, A and Chang, EF and Mesgarani, N}, title = {Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments.}, journal = {Nature neuroscience}, volume = {}, number = {}, pages = {}, pmid = {42115433}, issn = {1546-1726}, support = {R01DC014279//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; DC018805//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; }, abstract = {Understanding speech in noisy environments is difficult for many people, and current hearing aids often fail because they amplify all sounds rather than the talker of interest. Auditory attention decoding (AAD) offers a potential solution by using the listener's brain signals to identify and enhance the attended speaker, but it has been unclear whether this can provide real-time perceptual benefits. Here we used high-resolution intracranial electroencephalography in patients undergoing neurosurgical procedures to implement a closed-loop system that achieves the decoding fidelity necessary to dynamically amplify the attended talker. Across multiple experiments, the system improved speech intelligibility, reduced listening effort and was consistently preferred by subjects. It also tracked both instructed and self-initiated attention shifts. By providing direct evidence that a real-time, brain-controlled hearing system can enhance perception, this work establishes a key performance benchmark for future auditory brain-computer interfaces and advances AAD from a theoretical concept to a validated solution for personalized assistive hearing.}, }
@article {pmid41993291, year = {2026}, author = {Mynhier, NA and Gamez, J and Pejsa, K and Bari, A and Murray, RM and Andersen, RA}, title = {The Compositional Encoding of Hand-Eye Coordinated Movements for Single Neurons in the Posterior Parietal Cortex.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {41993291}, issn = {2692-8205}, abstract = {Human posterior parietal cortex (PPC) is thought to play an important role in hand-eye coordination, yet the underlying encoding mechanisms remain uncertain. We recorded 412 single neurons across 11 sessions from motor cortex (MC; n=251) and PPC (n=161) in a single human participant performing a hand-eye (H-E) coordinated center-out task. While MC neurons showed little to no modulation by eye movements, 79% of PPC neurons had neural representations that were additively separable into independent hand- and eye-movement tuning curves. Due to this separability, neural representations could be separated and additively recomposed while maintaining structure similarity. Consequently, compositional decoders trained solely on single-effector movements could match the performance of decoders trained on coordinated H-E movements (hand: 66% vs 69%; eye: 34% vs 36%). These results show that, during simple center-out tasks, MC hand movement codes are unaffected by eye movements and that compositionality can be used to modularly decode H-E coordinated movements in PPC.}, }
@article {pmid42043991, year = {2026}, author = {Blanco-Diaz, CF and Vendrame, E and Cipriani, C and Dosen, S and Cappello, L}, title = {Recent Advances in Supplementary Haptic Feedback for Human-Machine Interfaces in Upper Limb Assistance and Rehabilitation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {34}, number = {}, pages = {2295-2314}, doi = {10.1109/TNSRE.2026.3687960}, pmid = {42043991}, issn = {1558-0210}, mesh = {Humans ; *Feedback, Sensory/physiology ; *Upper Extremity/physiology ; User-Computer Interface ; Brain-Computer Interfaces ; Touch ; *Man-Machine Systems ; Artificial Limbs ; *Rehabilitation ; }, abstract = {Despite the rapid technological advancements we witnessed in the last few decades, effective regaining or substituting the impaired sensorimotor function of the upper limb is still a dream for many patients and researchers worldwide. While technology-aided motor therapy and advanced human-machine interfaces have significantly evolved, the efforts to integrate supplementary sensory feedback (SSF) to promote sensorimotor restoration after neurological or orthopedic damage became relevant only in recent years. In this review, we examine emerging strategies for encoding and delivering somatosensory information to users of prosthetic, orthotic, and rehabilitation systems, highlighting advances in electrotactile, vibrotactile, mechanotactile, and neurostimulation-based approaches. We synthesize cross-disciplinary findings from neuroscience, haptics, and clinical bioengineering to outline how SSF influences embodiment, motor learning, user acceptance, and real-world performance. Despite rapid technical progress, major gaps persist, including limited long-term evaluation, narrow user representation, and a lack of standardized methods for characterizing sensations and benchmarking device performance. We discuss the scientific and translational barriers that currently constrain widespread adoption of SSF technologies and identify promising directions for future research, including unified assessment frameworks, personalization strategies, and the development of richer haptic vocabularies to enhance the functionality and clinical relevance of next-generation sensorimotor interfaces.}, }
@article {pmid42102075, year = {2026}, author = {Jiang, X and Zhou, J and Duan, Y and Zhao, Z and Chang, YC and Do, T and Lin, CT}, title = {Neural Spelling: A Spell-Based BCI System for Language Neural Decoding.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3691322}, pmid = {42102075}, issn = {1558-2531}, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) support the study of communication-oriented neural decoding by translating neural activity into text, yet existing non-invasive systems rarely cover the full alphabet in handwriting-based settings.<br><br>METHODS: We propose a novel non-invasive EEG-based BCI framework, Curriculum-based Neural Spelling (CNS), that decodes all 26 English letters by first learning neural patterns associated with handwriting trajectories. A Generative AI (GenAI) module based on large language models (LLMs) is then integrated to transform noisy letter-level neural predictions into sentence-level outputs under explicit neural constraints.<br><br>RESULTS: The proposed system achieves robust letter-level decoding and improved sentence-level reconstruction under controlled offline evaluation, outperforming conventional EEGNet and hybrid CNN-RNN baselines. GenAI correction further reduces word error rates and enhances decoding fluency.<br><br>CONCLUSION: Combining EEG-based neural spelling with generative language modeling supports the study of full-alphabet decoding and improves sentence-level linguistic metrics in a controlled non-invasive EEG setting, but does not by itself establish clinical or real-world usability.<br><br>SIGNIFICANCE: This work demonstrates how integrating GenAI with neural decoding can bridge the gap between noisy signal-level predictions and coherent language-level outputs, establishing a system-level framework for full-alphabet neural spelling and adaptive language-level correction under non-invasive EEG constraints.}, }
@article {pmid42102610, year = {2026}, author = {Wang, W and Ji, Y and Yu, R and Chen, J and Chen, H and Huangfu, C and Zhu, C and Li, Q and Zuo, Z}, title = {SMYD3-mediated H3K4 trimethylation aggravates hypertension-induced renal injury via TXNIP transcriptional activation.}, journal = {International immunopharmacology}, volume = {182}, number = {}, pages = {116800}, doi = {10.1016/j.intimp.2026.116800}, pmid = {42102610}, issn = {1878-1705}, abstract = {Hypertensive renal disease (HRD) is the second leading cause of end-stage renal disease (ESRD) following diabetes mellitus, with oxidative stress and inflammation serving as synergistic pathogenic drivers, the molecular mechanisms of which remain incompletely elucidated. Epigenetic regulation (especially histone methylation) is pivotal in chronic kidney disease (CKD). SMYD3, a histone methyltransferase, has been reported to modulate oxidative stress and inflammation via mediating H3K4me3 modification, while its specific role and regulatory mechanism in HRD remain largely unclear. This study explored SMYD3's function using angiotensin II (Ang II)-induced HRD models (28-day subcutaneous Ang II-infused mice in vivo; HK-2 cells in vitro). Mice were grouped into Control, HRD, HRD + MTA (an H3K4 methylation inhibitor), BCI-121 (a SMYD3 inhibitor), and HRD + BCI-121. In vivo, blood, urine, and kidney samples were analyzed via biochemical assays (creatinine, BUN, oxidative stress biomarkers) and histopathology (HE, PAS, Masson staining). In vitro, SMYD3 was inhibited by BCI-121 or siRNA, with Western blotting, co-IP, and ChIP detecting interactions among SMYD3, H3K4me3, TXNIP promoter, and JAK2/STAT3 pathway-related molecules. Ang II infusion aggravated renal dysfunction (elevated creatinine, BUN, urinary albumin), pathological damage, oxidative stress, inflammation, and cellular senescence, accompanied by increased SMYD3 and H3K4me3. Treatment with MTA/BCI-121 alleviated these changes, and SMYD3 knockdown/inhibition reversed Ang II-induced injuries in HK-2 cells. Mechanistically, SMYD3 was associated with enhanced TXNIP transcription via H3K4me3 methylation, activating NLRP3 inflammasome and oxidative stress pathways. SMYD3 was regulated by the JAK2/STAT3 pathway; STAT3 inhibitor S3I-201 reduced SMYD3 and H3K4me3, indicating that JAK2/STAT3 upregulates SMYD3 to exacerbate HRD. In conclusion, our findings demonstrate that SMYD3 acts as a key responsive mediator of Ang II-induced renal oxidative stress and inflammation, which is closely associated with the promotion of H3K4me3 enrichment at the TXNIP promoter. We also identify that the Ang II-activated JAK2/STAT3 axis may function as an upstream regulator of SMYD3 expression, thus providing novel insights and potential therapeutic targets for HRD.}, }
@article {pmid42102832, year = {2026}, author = {Yang, Y and Liao, Y and Han, Q and Peng, J and Huang, L}, title = {ASEAF: Attention-SincNet driven EEG-audio fused target speaker extraction network.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae6aa0}, pmid = {42102832}, issn = {2057-1976}, abstract = {This study addresses the challenge of selective auditory attention in noisy environments by proposing an EEG-based target speaker extraction model, ASEAF, designed to mimic neural decoding through tailored spatio-temporal feature extraction and cross-modal fusion. The model achieves precise extraction of the target speaker's speech by simultaneously processing EEG and audio signals. ASEAF comprises four modules: an EEG encoder using CNN and self-attention for spatio-temporal features, an audio encoder with SincNet for frequency-aware processing, a dual-path LSTM speaker extractor for fused feature masking, and a CNN decoder for waveform reconstruction. This innovative integration advances neural-signal-based speech reconstruction by providing insights into cross-modal interactions. Experiments on the Cocktail Party dataset, KUL dataset and DTU dataset demonstrate that ASEAF outperforms state-of-the-art models across multiple metrics, with an average improvement of 11.5% in scale-invariant signal-to-distortion ratio (SI-SDRi). This work offers a more effective hearing aid solution for individuals with hearing impairments and advances the field of brain-computer interfaces.}, }
@article {pmid42106089, year = {2026}, author = {Wang, L and An, X and Zhao, L and Liu, S and Ming, D}, title = {An fMRI-based study of the effect of audiovisual stimulus temporal pacing on brain responses.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121972}, doi = {10.1016/j.neuroimage.2026.121972}, pmid = {42106089}, issn = {1095-9572}, abstract = {Research on the effect of stimulus temporal pacing on brain states is a central topic in neuroscience and psychology. Studies of audiovisual integration (AVI) in the fields of Brain-Computer Interfaces (BCIs) and neuropsychology have often yielded inconsistent findings, potentially due to variations in stimulus temporal pacing. Although a number of psychological experiments have investigated the effects of stimulus temporal pacing on brain activity, the underlying neural mechanisms remain poorly understood. This study aims to investigate how stimulus temporal pacing modulates the dynamic reconfiguration of brain activity and connectivity using functional magnetic resonance imaging (fMRI). A multimodal audiovisual oddball paradigm was employed, presenting stimuli at two temporal pacing conditions (rapid and slow) across three sensory modalities (visual, auditory, and audiovisual) to compare brain activation and functional connectivity across conditions. Results showed that in the unimodal condition, rapid stimuli preferentially engaged primary sensory cortices, indicating efficient perceptual encoding under high temporal pressure. In contrast, slow stimuli shifted processing toward higher-order cognitive regions, suggesting greater engagement in higher-order cognitive regions and enhance global network efficiency. For audiovisual condition, both rapid and slow stimuli elicit comparable functional connectivity patterns, whereas slow stimuli showed stronger connectivity in specific regions (e.g., occipital-motor areas, STG-DMN nodes), suggesting that the core audiovisual network and the extended whole-brain networks act in concert, forming a dual-layer processing mechanism. These findings provide a neural basis for understanding how stimulus temporal pacing acts as a modulator, shaping the dynamic balance between localized sensory analysis and integrated global processing.}, }
@article {pmid42107264, year = {2026}, author = {Liao, Y and Zhang, Y and Zhang, H and Li, Y and Guan, DX and Yan, Y and Chai, YL and Lai, MKP and Xiao, S and Chen, CLH and Xu, X}, title = {Phenotyping of mild behavioral impairment domains in multi-regional dementia-free older adults of Chinese ethnicity: impulse dyscontrol as the leading domain.}, journal = {The journal of prevention of Alzheimer's disease}, volume = {13}, number = {7}, pages = {100589}, doi = {10.1016/j.tjpad.2026.100589}, pmid = {42107264}, issn = {2426-0266}, abstract = {BACKGROUND: Mild behavioral impairment (MBI) is an early neurobehavioral marker of dementia, yet MBI domain patterns remain underexplored among populations of Chinese ethnicity. This study aimed to characterize MBI domain phenotypes by examining the prevalence of MBI domains and identifying the leading domain across multi-regional cohorts of dementia-free older adults of Chinese ethnicity.<br><br>METHODS: Data from three previously unpublished datasets (Hangzhou community cohort, China Longitudinal Aging Study and Singapore memory clinic cohort) and three published studies were integrated to estimate the MBI domain prevalence, measured by the Neuropsychiatric Inventory (NPI) and/or MBI-Checklist (MBI-C), through a random-effects meta-analysis. Within the Hangzhou cohort, cross-instrument consistency was evaluated. Exploratory analyses were performed in the Singapore cohort on associations between MBI domains and incident dementia.<br><br>RESULTS: Among 1817 participants, impulse dyscontrol was the most prevalent MBI domain, followed by affective dysregulation and decreased motivation, consistently across instruments and cognitive status. In the exploratory longitudinal analyses, impulse dyscontrol was associated with a greater likelihood of incident dementia (HR = 5.05, 95%CI = 2.92 - 8.73).<br><br>CONCLUSIONS: Impulse dyscontrol was the leading MBI domain among older adults of Chinese ethnicity, with potential clinical relevance for early identification and dementia risk stratification.}, }
@article {pmid42107656, year = {2026}, author = {Chen, H and Zhang, X and Hong, S and Yu, Z and Chen, W}, title = {Divergence of clinical and autonomic recovery in adolescent major depressive disorder: A 10-week prospective heart rate variability study with nocturnal electrocardiogram monitoring.}, journal = {Journal of affective disorders}, volume = {}, number = {}, pages = {121940}, doi = {10.1016/j.jad.2026.121940}, pmid = {42107656}, issn = {1573-2517}, abstract = {BACKGROUND: Adolescence is a critical window for autonomic nervous system (ANS) development, which can be disrupted by major depressive disorder (MDD). Heart rate variability (HRV) is a promising biomarker, but adolescent findings are inconsistent with scarce longitudinal treatment data. This study longitudinally assessed autonomic function in adolescents with MDD via nocturnal electrocardiogram (ECG).<br><br>METHODS: We enrolled 43 adolescents aged 12-18&#x202f;years with MDD and 43 healthy controls. MDD participants received routine, non-study-mandated selective serotonin reuptake inhibitors (SSRIs) treatment, and completed 3 consecutive nights of nocturnal ECG monitoring at both baseline and 10-week post-treatment follow-up. Controls completed only the baseline monitoring protocol. We performed between-group, pre-post within-MDD group, and correlation analyses between HRV and clinical features.<br><br>RESULTS: 10-week treatment significantly reduced 17-item Hamilton Depression Rating Scale (HAMD-17) and Hamilton Anxiety Rating Scale (HAMA) scores, though only 7.0% of patients achieved early clinical response. Baseline HRV deficits in MDD persisted post-treatment, with no reversal of autonomic impairment and further pathological sympathetic predominance. Notably, somatic anxiety reduction was negatively correlated with post-treatment low-frequency/high-frequency ratio (LF/HF) and normalized low-frequency power (LFn) (P&#x202f;<&#x202f;0.05), indicating mitigation of sympathetic pathological deterioration rather than autonomic normalization.<br><br>CONCLUSION: This study shows dissociation between partial clinical improvement and persistent autonomic dysfunction in adolescent MDD. HRV may be a useful adjunct biomarker, and somatic anxiety-targeted interventions may facilitate recovery.}, }
@article {pmid42108231, year = {2026}, author = {Fan, L and Xu, W and Wang, R and Dong, Y and Wu, Z}, title = {Age-related divergence of lipid dysregulation in Chinese patients with Wilson's disease.}, journal = {Chinese medical journal}, volume = {}, number = {}, pages = {}, pmid = {42108231}, issn = {2542-5641}, }
@article {pmid42109929, year = {2026}, author = {Wang, M and Zhang, L and Liang, Z and Huang, G}, title = {Fundamental questions on closed-loop neuromodulation: a control theory perspective.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {88}, pmid = {42109929}, issn = {1871-4080}, abstract = {Closed-loop neuromodulation aims to adjust therapeutic stimulation in real time based on ongoing neural or physiological signals. Despite growing clinical adoption, most implementations rely on heuristic rules rather than a principled systems-and-control formulation. This paper, motivated by discussions from the Brain Theory Seminar (Shanghai, March 2025), develops such a formulation around seven fundamental questions-mechanism (Q1), plant nature (Q2), state measurement (Q3), actuation (Q4), modeling (Q5), objectives (Q6), and constraints (Q7)-and, for each, provides a knowledge-based review synthesizing current understanding together with a prospective scientific opinion on unresolved issues. Five recurring themes unify the seven questions: (i) nonstationarity as the default operating condition, (ii) structural partial observability and under-actuation, (iii) closed-loop confounding between stimulation and measurement, (iv) the primacy of hard constraints over unconstrained optimization, and (v) the necessity of layered governance separating performance seeking from safety enforcement. We argue that the neural plant is fundamentally different from classical engineered systems in ways that reshape what can be sensed, modeled, actuated, and verified; accordingly, we reframe therapeutic goals from setpoint tracking toward set-based regulation within a therapeutic window, and we treat safety, ethics, and accountability not as external add-ons but as architectural primitives that define the admissible design space. We close with a discussion synthesizing system-level barriers and near-term architectural directions, including bidirectional brain-computer interfaces, hybrid learning-and-control pipelines with independent safety supervision, and digital twins as regulated test harnesses.}, }
@article {pmid42110188, year = {2026}, author = {Abudu, H and Liu, Z and Niu, Y and Xu, G and He, K and Dong, J and Tuerxun, T and Hua, G and Yan, X and Fan, H}, title = {Development of a Clinical Prediction Model for Acute Kidney Injury Among In-Hospital Cardiac Arrest Patients During Intensive Care Unit Hospitalization.}, journal = {Reviews in cardiovascular medicine}, volume = {27}, number = {4}, pages = {47434}, pmid = {42110188}, issn = {2153-8174}, abstract = {BACKGROUND: Acute kidney injury (AKI) is a significant cause of mortality among post-cardiac arrest patients. However, clinical prediction models for assessing AKI risk for in-hospital cardiac arrest (IHCA) patients remain limited. Thus, this retrospective study aimed to develop a nomogram that uses readily available clinical characteristics to predict the likelihood of AKI in this group of patients during intensive care unit (ICU) hospitalization.<br><br>METHODS: This study constructed a nomogram based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and conducted variable selection through Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by univariate and multivariate logistic regression analyses on the selected variables. Model performance was evaluated by calculating sensitivity, specificity, and the Youden index, and by performing decision curve analysis (DCA), clinical impact curve (CIC), and receiver operating characteristic (ROC) curve analysis.<br><br>RESULTS: This study included 1427 cardiac arrest (CA) patients, who were randomly allocated into a training cohort (n = 999) and a validation cohort (n = 428). We identified five independent predictors for post-cardiac arrest AKI: weight (adjusted odds ratio (aOR): 1.016, 95% confidence interval (CI): 1.009-1.024), peripheral capillary oxygen saturation (SpO2) (aOR: 1.044, 95% CI: 1.026-1.063), sodium (aOR: 0.947, 95% CI: 0.919-0.975), Sequential Organ Failure Assessment (SOFA) score (aOR: 1.134, 95% CI: 1.083-1.190), and Oxford Acute Severity of Illness Score (OASIS) score (aOR: 1.080, 95% CI: 1.059-1.103). The model demonstrated strong performance, with area under the curve (AUC) values of 0.920 and 0.875 in the training and validation cohorts, respectively. Upon validation, the specificity, sensitivity, and Youden index for the model were 0.837, 0.781, and 0.618, respectively. The calibration curve indicated good agreement between predictions and observations. The DCA and CIC confirmed the clinical utility of the model.<br><br>CONCLUSION: The developed prediction model exhibits high predictive performance for predicting AKI in IHCA patients.}, }
@article {pmid42110893, year = {2026}, author = {Lin, J and Deng, L and Li, M and Wang, Q and Zhao, L and Yu, H and Li, X and Deng, W and Guo, W and Li, T and Ni, P and Wei, W}, title = {Age-Dependent Corpus Callosum Thickness Abnormalities and Clinical Implications in Treatment-Naïve First-Episode Schizophrenia.}, journal = {Alpha psychiatry}, volume = {27}, number = {2}, pages = {48363}, pmid = {42110893}, issn = {2757-8038}, abstract = {BACKGROUND: Although morphological abnormalities of the corpus callosum (CC) have been reported in schizophrenia, findings across studies have been inconsistent. We systematically examined whether these morphological alterations are influenced by age.<br><br>METHODS: A total of 151 individuals with treatment-na&#xef;ve first-episode schizophrenia (FES) and 278 healthy controls were included. T1-weighted structural MRI scans were used to segment the CC on the midsagittal plane into 100 equidistant points, and CC thickness was estimated at each point. To determine whether CC thickness abnormalities associated with schizophrenia were moderated by age, we applied the Johnson-Neyman technique. Additionally, we investigated the relationship between age-dependent CC thickness abnormalities and clinical symptoms using partial least-squares correlation analysis.<br><br>RESULTS: Abnormal CC thickness was observed in individuals with treatment-na&#xef;ve FES, specifically within the rostral body, anterior midbody, isthmus, and splenium. These regions were thinner in younger patients compared with healthy controls but appeared thicker in older patients. Furthermore, increased CC thickness in older patients was associated with greater clinical symptom severity, whereas this association was not observed in younger patients.<br><br>CONCLUSIONS: Our findings demonstrate that CC thickness abnormalities in treatment-na&#xef;ve FES are age-dependent. The relationship between CC thickness and symptom severity also varies as a function of age. These results suggest that the CC may represent a critical biological target for age-sensitive, individualized therapeutic interventions in schizophrenia.}, }
@article {pmid42111074, year = {2026}, author = {Chu, X and Sun, R and Han, S and Yang, Z and Bai, Y and Zhao, S and Yuan, J and Xing, Z and Wang, W and Zhang, L and Li, Q}, title = {Observation on the efficacy of combined electrical stimulation in the treatment of incomplete spinal cord injury: a randomized controlled trial.}, journal = {Frontiers in neurology}, volume = {17}, number = {}, pages = {1774055}, pmid = {42111074}, issn = {1664-2295}, abstract = {OBJECTIVE: This research evaluates the effectiveness of combined electrical stimulation therapy in patients with incomplete spinal cord injury.<br><br>METHODS: Twenty-five eligible patients from Tianjin Hospital's Rehabilitation Department were randomly divided into an experimental group (n&#x202f;=&#x202f;13) and a control group (n&#x202f;=&#x202f;12). The experimental group received combined electrical stimulation, while the control group had conventional needle electrode therapy. Both groups were treated 5 times weekly for 30&#x202f;min each session over 8&#x202f;weeks. Sensory and motor functions were evaluated using the ASIA scales, and the BioNeuro Infiniti system measured RMS values and integrated electromyography.<br><br>RESULTS: Post-treatment, the experimental group demonstrated improved sensory and motor functions, functional independence, and biochemical blood markers compared to the control group (p&#x202f;<&#x202f;0.05).<br><br>CONCLUSION: Both combined electrical stimulation and needle electrode therapy effectively treat incomplete spinal cord injuries over 8&#x202f;weeks, with combined electrical stimulation showing greater efficacy.<br><br>CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/showproj.html?proj=129549, identifier ChiCTR2100052017.}, }
@article {pmid42095521, year = {2026}, author = {Zhang, C and Xiao, H and Jiang, X and Ta, D}, title = {Integrated Ultrasonic Platform for Bioelectronic Control through Biological Barriers Based on Metasurface.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e75563}, doi = {10.1002/advs.75563}, pmid = {42095521}, issn = {2198-3844}, support = {2023YFA1407800//National Key R&amp;D Program of China/ ; 123B2070//National Natural Science Foundation of China/ ; 12474451//National Natural Science Foundation of China/ ; T2321003//National Natural Science Foundation of China/ ; T2222024//National Natural Science Foundation of China/ ; 21TQ1400100//Shanghai Pilot Program for Basic Research-Fudan University/ ; 25TQ001//Shanghai Pilot Program for Basic Research-Fudan University/ ; //Shanghai QiYuan Innovation Foundation/ ; }, abstract = {Closed-loop bioelectronic systems that adapt stimulation to real-time physiological feedback hold transformative potential for treating neurological and cardiac disorders and are emerging as key components of future ultrasonic brain-machine interfaces (uBMIs). Realizing this requires the simultaneous achievement of millimeter‑scale deep-tissue targeting, artifact-free physiological feedback, and robust wireless power and data transfer, which remain elusive with current methods. Here, we present an integrated ultrasonic platform engineered to overcome these fundamental limitations. We propose a physics-constrained metasurface design framework to enable high-resolution multifocal ultrasound energy delivery through highly aberrating biological barriers such as the skull and ribs, achieving improved experimental targeting accuracy (e.g., ±6.5% intensity uniformity across multiple foci). We demonstrate the platform's adaptive stimulation capabilities through two distinct paradigms: attention-based ultrasound stimulation and cardiac-synchronized ultrasound stimulation. Furthermore, we introduce a novel dual-channel acoustic link that enables continuous wireless power and wireless data streaming through the skull with a single acoustic metasurface, demonstrating robustness even with a 400-fold power differential. This integrated ultrasonic framework, providing seamless integration of precise spatial targeting through biological barriers, adaptive physiological feedback, and untethered operation, contributes to the development of next-generation uBMIs and closed-loop bioelectronic therapies.}, }
@article {pmid42096275, year = {2026}, author = {Fu, Y and Shi, F and Sha, L and Ma, Y and Lin, W and Li, X and Yan, H and Wang, P and Fang, J and Huang, Q and Chen, F and Li, Y and Kong, Q and Huang, H and Hu, X and Liu, C and Wang, J and Xiao, X and Zhang, Q and Mei, R and Han, Y and Wu, Y and He, S and Zhang, H and Wang, K and Zhu, Y and Lin, W and Peng, Z and Zhu, X and Wu, X and Yu, M and Zou, M and Zou, X and Wu, T and He, X and Guo, H and Zhong, M and Zhang, Q and Su, Y and Liu, Y and Feng, Q and Wang, H and Chen, W and Sun, Y and Sun, M and Zhou, J and Zhao, H and Guo, C and Gao, J and Guo, Y and Huang, J and Sun, H and Luo, X and Yang, R and Qin, H and Tomson, T and Chen, L}, title = {Folic acid supplementation and prevention of adverse offspring outcomes among women with epilepsy: An observational study.}, journal = {Epilepsia}, volume = {}, number = {}, pages = {}, doi = {10.1002/epi.70264}, pmid = {42096275}, issn = {1528-1167}, support = {2024ZDZX0018//Sichuan Province Science and Technology Support Program/ ; }, abstract = {OBJECTIVE: Folic acid (FA) is essential for fetal development, while the benefits and optimal dose in pregnant women with epilepsy (PWWE) remain unclear. This study explores effects of FA supplementation, dose, and initiation time on offspring outcomes in PWWE.<br><br>METHODS: This multi-center cohort recruited PWWE from 58 hospitals in China. Anti-seizure medication (ASM) and FA exposures were categorized by first-trimester use. The primary outcome was a composite of preterm birth, low birth weight (LBW), major congenital anomalies (MCAs), fetal death, and neurodevelopmental delay. Logistic regression models assessed the associations between FA exposure, dose, initiation time, and adverse outcomes, adjusting for demographics and epilepsy characteristics, with stratification by maternal ASM use. Dose-response relationships were analyzed using restricted cubic splines.<br><br>RESULTS: Among 1013 women with 1209 pregnancies, 952 received FA. In ASM-exposed pregnancies, FA supplementation was associated with lower risks of composite adverse offspring outcomes (adjusted odds ratio [aOR] .59, 95% confidence interval [CI] .387-.911) and fetal death (aOR .127, 95% CI .054-.296), whereas no significant differences were observed between preconception and first-trimester initiation. Compared to no supplement, supplementation with .4&#x2009;mg/day protected against fetal death (aOR .185, 95% CI .078-.428); doses exceeding .4&#x2009;mg/day&#xa0;further reduced risk of composite adverse outcomes (aOR .343, 95% CI .162-.675), and doses above 1&#x2009;mg additionally showed trends toward decreased preterm birth in ASM-exposed pregnancies (aOR .338, 95% CI .104-.943). Compared with .4&#x2009;mg&#xa0;supplementation, doses above 1&#x2009;mg/day&#xa0;were associated with a lower risk of LBW (aOR .208, 95% CI .05-.58).<br><br>SIGNIFICANCE: FA supplementation was associated with lower risks of composite adverse offspring outcomes in ASM-exposed pregnancies, specifically at doses exceeding .4&#x2009;mg. No such associations were observed in pregnancies not exposed to ASMs. However, the optimal upper limit of high-dose FA supplementation requires further investigation.}, }
@article {pmid42096402, year = {2026}, author = {Dong, YJ and Xi, K and Zhang, YZ and Xue, JH and Shen, DD and Zang, SK and Zhao, R and Qi, H and Mao, C and Wang, WW and Zhang, Y}, title = {Structured water molecules drive activation and G protein selectivity in the GPR174 receptor.}, journal = {PLoS biology}, volume = {24}, number = {5}, pages = {e3003447}, pmid = {42096402}, issn = {1545-7885}, mesh = {*Receptors, G-Protein-Coupled/metabolism/chemistry/genetics ; Humans ; Cryoelectron Microscopy ; *Water/chemistry/metabolism ; Molecular Dynamics Simulation ; Signal Transduction ; Lysophospholipids/metabolism ; HEK293 Cells ; *GTP-Binding Proteins/metabolism ; Protein Binding ; Binding Sites ; Protein Conformation ; Amino Acid Sequence ; }, abstract = {G protein-coupled receptor 174 (GPR174), a key modulator of autoimmune responses, maintains immune homeostasis through distinct G protein signaling pathways, particularly Gs and Gi. Although the structural mechanism of lysophosphatidylserine (LysoPS)-activated GPR174 in the Gs pathway has been characterized, how hydration-mediated interactions influence GPR174 activation and signaling selectivity remains unclear. Here, we determined high-resolution cryo-electron microscopy (cryo-EM) structures of LysoPS-activated human GPR174 bound to Gs (2.0 Å) and Gi (3.4 Å), revealing a continuous hydration-mediated signal transduction network that bridges the sodium-binding pocket, the NPxxY and DRY motifs, and the G protein-binding interface. This network stabilizes the active-state conformation of GPR174 and dynamically reshapes the intracellular cavity, thereby enabling differential engagement of Gs and Gi. Molecular dynamics simulations and functional assays demonstrated that the hydration network is essential for receptor activation and selectively modulates G protein coupling. To evaluate its conservation, we performed sequence alignments and structural analyses across class A GPCRs, defining three hydration cavities: the conserved water cavity (CWC), the junctional water cavity (JWC), and the extended water cavity (EWC), whose hydration is determined by residue properties at position 5.58. Together, our study reveals a hydration-driven molecular mechanism that underlies the activation of GPR174 and its dual G protein selectivity. These findings advance the understanding of hydration-mediated signaling in GPR174 and provide a framework for investigating water-mediated regulation across class A GPCRs.}, }
@article {pmid42100576, year = {2026}, author = {Jain, A and Raveendran, S and Nair, KPS and Ramakrishnan, S}, title = {Brain-computer interface: an update for the clinicians.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1777024}, pmid = {42100576}, issn = {1662-5161}, abstract = {This narrative review critically examines the fundamental principles and clinical applications of Brain-Computer Interfaces (BCIs) in neuroscience and mental health. We searched PubMed, Scopus, and PEDro databases using pre-defined keywords, with inclusion restricted to clinical studies. The manuscript provides an evidence-based assessment of current indications, technological limitations, and emerging solutions, offering insights into both the opportunities and challenges for clinical integration. Clinical decision-making pathways are outlined to guide the adoption of BCI technologies in patient care. This article aims to increase awareness among clinicians and to equip them with the essential knowledge required as BCI systems advance toward mainstream clinical use.}, }
@article {pmid42092952, year = {2026}, author = {Song, Y and Yang, C and Tu, J and Dong, J and Zhang, X}, title = {Multi-omics analysis of deep brain stimulation associated with brain-gut axis modulation and symptom amelioration in a Parkinson's disease mouse model.}, journal = {Biology direct}, volume = {}, number = {}, pages = {}, doi = {10.1186/s13062-026-00778-4}, pmid = {42092952}, issn = {1745-6150}, support = {2024ZD0524402//the Noncommunicable Chronic Diseases-National Science Technology Major Project/ ; }, abstract = {This study aimed to systematically elucidate the molecular mechanisms underlying PD-associated brain-gut dysfunction through multi-omics analyses and to evaluate the therapeutic potential of combined Deep Brain Stimulation (DBS) and Brain-Computer Interface (BCI) interventions. Transcriptomic and 16S rRNA datasets from Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) were integrated and analyzed using DESeq2, limma, Gene Set Enrichment Analysis (GSEA), and PICRUSt2 to identify disrupted pathways and microbial functional features. In the 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mouse model, four groups (Normal, MPTP, MPTP + DBS, and MPTP + DBS+BCI) were assessed using behavioral testing, Local Field Potentials (LFP) recordings, molecular assays, and histological analysis. The findings revealed synaptic damage and metabolic pathway disruptions in PD brains, accompanied by reduced abundance of Short-Chain Fatty Acid (SCFA)-producing gut microbes. Combined DBS and BCI markedly improved motor deficits, suppressed aberrant β oscillations, restored gut barrier integrity and microbial homeostasis, and reduced pathological α-synuclein (αSyn) aggregation. Collectively, these results demonstrate that DBS + BCI is associated with improvements across neural, microbial and inflammatory readouts, supporting a correlative brain-gut-immune framework.}, }
@article {pmid42093854, year = {2026}, author = {Li, Y and Chen, J and Wang, Y and Huang, J and Fang, F}, title = {Mapping knowledge structure and emerging trends in non-invasive brain-computer interface for stroke rehabilitation.}, journal = {IBRO neuroscience reports}, volume = {20}, number = {}, pages = {662-672}, pmid = {42093854}, issn = {2667-2421}, abstract = {OBJECTIVE: To explore the current research landscape and emerging frontiers in the application of non‑invasive brain-computer interface (BCI) technology in the field of stroke.<br><br>METHODS: Publications related to non&#x2011;invasive BCI technology in stroke were retrieved from the Web of Science Core Collection database between January 2014 and March 2025. Only English articles and reviews were included; conference papers, editorials, and corrections were excluded.Bibliometric software was employed to construct visual knowledge maps based on authors, institutions, keywords, and other metrics.<br><br>RESULTS: After excluding items such as publisher corrections, editorial materials, and conference papers, 587 publications were included. Over the past decade, the annual number of publications showed an upward trend. China (177 publications) contributed the highest volume of output globally. The most prolific author was Jochumsen, Mads (17 publications), and Aalborg University (31 publications) was the leading institution. The journal with the highest number of publications was IEEE Transactions on Neural Systems and Rehabilitation Engineering(60 articles), while the Journal of Neural Engineeringreceived the most citations (2129). Keyword analysis and burst detection revealed that research hotspots mainly focus on signal acquisition methods, EEG&#x2011;based signal types, neural mechanisms, algorithms, external devices, and their impact on functional rehabilitation after stroke.<br><br>CONCLUSION: Over the past ten years, advances in technology and interdisciplinary collaboration between medicine and engineering have provided new opportunities for stroke rehabilitation through non&#x2011;invasive BCI. This technology shows great clinical value in promoting neural plasticity and functional recovery in stroke patients.It is projected that future research will emphasize multimodal integration, innovations in algorithms such as deep learning, and breakthroughs in material technology, which are expected to represent major research directions and hotspots in the field.}, }
@article {pmid42094053, year = {2026}, author = {Cajigas, I and Borges, P and Qureshi, Q and Davis, P and Wang, Z and Sargur, K and Haggerty, J and Wingel, K and Kim, MJ and Pisano, T and Ho, E and Barth, K and Byun, YW and Miller, J and Dister, J and Anushiravani, R and Murphy, M and Poole, A and Strauss, J and Mermel, C and Qiu, L and Halpern, C and Yoshor, D and Beauchamp, M and Pesaran, B and Rapoport, B}, title = {Microscale organization and separability of upper extremity representations in the human motor homunculus.}, journal = {Research square}, volume = {}, number = {}, pages = {}, doi = {10.21203/rs.3.rs-9528027/v1}, pmid = {42094053}, issn = {2693-5015}, abstract = {Understanding the microscale spatial organization of the human motor homunculus is essential for designing surface-based brain-computer interfaces (BCIs). We investigated these dynamics using the highest-density clinically available subdural microelectrode arrays (1024 channels, 400 micrometers pitch) temporarily implanted in 11 neurosurgical patients undergoing awake surgery. We mapped broadband high gamma activity (>80 Hz) during upper extremity movements across 9 joints and hand gestures (rock, paper, scissors). Gestures produced consistent, localized spatial patterns in M1/S1, revealing shared microscale hand somatotopy across participants. Joint mapping revealed somatotopic representations organized as concentrically larger activation regions from distal to proximal joints. We characterized persistent spatial gradients in high gamma activity and representational overlap at microscale resolution. While previous macroscale studies showed overlapping motor representations, our high-density recordings provided a much finer mapping of this overlap and revealed a relationship between overlap degree and decoding performance. Our findings reveal a previously unobserved microscale mapping of motor commands in M1 and S1 and suggest that finer spatial resolution is necessary to decode complex movements from the brain surface.}, }
@article {pmid42094582, year = {2026}, author = {Gusman, JT and Beckman, ZC and Singer-Clark, TS and Paulk, AC and Kapitonava, A and Hosman, T and Allcroft, S and Acosta, AJ and Nicolas, C and Rubin, DB and Donoghue, JP and Vargas-Irwin, CE and Hochberg, LR}, title = {Observation-Related Activity in Human Motor Cortex Increases with Effector Anthropomorphicity.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.04.24.720491}, pmid = {42094582}, issn = {2692-8205}, abstract = {UNLABELLED: Neurons in motor cortex can be engaged not only in motor execution but also during observation of movements performed by other anthropomorphic agents (i.e. humans or monkeys). However, it is unknown how motor cortical neurons respond during observation of the range of assistive or prosthetic devices controlled by people using intracortical brain-computer interfaces (iBCIs). We recorded single-unit activity in the precentral gyrus while iBCI users viewed grasp-like movements performed by a spectrum of virtual effectors that included human, robotic, and hand-like dot stimuli. We found a relationship between neural modulation and effector anthropomorphicity (i.e. human-likeness) that existed on an ensemble-wide and individual neuron level, suggesting that human motor cortex activity incrementally increases in response to the visually observed agent's human-likeness. Both solicited and spontaneous feedback from the participant indicated a relationship between neural activity and subjective assessments of anthropomorphicity, revealing a powerful contribution of context on observation-induced activity in motor cortex. The activity of motor cortex remained similar during attempted hand movements while different effectors were being observed, suggesting that intuitive external device control via iBCIs may not be overtly affected by the anthropomorphicity of the effector.<br><br>SIGNIFICANCE STATEMENT: The tendency for neurons in motor cortex to respond during movement observation has been proposed to underlie cognitive processes from motor learning and language development to empathy and theory of mind. Understanding how the motor cortex is engaged during observation of abstract and anthropomorphic agents informs our understanding of these processes and may guide development of neural prostheses which harness the activity of motor cortical neurons to restore lost neurologic function. Here we provide unique neuron-level evidence that human motor cortex activity is gradually modulated by how human-like an observed agent appears and moves. This finding advances our interpretation of "mirror" activity in the brain and could help guide the design of brain-controlled prostheses used by people with tetraplegia.}, }
@article {pmid41134956, year = {2026}, author = {Jia, T and Long, H and McGeady, C and Yang, X and Colacrai, F and Wang, J and Ji, L and Li, C and Farina, D}, title = {Physiology-Inspired EEG Transformer for Predicting Movement Transitions in Bimanual Tasks.}, journal = {IEEE journal of biomedical and health informatics}, volume = {30}, number = {5}, pages = {4108-4119}, doi = {10.1109/JBHI.2025.3622729}, pmid = {41134956}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; Movement/physiology ; Male ; Adult ; Female ; *Signal Processing, Computer-Assisted ; Young Adult ; *Brain-Computer Interfaces ; Motor Cortex/physiology ; Hand/physiology ; }, abstract = {Human-machine interfaces (HMIs) have been widely integrated with motor rehabilitation and augmentation systems. Forecasting movement transitions during human-robot interaction is crucial to ensure system safety, intuitiveness, and reactivity, particularly in anticipating human motor intentions under sudden perturbations or emergency scenarios. In this study, we investigated pre-movement neural signatures preceding sudden movement transitions during ongoing bimanual tasks. Informed by these findings, we propose a physiology-informed EEG Transformer (PI-EEGformer) for EEG-based motor intention recognition. An EEG dataset collected from a bimanual movement task, where one hand was required to switch motor states in response to unexpected cues, was used to evaluate the performance of the PI-EEGformer in comparison with seven state-of-the-art models. Results showed that, prior to the movement transition, EEG power spectrum decreased, and movement-related cortical potentials (MRCPs) could be accurately extracted from the contralateral motor cortex. PI-EEGformer reached an average accuracy of 0.912 in inter-subject tests and 0.829 in cross-subject tests in detecting movement transitions using EEG from 500 ms to 100 ms prior to the actual movement. This performance was superior to all the state-of-the-art models tested. These results demonstrate that EEG neural signatures can predict sudden movement transitions during ongoing bimanual tasks. The PI-EEGformer, designed with these physiological signatures, can enable accurate prediction of sudden movement transitions. This study will help improve the response of HMI systems to sudden disturbances, contributing to a more realistic HMI system.}, }
@article {pmid42080051, year = {2026}, author = {Duan, S and Li, P and Yuan, D and Wang, K and Yu, D and Cheng, L}, title = {Multi-Scale convolutional neural networks integrated with self-attention for motor imagery EEG decoding.}, journal = {Biomedical engineering letters}, volume = {16}, number = {3}, pages = {719-734}, pmid = {42080051}, issn = {2093-985X}, abstract = {Brain-computer interface (BCI), as a cutting-edge technology with great application prospects, has received widespread attention in recent years. Motor imagery (MI) electroencephalography (EEG) classification is a key component of brain-computer interfaces, widely used in applications such as assisting people with disabilities, controlling devices, and interacting with environments. However, since convolutional neural networks (CNNs) extract only local temporal features, they may be unable to capture the long-term dependencies used for EEG decoding, which can have an impact on the decoding performance. In order to address this problem, this paper proposes a novel deep learning network that combines a multi-scale convolutional neural network with an attention mechanism to capture temporal information and global dependencies. First, a multi-scale structure is designed to extract spatial-temporal information at different scales and multimodal information from both the mean and variance perspectives. Second, a squeeze-excite-compress (SEC) module is used to enhance the feature response of each branch and reduce information redundancy. Finally, an encoder with a multi-head attention mechanism extracts more discriminative features and highlights the most valuable information in MI-EEG data. In addition, this paper uses a data augmentation method of signal reorganization to expand the dataset and further enhance the generalization ability of the network. Our method was evaluated by performing experiments on the BCI Competition IV-2a (BCI-IV-2a) and High Gamma Dataset (HGD) with classification accuracies of 85.26% and 95.86%, respectively. The experimental results show that our method achieves state-of-the-art performance and has great potential to be a new baseline for general EEG decoding.}, }
@article {pmid42080269, year = {2026}, author = {Liu, J and Yang, X and Li, M and Zheng, C and Wang, K and Ren, X and Zhang, B and Li, H and Jiang, D and Li, W and Xu, M}, title = {Application of Graphene Dry Electrode in 512-Lead EEG Cap and Real-Time Monitoring EEG System.}, journal = {ACS applied materials &amp; interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.5c25282}, pmid = {42080269}, issn = {1944-8252}, abstract = {Dry electroencephalography (EEG) electrodes with low noise and minimal potential drift are crucial for daily wearable and high-density noninvasive brain-computer interfaces. In this study, a Na-doped vertical graphene dry electrode with a diameter of 2.8 mm was prepared to construct a 512-lead ultrahigh-density EEG cap and wireless 8- and 32-lead EEG headbands. The Na-doped vertical graphene layer has a three-dimensional architectural structure that absorbs sweat from the scalp and converts it into an Na[+]-mediated solid electrolyte, electrically connecting the device to the scalp. The optimized graphene dry electrodes exhibited low scalp-contact resistance (dry: 3.8-6.5 kΩ, H2O: 4.5 kΩ), self-noise (11.1 μV), DC offset voltage (15.6 mV), and potential drift (189.9 μV). The EEG cap, composed of 512 dry graphene electrodes, recorded different rhythm signals with a high signal-to-noise ratio, demonstrating excellent repeatability and long-term stability over 103 days. In addition, a task-state strategy was designed that combined the intensity ratio of fast and slow waves with frequency-domain event-related potentials, demonstrating the reliability of dry electrode headband systems for rapid attention analysis during daily wear. This wearable metal-doped vertical graphene dry-electrode device, especially the 512-lead ultrahigh-density dry-electrode EEG cap, holds promise for applications in brain function research, neuroimaging, and brain-computer interface control.}, }
@article {pmid42081511, year = {2026}, author = {Zhong, M and Jiang, Y and Huang, S and Chen, P and Liu, H and Zhang, Y and He, X and Yang, F and Fu, Q and Zheng, Y and Guo, Y and Lin, Q}, title = {A Closed-Loop ta-VNS System Synchronized with BCI-Based Motor Training for Post-Stroke Upper Limb Rehabilitation.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {230}, pages = {}, doi = {10.3791/69272}, pmid = {42081511}, issn = {1940-087X}, mesh = {*Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Electroencephalography/methods ; Humans ; *Upper Extremity/physiopathology ; *Vagus Nerve Stimulation/methods/instrumentation ; Stroke/physiopathology ; *Transcutaneous Electric Nerve Stimulation/methods/instrumentation ; }, abstract = {Transcutaneous auricular vagus nerve stimulation (ta-VNS) involves applying electrical stimulation via electrodes to the auricular concha. This activates vagal afferent fibers, initiating an ascending pathway from the periphery to the brainstem, which ultimately stimulates central vagal projections and promotes neural plasticity. Previous studies have demonstrated that combining ta-VNS with motor training offers synergistic benefits for motor recovery after stroke. However, these combined approaches typically employ open-loop stimulation with fixed parameters, lacking real-time closed-loop responsiveness to dynamic neural activity. To address this limitation, we developed a novel closed-loop ta-VNS system synchronized with electroencephalography (EEG)-triggered brain-computer interface (BCI) motor training. This system was designed to enhance corticospinal coupling and promote synaptic plasticity. We established a standardized protocol for applying this closed-loop ta-VNS system synchronized with BCI-based motor training in stroke patients. Using EEG-based functional assessment, we compared the effects of the closed-loop ta-VNS system synchronized with BCI-based motor training to those of sham ta-VNS synchronized with BCI-based motor training. This work provides the methodological and theoretical groundwork for the clinical application of this approach in stroke rehabilitation.}, }
@article {pmid42082585, year = {2026}, author = {Wang, G and Huang, Y and Muckli, L and Faccio, D}, title = {Symbiotic brain-machine drawing via visual brain-computer interfaces.}, journal = {npj biomedical innovations}, volume = {3}, number = {1}, pages = {}, pmid = {42082585}, issn = {3005-1444}, support = {EP/T00097X/1, EP/Y029097/1, EP/ Z533166/1//UK Research and Innovation/ ; }, abstract = {Brain-computer interfaces (BCIs) are evolving from research prototypes into clinical, assistive, and performance enhancement technologies. Despite the rapid rise and promise of implantable technologies, there is a need for better and more capable wearable and non-invasive approaches whilst also minimising hardware requirements. We present a non-invasive BCI for iterative selection-based mind-drawing that infers a subject's internal visual intent through iterative selection of adaptive visual probes presented on a screen encoded at different flicker-frequencies and analyses the steady-state visual evoked potentials (SSVEPs). Gabor-inspired or machine-learned policies dynamically update the spatial placement of the visual probes on the screen to explore the image space and reconstruct simple imagined shapes within approximately two minutes or less using just single-channel EEG data. Additionally, by leveraging stable diffusion models, reconstructed mental images can be transformed into realistic and detailed visual representations. Whilst we expect that similar results might be achievable with e.g. eye-tracking techniques, our work shows that symbiotic human-AI interaction can increase BCI bit-rates by more than a factor 5x, providing a platform for future development of AI-augmented BCI.}, }
@article {pmid42088024, year = {2026}, author = {Kazazian, K and Kolisnyk, M and Gupta, G and de Jeu, J and Abdalmalak, A and Owen, AM}, title = {Towards the use of functional near-infrared spectroscopy as an assessment tool in disorders of consciousness.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {4}, number = {}, pages = {}, pmid = {42088024}, issn = {2837-6056}, abstract = {Functional near-infrared spectroscopy (fNIRS) has emerged as a promising neuroimaging tool for assessing patients with disorders of consciousness (DoC). While functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have advanced the detection of covert brain function, their use is often constrained by accessibility, medical and physical contraindications, and practical limitations. fNIRS offers a portable, safe, and cost-effective alternative capable of measuring hemodynamic responses at the bedside. In this perspective, we discuss the clinical motivation for integrating fNIRS into DoC patient assessments, summarize recent advancements in the application of fNIRS for examining brain function, and outline the clinical and technical advantages. We highlight key future directions of fNIRS research, including large-scale validation, multimodal integration, and the development of fNIRS-based brain-computer interfaces. Finally, we address the ethical imperative to ensure equitable access to neurotechnologies capable of detecting covert brain function. With continued methodological refinement and standardization, fNIRS may significantly transform the diagnostic, prognostic, and communicative landscape of DoC care.}, }
@article {pmid42091832, year = {2026}, author = {Jin, J and Dai, L and Wang, Z and Xiao, Q and Wang, A}, title = {Understanding loss aversion by using tDCS stimulation on DLPFC and multiple ERP measures: A tDCS-EEG study.}, journal = {Cognitive, affective &amp; behavioral neuroscience}, volume = {}, number = {}, pages = {}, pmid = {42091832}, issn = {1531-135X}, support = {22dz2261100//Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; 72271166//National Nature Science Foundation of China/ ; }, abstract = {Loss aversion is a crucial aspect of risky decision-making; yet its neural underpinnings remain unclear, particularly regarding the functional relationship between neural activity and behavior. This study employed bihemispheric DLPFC transcranial direct current stimulation (tDCS) and electroencephalogram (EEG) to deeply understand the neural mechanism of loss aversion from three aspects: 1) functional relationship of dorsolateral prefrontal cortex (DLPFC) on loss aversion; 2) comprehensive neural basis of loss aversion; 3) neural evidence of functional effect of DLPFC on loss aversion. Twenty-five healthy subjects underwent three stimulations, i.e., right anodal/left cathodal (right stimulation), left anodal/right cathodal (left stimulation), and sham stimulation targeted bilateral DLPFC on separate days with 7- to 14-day intervals. Participants performed a mixed gamble task poststimulation while EEG was recorded. Behaviorally, right stimulation reduced acceptance rate and increased loss aversion coefficients compared with sham and left stimulation. Moreover, both average and single-trial ERP analysis revealed enhanced feedback-related negativity difference (d-FRN) deflections following right stimulation, whereas no significant error-related negativity (ERN) effect was found. These findings suggested that right DLPFC is a key region driving loss aversion by increasing sensitivity to losses and modulating negative emotional responses.}, }
@article {pmid42092107, year = {2026}, author = {Dong, X and Huang, W and Chen, HJ and Zhang, Y and Liu, B and Zhang, D and Zhang, Z and Ma, G and Shu, N}, title = {Shared genetic architecture between the topology of brain white matter structural connectome and fluid intelligence.}, journal = {Communications biology}, volume = {}, number = {}, pages = {}, doi = {10.1038/s42003-026-10131-0}, pmid = {42092107}, issn = {2399-3642}, support = {82301608//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32271145//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81871425//National Natural Science Foundation of China (National Science Foundation of China)/ ; 210510238//National Natural Science Foundation of China (National Science Foundation of China)/ ; L252087//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; }, abstract = {White matter (WM) connections support efficient interregional communication and form the structural basis of human fluid intelligence. However, the shared genetic architecture between WM structural connectome and fluid intelligence remains largely unknown. In this study, we analyzed diffusion-weighted MRI data from 26,655 UK Biobank participants to construct individual WM connectome and performed genome-wide association analyses on global and regional network topology. We identified 41 single nucleotide polymorphisms (SNPs) significantly associated with global efficiency and 45 SNPs linked to nodal efficiency. Genetic correlations with fluid intelligence were observed for 128 brain regions, with 44 and 3 regions sharing SNPs within chromosomes 6q21 and 3p21.1, respectively. Mendelian randomization revealed causal effects from WM connectome to fluid intelligence, particularly in the orbital and superior frontal gyrus. Finally, integrating polygenic scores with network efficiency improved the prediction of individual fluid intelligence. These findings highlight the genetic basis linking WM connectome topology and fluid intelligence, providing new insights into the neurogenetic underpinnings of fluid intelligence.}, }
@article {pmid42092502, year = {2026}, author = {Damiano, RJ and Philpott, JM and Moront, MG and Murphy, ET and Bailey, SH and Ramlawi, B and Willekes, C and Melnitchouk, S and Heimansohn, D and Hu, Y and Lehr, EJ}, title = {A Prospective, Multicenter Trial of Irrigated Radiofrequency Ablation and Cryoablation to Treat Non-Paroxysmal Atrial Fibrillation.}, journal = {The Journal of thoracic and cardiovascular surgery}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.jtcvs.2026.04.010}, pmid = {42092502}, issn = {1097-685X}, abstract = {OBJECTIVE: Concomitant surgical ablation of atrial fibrillation (AF) improves AF-free survival, decreases stroke risk, and improves quality of life (QOL). This clinical trial evaluated the efficacy and safety of irrigated radiofrequency (iRF) ablation and cryoablation for the treatment of non-paroxysmal AF (NPAF).<br><br>METHODS: In this prospective, multicenter study, a Cox-Maze IV lesion set using iRF and cryoablation was performed to treat NPAF in patients undergoing concomitant cardiac surgery. Pulmonary vein isolation (PVI) was assessed intraoperatively. The primary efficacy endpoint was freedom from AF/atrial flutter/atrial tachycardia (ATAs) of &#x2265;30 seconds after a 90-day washout from antiarrhythmic drugs (AADs) through 12 months. The primary safety endpoint was the rate of major adverse events (MAEs) at 30 days/discharge. MAEs and rhythm assessments were adjudicated independently.<br><br>RESULTS: Among 94 treated patients, mean age was 69&#xb1;7 years, and 33 (35%) patients were female. Fifty-one patients (54%) had persistent AF, and 43 (46%) had long-standing persistent AF. PVI was confirmed in 100% of patients tested (65/65). Freedom from ATAs through 12 months was 76.2% (64/84; 95% Bayesian credible interval: 66.0%-84.0%). Seven of 93 patients 7.5% (95% BCI: 3.75%-14.7%) had 10 MAEs through 30 days. The &#x2264;30-day mortality rate was 4% (2%-11%). QOL scores at 12 months (n=81) improved significantly from baseline (p<0.001).<br><br>CONCLUSIONS: This trial showed a high rate of success at restoring sinus rhythm with a low complication rate and an improvement in QOL when treating NPAF with iRF clamps and cryoablation. These excellent results support wider adoption of concomitant AF ablation.}, }
@article {pmid42079853, year = {2026}, author = {Ma, T and Huggins, JE and Kang, J}, title = {Bayesian Signal Matching for Transfer Learning in ERP-Based Brain Computer Interface.}, journal = {Journal of the American Statistical Association}, volume = {121}, number = {553}, pages = {100-112}, pmid = {42079853}, issn = {0162-1459}, support = {R01 DA048993/DA/NIDA NIH HHS/United States ; R01 MH105561/MH/NIMH NIH HHS/United States ; }, abstract = {An Event-Related Potential (ERP)-based Brain-Computer Interface (BCI) Speller System assists people with disabilities to communicate by decoding electroencephalogram (EEG) signals. A P300-ERP embedded in EEG signals arises in response to a rare, but relevant event (target) among a series of irrelevant events (non-target). Different machine learning methods have constructed binary classifiers to detect target events, known as calibration. The existing calibration strategy uses data from participants themselves with lengthy training time. Participants feel bored and distracted, which causes biased P300 estimation and decreased prediction accuracy. To resolve this issue, we propose a Bayesian signal matching (BSM) framework to calibrate EEG signals from a new participant using data from source participants. BSM specifies the joint distribution of stimulus-specific EEG signals among source participants via a Bayesian hierarchical mixture model. We apply the inference strategy. If source and new participants are similar, they share the same set of model parameters; otherwise, they keep their own sets of model parameters; we predict on the testing data using parameters of the baseline cluster directly. Our hierarchical framework can be generalized to other base classifiers with parametric forms. We demonstrate the advantages of BSM using simulations and focus on the real data analysis among participants with neuro-degenerative diseases. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.}, }
@article {pmid42080047, year = {2026}, author = {Du, X and Wang, H and Xi, M and Qiu, S and Lv, Y}, title = {SSA-DCNet: a cross-session MI-EEG classification network based on deformable convolution and spatial-shift attention.}, journal = {Biomedical engineering letters}, volume = {16}, number = {3}, pages = {769-780}, pmid = {42080047}, issn = {2093-985X}, abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) electroencephalogram (EEG) signals have shown tremendous potential in neurorehabilitation due to their non-invasive acquisition and ease of use. However, the cross-session nature of EEG signals-where recordings from the same subject at different sessions may vary due to fluctuations in physiological state and environmental conditions-presents a significant challenge. Efficient extraction and preservation of temporal and spatial features from EEG signals can capture invariant neural activation patterns while suppressing session-dependent noise and variability, thereby greatly enhancing the robustness of cross‑session motor imagery classification. To address the suboptimal performance of existing models in cross-session MI-EEG classification, this paper proposes Spatial-Shift Attention Deformable Convolution Network-SSA-DCNet, a compact convolutional neural network in which temporal filtering is implemented via a two-dimensional deformable convolution of size 1 × 64, so that the sampling grid dynamically adapts to the non-uniform distributions of informative EEG segments while operating on a 1 × 64 kernel along the temporal axis. Thereafter, a spatial-shift attention architecture expands each intermediate feature map from C to 3 C channels, evenly splits them into three subsets, applies distinct spatial-shift operations to each subset, and finally merges them via a split-attention that recalibrates channel weights to emphasize spatial patterns stable across sessions. On the public BCI Competition IV-2a and 2b datasets, SSA-DCNet achieved classification accuracies of 84.72% and 90.45%, respectively. Moreover, t-SNE visualizations provide intuitive evidence, underscoring its superior discriminative power and robust cross-session generalization.}, }
@article {pmid42080048, year = {2026}, author = {Wang, C and Li, M and Zhang, P and Zhang, Z and Wang, F and Kang, F}, title = {A Multi-perception fusion using shared-control method for brain-mobile robot.}, journal = {Biomedical engineering letters}, volume = {16}, number = {3}, pages = {781-798}, pmid = {42080048}, issn = {2093-985X}, abstract = {For human-robot collaboration, brain-computer interface is promising to express human perception to improve the adaptability of human-robot collaboration in complex environments. In this study, a multi-perception fusion using shared control method (MPF-SC) is proposed to accurately integrate human perception and robot perception. This MPF-SC is applied in brain-controlled mobile robots to accomplish navigation and obstacle avoidance in complex terrain with multiple undetectable obstacles. The MPF-SC establishes a mapping relationship between visual stimulus interface and environment by computer vision, and utilizes a grid costmap to describe the human perception. It integrates EEG and EMG signals with user intent to dynamically adjust the grid costmap, mapping obstacle regions and integrating robot navigation to jointly accomplish driving tasks-with the aim of achieving human-machine shared perception. Sixteen subjects participated in an online obstacle avoidance experiment and compared the performance of the proposed method with two traditional methods. The research results show that the MPF-SC can generate smoother trajectories, achieve a significantly reduced collision rate during navigation, and significantly enhance user comfort. The MPF-SC based on brain-computer interface, fully leverages human anticipation of risks and the robot's perception of obstacle environments, demonstrating that bilateral intelligence is capable of adapting to increasingly complex environments, thereby offering a novel avenue and intuitive avenue for human-machine shared control.}, }
@article {pmid41671386, year = {2026}, author = {Li, M and Chen, Y and Liu, A and Wu, Q and Huang, C and Song, D and Hu, F and Lan, J and Huang, C and Hu, J and Wang, G}, title = {SAICAR Drives T Regulatory Cell Differentiation and FOXP3 Maintenance to Promote Immunotherapy Resistance.}, journal = {Cancer research}, volume = {86}, number = {9}, pages = {2218-2236}, doi = {10.1158/0008-5472.CAN-25-4373}, pmid = {41671386}, issn = {1538-7445}, support = {82425041//National Natural Science Foundation of China (NSFC)/ ; 82330084//National Natural Science Foundation of China (NSFC)/ ; 82403349//National Natural Science Foundation of China (NSFC)/ ; 82504215//National Natural Science Foundation of China (NSFC)/ ; 82503378//National Natural Science Foundation of China (NSFC)/ ; 2022YFA1105303//National Key Research and Development Program of China (NKPs)/ ; 2023yfc3402100//National Key Research and Development Program of China (NKPs)/ ; SCZ202409//Major Technology Innovation of Hubei Province/ ; 2021CFA006//Natural Science Foundation of Hubei Province ()/ ; 2024AFB048//Natural Science Foundation of Hubei Province ()/ ; 2024AFB079//Natural Science Foundation of Hubei Province ()/ ; 2023BR036//Huazhong University of Science and Technology (HUST)/ ; }, mesh = {Animals ; *T-Lymphocytes, Regulatory/immunology/metabolism/drug effects ; Mice ; Humans ; *Forkhead Transcription Factors/metabolism/genetics ; Immunotherapy/methods ; Cell Differentiation/immunology/drug effects ; Tumor Microenvironment/immunology ; *Drug Resistance, Neoplasm/immunology ; Mice, Inbred C57BL ; Cell Line, Tumor ; Female ; }, abstract = {UNLABELLED: Regulatory T (Treg) cells within the tumor microenvironment critically undermine the efficacy of PD-1 immune checkpoint blockade. Metabolic reprogramming has emerged as a critical determinant of antitumor immunity, highlighting the need to define the metabolic cues that program Treg differentiation in cancer. In this study, we identified the purine biosynthesis intermediate succinylaminoimidazole carboxamide ribose-5'-phosphate (SAICAR) as a key metabolic driver of Treg induction and resistance to anti-PD-1 immunotherapy. Mechanistically, SAICAR directly bound to the serine/threonine phosphatase PPM1A, inhibiting SMAD3 dephosphorylation and thereby sustaining TGFβ-SMAD3 signaling. Persistent SMAD3 activation enhanced FOXP3 transcription and stabilized the Treg lineage. In both human tumors and mouse models, elevated intratumoral SAICAR levels were associated with increased Treg accumulation, suppression of effector T-cell function, and failure of PD-1 blockade. Genetic or pharmacologic reduction of SAICAR restored antitumor immunity and sensitized tumors to PD-1 therapy. Notably, low-dose 6-mercaptopurine disrupted SAICAR-driven immunosuppression and synergized with anti-PD-1 treatment without inducing systemic immune toxicity. Together, these findings establish SAICAR as an immunometabolic regulator that links purine metabolism to immune evasion and highlight a therapeutically actionable pathway to overcome metabolite-driven resistance to immune checkpoint blockade.<br><br>SIGNIFICANCE: SAICAR is necessary and sufficient to drive Treg-mediated immunosuppression in the tumor microenvironment, linking tumor metabolism and immunosuppression and providing mechanistic insights for metabolism-guided combination immunotherapy.}, }
@article {pmid42061401, year = {2026}, author = {Xu, A and Zhang, J and Wu, B and Xu, M and Wang, T and Shao, C and Bing, S and Huang, Y and Yao, Y and Wang, J and Tang, Y and Cao, J and Yang, B and Shao, X and He, Q and Ying, M}, title = {Acyltransferase ZDHHC22 promotes N-Myc transcriptional activation to drive neuroblastoma progression and chemoresistance.}, journal = {Molecular cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.molcel.2026.04.002}, pmid = {42061401}, issn = {1097-4164}, abstract = {MYCN-amplified neuroblastoma is one of the most lethal pediatric malignancies, where aberrant N-Myc-driven transcription promotes tumor progression. As direct targeting of N-Myc has proven challenging, current approaches prioritize understanding the mechanisms that regulate its activity, which remain poorly understood. Here, we demonstrate a crucial role of S-acylation in regulating N-Myc transcriptional activity and identify the acyltransferase zinc finger DHHC-type containing 22 (ZDHHC22) as a key regulator of this process. Mechanistically, ZDHHC22 catalyzes the S-acylation of N-Myc, which enhances its transcriptional activity by facilitating the recruitment of coactivators such as TIP60 and GCN5. Furthermore, N-Myc transcriptionally upregulates ZDHHC22, establishing a feedback loop that contributes to chemoresistance in high-risk neuroblastoma. Targeting ZDHHC22 suppresses neuroblastoma cell growth in vitro and in vivo, particularly in refractory patient-derived models. Collectively, our findings uncover a biological function of ZDHHC22 in regulating N-Myc transcriptional activation and indicate that ZDHHC22 is a promising therapeutic target for N-Myc-driven high-risk neuroblastoma, especially in MYCN-amplified patients.}, }
@article {pmid42061591, year = {2026}, author = {Zhang, Y and Li, X and Jin, Z and Zhang, J and Li, L}, title = {Distinct frontal lobe subregions mediate the emergence and reporting of visual consciousness.}, journal = {NeuroImage}, volume = {334}, number = {}, pages = {121964}, doi = {10.1016/j.neuroimage.2026.121964}, pmid = {42061591}, issn = {1095-9572}, abstract = {Persistent debate surrounds whether the frontal lobe supports the emergence or reporting of consciousness, raising the hypothesis that distinct frontal subregions may support these processes. We addressed this by combining electroencephalography (EEG) with eye-tracking in Report and No-Report paradigms. Eye-movement features distinguished conscious and unconscious trials in the no-report task. Event-related potential analyses showed that the Visual Awareness Negativity (VAN) was independent of reporting, whereas P3b occurred only with explicit reports. Importantly, the frontal Dorsal Attention Network (DAN) supported the emergence of consciousness, independent of post-perceptual reporting, as shown by multivoxel pattern analysis showing that a classifier's ability to decode visual consciousness generalized bidirectionally between report and no-report tasks. In contrast, frontal components of the Default Mode Network (DMN) and Frontoparietal Control Network (FPN) encoded visual consciousness only when explicit reports were required, indicating roles in reporting. These findings demonstrate a functional dissociation within the frontal lobe and refine the anatomical framework for the neural basis of visual consciousness.}, }
@article {pmid42062414, year = {2026}, author = {Turay, T}, title = {A novel 3D region-based speller paradigm for BCI systems.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-49989-9}, pmid = {42062414}, issn = {2045-2322}, abstract = {This study proposes and evaluates a novel three-dimensional region-based (3D-RB) speller paradigm designed to enhance classification performance. EEG data were recorded from 15 participants using 32 channels. Classification accuracy was examined across both single electrodes and predefined electrode groups. Subject-dependent analyses revealed that electrodes located in the parietal and occipital regions (e.g., Pz, P7, P8, O1, O2, Oz) achieved the highest single-channel accuracies (approximately 80-85%), whereas central electrodes (e.g., Cz, C3, C4) yielded lower accuracies (around 70-73%). Electrode grouping provided a distinct advantage; for most participants, Group 4 (Parietal + Occipital) and Group 5 (Parietal + Occipital + Central) achieved the highest performance, reaching nearly 99% accuracy. Notably, despite including fewer electrodes, Group 4 performed nearly as well as Group 5, underscoring the practical benefit of optimized electrode selection. Subject-independent (LOSO) analyses showed similar trends. Among single electrodes, P7, P8, O1, and O2 achieved the highest accuracies (approximately 78-79%), while central electrodes (e.g., Cz, Cp1, Cp2, C3, C4) remained lower (70-73%). Electrode groups again outperformed single channels, with Group 4 and Group 5 reaching approximately 89-91% accuracy. The comparable performance of Group 4, despite fewer electrodes, highlights its practical advantage for real-world applications. Grand Average ERP analyses indicated that differences between target and non-target stimuli primarily emerged within early and mid-latency time windows, with these effects being more pronounced over parietal and occipital regions. Taken together, these findings demonstrate that incorporating three-dimensional visual effects within a region-based paradigm significantly enhances classification performance by leveraging parietal-occipital activity. The proposed 3D-RB paradigm therefore offers an efficient and user-friendly approach for future BCI speller designs.}, }
@article {pmid42062474, year = {2026}, author = {Jin, JY and Song, YX and Lu, JB and Li, GQ and Wang, JQ and Feng, XJ and Luo, PH and Yang, B and Xu, ZF and Yan, H and He, QJ and Yang, XC}, title = {Deficient chaperone-mediated autophagy drives multiorgan fibrogenesis via SMAD2/4 stabilization to sustain TGFβ-SMAD signaling.}, journal = {Acta pharmacologica Sinica}, volume = {}, number = {}, pages = {}, pmid = {42062474}, issn = {1745-7254}, abstract = {Fibrotic diseases, driven by excessive extracellular matrix deposition, account for substantial global morbidity and mortality, yet effective therapies remain elusive. Emerging evidence highlights impaired protein homeostasis as a key contributor to fibrosis, prompting exploration of autophagy-mediated degradation pathways. Here, we investigate the role of chaperone-mediated autophagy (CMA), a selective lysosomal degradation mechanism, in fibrosis progression. We demonstrate that CMA activity is suppressed in fibrotic tissues from experimental mice and human patients, correlating with pathological SMAD2/4 accumulation. Mechanistically, CMA deficiency impedes SMAD2/4 degradation, amplifying TGF-β signaling and collagen overproduction. AAV-mediated LAMP2A overexpression to restore CMA activity alleviated bleomycin-induced pulmonary fibrosis and carbon tetrachloride-induced hepatic fibrosis in mice. Furthermore, we identify sunitinib, an FDA-approved tyrosine kinase inhibitor, as a novel CMA activator that enhances LAMP2A transcription via targeting the transcription factor JUND, reduces SMAD2/4 levels, and mitigates fibrosis in vivo. Our findings establish CMA dysfunction as a common pathological hallmark of fibrotic diseases and unveil therapeutic strategies targeting CMA to restore protein homeostasis. This study provides critical insights into fibrosis pathogenesis and positions pharmacological CMA activation as a promising treatment avenue. CMA is impaired across fibrotic tissues, driving disease progression. Sunitinib activates CMA by targeting JUND to promote SMAD2/4 degradation, suppressing TGFβ-SMADs-fibrosis signaling. CMA, chaperone-mediated autophagy; IPF, idiopathic pulmonary fibrosis; PF, pulmonary fibrosis; HF, hepatic fibrosis.}, }
@article {pmid42065981, year = {2026}, author = {Wang, Z and Shen, L and Mi, X and Cheng, L and Yang, Y and Wang, B and Jung, TP and Wan, F}, title = {Unified Online Adaptation Framework for Correlation Analysis-based Spatial Filtering Methods in SSVEP-based BCIs.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3689883}, pmid = {42065981}, issn = {2168-2208}, abstract = {Online adaptation is a promising technique for achieving calibration-free recognition in user-friendly brain-computer interfaces (BCIs) but remains underexplored for steady-state visual evoked potential (SSVEP) recognition. In our previous work on online multi-stimulus canonical correlation analysis (OMSCCA), we introduced a state-of-the-art scheme for the online adaptation of SSVEP spatial filters. Despite its effectiveness, this approach can not be directly extended to other advanced spatial filtering methods, thereby seriously limiting the broader development of calibration-free algorithms. To address this limitation, we propose a unified online adaptation frame work for correlation analysis (CA)-based spatial filtering methods, encompassing both spatial filter computation and utilization. Specifically, we extend the least-squares (LS) unified framework originally designed for full calibration with large amounts of training data to the online adaptation scenario without any pre-calibration, thereby enabling continuous updates of spatial filters. Moreover, to sufficiently utilize spatial filters, we introduce a cross-stimulus transfer method for online adaptation of the common impulse response and generation of user-specific templates for all stimuli using limited online unlabeled data. Finally, leveraging the proposed unified framework, we adapt three advanced spatial filtering methods from their calibration based counter parts to online adaptation paradigms and validate their performance through simulation studies. Our results demonstrate the framework's effectiveness in promoting the development ofzero-calibration SSVEP-based BCIs. Compared to the OMSCCA, the proposed online adaptation methods canimprove the recognition performance by more than 12%. This work provides a generalizable approach for transforming existing calibration-based methods into adaptive, user-friendly solutions for practical BCI applications.}, }
@article {pmid42066562, year = {2026}, author = {Li, K and Zhang, C and Li, R and Yuan, X and Jia, A and Zhang, K and Deng, W}, title = {Disrupted global and local brain functional network dynamics in adolescents with obsessive-compulsive disorder.}, journal = {Comprehensive psychiatry}, volume = {148}, number = {}, pages = {152702}, doi = {10.1016/j.comppsych.2026.152702}, pmid = {42066562}, issn = {1532-8384}, abstract = {BACKGROUND: Obsessive-compulsive disorder (OCD) frequently emerges during adolescence, a critical period for the development of static and dynamic properties of large-scale brain networks. Although previous studies have reported altered static connectivity in adolescents with OCD, the temporal organization of functional networks during this stage remains largely unexplored.<br><br>METHODS: We analyzed resting-state fMRI data from 40 adolescents with OCD and 40 age- and sex-matched healthy controls. Group independent component analysis (ICA) was used to identify intrinsic connectivity networks (ICNs). A sliding-window approach and k-means clustering were applied to derive dynamic brain states, while graph-theoretical metrics (strength, local efficiency, clustering coefficient) were computed to assess nodal variability over time. Group comparisons were performed using general linear models controlling for age and sex, and symptom correlations were tested using partial correlation analyses.<br><br>RESULTS: Compared to controls, OCD patients spent significantly less time in a globally integrated brain state characterized by strong intra- and inter-network connectivity. At the local level, reduced temporal variability was observed in the striatum, thalamus, and dorsolateral prefrontal cortex, key nodes of the cortico-striato-thalamo-cortical (CSTC) circuit. Notably, reduced striatal variability correlated with greater OCD symptom severity and decreased time in the integrated brain state.<br><br>CONCLUSIONS: These findings reveal disrupted dynamic network integration and reduced functional flexibility in adolescents with OCD, both globally and locally. This multilayered impairment may reflect early pathophysiological mechanisms and offers potential targets for age-sensitive neuromodulation strategies.<br><br>CLINICAL TRIAL REGISTRATION: ChiCTR2400092275, Chinese Clinical Trial Registry (www.chictr.org.cn).}, }
@article {pmid42068651, year = {2026}, author = {Jia, J and Wang, S and Chen, Y and Li, H and Zheng, C and Deng, J and Zhao, F}, title = {Heavy metals, gastrointestinal polymer-related materials, and gut microbiome in an Indo-Pacific bottlenose dolphin (Tursiops aduncus) recovered from a fisheries bycatch-related event in the East China Sea.}, journal = {Ecotoxicology and environmental safety}, volume = {317}, number = {}, pages = {120191}, doi = {10.1016/j.ecoenv.2026.120191}, pmid = {42068651}, issn = {1090-2414}, abstract = {Incidental cetacean bycatch provides irreplaceable opportunities to investigate population dynamics, mortality, and health. This multidisciplinary study examined morphology, age, gut microbiome, heavy metals, and gastrointestinal polymer-related materials in an immature male Indo-Pacific bottlenose dolphin (Tursiops aduncus, 248 cm, 114 kg, 5 years) accidentally captured in the East China Sea. Morphometrics indicated excellent body condition (BCI = 0.506) and superior dorsal fin shape compared to captive individuals, highlighting the role of natural environments in development. The gut microbiome was dominated by Proteobacteria and Firmicutes, showing segment-specific variation. Heavy metals accumulated mainly as Cd in kidneys and Cu and Zn in liver, with overall levels lower than those in other Chinese marine regions. LDIR analysis indicated the presence of polymer-related materials in the gastrointestinal tract, including reported matches to polyamide and chlorinated polyethylene, which may be associated with fisheries activities. These findings provide critical baseline ecotoxicological data for the East China Sea and underscore the importance of standardized passive biomonitoring networks that transform bycatch events into valuable scientific and conservation resources.}, }
@article {pmid42072234, year = {2026}, author = {Ou, HY and Hasegawa, T and Fukayama, O and Miyashita, E}, title = {Neuroscience-Inspired Deep Learning Brain-Machine Interface Decoder.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {13}, number = {4}, pages = {}, doi = {10.3390/bioengineering13040440}, pmid = {42072234}, issn = {2306-5354}, support = {JP23ym0126812//Japan Agency for Medical Research and Development/ ; JP24ym0126812//Japan Agency for Medical Research and Development/ ; }, abstract = {Brain-machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In this work, we propose a Single-Direction CNN-LSTM decoder inspired by motor cortex encoding mechanisms, which separately models extension and flexion dynamics through parallel CNN-LSTM branches. Each branch extracts spatial-temporal features from neural spike data and predicts directional joint variables, which are then combined by subtraction to yield the net angular velocity and torque of upper-limb joints. Using invasive recordings from a macaque during a 2D center-out reaching task, we demonstrate that our decoder achieves comparable performance to a conventional CNN-LSTM when trained on all tasks, while significantly outperforming both CNN-LSTM and linear regression baselines in cross-target generalization scenarios. Moreover, the model can capture physiologically meaningful co-contraction patterns, providing richer insights into motor control. These results suggest that incorporating neuroscience-inspired modular decoding into deep neural architectures enhances robustness and adaptability across tasks, offering a promising pathway for BMI applications in prosthetics and rehabilitation.}, }
@article {pmid42074167, year = {2026}, author = {Melnikova, AA and Egorchev, AA and Rosin, AA and Nurullin, LF and Lipachev, NS and Vedischeva, DS and Derzhavin, DV and Perepechenov, SS and Sukhodolova, EA and Shabernev, GV and Titova, AA and Kiyamova, RG and Kiyasov, AP and Chickrin, DE and Aganov, AV and Samigullin, DV and Popova, IY and Paveliev, M}, title = {Foreign Body Response to Neuroimplantation: Machine Learning-Assisted Quantitative Analysis of Astrogliosis.}, journal = {International journal of molecular sciences}, volume = {27}, number = {8}, pages = {}, doi = {10.3390/ijms27083524}, pmid = {42074167}, issn = {1422-0067}, support = {24-75-00123//Russian Science Foundation/ ; }, mesh = {*Machine Learning ; *Astrocytes/metabolism/pathology ; *Gliosis/pathology/etiology/metabolism ; Animals ; Glial Fibrillary Acidic Protein/metabolism ; *Foreign-Body Reaction/pathology/etiology/metabolism ; Male ; Rats ; }, abstract = {Neuroimplants represent an emerging medical technology, offering new therapeutic approaches for severe neurological and psychiatric disorders. One of the key limitations to long-term neuroimplant performance is the foreign body response elicited by intracortical implantation. Among the contributing cell types, astrocytes play a central role in glial scar formation around the implant, which can compromise device functionality. Immunofluorescence of glial fibrillary acidic protein (GFAP) provides a well-established marker of astrogliosis (neuroinflammation), yet quantitative and reproducible assessment of astrocyte morphology remains challenging due to the complexity and variability of image analysis approaches. Here, we aimed to quantitatively assess implantation-induced astrogliosis and to determine how classifier training strategy influences segmentation outcomes and morphometric measurements. We present a machine learning-assisted pipeline based on the LabKit plugin in Fiji for segmentation and morphometric analysis of GFAP-positive astrocytes in peri-implant scar versus distant cortical regions. Using this approach, we demonstrate an increase in GFAP expression, cell area, and astrocytic process length as well as the redistribution of GFAP signal along astrocytic processes within scar regions. We show that different classifier training strategies produce systematically distinct segmentation outcomes, with rule-compliant annotation improving agreement with manually defined ground truth. These findings highlight the critical role of annotation strategy in shallow learning-based segmentation and provide a practical framework for improving reproducibility of astrocyte morphometry in studies of neuroinflammation and neuroimplant biocompatibility.}, }
@article {pmid42074318, year = {2026}, author = {Wang, Y and Lu, J and Feng, X and Yang, B and He, Q and Luo, P and Yang, X}, title = {ATF3/SLC31A1-Mediated Cuproptosis Contributes to Bortezomib-Induced Peripheral Neurotoxicity and Intervention by (-)-Epigallocatechin Gallate.}, journal = {International journal of molecular sciences}, volume = {27}, number = {8}, pages = {}, doi = {10.3390/ijms27083680}, pmid = {42074318}, issn = {1422-0067}, support = {82274018//National Natural Science Foundation of China/ ; }, mesh = {Animals ; *Bortezomib/adverse effects ; Mice ; *Activating Transcription Factor 3/metabolism/genetics ; *Catechin/analogs &amp; derivatives/pharmacology ; Male ; *Copper Transporter 1/metabolism/genetics ; Copper/metabolism ; *Neurotoxicity Syndromes/metabolism/etiology/drug therapy ; *Peripheral Nervous System Diseases/chemically induced/metabolism/drug therapy ; Mice, Inbred C57BL ; }, abstract = {Bortezomib (BTZ), the first-generation proteasome inhibitor, has been approved for the treatment of relapsed, refractory, and newly diagnosed multiple myeloma. Despite its remarkable antitumor efficacy, BTZ treatment is severely limited by a high incidence of systemic adverse reactions, primarily due to its non-selective cytotoxicity toward rapidly dividing normal cells and its potent neurotoxic effects on peripheral neurons. Bortezomib-induced peripheral neurotoxicity (BIPN) manifests as neuropathic pain and sensory abnormalities, affecting up to 31% to 64% of patients and limiting BTZ's clinical use. Currently, the underlying mechanisms of BIPN are poorly understood. To evaluate the effects of BTZ on the functions of peripheral nerves in mice, we administered an intraperitoneal injection treatment for four weeks. Results indicated that BIPN caused mechanical allodynia, gait abnormalities, and pathological changes in myelin and axons in mice. This study confirms that BTZ upregulates the expression of the activating transcription factor 3 (ATF3), which in turn mediates the increased expression of the copper transporter SLC31A1, causing dysregulation of intracellular copper ion homeostasis and subsequent copper accumulation, and ultimately inducing the development of peripheral neurotoxicity. Elevated intracellular copper concentration exerts a dual effect: it directly promotes the oligomerization of Dihydrolipoamide S-acetyltransferase (DLAT) and concurrently damages the iron-sulfur cluster protein ferredoxin 1 (FDX1), collectively triggering the onset of cuproptosis. Green tea has garnered attention for its rich content of catechins, with (-)-Epigallocatechin Gallate (EGCG) being the most abundant catechin present. This study uncovers the molecular mechanism by which EGCG inhibits BTZ-induced cuproptosis through targeted regulation of copper homeostasis. Analyses demonstrate that EGCG significantly downregulates the expression of the copper transporter SLC31A1, thereby effectively suppressing transmembrane influx of extracellular copper ions. This intervention markedly reduces intracellular copper overload, eliciting a dual regulatory effect: on one hand, the decreased copper concentration directly inhibits the oligomerization of DLAT; on the other hand, it effectively protects the iron-sulfur cluster protein FDX1 from damage. This study aims to systematically elucidate the molecular mechanisms underlying BIPN and to evaluate the therapeutic potential of EGCG in alleviating BIPN, offering a novel therapeutic strategy for the prevention and treatment of BIPN.}, }
@article {pmid42074864, year = {2026}, author = {Cywka, KB and Skarzynski, PH and Czaplicka, EA and Skarzynski, H}, title = {Outcomes of Bonebridge Implantation in 10 Patients with Rare Genetic Syndromes and Difficult Anatomy.}, journal = {Journal of clinical medicine}, volume = {15}, number = {8}, pages = {}, doi = {10.3390/jcm15083064}, pmid = {42074864}, issn = {2077-0383}, abstract = {Background: Congenital hearing loss occurs in about 2 of every 1000 newborns, of which half probably have a genetic origin. In syndromic patients, hearing impairment often results from craniofacial malformations affecting the outer and middle ear. Anatomical limitations such as microtia or external auditory canal atresia often preclude conventional air-conduction hearing aids, leaving bone-conduction devices as one viable option. However, surgical intervention in such patients is challenging. This study aimed to evaluate the audiological outcomes, safety, and effectiveness of the Bonebridge BCI 602 implant in 10 patients with genetic syndromes. Methods: The case series was made up of 10 patients aged 6-45 years, each diagnosed with a congenital syndrome affecting the external and/or middle ear. All cases involved surgical implantation of the Bonebridge system. Audiological outcomes were evaluated in free-field conditions on the day of sound processor activation and at 3-6 months follow-up via pure-tone and speech audiometry. Results: All surgical procedures were completed without serious adverse events, and the incidence of postoperative complications was low. Audiological outcomes showed clinically significant hearing improvement in all patients following Bonebridge implantation. Post-implantation hearing thresholds ranged from 25 to 40 dB HL, with notable gains in speech perception in both quiet and noisy environments. Conclusions: The Bonebridge implant appears to be a safe and effective option for auditory rehabilitation in patients with hearing loss associated with various genetic syndromes involving craniofacial malformation. However, this complex patient population requires individual assessment, interdisciplinary evaluation, and careful surgical planning.}, }
@article {pmid42076534, year = {2026}, author = {Alzahrani, SI and Alomari, N and Alkilani, S and Alghamdi, L and Melhem, B}, title = {Design and Performance Evaluation of a Low-Cost High-SNR EOG Sensing System for Arabic Locked-In Syndrome Communication.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {8}, pages = {}, doi = {10.3390/s26082425}, pmid = {42076534}, issn = {1424-8220}, mesh = {Humans ; *Electrooculography/methods/instrumentation ; Signal-To-Noise Ratio ; *Locked-In Syndrome/physiopathology/diagnosis ; Adult ; Male ; Eye Movements/physiology ; Female ; Communication Devices for People with Disabilities ; Signal Processing, Computer-Assisted ; Communication ; Equipment Design ; }, abstract = {Locked-in Syndrome (LIS) is a neurological condition in which individuals remain conscious but experience complete paralysis of voluntary muscles, except for eye movements-highlighting the need for reliable assistive communication technologies. This study presents the design and evaluation of an Arabic electrooculogram (EOG)-based communication system with adaptive classification capabilities for LIS applications. A custom-designed EOG acquisition circuit incorporating filtering and amplification stages was implemented and compared with the OpenBCI Cyton board. The system employed a hybrid classification approach combining amplitude, temporal, and statistical features to distinguish between blinks and voluntary vertical eye movements. Testing with ten healthy subjects yielded a mean classification accuracy of 83.96% ± 4.59% and an information transfer rate of 10.43 letters per minute, corresponding to a 30.38% improvement over conventional approaches. The custom-designed circuit achieved a signal-to-noise ratio of 25.21 dB, outperforming the OpenBCI Cyton board by 8% while reducing system cost by 62%. The integration with a Morse code-based interface enabled Arabic letter composition, while the system incorporated auto-completion and text-to-speech functionalities to further enhance communication efficiency. This cost-effective solution addresses a critical gap in assistive technologies for Arabic-speaking individuals with LIS and shows strong potential for enhancing their communication abilities and overall quality of life.}, }
@article {pmid42076632, year = {2026}, author = {Liu, X and Chen, R and Wu, F and Yu, B and Zhou, G and Hu, S and Zhang, H and Wang, P and Xu, B and Zhuang, L}, title = {Advanced Sensing and Delivery Technologies for Nose-to-Brain Administration: From Nanocarriers to Sensor-Integrated Organ-on-Chips.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {8}, pages = {}, doi = {10.3390/s26082523}, pmid = {42076632}, issn = {1424-8220}, mesh = {Humans ; *Administration, Intranasal/methods ; *Drug Delivery Systems/methods ; Blood-Brain Barrier/metabolism ; *Drug Carriers/chemistry ; *Brain/metabolism/drug effects ; *Lab-On-A-Chip Devices ; Animals ; *Biosensing Techniques ; Nanotechnology ; *Nanoparticles/chemistry ; }, abstract = {Central nervous system (CNS) disorders represent a growing healthcare burden, and various drugs are developed for their treatment. However, the blood-brain barrier (BBB) prevents over 98% of therapeutics from reaching brain tissue. Intranasal delivery provides a promising alternative by exploiting olfactory and trigeminal nerve pathways to circumvent the BBB. This review surveys recent advances in nose-to-brain delivery technologies, from carrier design to evaluation methods. Polymeric and lipid-based nanocarriers show enhanced mucosal penetration and prolonged residence time, and microneedle platforms further enable controlled drug release with minimal discomfort. To evaluate these delivery strategies, sensor-integrated organ-on-chip models provide more physiologically relevant testing than static cultures. Although persistent challenges such as rapid mucociliary clearance and formulation stability remain, combining nanotechnology with microfluidic devices and computational modeling shows potential for developing patient-specific therapeutics.}, }
@article {pmid42077356, year = {2026}, author = {Yuan, Z and Shi, Z and Wang, Z}, title = {Brain-computer interfaces: an engineering black-box swindle or a lone advance guided by deep learning.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1783020}, pmid = {42077356}, issn = {1662-4548}, }
@article {pmid42077635, year = {2025}, author = {Herbert, C and Acuna, VR and Kneipp, RRK and Kapfer, NI}, title = {Exploring individual biases in BCI research and users: Does gender matter?.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1695370}, pmid = {42077635}, issn = {1662-5161}, abstract = {OBJECTIVE: Brain-Computer Interface (BCI) is an interdisciplinary research field characterized by rapid technological advances and collaborative efforts to develop user-friendly, adaptive devices that enable healthy and non-responsive users to communicate and interact with their environment through brain signals elicited by specific instructions or tasks. However, research often shows gender bias, especially in scientific disciplines with strong technological, medical, or social foundations. Gender biases have been found among scientists conducting and publishing research. They may also exist among examiners and study participants.<br><br>RESEARCH QUESTION AND METHODS: This study investigates whether gender biases are present in BCI research, particularly in the distribution of women and men across editorial boards and authorship of studies focusing on psychological human factors that influence BCI performance and usability. We systematically analyzed the gender distribution in neuroscientific journals that accept BCI research or have a strong focus on BCI, reviewed their editorial boards, analyzed BCI publications -including those related to psychological human factors-and examined gender biases among study participants. Additionally, we reviewed EEG studies investigating sex- or gender-related differences in EEG signals relevant to BCI research.<br><br>RESULTS: We observed significant differences in the representation of women and men among editorial board members and BCI authors, including first-, co-, and last-authorship. Similarly, there were differences in the gender distribution of participants in BCI studies. Moreover, the literature review suggests potential differences in brain signals between women and men within the studied samples. The impact of these differences on performance in BCIs, such as motor-imagery SMR-BCIs, SSVEP-BCIs, and P300-BCIs, as well as training methods and BCI usability, still needs to be explored.<br><br>CONCLUSION: Our findings emphasize the importance of increasing awareness of gender-, sex-, and user-related factors in BCI research. In line with recent perspectives that highlight the need to address gender biases and individual differences in the language of the user, their motivation or cultural background, future BCI research should focus on systematically examining gender and sex differences. This will help promote gender equality in BCI research and lead to a better understanding of users' needs, preferences, and individual characteristics.}, }
@article {pmid42079191, year = {2026}, author = {Hakim, R and Jaggi, A and Heo, G and Matsumoto, H and Uchida, N and Watabe-Uchida, M and Datta, SR and Musall, S and Sabatini, BL}, title = {Spectral envelopes of facial movements predict intention, cortical representations, and neural prosthetic control.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.09.10.675423}, pmid = {42079191}, issn = {2692-8205}, abstract = {1Animals, including humans, use coordinated facial movements to sample the environment, ingest nutrients, and communicate. Rodents, in particular, produce rhythmic facial movements during spontaneous behavior and cognitive tasks. Measuring these movements precisely and linking them to neural activity remains challenging. We introduce face-rhythm, an unsupervised pipeline that combines markerless point tracking, spectral analysis, and non-negative tensor component analysis to decompose facial video into a small set of interpretable components. Applied to videos of mice during a Pavlovian odor-reward task, a brain-machine interface (BMI) task, and free behavior, face-rhythm recovers human-interpretable behaviors such as whisking, sniffing, licking, and more subtle behaviors. The resulting components are consistent across animals, are sufficient to decode task variables or internal belief states, and explain cortical activity using a low-rank representation. We also find that the activity of neurons in face-associated primary motor cortex (M1) is predicted well by a phase-invariant spectral transformation of facial movements above ~ 0.5 Hz, while slower movements retain a phase-variant representation better predicted by the instantaneous position of the face; individual neurons can show either or both forms of tuning. A systematic comparison against deep-learning point-tracking models, contrastive-learning embeddings, and vision-transformer features places face-rhythm competitively across tasks while also achieving the goal of producing a low-dimensional, interpretable description of rodent facial behavior that is closely linked to cortical activity.}, }
@article {pmid42060724, year = {2026}, author = {Wang, Y and Cheng, L and Li, D and Lu, Y and Hopkins, WD and Sherwood, CC and Xu, T and Liu, C and Paxinos, G and Jiang, T and Chu, C and Fan, L}, title = {Homologous specialization of arcuate fasciculus ventrolateral frontal connectivity in marmosets and humans.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {123}, number = {18}, pages = {e2600429123}, doi = {10.1073/pnas.2600429123}, pmid = {42060724}, issn = {1091-6490}, support = {2021ZD0200203//Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project/ ; 82472061//Natural Science Foundation of China/ ; 82202253//Natural Science Foundation of China/ ; 62250058//Natural Science Foundation of China/ ; 2022M722915//China Postdoctoral Science Foundation ()/ ; 2024M761725//China Postdoctoral Science Foundation ()/ ; AD22035125//Guangxi Science and Technology Base and Talent Special Project/ ; }, mesh = {Animals ; Humans ; *Callithrix/physiology/anatomy &amp; histology ; *Frontal Lobe/physiology/anatomy &amp; histology ; Male ; Neural Pathways/physiology ; Female ; Language ; Pan troglodytes ; Brain Mapping ; Speech/physiology ; Macaca ; Nerve Net/physiology ; }, abstract = {The arcuate fasciculus (af) is a crucial dorsal pathway underpinning human language, yet its weak frontal connectivity in macaques-the standard primate model-creates an evolutionary puzzle. Here, we investigate the common marmoset, a distantly related platyrrhine with high vocal complexity, to test for convergent neural adaptations. By integrating retrograde and anterograde tracing with ultra-high-resolution diffusion MRI, we identified a robust af homolog in marmosets that is anatomically distinct from the superior longitudinal fasciculus. Comparative mapping across marmosets, macaques, chimpanzees, and humans reveals a notable similarity in connectivity patterns: The marmoset af terminates extensively in the ventrolateral frontal cortex, exhibiting a connectivity profile significantly more similar to humans than to that of the phylogenetically closer macaque. Functionally, this pathway targets cortical regions activated during vocal exchanges, partially overlapping with the human speech network. These findings suggest that the frontal connectivity of the dorsal audio-motor pathway is not strictly determined by phylogenetic proximity but represents an evolutionarily labile scaffold that undergoes lineage-specific elaboration under pressure associated with complex vocal communication.}, }
@article {pmid42059314, year = {2026}, author = {Maya, I and Noiret, B and Merlot, B and Denost, Q}, title = {Robotic posterior pelvic exenteration with perineal reconstruction with a fasciocutaneous flap - A video vignette.}, journal = {Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland}, volume = {28}, number = {5}, pages = {e70470}, doi = {10.1111/codi.70470}, pmid = {42059314}, issn = {1463-1318}, }
@article {pmid42060426, year = {2026}, author = {Lou, X and Li, X and Meng, H and Li, Z}, title = {Subject-Independent Deep Learning Framework for Motor Imagery Electroencephalogram Decoding in Neurorehabilitation.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3689121}, pmid = {42060426}, issn = {2168-2208}, abstract = {Motor imagery (MI) has emerged as a pivotal paradigm in non-invasive brain-computer interfaces (BCIs) for neurorehabilitation, enabling motor function restoration through mental rehearsal of movements. However, traditional MI electroencephalogram (EEG) classification models face significant challenges due to high inter-subject variability and the expensive requirement of annotated EEG data for each new subject. To tackle these limitations, we introduce a deep learning framework, the Dual-branch Subject-aligned Generalization Network (DSGNet). DSGNet simultaneously extracts temporal and spectral EEG features through dual complementary convolutional branches and incorporates a novel class alignment loss to enforce domain-invariant representation across subjects, enabling generalization to unseen individuals without requiring subject-specific labeled data. We evaluate DSGNet on four public MI-EEG datasets-OpenBMI, BCI Competition IV 2a, SHU Version 5, and BCI Competition IV 2b-under a rigorous leave-one-subject-out cross-validation protocol. Experimental results show that DSGNet achieves the highest accuracy on the three-class and four-class datasets, with improvements of 0.22% and 2.15% over the strongest baselines, respectively, while maintaining comparable performance on the binary-class dataset. These findings highlight the effectiveness of class-structure alignment in developing reliable subject-independent BCI systems for neurorehabilitation.}, }
@article {pmid42051419, year = {2026}, author = {Sun, M and Zhang, H and He, X and Wei, X and Cui, B and Huang, H and Li, H and Lin, Y and Zhang, S and Li, ZA and Shi, P and Xu, L}, title = {Ultrathin and ultrastrong hydrogel bioelectronic membranes.}, journal = {National science review}, volume = {13}, number = {8}, pages = {nwag105}, pmid = {42051419}, issn = {2053-714X}, abstract = {Hydrogels are promising materials for constructing next-generation bioelectronics because of their excellent biocompatibility and mechanical compliance. Yet, creating robust and multifunctional hydrogel devices that conform to the surface of 3D organs remains challenging. Here, we report a biomimetic strategy for engineering ultrathin and ultrastrong hydrogel membranes as an advanced platform for organ-conformal bioelectronics. In these hydrogels, self-organized nanofiber networks confer strain-stiffening characteristics with a phenomenal combination of high mechanical strength (∼13.65 MPa), fracture toughness (∼21 573 J/m[2]), and low initial stiffness (∼600 kPa), which accommodates the construction of ultrathin membranes (∼10 μm thickness) reconciling mechanical robustness and 3D conformability. Theoretical simulations reveal unique strengthening mechanisms originating from the topological reconfiguration of fibrillar joints, indicating a widely applicable principle for designing soft composites involving 3D fibrillar networks. We show that various electronic components, including conducting polymers and wafer-fabricated microelectronic sensors, can be integrated on the ultrathin hydrogel membranes, providing means for multimodal physiological sensing and stimulation. These hydrogel membranes open paths to robust, functional and biocompatible interfaces with 3D soft organs and tissues, which are useful for epidermal electronics, implantable brain-machine interfaces, peripheral nerve stimulation, and many other bioelectronic applications.}, }
@article {pmid42052286, year = {2026}, author = {Rossi, A}, title = {Commentary: Editorial: The convergence of AI, LLMs, and industry 4.0: enhancing BCI, HMI, and neuroscience research.}, journal = {Frontiers in computational neuroscience}, volume = {20}, number = {}, pages = {1810869}, pmid = {42052286}, issn = {1662-5188}, }
@article {pmid42052812, year = {2026}, author = {Li, R and Liu, Z and Yan, S and Zhang, L and Zhang, R and Chen, M and Hu, Y and Shi, L}, title = {Recurrent Processing Dynamics in Occluded Object Recognition Revealed by Electroencephalography and Deep Neural Networks.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2650035}, doi = {10.1142/S0129065726500358}, pmid = {42052812}, issn = {1793-6462}, abstract = {The human visual system excels at recognizing occluded objects, yet the temporal dynamics of recurrent processing in this task remain unclear. Using high-temporal-resolution Electroencephalography (EEG), backward masking, and deep neural networks (DNNs), we employed a two-stage paradigm to investigate recurrent processing in occluded object recognition. In Experiment 1, we manipulated occlusion levels and applied multivariate pattern analysis (MVPA) and temporal generalization analysis (TGA) to investigate the neural differences in object recognition across varying degrees of occlusion. In Experiment 2, backward masking was used to dissociate feedforward and recurrent contributions, assessed via representational similarity analysis (RSA). Results revealed a distinct shift in processing mechanisms: While low occlusion primarily relied on a rapid feedforward sweep, higher occlusion necessitated the recruitment of additional processing. Further characterization of this processing based on TGA and RSA under mask conditions revealed a two-stage recurrent process: An early stage (200-300[Formula: see text]ms) associated with low-level features, and a late stage (300-500[Formula: see text]ms) involved mid- and high-level representations, reflecting cross-hierarchical recurrent interactions. The early mask condition disrupted this coordination, highlighting the essential role of recurrent processing. These findings clarify the temporal dynamics of recurrent processing in occluded object recognition and emphasize the critical role of recurrence in achieving robust biological vision.}, }
@article {pmid42054384, year = {2026}, author = {Huang, Y and He, Z and Ding, C}, title = {A GNN-based approach for accurate trade balance forecasting and interpretable analysis.}, journal = {PloS one}, volume = {21}, number = {4}, pages = {e0346324}, pmid = {42054384}, issn = {1932-6203}, mesh = {*Commerce ; *Neural Networks, Computer ; Forecasting/methods ; Machine Learning ; Humans ; }, abstract = {In this study, we developed a machine learning pipeline to predict trade balances across 229 countries, utilizing a Graph Neural Network (GNN), and compared it with several deep learning and regression-based models. The data preprocessing involved handling missing values, normalizing features, and conducting exploratory data analysis to uncover key patterns. Feature selection was performed using a Random Forest Regressor to identify the most influential predictors of trade balances. We then evaluated multiple models, including a complex Deep Neural Network (DNN), Transformer with multi-head attention, Random Forest, and a hybrid ensemble model, using various regression metrics. Among these, the GNN proved to be the most effective model, achieving an MSE of 0.06, RMSE of 0.26, MAE of 0.18, and an R[2] of 0.91. These results demonstrate that GNN outperforms other models in terms of accuracy, robustness, and consistency in predicting trade balances. We compared models across several key evaluation metrics and conducted a detailed comparison of residual plots to assess prediction quality and error distribution. Residual plots and ROC curves were used to validate the reliability and performance of the GNN and other models, ensuring robust and accurate predictions across the board. This study highlights the potential of machine learning techniques to improve trade balance forecasting, providing policymakers and economists with a more adaptable and precise tool for navigating complex global trade dynamics. The findings contribute to more informed economic strategies and enhanced forecasting methodologies.}, }
@article {pmid42054886, year = {2026}, author = {Xu, Z and Zhang, Y and Li, B and Zhou, G and Lu, X and Lukasiewicz, T}, title = {Advancing federated semi-supervised medical image segmentation: A duo of interactive denoising pseudo-labels and convolutional contrastive learning.}, journal = {Medical image analysis}, volume = {112}, number = {}, pages = {104091}, doi = {10.1016/j.media.2026.104091}, pmid = {42054886}, issn = {1361-8423}, abstract = {Many existing studies on federated learning (FL) for segmentation primarily assume that all client data are labeled. However, in reality, due to the high cost of hospital construction and the scarcity of expert annotators, many medical sites can only provide unlabeled data. Therefore, in our work, we focus on a more practical and challenging problem, namely federated semi-supervised segmentation (FSSS), where only a subset of clients possesses labeled data while the remaining clients contribute unlabeled data. To tackle this problem, we propose an effective and generalizable FSSS framework. Specifically, labeled clients are first aggregated to construct a label-based aggregation model, which serves to guide the pseudo-label generation for unlabeled clients. Since the generated initial pseudo-labels often suffer from feature offset, we develop a pixel-level denoising method based on uncertainty feature map estimation, which enhances the quality of pseudo-labels by leveraging local data. Second, we design a model-convolutional contrastive learning to endow unlabeled clients with enhanced feature discrimination capabilities, thereby correcting their inaccurate representations. Finally, an effective dynamic model aggregation method is devised to adjust the aggregation weight of each client by considering the contribution quantified via a one-hot scheme. We comprehensively evaluate our method from multiple perspectives on three non-independent and identically distributed (Non-IID) segmentation tasks, and the experimental results confirm the effectiveness of our method. The codes of this work has been released at the following link: https://github.com/ZhenghuaXu/FedDPCon.}, }
@article {pmid42055147, year = {2026}, author = {Nishi, A and Yanagisawa, T and Fukuma, R and Yamamoto, S and Tani, N and Kishima, H}, title = {Decreased gamma band power and increased betagamma phaseamplitude coupling are characteristic of brain activity in patients with chronic spinal cord injury.}, journal = {Brain research bulletin}, volume = {}, number = {}, pages = {111899}, doi = {10.1016/j.brainresbull.2026.111899}, pmid = {42055147}, issn = {1873-2747}, abstract = {Neurophysiological biomarkers are needed to characterize the condition of patients with spinal cord injury (SCI), for which effective symptomatic biomarkers are lacking. We recorded the resting-state magnetoencephalography data of 22 patients with SCI and 29 healthy controls. Power spectral density and phase-amplitude coupling (PAC) were assessed for six frequency bands using source-reconstructed cortical currents. Compared with controls, SCI patients exhibited significantly reduced gamma band power and increased beta-gamma PAC in the frontal cortex, including the primary motor area (q < 0.05, FDR corrected). No significant differences were observed in alpha or beta power. These results suggest that decreased gamma power and increased beta-gamma coupling reflect altered cortical dynamics after SCI and may serve as potential neurophysiological signatures for chronic cortical adaptation.}, }
@article {pmid42055968, year = {2026}, author = {Yu, X and He, X and Huang, B and Li, G and Yu, X}, title = {Brain-Controlled Wheeled Mobile Robots: A Shared Control Framework Integrating Event-Triggered Mechanism and Deep Reinforcement Learning.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3689068}, pmid = {42055968}, issn = {1558-0210}, abstract = {This study addresses the problem of quantifying user control authority in brain-computer shared control by integrating Event-Triggered Control (ETC) with Deep Reinforcement Learning (DRL). Firstly, an ETC-based brain-computer shared-control framework is developed for a wheeled mobile robot (WMR). In this framework, the Steady-State Visual Evoked Potential brain-computer interface (SSVEP-BCI) directly controls the WMR during non-triggered intervals, while control is transferred to a model predictive controller (MPC) once an event is triggered. Secondly, to overcome the limited adaptability of the Fixed Threshold (FT) triggering mechanism, a DRL-based adaptive triggering strategy is introduced to replace manually designed threshold rules. A grouped training strategy is further adopted to account for inter-subject differences in SSVEP-BCI decoding reliability during DRL training. Finally, experimental results demonstrate that integrating ETC into the SSVEP-BCI shared-control system improves the path-tracking performance of brain-controlled WMRs while enabling explicit quantification of user control authority. Specifically, compared with the FT-based strategy, the proposed DRL-based method achieves comparable lateral tracking performance, reduces heading error by 32.34%, and lowers intrusion rate by 57.85%. In addition, compared with the Time-Triggered Shared Control baseline, the cumulative execution time is reduced by 82.38%. These results indicate that the proposed framework achieves a favorable trade-off among tracking performance, computational cost, and preservation of user control authority.}, }
@article {pmid42058018, year = {2026}, author = {Kokorina, A and Syrov, N and Yakovlev, L and Lebedev, M}, title = {Case Report: post-stroke rehabilitation with a visuomotor transformation-based brain-computer interface.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1774409}, pmid = {42058018}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) are increasingly explored as tools for post-stroke neurorehabilitation. Motor imagery (MI)-based paradigms are widely used but may be difficult for some patients to perform reliably, motivating the exploration of alternative control strategies. This study presents a retrospective exploratory case series (n = 5) evaluating the feasibility and safety of a P300-based BCI paradigm designed to engage visuomotor transformation processes during upper limb rehabilitation. Two patients underwent rehabilitation using the P300-based paradigm, while three patients used an MI-based BCI within the same rehabilitation framework. In both conditions, BCI control was integrated with a robotic orthosis and an immersive virtual reality (VR) environment. BCI performance, neurophysiological responses (event-related potentials and event-related desynchronization), and clinical measures (Fugl-Meyer Assessment of the Upper Extremity, NIHSS) were assessed before and after a 10-session rehabilitation course. All participants were able to achieve BCI control above chance level. Across cases, changes in clinical scores and consistent neurophysiological patterns associated with task engagement were observed. No adverse events or clinically significant safety concerns were identified. These findings suggest that a P300-based BCI paradigm incorporating visuomotor transformation can be feasibly implemented within a VR-assisted robotic rehabilitation framework. Given the exploratory design, small sample size, and heterogeneity of the cohort, the results should be interpreted as hypothesis-generating. Further controlled studies are required to determine the clinical relevance and potential applications of this approach.}, }
@article {pmid42058718, year = {2026}, author = {G, A and K, D}, title = {Multi-source domain generalization with few-shot fine-tuning (MSDG-FT) for cross-dataset EEG mental workload classification.}, journal = {MethodsX}, volume = {16}, number = {}, pages = {103913}, pmid = {42058718}, issn = {2215-0161}, abstract = {EEG-based mental workload (MWL) classifiers consistently achieve high within-dataset accuracy but collapse when applied across datasets recorded under different paradigms or hardware. This cross-domain generalisation gap limits real-world deployment of passive brain-computer interfaces. We evaluate transfer strategies across three publicly available EEG-MWL datasets - CogBCI (29 subjects, 3 sessions), Neuro2021 (15 subjects), and STEW - revealing a mean within-domain accuracy of 78.8% versus cross-domain accuracy of only 44.0%, a gap of 34.8 percentage points. We propose Multi-Source Domain Generalisation with Few-Shot Fine-Tuning (MSDG-FT), which reduces this gap to 6.6 percentage points using as few as 50 labelled calibration samples. Cross-session drift on CogBCI is further characterised across all six session-pair directions, showing near-chance baseline accuracy (36.0%) that recovers to 51.6% with minimal calibration.•A 3 × 3 cross-domain transfer matrix quantifies generalisation failure across three heterogeneous EEG-MWL datasets and establishes a reproducible benchmark for future methods.•Multi-source pre-training combined with few-shot target-domain fine-tuning (MSDG-FT) closes the 34.8% transfer gap to 6.6% using only 50 labelled samples from the target domain.•Random calibration (20 samples) matches sophisticated confidence-weighted selection (p = 0.28), demonstrating simple baselines suffice. Cross-session benefits vary by dataset: CogBCI +15.6%, Neuro2021 +3.5%, indicating task-dependent effectiveness.}, }
@article {pmid42045070, year = {2026}, author = {Canfield, RA and Ouchi, T and Fang, H and Macagno, B and Smith, LI and Scholl, LR and Orsborn, AL}, title = {The spatiotemporal structure of neural activity in motor cortex during reaching.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.1965-25.2026}, pmid = {42045070}, issn = {1529-2401}, abstract = {Intracortical brain-computer interfaces (BCI) leverage knowledge about neural representations to translate movement-related neural activity into actions. BCI implants have targeted broad cortical regions known to have relevant motor representations, but emerging technologies allow flexible targeting to specific neural populations. The structure of motor representations in neural populations across frontal motor cortices, which span centimeters, has not been well characterized. We investigate how motor representations and population dynamics (temporal coordination) vary across a large expanse of frontal motor cortices. We used high-density, laminar, microelectrode arrays to record many neurons, sampling neural populations across frontal motor cortex while two male monkeys performed a reaching task. Our experiments allowed us to map neuronal activity across three spatial dimensions and relate them to movement. Target decoding analysis revealed that target direction information (one key aspect of task information) was heterogeneously distributed across the cortical surface and in depth. Similarly, we found that the temporal dynamics of different neural populations was highly variable, but that the amount of task information predicted which neural populations had similar dynamics. The neural populations with the most similar dynamics were composed of neurons with high task information regardless of spatial location. Our results highlight the spatiotemporal complexity of motor representations across frontal motor cortex at the level of neurons and neural populations, where well-learned movements consistently recruit a spatially distributed subset of neurons. Further insights into the spatiotemporal structure of neural activity patterns across frontal motor cortex will be critical to guide future implants for improved BCI performance.Significance Statement Motor brain-computer interfaces (BCI) translate neural activity into movement, but how to target implants within motor cortices to maximize performance remains unclear. We used high-density recordings of neural activity spanning a large cortical area and related them to movement to map the spatial distribution of task information and the evolution of neural population activity over time. Our measurements revealed that neurons with the most task information were highly distributed across cortex yet also evolved coherently in time, suggesting that spatially distributed neurons coordinate to control movements. Our results provide new links between neuron- and population-level maps of motor representations, and highlight the complex spatiotemporal structure of activity that may need to be considered when designing next-generation BCIs.}, }
@article {pmid42045188, year = {2026}, author = {Wang, H and Zhang, Y and Chai, Q and He, Q and Hu, J and Bai, Y and Liu, G and Li, Z and Chai, J and He, X and Zhao, M and Xue, G and Liu, K and Fu, Y and Tang, H and Xu, Y and Yu, B}, title = {Artificial plateau neurons with in-situ spike-malleability for rhythmic quadrupedal locomotion.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-72428-2}, pmid = {42045188}, issn = {2041-1723}, support = {DT23F0401//National Natural Science Foundation of China (National Science Foundation of China)/ ; LDT23F04011F04//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {Whole-body intelligent locomotion systems face persistent challenges of redundant actuation and poor energy efficiency, limiting real-world deployment. Bio-inspired central pattern generators offer a promising framework for rhythmic control, yet hardware implementations struggle to match the efficiency and adaptability of biological systems. Here, we introduce an in-situ spike-malleable artificial plateau neuron integrating a bistable plateau gate with a transient threshold-switch. The neuron generates amplitude-programmable rhythmic spike bursts, achieving energy-efficient, antagonistic activation of extensors and flexors via a scalable circuit comprising two paired units (plateau gate and threshold-switch). The design leverages distributed encoding for coordinated muscle control, operating at ultra-low energy dissipation (141.37 pJ/spike). An expanded four-unit circuit enhances dynamic spike malleability, enabling parallel processing for multi-joint coordination. On a quadruped robot (Unitree Go2), these distributed circuits directly drive joint-level proportional derivative controllers using the Gaussian-filtered rhythmic spikes, enabling energy-efficient trotting without centralized computation. Critically, the system achieves stable on-ground locomotion and demonstrates adaptive gait transitions in real-world environments. Our approach merges ultra-compact hardware with bio-inspired architecture, advancing neuromorphic systems for energy-efficient autonomous robotics.}, }
@article {pmid42046874, year = {2026}, author = {Kong, L and Yang, Y and Zhou, W and Hu, S}, title = {Sporadic Alzheimer's disease with bipolar-like features: a case report and a brief review of the current research status.}, journal = {Journal of Zhejiang University. Science. B}, volume = {27}, number = {4}, pages = {416-425}, pmid = {42046874}, issn = {1862-1783}, mesh = {*Alzheimer Disease/pathology/diagnosis ; Humans ; Amyloid beta-Peptides/metabolism ; Female ; tau Proteins/metabolism ; Aged ; Male ; Oxidative Stress ; Brain/pathology ; }, abstract = {Alzheimer's disease (AD) is among the main causes of cognitive impairment, memory loss, and dementia, particularly in old adults. It has been listed as one of the most expensive, lethal, and burdening diseases of the 21st century and develops with the process of aging worldwide (Scheltens et al., 2021). Currently, it is widely acknowledged that the typical pathogenesis of AD involves the deposition of amyloid-β (Aβ) and Tau proteins in the cerebral parenchyma and vasculature, intraneuronal neurofibrillary tangles, and the gradual degeneration of synapses (Scheltens et al., 2016; Rostagno, 2022). According to several hypotheses, abnormalities and dysfunctions in vascular structure, mitochondrial metabolism, oxidative stress, glucose utilization, and neuroinflammation are considered fundamental for AD pathology (Scheltens et al., 2016).}, }
@article {pmid42048458, year = {2026}, author = {Zhu, R and Hu, Z and Lou, Z and Xie, F and Zhao, S and Jiao, X and Wang, J and Fukuda, K and Chen, X and Hu, W and Cheng, HM and Li, X and Someya, T and Xu, X}, title = {An exceptionally conductive hydrogel for all-organic, ultraflexible, and chronic neural interfaces.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {123}, number = {18}, pages = {e2532840123}, doi = {10.1073/pnas.2532840123}, pmid = {42048458}, issn = {1091-6490}, support = {2023YFE0101400//Ministry of Science and Technology of the People's Republic of China (MOST)/ ; JPMJCR21P2//JST, CREST/ ; 52273249//National Natural Science Foundation of China/ ; 2021ZT09L197//GDSTC | Guangdong Provincial Introduction of Innovative Research and Development Team (Guangdong Innovative Research Team Program)/ ; KQTD20210811090112002//| Natural Science Foundation of Shenzhen Municipality (Shenzhen Natural Science Foundation)/ ; 2308085MA19//Anhui Provincial Natural Science Foundation/ ; }, mesh = {*Hydrogels/chemistry ; Electric Conductivity ; *Brain-Computer Interfaces ; Animals ; *Electrocorticography/methods/instrumentation ; Electrodes, Implanted ; }, abstract = {Chronic neural interfaces are essential for advancing brain-computer interfaces, neuroprosthetics, and neuromodulation technologies. However, a long-standing trade-off between performance and longevity persists due to the scarcity of materials that simultaneously achieve superior electrical performance, mechanical compliance, and biocompatibility. Here, we overcome this limitation with an all-organic, ultraflexible electrocorticography (ECoG) design that features a thickness of only 9 µm, achieving low electrode-tissue impedance and durability in vivo. Central to this design is a conductive hydrogel featuring an interfacial percolation (CHIP) microstructure, with tunable hydration levels and softness, achieving a highest in-plane electrical conductivity of 2,512 S cm[-1]. We further developed an in-plane swelling control with a dry, soft-protective etching strategy that preserves the structural integrity during hydrogel processing. The resulting all-organic ECoG array conforms to the cortical surface, minimizing foreign body response and providing exceptional signal quality, with the longest record up to 550 d.}, }
@article {pmid42049850, year = {2026}, author = {Xu, G and Yu, C and Shao, G and Pan, G and Wang, Y and Hao, Y}, title = {Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces.}, journal = {Communications biology}, volume = {}, number = {}, pages = {}, doi = {10.1038/s42003-026-10144-9}, pmid = {42049850}, issn = {2399-3642}, abstract = {Intracortical brain-computer interfaces (iBCIs) demand computationally efficient feature extraction methods to process high-bandwidth neural signals in resource-constrained implantable systems. We present the mean absolute of n-th difference (MAND), a feature extraction technique that utilizes optimized differencing operations to isolate spiking-band activity with minimal computational overhead. Through theoretical and empirical validation across multiple datasets encompassing human handwriting, primate reaching/grasping, and rodent cognitive tasks, MAND demonstrated superior performance compared to state-of-the-art features, significantly reducing velocity reconstruction error and improving classification accuracy. An extended MAND variant, incorporating a weighted sum of dual-differencing, achieved additional performance gains through enhanced spectral alignment with neural spiking activity. Hardware implementation on FPGA/MCU platforms confirmed MAND's exceptional efficiency - processing 10-second neural recordings in just 6 ms while consuming only 3 mW of power - representing orders-of-magnitude improvements in both speed and energy efficiency compared to conventional methods. These results establish MAND as a breakthrough solution that enables superior decoding performance with exceptional computational efficiency, paving the way for next-generation, fully implantable iBCI systems.}, }
@article {pmid42049861, year = {2026}, author = {Tao, X and Pu, Y and Kong, XZ}, title = {Rebalancing psychology in China.}, journal = {Communications psychology}, volume = {4}, number = {1}, pages = {}, pmid = {42049861}, issn = {2731-9121}, }
@article {pmid42050355, year = {2026}, author = {Zhao, X and Lin, Z and Zhang, H and Zhang, W and Liu, Z and Chen, C and Ji, H and Hu, S and Xu, X}, title = {Longitudinal associations of cardiovascular-kidney-metabolic syndrome with midlife or late-life mental disorders and dementia, and the mediating role of metabolomic signature.}, journal = {Communications medicine}, volume = {}, number = {}, pages = {}, doi = {10.1038/s43856-026-01608-4}, pmid = {42050355}, issn = {2730-664X}, abstract = {BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome assesses the interconnections among metabolic, kidney, and cardiovascular diseases, rendering significant prognostic value for age-related chronic diseases and mortality. We aimed to investigate the effects of CKM syndrome on transitions between healthy status, mental disorders, and dementia and evaluate the potential mediating role of a CKM-related metabolomic signature in these associations.<br><br>METHODS: This prospective longitudinal study used UK Biobank data from 375,203 midlife and older adults at baseline and 188,018 with metabolomic information. CKM was staged from 0 to 4. Mental disorders and dementia were identified via ICD-10. Multi-state models analyzed the impact of CKM on transitions from healthy status to mental disorders and dementia. Competing risk (death) models assessed the associations of CKM with specific mental disorders and dementia. Mediation role of CKM-related metabolomic signature was evaluated.<br><br>RESULTS: We show that per-stage CKM increase elevates hazards of transitioning from healthy to mental disorders (HR&#x2009;=&#x2009;1.24[1.22-1.26]) and subsequently to dementia (HR&#x2009;=&#x2009;1.38[1.21-1.58]), or directly to dementia (HR&#x2009;=&#x2009;1.27[1.21-1.33]). Worsening CKM stages are associated with bipolar, depressive, and anxiety disorders; whilst only advanced stages (3/4) associated with all dementia types. The CKM metabolomic signature mediates 34.9% and 8.1% of associations of CKM with pre-dementia mental disorders and dementia, respectively.<br><br>CONCLUSIONS: CKM syndrome is associated with pre-dementia mental disorders and dementia, emphasizing the need for regular monitoring and early intervention to manage CKM progression and reduce geriatric neuropsychiatric disturbances.}, }
@article {pmid41996427, year = {2026}, author = {Campbell, E and Eddy, E and Scheme, E}, title = {Transformer-Based Context-Informed Incremental Learning With sDTW Alignment Unlocks Fast and Precise Regression-Based Myoelectric Control.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {34}, number = {}, pages = {2118-2129}, doi = {10.1109/TNSRE.2026.3685074}, pmid = {41996427}, issn = {1558-0210}, mesh = {Humans ; *Electromyography/methods ; Male ; Adult ; Female ; Algorithms ; Young Adult ; Neural Networks, Computer ; Regression Analysis ; User-Computer Interface ; *Machine Learning ; Calibration ; Brain-Computer Interfaces ; }, abstract = {Regression-based myoelectric interfaces hold the promise of enabling intuitive proportional and simultaneous control but remain limited by calibration sensitivity, unpredictable dynamics, and inconsistent user behaviours. Temporal neural architectures have the potential to substantially improve these controllers by capturing the temporal structure of user behaviours, provided they are trained using dynamics that are sufficiently representative of closed-loop use. Context-informed incremental learning (CIIL) offers a mechanism for acquiring such data online; however, its reliance on environment-derived pseudo-labels makes it vulnerable to temporal deviations between assumed and true user intent. This study introduces T-sDTW-CIIL, a transformer-based incremental learning framework that integrates temporal modelling, closed-loop learning, and soft dynamic time warping (sDTW) to enable tolerant label alignment. Twelve participants completed an adaptive regression-based cursor-control task using four pipelines: static and CIIL variants of both MLP and transformer models. T-sDTW-CIIL achieved significantly higher success rates, throughputs, efficiencies, and simultaneity gains when evaluated in a high precision ISO-Fitts' environment. T-sDTW-CIIL achieved throughputs of $2.0\times $ , $2.4\times $ , and $3.7\times $ those of an MLP trained using conventional screen-guided training when acquiring large, medium, and small targets, respectively. Perhaps more importantly, it maintained success rates of 98.4% for small targets, whereas the static MLP degraded to only 23.4% success. T-sDTW-CIIL-based adaptation also reduced overall contraction intensities by ~10%. These results demonstrate the powerful combination of temporal learning with context-informed co-adaptation. T-sDTW-CIIL overcomes key limitations of existing regression-based myoelectric controllers, enabling robust, low-intensity human-computer interaction.}, }
@article {pmid42041797, year = {2026}, author = {Zhang, H and Tao, C}, title = {Deep Learning Decoding of Steady-State Visual Evoked Potential (SSVEP) for Real-Time Mobile Brain-Computer Interfaces: A Narrative Review from Laboratory Settings to Lightweight Engineering Applications.}, journal = {Brain sciences}, volume = {16}, number = {4}, pages = {}, pmid = {42041797}, issn = {2076-3425}, support = {QY25232//National Natural Science Foundation of China/ ; }, abstract = {Background/Objectives: SSVEP-BCI has broad application potential in mobile human-computer interaction due to its high information transfer rate and stable signal characteristics. The introduction of deep learning technology has significantly advanced SSVEP decoding performance, offering novel approaches for processing short-duration signals and tackling complex classification tasks. The establishment of the Tsinghua Benchmark dataset provides a standardized benchmark for evaluating algorithm performance, accelerating the development of deep learning-based SSVEP decoding. However, a summary of SSVEP deep learning decoding technologies for real-time mobile applications is lacking. Methods: We conducted a comprehensive literature review of SSVEP deep learning decoding studies published since 2023, using the Tsinghua Benchmark dataset. This review focuses on technical developments targeting real-time performance, low computational complexity, and high robustness. Results: We summarize the key technologies developed for real-time mobile SSVEP decoding. Our analysis thoroughly examines how these techniques address core challenges in the engineering implementation of mobile brain-computer interfaces, including real-time processing requirements, resource constraints, and environmental robustness. Conclusions: This review provides a comprehensive overview of SSVEP deep learning decoding technologies for mobile applications, establishing a technical foundation to advance mobile brain-computer interfaces from laboratory settings to practical deployment.}, }
@article {pmid42041805, year = {2026}, author = {Christodoulides, P and Peschos, D and Zakopoulou, V}, title = {The Use of EEG in the Study of Emotional States and Visual Word Recognition with or Without Musical Stimulus in University Students with Dyslexia.}, journal = {Brain sciences}, volume = {16}, number = {4}, pages = {}, pmid = {42041805}, issn = {2076-3425}, abstract = {This study investigated neural oscillatory dynamics underlying visual word recognition in university students with dyslexia using a portable brain-computer interface (BCI) EEG system. The sample included university students with dyslexia (N = 12) and matched controls (N = 14) who completed auditory discrimination and visual word recognition tasks, with and without musical accompaniment. Through these experimental conditions, the researchers assessed (a) the cortical activation across frequency bands, (b) the modulatory effect of background music, and (c) the relationship between emotional states and brain activity. Results revealed significant group differences in oscillatory patterns, with reduced β- and γ-band activity in the left occipito-temporal cortex among participants with dyslexia, confirming disrupted temporal coordination in posterior reading networks. Compensatory right-hemisphere activation was observed, particularly under musical conditions, accompanied by increased α-band power and reduced δ activity, indicating enhanced attentional engagement and reduced cognitive fatigue. Emotional assessment using the DASS-21 revealed higher stress and anxiety scores in the dyslexic group, suggesting that affective factors may modulate oscillatory dynamics. The presence of background music appeared to attenuate these effects, supporting improved emotional regulation and cognitive focus. These findings demonstrate that dyslexia reflects a distributed disruption in neural synchrony and cross-frequency coupling, influenced by both cognitive and affective mechanisms. The integration of portable EEG technology with rhythmic auditory stimulation offers new insights into the neurophysiological and emotional aspects of dyslexia, highlighting the potential of rhythm- and music-based approaches for both diagnostic and therapeutic applications.}, }
@article {pmid42041832, year = {2026}, author = {Padilla, GL and Farfán, FD}, title = {Stimulus Size Modulates Periodic and Aperiodic EEG Components in SSVEP-Based BCIs.}, journal = {Brain sciences}, volume = {16}, number = {4}, pages = {}, pmid = {42041832}, issn = {2076-3425}, abstract = {Background/Objectives: Steady-State Visual Evoked Potential-based Brain-Computer Interfaces face a critical trade-off between system accuracy and user visual fatigue. To address this challenge, the objective of this study was to determine how the spatial manipulation of stimulus size modulates the full spectral dynamics of the Electroencephalogram, encompassing both the periodic oscillatory response and the aperiodic (1/f) background noise. Methods: Twenty-two healthy subjects completed a sustained visual attention task using a competitive stimulus paradigm (20 Hz and 30 Hz) presented in three spatial dimensions (Small, Medium, and Big). Parieto-occipital brain signals were decomposed using the spectral parameterization algorithm (SpecParam) to extract frequency-specific visually evoked response power and the aperiodic slope, while visual fixation was continuously monitored via eyetracking. Results: Increasing stimulus size induced a statistically significant gain in the power of the attended signal (Target) without increasing the response of the peripheral distractor. Simultaneously, larger stimuli produced a significant increase in the aperiodic slope during 20 Hz attention and visual rest, suggesting increased cortical inhibition and a reduction in broadband neural activity. This aperiodic modulation was not observed at 30 Hz. Conclusions: The improvement in Signal-to-Noise Ratio with increasing stimulus size arises from a dual neurophysiological mechanism: enhancement of the periodic evoked response together with a reduction in background neural noise.}, }
@article {pmid42042585, year = {2026}, author = {Urfy, M and Mir, MT}, title = {A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.}, journal = {Journal of personalized medicine}, volume = {16}, number = {4}, pages = {}, pmid = {42042585}, issn = {2075-4426}, abstract = {Background/Objectives: Stroke generates 157 million disability-adjusted life-years (DALYs) annually, making it the leading neurological cause of global disease burden. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies across the stroke care continuum. This narrative review maps the trajectory of AI in stroke medicine over the decade from 2015 to 2025. Methods: We conducted a narrative review with a structured, pre-specified search strategy across eight pre-specified thematic clusters using PubMed/MEDLINE (January 2015-December 2025), identifying 8549 records and including 1335 studies after screening. Inclusion criteria encompassed primary research articles, systematic reviews, meta-analyses, and RCTs reporting quantitative performance metrics or clinical outcome data for AI/ML in stroke. Results: Stroke imaging AI is the most commercially mature domain, with over 30 FDA-cleared tools. Automated ASPECTS scoring reduced radiologist reading time by 74.8% (AUC 84.97%; 95% CI: 83.1-86.8%). The only triage AI RCT demonstrated an 11.2 min reduction in door-to-groin time without significant improvement in 90-day functional independence (OR 1.3, 95% CI 0.42-4.0). Brain-computer interface rehabilitation showed significant upper limb recovery in a 17-center RCT (FMA-UE mean difference +3.35 points, 95% CI 1.05-5.65; p = 0.0045). AF detection AI is FDA-cleared and RCT-validated. LLMs and federated learning are pre-regulatory but growing exponentially. Conclusions: AI in stroke has achieved diagnostic maturity but therapeutic immaturity. Bridging algorithmic performance to patient outcomes, addressing equity gaps, and building the economic evidence base for scalable deployment are the defining challenges of the next decade.}, }
@article {pmid42044749, year = {2026}, author = {Pan, X and Zhang, R and Xia, X and Cui, H and Liu, L and Chen, X}, title = {Feasibility of a Hybrid SSVEP-Motor Imagery BCI with Robotic Feedback for Upper Limb Motor Rehabilitation in Stroke Patients.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110780}, doi = {10.1016/j.jneumeth.2026.110780}, pmid = {42044749}, issn = {1872-678X}, abstract = {BACKGROUND: Stroke remains a leading cause of long-term disability, necessitating innovative neurorehabilitation strategies to address persistent motor deficits. Traditional therapies often exhibit limited efficacy due to therapeutic plateau, highlighting the critical need for alternative rehabilitation paradigms.<br><br>NEW METHOD: This study assesses the feasibility of a novel hybrid brain-computer interface (BCI) that integrates motor imagery (MI) and steady-state visual evoked potentials (SSVEP), with robotic glove-assisted feedback used to optimize overall system performance. Thirty-two stroke patients were divided into a control group (conventional therapy) and an experimental group (conventional therapy plus BCI intervention with 10- or 20-day cycles).<br><br>RESULTS: Outcomes assessed via Fugl-Meyer Assessment (FMA) scores, electroencephalography (EEG) classification accuracy, laterality coefficients (LC), and weighted brain connectivity analysis indicated promising trends. The experimental group showed considerable improvements in FMA scores compared with the control group. The proposed BCI system successfully achieved satisfactory EEG classification accuracy (maximum value of 98.08%) and robust system operation. Furthermore, increases in EEG accuracy, normalization of laterality coefficients (LC), and reinforcement of task-specific weighted brain connectivity were observed, particularly after prolonged training.<br><br>The proposed hybrid BCI system demonstrates a potential to overcome the limitations of conventional therapies and traditional single-modality BCIs, offering a more engaging and adaptive rehabilitation approach.<br><br>CONCLUSIONS: These findings demonstrate the feasibility of the proposed hybrid BCI system and the observed improvements in motor function, neurophysiological markers, and brain connectivity underscore its promise as a novel paradigm to enhance neuroplasticity and recovery outcomes.}, }
@article {pmid42032610, year = {2026}, author = {Bourgeois, A and Maiani, M and Minhas, A and Shaw, P and Nikitovic, D and Irvine, B and Robu, I and Brand, N and Jadavji, Z and Wilding, G and Ambrogiano, M and Schrag, E and Hilderley, A and Márquez, DC and Carlson, HL and Romanow, N and Kirton, A and Kinney-Lang, E}, title = {Creating an engaging brain computer interface, electrical stimulation therapy for children with hemiparesis: a pilot study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01990-z}, pmid = {42032610}, issn = {1743-0003}, }
@article {pmid42034279, year = {2026}, author = {Long, Y and Yao, P and Zou, G and Guo, Y and Shao, Y and Li, Y and Liu, J and Zhou, S and Xu, J and Sun, H and Zou, Q and Gao, JH}, title = {EEG microstates during wake and NREM sleep in insomnia disorder.}, journal = {Progress in neuro-psychopharmacology &amp; biological psychiatry}, volume = {}, number = {}, pages = {111718}, doi = {10.1016/j.pnpbp.2026.111718}, pmid = {42034279}, issn = {1878-4216}, abstract = {Insomnia disorder (ID) is prevalent and debilitating, yet its neurophysiological basis remains unclear. Abnormalities in temporal parameters of electroencephalography (EEG) microstates have been linked to diverse neuropsychiatric disorders, but their dynamics across wakefulness and non-rapid eye movement (NREM) sleep in chronic insomnia remain unexplored. In this study, EEG microstate dynamics across wakefulness and NREM sleep were examined in adults with ID (n = 33) and healthy controls (HC; n = 29). Simultaneous EEG and functional magnetic resonance imaging (fMRI) were acquired during nocturnal sleep. Microstate parameters were tested for group differences, diagnostic classification, and associations with insomnia symptom course. Six stable microstates across stages were identified in ID. Linear mixed-effects models revealed significant interactions for Group × Map and main effects of Group on duration and occurrence of the microstates. Compared to HC, ID exhibited significantly shorter mean duration of microstates 1-3, and higher overall occurrence rates of microstates 4-6. The best-performing classifier achieved 90.0% accuracy in distinguishing ID from HC. The most influential predictor was the mean duration of Microstate 2, which was negatively associated with insomnia course. Together, these findings suggest stage- and map-dependent alterations in sleep microstate dynamics in ID, especially during N2 sleep.}, }
@article {pmid42034616, year = {2026}, author = {Zhai, X and Guo, J and Shen, Q and Chen, LN and Wang, G and Shen, DD and Zhang, C and Xu, X and Mao, C and Zhang, Y and Liu, Z}, title = {Core conformation of arrestin coupling to parathyroid hormone type 1 receptor.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-72448-y}, pmid = {42034616}, issn = {2041-1723}, abstract = {The recruitment of β-arrestin (βarr) by G-protein-coupled receptor (GPCR) holds imperative importance in physiological processes, while the mechanisms underlying arrestin engagement with receptors remain obscure. The parathyroid hormone type 1 receptor (PTH1R), as a prototypical class B1 receptor, incorporates arrestin for signaling and regulates G-protein signaling by distinct mechanisms. Here, we report three cryo-electron microscopy structures of β-arrestin1 (βarr1) engaged with the activated wild-type and chimeric PTH1R in core conformation, revealing a distinctive binding mode of βarr1 coupling to PTH1R compared to other GPCRs. In addition to the pronounced kinking of transmembrane (TM) 6, βarr1 establishes extensive interactions with the core cavity of PTH1R by promoting the outward movement of TM5 and intracellular loop (ICL) 2, stabilizing the core conformation of the complex. Further, our work shows that the core coupling mode of βarr with PTH1R mediates receptor internalization and trafficking. Collectively, our work offers a paradigm for the arrestin coupling to class B1 GPCR and regulating the signaling transduction.}, }
@article {pmid42035423, year = {2026}, author = {Ji, YL and Yin, B and Zhu, G and Wang, X and Zhang, Y and Zhao, Q}, title = {Discontinuous 3D Printing of Amorphous Photonic Crystal Hydrogels for Multifunctional Applications.}, journal = {Small (Weinheim an der Bergstrasse, Germany)}, volume = {}, number = {}, pages = {e73576}, doi = {10.1002/smll.73576}, pmid = {42035423}, issn = {1613-6829}, support = {62575143//National Natural Science Foundation of China/ ; 62174085//National Natural Science Foundation of China/ ; 62288102//National Natural Science Foundation of China/ ; BK20240034//Natural Science Foundation of Jiangsu Province/ ; }, abstract = {Amorphous photonic crystals (APCs) offer angle-independent structural color for reliable sensing, yet their precise 3D fabrication remains challenging due to the tendency of particles to self-assemble into ordered structures. We develop a discontinuous digital light processing 3D printing strategy combining discrete ink reflow and rapid curing to construct disordered APCs hydrogels. The printed hydrogels integrate single-sized polymer nanospheres and MXene nanosheets to achieve structural color, mechanical robustness, and interactive optical/electrical responsiveness. The structural color responds to moisture-induced swelling yet remains unchanged under mechanical deformation because of strain-accommodating microcracks. These features ensure reliable visual feedback without strain interference. Meanwhile, mechanical deformation modulates the conductive network and thus provides a complementary electrical response. In diabetic wound models, the hydrogel enables precise electrical stimulation and provides visual alerts of micro-swelling to prevent secondary damage from unnoticed volumetric changes. This strategy provides a generalizable pathway for precise intervention and real-time monitoring in wound management.}, }
@article {pmid42035944, year = {2026}, author = {Poslu Karademir, F and Özçelik, SS and Efe, A&#xc7; and Ulaş, MG and Diri, &#x130; and Kaynak, P and Taşkapılı, M}, title = {Outcomes of probing with or without bicanalicular intubation in children aged three years and older: a decade of experience at a tertiary eye hospital.}, journal = {Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus}, volume = {}, number = {}, pages = {104841}, doi = {10.1016/j.jaapos.2026.104841}, pmid = {42035944}, issn = {1528-3933}, abstract = {PURPOSE: To evaluate the clinical efficacy of probing with or without bicanalicular intubation (BCI) for congenital nasolacrimal duct obstruction (CNLDO) in children at least 3 years of age and to identify factors influencing surgical success.<br><br>METHODS: The medical records of children treated between 2014 and 2024 at Health Sciences University Beyo&#x11f;lu Eye Training and Research Hospital were reviewed retrospectively. All patients underwent probing with or without bicanalicular silicone intubation (BCI) using the square knot technique. Surgical success was defined as resolution of symptoms and a normal fluorescein dye disappearance test.<br><br>RESULTS: A total of 95 children (116 eyes) were included. Mean patient age was 4.57 &#xb1; 1.98 years (range, 3-14). Mean follow-up was 15.5 &#xb1; 15.4 months. BCI was performed initially in 102 eyes. Mean tube retention was 66.8 &#xb1; 43.0 days. Overall success was 87%, increasing to 95% after reprobing and BCI in failed cases. Age, sex, obstruction type, canalicular stenosis, Rosenm&#xfc;ller's valve hypertrophy, and inferior turbinate infracture were not significantly associated with success (P > 0.05). Tube retention for 45-90 days was significantly associated with higher success compared with retention <45 days (P = 0.013; OR = 12.75; 95% CI, 1.72-94.48).<br><br>CONCLUSIONS: In our study cohort of children undergoing surgery for CNLDO at 3 years of age and older, probing and BCI achieved high success, especially if the tube was successfully retained for at least 45 days. Reintubation in failed cases can improve outcomes.}, }
@article {pmid42036262, year = {2026}, author = {Wang, H and Liu, S and Bai, L}, title = {A dimmer switch for reward: the vagus sets the gain.}, journal = {Trends in neurosciences}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tins.2026.04.002}, pmid = {42036262}, issn = {1878-108X}, abstract = {The vagus nerve, among its various functions, carries gut nutrient signals to brain reward circuits. In a recent study, Onimus and colleagues have shown it is more than a simple relay: vagal integrity is required for maintaining dopamine release and the circuit structure of the mesolimbic system to mount reward responses. These findings reveal the vagus as a tonic gatekeeper of motivation and reward.}, }
@article {pmid42036584, year = {2026}, author = {Tsai, HJ and Tsai, SJ}, title = {Neurofeedback in Major Depression.}, journal = {Advances in experimental medicine and biology}, volume = {1502}, number = {}, pages = {461-475}, pmid = {42036584}, issn = {0065-2598}, mesh = {Humans ; *Neurofeedback/methods ; *Major Depressive Disorder/therapy/physiopathology/psychology ; *Brain/physiopathology ; Electroencephalography ; Brain-Computer Interfaces ; Magnetic Resonance Imaging ; Neuronal Plasticity/physiology ; Emotions/physiology ; }, abstract = {Emerging evidence highlights the significant interplay between mental health and brain health, underscoring the potential of non-pharmacological interventions for major depressive disorder. Brain-computer interfaces offer a promising avenue in psychiatry, advancing self-regulation techniques to elucidate relationships among human behavior, emotional processes, and brain functionality. By visualizing brain function and enabling active modulation of cortical activity through real-time feedback, neurofeedback utilizes signals derived from electroencephalography and/or real-time functional magnetic resonance imaging, providing patients with interactive indicators for self-brain training. This closed-loop system targets specific brain activity or regions known to be associated with depression for upregulation or downregulation, thereby enhancing emotion regulation and executive functioning via mechanisms underlying neuroplasticity. Clinical evidence demonstrates promising outcomes, including strengthened neural connectivity, symptom improvement, and increased remission rates in depression. By coupling the advantages of psychotherapy and neuromodulation, neurofeedback aligns with the field's shift toward personalized, technology-driven psychiatry. This chapter also addresses the practical challenges, including protocol standardization, precision targeting, long-term assessment, and scalable delivery, that are essential for translating promising pilot data into routine clinical practice and for empowering patients to actively engage in their brain health toward mental health improvement.}, }
@article {pmid42037083, year = {2026}, author = {Eilts, H and Ivucic, G and Koenen, N and Wright, MN and Schultz, T and Putze, F}, title = {Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates.}, journal = {Human brain mapping}, volume = {47}, number = {6}, pages = {e70528}, doi = {10.1002/hbm.70528}, pmid = {42037083}, issn = {1097-0193}, support = {459360854//Deutsche Forschungsgemeinschaft/ ; 447089431//Deutsche Forschungsgemeinschaft/ ; }, mesh = {Humans ; *Electroencephalography/methods/classification ; *Deep Learning ; *Neural Networks, Computer ; Adult ; *Attention/physiology ; *Imagination/physiology ; *Cerebral Cortex/physiology ; Male ; Young Adult ; Female ; }, abstract = {While deep learning has drastically improved the performance of electroencephalography (EEG) analysis, it remains unclear what these models, such as EEGNet, learn from the data and how their learned features relate to neuroscientific concepts. In this work, we introduce a comprehensive interpretability framework for deep learning models of neural data based on Concept Relevance Propagation (CRP), an extension of layer-wise relevance propagation that enables the analysis of abstract concepts encoded by individual neurons and filters. We apply CRP to individual filters of convolutional neural networks (EEGNet) trained using leave-one-out cross-validation. To identify common classification strategies across models, we guide the selection of representative data for individual filters using relevance maximization, reduce dimensionality via UMAP, and identify clusters of filters encoding similar concepts through density-based clustering. To gain insight into the neural correlates of these tasks, we analyze the learned features across multiple data domains without requiring model retraining. We integrate a virtual inspection layer to project explanations into the frequency domain, enabling the simultaneous analysis of spatial, temporal, and spectral aspects using topographic maps, functional grouping, and independent component analysis (ICA). Using three EEG classification tasks-auditory attention, internal/external attention, and motor imagery-we demonstrate that our approach reveals interpretable, task-relevant neural patterns that generalize across participants. Overall, this framework provides a step toward understanding the models itself and gaining insights into the tasks in terms of neuroscience.}, }
@article {pmid42037346, year = {2026}, author = {Xue, Y and Li, Z and Wang, F and Zhao, L and Li, T and Gong, A and Nan, W and Fu, Y}, title = {[Ethical risks and regulatory considerations in neurofeedback technology].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {43}, number = {2}, pages = {414-420}, doi = {10.7507/1001-5515.202507052}, pmid = {42037346}, issn = {1001-5515}, mesh = {Humans ; *Neurofeedback/ethics ; Informed Consent/ethics ; Brain/physiology ; Personal Autonomy ; }, abstract = {Neurofeedback transforms real-time brain activity features into multimodal feedback to guide self-regulation of brain function, showing potential applications in neuropsychiatric treatment and cognitive enhancement. However, its use entails ethical risks including cognitive autonomy, personal identity integrity, safety and efficacy, privacy protection, and the safeguarding of vulnerable populations, with informed consent challenges being particularly pronounced in implicit neurofeedback. Based on these risks, this paper proposes establishing an ethical evaluation framework for neurofeedback, promoting ethics-embedded design, and strengthening international cooperation and public education, emphasizing responsible innovation to align technological development with ethical safeguards.}, }
@article {pmid42037366, year = {2026}, author = {Perdigão, B and Chang, B and Anand, GAE and Tao, L and Kim, K and Eriksen, AZ and Schönhoff, MA and Ferreira, G and Liu, X and Genelioglu, S and Lyngholm-Kjærby, J and Zahoor, N and Lind, JU and Fanta, ABDS and Hansen, TW and Cai, C and Han, A}, title = {Solid Ethanol as a Renewable, Low-Toxicity, Electron-Beam Direct Write, and Biomedical Material.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e75341}, doi = {10.1002/advs.75341}, pmid = {42037366}, issn = {2198-3844}, support = {R345-2020-1440//Lundbeck Foundation/ ; NNF24OC0092323//Novo Nordisk Foundation/ ; NNF20OC0064289//Novo Nordisk Foundation/ ; NNF24OC0095407//Novo Nordisk Foundation/ ; 1133-00016B//Danish Medical Research Council/ ; }, abstract = {3D ice lithography (3DIL) is an emerging direct-write technique that fabricates intricate 3D structures using frozen precursors. Here, we report the use of ethanol as a renewable and low-toxicity precursor for 3DIL, intended for the first time for the fabrication of intricate porous microstructures for in vitro and in vivo biomedical applications. The first nanoindentation analysis of 3DIL materials reveals mechanical properties (Young's modulus 2-4 GPa) comparable to biocompatible polymers. TEM shows that the material is an amorphous carbon that undergoes controlled graphitization under annealing at very high temperatures (1300°C). Due to its chemical composition, mechanical properties, and stability in water, cross-linked ethanol scaffolds support in vitro endothelial cell adhesion and proliferation with high confluency. Patterned neurostimulation electrodes implanted in mouse brains elicit no significant increase in astrocytic or microglial activation, indicating excellent in vivo biocompatibility. Additionally, we present for the first time the use of optically transparent substrates and the first patterning of neurostimulation electrodes using 3DIL. This study positions 3DIL using ethanol as a versatile, direct-write technique using renewable precursors to produce novel microdevices in biomedical engineering.}, }
@article {pmid42037370, year = {2026}, author = {Xing, Y and Yang, Y and Hong, Z and Tian, C and Chu, H and Prokop, SC and Cai, H and Gu, M and Tchieu, J and Mackie, K and Guo, F}, title = {Organoid Brain-Machine-Interface Devices for Central Nervous System Repair.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e75444}, doi = {10.1002/advs.75444}, pmid = {42037370}, issn = {2198-3844}, support = {U54AG090792//National Institute of Health Awards/ ; R01GM160423//National Institute of Health Awards/ ; U01DA056242//National Institute of Health Awards/ ; EFRI2422149//National Science Foundation Award/ ; }, abstract = {Central nervous system (CNS) repair and regeneration suffer from tremendous clinical challenges due to current limitations in replacing lost neural tissues and restoring long-term neural circuits. Neural organoids, 3D lab-cultured neural tissues derived from stem cells, can recapitulate key cellular, structural, and physiological features of the human CNS, showing promising potential for neural regeneration. Here, we envision organoid brain-machine-interface (Organoid-BMI) devices as a new kind of neuroelectrical interface for CNS repair. The Organoid-BMI devices employ neural organoids and bioelectrodes as biohybrid bidirectional communication pathways to connect the human CNS and the external world. Acting as a biologically compatible intermediate, this approach may facilitate structural incorporation and functional alignment with host neural circuits for addressing persistent challenges of CNS repair including graft-host mismatch and long-term circuit stability. Through implementing adaptive and closed-loop strategies, this approach can modulate interaction and functional communication with the host for promoting CNS circuit remodeling and functional recovery. Together, this innovative technology may open new avenues for personalized regenerative medicine.}, }
@article {pmid42037657, year = {2026}, author = {Aabedi, A and Mashiach, D and Fraix, MP and Agrawal, DK}, title = {Most Effective Interventions for Improving Upper Extremity Function in Patients with Hemiparesis.}, journal = {Cardiology and cardiovascular medicine}, volume = {9}, number = {6}, pages = {504-511}, pmid = {42037657}, issn = {2572-9292}, abstract = {Hemiparesis, commonly resulting from stroke, leads to significant impairments in upper extremity function, limiting daily activities and reducing quality of life. Effective rehabilitation strategies are essential to enhance motor recovery and restore functional independence. This review evaluates the most effective interventions for improving upper extremity function in patients with hemiparesis. A comprehensive literature review was conducted, analyzing systematic reviews, randomized controlled trials, and clinical guidelines. The efficacy of various interventions, including task-specific training, constraint-induced movement therapy (CIMT), neuromuscular electrical stimulation (NMES), mirror therapy, virtual reality, bilateral arm training, pharmacological approaches, and robotic-assisted rehabilitation, was assessed based on their impact on motor function and daily activities. The review highlights the role of neuroplasticity in motor recovery, emphasizing interventions that promote cortical reorganization. Task-specific training, CIMT, and NMES demonstrate strong evidence in enhancing motor function. Emerging technologies, such as brain-computer interfaces and robotics, show promise in optimizing rehabilitation outcomes. Factors influencing recovery, including stroke severity, time since onset, and patient motivation, are discussed. Studies consistently support the effectiveness of CIMT and task-specific training in improving upper extremity function. NMES and mirror therapy are beneficial adjunct therapies, particularly for patients with moderate impairment. Virtual reality and robotics enhance engagement and motor learning, while pharmacological and stem cell therapies are emerging areas with potential but require further research. A multimodal rehabilitation approach combining task-oriented therapies, neuromodulation, and emerging technologies yields the best outcomes for upper extremity recovery in hemiparesis patients. Future research should focus on optimizing individualized treatment plans and integrating novel therapeutic modalities to maximize functional gains.}, }
@article {pmid42039372, year = {2026}, author = {Zhao, P and Liang, T and Jia, H and Dayan, A and Dinarès-Ferran, J and Solé-Casals, J}, title = {MCFANet: a multi-class fusion attention network for motor imagery EEG classification.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1811759}, pmid = {42039372}, issn = {1662-5161}, abstract = {INTRODUCTION: This paper proposes a Multi-Class Fusion Attention Network (MCFANet) that combines the multi-class spatial filtering outputs of FBCSP with the spatiotemporal feature extraction capability of convolutional neural networks for multi-class motor imagery EEG classification. In multi-class motor imagery decoding, traditional spatial filtering methods extract effective discriminative spatial features but decompose the task into independent binary subproblems, and typically retain only energy statistics while discarding temporal dynamics. Deep learning methods can learn spatiotemporal features but must learn spatial patterns from the beginning, making it difficult to fully capture established neurophysiological priors under limited training samples.<br><br>METHODS: MCFANet concatenates the spatial filtering outputs from all classes and sub-bands along the channel dimension to construct a virtual channel representation containing the discriminative responses of all classes. The full time series is preserved and fed into a convolutional module for spatiotemporal feature extraction, and a channel attention module adaptively reweights the feature maps to focus on the most discriminative representations. Four-class classification experiments were conducted on two public datasets.<br><br>RESULTS: On Dataset 2a, MCFANet achieved an accuracy of 67.94% &#xb1;13.70, outperforming FBEEGNet (63.98%) and EEGNet (58.79%). On the High Gamma Dataset, MCFANet achieved 87.10% &#xb1;10.09, improving over FBEEGNet by approximately 2.5 percentage points. Paired t-tests and effect size analysis confirm that the improvements over the main baseline methods are statistically significant.<br><br>DISCUSSION: The results suggest that reorganizing multi-class spatial discriminative responses into a unified representation that preserves temporal dynamics provides an effective path for bridging traditional spatial filtering and deep learning.}, }
@article {pmid42039954, year = {2026}, author = {Yıldırım, Y and Ertaş, E}, title = {Surgical outcomes and the role of probe exit site in nasal endoscopy-guided interventions for congenital nasolacrimal duct obstruction: a cross-sectional study.}, journal = {International journal of ophthalmology}, volume = {19}, number = {5}, pages = {901-908}, pmid = {42039954}, issn = {2222-3959}, abstract = {AIM: To evaluate the clinical presentation, nasal endoscopic findings, and surgical outcomes of probing surgery (PS) or bicanalicular silicone tube intubation (BCI) performed under nasal endoscopic guidance (NEG) in pediatric patients with congenital nasolacrimal duct obstruction (CNLDO), regardless of previous surgical history.<br><br>METHODS: This retrospective cross-sectional study included CNLDO patients with data on demographics, fluorescein dye disappearance test (FDDT) results, dacryoscintigraphy findings, prior surgeries, and outcomes of NEG-PS or NEG-BCI. NEG-BCI using Crawford stents was performed intraoperatively in complex cases. Intraoperative and postoperative complications were recorded. Surgical success was evaluated clinically and with FDDT at postoperative months 1 and 6. Stents were retained for a minimum of 12wk, with follow-up for at least 6mo after removal.<br><br>RESULTS: Of the 67 pediatric patients (67 eyes, mean age 37.4&#xb1;17.5mo), 44 (65.7%) were female. Preoperative FDDT was graded 3+ in 85.1% of cases, and dacryoscintigraphy confirmed obstruction in 92.5%. Nine patients (13.4%) had a history of PS. At 6mo, surgical success was achieved in 96.6% (28/29) of the NEG-PS group and 71.1% (27/38) of the NEG-BCI group (P=0.009). All cases with probe exit through the inferior meatus (IM) were successful, whereas exits through the inferior concha (IC) or submucosal IM (SM) were significantly associated with failure (P<0.001).<br><br>CONCLUSION: NEG allows intraoperative classification of CNLDO and selection of surgical method based on real-time anatomical findings. Probe exit through the IM predicts a high likelihood of success, whereas IC or SM exits are risk factors for failure. Incorporating NEG into routine practice may improve surgical precision and reduce the need for repeated procedures.}, }
@article {pmid42040188, year = {2026}, author = {Wang, N and Chai, X and He, Y and Song, J and Cao, T and He, Q and Zhu, S and Jia, Y and Si, J and Yang, Y and Zhao, J}, title = {Graph-Theoretical Signature from Neural and Vascular Signals Reveals Spinal Cord Stimulation Frequency-Specific Brain Network in Disorders of Consciousness Patients.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {7}, number = {}, pages = {0539}, pmid = {42040188}, issn = {2692-7632}, abstract = {Introdution: Spinal cord stimulation (SCS) has emerged as a promising neuromodulatory intervention for patients with disorders of consciousness (DoC). However, the identification of optimal stimulation frequencies remains a subject of ongoing debate. Although previous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) studies have suggested the therapeutic efficacy of 5- and 70-Hz, respectively, the integrative neurovascular mechanisms and frequency-specific network dynamics underlying these effects remain to be elucidated. Objective and Impact Statement: This study aims to characterize frequency-dependent network reconfiguration in DoC using simultaneous EEG-fNIRS recordings and graph theoretical analysis. By delineating distinct neurophysiological and hemodynamic signatures, our findings establish a mechanistic framework for the optimization of SCS parameters, thereby advancing personalized neuromodulation strategies for the promotion of consciousness recovery. Methods: This prospective trial used simultaneous EEG-fNIRS and graph theory in 16 patients with DoC undergoing multifrequency SCS at 5, 20, 70, and 100 Hz to decode frequency-specific network dynamics. Our integrated EEG-fNIRS analysis revealed 3 principal advances. First, multimodal cortical mapping via a unified anatomical atlas quantified frequency-dependent network reconfiguration, generating graph-theoretical metrics (global and nodal efficiency, characteristic path length, and clustering coefficients) from source-localized EEG (delta-gamma bands) and fNIRS (oxyhemoglobin and deoxygenated) data. Second, we identified frequency-dependent neurophysiological profiles. Results: Five-hertz stimulation produced acute enhancement of theta-band global network efficiency coupled with elevated gamma-band nodal efficiency in the right cingulate motor area, indicating immediate frontolimbic engagement. Conversely, 70-Hz stimulation selectively evoked delayed hemodynamic responses in the visual cortices and increased occipital hemoglobin oxygenation without concomitant EEG alterations, suggesting preferential retinotopic pathway recruitment. Conclusion: Multimodal EEG-fNIRS analysis elucidates frequency-specific SCS mechanisms, where 5-Hz stimulation optimizes local information integration through theta and gamma modulation, while 70-Hz enhances long-range connectivity, exposing frequency-specific neural plasticity mechanisms.}, }
@article {pmid42041774, year = {2026}, author = {Wang, Z and Ma, Y and Du, Y and She, Q}, title = {A Band-Aware Riemannian Network with Domain Adaptation for Motor Imagery EEG Signal Decoding.}, journal = {Brain sciences}, volume = {16}, number = {4}, pages = {}, doi = {10.3390/brainsci16040363}, pmid = {42041774}, issn = {2076-3425}, support = {LZ26F010007//Zhejiang Provincial Natural Science Foundation/ ; 62371172//National Natural Science Foundation of China/ ; 2025ZY01045//Central Government-Guided Local Science and Technology Development Fund/ ; 2025E10015//Zhejiang Provincial Key Laboratory of Brain Computer Collaborative Intelligence Technology and Applications/ ; }, abstract = {BACKGROUND: The decoding of motor imagery electroencephalography (MI-EEG) is constrained by core issues including low signal-to-noise ratio (SNR) and cross-session as well as cross-subject domain shift, which seriously impedes the practical deployment of brain-computer interfaces (BCIs).<br><br>METHODS: To address these challenges, this paper proposes a novel end-to-end MI-EEG decoding method named BARN-DA. Two innovative modules, Band-Aware Channel Attention (BACA) and Multi-Scale Kernel Perception (MSKP), are designed: one enhances discriminative channel features by modeling channel information fused with frequency band feature representation, and the other captures complex data correlations via multi-scale parallel convolutions to improve the discriminability of the network's feature extraction. Subsequently, the features are mapped onto the Riemannian manifold. For the source and target domain features residing on this manifold, a Riemannian Maximum Mean Discrepancy (R-MMD) loss is designed based on the log-Euclidean metric. This approach enables the effective embedding of Symmetric Positive Definite (SPD) matrices into the Reproducing Kernel Hilbert Space (RKHS), thereby reducing cross-domain discrepancies.<br><br>RESULTS: Experimental results on four public datasets demonstrate that the BARN-DA method achieves average cross-session classification accuracies of 84.65% &#xb1; 8.97% (BCIC IV 2a), 89.19% &#xb1; 7.69% (BCIC IV 2b), and 61.76% &#xb1; 12.68% (SHU), as well as average cross-subject classification accuracies of 65.49% &#xb1; 11.64% (BCIC IV 2a), 78.78% &#xb1; 8.44% (BCIC IV 2b), and 78.14% &#xb1; 14.41% (BCIC III 4a). Compared with state-of-the-art methods, BARN-DA obtains higher classification accuracy and stronger cross-session and cross-subject generalization ability.<br><br>CONCLUSIONS: These results confirm that BARN-DA effectively alleviates low SNR and domain shift problems in MI-EEG decoding, providing an efficient technical solution for practical BCI systems.}, }
@article {pmid42028018, year = {2026}, author = {Zhang, J and Zhang, Y and Zhang, X and Xu, B and Zhao, H and Sun, T and Wang, J and Lu, S and Shen, X}, title = {Erratum: A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking.}, journal = {iScience}, volume = {29}, number = {5}, pages = {115705}, doi = {10.1016/j.isci.2026.115705}, pmid = {42028018}, issn = {2589-0042}, abstract = {[This corrects the article DOI: 10.1016/j.isci.2024.110164.].}, }
@article {pmid42028123, year = {2026}, author = {Mediana, E and Hamid, ARAH and Rahman, F and Mochtar, CA and Umbas, R and Abol-Enein, H}, title = {Quality of Life After Radical Cystectomy: Meta-analysis of Neobladder and Ileal Conduit Outcomes Across Multiple Assessment Tools.}, journal = {European urology open science}, volume = {87}, number = {}, pages = {115-124}, pmid = {42028123}, issn = {2666-1683}, abstract = {BACKGROUND AND OBJECTIVE: Radical cystectomy (RC) requires urinary diversion, commonly orthotopic neobladder (ONB) or ileal conduit (IC). While ONB preserves natural voiding, IC is technically simpler. This study aimed to compare long-term (>12 mo) quality of life (QoL) outcomes between ONB and IC to aid preoperative shared decision-making.<br><br>METHODS: Following PRISMA guidelines, we searched PubMed, Cochrane Library, and Google Scholar up to September 15, 2025. We included studies comparing ONB and IC in adults with follow-up >12&#xa0;mo. Heterogeneity was explored using meta-regression. The Newcastle-Ottawa Scale assessed bias, and Review Manager v5.4 was used for analysis.<br><br>KEY FINDINGS AND LIMITATIONS: Nineteen studies involving 2379 patients were analyzed. For all assessment tools used (EORTC QLQ-C30, FACT-BL, SF-36, and Bladder Cancer Index [BCI]), higher scores indicate better QoL or function. Pooled analysis showed that ONB was associated with higher global health status (EORTC QLQ-C30: mean difference [MD]&#xa0;=&#xa0;-9.42, p&#xa0;=&#xa0;0.009; negative value indicates higher score in ONB) and functional well-being (FACT-BL -2.60, p&#xa0;=&#xa0;0.010). Conversely, the IC group demonstrated higher scores in urinary outcomes (BCI Urinary: MD&#xa0;=&#xa0;22.81, p&#xa0;=&#xa0;0.02; positive value indicates higher score in IC). Heterogeneity among studies was moderate to high. Meta-regression indicated geographic location and tumor characteristics influenced heterogeneity. Limitations include observational design and potential selection bias.<br><br>ONB reconstruction is associated with higher overall QoL scores, while IC is associated with higher urinary scores. These findings represent clinical trade-offs rather than superiority. Surgical selection should be individualized, balancing patient preference for body image against the risk of functional management challenge.}, }
@article {pmid42031565, year = {2026}, author = {Haller, D and Beermann, F and Sîmpetru, RC and Hofbeck, L and Enoka, RM and Del Vecchio, A}, title = {Voluntary Dissociation of Motor Unit Activity in the Vastii Muscles.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.1982-25.2026}, pmid = {42031565}, issn = {1529-2401}, abstract = {The CNS controls movement with consistent activation patterns across muscles and motor units (MU), suggesting the presence of a relatively fixed and high-dimensional number of neural constraints on voluntary actions. In the human quadriceps, the vastus medialis (VM) and vastus lateralis (VL) contribute to the knee extensor torque and are considered a synergistic pair largely activated by shared neural inputs. However, some evidence suggests that these muscles, or even subregions within them, can be controlled independently. We investigated whether humans can dissociate neural input to VM and VL during isometric contractions. Ten participants (6 males, 4 females) received real-time feedback from multiple intramuscular electromyography (EMG) electrodes inserted into different regions of the VM and VL while attempting to activate each muscle or region selectively. Nine out of ten participants were able to separate VM and VL activity based on the intramuscular EMG feedback. However, MU decomposition from the intramuscular EMGs revealed that selective recruitment of a unique set of MUs was possible only within the proximal region of VM. In contrast, we found highly correlated activity between MUs in VL and distal VM. Correlation analyses confirmed that the proximal VM exhibited distinct activation profiles compared with both distal VM and VL, supporting the existence of compartmentalized control within VM. These findings demonstrate that it is possible to dissociate the activation of MUs within this synergistic muscle group during low-force isometric contractions.Significance Statement Humans are typically thought to lack voluntary control over individual quadriceps muscles due to a shared neural input and a common distal tendon. With real-time EMG feedback from multiple muscle implants we found that participants were able to activate distinct MU populations within vastus medialis, partially dissociating its activity from the vastus lateralis. These results reveal a relatively flexible, region-specific neural control within a pair of synergistic muscles that offers new perspectives for motor learning and targeted rehabilitation.}, }
@article {pmid42031765, year = {2026}, author = {Kim, C and Yeh, JY}, title = {Molecular evolution and antigenic stability of ibaraki virus: evidence for regional circulation from a South Korean whole-genome analysis.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-47936-2}, pmid = {42031765}, issn = {2045-2322}, support = {RS-2025-02304897//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry/ ; 2020-0325//Incheon National University/ ; }, }
@article {pmid42032248, year = {2026}, author = {Tang, Y and Wang, Y and Zhang, W and Liu, X and Li, Y and Hu, W and Ding, L and Feng, F and Chen, X and Feng, J and Xu, S and Chen, S and Wang, J}, title = {Magnetic NeuroRing: a portable adaptive brain-computer interface for real-time transcranial magnetic stimulation in post-stroke motor rehabilitation.}, journal = {npj biomedical innovations}, volume = {3}, number = {1}, pages = {}, pmid = {42032248}, issn = {3005-1444}, support = {82202798, 5230130320, 22205254//National Natural Science Foundation of China/ ; 82202798, 5230130320, 22205254//National Natural Science Foundation of China/ ; 82202798, 5230130320, 22205254//National Natural Science Foundation of China/ ; 24YL1900202//the Project of Shanghai Science and Technology Commission/ ; 22YF1404200//Shanghai Sailing Program/ ; }, abstract = {Stroke often causes persistent upper limb and hand motor dysfunction due to disrupted neural reorganization. To address this, we developed the Magnetic NeuroRing: a portable brain-computer interface integrating real-time electroencephalogram (EEG) with closed-loop continuous theta burst stimulation (cTBS) for adaptive transcranial magnetic stimulation (TMS). A multi-channel EEG array over motor cortical regions (FC3, FC4, CP3, CP4, FT7, FT8, TP7, TP8) detects event-related desynchronization (ERD), indicating motor intent. When ERD/ERS falls below a threshold (ERD/ERS < 0 over five consecutive activations), the system delivers inhibitory cTBS to hyperactive regions, aiming to rebalance stroke-impaired interhemispheric dynamics. The lightweight, patient-specific headgear uses magnetic levitation for precise targeting and EEG-TMS synchronization. In healthy subjects, adaptive cTBS significantly modulated resting-state and task-related neural metrics, aligning with prior large-device findings and demonstrating feasibility for inducing neuroplastic changes. By bridging real-time diagnostics with targeted neuromodulation, the Magnetic NeuroRing enables dynamic, data-driven rehabilitation across clinical and home settings.}, }
@article {pmid42032320, year = {2025}, author = {Li, J and Chen, G and Li, G and Xiao, L and Jia, R and Zhang, K}, title = {Flexible brain electronic sensors advance wearable brain-computer interface.}, journal = {npj biomedical innovations}, volume = {2}, number = {1}, pages = {}, pmid = {42032320}, issn = {3005-1444}, support = {2024NSFJQ0048//Sichuan Provincial Science and Technology Support Program/ ; 82022033//National Natural Science Foundation of China/ ; }, abstract = {The emerging field of wearable brain-computer interface (BCI) strives to achieve both high spatial and temporal resolution. The performance of flexible brain electronic sensor (FBES) has been validated across a variety of experimental settings, demonstrating their potential for real-world applications. As a result, FBES are increasingly shaping the landscape of health monitoring and disease treatment by enabling non-invasive, precise neural data acquisition. This review summarizes recent studies recent progress in wearable brain computer interface technology and FBES development, while provides insights into future clinical application of FBES within BCI systems. Additionally, we propose strategic directions to bridge the gap between laboratory research and practical healthcare implementations.}, }
@article {pmid42019892, year = {2026}, author = {Wang, M and Xu, S and Ball, LJ}, title = {Frequency-specific prefrontal inter-brain synchrony and reinforcement learning signatures differentiate cooperative and competitive risky decision-making: an fNIRS hyperscanning study.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121942}, doi = {10.1016/j.neuroimage.2026.121942}, pmid = {42019892}, issn = {1095-9572}, abstract = {The neural and computational mechanisms that distinguish cooperative from competitive strategies in risky decision-making remain incompletely understood. In this study, we combine frequency-specific prefrontal inter-brain synchrony (IBS) measured via functional near-infrared spectroscopy (fNIRS) hyperscanning with reinforcement learning modeling to examine how social context shapes dyadic choice. Sixty female dyads performed cooperative or competitive variants of a modified Iowa Gambling Task (IGT). Behaviorally, competitive pairs achieved significantly higher cumulative earnings than cooperative pairs. Reinforcement learning analyses indicated that the Outcome Representation Learning (ORL) model provided the best account of behavior. Cooperative dyads showed increased sensitivity to win frequency (βfre), suggesting a tendency to favor frequent but suboptimal gains. In contrast, competitive dyads adopted more flexible strategies that were less dependent on reward frequency. Neuroimaging results revealed dissociable frequency related patterns. Ultra-low frequency coupling in the dorsolateral prefrontal cortex (DLPFC) within the range of 0.015 to 0.017 Hz was associated with goal directed control and higher earnings. Higher frequency coupling in the frontopolar cortex (FPC) within the range of 0.340 to 0.381 Hz was associated with opponent monitoring and sustained competitive engagement, and was reduced during cooperation, consistent with reduced individual responsibility. These findings support a dual pathway account in which competition engages both control and monitoring processes to facilitate performance, whereas cooperation may incur performance costs through socially shaped learning biases. The results provide mechanistic insight into social decision making and identify candidate neural markers for adaptive behavior in interactive contexts.}, }
@article {pmid42020458, year = {2026}, author = {Perez-Blanco, JG and Huegel, JC and Hernández-Rojas, LG and Valdez-Calderón, A and Lizárraga-Torreblanca, H and Cruz-Ortiz, D and Ballesteros, M and Gomez-Correa, M and Antelis, JM}, title = {An EEG-EMG-kinematics dataset from wrist pointing tasks for biomarker research in neurorehabilitation.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-026-07287-z}, pmid = {42020458}, issn = {2052-4463}, support = {1278020//SECIHTI/ ; SECTEI/081/2024//SECTEI/ ; }, abstract = {This work presents a multimodal dataset containing synchronized electroencephalography (EEG), electromyography (EMG), and kinematic recordings acquired during wrist motor tasks performed with a three degree of freedom robotic exoskeleton (BiomechWrist) coupled to a virtual interface. Designed as a normative baseline and benchmark resource for studying electrophysiological biomarkers and motor performance in healthy individuals, the dataset includes recordings from 45 healthy participants, each completing 320 trials of standardized wrist movements. The exoskeleton operated in transparent mode (actuators de-energized) to capture voluntary movements through high resolution encoders. Data are formatted according to the Brain Imaging Data Structure (BIDS) standard and follow FAIR principles, comprising raw biosignals, encoder trajectories, event markers, and derived performance metrics. To assess data quality, we provide subject level validation analyses, including power spectral density (PSD) and event related desynchronization/synchronization (ERDS) for EEG, as well as an EMG-Kinematic coupling analysis through Electromechanical Delay (EMD), and kinematic trajectory evaluation with performance metrics (accuracy, execution time, trajectory efficiency). This dataset supports research on wrist rehabilitation technologies and biomarker driven neuromodulation therapies, while also enabling studies in biosignal processing, artifact removal, machine learning for motor intention decoding, and the development of brain computer interfaces (BCI) and assistive devices targeting wrist mobility.}, }
@article {pmid42020535, year = {2026}, author = {Margaret, MJ and Banu, NMM and Madhumithaa, S and Pathan, AK}, title = {On the prediction models for brain signal-based emotion recognition.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-47622-3}, pmid = {42020535}, issn = {2045-2322}, }
@article {pmid42021568, year = {2026}, author = {Li, Z and Ge, R and Zhao, Z and Xiao, H and Du, C and Lai, Y and Wang, L}, title = {From Bio-Interface Materials to Neural Integration: The Next-Generation Brain-Machine Interfaces Powered by Hydrogels.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e23422}, doi = {10.1002/adma.202523422}, pmid = {42021568}, issn = {1521-4095}, support = {22322803//National Natural Science Foundation of China/ ; 22375047//National Natural Science Foundation of China/ ; 22361162607//International Cooperation and Exchanges NSFC/ ; 20240305028YY//Key Research Development Program of Jilin Province/ ; 2022YFB3804905//National Key Research and Development Program of China/ ; 2022YFB3804900//National Key Research and Development Program of China/ ; //Graduate Innovation Fund of Jilin University/ ; FZ2025038//State Key Laboratory of New Textile Materials and Advanced Pro- cessing/ ; }, abstract = {Brain-machine interfaces (BMIs), which serve as revolutionary tools for neural recording, modulation, and rehabilitation, are highly dependent on the biocompatibility and mechanical suitability of their electrode materials. Although traditional metal electrodes possess excellent conductivity, their inherent rigidity causes a substantial mechanical mismatch with soft neural tissue, leading to chronic inflammatory responses and poor long-term stability. The emergence of hydrogel electrodes has provided a breakthrough solution to this fundamental limitation. Hydrogels, characterized by their softness, high ionic conductivity, and tissue-like compliance, offer a viable solution to mitigate these issues. This review systematically explores the material properties of hydrogel-integrated BMIs, providing an in-depth investigation of key hydrogel characteristics, including toughness, adhesion, conductivity, and biocompatibility. Additionally, hydrogel-based BMIs are categorized into non-invasive and invasive systems, each defined by its characteristic operational principles and signal-acquisition mechanisms. The study further reviews critical issues, including surgical implantation strategies, multimodal data fusion, integration of artificial intelligence, as well as system integration and clinical translation. From a therapeutic perspective, this work highlights the application of BMIs in treating neurological disorders such as Alzheimer's disease, Parkinson's disease, epilepsy, stroke, neuropathic pain, and depression. Furthermore, this review critically examines the persistent challenges faced by hydrogel-based BMIs and proposes innovative strategies for future development. Ultimately, it outlines a developmental roadmap for next-generation hydrogel-based biotherapeutic technologies aimed at achieving high-fidelity, stable and clinically translatable BMI systems.}, }
@article {pmid42022239, year = {2026}, author = {Roualdes, V and Moussaoui, S and Normand, JM and Kuhn, E and Nizard, J and Van Langhenhove, A}, title = {EEG-based brain-computer interface with immersive virtual reality for phantom limb pain: a single-center pilot neurofeedback trial.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1697837}, pmid = {42022239}, issn = {1662-5161}, abstract = {BACKGROUND: Phantom limb pain (PLP) is a challenging neuropathic pain condition following limb amputation or brachial plexus injury. Non-pharmacological interventions such as motor imagery training, phantom motor execution and mirror therapy have shown potential to alleviate PLP by engaging sensorimotor circuits, but their effects are debated. We developed GHOST, a portable EEG-based brain-computer interface (BCI) coupled with immersive virtual reality (VR), allowing patients to control a virtual limb via motor imagery in real time, as a neurofeedback-based rehabilitation tool.<br><br>METHODS: We conducted a single-center exploratory pilot trial to assess the feasibility and preliminary efficacy of this device. Seven patients with chronic upper-limb PLP (amputees or brachial plexus avulsion, pain &#x2265;3/10) underwent 10 training sessions over 2&#x202f;weeks. Daily pain diaries (distinguishing continuous pain vs. paroxysmal pain episodes) were recorded for 1&#x202f;month before and 1&#x202f;month after the intervention, with follow-up to 6&#x202f;months. Motor imagery ability, anxiety-depression (HADS), and quality of life (SF-36) were also evaluated.<br><br>RESULTS: Six patients completed &#x2265;8 sessions. All participants achieved BCI control of the virtual hand, with high success rates (>70%) even as task difficulty increased, demonstrating system feasibility. No adverse events occurred. Compared to baseline, patients experienced a significant short-term reduction in paroxysmal pain (frequency and intensity of pain "flare-ups"), with up to >80% median decrease in weekly cumulated pain episode intensity (p&#x202f;<&#x202f;0.001). Three of five patients also reported around 30% improvement in average daily pain during the first post-training month. HADS anxiety/depression scores showed a non-significant improving trend. By 3-6&#x202f;months post-training, pain levels had largely returned to pre-intervention values.<br><br>CONCLUSION: This pilot study supports the safety and feasibility of EEG-BCI with VR for PLP and suggests that it can yield short-term analgesic effects, particularly on paroxysmal pain. These findings support the hypothesis that sensorimotor re-engagement could effectively target maladaptive neural processes underlying PLP, while warranting confirmation in controlled trials. Future work will optimize the training protocol and investigate neuroplastic changes associated with this BCI-VR intervention.}, }
@article {pmid42023246, year = {2026}, author = {Kumaresan, V and Pahari, S and Hung, CY and Hermann, BP and Schlesinger, LS and Seshu, J}, title = {Role of dual specificity phosphatase 1 in influencing inflammatory pathways in macrophages modulated by Borrelia burgdorferi lipoproteins.}, journal = {Frontiers in immunology}, volume = {17}, number = {}, pages = {1766756}, pmid = {42023246}, issn = {1664-3224}, mesh = {Animals ; *Borrelia burgdorferi/immunology ; *Macrophages/immunology/metabolism ; *Lipoproteins/immunology ; Mice ; Signal Transduction/immunology ; *Lyme Disease/immunology/microbiology ; *Dual Specificity Phosphatase 1/metabolism/genetics/immunology ; *Inflammation/immunology ; Humans ; Proteomics ; Host-Pathogen Interactions/immunology ; Mice, Inbred C57BL ; }, abstract = {Borrelia burgdorferi (Bb), the spirochetal agent of Lyme disease, has a large array of lipoproteins that play a significant role in mediating host-pathogen interactions within ticks and vertebrates. While prior work has established that borrelial lipoproteins (BbLP) modulate immune signaling pathways, the broader transcriptional and proteomic programs induced by these molecules in macrophages are unclear. Here, we used integrated multi-omics approaches to characterize host signaling pathways activated specifically by purified borrelial lipoproteins in murine bone marrow derived macrophages (BMDMs). Single-cell RNA-Seq (scRNA-Seq) performed on BMDMs treated with various concentrations of borrelial lipoproteins revealed macrophage subsets within the BMDMs. Differential expression analysis showed that genes encoding various receptors, type I IFN-stimulated genes, signaling chemokines are upregulated while mitochondrial and ribosomal genes are downregulated in BMDMs in response to lipoproteins. Unbiased proteomics analysis of lysates of BMDMs treated with lipoproteins corroborated several of these findings. Notably, dual specificity phosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to BbLP. Pharmacological inhibition with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to BbLP, demonstrated that DUSP1 negatively regulates NLRP3-mediated pro-inflammatory signaling and positively regulates the expression of interferon-stimulated genes and those encoding Ccl5, Il1b, and Cd274. Using human monocytic reporter cell lines, we showed MyD88- and IKK-dependent pathways contribute to mitochondrial alterations upon stimulation with lipoproteins. Extracellular flux analysis using the Seahorse assay revealed decreased oxygen consumption rate (OCR) and increased extracellular acidification rate (ECAR), indicating time-dependent metabolic reprogramming and a shift toward a glycolytic, pro-inflammatory metabolic state in BMDMs following BbLP stimulation. Collectively, these findings define signaling networks, regulatory nodes and metabolic alterations induced by borrelial lipoproteins in macrophages and highlight DUSP1 as a key modulator of lipoprotein-driven innate immune responses. This work provides a mechanistic framework for understanding how borrelial lipoproteins shape macrophage signaling, independent of the broader complexity of infection with intact pathogen.}, }
@article {pmid42024948, year = {2026}, author = {Ma, W and Zhang, H and Li, Y and Wei, M}, title = {NeuroDecoder: A new framework for image decoding and reconstruction of EEG signals.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3686624}, pmid = {42024948}, issn = {2168-2208}, abstract = {Brain-Computer Interface (BCI) technology holds great promise for enhancing human health and quality of life, with visual stimulus reconstruction from EEG signals being a key application. However, the complexity and noise of EEG data challenge existing reconstruction methods. To address these issues, we propose NeuroDecoder, an end-to-end multimodal guidance generation framework that produces high-quality images from EEG signals. The key innovation is the collaborative mitigation of EEG noise and cross-modal representation discrepancies through a noise-robust encoder, mask-based triple-contrastive alignment, and a fixed generative model. Specifically, NeuroDecoder consists of three integrated learning stages: 1) EEG Decoding, 2) Modality Alignment, and 3) Image Reconstruction. In the decoding stage, a novel visual decoding model extracts visually relevant features with superior classification accuracy. In the alignment stage, a mask-based triple contrastive learning strategy achieves efficient cross-modal alignment of EEG, text, image, and edge map embeddings into a unified space. In the generation stage, a new reconstruction pipeline feeds the aligned EEG embeddings into a pre-trained stable diffusion model, enabling high-quality visual stimulus reconstruction with enhanced semantic and structural fidelity, without fine-tuning the generative model. On three EEG datasets, NeuroDecoder achieved subject-dependent classification accuracies of 99.76%, 94.41%, and 56.67%, respectively; in the subject-independent setting, it performed near random on EEGCVPR40 but reached 91.61% and 37.63% on the other two. For image reconstruction, it obtained Fréchet Inception Distance of 62.84 and 63.12 on the first two datasets. Extensive experiments demonstrate that NeuroDecoder outperforms prior methods in both EEG classification accuracy and image reconstruction quality.}, }
@article {pmid42026803, year = {2026}, author = {Zhang, F and Hu, K and Sun, C and Chen, R and Ni, G and Liu, X and Wei, L and Su, R}, title = {Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.}, journal = {Gut microbes}, volume = {18}, number = {1}, pages = {2662690}, doi = {10.1080/19490976.2026.2662690}, pmid = {42026803}, issn = {1949-0984}, mesh = {Humans ; *Immune Checkpoint Inhibitors/therapeutic use ; *Gastrointestinal Microbiome/genetics/drug effects ; *Neoplasms/drug therapy/microbiology ; Deep Learning ; Biomarkers, Tumor/genetics ; *Bacteria/classification/genetics/isolation &amp; purification ; Female ; Male ; Metagenomics ; }, abstract = {Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has improved survival across multiple cancer types, but the variability in patient response highlights the need for better predictive biomarkers. Existing studies rely on taxonomic abundance derived from reference genome databases, limiting the discovery and functional interpretation of uncharacterized microbes. Here, we integrated metagenomic data from multiple ICI-treated cohorts spanning diverse cancer types and geographic regions and developed a deep learning model, named BioP-VAE, that incorporates biological prior knowledge via protein sequence embeddings and uses gene-level microbial abundance features as input. Gene-level microbial abundance outperformed taxonomy abundance in predicting both ICI response and 12-month progression-free survival (PFS). In patients receiving combination immune checkpoint blockade (CICB), BioP-VAE achieved a mean AUC of 0.89 in intracohort and 0.88 in cross-cohort evaluation. Notably, in the monotherapy-treated intracohorts, BioP-VAE achieved a mean AUC of 0.97. Feature attribution analysis revealed key microbial genes. Additionally, we identified distinct predictive microbial signatures via age-stratified analysis, suggesting that host age may modulate microbiome‒immune interactions. Importantly, this is the first large-scale study to evaluate gene-level microbial abundance features for ICI response prediction across multiple cancer types by deep learning. Our findings demonstrate that incorporating biological prior knowledge into deep learning models can improve the discovery of microbial biomarkers that can be generalized across cancer types and treatment settings, offering a novel strategy for patient stratification in immunotherapy.}, }
@article {pmid42018586, year = {2026}, author = {Zhao, Y and He, D and Ren, F and Xia, Q and Xu, L and Xie, G and Zhang, X and Yang, R and Zou, S and Jiang, B}, title = {RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.}, journal = {PloS one}, volume = {21}, number = {4}, pages = {e0347671}, pmid = {42018586}, issn = {1932-6203}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; *Imagination/physiology ; }, abstract = {Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). However, deep learning models often struggle with inter-subject variability, which limits their ability to generalize across subjects. This study proposes RMETNet, a novel framework that integrates TSLANet, a spatio-temporal convolution module, and a multi-scale Riemannian geometry feature module. TSLANet suppresses noise and captures complex temporal patterns for preliminary signal decoding, while the spatio-temporal convolution module extracts higher-order representations. The Riemannian branch learns geometry-based distribution features across subjects, and the fused features are used for classification. To address inter-subject distribution shifts, RMETNet incorporates Maximum Mean Discrepancy (MMD) loss for domain adaptation, aligning feature distributions between source and target domains. Experiments show that on the four-class BCI Competition IV 2a (BCICIV2a) dataset, RMETNet achieved accuracies of 71.39% in the cross-subject setting and 80.71% in the subject-dependent setting; on the two-class BCI Competition IV 2b (BCICIV2b) dataset, it achieved 80.93% and 86.76%, respectively. The model consistently outperformed baseline algorithms. Ablation and visualization analyses further validated its effectiveness in reducing inter-subject feature distribution disparities and enhancing MI-EEG decoding. The code is available at: https://github.com/rokanfeermecer486/RMETNet.}, }
@article {pmid42019053, year = {2026}, author = {Zhang, S and Zhang, H and Wei, M and Yang, C}, title = {An Optimized Encoding BCI Framework: Implementing Massive Command with Minimal Calibration.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3686710}, pmid = {42019053}, issn = {1558-0210}, abstract = {As a critical metric for brain-computer interfaces (BCIs), the number of commands directly defines the control capacity for practical applications. However, existing BCIs often suffer from limited command sets and prohibitive calibration costs. To address these problems, this study presents a functional optimizationbased encoding framework to generate massive com8 mands with high discriminability while minimizing calibration burden. Specifically, a functional optimization theory enhances command distinguishability by optimizing the encoding function, while a few-shot training strategy leverages symbol reusability to reduce calibration data. Additionally, a symbol-joint decoding approach improves recognition accuracy. Using this framework, we developed an online BCI system with an unprecedented 1,008 commands-establishing a dual state-of-the-art (SOTA) in both command scale and calibration efficiency for large-scale BCIs (>100 commands). Comparative analysis shows that the functional optimization strategy improved accuracy by 13.94% and the information transfer rate (ITR) by 26.12% over the widely adopted baseline. Remarkably, with only 72 seconds of calibration data, the system achieved an average accuracy of 86.60 ± 13.35% and an average ITR of 122.74 ± 24.64 bits/min across 15 subjects, peaking at 100%. The framework features high flexibility in command encoding and robust cross-paradigm compatibility, significantly enhancing BCI performance and practicality.}, }
@article {pmid42019267, year = {2026}, author = {Cui, Y and Yun, R and Zhang, S and Gong, Y and Li, Z and Chen, Y and Su, M and Wu, D and Wu, J and Wang, Q and Wang, J and Tian, Q and Yuan, Y and Mei, S and Wu, L and Li, X and Zhang, B and Guo, T and Sun, J}, title = {EEG predict response to transcutaneous auricular vagus nerve stimulation in treatment-resistant schizophrenia.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {187}, number = {}, pages = {2111892}, doi = {10.1016/j.clinph.2026.2111892}, pmid = {42019267}, issn = {1872-8952}, }
@article {pmid42019559, year = {2026}, author = {Xu, P and Chen, Y and Wei, X and Qi, J and Chen, Y and Li, L}, title = {Resting-State EEG Networks Predict Individual Differences in Cognitive Flexibility.}, journal = {Brain research bulletin}, volume = {}, number = {}, pages = {111895}, doi = {10.1016/j.brainresbull.2026.111895}, pmid = {42019559}, issn = {1873-2747}, abstract = {Cognitive flexibility, the ability to adapt behavior and switch between tasks in response to changing goals, is a core component of executive function. However, the multiscale resting-state mechanisms underlying individual differences remain poorly understood. Here, resting-state electroencephalography (EEG) from 128 healthy participants (66 male; age 18-35 years) was analyzed to characterize frequency-specific connectivity and network topology. Results show that, delta-band fronto-temporal connectivity and associated graph metrics associated with repeat task performance, whereas beta-band fronto-parietal, fronto-occipital, and prefronto-frontal connections associated with shift task performance. Individuals with low switching costs exhibited stronger intra- and inter-hemispheric alpha-, beta-, and gamma-band connectivity, which were associated with more efficient cognitive flexibility. Multivariate models using connectivity features reliably predicted repeat RT and shift RT. Together, these findings indicate that hierarchical, frequency-specific resting-state networks constitute core neural mechanisms of cognitive flexibility and highlight the potential for resting-state EEG networks to account for individual differences in executive function.}, }
@article {pmid42012068, year = {2026}, author = {Ye, Y and Wu, J and Zhang, Y and Ma, H and Deng, Q and Jian, J and Tang, R and Sun, B and Zeng, Y and Song, Y and Wang, J and Lin, H and Zhao, S and Li, L}, title = {Reconfigurable in-Sensor Image Enhancement Based on Tunable Band Alignment of In2Se3/PdSe2 Heterojunction.}, journal = {Nano letters}, volume = {}, number = {}, pages = {}, doi = {10.1021/acs.nanolett.5c06055}, pmid = {42012068}, issn = {1530-6992}, abstract = {In-sensor computing has emerged as a promising paradigm to overcome power consumption and latency bottlenecks in vision systems. Here, we demonstrate a reconfigurable in-sensor image enhancement strategy based on an In2Se3/PdSe2 ferroelectric heterojunction. The photodetector exhibits a broadband spectral response (400-1550 nm) and a high external quantum efficiency exceeding 10[4]%. By synergistically leveraging electrostatic and ferroelectric fields to tune the band alignment, we achieve programmable carrier collection efficiency, leading to a gate-tunable nonlinear photocurrent response. This hardware-level nonlinearity enables dual imaging modes for adaptive imaging: a low-light signal amplification mode to boost brightness and an overexposure recovery mode to compress contrast. By implementing a programmable photoresponse into a single photodetector, our approach bypasses redundant data transmission, providing a compact and energy-efficient solution for intelligent vision systems.}, }
@article {pmid42013255, year = {2026}, author = {Wu, X and Daly, I and Lau, AT and Chen, W and Wang, C and Cichocki, A and Jin, J}, title = {Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks with Pyramid Squeeze Attention.}, journal = {IEEE transactions on image processing : a publication of the IEEE Signal Processing Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TIP.2026.3684399}, pmid = {42013255}, issn = {1941-0042}, abstract = {Steady state visual evoked potential (SSVEP)-based brain-computer interfaces have been widely studied for their fast response speeds and high information transfer rates. However, how to fully utilize the potential information of existing subjects to realize the mining of common information among different subjects and then realize the information migration in a small amount of data scenarios is a difficult problem faced by current research. In order to solve the above problems, this study proposes a deep neural network based on the pyramid squeeze attention (PSA-DNN) mechanism to enhance the performance of SSVEP-BCI through common information migration. Specifically, the band-pass filtered EEG signals were first Fourier transformed to obtain the frequency domain information; subsequently, the frequency domain information is input into a deep neural network, followed by a spatial convolution step to extract spatial domain information. In order to further enhance the quality of information extraction, a pyramid attention module is introduced into the network to realize the enhancement of frequency domain and spatial domain information. Time domain information from the EEG signals is then mined using temporal convolution. Finally, the full connectivity layer is used to output the recognition results. The model is trained in a three-stage stepped approach for SSVEP target recognition. The first stage uses data from all participants in the training set for common information learning and transfers the model parameters trained in the first stage to the network model in the second stage. In the second stage, some of the information from participants in the test set is used for fine-tuning and to mine personalized information from these new participants. The third stage uses the remaining data from participants in the test set to produce classification results. The proposed method is systematically evaluated using the Benchmark and BETA datasets, where it demonstrates favorable performance compared to established baselines. These findings contribute theoretical insights and methodological references for the application of SSVEP-based brain-computer interfaces in real-world scenarios.}, }
@article {pmid42013272, year = {2026}, author = {Zhang, J and Liu, J and Wang, L and Peng, Y and Kong, W and Cichocki, A}, title = {BR-SFDA: A Source-Target Bidirectional Refined SFDA for Privacy Preserving EEG-based BCIs.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3686008}, pmid = {42013272}, issn = {2168-2208}, abstract = {Due to significant inter-subject variability in feature distributions caused by the diversity of neural activity patterns, Electroencephalography (EEG)-based brain-computer interface (BCI) systems face considerable challenges in cross-subject EEG decoding. Though transfer learning has been widely introduced for knowledge transfer from source subject(s) to target subject and exhibited great success, a non-negligible issue is that source subjects' EEG data usually contains privacy information and should be protected. To address both issues, we propose a source-target bidirectional refined source-free domain adaptation (BR-SFDA) framework in this paper for privacy preserving cross-subject EEG classification. BR-SFDA makes improvements from two aspects under the popular 'pretraining and fine-tuning' paradigm. On one hand, it locally performs data augmentation and builds a multi-criteria fused metric to select representative EEG sample for model pre-training. On the other hand, a structured graph learning strategy is employed to better guide the model finetuning in a self-supervised manner. Both improvements collaborate respectively from the front-end and back-end, leading to a bidirectional refined SFDA framework. Extensive experiments are conducted on two tasks of cross-subject motor imagery decoding and emotion recognition, and the results on four datasets demonstrate that BR-SFDA achieves superior performance to some competitive models. Besides, the effectiveness of data augmentation and filtering, structured graph learning and domain adaptation is well evaluated.}, }
@article {pmid42013704, year = {2026}, author = {Han, L and Smagghe, G and Yang, J and Yu, J and Zheng, H and Chen, X and Wu, X}, title = {Synergistic removal of morpholine fungicides and cadmium from agricultural water by a biochar-immobilized bacterial-duckweed system: Quantifying roles of biodegradation, adsorption, and phyto-uptake.}, journal = {Journal of hazardous materials}, volume = {510}, number = {}, pages = {142125}, doi = {10.1016/j.jhazmat.2026.142125}, pmid = {42013704}, issn = {1873-3336}, abstract = {The co-contamination of agricultural water by morpholine fungicides (e.g., flumorph and dimethomorph) and cadmium (Cd) poses significant ecological threats, challenging conventional treatment approaches. This study developed an innovative bioremediation system integrating biochar-immobilized microbial consortia with phytoremediation, and quantified the individual contributions of biodegradation, biosorption, phyto-uptake, and biochar-adsorption to the synergistic removal of pesticide and Cd co-contaminants. A novel Cd-tolerant and flumorph-degrading bacterium, Alcaligenes faecalis X4, was combined with a dimethomorph-degrading strain (Bacillus cereus WL08) to form a stable consortium. This consortium was capable of simultaneously metabolizing both fungicides into less toxic products and adsorbing cadmium. The consortium was immobilized on bamboo charcoal to produce a biocomposite (BCI-X4 + WL08), which achieved high removal efficiencies under optimized conditions: 97.65% for flumorph (50 mg/L), 94.23% for dimethomorph (50 mg/L), and 82.68% for cadmium (10 mg/L). Subsequent introduction of duckweed (Lemna minor) contributed an additional 15.40-28.00% removal via phyto-accumulation. Partitioning analysis confirmed true synergistic interactions-rather than merely additive effects-enhancing overall removal by up to 3.27-fold while alleviating oxidative stress in the plants. A compound ecological filter bed incorporating both BCI-X4 + WL08 and duckweed demonstrated practical applicability under outdoor conditions, achieving average simultaneous removal rates of 94.96% (flumorph), 91.43% (dimethomorph), and 85.42% (Cd) across three consecutive seasons, along with improved water quality parameters. This work presents a scalable, eco-friendly strategy for the in situ remediation of surface waters co-contaminated with pesticides and heavy metals, and provides a quantitative assessment of the distinct microbial, plant, and biochar contributions to the synergistic remediation process.}, }
@article {pmid42014579, year = {2026}, author = {Miao, Y and Fu, Z and Zhang, J and Tao, Y and Pang, K and Wang, C and Jiang, Q and Shen, L and Xia, T and Lu, P and Xu, Z and Xia, L and Zuo, L and Dong, R and Liu, Y and Wang, Z and Zhang, N and Song, J and Gao, C and Jiang, R and Deng, D and Zhu, Y}, title = {Theoretical quantitative model and clinical outcome predictions of conductive cardiac patches for electrophysiological treatments.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {42014579}, issn = {2157-846X}, support = {2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2019YFE0117400//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&amp;T for Social Development)/ ; 2019YFE0117400//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&amp;T for Social Development)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Myocardial infarction (MI) impairs cardiac electrical signal transmission, which could be partially remedied by implantable electroactive biomaterials. Here we characterize electroactive cardiac patches (eCarPs) with conductivities spanning five orders of magnitude both in vitro and in rat models. In contrast to common belief, we reveal that highly conductive eCarPs are more effective in lowering the risk of post-MI arrhythmia and preserving cardiac function with respect to eCarPs with conductivity similar to normal myocardium. We show that highly conductive eCarPs restore electrical signal conduction velocity across infarcted myocardium to healthy levels, while less conductive eCarPs fail to do this. We quantitatively demonstrate that three-dimensional cardiac simulation based on the monodomain model accurately replicates the effect of high-conductivity patches in eliminating conduction blocks in porcine myocardium and the locations of reentrant circuits in patients with MI. Our results suggest that eCarP conductivity higher than healthy human myocardium is preferred for lowering the risk of arrhythmia in patients by reducing the number of reentrants and stabilizing the reentrant routes.}, }
@article {pmid42014794, year = {2026}, author = {Zou, J and Poeppel, D and Ding, N}, title = {Constituent-constrained word prediction during language comprehension.}, journal = {Nature neuroscience}, volume = {}, number = {}, pages = {}, pmid = {42014794}, issn = {1546-1726}, abstract = {Next-word prediction has been hypothesized as the central computational objective of the human language system, akin to that of current large language models. Here we put this conjecture to the test, investigating whether the brain predicts each upcoming word as precisely as possible when listening to connected speech. In three magnetoencephalography experiments with Mandarin Chinese speakers, we demonstrate that the response related to word unpredictability, that is, word surprisal calculated using large language models, is significantly stronger for words within an ongoing constituent than words across a major constituent boundary, and this effect is further modulated by the certainty of a constituent boundary. This constituent-boundary effect is also observed behaviorally unless speech is very slowly presented, and it is confirmed by analyzing a dataset of electrocorticography responses to natural English narratives. The constituent-boundary effect demonstrates that the language system does not solely optimize word-prediction precision; rather, it balances word-prediction contributions by constituent-constrained management of linguistic contextual representations.}, }
@article {pmid42014805, year = {2026}, author = {Nasiraee, H and Nazari, F and Samsami-Khodadad, F and Liu, X}, title = {Neural-LWE: a biometric-anchored authenticated key agreement for post-quantum brain-computer interfaces.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-48527-x}, pmid = {42014805}, issn = {2045-2322}, }
@article {pmid42016063, year = {2026}, author = {Fankhauser, CD and Röthlin, K and Baumeister, P and Mattei, A and Piatti, P and Chew, YC and Kaufmann, E}, title = {Validation of the diagnostic accuracy of a urine-based DNA methylation marker test in patients with upper urinary tract lesions.}, journal = {BJUI compass}, volume = {7}, number = {3}, pages = {e70195}, pmid = {42016063}, issn = {2688-4526}, abstract = {OBJECTIVES: This study aims to validate the diagnostic accuracy of a novel urine-based DNA methylation test in patients with suspected upper tract urothelial carcinoma (UTUC) on CT urography and to assess its potential to eliminate the need for diagnostic ureterorenoscopy (URS) in selected patients, expedite treatment and identify high-grade tumours suitable for neoadjuvant chemotherapy.<br><br>PATIENTS AND METHODS: We prospectively collected urine samples from 46 consecutive patients with suspected UTUC in computed tomography and analysed them using the Bladder CARE&#x2122; methylation test. Test performance was evaluated against final pathology from URS biopsies and/or surgical specimens. We performed Youden Index analysis to optimise diagnostic cut-off values and assessed correlations between Bladder CARE Index (BCI) levels and tumour characteristics, particularly grade differentiation.<br><br>RESULTS: Using the manufacturer's cut-off (BCI&#x2009;>&#x2009;2.5), the test demonstrated 95% sensitivity, 69% specificity, 70% positive predictive value and 95% negative predictive value (NPV), significantly outperforming cytology (11% sensitivity). An optimised, study-derived cut-off (4.35) further improved specificity to 92% with sensitivity and NPV remaining &#x2265;95%. Importantly, a higher threshold (BCI&#x2009;>&#x2009;10) yielded 100% specificity and 100% PPV, although at the expense of sensitivity (65%). Median BCI values differed between high-grade (38.6) and low-grade tumours (9.45), suggesting utility for non-invasive grade assessment. BCI also correlated with tumour size (&#x3b2;&#x2009;=&#x2009;12&#x2009;mm per log10 increase, p&#xa0;=&#x2009;0.08).<br><br>CONCLUSION: This novel urine-based DNA methylation test offers high diagnostic accuracy for UTUC detection. However, clinical interpretation should be threshold dependent. While BCI values >2.5 show high sensitivity, the PPV of 70% indicates a relevant proportion of false-positive results, and diagnostic URS remains warranted in this range. In contrast, high positive values (BCI&#x2009;>&#x2009;10) demonstrated 100% specificity and PPV and could enable direct progression to definitive surgery without diagnostic URS, avoiding procedure-related complications and expediting treatment. The correlation with tumour grade addresses a critical need for identifying candidates for neoadjuvant chemotherapy without invasive tissue diagnosis.}, }
@article {pmid42000762, year = {2026}, author = {Li, S and Jin, Z and Gu, S and Zhang, RY and Li, Y}, title = {A large-scale fMRI dataset for vision-language semantic association.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-026-07248-6}, pmid = {42000762}, issn = {2052-4463}, support = {2025ZD0217000//National Science and Technology Major Project/ ; 32371154//National Natural Science Foundation of China/ ; 24QA2705500//Science and Technology Commission of Shanghai Municipality/ ; LG-GG-202402-06//Lin Gang Laboratory/ ; }, abstract = {Understanding the neural coding and association of visual and language information benefits from the development of deep learning models and the collection of massive datasets with extensive sampling of brain activity. Large-scale functional magnetic resonance imaging (fMRI) datasets with naturalistic stimuli provide more ecologically relevant experimental conditions and promote more reproducible research into the neural basis of sensory perception. Here, unlike most previous datasets restricted to isolated modalities, we present the Caption Scene Dataset (CSD), a large-scale fMRI dataset for vision-language semantic association, in which neural responses to 4,400 pairs of Chinese captions and naturalistic scenes were acquired from eight healthy participants. The participants were instructed to determine whether the semantics in the caption and the image are consistent. To illustrate the utility of the CSD dataset, we demonstrated that deep neural encoding models effectively predicted neural responses to both caption and image stimuli across different cortical regions. This dataset provides a platform for the investigation of the neural basis of semantic association across vision and language, facilitating cross-disciplinary advances between vision neuroscience and artificial intelligence.}, }
@article {pmid42000809, year = {2026}, author = {Lomele, G and Lencioni, T and D'Ambrosio, S and Comanducci, A and Lucchetti, F and Marzegan, A and Derchi, C and Garzonio, S and Atzori, T and Rabuffetti, M and Castiglioni, P and Ferrarin, M and Fornia, L}, title = {High-Density EEG and Multi-Muscle EMG Dataset during Object Prehension with a sensorized Grasping Box in Humans.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-026-07242-y}, pmid = {42000809}, issn = {2052-4463}, support = {project MNESYS (PE0000006)//Ministry of University and Research (MUR)/ ; ECS_00000035//European Union - NextGenerationEU/ ; }, abstract = {Understanding how cortical areas control prehension movements requires synchronized neural and muscular data. For this aim, we introduce a novel open-access dataset of synchronized EEG and EMG recordings during prehension movements. The dataset combines high-density EEG (64 channels) with EMG recordings from 13 upper-limb muscles collected during prehension movements associated with 3 grip types: precision grip (thumb-index, PG), whole-hand power grasp (WH), and an unconventional grip (thumb-ring finger, UG). Data were acquired from 14 healthy participants performing visually guided prehension using a custom sensorized device that precisely timestamps action events, including go signals, object contacts, and lift completions. Each trial was divided into a dynamic phase (reaching, grasping, lifting) and a final isometric phase (holding), enabling investigation of transient and sustained motor activity. The extensive multi-muscle EMG recordings allow extraction of muscle synergy patterns that can be analyzed alongside EEG features to study cortico-muscular interactions. This dataset supports research on the neural control of complex hand movements, sensorimotor integration, and adaptive brain-computer interfaces. It provides a comprehensive resource for neuroscientists, engineers, and clinicians interested in motor control and its translation into rehabilitation practice.}, }
@article {pmid42000827, year = {2026}, author = {Razaghi, Z and Faraji, M and Mohammadpour, R and Ebrahimpour, R and Zad, AI}, title = {A systematic evaluation of EEG electrode geometry for enhanced signals: an experimental approach.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-47459-w}, pmid = {42000827}, issn = {2045-2322}, abstract = {The advancement of neural recording technologies is critical for improving our understanding of brain function and enhancing the efficacy of brain-computer-interfaces. Electroencephalography (EEG), is a non-invasive technique for recording brain electrical activity via scalp electrodes. The geometry and material of electrodes significantly influence signal quality and noise. Here, we investigated the effect of electrode geometry on EEG performance. Time-domain analysis of the selected nanostructured pin-shaped gold-based dry electrodes, showed an in-phase conductance of 0.9 mS (effective resistance ~ 1.1 kΩ) without applied pressure. Also, impedance spectroscopy revealed a reduced electrode-skin impedance of ~ 300 kΩ at 100 Hz, with the bulk resistance of ~ 1.95 Ω, consistent with expected interface behavior. EEG performance was evaluated using steady-state visually evoked potential (SSVEP) and alpha modulation paradigms. In the SSVEP task (15 Hz stimulus), dry electrodes achieved a signal-to-noise ratio of 1.27 dB, comparable to 1.30 dB for standard wet Ag/AgCl electrodes. Alpha modulation analysis showed alpha-ratio of 2.43 a.u. vs. 1.83 a.u. for wet electrodes, and random forest classifier which completely distinguished eyes-open/eyes-closed states, confirmed the high-fidelity signal capturing. The scalable and cost-effective fabrication process offers long-term stability for clinical and research EEG, paving the way for future innovations in neural recording devices.}, }
@article {pmid42002556, year = {2026}, author = {Selvam, AK and Loganathan, A}, title = {An intelligent EEG-based ensemble framework for communication assistance in Locked-In Syndrome patients.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-47041-4}, pmid = {42002556}, issn = {2045-2322}, }
@article {pmid42003430, year = {2026}, author = {Maya, I and Noiret, B and Denost, Q}, title = {How to avoid APR after failure of organ preservation in ultra-low rectal cancer? A video vignette.}, journal = {Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland}, volume = {28}, number = {4}, pages = {e70458}, doi = {10.1111/codi.70458}, pmid = {42003430}, issn = {1463-1318}, }
@article {pmid42004294, year = {2024}, author = {Collyer, J}, title = {Fasciola hepatica: can the coproantigen ELISA replace the faecal egg sedimentation test?.}, journal = {Veterinary evidence}, volume = {9}, number = {4}, pages = {}, pmid = {42004294}, issn = {2396-9776}, abstract = {PICO QUESTION: In adult cattle, is the sensitivity of the coproantigen ELISA test equal or superior to the sensitivity of the faecal egg sedimentation test for the diagnosis of Fasciola hepatica?<br><br>CATEGORY OF RESEARCH: Diagnosis.<br><br>Three studies were appraised. This included two cross-sectional diagnostic accuracy studies and one case control diagnostic accuracy study.<br><br>STRENGTH OF EVIDENCE: Moderate.<br><br>OUTCOMES REPORTED: The first study reported the findings from 619 tested cattle over 3 sample periods comparing the sensitivity and specificity of the different tests. The sensitivity of the faecal egg sedimentation test varied greatly between the sample periods from 0.81 (95% beta coefficient (BCI) 0.72-0.90) to 0.58 (95% BCI 0.43-0.72) with the coproantigen ELISAs sensitivity remaining consistent at 0.77 (95% BCI 0.64-0.88) throughout.The second study reported the findings of 200 tested cattle over 2 sampling periods comparing the sensitivity and specificity of the different tests. The mean sensitivity of the coproantigen ELISA was significantly higher than the 4 g/10 g preparations of the faecal egg sedimentation tests at 94% (95% CI 87%-98%) (P < 0.001). The third study reported the findings of Coproantigen ELISA testing on 250 bovine faecal samples with 94 confirmed positive for liver fluke via faecal sedimentation testing. The sensitivity of the coproantigen ELISA was 80% and the specificity was 100% compared with 70% and 80% respectively for the faecal egg sedimentation test.<br><br>CONCLUSION: All three studies demonstrated either an increased or equivalent sensitivity of the coproantigen ELISA to the faecal sedimentation test, but only one study reported a statistically significant increase in test sensitivity. Whilst all three studies were diagnostic accuracy validity studies, the systematic sampling strategy of one study was superior to the convenience sampling method of one of the other studies and to the case control method of the other.Several sources of bias also exist within the included studies. Sampling and selection bias is present in the two of studies due to the animals selected only being sampled over one year. The results of these studies are susceptible to changes in the fluke lifecycle of that year, and the sampled animals are more likely to be fit and well-conditioned as they are presenting for slaughter, and as such are less likely to carry significant/chronic fluke burdens. All three studies are susceptible to validity issues due to an absence of clinical information regarding flukicide treatment and concurrent parasitic diseases which, whilst not impacting the efficacy of diagnostic testing, may cause issues if the studies are to be repeated.The coproantigen ELISA can be utilised as a suitable adjunctive test to aid in the diagnosis of Fasciola hepatica in adult cattle and has the scope to be used as an early diagnostic test, but whilst the results of the reported studies indicate that the coproantigen ELISA is an accurate and reliable test, it does not provide definitive evidence to warrant the discontinuation of the simple and affordable faecal egg sedimentation test. In order to come to a conclusion regarding the more sensitive test more literature is required that directly compares the coproantigen ELISA to the faecal egg sedimentation test in different clinical scenarios and exploring different diagnostic techniques.}, }
@article {pmid42004547, year = {2026}, author = {Andong, FA and Mayowa, ES and Nwanozie, PO and Ejere, VC and Afyare, AAA}, title = {Double burden: microfilariae infection amplifies metabolic costs of moult in breeding male village weavers (Ploceus cucullatus).}, journal = {Biochemistry and biophysics reports}, volume = {46}, number = {}, pages = {102576}, pmid = {42004547}, issn = {2405-5808}, abstract = {Breeding male birds face high energetic demands due to simultaneous investment in reproduction and feather moult, yet the metabolic consequences of parasitic infection during this period are poorly understood. To address this gap, we focused on non-moulting and actively moulting breeding adult male village weavers (Ploceus cucullatus) to investigate how microfilariae infection affects host biochemical energy status and overall condition. Using plasma glucose, triglycerides, β-hydroxybutyrate, and body mass adjusted for structural size as integrative markers, we examined how infection influences energy allocation and imposes physiological costs during this critical life-history stage. Specifically, we aimed to: (i) determine whether microfilariae infection and active moult influence short-term energy availability by examining plasma glucose concentrations, and whether absolute body mass modulates the effect of infection; and (ii) evaluate the combined and independent effects of infection and moult on lipid and ketone metabolism, while incorporating absolute body mass and size-corrected body condition index (BCI) to assess overall energetic reserves and physiological trade-offs. A total of 128 breeding males were trapped and screened for microfilariae and moult status. Our results indicate infected birds that are actively moulting experienced higher β-hydroxybutyrate, lower glucose and reduced BCI, when compared with the non-infected birds that were non-moulting. On the other hand, non-infected male birds that were also non-moulting maintained higher triglyceride levels. Our regression analyses indicate both infection and moult independently increased ketone concentrations and decreased triglycerides (P < 0.05), with no significant interaction for most markers. However, for β-hydroxybutyrate, the interaction may approach significance (P = 0.08), which suggest a marginal tendency toward non-additive effects. These results highlight a 'double burden,' where concurrent parasitism and moult constrain energy allocation, shifting metabolism from carbohydrates toward lipid catabolism. This study may provide mechanistic insight into how microfilariae infection amplifies energetic costs during high-demand life-history stages in breeding male village weavers.}, }
@article {pmid42006915, year = {2026}, author = {Taquet, L and Conway, BJ and Boerger, TF and Goetschel, K and Young, SC and Botros, NE and Raghavan, M and Schmit, BD and Krucoff, MO}, title = {The frequency-dependent effects of primary hand motor cortex stimulation on volitional finger movement.}, journal = {Clinical neurophysiology practice}, volume = {11}, number = {}, pages = {252-261}, pmid = {42006915}, issn = {2467-981X}, abstract = {OBJECTIVE: We conducted a prospective study in human patients undergoing awake craniotomies to examine whether the effects of cortical stimulation in hand primary motor cortex (M1) can be (1) frequency dependent and (2) inhibitory.<br><br>METHODS: In 11 participants undergoing clinically indicated awake craniotomies, we delivered bursts of 1-333&#xa0;Hz stimulation during a finger-flexion task. Synchronized electrocorticography (ECoG), finger joint kinematics, electromyography (EMG), and video were recorded.<br><br>RESULTS: Inability to flex the index finger during subthreshold stimulation was noted in 3 participants at frequencies >250&#xa0;Hz when the electrodes were in locations that induced extension of the forefinger at higher amplitudes. Other than these trials, all stimulation events either induced muscle contractions or had no measurable effect.<br><br>CONCLUSION: Data presented here represent the first evidence of (1) movement inhibition of the human hand caused by electrical stimulation of M1, as well as (2) the frequency-dependence of net downstream effects of hand M1 stimulation during task. Our findings support the hypothesis that the mechanism of movement inhibition may be activation of indirect, net-inhibitory mechanisms, as opposed to direct inhibition of the stimulated motor neurons.<br><br>SIGNIFICANCE: There is growing interest in using continuous electrical stimulation of the brain to remap anatomical-functional relationships away from invasive lesions. Achieving this type of neuroplasticity requires a better understanding of the direct and indirect effects of cortical stimulation. Here we demonstrate the frequency-dependent effects of cortical M1 stimulation on volitional finger movement.}, }
@article {pmid42009354, year = {2026}, author = {Kim, M and Heo, D and Kim, J and Kim, SP}, title = {Enhancing the Performance of Event-Related Potential-Based Brain-Computer Interfaces under Cognitive Distraction: A Multiwindow Adaptive Approach.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3685282}, pmid = {42009354}, issn = {1558-0210}, abstract = {Event-related potential (ERP)-based brain- computer interfaces (BCIs) require focused attention to presented stimuli. However, their applications in real life frequently involve environments that demand multitasking and impose cognitive distraction. Such distractions degrade ERP amplitudes and consequently reduce BCI performance. This study proposes a multiwindow adaptive model to mitigate the adverse effects of cognitive distraction on visual ERP-based BCIs. The proposed approach divides poststimulus intervals into multiple overlapping windows, each with dedicated spatial filters and classifiers that continuously update through adaptive semi-supervised learning. Offline experiments on a BCI control dataset collected during concurrent speaking demonstrate that the proposed method significantly outperforms single-window or fixed (i.e., nonadaptive) models, yielding an accuracy of 91.08%. Further validation in an online experiment confirms that the multiwindow adaptive approach effectively restores BCI performance, achieving an accuracy of 93.20% despite cognitive distraction. These findings highlight the practical benefits of temporally tailored feature extraction and continuous adaptation for real-world ERP-based BCIs, enabling robust performance even under cognitive distraction.}, }
@article {pmid42010188, year = {2026}, author = {Mesgarani, N}, title = {From Selective Listening to Brain-Controlled Hearing: A Perspective on the Future of Auditory Technology.}, journal = {Journal of the Association for Research in Otolaryngology : JARO}, volume = {}, number = {}, pages = {}, pmid = {42010188}, issn = {1438-7573}, abstract = {Understanding speech in noisy environments is a major challenge for millions, a problem that conventional hearing aids often exacerbate by amplifying all sounds indiscriminately. Auditory Attention Decoding (AAD) offers a revolutionary alternative: a brain-computer interface that decodes a listener's attentional focus from their neural signals to selectively enhance the desired sound source. For over a decade, research has demonstrated the scientific feasibility of attention decoding, yet the field has faced a critical barrier in translating this promise into a real-time system that provides a demonstrable perceptual benefit in real-world listening conditions. This perspective charts the journey of AAD, from its foundational neuroscientific discoveries to the current engineering hurdles that must be overcome for real-world deployment. We outline the key remaining challenges, including the need to define user-centric metrics for success, develop practical and power-efficient wearable sensors, design low-latency and computationally efficient decoding algorithms, and ensure robust performance in complex, naturalistic scenes. By addressing these questions, the field can move beyond passive amplification and create the next generation of assistive technology: one that listens with the brain to restore or augment the hearing experience, making it fully aligned with the user's intent.}, }
@article {pmid41993643, year = {2026}, author = {Wang, Y and Zhai, Y and Zheng, Z and Wang, N and Chai, X and Niu, H and Jia, Y and Zhu, S and Shang, Y and Wan, K and Cao, T and He, Q and Zhang, T and Qiu, H and Yang, Y}, title = {Progress in non-invasive neuromodulation based on consciousness-related neural circuits: a narrative review.}, journal = {Frontiers in neurology}, volume = {17}, number = {}, pages = {1770928}, pmid = {41993643}, issn = {1664-2295}, abstract = {Disorders of Consciousness (DOC) are characterized by abnormal function or disrupted connectivity of consciousness-related neural circuits, mainly presenting as Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) and Minimally Conscious State (MCS), which impose a heavy burden on patients' families and society. Non-Invasive Brain Stimulation (NIBS) has emerged as a core research direction for DOC treatment due to its non-invasiveness, ease of operation, and favorable safety profile. Based on the classification of consciousness-related neural circuits, this review systematically summarizes the research progress of central and peripheral non-invasive neuromodulation techniques, including their potential regulatory mechanisms on core circuits (such as the frontoparietal network, cortico-thalamocortical circuit, and ascending reticular activating system), clinical evidence, and synergistic effects of combined therapies. Studies have shown that techniques like Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS) targeting the frontoparietal network, Low-Intensity Transcranial Focused Ultrasound (LITUS, also referred to as Transcranial Focused Ultrasound [TUS]/transcranial Focused Ultrasound [tFUS] in the field) and Temporal Interference (TI) regulating the cortico-thalamocortical circuit, and Median Nerve Stimulation (MNS) activating the ascending reticular activating system have demonstrated certain efficacy in improving consciousness in MCS patients, while the evidence for efficacy in VS/UWS patients remains weak due to small sample sizes, lack of control groups and insufficient statistical power. Combined therapies such as TMS + MNS and Transcranial Focused Ultrasound LITUS+TMS exhibit significantly superior synergistic effects compared to monotherapies. By horizontally comparing the advantages and limitations of various techniques, this review proposes personalized treatment recommendations based on the characteristics of neural circuit damage. It also points out that future research should optimize stimulation parameters, clarify the specificity of circuit regulation, and verify long-term efficacy through large-sample randomized controlled trials (RCTs), aiming to provide a reference for the standardized and precise application of NIBS in DOC treatment.}, }
@article {pmid41997170, year = {2026}, author = {Ma, J and Ruotsalo, T}, title = {Adapting frozen foundation models for montage-agnostic high-resolution EEG event segmentation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae6142}, pmid = {41997170}, issn = {1741-2552}, abstract = {Deploying brain-computer interfaces (BCIs) outside controlled laboratories requires detecting neural events in continuous electroencephalography (EEG) without relying on time-locked synchronization, while simultaneously generalizing across the diverse electrode montages encountered with different acquisition hardware. We investigate whether frozen EEG foundation models can be adapted to perform high temporal resolution event segmentation across unseen montages and datasets without subject-specific calibration. Approach. We introduce a lightweight, parameter-efficient preprocessing layer that interpolates learned channel embeddings based on electrode coordinates, enabling any frozen foundation model backbone to accept arbitrary montages. A shallow segmentation head is attached to produce a label every 4\,ms of continuous EEG, and overlapping predictions are consolidated via sliding-window majority voting. Main results. Evaluated on eight public corpora spanning P300, steady-state visually evoked potential (SSVEP) and motor imagery (MI) paradigms, our method consistently outperforms the original foundation models (BIOT, EEGPT) and classical baselines (EEGNet), achieving a mean macro F1 of 0.492 and Intersection over Union (IoU) of 0.361 in cross-subject evaluation, and F1\,=\,0.462, IoU\,=\,0.319 in calibration-free cross-dataset generalization. Significance. By decoupling the electrode montage from the pre-trained feature extractor through a plug-in adapter rather than massive retraining, our framework enables practical, resource-efficient BCI applications that operate without time-locked synchronization or montage-specific calibration, laying the groundwork for bridging the lab-to-field gap. The code and pre-processed datasets are available at: https://anonymous.4open.science/r/VewOdXnk669E17342jch-F1BD.}, }
@article {pmid41998183, year = {2026}, author = {du Bois, N and Korik, A and Hodge, S and Hudson, L and Elahi, AS and Bigirimana, A and Dayan, N and Sanchez-Bornot, JM and McCann, A and Yelden, K and Bradley, L and Nair, KPS and Judge, S and Hoad, D and Vines, E and Harilal, V and Parke, S and Johnson, P and Pogue, J and Dodds, E and Salawu, A and Carson, R and McCreadie, K and Stow, J and McElligott, J and Carroll, A and Coyle, D}, title = {Advancing EEG-based assessment of consciousness and cognition in prolonged disorders of consciousness.}, journal = {Communications medicine}, volume = {}, number = {}, pages = {}, doi = {10.1038/s43856-026-01574-x}, pmid = {41998183}, issn = {2730-664X}, support = {EP/T022175//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/ ; EP/V025724/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/ ; }, abstract = {BACKGROUND: Accurate assessment of residual awareness in patients with Prolonged Disorders of Consciousness (PDoC) remains a major clinical challenge, as conventional behavioural tools can underestimate covert cognition. This study evaluates whether a structured, multi-phase motor imagery Brain-Computer Interface (MI-BCI) protocol provides objective electroencephalography (EEG)-based indicators of awareness that complement behavioural assessments.<br><br>METHODS: Forty-four participants (N&#x2009;=&#x2009;44) completed repeated imagined-movement tasks using wearable EEG (PDoC: Unresponsive Wakefulness Syndrome (UWS, n&#x2009;=&#x2009;14), Minimally Conscious State (MCS, n&#x2009;=&#x2009;17), Locked-In Syndrome (LIS, n&#x2009;=&#x2009;11); two able-bodied participants as benchmarks; ClinicalTrials.gov: NCT03827187; 30-01-2019). The protocol assessed sensorimotor rhythm modulation, training with and without neurofeedback, and binary question answering across phases. Standard behavioural assessments (CRS-R and WHIM) were administered at each session.<br><br>RESULTS: Significant MI-BCI decoding accuracy (DA) is achieved by 73.8% of patients, of whom 90% progress to Q&amp;A testing and frequently exceed the 70% usability threshold, revealing marked inter-individual heterogeneity. For significant MI-BCI runs, LIS outperform MCS (p&#x2009;=&#x2009;0.007) and UWS (p&#x2009;=&#x2009;0.048), while UWS exceed MCS during Q&amp;A (p&#x2009;=&#x2009;0.049), driven by familiar-voice stimuli. Using leave-one-subject-out cross-validation, combining predictions from DA and behavioural assessments improves balanced diagnostic accuracy to 62% (from 55%), increasing sensitivity to MCS (39% to 69%), with a modest reduction in LIS sensitivity (78% to 67%). Task-related activity over sensorimotor and parietal cortices differentiate diagnostic groups.<br><br>CONCLUSIONS: The structured MI-BCI protocol demonstrates potential as a movement-independent, EEG-based tool for distinguishing UWS, MCS and LIS. Integrating DA and spatial patterns yields diagnostic information that may augment behavioural assessment and advance objective tools for evaluating awareness in PDoC.}, }
@article {pmid42000335, year = {2026}, author = {Gan, Z and Xu, X and Chen, X}, title = {Surviving Toward Recovery: Redefining the Surgical Goals in Spontaneous Intracerebral Hemorrhage in the Era of Brain-Computer Interface.}, journal = {World neurosurgery}, volume = {210}, number = {}, pages = {124969}, doi = {10.1016/j.wneu.2026.124969}, pmid = {42000335}, issn = {1878-8769}, }
@article {pmid42000756, year = {2026}, author = {Zhan, L and Wu, X and Wang, X and Xiao, H and Wang, S and Zheng, L and Wang, H}, title = {A midbrain circuit for high-fat-food induced conditioned taste aversion.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-72107-2}, pmid = {42000756}, issn = {2041-1723}, support = {32171014, 31970940//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Conditioned taste aversion (CTA) is a survival mechanism that prevents consumption of harmful foods. Yet its neural circuits, especially those for solid food aversion, are poorly understood. Using a male mouse model where high-fat food (HFF) was paired with LiCl injections, we identified the median raphe region (MRR) as essential for CTA. Optogenetic activation of MRR glutamatergic neurons replaced LiCl injections, inducing robust HFF aversion. Calcium signaling in MRR neurons increased upon HFF approach post-CTA. We uncovered a necessary glutamatergic projection from the medial preoptic area (MPOA) to the MRR; stimulating this circuit mimicked LiCl, to elicit HFF aversion. Following CTA, synaptic changes in MRR neurons included an increased mEPSC frequency and an altered paired-pulse ratio in the MPOA[VgluT2]-MRR pathway. Finally, MRR projections to the medial septum and lateral habenula differentially encode and retrieve CTA memory. These findings define a circuit for aversion learning, offering insights into maladaptive eating behaviors.}, }
@article {pmid41991043, year = {2026}, author = {Han, J and Huang, X and Wu, N and Xi, L and Xu, H and Zhang, E}, title = {Effort enhances feedback expectation but reduces feedback evaluation in prosocial contexts: A behavioral and ERP study.}, journal = {Biological psychology}, volume = {}, number = {}, pages = {109263}, doi = {10.1016/j.biopsycho.2026.109263}, pmid = {41991043}, issn = {1873-6246}, abstract = {Previous research has shown that effort expenditure influences feedback processing in prosocial behavior. However, it remains unclear whether effort enhances or reduces feedback expectations and evaluations in prosocial behavior. Therefore, using event-related potentials (ERP) and multivariate pattern analysis (MVPA), the current study employed a modified prosocial effort task to examine how effort separately affects feedback expectation and evaluation in prosocial behavior. Behaviorally, high effort (vs. low effort) reduced participants' satisfaction when the beneficiary was another person, no such effect was observed when the beneficiary was the self. On the electrophysiological level, during the anticipation stage, the stimulus-preceding negativity (SPN) was more negative under high effort (vs. low effort) conditions. Conversely, during the evaluation stage, the feedback P300 (Fb-P3) and late positive potential (LPP) exhibited smaller responses after exerting high effort (vs. low effort). Moreover, the N2 and theta power were larger when the beneficiary was another person (vs. the self). MVPA revealed that effort distinguishing could be reliably decoded from electroencephalography (EEG) signals, regardless of whether the beneficiary was the self or another person. Taken together, our findings suggest that effort expenditure enhances feedback expectations but reduces subsequent feedback evaluations in prosocial behavior.}, }
@article {pmid41991955, year = {2026}, author = {Wang, X and Ding, Y and Ban, Y and Wang, L and Chen, F}, title = {An Open Non-Invasive EEG Dataset for Spontaneous Auditory Attention Switch Decoding.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-026-07244-w}, pmid = {41991955}, issn = {2052-4463}, support = {No. 2023YFF1203502//the National Key Research and Development Program of China/ ; }, abstract = {Auditory brain-computer interfaces (BCIs) based on non-invasive electrodes offer significant potential for advancing the understanding of selective auditory attention mechanisms and enabling natural human-computer interaction. Despite growing interest in auditory attention research, publicly available datasets focused on spontaneous auditory attention switching remain limited, particularly those with high-quality electroencephalography (EEG) recordings in realistic listening environments. To address this gap, we present the Auditory Attention Switching Dataset (AASD), a non-invasive EEG dataset designed to investigate spontaneous selective auditory attention switching during naturalistic auditory processing. The dataset captures both sustained attention and spontaneous attention switching events through EEG signals. A baseline decoding model is introduced to verify data integrity and demonstrate its potential for practical applications. This open-access resource lays the foundation for developing algorithms for spontaneous auditory attention switching and advancing research in natural-scenario auditory BCIs.}, }
@article {pmid41992015, year = {2026}, author = {}, title = {Brain-machine interface reveals the origin of a widely used neural signal.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {41992015}, issn = {1476-4687}, }
@article {pmid41993020, year = {2026}, author = {Shuolin, L and Chuanyin, L and Zhu, M and Zhu, T and Li, YE and Liu, Y and Xu, C and Wang, H and Hu, R and Ge, J and Zhang, Y}, title = {Targeting E3 Ubiquitin Ligase Hrd1 Prevents Myocardial Ischemia-Reperfusion Injury Through Enhancing ALDH2 Enzymatic Activity.}, journal = {Circulation}, volume = {}, number = {}, pages = {}, doi = {10.1161/CIRCULATIONAHA.125.074399}, pmid = {41993020}, issn = {1524-4539}, abstract = {BACKGROUND: Myocardial ischemia-reperfusion (I/R) injury presents a significant clinical challenge characterized by a complex pathological mechanism. The role of protein ubiquitination in I/R injury has not been systematically investigated. Global ubiquitinome profiling was conducted to identify the potential key players in myocardial I/R injury.<br><br>METHODS: The ubiquitination levels of proteins in mouse hearts subjected to either sham surgery or I/R injury were analyzed using ubiquitinome. A combined analysis of ubiquitinome, single-cell RNA sequencing (RNA-seq), and proteomics data was employed to predict potential E3 ubiquitin ligases associated with myocardial I/R injury. Global heterozygous 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase degradation 1 (Hrd1) knockout, endothelial cell (EC)-specific Hrd1 deficiency (Hrd1[f/f]; Cdh5[Cre]), and EC-specific Hrd1 overexpression (AAV-EC-Hrd1) mice were used to assess the role of Hrd1 in myocardial I/R injury. Mass spectrometry and immunoprecipitation were used to elucidate the interaction between Hrd1 and aldehyde dehydrogenase 2 (ALDH2). Additionally, we assessed ubiquitination and vasomotor reactivity to clarify the mechanisms by which Hrd1 regulates ALDH2 activity and EC dysfunction during I/R injury.<br><br>RESULTS: Ubiquitinome analysis revealed that protein ubiquitination exacerbates endothelial dysfunction after myocardial I/R injury. Integrative analysis of the ubiquitinome, proteomics, and single-cell RNA-seq revealed a significant upregulation of the E3 ubiquitin-protein ligase Hrd1 in CD45[+] ECs. In both humans and mice, the level of endothelial Hrd1 protein was found to increase in response to I/R in vivo. Genetic ablation of Hrd1 significantly alleviated myocardial infarction, endothelial dysfunction, and infiltration of inflammatory cells after I/R injury. Mechanistically, Hrd1 promoted the K33-linked polyubiquitination of ALDH2 and then inhibited the formation of its active tetramers, which reduced the apoptosis of CD45[+] ECs and exacerbated endothelial dysfunction through the NO/cGMP/PKG (nitric oxide-cyclic guanosine monophosphate-protein kinase G) signaling pathway. Furthermore, our findings demonstrated that pharmacological inhibition of Hrd1 robustly ameliorated myocardial I/R injury and endothelial dysfunction.<br><br>CONCLUSIONS: Our findings demonstrated a previously unidentified crucial role of cardiac EC Hrd1 in myocardial I/R injury. Hrd1 may serve as a therapeutic target for preventing myocardial I/R injury.}, }
@article {pmid41989905, year = {2026}, author = {Li, Y and Su, D and Wang, X and Wei, F and Zhao, H and Zhang, J}, title = {Zero-Calibration MI Decoding via Self-Supervised Representation and Ensemble Learning.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3681334}, pmid = {41989905}, issn = {1558-2531}, abstract = {Motor Imagery (MI) is a core task in brain computer interface (BCI) research. Zero-calibration MI de coding, which eliminates the need for training subject specific models, significantly reduces data annotation and training time, thus attracting widespread attention. However, this paradigm demands a higher level of precision in extracting invariant features. This study proposes an efficient strategy that combines self-supervised representation learning with supervised classification learning to accurately extract intrinsic invariant features from EEG signals. Specifically, through random masking and feature reconstruction mechanisms, the encoder performs self-supervised learning to uncover universal EEG signal features. Additionally, an ensemble learning classifier is used to further compress features and significantly enhance model performance through multi-branch comprehensive decision-making. Our method employs a pure convolutional neural network (CNN) architecture, achieves excel lent performance in three MI tasks: in the complex four class classification task, it is the only method to achieve an accuracy exceeding 60%; in the two binary classification tasks, accuracies of 85.50% and 82.98% were achieved. In the comparison, the p-values of almost all methods were less than 0.05, demonstrating significant statistical significance. This study provides an innovative and efficient solution for cross-subject zero-calibration MI decoding.}, }
@article {pmid41990730, year = {2026}, author = {Li, K and Wei, Y and Ni, JD}, title = {A sensor for heart filling.}, journal = {Neuron}, volume = {114}, number = {8}, pages = {1335-1337}, doi = {10.1016/j.neuron.2026.03.030}, pmid = {41990730}, issn = {1097-4199}, mesh = {Animals ; Humans ; *Heart/innervation/physiology ; *Vagus Nerve/physiology ; *Ion Channels/metabolism ; *Blood Volume/physiology ; }, abstract = {Cardiovascular sensory pathways are essential for maintaining tissue perfusion under physiological perturbation. A new study identifies PIEZO2-positive vagal afferents as candidate cardiac blood-volume receptors that detect reduced filling and trigger protective responses during postural challenge and hemorrhage.}, }
@article {pmid41990822, year = {2026}, author = {Klee, D and Memmott, T and Oken, B}, title = {Measures of fatigue and performance are related to user interface and task in a communication BCI.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae60d2}, pmid = {41990822}, issn = {1741-2552}, abstract = {OBJECTIVE: This exploratory study compared two non-implantable Communication Brain-Computer Interfaces (cBCIs) to determine whether physiologic and self-report measures of mental fatigue, effort, and boredom were greater during calibration than during copy-spelling and whether there were differences between two common cBCI interfaces, Rapid Serial Visual Presentation (RSVP) and Single-Character Presentation Matrix (SCP-Matrix).<br><br>APPROACH: Twenty-three healthy adults successfully utilized both RSVP and SCP-Matrix speller cBCIs in a single experimental session. Participants completed a calibration task and three online (closed-loop) copy-spelling tasks for each interface and provided self-report data on state mental fatigue, effort, and boredom. Physiological measures included EEG recordings alongside autonomic markers, including blood pressure, heart rate, respiration rate, and pulse rate variability (PRV).<br><br>MAIN RESULTS: Participants reported significant increases in perceived mental fatigue, effort, boredom, and sleepiness during the session, with significant increases during calibration compared to copy-spelling. On average, users typed 1.5 more correct characters per copy-spelling phase using the SCP-Matrix interface than when using RSVP. Results for autonomic and self-report metrics were consistent with fatigue being increased during calibration tasks relative to copy-spelling. EEG measures showed increased absolute and relative alpha activity and decreased relative theta activity during calibrations compared to copy-spelling, and increased absolute and relative alpha activity and decreased relative theta activity during RSVP, compared to Matrix. P300 amplitude on average was greater during copy spelling tasks than during calibrations.<br><br>SIGNIFICANCE: Participants demonstrated increased fatigue while using non-implantable cBCIs. Evidence suggested that calibration tasks for both interfaces were more fatiguing, required more mental effort, and were less engaging than copy-spelling tasks. Increased user fatigue and perceived mental effort remain significant barriers to sustained use of non-implantable cBCI systems. Though limited, the current study enhances our understanding of user experience with cBCIs and emphasizes the need to design more engaging and concise calibration procedures.}, }
@article {pmid41990825, year = {2026}, author = {Zhang, J and Shi, Z and Xu, H and Rao, Z and Bai, S and Gao, J}, title = {Kalman phase transfer entropy (KTE-TP): a novel measure for information transfer measurement to enhance brain-computer decoding.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae60d1}, pmid = {41990825}, issn = {1741-2552}, abstract = {Objective, To address the limitation that existing brain network analysis methods relying on relatively simplistic data processing models fail to capture the multimodal information embedded in neural signals, this study introduces a multidimensional information transfer measurement method - Kalman Time Phase Transfer Entropy (nKTE-TP), and applies it to Enhanced Brain Computer Interface (BCI) decoding. Approach, Built upon the transfer entropy algorithm, this method leverages the Kalman filter to dynamically weight signal amplitude and phase information, while taking both the temporal and spectral characteristics of signals into account, thus effectively integrating amplitude energy-driven features with phase temporal properties. The effectiveness of the nKTE-TP method was validated through multivariable autoregressive model (MVAR) simulation experiments and three different types of publicly available BCI datasets, and compared with existing algorithms. Main results, MVAR simulation results demonstrate that this method robustly assesses causal information and exhibits superior stability and accuracy in evaluating complex systems compared to existing algorithms. In addition, the validation results of three publicly available BCI datasets show that compared with other causal algorithms, the nKTE-TP network features exhibit higher discriminative ability and generate more robust recognition models. Significance, This method offers a more comprehensive metric for investigating information dependence and integration across different brain regions, while simultaneously providing an effective decoding strategy for BCI neural signal analysis.}, }
@article {pmid41880655, year = {2026}, author = {Van Ransbeeck, W and Yuan, Z and Maes, PJ and Leman, M and Verhulst, S and Botteldooren, D}, title = {An EEG-based framework for exploring adaptive rhythmic human-machine interaction.}, journal = {Journal of neural engineering}, volume = {23}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ae573d}, pmid = {41880655}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; *Brain-Computer Interfaces ; *Periodicity ; Photic Stimulation/methods ; *Man-Machine Systems ; }, abstract = {Objective.Understanding rhythmic human-human interaction and its underlying mechanisms can enhance experiential value and enjoyment by providing a tailored experience and supporting applications in medical human-machine contexts. Existing experimental paradigms often lack a unified and holistic analysis, characterised by limited ecological validity in partner realism, active engagement, and visual interaction. These can produce hidebound insights due to variable partner behaviour, inflexible design, or insufficient user experience analysis. The study presents and validates a multimodal paradigm that addresses these limitations and enables controlled evaluation of human-human rhythm interaction and its extension to virtual AI agents.Approach.Participants completed a tapping paradigm with an audio-visual drum animation driven by either a human or AI-based partner under simple and complex (polyrhythmic) conditions. Portable electroencephalography (EEG) recordings and post-trial questionnaires assessed neural and subjective responses.Main results.The framework improves ecological validity relative to existing approaches and effectively masks partner identity (human vs AI) without reducing experienced flow, arousal, or enjoyment, which remained positive overall. Notably, the AI-based partner considered a first attempt to create a virtual AI-driven interacting drummer, suitable for future consideration of alternative algorithms. Additionally, the design supports unobtrusive, portable EEG measurement of neural modulation and temporal alignment with both performed and presented stimuli.Significance.This paradigm offers a flexible foundation for studying rhythmic interaction in human-machine systems, balancing ecological realism with experimental partner control while supporting future adaptive or biofeedback-driven systems that optimise rhythm interaction in real-time.}, }
@article {pmid41984613, year = {2026}, author = {Wei, Y and Luo, R and Xia, Y and Mai, X and Zhu, X and Meng, J}, title = {Rhythmic Motor Imagery Boosts Accuracy and Efficiency in Noninvasive Brain-Computer Interfaces.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3684384}, pmid = {41984613}, issn = {2168-2208}, abstract = {OBJECTIVE: Decoding motor intentions from noninvasive brain recordings remains a longstanding challenge in neural engineering, particularly in advancing brain-computer interfaces (BCIs) for motor assistance and rehabilitation. The traditional motor imagery (MI) paradigm faces limitations due to ill-defined mental tasks and the variability of the induced sensorimotor rhythm (SMR) features. Studies involving large-scale subject cohorts have reported that conventional MI-BCI achieves only around 70 75% accuracy in binary classification, with an inefficiency rate of 35%-50%.<br><br>METHODS: Here, we introduce a rhythmic MI paradigm which can induce steady-state movement-related rhythms (SSMRR). A comprehensive evaluation involving 65 BCI-na&#xef;ve participants was conducted to investigate whether rhythmic MI with SSMRR features can enhance MI-BCI's performance.<br><br>MAIN RESULTS: Our results demonstrate a 4 class online decoding accuracy of 78.88%&#xb1;14.80% and a binary offline decoding accuracy of nearly 90%, with an inefficiency rate below 10%, marking a substantial improvement over conventional MI-BCI. Offline evaluations also show the potential of rhythmic MI for cross-subject generalization. Furthermore, we show that the proposed rhythmic MI tasks can help participants better modulate SMR, reducing the SMR inefficiency rate from 50.77% to 23.08%. Lastly, we validate phase consistency as a neurophysiological predictor of SSMRR-based decoding, offering insights for further refining mental tasks and improving decoding algorithms.<br><br>SIGNIFICANCE: Overall, our findings demonstrate that rhythmic MI can facilitate a noninvasive BCI with high decoding accuracy and low inefficiency rate, unlocking new possibilities for human machine interaction and clinical applications such as neurorehabilitation.}, }
@article {pmid41984955, year = {2026}, author = {Saussus, O and De Schrijver, S and Ramirez, JG and Decramer, T and Janssen, P}, title = {Intracortical brain-computer interface for navigation in virtual reality in macaque monkeys.}, journal = {Science advances}, volume = {12}, number = {16}, pages = {eadw3876}, doi = {10.1126/sciadv.adw3876}, pmid = {41984955}, issn = {2375-2548}, mesh = {Animals ; *Brain-Computer Interfaces ; *Virtual Reality ; Macaca mulatta ; *Motor Cortex/physiology ; Male ; Macaca ; *Spatial Navigation ; }, abstract = {We present an innovative intracortical brain-computer interface (BCI) to bridge the gap between laboratory settings and real-world applications. This BCI approach introduces three key advancements. First, we used neural signals from three macaque brain regions-primary motor, dorsal, and ventral premotor cortex-enabling precise and flexible decoding of real-time three-dimensional (3D) sphere/avatar velocities. Second, we developed a realistic, immersive 3D virtual reality setup with dynamic camera tracking, allowing continuous navigation and obstacle avoidance that closely mimic real-world scenarios. Last, our BCI approach is very well suited for use by paralyzed patients, featuring a brief passive fixation without overt movements and closed-loop operation without retraining of the decoder during online decoding, relying on the user's neural plasticity and the decoder's robust generalization across tasks. Our BCI adapted to different environments, targets, and obstacles, illustrating its potential to substantially enhance the quality of life for paralyzed patients by enabling natural, reliable, and flexible control in complex settings.}, }
@article {pmid41985514, year = {2026}, author = {Porcaro, C and Bertoldo, A}, title = {Quantifying Fractal and Oscillatory Components in Neural Signals for Biomarker Development.}, journal = {Progress in biomedical engineering (Bristol, England)}, volume = {}, number = {}, pages = {}, doi = {10.1088/2516-1091/ae6003}, pmid = {41985514}, issn = {2516-1091}, abstract = {Neural activity encompasses both rhythmic oscillations and aperiodic background dynamics, reflecting complex brain function beyond traditional rhythm-centric views. The aperiodic component, once considered noise, is now recognised as a meaningful signal indicative of excitation-inhibition balance and intrinsic neural timescales. Here, we review advanced signal processing frameworks, including spectral parameterisation and burst detection algorithms, that disentangle these periodic and aperiodic components. We critically evaluate evidence suggesting that aperiodic parameters track neurodevelopment and serve as candidate biomarkers for Alzheimer's Disease and Parkinsonism. Furthermore, we highlight how neuroengineering interventions, such as Deep Brain Stimulation and acupuncture, actively modulate these features. Crucially, we address the current methodological heterogeneity in the field, proposing a standardized roadmap for estimation to resolve conflicting interpretations. These findings underscore the complementary roles of oscillatory and aperiodic dynamics, offering novel avenues for closed-loop brain-computer interfaces (BCIs) and personalized neurotherapeutics.}, }
@article {pmid41985678, year = {2026}, author = {Wang, J and Peng, Y and Lv, B and Yao, D}, title = {Multi-scale EEG evidence for attention enhancement following long-term action video game training.}, journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology}, volume = {225}, number = {}, pages = {113384}, doi = {10.1016/j.ijpsycho.2026.113384}, pmid = {41985678}, issn = {1872-7697}, abstract = {Action video games (AVGs), characterized by a fast pace and high perceptual load, have been increasingly applied to probe cognitive enhancement and neural plasticity. While substantial behavioral evidence confirms that AVG training enhances various cognitive functions such as attention, the underlying mechanisms of neural plasticity, particularly from the perspective of dynamic, large-scale functional coordination, remain incompletely understood. Existing studies are largely limited by short training paradigms and an emphasis on local EEG measures, making it difficult to capture the systemic neural remodeling associated with prolonged training. In this single-group longitudinal intervention study, a six-month AVG training program (120 h in total) was conducted in healthy young adults to examine neural and behavioral changes related to long-term AVG training. Behavioral performance improved across the training period in both the d2 Test of Attention and the Attention Breadth task. Resting-state EEG was analyzed using a multi-level framework that combined PSD-based local analysis with network-based indices of large-scale functional organization. Association models were constructed to link neural features with behavioral gains, and model performance was evaluated using leave-one-subject-out cross-validation based on the correlation between predicted and observed behavioral improvements. These findings may help to better characterize the neurophysiological basis of cognitive training and may provide preliminary insights for the development of personalized cognitive rehabilitation strategies.}, }
@article {pmid41986129, year = {2026}, author = {Laureys, S and Guger, C and Meng, J}, title = {Transformative Technologies in Brain-Computer Interfaces-Innovations, Applications, and Challenges.}, journal = {Brain connectivity}, volume = {}, number = {}, pages = {21580014261437968}, doi = {10.1177/21580014261437968}, pmid = {41986129}, issn = {2158-0022}, }
@article {pmid41986329, year = {2026}, author = {Srinivasan, A and Wairagkar, M and Iacobacci, C and Hou, X and Card, NS and Jacques, BG and Pritchard, AL and Bechefsky, PH and Hochberg, LR and AuYong, N and Pandarinath, C and Brandman, DM and Stavisky, SD}, title = {Encoding of speech modes and loudness in ventral precentral gyrus.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-71284-4}, pmid = {41986329}, issn = {2041-1723}, abstract = {The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with 94% and 89% accuracy for the two participants, and corresponding neural preparatory activity at 640 ms and 270 ms before speech onset enabled 80% decoding accuracy, respectively. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.}, }
@article {pmid41986769, year = {2026}, author = {Hadke, SS and Klingler, CN and Brown, ST and Holla, M and Zhuang, X and Li, L and Utama, MIB and Diaz-Arauzo, S and Chapagain, A and Li, S and Lee, JH and Raman, IM and Sangwan, VK and Hersam, MC}, title = {Printed MoS2 memristive nanosheet networks for spiking neurons with multi-order complexity.}, journal = {Nature nanotechnology}, volume = {}, number = {}, pages = {}, pmid = {41986769}, issn = {1748-3395}, support = {EFMA-2317974//National Science Foundation (NSF)/ ; DMR-2308691//National Science Foundation (NSF)/ ; ECCS-2025633//National Science Foundation (NSF)/ ; DE-AC02-06CH11357//U.S. Department of Energy (DOE)/ ; R35-NS116854//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; }, abstract = {Artificial neurons that reproduce the rich dynamical behaviour of biological spiking are essential for neuromorphic hardware and biohybrid interfaces, yet scalable solution-processed devices with physiologically relevant spiking characteristics remain elusive. Here we demonstrate aerosol-jet-printed memristive networks of MoS2 nanosheets that exhibit thermally activated filamentary switching and snap-back negative differential resistance, enabling volatile threshold switching in fully printed graphene/MoS2/graphene devices on flexible substrates. In situ thermal imaging and circuit modelling reveal that current-constricted filaments formed through Joule heating govern the nonlinear switching dynamics. These printed memristors enable oscillatory and spiking neuron circuits with tunable frequencies up to 20 kHz and stable operation over more than 10[6] cycles. Simple neuristor circuits realize first-, second- and third-order spiking complexity, including integrate-and-fire behaviour, spike latency, tonic firing, class 1 excitability, tonic bursting and phasic dynamics. The generated spike waveforms match physiological timescales and stimulate Purkinje neurons in mouse cerebellar slices. Our results establish printed nanosheet memristive networks as a scalable platform for bio-realistic neuromorphic hardware and flexible brain-machine interfaces.}, }
@article {pmid41987469, year = {2026}, author = {Huang, Q and Liu, X and Ji, X and Jin, R and Liu, C and Jin, K and Hu, S}, title = {[Progress and Prospects of Ultrasound Brain-Computer Interface Medical Devices Technology].}, journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation}, volume = {50}, number = {2}, pages = {181-187}, doi = {10.12455/j.issn.1671-7104.250652}, pmid = {41987469}, issn = {1671-7104}, mesh = {*Brain-Computer Interfaces ; Humans ; Ultrasonography ; Brain ; Deep Brain Stimulation ; }, abstract = {Brain-computer interface (BCI) medical devices represent a category of innovative medical devices. They assist in treating neurological disorders by leveraging human-machine interaction to improve patients' quality of life. Simultaneously, ultrasound is a non-invasive and effective medical physics technology. It enables imaging of cerebral hemodynamic changes and neuromodulation, specifically for acquiring brain activity information and stimulating deep brain tissues. Notably, ultrasound BCI medical devices targeting deep brain stimulation remain in the research and clinical trial phases. Consequently, ultrasound BCI technology has significant research prospects. In this paper, different types of ultrasound BCI medical device technologies are firstly summarized and organized, followed by a review of their current research status. Then, the regulatory laws and regulations for ultrasound-based BCI devices are briefly discussed, and finally the future regulatory requirements for related products are prospected.}, }
@article {pmid41987849, year = {2026}, author = {Xu, Y and Chen, D and Ye, Q and Zhang, P and Shi, J and Li, S and Sun, Y and Zhao, Z and Tang, Y and Zhang, P and Tang, Z}, title = {Emerging Neural Recording and Neurostimulation Technologies Based on Brain-Computer Interface: A Promising Approach for Neuropsychiatric Disorders.}, journal = {MedComm}, volume = {7}, number = {}, pages = {e70739}, pmid = {41987849}, issn = {2688-2663}, abstract = {Neurological and psychiatric disorders, arising from disruptions in neural circuitry, pose a major and growing challenge to global healthcare systems. Brain-computer interface (BCI) technology has emerged as a promising approach, enabling direct communication between the brain and external devices. By facilitating bidirectional interaction with the nervous system, BCIs open new avenues for both diagnosis and treatment. In this review, we examine recent advances in recording and stimulation technologies within the BCI framework and evaluate their therapeutic potential across major neuropsychiatric disorders. We focus particularly on post-stroke motor rehabilitation as a representative paradigm, providing detailed analysis of the mechanisms, clinical evidence, and future prospects of endovascular BCI, BCI-integrated epidural spinal cord stimulation, and BCI-driven deep brain stimulation. We further extend the discussion to movement disorders such as Parkinson's disease and epilepsy, as well as cognitive and psychiatric conditions including Alzheimer's disease and depression, highlighting how BCI-based approaches enable symptom detection and closed-loop neuromodulation. Additionally, we address ethical and societal considerations accompanying clinical translation of these advanced neurotechnologies. By integrating current evidence, this review highlights a paradigm shift toward more active, precise, and personalized neural rehabilitation enabled by BCI systems, while outlining key challenges and future directions for research and clinical application.}, }
@article {pmid41989053, year = {2026}, author = {Perez, PN and Smesseim, I and Sterman, DH}, title = {The role of bronchoscopic cryoimmunotherapy in non-small cell lung cancer: current evidence and future perspectives.}, journal = {Immunotherapy}, volume = {}, number = {}, pages = {1-11}, doi = {10.1080/1750743X.2026.2651072}, pmid = {41989053}, issn = {1750-7448}, abstract = {Lung cancer is the leading cause of cancer deaths, and despite therapeutic advances, recurrence and resistance persist. Local tumor ablation can function as an in situ vaccine, but thermal techniques may disrupt antigen and extracellular matrix integrity, potentially limiting immunogenicity, whereas cryoablation has been shown to preserve tumor antigens and matrix architecture while inducing immunogenic cell death. Bronchoscopic cryoimmunotherapy (BCI) aims to prime antitumor immunity rather than achieve complete tumor eradication. We review preclinical and clinical studies evaluating cryoablation and BCI in non-small cell lung cancer (NSCLC), focusing on immune mechanisms, delivery approaches, and combination with systemic therapies, particularly immune checkpoint inhibitors (ICIs). Preclinical models demonstrate that cryoablation releases danger signals and intact tumor antigens, drives dendritic cell maturation, expands effector CD8+ T cells, and activates STING-dependent type I interferon pathways. Early-phase human studies of BCI monotherapy show systemic immune stimulation, including reductions in the derived neutrophil-to-lymphocyte ratio and expansion of CD8+ effector memory populations. Combination cryoablation-ICI regimens have revealed improved response rates in some cohorts, although clinical outcomes have been limited by small, heterogeneous, and non-randomized studies. BCI is a mechanistically compelling, minimally invasive therapy, but its clinical benefit remains unproven and warrants rigorous randomized evaluation.}, }
@article {pmid41989127, year = {2026}, author = {Rao, H and Mao, T}, title = {Decoding the Adenosinergic Paradox of Chronic Sleep Restriction.}, journal = {Sleep}, volume = {}, number = {}, pages = {}, doi = {10.1093/sleep/zsag099}, pmid = {41989127}, issn = {1550-9109}, }
@article {pmid41978994, year = {2026}, author = {Li, J and Yi, Y and Gan, L and Bezgin, G and Chan, T and Rahmouni, N and Wang, YT and Aumont, E and Hosseini, SA and Hall, BJ and Trudel, L and Therriault, J and Macedo, AC and Socualaya, KMQ and Arias, JF and Zheng, Y and Olivia-Lopez, D and Hopewell, R and Hsiao, CH and Zou, T and Soucy, JP and Gauthier, S and Vitali, P and Pascoal, TA and Razlighi, QR and Montembeault, M and Li, R and Rosa-Neto, P}, title = {Elevation in network dynamics amplifies amyloid-dependent tau pathology.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {22}, number = {4}, pages = {e71354}, pmid = {41978994}, issn = {1552-5279}, support = {//Weston Brain Institute/ ; MOP-11-51-31/CAPMC/CIHR/Canada ; RFN 152985/CAPMC/CIHR/Canada ; 159815/CAPMC/CIHR/Canada ; 162303/CAPMC/CIHR/Canada ; MOP-11-51-31 -team 1//Canadian Consortium of Neurodegeneration and Aging/ ; NIRG-12-92090//the Alzheimer's Association/ ; NIRP-12-259245//the Alzheimer's Association/ ; CFI Project 34874//Brain Canada Foundation/ ; 33397//Brain Canada Foundation/ ; Chercheur Boursier 2020-VICO-279314//the Fonds de Recherche du Qu&#xe9;bec-Sant&#xe9;/ ; 2024-VICO-356138//the Fonds de Recherche du Qu&#xe9;bec-Sant&#xe9;/ ; 2022ZD0208903//Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project/ ; 823720853//National Natural Science Foundation of China/ ; LGL-1630-05//Lingang Laboratory/ ; //Fondation Brain Canada/ ; //Consortium canadien en neurod&#xe9;g&#xe9;n&#xe9;rescence associ&#xe9;e au vieillissement/ ; }, mesh = {Humans ; Male ; *tau Proteins/metabolism ; Female ; Cross-Sectional Studies ; *Amyloid beta-Peptides/metabolism ; Magnetic Resonance Imaging ; Positron-Emission Tomography ; Aged ; *Brain/diagnostic imaging/metabolism/pathology ; *Alzheimer Disease/pathology/diagnostic imaging/metabolism ; Middle Aged ; *Nerve Net/diagnostic imaging ; Biomarkers ; }, abstract = {INTRODUCTION: The role of brain network dynamics in relation to amyloid beta (Aβ) and tau pathology across Braak stages remains unclear.<br><br>METHODS: In this cross-sectional study of 216 participants from Translational Biomarkers of Aging and Dementia (TRIAD) cohort, we analyzed resting-state functional magnetic resonance imaging using a multilayer modularity algorithm to assess brain network dynamics across 10 predefined functional networks, stratified by amyloid and tau positron emission tomography biomarkers and Braak stages.<br><br>RESULTS: Switching rates were significantly elevated in A&#x3b2;-positive/tau-positive individuals relative to A&#x3b2;-negative/tau-negative individuals, and increased progressively with advancing Braak stages. Elevated switching rates were strongly correlated with A&#x3b2; and tau burden in dorsal attention network and sensorimotor network, as well as with cognitive severity. Importantly, the interaction between network switching rate and A&#x3b2; burden synergistically contributed to accelerated tau accumulation in Braak stage III to V regions.<br><br>DISCUSSION: These findings support the framework that increased network switching may amplify A&#x3b2;-related tau load and cognitive deterioration in Alzheimer's disease.}, }
@article {pmid41979953, year = {2026}, author = {Cao, B and Guo, Y and Tian, Y and Cao, F and Chen, Y and Lin, CT}, title = {A Hybrid Covert Attention-Augmented Motor Imagery Paradigm for Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3683767}, pmid = {41979953}, issn = {1558-0210}, abstract = {Motor imagery (MI) is widely used in brain-computer interfaces (BCIs) and has been applied in neurorehabilitation to support motor recovery in stroke patients. Although numerous decoding algorithms have been developed and achieve comparable performance, their effectiveness fundamentally depends on the availability of robust bio-markers elicited during MI. However, inexperienced users often evoke weak MI-related bio-markers, making reliable classification difficult even with advanced algorithms. Consequently, inaccurate classification lead to erroneous feedback to users, which further undermines the usability of MI. From the perspective of feature representation, augmenting MI with additional cognitive bio-markers can enrich neural features, improve feedback reliability, and facilitate more effective MI training and use. Therefore, a covert attention-augmented motor imagery (CAA-MI) paradigm is proposed as a hybrid BCI approach, which integrates covert spatial attention (CSA) with MI to introduce additional bio-markers and enrich the features used for classification. A transformer-based multi-branch EEG fusion network (TMEF-Net) is developed to fully leverage the diverse EEG features elicited by the proposed paradigm for decoding. Experiments involving 17 subjects demonstrate that CAA-MI consistently outperforms traditional MI (T-MI) in both intra-subject and inter-subject evaluations, particularly under short decoding windows. With a 3-s input, CAA-MI achieved intra-subject and inter-subject accuracies of 89% and 81%, outperforming T-MI (82% and 76%). Neurophysiological analysis further reveals that CAA-MI evokes broader activations across occipital-parietal and sensorimotor regions, providing more discriminative EEG features. These findings suggest that the proposed paradigm enables more robust neural responses and offers a promising strategy for neurorehabilitation-oriented BCIs.}, }
@article {pmid41980295, year = {2026}, author = {Xu, W and Lin, Y and Wang, X and Liu, J and Ren, Y}, title = {PR[2]DM: Position-aware robust reconstruction with diffusion model for emotion recognition From EEG.}, journal = {Computer methods and programs in biomedicine}, volume = {281}, number = {}, pages = {109365}, doi = {10.1016/j.cmpb.2026.109365}, pmid = {41980295}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Emotion recognition is significant in domains like smart education, mental health assistance, and brain-computer interface. Electroencephalography-based emotion recognition methods provide an objective approach to evaluating emotional states by analyzing the brain's electrophysiological activity. In this research, we aim to address the challenges of EEG data scarcity and noise that vary depending on the electrode position, both of which significantly impact the quality of emotion recognition.<br><br>METHODS: We propose position-aware robust reconstruction with diffusion model (PR[2]DM) for emotion recognition from EEG. PR[2]DM integrates electrode positional encoding with features in each level and fuses various temporal scale features, reconstructing noise-independent features from the corrupted features to enrich the training sample. Additionally, we propose a spatial-temporal adaptive convolutional network that adaptively adjusts parameters based on EEG features for more precise classification.<br><br>RESULTS: The developed framework is validated on DEAP and DREAMER datasets, demonstrating excellent performance in both valence and arousal classification tasks, achieving 95.01% and 95.62% accuracy on DEAP, and 84.77% and 87.30% accuracy on DREAMER, respectively. A comparative analysis of EEG topographic maps further confirms the model's effectiveness in reconstructing noise-independent EEG features. Ablation experiments further explored the contribution of each component to model performance, proving that our proposed method effectively enhances the performance of emotion recognition.<br><br>CONCLUSION: The proposed PR[2]DM mitigates the impact of data scarcity in EEG signals and inconsistent noise distribution across electrode positions, suggesting improvements in emotion recognition accuracy based on EEG signals.}, }
@article {pmid41980782, year = {2026}, author = {Liao, YY and Che, J and Gao, YT and Xue, J and Li, L and Wu, LL and Hu, JX and Hu, MT and Xie, L and Zhang, H and Shen, DD and Dong, Y and Zang, S and Zhang, N and Wang, H and Zhang, Y and Dong, X and Li, XM}, title = {Rational design of Gi-biased CB1 agonist with reduced side effects.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2026.03.020}, pmid = {41980782}, issn = {1097-4172}, abstract = {The cannabinoid receptor 1 (CB1) has emerged as a promising candidate for next-generation non-opioid therapies. However, the development of therapeutics targeting CB1 has been consistently hindered by significant adverse effects. Here, through structure-activity relationship analyses focused on biased signaling, we rationally design two Gi-biased CB1 agonists, LZD503 and LZD505. Our design strategy employed structural spatial tuning of the agonist scaffold to disrupt specific molecular interactions and minimize steric conflicts with critical tip residues within the ligand-binding pocket, thereby promoting preferential Gi-pathway signaling. Cryo-electron microscopy structures of the CB1-G-protein complexes bound to these designed agonists confirmed that their anticipated conformational poses favored Gi-biased signaling. Both designed compounds demonstrated promising results by alleviating pain and mitigating unwanted responses in mice. The elucidated CB1 complex structures and the resulting insights establish a comprehensive framework for the structure-guided development of innovative CB1-targeted analgesics with reduced adverse effect profiles.}, }
@article {pmid41982007, year = {2026}, author = {Aboubakr, O and Mokhtari, K and Nichelli, L and Bielle, F and Demeret, S and Dubessy, AL and Alamowitch, S and Pineton De Chambrun, M and Le Joncour, A and Pourcher, V and Idbaih, A and Mathon, B}, title = {Early brain biopsy in neurological diseases of unknown etiology improves functional outcome.}, journal = {European journal of internal medicine}, volume = {}, number = {}, pages = {106878}, doi = {10.1016/j.ejim.2026.106878}, pmid = {41982007}, issn = {1879-0828}, abstract = {BACKGROUND: The indications and timing of brain biopsy in adults with neurological diseases of unknown etiology remain controversial. We aimed to determine diagnostic yield, complications, outcomes, and survival after brain biopsy and evaluate whether early biopsy improves prognosis.<br><br>METHODS: We analyzed adults who underwent brain biopsy (2008-2024) after non-diagnostic workup at our institution. Primary outcomes were diagnostic yield, 6-month functional status using modified Rankin Scale (mRS), and overall survival (OS). Early biopsy was defined as &#x2264;1 month after symptom onset. Multivariable logistic regression identified predictors of favorable outcomes (mRS&#x2264;2), and Cox models assessed OS.<br><br>RESULTS: Among 3014 biopsies, 294 met inclusion criteria (mean age 50.6 &#xb1; 15.3 years; 47% immunocompromised). Biopsy provided a contributory diagnosis in 69% of patients and changed management in 71%. Symptomatic complications occurred in 3.4% of patients. Functional independence (mRS&#x2264;2) increased from 44.6% at biopsy to 54.1% at 6 months (p = 0.003), with 22% mortality. Baseline independence (OR 7.15, 95%CI 3.94-12.97) and early biopsy (OR 2.03, 95%CI 1.05-3.93) predicted favorable outcomes, whereas solid organ tumor history (OR 0.32) and altered consciousness (OR 0.53) predicted worse recovery. Early biopsy yielded a higher diagnostic success rate (82% vs. 65%, p = 0.005). During 37.9-month follow- up, 31% died (mean OS 9.3 months). Longer OS was associated with baseline independence and autoimmune/inflammatory diagnosis, whereas solid-organ tumors, altered consciousness, and coma predicted shorter OS.<br><br>CONCLUSIONS: Brain biopsy is safe and diagnostically useful for cryptogenic neurological diseases. Early biopsy independently predicts better functional outcomes and higher diagnostic yield, supporting earlier tissue sampling after inconclusive noninvasive evaluation.}, }
@article {pmid41983319, year = {2026}, author = {Zhang, H and Xiang, Y and Zhang, X and Zhang, L and Xue, Q and Zhu, X and Chang, W and Li, T and Yu, X and Yang, C and Lin, Y and Zhang, M and Wang, R and Xiao, K}, title = {An Artificial Dopamine-Ionic Cascade Synapse for Adaptive Neuromorphic Attention.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e21464}, doi = {10.1002/adma.202521464}, pmid = {41983319}, issn = {1521-4095}, support = {B2401005//Shenzhen Medical Research Fund/ ; 22275079//National Natural Science Foundation of China/ ; 22474053//National Natural Science Foundation of China/ ; 2023ZT10C027//Guangdong Innovative and Entrepreneurial Research Team Program/ ; KQTD20221101093559017//Shenzhen Science and Technology Program/ ; JCYJ20230807093205011//Shenzhen Science and Technology Program/ ; RCYX20231211090432060//Shenzhen Science and Technology Program/ ; 2024A1515012600//Guangdong Basic and Applied Basic Research Foundation/ ; 2022B1212010003//Guangdong Provincial Key Laboratory of Advanced Biomaterials/ ; G03050K002//High Level of Special Funds from Southern University of Science and Technology (SUS-Tech)/ ; }, abstract = {Biological intelligence operates through chemo-ionic signal processing, where neurotransmitters encode information as spatiotemporal chemical gradients that regulate ionic dynamics across neural synapses. Given the diversity of chemical neurotransmitters and ion species, developing an artificial chemo-ionic cascade synapse that can translate biochemical signals into tunable synaptic weights will be of great significance for brain-inspired computing and brain-computer interfaces. Here, we present an artificial dopamine (DA)-ionic cascade synapse by integrating a sensitive DA sensor with an ionic elastomer-based neuromorphic device. The oxidation of DA generates localized electric fields that electrostatically modulate ion migration within the ionic elastomer device, enabling chemical-to-ionic signal transduction and dynamic plasticity control. Consequently, biochemical cues like DA concentration can be directly reflected in tunable ionic synaptic weights, which can then be used to control a robotic platform for recognition tasks. This artificial synapse exhibits biochemical signal-driven behavioral selectivity in an object-grasping task, completing a perception-decision-execution loop. This work establishes a framework for processing biochemical information via native ionic dynamics, paving the way for chemically neuromorphic systems and embodied human-machine interaction.}, }
@article {pmid41983445, year = {2026}, author = {Zhu, JC and Zhu, MJ and He, QZ and Wu, P and Niu, XK and Wang, JT and Wang, ZZ}, title = {Multidimensional visual feature encoding and functional organization in the pigeon entopallium.}, journal = {Zoological research}, volume = {47}, number = {2}, pages = {487-502}, doi = {10.24272/j.issn.2095-8137.2025.217}, pmid = {41983445}, issn = {2095-8137}, mesh = {Animals ; *Columbidae/physiology ; *Visual Perception/physiology ; Neurons/physiology ; Photic Stimulation ; *Visual Pathways/physiology ; }, abstract = {Understanding how birds perceive and recognize visual objects remains a fundamental question in neuroscience. The entopallium, a key node in the avian tectofugal pathway, has long been implicated in complex visual processing, yet its internal functional architecture remains incompletely understood. In this study, neuronal activity in the pigeon entopallium was systematically mapped using controlled visual stimuli that independently varied in color, shape, and motion. Recordings revealed marked hue selectivity that remained invariant across luminance levels, pronounced orientation tuning in response to shape stimuli, and robust direction selectivity for moving stimuli. Spatial mapping further revealed distinct functional segregation, with color-selective neurons localized anteroventrally, shape-selective neurons dorsally, and motion-selective neurons posteriorly. At the same time, partial overlap among these response classes was observed, with a subset of neurons exhibiting joint tuning across stimulus dimensions, suggesting an organizational scheme characterized by regional specialization and partial cross-feature integration. Notably, entopallium neurons exhibited a moderate level of visual feature integration and shared important functional properties with early to intermediate stages of mammalian visual processing. Together, these findings establish the entopallium as a major site for multidimensional visual analysis in birds and provide evidence for convergent principles underlying the evolution of complex visual systems across vertebrates.}, }
@article {pmid41983993, year = {2026}, author = {Liang, F and Chen, X and Li, B and Yang, B}, title = {Brain-Computer Interface Combined with Functional Electrical Stimulation for Post-Stroke Upper Limb Motor Recovery: A Systematic Review and Meta-Analysis.}, journal = {Clinical EEG and neuroscience}, volume = {}, number = {}, pages = {15500594261441055}, doi = {10.1177/15500594261441055}, pmid = {41983993}, issn = {2169-5202}, abstract = {BackgroundBrain-computer interface-driven functional electrical stimulation (BCI-FES) is a promising approach for post-stroke upper limb rehabilitation. However, considerable variability exists in stimulation parameters and task designs across studies, and evidence remains insufficient to support definitive protocol recommendations.MethodsWe searched PubMed, Embase, Web of Science, and the Cochrane Library for randomized controlled trials (RCTs) up to September 2025. Eligible studies applied BCI-FES and reported the Fugl-Meyer Assessment for the upper extremity (FMA-UE). Risk of bias was assessed with the PEDro scale, and evidence certainty graded with GRADE. Random-effects meta-analyses were performed.ResultsTwelve RCTs (n = 619) showed BCI-FES improved FMA-UE scores versus controls (MD = 5.82, 95% CI 3.04-8.59, p < 0.00001; I[2] = 39%), with larger benefits in subacute stroke (MD = 8.45). Dynamic-threshold paradigms and motor imagery were associated with higher effect sizes. Higher stimulation frequency (>50 Hz), narrow-pulse width (150 µs) more frequent sessions (≥5/week), shorter session duration (≤30 min), greater total sessions (>20), and longer intervention (>4 weeks) tended to be associated with larger effect sizes, though evidence is limited and based on few studies. Secondary outcomes (ARAT, WMFT, MBI) improved, and no serious adverse events were reported. Evidence certainty was moderate.ConclusionBCI-FES was associated with improvements in upper limb motor recovery after stroke, especially in subacute patients. Some stimulation and training features may relate to greater effects, but current evidence remains insufficient for definitive clinical guidance. Larger multicenter RCTs are needed to clarify dose-response relationships and support biomarker-guided, personalized interventions.}, }
@article {pmid41984214, year = {2026}, author = {Garvayo, M and Dupont, S and Frazzini, V and Bielle, F and Adam, C and El Bendary, Y and Méré, M and Samson, S and Guesdon, A and Navarro, V and Mathon, B}, title = {Correction: Resective surgery for mesial temporal lobe epilepsy associated with hippocampal sclerosis in patients over 50 years: a case-control study.}, journal = {Journal of neurology}, volume = {273}, number = {5}, pages = {}, doi = {10.1007/s00415-026-13736-x}, pmid = {41984214}, issn = {1432-1459}, }
@article {pmid41973558, year = {2026}, author = {Kwon, BH and Jeong, JH and Lee, SW}, title = {E2T: EEG-to-Trajectory Transformer for Motor Imagery-based Fully-DoF Motion Prediction.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3683431}, pmid = {41973558}, issn = {1558-0210}, abstract = {Brain-computer interfaces (BCIs) using electroen-cephalography (EEG) enable non-invasive, real-time interaction for individuals with motor impairments by decoding neural signals associated with movement intention. Although traditional classification-based motor imagery (MI) BCIs translate discrete EEG patterns into predefined commands, such approaches inherently lack the flexibility required for continuous and naturalistic movement control. To overcome these limitations, we propose a novel EEG-based decoding framework that directly predicts continuous 3D upper limb trajectories using a hybrid deep learning architecture. The model integrates frequency-adaptive Sinc convolutional filters, a Transformer-based temporal encoder, and ensemble learning to extract meaningful spatiotemporal features from raw EEG signals. To ensure balanced spatial coverage, we developed a task-specific experimental paradigm in which each trial comprised sequential motor execution (ME) and MI phases based on one of four predefined 3D directions: forward-vertical, forward-leftward, forward-rightward, and lateral-vertical. The MI phase was placed after the ME phase to reinforce vivid internal representations of movement. This approach allowed the model to capture continuous 3D trajectories across the X, Y, and Z axes without directional bias. Comprehensive evaluation under both ME and MI conditions demonstrates robust decoding performance, achieving average correlation coefficients of 0.7728 (ME) and 0.7110 (MI), significantly outperforming existing baseline models. Furthermore, the framework generalizes well to unseen MI tasks, demonstrating its feasibility for real-world deployment in assistive neuroprosthetic systems with a reduced calibration session.}, }
@article {pmid41973582, year = {2026}, author = {Pan, H and Teng, B and Li, Z and Li, L and Guo, Q and Lei, X and Mi, W}, title = {MBGFF Model for EEG-Text Generation Under Multi-Task Parallel Imagination.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3683557}, pmid = {41973582}, issn = {1558-2531}, abstract = {Brain-computer interface (BCI)-based text generation provides a promising communication pathway for individuals with severe motor or speech impairments. However, most existing EEG-based text generation studies rely on a single imagery modality, which limits information richness, decoding robustness, and practical communication applicability. To address these issues, this study proposes a text generation framework based on a Multi-Branch Global Feature Fusion (MBGFF) model under a multi-task parallel imagery paradigm that jointly incorporates visual, speech, and motor imagery. To reduce inter-subject variability, a personalized channel layout strategy based on one-way analysis of variance is designed for subject-specific EEG acquisition. The proposed MBGFF model further introduces a tri-branch architecture that combines multi-scale local feature extraction and global feature fusion through convolutional blocks, multi-head attention, and a global convolution block, thereby enhancing multi-character EEG decoding. In addition, an online EEG-to-text platform with real-time signal acquisition, decoding, and dual-model language correction is developed to support practical communication scenarios. Experiments on a 29-character EEG dataset collected from 10 subjects show that the proposed method achieves an average offline decoding accuracy of 71.48%. In online experiments, the average decoded character accuracy rate reaches 64.70%, and the average corrected character accuracy rate further improves to 77.39% after language correction. These results demonstrate that the proposed framework is effective for multi-character EEG decoding and shows strong potential for practical assistive EEG-based text generation.}, }
@article {pmid41974133, year = {2026}, author = {Tajik Mansouri, Z and Dion, JP and Escabí, MA and Stevenson, IH}, title = {Parametric models for predicting nonstationary spike-spike correlations with local field potentials.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae5ea5}, pmid = {41974133}, issn = {1741-2552}, abstract = {Objective Correlations between the spiking of pairs of neurons are often used to study the brain's representation of sensory or motor variables and neural circuit function and dysfunction. Previous statistical techniques have shown how time-averaged spike-spike correlations can be predicted by the time-averaged relationships between the individual neurons and the local field potential (LFP). However, spiking and LFP are both nonstationary, and spike-spike correlations have nonstationary structure that cannot be accounted for by time-averaged approaches. Here our goal is to develop parametric models that predict spike-spike correlations using a small number of LFP-based predictors and apply these models to the problem of tracking changes in spike-spike correlations over time. Approach Parametric models allow for flexibility in the choice of which LFP recording channels and frequency bands to use for prediction, and coefficients directly indicate which LFP features are associated with correlated spiking. Here we demonstrate our methods in simulation and test the models on experimental data from large-scale multi-electrode recordings in the mouse hippocampus and visual cortex. Main results In single time windows, we find that our parametric models can be as accurate as previous nonparametric approaches, while also being flexible and interpretable. We then demonstrate how parametric models can be applied to describe nonstationary spike-spike correlations measured in sequential time windows. We find that although the patterns of both cortical and hippocampal spike-spike correlations vary over time, these changes are, at least partially, predicted by models that assume a fixed spike-field relationship. Significance This approach may thus help to better understand how the dynamics of spike-spike correlations are related to functional brain states. Since spike-spike correlations are increasingly used as features for decoding external variables from neural activity, these models may also have the potential to improve the accuracy of adaptive decoders and brain machine interfaces.}, }
@article {pmid41974139, year = {2026}, author = {Grevet, E and Izac, M and Py, J and Amadieu, F and Glize, B and Gasq, D and Jeunet-Kelway, C}, title = {Bridging innovation and adoption: a mixed-methods investigation into stroke survivors' acceptability of brain-computer interface-based rehabilitation interventions.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae5ea6}, pmid = {41974139}, issn = {1741-2552}, abstract = {Objective Brain-computer interface (BCI)-based interventions show growing evidence of efficacy for post-stroke upper-limb rehabilitation by closing the sensorimotor loop and promoting neuroplasticity. Despite this promise, their clinical uptake remains limited. This study aimed to identify the determinants of BCI acceptability among stroke survivors in order to inform user-centred neurotechnology design and facilitate clinical adoption. Approach We conducted a mixed-methods study combining a large-scale questionnaire (N = 140) and semi-structured interviews (N = 12) with stroke survivors. The questionnaire was grounded in a validated theoretical model of BCI acceptability and assessed the determinants of three core constructs: intention to use (IU), perceived usefulness (PU), and perceived ease of use (PEOU). Qualitative data were analysed using thematic framework analysis to enrich and contextualise the quantitative findings. Main results Overall, stroke survivors reported high acceptability of BCI-based rehabilitation, with strong intention to use (mean IU = 8.48/10). Quantitative analyses showed that IU was primarily driven by PU, itself strongly influenced by perceived scientific relevance and, to a lesser extent, by individual factors such as autonomy, self-efficacy, and technology-related anxiety. PEOU was mainly determined by ease of learning and playfulness, but did not directly predict IU. Qualitative findings complemented these results by highlighting the importance of perceived innovativeness, the perception of directly engaging brain activity, visibility of progress, and clear scientific evidence. Interviews also emphasised the need for intuitive interfaces, clear instructions, short sessions, and appropriate therapeutic support to sustain engagement. Significance These findings underscore that successful adoption of BCI-based rehabilitation requires more than technological performance alone. Enhancing acceptability among stroke survivors calls for: (i) goal-oriented and evidence-based rehabilitation protocols; (ii) clear communication of scientific rationale and outcomes to reinforce trust and social acceptance; and (iii) user-friendly system design that supports learning, autonomy, and self-efficacy while minimising cognitive and physical burden.}, }
@article {pmid41975456, year = {2026}, author = {Tscherniak, IW and Thiemann, NC and McWhinnie-Fernández, A and Curcean, I and Jokinen, LLJ and Hodzic, S and Huber, TE and Pavlov, D and Methasani, M and Marcolongo, P and Krafczyk, GV and Rivera, OOS and Le, T and Pallotti, F and Fazzi, EA and , }, title = {Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01969-w}, pmid = {41975456}, issn = {1743-0003}, }
@article {pmid41977910, year = {2026}, author = {Huggins, JE and Biswas, P and Huggins, JK and Chandel, R}, title = {A Flash Group Creation Algorithm for P300 Brain-Computer Interface Integration with Irregular Assistive Technology Keyboard Layouts.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {7}, pages = {}, doi = {10.3390/s26072123}, pmid = {41977910}, issn = {1424-8220}, support = {R42DC015142/DC/NIDCD NIH HHS/United States ; SB1DC015142/DC/NIDCD NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Algorithms ; *Event-Related Potentials, P300/physiology ; Electroencephalography ; Communication Devices for People with Disabilities ; *Self-Help Devices ; }, abstract = {An event-related potential (ERP)-based brain-computer interface (BCI), or P300 BCI, has long been intended for communication access for individuals with severe motor impairments. BCI access to communication tools, websites, and augmentative and alternative communication (AAC) keyboards requires aligning BCI stimuli to screens with differing numbers of various-sized keys in partially populated grid layouts. Six design priorities were defined for creating and ordering flash groups: identifiability, unpredictability, perceptibility, minimality, anti-adjacency, and equality. Building on the checkerboard paradigm, multiple algorithmic approaches were evaluated on simulated AAC screens to create the magic square paradigm (MSP) for flash group creation for irregular key layouts. The MSP algorithm was then used for BCI access to the dynamic screens of a commercial AAC device that combines text-based and icon-based language representations and the resulting flash groups analyzed for design priorities of anti-adjacency and equality. The 126,944 flash groups created for 5778 selections on AAC screens had 0 groups with side-by-side adjacency, 0.02% with adjacency to an amalgamated key, and 6% with diagonally adjacent keys. The average difference between the shortest and longest flash groups was 1.9 keys. The MSP provides a novel method to access dynamic AAC keyboards with irregular layouts and multiple key sizes.}, }
@article {pmid41977996, year = {2026}, author = {Saichoo, T and Siribunyaphat, N and Sahoh, B and Efendi, MA and Punsawad, Y}, title = {Electroencephalography-Based Brain-Computer Interface System Using Tongue Movement Imagery for Wheelchair Control.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {7}, pages = {}, doi = {10.3390/s26072211}, pmid = {41977996}, issn = {1424-8220}, support = {N42A680142//National Research Council of Thailand/ ; WU66252//Research and Innovation Institute of Excellence, Walailak University/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Tongue/physiology ; *Wheelchairs ; Male ; Adult ; Movement/physiology ; Female ; Support Vector Machine ; Young Adult ; Neural Networks, Computer ; Motor Cortex/physiology ; Imagination/physiology ; }, abstract = {Brain-computer interfaces (BCIs) are essential in assistive technologies to restore mobility in individuals with motor impairments. Although electroencephalography (EEG)-based brain-controlled wheelchairs have been extensively studied, most tongue-controlled systems rely on physical tongue movements, intraoral devices, or limited offline commands, which reduces the usability and comfort. This study introduces an EEG-based tongue motor imagery (MI) BCI for intuitive and entirely mental wheelchair control. By leveraging preserved motor function and the cortical representation of the tongue, the system enables natural four-directional control through imagined tongue movements. Six imagined tongue actions-touching the left and right mouth corners, the upper and lower lips, and producing left and right cheek bulges-were designed to elicit alpha-band event-related desynchronization (ERD) patterns over the tongue motor cortex. EEG data were collected from 15 healthy participants using a 14-channel consumer-grade EMOTIV EPOC X headset. Alpha-band ERD features were extracted and classified using linear discriminant analysis, support vector machine, naïve Bayes, and artificial neural networks (ANNs). Simpler command sets yielded the highest accuracy: two-class tasks achieved 76.19%, while the performance decreased with increasing task complexity. The ANN achieved superior results in multi-class scenarios. The proposed tongue MI method offers initial support for developing a BCI control strategy for assistive technology; however, further improvements in classification techniques, user training, and real-time validation are needed to improve the robustness and practical usability.}, }
@article {pmid41978050, year = {2026}, author = {Ahn, JW and Yu, GY and Kim, SW and Seok, YS and Byun, KM and Choi, SH}, title = {A Cheonjiin Layout Mental Speller: Developing a Simple and Cost-Effective EEG-Based Brain-Computer Interface System.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {7}, pages = {}, doi = {10.3390/s26072265}, pmid = {41978050}, issn = {1424-8220}, support = {RS-2026-25519175//Rural Development Administration/ ; IITP-2026-RS-2024-00438239//Ministry of Science and ICT/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; *Brain/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Cost-Benefit Analysis ; Photic Stimulation ; Algorithms ; }, abstract = {A brain-computer interface (BCI) enables direct communication between the brain and external devices by translating neural activity into executable control commands. Among electroencephalography (EEG)-based paradigms, steady-state visual evoked potential (SSVEP) is widely adopted due to its high signal-to-noise ratio, robustness, and minimal calibration requirements. While SSVEP-based spellers have been extensively investigated, many existing systems rely on high-channel-density EEG recordings and computationally complex processing pipelines, and are primarily designed for alphabetic input structures. In this study, we present an SSVEP-based Korean speller that integrates the Cheonjiin keyboard layout to support intuitive composition of Hangul syllables. The proposed system adopts a simple configuration, employing only five visual stimulation frequencies (6.67-12 Hz) and two occipital EEG channels (O1 and O2), with real-time frequency recognition performed using canonical correlation analysis (CCA) within a 1.5 s sliding window. EEG signals were acquired at 200 Hz using an OpenBCI Ganglion board, band-pass filtered (5-45 Hz), and processed with harmonic sinusoidal reference templates for multi-frequency classification. The proposed interface generates five control commands (up, down, left, right, and select), enabling directional cursor navigation and character confirmation on a 4 × 4 virtual Cheonjiin keyboard. Experimental validation with three healthy participants demonstrated an average classification accuracy of approximately 82% and an information transfer rate (ITR) of 31.2 bits/min. Frequency-domain analysis revealed clear spectral peaks at the stimulation frequencies and their harmonics, indicating reliable SSVEP responses. The proposed system employs a simple two-channel configuration integrated with a Korean language-specific input structure, demonstrating that reliable SSVEP-based communication can be realized without computationally intensive algorithms or high-cost EEG acquisition systems. These findings demonstrate that reliable SSVEP-based communication can be achieved using a low-channel configuration without reliance on high-cost EEG equipment.}, }
@article {pmid41978246, year = {2026}, author = {Yang, D and Liu, X and Hu, J and Zhang, W}, title = {Review of Recent Advances in Implantable Brain-Computer Interfaces for the Restoration of Motor Function in Patients With Paralysis.}, journal = {Medical science monitor : international medical journal of experimental and clinical research}, volume = {32}, number = {}, pages = {e951925}, doi = {10.12659/MSM.951925}, pmid = {41978246}, issn = {1643-3750}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Paralysis/physiopathology/therapy/rehabilitation ; Electroencephalography/methods ; Recovery of Function/physiology ; Electrodes, Implanted ; Electromyography/methods ; Movement/physiology ; Animals ; Neuronal Plasticity ; }, abstract = {Implantable brain-computer interfaces (BCIs) - positioned at the intersection of neuromedicine and clinical neurorehabilitation - have achieved notable advances in restoring motor function for individuals with paralysis. By using invasive electrodes to directly sample cortical neuronal activity and translating these signals into control commands for external effectors, BCIs offer a viable therapeutic pathway for severe motor impairment. On the mechanistic front, steady improvements in neural signal acquisition and decoding have enabled more precise capture of movement intent and real-time control of robotic manipulators, exoskeletons, and functional electrical stimulation systems, thereby supporting partial restoration of motor function. Evidence from animal studies and early clinical investigations indicates that long-term implanted electrodes provide distinctive advantages in signal stability, spatial resolution, and the induction of neuroplasticity, which collectively strengthen basic mechanistic inquiry and translational application. At the application level, recent work combining high-density electrode arrays with deep learning-based decoding strategies has demonstrated near real-time, multi-degree-of-freedom control of hand and upper-limb movements. In parallel, hybrid interfaces integrating electroencephalography and electromyography, together with closed-loop neuromodulatory paradigms, further extend the rehabilitative potential of BCI systems. In summary, implantable BCIs show substantial promise for motor recovery in paralysis and are progressing from laboratory demonstrations toward bedside deployment. With continued convergence of materials science, artificial intelligence, and clinical neuroscience, BCIs are poised to play an increasingly pivotal role in improving quality of life and advancing the practice of neurorehabilitation. This article aims to review recent advances in implantable BCIs for the restoration of motor function in patients with paralysis.}, }
@article {pmid41929000, year = {2026}, author = {Replogle, JM and Marks, JD and Fernandez, MG and Yuan, H and Yu, D and Winters, E and Jawahar, VM and Deshmukh, R and Sutanto, R and Kowal, I and Frankenfield, A and Shi, R and Carlomagno, Y and Jansen-West, K and Todd, TW and Kopach, A and Sandra Ndayambaje, I and Qi, YA and Shantaraman, A and Pozo-Cabanell, I and Sheth, U and Yue, M and Duong, D and Ferguson, SM and Bennett, DA and Damme, M and Boeve, BF and Day, GS and Kellman, B and Skarnes, WC and Petersen, RC and Josephs, KA and Graff-Radford, NR and McDonough, JA and Prudencio, M and Barmada, SJ and Zhang, Y and Hao, L and DeTure, M and Rawlinson, B and Engelberg-Cook, E and Casey, MC and Perez, N and Dickson, DW and Wingo, A and Liu, Y and Seyfried, NT and Wingo, TS and Mosalaganti, S and Petrucelli, L and Ward, ME}, title = {Neurodegeneration risk variants promote lysosomal TMEM106B fibril accumulation.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {41929000}, issn = {2692-8205}, abstract = {Variants in TMEM106B and GRN, which encode lysosomal proteins, interact through unknown mechanisms to increase the risk of age-related cognitive decline and neurodegeneration. Here, we show that these variants converge on a single molecular intermediate: the cleaved intra-lysosomal fibril core of TMEM106B, a precursor to amyloid fibrils that accumulate in the aging brain. A protein-coding TMEM106B risk variant (p.T185) drives fibril core accumulation by impairing its degradation and GRN risk variants amplify this effect. Mice over-expressing the fibril core develop hallmarks of neurodegeneration, and cryo-electron tomography reveals intra-lysosomal fibrils in cultured neurons, mice, and diseased human brain. In GRN-mutation carriers, in whom fibril burden is greatest, fibrils extrude through ruptured lysosomal membranes. These findings identify intra-lysosomal TMEM106B fibrillization as a convergent neurodegeneration mechanism and potential therapeutic target.}, }
@article {pmid41964479, year = {2026}, author = {Majumder, S and Halder, A and Bisht, P and Roy, M and Biswas, U}, title = {Wheelchair movement signal classification from EEG for motor-impaired individuals using novel deep learning architecture.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {}, number = {}, pages = {1-20}, doi = {10.1080/17483107.2026.2651889}, pmid = {41964479}, issn = {1748-3115}, abstract = {Purpose: Traditional wheelchair controls often limit independence and pose safety risks for motor-impaired users. To address these challenges, this study explores the potential of EEG-based control systems that allow users to operate powered wheelchairs through brain signals rather than physical movements. Materials and Methods: We developed a hybrid deep learning model that integrates Long Short-Term Memory (LSTM) and 1D-Convolutional Neural Networks (1D-CNN) with skip connections to capture both temporal and spatial EEG signal features. The model was trained and evaluated on a public EEG dataset to classify intended wheelchair movements. Performance metrics, including accuracy, precision, recall, and F1 score, were computed. Confidence interval tests and ablation studies were conducted to assess statistical reliability and component contribution. Results: The proposed model achieved an accuracy of 98.08% with 0.98 precision, recall, and F1 score, outperforming ten state-of-the-art methods. Confidence interval analysis confirmed the model's statistical superiority, while ablation results demonstrated the importance of the LSTM-CNN fusion and skip connections in enhancing prediction performance. Conclusions: The LSTM-CNN architecture with skip connections offers a reliable and accurate EEG-based control approach for powered wheelchairs, improving safety and independence for users with severe motor impairments. In future, proposed model may lead to EEG-responsive wheelchair systems to aid mobility and self-directed activity, contributing to improved quality of life and rehabilitation outcomes.}, }
@article {pmid41964718, year = {2026}, author = {Mathon, B and Jacquens, A and Gourvennec, E and Carpentier, A}, title = {Outpatient supratentorial craniotomy for brain lesions: a pilot feasibility and safety study.}, journal = {Neurosurgical review}, volume = {49}, number = {1}, pages = {}, pmid = {41964718}, issn = {1437-2320}, }
@article {pmid41965314, year = {2026}, author = {Hosoo, H and Araki, K and Masuda, Y and Ishida, H and Matsumaru, Y}, title = {Endovascular neural interfaces: current platforms and clinical readiness.}, journal = {Journal of neurointerventional surgery}, volume = {}, number = {}, pages = {}, doi = {10.1136/jnis-2025-024870}, pmid = {41965314}, issn = {1759-8486}, abstract = {Neurointerventional techniques are facilitating a new class of neural interfaces that record and stimulate brain activity from within the cerebral vasculature. Conventional scalp electroencephalography (EEG) is safe and widely scalable but is limited by skull attenuation and volume conduction, whereas electrocorticography and stereoelectroencephalography provide higher-amplitude signals at the cost of craniotomy or stereotactic depth implantation and procedure-related morbidity. Endovascular approaches offer a distinct access paradigm by leveraging familiar catheter-based workflows to reach cortical veins and dural sinuses. They occupy a practical middle ground that enhances signal quality relative to scalp EEG while mitigating some of the procedural risks associated with open or multi-trajectory intracranial implants. This narrative review summarizes the historical evolution and major device classes, including catheter-based electrodes, stent-electrode arrays, and emerging leadless or wireless systems, with emphasis on leading clinical platforms such as Stentrode (a stent-electrode recording array from Synchron, New York, USA), and EP-01 (an EEG device from Epsilon Medical, Japan). We synthesize evidence on implantation targets, deliverability, signal characteristics relevant to epilepsy evaluation and brain-computer interface applications, stimulation feasibility, and translational constraints governing clinical adoption, including antithrombotic management, vascular patency, imaging surveillance, complications, and device failure modes. We highlight decision-linked endpoints, particularly concordance with conventional intracranial EEG for seizure lateralization, and outline essential reporting elements needed to compare studies across anatomical locations, referencing strategies, and artifact environments. Finally, we provide pragmatic recommendations for neurointerventional adoption and identify priorities for next-generation device development, registries, and multicenter prospective trials.}, }
@article {pmid41965644, year = {2026}, author = {Jiangyi, L and Shuping, L and Yulei, S and Congdon, N and Lei, C and Yuanbo, L}, title = {Retinal nerve fiber layer thickness in epilepsy: a meta-analysis comparing affected patients with healthy controls.}, journal = {BMC ophthalmology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12886-026-04789-7}, pmid = {41965644}, issn = {1471-2415}, }
@article {pmid41969217, year = {2026}, author = {Alsubaie, M and Alshammari, S and Ahmed, Y and Esmail, S and Adams, K and Rouhani, H and Ríos Rincón, A}, title = {Brain-Computer Interface Games for Cognitive Assessment: A Scoping Review.}, journal = {The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques}, volume = {}, number = {}, pages = {1-44}, doi = {10.1017/cjn.2026.10606}, pmid = {41969217}, issn = {0317-1671}, }
@article {pmid41970658, year = {2026}, author = {Liu, M and Wang, S and Cui, W and Zhang, S and Zhao, L and Beyette, FR}, title = {Render EEG-Based Brain-Computer Interfaces Calibration-Free: Trade Space for Time in EEG Decoding.}, journal = {IEEE open journal of engineering in medicine and biology}, volume = {7}, number = {}, pages = {94-105}, pmid = {41970658}, issn = {2644-1276}, abstract = {Goal: Electroencephalogram-based brain-computer interfaces (EEG BCIs) have broad applications in neurorehabilitation, clinical assessment, and assistive technologies. However, their practical deployment is severely limited by subject-specific calibration, which requires time-consuming data collection and model retraining for each user, significantly reducing usability. This reliance on calibration arises from the conventional "one-model-fits-all" strategy: "relying on a single general model to handle all data complexity like subject variability. When its limited generalization falls short, time must be spent on calibration to adapt the model." Methods: To address this limitation, we propose a trade-space-for-time strategy for calibration-free EEG decoding: "Instead of adapting one model to every user, we maintain a pool of compact models, including a general model and multiple biased models, where each biased model specializes in decoding a specific type of subject pattern. For a new input, the system automatically selects the most suitable model based on data characteristics, enabling instant adaptation without retraining." Compact deep learning models make this design feasible by allowing fast switching and low storage cost, which would be impractical with large-scale architectures. Results: Experiments on multiple public EEG datasets show that the proposed strategy achieves performance comparable to within-subject decoding: slightly higher in one dataset (0.7672 vs. 0.7601), nearly identical in another (0.7568 vs. 0.7572), and marginally lower in a third (0.8804 vs. 0.8888). Conclusions: These results demonstrate that our approach effectively eliminates calibration while preserving accuracy, providing a practical and scalable alternative for EEG BCIs. The framework also has potential applications in other neuroimaging modalities such as fMRI and fNIRS.}, }
@article {pmid41970718, year = {2026}, author = {Han, F and Yu, Q and Zheng, H and Li, G and Li, Q and Tian, Q and Zhang, Y and Liu, Z}, title = {A new paradigm for brain-machine interface electrodes: From static to dynamic, advancing toward embodied intelligence.}, journal = {Innovation (Cambridge (Mass.))}, volume = {7}, number = {4}, pages = {101258}, pmid = {41970718}, issn = {2666-6758}, }
@article {pmid41971043, year = {2026}, author = {Kiser, A and Cantürk, A and Volosyak, I}, title = {SSVEP-driven BCI authentication with reduced number of EEG electrodes across high and low frequency ranges.}, journal = {Frontiers in neuroergonomics}, volume = {7}, number = {}, pages = {1741655}, pmid = {41971043}, issn = {2673-6195}, abstract = {Growing concerns over data privacy, credential theft, and spoofing attacks have highlighted the limitations of traditional authentication methods in high-security settings. To address these challenges, we propose a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) authentication system that verifies short-lived, session-specific identity prompts using neural activity. The proposed system uses a single flickering visual stimulus to encode a unique, system-generated random code that remains unknown to the user. Instead of relying on conscious input, the system directly extracts the user's brain responses to the stimulus. Authentication is achieved by matching the frequency components of the recorded electroencephalography (EEG) signals to those embedded in the visual stimulus, enabling implicit verification without prior training or manual interaction. In an online BCI study involving 21 healthy participants, we evaluated four configurations differing in stimulation frequencies and EEG electrode count. Mean symbol-level accuracy reached 99% (95% CI: 98.3 -99.6) for high-frequency stimulation with three electrodes, 95% (95% CI: 91.1 -98.2) for high-frequency stimulation with a single electrode, 97% (95% CI: 95.1 -98.4) for low-frequency stimulation with three electrodes, and 96% (95% CI: 94.5 -97.8) for low-frequency stimulation with a single electrode. The corresponding mean trial durations were 38.6 s, 76.6 s, 17.2 s, and 27.1 s, respectively. Participants generally rated high-frequency flickering stimuli as more comfortable, whereas setup time and EEG wearability were identified as the main barriers to usability. These findings demonstrate that SSVEP-based authentication can provide accurate and training-free implicit authentication, while also offering potential resistance to spoofing attacks. The results suggest that this passive BCI approach is a promising direction for secure authentication, although practical deployment will require further improvements in speed, comfort, and wearability.}, }
@article {pmid41971081, year = {2026}, author = {Feng, X and Jin, J and Ye, X and Wu, J and Yang, B and He, Q and Luo, P and Lu, J and Yang, X}, title = {Exploring the anti-diabetic potential of peimisine through bioinformatics analysis and in vitro studies.}, journal = {Frontiers in pharmacology}, volume = {17}, number = {}, pages = {1766852}, pmid = {41971081}, issn = {1663-9812}, abstract = {Fritillariae Cirrhosae Bulbus is a traditional herb with diverse activities, yet its active metabolites against type 2 diabetes (T2D) remain unclear.<br><br>OBJECTIVE: This study aimed to identify key bioactive metabolites from Fritillariae Cirrhosae Bulbus through database mining, and to evaluate the therapeutic potential of the selected metabolite peimisine against T2D through bioinformatics and experimental validation.<br><br>METHODS: Metabolites were retrieved from TCMSP. Following ADME screening and literature validation, six metabolites were identified, from which peimisine was selected based on AlogP. Its targets were predicted using multiple databases, followed by GO and KEGG enrichment analyses and disease association analyses. Glucose uptake and gluconeogenesis assays were conducted in HepG2 cells, and key targets were further analyzed via PPI network and molecular docking.<br><br>RESULTS: Six metabolites were identified, with peimisine selected as the most promising candidate. Bioinformatics analysis predicted 48 potential targets, with enrichment in metabolic pathways and a strong association with T2D. Experimentally, peimisine at 20&#xa0;&#x3bc;M increased glucose uptake by up to 36.30% and reduced medium glucose by 57.65% under normal conditions; in an insulin-resistance model, it restored uptake by 42.82% and lowered glucose by 15.32%. It also significantly suppressed gluconeogenic enzymes, reducing PEPCK mRNA by 80% and G6PD by 31% relative to control. HSP90AA1 was identified as a central target, with a docking score of -7.9&#xa0;kJ/mol.<br><br>CONCLUSION: Peimisine, a metabolite of Fritillariae Cirrhosae Bulbus, demonstrates anti-T2D potential by enhancing glucose uptake and suppressing gluconeogenesis, likely through targeting HSP90AA1, supporting its development as a phytotherapeutic candidate for T2D.}, }
@article {pmid41971349, year = {2026}, author = {Zhong, Y and Wang, Z and Zhao, X and Xu, T and Zhou, T and Hu, H}, title = {Electroencephalogram-based multimodal attention level classification using deep learning techniques.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1791677}, pmid = {41971349}, issn = {1662-5161}, abstract = {This study aims to develop a novel attention level prediction method using a multimodal brain-computer interface system that integrates electroencephalogram (EEG), electrocardiogram (ECG), and electrooculogram (EOG) signals to enhance prediction accuracy and robustness. We propose the Multi-Feature Enhanced Attention Network (MEAN), which leverages the complementary strengths of these signals: EEG provides insights into brain electrical activity, ECG captures heart rate variability to reflect emotional and cognitive states, and EOG records eye movements for contextual attention level information. The model is designed to address the limitations of single-modality signals, such as noise susceptibility and limited information range. Experimental results demonstrate that MEAN achieves an average accuracy of 0.9524, outperforming traditional models. The model exhibits superior adaptability, particularly in handling EEG and multimodal data, and shows enhanced predictive performance compared to existing approaches. In conclusion, the proposed MEAN model effectively integrates multimodal physiological signals to improve attention level prediction, offering a robust and accurate solution for applications requiring attention level monitoring. This research provides a foundation for advancing applications in education, work efficiency assessment, and cognitive enhancement technologies, highlighting the potential of multimodal approaches for understanding and predicting attention states.}, }
@article {pmid41971353, year = {2026}, author = {Tou, SLJ and Warschausky, SA and Karlsson, P and Huggins, JE}, title = {Individualized electrode subset improves the calibration accuracy of an EEG P300-design brain-computer interface for people with severe cerebral palsy.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1720969}, pmid = {41971353}, issn = {1662-5161}, abstract = {INTRODUCTION: This study examined the effect of individualized electroencephalogram (EEG) electrode location selection for non-invasive P300-design brain-computer interfaces (BCIs) in people with varying severity of cerebral palsy (CP) in a post-hoc offline analysis.<br><br>METHODS: A forward selection algorithm was used to select the best performing eight electrodes (of an available 32) to construct an individualized electrode subset for each participant. Custom electrode subset size was chosen to be 8 because BCI accuracy of the individualized subset was compared to accuracy of a widely used default subset.<br><br>RESULTS: Across 51 participants, individualized subsets improved calibration accuracy only for the severe CP cohort (mean +28.6% absolute; 95% CI [13.4%, 46.1%]; p < 0.0001). No group-level benefit was detected for mild CP or typically developing controls, although several individuals in these groups improved (2/17 mild CP; 1/10 controls). In the subset with held-out testing data (mild CP and controls), calibration gains did not translate to higher testing accuracy; among controls, the subset effect was reduced on testing (-9.6%, 95% CI [-13.3%, -5.8%], p < 0.0001), with no evidence of change for mild CP. Participants with severe CP typically required larger subsets to approach asymptotic accuracy, whereas &#x2264; 8 electrodes were sufficient for most others.<br><br>DISCUSSION: The findings suggested that electrode selection can accommodate atypical neuroanatomy in people with severe CP, while the default electrode locations are sufficient for people with milder impairments from CP and typically developing individuals.}, }
@article {pmid41971720, year = {2026}, author = {Luo, TJ}, title = {Domain generalized feature embedded learning for calibration-free event-related potentials recognition.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {77}, pmid = {41971720}, issn = {1871-4080}, abstract = {Event-related potentials (ERPs) play an important role for building EEG-based brain computer interfaces (BCIs). However, due to the complex and varied spatio-temporal characteristics of ERPs across subjects, data distribution across subjects a very important issue to be solved for constructing calibration-free BCIs. To achieve calibration-free ERPs recognition, we propose a Domain Generalized Feature Embedded Learning (DGFEL) method. First, we align the ERPs of each existed subject based on covariance centroids. Then, we enhanced the aligned samples based on xDAWN filter and extract spatio-temporal features. Finally, the spatio-temporal features are further generalized by the decomposed adversarial loss, and we construct a neural network embedding backbone to implement features generalization across subjects. The proposed method has been systematically validated on two benchmark EEG-based ERP datasets, and its classification performance surpasses several state-of-the-art methods as well as deep learning models. Moreover, it effectively captures robust features from existed source subjects, and can be generalized to new subjects without accessing target ERP samples. Our method therefore provides a novel selection to construct calibration-free ERP-BCIs.}, }
@article {pmid41973557, year = {2026}, author = {Kroflic, N and Kunavar, T and Pauw, K and Babic, J}, title = {Feedback-Related Dynamics of Hierarchical Error Processing in Goal-Directed Action.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3683510}, pmid = {41973557}, issn = {1558-0210}, abstract = {The performance monitoring system is essential for adaptive behavior and the development of brain-machine interfaces that utilize neural feedback signals. The posterior medial frontal cortex generates different error-related potentials (ErrP), including error-related negativity (ERN), N2, and feedback-related negativity (FRN), which encode specific aspects of performance evaluation. In this study, we reexamine the hierarchical framework of error processing by investigating how low-level execution error detection and correction influence high-level outcome evaluation as reflected in FRN dynamics. Furthermore, we examine whether neural signals associated with outcome errors maintain consistent or distinct feature representations under different experimental conditions of altered feedback availability. Using a visuomotor rotation task, we manipulated the availability of visual feedback in three blocks to examine how immediate sensory error detection and corrective actions interact with outcome processing. Participants (n = 16) performed reaching movements while experiencing unexpected cursor rotations (±20° and ±40°; 20% probability) that challenged their sensorimotor control and task success. EEG recordings revealed that the FRN showed valence sensitivity in Blocks 1 and 3, while Block 2 exhibited a surprise-driven response without outcome differentiation. In contrast, posterior negativity appeared only in Blocks 1 and 3, where participants could detect and correct movement errors. This posterior response emerged on trials requiring corrective movements, regardless of final outcome, and appears to be driven by the availability of sensory feedback and error correction rather than by outcome valence. Furthermore, we demonstrate robust classification between low-level and high-level error signals and their conditional outcome-related variations, providing a foundation for more informative feedback in adaptive neural interfaces.}, }
@article {pmid41961852, year = {2026}, author = {Iwama, S and Matsuoka, A and Ushiba, J}, title = {Brain-computer interface-based neurofeedback training enables transferable control of cortical state switching in humans.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {123}, number = {15}, pages = {e2525769123}, doi = {10.1073/pnas.2525769123}, pmid = {41961852}, issn = {1091-6490}, support = {JPMJPR23I1//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; 20H05923//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Neurofeedback/methods/physiology ; Male ; Female ; Adult ; Young Adult ; Learning/physiology ; Electroencephalography ; Double-Blind Method ; }, abstract = {Behavioral flexibility relies on transient neural dynamics that govern cortical state transitions. However, whether humans can deliberately learn to control such state transitions and generalize trained neural dynamics beyond contexts remains unclear. Here, we demonstrate that operation of a brain-computer interface (BCI) which links time evolution of sensorimotor activity with real-time feedback enables volitional control over the targeted neural population. Compared with a double-blind sham control group, trained participants modulated sensorimotor oscillations in the absence of BCI. Data-driven latent-state analysis further revealed stronger interregional phase coupling and steeper broadband spectral slope in the medial frontal cortex during transitions. The training-induced reorganization of sensorimotor dynamics was found during movement execution and associated with performance improvement, indexed by reduced reaction times for both muscle contraction and relaxation. These findings provide evidence that learned control over cortical state transitions enhances behavioral flexibility beyond the training context.}, }
@article {pmid41962159, year = {2026}, author = {Chen, SW and Gallo, P and Afshari, FT and Herbert, K and Lo, WB and Rodrigues, D and Solanki, G and Pepper, J}, title = {Ventricular indices in infants with enlargement of the subarachnoid space.}, journal = {Journal of neurosurgery. Pediatrics}, volume = {}, number = {}, pages = {1-9}, doi = {10.3171/2025.11.PEDS25305}, pmid = {41962159}, issn = {1933-0715}, abstract = {OBJECTIVE: The aim of this study was to characterize ventricular measurements in children diagnosed with enlargement of the subarachnoid spaces (ESS) to determine ventricular morphology.<br><br>METHODS: Children diagnosed with ESS were retrospectively identified between 2015 and 2023. Inclusion required a craniocortical width > 5 mm on neuroimaging. Demographic data and developmental outcomes were collected. Referrals to therapy services, including speech and language therapy, occupational therapy, or physiotherapy, were recorded. Ventricular size was quantified using the Evans Index (EI), bicaudate index (BCI), and cella media index (CMI) measured on axial T2-weighted MR images.<br><br>RESULTS: Of 101 children, 98 presented with macrocephaly; 3 were diagnosed incidentally through imaging. The mean age at referral was 9.0 &#xb1; 5.8 months, with a mean follow-up of 26.3 months. The median initial and final occipitofrontal circumference percentiles were 99.2 (IQR 5.6) and 99.6 (IQR 1.9), respectively. The mean craniocortical width was 9.92 mm. Ventricular indices were near or slightly above normal limits. In males, the mean EI, BCI, and CMI were 0.30 (range 0.22-0.38), 0.15 (range 0.09-0.21), and 4.43 (range 2.29-6.57), respectively. In females, the mean EI, BCI, and CMI were 0.29 (range 0.23-0.35), 0.15 (range 0.11-0.19), and 4.18 (range 2.38-5.98), respectively. No child required neurosurgical intervention. Developmental concerns prompted referrals to speech and language therapy in 56.4% of patients, physiotherapy in 16.8%, and occupational therapy in 13.9%; 4% had referrals across multiple domains.<br><br>CONCLUSIONS: This study presents one of the largest studies evaluating ventricular indices in children diagnosed with ESS. Despite mild ventriculomegaly and macrocephaly, no children underwent neurosurgical intervention. However, the association with therapy input supports a shift of focus to one of facilitating the children to achieve their developmental potential, best delivered by the pediatric and/or community service. Continued neurosurgical monitoring should be reserved for children in whom the diagnosis of ESS is not secure and concerns of raised intracranial pressure or hydrocephalus persist.}, }
@article {pmid41962830, year = {2026}, author = {Zhang, X and Wang, X and Zhu, H and Yu, H and Fu, L and Shu, W and Ye, X and Lim, Z and Saeed, S and Nguyen, HL and Pistoor, J and Rong, D and Lai, J and Wang, Z and He, Y and Wang, Y and Shen, Y and Zhou, Y and Hu, S and Tong, J}, title = {Eye-brain axis: Ocular and visual pathophysiology as driver and therapeutic target across the mood disorder trajectory.}, journal = {Progress in retinal and eye research}, volume = {112}, number = {}, pages = {101467}, doi = {10.1016/j.preteyeres.2026.101467}, pmid = {41962830}, issn = {1873-1635}, abstract = {In recent years, the promotion of multidisciplinary care and the heightened focus on patients' physical and mental well-being have sparked increased research interest in the mental health burden associated with ophthalmic diseases. In response, we assembled a multidisciplinary team of ophthalmologists, psychiatrists, neurobiologists, and computer scientists to create a systematic and forward-looking overview aimed at guiding future research in both fundamentals of life sciences and brain-computer interface as well as clinical practice. This overview centers on mood disorders, the most prevalent psychiatric conditions among this population. We integrate evidence on the neural, humoral, and inflammatory mechanisms that connect eye disease to mood dysregulation, while also detailing the ocular manifestations typical of mood-disordered patients, including their unique features and underlying mechanisms. Furthermore, we catalog current and emerging ophthalmic and psychiatric diagnostic tools and therapeutic strategies. Finally, we propose a comprehensive multidisciplinary framework for screening, treatment, patient education, and long-term follow-up, providing researchers and clinicians with an evidence-based resource for integrated care.}, }
@article {pmid41963318, year = {2026}, author = {Liu, S and Li, YE and Zhu, T and Zhang, L and Yang, C and Bi, Y and Bao, C and Hu, R and Ge, J and Zhang, Y}, title = {MARCH2 prevents doxorubicin-induced cardiomyopathy by stabilizing NR1H2 and promoting clearance of apoptotic cardiomyocytes.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-71580-z}, pmid = {41963318}, issn = {2041-1723}, support = {82425005//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Doxorubicin-induced cardiomyopathy (DiCM) involves impaired clearance of apoptotic cardiomyocytes (efferocytosis) by cardiac macrophages. This study reveals a central role for the MARCH2-NR1H2 axis in this process. We find that MARCH2 expression is significantly reduced in cardiac macrophages from DiCM mice and human dilated cardiomyopathy patients. Genetic ablation of MARCH2, either globally (MARCH2[-/-]) or specifically in resident cardiac macrophages (MARCH2[f/f]; CX3CR1[Cre]), exacerbates DiCM, impairs efferocytosis, and increases inflammation. Mechanistically, MARCH2 enhances the protein stability of the nuclear receptor NR1H2 via K27-linked polyubiquitination, leading to upregulation of the efferocytosis receptor MERTK. Conversely, macrophage-specific NR1H2 deficiency (NR1H2[f/f]; CX3CR1[Cre]) suppresses efferocytosis and worsens cardiac dysfunction. Importantly, pharmacological activation of NR1H2 attenuates DiCM progression. These findings identify the MARCH2-NR1H2 axis as a key regulator of macrophage efferocytosis and a potential therapeutic target for DiCM.}, }
@article {pmid41952690, year = {2026}, author = {McDaid, J and Bailes, JE and Jha, NK and Bobustuc, G and Berlet, R and Kessinger, C and Whitcomb, E and Lee, JM and Aksenov, DP and Walker, M and Azapagic, A and Bookwalter, J and Ullah, A and Sant, H and Shea, J and Gale, BK}, title = {Correction: Efficacy of local convection enhanced delivery of chemotherapy using an intracerebral osmotic pump in a rat model of glioblastoma.}, journal = {Frontiers in oncology}, volume = {16}, number = {}, pages = {1828465}, doi = {10.3389/fonc.2026.1828465}, pmid = {41952690}, issn = {2234-943X}, abstract = {[This corrects the article DOI: 10.3389/fonc.2026.1775053.].}, }
@article {pmid41952871, year = {2026}, author = {Wei, JX and Zhang, YM and Liang, SS and Li, QF and Huang, L and Dai, YH and Bi, ZT and Xiao, JH and Xu, JW and Zhang, YS}, title = {Effects of brain-computer interface-based rehabilitation on upper limb function, activities of daily living, and adverse events in patients with early stroke: a systematic review and meta-analysis.}, journal = {Frontiers in aging neuroscience}, volume = {18}, number = {}, pages = {1737740}, pmid = {41952871}, issn = {1663-4365}, abstract = {BACKGROUND: Brain-computer interface-based rehabilitation represents an emerging neurorehabilitation approach for post-stroke motor recovery, yet its comprehensive effects on patients in the early phase after stroke, typically defined as within 3 months of onset, remain to be fully established. This systematic review and meta-analysis evaluated effects of this intervention on upper limb function, activities of daily living, and adverse events in individuals with early stroke.<br><br>METHODS: This study was conducted following PRISMA guidelines. Eligibility criteria were established for randomized controlled trials that encompassed: (1) participants were adults (&#x2265;18 years) within 3 months of stroke onset with upper limb motor impairment; (2) interventions included brain-computer interface-based rehabilitation, and (3) outcomes that measured upper limb function, activities of daily living, and adverse events. A systematic search was performed across PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, and China National Knowledge Infrastructure databases from their inception to August 23, 2025. Two independent reviewers assessed eligibility, compiled data, and appraised methodological rigor, potential bias, and reliability of the evidence. Meta-analysis was performed using RevMan 5.4 (Cochrane Collaboration, UK) and Stata 18 (StataCorp., USA), applying random-effects models to calculate mean differences (MD) or risk ratios (RR) with 95% confidence intervals (CI). Subgroup analyses, meta-regression, sensitivity analyses, and publication bias assessments were conducted where appropriate.<br><br>RESULTS: Nine studies involving 642 participants (212 females and 430 males) with a mean age of 59.77 years were included. For primary outcomes, brain-computer interface-based rehabilitation significantly improved upper limb function in patients with early stroke (MD = 5.02, 95% CI: 3.20, 6.84). Subgroup analyses revealed that no statistically significant differences were observed in the improvement of upper limb functionality among various patient demographics and intervention characteristics (all p > 0.05). For secondary outcomes, the pooled analysis suggested a potential improvement in activities of daily living with BCI-based rehabilitation (MD = 7.68, 95% CI: 0.32, 15.03), although this finding was accompanied by very high heterogeneity (I [2] = 88%) and was not robust in sensitivity analyses, indicating low certainty of evidence. Subgroup analyses indicated that greater benefits might be observed in patients within 30 days after stroke onset and with intervention durations not exceeding 3 weeks. Regarding safety, preliminary data from a single study suggested no significant difference in adverse events between groups (p = 0.87), but the evidence base is currently insufficient to draw firm conclusions.<br><br>CONCLUSIONS: Brain-computer interface-based rehabilitation is effective in improving upper limb motor function in patients with early stroke. Current evidence suggests a potential benefit for activities of daily living, but the evidence is of low certainty due to substantial heterogeneity and limited robustness. Subgroup analyses identified time from onset and intervention duration as potential effect modifiers for activities of daily living. Preliminary safety data from a single study are encouraging but insufficient to establish a safety profile. Further well-designed randomized controlled trials are needed to establish optimal brain-computer interface-based rehabilitation protocols, to confirm the potential benefit on activities of daily living with more robust evidence, and to evaluate long-term efficacy and safety.<br><br>PROSPERO [Register number: CRD420251144151].}, }
@article {pmid41956899, year = {2026}, author = {Park, YJ and Kwon, J and Lee, G and Meng, K and Chung, CK}, title = {Spatiotemporal Dynamics in Pre-speech Semantic Category Decoding: An intracranial EEG Study.}, journal = {eNeuro}, volume = {}, number = {}, pages = {}, doi = {10.1523/ENEURO.0254-25.2026}, pmid = {41956899}, issn = {2373-2822}, abstract = {Despite major advances in brain-computer interfaces (BCIs), decoding high-level language representations prior to speech remains challenging. While prior efforts have primarily focused on acoustic or articulatory features, how semantic categories are decoded in time and space remains unclear. Here, we investigated how semantic representations unfold over time by analyzing high-gamma (HG, 70-170 Hz) electrocorticography (ECoG) signals from twenty subjects (7 females and 13 males) performing a word-reading task involving body- and non-body-related words. HG activity was examined from word presentation to 500 ms, capturing the pre-speech window. Group-level time-resolved decoding, pooling features across subjects within each Brodmann area (BA), revealed significant classification accuracy above chance in both hemispheres (p<0.05, FDR-corrected). In the left hemisphere, peak-performing BAs followed a frontal-temporal-occipital-parietal cascade: dorsolateral prefrontal cortex (dlPFC) (50 ms), inferior temporal and fusiform gyri (350-400 ms), and supramarginal gyrus (SMG) (500 ms). In contrast, the right hemisphere exhibited an occipital-temporal-frontal-temporal-parietal sequence: visual and temporal pole (TP) regions (50-100 ms), dlPFC (200 ms), fusiform gyrus (FG) (400 ms), and angular gyrus (450 ms). This progression contrasts with the frontal-initiated cascade of the left hemisphere, underscoring hemispheric differences in the timing of peak decoding loci. Cross-temporal regression revealed predictive interregional engagement. In the left hemisphere, early dlPFC activity (0-150 ms) predicted later SMG responses (300-350 ms). In the right, a strong but brief predictive link emerged from the TP to the angular gyrus (200-300 ms; peak R[2] ≈ 0.70). These findings demonstrate that semantic category decoding relies on temporally structured interregional interactions, revealing distinct hemispheric patterns.Significance statement This study investigates spatiotemporal dynamics in decoding semantic categories during the pre-speech interval using high-resolution intracranial EEG. We reveal a left-hemisphere cascade beginning in frontal areas and extending to temporal, occipital, and parietal regions, and a distinct right-hemisphere cascade involving early occipital and temporal pole activity. Cross-temporal regression reveals sustained left-lateral predictive temporal pattern and a brief but high-precision right-hemisphere link. These findings advance our understanding of how semantic categories are constructed in the brain over time and may inform future efforts to develop neural decoding frameworks that operate before speech output.}, }
@article {pmid41957013, year = {2026}, author = {Zhou, T and Yu, R and Bai, X and Yang, Y and Fan, Z and Du, X and Chen, Y and Wang, Y and Yang, Z and Sun, X and Zhu, M and Pan, S}, title = {Tissue-adaptive bioelectronic fibers with temperature-induced self-tightening enable ultrastable neural interface.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-71689-1}, pmid = {41957013}, issn = {2041-1723}, support = {52373201,52103252//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Neural interfaces are essential for brain-machine communication and closed-loop neuromodulation. However, achieving durable interfaces between neural tissue and bioelectronics remains a key challenge, as conventional electronics do not actively conform to the soft, tortuous 3D architecture of neural tissue. We report a tissue-adaptive bioelectronic fiber that actively contracts to wrap around neural tissues, forming ultrastable neural-electronic interfaces, and enabling highly reliable neural stimulation and recording. This fiber is fabricated via wet spinning from a precursor integrating a thermoresponsive polymer and electroactive materials, and exhibits an ultralow modulus of 0.16 MPa and a phase transition temperature of 26.7 °C. Upon contact with rat tissue, the polymer chains undergo a hydrophilic-to-hydrophobic transition, expelling water and contracting the fiber to conform tightly to the sciatic nerve. This ultrastable biointerface demonstrates reliable neural stimulation, producing stable hindlimb bending responses, while sciatic nerve action potential recordings show 99.5% signal retention under successive stimulations.}, }
@article {pmid41957822, year = {2026}, author = {Aljanahi, A and Dalrymple, KV and Dimidi, E and Sullivan, ES}, title = {Methodologies for establishing and validating cut-points and comparative standards in medical imaging-based body composition analysis: a scoping review protocol.}, journal = {Systematic reviews}, volume = {}, number = {}, pages = {}, doi = {10.1186/s13643-026-03096-y}, pmid = {41957822}, issn = {2046-4053}, abstract = {BACKGROUND: Medical imaging-based body composition analysis (BCA) has shown promise in offering detailed, noninvasive assessments of fat, muscle, and bone, but challenges persist in establishing consistent comparative standards. Current studies reveal significant variability in methodologies, which limits comparability and clinical application. This highlights the need for a comprehensive review to explore these methodologies and address the gap in standardisation. The aim of the study is to identify and map the methodologies used in body composition imaging to establish and validate comparative standards (such as cut-points, thresholds, or normative values) and to catalogue the proposed comparative standards.<br><br>METHODS: This scoping review will be conducted following JBI methodology. The following eligibility criteria will be applied: Population: healthy subjects with no major comorbidities or individuals with cancer assessed using body composition imaging (BCI) and concept: methodologies for establishing BCI comparative standards and/or formally validating them against any outcome or other BCA reference standard. This scoping review will consider studies across all clinical settings. There will be no restrictions on the setting or purpose of the original study. Validation studies using BCI as the reference standard will not be included unless the comparative standard being validated is another BCI feature. The electronic databases to be searched are Ovid MEDLINE, Ovid Embase, Scopus, EBSCOhost CINAHL, Web of Science, Cochrane Library, and IEEE Xplore. Grey literature sources will not be included. Studies published in English will be considered, with no date restrictions applied. Two independent reviewers will screen all titles and abstracts, followed by full-text articles, and will undertake data extraction. Data extracted will be presented in tabular and/or diagrammatic form for comprehensive narrative synthesis.<br><br>DISCUSSION: The scoping review will summarise existing evidence on BCI. It will identify potential methodological gaps, describe current proposed thresholds or normative values, and highlight areas for further research to establish validated cut-points.<br><br>OSF https://doi.org/10.17605/OSF.IO/QZMN2.}, }
@article {pmid41958205, year = {2026}, author = {Zhang, Z and Zheng, Y and Guo, K and Liang, J and Dong, M}, title = {A Few-Layer Multilayer Perceptron is Worth Attention for EEG Classification in Rapid Serial Visual Presentation Task.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2650030}, doi = {10.1142/S0129065726500309}, pmid = {41958205}, issn = {1793-6462}, abstract = {Rapid serial visual presentation (RSVP) enables efficient electroencephalography (EEG)-based brain-computer interfaces, yet single-trial decoding remains difficult due to signal overlap and multicomponent entanglement. This work developed DisCo-Former, a Transformer-based framework incorporating three priors-guided components, including trend-periodicity disentanglement, channel-level embeddings that preserve global temporal pattern, and contrastive learning that exploits target-adjacent nontargets. Although DisCo-Former surpassed existing approaches, analysis revealed a consistent attention collapse: attention maps became nearly uniform, and value projection weights shrank toward zero. Removing the Transformer encoder yields DisCo-MLP, a purely multilayer perceptron (MLP) variant that preserves all remaining modules. Across two datasets and three evaluation regimes, DisCo-MLP matched or outperformed its Transformer-based counterpart. In within-subject decoding, mean AUCs ranged from approximately 0.94 to 0.98 across two datasets, consistently exceeding strong baselines. These results indicate that, for RSVP-EEG decoding, effectiveness stems less from architectural complexity and more from modeling the signal's structure. Simplicity motivated by paradigm-specific neurophysiological priors offers a practical path to state-of-the-art performance in EEG-based interfaces.}, }
@article {pmid41958714, year = {2025}, author = {Liang, C and Silva, RF and TulayAdali,  and Jiang, R and Zhang, D and Qi, S and Calhoun, VD}, title = {Multimodal data fusion in neuroscience: promises, challenges and future directions.}, journal = {IEEE signal processing magazine}, volume = {42}, number = {5}, pages = {8-21}, pmid = {41958714}, issn = {1053-5888}, abstract = {Multimodal fusion provides significant benefits over single modality analysis by leveraging both shared and complementary information across diverse data sources. In this article, we systematically review methods for fusion of heterogonous multimodal biomedical data of varying dimensionality (including neuroimaging, biomics, clinical phenotypes and text), with a focus on neuroscience. We discuss the strengths and limitations of these strategies based on a survey of 302 research articles. Next, we examine the applications of these methods to a variety of scenarios spanning a continuum from scientific research to clinical practice. Finally, an in-depth discussion of common challenges and promising directions for future development of multimodal biomedical data fusion are provided. Overall, multimodal fusion shows substantial benefits and transformative potential in the field of neuroscience. Future research should prioritize improving model generalization, enhancing interpretability, addressing inherent data limitations, and developing unified platforms alongside multimodal foundational models to bridge the gap between fusion techniques, research, and application to various domains.}, }
@article {pmid41960599, year = {2026}, author = {Ronca, V and Longo, L and Capotorto, R and Aricò, P}, title = {Editorial: Passive brain-computer interfaces: moving from lab to real-world application.}, journal = {Frontiers in computational neuroscience}, volume = {20}, number = {}, pages = {1826791}, pmid = {41960599}, issn = {1662-5188}, }
@article {pmid41961787, year = {2026}, author = {İşcan, Z}, title = {Evaluation of long-range temporal correlations during overt and covert attention in a steady state visual evoked potential based brain-computer interface.}, journal = {PloS one}, volume = {21}, number = {4}, pages = {e0345793}, doi = {10.1371/journal.pone.0345793}, pmid = {41961787}, issn = {1932-6203}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Attention/physiology ; Male ; Female ; Adult ; Electroencephalography ; Young Adult ; Photic Stimulation ; Signal-To-Noise Ratio ; *Brain/physiology ; }, abstract = {Gaze control is required for successful brain-computer interface (BCI) operation in different paradigms. It has been shown that the performance of a steady-state visual evoked potential-based BCI is lower in covert attention when the participants attend to the stimuli covertly, without the need to move their eyes. Some studies in the literature have tried to find the brain regions that are affected by covert attention. Moreover, it has been shown that the signal-to-noise (SNR) ratio is smaller in covert attention than in overt attention. Based on the fact that brain oscillations exhibit long-range temporal correlations (LRTCs), which can be measured by the Hurst exponent, and estimated using the detrended fluctuation analysis (DFA), this is the first study focusing on the DFA differences in overt and covert attention in an SSVEP-based BCI experiment. The main hypothesis is that there should be differences between DFA exponents of EEG in overt and covert attention, as there are differences in SNR between these attentional states. Gender differences between overt and covert attention were also evaluated using DFA. The results revealed significant differences in LRTCs depending on the gender and the attentional state. These results could be taken into account for an efficient SSVEP-based BCI design.}, }
@article {pmid41950858, year = {2026}, author = {Alam, W and Song, KD and Ali, S and Ahmad, W and Koziol, MJ}, title = {Fusion-m6A: A lightweight hybrid deep learning framework for RNA m6A site prediction.}, journal = {Computers in biology and medicine}, volume = {208}, number = {}, pages = {111669}, doi = {10.1016/j.compbiomed.2026.111669}, pmid = {41950858}, issn = {1879-0534}, abstract = {N6-methyladenosine (m6A) is the most common mRNA modification and plays key role in RNA metabolism, gene regulation, and disease. Accurate identification of m6A sites is critical for understanding their functional and biological significance. Although experimental techniques such as Nanopore direct RNA sequencing (DRS) have advanced m6A profiling, they remain costly and laborious. Computational approaches provide scalable alternatives, but many depend on handcrafted features or computationally expensive transformer-based models. We present Fusion-m6A, a hybrid deep learning framework that integrates Word2Vec-based sequence embeddings, convolutional layers for local motif detection, bidirectional gated recurrent unit with attention for capturing long-range dependencies, and auxiliary k-mer features. The fused representations are passed through fully connected layers to predict m6A sites with high accuracy. Benchmarking across multiple human tissues and cell lines shows that Fusion-m6A consistently outperforms state-of-the-art predictors in accuracy and Matthews correlation coefficient. Crucially, the model achieves faster inference and requires substantially less memory, offering a practical and robust solution for large-scale and tissue-specific m6A site prediction. The implementation of Fusion-m6A is publicly available for reproducibility at: https://github.com/waleed551/Fusion_m6A.}, }
@article {pmid41951385, year = {2026}, author = {Sun, SH and Ibbotson, MR}, title = {Extracellular Spike Waveforms: Morphology, Biophysics, and Classification Strategies.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {46}, number = {14}, pages = {}, doi = {10.1523/JNEUROSCI.1741-25.2025}, pmid = {41951385}, issn = {1529-2401}, mesh = {Animals ; Humans ; *Neurons/physiology/classification ; *Action Potentials/physiology ; Biophysics ; Biophysical Phenomena/physiology ; }, abstract = {Extracellular spike waveforms provide critical insights into neuronal activity, morphology, and function. Their shape can reveal cell-type identity, excitatory versus inhibitory function, and afferent projections from distal regions. The development of dense, high-channel-count probes now permits recordings from thousands of sites simultaneously, revealing a wider diversity of waveform types than previously appreciated. These advances provide an unprecedented opportunity to link waveform shape to the underlying biophysical processes of neurons and their spatial arrangement relative to the recording electrode. This review examines and catalogs the diversity of extracellular waveforms (including negative, triphasic, and positive spike waveforms), focusing on their biophysical origins and roles in neural compartments. We also discuss classification strategies, ranging from traditional feature-based approaches that use specific waveform features (such as spike duration and peak-to-trough ratios) to emerging machine learning and multimodal methods that integrate waveform shape with firing dynamics and anatomical localization. These new approaches reveal novel neuronal populations but also highlight a pressing need for standardized classification frameworks to ensure reproducibility and facilitate cross-study comparisons. Finally, we review how experimental factors such as filtering, sampling biases, and spike-sorting algorithms shape the observed diversity of extracellular waveforms. By consolidating recent progress in both experimental and computational approaches, this review provides a comprehensive resource for interpreting extracellular recordings. A deeper understanding of waveform diversity will advance basic neuroscience and accelerate applications in brain-machine interfaces, diagnostics, and neural prosthetics.}, }
@article {pmid41951594, year = {2026}, author = {Li, K and Zhang, C and Li, R and Yuan, X and Zhou, C and Qian, L and Zhang, K and Deng, W}, title = {Distinct neural substrates of obsessions and compulsions in adolescent obsessive compulsive disorder.}, journal = {Translational psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41398-026-04024-3}, pmid = {41951594}, issn = {2158-3188}, abstract = {Adolescent obsessive-compulsive disorder (OCD) is characterized by notable clinical heterogeneity, which may limit treatment precision. Although traditional content-based models have improved symptom characterization, they may overlook key pathophysiological features. A complementary process-based framework that distinguishes between obsessions and compulsions offers a promising alternative, but the neurobiological correlates of these dimensions remain poorly understood. To address this gap, we recruited 40 adolescents with OCD and 40 matched healthy controls and conducted connectome-wide association studies (CWAS) using multivariate distance matrix regression (MDMR) to identify brain regions whose whole-brain connectivity patterns were associated with obsessive or compulsive symptom severity. Follow-up seed-based analyses were then performed to delineate the relevant circuits, and cross-modal comparisons were further used to examine the alignment of symptom-connectivity association maps with neurotransmitter receptor distributions and gene expression profiles. We found that obsessive symptoms were associated with altered connectivity patterns centered on the Dorsolateral Prefrontal Cortex and Cerebellum Posterior Lobe, whereas compulsive symptoms were linked to the Ventrolateral Prefrontal Cortex. In both cases, connectivity between each symptom-specific target and the default mode network (DMN) was negatively correlated with the severity of its corresponding symptom dimension. Moreover, the symptom-connectivity association maps for obsessions and compulsions showed distinct associations with neurotransmitter systems and transcriptomic signatures. Together, these findings provide novel evidence for distinct neurobiological substrates underlying obsession and compulsion dimensions in adolescent OCD, support the utility of process-based symptom modeling, and suggest potential targets for dimension-specific intervention.}, }
@article {pmid41951666, year = {2026}, author = {Li, Y and Yang, Y and Wang, C and Dai, Y and Yan, X and Kong, D and Liao, Z and Wang, S and Ruan, GJ and Wang, P and Cheng, B and Liang, SJ and Miao, F}, title = {Massively parallel in-sensor skinomorphic computing.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-71697-1}, pmid = {41951666}, issn = {2041-1723}, support = {62034004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62304104//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62305155//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62304105//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Real-time sensing and processing of a large amount of tactile information is essential for intelligent robotics and wearable technology. However, physical separation between sensors and processors in the traditional tactile sensing scheme makes these functionalities inaccessible, posing a major roadblock to the rapid advance of skinomorphic electronics. Here, we propose a massively parallel in-sensor skinomorphic computing scheme and demonstrate its promising applications in intelligent tactile perception. This scheme allows for achieving parallel sensing and processing of tactile information directly within sensor. We implement this proposed scheme by fabricating a 32×32 flexible capacitive pressure sensors array with excellent uniformity and endurance, and by cascading the sensors array with a memristive crossbar array. We experimentally demonstrate that the broken pressure patterns of the letter 'NJU' loaded on the sensors array can be sensed and restored in parallel, which is inaccessible with previously reported tactile technologies. Moreover, by networking the pressure sensors array with two memristive crossbar arrays, we show that textural features of the loaded complex pressure patterns can be directly extracted in a parallel manner and the tactile information can thus be compressed. Our work opens up an avenue for developing intelligent skins capable of real-time and high-throughput tactile perception.}, }
@article {pmid41952678, year = {2026}, author = {McDaid, J and Bailes, JE and Jha, NK and Bobustuc, G and Berlet, R and Kessinger, C and Whitcomb, E and Lee, JM and Aksenov, DP and Walker, M and Azapagic, A and Bookwalter, J and Ullah, A and Sant, H and Shea, J and Gale, BK}, title = {Efficacy of local convection enhanced delivery of chemotherapy using an intracerebral osmotic pump in a rat model of glioblastoma.}, journal = {Frontiers in oncology}, volume = {16}, number = {}, pages = {1775053}, pmid = {41952678}, issn = {2234-943X}, abstract = {BACKGROUND: Modern protocols for the treatment of glioblastoma multiforme (GBM) involve resection surgery, followed by chemotherapy and radiation therapy and subsequently adjuvant chemotherapy. While modestly successful in prolonging overall survival, peripherally administered chemotherapy drugs have limited ability to cross the blood brain barrier (BBB), limiting their bioavailability, and thus efficacy, at the tumor site. One way of circumventing the BBB is direct delivery of chemotherapy to the tumor site. Direct application of chemotherapy into the resection cavity during surgery in the form of carmustine/bis-chloroethylnitrosourea (BCNU) wafers has had limited success, in part due to the need for wafer solubilization, which restricts drug distribution and efficacy. The primary limitation, however, is that the drug is only distributed over short distances, for a short time.<br><br>METHODS: In this study, we evaluated the efficacy of drug perfusion into the tumor resection cavity in a rat glioma model through convection enhanced delivery (CED), using an implanted microfluidic osmotic pump. We compared the effects of two alkylating agents, BCNU and temozolomide (TMZ), on tumor recurrence and survival.<br><br>RESULTS: Using pumps containing a high concentration of ferumoxytol - a superparamagnetic iron oxide nanoparticle (SPION) - tissue perfusion was demonstrated in vivo by MRI and by post-mortem histology, confirming the effectiveness of the microfluidic pump as a drug delivery device. When delivered by implanted pumps, BCNU (4mg/ml) showed significantly greater efficacy against tumor recurrence than either TMZ; 2-4mg/ml or control (a low concentration of SPION).<br><br>CONCLUSION: BCNU may be an effective choice for CED-driven, locally delivered chemotherapy in GBM.}, }
@article {pmid41942448, year = {2026}, author = {Niu, J and Xia, J and Liu, Q and He, Y and Li, W and Chen, K and Zhang, X and Qiu, J and Chen, H and Li, J and Liao, W}, title = {Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.}, journal = {Translational psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41398-026-04025-2}, pmid = {41942448}, issn = {2158-3188}, abstract = {Aberrant dynamic shifts in brain states are a hallmark of cognitive and behavioral dysfunctions in major depressive disorder (MDD), yet the underlying mechanisms of these disturbances remain elusive. Leveraging network control theory of morphological networks, we characterized aberrant brain dynamics and energy deficits of MDD patients in two independent cohorts. MDD patients exhibited reduced dynamic stability, characterized by elevated intra-state transitions and diminished inter-state transitions, which were associated with impaired control energy. Region-specific deficits of energy regulation capacity were observed in key nodes of the default mode and limbic networks, including the posterior cingulate cortex and temporal pole, which correlated with cognition and clinical symptoms in MDD patients. MDD-related energy inefficiency was related to multiscale energy architectures at cellular, molecular, and biological levels, including mitochondrial morphologies and functions, energy metabolism pathways, and brain metabolic patterns. Additionally, we demonstrated an association between energy demands and cortical dynamics, indicating a disrupted energy-dependent neurophysiological activity in MDD patients. Together, these results identified the energetic fundamentals underlying pathological brain-state transitions in MDD patients. Identifying energy-vulnerable nodes from a controllability perspective may therefore provide valuable targets for restoring normative neural dynamics in MDD.}, }
@article {pmid41942685, year = {2026}, author = {Lim, Z and Nguyen, HL and Zeng, Y and Qu, Q and Le, Y and Sun, M and Wang, X and Zhu, H and Qian, Y and Saeed, S and Wang, H and Rong, D and Wang, Y and Zhang, X and Hu, S}, title = {Life Cycle and Circadian Rhythms in Central Resident Immunity and Neuropsychiatric Pathology.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {41942685}, issn = {1995-8218}, abstract = {The central resident immune system, commonly known as the glial system, comprises various glial cells that play a critical role in neuropsychiatric disorders. However, a systematic review exploring the relationships between the life cycles and daily rhythms of these immune cells and the pathological features of neuropsychiatric disorders is lacking. These immune cells exhibit unique developmental origins and circadian characteristics, resulting in rhythmic variations in functions such as phagocytosis, immune clearance, neurogenesis, and neurotransmitter recycling. These properties are crucial for understanding the pathological mechanisms underlying developmental disorders like major depressive disorder, autism spectrum disorder, and schizophrenia, as well as age-related conditions such as Alzheimer's and Parkinson's diseases. The daily rhythms of these immune cells correlate with diurnal variations in emotion, cognition, and motor function, involving shared processes like oxidative stress and neuroinflammation. This article systematically reviews the composition, life cycle changes, and circadian characteristics of central immune cells, highlighting their roles in neuropsychiatric diseases.}, }
@article {pmid41943963, year = {2026}, author = {Kotov, SV and Isakova, EV and Ponomareva, ES}, title = {[Bimanual interaction as an illustration of an integrative approach in post-stroke neurorehabilitation].}, journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova}, volume = {126}, number = {3. Vyp. 2}, pages = {62-68}, doi = {10.17116/jnevro202612603262}, pmid = {41943963}, issn = {1997-7298}, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology ; Transcranial Magnetic Stimulation ; Neuronal Plasticity ; Transcranial Direct Current Stimulation ; Brain-Computer Interfaces ; *Neurological Rehabilitation/methods ; Upper Extremity/physiopathology ; Functional Laterality ; Corpus Callosum/physiopathology ; }, abstract = {The review addresses the roles of interhemispheric asymmetry and interhemispheric interaction in the pathogenesis and medical rehabilitation after stroke, with a focus on bimanual training to restore upper-extremity motor function. After a stroke, 50-80% of patients persist with movement disorders affecting not only the paretic but also the «healthy» ipsilateral limb, leading to the avoidance of bilateral patterns in daily activities. Interhemispheric asymmetry is a fundamental property of the brain that is disrupted during a stroke, leading to an imbalance with increased excitability in the intact hemisphere and suppression of the affected hemisphere's functions, which aggravates motor and cognitive deficits after a stroke. Interhemispheric interaction, mainly through the corpus callosum, provides coordination of the brain hemispheres; however, in stroke it leads to an abnormal imbalance, reducing plasticity. Methods for restoring interhemispheric communication include noninvasive brain stimulation (transcranial magnetic stimulation and transcranial direct current stimulation), brain-computer interfaces, and physical activity modulating cerebral hemispheric connectivity. Particular attention is paid to bimanual motor training, which stimulates neuroplasticity, improves bimanual coordination, and reduces the severity of interhemispheric asymmetry. Neuromotorics, a set of bimanual exercises for training fine motor skills, is described. These approaches, especially when combined, are effective for motor recovery but depend on the stroke period and individual factors. The practical significance of integrative rehabilitation in overcoming interhemispheric imbalance and improving patients' functionality and quality of life is emphasized. Further research is needed to optimize bimanual therapy.}, }
@article {pmid41946420, year = {2026}, author = {Guo, X and Zhang, L and Zhang, Q and Kendrick, KM and Yao, S}, title = {A systematic review and meta-analysis of oxytocin modulation of amygdala responses to emotional stimuli and implications for anxiolytic effects.}, journal = {Neuroscience and biobehavioral reviews}, volume = {}, number = {}, pages = {106679}, doi = {10.1016/j.neubiorev.2026.106679}, pmid = {41946420}, issn = {1873-7528}, abstract = {Oxytocin (OT), a neuropeptide essential for social and emotional functions, has been proposed to be anxiolytic as indicated by its effects on inhibiting amygdala activity. However, findings are inconsistent which may be contributed to by variabilities across studies. To obtain a comprehensive overview and a more reliable assessment of the extent to which OT's anxiolytic effects are convergent, we conducted a systematic review and meta-analyses in 55 neuroimaging studies (3337 participants) examining OT's effects on brain responses to negative or stressful emotional stimuli. Results showed a gender-dependent effect of OT on modulating amygdala activity. While OT showed a significant effect on inhibiting amygdala activity in males, an enhancement effect was found in females. An activation likelihood estimation analysis further revealed that OT reduced amygdala activity in the centromedial subregion and could either decrease or increase activity in the basolateral subregion. Our study provides evidence for a gender-dependent anxiolytic effect of OT and its targeting substrates. These findings provide preliminary support for taking individual differences into consideration when developing OT-based therapeutic strategies.}, }
@article {pmid41946820, year = {2026}, author = {Khanal, R and van Schooten, KS and Piovezan, R and Adams, R and Sansom, K and Vakulin, A}, title = {Obstructive sleep apnea risk is associated with poor physical performance: a cross-sectional analysis of the U.S. health and retirement study.}, journal = {Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine}, volume = {22}, number = {1}, pages = {}, pmid = {41946820}, issn = {1550-9397}, mesh = {Humans ; Cross-Sectional Studies ; Female ; Male ; *Sleep Apnea, Obstructive/complications/physiopathology/epidemiology ; Aged ; Middle Aged ; United States/epidemiology ; Hand Strength/physiology ; Postural Balance/physiology ; Risk Factors ; *Physical Functional Performance ; Surveys and Questionnaires ; Accidental Falls/statistics &amp; numerical data ; }, abstract = {STUDY OBJECTIVES: Obstructive sleep apnoea (OSA) may be linked to poor physical performance and fall risk, yet this association remains underexplored. This study examined associations between OSA risk, balance, gait speed and handgrip strength (HGS) in community living adults across age-groups and sexes.<br><br>METHODS: Cross-sectional data from the 2016 Health and Retirement Study were analysed. Probable OSA was estimated with an adapted STOP-Bang questionnaire. Poor balance was defined as the inability to hold a semi-tandem stance for 10&#xa0;s; slow gait speed as walking&#x2009;<&#x2009;0.8&#xa0;m/s over 2.5&#xa0;m; and weak HGS as HGS-to-body mass index ratio&#x2009;<&#x2009;1.00 m[2] for males and&#x2009;<&#x2009;0.56m[2] for females.<br><br>RESULTS: 6,918 participants (mean age 66&#x2009;&#xb1;&#x2009;11&#xa0;years; 57% female) were included. Probable OSA was associated with higher odds of: (i) poor balance in the overall sample (OR:1.23, 95% bootstrapped confidence interval (BCI):1.07-1.39, p&#x2009;=&#x2009;0.002), 50-64&#xa0;years (OR: 1.41, BCI: 1.15- 1.72, p&#x2009;<&#x2009;0.001) and females (OR: 1.30, BCI: 1.10-1.56, p&#x2009;=&#x2009;0.004); (ii) slow gait speed in the overall sample (OR:1.29, BCI:1.07-1.57, p&#x2009;=&#x2009;0.007), 80&#x2009;+&#x2009;years (OR:1.61, BCI:1.07-2.42, p&#x2009;=&#x2009;0.028) and females (OR:1.39, BCI:1.03-1.91, p&#x2009;=&#x2009;0.024); and (iii) weak HGS in the overall sample (OR:2.22, BCI:1.90-2.63, p&#x2009;=&#x2009;0.001), 50-64&#xa0;years (OR:3.40, BCI: 2.58-4.61, p&#x2009;<&#x2009;0.001), 65-79&#xa0;years (OR: 1.93, BCI:1.52- 2.47, p&#x2009;<&#x2009;0.001), males (OR&#x2009;=&#x2009;1.87, BCI:1.49-2.35, p&#x2009;<&#x2009;0.001) and females (OR&#x2009;=&#x2009;2.67, BCI 2.15-3.33, p&#x2009;<&#x2009;0.001).<br><br>CONCLUSIONS: Poor balance, slow gait speed and weak HGS are common among older adults at high risk of OSA. Further research should evaluate causality and assess co-screening to potentially enable early detection of fall risk in older adults.<br><br>STUDY RATIONALE: OSA is a common but often undiagnosed condition that may contribute to accelerated age-related physical decline and increased fall risk. Despite known links between diagnosed OSA and motor deficits, little is known about how undiagnosed OSA relates to fall-related physical performance measures in large, community-based populations. Study Impact: This study suggests that individuals at high risk of OSA are more likely to have poor balance, slow gait speed, and weak handgrip strength, which are key predictors of fall risk. The observation of these associations in adults as young as 50&#xa0;years of age warrants future research to evaluate causality and determine if co-screening of OSA and fall risk can help identify those most vulnerable.}, }
@article {pmid41947853, year = {2026}, author = {Huang, J and Zou, J and Li, X and Cai, Y and Lin, B and Li, Y and Xia, X}, title = {APCformer: an aggregation-perception enhanced convolutional transformer network for MI-EEG decoding.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1766883}, pmid = {41947853}, issn = {1662-4548}, abstract = {Electroencephalogram (EEG) decoding is essential for Brain-computer interfaces (BCI) systems to predict brain activity. However, existing methods usually suffer from two core problems: (1) existing networks lack effective interaction mechanisms and insufficiently capture spatial-temporal dynamic features, leading to the loss of critical fine-grained information; (2) the modeling of long-range dependencies and local features is unbalanced, making it difficult to adapt to the temporal characteristics of EEG signals. To address these issues, this paper proposes an Aggregation-Perception Enhanced Convolutional Transformer (APCformer) network. The network adopts a branch-interactive structure as its main body and jointly extracts shallow features via multi-scale spatial-temporal convolution; an Adaptive Feature Recalibration (AFR) module is embedded to realize cross-scale feature interaction and enhancement of critical fine-grained features. The Position-aware Enhancement (PAE) module is utilized to integrate learnable positional encoding, improving the ability of deep networks to characterize the temporal positional relationships of EEG sequences and enhancing adaptability to temporal dynamic features. We further propose a Sparse Information Aggregation Transformer (SAT), which combines the attention mechanism with the maximum attention mechanism to achieve a balanced modeling of global long-term dependencies and local fine-grained features. Experimental results on the public BCI-IV 2a and BCI-IV 2b datasets show that APCformer achieves superior performance in EEG decoding tasks, with average decoding accuracies of 85.53% and 89.15%, respectively. These results highlight APCformer's strong capability in handling complex EEG features and dynamic patterns, effectively improving the efficiency and accuracy of EEG decoding.}, }
@article {pmid41838514, year = {2026}, author = {Malcolm, K and Uribe, CA and Yamagami, M}, title = {Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {34}, number = {}, pages = {1814-1825}, doi = {10.1109/TNSRE.2026.3674102}, pmid = {41838514}, issn = {1558-0210}, mesh = {*Electromyography/methods ; Humans ; Algorithms ; Male ; *Privacy ; Adult ; Female ; Computer Simulation ; Young Adult ; *Machine Learning ; Brain-Computer Interfaces ; Federated Learning ; }, abstract = {Neural interfaces offer a pathway to intuitive, high-bandwidth interaction, but the sensitive nature of neural data creates significant privacy hurdles for large-scale model training. Federated learning (FL) has emerged as a promising privacy-preserving solution, yet its efficacy in real-time, online neural interfaces remains unexplored. In this study, we 1) propose a conceptual framework for applying FL to the distinct constraints of neural interface applications and 2) provide a systematic evaluation of FL-based neural decoding using high-dimensional surface electromyography across both an offline simulation and a real-time, online user study. While offline results suggest that FL can simultaneously enhance performance and privacy, our online experiments reveal a more complex landscape. We found that standard FL assumptions struggle to translate to real-time, sequential interactions with user-decoder co-adaptation. Our results show that while FL retains privacy advantages, it introduces performance tensions not predicted by offline simulations. These findings identify a critical gap in current FL methodologies and highlight the need for specialized algorithms designed to navigate the unique co-adaptive dynamics of online neural decoding.}, }
@article {pmid41855656, year = {2026}, author = {Inoue, M and Hatakeyama, E and Kita, Y and Sasai, S}, title = {Large-scale training data enhances silent speech decoding with around-ear EEG.}, journal = {Journal of neural engineering}, volume = {23}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ae54d0}, pmid = {41855656}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods/instrumentation ; Male ; Female ; Adult ; *Speech/physiology ; Young Adult ; *Brain-Computer Interfaces ; Middle Aged ; Wearable Electronic Devices ; Electromyography/methods ; }, abstract = {Objective. Silent speech decoding (SSD) offers a potential communication alternative for individuals with impaired vocalization. However, conventional multi-electrode electroencephalography (EEG) or facial electromyography (EMG) systems require cumbersome preparation and are unsuitable for daily use. This study evaluates the practicality of SSD using a wearable around-ear EEG device, focusing on data scaling, cross-subject transfer, vocabulary extensibility, and online decoding performance.Approach. We collected 72 h of around-ear EEG from 24 healthy participants and one individual with incomplete locked-in syndrome (LIS) during silent, vocalized, and attempted speech, and integrated these around-ear EEG recordings with prior EMG + high-density EEG datasets, yielding 282.4 total h of training data. Using a 64-word classification task as the evaluation metric, we assessed: (1) whether larger datasets improve around-ear EEG-based SSD, (2) whether healthy-participant data supplement limited LIS-participant data despite articulatory differences, (3) transferability to unseen vocabulary, and (4) online user-interface performance.Main results. Large-scale EEG/EMG data improved SSD accuracy in both healthy participants and the LIS participant. Training on the heterogeneous dataset achieved 56.6% accuracy for healthy users and 47.3% for the LIS participant. Fine-tuning this decoder for new vocabulary increased the accuracy by 22 percentage points relative to training from scratch. Regression analysis showed that, for decoding in the LIS participant, data from the LIS participant contributed approximately four times the weight of healthy-participant data, quantifying data strategies for SSD. Online experiments achieved top-1/top-5 accuracies of 47.2%/76.0% for healthy users and 26.5%/49.1% for the LIS participant.Significance. The results indicate that lightweight, commercially feasible around-ear EEG can enable practical SSD when combined with large-scale healthy-participant data, supporting online operation. Moreover, models trained on a 64-word vocabulary facilitate decoding of a new vocabulary, providing a path toward SSD systems requiring minimal LIS-participant data. This study advances non-invasive SSD systems suitable for everyday communication.}, }
@article {pmid41936725, year = {2026}, author = {Zhuang, JR and Guo, PC}, title = {Attention-Enhanced U-Net for Sensor-Efficient High-Density EEG Reconstruction in Wearable Brain Monitoring Systems.}, journal = {Journal of medical systems}, volume = {50}, number = {1}, pages = {}, pmid = {41936725}, issn = {1573-689X}, abstract = {UNLABELLED: High-channel-density (HCD) electroencephalography (EEG) enables fine-grained neural sensing but is constrained by high hardware costs, spatial complexity, and limited portability. This study developed a deep learning-based method to reconstruct high-density EEG signals from low-channel-density (LCD) inputs, enabling more practical and affordable brain-monitoring systems. This study introduces VEEG-A-U-Net, a lightweight U-Net architecture enhanced with attention gates and residual learning. The model combined spherical spline interpolation with a learnable correction signal to adaptively model spatial-temporal features. The framework was trained and evaluated on the SEED dataset, using normalized mean square error (NMSE), signal-to-noise ratio (SNR), and Pearson correlation coefficient (PCC) to assess reconstruction performance. Validation was conducted through leave-one-subject-out cross-validation (LOSO-CV) and cross-dataset experiments to examine generalizability. Under the same reconstruction setting (scale factor = 2), VEEG-A-U-Net achieved competitive reconstruction performance compared with state-of-the-art methods, while requiring substantially fewer parameters and computational operations. Cross-dataset evaluations confirmed stable performance across different EEG paradigms. Inference-time analysis showed low computational latency, indicating practical feasibility for deployment in resource-constrained and edge computing environments. A preliminary clinical EEG evaluation was also conducted to explore feasibility in clinical settings.The proposed framework offers an effective and lightweight solution for reconstructing high-density EEG from sparse measurements. These findings may support the development of sensor-efficient and portable EEG systems for practical neuroengineering and brain–computer interface applications.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10916-026-02374-5.}, }
@article {pmid41940909, year = {2026}, author = {Mukhtiar, A and Mubarak, NM and Aly Saad Aly, M}, title = {MXene Nanomaterial Interfaces: Pioneering Neural Signal Recording for Brain-Computer Interfaces and Cognitive Therapy.}, journal = {Topics in current chemistry (Cham)}, volume = {384}, number = {2}, pages = {}, pmid = {41940909}, issn = {2364-8961}, support = {IMSIU-DDRSP2601//Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU)/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Nanostructures/chemistry ; Animals ; Brain/physiology ; Nitrites ; Transition Elements ; }, abstract = {The development of cost-effective, high-accuracy MXene-based electrode devices is a promising approach for monitoring brain activity. The high conductivity and controllable surface chemistry make MXenes viable for neural stimulation and recording applications. In this review article of MXene integration into neural devices, we analyze the role of MXenes in advancing next-generation brain-computer interfaces (BCIs). High-resolution neural interfaces can be studied through cognitive rehabilitation investigations that examine real-time signal decoding capabilities and feedback systems in these devices. In addition to a summary of recent experimental findings from in vitro and in vivo models, the article also discusses engineering strategies for optimizing MXene-based systems for neural applications. The clinical implementation of future technologies must address challenges related to material stability and compatibility with biological tissues, as well as device miniaturization requirements. This investigation aims to evaluate MXenes as transformative materials that could drive breakthroughs in neural interface technology while advancing brain-machine interface functionality.}, }
@article {pmid41941779, year = {2026}, author = {Chen, W and Daly, I and Chen, Y and Wu, X and Liang, W and He, X and Wang, X and Cichocki, A and Jin, J}, title = {Enhancing the Capability and Accuracy of Motor Imagery Classification: A Deep Neural Network-Powered Multifaceted Strategy Model.}, journal = {IEEE transactions on cybernetics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TCYB.2026.3678659}, pmid = {41941779}, issn = {2168-2275}, abstract = {Motor imagery (MI) is a popular noninvasive brain computer interface (BCI) paradigm, yet its decoding accuracy remains hindered by the inherent nonstationarity and low signal-to-noise ratio of electroencephalogram (EEG) signals. Current decoding frameworks often fail to fully exploit the intricate spatial-temporal dependencies, leading to suboptimal feature representation and the omission of latent discriminative cues. To address these challenges, we introduce a deep neural network-powered multifaceted strategy (DPMS-Net) model, a novel approach that employs dynamic convolution to unearth effective discriminative cues across multiple dimensions, including the temporal, spatial, and frequency domains. This model synergizes channel and temporal attention mechanisms to adeptly capture the salient features of EEG signals across diverse spatial-temporal dimensions, thereby mitigating the risk of omitting critical information. Furthermore, we introduce a spectral-domain analysis component that unearths subtle oscillatory signatures hidden within the EEG spectrum, providing enriched evidence for classification. We evaluated the performance of DPMS-Net on two publicly available datasets and a self-collected dataset from stroke patients. On the BCI Competition IV 2a and BCI Competition IV 2b datasets, DPMS-Net achieved subject-dependent classification accuracies of 83.93% and 88.38%, respectively, alongside subject-independent classification accuracies of 65.88% and 76.01%. In the stroke patient dataset, DPMS-Net attained a subject-dependent classification accuracy of 67.67% and a subject-independent classification accuracy of 57.58%. Experimental results indicate that DPMS-Net possesses efficient decoding capabilities and robust stability, reflecting its potential for deployment in neurorehabilitation BCI systems.}, }
@article {pmid41941946, year = {2026}, author = {Zhou, X and Wang, L and Zhang, L and Yao, W and Li, G}, title = {Dynamic source domain selection: An adaptive EEG transfer learning framework for mitigating negative transfer.}, journal = {Journal of neuroscience methods}, volume = {432}, number = {}, pages = {110768}, doi = {10.1016/j.jneumeth.2026.110768}, pmid = {41941946}, issn = {1872-678X}, abstract = {BACKGROUND: Electroencephalography (EEG) is widely used in brain-computer interfaces (BCIs). Current transfer learning (TL) methods often merge multiple source domains, underutilizing diverse information and risking negative transfer when source-target similarity is low. Moreover, inter-subject variability further reduces TL effectiveness in motor imagery BCIs (MI-BCIs).<br><br>NEW METHOD: To address the above issues, we propose an adaptive EEG dynamic transfer learning framework. The framework first performs time-frequency decomposition on EEG signals using wavelet transform convolution. It then realizes dynamic adaptive matching of features between the source domain and the target domain, thereby reducing negative transfer. Specifically, a feature extractor maps EEG signals to a latent space with discriminative representations. Next, the dynamic migration-based attention module matches source and target domain samples within this latent space, ensuring a high degree of alignment. Finally, a novel combined loss function is co-optimized to reduce both marginal and class-conditional discrepancies arising from the multimodal structure of EEG signals.<br><br>RESULTS: The model is validated on the BNCI2014001, BNCI2014002, and BNCI2015001 datasets to assess its classification performance. The accuracy rates of the three datasets are 78.78%, 82.11%, and 78.19%, respectively.<br><br>The results indicate that the method is robust to subject variability. The average accuracy of the proposed method outperforms the baseline algorithms, with improvements ranging from 0.13% to 27.7%.<br><br>CONCLUSIONS: for research articles: Our approach addresses the domain shift challenge in MI-BCIs by enabling effective cross-domain knowledge transfer. This capability to bridge distribution disparities significantly enhances the real-world applicability of such systems.}, }
@article {pmid41929704, year = {2026}, author = {Lin, D and Tran, T and Thaploo, S and Matias, JGE and Pixley, JE and Nenadic, Z and Do, AH}, title = {Perception of brain-computer interface implantation surgery for motor, sensory, and autonomic restoration in spinal cord injury and stroke.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1678175}, pmid = {41929704}, issn = {1662-4548}, abstract = {INTRODUCTION: Stroke and spinal cord injury (SCI) can profoundly diminish quality of life across physical and psychosocial domains, with motor and sensory deficits often persisting despite current therapies. Invasive brain-computer interface (BCI) systems, particularly electrocorticography (ECoG)-based approaches, offer a potential means to bypass neural injury and restore function. To inform development and deployment, it is critical to understand candidate users' willingness to adopt such technology and how that willingness relates to their functional goals and rehabilitation priorities.<br><br>METHODS: We conducted a survey assessing receptiveness to surgical implantation of ECoG grids for BCI use and eliciting participants' rehabilitative goals and perceived priorities across motor and sensory domains. We examined associations between willingness to undergo implantation and (1) the level of functional recovery hypothetically offered, (2) stated rehabilitative priorities, and (3) self-reported disability.<br><br>RESULTS: We surveyed 71 participants: stroke (n = 33), SCI (n = 37), and both stroke and SCI (n = 1). Across this cohort, respondents reported a high willingness to undergo surgery for ECoG-based BCI if it could restore basic functions, including upper-extremity control, gait, bowel/bladder function, and sensation. Willingness to pursue implantation showed no correlation with the degree of functional recovery promised by the hypothetical BCI. Likewise, willingness did not correlate with participants' rehabilitative priorities or their level of disability.<br><br>DISCUSSION: These findings indicate a strong interest in invasive BCIs even when only basic functions may be restored, independent of disability severity or stated priorities. This suggests that first-generation commercial invasive BCIs with limited functionality may still find receptive users. However, stated interest may not translate to informed surgical consent in real-world contexts, thereby highlighting the risk of overly optimistic expectations. Hence, robust, transparent consent frameworks and balanced communication are essential as invasive BCIs move toward clinical deployment.}, }
@article {pmid41932127, year = {2026}, author = {Cheng, C and Shang, R and Wang, Z and Li, H and Jia, Z}, title = {MBDA: A modality-balanced framework with data augmentation and alignment for multimodal emotion recognition.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {201}, number = {}, pages = {108852}, doi = {10.1016/j.neunet.2026.108852}, pmid = {41932127}, issn = {1879-2782}, abstract = {Multimodal Emotion Recognition (MER) aims to infer human emotional states by integrating complementary information from heterogeneous modalities. However, existing MER methods often suffer from modality imbalance, cross-modal misalignment, and limited data diversity, which hinder their robustness and generalization. To address these issues, we propose a Modality-Balanced framework with Data Augmentation and Alignment (MBDA), which integrates modality-aware augmentation, feature alignment, and counterfactual knowledge distillation into a unified framework in a progressive learning manner. MBDA boosts data diversity while preserving semantic consistency through modality-aware augmentation, enforces robust multi-level alignment across modalities, and adaptively rebalances modality contributions through counterfactual knowledge distillation. Experiments on the DEAP and SEED-IV datasets demonstrate that MBDA consistently outperforms state-of-the-art methods, achieving accuracies of 93.86%, 95.11%, 91.02%, and 92.66% on DEAP-A, DEAP-V, DEAP-AV, and SEED-IV, respectively.}, }
@article {pmid41932888, year = {2026}, author = {Ji, SY and Wang, WW and Yang, Y and Xu, P and Zhang, J and Zhao, X and Xi, K and Zang, SK and Shen, DD and Mao, C and Shen, Q and Zhang, Y}, title = {Dynamic monomer-dimer transition in ligand-induced apelin receptor activation.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-71325-y}, pmid = {41932888}, issn = {2041-1723}, support = {92353303//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32141004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81922071//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {G-protein-coupled receptors (GPCRs) are significant signal transducers that exist as monomers and in multiple oligomeric forms. However, molecular mechanism driving their dynamic interconversion to regulate intricate signaling in class A GPCRs remains elusive, compounding our understanding of their related pathophysiological functions. Here, we present a set of 12 assemblies of the apelin receptor (APLNR), including dimeric apo state, agonistic small molecule- or nanobody-bound state of monomeric and dimeric APLNR with and without G-proteins, providing a detailed dynamic view of the monomer-dimer transition. High-resolution cryo-EM structures reveal that different ligands induce varying degrees of pre-dissociation of dimers in the absence of G-protein, with G-protein coupling facilitating the transition from dimeric to monomeric receptor. These insights enhance our understanding of the dynamic regulation of class A GPCRs between monomeric and dimeric forms and advance the rational drug design strategies aimed at selectively modulating of APLNR signaling.}, }
@article {pmid41933048, year = {2026}, author = {Wang, T and Gong, H and Ye, G and Chen, R and Sun, S and Huang, X and Zhang, B and Jiang, L and Zhang, Y and Chen, T and Pan, Y and Xu, J and Jin, M and Chen, K and Mao, W and Xu, Q}, title = {Multiple pathways of CD34[+] cell differentiation during embryogenesis.}, journal = {Cell death and differentiation}, volume = {}, number = {}, pages = {}, pmid = {41933048}, issn = {1476-5403}, support = {82030008//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31830039//National Natural Science Foundation of China (National Science Foundation of China)/ ; W2541023//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82400490//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82200479//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82400575//National Natural Science Foundation of China (National Science Foundation of China)/ ; U24A20799//National Natural Science Foundation of China (National Science Foundation of China)/ ; LMS25H020009//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {CD34 has long been defined as a canonical marker for endothelial progenitors as well as hematopoietic stem cells, implicating its role in vascular development and hematopoiesis. However, the precise developmental hierarchy and lineage potential of CD34[+] cells remain controversial. In this study, we integrated inducible genetic lineage tracing techniques, proteomics and single-cell RNA-seq (scRNA-seq) analyses to elucidate the dynamic developmental trajectory of CD34[+] cells during various embryonic periods in both humans and mice. Remarkably, our analyses indicated that the progeny of CD34[+] cells marked distinct, spatiotemporally restricted progenitor waves with divergent fates, at which point cells adopted endothelial, hematopoietic and fibroblastic fates, respectively. During gastrulation (E6.5-E8.5), an initial wave of CD34[+] progenitors predominantly orchestrates vasculogenesis via a Kdr-dependent mechanism. Subsequently, from E9.5 to E14.5, cell cycle activation serves as a molecular switch, facilitating the endothelial-to-hematopoietic transition (EHT) of CD34[+] progenitors. Unexpectedly, we identify a wave of CD34[+] progenitors in late embryogenesis that gives rise to fibroblasts, distinct from earlier endothelial or hematopoietic lineages. Furthermore, because umbilical cord blood is a valuable source of different circulating stem/progenitor cells, we distinguish circulating endothelial progenitors from fibroblast progenitors in human cord blood by unique molecular signatures, with GFPT2 specifically marking the fibroblast progenitors. Collectively, our study provides a high-resolution spatiotemporal atlas of CD34[+] cells during embryogenesis, redefining the temporal shifts of CD34[+] cells in cell states and offering a precise framework for manipulating CD34[+] cells in regenerative medicine.}, }
@article {pmid41933504, year = {2026}, author = {Torbahn, G and Schoene, D and Ernst, IG and Schwingshackl, L and Rücker, G and Knüttel, H and Kemmler, W and Sieber, CC and Batsis, JA and Villareal, DT and Stroebele-Benschop, N and Volkert, D and Kiesswetter, E}, title = {EffectS of Lifestyle Interventions in Older PEople With Obesity (Effective SLOPE): a Systematic Review With Network Meta-Analyses.}, journal = {Obesity reviews : an official journal of the International Association for the Study of Obesity}, volume = {}, number = {}, pages = {e70123}, doi = {10.1111/obr.70123}, pmid = {41933504}, issn = {1467-789X}, support = {01KG1903//German Federal Ministry of Education and Research (BMBF)/ ; }, abstract = {BACKGROUND/AIM: We conducted a systematic review with network meta-analyses (NMA) summarizing the effects and safety of lifestyle interventions containing nutrition (NUT; e.g., calorie restriction), exercise (EX; e.g., aerobic/resistance exercise) and behavior change interventions (BCI; e.g., behavioral therapy) on physical function, body composition, quality of life, psychosocial outcomes, health and adverse events in community-dwelling older adults with obesity.<br><br>METHODS: We used the methodology proposed by Cochrane and searched six databases and one trial registry for eligible randomized controlled trials (RCTs; intervention duration &#x2265;&#x2009;12&#x2009;weeks) up to May 2022 with a full new search in MEDLINE and a re-assessment of previously identified eligible trial registry entries in October 2025. Random-effects NMA ((standardized) mean difference ((S)MD), 95% confidence intervals) were conducted if possible.<br><br>RESULTS: We included 72 RCTs (n&#x2009;=&#x2009;6716) for descriptive summaries and 54 RCTs (n&#x2009;=&#x2009;4249) for NMA. NUT+EX+BCI improved physical function (performance batteries) compared to control (SMD 3.37 [1.76;4.97]; high certainty of evidence). NUT+EX+BCI may reduce body (MD -8.69 [-13.14;-4.25]) and fat mass (MD -6.58 [-10.44;-2.73]) while not negatively affecting fat-free mass (MD -1.38 [-3.52;0.76]) or bone mineral density (MD -0.01 [-0.05;0.02]) (evidence very uncertain). Other interventions (single/combined) may also be effective; however, effects were often imprecise. For psychosocial outcomes, quality of life, and health events, data were insufficient or too heterogeneous to derive clear results.<br><br>CONCLUSION: The evidence suggests that NUT+EX+BCI interventions are most suitable for the management of obesity in older adults. Nevertheless, further RCTs-especially in frail populations and on patient-relevant outcomes-are needed.}, }
@article {pmid41937954, year = {2026}, author = {Esteves, D and Vourvopoulos, A}, title = {EEG biomarkers of the sense of embodiment: methodological gaps and evidence-based recommendations from a systematic review.}, journal = {Frontiers in systems neuroscience}, volume = {20}, number = {}, pages = {1756407}, pmid = {41937954}, issn = {1662-5137}, abstract = {INTRODUCTION: The sense of embodiment (SoE), describing the experience of owning, controlling, and being located within a body, underpins virtual reality (VR) interaction, brain-computer interfaces (BCIs), and multisensory body-illusion research. Although SoE is typically assessed through subjective questionnaires, their variability and limited validity have motivated the search for objective neural markers. Electroencephalography (EEG) has become the most widely used technique given its portability and high temporal resolution; however, the existence of a consistent EEG correlate of embodiment remains unclear.<br><br>METHODS: This systematic review summarizes 35 EEG studies (2010-June 2025) identified through structured database searches, examining SoE across immersive and non-immersive VR, augmented reality, and non-VR paradigms. We analyze EEG features including spectral power, event-related desynchronization/synchronization (ERD/ERS), connectivity, and temporal dynamics, and examine methodological variability in illusion induction and SoE assessment.<br><br>RESULTS: Across studies, the reduction of the alpha-band over central-parietal regions emerges as the most recurrent correlate of embodiment. Beta-band decreases and gamma-band increases appear in several studies but lack consistent replication, while findings in Delta and Theta bands remain sparse and contradictory. Considerable heterogeneity is found in VR paradigms, EEG setups, preprocessing, and psychometric tools, contributing to inconsistent results and limiting cross-study comparability.<br><br>DISCUSSION: Critically, no EEG feature demonstrates sufficient reproducibility to qualify as a universal biomarker of SoE, and no standardized protocol for EEG-based embodiment assessment currently exists. Overall, this review highlights both the promise and current limitations of EEG-based approaches to measuring embodiment. We conclude by identifying methodological gaps and outlining recommendations to support the development of reliable EEG markers for future applications in VR rehabilitation, MI-BCIs, cognitive neuroscience, and clinical interventions.}, }
@article {pmid41938598, year = {2026}, author = {Paneru, B}, title = {A multi-dimensional CNN-Bi-GRU for IoT-based brain-computer interface in early epileptic seizure detection.}, journal = {Biology methods &amp; protocols}, volume = {11}, number = {1}, pages = {bpag010}, pmid = {41938598}, issn = {2396-8923}, abstract = {The study focuses on seizure detection using EEG data from Mendeley. An early-alert IoT-BCI system is designed to simulate real-time support for patients during seizures. The proposed Multi-Dimensional CNN-Bi-GRU (MDCBG) outperforms hybrid deep learning models, achieving 97.43% accuracy, surpassing baseline EEGNet (92.17%) and CTNET (85.11%), along with models evaluated through ablation studies on seizure vs. non-seizure prediction. The proposed model, along with other models like Bi-GRU with attention, Bi-LSTM-GRU, and XGBoost, also performs well on classifying various types of seizures. SHAP analysis shows Channel 5 contributes most to predictions. An IoT-based automation system is simulated on seizure detection for triggering micro devices near the patient's environment. This approach supports early seizure warning and guides home-automation strategies to assist patients.}, }
@article {pmid41939130, year = {2024}, author = {Xu, S and Scott, K and Manshaii, F and Chen, J}, title = {Heart-brain connection: How can heartbeats shape our minds?.}, journal = {Matter}, volume = {7}, number = {5}, pages = {1684-1687}, pmid = {41939130}, issn = {2590-2385}, abstract = {Recent neuroscience reveals the heart's impact on brain activity through blood pulsations, affecting mitral cells in the olfactory bulb. This connection, involving mechanosensitive ion channels like Piezo2, links cardiovascular dynamics to neuronal function, offering new treatments for neurological disorders, advancing closed-loop brain-computer interfaces, and emphasizing the body-mind interconnectivity.}, }
@article {pmid41940264, year = {2026}, author = {Li, P and Qi, G and Zhao, S and Huang, A and Guan, W}, title = {EEG-based brain functional connectivity dynamics in manual and video-based car-following observation among young drivers.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {72}, pmid = {41940264}, issn = {1871-4080}, abstract = {UNLABELLED: Understanding the neurophysiological mechanisms underlying driving behavior in young drivers is essential for improving cognitive-aware driver assistance and vehicle-human interaction systems. This study systematically examines EEG dynamics and functional brain network reconfigurations across both manual and video-based car-following observation, providing a neurophysiological framework for differentiating driving modes among young adult drivers. EEG characteristics were analyzed under three car-following strategies-aggressive, conservative, and personalized-implemented within a simulated driving environment, to capture the variability of cognitive engagement during distinct control demands. Key findings reveal that power spectral density (PSD) in the θ, β, and γ bands, combined with brain functional connectivity (BFN) measures, effectively characterizes workload-related modulation and attentional resources across driving conditions. A novel computational framework integrating Time-Frequency Common Mutual Information (TFCMI) features with a Parallel Compact Convolutional Neural Network (PCNet) achieved an average classification accuracy of 85.26%, surpassing traditional single-modality approaches. Neurotopographic results further indicate context-dependent functional specialization: frontal regions showed stronger activation and connectivity during manual control, while occipital regions exhibited enhanced synchronization during video-based car-following observation tasks. Collectively, these findings advance the understanding of driving-related cognitive processes in young drivers and provide neuroergonomic insights for designing adaptive human-machine interfaces in future intelligent transportation systems.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10442-2.}, }
@article {pmid41940265, year = {2026}, author = {Huang, K and Yang, H and Zhu, S and Chen, Y and Li, T and Zhao, L and Gong, A and Nan, W and Xu, J and Fu, Y}, title = {Ethical risks and considerations of brain-controlled and neuromodulation technologies.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {74}, pmid = {41940265}, issn = {1871-4080}, abstract = {Brain-controlled technology (BCT), centered on brain-computer interfaces (BCI), acquires and decodes neural signals to convert subjective intentions into control commands for external devices, establishing an intention output loop. In contrast, neuromodulation technology applies external physical stimuli to the central nervous system to regulate neuronal excitability and brain network states, achieving energy input for functional modulation and therapeutic purposes. The inherent differences in mechanisms and application goals determine that the ethical risk profiles and governance priorities of these two technologies cannot be conflated. Current public communication is characterized by terminology misuse and concept generalization, notably the misinterpretation of neuromodulation as controlling the brain. In response to the resulting ethical anxiety caused by capability extrapolation, this paper first clarifies the functional positioning of both technologies. Subsequently, a three-dimensional assessment model based on reality, reversibility, and technological dependence is constructed to map a stratified ethical risk landscape. The analysis reveals a significant asymmetry in risk distribution: risks of BCT are primarily concentrated on neural privacy leakage and responsibility attribution dilemmas within the intention decoding process, whereas risks of neuromodulation are deeply embedded in the potential erosion of personal identity and subject autonomy induced by external stimuli. To address institutional gaps in the current regulatory system regarding consumer-grade devices and long-term effects, this paper proposes a differentiated tiered governance strategy. It advocates establishing terminology demystification and conceptual rectification as the frontline defense for risk governance. On this basis, the strategy enforces physical defense mechanisms such as hardware fusing and parameter safety windows on the technical side, and strengthens data desensitization and algorithmic accountability on the data side. Ultimately, a multi-subject synergistic governance mechanism covering the full lifecycle from research and development and clinical trials to social application is constructed to provide institutional support for responsible innovation in neurotechnology.}, }
@article {pmid41940343, year = {2026}, author = {Otake, H and Senta, N and Ushiba, J and Takemi, M}, title = {Neural correlates of individual differences in motor learning under reinforcement contexts.}, journal = {iScience}, volume = {29}, number = {4}, pages = {115336}, pmid = {41940343}, issn = {2589-0042}, abstract = {Rewards and punishments shape motor learning, yet individuals vary in their adaptation speed and skill retention. Previous studies have linked these processes to two electroencephalographic signatures: feedback-related negativity (FRN) and sensorimotor event-related desynchronization (ERD). However, their roles in individual learning differences remain unclear. We recorded electroencephalography while 64 adults performed a visuomotor rotation task where gains or losses scaled with movement error. Using Lasso regression, we examined whether these neural markers accounted for individual variability in learning and retention. Results demonstrated that the interaction between sensorimotor alpha-ERD during movement preparation in late adaptation and feedback condition explained retention. Stronger alpha-ERD predicted better retention only in the reward condition, whereas neither ERD nor FRN explained adaptation rates. These findings indicate that late-phase alpha-ERD reflects neural mechanisms supporting motor memory stabilization, which becomes behaviorally relevant specifically under positive reinforcement. Thus, pairing reward with interventions enhancing sensorimotor cortical excitability may facilitate skill maintenance.}, }
@article {pmid41929610, year = {2026}, author = {Abdalla, N and El Arab, RA and Abdrbo, A and Almari, M and Ayoub, MY and Alsaaideh, B and Dagamseh, MS and Almagharbeh, WT and Abuadas, F and Abu Mahfouz, MS and Gaballah, MK}, title = {Artificial intelligence in rehabilitation: a review of clinical effectiveness, real-world performance, safety, and equity across modalities and settings.}, journal = {Frontiers in digital health}, volume = {8}, number = {}, pages = {1737957}, pmid = {41929610}, issn = {2673-253X}, abstract = {BACKGROUND: Rehabilitation faces a scale problem: millions who could benefit lack timely, effective services. Artificial intelligence (AI) and device-based modalities (e.g., robotics and VR) can extend reach and personalise care when validated, yet decision-makers lack a consolidated view of clinical usefulness, translation to practice, safety, equity, and cost.<br><br>METHODS: We conducted an umbrella review of reviews using a Population-Exposure-Outcome framework. Searches span biomedical, allied health, and engineering databases from inception to September 1, 2025. We distinguished AI-enabled (ML/DL) interventions from technology-assisted (no ML demonstrated) modalities and synthesised outcomes across impairment, activity, independence, usability/safety, equity, and economics.<br><br>FINDINGS: The most reproducible clinical signal is activity improvement for post-stroke upper limb with technology-assisted training (robotics with or without VR) that increases task-specific practice; effects on impairment and independence are inconsistent once dose is matched and assessors are blinded. Claims of non-inferiority are not established when prespecified margins and confidence-interval testing are absent, so parity is interpreted as no between-group advantage under those conditions. Across AI-enabled domains, a development-to-deployment performance drop is evident most notably for brain-computer-interface classifiers and computer-vision movement evaluation limiting immediate clinical impact. Imaging-based decision support (radiomics/CNN) is closer to practice but varies by software and site, requiring local calibration and impact evaluation before pathway change. Reported adverse events are generally mild, yet usability, adherence, equity, and cost are under-measured, particularly in home and hybrid delivery. Prediction-model and trial reporting frequently fall short of contemporary AI standards; representation skews toward high-income settings, and subgroup performance is seldom reported.<br><br>CONCLUSION: An adjunct-first posture is warranted. Adoption should be gated by minimum clinically important difference-anchored benefit under dose symmetry and blinded assessment; external, multi-site validation with declared lab-to-clinic performance loss; subgroup fairness with mitigation; decision-grade economic value; interoperability; and readiness for regulation, change control, and cybersecurity. Priorities include pragmatic, multi-site, assessor-blinded, dose-matched trials; standardised safety/usability capture for home use; and a public, living evidence atlas. AI can expand rehabilitation when held to clinical standards that matter to patients and services. With clear adoption gates and continuous post-market monitoring, systems can extend access and independence without sacrificing rigour, safety, equity, or fairness.}, }
@article {pmid41926164, year = {2026}, author = {Traoré, N and Zabré, P and Millogo, O and Sié, A and Vounatsou, P}, title = {Assessing the role of interventions and climate on malaria mortality among children under five years of age: insights from two decades of data from the Health Demographic Surveillance System of Nouna, Burkina Faso.}, journal = {Journal of global health}, volume = {16}, number = {}, pages = {04080}, pmid = {41926164}, issn = {2047-2986}, mesh = {Humans ; Burkina Faso/epidemiology ; Child, Preschool ; *Malaria/mortality/prevention &amp; control ; Infant ; Seasons ; *Climate ; Insecticide-Treated Bednets/statistics &amp; numerical data ; Female ; Male ; Infant, Newborn ; Population Surveillance ; }, abstract = {BACKGROUND: Malaria is a preventable disease that causes serious illness and death. In 2022, it remained the leading cause of death among children under five years of age in Burkina Faso, despite significant intervention efforts over the past two decades. Research on the effects of interventions and climatic factors on malaria morbidity has expanded, but their effects on malaria mortality remain unclear. We aimed to estimate the effects of interventions and lagged climatic factors on malaria mortality among children under five years of age in northwest Burkina Faso. We further evaluated the role of climatic seasonality in patterns of malaria mortality.<br><br>METHODS: We investigated the seasonal patterns of malaria mortality among children under five years of age and their association with climatic factors, such as rainfall and land surface temperature (LST), using wavelet analysis on mortality data from the Nouna Health Demographic Surveillance System spanning 2002-2021. Furthermore, we assessed the effects of interventions, including coverage of insecticide-treated nets (ITNs) and artemisinin-based combination therapies (ACTs), on malaria mortality alongside climate effects using Bayesian negative binomial temporal models for the period 2013-2021.<br><br>RESULTS: The lag time in the effects of climatic factors varied over time. Malaria mortality, rainfall, and LST showed a 12-month seasonal cycle throughout the years, while LST also had a six-month cycle in specific years. Rainfall lagged by 1.5 to 2 months and LST by 1 to 1.5 months, depending on the seasonal cycle and year. Rainfall was positively associated with malaria mortality (mortality rate ratio (MRR)&#x2009;=&#x2009;1.59; 95% Bayesian credible interval (BCI)&#x2009;=&#x2009;1.18, 1.95), LST showed a decrease in mortality (MRR&#x2009;=&#x2009;0.68; 95% BCI&#x2009;=&#x2009;0.52, 0.86), and ITN was associated with a reduction in mortality (MRR&#x2009;=&#x2009;0.59; 95% BCI&#x2009;=&#x2009;0.42, 0.79); however, ACT was not statistically important.<br><br>CONCLUSIONS: We found that ITN was more effective in reducing malaria mortality than temperature, but rainfall had a greater opposing impact on increasing malaria mortality. The seasonal mortality pattern was more influenced by rainfall than by temperature. Varying climatic lag times highlight the need for adaptive strategies. Policymakers should focus on climate-informed planning, sustained ITN coverage, and reassessment of ACT strategies to further reduce malaria mortality.}, }
@article {pmid41926597, year = {2026}, author = {Samal, S and Xiao, S and Nelson, S and Kolhe, O and Khan, HF and Matin, MH and Lee, WJ and Ahmed, M and Wang, D and Wang, T and Pikes, T and Scott, AN and Rodriguez, JA and Olson, MR and Deng, Q and Parkinson, EI and Rochet, JC and Jayant, K and Mei, J}, title = {Blood-catalyzed n-doped polymers for reversible optical neural control.}, journal = {Science (New York, N.Y.)}, volume = {392}, number = {6793}, pages = {eadu5500}, doi = {10.1126/science.adu5500}, pmid = {41926597}, issn = {1095-9203}, mesh = {Animals ; Mice ; Zebrafish ; *Polymers/chemistry ; Polymerization ; Catalysis ; Infrared Rays ; *Biocompatible Materials/chemistry ; Sodium Channels ; Neurons/physiology ; }, abstract = {Biocompatible integration of synthetic materials with living tissue remains a major challenge for bioelectronics. In this case, substrate-free conducting polymer (CP) interfaces could help bridge this gap. We report in vivo assembly of n-doped poly(benzodifurandione) (n-PBDF) using whole blood-catalyzed polymerization in awake zebrafish and mice. This approach leverages endogenous catalysts, specifically hemoproteins, to form stable, thermally and ionically sensitive CP networks, ensuring long-term compatibility throughout the lifespan. We showcase the impact of this interface through reversible, cellular, and subcellular neuromodulation using near-infrared (NIR) light, including in vivo polymerized n-PBDF. Electrophysiological studies confirmed that n-PBDF alters intrinsic sodium ion channel excitability, and NIR light stimulation amplifies this modulation through thermoionic-induced shunting, providing on-demand, millisecond-scale reversible inhibitory control of excitability, a feature recapitulated in actively behaving mice.}, }
@article {pmid41927190, year = {2026}, author = {Rustamzadeh, O and Hosseini, SA and Tanha, RR and Akbarfahimi, N}, title = {Robotic rehabilitation and intelligent algorithms improving the performance skills of stroke patients: a scoping review.}, journal = {Journal of bodywork and movement therapies}, volume = {46}, number = {}, pages = {308-331}, doi = {10.1016/j.jbmt.2025.09.037}, pmid = {41927190}, issn = {1532-9283}, mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; *Robotics/methods ; Hand Strength/physiology ; Algorithms ; Upper Extremity/physiopathology ; Range of Motion, Articular/physiology ; Exoskeleton Device ; }, abstract = {BACKGROUND: This scoping review highlights major advances and persisting gaps in robotic and AI-driven rehabilitation for stroke, evaluating their impact on hand strength, dexterity, and ROM, and offering clinicians practical, updated guidance.<br><br>METHODS: Studies that focused on robotic-assisted technologies (RATs) in upper limb rehabilitation for stroke survivors (2014-2024) were included. Study designs unrelated to stroke, animal studies, and conference abstracts were excluded. Systematic searching in PubMed, Web of Science, Scopus, and Google Scholar employed robotic rehabilitation, AI, hand function, and stroke recovery-related terms. Data extraction encompassed intervention type, duration of treatment, dosage of therapy, outcome measures, cost-effectiveness, and patient satisfaction. Types of robotic rehabilitation: end-effector robots, exoskeletons, soft robotic gloves (SRGs), brain-computer interfaces (BCIs), and AI-enhanced virtual reality (AIVR).<br><br>RESULTS: These devices can augment motion, grip strength, and functional independence, especially in chronic and subacute stroke patients. Therapies are made fine-grained by algorithms to balance challenge and engagement, thus lightning therapists' burdens. Conventional energy sources may offer a more attractive option at shorter timelines and with reasonably predictable availability. Models that can be done at home enhance adherence at that higher level, though usability appears high for most models. Still, challenges with setup and independence for participants remain.<br><br>CONCLUSION: Robotic rehabilitation has a significant impact on motor function (MF) among stroke patients. Despite this, obstacles such as cost, accessibility, and long-term efficacy need even more research. Therapy dose optimization, adaptive AI integration, and cognitive-emotional outcome assessment are all areas of gaps in robotic rehabilitation that still need to be addressed.}, }
@article {pmid41927340, year = {2026}, author = {Amlie-Lefond, C and Cooper, A and Barry, D and Shaw, DWW}, title = {Fluid-Attenuated Inversion Recovery Correlates with Stroke Onset in Childhood Arterial Ischemic Stroke.}, journal = {AJNR. American journal of neuroradiology}, volume = {47}, number = {4}, pages = {1089-1092}, pmid = {41927340}, issn = {1936-959X}, mesh = {Humans ; Child ; Female ; Male ; Adolescent ; Child, Preschool ; *Ischemic Stroke/pathology/diagnostic imaging ; *Magnetic Resonance Imaging/methods ; Infant ; Bayes Theorem ; }, abstract = {BACKGROUND AND PURPOSE: In adults, time since stroke onset correlates with safety and efficacy of recanalization therapies, and the appearance of FLAIR hyperintense signal is considered a proxy for time. The time to FLAIR hyperintensity in childhood stroke is unknown but is of interest because time of stroke onset in childhood stroke is often unknown.<br><br>MATERIALS AND METHODS: Time to FLAIR hyperintensity on brain MRI performed on children within 24 hours of stroke onset was studied with Bayesian accelerated failure time models.<br><br>RESULTS: A total of 82 MRIs with FLAIR imaging were available from 72 children (37 girls), median age of 10.9 years (range: 0.8-18.0 years). Seventy-two percent (52/72) of children had anterior circulation stroke. Median time between stroke onset and MRI was 7 hours (range: 0.5-23.5 hours). The median estimated time to FLAIR presence was 5.4 hours (50% Bayesian credible interval [BCI], 2.9-8.8; 90% BCI, 0.7-16.7) for all patients, and 6.0 hours (50% BCI, 4.4-7.3; 90% BCI, 1.9-8.8) for anterior circulation only strokes. For all patients, when no signal hyperintensity on FLAIR is observed, there is 50% chance the stroke occurred more than 5.4 hours ago and a 25% chance the stroke occurred more than 8.8 hours ago. For anterior circulation only strokes without FLAIR hyperintense signal, there is a 50% chance the stroke occurred more than 6.0 hours ago, and a 25% chance the stroke occurred more than 7.3 hours ago.<br><br>CONCLUSIONS: FLAIR signal hyperintensity can be used to estimate time since stroke ictus in childhood stroke. Children may have a similar FLAIR signal change timing compared with adults, suggesting that they may have a similar window for effective recanalization therapies.}, }
@article {pmid41927547, year = {2026}, author = {Malpas, SC and Wright, BE and Guild, SJ and Heppner, P and Gallichan, RJ and Leung, DP and Kim, SH and Boesley, Q and Tan, S and Kondo, M and McAllister, DJ and Windsor, JA and Campbell, D and Alan Barber, P and McCormick, D}, title = {Long-term brain pressure monitoring via a discrete microimplant; a first-in-human safety and initial efficacy trial in adults and children with hydrocephalus.}, journal = {Nature communications}, volume = {17}, number = {1}, pages = {}, pmid = {41927547}, issn = {2041-1723}, mesh = {Humans ; *Hydrocephalus/physiopathology/diagnosis/surgery ; *Intracranial Pressure/physiology ; Child ; Adult ; Male ; Female ; Monitoring, Physiologic/instrumentation/methods ; Child, Preschool ; Adolescent ; Middle Aged ; Young Adult ; Aged ; Prostheses and Implants ; Infant ; Brain ; }, abstract = {Emerging neurotechnologies such as brain-computer interfaces and implantable sensors offer considerable promise in the treatment of a broad range of neurological conditions. The key challenges are reducing the implant size, powering it, and confirming long-term accuracy and safety. Here we report the development of a novel type of implantable medical device that measures intracranial pressure long term and which weighs only 0.28 g. Currently the management of hydrocephalus patients relies heavily on non-specific symptoms e.g. headache and there is a lack of actionable data to drive decisions that are not solely hospital based such as imaging. The implant is designed to sit within the cerebral cortex. In a group of 10 adults and 10 children with hydrocephalus we demonstrated that the device was safe and capable of remotely monitoring intracranial pressure in patients at home for up to 18 months (ClinicalTrial.gov NCT06402786). In several children shunt failures occurred and these were associated with raised ICP. Instead of relying on non-specific symptoms such as headache, physicians were able to obtain real-time intracranial pressure readings that can lead to changes in the management of these complex patients.}, }
@article {pmid41928763, year = {2026}, author = {Tsubaki, T and Kashihara, S and Asai, T and Imamizu, H and Nambu, I}, title = {Polarity-considered EEG microstates improve classification accuracy of oddball stimulus.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1712380}, pmid = {41928763}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) require efficient feature extraction and dimensionality reduction from high-dimensional neural signals. Electroencephalogram (EEG) microstate analysis is a rapid and noise-resistant approach that classifies instantaneous EEG states into several spatial distribution patterns (templates). Previous BCI studies using the EEG microstate approach have typically used aggregated metrics, such as duration, frequency of occurrence, or time coverage, and have rarely applied pointwise microstate labeling as temporally ordered, one-dimensional sequences for robust classification. Moreover, the physiological relevance of EEG topographic polarity has often been overlooked, despite its potential to reveal smoother state transitions and align with event-related potential components. In this study, we applied polarity-considered microstate labeling to stimulus-driven classification in an oddball paradigm. EEG data from 40 healthy participants (20 per response type) were analyzed across three factors: stimulus modality (auditory or visual), modality condition (unimodal or cross-modal), and response type (key-response task or mental counting task). Preprocessed 32-channel EEG data were labeled with microstate templates (A-E ± topographical polarity) using a winner-take-all approach, and the resulting sequences were classified using multiple machine-learning models. The results showed that tree-based ensemble models (Random Forest, XGBoost, and CatBoost) achieved the most stable and accurate performance in the key-response task with cross-modal visual targets. These models reached an area under the receiver operating characteristic curve above 0.8 and a mean F1 score of 0.83. Preserving polarity improved classification by approximately 20% across tasks, doubling the label-space granularity and revealing temporal patterns aligned with the N200 and P300 components. Visual stimuli generally outperformed auditory stimuli, and cross-modal benefits emerged primarily in key-response tasks. These findings demonstrate that polarity-considered microstate labeling enhances classification accuracy and interpretability in BCIs. This method highlights the potential for real-time applications, such as P300 spellers and multimodal attention monitoring.}, }
@article {pmid41928799, year = {2026}, author = {Ouyang, Z and Walmsley, K and Luo, S and Tippett, D and Wyse-Sookoo, K and Fifer, M and Vansteensel, MJ and Angrick, M and Ramsey, N and Crone, NE}, title = {Stable speech BCI performance during slow progression of ALS: A longitudinal ECoG study.}, journal = {Research square}, volume = {}, number = {}, pages = {}, doi = {10.21203/rs.3.rs-9156039/v1}, pmid = {41928799}, issn = {2693-5015}, abstract = {Background Electrocorticographic (ECoG) speech brain-computer interfaces (BCIs) show promise for restoring communication in amyotrophic lateral sclerosis (ALS), but the long-term stability of speech-related neural signals and decoding performance during disease progression remains unclear. We tracked signal characteristics and decoding over 25 months in a participant with ALS to determine how high-gamma (HG, 70-170 Hz) activity changes over time and whether these changes affect offline speech decoding. Methods We implanted two 8×8 subdural ECoG grids over left sensorimotor cortex (SMC) in a participant with slowly progressive bulbar variant ALS. Across 25 months, the participant performed an overt syllable-repetition task (12 consonant-vowel tokens) during simultaneous ECoG and audio recording. We quantified HG activation ratio (ActR), spectral signal-to-noise ratio (SNR; HG/HF, where HF = 300-499 Hz), and peak z-scored HG responses. Speech acoustics were evaluated using first/second formants (F1/F2) and the triangular vowel space area (tVSA). Offline EEGNet-based decoders were assessed in two stages: models trained on post-implant months 1-6 were tested on months 7-25, while models trained on stabilized data (months 7-11) were tested on the remaining period (months 12-25). Electrode-level saliency assessed spatial contributions to decoding. Results Acoustic analyses showed a significant reduction in tVSA over two years (-44.6 Hz[2]/day; P < 10 [-] [7]), consistent with mild intelligibility decline. Neural metrics (ActR and SNR) followed a biphasic trajectory: increasing during the first 6 months, after which ActR stabilized (0.041%/day; P = 0.13), and SNR declined gradually (-0.46%/day, P < 10 [- 4]). The model trained on months 1-6 achieved 55.7% accuracy (chance: 8.33%), but performance declined over time (-0.019%/day; P = 2.1×10 [-] [4]). Conversely, the model trained on months 7-11 achieved higher accuracy (65.9%) on subsequent data with no significant temporal decline (P = 0.23). Conclusions Speech-related HG features exhibited an initial unstable period followed by a long-term gradual SNR reduction, potentially reflecting disease progression. Models trained after signal stabilization generalized robustly to data recorded over a year later. These findings confirm that despite reduced absolute HG power and mild acoustic degradation of speech, cortical features remain stable enough to support durable ECoG speech BCIs without frequent recalibration. These findings will motivate future adaptive calibration algorithms that account for slow signal changes while leveraging stable spatial representations in ventral SMC. ClinicalTrials.gov Identifier NCT03567213.}, }
@article {pmid41928997, year = {2026}, author = {Emonds, AMX and Okorokova, EV and Blumenthal, GH and Collinger, JL and Bensmaia, SJ and Miller, LE and Downey, JE and Sobinov, AR}, title = {Overlap in neural representations of coordinated wrist and finger movements in human motor cortex.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.03.19.712976}, pmid = {41928997}, issn = {2692-8205}, abstract = {Dexterous hand function underlies many essential human activities, from tool use to expression through gestures. Coordinated digit movements are enabled by the intricate musculature of the hand and forearm, which also imposes mechanical coupling between the digits and wrist, constraining their independent control. It remains unclear whether motor cortex inherits these constraints in its activity or encodes digit and wrist independently. To address this problem, we asked individuals with intracortical microelectrode arrays implanted in motor cortex to attempt flexion and extension of individual digits, either in isolation or in combination with attempted wrist movements. We could accurately decode which digit was moving based on cortical recordings, and channels selective for digit identity were arranged somatotopically across the recording arrays. Nevertheless, the activity during flexion or extension overlapped between digits, and movement direction of a given digit could be reliably inferred by a decoder trained on movements of other digits. This directional signal was largely invariant to the digit's initial posture. The population axis describing digit movement direction was aligned with the axes associated with wrist flexion-extension or pronation-supination. This alignment persisted during simultaneous wrist and digit movements, which complicated efforts to control them individually. However, by decoding wrist and digit motion from activity orthogonal to the shared direction axis, a participant was able to achieve continuous control of virtual hand movements with improved speed and reduced unintended movements. Together, the results identify both a code for digit identity and a low-dimensional flexion-extension signal which is shared across the digits and wrist. This arrangement is consistent with muscle-like biomechanical constraints on motor cortical activity, which must be accounted for to improve coordinated BCI control.}, }
@article {pmid41929051, year = {2026}, author = {Karrenbach, MA and Wang, H and Johnson, Z and Ding, Y and He, B}, title = {EEG Foundation Model Improves Online Directional Motor Imagery Brain-computer Interface Control.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.03.24.714020}, pmid = {41929051}, issn = {2692-8205}, abstract = {Brain-Computer interfaces (BCIs) offer a link between neural signals and external computation, enabling control of devices for the purposes of restoring function to motor-affected individuals and enhancing capabilities of a wider set of populations. Electroencephalography (EEG) offers a high temporal resolution for dynamic and potential real-time feedback for non-invasive systems. However, its practical efficacy remains limited due to low spatial resolution and poor signal-to-noise ratio, leading to insufficient decoding accuracy and unintuitive control paradigms that hinder reliable user interaction. In this study, we present a framework for an online EEG foundation model by creating a custom foundation model through spectrogram reconstruction of compact temporal windows and online constraints during pretraining. We evaluate the performance of the model in a challenging control paradigm of single-arm, directional motor imagery with dynamic movements for guided and free movement cursor control tasks. Our foundation model approach achieved a final average accuracy of 51.3% during a goal-oriented guided control task. This represents a 15.8% increase over a conventional deep learning framework and a 26.3% increase above chance level, evaluated in a cohort of 11 human participants. During the free movement task, the foundation model invoked a higher rate of completion and lower completion times. Furthermore, the custom EEG foundation model demonstrated superior adaptability from same-session finetuning and indicated an enhanced capability to assist subject learning. These findings highlight the potential of EEG foundation models to support more robust and intuitive non-invasive BCI systems, providing a promising modelling framework for future BCI development.}, }
@article {pmid41920806, year = {2026}, author = {Kostoglou, K and Müller-Putz, GR}, title = {Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training.}, journal = {PLoS computational biology}, volume = {22}, number = {4}, pages = {e1014112}, doi = {10.1371/journal.pcbi.1014112}, pmid = {41920806}, issn = {1553-7358}, abstract = {Brain-computer interfaces (BCIs) depend on the reliable decoding of brain activity, yet key rhythms like alpha and mu are not spectrally static and can shift with cognitive and motor demands. Here, we investigated within-session changes in instantaneous alpha/mu frequency and magnitude during motor-related BCI calibration using an oscillator-tracking framework based on an extended Kalman filter (EKF). We applied this method to four public EEG datasets spanning motor execution and imagery tasks. Across all datasets, we observed consistent increases in mu instantaneous frequency and magnitude over central sensorimotor regions, indicative of motor engagement and possible training-related neuroplasticity. In contrast, posterior and surrounding cortical areas often showed alpha slowing, suggestive of declining vigilance or cognitive fatigue, or alternatively, resource reallocation via inhibition of task-irrelevant regions. These opposing spatial trends underscore the functional heterogeneity of alpha-band activity across the cortex. Our results highlight the potential of real-time frequency tracking not only to improve decoding accuracy but also to monitor neurophysiological state changes and guide adaptive adjustments in BCI calibration paradigms.}, }
@article {pmid41922362, year = {2026}, author = {Chen, J and Qi, Y and Wang, Y and Pan, G}, title = {Human-like cognitive generalization for large models via mental representation-guided supervision.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-71267-5}, pmid = {41922362}, issn = {2041-1723}, support = {LR24F020002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 624B2127//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Recent advancements in deep neural networks (DNNs), particularly large-scale language models, have demonstrated remarkable capabilities in image and natural language understanding. Although scaling up model parameters with increasing volume of training data has progressively improved DNN capabilities, achieving complex cognitive abilities-such as understanding abstract concepts, reasoning, and adapting to novel scenarios, which are intrinsic to human cognition-remains a major challenge. In this study, we show that mental representation-guided supervised learning, utilizing a small set of brain signals, can effectively transfer human conceptual structures to DNNs, significantly enhancing their comprehension of abstract and even unseen concepts. Experimental results further indicate that the enhanced cognitive capabilities lead to substantial performance gains in challenging tasks, including few-shot/zero-shot learning and out-of-distribution recognition, while also yielding highly interpretable concept representations. These findings highlight that mental representation-guided supervision can effectively augment the complex cognitive abilities of large models, offering a promising pathway toward developing more human-like cognitive abilities in artificial systems.}, }
@article {pmid41922634, year = {2026}, author = {Zhi, J and Zhang, Q and Li, Y and Zhang, J and Liu, P and Nan, J and Li, Y and Li, D}, title = {Joint MVMD-based optimal feature selection and FW-LS-TWSVM for motor imagery recognition.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-46642-3}, pmid = {41922634}, issn = {2045-2322}, support = {262102211021//the Key Science and Technology Program of Henan Province/ ; 262102211056//the Key Science and Technology Program of Henan Province/ ; 25A520003//the Key Science Research Project of Colleges and Universities in Henan Province/ ; }, abstract = {The Motor Imagery-Brain Computer Interface (MI-BCI) system is an effective approach for motor neurorehabilitation training and human-machine collaborative control. However, the current MI-BCI systems' decoding accuracy and real-time performance still fall short of practical requirements. To address this issue, this study proposes a model combining MVMD-based optimal feature selection and the Fuzzy Weighted Least Squares Twin Support Vector Machine (FW-LS-TWSVM). First, raw data is decomposed into multiple Intrinsic Mode Functions (IMFs) using Multivariate Variational Mode Decomposition (MVMD). Then, Common Spatial Pattern (CSP) is employed to extract features from each IMF, and a feature selection method based on F-statistics is used to adaptively identify the optimal IMFs and their corresponding features, thereby extracting optimal frequency information. Subsequently, this paper introduces, for the first time, the application of the FW-LS-TWSVM to MI-BCI EEG decoding, aiming to enhance the identification efficiency of outliers. The proposed method was validated on two publicly available motor imagery datasets, achieving accuracies of 87.40% and 88.48%, respectively. Comparative analysis revealed that both the frequency band decomposition method and the FW-LS-TWSVM classification model contributed significantly to the decoding accuracy. Compared to traditional frequency band decomposition, SVM, and its improved variants, the proposed method not only achieved higher accuracy but also required relatively less training time. These results indicate that the proposed model can facilitate the development of MI-BCI systems, enhance the behavioral capabilities of healthy individuals, and help improve the quality of life for patients with neurological disabilities.}, }
@article {pmid41922703, year = {2026}, author = {Aczel, B and Szaszi, B and Clelland, HT and Kovacs, M and Holzmeister, F and van Ravenzwaaij, D and Schulz-Kümpel, H and Hoffmann, S and Nilsonne, G and Kosa, L and Torma, ZA and Abdelfatah, Y and Aberson, CL and Acar, OA and Acem, E and Adamkovic, M and Adamovich, T and Adiasto, K and Ahnström, L and Akil, AM and Al-Busaidi, AS and Al-Hoorie, AH and Albers, CJ and Allen, PJ and Alsalti, T and Altman, M and Alzahawi, S and Ambrosini, E and Anafinova, S and Anand, R and Angerer, M and Angulo-Brunet, A and Antonietti, A and Arato, J and Arenas, A and Aviña, MM and Azevedo, F and Bachl, M and Bago, B and Bahník, &#x160; and Baker, BJ and Balayan, E and Baldwin, CL and Banai, B and Banas, K and Bartoš, F and Baskin, E and Bastiaansen, JA and Bault, N and Bauman, CW and Beazer, QH and Behnke, M and Bendixen, T and Berger, S and Bernard, A and Bernardic, U and Bloom, PA and Boldt, A and Bosch-Rosa, C and Botvinik-Nezer, R and Bouyamourn, A and Bozkurt, O and Brehm, L and Breuer, J and Briggs, R and Brohmer, H and Buchanan, E and Buckenmaier, J and Buckley, J and Buczny, J and Burghart, M and Butt, BH and Byrd, N and Cafarelli, V and Callahan, P and Capitán, T and Carriere, K and Cataldo, AM and Cepaluni, G and Chan, E and Chandler, JJ and Chang, CC and Chen, X and Chen, SS and Chen, F and Chen, H and Chirkov, V and Cialfi, D and Clarke, B and Coelho, SG and Cohen, C and Collins, J and Cook, SW and Corlazzoli, G and Cummins, J and Czymara, C and D'hondt, J and Rosa, AD and Davis, AMB and Davis, CP and Day, MV and De Keyzer, F and de Leeuw, JR and de Vries, TR and Debnath, R and Dechterenko, F and Demiral, EE and Desgroseilliers, M and Dianovics, D and Diveica, V and Dochow-Sondershaus, S and Dohle, S and Dong, L and Dora, J and Dorrough, AR and Dreber, A and Du, H and Edlund, JE and Eerland, A and Efendić, E and Elder, J and Elsherif, MM and Ernst, M and Estrada, E and Eudave, L and Evans, TR and Farrera, A and Ferrouhi, EM and Fiala, L and Fialho, FM and Fiechter, JL and Fišar, M and Flores-Kanter, PE and Folwarczny, M and Fossum, JL and Franco, VR and Freichel, R and Freire, D and Frese, J and Furnas, AC and Gaebler, JD and Gajary, LC and Galang, CM and Ganschow, B and Garrison, SM and Gasiorowska, A and Ponne, BG and Gauriot, R and Geminiani, A and Geraldes, D and Gernsbacher, MA and Giani, C and Glerean, E and Gligorić, V and Gnambs, T and Godefroidt, A and González-Bustamante, B and Goreis, A and Graf-Vlachy, L and Grieder, M and Grigoryev, D and Grinschgl, S and Grüning, DJ and Guassi Moreira, JF and Guichet, C and Gurgand, L and Habibnia, H and Hafenbrack, AC and Hafenbrädl, S and Häffner, C and Hagemeister, F and Haigh, M and Hajdu, N and Hajimoladarvish, N and Hall, JD and Hamjediers, M and Hardwick, RM and Harma, M and Harp, NR and Hartvig, &#xc1;D and Heiberger, RH and Heim, A and Hernæs, &#xd8; and Hernaus, D and Heyman, T and Hicks, J and Hogeveen, J and Höpler, J and Houlihan, SD and Huber, C and Hughes, C and Hummler, T and Huth, K and Ingendahl, M and Ishii, T and Isler, O and Izydorczak, K and Jackson, IR and Jahn, A and Jain, M and Jakubow, A and Jang, D and Jang, J and Jekel, M and Jia, F and Jiménez-Leal, W and Johnson, R and Jones, A and Jungkunz, S and Kačmár, P and Kaiser, C and Kalaycı, Y and Kantorowicz, J and Karabulut, A and Karch, JD and Karimi-Rouzbahani, H and Karl, JA and Kažemekaitytė, A and Kazlou, A and Kekecs, Z and Kim, J and Kirchler, MH and Kiss-Dobronyi, B and Klasmeier, KN and Klein, JW and Koba, C and Kołczyńska, M and Kolias, P and Kolouch Grabovský, M and Korbmacher, M and Korda, &#x17d; and Kowal, M and Kretzschmar, A and Krivoshchekov, V and Krypotos, AM and Kubsch, M and Kunisato, Y and Lacko, D and Landwehr, JR and Lange, M and Lee, H and Lee, D and Lee, S and Lemay, EP and Lempert, D and Leo, A and Lesage, E and Levin, JM and Li, P and Lin, J and Lindsay, L and Lisovoj, D and Liu, M and Liu, S and Liu, T and Iacono, SL and Lodder, P and López-Bueno, R and Lopez-Nicolas, R and Loter, K and Lou, NM and Lovakov, A and Lu, JG and Ludwig, J and Luebber, F and Lukavský, J and Luo, CQ and Lyu, X and Maassen, E and Máčel, M and Mack, ML and Madan, CR and Mädebach, A and Maffly-Kipp, J and Mallinson, DJ and Marchetti, I and Marghetis, T and Marini, MM and Fages, DM and Martínez, M and Martinoli, M and Masiliunas, A and Massoni, S and Mathieu, KC and Mayer, S and Mayer, DJ and Mayer, M and McCormick, EM and McDonough, IM and McGowan, AL and McIntyre, MM and McKee, P and Meier, AN and Meier, PF and Melero, H and Merkle, C and Merz, R and Michaelides, MP and Michaelsen, P and Mikolajczak, G and Mill, W and Millroth, P and Miroshnik, KG and Misiak, M and Mora, YL and Moreau, D and Moreh, C and Morvinski, C and Mushtaq, F and Nagy, T and Nater, C and Naumann, E and Navarrete, G and Nebe, S and Nedderhoff, A and Nennstiel, R and Neugebauer, M and Nicolaisen-Sobesky, E and Nielsen, YA and Niso, G and Nowak, B and Okan, M and Ong, K and Onicas, AI and Oswald, C and Otten, K and Pandey, S and Pantazi, M and Papale, P and Pärnamets, P and Pauer, S and Pavlov, YG and Pawel, S and Peelle, JE and Peetz, HK and Peez, A and Pesciarelli, F and Peterson, BD and Petruželka, B and Petter, J and Pfänder, J and Pfuhl, G and Phillips, J and Pietryka, MT and Pirrone, A and Pit, IL and Plachti, A and Plank, IS and Ploner, M and Poldrack, RA and Pollmann, MMH and Porcher, S and Präg, P and Pua, AAY and Pugel, J and Puri, R and Püski, M and Radkani, S and Raes, L and Rafaï, I and Raiber, K and Rathje, S and Rehms, R and Reshetnikov, M and Reynolds, CJ and Reynolds, JP and Rigaud, K and Rioux, C and Rivera, S and Robertson, O and Román-Caballero, R and Ropovik, I and Röseler, L and Ross, RM and Rotella, A and Rüffer, FF and Rusche, F and Rusconi, M and Russo, I and Sahm, AHJ and Salamon, J and Samahita, M and Sanaei, A and Sangchooli, A and Sarafoglou, A and Scandola, M and Schaak, H and Schaerer, M and Schares, E and Schilling, HT and Schmalz, X and Schmidt, K and Schonberg, T and Schreiner, MR and Schröder, JM and Schubert, AL and Schuetze, B and Schultz, DH and Schulze, L and Schwartz, ST and Schwitter, N and Scoggins, B and Seetahul, Y and Seri, R and Shanks, DR and Shaw, ST and Shaw, J and Shen, Q and Siemroth, C and Sladekova, M and Somo, A and Sondhi, A and Sonmez, B and Spantig, L and Speekenbrink, M and Stamos, A and Stasielowicz, L and Steckermeier, LC and Steinkamp, SR and Stoevenbelt, AH and Street, CNH and Suchow, JW and Sunde, HF and Sundquist, J and Suschevskiy, V and Swain, SD and Szecsi, P and Szekely-Copîndean, RD and Szumowska, E and Tacconelli, A and Talbert, E and Tang, JP and Tendeiro, JN and Testori, M and Toffalini, E and Tomašević, A and Topel, S and Torkkeli, L and Tozzi, L and Traczyk, J and Trinidad, A and Trübutschek, D and Turek, K and Uhlich, M and Uhlmann, EL and Urbanska, K and Van Assche, J and van Assen, MALM and van Dongen, NNN and van Lieshout, K and van Veldhuizen, R and Varga, MA and Vaughn, LA and Venczel, F and Vezzoli, M and Vierus, P and Visalli, A and Voldal, E and Votta, F and Wagenmakers, EJ and Waldendorf, A and Walker, MJ and Wall, MB and Wallen, H and Wang, K and Wang, I and Wang, YA and Weinmann, M and Weiß, M and Westheide, C and Wichman, A and Wilcke, JC and Williams, BJ and Wisniewski, D and Woiczyk, TKA and Woźniak, M and Wright, JD and Youyou, W and Wulff, JN and Yang, T and Yeung, SK and Yuen, KSL and Zawistowski, M and Zein, RA and Zhao, X and Zheng, Z and Zhou, S and Ziller, C and Zimmerman, D and Zogmaister, C and Zultan, R and Fox, N and Errington, TM and Nosek, BA}, title = {Investigating the analytical robustness of the social and behavioural sciences.}, journal = {Nature}, volume = {652}, number = {8108}, pages = {135-142}, pmid = {41922703}, issn = {1476-4687}, support = {//Amsterdam Brain and Cognition/ ; //Czech Science Foundation/ ; //Marie Sk&#x142;odowska-Curie grant/ ; }, mesh = {*Social Sciences/standards/statistics &amp; numerical data/methods ; *Behavioral Sciences/standards/statistics &amp; numerical data ; Reproducibility of Results ; Humans ; *Research Design/standards ; *Behavioral Research/standards ; }, abstract = {The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science[1-3]. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts' choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen's d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses[4]. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.}, }
@article {pmid41922776, year = {2026}, author = {Lei, T and Scheid, MR and Flint, RD and Glaser, JI and Slutzky, MW}, title = {Active dissociation of intracortical spiking and high gamma activity.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {41922776}, issn = {1476-4687}, abstract = {Cortical high gamma-band activity (HGA) is used in many scientific investigations[1-18], yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly-that it predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. To test these hypotheses, here we trained monkeys (Macaca mulatta) to decouple local spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them suggested that HGA is probably not generated simply by summed local spiking. Instead, HGA correlated with co-firing of neuronal populations that were widely distributed across millimetres of cortex. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises mainly from summed postsynaptic potentials triggered by the synchronous co-firing of widely distributed neurons.}, }
@article {pmid41924028, year = {2026}, author = {Zheng, Y and Wang, X and Zheng, L and Zhang, H and Wang, F and Zhuo, Y}, title = {Multidimensional dynamic characterization and decoding of finger movements using magnetoencephalography.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {4}, number = {}, pages = {}, pmid = {41924028}, issn = {2837-6056}, abstract = {The similarity of neural activity in finger movements poses challenges for accurate decoding using many non-invasive imaging techniques. Magnetoencephalography (MEG), with its relatively high spatial resolution, offers the potential to capture the underlying dynamic neural differences. In this study, we recorded MEG signals during single extension movements of the right-hand fingers, examining the time-varying cortical activation patterns across different frequency bands and their contribution to decoding finger movements. Our results demonstrate that signals below 8 Hz not only enable effective movement classification but also reveal millisecond-scale neural activation patterns in the sensorimotor cortex. Furthermore, incorporating the spatiotemporal dynamics of neural activity may enhance decoding performance for fine motor control. These findings highlight the value of integrating temporal, frequency, and spatial dimensions in studying motor neural activity and underscore MEG's potential for broader applications in movement-related neurophysiology and brain-computer interface research.}, }
@article {pmid41915503, year = {2026}, author = {Wang, H and Jia, Z and Shen, Y and Wang, Z and Li, S and Tang, Z and Shu, K and Hu, F and Wu, D}, title = {SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3678858}, pmid = {41915503}, issn = {1558-2531}, abstract = {OBJECTIVE: Speech disorders such as dysarthria and anarthria can severely impair patients' ability to communicate verbally. Speech decoding brain-computer interfaces (BCIs) offer a potential alternative by directly translating speech intentions into spoken words, serving as speech neuroprostheses.<br><br>METHODS: This paper reports an experimental protocol for Mandarin Chinese speech decoding BCIs and proposes a contrastive learning-based decoding algorithm termed SEEG and Audio Contrastive Matching (SACM). Stereo-electroencephalography (SEEG) and synchronized audio data were collected from ten patients with drug-resistant epilepsy as they performed a word-level reading task. SACM leverages the cross-modal correlation between neural activity and audio signals to decode matched speech segments.<br><br>RESULTS: The proposed framework achieved accuracies significantly exceeding random matching in both isolated-word and continuous speech decoding tasks, and outperformed SEEG-only baselines across seven backbone architectures in the isolated-word setting. Electrode- wise analysis revealed that a single ventral sensorimotor cortex electrode achieved performance comparable to that of the full electrode array. Our code is publicly available.<br><br>SIGNIFICANCE: To our knowledge, this is the first work on multimodal decoding for tonal speech BCIs.}, }
@article {pmid41916314, year = {2026}, author = {Li, XY and Bao, YF and Hu, CC and Dong, Y and Wu, ZY}, title = {Pyramidal signs in Huntington's disease: An early clinical indicator associated with proximity to disease onset.}, journal = {Med (New York, N.Y.)}, volume = {}, number = {}, pages = {101071}, doi = {10.1016/j.medj.2026.101071}, pmid = {41916314}, issn = {2666-6340}, abstract = {BACKGROUND: Previous studies in Huntington's disease (HD) have primarily focused on striatal degeneration, while pyramidal signs remain insufficiently characterized. Understanding the clinical significance of pyramidal signs in HD is crucial for elucidating the disease's pathogenesis.<br><br>METHODS: In a cross-sectional cohort, 29 individuals with premanifest HD (pre-HD) and 196 patients with manifest HD underwent standardized neurological examination. Serum neurofilament light-chain (sNfL) levels, clinical assessments, and MRI were obtained. In addition, a prospective longitudinal cohort including 15 individuals with pre-HD and 51 patients with manifest HD was followed up.<br><br>FINDINGS: In the cross-sectional cohort, the prevalence of pyramidal signs was 44.8% in individuals with pre-HD and 63.3% in patients with manifest HD. Individuals with pre-HD with pyramidal signs exhibited significantly higher sNfL levels (17.4 vs. 9.3 pg/mL, p = 0.0010) and were closer to predicted motor onset (p = 0.0005) compared with those without pyramidal signs. In the longitudinal analysis, linear mixed-effects models revealed that the rates of disease progression did not differ significantly between pyramidal-sign-positive and -negative patients with manifest HD. Cox regression analysis further indicated that pyramidal signs emerged approximately 13.3 years before the predicted motor onset, in a manner dependent on CAG repeat length.<br><br>CONCLUSIONS: The presence of pyramidal signs in pre-HD reflects closer proximity to disease onset, but it lacks prognostic value in manifest HD. These signs typically emerge more than a decade before motor symptom onset and serve as a simple and predictive marker for physicians.<br><br>FUNDING: This study was supported by the National Natural Science Foundation of China to Z.-Y.W. (82230062, Beijing).}, }
@article {pmid41916607, year = {2026}, author = {Xue, Y and Cai, X and Liu, H}, title = {Passive pitch rotation enables optimal vibrational stabilization in hawkmoth forward flight.}, journal = {Journal of the Royal Society, Interface}, volume = {23}, number = {237}, pages = {}, doi = {10.1098/rsif.2025.1011}, pmid = {41916607}, issn = {1742-5662}, support = {//Japan Society for the Promotion of Science/ ; }, mesh = {Animals ; *Flight, Animal/physiology ; *Wings, Animal/physiology ; Vibration ; *Models, Biological ; *Manduca/physiology ; Biomechanical Phenomena ; Rotation ; }, abstract = {Flying insects maintain stable flight through both active control and passive mechanisms that exploit natural wing and body vibrations. One such mechanism, vibrational stabilization, uses high-frequency wing vibrations to create a virtual spring effect that helps insects like hawkmoths stay stable during hovering. In addition, the flexible musculoskeletal system contributes pitch stiffness to add a stabilizing effect that may vary with forward flight speed but has not been fully explored. This study develops a fluid-structure interaction model that integrates the dynamics of an elastic wing hinge with unsteady flapping aerodynamics. We introduce a vibrational stabilization framework to investigate the passive stability of the hawkmoth Manduca sexta across a broad range of forward flight velocities. The framework reveals that natural wing vibrations enhance flight stability at all speeds. At low speeds, vibrational stiffness generates a restorative pitching moment, while at higher speeds, damping effects from wing vibrations dominate. The model shows that biologically realistic hinge stiffness values optimize vibrational stabilization throughout the flight envelope. This flexible-vibrational mechanism significantly improves robustness against external pitch disturbances, reducing reliance on active neural control. These findings offer useful design principles for biomimetic flying robots, potentially simplifying their control architectures.}, }
@article {pmid41916974, year = {2026}, author = {Lai, S and Huang, Y and Ma, S and Hao, H and Dai, A and Yan, X and Yang, J and Wang, S and Ren, Q and Zhang, Y and Hu, P and Li, J and Zheng, X and Brosius, J and Deng, C}, title = {FSHR and LHR functional compensation reveals the mechanism and treatment of Ovarian Hyperstimulation Syndrome.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-71338-7}, pmid = {41916974}, issn = {2041-1723}, support = {32270438//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170498//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Gain-of-function mutations in the human follicle-stimulating hormone receptor (FSHR) cause spontaneous ovarian hyperstimulation syndrome (OHSS), a serious reproductive disorder. However, the molecular physiology and treatment options for OHSS remain elusive. Notably, estrildid finches naturally carry an FSHR variant (Thr449Ala) analogous to the pathogenic mutation in humans yet are resistant to OHSS. Here we show that this resistance stems from significantly reduced luteinizing hormone receptor expression in estrildid ovarian granulosa cells. Furthermore, treatment with the luteinizing hormone receptor antagonist alleviates OHSS symptoms in mouse models. Single-cell RNA transcriptomic reveals functional compensation of the two receptors to regulate estrogen production and vascular permeability, resembling the adaptive mechanisms observed in estrildid finches. Our study unravels the molecular mechanism underlying the physiological adaptation of estrildid ovaries to high FSHR constitutive activity and is a example of how the concept of Darwinian Medicine could be exploited to identify novel drug targets for ovarian hyperstimulation syndrome treatment.}, }
@article {pmid41917049, year = {2026}, author = {Pollina, L and Struber, L and de Seta, V and Russo, E and Karakas, S and Chabardes, S and Aksenova, T and Charvet, G and Shokur, S and Micera, S}, title = {Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-71234-0}, pmid = {41917049}, issn = {2041-1723}, abstract = {The brain coordinates multiple parallel motor programs, ensuring synergy and preventing interference during movements. Yet, performance often degrades when brain-machine interfaces are used during concurrent tasks or ongoing movements. We suggest that latent neural representations may represent a strategy to solve this issue. In this study, we addressed this question using neural signals from a tetraplegic individual with partial residual motor function, implanted with a wireless epidural electrocorticography (ECoG) device. By adapting dimensionality reduction techniques, we found that motor execution and motor imagery span partially overlapping subspaces in mesoscale neural signals, shaped by specific frequency band contributions. Despite substantial shared variance, we show that identifying orthogonal, condition-specific dimensions enables successful decoding of executed and imagined movements, even when performed simultaneously. These findings show that ECoG signals can expose separable neural subspaces, allowing executed and imagined actions to be harnessed independently and in concert. This opens a promising avenue to develop brain-machine interfaces that can simultaneously control multiple external devices or operate alongside natural movements.}, }
@article {pmid41918517, year = {2026}, author = {Guerrero, AI and Vivanco, C and Pavez, G and Sabat, P and Maldonado, K and Barilari, MF and Quiñones, RA and Carrasco, P and Toro, F and Gutiérrez, J and Sepúlveda, M}, title = {Drivers of body condition in South American sea lion pups along a latitudinal gradient.}, journal = {Conservation physiology}, volume = {14}, number = {1}, pages = {coag018}, pmid = {41918517}, issn = {2051-1434}, abstract = {Body condition is a key proxy of fitness in pinnipeds, reflecting nutritional status and maternal investment. In the South American sea lion (Otaria flavescens), pup growth and survival depend on maternal foraging success, making pup condition a sensitive indicator of local environments. We quantified spatial and interannual variation in pup body condition across five Chilean breeding colonies spanning 21-53°S during the austral summers of 2024 and 2025. We captured 157 live pups (95 males, 62 females), measured morphometrics and calculated a body condition index (BCI = mass/length). To account for seasonal effects, BCI values were standardized to allow comparisons across sites and years. We tested the effects of sex, year, locality and satellite-derived net primary productivity (NPP). Male pups consistently showed higher standardized BCI than females. Locality was the strongest predictor: Isla Marta (southern limit) exhibited significantly higher values than all other sites, followed by Isla Metalqui. Cobquecura, Isla Choros and Punta Lobos showed lower or intermediate values. Year alone had no effect, but a significant locality × year interaction indicated interannual variability in northern colonies, particularly Punta Lobos. NPP was not retained in top-ranked models, suggesting broad-scale productivity does not directly predict pup condition at this resolution. The pronounced latitudinal gradient, with larger, better-conditioned pups at higher latitudes, is consistent with expectations under Bergmann's rule, which refers to the tendency of animals to be larger in colder climates and smaller in warmer ones. These results underscore the combined influence of local ecological conditions, maternal effects and intrinsic sex differences on pup condition and reinforce the value of South American sea lion pups as sentinels of ecosystem variability along the Chilean coast.}, }
@article {pmid41920177, year = {2026}, author = {Van Damme, S and Mumford, L and Thompson, A and Murphy, C and Ali, T and Hegazi, A and Wang, R and Chang, K and Raza, IA and Chau, T and Kingsnorth, S}, title = {Family Experiences in a Pediatric Clinical Brain-Computer Interface Program: A Qualitative Study.}, journal = {The American journal of occupational therapy : official publication of the American Occupational Therapy Association}, volume = {80}, number = {3}, pages = {}, doi = {10.5014/ajot.2026.051474}, pmid = {41920177}, issn = {0272-9490}, mesh = {Humans ; Child ; Female ; Male ; *Brain-Computer Interfaces/psychology ; Adolescent ; Qualitative Research ; *Children with Disabilities/rehabilitation ; *Occupational Therapy ; *Parents/psychology ; Interviews as Topic ; Adult ; }, abstract = {IMPORTANCE: Brain-computer interfaces (BCIs) are access technologies that can improve the occupational participation of children with disabilities. The research on the experiences of pediatric BCI users and their families is currently limited.<br><br>OBJECTIVE: To explore experiences of pediatric BCI use and future expectations through caregiver perspectives.<br><br>DESIGN: A qualitative, descriptive study using purposeful sampling and inductive thematic analysis. Investigator triangulation and reflexivity enhanced credibility.<br><br>SETTING: Zoom for Healthcare virtual platform.<br><br>PARTICIPANTS: Fifteen parents (12 mothers and 3 fathers) of children and youth with disabilities (ages 6-18 yr; 9 females and 6 males) who participated in a recreational BCI program at a pediatric rehabilitation hospital, with the option of additional at-home BCI use, were selected via purposive sampling.<br><br>OUTCOMES AND MEASURES: In-depth, semistructured interviews were used to collect data.<br><br>RESULTS: Three major themes emerged from the central topic of experiencing play using BCIs: (1) transformative experiences, (2) personalization for success, and (3) future hopes.<br><br>CONCLUSIONS AND RELEVANCE: By documenting family experiences with and expectations of BCIs, these findings can guide the development of BCI use in clinical and recreational programs. Occupational therapy practitioners can use the transformative potential of BCI technology to create new pathways for participation and empowerment in the lives of children and youth with disabilities. Plain-Language Summary: Children with complex disabilities often cannot take part in play and recreation. Many activities are not accessible to them. Brain-computer interface (BCI) technologies can help kids play without needing to move or speak. We asked families using BCIs about their experiences. They shared that use of a BCI empowered their child and allowed others to consider them in a new light. Some families enjoyed the programming, and others found the activities too simple over time. Many families shared that BCI headsets were uncomfortable. A better design for kids with disabilities is important. Families hope that BCIs will help kids control their environment in the future. Occupational therapists should understand how kids and families feel about using BCIs. This study helps occupational therapists learn about the benefits of BCIs in their practice and the challenges of using them.}, }
@article {pmid41920265, year = {2026}, author = {Wang, C and Lu, J and Fu, H and Feng, X and Xu, Z and Luo, P and Yang, B and He, Q and Yang, X}, title = {Glycyrrhizic Acid Alleviates Osimertinib-Induced Cutaneous Toxicity by Inhibiting Keratinocyte Apoptosis and Inflammation.}, journal = {Phytotherapy research : PTR}, volume = {}, number = {}, pages = {}, doi = {10.1002/ptr.70310}, pmid = {41920265}, issn = {1099-1573}, support = {No. 82274018//National Natural Science Foundation of China/ ; 2020YFE0204300//National Key Research and Development Program of China/ ; }, abstract = {Osimertinib is a primary treatment for patients with EGFR-mutated non-small cell lung cancer. But a significant number of patients receiving Osimertinib treatment suffer from cutaneous toxicity, which includes symptoms such as rash, itching, and hair loss. This study aims to help clinical patients suffering from cutaneous toxicity to improve their quality of life. Mice treated with 50 mg/kg/day Osimertinib for 42 days exhibited different levels of cutaneous toxicity. PI/Annexin-V apoptosis assay and western blotting were used to assess keratinocyte apoptosis and DNA damage. Osimertinib upregulated inflammatory factors including CCL2, CCL27, and IL18. Glycyrrhizic acid (GA) is the most important active ingredient in licorice with pharmacological effects such as anti-inflammatory, antiviral, and anti-apoptotic. Due to its rich bioactivity, the research about GA has always been popular. However, the effects of it on relieving cutaneous toxicity have not been studied yet. We have explored the therapeutic effects and mechanisms of GA on keratinocytes and C57BL/6 mice. Thirty milligrams/kg/day of GA could effectively reduce the frequency and severity of cutaneous toxicity induced by Osimertinib, restore epidermal thickness in mice, reduce DNA damage, and lower the expression levels of inflammatory factors. Our results indicated that GA could potentially mitigate the cutaneous toxicity caused by Osimertinib, which could position it as a promising adjunct in clinical practice.}, }
@article {pmid41920737, year = {2026}, author = {Jude, JJ and Haro, S and Levi-Aharoni, H and Hashimoto, H and Acosta, AJ and Card, NS and Wairagkar, M and Brandman, DM and Stavisky, SD and Williams, ZM and Cash, SS and Simeral, JD and Hochberg, LR and Rubin, DB}, title = {Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome.}, journal = {Cell reports}, volume = {45}, number = {4}, pages = {117162}, doi = {10.1016/j.celrep.2026.117162}, pmid = {41920737}, issn = {2211-1247}, abstract = {Intracortical brain-computer interfaces (iBCIs) for decoding intended speech have provided individuals with ALS and severe dysarthria an intuitive method for high-throughput communication. These advances have been demonstrated in individuals who are still able to vocalize and move speech articulators. Here, we decoded intended speech from an individual with long-standing anarthria, locked-in syndrome, and ventilator dependence due to advanced symptoms of ALS. We found that phonemes, words, and higher order language units could be decoded well above chance. While sentence decoding accuracy was below that of demonstrations in participants with dysarthria, we attained an extensive characterization of neural signals underlying speech in a person with locked-in syndrome and identify directions for future improvement. These include closed-loop speech imagery training and decoding linguistic (rather than phonemic) units from neural signals in middle precentral gyrus to augment decoding at the sentence level. These results demonstrate that usable speech decoding from motor cortex may be feasible in people with anarthria and ventilator dependence.}, }
@article {pmid41909202, year = {2026}, author = {Ming, G and Pei, W and Tian, S and Chen, X and Gao, X and Wang, Y}, title = {A High-Speed Visual BCI Based on Hybrid Frequency-Phase-Space Encoding and High-Density EEG Decoding.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {7}, number = {}, pages = {0555}, pmid = {41909202}, issn = {2692-7632}, abstract = {Brain-computer interface (BCI) technology establishes a direct communication pathway between the brain and external devices. Current visual BCI systems suffer from insufficient information transfer rates (ITRs) for practical use. Spatial information, a critical component of visual perception, remains underexploited in existing systems because the limited spatial resolution of recording methods hinders the capture of the rich spatiotemporal dynamics of brain signals. This study proposed a hybrid frequency-phase-space encoding method, integrated with high-density electroencephalogram (EEG) recordings, to develop high-speed BCI systems. EEG data were recorded using a 256-channel standard cap, and 4 electrode configurations comprising 66, 32, 21, and 9 parieto-occipital electrodes, extracted from 256-, 128-, and 64-channel caps (abbreviated as 66/256, 32/128, 21/64, and 9/64), were systematically compared. In the classical frequency-phase encoding the 40-target BCI paradigm, the 66/256, 32/128, and 21/64 electrode configurations brought theoretical ITR increases of 83.66%, 79.99%, and 55.50% over the traditional 9/64 setup. In the proposed frequency-phase-space encoding 200-target BCI paradigm, these increases climbed to 195.56%, 153.08%, and 103.07%, respectively. The online BCI system achieved an average actual ITR of (472.72 ± 15.06) bits per minute. Taken together, these findings clarify how the spatiotemporal encoding strategy and electrode density jointly determine achievable ITRs and provide quantitative design guidelines for future high-speed visual BCIs.}, }
@article {pmid41909203, year = {2026}, author = {Ma, Y and Zhang, C and Nie, F and Qin, H and Zhang, Q and Zhang, Y and Yang, L and Liu, L}, title = {Construction, Control, and Application of Cyborg Animal Composed of Biological and Electromechanical Systems.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {7}, number = {}, pages = {0486}, pmid = {41909203}, issn = {2692-7632}, abstract = {The limitations of biohybrid and mechanical robots, including insufficient control accuracy, limited flexibility, long-term stability, and endurance, have spurred considerable research interest in cyborg animals, which leverage the innate locomotion capabilities, physiological systems, and natural intelligence of organisms to perform tasks with high adaptability, superior performance, and extended endurance. This study provides a comprehensive overview of cyborg animals within the framework of animal taxonomy, summarizing the current state of research from a zoological perspective. Subsequently, the effect of different control techniques on the locomotion performance of cyborg animals was examined, with a special emphasis on 2 prominent research areas: brain-computer interfaces and muscle-receptor electrical stimulation. In addition, the role of advances in electronic backpack design and navigation control algorithms in enabling closed-loop control and applications, including swarm robotics, environmental exploration, and human-machine interaction, is also introduced, offering valuable insights for developing cyborg animals. This study highlights 4 critical aspects essential for the future advancement of cyborg animals by synthesizing recent progress and clarifying technical distinctions: adaptation between control strategies and animals, biocompatibility and stability of electronic backpacks, construction of interactive hybrid robotic systems, and ethical and welfare considerations related to the experimental animals, with the hope of facilitating the optimization and application of cyborg animal systems.}, }
@article {pmid41911327, year = {2026}, author = {Zhang, R and Zhou, W and Wang, Y and Liu, G}, title = {STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {229}, pages = {}, doi = {10.3791/70425}, pmid = {41911327}, issn = {1940-087X}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Software ; }, abstract = {Motor imagery-brain-computer interfaces (MI-BCIs) have demonstrated significant potential for neurorehabilitation and cognitive neuroscience. However, a standardized and reproducible MI-EEG workflow for configurable spatial-temporal-frequency feature analysis remains limited, and many pipelines require complex configuration and parameter tuning with limited interpretability, hindering practical deployment and generalization. To address these challenges, an STFEEG-Tool was developed to provide a user-friendly, standardized, and interpretable workflow for EEG decoding in MI paradigms. STFEEG-Tool enables fine-grained configuration of temporal, frequency-band, and spatial segmentation, allowing the extraction of multiscale MI features. The toolbox integrates multiple feature extraction algorithms, including common spatial patterns (CSP) and divergence-based CSP (div-CSP), along with various classifiers, such as support vector machines (SVMs), Ridge Regression Classifier, and Lasso Classifier. A dynamic time-frequency scalp topographical map is provided to summarize spatial patterns across time-frequency segments and support interpretation of decoding results. Overall, STFEEG-Tool serves as a reproducible and extensible platform for fine-grained MI-EEG analysis, facilitating the translation of fine-grained decoding pipelines into practical, user-oriented applications.}, }
@article {pmid41912561, year = {2026}, author = {Elwasify, F and Shaaban, E and Abdelmoneem, RM}, title = {EEG imagined speech neuro-signal preprocessing and deep learning classification.}, journal = {Scientific reports}, volume = {16}, number = {1}, pages = {}, pmid = {41912561}, issn = {2045-2322}, }
@article {pmid41912889, year = {2026}, author = {Kim, HR and Kang, D and Kim, DH and Jeong, B and Kim, K and Park, BS and Yang, HR and Kwon, HM and Koch, M and Young, CN and Lee, BJ and Kim, JG}, title = {TonEBP as a key regulator of hypothalamic leptin signaling and resistance.}, journal = {Cellular and molecular life sciences : CMLS}, volume = {}, number = {}, pages = {}, doi = {10.1007/s00018-026-06150-z}, pmid = {41912889}, issn = {1420-9071}, support = {Research Assistance Program (2021) in the Incheon National University//Incheon National University/ ; }, }
@article {pmid41914668, year = {2026}, author = {Li, D and Wang, J and Xu, J and Zhang, Y}, title = {Feature alignment and enhancement network with guided tuning for non-stationary EEG classification.}, journal = {Journal of neural engineering}, volume = {23}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ae512e}, pmid = {41914668}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods/classification ; *Brain-Computer Interfaces/classification ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain/physiology ; }, abstract = {Objective.Electroencephalogram (EEG) signal variability caused by external factors and subject differences limits the adaptation of motor imagery (MI) classification models in brain-computer interfaces (BCIs). Existing domain alignment methods often inadequately utilize critical source and target domains information, leading to negative transfer problems. This paper proposes a Feature Alignment and Enhancement Framework for cross-domain MI-EEG classification to address these limitations.Approach.First, by aligning the covariance matrices of the source and target domains, the spatial distributions of the two domains are preliminarily aligned, establishing a consistent foundation for feature mapping. Second, a conditional domain adversarial network optimizes cross-domain representations, reducing distribution discrepancies while enhancing discriminability. Finally, this paper introduces an EEG feature-based guided tuning method. This method extracts high-confidence features from both the source and target domains and generates centroid features to construct cross-domain feature banks. The input feature representations are dynamically optimized by attending to the relationships between centroid features, thus enhancing the model's adapt-ability to target domain tasks.Main results.Experimental data show that in the four-class MI task of the BCI Competition IV-2a dataset, the cross-session and cross-subject model classification accuracies were 76.89% and 57.91%, respectively. The model achieved accuracy rates of 84.61% and 82.78% on the BCI Competition IV-2b datasets and the High Gamma Datasets, respectively, as well as 84.09% and 70.81%.Significance.The proposed framework effectively mitigates cross-domain variations, providing a reliable solution for cross-session and cross-subject MI-EEG classification.}, }
@article {pmid41772194, year = {2026}, author = {Bagnato, S and Boccagni, C and Bonavita, J}, title = {Eye movements as indicators of awareness in prolonged disorders of consciousness: a scoping review of behavioral and neural evidence.}, journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology}, volume = {47}, number = {3}, pages = {}, pmid = {41772194}, issn = {1590-3478}, abstract = {BACKGROUND: In prolonged disorders of consciousness (DoC), visual fixation and smooth pursuit are among the earliest signs of re-emerging awareness. Yet, because their physiology relies heavily on brainstem–cerebellar circuitry, it remains debated whether these ocular behaviors invariably reflect consciousness or, after severe brain injury, also arise from reflexive/subcortical mechanisms.<br><br>METHODS: To address this question, we conducted a PRISMA-ScR scoping review of PubMed/MEDLINE and Scopus to verify the behavioral and neural correlates of fixation and pursuit. Ocular behavior evidence was classified as cognitively mediated, not cognitively mediated or indeterminate for each study.<br><br>RESULTS: We included 24 studies spanning clinical assessment, instrumented eye&#x2011;tracking, neurophysiology, neuroimaging and brain&#x2013;computer interfaces. Clinically, fixation and pursuit were the most common signs accompanying transition from unresponsive wakefulness syndrome to the minimally conscious state; still, in isolation their specificity remained undetermined. Eye-tracking improved detection and, under explicit, goal&#x2011;directed tasks, demonstrated task&#x2011;contingent responses, occasionally prompting diagnostic reclassification. Neural measures showed that task&#x2011;locked ocular behaviors frequently co&#x2011;occurred with cortical responses, whereas some studies&#x2014;especially for visual fixation&#x2014;found no task&#x2011;linked neural correlates.<br><br>CONCLUSIONS: Overall, fixation and pursuit are sensitive, although context&#x2011;dependent, indicators of awareness. Isolated visual signs&#x2014;especially simple fixation&#x2014;warrant cautious interpretation: absent convergent neural signatures may reflect either limited sensitivity to minimal consciousness or genuinely reflexive/subcortical control. Further studies are needed to quantify cognitive involvement in cases of isolated fixation or pursuit.}, }
@article {pmid41904270, year = {2026}, author = {Mei, S and He, N and He, W and Yan, J and Chu, C and Zeng, Z and Yan, F and Li, D and Pu, Y and Zhang, C and Kong, XZ}, title = {Pallidal and subthalamic stimulations modulate inter-hemispheric interaction and asymmetry in Parkinson's disease.}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {41904270}, issn = {1476-5578}, support = {32400882//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Substantial asymmetries of motor dysfunction are evident in patients with Parkinson's disease (PD), the mechanisms of which remain largely unexplored. This study investigated how deep brain stimulation (DBS) targeting the globus pallidus interna (GPi) and subthalamic nucleus (STN) modulates characteristics of hemispheric lateralization in PD patients, with particular emphasis on motor asymmetries and hemispheric integration (via homotopic functional connectivity) and segregation (via hemispheric asymmetry in connectivity). Resting-state functional magnetic resonance imaging (fMRI) and Unified Parkinson's Disease Rating Scale (UPDRS) III scores were analyzed from 55 PD patients who underwent either bilateral GPi- or STN-DBS. Both targets produced significant improvements in motor function. Notably, stimulation effects on motor asymmetry depend on patients' baseline asymmetry direction (DBS OFF): STN-DBS consistently reduced asymmetry in the leftward-asymmetry patients, whereas GPi-DBS has stronger effects in rightward patients. In both cases, stimulation led to a more symmetric pattern. Beyond motor outcomes, motor gains were associated with changes in homotopic connectivity in the lateral occipital region, overlapping the extrastriate body area, suggesting a compensatory role of visual networks. These findings highlight the contribution of the visual networks to motor improvement and reveal target-dependent effects of DBS on both motor asymmetry and non-motor cognitive domains.}, }
@article {pmid41905427, year = {2026}, author = {Yan, H and Chai, B and Yu, M and Chen, J and Wang, Y and Zhang, Z and Yang, W and Lu, J and Yang, B and He, Q and Luo, P and Yang, X}, title = {Duvelisib upregulates p27 expression and leads to intestinal damage via the NEDD4L/CK1ε axis.}, journal = {Biochemical pharmacology}, volume = {}, number = {}, pages = {117939}, doi = {10.1016/j.bcp.2026.117939}, pmid = {41905427}, issn = {1873-2968}, abstract = {Duvelisib-induced enterotoxicity is one of the most noteworthy clinical concerns, yet due to its unclear mechanism, effective intervention strategies remain lacking. Here, we demonstrated that duvelisib increased the protein stability of casein kinase 1ε (CK1ε) by down-regulating the expression of NEDD4 like E3 ubiquitin protein ligase (NEDD4L), which in turn induced p27-dependent G0/G1 cell cycle arrest in small intestinal epithelial cells and led to intestinal injury. Meanwhile, we found that β, β-dimethyl-acryl-alkannin (ALCAP2), which could upregulate the protein level of NEDD4L, had protective effect against the toxicity in vivo. Collectively, our findings identified the excessive accumulation of CK1ε as a key cause of duvelisib-induced enterotoxicity, and reduction in the protein stability of CK1ε represents a potential therapeutic strategy to prevent duvelisib-induced enterotoxicity.}, }
@article {pmid41905536, year = {2026}, author = {Guo, W and Zhao, X and Xu, G and Wang, Z and Zhou, T and Zhou, W and Liu, H and Xu, T and Hu, H}, title = {Real-time channel selection for enhanced steady-state visual evoked potentials online brain-computer interface systems.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110757}, doi = {10.1016/j.jneumeth.2026.110757}, pmid = {41905536}, issn = {1872-678X}, abstract = {BACKGROUND: In recent years, researchers have actively developed new brain-computer interface (BCI) systems while seeking optimal channel combinations. Although channel selection has advanced significantly in BCI systems, few studies have specifically addressed channel selection for steady-state visual evoked potentials-BCI (SSVEP-BCI).<br><br>NEW METHOD: This study proposes an online SSVEP-BCI system with dynamic channel selection during experiments. The proposed method constructs a multidimensional feature framework that incorporates signal energy, stability, and inter-channel correlation. The abnormality of each feature is then quantified to generate corresponding anomaly scores. These anomaly scores are integrated through a hierarchical decision mechanism to produce a comprehensive channel quality score. Based on this score, bad channels are precisely identified and removed, enabling training-free and dynamic channel selection.<br><br>RESULTS: The proposed multi-Adaptive priority-based SSVEP channel selection (MAPS-CS) method achieves the best performance compared with three existing channel selection methods. For the standard filter bank canonical correlation analysis (FBCCA), the accuracy of the proposed method was increased for four stimulus durations. Compared with the channel ensemble (CE) method, the proposed method achieved accuracy improvements of 3.5%, 4.1%, 4.4%, and 6.5% for stimulus durations of 2 s, 1.5 s, 1 s, and 0.5 s, respectively.<br><br>The proposed method provides the best performance in FBCCA compared with CE, binary harmony search (BHS) and TOP-K local optimization channel selection (TOP-K-LOCS).<br><br>CONCLUSIONS: These results confirm that the system can effectively detect bad channels in laboratory-based online experiments.}, }
@article {pmid41906020, year = {2026}, author = {Xue, W and Lu, W and Zhang, X and Wang, H}, title = {Fixed-time formation behavior control for unmanned ground vehicle-manipulators.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-43223-2}, pmid = {41906020}, issn = {2045-2322}, support = {360302022401//the Efficient space-time coordination of swarm aircraft/ ; 82260364//the National Natural Science Foundation of China/ ; 360302042406//the Research on Motion Control Mechanism and Regulation Strategy for Brain Computer Interface/ ; }, abstract = {Unmanned ground vehicle-manipulators (UGVMs), which integrate mobility and dexterous manipulation, are increasingly deployed in complex environments. However, their formation control is challenged by nonholonomic constraints, external disturbances, and multi-task conflicts. This paper proposes a fixed-time formation behavior control (Fixed-FBC) method UGVMs operating in static obstacle environments under external disturbances and system uncertainties. The approach introduces a nonholonomic null-space behavioral control (N-NSBC) framework that integrates fixed-time stability strategy for rapid convergence, systematic incorporation of nonholonomic constraints to inherently avoid local minima by resolving yaw-position coupling, and transformation of multi-objective coordination into unified velocity commands. To address dynamic uncertainties, an adaptive fixed-time tracking controller is developed that employs adaptive laws to estimate unknown system parameters in real-time while utilizing sliding mode techniques to reject external disturbances. Simulation results demonstrate a [Formula: see text] reduction in settling time compared to conventional methods, along with effective coordination of formation maintenance, obstacle avoidance, and manipulator control.}, }
@article {pmid41907360, year = {2026}, author = {Corti, S and Ferrucci, R and Angotzi, GN and Arighi, A and Brambilla, P and Buijs, E and Carrafiello, G and Crippa, M and De Momi, E and Del Debbio, P and Folgieri, R and Giachetti, M and Giannì, AB and Magnoni, W and Marceglia, S and Massimini, M and Stigliani, D and Stocco, M and Tanga, A and Ottoboni, L and Barbieri, S}, title = {Minds and machines: AI's transformative role in human identity and medicine.}, journal = {Digital health}, volume = {12}, number = {}, pages = {20552076251390473}, pmid = {41907360}, issn = {2055-2076}, abstract = {The application of artificial intelligence (AI) in medicine presents unprecedented potential but challenges traditional notions of human identity and medical ethics. Through a systematic literature review and thematic analysis of the interdisciplinary conference "Minds and Machines", we examine the transformative impact of AI on medical practice, consciousness, and human enhancement considering the clinical, ethical, and regulatory contexts. Successful integration of AI requires a delicate balance between innovation and safeguarding the human component in healthcare through robust ethical frameworks, enhanced medical education, and person-centered implementation.}, }
@article {pmid41907799, year = {2026}, author = {Khan, H and Nazeer, H and Mirtaheri, P}, title = {Open access individual finger movement dataset with fNIRS.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1747655}, pmid = {41907799}, issn = {1662-5161}, }
@article {pmid41901024, year = {2026}, author = {Mac-Auliffe, D and Surapaneni, A and Millán, JDR}, title = {Reopening Motor Learning Windows: Targeted Re-Engagement of Latent Pathways via Non-Invasive Neuromodulation.}, journal = {Life (Basel, Switzerland)}, volume = {16}, number = {3}, pages = {}, pmid = {41901024}, issn = {2075-1729}, abstract = {Motor recovery after stroke, spinal cord injury, or traumatic brain injury reflects relearning rather than simple restitution, as surviving circuits retain plastic potential that can be re-engaged through temporally precise stimulation. This review synthesizes convergent findings demonstrating that Hebbian and spike-timing-dependent mechanisms govern reorganization across cortical, striatal, and spinal levels. Leveraging these timing rules to shape excitability during receptive network states enables durable changes in connectivity and behavior. This effect depends on temporal precision, physiological state, and reinforcement-not stimulus intensity alone-within plasticity windows regulated by metaplastic mechanisms that determine whether Hebbian processes are expressed. Together, these principles define a translational framework for neurorehabilitation, emphasizing biomarker-guided, adaptive, and scalable strategies aligned with intrinsic rules of experience-dependent reorganization.}, }
@article {pmid41901531, year = {2026}, author = {Turan, S and Çıray, RO}, title = {Comparative Effects of BCI-Based Attention Training, Methylphenidate, and Citicoline on Attention and Executive Function in School-Age Children: A Quasi-Experimental Study.}, journal = {Medicina (Kaunas, Lithuania)}, volume = {62}, number = {3}, pages = {}, pmid = {41901531}, issn = {1648-9144}, support = {THIZ-2023-1670//Bursa Uluda&#x11f; &#xdc;ni&#x307;versi&#x307;tesi&#x307;/ ; }, mesh = {Humans ; *Methylphenidate/therapeutic use/pharmacology ; Child ; Male ; Female ; *Executive Function/drug effects ; *Attention Deficit Disorder with Hyperactivity/drug therapy/therapy/psychology ; *Attention/drug effects ; *Cytidine Diphosphate Choline/therapeutic use/pharmacology ; Central Nervous System Stimulants/therapeutic use ; }, abstract = {Background and Objectives: Attention-Deficit Hyperactivity Disorder (ADHD) is a neurological condition characterized by cognitive task difficulty, impulsivity, hyperactivity and loss of attention. This study compared four approaches for improving attention and related skills in school-age children: COGO Brain-Computer Interface (BCI)-based attention training, methylphenidate, citicoline, and their combined use. Materials and Methods: A quasi-experimental pre-post design was used with four groups: COGO + methylphenidate (n = 44), COGO + citicoline (n = 44), COGO-only (n = 44), and citicoline-only (n = 42). Children completed baseline and post-treatment assessments, including the CPT-3 and several behavioral and emotional rating scales. Analyses included paired t-tests, ANCOVA, and repeated-measures ANOVA, adjusting for age. Results: The strongest improvements appeared in the COGO + methylphenidate group, especially in measures of sustained attention and reaction time consistency. The COGO + citicoline group showed clearer gains in inhibitory control (fewer commission errors) and reductions in anxiety/emotional symptoms. The COGO-only and citicoline-only groups showed little to no measurable change. Despite these within-group patterns, there were no significant differences between groups on CPT-3 outcomes or behavioral/emotional scales. Conclusions: This trial showed that combining COGO-based attention training with medication is both feasible and well-tolerated in children with attention and executive function difficulties. Moreover, the integrated approach produced measurable improvements across attentional performance and behavioral regulation domains.}, }
@article {pmid41901953, year = {2026}, author = {Hu, C and Liu, Q and Xu, C and Li, G and Li, Y}, title = {Dual-Manifold Contrastive Learning for Robust and Real-Time EEG Motor Decoding.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {6}, pages = {}, pmid = {41901953}, issn = {1424-8220}, support = {2022ZD0210400//STI2030-Brain Science and Brain-Inspired Intelligence Technology/ ; 2023QN10Y209//GUANGDONG TALENTS PROGRAM/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; XMHT20230115002//Shenzhen Strategic Emerging Industry Support Plans/ ; \#2023A1515011478//Guangdong Basic and Applied Basic Research Foundation/ ; ZR2021ZD40//Major Basic Research Project of Shandong Natural Science Foundation/ ; pdjh2024a135//Guangdong Climbing Program Special Funds/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; *Machine Learning ; Signal Processing, Computer-Assisted ; Brain/physiology ; Male ; Adult ; }, abstract = {Brain-computer interfaces (BCIs) have great potential for consumer electronics, as they enable the decoding of brain activity to control external devices and assist human-computer interaction. However, current decoding methods for BCIs face several challenges, such as low accuracy, poor stability under electrode shift, and slow processing for real-time use. In this paper, we propose a hybrid decoding framework designed to address the challenges of current EEG decoding methods. Our method combines manifold learning with contrastive learning. The core of our method lies in a dual-manifold model that uses non-negative matrix factorization (NMF) and a contrastive manifold learning framework to extract clear and useful features from brain signals. To improve decoding stability, we introduce a joint training strategy that enhances feature learning. Furthermore, the system is optimized for real-time interaction, reducing the system latency to 100 ms. We collect EEG signals from 15 subjects performing motor execution tasks and 10 subjects performing motor imagery tasks to construct a motor EEG dataset. On this dataset, the proposed method achieves superior decoding performance, reaching F1-scores of 0.7382 for the motor imagery tasks and 0.8361 for the motor execution tasks. Furthermore, the method maintains robustness even with reduced electrode counts and altered spatial distributions, highlighting its potential as a decoding solution for reliable and portable BCI systems.}, }
@article {pmid41902016, year = {2026}, author = {Althobaiti, M}, title = {Unsupervised Dynamic Time Warping Clustering for Robust Functional Network Identification in fNIRS Motor Tasks.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {6}, pages = {}, pmid = {41902016}, issn = {1424-8220}, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; Brain-Computer Interfaces ; Cluster Analysis ; Male ; Adult ; Female ; Foot/physiology ; Hand/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Young Adult ; }, abstract = {Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive modality for brain-computer interfaces (BCIs), but robust signal interpretation is challenged by the significant temporal variability of the hemodynamic response. Standard linear methods, such as Pearson correlation, often fail to capture functional connectivity when signals exhibit temporal jitter. This study validates an unsupervised Dynamic Time Warping (DTW) clustering framework to robustly identify motor networks from fNIRS data by accommodating non-linear temporal shifts. We analyzed a public fNIRS dataset (N = 30) across right-hand (RHT), left-hand (LHT), and foot tapping (FT) tasks. A robust preprocessing pipeline was implemented, including Wavelet Motion Correction and Common Average Referencing (CAR) to remove artifacts and global systemic noise. The core method involved computing Z-score normalized DTW distance matrices, followed by hierarchical clustering. To validate the framework, we benchmarked it against a standard Pearson Correlation method. Results show that the unsupervised DTW framework achieved a network identification accuracy of 53.17%, significantly outperforming the standard Pearson correlation benchmark (48.06%) with a statistically significant difference (p < 0.05). The framework successfully detected distinct, somatotopically correct modulations: superior-medial activation during foot tapping and lateralized activation during hand tapping. These findings demonstrate that unsupervised DTW clustering is a robust, data-driven approach that outperforms conventional linear methods in capturing functional networks during motor tasks, showing significant potential for next-generation asynchronous BCIs.}, }
@article {pmid41902060, year = {2026}, author = {Hristov, H and Minchev, Z and Shoshev, M and Kancheva, I and Koleva, V and Vakarelsky, T and Dimitrov, K and Prodanov, D}, title = {Sensing Cognitive Responses Through a Non-Invasive Brain-Computer Interface.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {6}, pages = {}, pmid = {41902060}, issn = {1424-8220}, support = {101086815//European Union's Horizon Europe program under grant agreement VIBraTE/ ; }, mesh = {Humans ; *Cognition/physiology ; Heart Rate/physiology ; Male ; Electroencephalography ; Female ; Adult ; *Brain-Computer Interfaces ; Young Adult ; Galvanic Skin Response/physiology ; }, abstract = {Cognitive stress, also known as mental workload, constitutes a central topic within the field of psychophysiology due to its role in modulating attention, autonomic regulation, and stress reactivity. Furthermore, it bears direct relevance to practical monitoring systems that employ non-invasive sensing techniques. This study investigates whether a multimodal, non-invasive measurement setup can detect systematic physiological differences between Resting periods and short episodes of cognitive load within the same individuals. Additionally, it explores the capacity of such a system to differentiate tasks characterized by varying cognitive demands. A sequential, within-subject protocol was employed, comprising five consecutive phases (rest 1, Stroop, rest 12, subtraction, rest 3), during which five modalities were recorded concurrently: EEG, heart rate (HR), galvanic skin response (GSR), facial surface temperature, and oxygen saturation (SpO2). Beyond phase-wise inspection of time-series data, an exploratory assessment of similarity across participants was conducted using correlation coefficients. The maximum cross-participant correlations observed were 0.88 (HR), 0.90 (GSR), 0.83 (facial temperature), and 0.77 (SpO2); however, these correlations were used only as exploratory descriptors of inter-individual similarity and did not imply a significant phase effect. For inferential analysis, phase-wise epoch means were evaluated through one-factor repeated-measures ANOVA. The heart rate exhibited a robust main effect of phase (F(4, 32) = 10.5862, p_GG = 0.01044, ηp[2] = 0.5696), with higher HR observed during cognitive load epochs (e.g., 77.841 ± 11.777 bpm at rest 1 versus 83.926 ± 14.532 bpm during subtraction). The relatively large standard deviation reflects variability between subjects rather than variability within epochs. Regarding processed baseline-referenced GSR, the omnibus phase effect was not statistically significant under the conservative Greenhouse-Geisser correction; therefore, GSR was interpreted as exploratory in this dataset. Facial temperature and SpO2 likewise did not show statistically significant omnibus phase effects under Greenhouse-Geisser correction (e.g., SpO2: p_GG = 0.1209). EEG-derived measures provide supplementary central evidence of task engagement; entropy variations within an approximate dynamic range of 0.2 to 0.8 were observed, and the α/θ ratios demonstrated nearly a twofold distinction between rest and cognitive load epochs across different leads.}, }
@article {pmid41902355, year = {2026}, author = {Yu, Y and Liu, W and Ju, S and He, L and Chen, N and Chernov, AN and Zhang, J and Mao, J and Liu, G}, title = {Global Trends in Research of Brain-Computer Interfaces in Neuroscience From 2014 to 2023: A Bibliometric Analysis.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {32}, number = {4}, pages = {e70851}, doi = {10.1002/cns.70851}, pmid = {41902355}, issn = {1755-5949}, support = {202510631036//National Training Program of Innovation and Entrepreneurship for Undergraduates/ ; }, mesh = {*Brain-Computer Interfaces/trends ; *Bibliometrics ; *Neurosciences/trends ; Humans ; *Biomedical Research/trends ; }, abstract = {AIM: Brain-computer interfaces (BCIs) represent a promising technology for addressing neurological disorders, with growing research interest globally. This study aimed to map global research trends in BCI neuroscience from 2014 to 2023 via bibliometric analysis, identifying key contributors and hot topics to inform future research.<br><br>METHODS: A total of 2386 publications related to BCIs in neuroscience were retrieved from the Web of Science Core Collection. Bibliometric analyses, including co-authorship networks, keyword co-occurrence, and burst detection, were performed using VOSviewer, R, and CiteSpace. The study analyzed publications by country, institution, journal, author, and keyword to map the landscape of global research activity.<br><br>RESULTS: China emerged as the country with the highest number of publications, and the International Journal of Neural Engineering was the most productive journal. Co-authorship analysis revealed collaborative networks across global institutions, while keyword co-occurrence and burst detection identified electroencephalography (EEG), rehabilitation, and motor cortex as the most prominent research hotspots in recent years.<br><br>CONCLUSION: This analysis provides a reference for researchers and data support for future studies, clarifying the global landscape and priorities in BCI neuroscience research.}, }
@article {pmid41903116, year = {2026}, author = {Zhang, E and Shotbolt, M and Abdel-Mottaleb, M and Chen, S and Andre, V and Tian, J and Shulgach, J and Murphy, M and Noga, B and Liang, P and Griffin, D and Weber, D and Pardo, M and Pane, S and Khizroev, S}, title = {Magnetoelectric Nanoparticle-Based Wireless Brain-Computer Interface: Underlying Physics and Projected Technology Pathway.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e24329}, doi = {10.1002/advs.202524329}, pmid = {41903116}, issn = {2198-3844}, support = {N66001-19-C-4019//Defense Advanced Research Projects Agency/ ; ECCS-211082//National Science Foundation/ ; 5P30240139-02/NH/NIH HHS/United States ; }, abstract = {Magnetoelectric nanoparticles (MENPs) provide a fully wireless and minutely invasive platform for bidirectional brain-computer interfaces (BCIs) by locally transducing magnetic fields into electric fields, and vice versa. The achievable spatial and temporal resolutions are governed by the control of magnetic field energy at the nanoparticle level. Since the introduction of the MENP concept a decade and a half ago, independent studies have demonstrated MENP-mediated neural activation in vitro and in vivo, establishing a strong proof of concept for wireless neuromodulation. In contrast, MENP-based neural recording remains largely theoretical, with existing models indicating that in vivo implementation is feasible. However, progress toward scalable and reliable MENP-based BCIs is hindered by an incomplete understanding of the nonlinear physics governing MENP operation and nanoparticle-cell interactions. This study addresses this gap by developing a comprehensive theoretical framework that explicitly incorporates nonlinear effects and correlates neuromodulation predictions with available experimental data. The analysis identifies nanoparticle properties and magnetic field amplitude and frequency as key performance determinants. Properly engineered MENPs are predicted to enable deepbrain and cortical neuromodulation and recording with submillimeter spatial resolution and millisecondscale temporal precision, offering a pathway toward clinically viable BCIs without implanted electrodes or genetic modification.}, }
@article {pmid41891008, year = {2026}, author = {Johnson, SN and Rybář, M and Greenspon, CM and Moore, DD and Downey, JE and Dekleva, BM and Hatsopoulos, NG}, title = {Limb state accounts for differences between motor imagery and action in motor cortex.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.03.13.26348353}, pmid = {41891008}, abstract = {The motor cortex is involved not only in movement execution but also in motor imagery, a process leveraged by decoding algorithms for brain-computer interface (BCI) applications in individuals with severe motor impairments. Previous work has shown that population activity during execution and imagery occupies partially overlapping regions of neural state space while also engaging distinct subspaces unique to each motor state, suggesting that decoders trained in one condition may not generalize to the other. Moreover, movement execution likely includes neural representations of both motor output and proprioceptive feedback, which themselves may occupy distinct or overlapping regions of neural state space. To explore these distinctions, we studied two individuals with incomplete spinal-cord injuries and partial residual proximal arm function performing a center-out reaching task in three conditions: motor imagery, active execution, and passive movement. We found that decoders trained on neural activity from motor imagery failed to generalize to either active or passive movements. In contrast, decoders trained on active or passive movement activity generalized reciprocally. Population analysis revealed distinct dynamics depending on limb state and proprioceptive feedback, which could explain this lack of generalization. These results suggest that motor imagery engages motor cortical representations distinct from those recruited during actual movements, either actively or passively generated, with important implications for the design of BCI decoders.}, }
@article {pmid41891367, year = {2026}, author = {Cho, W and Jung, M and Chung, TD}, title = {Janus Synapses as Modular Neurointerfaces.}, journal = {ACS applied materials &amp; interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.6c01266}, pmid = {41891367}, issn = {1944-8252}, abstract = {The nervous system processes information by translating chemical signals into electrical and biochemical responses, ultimately driving biological adaptation and computation. Chemical synapses are the primary communication channels between neurons, operating with remarkable speed and precision to enable complex neural information processing. In this perspective, we focus on these native signaling principles and explore the potential of synaptic structures as neurointerface modules. Building on this view, we argue that electrodes can be engineered to function as complementary synaptic terminals, enabling neuron-device communication that directly leverages the chemical, electrical, and biological logic of neural systems. In particular, we discuss whether synaptic cell adhesion molecules can be harnessed as synaptogenic cues to redefine electrode surfaces as functional synaptic counterparts of neuronal terminals, and we examine the distinctive properties and emerging applications of such interfaces.}, }
@article {pmid41892049, year = {2026}, author = {Ghosh, S and Bhuvanakantham, R and Sindhujaa, P and Harishita, PB and Mohan, A and Gulyás, B and Máthé, D and Padmanabhan, P}, title = {A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System.}, journal = {Biosensors}, volume = {16}, number = {3}, pages = {}, doi = {10.3390/bios16030157}, pmid = {41892049}, issn = {2079-6374}, mesh = {*Biosensing Techniques ; *Brain-Computer Interfaces ; *Cloud Computing ; Humans ; Electroencephalography ; Signal Processing, Computer-Assisted ; }, abstract = {BCI biosensors enable continuous monitoring of neural activity, but existing systems face challenges in scalability, latency, and reliable integration with cloud infrastructure. This work presents a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors, validated at the system level through a full physical prototype. The system integrates the BioAmp EXG Pill for signal acquisition with an RP2040 microcontroller for local preprocessing using edge-resident TinyML deployment for on-device feature/inference feasibility coupled with environmental context sensors to augment signal context for downstream analytics talking to the external world via Wi-Fi/4G connectivity. A tiered data pipeline was implemented: SD card buffering for raw signals, Redis for near-real-time streaming, PostgreSQL for structured analytics, and AWS S3 with Glacier for long-term archival. End-to-end validation demonstrated consistent edge-level inference with bounded latency, while cloud-assisted telemetry and analytics exhibited variable transmission and processing delays consistent with cellular connectivity and serverless execution characteristics; packet loss remained below 5%. Visualization was achieved through Python 3.10 using Matplotlib GUI, Grafana 10.2.3 dashboards, and on-device LCD displays. Hybrid deployment strategies-local development, simulated cloud testing, and limited cloud usage for benchmark capture-enabled cost-efficient validation while preserving architectural fidelity and latency observability. The results establish a scalable, modular, and energy-efficient biosensor framework, providing a foundation for advanced analytics and translational BCI applications to be explored in subsequent work, with explicit consideration of both edge-resident TinyML inference and cloud-based machine learning workflows.}, }
@article {pmid41892610, year = {2026}, author = {Yang, S and Zhang, L and Cheng, Y and Zheng, Y and Zheng, S and Guo, J and Zheng, L}, title = {STHMA: Decoupling Spatio-Temporal Dynamics in EEG via Hybrid State Space Modeling.}, journal = {Brain sciences}, volume = {16}, number = {3}, pages = {}, doi = {10.3390/brainsci16030267}, pmid = {41892610}, issn = {2076-3425}, support = {2023B0303040002//Guangdong S&amp;T Program/ ; }, abstract = {Background/Objectives: Decoding affective states from Electroencephalography (EEG) signals is fundamental to non-invasive Brain-Computer Interfaces. Despite recent advances, accurate recognition is impeded by the inherently non-stationary nature of physiological signals and the entanglement of spatio-temporal dynamics within high-dimensional recordings. While Transformers excel at global modeling, they often neglect the continuous dynamical properties of neural signals and suffer from quadratic complexity. Methods: In this paper, we propose the Spatio-Temporal Hybrid Mamba-Attention (STHMA), a framework designed to explicitly disentangle and model EEG dynamics via linear-complexity State Space Models. First, to incorporate domain knowledge, we introduce a Dual-Domain Physics-Aware Embedding module. This module fuses learnable temporal convolutions with explicit frequency-domain spectral features, ensuring fidelity to neurophysiological principles. Second, we propose a novel Decoupled Spatial-Temporal Scanning strategy. By dynamically reconfiguring the serialization of the data tensor, our model strictly separates the learning of instantaneous functional connectivity from the tracking of emotional state evolution, thereby preventing the structural collapse common in 1D sequence models. Results: Extensive experiments on the FACED and SEED-V datasets demonstrate that the STHMA achieves state-of-the-art performance, significantly exceeding the random chance baselines (11.11% for 9-class FACED and 20.00% for 5-class SEED-V). Conclusions: The results validate that combining Physics-Aware Embeddings with decoupled state-space modeling offers a scalable and effective paradigm for EEG emotion recognition.}, }
@article {pmid41893390, year = {2026}, author = {Hu, C and Wang, X and Pan, T and Dong, Y and Zhang, X}, title = {Application of Brain-Computer Interactive Rehabilitation Training Combined With a Gait Robot: A Randomized Controlled Trial.}, journal = {Archives of physical medicine and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.apmr.2026.02.008}, pmid = {41893390}, issn = {1532-821X}, abstract = {OBJECTIVE: To explore the effects of brain-computer interactive (BCI) rehabilitation training combined with gait robot (GR) on gait recovery in patients with hemiplegia after stroke.<br><br>DESIGN: Randomized controlled trial.<br><br>SETTING: Hospital settings across the Shandong Provincial Third Hospital.<br><br>PARTICIPANTS: A total of 120 eligible subjects were enrolled and randomly allocated, via random-number table, into 4 equal groups (n=30 each): control (routine training), BCI, GR, and BCI-GR (BCI combined with GR training).<br><br>INTERVENTIONS: Each group received its designated training once daily, 6 d/wk, for 8 consecutive weeks.<br><br>MAIN OUTCOME MEASURES: Fugl-Meyer Assessment-Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Functional Ambulation Category (FAC), integrated electromyography (IEMG), and stride parameters. Assessments were conducted at baseline, 4 wk, and 8 wk.<br><br>RESULTS: After 8 wk, all interventions-BCI, GR, and BCI-GR-significantly improved lower-limb function, muscle activity, and gait compared with control (all P<.01). FMA-LE increased by +12.93 (BCI-GR), +12.16 (BCI), and +12.07 (GR) versus +8.20 in control; BBS improved by +14.53, +13.63, and +14.30 versus +9.53; FAC improved by +1.73, +1.73, and +1.77 versus +1.37. IEMG of tibialis anterior and gastrocnemius increased most in BCI-GR (+0.156 and +0.063), significantly higher than BCI and GR (P<.05), whereas co-contraction ratio decreased most in BCI-GR (-18.59%; P<.05). Stride parameters (step frequency, step length, step width, and walking speed) improved in all intervention groups, with BCI-GR showing greater gains in step frequency (+25.90) and step width reduction (-12.87) versus single interventions (P<.05). No other significant differences among intervention groups were observed at week 8.<br><br>CONCLUSIONS: BCI and GR interventions significantly improve lower-limb motor function, muscle activation, and gait in poststroke patients. Combined BCI-GR training accelerates early functional recovery and more effectively enhances muscle activation patterns compared with single interventions, providing a promising strategy for poststroke rehabilitation.}, }
@article {pmid41895045, year = {2026}, author = {Sabourin, CJ and Lomber, SG and Negandhi, J and Cushing, SL and Papsin, BC and Gordon, KA}, title = {Assessment of neural and MAP level asymmetries in a large cohort of children with bilateral cochlear implants.}, journal = {Hearing research}, volume = {475}, number = {}, pages = {109620}, doi = {10.1016/j.heares.2026.109620}, pmid = {41895045}, issn = {1878-5891}, abstract = {Bilateral cochlear implants (BCIs) are provided to children who are deaf in both ears to restore binaural/spatial hearing, but interaural stimulation mismatches can limit potential benefits. This study aimed to: 1) investigate how the programming of stimulation parameters in BCI users differs between bilateral pairs of electrodes, and (2) evaluate the impact of sequential implantation and mismatched array types on asymmetries. A mixed effects modeling analysis assessed cochlear implants (CI) stimulation parameters and peripheral neural responses retrospectively collected (September 2003-July 2022) in 542 children with BCIs (n = 157 sequentially implanted, n = 385 simultaneously implanted, n = 465 with matched perimodiolar arrays, and n = 77 with one perimodiolar and one straight array (mismatched)). Peripheral neural measures were similar between BCIs although asymmetries in auditory nerve thresholds were measured in children implanted sequentially with mismatched arrays (mean(SE) = -2.02(0.90) dB, p < 0.05). Children with sequential BCIs had greater maximum stimulation levels (C-levels) in the first implanted ear than the second (mean(SE) = 1.64(0.03) dB, p < 0.0001) whereas C-levels were similar between ears in children with simultaneous BCIs (mean(SE) = 0.07(0.01) dB, p < 0.0001). Programming asymmetries were comparable between matched and mismatched arrays in sequential BCIs (F(1, 149.9) = 0.01, p = 0.92) and simultaneous BCIs (F(1, 377) = 0.001, p = 0.98). Overall, programming asymmetries reflect implantation sequence more than array type differences. Similar neural responses bilaterally suggest programming asymmetries arise from central effects of prior unilateral hearing, consistent with the aural preference syndrome.}, }
@article {pmid41898344, year = {2026}, author = {Zhao, X and Li, M and Wang, Q and Deng, L and Zhao, L and Yu, H and Li, X and Deng, W and Guo, W and Li, T and Wei, W}, title = {Individualized DTI-ALPS Identifies Phase-Specific Glymphatic Dysfunction in Early-Stage Bipolar Disorder.}, journal = {Biomedicines}, volume = {14}, number = {3}, pages = {}, doi = {10.3390/biomedicines14030699}, pmid = {41898344}, issn = {2227-9059}, support = {82230046//Key Project of National Natural Science Foundation of China/ ; 20241203A14//Agriculture and Social Development of Hangzhou Science and Technology Bureauand/ ; 2024ZY01010//the Zhejiang Central Guiding Local Technology Development/ ; 2025HZGF10//the Construction Fund of Key Medical Disciplines of Hangzhou/ ; 2025HZZD14//the Construction Fund of Key Medical Disciplines of Hangzhou/ ; 2024E10107//the Zhejiang Provincial Key Laboratory of Clinical and Basic Research on Mental Disorders/ ; 82501795//the National Natural Science Foundation of China/ ; }, abstract = {Background: The glymphatic system, essential for brain waste clearance and neuroimmune regulation, remains underexplored in the context of bipolar disorder (BD) among young populations. Methods: Using diffusion tensor image analysis along the perivascular space (DTI-ALPS), we compared ALPS indices derived from the conventional FSL-based (cFSL) pipeline with those from the individualized ALPS (iALPS) pipeline. A cohort of young adults comprising 77 individuals with BD and 289 healthy controls was analyzed to evaluate methodological consistency and to identify disorder-specific alterations in glymphatic function. Results: The two pipelines showed only moderate agreement (Lin's concordance correlation coefficient = 0.52-0.60), suggesting that differences in ROI placement strategies significantly affect ALPS estimation. While the cFSL pipeline detected no group differences, the iALPS pipeline identified a trend-level reduction in ALPS index in patients with BD during depressive episodes, particularly in the right hemisphere (p = 0.036, uncorrected, FDR-adjusted p = 0.071). No significant glymphatic alterations were observed in individuals with early-stage BD. Conclusions: These findings suggest that glymphatic dysfunction in psychiatric disorders may be phase-specific on illness. The use of individualized and automated analytical strategies, such as the iALPS pipeline, appears to enhance sensitivity to subtle, state-related brain changes that conventional methods may overlook. This methodological advancement provides a more biologically informed framework for future large-scale and longitudinal studies aimed at elucidating the role of glymphatic function in the pathophysiology of psychiatric disorders.}, }
@article {pmid41899754, year = {2026}, author = {Daube, A and Lima-Carmona, YE and Hernández Solís, DG and Contreras-Vidal, JL}, title = {A Systematic Review and Meta-Analysis of EEG, fMRI, and fNIRS Studies on the Psychological Impact of Nature on Well-Being.}, journal = {International journal of environmental research and public health}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/ijerph23030377}, pmid = {41899754}, issn = {1660-4601}, support = {1757949//U.S. National Science Foundation/ ; 2137255//U.S. National Science Foundation/ ; }, mesh = {Humans ; *Electroencephalography ; Spectroscopy, Near-Infrared ; *Magnetic Resonance Imaging ; *Nature ; *Brain/physiology/diagnostic imaging ; }, abstract = {Exposure to nature has been associated with benefits to human well-being, commonly evaluated using standardized psychological assessments and, more recently, neuroimaging modalities such as Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), and functional Near-Infrared Spectroscopy (fNIRS). This systematic review and meta-analysis addresses the following questions. (1) How is the impact of nature on well-being studied using psychological and neuroimaging modalities and what does it reveal? (2) What are the challenges and opportunities for the deployment of wearable neuroimaging modalities to understand the impact of nature on the brain's health and well-being? A search on PubMed, IEEE Xplore, and ClinicalTrials.gov (March 2024) identified 33 studies combining neuroimaging and psychological assessments during exposure to real, virtual or imagined natural environments. Studies were analyzed by tasks, populations, neuroimaging modality, psychological assessment, and methodological quality. Most studies were conducted in Asia (n = 23 or 70%). Healthy participants were the dominant target population (70%). In total, 61% of the studies were conducted in natural settings, while 39% used visual imagery. EEG was the most common modality (82%). STAI (n = 8) and POMS (n = 8) were the most common psychological assessments. Only seven studies included clinical populations. Two separate meta-analyses of nine studies with explicit experimental and control groups revealed a significant positive effect of nature exposure on psychological outcomes (Hedges' g = 0.30; p = 0.0021), and a larger effect on neurophysiological outcomes (Hedges' g = 0.43; p = 0.0004), both with moderate-to-high heterogeneity. Overall, exposure to nature was associated with reductions in negative emotions in clinical populations. In contrast, healthy populations showed a more balanced psychological response, with nature exposure being associated with both increases in positive emotions and reductions in negative emotions. Notably, 88% of the studies presented methodological weaknesses, highlighting key opportunities for future neuroengineering research on the neural and psychological effects of nature exposure.}, }
@article {pmid41899838, year = {2026}, author = {Tibermacine, A and Naidji, I and Tibermacine, IE and Mamen, L and Rabehi, A and Habib, M}, title = {EEG-TriNet++: A Transformer-Guided Meta-Learning Framework for Robust and Generalizable Motor Imagery Classification.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {13}, number = {3}, pages = {}, doi = {10.3390/bioengineering13030307}, pmid = {41899838}, issn = {2306-5354}, abstract = {Motor imagery (MI) classification using EEG signals is central to brain-computer interfaces but remains challenging due to low signal-to-noise ratio, non-stationarity, and high inter-subject variability. We introduce EEG-TriNet++, a multi-branch deep learning architecture that enhances both classification accuracy and cross-subject generalization. The model integrates three complementary components: convolutional spatial-spectral encoders for channel-wise and frequency-specific patterns, bidirectional LSTMs to model temporal dynamics, and a Transformer head for global relational reasoning. A patchwise tokenization strategy and neural architecture search optimize the trade-off between efficiency and representational capacity. To address individual differences, a model-agnostic meta-learning (MAML) module enables rapid adaptation to new users with limited data. Evaluated on two public MI datasets under within-subject and leave-one-subject-out (LOSO) protocols, EEG-TriNet++ achieves 79.1% and 78.6% accuracy in within-subject tasks, and 72.4% and 71.3% in LOSO settings. Ablation studies validate the contribution of each module, and comparisons with state-of-the-art methods demonstrate consistent performance gains under identical conditions.}, }
@article {pmid41899962, year = {2026}, author = {Xu, Z and Yu, Z}, title = {Entropy-Based Dual-Teacher Distillation for Efficient Motor Imagery EEG Classification.}, journal = {Entropy (Basel, Switzerland)}, volume = {28}, number = {3}, pages = {}, doi = {10.3390/e28030310}, pmid = {41899962}, issn = {1099-4300}, support = {2022ZD0211700//The Technology Innovation 2030/ ; }, abstract = {Motor imagery (MI) EEG classification is a key component of noninvasive brain-computer interfaces (BCIs) and often must satisfy strict latency constraints in online or edge deployments. Although ensembling can reliably improve MI decoding accuracy, its inference cost grows linearly with the number of ensemble members, making it impractical for low-latency applications. To address these issues, we propose an entropy-based dual-teacher distillation framework that transfers ensemble teacher knowledge to a single deployable backbone. From an information theoretic perspective, two failure modes are common in small and noisy MI datasets: elevated predictive entropy (noisy decisions) and large fluctuation across late training epochs (unstable convergence and unreliable checkpoint selection). Thus, we introduce an exponential moving average (EMA) teacher with entropy-gated activation as a low-pass filter in parameter space to reduce the student's prediction noise. In addition, a two-stage cosine annealing schedule is employed to suppress late-stage oscillations and improve the robustness of final checkpoint selection. Experiments on two public MI benchmarks (BCI Competition IV-2a and IV-2b) with three representative backbones (EEGNet, ShallowConvNet, and ATCNet) under the subject dependent protocol show consistent accuracy gains over the ensemble teacher and strong distillation baselines. On IV-2a, our method achieves an average accuracy of 0.7713 across the backbones, surpassing both the original models (0.7222) and the corresponding ensembles (0.7482); on IV-2b, it achieves 0.8583 versus 0.8432 (original) and 0.8529 (ensemble).}, }
@article {pmid41900188, year = {2026}, author = {Zhang, S and Shan, J and Lv, S and Liu, Y and Miao, J and Liu, Z and Ning, E and Xu, Z and Liu, J and Wang, M and Jin, H and Cai, X and Song, Y}, title = {A Comb-Shaped Flexible Microelectrode Array for Simultaneous Multi-Scale Cortical Recording.}, journal = {Micromachines}, volume = {17}, number = {3}, pages = {}, doi = {10.3390/mi17030301}, pmid = {41900188}, issn = {2072-666X}, support = {62333020, 62121003, T2293730, T2293731, 62471291, 62171434, 62501572, 62374004//National Natural Science Foundation of China/ ; 2021ZD02016030//Major Program of Scientific and Technical Innovation 2030/ ; 8091A170201//Joint Foundation gram of the Chinese Academy of Sciences/ ; F252069//Natural Science Foundation of Beijing/ ; }, abstract = {High-resolution, multi-modal neural interfaces are essential for advancing systems neuroscience and brain-computer interface technologies. This study designed and fabricated a 128-channel comb-shaped flexible micro-electrode array. The device integrates a biocompatible Parylene substrate with a flexible thin-film microprobe array, enabling simultaneous recording of electrocorticography (ECoG), intracortical local field potentials (LFP), and neuronal action potentials (spikes) from the cortical surface and superficial layers. Microelectrode sites were modified with platinum black nanoparticles, significantly reducing impedance. In vivo experiments in rats demonstrated the array's ability to capture high-fidelity signals across different recording depths. Key findings included the acquisition of opposing LFP trends and polarity reversals between adjacent channels, reflecting local microcircuit dynamics. The array also reliably recorded neural activity during audiovisual cross-modal sensory stimulation. These results validate the device as an effective tool for multi-scale electrophysiology, successfully balancing high spatial resolution and signal quality with minimal tissue invasiveness, thereby offering significant potential for fundamental research and neural engineering applications.}, }
@article {pmid41900229, year = {2026}, author = {Shang, L and Liu, J and Lv, S and Jiang, L and Liu, Y and Hua, S and Luo, J and Cai, X}, title = {From Physical Replacement to Biological Symbiosis: Evolutionary Paradigms and Future Prospects of Auditory Reconstruction Brain-Computer Interfaces.}, journal = {Micromachines}, volume = {17}, number = {3}, pages = {}, doi = {10.3390/mi17030343}, pmid = {41900229}, issn = {2072-666X}, support = {62121003, T2293730, T2293731, 62333020, 62171434, and 62471291//National Natural Science Foundation of China/ ; F252069//Natural Science Foundation of Beijing/ ; 2022YFC2402501, 2022YFB3205602//National Key Research and Development Program of China/ ; No.PTYQ2024BJ0009//Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; 2021ZD0201600//Major Program of Scientific and Technical Innovation 2030/ ; }, abstract = {Auditory Brain-Computer Interfaces (BCIs) constitute the vital intervention for profound sensorineural hearing loss where the auditory nerve is compromised, yet their clinical efficacy remains restricted by substantial biological bottlenecks and limited spectral resolution. This review critically examines the evolutionary paradigm of auditory restoration, tracing the transition from static physical replacement to dynamic biological symbiosis. We systematically analyze physiological barriers across cochlear, brainstem, and cortical levels, elucidating how rigid interfaces provoke chronic tissue responses and why linear encoding protocols fail in distorted central tonotopy. The article synthesizes emerging methodologies in material science, demonstrating how soft, bio-integrated electronics and biomimetic topologies effectively address mechanical impedance mismatches. Furthermore, the trajectory of neural encoding is evaluated, highlighting the paradigm shift from traditional envelope extraction to deep learning-driven non-linear mapping and adaptive closed-loop neuromodulation. Finally, the potential of high-resolution modulation techniques, including optogenetics and sonogenetics, alongside AI-facilitated intent perception for active listening, is assessed. It is concluded that future neuroprostheses must evolve into symbiotic systems capable of seamlessly integrating with neural plasticity to enable high-fidelity cognitive reconstruction.}, }
@article {pmid41883146, year = {2026}, author = {Akbar, TF and Jimenez-Rodriguez, CA and Biktimirova, R and Hermes, I and Kurth, T and Pham, MD and Tsurkan, M and Friedrichs, J and Morgan, FLC and Kleemann, H and Guskova, O and Freudenberg, U and Fratzl, P and Werner, C and Tondera, C and Minev, IR}, title = {Conductive Hydrogels for Exogenous Sensing and Cell Fate Control.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e72866}, doi = {10.1002/adma.72866}, pmid = {41883146}, issn = {1521-4095}, support = {101125081/ERC_/European Research Council/International ; 518476867//German Research Foundation/ ; }, abstract = {Next generation technologies linking living systems to computers will require materials built on biology, an approach that may address persistent challenges in stable and multimodal information exchange. Here, we present a semi-synthetic hydrogel, designed to emulate key features of native extracellular matrix (ECM) while offering electrically tunable functionality. We engineer interactions between sulfated glycosaminoglycans (sGAGs) and a semiconducting organic polymer (poly(3,4-ethylenedioxythiophene), PEDOT) within a soft hydrogel network (PEDOT:sGAGh). We demonstrate control over the material's nanoarchitecture, electrochemical behavior, and biomolecular interactions. In particular, PEDOT:sGAGh exhibits affinity for bioactive proteins, including growth factors, and allows their release or retention to be modulated by low-voltage stimulation. This enables electrical control over macromolecular cues for cell differentiation, a capability not found in natural ECM or conventional conductive hydrogels. These functions are achieved with ultra-low PEDOT content (≈1 wt.%), preserving the hydrogel's tissue-like softness and high water content. The PEDOT:sGAGh material can be integrated as a bioactive coating on electrodes, or into 3D organic electrochemical transistors (OECTs). Our results position PEDOT:sGAGh as a versatile platform for realizing biohybrid circuits that bridge molecular signaling and solid-state electronics, thus paving the way for brain-machine interfaces that operate beyond purely electrical modes of interaction.}, }
@article {pmid41883491, year = {2026}, author = {Zhang, T and Zhang, R and Zeng, X and Zeng, M and Xu, Y and Xiong, Y and Zhang, G and Guo, D and Yao, D}, title = {A new BCI paradigm based on biological brain - digital twin brain dialogue.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {70}, pmid = {41883491}, issn = {1871-4080}, abstract = {Brain-computer interface (BCI) establishes a bidirectional pathway between the brain and external devices. Its applications fall into two main categories: utilizing the brain as a controller (e.g., for prosthetics) or as a modulation target (e.g., for cognitive regulation). Progress in BCI is constrained by two core bottlenecks: in brain control, limited understanding of neural coding mechanisms restricts improvements in the accuracy and robustness of encoding/decoding algorithms; in brain regulation, one-size-fits-all regulatory strategies struggle to address significant individual variability, resulting in heterogeneous therapeutic responses. Inspired by neuroscience advances, this perspective proposes a new biological brain - digital twin brain based BCI (BDBCI) paradigm. Here, the biological brain acts as an empirical anchor and ultimate validation platform, while a high-fidelity digital twin brain (DTB) serves as a theoretical inference engine and virtual testbed. Specifically, experimental induction is applied to the biological brain to distill preliminary conclusions, such as brain-behavior mappings and brain-stimulation causal relationships, which are then used to construct and calibrate the DTB model. Subsequently, on the DTB platform, large-scale model deduction is conducted to validate and deepen these preliminary insights mechanistically, thereby optimizing control/regulation parameters or informing the parameter ranges for the next round of experimental induction and model deduction. Through this BDBCI paradigm, we aim to advance BCI research from empirical trial-and-error toward a new era of model-driven, predictable, and explainable precision science.}, }
@article {pmid41886100, year = {2026}, author = {Zou, G and Chen, L and Tan, H and Zeng, F and Dong, A and Li, G and Fu, J and Yu, K and Du, L and Liu, Q and Chen, X and Wang, H}, title = {A GRASS-guided phased progressive brain-computer interface approach for post-infarction hand motor recovery.}, journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology}, volume = {47}, number = {4}, pages = {}, pmid = {41886100}, issn = {1590-3478}, support = {82102665//the National Natural Science Foundation of China/ ; 2024CZ010198//the 2024 Huizhou Science and Technology Bureau Scientific Research Project/ ; 20231345//the 2023 Guangdong Provincial Administration of Traditional Chinese Medicine Scientific Research Project/ ; 21YF1404600//the Shanghai Sailing Program/ ; }, }
@article {pmid41886201, year = {2026}, author = {Jihen, S and Karmani, S and Belwafi, K and Jemmali, M and Djemal, R}, title = {Filter bank CSP with Riemannian weighting for disability-centric motor imagery brain computer interface.}, journal = {Brain informatics}, volume = {}, number = {}, pages = {}, doi = {10.1186/s40708-026-00295-0}, pmid = {41886201}, issn = {2198-4018}, support = {2502150165//University of Sharjah/ ; }, abstract = {Brain-computer interfaces (BCIs) were initially created to help individuals with disabilities control devices and communicate without muscle movement. Today, BCIs are used for prosthetic control, cognitive enhancement, and neurological rehabilitation. The BCI system depends on analyzing electroencephalogram (EEG) signals captured from the brain. Decoding these EEG signals is a complex process that combines multiple algorithms to extract meaningful information from these intricate and noisy signals. One of the most popular techniques is the Common Spatial Patterns (CSP), which helps preserve useful and sensitive information. This paper presents an optimized extension of the CSP model for extracting EEG data features in a multiclass setting using Riemannian geometry-based weighting. The use of weighting based on Riemannian geometry enhances the robustness of covariance matrix computation, thereby decreasing the influence of noise that can significantly distort the mean of covariance matrices in the traditional CSP method. The proposed approach is also extended by the integration of a multi-band filter bank, providing a more detailed examination of EEG signals. Three classifiers, Linear Discriminant Analysis (LDA), Random Forest Classifier (RFC), and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery tasks. LDA achieves an accuracy of 80.40%, while MLP and RFC reach 80.02% and 80.90%, respectively. The results obtained using a majority vote combining the decisions of the three classifiers are 81.83% for accuracy and Recall, 82.74% for precision, and 81.87% for F1-score. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.}, }
@article {pmid41886379, year = {2026}, author = {Obaid, A and Hanna, ME and Huang, SW and Hu, YT and Jáidar, O and Nix, W and Ding, JB and Melosh, NA and Wu, YW}, title = {Ultrasensitive measurement of brain penetration mechanics and blood vessel rupture with microscale probes.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {123}, number = {13}, pages = {e2529147123}, doi = {10.1073/pnas.2529147123}, pmid = {41886379}, issn = {1091-6490}, support = {NS014861//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; Seed Grant//SU | Wu Tsai Neurosciences Institute, Stanford University (Wu Tsai Neurosciences Institute)/ ; Startup Fund//Academia Sinica (AS)/ ; 114-2321-B-001 -005//National Science and Technology Council (NSTC)/ ; gift fund//GG gift fund/ ; 113-2321-B-001-012//National Science and Technology Council (NSTC)/ ; }, mesh = {Animals ; *Brain/blood supply/physiology ; Microelectrodes ; *Electrodes, Implanted/adverse effects ; Rats ; *Blood Vessels ; Biomechanical Phenomena ; Male ; Brain-Computer Interfaces ; }, abstract = {Microscale electrodes, on the order of 10 to 100 µm, are rapidly becoming critical tools for neuroscience and brain-machine interfaces for their high channel counts and spatial resolution, yet the mechanical details of how probes at this scale insert into brain tissue are largely unknown. Here, we performed quantitative measurements of the force and compression mechanics together with real-time microscopy for in vivo insertion of a systematic series of microelectrode probes as a function of diameter (7.5 to 100 µm and rectangular Neuropixels) and tip geometry (flat, angled, and electrochemically sharpened). These results elucidated the role of tip geometry, surface forces, and mechanical scaling with diameter. Surprisingly, the insertion force postpia penetration was constant with distance and did not depend on tip shape. Real-time microscopy revealed that at small enough lengthscales (<25 µm), blood vessel rupture and bleeding during implantation could be entirely avoided. This appears to occur via vessel displacement, avoiding capture on the probe surface which led to elongation and tearing for larger probes. We propose a three-zone model to account for the probe size dependence of bleeding, and provide mechanistic guidance for probe design.}, }
@article {pmid41887546, year = {2026}, author = {Chen, L and Tang, C and Gao, H and Zhang, L and Cheng, S and Wang, Z and Liu, S and Ming, D}, title = {Transcutaneous auricular vagus nerve stimulation facilitates visuomotor association learning: Behavioral and electrophysiological evidence.}, journal = {NeuroImage}, volume = {331}, number = {}, pages = {121879}, doi = {10.1016/j.neuroimage.2026.121879}, pmid = {41887546}, issn = {1095-9572}, abstract = {Associating visual cues with appropriate motor responses is a fundamental adaptive skill. Transcutaneous auricular vagus nerve stimulation (taVNS) may enhance visuomotor association (VMA) learning, though its neural mechanisms remain unclear. Electroencephalogram (EEG), with its millisecond temporal resolution, offers unique advantages for elucidating the neurodynamic of VMA plasticity. This single-blind, sham-controlled, between-subjects study investigated whether taVNS facilitates VMA learning through behavioral and EEG analysis. Participants (each group N = 19) performed a VMA task (associating five oracle pictures with five keyboard keys) before and after 20-min active/sham taVNS. Behavioral results revealed that compared to the sham group, the active group exhibited shorter reaction time, higher response accuracy and larger learning curve integration, confirming the positive effect of taVNS on VMA learning. Neurophysiologically, taVNS reduced the P200 and P300 amplitudes, enhanced N170 negativity and attenuated error-related negativity. Cross-regional-frequency phase-amplitude coupling results demonstrated enhanced synchronization of frontal-parietal-occipital neural cross-frequency activity. Additionally, parietal-occipital θ, α, β band inter-trial phase coherence was enhanced in the active group. These findings demonstrate that taVNS enhances VMA acquisition through optimizing visual and error processing efficiency. This study establishes a neurophysiological basis for taVNS's cognitive enhancement potential, suggesting its utility in rehabilitative paradigms targeting associative learning deficits.}, }
@article {pmid41889816, year = {2026}, author = {Posani, L}, title = {Decodanda: a Python toolbox for best-practice decoding and geometric analysis of neural representations.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.03.16.711920}, pmid = {41889816}, issn = {2692-8205}, abstract = {Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for studying the computationally relevant properties of representational geometry, revealing not only whether a variable is encoded, but also how it is encoded and which computations the collective activity of neural populations may support. However, decoding analyses present several technical challenges and common pitfalls that can lead to misleading conclusions if not handled carefully. Here, we introduce Decodanda, a Python toolbox for decoding and geometric analysis of neural population activity. Decodanda provides functions for decoding arbitrary variables and for quantifying geometric features of neural representations, including shattering dimensionality and cross-condition generalization performance (CCGP). Importantly, the package automates several essential best-practice safeguards, including trial-based cross-validation to avoid training-testing leakage from temporally correlated neural traces (a particularly important issue for calcium imaging data), null models for statistical significance, pseudo-population pooling, and cross-variable balancing to determine which of a set of correlated variables is genuinely encoded in the activity. Decodanda is agnostic to the specific classifier used for decoding, and it is designed to be both user-friendly and highly customizable, allowing researchers to assemble flexible analysis pipelines from modular building blocks. Here, we provide an overview of the design principles of Decodanda and illustrate its use cases in neuroscience research. Documentation, example notebooks, and source code are available at github.com/lposani/decodanda .}, }
@article {pmid41889927, year = {2026}, author = {Fan, Y and Ma, Y and Zolotavin, P and Topalli, G and Wang, W and Karlsson, M and Karlsson, M and Luan, L and Xie, C and Chi, T}, title = {High-channel-count neural recording and stimulation platform with 5,376 simultaneous recording channels.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.03.13.709972}, pmid = {41889927}, issn = {2692-8205}, abstract = {Advancing neural interfaces requires large-scale, high-density recording technologies capable of capturing full-spectrum neural activity across cortical and subcortical regions. Here, we present a scalable approach to integrate neural electrodes with advanced application-specific integrated circuits (ASICs). Specifically, we custom-designed an ASIC with 5,376 simultaneous channels, each sampling at 20 kS/s and enabling >1.3 Gb/s total data streaming throughput. The ASIC incorporates in-pixel amplification, time-division multiplexed ADCs, and on-chip stimulation capabilities, ensuring precise signal acquisition with minimal power consumption while maintaining a low noise level of 5.5 $\mu$Vrms. We further developed an interconnect strategy using gold bump bonding, which allows for high-density integration of the flexible probe and rigid chip. We demonstrate the capacity of this platform through the integration with a flexible $\mu$ECoG array. The resulting device allows for the high-resolution mapping of in vivo field potentials on the cortical surfaces of rat brains, supported by the precise localization of evoked sensory activities. These results prove an effective approach towards highly integrated neural interfaces with applications in brain-computer interfaces, neuroprosthetics, and large-scale functional brain mapping.}, }
@article {pmid41876829, year = {2026}, author = {Zou, Z and Wang, B and Chen, T and Fan, S and Ye, B}, title = {A brain-edge co-evolution framework for zero-trust real-time hot patching in power equipment.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-45643-6}, pmid = {41876829}, issn = {2045-2322}, support = {SGXJDK00DWJS2500136//the Science and Technology Project of State Grid Xinjiang Electric Power Co., Ltd/ ; }, }
@article {pmid41877259, year = {2026}, author = {Toppi, J and Pichiorri, F and Ciaramidaro, A and Mohebban, S and Patarini, F and Tagliamonte, NL and Di Tommaso, F and Ferrara, M and Scorza, M and Bigioni, A and Serratore, G and Guredda, G and Scivoletto, G and Mattia, D and Tamburella, F}, title = {Investigating the role of therapist-patient interaction during robot-assisted gait training after incomplete spinal cord injury: the INTER-RO-GAIT randomized controlled trial.}, journal = {Trials}, volume = {}, number = {}, pages = {}, doi = {10.1186/s13063-026-09644-0}, pmid = {41877259}, issn = {1745-6215}, support = {GR-2019-12369207//Ministero della Salute/ ; }, abstract = {BACKGROUND: In the neurorehabilitation framework of treadmill-based robot-assisted gait training (t-RAGT), a threefold relationship among physiotherapist (Pht), patient (Pt), and the selected robotic device should be considered. Furthermore, the type of visual FeedBack (FB) selected for the training and how the Pht guides and supports the Pt have an important impact on Pt's engagement. Pht-Pt interaction is mostly effective when FB with high technical content is employed, and it affects Pt's visual attention and emotional experience during training. The INTER-RO-GAIT project proposes an experimental modulation of Pht-Pt interaction during the training with the Lokomat device, to primarily investigate its role in the effectiveness of t-RAGT for individuals with subacute and chronic incomplete spinal cord injury (i-SCI) through a longitudinal randomized controlled trial (RCT), by means of clinical scales and biomechanical data. Timed walking tests for gait speed evaluation (10-Meter Walking Test and 6-Minute Walking Test) are considered as primary outcome measures, while clinical scales for the assessment of lower limbs' force, spasticity, pain, clonus, spasms, and independence in activities of daily living are selected as secondary outcome measures. The biomechanical assessment includes overground gait analysis to assess recovery of motor functions, and human-Lokomat interaction analysis to measure the active Pt participation in the exercise and evaluate its evolution along training. Secondary aims are as follows: (i) to identify neurophysiological indices derived from electroencephalography (EEG) hyperscanning data monitoring the Pht-Pt relationship along t-RAGT; (ii) to evaluate the Pt's engagement in terms of Visual Attention during the RAGT; (iii) to investigate the correlation between the rehabilitation outcome and the neurophysiological indices or the psychological metrics referring to Pht-Pt relationship.<br><br>METHODS: Fifty participants from I.R.C.C.S. Fondazione Santa Lucia (Rome, Italy) will be enrolled and randomized into a single-blind RCT to investigate the effects of 12 Lokomat t-RAGT sessions administered with two different levels of Pht-Pt interaction (high level of interaction for the experimental (EXP) group and low level of interaction for the control (CTRL) group), as an add-on training to conventional rehabilitation. Before and after the whole t-RAGT, as well as at the first, the mid, and the last training session, a battery of clinical, biomechanical, psychological, and neurophysiological assessments will be conducted.<br><br>DISCUSSION: Given that incomplete subacute or chronic SCI may lead to long-term disability for which cost-effective rehabilitation options are critically needed, INTER-RO-GAIT aims at providing evidence for an optimal Pht-Pt interaction to potentially boost the t-RAGT effects on Pts' performance, improving robotic rehabilitation protocols and devices development even beyond the specific gait application.<br><br>TRIAL REGISTRATION: Patient-therapist INTERaction During RObotic GAIT Rehabilitation After Spinal Cord Injury (INTER-RO-GAIT); ClinicalTrial.gov platform registration number: GR-2019-12369207 on 31st July 2024.}, }
@article {pmid41877429, year = {2026}, author = {Ke, S and Li, Y and Qu, Y and Huang, H and Hao, M and Yang, L and Wu, Q and Ye, C and Chu, PK and Yu, XF and Wang, J}, title = {Spectrally Defined Bipolar Black Phosphorus Memristor Enables All-Optical Boolean Logic and Multispectral Computing.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e22710}, doi = {10.1002/adma.202522710}, pmid = {41877429}, issn = {1521-4095}, support = {2024YFB3614200//National Key R&amp;D Program of China/ ; 62274058//National Natural Science Foundation of China/ ; 62404237//National Natural Science Foundation of China/ ; 32471459//National Natural Science Foundation of China/ ; 2024A1515030176//Guangdong Basic and Applied Basic Research Foundation/ ; 2025B1515020088//Guangdong Basic and Applied Basic Research Foundation/ ; 2023A1515110590//Guangdong Basic and Applied Basic Research Foundation/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; JQ0209-2025//Original Innovation Project in SIAT/ ; RCJC20200714114435061//Shenzhen Science and Technology Program Grants/ ; DON-RMG 9229021//City University of Hong Kong Donation Research Grants/ ; 9220061//City University of Hong Kong Donation Research Grants/ ; GZC20241837//China Postdoctoral Science Foundation/ ; 2025WK2013//Natural Science Foundation of Hunan Province/ ; B2302028//Shenzhen Medical Research Fund/ ; }, abstract = {Although optoelectronic memristors with nonvolatile bipolar photoconductivity enable in-sensor vision-centric neuromorphic hardware, achieving wavelength-defined polarity inversion across a broad spectrum remains a challenging task. Herein, a stable optoelectronic memristor composed of nonstoichiometric lead oxide (PbOx) coated black phosphorus (BP) nanosheets is demonstrated. The optoelectronic processes in the PbOx-BP heterostructure result in programmable polar photoresponses across the 365 nm - 1,550 nm wavelength range. Visible light causes positive photoconductance via photoelectrochemical Ag[+] reduction and conductive filament reconstruction. Conversely, ultraviolet light drives the reverse photogenerated electron transfer to chemically oxidize the Ag CFs, while infrared light induces their localized melting via the photothermal effect. This bipolar optoelectronic tunability enables all-optical Boolean logic operations, allowing for the realization of 14 binary functions through optical reconfiguration. Furthermore, multispectral computing tasks, including edge extraction and spectral noise suppression, are performed, yielding a classification accuracy of up to 98.6% for 16 crop species using an all-optical convolutional neural network. The ultra-thin oxide coating presents an effective surface modification approach to improve two-dimensional devices, while the optoelectronic bipolarity establishes a framework for all-optical modulation in neuromorphic machine vision.}, }
@article {pmid41878268, year = {2026}, author = {Velut, S and Thielen, J and Chevallier, S and Corsi, MC and Dehais, F}, title = {Neurophysiological screening of individual variability for robust decoding in c-VEP-based BCI.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {4}, number = {}, pages = {}, pmid = {41878268}, issn = {2837-6056}, abstract = {Code-modulated visual evoked-potential (c-VEP)-based reactive brain-computer interfaces (BCIs) deliver high information-transfer rates with minimal calibration, yet performance often collapses when models are transferred between users. We, therefore, pursue a two-fold aim: first, to pinpoint neurophysiological predictors that explain this inter-participant variability; second, to identify a decoding pipeline that sustains accuracy across users in a burst-c-VEP paradigm (brief, aperiodic flashes at 3 Hz). From 24 participants, we find that stronger inter-epoch correlation (R ≈ 0.80), larger peak-to-peak amplitude of the flash-VEP, larger α bandpower, larger θ bandpower, and lower δ bandpower are five neurophysiological predictors that correlate between high performers (> 90% accuracy) and low performers (< 70%), enabling a 22 s "go/no-go" calibration. We then compare three preprocessing schemes (small, combined, participant-specific) paired with three decoders-a convolutional neural network, a Riemannian xDAWN-LDA baseline, and GREEN, a wavelet-based symmetric positive definite neural network. Subject-specific alignment plus GREEN achieves 93% trial-level accuracy in both intra- and cross-participant settings, eliminating the 15-20% transfer loss obtained with the other tested decoding models while keeping the total calibration under 1 min. In conclusion, rapid user screening with these neurophysiological predictors, followed by this lightweight, user-specific pipeline, yields burst-c-VEP control that is fast to deploy and robust across individuals.}, }
@article {pmid41880939, year = {2026}, author = {Zhang, HG and Jialin, A and Chen, ZR and Zhang, JQ and Wang, C and Cao, MN and Li, XJ and Yin, XW and Ye, JX and Xue, C and Zhong, BL and Deng, W}, title = {Left cortical activation and combined diagnostic utility during three verbal fluency tasks in major depressive disorder: A multi-channel fNIRS study.}, journal = {Psychiatry research}, volume = {360}, number = {}, pages = {117101}, doi = {10.1016/j.psychres.2026.117101}, pmid = {41880939}, issn = {1872-7123}, abstract = {BACKGROUND: Recent functional near-infrared spectroscopy (fNIRS) studies have shown reduced left cortical hemodynamic responses in major depressive disorder (MDD), suggesting a promising neuroimaging biomarker for diagnosis. However, given MDD's pronounced clinical heterogeneity and widespread cognitive impairments, reliance on a single task-based activation index may be insufficiently sensitive. Therefore, this study aims to combine three Chinese verbal fluency tasks (VFTs) with distinct cognitive demands to delineate MDD-related aberrant neural response patterns and to derive more comprehensive, robust fNIRS biomarkers for objective diagnostic classification.<br><br>METHODS: This study recruited 60 patients with MDD and 60 demographically matched healthy controls (HCs). Hemodynamic changes in the left cortex were measured using a 48-channel fNIRS during the three VFTs. Demographics information, clinical characteristics and VFT performance were also collected.<br><br>FINDINGS: Each Chinese VFT variant elicited a different pattern of left cortical activation. Relative to HCs, patients with MDD exhibited significantly reduced activation in both the left dorsolateral and medial prefrontal cortices. Moreover, integrating neural activation indices across all three VFTs substantially enhanced the discrimination between MDD patients and HCs compared with any single task.<br><br>CONCLUSIONS: In light of the heterogeneous nature of depression and its broad impact on multiple cognitive domains, combining multiple cognitive paradigms may develop richer and more reliable fNIRS-based biomarkers for the identification of MDD.}, }
@article {pmid41881762, year = {2026}, author = {Zhao, W and Rao, J and Wang, R and Chai, Y and Mao, T and Quan, P and Deng, Y and Chen, W and Wang, S and Guo, B and Zhang, Q and Rao, H}, title = {Retraction notice to "Test-retest reliability of coupling between cerebrospinal fluid flow and global brain activity after normal sleep and sleep deprivation" [NeuroImage 309 (2025) 121097].}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121851}, doi = {10.1016/j.neuroimage.2026.121851}, pmid = {41881762}, issn = {1095-9572}, }
@article {pmid41882308, year = {2026}, author = {Machhi, V and Shah, A}, title = {Emotion detection unveiled: A cognitive-computational synthesis of physiological models, machine learning, and datasets.}, journal = {Cognitive, affective &amp; behavioral neuroscience}, volume = {}, number = {}, pages = {}, pmid = {41882308}, issn = {1531-135X}, abstract = {This comprehensive survey synthesizes state-of-the-art advancements in emotion recognition based on physiological signals, specifically focusing on the paradigm shift occurring between 2021 and 2025. Crucially, we move beyond a technical review by establishing a novel Cognitive-Computational Synthesis Framework (CCSF). This framework explicitly maps multimodal physiological manifestations (e.g., electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR)) to underlying cognitive processes, such as attentional allocation, arousal regulation, and perceptual bias, providing a theoretical foundation for explainable AI (XAI) in affective computing. We meticulously examine the transition from traditional machine learning to advanced deep learning architectures, highlighting how recent innovations in Transformers, self-supervised learning, and diffusion models have shattered previous performance plateaus. While earlier dimensional models were often limited to 70-75% accuracy, this survey details how modern architectures now achieve benchmarks exceeding 95% on seminal datasets like SEED and DREAMER. Furthermore, the survey provides a rigorous analysis of 40 key studies (identified via PRISMA protocols), evaluating them based on their validation strategies, cross-subject generalizability, and adversarial robustness. By bridging the gap between raw physiological data and cognitive theory, this work offers a strategic roadmap for the next generation of robust, interpretable, and real-time emotion recognition systems.}, }
@article {pmid41882345, year = {2026}, author = {Haggerty, J and Qureshi, Q and Gabriel, ED and Borges, PG and Davis, P and Wingel, K and Cai, J and Sargur, K and Kim, MJ and Dubey, A and Garwood, I and Vaz, A and Richardson, AG and Chen, HI and Hammer, LH and Gold, J and Litt, B and Yoshor, D and Beauchamp, M and Halpern, C and Pesaran, B and Cajigas, I}, title = {Thalamus: a real-time system for synchronized, closed-loop multimodal behavioral and electrophysiological data capture.}, journal = {Communications engineering}, volume = {}, number = {}, pages = {}, doi = {10.1038/s44172-026-00646-z}, pmid = {41882345}, issn = {2731-3395}, support = {5K12NS129164-02//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; }, abstract = {Precise and synchronized multimodal data capture in neurosurgical environments is essential for further understanding brain function and will be crucial to advancing the development of brain-computer interface technology. We have developed an open-source software platform named Thalamus, for multimodal data capture integrated with existing sensors and hardware commonly utilized in the operating room and other clinical environments such as pulse oximeters, inertial sensors, electromyography and neural electrophysiology. Thalamus facilitates synchronous recording of neural and behavioral data, enabling real-time computation for closed-loop experiments and detailed analysis of complex motor functions and neural activity. Thalamus uses a modular, configurable node-based pipeline with a tiered Python and C + + architecture. These design elements allow Thalamus to support a wide range of high-resolution sensors for diverse behavioral data types and enable robust closed-loop synchronization of various data streams. Validation experiments demonstrate that Thalamus is capable of data integration and concurrent analysis with up to sub-millisecond precision, offering great potential for enhancing neurosurgical research and clinical applications. By leveraging conventional sensors and hardware already in use, Thalamus supports adoption into the clinical environment, paving the way for more comprehensive, data-driven approaches to neurological care and improving the personalization and rigor of treatment strategies.}, }
@article {pmid41870922, year = {2026}, author = {Wei, Y and Mai, X and Li, Y and Luo, R and Cheng, R and Meng, J}, title = {High-Performance Cross-Subject Decoding of Multiclass Rhythmic Motor Imagery Using EEG Data from 100 Subjects.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3676837}, pmid = {41870922}, issn = {1558-0210}, abstract = {OBJECTIVE: Effective cross-subject decoding is essential for reducing calibration time and enhancing the practical usability of brain-computer interfaces (BCIs). However, large inter-subject variability in EEG features poses a major challenge, particularly for motor imagery (MI) paradigms. Recent studies have shown that rhythmic MI can induce steady-state movement-related rhythms (SSMRR), which provide more structured electrophysiological features than conventional sensorimotor rhythms (SMR) and may offer a promising basis for efficient cross-subject decoding.<br><br>METHODS: In this study, we comprehensively explored ways to achieve high-performance cross-subject decoding based on the rhythmic MI paradigm from both model and data perspectives.<br><br>RESULTS: We achieved an encouraging cross-subject four-class decoding accuracy of 72.94%&#xb1;13.80% using a streamlined multilayer perceptron (MLP)-based network on a self-collected dataset comprising 100 BCI-na&#xef;ve participants. From a model perspective, networks composed of simple MLP-based functional modules can achieve results comparable to, or even superior to, those of several state-of-the-art (SOTA) models. From a data perspective, increasing the training set size substantially improves cross-subject decoding performance (from 61.78% to 72.94%). Moreover, we revealed a strong positive correlation between EEG feature consistency and cross-subject decoding accuracy, providing a physiological explanation for why enlarging the training data scale enhances cross-subject generalization. Finally, we explored strategies for selecting high-quality training data. We found that feature-consistency-based selection serves as a more reliable criterion than within-subject decoding accuracy.<br><br>SIGNIFICANCE: Overall, our study provides novel insights into cross-subject EEG decoding from the perspectives of model design, data scale and quality. The code is available in https://github.com/SJTUwyxuan/RhythmicMI-CrossSubject.}, }
@article {pmid41871461, year = {2026}, author = {Mei, T and Wang, Y and Gou, H and Chang, C and Hu, S and Zhang, X}, title = {EEG-CMT: Spatial-Temporal Representation of EEG for Emotion Recognition Using Convolutional Neural Networks and Vision Transformers.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae55aa}, pmid = {41871461}, issn = {2057-1976}, abstract = {Background Recent researches on electroencephalogram (EEG) based emotion recognition face challenges in effectively mapping the spatial positional relationships of EEG acquisition electrodes. Additionally, conventional models struggled to simultaneously capture both fine-grained temporal-spatial features and long-range dependencies in EEG signals. New method To address these limitations, we propose a novel EEG data processing method that incorporates spatial relative position encoding and a hybrid neural architecture integrating convolutional neural networks (CNNs) with self-attention mechanisms. This approach systematically encodes the spatial topology of electrodes to enhance the representation of temporal-spatial information. CNNs are employed to extract localized temporal-spatial micro-patterns, while self-attention modules model global contextual dependencies across extended sequences, thereby enhancing model's representational capacity. Results The experimental results and feature visualizations demonstrate that our method achieves state-of-the-art performance on two benchmark emotion recognition datasets, reaching an average accuracy of 97.51% on the SEED dataset and 96.13% on the SEED-IV dataset. Moreover, the learned spatial features align well with known neuroscientific patterns of emotional processing. Comprehensive ablation studies further validate the necessity and effectiveness of both the spatial-encoded data processing strategy and the hybrid architecture design. Comparison with Existing Methods Compared to other hybrid neural network models, our proposed method (EEG-CMT) achieves the highest classification accuracy. Specifically, it outperforms baseline algorithms by margins ranging from 0.86% to 11.43% on the SEED dataset, and from 9.49% to 39.52% on the SEED-IV dataset. Conclusions The proposed method effectively addresses key limitations in existing EEG-based emotion recognition models by jointly leveraging spatial topology and hybrid modeling techniques. These innovations significantly improve the model's ability to recognize emotions from EEG data and provide neural interpretable insights, offering a promising direction for future research in affective brain-computer interfaces.}, }
@article {pmid41872323, year = {2026}, author = {Qiu, S and Liu, L and Xiang, B and Jin, Z and Li, Y and Li, D and Hou, H and Li, K and Wei, G and Xie, J and Li, S and Liu, S and Chen, C and Liang, X and Sun, Q and Xiong, W}, title = {Template-independent genome editing and restoration for correcting frameshift disorders.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {41872323}, issn = {2157-846X}, support = {2021ZD0203304//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; U23A20442//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Frameshift mutations, responsible for >20% of Mendelian inherited diseases, pose substantial therapeutic challenges. Here we developed Template-Independent Genome Editing for Restoration (TIGER), a platform for the efficient and precise correction of frameshift mutations across various models. By identifying reproducible nucleotide-level factors that influence therapeutic efficacy across cells and tissues, we developed a scoring system for guide RNA (gRNA)-Cas9 outcomes. Approximately 75% of deletion and 50% of insertion mutations produced ≥30% in-frame products, sufficient for phenotypic restoration, with 38% and 65% achieving wild-type correction, respectively. To expand the applicability of TIGER across species and genome wide, we retrained the inDelphi algorithm to predict therapeutic gRNAs for single-nucleotide frameshifts. In a mouse model of deafness, delivery of SpCas9 and optimal gRNA via dual adeno-associated virus restored hearing thresholds to wild-type levels, with ~90% of in-frame edits being wild type. TIGER provides a robust and broadly applicable strategy for in vivo correction of inherited frameshift diseases.}, }
@article {pmid41874079, year = {2026}, author = {Lorente-Piera, J and Manrique-Huarte, R and Picciafuoco, S and Lima, JP and Serra, V and Manrique, M}, title = {Beyond the Air-Bone Gap: The Role of Bone Conduction Thresholds in Predicting Functional Outcomes and Guiding Surgical Decision-Making in Active Middle Ear and Bone Conduction Implants.}, journal = {Audiology research}, volume = {16}, number = {2}, pages = {}, pmid = {41874079}, issn = {2039-4330}, abstract = {Introduction: In patients with conductive and mixed hearing loss, implantable hearing devices such as active middle ear implants (AMEIs) and bone conduction implants (BCIs) are established alternatives when conventional hearing aids fail. Although bone conduction (BC) thresholds are routinely used as eligibility criteria, their role as frequency-specific predictors of postoperative functional outcomes remains poorly defined. This study aimed to evaluate the influence of preoperative BC thresholds across the audiometric spectrum on postoperative speech recognition outcomes after implantation with AMEIs and BCIs. Methods: A retrospective observational study was conducted at a tertiary referral center including patients implanted with BCIs or AMEIs. Pre- and postoperative audiological data were analyzed, including air and bone conduction thresholds, frequency-segmented BC measures (low, mid, and high frequencies), cochlear frequency gradient (ΔBC Slope), and speech recognition scores (SRSs) at 65 dB HL one year after implantation. Results: 102 patients were included (50 BCI, 52 AMEI). Both implant types achieved significant postoperative improvements in tonal thresholds and SRS compared with pre-implantation values (all p < 0.001). High-frequency BC thresholds (BC-High, 4-6 kHz) showed a significant inverse correlation with postoperative SRS in both BCI (r = -0.382, p = 0.001) and AMEI users (r = -0.398, p < 0.001), and emerged as the only independent predictor in multivariable models (BCI: β = -0.533, p = 0.022; AMEI: β = -0.491, p = 0.020). Low- and mid-frequency BC measures were not associated with postoperative speech outcomes (all p > 0.05). ROC analyses demonstrated excellent discriminative performance of BC-High for identifying suboptimal outcomes, with area under the curve values of 0.92 for BCI (p = 0.001) and 0.94 for AMEI (p = 0.002), and implant-specific cutoff values of >47 dB HL and >61 dB HL, respectively. Conclusions: High-frequency BC thresholds showed the strongest association with postoperative speech recognition after implantable hearing rehabilitation. BC-High could function as a prognostic marker of functional outcome rather than an eligibility criterion, providing clinically meaningful information to refine preoperative counseling and individualized decision-making within current indication frameworks.}, }
@article {pmid41875491, year = {2026}, author = {Haxel, L and Kapoor, J and Ziemann, U and Macke, JH}, title = {EDAPT: Towards calibration-free BCIs with continual online adaptation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae5689}, pmid = {41875491}, issn = {1741-2552}, abstract = {Brain-computer interfaces (BCIs) suffer from accuracy degradation as neural signals drift over time and vary across users, requiring frequent recalibration that limits practical deployment. Our goal is to develop a framework that eliminates the need for separate calibration phases by enabling continual, real-time model adaptation to new users and changing signal characteristics. Approach. We propose EDAPT, a task- and model-agnostic framework for continual online learning. EDAPT first establishes a robust baseline decoder through population-level pretraining on data from multiple users. It then personalizes this model during deployment using supervised continual finetuning on a trial-by-trial basis. Due to its modular design, EDAPT can be composed with unsupervised domain adaptation techniques to further address distribution shifts. Main results.We validate EDAPT across nine datasets, three BCI paradigms, and four deep learning architectures. EDAPT consistently improves decoding accuracy over static models for nearly all subjects and datasets, raising mean balanced accuracy from 0.80 to 0.87 on representative datasets (Table 3). Ablation studies confirm that the combination of population-level pretraining and online finetuning is the primary driver of this performance gain, with further improvements on some datasets when using unsupervised domain adaptation techniques. We demonstrate real-time feasibility of the framework, with adaptation latencies under 200 milliseconds on consumer-grade hardware. Our scaling analysis further reveals that decoding accuracy is primarily determined by the total pretraining data budget, rather than its specific allocation between subjects and trials. Significance. These findings demonstrate that continual online learning is a practical and effective strategy for creating high-performance, user-adaptive BCIs. By systematically addressing the bottleneck of model recalibration, EDAPT reduces a major barrier to the widespread adoption of BCI technology and helps advance neurotechnology toward robust, user-friendly, real-world applications.}, }
@article {pmid41875494, year = {2026}, author = {Kim, D and Song, CY and Hsieh, HL and Shanechi, MM}, title = {Unsupervised learning of multiscale switching dynamical system models from multimodal neural data.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae5688}, pmid = {41875494}, issn = {1741-2552}, abstract = {OBJECTIVE: Neural population activity often exhibits regime-dependent non-stationarity in the form of switching dynamics. Learning accurate switching dynamical system models can reveal how behavior is encoded in neural activity. Existing switching approaches have primarily focused on learning models from a single neural modality, either continuous Gaussian signals such as local field potentials (LFPs) or discrete Poisson signals such as spiking activity. However, multiple neural modalities are often recorded simultaneously to measure different spatiotemporal scales of brain activity, and all these modalities can encode behavior. Moreover, regime labels are typically unavailable in training data, posing a significant challenge for learning models of regime-dependent switching dynamics. These gaps highlight the need for a new unsupervised method that can learn switching dynamical system models for multiscale data and do so without requiring regime labels.<br><br>APPROACH: We develop a novel unsupervised learning algorithm that learns the parameters of switching multiscale dynamical system models using only multiscale neural observations. Doing so, the algorithm can not only fuse multiscale neural information but also account for regime-dependent switches in multiscale neural dynamics.<br><br>MAIN RESULTS: We demonstrate our method using both simulations and two distinct experimental datasets with multimodal spike-LFP observations during different motor tasks. We find that our switching multiscale dynamical system models more accurately decode behavior than switching single-scale dynamical models, showing the success of multiscale neural fusion. Further, our models outperform stationary multiscale models, illustrating the importance of tracking regime-dependent nonstationarity in multimodal neural data.<br><br>SIGNIFICANCE: The developed unsupervised learning framework enables more accurate modeling of complex multiscale neural dynamics by leveraging information in multimodal recordings while incorporating regime switches. This approach holds promise for improving the performance and robustness of brain-computer interfaces over time and for advancing our understanding of the neural basis of behavior.}, }
@article {pmid41778805, year = {2026}, author = {Bai, K and Ge, T and Wang, C-X and Dou, Y-Y and Zhang, J-X and Li, P and Feng, X-L and Han, Y and Zhao, S-S and Su, K-M and Shang, Y-X and Yu, X and Li, S-R and Su, D and Song, J-J and Qin, X and Yu, J and Yang, C-B and Zhang, J-P and Wang, W}, title = {EEG and gut microbiota response patterns in high-altitude indigenous populations.}, journal = {mSystems}, volume = {11}, number = {3}, pages = {e0169225}, doi = {10.1128/msystems.01692-25}, pmid = {41778805}, issn = {2379-5077}, support = {2020QZDY002//Tangdu Hospital, Fourth Military Medical University/ ; axjhww//Hovering Program of Fourth Military Medical University/ ; 2018BJ003//Talent Foundation of Tangdu Hospital/ ; 2025PT-08//7T MRI Precision Neurology Platform of Shaanxi Province/ ; //Innovative Team for Early Warning and Rehabilitation of Mental Fatigue Using BCI and Virtual Reality/ ; 2024SF2-GJHX-71//Key Core Technique Program of Shaanxi Province/ ; 61240302//Science and Technology Research Project of Shaani Nuclear Industry Group Co., Ltd/ ; }, mesh = {Humans ; *Gastrointestinal Microbiome/physiology ; *Altitude ; *Electroencephalography ; Male ; Adult ; *Brain/physiology ; Female ; Young Adult ; RNA, Ribosomal, 16S/genetics ; Cognition/physiology ; }, abstract = {Indigenous high-altitude populations maintain relatively normal brain function despite chronic hypoxia, yet the underlying neurophysiological mechanisms and the potential role of gut-brain interaction remain unclear. This study combined 16S rRNA gut microbiota profiling in 211 high-altitude indigenous populations at 2, 3, and 4 km altitudes with resting-state and task-based electroencephalography recordings in 135 of them. Residents at 4 km showed enhanced delta (1-4 Hz) power across most brain regions along with increased frontal-occipital functional connectivity (FC) during resting state. During a cognitive oddball task, the 4 km group exhibited elevated P3 amplitude in response to oddball stimuli, together with larger parietal delta power. In parallel, the 4 km group displayed higher species richness and an elevated abundance of short-chain fatty acid-producing genera such as Roseburia, Blautia, and Coprococcus. Furthermore, the abundance of Blautia was positively associated with resting-state FC, a relationship that may further influence anxiety and sleep quality. Our findings demonstrate a coordinated gut-brain interaction adaptation to high altitude, highlighting the homeostatic role of microbial pathways.IMPORTANCEIndigenous high-altitude populations maintain normal cognitive function under chronic hypoxia, a process potentially involving the gut microbiota. Our study added evidence that the neural activity patterns and gut microbiota structure may work in coordination to assist the host in adapting to extreme environments.}, }
@article {pmid41867018, year = {2026}, author = {Yu, R and Shen, R and Chen, L and Li, P}, title = {Insights Into the Inhibitory Effect of Ofloxacin on Pepsin Through Peptidomics and Bioinformatics Approaches.}, journal = {Journal of biochemical and molecular toxicology}, volume = {40}, number = {4}, pages = {e70788}, doi = {10.1002/jbt.70788}, pmid = {41867018}, issn = {1099-0461}, support = {2024X007-KXZ//Beijing Polytechnic University/ ; 2023R008-JFQB//Youth Top Talent Cultivation Plan/ ; }, mesh = {*Pepsin A/chemistry/antagonists &amp; inhibitors ; Animals ; *Ofloxacin/pharmacology/chemistry ; Cattle ; Molecular Docking Simulation ; *Proteomics/methods ; *Computational Biology/methods ; Hydrolysis ; *Peptides/chemistry ; Serum Albumin, Bovine/chemistry ; Tandem Mass Spectrometry ; }, abstract = {The hydrolysis of proteins by pepsin is of great significance for the biological utilization of proteins and the discovery of functional peptide molecules. Bovine serum albumin (BSA) and bovine collagen I (BCI) are both commonly used natural source proteins for studying the hydrolysis characteristics of pepsin. UHPLC - MS/MS, peptidomics, and molecular docking technologies were employed to investigate the underlying mechanism responsible for the inhibitory effect of ofloxacin on pepsin. The molecular weight distribution of peptides produced by pepsin in this study was mostly in the range of 600 Da to 1800 Da, and peptide segments were mostly composed of 9-11 amino acids. The predominant terminal amino acids were proline, glycine, leucine, valine, serine, and threonine. Ofloxacin led to conformational changes of the hydrolysis active sites of pepsin by forming hydrogen bonds with aspartic acids. When the key aspartic acid residues in the active center of pepsin were inhibited, the numbers of peptides TPAQD, VSVDAA, TVLFD, and TVIFD were upregulated. The hydrolysis characteristics of pepsin were changed, shown as an increase in the proportion of low molecular weight peptides and a decrease in the hydrophobicity of peptide segments in the hydrolysates. The study contributed to the evaluation of the activity of peptides from homologous protein hydrolysis by pepsin and the elucidation of the inhibitory mechanism of ofloxacin on pepsin.}, }
@article {pmid41867762, year = {2026}, author = {Barzon, G and De, A and Moran, I and Carnahan, C and Mazzucato, L and Kiani, R}, title = {Control of cortical population activity with patterned microstimulation.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.03.02.709018}, pmid = {41867762}, issn = {2692-8205}, abstract = {Closed-loop control of cortical activity is a central goal in systems neuroscience and clinical neuromodulation, but most approaches either rely on detailed circuit models that are unattainable in vivo or on open-loop stimulation tuned by trial and error. Here we introduce REACHable manifold Control (REACH-Ctrl), a data-driven brain-computer interface that achieves real-time control of population spiking activity using patterned microstimulation and multi-electrode recordings. REACH-Ctrl learns a finite-horizon controllability map directly from short training epochs in which random multi-electrode pulse sequences are delivered through a subset of electrodes while recording evoked responses. From these input-output data, it identifies the "reachable manifold" of population states and computes low-current microstimulation sequences that steer activity toward designated targets, without explicit knowledge of the underlying connectivity or dynamics. We test REACH-Ctrl in macaque prefrontal cortex, demonstrating high control accuracy, robust across sessions and stimulation parameters. Geometric analyses showed that multi-pulse sequences traverse a well-defined reachable manifold with substantial, but incomplete, overlap with the intrinsic neural activity manifold, revealing both on- and off-manifold components of control. Encoding models further revealed that, in our weak-stimulation regime, population responses to multi-electrode sequences are well approximated by the linear sum of localized "stimulation fields" with modest history dependence, explaining the success of our linear control approach. These results demonstrate precise, sample-efficient control of cortical population activity with clinically relevant microstimulation hardware, and provide a general blueprint for designing perturbations for sparsely observed neural circuits.}, }
@article {pmid41868420, year = {2026}, author = {Rodino, F and Briki, M and Buclin, T and Guidi, M and Carrara, S}, title = {Dual-Biosensor for Five Drugs Detection in Precision Oncology.}, journal = {BioNanoScience}, volume = {16}, number = {4}, pages = {258}, pmid = {41868420}, issn = {2191-1630}, abstract = {ABSTRACT: The increasing demand for precision medicine, particularly in oncology, requires innovative solutions to address patient-specific inter-individual variability in drug response. Therapeutic drug monitoring (TDM) is crucial for optimizing treatment efficacy and minimizing toxic side effects, enabling precise dosage adjustments tailored to the patient's individual metabolic profile. Electrochemical biosensors offer a cost-effective, simple, and portable solution with rapid response times, making them ideal for point-of-care applications. In this work, we propose a novel dual-biosensor platform for TDM, designed to simultaneously detect multiple chemotherapeutic agents-cyclophosphamide, ifosfamide, etoposide, methotrexate, and 5-fluorouracil-for precision oncology. Following real clinical treatment scenarios, the system uses only two working electrodes integrated into a single electrochemical sensing platform, significantly reducing complexity and cost. By integrating MWCNTs with cytochrome P450 enzymes (CYP3A4 and CYP2B6), our platform achieves enhanced electron transfer and substrate specificity, enabling sensitive and selective detection of the five chemotherapeutic drugs, individually and in combination, within clinically relevant ranges. Designed for portability and rapid analysis, this dual-biosensor platform enables real-time, cost-effective drug monitoring at the point-of-care, advancing personalized cancer treatment with greater precision and accessibility.}, }
@article {pmid41869230, year = {2026}, author = {Abazovic Bihorac, A and Kovacevic, M}, title = {Acute Ischemic Stroke: A Retrospective Study Comparing Clinical Characteristics and Outcomes in Patients With and Without Complications.}, journal = {Cureus}, volume = {18}, number = {2}, pages = {e103902}, pmid = {41869230}, issn = {2168-8184}, abstract = {BACKGROUND: Acute ischemic stroke (AIS) is a leading cause of morbidity and mortality. Post-stroke complications, both neurological and systemic, negatively affect patient outcomes, prolong hospitalization, and increase healthcare costs. Identifying high-risk patients is essential for early intervention.<br><br>AIM: To compare clinical, radiological, laboratory characteristics, and in-hospital outcomes between patients with AIS who developed complications and those who did not.<br><br>METHODS: This retrospective cohort study included 150 patients with confirmed first AIS admitted between October 2023 and October 2024. Patients were divided into two groups: Group 1 (n = 73) with in-hospital complications and Group 2 (n = 77) without complications. Demographic data, comorbidities, National Institutes of Health Stroke Scale (NIHSS) scores, brain computer tomography (CT) findings, laboratory parameters, blood pressure, complications, and outcomes were analysed. Continuous variables are presented as median (interquartile range) and categorical variables as number (%). A P-value < 0.05 was considered statistically significant.<br><br>RESULTS: Group 1 patients were older (73.0 (interquartile range (IQR) 66.5-79.0) vs. 69.0 (IQR 62.0-73.0) years; P < 0.001) and had higher NIHSS scores at admission (10.0 (IQR 5.0-16.0) vs. 5.0 (IQR 4.0-7.0); P < 0.001) and follow-up (6.0 (IQR 4.0-11.0) vs. 3.0 (IQR 2.0-5.0); P < 0.001). Large infarctions were more frequent in Group 1 (57.5% vs. 27.3%; P < 0.001), and glucose levels were higher (14.0 (IQR 10.1-16.3) vs. 6.8 (IQR 5.95-9.65) mmol/L; p = 0.027). Length of hospital stay and in-hospital mortality were greater in Group 1 (14.0 (IQR 10.0-17.0) vs. 7.0 (IQR 6.0-10.0) days; P < 0.001; 17.8% vs. 3.9%, respectively).<br><br>CONCLUSIONS: Patients with AIS who develop complications have distinct clinical and laboratory profiles, more severe neurological deficits, and worse in-hospital outcomes. Early risk identification may improve management and patient care.}, }
@article {pmid41862465, year = {2026}, author = {Shen, C and Ding, H and Zhang, S and Xu, C and Zou, B and Ji, S and Liu, YR and Li, Y and Zhou, R and Liang, J and Shen, DD and Liu, Y and Chen, X and Rondard, P and He, J and Zhang, Y and Pin, JP and Liu, J}, title = {Functional and structural basis of a negative allostery within GABAB hetero-tetramers.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-70640-8}, pmid = {41862465}, issn = {2041-1723}, abstract = {G protein coupled receptors (GPCRs) oligomerization may allow signal integration from different GPCR units. The GABAB receptor, activated by the main inhibitory transmitter, GABA, is an obligatory heterodimer. It is the target of two therapeutic drugs, baclofen and GHB, and can form stable oligomers. The existence, roles, and possible allosteric interaction of GABAB oligomers remain elusive. Here, we show that GABAB oligomers exist in neurons. Their function can be specifically affected by human disease-associated mutations, demonstrating their essential role for normal brain function. The cryo-EM structure of a hetero-tetramer in the apo state reveals the heterodimers interacting in an asymmetrical way to prevent one unit from being activated. This represents a nice example of a negative allosteric interaction between GPCRs related to human diseases.}, }
@article {pmid41862569, year = {2026}, author = {Liang, S and Tan, ZL and Ding, J and Dai, Y and Xu, Y and Ma, J and Song, XM and Yeo, BTT and Li, T}, title = {Peripheral immune-redox signatures associate with cortical network alterations in anhedonic depression.}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {41862569}, issn = {1476-5578}, support = {82230046//National Natural Science Foundation of China (National Science Foundation of China)/ ; LTGY24H090012//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; LTGY23C090002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {Anhedonia is a core feature of major depressive disorder (MDD), yet links between peripheral molecular signatures and cortical network architecture remain poorly defined. We enrolled 210 participants, including 56 unmedicated MDD patients with high-anhedonia (HA), 61 with low-anhedonia (LA), and 93 healthy controls (HC). Morphometric similarity networks (MSNs) from structural MRI were compared between HA and LA. MSNs index individual-level network organization by quantifying inter-regional morphometric similarity. Regional MSN patterns were linked to Allen Human Brain Atlas using Spearman correlations with spin tests and a multi-K stability screen. Whole-blood RNA-seq (n = 199) was integrated with MSN features via sparse partial least squares-canonical correlation (sPLS-C), with key blood analyses repeated after leukocyte-composition adjustment. Gene Ontology over-representation and MAGMA gene-level analyses provided pathway context. HA showed greater MSN integration than LA, particularly within default-mode and somatomotor networks. MSN maps were negatively correlated with dopamine-transporter and kappa-opioid-receptor densities. Imaging-derived gene associations were enriched for regulation of Toll-like-receptor-3 signaling. In blood, sPLS-C revealed coupling between MSN features and a transcriptomic signature enriched for T-cell activation/differentiation and lymphocyte-apoptosis pathways. After composition adjustment, the pre-specified blood signature did not differ between HA and LA, indicating that between-group differences were largely composition-driven. As supportive genetic context, over-representation on MAGMA FDR-significant genes suggested protocadherin-mediated homophilic adhesion. Peripheral immune-redox pathway enrichment aligns with anhedonia-related cortical network alterations, whereas between-group blood differences are chiefly composition-driven. Adjusting for blood-cell composition is essential, this multimodal framework nominates immune-modulatory/redox targets and synaptic-adhesion biology for precision stratification and intervention.}, }
@article {pmid41863372, year = {2026}, author = {Yin, Y and Wei, W and Deng, L and Li, X and Ma, X and Zhao, L and Deng, W and Guo, W and Sham, PC and Wang, Q and Li, T}, title = {Disrupted Structural Covariance in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder.}, journal = {Schizophrenia bulletin}, volume = {52}, number = {2}, pages = {}, pmid = {41863372}, issn = {1745-1701}, support = {82230046//National Natural Science Foundation of China/ ; U25A2079//National Natural Science Foundation of China/ ; 82171499//National Natural Science Foundation of China/ ; 82571712//National Natural Science Foundation of China/ ; 82001410//National Natural Science Foundation of China/ ; 2021ZD0200404//STI 2030-Major Projects/ ; 2021ZD0200800//STI 2030-Major Projects/ ; 20241203A14//Key Research and Development by Hangzhou Science and Technology Bureau/ ; CXTD202501053//Zhejiang Clinovation Pride/ ; 2022WJC265//Hangzhou Biomedical and Health Industry Development Support Science and Technology Project/ ; 2025HZGF10//Construction Fund of Key Medical Disciplines of Hangzhou/ ; }, mesh = {Humans ; *Bipolar Disorder/physiopathology/diagnostic imaging/pathology ; *Major Depressive Disorder/physiopathology/diagnostic imaging/pathology ; *Schizophrenia/physiopathology/diagnostic imaging/pathology ; Male ; Female ; Adult ; Magnetic Resonance Imaging ; *Nerve Net/diagnostic imaging/physiopathology/pathology ; Young Adult ; Middle Aged ; *Default Mode Network/diagnostic imaging/physiopathology ; }, abstract = {BACKGROUND AND HYPOTHESIS: Shared clinical features and genetic factors in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) have led to the hypothesis of common pathophysiological mechanisms. This study aims to elucidate aberrant transdiagnostic structural covariance patterns across these disorders employing a multivariate analytical approach.<br><br>STUDY DESIGN: Structural magnetic resonance imaging data were acquired from a sample of 704 subjects, comprising 244 healthy controls, 119 first-episode treatment-na&#xef;ve SCZ individuals, 159 BD individuals, and 182 treatment-na&#xef;ve MDD individuals. Seed-based partial least squares correlation analysis was applied to construct structural covariance networks (SCNs) across 6 predefined functional networks: the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), somatomotor network (SMN), ventral attention network (VAN), and visual network. Network seeds were selected based on functional network definitions. Spatial distributions of SCNs were calculated, and individual network integrity indices were derived as measures of SCN strength. Group comparisons of network integrity were performed using multiple t-tests to identify network-specific alterations across the diagnostic groups.<br><br>STUDY RESULTS: Structural covariance patterns exhibited spatial distributions akin to those of functional networks. Network integrity showed common reductions across all 3 disorders in DMN, DAN, and FPCN, while BD showed specific reductions in the SMN, and both BD and MDD showed reductions in the VAN. Furthermore, there was a significant correlation between individualized network integrity and clinical and cognitive manifestations.<br><br>CONCLUSIONS: Our results highlight the potential of the integrity of SCNs as transdiagnostic biomarkers.}, }
@article {pmid41864054, year = {2026}, author = {Kaur, A and Garg, R and Prasad, S}, title = {A comprehensive review of EMG/EEG based wheelchair control systems for individuals with disabilities: HMI and BCI perspectives.}, journal = {Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology}, volume = {88}, number = {}, pages = {103134}, doi = {10.1016/j.jelekin.2026.103134}, pmid = {41864054}, issn = {1873-5711}, abstract = {Human-machine interface (HMI) and brain-computer interface (BCI) are proving to help make technologies better and helpful for people with disabilities. These systems give individuals the ability to easily control wheelchair, and enhance their quality of life. This review focuses on the use of EMG (muscle activity) and EEG (brain activity) signals, considered primarily as individual modalities, for wheelchair control. EMG signals facilitate muscle control, which is particularly useful for individuals with motor impairments or impaired limb function. On the other hand, EEG-based BCIs enable independent navigation for individuals with severe motor disorders by systematically analyzing brainwave patterns. This review covers the literature from 2014 to 2024 and focuses on signal acquisition, filtering, feature extraction, and classification techniques. It also highlights the challenges of signal processing, inter-subject interaction, and real-time optimization. Based on the analyzed studies, research gaps are identified, and future directions are outlined, including the potential integration of multimodal EEG-EMG approaches as an emerging research trend for developing more user-centric and adaptive wheelchair systems.}, }
@article {pmid41864786, year = {2026}, author = {Hu, X and He, J and Li, N and Mo, J and Yao, S and Lu, Y and Huang, M and Jiang, P and Pang, M and He, L and Gong, J and Liu, Z and Xie, X and Xu, J and Hu, X and Krassioukov, AV and Zhang, L and Liu, B and Rong, L}, title = {Bridging cortical intentions: brain-computer interfaces for spinal cord injury recovery.}, journal = {Science bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.scib.2026.03.016}, pmid = {41864786}, issn = {2095-9281}, }
@article {pmid41865023, year = {2026}, author = {Khalikov, R and Soghoyan, G and Sintsov, M and Lebedev, M}, title = {Wearable optomyography enables continuous neuroprosthetic control.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-32646-y}, pmid = {41865023}, issn = {2045-2322}, support = {21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; }, abstract = {Wearable devices are increasingly used to enable human-machine interfaces, such as typing or cursor control, through wristbands that translate surface electromyographic (sEMG) signals into computer commands. However, traditional sEMG techniques face several limitations, including challenges with sensor fixation, signal cross-talk, instability over time, and susceptibility to electrical and mechanical artifacts. In this study, we propose an alternative approach to capturing and interpreting muscle activity using optomyography (OMG). Our OMG system - a wristband with 50 data channels, facilitates various computer mouse-like controls. Decoding is achieved through an efficient, compact, fully connected neural network trained on data from a center-out task performed with hand gestures. Eight able-bodied participants and one individual with limb loss successfully mastered OMG-based controls in tasks such as acquiring targets across various screen positions and playing Tetris. Performance improvements with training were assessed using metrics such as deviations from a straight trajectory, temporal deviation from an optimal path, and dwell time near the target prior to successful selection. These results highlight the potential of next-generation wearable devices to exceed conventional approaches in performance, accuracy, stability, and versatility.}, }
@article {pmid41860566, year = {2026}, author = {Lee, H and Lee, S and Hwang, KS and Kim, G and Hong, Y and Kim, M and Eun, J and Kim, HN and Chou, N and Shin, H}, title = {Biocompatible Lubricant-Coated Flexible Neural Probes with Enhanced Long-Term Recording Stability.}, journal = {ACS applied bio materials}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsabm.5c02232}, pmid = {41860566}, issn = {2576-6422}, abstract = {Implantable neural probes enable high-resolution, multi-unit recordings and are essential tools for studying neurological disorders and developing brain-machine interface (BMI) technologies. However, conventional metal- or silicon-based probes exhibit significant mechanical mismatch with brain tissue, both of which elicit inflammatory responses and compromise long-term recording stability. Here, we introduce a flexible neural probe fabricated through a commercial flexible printed circuit board (FPCB) process and functionalized with a biocompatible lubricant coating to overcome these challenges. The inherent flexibility of the FPCB minimizes mechanical mismatch with brain tissue, while the coating enhances surface hydrophobicity and reduces insertion friction, thereby minimizing tissue damage during implantation. Its resistance to water ingress contributes to maintaining the probe's electrical insulation stability, supporting stable long-term performance. In chronic mouse hippocampal implants, lubricant-coated probes maintained consistent neural signal quality for several weeks, while immunohistochemical analysis revealed markedly reduced astrocytic and microglial activation (GFAP/Iba1) compared with uncoated controls, indicating effective mitigation of neuroinflammation. In vitro cell viability assays further confirmed the high biocompatibility of the coated devices. Importantly, because this approach leverages scalable and cost-effective FPCB manufacturing, it enables the production of flexible neural interfaces that combine long-term electrical and biological stability with manufacturing practicality. This work establishes a broadly applicable strategy for next-generation neural probes, offering durable, minimally invasive, and scalable solutions for chronic recordings in BMI systems, deep brain stimulation, and neurological disease models.}, }
@article {pmid41861408, year = {2026}, author = {Ukaegbu, UFF and Houshmand, S and Hammond, L and Adams, K and Andersen, J and Rouhani, H}, title = {Navigation Paradigms for Non-invasive BCI-controlled Wheelchairs: A Systematic Review.}, journal = {Progress in biomedical engineering (Bristol, England)}, volume = {}, number = {}, pages = {}, doi = {10.1088/2516-1091/ae5563}, pmid = {41861408}, issn = {2516-1091}, abstract = {Brain-controlled powered wheelchairs represent a promising advancement for individuals with neurological conditions that significantly impair motor function. Despite substantial progress, brain-controlled wheelchairs have not been adapted for real-world settings. This article systematically reviews recent trends in brain-computer interface (BCI) technology for wheelchair navigation and control, highlighting the contributions and limitations of various navigation paradigms. This review was conducted in accordance with the PRISMA guidelines, sourcing studies from four databases (PubMed, Scopus, IEEE Xplore, Google Scholar) published between 2000 and April 2025. This review focused on non-invasive BCI paradigms and real-world navigation experiments. The results were narratively synthesized and classified into two primary categories: BCI-based navigation paradigms and wheelchair-based navigation paradigms, along with intersecting concepts such as single-variant BCI, hybrid BCI, control switches, and proportional control. Of the 149 full-text articles reviewed, 47 were included and categorized by navigation paradigm, comprising 20 BCI-based and 27 wheelchair-based studies, with 6 involving participants with motor disabilities. Quality assessment scores ranged from 40% to 95%, with approximately 40% of the studies demonstrating a low risk of bias. The findings indicate that low-level navigation control was predominant in BCI wheelchair studies, with 31 studies employing minimal or no obstacle avoidance. Most studies (57%) integrated sensors for obstacle avoidance, localization, mapping, and autonomous navigation. Twenty-two studies utilized control switches, and five incorporated proportional control for wheelchair navigation. Additionally, motor imagery and steady-state visually evoked potential (SSVEP) paradigms have emerged as the most common approaches for generating control commands, highlighting their potential for effective navigation. Given the potential societal impact on a large number of individuals, future research should prioritize enhancing the reliability and adaptability of BCI wheelchair systems in real-world environments. .}, }
@article {pmid41861827, year = {2026}, author = {Liu, V and Kong, Z and Fu, J and Zheng, L and Wang, I and Wang, M and Du, Y and Zuo, L and Qiu, B and Zhong, C and Zhu, L and Yuan, Z and Zhang, X and Hongwen Song, }, title = {Moral inconsistency is based on the vmPFC's insufficient representation across tasks and connectedness.}, journal = {Cell reports}, volume = {}, number = {}, pages = {117058}, doi = {10.1016/j.celrep.2026.117058}, pmid = {41861827}, issn = {2211-1247}, abstract = {Moral inconsistency-misaligning one's behavior with the same moral principle of judging others-undermines personal reputations and social relationships. This study explores the neural underpinnings of moral inconsistency in a profit-honesty trade-off setting with functional magnetic resonance imaging and transcranial temporal interference stimulation (tTIS). Experiment 1 demonstrated that participants showed inconsistent sensitivity to profit and honesty between moral behavior and moral judgment tasks. Furthermore, multivariate pattern analyses showed that participants with higher moral inconsistency exhibited reduced judge score representation across tasks and weaker connectedness during the moral behavior task in the ventromedial prefrontal cortex (vmPFC). Experiment 2 showed that tTIS modulation of the vmPFC increased moral inconsistency. These findings indicate the vmPFC's involvement in the neural basis of moral inconsistency. While individuals with higher moral inconsistency may be aware of moral principles when making decisions, they consider moral principles less and do not integrate them into their own behaviors.}, }
@article {pmid41862057, year = {2026}, author = {Canal-Rivero, M and Baca-García, E and Barrigón, ML and Ruiz-Veguilla, M and Crespo-Facorro, B}, title = {Shifting vulnerabilities in suicide mortality from the COVID-19 crisis to the socioeconomic aftermath in Spain (2016-2024): A Bayesian triple-interaction analysis.}, journal = {Journal of affective disorders}, volume = {405}, number = {}, pages = {121650}, doi = {10.1016/j.jad.2026.121650}, pmid = {41862057}, issn = {1573-2517}, abstract = {BACKGROUND: The transition from the acute Coronavirus Disease 2019 (COVID-19) crisis to the subsequent socioeconomic aftermath introduced complex stressors. We aimed to determine the differential impacts of pandemic onset (March 2020) and the socioeconomic aftermath (July 2021) on suicide mortality in Spain, examining heterogeneous effects by sex and age.<br><br>METHODS: We analysed 108&#xa0;months (2016-2024) of national registry data. Using a Bayesian Interrupted Time-Series (ITS) design with a Triple Interaction framework (Sex&#xd7;Age&#xd7;Event), we isolated immediate (level) and long-term (trend) risk trajectories, adjusting for Gross Domestic Product (GDP), Public Health Expenditure (PHE), and (COVID-19) mortality. Leave-One-Out Cross-Validation (LOO-CV) was used to validate the complex specification against simpler models.<br><br>RESULTS: Impacts differed fundamentally across demographics. Pandemic onset was associated with an immediate increase in men aged 80+ (Rate Ratio [RR]&#xa0;=&#xa0;1.46; 95% BCI 1.13-1.90), while other male groups remained stable. Conversely, the socioeconomic aftermath triggered a delayed acute shock in women, specifically aged 15-29 (RR&#xa0;=&#xa0;1.66; 95% BCI 1.05-2.68). Bayesian comparison confirmed simpler models failing to account for triple interactions obscured these effects.<br><br>LIMITATIONS: The ecological design precludes causal inference at the individual level.<br><br>CONCLUSIONS: Suicide risk pathways were highly heterogeneous: male vulnerability was concentrated in the elderly during the initial viral threat, whereas female vulnerability emerged later as a delayed response to the socioeconomic aftermath. Prevention requires adapting strategies to the distinct nature of immediate isolation in older men versus delayed socioeconomic strain in women.}, }
@article {pmid41853193, year = {2026}, author = {Khanam, H and Hoque, A and Jafar Mazumder, MA and Arafat, MT}, title = {Catechol functionalized polyguluronate enriched sodium alginate wetspun fibers with immobilized platelet lysate for diabetic wound healing.}, journal = {RSC advances}, volume = {16}, number = {16}, pages = {14328-14349}, pmid = {41853193}, issn = {2046-2069}, abstract = {The development of advanced wound dressings with multifunctional properties is crucial for accelerating healing in diabetic wounds. Platelet lysate contains many biologically active substances, which have tremendous clinical benefits in treating diabetic wounds. However, its clinical use and therapeutic efficacy are severely limited by its poor mechanical qualities and the sudden release of active chemicals. To address these challenges and minimize the risk of wound infection, sodium alginate-polyethylene glycol wetspun fibers were developed and immobilized with platelet lysate. Furthermore, surface modification with dopamine introduced catechol groups, enhancing interfacial adhesion and bioactivity to promote effective healing in diabetic wounds. Morphological and physicochemical analyses confirmed improved thermal stability and crystalline behavior in the dopamine modified fibers (SA-PEG-D-PL). The modified fibers achieved sustained PL release over 18 days with 90% cumulative release, a 30% improvement over free PL and a 20% improvement over unmodified fibers. The whole blood clotting index demonstrated a notably lower BCI of 15% for dopamine functionalized fibers, indicating enhanced coagulation potential due to increased surface striation and water absorption. Moreover, in a diabetic mice wound model, the functionalized fibers drove >85% wound closure by day 7 and complete reepithelialization by day 14, while reducing scar formation to a scar index of 7.3, significantly lower than controls (22-42.6). These outcomes suggest that the synergistic effects of dopamine functionalization and PL immobilization on alginate based fibrous matrices not only improve mechanical and biological responses but also accelerate wound closure and minimize scarring. Overall, the developed dopamine modified fibers demonstrate high potential as an advanced wound care material for diabetic patients.}, }
@article {pmid41855051, year = {2026}, author = {He, X and Daly, I and Gu, W and Chen, Y and Wu, X and Chen, W and Wang, X and Cichocki, A and Jin, J}, title = {TBMSCCN: Two-Branch Multi-Scale Convolutional Correlation Network for Steady-State Visual Evoked Potential Classification.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3676014}, pmid = {41855051}, issn = {1558-2531}, abstract = {In recent years, artificial neural networks have been effectively used to improve the target recognition performance of steady-state visual evoked potential (SSVEP) based Brain-Computer interfaces (BCIs). However, these models require the collection of a large number of calibration trials from users, which typically results in a poor user experience. When fewer calibration trials are acquired this leads to insufficient training of model parameters and weak recognition performance. To tackle these issues, this study proposes a two-branch multi-scale convolutional correlation network (TBMSCCN) in which a correlation network framework is introduced to reduce the model training parameters and prior knowledge of the SSVEP is used to enhance the model representation ability and convergence. First, a multi-scale temporal convolution module is designed to learn local temporal dependencies in a parallel two-branch feature extraction module. Next, a contrastive loss function is constructed in the latent feature space, which can guide the model to learn the intra-class consistent features while speeding up model convergence. Finally, a group convolution module is used as a decision layer to reduce the network parameters, while learning distinguishability features between targets and non-targets. Our offline tests on two public datasets show that proposed TBMSCCN method outperforms TRCA, eTRCA, DNN, Conv-CA and Bi-SiamCA in individual calibration scenarios, which can achieve an average information transform rates (ITRs) of 378.03 ± 139.18 bit/min and 198.92 ± 111.27 bit/min on the "Benchmark" dataset and the "Beta" dataset respectively. Additionally, proposed TBMSCCN method outperform FBCCA, ttCCA, EEGNet, and TST-CFSR in calibration-free scenarios. Furthermore, an online Chinese spelling experiment confirmed the real-world effectiveness of the proposed method. The proposed model has the characteristics of low parameter and strong robustness, which can facilitate the practical engineering application of SSVEP-Based-BCI system. The code is available at https://github.com/xinjieHe123/TBMSCCN.}, }
@article {pmid41855458, year = {2026}, author = {Song, S and Li, X and Pan, P}, title = {Application and prospects of brain-computer interface technology for motor function reconstruction after brachial plexus injury.}, journal = {Annals of medicine}, volume = {58}, number = {1}, pages = {2646355}, doi = {10.1080/07853890.2026.2646355}, pmid = {41855458}, issn = {1365-2060}, mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Brachial Plexus/injuries/physiopathology ; Recovery of Function/physiology ; *Brachial Plexus Neuropathies/rehabilitation/physiopathology ; }, abstract = {BACKGROUND: Brachial plexus injury (BPI) is a severe peripheral nerve disorder leading to significant upper limb motor dysfunction. While traditional surgeries like nerve grafting and tendon transfer exist, functional outcomes are often suboptimal due to biomechanical limitations and slow neural recovery. Brain-computer interface (BCI) technology has emerged as a promising innovative pathway for motor function reconstruction.<br><br>OBJECTIVE: This review systematically evaluates the current applications, physiological mechanisms, and technical challenges of BCI technology specifically within the clinical framework of BPI rehabilitation.<br><br>METHODS: We analysed recent research breakthroughs focusing on neural repair mechanisms, clinical translational applications of BCI-controlled neuroprosthetics, and the integration of novel biomaterials.<br><br>RESULTS: BCI technology facilitates cortical remapping after standard BPI procedures like nerve transfers by providing synchronised closed-loop feedback. Unlike applications for amputees that drive external prosthetics, BCI in BPI focuses on in-situ muscle activation via a "neural bypass" to prevent disuse atrophy and restore a sense of agency. Furthermore, BCI-mediated neuromodulation shows unique potential in alleviating chronic deafferentation pain by down-regulating pathological cortical hyperexcitability. Emerging technologies like conductive hydrogels and hybrid BCI systems are addressing current bottlenecks in signal stability and control accuracy.<br><br>CONCLUSION: BCI technology represents a transformative approach for BPI rehabilitation, moving from mechanical substitution to biological reactivation. Overcoming technical barriers in signal reliability and establishing personalised rehabilitation systems are essential for their broad clinical translation.}, }
@article {pmid41856938, year = {2026}, author = {Wang, G and Song, X and Jiang, L and Zhang, Y and Yao, D and Lu, J and Xu, P and Li, F}, title = {A Lightweight Dual-Attention Neural Network for Robust and Efficient EEG Motor Imagery Decoding.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2650026}, doi = {10.1142/S0129065726500267}, pmid = {41856938}, issn = {1793-6462}, abstract = {Motor imagery-based brain-computer interface (MI-BCI) faces a critical challenge in achieving effective spatial-temporal feature modeling while maintaining a compact model parameterization. Herein, a lightweight model was proposed, termed as Dual-Attention-EEGNet (DA-EEGNet), which extends the EEGNet backbone by integrating a channel attention module and a depth attention module to selectively emphasize informative electrodes and temporally discriminative features. Two widely used MI benchmark datasets and three evaluation strategies, i.e. subject-dependent scenario, subject-independent scenario, and dataset-independent classification scenario, were utilized to verify the model's performance. Despite its compact design, DA-EEGNet contains merely 3.97[Formula: see text]k trainable parameters and achieves average classification accuracies of [Formula: see text] and [Formula: see text], outperforming or matching existing deep learning approaches that rely on substantially larger parameter counts. Ablation studies further confirm the complementary contributions of the channel and depth attention modules. In addition, visualization analyses, including temporal attention heatmaps and motor-area topographies, demonstrate that DA-EEGNet captures neurophysiologically meaningful spatial-temporal patterns consistent with MI-related brain activity. These results indicate that DA-EEGNet provides a favorable parameter-accuracy trade-off and serves as an efficient and interpretable baseline for MI-BCI applications.}, }
@article {pmid41857029, year = {2026}, author = {Liu, W and Chen, Y and Wang, X and Fang, T and Wang, R and Cheng, Y and Zhao, X and Fan, Q and Gao, W and Ming, D}, title = {Dual-axis myelination covariance drives the functional connectivity emergence during infancy.}, journal = {Nature communications}, volume = {17}, number = {1}, pages = {}, pmid = {41857029}, issn = {2041-1723}, support = {82202249//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Myelin Sheath/physiology/metabolism ; White Matter/physiology/growth &amp; development/diagnostic imaging ; Gray Matter/physiology/growth &amp; development/diagnostic imaging ; Connectome/methods ; Infant, Newborn ; Male ; Female ; Infant ; Magnetic Resonance Imaging ; *Brain/physiology/growth &amp; development ; *Nerve Net/physiology ; }, abstract = {The mechanisms linking structural maturation to the emergence of functional networks in the perinatal brain remain unresolved. While prevailing models attribute functional connectivity to white matter myelination, neonates paradoxically exhibit adult-like resting-state networks despite profoundly immature white matter tracts. Here, we proposed gray matter myelination covariance as a critical basis of early functional connectivity emergence. We introduced a dual-axis myelination covariance framework and derived a myelination-function coupling (MFC) index specific to the newborn brain. Results revealed that the MFC exhibited distinct spatial patterns dominated by primary sensory and motor cortices, increased with age, and showed a distance-dependent strength. Crucially, neonatal MFC patterns showed a strong spatial correlation with gene expression profiles implicated in neurovascular coupling and specifically predicted later behaviors. These findings suggest that during infancy, the integration of brain function is not initially dominated by only the white matter connections but is also shaped by the synchrony of intracortical microstructure that reflects shared developmental trajectories, which offers a framework for understanding the formation of the developmental connectome.}, }
@article {pmid41857304, year = {2026}, author = {Wang, Z and Xu, M and Yao, J and Yu, Y and Hu, B and Wang, Y and Wang, Y and Zhang, X}, title = {Review of electroencephalography and electromyography research in robotics: opportunities and challenges.}, journal = {Visual computing for industry, biomedicine, and art}, volume = {9}, number = {1}, pages = {}, pmid = {41857304}, issn = {2524-4442}, support = {62072388//National Natural Science Foundation of China/ ; 2024HZ01040037//Fujian Provincial Science and Technology Major Project/ ; 20244BAB28039//Jiangxi Provincial Natural Science Foundation Key Project/ ; 3502Z20231043//Xiamen Public Technology Service Platform/ ; }, abstract = {In the evolving nexus of neuroscience and robotics, the symbiotic fusion of electroencephalography (EEG) and electromyography (EMG) is emerging as a paradigm-shifting avenue for enhancing human-machine interfaces. While EEG, which captures the subtle electrical nuances of the brain, offers a potent channel for nuanced brain-machine communication, EMG serves as a bridge, converting neuromuscular intentions into actionable directives for robotic apparatuses. This review highlights the current methodologies in which EEG and EMG not only function in silos but also converge harmoniously to dictate robotic control. By delving deeper into this, the intricate synergy between cognitive processes, muscular responses, and machine actions can be unraveled. Subsequently, the discourse also navigates through the myriad challenges encountered in realizing real-time, seamless integration of these bio-signals with robotics and the innovative solutions poised to address them. The aim is to provide a comprehensive understanding of the interplay between neuroscience and robotics. This insight will help drive breakthroughs in adaptive human-machine collaboration.}, }
@article {pmid41857397, year = {2026}, author = {Liu, J and Peng, F and Li, P and Yao, C and Jin, S and Wu, G and Zhang, T and Liang, Q and Wang, X and Du, X}, title = {Mechanistic insights into cannabidiol-mediated TrkB activation via FRS2 interaction in attenuating Alzheimer's disease pathology and cognitive impairment.}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {41857397}, issn = {1476-5578}, support = {82550005//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Alzheimer's disease (AD) is characterized by progressive synaptic failure, neuroinflammation, amyloid and tau pathology, yet effective disease-modifying therapies remain limited. Cannabidiol (CBD) has shown neuroprotective potential in AD, but its direct molecular targets and signaling mechanisms remain unclear. Here, we demonstrate that CBD ameliorates cognitive and emotional deficits in 3×Tg-AD mice by restoring synaptic integrity and plasticity. At the mechanistic level, CBD activated TrkB signaling independently of BDNF, leading to suppression of tau hyperphosphorylation via the PI3K/AKT/GSK3β pathway and attenuation of neuroinflammation and amyloid pathology through inhibition of the JAK2/STAT3/SOCS1 axis. Using isothermal shift assays combined with biophysical binding analyses, we identified FRS2, a core adaptor protein of TrkB, as a direct molecular target of CBD. Molecular dynamics simulations further revealed that CBD stabilizes the FRS2-TrkB interface, thereby facilitating TrkB activation. Importantly, genetic knockdown of FRS2 abolished CBD-induced TrkB signaling and its downstream neuroprotective effects in both cellular and in vivo AD models. Together, these findings identify FRS2 as a critical signaling node mediating BDNF-independent TrkB activation by CBD and establish a mechanistic framework linking CBD to disease-modifying pathways in AD.}, }
@article {pmid41858309, year = {2026}, author = {Xiao, Y and Yang, L and Qu, Y and Zhang, S and Ke, S and Ke, C and Li, Y and Hao, M and Wang, C and Xue, P and Zhang, Z and Huang, H and Liu, Y and Cheng, Z and Ye, C and Chu, PK and Yu, XF and Wang, J}, title = {Natural Superlattice 2D Materials-based Volatile Memristor Promotes Artificial Nociceptor.}, journal = {Small (Weinheim an der Bergstrasse, Germany)}, volume = {}, number = {}, pages = {e14931}, doi = {10.1002/smll.202514931}, pmid = {41858309}, issn = {1613-6829}, support = {2024YFB3614200//National Key R&amp;D Program of China/ ; 62365010//National Natural Science Foundation of China/ ; 62274058//National Natural Science Foundation of China/ ; 2023A1515110590//Guangdong Basic and Applied Basic Research Foundation/ ; 2024A1515030176//Guangdong Basic and Applied Basic Research Foundation/ ; 2025B1515020088//Guangdong Basic and Applied Basic Research Foundation/ ; 20232BCJ23011//Jiangxi Provincial Cultivation Program for Academic and Technical Leaders of Major Disciplines/ ; JCYJ20220818100806014//Shenzhen Science and Technology Program/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; DON-RMG 9229021//City University of Hong Kong Donation Research Grants/ ; 9220061//City University of Hong Kong Donation Research Grants/ ; }, abstract = {Memristors show promise in neuromorphic computing because of their resistive switching properties and memory functions. The integration of high-performance memristor devices with sensors offers an effective pathway toward energy-efficient edge-computing systems. Herein, using the natural superlattice 2D material of BiTiS3 composed of alternating BiS and TiS2 sublayers, a volatile memristor with a low operating voltage is designed and demonstrated. The lattice distortion and sulfur vacancies in BiTiS3 enhance ion migration and filament formation, as verified by conductive atomic force microscopy and X-ray photoelectron spectroscopy. This defect-induced enhancement of ion transport promotes the rapid formation and dissolution of conductive filaments, thereby implementing the memristors' volatile switching behavior. The nociceptive functions, such as pain hypersensitivity and allodynia, are mimicked. This biomimetic nociceptor system effectively emulates the biological pain response pathways, converts physical stimuli into electrical signals, and generates the appropriate neural-like outputs. Our results highlight the potential of memristors in bioinspired electronics and reveal a new strategy for intelligent bionic devices and artificial sensing systems.}, }
@article {pmid41859480, year = {2026}, author = {Shao, Z and Gu, Z and Che, L and Yu, Z and Li, Y}, title = {Dynamic graph based attention spectral network for motor imagery-brain computer interface.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1755549}, pmid = {41859480}, issn = {1662-5161}, abstract = {Motor imagery-based brain computer interface (MI-BCI) have been increasingly adopted in neurorehabilitation and related fields. The performance of MI-electroencephalogram (MI-EEG) decoding algorithms is central to the advancement of MI-BCI. However, current studies often lack rigorous investigation into the brain's complex network organization. Moreover, most existing methods do not incorporate the cross-frequency coupling (CFC) phenomena that occur during MI into their algorithmic designs, nor do they adequately account for how temporal dynamics across different MI stages influence decoding outcomes. To address these limitations, we propose the Dynamic Spectral-Spatial Interaction Convolution Neural Network (DSSICNN), a parameter-efficient MI-EEG decoding framework that jointly extracts temporal-spectral-spatial features. DSSICNN adopts a dual-branch parallel architecture to concurrently learn spatial representations in both Euclidean and non-Euclidean domains. It further integrates a CFC-inspired attention module to model cross-spectral interactions, followed by an additional attention mechanism that quantifies the contributions of distinct MI stages to decoding performance. DSSICNN achieves decoding performance on two public datasets that surpasses the current state-of-the-art (SOTA) under both session-dependent and session-independent settings. Beyond its empirical advantages, DSSICNN offers design insights for developing Graph Neural Network (GNN)-based MI-EEG decoding algorithms and provides a network neuroscience-inspired perspective for understanding the neurophysiological mechanisms underlying MI.}, }
@article {pmid41846942, year = {2026}, author = {Daie, K and Aitken, K and Rózsa, M and Bull, MS and Humphreys, PC and Wang, ZC and Kinsey, L and Kulkarni, M and Stachenfeld, KL and Eckstein, MK and Kurth-Nelson, Z and Clopath, C and Lillicrap, TP and Botvinick, M and Golub, M and Mihalas, S and Svoboda, K}, title = {Functional reorganization of motor cortex connectivity during learning.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.03.03.709199}, pmid = {41846942}, issn = {2692-8205}, abstract = {Learning new tasks requires the brain to reshape the flow of neural activity, but how these changes arise from dynamic neural connectivity remains unclear. Here, we used two-photon photostimulation and calcium imaging to map learning-related changes in connectivity in layer 2/3 of mouse motor cortex, induced by learning of an optical brain-computer interface (BCI) task. Mice rapidly (within minutes) learned to change activity in a conditioned neuron to earn rewards. Activity changes were sparse; the conditioned neuron increased activity more than surrounding neurons. Mapping connectivity before and after learning revealed changes in motor cortex connectivity, enriched in neurons that were active before trial initiation, analogous to motor cortex populations that are active preceding movement. Motor cortex plasticity reroutes preparatory activity to neurons that are active later and control the conditioned neuron. Our findings show how rapid learning can be achieved through structured changes in motor cortex connectivity.}, }
@article {pmid41849802, year = {2026}, author = {Shaikh, UQ and Kalra, A and Lowe, A and Niazi, IK}, title = {Multi-head noise regression for single-channel EEG: estimating ocular and muscle contamination to guide artifact removal.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae541d}, pmid = {41849802}, issn = {1741-2552}, abstract = {EEG is often contaminated by ocular (EOG) and muscle (EMG) artifacts, yet many pipelines apply uniform denoising, risking distortion of clean neural activity. We propose a two-head, single-channel regressor that estimates EOG and EMG noise-to-signal ratio (NSR, dB) from short segments and test whether it can guide selective artifact reduction, including downstream BCI decoding. Approach. Using EEGdenoiseNet clean EEG and artifact exemplars, we synthesised 2-s single-channel mixtures with known EOG/EMG NSR spanning -10 to +10 dB and trained several model families to jointly regress both NSRs. Generalisation was evaluated on an independent eyeblink dataset via agreement with regression-based ocular-reference topographies, and in two applications: (i) gating stationary wavelet blink removal on a P3 ERP dataset and (ii) gating the same denoiser on a 55-subject RSVP P300 speller dataset (FP1/FP2). Main results. A dilated temporal convolutional network (TCN) performed best (EOG: MAE ≈ 1.8 dB, R[2] ≈ 0.82; EMG: MAE ≈ 1.0 dB, R[2] ≈ 0.94) with low bias across NSR. The EOG head recovered blink topographies (median spatial correlation ≈ 0.91). On the P3 dataset, indiscriminate wavelet denoising reduced significant ERP channels, whereas TCN-guided gating preserved 22-23 of 24 while processing ~9-20% of segments. On the speller dataset, denoising all epochs reduced decoding, while selective denoising improved AUC (θ = 9 dB: ΔAUC = 0.327, p = 0.0040) while denoising 12.45 ± 9.29% of test segments. Significance. Multi-head noise regression provides interpretable, continuous ocular and muscle contamination estimates that can act as control signals for conservative, noise-aware artifact handling under constrained-lead conditions. .}, }
@article {pmid41850148, year = {2026}, author = {Oullier, O and Roser, F and Barbaste, P and Vasques, X}, title = {Improving consciousness assessment through neuroadaptive artificial intelligence and quantum-enhanced brain-computer interfaces.}, journal = {Clinical neurology and neurosurgery}, volume = {266}, number = {}, pages = {109396}, doi = {10.1016/j.clineuro.2026.109396}, pmid = {41850148}, issn = {1872-6968}, abstract = {Accurate assessment of consciousness in patients with disorders of consciousness (DoC) remains a major clinical challenge, particularly when motor impairment masks evidence of preserved awareness. Recent advances in neuroadaptive artificial intelligence (NA-AI) may help transform brain-computer interfaces (BCIs) from experimental systems into more clinically scalable tools tailored to each patient, continuously adjusting their models in real time to changes in an individual's (neuro)physiological signals. Generative and self-adapting AI models can account for inter-individual variability and temporal instability in neural signals, enabling faster calibration, improved robustness and personalized decoding of conscious intent. AI world-model approaches further enable realistic and dynamic representations of a patient's neurophysiology, allowing BCIs to interpret neural activity in the context of evolving brain states rather than static classifications of consciousness levels. Emerging work in quantum-enhanced machine and deep learning suggests that some current computational bottlenecks in BCIs, including high-dimensional optimization and complex pattern discovery, may be further alleviated. We argue that the convergence of neuroadaptive AI and quantum-enabled computation could improve the sensitivity, speed and reliability of consciousness assessments. Given the exploratory stage of quantum-AI research, rigorous clinical validation and governance frameworks will be required to ensure safe deployment and improved patient outcomes. If validated, quantum-AI BCIs could reduce diagnostic uncertainty, improve prognostication and support ethically grounded decision-making for patients unable to communicate.}, }
@article {pmid41850543, year = {2026}, author = {Li, Y and Li, S and Xie, J and Yao, D and Li, F and Xu, P and Wu, J and Jiang, L}, title = {Unlocking Interbrain Neural Signatures Differences During Triadic Cooperation and Competition: Evidence from EEG Hyperscanning.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121865}, doi = {10.1016/j.neuroimage.2026.121865}, pmid = {41850543}, issn = {1095-9572}, abstract = {Cooperation and competition are fundamental to human social interaction. While recent hyperscanning studies have linked stronger interbrain synchrony (IBS) to successful cooperation, most have focused on dyadic interactions, leaving the underlying neural mechanisms of group-level social behavior largely unknown. Here, we employed EEG hyperscanning to investigate interbrain neural dynamics of triadic cooperative and competitive interactions. Distinct interbrain network patterns emerged in the delta and beta bands, with cooperation showing enhanced frontal-parietal IBS and more efficient network properties. Non-parametric cluster-based permutation tests further identified significant regional differences in a left-lateralized frontal-temporal-parietal cluster in both bands. Crucially, increased delta-band frontal-parietal IBS was closely associated with better group-level cooperative performance. Moreover, classification and prediction models based on delta-band interbrain metrics successfully distinguished interaction types and predicted cooperative outcomes. These findings uncover interbrain neurocognitive traits that reflect specific social behavioral contexts, highlighting the pivotal role of frontal-parietal synchrony and delta-band modulations in supporting group cooperation. Together, our results advance the understanding of the neural basis of triadic social interaction and underscore the potential of interbrain network signatures as biomarkers for decoding and predicting complex social behaviors.}, }
@article {pmid41851249, year = {2026}, author = {Leinders, S and Aarnoutse, EJ and Branco, MP and Freudenburg, ZV and Geukes, SH and Schippers, A and Verberne, MSW and van den Boom, MA and van der Vijgh, BH and Crone, NE and Denison, T and Ramsey, NF and Vansteensel, MJ}, title = {Implanted brain-computer interface functionality during nighttime in late-stage amyotrophic lateral sclerosis.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-44228-7}, pmid = {41851249}, issn = {2045-2322}, support = {UH3NS114439/NS/NINDS NIH HHS/United States ; ADV 320708/ERC_/European Research Council/International ; UGT7685//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; U01DC016686/DC/NIDCD NIH HHS/United States ; }, abstract = {Brain-computer interfaces (BCIs) hold promise as assistive communication technology for people with severe paralysis. Although such BCIs should be available 24/7, feasibility of nocturnal BCI use has not been investigated. Here, we addressed this question using data from an electrocorticography-BCI user with amyotrophic lateral sclerosis. We investigated nocturnal dynamics of neural signal features used for BCI control. Additionally, we assessed nocturnal performance of a decoder trained on daytime data, by quantifying the number of unintentional BCI activations at night. Finally, we developed a nightmode functionality and assessed its performance. Mean and variance of low and high frequency band power were significantly higher at night than during the day. When applied to night data, daytime decoders caused unintentional BCI activations in 100% of nights (245 unintended click-commands and 13 unintended caregiver-calls per hour). The specifically developed nightmode functionality, however, functioned error-free in 79% of nights over a period of ± 1.5 years, allowing the user to reliably call the caregiver. Reliable nighttime use of a BCI requires strategies to adjust to circadian and sleep-related signal changes. This demonstration of a reliable nightmode and its long-term use by an individual with amyotrophic lateral sclerosis underscores the importance of 24/7 BCI reliability.}, }
@article {pmid41851364, year = {2026}, author = {Yang, P and Duan, Y and Wang, L and Gao, Y and Zhang, Y and Liang, Z and Zhou, X and Wang, D and Yang, J}, title = {An early detection framework for young Chinese learners at risk of reading difficulty using fNIRS and deep learning.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-44379-7}, pmid = {41851364}, issn = {2045-2322}, support = {JWC20240116//teaching reform and research projects of Sichuan Normal University/ ; 23YJC880062//Research project of Ministry of Education of China/ ; BG2024025//Major Science and Technology Special Program of Jiangsu Province/ ; }, abstract = {Reading difficulty (RD), a neurodevelopmental disorder affecting language acquisition in children, necessitates early screening for effective educational interventions. This study proposes the RD-risk Classifier (RDr-C), a novel framework integrating functional near-infrared spectroscopy (fNIRS) with deep learning, specifically combining a dual-layer graph convolutional network (GCN), a bidirectional long short-term memory network (BiLSTM), and multi-head self-attention mechanisms (MSA) for 7-8-year-old children's literacy assessment. The model was validated using fNIRS signals from 30 participants (16 experimental group, 14 control group) during the visual sign recognition test and phonetic discrimination test, with performance evaluated through 5 runs × 5-fold cross-validation experiments. Results show that RDr-C achieved a mean classification accuracy of 99.60% and 99.66% in visual and auditory tests, respectively, significantly outperforming traditional convolutional neural networks (CNN), long short-term memory networks (LSTM), and existing fNIRS classification models (e.g., fNIRS-T, fNIRSNet). Furthermore, leave-one-subject-out cross-validation demonstrates that RDr-C achieves global accuracies of 89.33% and 87.93% on visual and auditory tasks, respectively, with corresponding Kappa coefficients of 0.78 and 0.76, confirming its robustness across individuals. Feature shuffling and wavelet transformation visualizations further confirm robust feature separation, highlighting the model's ability to capture distinct hemodynamic patterns associated with RD. By integrating the spatial feature extraction of GCN, the temporal modeling of BiLSTM, and the global dependency capture of MSA, this work establishes a non-invasive neuroimaging paradigm for educational neuroscience. The high-precision classification lays a technical foundation for early screening tools, with future applications extending to multimodal brain-computer interfaces and longitudinal intervention monitoring.}, }
@article {pmid41851425, year = {2026}, author = {Springer, J and Steinbrink, GM and Tetmeyer, L and Mellen, K and Marcussen, B and Bond, DS and Wu, Y and Carr, LJ}, title = {Feasibility and preliminary efficacy of a 12-week primary care-based behavioral counseling intervention among adults with cardiovascular disease risk factors.}, journal = {Journal of behavioral medicine}, volume = {}, number = {}, pages = {}, pmid = {41851425}, issn = {1573-3521}, support = {Google//Google/ ; }, abstract = {Physical activity (PA) and dietary counseling are recommended for adults with cardiovascular disease (CVD) risk factors. However, these programs are seldom implemented in primary care. This study evaluated the feasibility and preliminary efficacy of a 12-week primary care-based behavioral counseling intervention (BCI) for adults with CVD risk factors. Participants were primarily recruited through a novel clinical screening and referral workflow implemented in six local Family Medicine clinics to participate in a single-arm, pre-post study. Participants received a 12-week, theory-based (Multi-Process Action Control), remotely-delivered BCI that included health education, health coaching, and a wearable activity and sleep monitor. Changes in psychosocial mechanisms of action (e.g., habits, identity), behavioral outcomes (PA, diet, sleep), and health outcomes (cardiometabolic and self-reported) were assessed with paired t-tests, and Cohen's d effect sizes were calculated. The relationships between baseline behaviors and observed changes in behaviors from pre-post intervention were tested with simple linear regression. Ninety-seven participants (mean age = 50.6 years, 64% women) completed the BCI. Moderate-large improvements were observed for behavioral regulation skills, health habits, and health identity psychosocial mechanisms of action (d = 0.75-1.03). Muscle-strengthening exercises, daily kilocalories, whole fruit and total protein intake, and several sleep parameters improved to a small-moderate degree (d = 0.23-0.64). Small-moderate improvements in diastolic blood pressure, body weight, total fat mass, depressive symptoms, fatigue, general health, and quality of life were also observed (d = 0.25-0.53). While no significant overall changes in device-based PA were observed, participants not meeting aerobic PA guidelines at baseline showed small-moderate improvements in daily steps and moderate-vigorous PA (d = 0.25-0.53). Participants with lower baseline steps and dietary quality showed greater improvements in these behaviors (r = - 0.54 and - 0.49, respectively), though regression to the mean may also explain these findings. Retention (85%) and adherence (e.g., 98% coaching attendance) were high. Results support the feasibility and preliminary efficacy of a 12-week, remotely-delivered BCI-mediated through primary care-to change targeted psychosocial mechanisms of action, and specific health behaviors and outcomes. Importantly, participants with less favorable behaviors at baseline benefited most. A randomized controlled trial is warranted to confirm these findings.}, }
@article {pmid41851546, year = {2026}, author = {Graham, F}, title = {Daily briefing: China approves world-first brain-computer interface device.}, journal = {Nature}, volume = {}, number = {}, pages = {}, doi = {10.1038/d41586-026-00888-z}, pmid = {41851546}, issn = {1476-4687}, }
@article {pmid41840016, year = {2026}, author = {Alhudhaif, A}, title = {Distance-based temporal similarity metrics for adaptive channel selection in multi-modal EEG-fNIRS BCI frameworks.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-44052-z}, pmid = {41840016}, issn = {2045-2322}, support = {PSAU/2024/01/31819//Adi Alhudhaif/ ; }, }
@article {pmid41840138, year = {2026}, author = {Jude, JJ and Levi-Aharoni, H and Acosta, AJ and Allcroft, SB and Nicolas, C and Lacayo, BE and Card, NS and Wairagkar, M and Levin, AD and Brandman, DM and Stavisky, SD and Willett, FR and Williams, ZM and Simeral, JD and Hochberg, LR and Rubin, DB}, title = {Restoring rapid natural bimanual typing with a neuroprosthesis after paralysis.}, journal = {Nature neuroscience}, volume = {}, number = {}, pages = {}, pmid = {41840138}, issn = {1546-1726}, support = {23SCEFIA1156586//American Heart Association (American Heart Association, Inc.)/ ; 23SCEFIA1156586//American Heart Association (American Heart Association, Inc.)/ ; A2295R, A4820R//Office of Research and Development (VHA Office of Research and Development)/ ; A2295R, A4820R, N2864C, A3803R//Office of Research and Development (VHA Office of Research and Development)/ ; A2295R, A4820R//Office of Research and Development (VHA Office of Research and Development)/ ; A4820R//Office of Research and Development (VHA Office of Research and Development)/ ; A2295R, A4820R, A3803R//Office of Research and Development (VHA Office of Research and Development)/ ; A2295R, A4820R, N2864C//Office of Research and Development (VHA Office of Research and Development)/ ; U01DC017844, R01DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844, R01DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; K23DC021297//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; Postdoctoral Fellowship//A.P. Giannini Foundation/ ; HT94252310153//United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs (CDMRP)/ ; Pilot Award from the Simons Collaboration for the Global Brain (872146SPI)//Simons Foundation/ ; }, abstract = {Here, recognizing keyboard typing as a familiar, high information rate communication paradigm, we developed an intracortical brain-computer interface (iBCI) typing neuroprosthesis providing bimanual QWERTY keyboard functionality for people with paralysis. Typing with this iBCI involves only attempted finger movements, which are decoded accurately with as few as 30 calibration sentences. Sentence decoding is improved using a 5-gram language model. This typing neuroprosthesis performed well for two iBCI clinical trial participants with tetraplegia-one with amyotrophic lateral sclerosis and one with spinal cord injury. Typing speed is user-regulated, reaching 110 characters per minute, resulting in 22 words per minute with a word error rate of 1.6%. This resembles able-bodied typing accuracy and provides higher throughput than current state-of-the-art hand motor iBCI decoding. In summary, a typing neuroprosthesis decoding finger movements, provides an intuitive, familiar and easy-to-learn paradigm for individuals with impaired communication due to paralysis.}, }
@article {pmid41840417, year = {2026}, author = {Cheng, XP and Wu, YQ and Luo, KL and Wu, D and Lv, L and Xie, LL and Zhan, LQ and Zhou, YZ and Ni, J and Chen, XY}, title = {Differences in brain function in cognitive impairment after stroke in different hemispheres of the brain: a functional near-infrared spectroscopy study.}, journal = {BMC neurology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12883-026-04827-3}, pmid = {41840417}, issn = {1471-2377}, support = {2023QH1112//the Startup Fund for Scientific Research of Fujian Medical University/ ; 61773124//the National Natural Science Foundation of China/ ; 82402952//the National Natural Science Foundation of China/ ; }, }
@article {pmid41840816, year = {2026}, author = {Hu, Z and Wang, J and Zhou, K and Ma, S and Hu, J}, title = {Application and Research Progress of BCI in Post-Stroke Psychiatric Disorders: A Narrative Review.}, journal = {Medical science monitor : international medical journal of experimental and clinical research}, volume = {32}, number = {}, pages = {e951399}, doi = {10.12659/MSM.951399}, pmid = {41840816}, issn = {1643-3750}, mesh = {Humans ; *Stroke/complications/psychology/physiopathology ; *Brain-Computer Interfaces ; *Mental Disorders/etiology/therapy/physiopathology ; Stroke Rehabilitation/methods ; Quality of Life ; }, abstract = {Post-stroke psychiatric disorders (PSPD), including depression, anxiety, and cognitive impairment, significantly hinder stroke survivors' rehabilitation and quality of life, with traditional interventions often showing limited efficacy. Brain-computer interface (BCI) technology has emerged as a promising tool for neurological regulation and rehabilitation, showing substantial potential in PSPD assessment and intervention. This narrative review comprehensively synthesizes the latest research advances in BCI applications for PSPD, covering underlying mechanisms, principal applications, clinical studies, technical challenges, and prospective directions. It highlights BCI's substantial potential in objective assessment, targeted neuromodulation, and promotion of neuroplasticity, while also addressing unresolved issues such as heterogeneous patient responses, technical limitations, and integration into routine clinical practice. By integrating current evidence and clarifying both achievements and gaps, this review provides theoretical insights and practical guidance for future basic and clinical research in the field.}, }
@article {pmid41844800, year = {2026}, author = {Feng, Y and Jia, N and Huang, P and Hu, S and Yang, S}, title = {Cross-ancestry genetic architecture reveals shared biological pathways of major psychiatric disorders.}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {41844800}, issn = {1476-5578}, abstract = {Psychiatric disorders, including bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ), share substantial genetic overlap. We conducted a cross-ancestry multivariate genome-wide association study (GWAS) integrating European and East Asian populations to uncover shared genetic underpinnings. Our analyses identified 403 loci associated with shared polygenic liability to psychiatric disorders, including 88 novel regions. Cross-ancestry fine-mapping highlighted robust shared signals, notably at VRK2 (rs7596038), consistently significant across ancestries. Gene prioritization revealed 90 high-confidence candidate genes enriched in neurodevelopmental pathways. Single-nucleus RNA sequencing implicated excitatory neurons and astrocytes as key cellular contexts, emphasizing NCAM1-FGFR1 and NEGR1-NEGR1 signaling pathways. Mendelian randomization analyses provided causal evidence linking shared genetic liability to structural brain alterations, particularly in regions crucial for emotion and cognition. Polygenic risk scores derived from shared genetic liability substantially enhanced predictive accuracy for BD and SCZ, demonstrating strong trans-ancestry validity. These results advance understanding of shared genetic architecture in psychiatric disorders, highlighting potential therapeutic targets and emphasizing the critical importance of diverse ancestry studies in precision psychiatry.}, }
@article {pmid41844810, year = {2026}, author = {Ullah, W and Dai, Q and Zulqarnain, RM and Fiidow, MA}, title = {Explainable artificial intelligence for early Alzheimer's diagnosis using enhanced grey relational features and multimodal data.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-43707-1}, pmid = {41844810}, issn = {2045-2322}, support = {62476126//National Natural Science Foundation of China/ ; }, abstract = {Alzheimer's disease, a progressive neurodegenerative disorder, presents a growing global health challenge due to its increasing prevalence and lack of accessible early diagnostic methods. Even though it has enhanced the diagnostic accuracy of machine learning, there is a major concern about striking a balance between predictive performance and interpretability. The proposed study presents an interpretable and sustainable machine learning architecture for early diagnosis of Alzheimer's disease based on multimodal, structured clinical and behavioral data, including demographics, vascular risk factors, lifestyle, and cognitive data. We perform extensive feature engineering to derive composite features, including blood pressure ratio, MMSE age ratio, cholesterol ratio, and cognitive decline score. The class imbalance is addressed using the Synthetic Minority Oversampling Technique. We also introduce a new strengthened Grey Relational Grade index based on the theory of grey system and the policy of sigmoid normalization. This greatly enhances the feature-diagnosis correlation (0.725 to 0.891), representing complicated nonlinear associations. This paper compared seven mainstream classifiers, such as Logistic Regression, Random Forest, Extreme Gradient Boosting, Light Gradient Boosting Machine, CatBoost, Stacking Ensembles, and Deep Neural Networks, in the context of model comparison. Among them, Deep Neural Networks achieve the best performance (accuracy: 98.01%, AUC: 99.43%), followed by a CatBoost-based Stacking Ensemble (Accuracy: 97.91%, AUC: 98.10%). Shapley Additive Explanations make models easier to understand by showing important modifiable predictors like family history, smoking, and early cognitive symptoms. This study presents that combining enhanced Grey Relational Grade metrics with robust machine learning and deep learning models produces an accurate, interpretable, and potentially effective framework for early AD risk assessment, which can be used to implement effective, behavior-centric prevention strategies in ageing demographics.}, }
@article {pmid41846866, year = {2026}, author = {Yang, CD and Guo, A and Lin, KY}, title = {Brain-Computer Interfaces for Vision Recovery in Precortical Vision Loss.}, journal = {Eye and brain}, volume = {18}, number = {}, pages = {561691}, pmid = {41846866}, issn = {1179-2744}, abstract = {INTRODUCTION: Precortical vision loss remains a major global health challenge. Advances in brain-computer interfaces (BCIs) offer a new pathway towards restoring functional vision by bypassing damaged structures in the visual pathway.<br><br>METHODS: This narrative review aims to synthesize the current evidence on BCIs for precortical vision recovery, including non-invasive and invasive techniques. Device design, testing, and outcomes are discussed, with an emphasis on developments in technology and engineering.<br><br>RESULTS: Non-invasive BCIs induce neuroplasticity and may restore vision in conditions of precortical vision loss such as glaucoma and optic neuropathy. Cortical visual prostheses demonstrate the ability to evoke visual precepts and recover functional vision. Integration of artificial intelligence and high-density electrode arrays has improved image encoding and device adaptability to enhance user experience and rehabilitation potential. Patient selection, safety, and long-term outcomes remain active areas of investigation.<br><br>DISCUSSION: BCIs present a paradigm shift in treating precortical blindness that offers hope for patients with no alternative options. Yet, challenges persist, including surgical risks, durability, and variability in response. Personalization of stimulation protocols and further technical refinement are needed to optimize efficacy and accessibility.<br><br>CONCLUSION: BCIs are a promising experimental modality for precortical vision restoration. Continued research and interdisciplinary collaboration are essential to address current limitations.}, }
@article {pmid41836667, year = {2026}, author = {Shang, W and Choi, B and Zhan, Q and Wu, J and Xu, D}, title = {Neuromodulation and rehabilitation of post-stroke cognitive impairment: challenges and prospects.}, journal = {Frontiers in psychiatry}, volume = {17}, number = {}, pages = {1780907}, pmid = {41836667}, issn = {1664-0640}, abstract = {It is essential to recognize the significant daily impact that post-stroke cognitive impairment (PSCI) has on patients and their families. Neuromodulation strategies have been increasingly applied in the clinical management of PSCI. This review outlines the mechanisms and brain function detection approaches through which neuromodulation promotes cognitive enhancement in stroke patients. For cognitive recovery, transcranial magnetic stimulation, transcranial electrical stimulation, vagus nerve stimulation, and brain-computer interfaces have shown promising results in clinical and preclinical studies. However, their efficacy remains unproven in large-scale pivotal trials. Preliminary clinical trials have shown that photobiomodulation enhances cognitive performance, but further investigation is required into the issue of skull attenuation of light. Transcranial ultrasound stimulation, a novel technology that overcomes the limitation of requiring deep electrode implantation for focal deep brain stimulation, still lacks scientific evidence. Chemogenetics and optogenetics provide methods for monitoring, disrupting, and regulating neural circuits after a stroke. To enhance the effectiveness of neuromodulation, it is recommended to implement multi-target stimulation, strengthen active participation in rehabilitation, and leverage cognitive-motor interactions to promote holistic recovery after stroke. Finally, we propose that neuromodulation will evolve toward brain-machine interaction neuromodulation, using artificial intelligence to develop a closed-loop strategy encompassing stimulation, detection, optimization, and re-stimulation.}, }
@article {pmid41838473, year = {2026}, author = {Searls, WC and Roderique, TJ and Cominos, ND and Khalil, LS}, title = {Editorial Commentary: Bio-Inductive Collagen Implant Augmentation Shows Long-Term Cost-Effectiveness, But Clinical Patient Outcomes and Careful Patient Selection Must Guide the Path Forward.}, journal = {Arthroscopy : the journal of arthroscopic &amp; related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association}, volume = {42}, number = {1}, pages = {83-86}, doi = {10.1002/arj.70031}, pmid = {41838473}, issn = {1526-3231}, mesh = {Humans ; Cost-Benefit Analysis ; *Collagen/economics ; *Patient Selection ; *Rotator Cuff Injuries/surgery/economics ; *Arthroscopy/methods/economics ; Treatment Outcome ; *Prostheses and Implants/economics ; }, abstract = {Arthroscopic rotator cuff repairs (ARCR) are fraught with low healing rates despite improvements in surgical techniques and constructs. Several studies have emerged showing significant improvements in failure to heal rates when incorporating bioinductive collagen implants (BCI) in the short term. Structural integrity following ARCR is paramount, as retear places exorbitant costs on the health care system and long-term studies have established that clinical outcomes are significantly worse in patients with structural retear. The up-front costs of biologic augmentation is cost-prohibitive in ambulatory surgery centers, where a large portion of ARCR occurs, despite the efficacy of improving rotator cuff repair tendon quality and integrity. This short-sighted, bundled reimbursement paradigm that omits BCI from Current Procedural Terminology coding must be revised considering the long-term cost effectiveness of reducing retear risk following ARCR. As BCI augmentation is established as a dominant strategy, strongly recommended by the American Academy of Orthopaedic Surgeons, to reduce retears and improve patient outcomes, it is critical that long-term clinical studies evaluating patient outcomes drive the indications for implementation of BCI in patients with high risk of repair failure.}, }
@article {pmid41838553, year = {2026}, author = {Hurley, ET and Ibán, M&#xc1;R and Oeding, JF and Navlet, MG and Lafuente, JL&#xc1; and Klifto, CS}, title = {Bio-Inductive Collagen Implant Augmentation for Arthroscopic Rotator Cuff Repair Is Cost-Effective in Medium to Large Tears for Reducing Retears: A Secondary Analysis of a Randomized Controlled Trial.}, journal = {Arthroscopy : the journal of arthroscopic &amp; related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association}, volume = {42}, number = {1}, pages = {73-82}, doi = {10.1002/arj.70000}, pmid = {41838553}, issn = {1526-3231}, mesh = {Humans ; Cost-Benefit Analysis ; *Rotator Cuff Injuries/surgery/economics ; *Arthroscopy/economics/methods ; Markov Chains ; Quality-Adjusted Life Years ; *Collagen/economics/therapeutic use ; Monte Carlo Method ; *Prostheses and Implants/economics ; Recurrence ; }, abstract = {PURPOSE: To perform a Markov model-based cost-effectiveness analysis comparing arthroscopic rotator cuff repair (ARCR) and bio-inductive collagen implant (BCI) to ARCR for symptomatic, medium-to-large rotator cuff tears.<br><br>METHODS: A Markov chain Monte Carlo probabilistic model was developed to evaluate the outcomes and costs of 1000 simulated patients undergoing ARCR&#x2009;+&#x2009;BCI versus ARCR for isolated, symptomatic, reparable, full-thickness, medium-to-large posterosuperior nonacute rotator cuff tears, with fatty infiltration &#x2264;2. Health utility values, transition probabilities, and costs were derived from the published literature. Outcome measures included costs, quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER). Ten-year costs for each patient in the microsimulation model were averaged by initial treatment strategy to capture costs of any subsequent treatments patients underwent as a result of retears. Cycle length was defined as 1&#x2009;year, with all costs and utilities discounted at 3% annually. Disutility was applied to patient health states involving conversion to reverse shoulder arthroplasty (RSA) for retears and postoperative complications.<br><br>RESULTS: Over the 10-year time horizon, mean total costs resulting from ARCR&#x2009;+&#x2009;BCI and ARCR were $49,240&#x2009;&#xb1;&#x2009;$8516 and $56,358&#x2009;&#xb1;&#x2009;$8665, respectively. On average, ARCR&#x2009;+&#x2009;BCI was associated with 5.6&#x2009;&#xb1;&#x2009;0.4 QALYs, while ARCR alone was associated with 4.3&#x2009;&#xb1;&#x2009;0.4 QALYs. Overall, ARCR&#x2009;+&#x2009;BCI was determined the preferred cost-effective strategy in 100% of patients included in the microsimulation model. Deterministic sensitivity analysis on the risk of retear associated with ARCR&#x2009;+&#x2009;BCI found that the recurrence risk associated with ARCR&#x2009;+&#x2009;BCI would need to be greater than 26.5% in order for ARCR without BCI augmentation to be more cost-effective than ARCR&#x2009;+&#x2009;BCI at a willingness-to-pay threshold of $50,000/QALY.<br><br>CONCLUSIONS: ARCR&#x2009;+&#x2009;BCI was determined to be the dominant, cost-effective treatment strategy to reduce retears for symptomatic, medium-to-large rotator cuff tears based on the Monte Carlo microsimulation and probabilistic sensitivity analysis. Patients treated ARCR alone faced higher retear rates, leading to greater downstream costs that ultimately exceeded those of the ARCR&#x2009;+&#x2009;BCI group.<br><br>LEVEL OF EVIDENCE: Level I, economic and decision analysis.}, }
@article {pmid41838798, year = {2026}, author = {Zhao, H and Zhang, X and Marin-Llobet, A and Lin, X and Liu, J}, title = {Benchmarking spike source localization algorithms in high density probes.}, journal = {PLoS computational biology}, volume = {22}, number = {3}, pages = {e1014059}, doi = {10.1371/journal.pcbi.1014059}, pmid = {41838798}, issn = {1553-7358}, abstract = {Estimating neuron location from extracellular recordings is essential for developing advanced brain-machine interfaces. Accurate neuron localization improves spike sorting, which involves detecting action potentials and assigning them to individual neurons. It also assists in monitoring probe drift, which affects long-term probe reliability. Although several localization algorithms are currently in use, the field is nascent and arguments for using one algorithm over another are largely theoretical or based on visual inspection of clustering results. We present a first-of-its-kind benchmarking of commonly used neuron localization algorithms. We assess these algorithms using two ground truth datasets: a biophysically realistic simulated dataset, and an experimental dataset pairing patch-clamp and extracellular Neuropixels recording data. We systematically evaluate the accuracy, robustness, and runtime of these algorithms in ideal recording conditions and long-term recording conditions with electrode degradation. Our findings highlight significant performance differences; while more complex and physically realistic models perform better in ideal conditions, models relying on simpler heuristics demonstrate superior robustness to noise and electrode degradation, making them more suitable for long-term neural recordings. This work provides a framework for assessing localization algorithms and developing robust, biologically grounded algorithms to advance the development of brain-machine interfaces.}, }
@article {pmid41839845, year = {2026}, author = {Wang, M and Jiang, H and Ni, C and Zhou, X and Xu, Y and Shang, S and You, X and Wang, W and Zhou, C and Zhang, W and Wang, X and Zhang, S and Shi, L and Ji, B}, title = {Conformal bumped electrode web for chronic ECoG recordings in swine.}, journal = {Microsystems &amp; nanoengineering}, volume = {12}, number = {1}, pages = {}, pmid = {41839845}, issn = {2055-7434}, support = {62204204//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {The acquisition of high-quality electrocorticogram (ECoG) signal is of great significance for the diagnosis and treatment of neurological diseases such as high amputation, visual injury, epilepsy and Parkinson's disease. Currently, flexible ECoG electrodes have received attention due to their low mechanical mismatch and minimally invasive characteristics. However, the traditional ECoG electrodes are non-stretchable planar structures that cannot be conformal with the cerebral cortex, which is in constant motion and has sulci and gyri structure. In this work, a flexible stretchable ECoG electrode with bumped electrodes was developed to alleviate these problems. Firstly, the mechanical simulation results show that this stretchable electrode structure can effectively reduce the stress mismatch between electrode and tissue interface. Secondly, the results of cyclic voltammetry scanning and mechanical tensile experiments show that the stretchable ECoG electrode structure can be conformally attached to the surface of the cerebral cortex and maintain good electrochemical stability during continuous stretching. Third, the bumped electrode has a larger adhesive force than the planar electrode and can significantly reduce the background noise by conformal attachment and electrochemical modification of PEDOT:PSS. Most importantly, in vivo animal experiments showed that the stretchable ECoG electrode can continuously record high-quality ECoG signals on the surface of the cerebral cortex of swine over an area of 22 × 22 mm[2] for more than 5 weeks.}, }
@article {pmid41839864, year = {2026}, author = {Yan, X and Li, Y and Zhao, Y and Pan, C and Yan, S and Yang, D and Ruan, GJ and Zhao, H and Chen, F and Yangdong, XJ and Wang, P and Yu, W and Yang, Y and Wang, C and Cheng, B and Liang, SJ and Miao, F}, title = {Light-programmable mechanical computing via polyaniline composite film.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-70425-z}, pmid = {41839864}, issn = {2041-1723}, abstract = {Mechanical computing represents a highly promising paradigm for environment-adaptive information processing. However, existing implementations are generally constrained by limited architectural scalability, and their modes of application in practical scenarios remain insufficiently defined. Here, we develop a light-programmable mechanical computing system that not only performs scalable logic operations but also enables environment-adaptive optical camouflage. The system is based on a polyaniline composite film (PCF) that integrates light-responsive expansion-contraction elements with a flexible conductive layer. Light illumination dynamically modulates the conductive pathways, giving rise to optically controlled single-pole single-throw (SPST) and single-pole double-throw (SPDT) relays that reconfigure signal transmission routes. Interconnecting these relays enables the construction of basic logic gates and 2-bit full-adder circuits, establishing a scalable paradigm for light-programmable mechanical computation. Moreover, we implement an adaptive camouflage function that senses environmental textures and generates matching optical patterns, demonstrating potential for intelligent skin applications capable of environmental interaction. This work establishes a light-programmable, pathway-reconfigurable mechanical computing framework, expanding possibilities for autonomous and adaptive intelligent systems.}, }
@article {pmid41839891, year = {2026}, author = {Li, H and Wang, S and Yu, Q and Zhao, H and Tang, Z and Lv, L and Han, F and Yang, R and Zhao, Y and Fu, Z and Shi, B and Li, G and Wang, C and Zhang, J and Song, K and Li, Y and Liu, Z}, title = {Implantable soft bladder-machine interface for neurogenic bladder dysfunction.}, journal = {Nature communications}, volume = {17}, number = {1}, pages = {}, pmid = {41839891}, issn = {2041-1723}, support = {//International Partnership Program of Chinese Academy of Sciences/ ; //Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; //Shenzhen Science and Technology Program/ ; }, mesh = {*Urinary Bladder, Neurogenic/therapy/physiopathology ; Animals ; *Urinary Bladder/physiopathology ; Rats ; Electromyography ; *Electric Stimulation Therapy/instrumentation/methods ; Female ; Rats, Sprague-Dawley ; *Prostheses and Implants ; Electric Stimulation ; Disease Models, Animal ; Muscle Contraction/physiology ; }, abstract = {Neurogenic bladder dysfunction impairs bladder sensation and contraction, causing severe renal complications. The bladder's large isotropic expansion hinders the development of implantable bioelectronic devices for monitoring and electrical stimulation. Addressing this, we report an implantable soft bladder-machine interface (BdMI) that integrates seamlessly with the bladder, providing monitoring and electrical stimulation. This BdMI features a conductive thin film capable of keeping functions under isotropic stretch up to 800%, created without the complex pre-stretching of its elastic substrate. We elucidate its stretchability mechanism and validate the BdMI in rat models, which enables simultaneous intravesical pressure detection, detrusor electromyographic monitoring, and electrical stimulation therapy. Implanted for 7 days, the BdMI operates efficiently and markedly reduces involuntary bladder contraction frequency post-stimulation. These findings validate the potential of BdMI in offering real-time, physiological feedback and electrical stimulation-based regulation for neurogenic bladder pathologies, marking a significant advancement in the field.}, }
@article {pmid41831590, year = {2026}, author = {Yang, W and Yuan, J and Ding, L and Keung Chow, SK}, title = {A neurofeedback-guided EEG and BCI framework for personalized attention rehabilitation in ADHD.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2026.03.010}, pmid = {41831590}, issn = {1873-7544}, abstract = {The integration of game-based cognitive training with electroencephalography (EEG)-based brain-computer interaction (BCI) has demonstrated potential for enhancing attention among individuals with attention-deficit hyperactivity disorder (ADHD). However, existing systems often lack adaptive difficulty regulation and rely solely on single-modal assessments, thereby limiting personalization and sustained engagement. This study developed and assessed an adaptive, multi-task EEG-BCI training system that combines real-time neurofeedback with machine learning-driven customization to bolster attentional capabilities. Fifty participants (25 with ADHD and 25 controls) completed attention-enhancement sessions utilizing SkiSport, a Unity-based skiing game that adjusts difficulty levels according to EEG-derived attention metrics obtained from the NeuroSky TGAM sensor. Support Vector Regression, XGBoost, and Multi-Layer Perceptron models were trained on behavioral and EEG data to predict optimal difficulty parameters. Attention and behavioural metrics were compared before and after personalisation. The findings indicated that EEG attention scores increased by an average of 15% (7.85% in controls, 21.5% in ADHD participants). The adaptive multi-task games yielded an additional 10% increase following personalization. Behavioral indices on reaction accuracy, game score, and completion time showed an overall improvement of 19%. XGBoost achieved the highest predictive accuracy on a held-out test set (R[2] value of 0.9826, RMSE of 0.8560, and MAE of 0.6417) for within-subject, window-level attention prediction. The proposed EEG-BCI game facilitated short-term enhancements in attention-related metrics among individuals with ADHD. The incorporation of machine learning-driven personalization into serious games offers a scalable, non-pharmacological strategy for short-term cognitive training and attentional modulation.}, }
@article {pmid41832156, year = {2026}, author = {Duhay, V and Tian, M and Kosieradzka, K and Ebner, M and Lo, WT and Krauss, M and Sprengel, HL and Voss, M and Riechmann, M and Savas, JN and Schwake, M and Haucke, V and Damme, M}, title = {Control of lysosome function by the GTPase-activating protein TBC1D9B and its binding partner TMEM55B.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-70345-y}, pmid = {41832156}, issn = {2041-1723}, support = {DA 1785/2-2//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; SCHW866/6-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; SCHW866/7-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; TRR186/A08//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; HA2686/26-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; }, abstract = {Lysosomes are highly dynamic organelles that serve antagonistic functions as terminal catabolic stations for the degradation of macromolecules and as central metabolic decision centers for anabolic growth signaling. Lysosome dysfunction is implicated in various human diseases. The physiological roles of lysosomes are linked to the control of lysosome position and dynamics via the activity of the kinesin-activating small GTPase ARL8. How the activity of ARL8 is regulated remains poorly understood. Here, we identify the GTPase-activating Tre-2/Bub2/Cdc16 (TBC) domain protein TBC1D9B as a critical negative regulator of ARL8B function. We demonstrate that TBC1D9B is associated with the lysosomal membrane protein TMEM55B, directly binds to ARL8B-GTP, and stimulates its GTPase activity. Knockout of TBC1D9B or its binding partner TMEM55B causes lysosome dispersion, defective autophagic flux, and impairs the adaptive degradative response of cells to limiting nutrient supply. These lysosomal phenotypes of TBC1D9B loss are occluded by concomitant depletion of ARL8 in cells. Collectively, our data unravel a key role for TBC1D9B in controlling lysosome function by serving as a negative regulator of ARL8 activity.}, }
@article {pmid41832195, year = {2026}, author = {Wang, Z and Xu, G and Yu, B and Xu, K and Zhu, J and Pan, G and Zhang, J and Wang, Y and Hao, Y}, title = {Cortical representation of multidimensional handwriting movement and implications for neuroprostheses.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-70536-7}, pmid = {41832195}, issn = {2041-1723}, abstract = {Handwriting brain-computer interfaces (BCIs) have enabled high performance brain-to-text communication for paralyzed individuals. However, the detailed parameters of handwriting movement and their cortical representations remain incompletely understood. Here, we recorded intracortical neural activity from a paralyzed subject and found distinct neural representations for strokes and pen lifts with respect to two-dimensional (2D) velocity on the writing plane, indicating that 2D kinematics alone cannot fully account for the observed neural variance. To address this, we acquired multidimensional handwriting data from healthy subjects, including 3D velocity, grip force, writing pressure, and multi-channel electromyographic (EMG) signals. Incorporating these additional dimensions beyond 2D velocity significantly improved the interpretability of neural signals for both strokes and pen lifts. We further leveraged these additional dimensions to enhance handwriting decoding performance. Together, our findings indicate the motor cortex encodes handwriting as multidimensional movement and highlight the importance of multidimensional features for improving the performance of handwriting BCIs.}, }
@article {pmid41832543, year = {2026}, author = {Hu, G and Tang, H and Zeng, F and Wen, X and Hou, W and Zhang, X}, title = {Brain responses to different action observation paradigms and assessing transferable cross-paradigm decoding.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01946-3}, pmid = {41832543}, issn = {1743-0003}, support = {62206032//the National Natural Science Foundation of China/ ; CSTB2025TIAD-JM011//Chongqing Key Project for Technology Innovation and Application Development/ ; }, }
@article {pmid41834060, year = {2026}, author = {Zhao, Z and Duan, X and Huang, H and Zhang, Y and Wang, M and Qin, J and Lin, S and Chen, H}, title = {Single-Nucleus Transcriptomics Reveals Microglial State Transitions and Astrocytic Trajectory Divergence During Glial Remodeling Induced by Intracortical Electrode Implantation.}, journal = {Glia}, volume = {74}, number = {5}, pages = {e70148}, doi = {10.1002/glia.70148}, pmid = {41834060}, issn = {1098-1136}, support = {32201095//National Natural Science Foundation of China/ ; 32127801//National Natural Science Foundation of China/ ; 62104051//National Natural Science Foundation of China/ ; }, mesh = {Animals ; *Microglia/metabolism ; *Electrodes, Implanted/adverse effects ; Rats ; *Astrocytes/metabolism ; *Transcriptome/physiology ; Male ; *Motor Cortex/metabolism ; *Neuroglia/metabolism ; Rats, Sprague-Dawley ; }, abstract = {The foreign body response to intracortical electrodes, characterized by chronic neuroinflammation and glial scar formation, remains a primary cause of long-term functional failure. However, neurons and glial cells' heterogeneity and intercellular signaling mechanisms following electrode implantation remain poorly resolved, which is responsible for direct dysfunction. Here, we applied single-nucleus RNA sequencing (snRNA-seq) to profile the peri-implant microenvironment in rat motor cortex tissue at 3, 25, and 50 days post-electrode implantation. Integrated bioinformatic analyses, including clustering, pseudotemporal trajectory reconstruction, and cell-cell communication inference, revealed a coordinated cellular response. We identified a pathologic microglial subpopulation (marked by Gpnmb, SPP1, and CD63) and a scar-associated astrocytic subtype (characterized by Mctp1 and Lrrc7) that progressively dominate the peri-implant niche. Crucially, we reveal that neurons orchestrate these processes via CX3CL1-CX3CR1 signaling, modulating microglial polarization and PTN-ALK/Ptpprz1 interaction, promoting astrogliosis and scar formation. These findings define the dynamic neuron-glia signaling landscape surrounding chronically implanted electrodes and provide mechanistic insight into how modulating cell-cell communication may improve the long-term biocompatibility of neural interfaces.}, }
@article {pmid41834064, year = {2026}, author = {Wang, X and Ciarlo, A and Lührs, M and Atanasyan, A and Böken, D and Roßmann, J and Schluse, M and Jäger, M and Nordt, M and Cong, F and Mathiak, K and Linden, DEJ and Goebel, R and Mehler, DMA and Zweerings, J}, title = {A Feasibility Study of Navigating Emotional States Using Real-Time Representational Similarity Analysis fMRI Neurofeedback.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2650018}, doi = {10.1142/S0129065726500188}, pmid = {41834064}, issn = {1793-6462}, abstract = {Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a promising noninvasive brain computer interface (BCI) technique for enhancing self-regulation of affective brain states. However, conventional univariate rt-fMRI-NF approaches struggle to discriminate distributed neural patterns underlying distinct emotions. This study implemented an rt-fMRI semantic neurofeedback (rt-fMRI-sNF) paradigm incorporating real-time representational similarity analysis (rt-RSA) to enable navigation among emotional states. Four emotion-specific base patterns were first derived from functional localizer runs and then used as target patterns during neurofeedback. Using an RSA-informed circular semantic map (CSM), participants received real-time visual feedback indicating both the similarity and intensity of their current brain activity relative to target patterns. Participants were instructed to use mental imagery to shift their brain activity toward the specific target pattern and enhance its intensity. Analyses of localizer data revealed overlapping regional activations across emotions and demonstrated that RSA reliably distinguished between emotional states. Group-level mixed-effects modeling of neurofeedback performance indicated significant within-run improvements and higher initial performance in the second run. Together, these results demonstrate the methodological feasibility of an RSA-informed rt-fMRI-NF framework for multivariate brain-state modulation and establish a foundation for future studies examining its transferability and clinical relevance.}, }
@article {pmid41835943, year = {2026}, author = {Zhang, Q and Cao, Z and Tian, S and Cai, Z and Shi, L and Qi, X}, title = {Comparative study of SSVEP characteristics in mixed versus virtual reality across varying depths.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1713018}, doi = {10.3389/fnins.2026.1713018}, pmid = {41835943}, issn = {1662-4548}, abstract = {Steady-state visually evoked potentials (SSVEP), owing to their high signal-to-noise ratio and low training cost, are widely regarded as an effective approach for constructing visually driven brain-computer interfaces (BCI), particularly in neurorehabilitation applications. However, the accommodation-vergence conflict (VAC) commonly present in mixed reality (MR) and virtual reality (VR) head-mounted displays may attenuate neural responses in the visual cortex, thereby compromising the long-term usability of such systems. This study aims to systematically evaluate the effects of MR and VR environments under different virtual depth conditions on SSVEP signal quality, classification performance, and visual comfort, providing parameter guidelines for the design of immersive visual BCIs in rehabilitation contexts. Green flickering stimuli at 7.5, 11.25, and 18 Hz were presented at three virtual depths of 0.4, 1.0, and 1.8 m. Feature extraction and classification were performed using canonical correlation analysis (CCA), Filter-Bank Canonical Correlation Analysis (FBCCA), and task-related component analysis (TRCA).The results showed a negative correlation between stimulus distance and SSVEP classification accuracy, with FBCCA achieving the highest accuracy at the 0.4 m depth (71.8% ± 33.8%). Overall, the signal-to-noise ratio (SNR) in the MR environment was higher than that in the VR environment, with the most pronounced difference observed under the 1.8 m condition, suggesting that MR is more effective in alleviating VAC and maintaining stable visual cortical responses. Among the three stimulation frequencies, 11.25 Hz elicited the highest SSVEP amplitude and SNR, indicating it as the optimal frequency band. Subjective visual fatigue assessments revealed higher scores for VR in terms of diplopia and fixation difficulty, with trends consistent with the observed SNR reduction. This study elucidates the interactive modulation effects of virtual depth, display modality, and flicker frequency on SSVEP, and demonstrates that MR outperforms VR in terms of signal stability, visual comfort, and potential rehabilitation usability. The derived parameters provide experimentally validated optimization strategies for stimulus depth and frequency in vision-based attention training, spatial orientation training, upper-limb interactive tasks, and immersive feedback systems in neurorehabilitation, thereby contributing to improved long-term adherence and clinical translational value of future rehabilitation BCI.}, }
@article {pmid41836195, year = {2026}, author = {Kunekar, P and Mankar, S and Cholke, P and Kulkarni, A and Nooji, P and Gadhave, R}, title = {MedIntelliCare: neurodynamic-inspired AI for medical decision support by integrating retrieval-augmented generation with multimodal cognitive processing.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {61}, doi = {10.1007/s11571-026-10429-z}, pmid = {41836195}, issn = {1871-4080}, abstract = {MedIntelliCare is an AI-powered medical assistant designed to enhance diagnostic accuracy, reduce cognitive load on healthcare professionals, and integrate real-time medical data. While current AI-driven medical systems focus on information retrieval and response generation, MedIntelliCare leverages Retrieval-Augmented Generation (RAG) combined with principles from neural computation and decision-making processes. This study explores the system's ability to simulate biologically inspired information processing by integrating brain-like computing, predictive modeling, and multimodal analysis, including EEG and neuroimaging data. By aligning MedIntelliCare with advances in computational neuroscience and intelligent diagnostics, we aim to establish a model that enhances clinical decision support through adaptive information retrieval. The system's future implications include cognitive disorder modeling, brain-computer collaboration, and advanced AI-driven diagnostics inspired by neural processing frameworks. Experimental validation using cosine similarity metrics demonstrates that MedIntelliCare achieves a 73% alignment with expert-generated reports, reinforcing its potential in neuro-inspired medical intelligence.}, }
@article {pmid41828036, year = {2026}, author = {Gao, Q and Jin, Y and Sun, Y and Jin, M and Tang, L and Chen, Y and She, Y and Li, M}, title = {Transforming Intracerebral Hemorrhage Care with Artificial Intelligence: Opportunities, Challenges, and Future Directions.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {16}, number = {5}, pages = {}, pmid = {41828036}, issn = {2075-4418}, support = {XY2025074//Scientific Research Fund of Zhejiang University/ ; }, abstract = {Spontaneous intracerebral hemorrhage (ICH) is associated with substantial mortality and morbidity. Current management paradigms rely heavily on the rapid interpretation of neuroimaging and clinical data, yet are frequently constrained by limitations in processing speed, diagnostic accuracy, and prognostic precision. Artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offers transformative potential to circumvent these challenges across the entire continuum of ICH care. This comprehensive review synthesizes the rapidly evolving landscape of AI applications in ICH management. Through a systematic evaluation of recent literature, we examine studies focused on the development, validation, or critical appraisal of AI-driven technologies for ICH care. Our analysis encompasses automated neuroimaging, computer-assisted surgical navigation, brain-computer interfaces (BCIs), prognostic modeling, and fundamental research into disease mechanisms. AI has demonstrated performance comparable to that of clinical experts in automating hematoma segmentation, predicting complications such as hematoma expansion, and refining surgical planning via augmented reality. Furthermore, BCIs present innovative therapeutic avenues for motor rehabilitation. However, the translation of these technological advances into routine clinical practice is impeded by substantial challenges, including data heterogeneity, model opacity ("black-box" issues), workflow integration barriers, regulatory ambiguities, and ethical concerns surrounding accountability and algorithmic bias. The integration of AI into ICH care signifies a paradigm shift from standardized treatment protocols toward dynamic, precision medicine. Realizing this vision necessitates interdisciplinary collaboration to engineer robust, generalizable, and interpretable AI systems. Key priorities include the establishment of large-scale multimodal data repositories, the advancement of explainable AI (XAI) frameworks, the execution of rigorous prospective clinical trials to validate efficacy, and the implementation of adaptive regulatory and ethical guidelines. By systematically addressing these barriers, AI can evolve from a mere analytical tool into an indispensable clinical partner, ultimately optimizing patient outcomes.}, }
@article {pmid41828065, year = {2026}, author = {Tasci, I and Sercek, I and Talu, Y and Barua, PD and Baygin, M and Tasci, B and Dogan, S and Tuncer, T}, title = {TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {16}, number = {5}, pages = {}, pmid = {41828065}, issn = {2075-4418}, support = {123E612//Scientific and Technological Research Council of Turkey/ ; TF.25.35//Scientific Research Projects Coordination Unit of Firat University/ ; }, abstract = {Objective: Accurate odor classification from EEG signals requires informative and interpretable features. Although Local Binary Pattern (LBP) and variants such as the center-symmetric binary pattern are widely used, they lack sufficient explainability and tensor-level implementations. Additionally, neuroscientific understanding of odor processing remains limited. Methods: We propose Tensor Center-Symmetric Binary Pattern (TensorCSBP), a novel tensor-based feature extractor designed for EEG odor analysis. TensorCSBP is integrated into an explainable feature engineering (XFE) pipeline with four steps: (1) TensorCSBP for feature generation, (2) CWNCA for feature selection, (3) tkNN classifier for decision making, and (4) DLob method for symbolic interpretability. Results: TensorCSBP XFE was evaluated on a newly collected 32-channel EEG dataset for odor detection. It achieved 96.68% accuracy under 10-fold cross-validation. Conclusions: The information entropy of the DLob symbol sequence was 3.5675, demonstrating the richness of the interpretability output. Significance: This study presents a high-accuracy, explainable, and computationally efficient model for EEG-based odor classification. TensorCSBP bridges low-level signal patterns with symbolic neuroscience insights, offering real-time potential for BCI and clinical applications.}, }
@article {pmid41829691, year = {2026}, author = {Gao, X and Cao, G and Ma, G}, title = {SFE-GAT: Structure-Feature Evolution Graph Attention Network for Motor Imagery Decoding.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {5}, pages = {}, pmid = {41829691}, issn = {1424-8220}, support = {2020YFB17122//Ministry of Science and Technology of the People's Republic of China/ ; 2021M692457//China Postdoctoral Science Foundation/ ; YDZJ202301ZYTS263//Department of Science and Technology of Jilin Province/ ; YDZJ202301ZYTS423//Department of Science and Technology of Jilin Province/ ; }, mesh = {Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; Brain-Computer Interfaces ; *Motor Cortex/physiology ; Nerve Net/physiology ; *Attention/physiology ; Algorithms ; Brain/physiology ; }, abstract = {Motor imagery EEG decoding often relies on static functional connectivity graphs that cannot capture the dynamic, stage-wise reorganization of brain networks during tasks. This paper aims to develop a graph neural network that explicitly simulates this neurodynamic process to improve decoding and provide computational insights. This paper proposes a Structure-Feature Evolution Graph Attention Network (SFE-GAT). Its inter-layer evolution mechanism dynamically co-adapts graph topology and node features, mimicking functional network reorganization. Initialized with phase-locking value connectivity and spectral features, the model uses a graph autoencoder with Monte Carlo sampling to iteratively refine edges and embeddings. On the BCI Competition IV-2a dataset, SFE-GAT achieved 77.70% (subject-dependent) and 66.59% (subject-independent) accuracy, outperforming baselines. Evolved graphs showed sparsification and strengthening of task-critical connections, indicating hierarchical processing. This paper advances EEG decoding through a dynamic graph architecture, providing a computational framework for studying the hierarchical organization of motor cortex activity and linking adaptive graph learning with neural dynamics.}, }
@article {pmid41830336, year = {2026}, author = {Momin, M and Feng, L and Chen, X and Ahmed, S and AlMahmood, B and Huang, LP and Ren, J and Wang, X and Lee, H and Cramer, SR and Zhang, N and Zhang, S and Zhou, T}, title = {3D-Printable, Honeycomb-Inspired Tissue-Like Bioelectrodes for Patient-Specific Neural Interface.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e16291}, doi = {10.1002/adma.202516291}, pmid = {41830336}, issn = {1521-4095}, support = {1R01HL171633/NH/NIH HHS/United States ; NTUT-PSU-113-01//National Taipei University of Technology-Penn State Collaborative Seed Grant Program/ ; //National Science Foundation/ ; }, abstract = {The unique gyral patterns of the human brain demand patient-specific neural interfaces to achieve precise neuromodulation, mitigate adverse tissue responses, and optimize therapeutic efficacy and safety. One-size-fits-all, conventional rigid electrocorticography (ECoG) electrodes, standardized for mass production through lithographic techniques, exhibit limited conformability to the brain's heterogeneous cortical topography. This mechanical mismatch results in poor electrode-tissue contact, signal loss, and foreign body responses. To address these limitations, we present an integrated novel platform, synergizing MRI-based anatomical mapping, finite element analysis (FEA)-optimized mechanical design, and direct ink writing (DIW) 3D printing to fabricate electrodes customized to individual gyral patterns. The resulting honeycomb-inspired printable gel electrode (HiPGE) employs a bioinspired honeycomb architecture with ultra-soft hydrogels, engineered to match the bending stiffness of brain tissue (0.1-10 kPa) while maintaining cost-efficiency and long-term durability. This mechanical congruence ensures exceptional cortical conformability and adaptive interfacing, circumventing the geometric and material limitations of traditional rigid electrodes. By combining patient-specific design with scalable fabrication, our platform establishes a transformative framework for neural interface engineering, enhancing precision, biocompatibility, and functional performance in neuromodulation therapies and neuroprosthetic applications.}, }
@article {pmid41826752, year = {2026}, author = {Li, Y and Si, Y and Pang, X and Li, S and Jiang, L and Yi, C and Yao, D and Li, F and Xu, P}, title = {EEG hyperscanning reveals dynamic interbrain network patterns during interactive social decision-making.}, journal = {Communications biology}, volume = {}, number = {}, pages = {}, doi = {10.1038/s42003-026-09852-z}, pmid = {41826752}, issn = {2399-3642}, abstract = {Social decision-making involves intricate and dynamic interactions between brains, yet prior hyperscanning research primarily concentrated on investigating the overall patterns of interbrain synchrony (IBS), leaving its fine-grained temporal dynamics unveiled. Here, after recording the electroencephalography of proposer-responder pairs who engaged in an iterated ultimatum game, time-varying IBS network architectures were explored by leveraging source-localized wavelet transform coherence and k-means clustering. Results revealed a sequence of temporally and functionally distinct IBS states along the response and feedback periods. Early states, occurring around stimulus onset, were dominated by a posterior parietal modular configuration, likely associated with shared attention and visual processing. In contrast, later states during the decision-feedback stage involved increased IBS in the frontal and temporoparietal regions, reflecting coordinated activity between interacting partners supporting decision execution and adaptive behavioral adjustments. Crucially, advantageous conditions (fair proposal or acceptance feedback) elicited more active and efficient dynamic IBS states than disadvantageous conditions (unfair proposal or rejection feedback), with greater IBS related to increased reciprocal behavior. These findings reveal recurring IBS patterns, suggesting that social decision-making is modulated not only by temporal fluctuations in IBS networks but also by flexible interbrain communication between key cortical regions.}, }
@article {pmid41826709, year = {2026}, author = {Jones, CT and Hill, ER}, title = {The evolution of speech communication devices for anarthria: a review.}, journal = {Journal of neurology}, volume = {273}, number = {3}, pages = {}, pmid = {41826709}, issn = {1432-1459}, mesh = {Humans ; *Communication Devices for People with Disabilities/trends ; *Facial Paralysis/rehabilitation/etiology ; *Communication Disorders/etiology ; }, abstract = {Anarthria is a lack of verbal communication caused by physiological disturbances in the motor pathway. While affected individuals retain the ability to comprehend and produce speech, orofacial paralysis renders them unable to execute speech. Anarthria can be caused by amyotrophic lateral sclerosis, stroke, traumatic brain injury, and other etiologies that affect the descending motor pathway. A wide range of technologies has been developed and tested to improve communication efficiency for patients with anarthria and accompanying paralysis. This review evaluates three key eras of communication device development. First, before implantation devices gained traction, many communication devices revolved around blinks, head and eye tracking, and non-invasive brain recording. Second, implanted cortical neuroprosthetics were designed to improve accuracy and speed of communication. Finally, the review analyzes the future era, where accessibility, patient comfort, and broader applications of neural analysis elevate communication for patients with anarthria to match fluid communication. Restoring speech communication in patients with anarthria is vital to improve their quality of life. Therefore, understanding communication device efficiency and its future trajectory is of utmost clinical importance.}, }
@article {pmid41825840, year = {2026}, author = {Miao, M and Fu, W and Zeng, H and Xu, B and Zhang, W and Hu, W}, title = {Meta-Learning Enhanced Multi-Source Domain Adaptation for zero-calibration motor imagery EEG decoding.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110742}, doi = {10.1016/j.jneumeth.2026.110742}, pmid = {41825840}, issn = {1872-678X}, abstract = {BACKGROUND: Motor imagery (MI) based brain-computer interface (BCI) holds promising application prospects for closed-loop neurorehabilitation in stroke recovery. Despite substantial progress, challenges such as inter-subject variability, lack of training data for specific subject, and the need for time-consuming calibration still hinder the practical deployment of MI-BCI systems.<br><br>NEW METHOD: In this work, aiming to address these issues, we propose a novel Meta-Learning Enhanced Multi-Source Domain Adaptation (MLEMSDA) framework that unifies cross-task, cross-dataset, and cross-subject domain adaptation with gradient-based meta-learning to enable calibration-free MI-EEG decoding. Specifically, two large public ME and MI EEG datasets are firstly used for pre-training to facilitate cross-task and cross-dataset knowledge transfer. Afterward, to further reduce the differences in feature distribution among different individuals, meta-learning based fine-tuning is performed using data from all subjects in the target dataset except the unseen subject. Finally, the obtained decoding model is tested on the unseen subject.<br><br>RESULTS: The proposed MLEMSDA framework was validated on a public stroke MI EEG dataset (CBCIC), our own collected MI EEG dataset, and BCI Competition IV dataset 2b using leave-one-out cross-validation method. DeepConvNet achieved the highest average accuracy of 77.87% on CBCIC dataset, EEGNet yielded the highest average accuracy of 75.54% on our own collected dataset, and ShallowConvNet obtained the highest average accuracy of 72.72% on BCI Competition IV dataset 2b.<br><br>With respect to classification accuracy in the zero-calibration scenario, our method outperforms all the competing methods.<br><br>CONCLUSION: These results clearly demonstrate the effectiveness and generalizability of our method, paving the way for more practical MI-BCI applications.}, }
@article {pmid41825227, year = {2026}, author = {Zhou, H and Bao, Y and Xu, J and Wang, D and Geng, F and Guo, W and Hu, Y}, title = {Test-retest reliability and symptom association of personalized depression TMS targets: A comparative study of refined seed-based (RSA) and hierarchical clustering (HCA) approaches.}, journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics}, volume = {23}, number = {2}, pages = {e00884}, doi = {10.1016/j.neurot.2026.e00884}, pmid = {41825227}, issn = {1878-7479}, abstract = {Personalized transcranial magnetic stimulation (TMS) targeting holds promise for improving depression treatment, but its clinical translation is hindered by limited open-source implementation and systematic comparisons of target reproducibility and clinical relevance. We implemented two leading personalized TMS-target generating approaches, namely refined seed-based (RSA) and hierarchical clustering (HCA) algorithms, and compared them on (1) test-retest reliability of derived targets, and (2) association of target-sgACC connectivity with depressive symptoms. Using resting-state fMRI data from healthy and depressed individuals, spatial reliability was quantified via inter-run Euclidean distances, and clinical relevance was assessed through correlations between depression severity and functional connectivity of targets with sgACC. Effects of global signal regression (GSR) were also evaluated. The results showed that RSA produced targets in more superior and postrior part of DLPFC and demonstrated significantly higher test-retest reliability than HCA (smaller inter-run Euclidean distances). Further, RSA-derived target-sgACC connectivity correlated positively with depression severity, which was absent in HCA-derived targets. In addition, GSR improved spatial reliability for RSA but not HCA. Our results indicate that RSA exhibits superior test-retest reliability and symptom association compared to HCA, yet large-scale clinical trials are warranted to determine which approach yields superior therapeutic efficacy, and open-sourced implementation may accelerate clinical adoption.}, }
@article {pmid41822235, year = {2026}, author = {Li, Y and Wang, R and Yan, C and Xu, X and Wang, Y and Pan, X and Song, Y and Zhang, B and Liu, Z}, title = {Application of neurodynamics theory in the study of neural circuits in major depressive disorder: a review on neural energy approaches.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {60}, pmid = {41822235}, issn = {1871-4080}, abstract = {Major depressive disorder (MDD) is accompanied by abnormal reward processing, altered dopamine transmission in the ventral tegmental area-nucleus accumbens-medial prefrontal cortex (VTA-NAc-mPFC) dopaminergic pathway, and disruptions in both neural dynamics and brain energy metabolism. Yet, how these abnormalities converge within a unified framework of neural dynamics and neural energy coding remains unclear. The purpose of this review is to integrate and critically assess computational models of neural dynamics and neural energy coding in MDD, with a particular emphasis on the multiscale modeling approaches developed in our recent work, and to organize these advances into a coherent conceptual framework linking dopamine-related circuit dysfunction to alterations in neural energy consumption. First, we constructed Hodgkin-Huxley (H-H) models for the NAc medium spiny neuron (MSN) to simulate its neurodynamics. Then, using the neural energy model, we explored the energy consumption characteristics of MSNs and found that, in the MDD condition, MSN energy consumption during spiking was lower than in controls, demonstrating the feasibility and sensitivity of this energy-based methodology. To further examine how these mechanisms scale to functional circuits, we constructed a neural network dynamical model for the VTA-NAc-mPFC dopaminergic pathway and applied an augmented neural-energy computation framework to characterize its energy consumption features. Simulations demonstrated that neural energy consumption was substantially lower in the MDD condition, primarily due to decreased mPFC energy expenditure. Distinct energy-coding patterns emerged across neuronal types, and the energy required to encode a single action potential in both MSNs and pyramidal neurons increased under MDD low dopamine situation, indicating reduced energy efficiency. Moreover, the correlation between membrane potential and instantaneous power was moderate (0.6-0.9) rather than tight, and it changed substantially with dopamine levels. This shows that neural energy consumption carries additional neural information that is not reflected directly in membrane potential signals. Together, these findings establish a unified computational framework that links dopamine deficiency, ion-channel-level dysfunction, microcircuit dynamics impairment, and large-scale reductions in neural energy consumption. Our work highlights neural energy coding as a promising mechanistic indicator and potential biomarker for MDD, and provides a generalizable methodology for investigating other neuropsychiatric disorders.}, }
@article {pmid41822138, year = {2026}, author = {Zhang, X and Wang, B and Zhang, L and Pu, Y and Kong, XZ}, title = {Successful Public Speaking Enhances Neural Alignment in Audience Language Networks.}, journal = {Neurobiology of language (Cambridge, Mass.)}, volume = {7}, number = {}, pages = {}, pmid = {41822138}, issn = {2641-4368}, abstract = {Public speaking is a fundamental form of communication across a wide range of domains; however, the neural mechanisms underlying audience engagement during different speeches remain poorly understood. In particular, it is unclear which functional brain networks support the dynamic fluctuations of audience engagement and what neurobiological processes underlie these effects. In this study, we used naturalistic fMRI combined with intersubject correlation (ISC) analysis to examine how carefully selected and matched speeches, with varying levels of audience engagement, influence neural activity. Our results revealed that the more engaging speech elicited significantly greater interbrain neural synchronization, as indexed by ISC, across a broad range of brain regions. Notably, these engagement-related effects were most prominent in networks associated with language processing and theory of mind, highlighting their critical roles in facilitating shared audience experiences during compelling public communication. A sliding-window analysis further revealed substantial temporal fluctuations in interbrain synchronization throughout the speech. Additionally, neurobiological annotation analyses identified strong associations between engagement-related ISC effects and molecular pathways involved in trans-synaptic signaling, suggesting that intrabrain neuronal communication may contribute to modulating interbrain synchronization. By integrating naturalistic fMRI with ISC analyses, this study offers a promising framework for investigating dynamic neural synchronization among audience members. These findings have broad implications for fields such as education and leadership development, where a deeper understanding of the neural basis of audience engagement could inform strategies to enhance public speaking and communication effectiveness.}, }
@article {pmid41821657, year = {2026}, author = {Prasanna, HS and Prasad, BNM and Ugalat, J and Vishnuvardhana,  and Shankarappa, TH and Shivanna, M and Manjunathagowda, DC and Narayanappa, MG and Lakshmana, VG}, title = {Differential responses of dark and white chia (Salvia hispanica L.) to elicitation: effects on seed quality and biochemical composition.}, journal = {3 Biotech}, volume = {16}, number = {4}, pages = {140}, pmid = {41821657}, issn = {2190-572X}, abstract = {UNLABELLED: The present study investigated the impact of exogenous elicitor application on enhancing chia seed quality. The application of chitosan (200 ppm) and PGPR consortia (5000 ppm) to black chia resulted in the most notable improvements. Application of chitosan improved swelling factor (12.03 cc g[-][1]), fiber content (44.35 g 100 g[-][1]), and oil content (36.08%). The PGPR consortia maximized α-linolenic acid (ALA) accumulation (66.74%), while methyl jasmonic acid increased protein content (33.17 g 100 g[-][1]). In contrast, elicitor application to white chia exhibited a distinct response pattern. Kinetin (100 ppm) recorded the highest swelling factor (11.98 cc g[-][1]), PGPR elevated protein content (34.03 g 100 g[-][1]), and chitosan increased fiber (49.09 g 100 g[-][1]) and oil content (35.78%). The study demonstrated a significant enhancement in the accumulation of secondary metabolites, specifically total phenols and flavonoids. In summary, the application of chitosan, PGPR consortia, and kinetin significantly improved the functional and nutraceutical qualities of both seed types.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-026-04746-7.}, }
@article {pmid41821632, year = {2026}, author = {Liu, C and Han, J and Wang, Y and Liang, X and Meng, X}, title = {Effects of brain-computer interface-based rehabilitation on lower limb function and activities of daily living after stroke: a systematic review and meta-analysis.}, journal = {Frontiers in neurology}, volume = {17}, number = {}, pages = {1746958}, pmid = {41821632}, issn = {1664-2295}, abstract = {BACKGROUND: Lower limb motor dysfunction is a common sequela of stroke that significantly impacts patients' walking safety and independence in daily living. Although brain-computer interface (BCI) technology has demonstrated efficacy in upper limb rehabilitation, its effects on lower limb recovery have not yet been systematically evaluated.<br><br>METHODS: A systematic literature search was conducted across seven databases (PubMed, Web of Science, Embase, China National Knowledge Infrastructure, SinoMed, VIP Database, and Wanfang Data.) to identify studies investigating BCI for post-stroke lower limb dysfunction, encompassing records published up to September 2025. All statistical analyses were performed using Review Manager software (version 5.4.1).<br><br>RESULTS: Thirteen studies involving 582 participants were included. BCI training significantly improved the scores of Fugl-Meyer Assessment for Lower Extremity (FMA-LE, MD = 2.67, 95%CI: 2.31-3.03, P < 0.00001, I [2] = 0%), Berg Balance Scale (BBS, MD = 7.04, 95%CI: 3.14-10.94, P = 0.0004), and Modified Barthel Index (MBI, MD = 6.72, 95%CI: 1.74-11.69, P = 0.008). Furthermore, a single study reported significant improvement in functional mobility measured by the Timed Up and Go Test (TUGT). Subgroup analysis for activities of daily living MBI showed that a cumulative training time of &#x2265; 500 min was associated with greater improvement.<br><br>CONCLUSION: BCI-based training is an effective approach for improving lower limb recovery after stroke, demonstrating benefits in motor function, balance, and functional mobility. While evidence for certain outcomes remains limited, the dose-dependent effect on daily living activities underscores the importance of sufficient training duration. Future research should validate these findings and clarify effects across a broader range of functional measures.<br><br>https://www.crd.york.ac.uk/PROSPERO/view/CRD420251150558, identifier: CRD420251150558.}, }
@article {pmid41821240, year = {2026}, author = {Lee, HY and Fahad, MAA and Park, M and Kang, HJ and Jahan, N and Shanto, PC and Kim, H and Lee, BT and Bae, SH}, title = {In Vitro and In Vivo Evaluation of Decellularized Porcine Femoral Aorta Reinforced With Electrospun Coarse Polycaprolactone Fibers for Vascular Graft Application.}, journal = {Artificial organs}, volume = {}, number = {}, pages = {}, doi = {10.1111/aor.70106}, pmid = {41821240}, issn = {1525-1594}, support = {2021R1G1A1094894//Ministry of Science and ICT, South Korea/ ; 2015R1A6A1A03032522//National Research Foundation of Korea/ ; //Soonchunhyang University, Republic of Korea/ ; }, abstract = {BACKGROUND: The clinical translation of small-diameter vascular grafts (SDVGs) is still limited due to severe complications, including thrombosis, intimal hyperplasia, and arteriosclerosis, commonly associated with synthetic polymer-based grafts. To address these challenges, combining synthetic polymers with naturally derived extracellular matrices (ECMs) offers a promising strategy to enhance biofunctionality and remodeling potential.<br><br>METHOD: This study developed a composite vascular graft by electrospinning a polycaprolactone (PCL) fibrous outer layer onto decellularized porcine femoral aorta extracellular matrix (PECM), generating a hybrid PCL-PECM graft. Decellularization was validated using H&amp;E staining and DNA quantification, ensuring effective cellular removal without compromising protein content. Scanning electron microscopy (SEM) was used to evaluate the interface between PCL and PECM. Mechanical properties were assessed via tensile testing. Hemocompatibility was evaluated by hemolysis testing and blood clotting index (%BCI). In&#xa0;vitro biocompatibility was assessed using cell culture assays, and in&#xa0;vivo remodeling was evaluated through subcutaneous implantation in a rat model, followed by histological analysis.<br><br>RESULTS: H&amp;E staining and DNA analysis confirmed complete decellularization. SEM images revealed no delamination between layers, and the PCL layer significantly enhanced the mechanical strength of the graft. Hemolysis ratio remained below 5%, and %BCI exceeded 80%, indicating excellent hemocompatibility. In&#xa0;vitro studies confirmed cytocompatibility, while histological staining of explanted grafts showed robust cell infiltration and ECM remodeling.<br><br>CONCLUSION: The PCL-PECM vascular graft demonstrates excellent structural integrity, mechanical performance, hemocompatibility, and remodeling potential, indicating its promise as a next-generation SDVG.}, }
@article {pmid41821070, year = {2026}, author = {Xie, W and Lei, H and Ning, C and Dong, D and Zhang, X and Rao, H}, title = {Network analysis of childhood trauma and meaning in life in adolescents with and without depression.}, journal = {BMC psychology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s40359-026-04218-w}, pmid = {41821070}, issn = {2050-7283}, support = {82371537//National Natural Science Foundation of China/ ; 2024JJ5496//Natural Science Foundation of Hunan Province/ ; kq2202408//Natural Science Foundation of Changsha City/ ; }, }
@article {pmid41820589, year = {2026}, author = {Kong, L and Zhuang, Y and Zhu, B and Wang, H and Chen, Y and Shen, Y and Feng, X and Hu, S and Lai, J}, title = {A multi-omics analysis of gut bacteriome, virome, and serum metabolome in bipolar depression.}, journal = {Npj mental health research}, volume = {5}, number = {1}, pages = {}, pmid = {41820589}, issn = {2731-4251}, support = {2023YFC2506200, 2023YFC2506203//National Key Research and Development Program of China/ ; 82571735, 82471542//National Natural Science Foundation of China/ ; 2024C03098, 2025C02109//Key Research &amp; Development Program of Zhejiang Province/ ; JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; }, abstract = {The involvement of microbiota-gut-brain axis in bipolar disorder (BD) has been uncovered, yet the specific tripartite interplay between the gut bacteriome, virome, and serum metabolome remains to be elucidated. We conducted a cross-sectional multi-omics analysis on 90 drug-free patients with bipolar depression and 30 healthy controls. A significant between-group difference in gut bacterial α-diversity was observed. Non-parametric test revealed 1929 bacterial and 134 viral species with significant inter-group difference, among which 249 bacterial and 7 viral species remained significant after FDR correction (Padjusted < 0.05). Metabolomic analysis identified 261 significantly differential serum metabolites, which were enriched in 70 biological pathways and 40 pathways remained significant after correction. Integration of the datasets revealed strong cross-omic correlations, while only eight significant viral-metabolic correlations were detected. Post-FDR significant correlations with clinical features were exclusively observed between differential metabolites and scores of disease severity, with a predominance of negative correlations. Clinically, a random forest model integrating bacteriome, virome, and metabolome features achieved superior discriminative power (AUC = 0.986) compared to single-omics models (metabolites: 0.970; bacteria: 0.823; viruses: 0.732). This work demonstrated a dysregulated bacteriome-virome-metabolome network of patients with bipolar depression, providing a robust panel of candidate biomarkers for the precise diagnosis of BD.}, }
@article {pmid41820551, year = {2026}, author = {Li, Y and Li, S and Li, Y and Pang, X and Yi, C and Jiang, L and Yao, D and Wu, W and Li, F and Xu, P}, title = {Temporal synchrony and spatial similarity of interbrain subnetworks predict dyadic social interaction.}, journal = {Communications biology}, volume = {}, number = {}, pages = {}, doi = {10.1038/s42003-026-09854-x}, pmid = {41820551}, issn = {2399-3642}, support = {W2411084//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82372084//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Human social behaviors involve complex interactions between individuals, and understanding how interbrain neural activity reflects and predicts these interactions is critical for advancing social cognitive neuroscience. While electroencephalography (EEG) hyperscanning has been widely used to explore interpersonal neural dynamics, most studies focus on pairwise regional coupling, overlooking the brain's intrinsic network-level organization. Here, we propose a spatiotemporal network analysis framework that combines Bayesian non-negative matrix factorization with EEG source imaging to identify interpretable subnetworks with spatiotemporal information. Applying this framework to dyadic EEG datasets from interactive decision-making tasks identifies eight task-relevant subnetworks, including the default mode network (DMN), somatosensory-motor network (SMN), and visual network (VN). Effective interpersonal coordination was associated with enhanced network-level time-domain interbrain synchrony and spatial-domain inter-subject similarity, and the fusion of these metrics reliably predicted interactive behaviors. Notably, synchrony and similarity involving DMN, VN, and SMN emerge as robust predictors of interactive behaviors, with spatiotemporal coupling most prominent within these subnetworks. These findings reveal spatiotemporal network signatures underlying interpersonal neural synchronization and demonstrate the importance of distributed subnetworks and their temporal and spatial alignment in achieving effective social interactions. This framework provides a useful computational tool for probing the neurobiological basis of social behaviors.}, }
@article {pmid41818827, year = {2026}, author = {Hore, A and Chakrabarti, S and Bandyopadhyay, S}, title = {Incorporating a variety of synaptic dynamics in neuromorphic hardware: Different types of inhibition and plasticity.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae512d}, pmid = {41818827}, issn = {1741-2552}, abstract = {OBJECTIVE: This study aims to design a CMOS-based circuit that mimics the behavior of real brain synapses, focusing on both plasticity and inhibi- tion. The goal is to improve the biological realism and learning ability of neuromorphic hardware.<br><br>APPROACH: &#xD;A unified CMOS-based synaptic architecture is proposed&#xD;that integrates short-term plasticity (STP) and long-term&#xD;plasticity (LTP) with two forms of synaptic inhibition:&#xD;divisive and subtractive. The STP circuit models short-&#xD;term depression (STD) and facilitation (STF), while the&#xD;LTP mechanism employs spike-timing-dependent plastic-&#xD;ity (STDP) to capture temporally driven synaptic mod-&#xD;ifications. Furthermore, a spiking neuronal network is&#xD;designed to demonstrate biologically accurate inhibitory&#xD;effects and to perform max pooling via divisive inhibition.&#xD;All circuits are implemented and simulated in TSMC 180&#xD;nm CMOS using Cadence Virtuoso.<br><br>MAIN RESULTS: The&#xD;proposed circuits successfully reproduce key biological&#xD;features of synaptic behavior. The STP and LTP blocks&#xD;enable time-dependent modulation of synaptic weights,&#xD;while the inhibitory networks exhibit both divisive and&#xD;subtractive control over postsynaptic firing frequency.&#xD;The maxpooling operation, achieved via divisive inhibi-&#xD;tion, allows the target neuron to respond to the input&#xD;with the highest spiking activity selectively. Simulation&#xD;results confirm the correct functional behavior of all&#xD;the designed circuits.<br><br>SIGNIFICANCE: This work provides&#xD;a simple and effective hardware solution for modeling&#xD;fundamental synaptic functions. It supports adaptive&#xD;learning and efficient processing in neuromorphic sys-&#xD;tems. The results can help build better brain-like systems&#xD;for AI, robotics, and brain-computer interfaces.}, }
@article {pmid41818825, year = {2026}, author = {Tidare, J and Johansson-Alvarez, M and Plantin, J and Palmcrantz, S and Astrand, E}, title = {Exploration of using "distance-to-bound" to manipulate the difficulty during motor imagery BCI training after stroke - A clinical two-cases study.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae512c}, pmid = {41818825}, issn = {1741-2552}, abstract = {Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) is a promising technology for neurorehabilitation after stroke. However, many face challenges in using a BCI because they fail to produce discriminable patterns in their brain activity. Personalizing the BCI task difficulty could help the learning process of these users but there is currently very limited knowledge on which methods can be used online. Our aim was to explore a distance-to-bound (DTB) approach for adapting MI BCI task difficulty in real time. Approach: Two chronic stroke patients performed 12 BCI training sessions over 4 weeks during which they performed MI of open- and close hand movements and received continual visual feedback based on multivariate decoding of ongoing electroencephalogram (EEG) activity. We increased the difficulty and maintained it by adapting it in real time based on DTB decoding metrics and, by using a multiple-session design, we investigated the stability of this approach and how it related to MI-related EEG activity of each patient. Main results: We show that patients had to produce stronger alpha and beta event-related desynchronisation/synchronisation (ERDS) pattern across the sensorimotor cortical areas of the brain to receive positive feedback. In addition, we show that the online adaptation converged within sessions as well as accommodating for drift in the data both within and between sessions. We suggest that the DTB approach can effectively be used to control BCI task difficulty which could, in future BCIs, serve as a potential tool to guide patients to produce functionally relevant activity patterns. However stronger sensorimotor ERDS did not correlate to improved motor function in one of our two patients. As this result is observational and cannot support causal claims, it exemplifies the need to individually tailor the translation of DTB outputs to feedback considering the stroke lesion and EEG activity profile of the specific patient. Significance: This study provides valuable insights and considerations for BCI difficulty adaptation in the aim of developing more effective training protocols in BCI-based stroke rehabilitation. .}, }
@article {pmid41815306, year = {2026}, author = {Ezzeldin, M and Hassan, AE and Ezzeldin, R and Adachi, K and Soliman, Y and Alshekhlee, A and Hussain, MS and Niazi, M and Sheriff, F and Bushnaq, S and Asif, K and Tanweer, O and Alaraj, A and Grandhi, R and Janjua, N and Vela-Duarte, D and Chaubal, V and Al Matairi, A and Mir, O and Mealer, L and Ezepue, C and AlMajali, M and Chaudhari, A and Martucci, M and Abdulrazzak, MA and Maud, A and Rodriguez, G and Miller, S and Quispe-Orozco, D and Suppakitjanusant, P and Froukh, M and Bains, N and Bhatti, I and Xu, J and Abou-Mrad, T and Salah, W and Shoraka, O and Ali, Z and Zaidat, O and Siddiq, F}, title = {Carotid Artery Stenting Outcomes in Comprehensive Stroke Hospitals (CASSH): A Prospective Multicenter Study.}, journal = {Stroke (Hoboken, N.J.)}, volume = {6}, number = {2}, pages = {e002201}, pmid = {41815306}, issn = {2694-5746}, abstract = {BACKGROUND: The CASONI study (Carotid Artery Stenting Outcomes by Neurointerventional Surgeons) showed that proceduralist experience significantly reduces complications in carotid artery stenting. The CASSH study (Carotid Artery Stenting Outcomes in Comprehensive Stroke Hospitals) prospectively evaluates real-world carotid artery stenting outcomes by fellowship-trained neurointerventionalists at comprehensive stroke centers across the United States to validate and expand on CASONI's findings.<br><br>METHODS: CASSH is a multicenter, prospective observational study conducted across 15 US comprehensive stroke centers from January 2023 to December 2024. Adults with symptomatic &#x2265;50% or asymptomatic &#x2265;70% carotid stenosis undergoing carotid artery stenting by fellowship-trained neurointerventionalists were included. The primary outcome was a 30-day composite of procedure-related death, stroke, or myocardial infarction. Secondary outcomes included nonprocedural mortality, access site complications, stent thrombosis, and other adverse events. Logistic regression identified predictors of adverse outcomes.<br><br>RESULTS: Among 889 patients (mean age 70.3&#xb1;9.9 years; 61.4% male), 87.1% had hypertension and 63.1% were symptomatic. The 30-day composite primary outcome occurred in 1.2% (mortality 0.8%, ischemic stroke 0.3%, hemorrhagic stroke 0.2%, myocardial infarction 0.2%). Composite secondary outcome occurred in 5.4%, most commonly access site complications (1.7%) and nonprocedural mortality (1.5%). Higher preprocedural modified Rankin Scale (odds ratio [OR], 1.42), National Institutes of Health Stroke Scale score (OR, 1.09), and longer fluoroscopy times (OR, 1.02) were associated with increased complication risk. Mortality was independently predicted by elevated modified Rankin Scale (OR, 1.72), higher National Institutes of Health Stroke Scale score (OR, 1.15), older age (OR, 1.05 per year), and lower ejection fraction (OR, 0.96). Postprocedural antiplatelet therapy was protective, reducing both complications (OR, 0.03) and mortality (OR, 0.07).<br><br>CONCLUSIONS: Carotid artery stenting performed by fellowship-trained neurointerventionalists at comprehensive stroke centers is associated with low rates of periprocedural stroke, myocardial infarction, and death. These outcomes align with the landmark CREST-2 trial (Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis Trial), particularly in asymptomatic patients, and are strongly influenced by preprocedural disability, stroke severity, age, and cardiac function, underscoring the importance of patient selection and optimized perioperative care.}, }
@article {pmid41812365, year = {2026}, author = {Khalili, MD and Abootalebi, V and Saeedi-Sourck, H and Santoro, A and Behjat, HH}, title = {Small-world scale-free brain networks from EEG with application to motor imagery decoding and brain fingerprinting.}, journal = {Computers in biology and medicine}, volume = {206}, number = {}, pages = {111606}, doi = {10.1016/j.compbiomed.2026.111606}, pmid = {41812365}, issn = {1879-0534}, abstract = {Developing individualized spatial models that capture the complex dynamics of multi-electrode EEG data is essential for accurately decoding global neural activity. A widely used approach is network modeling, where electrodes are represented as nodes. A key challenge lies in defining the network edges and weights, as precise connectivity estimation is critical for enhancing neural characterization and extracting discriminative features, such as those needed for task decoding. Traditional EEG-derived brain graphs often fail to capture biologically grounded organizational principles such as small-world structure and heavy-tailed (scale-free) connectivity patterns. To address this gap, we introduce a framework for inferring subject-specific EEG-based brain graphs that are explicitly designed to exhibit small-world and scale-free properties. Our approach begins by computing phase-locking values (PLV) between EEG channel pairs to build a backbone graph, which is then refined into an individualized small-world and scale-free network. To reduce computational complexity while preserving subject-specific characteristics, we apply Kron reduction to the resulting graph. Using two public EEG datasets, we evaluate the proposed method on motor imagery (MI) decoding and brain fingerprinting tasks. Our approach improves MI classification accuracy by 4-7% compared to conventional PLV, small-world, and scale-free graph models, and enhances differential identifiability in fingerprinting by 8-20% across six canonical frequency bands. These gains were statistically significant in both applications. Moreover, integrating graph signal processing features derived from our constructed graphs with classical EEG features further boosts performance. Overall, our findings highlight the potential of the proposed graph construction framework to enhance EEG analysis. By jointly capturing local segregation, global integration, and hub-driven hierarchical organization, the method strengthens downstream decoding and identification tasks, with promising implications for a wide range of applications in cognitive neuroscience and brain-computer interface research.}, }
@article {pmid41811559, year = {2026}, author = {Chen, Q and Jing, Y and Bu, W and Zhang, J and Liu, W and Shi, C and Liu, C and Su, D}, title = {RELA as a Diagnostic Biomarker for Parkinson's Disease by Integrating Ferroptosis, Lipid Metabolism, and Neuroinflammation.}, journal = {Inflammation}, volume = {}, number = {}, pages = {}, doi = {10.1007/s10753-026-02478-7}, pmid = {41811559}, issn = {1573-2576}, support = {2024202003//Jinan Municipal Health Commission Science and Technology Plan Project/ ; 202204040490//Shandong Provincial Medical and Health Science and Technology Development Plan Projec/ ; }, }
@article {pmid41810068, year = {2026}, author = {Hao, H and Jiao, X and Zhou, G and Chen, L and Wang, M and He, J and Lang, X and Zhang, J and Shi, L and An, M and Yan, L and Zhu, Y and Yang, Y}, title = {An Analogue Memristor Based on Conjugated Porous Polymer Composite for Artificial Synapse.}, journal = {Exploration (Beijing, China)}, volume = {6}, number = {1}, pages = {20250234}, pmid = {41810068}, issn = {2766-2098}, abstract = {Artificial synapses have emerged as a pivotal technological advancement in mimicking brain functions. Organic memristors are desirable for hardware implementation of artificial synapses, owing to their remarkable mechanical flexibility, high biocompatibility at cell-device interfaces, and adjustable material structure. Developing appropriate organic polymers with carbon dots modification will enable the memristor to possess analog-type resistive switching behavior, crucial for realizing brain-like associative learning and adapting dynamic variations of neuron connection strength. In this work, an artificial synapse based on the analogue organic memristor integrating neuromorphic computing and neural interface functions is proposed, utilizing synthetic conjugated porous polymers to construct composites with boron-doped carbon dots. The structure-property relationship of alkynyl and alkyl chains in polymers is elucidated, alongside the synergistic effect of local photoinduced redox and hole templating in composites that endows the device with analog-type resistive switching behavior. Moreover, the memristor presents impressive synaptic plasticity and associative memory learning potential for neuromorphic computing, and further serves as a core unit in flexible artificial neural interface chips, demonstrating dynamic information transmission with neural systems. This study will promote the further development of organic artificial synapses for neuromorphic computing and brain-machine interfaces.}, }
@article {pmid41809911, year = {2026}, author = {Reyes-Jiménez, F and Rosas-Agraz, F and Macias-Naranjo, E and Alvarado-Rodríguez, FJ and Vélez-Pérez, H and Romo-Vázquez, R and Guzmán-Quezada, E}, title = {EEG and EMG dataset for analyzing movement-related cortical potentials in hand gesture tasks.}, journal = {Data in brief}, volume = {65}, number = {}, pages = {112596}, pmid = {41809911}, issn = {2352-3409}, abstract = {This dataset contains electroencephalography (EEG) and electromyography (EMG) recordings acquired during the execution of specific motor tasks aimed at eliciting movement-related cortical potentials (MRCP). The goal is to provide an accessible resource for research in brain-computer interfaces (BCI), neurorehabilitation, and EEG-based prosthetic control. Data were collected from 40 healthy participants aged 18-30 years across five sessions, each comprising ten right-hand fist closure movements, guided by a custom Python-based visual interface. EEG signals were recorded using a 32-channel EMOTIV Flex 2 wireless system following the international 10-10 system, with a sampling rate of 128 Hz and electrode placement focused on the central cortical areas. All recordings, including raw EEG, raw EMG, and event triggers synchronized with the visual interface, were stored in .CSV format. To demonstrate that the EEG recordings in the dataset contain sufficient low-frequency information for MRCP analysis, we applied a standard preprocessing pipeline consisting of a common average reference (CAR), a Anti-Laplacian spatial filter, and a 0.1-1 Hz Butterworth band-pass filter. This procedure was used only for internal validation, allowing us to visualize the expected MRCP components from the nine motor-related electrodes. It is important to emphasize that these processed signals are not included in the database. The public dataset contains only the raw EEG and EMG recordings, so that users may apply their preferred preprocessing and analysis methods. The dataset was collected under controlled laboratory conditions at the Medical Devices Laboratory, Universidad Autónoma de Guadalajara, and represents a valuable contribution to the understanding and application of MRCP in BCI research.}, }
@article {pmid41809488, year = {2026}, author = {Al-Sheikh, U and Cheng, H and Bakrbaldawi, AAA and He, L and Chen, D and Zhan, R and Kang, L and Zhang, Y}, title = {A transcriptomic resource for glial GABA-associated ASH neuronal aging and candidate pathways.}, journal = {Frontiers in aging neuroscience}, volume = {18}, number = {}, pages = {1677754}, pmid = {41809488}, issn = {1663-4365}, abstract = {INTRODUCTION: Neuronal aging is tightly linked to neurodegeneration with dysregulation of GABA (gamma-aminobutyric acid), the primary inhibitory neurotransmitter, contributing to age-associated neuronal impairment. Our prior work demonstrated that restoring the key GABA-synthesizing enzyme UNC-25 (glutamic acid decarboxylase, GAD) in Caenorhabditis elegans AMsh glia mitigates age-related neurodegeneration. This study aims to provide a transcriptomic resource and identify potential pathways associated with glial GABA modulation during neuronal aging.<br><br>METHODS: ASH neurons from day 1 and day 7 nematodes were isolated and FACS-purified (Psra-6::RFP+/Pgpa-4::GFP-) from three distinct groups: Wild-type, unc-25 mutants, unc-25 mutants with AMsh glia-specific UNC-25 rescue. RNA-seq used Illumina NovaSeq (150 bp PE reads, aligned to WormBase WS293). DESeq2 identified DEGs (FDR < 0.05, fold-change &#x2265; 1); clusterProfiler performed GSEA and pathway enrichment. Comparisons also included AMsh glia vs. ASH neurons in wild young adults.<br><br>RESULTS: Here, we present transcriptomic data of glutamatergic ASH sensory neurons (a critical target of aging-related neurodegeneration) from three aging groups: wild-type worms, unc-25 (GABA-deficient) mutants, and unc-25 mutants with AMsh glia-specific UNC-25 rescue. Transcriptomic analyses revealed distinct transcriptional profiles across groups. Notably, the Hedgehog signaling pathway and its transcriptional effector TRA-1/GLI, the C. elegans GLI ortholog, were specifically upregulated in the glial rescue group, while the neuroprotective transcription factor HSF-1 was downregulated, suggesting these pathways as potential mediators of glial GABA-associated neuroprotection. We also provide transcriptomic comparisons between AMsh glia and ASH neurons in young worms, laying a foundation for understanding glia-neuron crosstalk.<br><br>CONCLUSIONS: This work establishes a valuable transcriptomic resource for glial GABA-associated ASH neuronal aging and identifies candidate pathways, offering critical molecular insights to dissect age-related neurodegeneration mechanisms and inform potential therapeutic targets.}, }
@article {pmid41809440, year = {2026}, author = {Yan, Y and Zhao, X and Zhang, Y and Li, W and Lin, Z and Zhou, Y and Fang, S and Huang, J and Chen, CL and Lin, Z and Xu, X}, title = {Cerebral oxygen extraction and blood flow in community-based older adults: associations with white matter hyperintensity and neurocognitive function.}, journal = {Brain communications}, volume = {8}, number = {2}, pages = {fcag056}, pmid = {41809440}, issn = {2632-1297}, abstract = {Cerebral oxygen extraction fraction (OEF) and cerebral blood flow (CBF) are key haemodynamic markers. Emerging evidence suggests that they may exert compensatory effects on small vessel disease and cognitive outcomes, with potentially nonlinear relationships, particularly in community-dwelling seniors. Therefore, we conducted a cross-sectional study of 296 participants from the Heritage Study in China. OEF was assessed using T2-relaxation-under-spin-tagging (TRUST) MRI, while CBF was measured using phase contrast MRI. White matter hyperintensity (WMH) volumes were segmented through T2 fluid-attenuated inversion recovery (FLAIR) imaging and log-transformed. Neurocognitive function was evaluated across multiple domains and summarized as a global composite Z-score. Based on the median values of CBF and OEF, participants were categorized into four quadrants and generalized linear models were used to examine associations between OEF CBF patterns and WMH and cognition. Participants with high OEF and low CBF had highest WMH volume (4.48 ± 8.02 cm3) and worse cognitive performance (-0.13 ± 1.04). Overall, higher OEF was significantly related to lower global cognition (P = 0.012), whereas lower CBF was significantly associated with greater WMH burden (P = 0.001). Compared with those in high OEF and low CBF, individuals in low OEF and high CBF exhibited significantly lower WMH volume (β = -0.55, 95% confidence interval (CI) = [-1.05, -0.05]) and better cognition (β = 0.28, 95% CI = [0.02, 0.54]). In contrast, low OEF and low CBF were associated with relative cognitive reserve (β = 0.32, 95% CI = [0.02, 0.61]) but higher WMH volume. Domain-based analyses for attention, visuospatial and memory functions showed similar results. To further explore potential nonlinear effects, response surface analysis was performed to investigate relationships among OEF, CBF, WMH, and global cognition, revealing a significant association between CBF and WMH (β = -1.42, 95% CI = [-2.85, -0.01]). For global cognitive performance, OEF was negatively associated with cognitive outcomes (OEF: β = -0.49, 95% CI = [-0.87, -0.11], OEF[2]: β = 0.01, 95% CI = [0.00, 0.01]), indicating a U-shaped association between OEF and cognition. Notably, when CBF was high, cognition was relatively preserved even under higher OEF. In summary, OEF emerged as a sensitive marker of cognitive vulnerability in community-based seniors, particularly in attention, executive function, visuospatial ability, and memory, while CBF was the primary determinant of WMH burden. Combined OEF CBF patterns enabled classification of at-risk community-dwelling individuals, with the 'misery perfusion' pattern (high OEF, low CBF) showing the most adverse profile and representing a promising target for early risk stratification.}, }
@article {pmid41809003, year = {2026}, author = {Mishler, J and Salimi, M and Koloski, M and Rembado, I and Shilyansky, C and Mishra, J and Ramanathan, D}, title = {Cortical oscillations reflect opponent ensemble dynamics through coordinated multifrequency activity.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.02.20.707132}, pmid = {41809003}, issn = {2692-8205}, abstract = {Neural oscillations are widely used as proxies for neuronal activity, where power in individual frequency bands is commonly interpreted as functionally indexing neural circuit engagement. However, power in individual frequency bands shows heterogeneous and sometimes opposing relationships with neuronal activity across regions and behavioral contexts, challenging the assumption of a stable frequency-to-circuit mapping. Here we show that glutamatergic population activity in rat medial prefrontal cortex is not stably linked with power in isolated frequency bands, but rather with dynamically recurring multi-frequency amplitude co-fluctuations. These multi-frequency patterns, termed spectral motifs, occurred in opponent pairs with nearly identical frequency composition but inverted relationships to population calcium activity. This opponent motif structure, observed across cortical regions and species, provides a key component for understanding how oscillations are linked to neuronal activity. We found that shifts in motif opponency balance explained changes in glutamatergic activity that occur during brain-computer interface learning better than models based on frequency band power alone. Furthermore, opponent motifs map selectively onto opponent cell ensembles and enable bidirectional mapping between local field potentials and ensemble activity. These findings identify multi-frequency opponent motifs as a conserved organizational principle linking oscillatory dynamics to population-level circuit states and challenge the notion that individual frequency bands can serve as interpretable functional units mapping onto neural circuit activity.}, }
@article {pmid41808199, year = {2026}, author = {Chen, X and Zeng, GQ and Ma, R and Li, N and Zhang, M and Zhang, X}, title = {The Distinct Roles of the Dorsolateral Prefrontal Cortex and Dorsal Anterior Cingulate Cortex in Cognitive Control: Evidence From Transcranial Temporal Interference Stimulation.}, journal = {Psychophysiology}, volume = {63}, number = {3}, pages = {e70269}, doi = {10.1111/psyp.70269}, pmid = {41808199}, issn = {1469-8986}, support = {2024YFF0507600//National Key R&amp;D Program of China/ ; 2021ZD0202101//The Chinese National Programs for Brain Science and Brain-like Intelligence Technology/ ; 32571266//The National Natural Science Foundation of China/ ; 32171080//The National Natural Science Foundation of China/ ; 32400919//The National Natural Science Foundation of China/ ; 32200914//The National Natural Science Foundation of China/ ; ZSYS[2024]001//the Project of Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing QianKeHe Platform/ ; 2408085QC081//Natural Science Foundation of Anhui Province/ ; 24YJCZH014//the Humanities and Social Science Fund of the Ministry of Education of China/ ; AHWJ2024Aa10016//Anhui Provincial Health Scientific Research Project/ ; //Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; }, mesh = {Humans ; *Gyrus Cinguli/physiology ; Male ; Adult ; Female ; *Dorsolateral Prefrontal Cortex/physiology ; *Memory, Short-Term/physiology ; Young Adult ; *Transcranial Direct Current Stimulation ; *Executive Function/physiology ; Stroop Test ; *Prefrontal Cortex/physiology ; Conflict, Psychological ; }, abstract = {Correlational evidence has accumulated on the distinct roles of dorsolateral prefrontal cortex (dlPFC) and dorsal anterior cingulate cortex (dACC) in cognitive control. However, causal evidence, especially regarding the dACC, is lacking. One of the main reasons is the limited focality and penetration depth of the conventional transcranial stimulation methods in targeting deep brain regions. This study aims to provide evidence for the dlPFC and dACC's roles in cognitive control using a novel transcranial stimulation method, i.e., temporal interference (TI) stimulation. By comparing pre- and post-stimulation effects on the conflict effect (CE) across individuals with different levels of working memory capacity (WMC), we seek to elucidate the differential impact of stimulating these brain regions and their interaction with WMC in enhancing cognitive control abilities. Cognitive control was assessed using the CE in a Stroop task. The study compared the pre- and post-stimulation effects of TI stimulation (dlPFC, dACC, and sham) on CE among individuals with varying levels of WMC. The results showed that dACC stimulation enhanced cognitive control regardless of WMC, while dlPFC stimulation improved control only in low WMC individuals. Distinct effects of dlPFC and dACC stimulation on cognitive control in varying WMC levels support the hypothesis that they play differing roles. TI stimulation shows promise for enhancing cognitive control.}, }
@article {pmid41806473, year = {2026}, author = {Cimarosto, P and Velut, S and Cabrera Castillos, K and Torre-Tresols, JJ and Roy, RN and Dehais, F}, title = {Near-invisible c-VEP-based passive BCI for mental workload monitoring.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae4ff6}, pmid = {41806473}, issn = {1741-2552}, abstract = {Flickering visual stimuli, either periodic (SSVEP) or aperiodic (c-VEP), have shown strong potential for implementing reactive Brain-Computer Interfaces (BCIs) and enabling hands-free interaction. Yet, their adaptation to passive BCIs remains limited, largely due to the distracting nature of flickers and its impact on visual comfort. Approach: In this study, we introduce an unobtrusive approach that embeds near--invisible, texture-based flickers over regions of interest in the user interface, combined with a c-VEP passive BCI pipeline to assess mental workload. We validated the approach in two experiments: (i) within an ecologically valid multitasking microworld of flying, and (ii) in a flight Simulator, where cognitive workload was systematically varied across three levels. Main results: Results at the group level disclosed that the amplitude of visual ERPs was significantly reduced under higher workload, providing an insightful neural marker for workload assessment. Moreover, results demonstrated that the proposed pipeline successfully enabled the derivation of indexes sensitive to workload-related modulation. Significance: These findings highlight the potential of textured flicker and c-VEP-based passive BCIs for monitoring cognitive workload in complex operational environments.}, }
@article {pmid41806395, year = {2026}, author = {Liu, C and Liu, K}, title = {A Lightweight Transformer Model for Robust EEG Emotion Recognition Using Channel-Wise Differential Entropy.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae4fc2}, pmid = {41806395}, issn = {2057-1976}, abstract = {With the increasing demand for emotion recognition technology in fields such as healthcare, humancomputer interaction, and education, efficiently and accurately decoding emotional information from EEG signals has become a research hotspot. This paper proposes a brain EEG emotion recognition model, Channel-wise Differential Entropy Transformer (CWDET), based on the combination of differential entropy (DE) features and Transformer encoder. In this method, DE features of EEG signals are first extracted in five frequency bands: δ, θ, α, β, and γ. Each channel is treated as an independent input token, and through simple but efficient embedding and positional encoding, low-dimensional information is mapped into highdimensional space. The multi-head self-attention mechanism is then employed to achieve global feature fusion across channels, effectively reducing data redundancy and computational cost. The experiments conducted on the SEED and SEED-IV datasets achieved high classification accuracies of 98.63% and 99.16%, respectively, with the model performing excellently in terms of standard deviation and stability. Further analysis of the attention weights reveals that the model automatically focuses on key brain regions such as the prefrontal area, central, and centralparietal junction. Even when selecting only a subset of channels, the model still maintained 93.44% recognition performance on the SEED-IV dataset. Comparative experiments with various existing advanced methods show that CWDET offers a simple structure and computational efficiency while maintaining high performance, providing a feasible low-resource solution for practical EEG emotion recognition applications. This work not only provides new theoretical and practical support for the development of EEG emotion recognition technology but also lays a solid foundation for future generalization research across subjects and sessions.}, }
@article {pmid41806126, year = {2026}, author = {Bai, S and Cao, X and Xie, M and Sun, G and Wang, X and Zheng, L and Li, X and Lin, Z and Gao, L}, title = {Acoustic Flutter Processing in the Inferior Colliculus of Awake Marmosets: Complementary Rate Coding Modulated by Acoustic Parameters.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {41806126}, issn = {1995-8218}, abstract = {The acoustic flutter is processed through complementary monotonic rate coding and cannot be modulated by other acoustic parameters in the auditory cortex (AC). However, it remains unclear how the inferior colliculus (IC) encodes acoustic flutter, especially when changing other acoustic parameters. Here, we recorded IC neural activity in response to acoustic flutter and determined the existence of conjunctive processing between repetition rate and other acoustic parameters. We found that most IC neurons also encode the repetition rate at the flutter range through complementary monotonic rate coding. In addition, although the acoustic parameters did not change their monotonicity, most IC neurons encode both repetition rate and other acoustic parameters, different from the flutter processing in AC. Thus, complementary monotonic rate coding for acoustic flutter was widespread in the auditory system; however, coding specificity for repetition rate increased from IC to AC, and the capacity for conjunctive coding with other acoustic parameters decreased.}, }
@article {pmid41805787, year = {2026}, author = {Wang, Y and Yu, X and Lu, S}, title = {Strategies for updating rules driven by reinforcement learning to solve social dilemmas.}, journal = {PloS one}, volume = {21}, number = {3}, pages = {e0341925}, pmid = {41805787}, issn = {1932-6203}, mesh = {Humans ; *Cooperative Behavior ; *Learning ; *Reinforcement, Psychology ; *Game Theory ; Algorithms ; }, abstract = {This study incorporates historical performance into traditional imitation rules and proposes a moderated strategy update rule. In this framework, an individual's temporal historical performance is calculated using the BM model. By adjusting the parameter δ, the influence of historical performance on strategy learning is determined, and the evolution of cooperation is subsequently observed. Results show that the proposed strategy update rule promotes cooperation more effectively than the traditional version, and systemic cooperation is further enhanced as δ increases. The reason why the proposed rule enhances cooperation is that it amplifies the evaluation of cooperative behavior while compressing the evaluation of defective behavior. Although establishing system objectives may hinder the diffusion of cooperative behavior, appropriate performance evaluation mechanisms can mitigate this adverse effect. Our results indicate that multidimensional evaluation can provide a theoretical basis for explaining cooperative behavior in complex environments.}, }
@article {pmid41804589, year = {2026}, author = {Koc, G and Yousif, MAA and Ozturk, M}, title = {Motor and Cognitive Imagery Detection from MEG Signals Using Wavelet-Based Common Spatial Pattern Analysis.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2650022}, doi = {10.1142/S012906572650022X}, pmid = {41804589}, issn = {1793-6462}, abstract = {Brain-computer interface (BCI) technology supports the interactions of individuals with severe neuromuscular limitations with their environment. This work presents a classification approach for distinguishing motor imagery (MI) from speech-related cognitive imagery (CI) such as word generation and arithmetic subtraction, using magnetoencephalography (MEG) signals. Differentiating MI and CI/SI processes is relevant for expanding command diversity in hybrid BCI systems and for clarifying the distinct neural mechanisms underlying motor versus verbal-semantic processing. Although a large proportion of noninvasive BCI studies focus on MI, this distinction has received relatively limited attention, particularly in MEG-based approaches. Making this distinction is important for increasing command diversity in hybrid BCI systems and for improving the understanding of neural mechanisms associated with motor and verbal-semantic processing. Tasks from an open-access MEG dataset were analyzed across six binary pairs (H-F, H-W, H-S, F-W, F-S, W-S). MEG signals were processed using two frequency-separation strategies: a broad-band configuration (FSB-1: 8-14[Formula: see text]Hz and 14-30[Formula: see text]Hz) and a narrow-band configuration (FSB-2: six sub-bands between 8 and 32[Formula: see text]Hz). Time-frequency features were extracted using continuous wavelet transform (CWT), and spatial features via the common spatial pattern (CSP) method. Feature selection followed a two-stage procedure: (i) t-test ranking to obtain a shared feature set for all task pairs; and (ii) subject- and task-specific optimization of feature number. The initial evaluation based on the shared feature set showed that the FSB-2/CWT approach yielded better classification accuracies compared to FSB-1/CWT (H-F: 56%, H-W: 71%, H-S: 66% versus 54%, 68%, 64%). With subject- and task-adaptive optimization, additional improvements were observed. Accuracies increased to 60%, 72%, and 69% for FSB-1, and to 63%, 75%, and 71% for FSB-2, for H-F, H-W, and H-S, respectively. Overall, the findings indicate that the proposed CWT[Formula: see text]CSP framework, particularly when combined with adaptive feature optimization, offers an interpretable analysis approach that can contribute to MI-CI discrimination in MEG-based BCI systems under limited data conditions.}, }
@article {pmid41804086, year = {2026}, author = {Yang, T and Cao, M and Qian, Z and Chen, J}, title = {Flavor-Oriented Brain-Computer Interface (Flavor-BCI): Neural Decoding of Eating and Sensory Perception With Emerging Applications in Food Evaluation.}, journal = {Comprehensive reviews in food science and food safety}, volume = {25}, number = {2}, pages = {e70442}, doi = {10.1111/1541-4337.70442}, pmid = {41804086}, issn = {1541-4337}, support = {82151311//National Natural Science Foundation of China/ ; 81827803//National Major Scientific Instruments and Equipment Development Project/ ; 81727804//National Major Scientific Instruments and Equipment Development Project/ ; BE2020705//Jiangsu Province Key Research and Development Program/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Taste Perception/physiology ; *Taste/physiology ; *Brain/physiology ; Eating ; }, abstract = {Flavor-induced sensory satisfaction is critical for food acceptance and market success. However, traditional sensory evaluation methods, relying heavily on subjective assessments, often fail to accurately reflect real-time, objective neural processing underlying complex multisensory flavor experiences. This limitation highlights the need for innovative methods that objectively quantify how flavors are perceived and integrated within the brain. In this review, we first examine the neural pathways underlying flavor perception, focusing on how gustatory, olfactory, and oral somatosensory inputs interact with reward and hedonic networks to form integrated flavor experience. Building on this foundation, we then outline the latest strategies for developing flavor-oriented brain-computer interface (flavor-BCI), summarizing key features of various neuroimaging techniques and associated technical implementation workflows. Finally, we assess emerging applications of flavor-BCI in sensory assessment and consumer decision-making and identify opportunities and challenges for future food design and product development. Flavor perception begins with parallel encoding of chemical stimuli in the primary gustatory and olfactory cortices and in trigeminal pathways. These signals are subsequently integrated in higher order regions, forming a distributed neural network across cortical, limbic, and subcortical structures that support flavor recognition, hedonic appraisal, and motivated eating. Flavor-BCI systems record neural activity from these regions using electrophysiology or neuroimaging and apply advanced algorithms to decode neural representations, translating them into objective sensory outputs. Relative to traditional evaluations, this approach enables real-time, precise quantification of flavor experience. Flavor-BCI thus offers promising avenues for intelligent sensory evaluation and novel human-machine interactions.}, }
@article {pmid41802861, year = {2026}, author = {Yang, J and Jiang, H and Bai, Y and Ni, G and Teng, X}, title = {Short-Term Perceptual Training Modulates Neural Responses to Deepfake Speech but Does Not Improve Behavioral Discrimination.}, journal = {eNeuro}, volume = {}, number = {}, pages = {}, doi = {10.1523/ENEURO.0300-25.2026}, pmid = {41802861}, issn = {2373-2822}, abstract = {Rapid advancements in artificial intelligence (AI) have enabled text-to-speech (TTS) systems to produce voices increasingly indistinguishable from humans, posing significant societal risks, particularly through potential misuse in fraud and deception. To address this concern, this study combined behavioral assessments and neural measures using electroencephalography (EEG) to examine whether short-term perceptual training enhances people's ability to distinguish AI-generated from human speech. Thirty participants (of either sex) listened to sentences produced by human speakers and corresponding AI-generated clones, judging each sentence as either human or AI-generated before and after a brief (∼12-minute) training session, during which voices were explicitly labeled as "human" or "AI". Behaviorally, participants showed consistently poor discrimination before and after training, with only minimal improvement. However, neural analyses revealed substantial training-induced changes. Specifically, temporal response function (TRF) analysis identified significant neural differentiation between speech types at early (∼55 ms, ∼210 ms) and later (∼455 ms) auditory processing stages following training. Additional EEG analyses, including spectral power and decoding, were conducted to further investigate training effects, but these measures revealed limited differentiation. The findings here highlight a dissociation between behavioral and neural sensitivity: while listeners struggle to behaviorally discriminate sophisticated AI-generated voices, their auditory systems rapidly adapt to subtle acoustic differences following short-term exposure. Understanding this neural-behavioral dissociation is crucial for developing effective perceptual training protocols and informing policies to mitigate societal threats posed by increasingly realistic synthetic voices.Significance Statement Artificial intelligence (AI)-generated voices are becoming increasingly indistinguishable from real human speech, raising serious concerns about fraud as scammers can convincingly impersonate trusted individuals. Our study shows that even when listeners cannot behaviorally distinguish AI-generated voices from real human voices, brief perceptual training enables their brains to detect subtle acoustic differences. Our findings thus reveal a dissociation between neural sensitivity and behavioral performance in recognizing AI-generated speech. By identifying this gap, we highlight an important opportunity: developing specialized training programs that guide listeners to recognize and utilize these subtle differences. Such targeted training could significantly enhance people's ability to identify synthetic voices, offering potential protection against the growing risks of scams and misinformation enabled by increasingly realistic AI speech technologies.}, }
@article {pmid41799891, year = {2026}, author = {Xiao, Y and Kellis, S and Reiche, CF and Solzbacher, F}, title = {Classifying motion states from neural activity of non-human primates for brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1714738}, pmid = {41799891}, issn = {1662-4548}, abstract = {INTRODUCTION: Brain-computer interface (BCI) systems commonly decode neural activity from sensorimotor areas to generate continuous control signals for cursors, robotic limbs, or other effectors. Although these decoders perform well during intended movement, neural activity persists during periods of intended non-movement, which can lead to unintended effector activation and reduced control stability. Accurately identifying intended stationary states therefore represents a key component for achieving stable and reliable BCI control.<br><br>METHODS: We propose a neural-state classification framework (cpSVM) that distinguishes stationary and movement states directly from intracortical neural activity. This model combines principal component analysis, correlation-based feature selection, and a linear support vector machine classifier. Offline evaluations were performed using multi-unit recordings from the premotor and primary motor cortices of two non-human primates during a center-out cursor task. Performance was compared against a conventional kinematics-based threshold-crossing method.<br><br>RESULTS: Correlation-informed dimensionality reduction revealed a clear low-dimensional separation between stationary and movement states, supporting the selection of task-relevant neural features. The cpSVM achieved high classification performance, with mean accuracies of 0.936 and 0.930 across the two subjects. Compared with the threshold-crossing method, the cpSVM consistently improved accuracy, sensitivity, specificity, and F-score, while substantially reducing spurious state transitions and improving output continuity.<br><br>DISCUSSION: These findings demonstrate that stationary and movement states can be reliably distinguished from intracortical neural signals using a low-dimensional, correlation-informed classification approach. The proposed framework provides a promising strategy to suppress unintended effector activation and improve continuity and stability in BCI control systems.}, }
@article {pmid41798213, year = {2026}, author = {Al-Bander, B}, title = {Editorial: Brain-Computer Interfaces (BCIs) for daily activities: innovations in EEG signal analysis and machine learning approaches.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1795349}, doi = {10.3389/fnhum.2026.1795349}, pmid = {41798213}, issn = {1662-5161}, }
@article {pmid41799923, year = {2024}, author = {Chen, X and Wang, R and Khalilian-Gourtani, A and Yu, L and Dugan, P and Friedman, D and Doyle, W and Devinsky, O and Wang, Y and Flinker, A}, title = {A neural speech decoding framework leveraging deep learning and speech synthesis.}, journal = {Nature machine intelligence}, volume = {6}, number = {4}, pages = {467-480}, pmid = {41799923}, issn = {2522-5839}, abstract = {Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies that aim to restore speech in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity and high dimensionality. Here we present a novel deep learning-based neural speech decoding framework that includes an ECoG decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable speech synthesizer that maps speech parameters to spectrograms. We have developed a companion speech-to-speech auto-encoder consisting of a speech encoder and the same speech synthesizer to generate reference speech parameters to facilitate the ECoG decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Our experimental results show that our models can decode speech with high correlation, even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. Finally, we successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with deficits resulting from left hemisphere damage.}, }
@article {pmid41798016, year = {2026}, author = {Benachour, A and Medvedev, V and Zinchenko, O}, title = {Correction: Mouse-tracking as a tool for investigating strategic behavior in Public Goods Game: an experimental pilot study.}, journal = {Frontiers in psychology}, volume = {17}, number = {}, pages = {1807328}, doi = {10.3389/fpsyg.2026.1807328}, pmid = {41798016}, issn = {1664-1078}, abstract = {[This corrects the article DOI: 10.3389/fpsyg.2025.1635677.].}, }
@article {pmid41795730, year = {2026}, author = {Li, C and Zhong, W and Li, H and Tao, Y and Huang, J and Lu, J and Zhang, X and Wu, J}, title = {Three-dimensional microsurgical anatomy of the cerebral hemisphere from medial to lateral: a fiber-dissection study.}, journal = {Acta neurochirurgica}, volume = {168}, number = {1}, pages = {}, pmid = {41795730}, issn = {0942-0940}, support = {2024AH051901//Scientific Research Project of Higher Education Institutions in Anhui Province/ ; H202429//Research Project of Wannan Medical College/ ; KF2024016//Research Project of Wannan Medical College/ ; 24dz2261500//Shanghai Science and Technology Committee&#xff0c;Shanghai Key Laboratory of Clinical and Translational Brain-Computer Interface Research/ ; 2023ZKZD13//Innovation Program of Shanghai Municipal Education Commission/ ; }, mesh = {Humans ; *Imaging, Three-Dimensional/methods ; Dissection/methods ; *Microsurgery/methods ; *White Matter/anatomy &amp; histology/surgery ; *Cerebrum/anatomy &amp; histology/surgery ; Male ; Adult ; Female ; Middle Aged ; *Gray Matter/anatomy &amp; histology/surgery ; Aged ; Cadaver ; }, abstract = {BACKGROUND: Accurate exposure of lesions on the medial cerebral hemisphere remains technically challenging, and current imaging cannot fully depict the subcortical intricate architecture extending from the medial surface outward. Although portions of this anatomy have been described, a comprehensive topographic characterization from medial to lateral is still lacking.<br><br>OBJECTIVES: To provide a systematic, layer-by-layer topographic analysis of the white-matter fiber tracts and deep gray-matter nuclei from the medial surface to the lateral convexity of the cerebral hemisphere by combining stepwise fiber dissection with three-dimensional (3D) photography.<br><br>METHODS: Twelve adult human cerebral hemispheres, fixed in 10% formalin and prepared with the Klingler fiber-dissection technique, were examined under 6&#xd7;&#x2009;- 40&#x2009;&#xd7;&#x2009;magnification. Dissection commenced at the medial surface and proceeded outward, exposing commissural, association, and projection fibers as well as adjacent subcortical nuclei. High-resolution stereoscopic images were captured after each stage to document 3D spatial relationships.<br><br>RESULTS: From medial to lateral, the hemisphere comprised orderly layers of commissural, association, and projection systems interwoven with deep nuclei, forming a complex but reproducible arrangement in all specimens. The study provides complete medial exposure of these structures and demonstrates consistent positional relationships among different specimens.<br><br>CONCLUSIONS: This 3D fiber-dissection study offers the layer-by-layer depiction of the cerebral hemisphere from medial to lateral, clarifying spatial relationships among key white-matter bundles and deep nuclei. The anatomic insights gained may facilitate safer, more precise neurosurgical approaches and refine understanding of hemispheric connectivity.}, }
@article {pmid41795668, year = {2026}, author = {Ling, Y and Sun, P and Wang, C and Peng, G and Wang, Y and Zhou, X and He, Z and Liu, B and Zhang, J and Yu, J and Su, Y and Li, K and Guo, T and Luo, B}, title = {A novel eye-tracking digital marker outperforms plasma biomarkers in detecting cognitive impairment.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {22}, number = {3}, pages = {e71253}, pmid = {41795668}, issn = {1552-5279}, support = {2022C03064//the Key Research and Development Program of Zhejiang/ ; 2025ZFJH01//the Fundamental Research for the Central Universities/ ; 2022KY067//Medical and Health Science and Technology Project of Zhejiang Province/ ; 82422027//the National Natural Science Foundation of China/ ; U24A20340//the National Natural Science Foundation of China/ ; 82171197//the National Natural Science Foundation of China/ ; 82371484//the National Natural Science Foundation of China/ ; 2023B1515020113//Guangdong Basic and Applied Basic Science Foundation for Distinguished Young Scholars/ ; }, mesh = {Humans ; *Biomarkers/blood ; *Cognitive Dysfunction/diagnosis/blood ; Male ; Female ; Amyloid beta-Peptides/blood ; Aged ; tau Proteins/blood ; *Eye-Tracking Technology ; Positron-Emission Tomography ; Magnetic Resonance Imaging ; Neuropsychological Tests ; Aged, 80 and over ; }, abstract = {INTRODUCTION: Detecting and monitoring cognitive performance in older adults is critical. In this study, we evaluated the validity of an eye-tracking tool in diagnosing cognitive impairment.<br><br>METHODS: We recruited 119 cognitively unimpaired (CU) individuals and 157 cognitively impaired (CI) patients who completed digital eye-tracking tests and cognitive scales. Of them, 154, 120, 53, and 146 underwent plasma biomarker tests, amyloid-&#x3b2; positron emission tomography (A&#x3b2;-PET) scans, tau-PET scans, and magnetic resonance imaging (MRI) scans. The diagnostic performance of eye-tracking markers and their relationships to Alzheimer's disease biomarkers and cognition were examined.<br><br>RESULTS: The eye-tracking panel exhibited better performance (area under the curve [AUC]&#xa0;=&#xa0;0.865) in classifying CI from CU compared to plasma A&#x3b2;42/40 (AUC&#xa0;=&#xa0;0.699), p-Tau217 (AUC&#xa0;=&#xa0;0.769), p-Tau217/A&#x3b2;42 (AUC&#xa0;=&#xa0;0.801), glial fibrillary acidic protein (GFAP; AUC&#xa0;=&#xa0;0.804), and neurofilament light chain (NfL) (AUC&#xa0;=&#xa0;0.826).<br><br>DISCUSSION: These findings demonstrate the validity of digital eye-tracking markers for screening patients with cognitive impairment, providing a novel digital marker for detecting cognitive decline in older adults.}, }
@article {pmid41793476, year = {2026}, author = {Wang, X and Wang, D and Zhang, C and Zhang, H and Wang, W and Qian, W and Zhou, J and Zhao, Y and Gao, J and Hu, Z and Qin, J and Wang, Z and Zheng, Y and Yin, G and Dong, H}, title = {miR-214-3p exacerbates mitochondrial dysfunction in parkinson's disease: a multi-omics and mechanistic study.}, journal = {Experimental brain research}, volume = {244}, number = {4}, pages = {}, pmid = {41793476}, issn = {1432-1106}, support = {YKK23132//Nanjing Health Science and Technology Development Project/ ; SLJ0216//Leading Talent Project of Jiangsu Province Traditional Chinese Medicine/ ; YKK20102//Nanjing Health Science and Technology Development Special Fund Project/ ; M2021088//General Program of the Jiangsu Commission of Health/ ; YKK21121//Nanjing Health Science and Technology Development General Project/ ; NA2021062071//Project of Nanjing Infectious Disease Clinical Medical Center Construction/ ; RCMS23010//Talent Lift Project of Nanjing Second Hospital/ ; XZR2024043//the Natural Science Foundation Project of Nanjing University of Chinese Medicine/ ; }, abstract = {UNLABELLED: Parkinson’s disease (PD) involves the loss of dopaminergic neurons, and prodromal PD exhibits elevated miR-214-3p, suggesting its role as a biomarker and pathogenic factor. This study investigated miR-214-3p’s effects on mitochondrial function in dopaminergic SH-SY5Y cells and mouse primary cortical neurons. In SH-SY5Y cells, proteomic/transcriptomic analyses and target prediction confirmed GFM1 as a direct target of miR-214-3p. miR-214-3p upregulation downregulated GFM1, causing severe mitochondrial bioenergetic impairment: increased reactive oxygen species (ROS), reduced oxygen consumption, diminished ATP production, and decreased respiratory chain complexes (RCC) I/IV expression. Critically, restoring GFM1 reversed these mitochondrial deficits and neuronal dysfunction. In mouse primary cortical neurons, miR-214-3p overexpression also impaired RCC I/IV but did not affect GFM1, revealing a cell type-dependent regulatory mechanism. These findings demonstrate that elevated miR-214-3p impairs mitochondrial function in a cell-specific manner. In dopaminergic cells, this damage is mediated by GFM1 downregulation, highlighting the miR-214-3p/GFM1 axis as a potential cell-type specific therapeutic target for PD and related dopaminergic neuronopathies.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00221-026-07267-0.}, }
@article {pmid41792767, year = {2026}, author = {Zhao, X and Zhou, H and Zhang, X and Tang, R and Gan, Y and Zhuang, T and Zhu, Y and Qin, Z and Chen, Y and Fu, Y and Zhang, D and Xu, L and Wang, S and Shen, Z and Hu, S and Wang, M}, title = {fMRI-guided V1-targeted rTMS improves depressive symptoms in adolescents and young adults with bipolar disorder: a double-blind randomized controlled trial.}, journal = {BMC medicine}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12916-026-04766-3}, pmid = {41792767}, issn = {1741-7015}, support = {2025KY1557//Zhejiang Province Medical and Health Science and Technology Plan Project/ ; 2024GZ86//Public Welfare Applied Research Project on Population Health (Medical &amp; Health Focus) of Huzhou Municipal Science and Technology Bureau/ ; 226-2022-00193, 226-2022-00002, 2023ZFJH01-01, 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; LTGY23H090013//Zhejiang Provincial Basic Public Research Program/ ; 2025C01137//Zhejiang Provincial Key Research and Development Program/ ; 52407261, 82201675//National Natural Science Foundation of China/ ; }, abstract = {BACKGROUND: Bipolar depression (BD-D) in adolescents and young adults is associated with disrupted neural circuits underlying affective regulation, particularly those involving the orbitofrontal cortex (OFC). Despite the promise of repetitive transcranial magnetic stimulation (rTMS) as a non-invasive intervention, effective targeting strategies that engage these dysfunctional circuits remain insufficiently explored. This study investigates the clinical efficacy of a novel rTMS protocol targeting the primary visual cortex (V1) node of the V1-OFC functional circuit in adolescents and young adults with BD-D.<br><br>METHODS: We conducted a double-blind randomized controlled trial. Fifty-two adolescents and young adults BD-D participants were randomized to active rTMS group (10&#xa0;Hz, 100% RMT) or sham rTMS group (20% RMT) targeting the V1 region that exhibited the strongest functional connectivity with the OFC (MNI:&#x2009;-&#x2009;12,&#x2009;-&#x2009;81, 6). rTMS was administered over 3&#xa0;weeks (5 sessions/week, 15 sessions in total), with all participants receiving adjunctive lurasidone (40-80&#xa0;mg/day). The primary outcome was the change in depressive symptoms measured by the Montgomery-&#xc5;sberg Depression Rating Scale (MADRS) at baseline, week 3, and week 8. Secondary outcomes included HAMD-24, QIDS-SR, and HAMA. Resting-state fMRI was performed at baseline and after the 3-week intervention to examine changes in functional connectivity related to rTMS.<br><br>RESULTS: A total of 43 participants completed a 3-week intervention, and 37 completed the 8-week follow-up. Compared with the sham group, the active rTMS group showed significantly greater reductions in depressive symptoms. Between-group differences were significant on the primary outcome MADRS at week 8 (t(35)&#x2009;= -&#x2009;3.595, pFDR&#x2009;<&#x2009;0.01), with a parallel effect detected for the secondary outcome on the QIDS-SR (t(35)&#x2009;= -&#x2009;3.653, pFDR&#x2009;<&#x2009;0.01). HAMD-24 scores also differed significantly at week 3 (t(35)&#x2009;= -&#x2009;3.921, pFDR&#x2009;<&#x2009;0.01). No significant changes were found in anxiety symptoms. Resting-state fMRI indicated altered connectivity in the anterior cingulate cortex and right superior occipital gyrus, suggesting modulation of mood-related visual circuits. No severe adverse effects were reported in all participants.<br><br>CONCLUSIONS: The study preliminarily demonstrated that the navigated rTMS precisely targeting the V1-OFC circuit may be a safe and potentially effective intervention for adolescents and young adults with BD-D.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05929183.}, }
@article {pmid41788543, year = {2026}, author = {Wang, Z and Liu, X and Xie, J and Lin, Y}, title = {Evoked potentials in stroke rehabilitation: current applications, emerging technologies, and future directions.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1758767}, pmid = {41788543}, issn = {1662-4548}, abstract = {Evoked potentials (EPs) are increasingly explored as objective neurophysiological biomarkers to complement scale-based assessment in stroke rehabilitation. This narrative review summarizes current evidence on the use of somatosensory evoked potentials (SEPs), motor evoked potentials (MEPs), and event-related potentials (ERPs) for monitoring recovery and guiding therapy. We first outline the physiological basis and stroke-relevant features of each modality, then synthesize data on how EP measures relate to motor, sensory, balance, cognitive and language outcomes, with particular emphasis on longitudinal changes during rehabilitation and responses to specific interventions, including neuromuscular electrical stimulation, robot-assisted training and non-invasive brain stimulation. Emerging applications such as perturbation-evoked cortical responses for postural control, EP-based brain-computer interfaces and EP-guided or closed-loop neuromodulation are discussed, together with advances in high-density recordings, connectivity analysis, and machine-learning-based multimodal prediction models. Finally, we highlight key methodological and practical challenges-protocol heterogeneity, small single-center studies, limited trial evidence, feasibility constraints and gaps in clinical integration-and propose priorities for standardization and translational research. Overall, EPs hold substantial promise as pathway-specific, temporally precise biomarkers to enable more mechanism-informed and individualized stroke rehabilitation monitoring.}, }
@article {pmid41788540, year = {2026}, author = {Lin, D and Zhang, Q and Chen, H and Lu, Y and Chen, H and Li, L and Mayet, AM and Zhang, G and Miao, X and Qiu, X}, title = {High accuracy EEG signal classification for brain computer interfaces using advanced neural architectures.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1752176}, pmid = {41788540}, issn = {1662-4548}, abstract = {INTRODUCTION: This study proposes advanced neural network architectures for classifying specific motor-related electroencephalography (EEG) tasks, employing deep feature extraction techniques. We analyzed EEG data from the MILimbEEG dataset, consisting of recordings from 60 individuals as they performed eight distinct motor movements: baseline with eyes open, left-hand closing, right-hand closing, dorsiflexion and plantarflexion of both the left and right feet, as well as rest periods between tasks. The high precision achieved in this study underscores the efficacy of sophisticated computational models like the GMDH network in enhancing the interpretation of EEG signals for the development of brain-computer interfaces (BCIs). This research significantly advances the potential of EEG as a reliable modality for BCIs, effectively translating brain activity into actionable commands suitable for neurorehabilitation and assistive technologies.<br><br>METHODS: For each of the 16 electrodes used in the recordings, 10 critical features were extracted, resulting in a comprehensive set of 160 features per sample that encapsulate the intricate brain activities associated with each task. A Group Method of Data Handling (GMDH) neural network, structured with eight hidden layers and a decremental arrangement of neurons from 40 in the first to 5 in the last, was utilized to classify these tasks.<br><br>RESULTS: This network configuration achieved an impressive classification accuracy of approximately 96%, demonstrating a robust capability to accurately decode EEG signals tied to specific motor actions.<br><br>DISCUSSION: The high precision achieved in this study underscores the efficacy of sophisticated computational models like the GMDH network in enhancing the interpretation of EEG signals for the development of brain-computer interfaces (BCIs). This research significantly advances the potential of EEG as a reliable modality for BCIs, effectively translating brain activity into actionable commands suitable for neurorehabilitation and assistive technologies. Our findings contribute substantially to the BCI field, promising to improve clinical outcomes by enabling more precise and effective interaction with neurorehabilitation devices.}, }
@article {pmid40329470, year = {2025}, author = {Ge, Y and Yang, Z and Su, H and Miu, J and Xie, J and Zhao, R and Liu, S and Han, C and Zhang, S and Xu, G}, title = {The critical role of melody and harmony in sleep induction: Direct evidence from electroencephalogram-based analysis.}, journal = {Annals of the New York Academy of Sciences}, volume = {1548}, number = {1}, pages = {233-247}, doi = {10.1111/nyas.15334}, pmid = {40329470}, issn = {1749-6632}, support = {2024QCY-KXJ-189//Qinchuangyuan Project/ ; 2022KXJ-129//Qinchuangyuan Project/ ; xzd012023015//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Music ; Male ; *Sleep/physiology ; Adult ; Female ; *Sleep Initiation and Maintenance Disorders/physiopathology/therapy ; Sleep Quality ; Young Adult ; Music Therapy ; }, abstract = {Insomnia, prevalent in contemporary society, is characterized by difficulties in sleep initiation and maintenance, leading to fatigue, depression, and impaired cognitive function. Although research has demonstrated the sleep induction effects of certain musical genres, the neurophysiological mechanisms through which musical constituents, namely, melody, harmony, and rhythm, induce sleep remain inconclusive. To elucidate how musical constituents influence sleep onset, we used both subjective and objective measures, including the Pittsburgh Sleep Quality Index and Karolinska Sleepiness Scale as the former and electroencephalography (EEG) analysis as the latter. The EEG data showed that melody and harmony significantly enhance sleep quality, particularly impacting the theta band and the dispersion entropy in the temporal lobes. Conversely, the effect of rhythm in sleep induction appeared less significant, with minimal activity observed in the frontal and parietal lobes. We believe the data provide a foundation for innovative approaches to music composition that emphasize melody and harmony to enhance the therapeutic potential of music in sleep management, while de-emphasizing the role of rhythm. The findings also support adapting traditional musical forms, such as classical music, for sleep induction purposes and the development of sleep aid music composition rooted in Western music theory.}, }
@article {pmid38591141, year = {2024}, author = {Xiang, Y and Zhao, Y and Cheng, T and Sun, S and Wang, J and Pei, R}, title = {Implantable Neural Microelectrodes: How to Reduce Immune Response.}, journal = {ACS biomaterials science &amp; engineering}, volume = {10}, number = {5}, pages = {2762-2783}, doi = {10.1021/acsbiomaterials.4c00238}, pmid = {38591141}, issn = {2373-9878}, mesh = {*Microelectrodes ; *Electrodes, Implanted ; Humans ; Animals ; Neurons/immunology/physiology ; Brain/immunology/physiology ; }, abstract = {Implantable neural microelectrodes exhibit the great ability to accurately capture the electrophysiological signals from individual neurons with exceptional submillisecond precision, holding tremendous potential for advancing brain science research, as well as offering promising avenues for neurological disease therapy. Although significant advancements have been made in the channel and density of implantable neural microelectrodes, challenges persist in extending the stable recording duration of these microelectrodes. The enduring stability of implanted electrode signals is primarily influenced by the chronic immune response triggered by the slight movement of the electrode within the neural tissue. The intensity of this immune response increases with a higher bending stiffness of the electrode. This Review thoroughly analyzes the sequential reactions evoked by implanted electrodes in the brain and highlights strategies aimed at mitigating chronic immune responses. Minimizing immune response mainly includes designing the microelectrode structure, selecting flexible materials, surface modification, and controlling drug release. The purpose of this paper is to provide valuable references and ideas for reducing the immune response of implantable neural microelectrodes and stimulate their further exploration in the field of brain science.}, }
@article {pmid30798604, year = {2019}, author = {Lu, L and Fu, X and Liew, Y and Zhang, Y and Zhao, S and Xu, Z and Zhao, J and Li, D and Li, Q and Stanley, GB and Duan, X}, title = {Soft and MRI Compatible Neural Electrodes from Carbon Nanotube Fibers.}, journal = {Nano letters}, volume = {19}, number = {3}, pages = {1577-1586}, doi = {10.1021/acs.nanolett.8b04456}, pmid = {30798604}, issn = {1530-6992}, abstract = {Soft and magnetic resonance imaging (MRI) compatible neural electrodes enable stable chronic electrophysiological measurements and anatomical or functional MRI studies of the entire brain without electrode interference with MRI images. These properties are important for many studies, ranging from a fundamental neurophysiological study of functional MRI signals to a chronic neuromodulatory effect investigation of therapeutic deep brain stimulation. Here we develop soft and MRI compatible neural electrodes using carbon nanotube (CNT) fibers with a diameter from 20 μm down to 5 μm. The CNT fiber electrodes demonstrate excellent interfacial electrochemical properties and greatly reduced MRI artifacts than PtIr electrodes under a 7.0 T MRI scanner. With a shuttle-assisted implantation strategy, we show that the soft CNT fiber electrodes can precisely target specific brain regions and record high-quality single-unit neural signals. Significantly, they are capable of continuously detecting and isolating single neuronal units from rats for up to 4-5 months without electrode repositioning, with greatly reduced brain inflammatory responses as compared to their stiff metal counterparts. In addition, we show that due to their high tensile strength, the CNT fiber electrodes can be retracted controllably postinsertion, which provides an effective and convenient way to do multidepth recording or potentially selecting cells with particular response properties. The chronic recording stability and MRI compatibility, together with their small size, provide the CNT fiber electrodes unique research capabilities for both basic and applied neuroscience studies.}, }
@article {pmid24276486, year = {1981}, author = {Singh, S}, title = {Single tester triple test cross analysis in spring wheat.}, journal = {TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik}, volume = {59}, number = {4}, pages = {247-249}, pmid = {24276486}, issn = {0040-5752}, abstract = {Two experiments, each including the same 30 homozygous varieties of spring wheat plus one separate tester variety, were conducted in order to detect epistasis and to test and estimate the additive and dominance components of genetic variation for five quantitative traits: final plant height, spike length, number of spikelets per spike, 100-kernel weight and grain yield per plant. Epistasis played a significant role in the control of 100-kernel weight and yield per plant. There was a gratifyingly good agreement between the two independent methods (2¯B1i - ¯f1i - ¯Pi and 2¯Bci - ¯F1i) used to test the presence of epistasis. In both experiments, there was a remarkably uniform high dominance ratio for most of the traits studied indicating that this test cross design is equally sensitive to both additive and dominance genetic variation.}, }
@article {pmid41788397, year = {2026}, author = {Zhang, X and Zhang, Y and Peng, H and Deng, T}, title = {Advancing individual finger classification through a sandwich enhanced CBAM network with ultra-high-density EEG data.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1751058}, pmid = {41788397}, issn = {1662-5161}, abstract = {INTRODUCTION: Ultra-High-Density Electroencephalography (uHD EEG) has gained increasing attention for its potential in individual finger decoding. However, accurately classifying these movements remains challenging due to the subtle spatial overlaps in cortical activity, which standard architectures often fail to isolate.<br><br>METHODS: To address this, we propose the Sandwich enhanced Convolutional Block Attention Module (SCBAM). The unique sandwich structure integrates dual attention mechanisms between convolutional layers, enabling the network to more effectively refine high-dimensional spatial features.<br><br>RESULTS AND DISCUSSION: The proposed network achieves an average accuracy of 78.63 (1.56)% in binary classification across ten finger pairs in five subjects, with the highest accuracy of 85% obtained at Thumb vs. Ring. The proposed network achieves an average accuracy of 61.12 (0.95)% in five-class classification across five subjects, with a highest accuracy of 62.36% on subject S2. The five-class classification is performed using 10 binary classifiers under a one-vs.-one strategy. Notably, five-class classification of individual fingers has not been extensively explored in the current literature, particularly with high-density EEG (HDEEG) data. This study addresses this gap, offering a valuable reference for future discussions. We conduct ablation studies to investigate the individual and synergistic effects of the modules in the proposed model. The results highlight the effects of two sequential attention mechanisms in this task. We conduct comparative experiments of our proposed model against six benchmark networks. The results from SCBAM significantly outperform these established models with FBCSP features. The proposed SCBAM significantly improves accuracy in binary finger classification compared to SVM and MLP using the same uHD EEG dataset. In summary, this study presents a high-performance hybrid network for individual finger classification and highlights the potential of uHD EEG for dexterous task decoding in Brain-Computer Interfaces (BCI).}, }
@article {pmid41788395, year = {2026}, author = {Li, J and Liu, X and Wu, X and Wang, Y and Huang, X}, title = {DSP-MCF: dual stream pre-training and multi-view consistency fine-tuning for cross-subject EEG emotion recognition.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1723907}, pmid = {41788395}, issn = {1662-5161}, abstract = {INTRODUCTION: Electroencephalogram (EEG) emotion recognition is attracting increasing attention in the field of brain-computer interface due to its strong objectivity and non-forgery. However, cross-subject emotion recognition is complicated by individual variability, limited availability of EEG data, and interference in certain channels during EEG acquisition.<br><br>METHODS: We propose a novel synergistic Dual Stream Pre-training and Multi-view Consistency Fine-tuning (DSP-MCF) framework. The DSP-MCF is based on a domain generalization architecture. The framework includes a dual stream pre-training stage, wherein the spatiotemporal encoder-decoder network extracts generalized spatiotemporal representations from masked channels and reconstructs EEG features from incomplete data. Then, a multi-view consistency loss function is proposed during the multi-view consistency fine-tuning. This loss function is essential for aligning the distribution of emotion predictions derived from various perspectives, specifically from actual and masked EEG data.<br><br>RESULTS: Experimental results demonstrate that the proposed DSP-MCF framework outperforms state-of-the-art methods in cross-subject EEG emotion recognition tasks. The model achieved an accuracy of 89.76% on the SEED dataset and 77.02% on the SEED-IV dataset.<br><br>DISCUSSION: The findings indicate that the DSP-MCF framework effectively addresses individual variability and maintains robust performance even under channel loss. By integrating spatiotemporal reconstruction with multi-view consistency, the model provides a reliable solution for handling incomplete or degraded EEG signals in practical BCI applications.}, }
@article {pmid41788156, year = {2026}, author = {Hira, R and Isomura, Y}, title = {Technical development of two-photon optogenetic stimulation and its potential application to brain-machine interfaces.}, journal = {Neurophotonics}, volume = {13}, number = {1}, pages = {010601}, pmid = {41788156}, issn = {2329-423X}, abstract = {Over the past decade, techniques enabling bidirectional modulation of neuronal activity with single-cell precision have rapidly advanced in the form of two-photon optogenetic stimulation. Unlike conventional electrophysiological approaches or one-photon optogenetics, which inevitably activate many neurons surrounding the target, two-photon optogenetics can drive hundreds of specifically targeted neurons simultaneously, with stimulation patterns that can be flexibly and rapidly reconfigured. In this review, we trace the development of two-photon optogenetic stimulation, focusing on its progression toward implementations in large field of view two-photon microscopes capable of targeted multi-neuron control. We highlight three principal strategies: spiral scanning, temporal focusing, and three-dimensional computer-generated holography, along with their combinations, which together provide powerful tools for causal interrogation of neural circuits and behavior. Finally, we discuss the integration of these optical technologies into brain-machine interfaces, emphasizing both their transformative potential and the technical challenges that must be addressed to realize their broader impact.}, }
@article {pmid41787678, year = {2026}, author = {Lei, J and Zhong, S and Fan, R and Shu, X and Wang, G and Guo, J and Xue, S and Zheng, L and Ren, A and Ji, J and Yang, B and Duan, S and Wang, Z and Guo, X}, title = {The Ubiquitin Ligase Zinc Finger SWIM Domain-Containing Protein 8 Regulates Oligodendrocyte Development Through the Argonaute2/MicroRNA-7 Axis.}, journal = {Glia}, volume = {74}, number = {5}, pages = {e70142}, doi = {10.1002/glia.70142}, pmid = {41787678}, issn = {1098-1136}, support = {31671039//National Natural Science Foundation of China/ ; 32071257//National Natural Science Foundation of China/ ; 2016YFA0501000//National Key Research and Development Program of China/ ; 2023YFF1204400//National Key Research and Development Program of China/ ; }, abstract = {Proteostasis of proteins with intrinsically disordered regions (IDRs) is of particular importance to the development and function of the central nervous system (CNS). The conserved ZSWIM8 ubiquitin ligase, an essential regulator of mammalian brain development, is known to target IDR proteins involved in neuronal cell migration. Here we show that ZSWIM8 is also indispensable for oligodendrocyte maturation and myelination in the CNS. Loss of ZSWIM8 in the brain causes gross accumulation of IDR-rich proteins including many RNA-binding proteins (RBPs). Substrate recognition by ZSWIM8 requires its own IDRs, while ZSWIM8-mediated ubiquitination of AGO2 also depends on microRNA binding. AGO2 stabilization in ZSWIM8-null tissues disrupts target-directed microRNA degradation (TDMD) of MiR7, leading to altered gene expressions and myelination defects in vivo. Together, these results not only establish ZSWIM8 as a versatile regulator of IDR proteins but also highlight the crucial roles of RBP/miRNA homeostasis in oligodendrocyte development.}, }
@article {pmid41787487, year = {2026}, author = {Chen, X and Qi, R and Zou, H and Jiang, L and Yang, B}, title = {Effect of Tai Chi Yunshou motor imagery training on upper limb motor dysfunction with stroke patients.}, journal = {BMC complementary medicine and therapies}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12906-026-05327-0}, pmid = {41787487}, issn = {2662-7671}, support = {2024YFF1206500//National Key Research and Development Program of China/ ; No. 32571279//National Natural Science Foundation of China/ ; No. 25YL1900100//Science and Technology Commission of Shanghai Municipality/ ; }, }
@article {pmid41787482, year = {2026}, author = {Wang, Z and Nan, J and Zhou, Y and Liu, J and Liu, S and Xu, M and He, F and Chen, L and Ming, D}, title = {EEG based multifunctional connectivity fusion across frequency bands and parameters promote motor function assessment in stroke: a pilot study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01914-x}, pmid = {41787482}, issn = {1743-0003}, support = {62376190//National Natural Science Foundation of China/ ; 62476193//National Natural Science Foundation of China/ ; 25ZXZSSS00020//Tianjin Science and Technology Program - State Key Laboratory Major Special Project/ ; }, }
@article {pmid41787382, year = {2026}, author = {Yan, C and Liu, Y and Zhao, J and Bao, M and Zhou, Q and Feng, S and Li, H and Pan, G and Yao, L and Wang, Y}, title = {Integrating single-channel EEG neurofeedback into video game-based digital therapeutics for ADHD.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01918-7}, pmid = {41787382}, issn = {1743-0003}, support = {32500952//National Natural Science Foundation of China/ ; 62336007//National Natural Science Foundation of China/ ; 2023C03003//Key Research and Development Program of Zhejiang/ ; ZJU-GENSCI2024YB003//Zju-GenSci Children's Health Research and Development Center/ ; 2021ZD0200400//STI 2030-Major Projects/ ; SN-ZJU-SIAS-002//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; }, abstract = {BACKGROUND: Digital therapeutics have emerged as a promising non-pharmacological intervention for children with attention-deficit/hyperactivity disorder (ADHD). Personalized adaptation is key to the success of digital therapeutics. However, most existing systems depend solely on observable performance rather than real-time internal attentional state, which lead to misinterpretation or delayed adaptation.<br><br>METHODS: In this study, we evaluated the effects of a tablet-based attention training game with and without EEG-informed real-time neurofeedback in children with ADHD. Participants were assigned to one of two groups: a neurofeedback group (NFb) in which the game adapted in real time based on single-channel frontal EEG signals and a standard game intervention group without neurofeedback (n-NFb). Attention and cognitive control were assessed before and after a one-month intervention.<br><br>RESULTS: All children showed improvements in attention in both parent report and children's performance in attentional tasks. The NFb group showed greater improvements in hitting accuracy (go trials) and less reductions in inhibition accuracy (no-go trials) than the n-NFb group. Both groups had significantly shorter reaction times after training. EEG analyses revealed greater improvement in attention index during training for NFb group.<br><br>CONCLUSION: Our findings suggest that video game-based digital therapeutics with EEG-informed real-time neurofeedback can effectively enhance attention in children with ADHD. The results support the potential of using adaptive neurofeedback with portable devices to enhance intervention effects.}, }
@article {pmid41785933, year = {2026}, author = {Scalabrini, A and Palladini, M and Poletti, S and Vai, B and Calesella, F and Paolini, M and Gulino, G and Masoumi, S and Zanardi, R and Colombo, C and Northoff, G and Benedetti, F}, title = {Shift to the core: Abnormal core-periphery global topography in unipolar and bipolar depression.}, journal = {Journal of affective disorders}, volume = {405}, number = {}, pages = {121550}, doi = {10.1016/j.jad.2026.121550}, pmid = {41785933}, issn = {1573-2517}, abstract = {This study explores the global signal topography of core and periphery brain networks in Major Depressive Disorder (MDD), Bipolar disorder (BD-Dep) and healthy controls (HC) using resting-state fMRI. In a sample of 140 depressed MDD and BD patients, and 70 HC, we observed a significant shift toward increased activity in the transmodal-core regions (e.g., default mode network, frontoparietal network) at the expense of unimodal-periphery regions (e.g., visual, sensory-motor cortices) in both depressed MDD and BD patients compared to HC. Whole brain machine learning analyses further demonstrated that altered global signal dynamics can effectively distinguish MDD and BD from HC (ACC = 79% and 77% respectively). Notably, we identified a significant negative correlation between global signal correlation in unimodal-periphery networks and depressive symptom severity. Additionally, in a smaller sample of BD during mania (N = 22) a distinct topographic pattern was observed, with increased global representation in the unimodal-periphery compared to depressive states, suggesting mood state-dependent shifts in network organization. To assess multivariate discriminability across diagnostic groups, a Partial Least Squares (PLS) analysis revealed that higher Core and related network activity (DMN, FPN) predicted diagnostic assignment to MDD and BD-Dep, whereas higher Periphery and related network (e.g., visual and sensory-motor networks) predicted assignment to BD-Man and HC. The Core-Periphery (C-P) ratio emerged as the strongest predictor (VIP = 1.65). These results underscore the critical role of global signal topography in mood disorders, particularly the imbalance between core and peripheral brain networks, as a potential neurobiological marker for depressive states.}, }
@article {pmid41785382, year = {2026}, author = {Kilwein, TM and Curry, KA and Sutcliffe, J and Manzler, CA}, title = {Beyond Averages: Uncovering Within-Person Links Between Sleep and Performance in Division I Collegiate Football Players.}, journal = {Research quarterly for exercise and sport}, volume = {}, number = {}, pages = {1-8}, doi = {10.1080/02701367.2025.2608371}, pmid = {41785382}, issn = {2168-3824}, abstract = {A robust body of research links greater sleep duration and quality to improved athletic performance and competitive outcomes. However, many athletes, particularly collegiate football players, struggle to achieve optimal sleep and accurately assess its quality. Despite the known sleep-performance relationship, little is known about how these variables manifest in real time among student-athletes. This study examined daily associations between objectively measured sleep and athletic performance in National Collegiate Athletic Association Division I football players. Sixty-five athletes aged 17 to 23 years (M = 19.88, SD = 1.41), representing a range of academic years and position groups, wore sensor-based devices over a three-week period to capture sleep metrics (sleep efficiency, latency, and total sleep time) and performance indicators (maximum acceleration, maximum velocity, and explosive movement). Multilevel modeling revealed no significant between-person effects, suggesting that athletes who slept better on average did not necessarily perform better on average. However, within-person analyses indicated that nights with longer sleep latency (estimate, -.007; 95% BCI, -.013, -.003) or lower sleep efficiency (estimate, .005; 95% BCI, .001, .010) predicted reduced maximum acceleration the next day. Conversely, days with lower maximum acceleration predicted shorter sleep latency (estimate, 6.869; 95% BCI, 3.998, 9.269) and higher sleep efficiency (estimate, -5.289; 95% BCI, -10.170, -1.027) that night. These findings underscore a dynamic, bidirectional relationship between sleep and performance at the daily level and highlight the need for individualized, athlete-centered sleep interventions that extend beyond sleep duration to include routine assessment, comprehensive education, and strategies to mitigate sleep disruptors.}, }
@article {pmid41784218, year = {2026}, author = {Li, J and Guo, C and Zhang, C and Chang, EF and Li, Y}, title = {High-fidelity neural speech reconstruction through an efficient acoustic-linguistic dual-pathway framework.}, journal = {eLife}, volume = {14}, number = {}, pages = {}, pmid = {41784218}, issn = {2050-084X}, support = {32371154//National Natural Science Foundation of China/ ; 2025ZD0217000//National Science and Technology Major Project/ ; 24QA2705500//Science and Technology Commission of Shanghai Municipality/ ; LG-GG-202402-06//Lin Gang Laboratory/ ; LGL-1987-18//Lin Gang Laboratory/ ; }, mesh = {Humans ; Electrocorticography ; *Speech/physiology ; *Brain-Computer Interfaces ; *Linguistics/methods ; Speech Intelligibility ; }, abstract = {Reconstructing speech from neural recordings is crucial for understanding human speech coding and developing brain-computer interfaces (BCIs). However, existing methods trade off acoustic richness (pitch, prosody) for linguistic intelligibility (words, phonemes). To overcome this limitation, we propose a dual-path framework to concurrently decode acoustic and linguistic representations. The acoustic pathway uses a long-short term memory (LSTM) decoder and a high-fidelity generative adversarial network (HiFi-GAN) to reconstruct spectrotemporal features. The linguistic pathway employs a transformer adaptor and text-to-speech (TTS) generator for word tokens. These two pathways merge via voice cloning to combine both acoustic and linguistic validity. Using only 20 min of electrocorticography (ECoG) data per human subject, our approach achieves highly intelligible synthesized speech (mean opinion score = 4.0/5.0, word error rate = 18.9%). Our dual-path framework reconstructs natural and intelligible speech from ECoG, resolving the acoustic-linguistic trade-off.}, }
@article {pmid41781649, year = {2026}, author = {Tang, X and Wu, H and Li, S and Bezerianos, A and Hu, R and Chen, Z and Wang, H}, title = {Time-frequency-spatial channel attention network for semantic decoding: an exploratory EEG study.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {41781649}, issn = {1741-0444}, support = {2024ZDJS033//Key Discipline Research Capability Enhancement Project in Guangdong Province/ ; 2025KCXTD048//Guangdong University Innovation Team Program/ ; }, abstract = {Semantic decoding is a crucial approach for investigating the neural mechanisms underlying language processing and representation. Informed by brain-computer interface (BCI) technology, this study investigated methods for decoding semantic information, with an emphasis on the neural representations of semantics in language perception. Due to the limited availability of electroencephalography (EEG) datasets containing Chinese linguistic stimuli, we have specifically designed a semantic task paradigm as a promising attempt to decode language comprehension and expression in patients with aphasia using scalp EEG. This paradigm fully incorporates the processes underlying both speech perception and speech imagery by adopting tasks such as overt speech perception and silent speech imagery. Firstly, Seventeen participants of aphasia patients and healthy subjects were recruited for EEG data collection. Secondly, we constructed a deep learning model termed Time-Frequency-Spatial Channel Attention Network (TFSANet), which processes both time-domain and frequency-domain features to extract key neural signatures associated with semantics. By optimizing the model and employing multidimensional feature extraction mechanisms, we significantly improved the model's ability to decode semantically relevant EEG features. Finally, the experimental results demonstrate the proposed TFSANet could decode semantic information from EEG for ten categories of four-word phrases under an "auditory-guided" paradigm with an accuracy of 60.73% and 75.09% for aphasia patients and healthy subjects respectively.}, }
@article {pmid41781440, year = {2026}, author = {Priori, S and Ricci, P and Consoli, D and Micheli, A and Merlini, A and Andriulli, FP}, title = {A visual imagery paradigm for BCI strategies using imagined flickering patterns.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-41324-6}, pmid = {41781440}, issn = {2045-2322}, support = {101046748//HORIZON EUROPE European Innovation Council/ ; ANR-10-LABX-07-01//Agence Nationale de la Recherche/ ; }, abstract = {Steady state visually evoked potentials (SSVEPs) are a popular type of control signals in brain-computer interfaces (BCIs), in which they are typically elicited by observing a visual stimulus flashing at a specific frequency. For some patients, using SSVEP as control signal for a BCI can be difficult, for instance if they are unable to focus their gaze over the visual stimuli. To address this issue, some approaches were presented to design a gaze-independent SSVEP-controlled BCI but some difficulties have been reported, for instance for patients suffering from locked-in syndrome. In this work we employ a visual imagery (VI) signal, in which the visual stimulus is imagined instead of observed, to drive a BCI system and offer an alternative for patients that encounter issues with standard SSVEP approaches. We tested the proposed approach with 20 untrained subjects within a 3-classes BCI resulting in an offline classification accuracy of 60.93%. These results demonstrate how this gaze-independent BCI can be used by inexperienced BCI users.}, }
@article {pmid41780622, year = {2026}, author = {Kromm, M and Branco, MP and Raemaekers, M and Ramsey, NF}, title = {Optimal Location for Gesture Decoding in the Sensorimotor Cortex and Implications for Brain-Computer Interface Research.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121837}, doi = {10.1016/j.neuroimage.2026.121837}, pmid = {41780622}, issn = {1095-9572}, abstract = {Implantable brain-computer interfaces (iBCIs) aim to restore communication in individuals with severe motor impairments. For good iBCI performance, it is important to target an optimal location. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging (fMRI) to map the spatial distribution of brain activity that can discriminate between a large number of hand gestures. Ten able-bodied participants performed 20 different unimanual hand gestures. Using support vector machines, we measured decodability across the cortex. The highest decoding performance was achieved in the hand region of the sensorimotor cortex. Moreover, we found that a subset of six well-distinguishable gestures could predict the optimal decoding location for the full set, suggesting that a carefully chosen subset can effectively guide pre-implantation mapping. Furthermore, while significant decoding was possible from sulcal as well as gyral regions of the precentral cortex, our analyses revealed that the sulcal area did not contribute unique information beyond that found in adjacent gyral regions. Similarly, decoding in the postcentral cortex was primarily driven by the gyrus. This indicates that surface recordings may suffice for iBCIs. Together, these findings offer practical guidance for future iBCI electrode placement, with the potential to improve communication and autonomy for individuals with severe motor impairments.}, }
@article {pmid41780166, year = {2026}, author = {Wellman, SM and Guzman, K and Suematsu, N and Thai, T and Tung, TH and Padilla, CG and Sridhar, S and Chen, K and Cambi, F and Kozai, TDY}, title = {Oligodendrocyte-specific Fus depletion preserves CA1 single-unit fidelity and stabilizes network dynamics during chronic recording.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae4d8b}, pmid = {41780166}, issn = {1741-2552}, abstract = {OBJECTIVE: Loss of oligodendrocytes (OLs) and myelin impairs neuronal firing and network stability, whereas enhancing oligodendrogenesis with clemastine improves electrophysiological stability in cortex and, to a lesser extent, hippocampus. Conditional depletion of Fus in OLs (FusOLcKO) drives developmentally regulated increases in myelin thickness via enhanced cholesterol biosynthesis. Here, we investigated whether Fus-depleted OLs differentially affect long-term extracellular recordings across cortical layers and hippocampal CA1.<br><br>APPROACH: We performed chronic electrophysiological recordings in visual cortex and CA1 of FusOLcKO mice and littermate controls over 16 weeks, combined with endpoint histology.<br><br>MAIN RESULTS: In FusOLcKO mice, visually-evoked single-unit detectability and firing rate in CA1 increased relative to wild-type littermates, whereas cortical recordings showed no improvement. At the population level, FusOLcKO cortex exhibited reduced firing rates and lower functional connectivity, indicating altered network dynamics. Post-mortem analysis revealed higher neuron density in recorded cortical regions acutely and greater excitatory synapse density in CA1 of FusOLcKO mice without significant changes in myelin profiles.<br><br>SIGNIFICANCE: Fus depletion in OLs enhances chronic hippocampal recordings but disrupts cortical network communication. These region-dependent effects highlight a differential role of OLs in supporting single-cell reliability versus population-level dynamics, offering novel insights into the interplay between oligodendrocytes, neural networks, and recording stability.}, }
@article {pmid41780162, year = {2026}, author = {Daly, I and Withanage, R and Oliveira, J and Barbera, T and Tallent, J}, title = {Brain state dependent repetitive transcranial magnetic stimulation improves motor learning outcomes.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae4dbe}, pmid = {41780162}, issn = {1741-2552}, abstract = {Objective Motor learning is key to successful neuro-rehabilitation. Combinations of Brain-Computer Interfaces (BCIs) and repetitive transcranial magnetic stimulation (rTMS) have been proposed for neurorehabilitation following conditions such as stroke. However, rTMS is typically delivered via a fixed protocol without taking into consideration the current brain states of participants. We propose a new BCI-based rTMS delivery protocol for supporting motor learning. Specifically, we propose BCI-based brain state dependent delivery of rTMS, in which a BCI system measures the event-related desynchronisation (\ERD; a neural marker of motor learning in the alpha band, selected because it is a robust, well-established real-time EEG correlate of motor activity and cortical excitability) in order to determine when to deliver rTMS. Approach We compare our proposed rTMS delivery protocol with two state of the art comparable protocols: delivery of rTMS prior to the BCI-based motor learning and delivery of rTMS at fixed times throughout the experiment, as well as a control condition in which no rTMS was used. Each protocol is tested with a different group (n=8) of participants (n=32 total participants). Main Results Our results reveal a significant effect of changing the rTMS delivery protocol ($p=0.005$) and that our proposed rTMS delivery protocol delivers better motor learning outcomes than other state of the art rTMS delivery protocols (e.g. BCI group vs. fixed times group: p=0.003, BCI group vs. no rTMS group: p=0.03). Inspection of ERD dynamics from each of our participant groups demonstrates that our BCI-based rTMS paradigm keeps corticospinal excitability relatively stable throughout the learning period, keeping the brain in a more optimal learning state for longer. Significance These findings suggest potential applications for adaptive rTMS-BCI systems in clinical neurorehabilitation, sports skill learning, and neuroprosthetic control.}, }
@article {pmid41779656, year = {2026}, author = {Zhang, R and Guo, X and Pan, Y and Gao, S}, title = {STAND-Net: A Spiking Temporal Attention autoeNcoDer Network for Efficient EEG Artifact Removal.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3670141}, pmid = {41779656}, issn = {2168-2208}, abstract = {Electroencephalography (EEG)-based brain computer interface (BCI) systems hold significant promise across diverse applications; however, their performance is compromised by pervasive physiological artifacts that degrade signal fidelity. While current deep neural networks (DNNs) improve artifact rejection, their high computational cost precludes deployment in wearable BCIs systems. Here, we introduce STAND-Net (Spiking Temporal Attention autoeNcoDer Network), a neuromorphic architecture that leverages event-driven spiking neurons to achieve ultra-efficient, high-fidelity EEG artifact removal. STAND-Net combines a spike-convolution encoder-decoder with leaky integrate-and-fire neurons to model spatiotemporal EEG dynamics, a dilation-enhanced residual backbone capturing long-range dependencies, and a spike-rate attention mechanism dynamically localizing artifacts via neuronal firing patterns. The system demonstrates >3.7 dB improvement in signal-to-distortion ratio over state-of-the-art methods across diverse artifacts while consuming 97.98% less power than comparable DNNs. Crucially, downstream BCI classification accuracy increased by 6.64% using STAND-Net-processed signals. This work establishes a neuromorphic framework for low-power and high quality EEG artifact removal in wearable BCI systems.}, }
@article {pmid41776620, year = {2026}, author = {Kim, KT and Jeong, JH and Sung, DJ and Lee, JY and Kim, L and Kim, DJ and Kim, SJ and Kim, H and Lee, SJ}, title = {Motor imagery BCI enables more practical and user-friendly exoskeleton control than smartwatch for users with spinal cord injury: a preliminary study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01924-9}, pmid = {41776620}, issn = {1743-0003}, support = {2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; }, }
@article {pmid41776592, year = {2026}, author = {Zhong, S and Tang, X and Cheng, X and Pan, Y}, title = {Bodily maps of subject-specific feelings and academic emotions among high school students.}, journal = {BMC psychology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s40359-026-04283-1}, pmid = {41776592}, issn = {2050-7283}, support = {24YJC190006//Humanities and Social Sciences Research Project of the Ministry of Education of China/ ; 226-2025-00127//Fundamental Research Funds for the Central Universities/ ; 62577047//National Natural Science Foundation of China/ ; LMS25C090002//Zhejiang Provincial Natural Science Foundation of China/ ; }, }
@article {pmid41775059, year = {2026}, author = {Wu, R and Berezutskaya, J and Freudenburg, ZV and Ramsey, NF}, title = {Across-speaker articulatory reconstruction from sensorimotor cortex for generalizable brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae4cca}, pmid = {41775059}, issn = {1741-2552}, abstract = {OBJECTIVE: Speech brain-computer interfaces (BCIs) can restore speech features like articulatory movements from brain activity. However, for individuals with vocal tract paralysis, lack of articulatory movements can pose a challenge for speech BCI development. To address this challenge, our study aims at extracting generalizable articulatory features from a group of native Dutch speakers and reconstructing these features from brain data of a separate group of able-bodied individuals.<br><br>APPROACH: We applied a tensor component analysis (TCA) model to extract generalisable articulatory features from a publicly available articulatory movement dataset. To reconstruct articulatory features from the brain, we analyzed data of three able-bodied participants P1, P2 and P3 with high-density electrocorticography (HD-ECoG) electrode arrays implanted over the sensorimotor cortex. For each participant, a separate TCA model was applied to extract neural features. A gradient boosting regression model was used to reconstruct articulatory features from neural features. Reconstruction performance was measured as Pearson's correlation coefficient (PCC) between the reconstructed and the generalizable articulatory features.<br><br>RESULTS: The extracted articulatory features showed even contributions across participants, indicating that these features captured generalizable articulatory kinematic patterns. By using these features, we were able to reconstruct articulatory features from brain data. PCC between the reconstructed and original articulatory features were significantly above chance for all three participants, with mean PCCs of 0.80, 0.75 and 0.76 for P1, P2 and P3 respectively.<br><br>SIGNIFICANCE: With the rapid development of speech BCI, our research demonstrates that speech-related articulatory features can be restored from HD-ECoG signal using generalizable articulatory features derived from able-bodied individuals. With the potential to reconstruct audio or speech-related facial movements from the reconstructed articulatory features, our framework may provide a new way for developing speech BCIs for people unable to produce mouth movements.}, }
@article {pmid41774933, year = {2026}, author = {Zhu, X and Yin, G and Shi, D and Wang, L and Yan, J and Feng, D and Wei, Z and Wang, Y and Wang, B and Tan, S and Zhao, Y}, title = {MAGCANet: A multiscale adaptive graph-convolutional attention network for MI-EEG decoding.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae4c94}, pmid = {41774933}, issn = {2057-1976}, abstract = {Motor imagery EEG (MI-EEG) decoding remains challenging due to low signal-to-noise ratios and pronounced inter-subject variability. Although end-to-end deep models reduce reliance on manual feature engineering, many existing architectures may introduce temporal leakage through non-causal operations and often rely on fixed spatial topologies that cannot accommodate subject- and trial-specific connectivity patterns. Approach. We propose MAGCANet, which integrates five core components: (i) a Multiscale Causal Convolution Module (MCCM) for hierarchical temporal encoding under explicit causal constraints, (ii) a Temporal Convolution Module (TCM) to capture complex temporal dynamics, (iii) an Adaptive Graph Convolution Module (AGCM) for sample-specific topology learning in latent space, (iv) a Multi-Head Self-Attention Module (MHSAM) for global feature aggregation, and (v) a Classification Block for final decision making. Together, these components enforce temporal causality, adapt spatial interactions to individual dynamics, and produce discriminative representations robust to inter-subject variability. Results. On the BCI Competition IV-2a and IV-2b datasets, MAGCANet achieves strong single-subject accuracies of 88.58\% and 91.13\%, respectively. Under Leave-One-Subject-Out (LOSO) evaluation, the model maintains accuracies of 70.49\% and 79.49\%, demonstrating competitive and stable cross-subject generalization. MAGCANet is highly lightweight, with only 0.0194M parameters, and achieves low inference latency (2.23 ms). Qualitative analyses, including feature clustering and channel occlusion, further highlight the model's interpretability and its ability to capture relevant EEG patterns. Significance. MAGCANet provides a robust and interpretable solution for MI-EEG decoding, balancing high precision with computational efficiency, and offering a reliable method for real-time BCI applications.}, }
@article {pmid41772895, year = {2026}, author = {Sun, WB and Xu, JJ and Chen, YL and Feng, ZC and Li, HF and Chen, DF and Wu, ZY}, title = {Heterozygous Loss-of-Function Variants of KCNJ10 Cause Paroxysmal Kinesigenic Dyskinesia.}, journal = {Movement disorders : official journal of the Movement Disorder Society}, volume = {}, number = {}, pages = {}, doi = {10.1002/mds.70247}, pmid = {41772895}, issn = {1531-8257}, support = {81330025//National Natural Science Foundation of China/ ; 82101526//National Natural Science Foundation of China/ ; 82301421//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholar of Zhejiang University/ ; }, abstract = {BACKGROUND: Heterozygous variants of potassium inwardly rectifying channel subfamily J member 10 (KCNJ10) were previously reported to be enriched in several patients with paroxysmal kinesigenic dyskinesia (PKD).<br><br>OBJECTIVES: The aim was to confirm the pathogenesis of KCNJ10 variants and the relationship between KCNJ10 variants and PKD phenotypes.<br><br>METHODS: The whole-exome sequencing followed by Sanger sequencing were used to screen the potential pathogenic KCNJ10 variants in a cohort of PKD patients. Functional studies were performed to check the pathogenicity of the variants. The clinical characteristics of KCNJ10-related PKD patients reported to date were reviewed.<br><br>RESULTS: Five heterozygous KCNJ10 variants including c.76C>T (p.R26*), c.436C>T (p.L146F), c.484A>G (p.T162A), c.524G>A (p.R175Q), and c.923del (p.G308Afs*17), were detected in five pedigrees and three sporadic patients. All variants had extremely low frequency in normal populations and were highly conserved between species. They influenced the location or expression of potassium inwardly&#xa0;rectifying channel (Kir) 4.1 and resulted in the Kir currents of cell decreased to varied degrees. Up to date, 31 KCNJ10 variants had been reported to manifest as PKD, and a significant majority (22/31, 71%) were in the cytoplasmic domain near the C-terminus. Notably, the KCNJ10-related PKD patients showed a pronounced male predominance.<br><br>CONCLUSIONS: The study confirmed the correlation between PKD and the loss-of-function of Kir4.1 resulted from heterozygous KCNJ10 variants. The distribution bias of PKD-related KCNJ10 variants as well as the male predominance in affected individuals shed light on the mechanism investigation of this subtype of PKD. &#xa9; 2026 International Parkinson and Movement Disorder Society.}, }
@article {pmid41771420, year = {2026}, author = {Lim, J and Wang, PT and Sohn, WJ and Lin, D and Thaploo, S and Bashford, L and Bjanes, DA and Nguyen, A and Gong, H and Armacost, M and Shaw, SJ and Kellis, S and Lee, B and Lee, D and Heydari, P and Andersen, RA and Nenadic, Z and Liu, CY and Do, AH}, title = {Real-Time Brain-Computer Interface Control of Walking Exoskeleton with Bilateral Sensory Feedback.}, journal = {Brain stimulation}, volume = {}, number = {}, pages = {103065}, doi = {10.1016/j.brs.2026.103065}, pmid = {41771420}, issn = {1876-4754}, abstract = {PURPOSE: Brain-computer interfaces (BCIs) offer a pathway to restore ambulation in indi-viduals with spinal cord injury (SCI). However, existing BCI systems for gait are unidirectional and lack sensory feedback. This study aimed to demonstrate that a bidirectional brain-computer interface (BDBCI) can simultaneously enable real-time brain-controlled walking and artificial leg sensation via electrical stimulation of the sensory cortex.<br><br>METHODS: Epilepsy patients undergoing bilateral interhemispheric subdural electrocorticog-raphy (ECoG) implantation were recruited for this proof-of-concept study. Motor mapping identified electrodes in the leg motor cortex for decoding stepping intent, while sensory stimu-lation mapping determined stimulation sites in the somatosensory cortex to elicit artificial leg percepts. A custom embedded BDBCI decoded motor intent in real time to actuate a robotic gait exoskeleton (RGE) from ECoG signals and delivered leg swing sensory feedback via direct cortical stimulation. Performance was assessed through correlations between cued and decoded states, sensory reliability tasks, and control experiments.<br><br>RESULTS: One subject was recruited and achieved a high decoding performance (&#x3c1; = 0.92 &#xb1; 0.04, lag of 3.5 &#xb1; 0.5 s) across 10 runs of operating the BDBCI-controlled RGE. Bilateral leg percepts were validated through a blind step-counting task (92.8% accuracy, p < 10[-6]). Control experiments verified that decoding was not affected by stimulation artifacts. No adverse events were reported.<br><br>DISCUSSION: This study establishes the feasibility of an embedded system BDBCI for restor-ing both motor control and artificial sensation of walking. Leveraging interhemispheric leg sen-sorimotor cortices is safe and yields superior decoding compared to prior lateral brain convexity approaches. These findings provide a foundation for translating BDBCI technology into fully implantable systems for SCI patients with paraplegia.}, }
@article {pmid41770462, year = {2026}, author = {Chen, J and Li, YW and Yao, ST and Huang, YH and Ma, WM and Li, ZH and Lan, Y and Xu, GQ and Ding, Q}, title = {The Influence of M1 and DLPFC iTBS on BCI Performance: A TMS and fNIRS Study.}, journal = {Translational stroke research}, volume = {17}, number = {2}, pages = {}, pmid = {41770462}, issn = {1868-601X}, support = {82472619 (YL)//National Natural Science Foundation of China/ ; 82072548 (GX), 82272588 (GX)//National Natural Science Foundation of China/ ; 82102678 (QD)//National Natural Science Foundation of China/ ; 202206010197 (YL) and 202201020378 (YL)//Guangzhou Municipal Science and Technology Program/ ; 2024A04J3082 (QD)//Guangzhou Municipal Science and Technology Program/ ; 2022YFC2009700 (YL)//Natural Key Research and Development Program of China/ ; A2024500(QD)//Guangdong Medical Research Foundation/ ; }, abstract = {Brain-computer interface (BCI) control inefficiency often occurs in stroke survivors due to insufficient sensorimotor activity generated during motor imagery. Previous studies focused on upregulating excitability of primary motor cortex (M1) alone. Dorsolateral prefrontal cortex (DLPFC), an important region for motor imagery, may be effective for improving BCI performance. This study is aimed at investigating how intermittent theta burst stimulation (iTBS) targeted on M1 and DLPFC influences BCI performance and its neural mechanisms.25 healthy subjects (9 males) received four types of iTBS (i.e., M1 iTBS, DLPFC iTBS, combination of M1 and DLPFC iTBS and sham iTBS) on separate days. BCI control testing, functional near-infrared spectroscopy assessment and single-pulse transcranial magnetic stimulation were performed before and immediately after iTBS in each session. Corticospinal excitability, brain activation, and functional connectivity were calculated. Our results revealed that corticospinal excitability was significantly increased after M1 iTBS (P = 0.016), with the magnitude of increase positively correlated with BCI performance (P = 0.013). Frontoparietal network functional connectivity was significantly increased after DLPFC iTBS (P's<0.05), with the magnitude of increase positively correlated with changes in BCI performance (P's<0.05). In conclusion, M1 iTBS and DLPFC iTBS alone influences BCI performance through specific neural mechanisms, and the combination of M1 and DLPFC iTBS did not induce any significant results. M1 iTBS could influence BCI performance by enhancing corticospinal excitability, while DLPFC iTBS could influence BCI performance by increasing frontoparietal network connectivity. These findings could contribute to the advancement of novel therapeutic strategies aimed at enhancing BCI effectiveness for neurological populations. Trial registration: The study was retrospectively registered in the Chinese Clinical Trial Registry (ChiCTR2500097678). Registration Date: 2025-02-24.}, }
@article {pmid41768366, year = {2026}, author = {Feng, Y and Guo, X and Huang, P and Ji, X and Jia, N and Yang, S and Hu, S}, title = {Cerebrospinal Fluid Genetics Enhance Risk Stratification in Bipolar Disorder.}, journal = {MedComm}, volume = {7}, number = {3}, pages = {e70629}, pmid = {41768366}, issn = {2688-2663}, abstract = {Bipolar disorder (BD) research confronts challenges: blood-based biomarkers offer limited insights into neurobiology, while cerebrospinal fluid (CSF) collection is clinically unusual. Linking genetic susceptibility to pathophysiology remains crucial for biologically informed risk stratification. We integrated cohort data and genome-wide association study (GWAS) summary statistics: the largest BD meta-analysis, CSF multi-omics profiles including 3107 proteomic and 2602 metabolomic participants, and a validation cohort of 247,834 UK Biobank participants. Unsupervised clustering revealed four single-nucleotide variant (SNV) clusters: metabolic-imbalance, metabolic-active, human leukocyte antigen (HLA)+immune, and HLA-immune. These clusters exhibited distinct clinical features, with the metabolic-imbalance cluster showing multi-directional associations with 21 psychiatric traits, while the HLA-immune cluster was associated with emotional instability in BD patients (odds ratio [OR] = 1.14, p = 0.027). The optimized multimodal cluster-specific polygenic risk scores (PRS) model significantly outperformed clinical-only prediction factors (C-index = 0.77), with the metabolic-imbalance PRS contributing a 22.6% incremental predictive value (hazard ratio [HR] = 1.23, 95% CI: 1.04-1.45, p = 0.016). Risk reclassification showed an 84% reduction in false-negative rates in the low-risk subgroup, identifying a high-risk layer with a 17.6-fold increased BD incidence. Altogether, genetically informed substitutes for CSF biomarkers emerged as a scalable tool for risk prediction, overcoming the barriers of CSF collection while capturing neurobiological heterogeneity.}, }
@article {pmid41767407, year = {2026}, author = {Gao, Y and Ma, Y and Liu, Y and Yin, G and Qin, Y}, title = {Multi-branch Domain Adversarial Neural Network with dynamic weight allocation for multi-source EEG classification.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {58}, pmid = {41767407}, issn = {1871-4080}, abstract = {To address challenges such as the strong non-stationarity and inter-subject distribution shifts of EEG data, as well as the limitations of conventional DANN-based methods in feature representation and multi-source domain adaptation, a Multi-branch Domain Adversarial Neural Network with Multi-scale Channel Attention (MBCA-DANN) is proposed. To enhance feature richness, a multi-scale channel attention Module (MSCA) is designed, which provides multi-scale features and adaptively adjusts the feature channel weights, improving the feature capture ability of the network. A multi-branch architecture is constructed by combining auxiliary Maximum Mean Discrepancy (MMD), domain discriminators, and label discriminators, ensuring optimal matching between the source and target domains. Furthermore, a multi-source domain method with dynamic weight allocation is introduced, enhancing classification performance and robustness. Experimental results demonstrate that the classification accuracy for single-source domain transfer on the MII and MIII datasets is 71.89% and 71.82%, respectively, while the multi-source domain transfer classification accuracy improves to 79.83% and 82.87%. The model achieves a classification accuracy of 98.69% on the fatigue detection dataset, outperforming all currently known state-of-the-art algorithms, validating its strong generalization ability and providing an effective solution for multi-source cross-subject EEG classification.}, }
@article {pmid41766383, year = {2026}, author = {Lecomte, A and Mazenq, L and Blatché, MC and Lecestre, A and Larrieu, G}, title = {Monolithic 3D Nanoelectrode Arrays on CMOS Circuitry for Scalable, High-Resolution Neural Recording.}, journal = {Small (Weinheim an der Bergstrasse, Germany)}, volume = {}, number = {}, pages = {e12016}, doi = {10.1002/smll.202512016}, pmid = {41766383}, issn = {1613-6829}, support = {863245//European Commission/ ; }, abstract = {Understanding brain function and neurodegenerative disorders, and accelerating preclinical drug development, demand neural interfaces that combine nanoscale sensitivity with high-resolution, large-scale recording capability. Here, we present a monolithically integrated high-density nanoelectrode array (HD-NEA) featuring vertical high-aspect ratio nanowire electrodes embedded within the back-end-of-line of commercial CMOS circuitry. Using a low-temperature (<400 °C), wafer-scale post-fabrication strategy, we decouple nanostructure formation from circuit integration while preserving CMOS functionality. The resulting 3D array, comprising 26,400 electrodes, achieves high yield and uniformity across 4-in. wafers. When interfaced with in vitro cortical neurons, the HD-NEA yields significantly higher spike amplitudes and signal-to-noise ratios than planar microelectrodes, without requiring electroporation. High-resolution spike mapping revealed steeper spatial signal decay, consistent with closer neuron-nanowires coupling, and enabled the detection of distinct waveform morphologies including putative dendritic signals. These results position HD-NEA as a scalable and CMOS-compatible nanobiointerface, enabling high-fidelity neural recording for neuroscience research, brain-machine interfacing, and bioelectronic diagnostics.}, }
@article {pmid41763275, year = {2026}, author = {Shi, B and Liu, M and Wang, Y}, title = {ATCRN: Attention-guided Temporal Convolutional Remix Network for P300 speller.}, journal = {Journal of neuroscience methods}, volume = {430}, number = {}, pages = {110727}, doi = {10.1016/j.jneumeth.2026.110727}, pmid = {41763275}, issn = {1872-678X}, abstract = {BACKGROUND: The P300 speller is a prominent brain-computer interface (BCI) that facilitates communication by detecting P300 event-related potentials. However, its performance is substantially constrained by the low signal-to-noise ratio of EEG signals and the inherent temporal variability of the P300 response.<br><br>NEW METHOD: We propose the Attention-guided Temporal Convolutional Remix Network (ATCRN), an end-to-end model that synergistically integrates a novel Temporal Convolutional Remix Network (TCRN) with a dual-attention framework. The TCRN employs multi-level skip connections to enable dynamic, cross-hierarchical fusion of local and global temporal features, addressing the variable latency of P300. Externally, the Convolutional Block Attention Module (CBAM) suppresses noise in spatial and channel dimensions. Internally, Efficient Channel Attention (ECA) modules within TCRN block perform dynamic channel recalibration.<br><br>RESULTS: On BCI Competition III Dataset II, ATCRN achieved character recognition rates of 99% and 98% for two subjects at the 15th repetition, and yielded superior information transfer rates. Evaluation across eight ALS patients showed robust P300 detection (average AUC-ROC 0.882).<br><br>ATCRN outperforms both established CNN/TCN benchmarks and recent Transformer-based models across two public datasets, achieving state-of-the-art results in P300 detection and character spelling.<br><br>CONCLUSION: The proposed ATCRN provides a novel, robust, and effective decoding framework for the P300 speller. The integration of TCRN for temporal feature fusion and the dual-attention mechanism for feature refinement offers a practical solution for advancing BCI applications.}, }
@article {pmid41762696, year = {2026}, author = {Song, D}, title = {Decoding Naturalistic Episodic Memory with Artificial Intelligence and Brain-Machine Interface.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e20125}, doi = {10.1002/advs.202520125}, pmid = {41762696}, issn = {2198-3844}, support = {N66001-14-C-4016//Defense Advanced Research Projects Agency (DARPA) Restoring Active Memory (RAM) program/ ; RF1DA055665/1R01EB031680//NIH/NIDA BRAIN Initiative - Theories, Models and Methods (TMM) program/ ; HR0011-25-3-0142//DARPA Investigating how Neurological Systems Process Information in REality (INSPIRE) program/ ; }, abstract = {Episodic memory integrates what, where, and when of experience into a coherent autobiographical narrative. Decades of research have identified hippocampal place, time, and concept cells as neural correlates of these components. Yet a major challenge remains: real-life memory encoding occurs in high-dimensional, naturalistic settings, where multimodal sensory, emotional, and cognitive processes intertwine across time and context. Traditional paradigms and analytical tools are insufficient to decode the neural activity underlying such complex experiences. Recent advances in artificial intelligence (AI) offer new means to address this challenge. AI models, such as variational autoencoders and multimodal alignment frameworks, can extract latent representations from neural and behavioral data, capturing the naturalistic structure of memory encoding. Large language models further provide powerful frameworks for interpreting subjective memory reports, linking verbal narratives to memory encoding. When integrated with closed-loop brain-machine interfaces (BMIs) capable of recording from and manipulating large populations of neurons in relevant brain regions, these tools make it possible to address the long-standing questions: how to decode memory codes during naturalistic behaviors and whether these memory codes causally generate memories rather than merely correlate with them. This integrated AI-BMI framework outlines a roadmap from mapping to engineering memory, with implications for Alzheimer's disease, traumatic brain injury, and PTSD.}, }
@article {pmid41761229, year = {2026}, author = {Song, J and Wang, N and Li, Z and Zhang, X and Lv, Z and Shan, X and Yang, Y and Liu, J and Chai, X}, title = {Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01920-z}, pmid = {41761229}, issn = {1743-0003}, support = {HX202409200057//Collaborative Research and Development Project "Deep Brain Stimulation System Development and Technical Research"/ ; 2025ZD0215200//National Key R&amp;D Program on Brain Science and Brain-Like Research: Precision Regulation Technology for Spinal Cord-Peripheral Nerve Integration Aimed at Motor Function Restoration/ ; }, }
@article {pmid41760680, year = {2026}, author = {Zhang, S and Liu, Z and Jiang, T and Wang, C and Wang, J and Wang, H and Fan, M and Yang, L and Li, Y and Ding, L and Yu, Y and Hao, X and Ma, S and Xu, B and Chen, X and Ye, C and Chen, X and Chu, PK and Jin, S and Ding, F and Yu, XF and Sun, Z and Wang, J}, title = {Strong optical anisotropy in one-dimensional phosphorus wavy tubes.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-70129-4}, pmid = {41760680}, issn = {2041-1723}, abstract = {Anisotropic materials with intrinsic one-dimensional architectures, where chains or tubes align along a crystallographic axis, exhibit direction-dependent optical responses and serve as ideal building blocks for polarization-sensitive optoelectronics. While progress exists in engineered compounds, discovering elemental crystals with naturally ordered one-dimensional building blocks exhibiting giant optical anisotropy remains challenging. Here, we report the synthesis of a direct-bandgap semiconducting one-dimensional phosphorus single crystal composed of unique wavy polygonal tubes. The monoclinic lattice structure is revealed by single-crystal X-ray diffraction and advanced transmission electron microscopy. The crystal exhibits giant birefringence in the visible and near-infrared regions, stemming from electron localization and anisotropic transitions of the phosphorus 3p orbital along the tube axis. The low-symmetry structure endows remarkable linear and nonlinear optical anisotropies, including orientation-dependent photoluminescence, Raman scattering, and second-harmonic generation. This study establishes a paradigm for designing giant optical anisotropies, opening avenues for on-chip polarization devices and nonlinear photonic circuits.}, }
@article {pmid41760219, year = {2026}, author = {Lai, Z and Feng, D and Liang, M and Liang, W and Xu, Y and Ke, J}, title = {[Research progress on flexible electrode technology in brain computer interface applications].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {43}, number = {1}, pages = {186-192}, doi = {10.7507/1001-5515.202508066}, pmid = {41760219}, issn = {1001-5515}, abstract = {Flexible electrode as a revolutionary brain computer interface (BCI) technology in the field of neural engineering, has achieved high-fidelity acquisition and long-term stable transmission of electroencephalographic signals through their exceptional bio-compatibility. This review systematically elucidates the design paradigms and material innovation systems of flexible electrodes, focusing on their transitional medical value from aspects such as electrode materials, signal acquisition and processing. It identifies the current technical bottlenecks that urgently need to be broken through and outlines the future development directions, hoping to provide a systematic technical road-map and evaluation framework for the technical development of next-generation BCI.}, }
@article {pmid41760218, year = {2026}, author = {Wang, Y and Li, W and Chen, X}, title = {[A review of noninvasive brain-computer interfaces combined with transcranial electrical stimulation for neural rehabilitation].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {43}, number = {1}, pages = {178-185}, doi = {10.7507/1001-5515.202509061}, pmid = {41760218}, issn = {1001-5515}, abstract = {The rehabilitation of motor dysfunction following stroke remains a major clinical challenge, underscoring the urgent need to develop novel therapeutic strategies to improve functional recovery in patients. Brain-computer interface (BCI) technology has emerged as a cutting-edge approach in neurorehabilitation, demonstrating significant potential for motor function restoration. Transcranial electrical stimulation (tES), a non-invasive neuromodulation technique, can promote neuroplasticity by regulating cortical excitability. In recent years, studies have begun to explore the combination of BCI with tES to synergistically enhance neural remodeling within the central nervous system. This integrated multi-technology strategy is increasingly becoming a key focus in the field of neurorehabilitation. This review systematically summarized recent advances in tES-BCI integrated systems for neurorehabilitation, with a particular emphasis on widely adopted BCI paradigms and tES parameter configurations and stimulation modalities. Based on a comprehensive synthesis of existing evidence, this review summarizes the efficacy of this combined intervention strategy in rehabilitating upper and lower limb motor functions following stroke, highlights the methodological limitations and clinical translation challenges present in current research, and aims to provide insights for mechanistic exploration, system optimization, and clinical translation of integrated BCI-tES technology.}, }
@article {pmid41760207, year = {2026}, author = {Qi, Q and Li, M}, title = {[A time-frequency transform and Riemannian manifold-based domain adaptation method for motor imagery in brain source space].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {43}, number = {1}, pages = {87-96}, doi = {10.7507/1001-5515.202507056}, pmid = {41760207}, issn = {1001-5515}, abstract = {To accurately capture and address the multi-dimensional feature variations in cross-subject motor imagery electroencephalogram (MI-EEG), this paper proposes a time-frequency transform and Riemannian manifold based domain adaptation network (TFRMDANet) in a high-dimensional brain source space. Source imaging technology was employed to reconstruct neural electrical activity and compute regional cortical dipoles, and the multi-subband time-frequency feature data were constructed via wavelet transform. The two-stage multi-branch time-frequency-spatial feature extractor with squeeze-and-excitation (SE) modules was designed to extract local features and cross-scale global features from each subband, and the channel attention and multi-scale feature fusion were introduced simultaneously for feature enhancement. A Riemannian manifold embedding-based structural feature extractor was used to capture high-order discriminative features, while adversarial training promoted domain-invariant feature learning. Experiments on public BCI Competition IV dataset 2a and High-Gamma dataset showed that TFRMDANet achieved classification accuracies of 77.82% and 90.47%, with Kappa values of 0.704 and 0.826, and F1-scores of 0.780 and 0.905, respectively. The results demonstrate that cortical dipoles provide accurate time-frequency representations of MI features, and the unique multi-branch architecture along with its strong time-frequency-spatial-structural feature extraction capability enables effective domain adaptation enhancement in brain source space.}, }
@article {pmid41760200, year = {2026}, author = {Song, L and Zhang, Y and Wei, Y and Liu, Y and Wang, C and Xu, G}, title = {[Microstate dynamics in motor imagery of stroke patients with transcranial alternating current stimulation modulation].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {43}, number = {1}, pages = {26-33}, doi = {10.7507/1001-5515.202508021}, pmid = {41760200}, issn = {1001-5515}, abstract = {Transcranial alternating current stimulation (tACS) holds significant potential for improving motor function in stroke patients, but its underlying mechanisms remain unclear. In this study, 20 Hz tACS was applied to 15 stroke patients, and their motor imagery (MI) signals were collected before and after stimulation, which were for assessment by combining with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE). Additionally, 11 subjects were recruited as a healthy control group. The study demonstrated that FMA-UE scores of stroke patients significantly increased after tACS intervention. The duration of EEG microstate C and F decreased significantly, while microstate D (coverage, duration, and occurrence probability) increased markedly, and microstate E decreased. The transition probabilities of C→D and D→B were positively correlated with FMA-UE scores. Based on these findings, this study concludes that 20 Hz tACS can enhance neuroplasticity and motor function in patients, and the transition probabilities (C→D/D→B) may serve as potential indicators for assessing motor function, providing experimental evidence for the clinical application of tACS and the development of rehabilitation brain-computer interfaces.}, }
@article {pmid41760197, year = {2026}, author = {Fu, Y and Cheng, T and Luo, R and Zhao, L and Li, T and Su, L and Xu, J}, title = {[A scientific definition of brain-computer interfaces (BCIs): Essential components, fundamental characteristics, capability boundaries, and scope delimitation].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {43}, number = {1}, pages = {1-7}, doi = {10.7507/1001-5515.202511002}, pmid = {41760197}, issn = {1001-5515}, abstract = {Brain-computer interfaces (BCIs) are communication and control systems centered on neural signals that incorporate both the user and the brain into a closed-loop interaction framework, and are widely regarded as a transformative paradigm in human-computer interaction. However, despite the existence of broadly accepted definitions within the research community, the rapid acceleration of BCI translation and commercialization has led to increasing ambiguity in scientific definitions, expansion of conceptual scope, and overstatement of technical capabilities. To address these issues, this paper proposed a scientifically grounded definition of BCIs and systematically analyzed their essential system components and fundamental characteristics. On this basis, the major and specific factors that constrain the capability boundaries of current and foreseeable BCI systems were examined. Furthermore, the scope of BCI was explicitly delineated by distinguishing BCIs from adjacent neurotechnologies based on their functional roles and system characteristics. This work aims to promote a more rigorous and coherent understanding of BCI definitions, scope, and capability limits within the academic community, and to provide essential theoretical foundations for responsible translation and long-term development. By clarifying conceptual boundaries and realistic expectations, it seeks to mitigate risks associated with conceptual generalization and distorted projections in both research and industrial practice, thereby fostering a more rational, robust, and sustainable ecosystem for the BCI field.}, }
@article {pmid41759685, year = {2026}, author = {Pang, Z and Li, Z and Zhang, R and Dong, Q and Cheng, Z and Cui, H and Chen, X}, title = {A High-Performance SSVEP-BCI System Based on High-Frequency Flickers in the Peripheral Visual Field.}, journal = {Brain research bulletin}, volume = {}, number = {}, pages = {111795}, doi = {10.1016/j.brainresbull.2026.111795}, pmid = {41759685}, issn = {1873-2747}, abstract = {BACKGROUND: The existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) primarily use central visual field flickers with a stimulus frequency of 8-20Hz, which is prone to exhibit strong flicker perception in users. Considering that, this study aims to develop an SSVEP-based BCI system which is both high-performance and low-flicker-perception by employing high-density electrodes and high-frequency flickers in the peripheral visual field.<br><br>METHODS: A custom-made electroencephalogram (EEG) cap with high-density electrodes was used to acquire more EEG data. To alleviate flicker perception, this study combined high-frequency visual stimulation with peripheral visual field stimulation. The proposed system encoded 40 targets using annuli with an angular range in 2.1&#xb0;-4.1&#xb0; and high-frequency flickers in the range of 32.00-36.68Hz. For signal decoding, the task-discriminant component analysis (TDCA) was first applied to the peripheral visual field SSVEP-based BCI system with weak response.<br><br>RESULTS: Through online experiments, the feasibility of this system was verified. It achieved an average classification accuracy of 83.22 &#xb1; 11.95% and an information transfer rate (ITR) of 178.21 &#xb1; 43.84 bits/min. Moreover, the role of high-density electrodes to obtain more useful EEG information and thus improving the classification accuracy has been proved.<br><br>The online ITR of this system was the highest for current peripheral visual field SSVEP-based BCIs.<br><br>CONCLUSION: The proposed system not only provides novel ideas for the design of BCI systems with weak flicker, but also provides reference value for the future application of high-density electrodes in SSVEP-based BCIs.}, }
@article {pmid41759684, year = {2026}, author = {Ye, J and Xu, M and Hu, J and Yu, H and Zhang, S and Jiang, L and Li, F and Xu, P and Dai, A}, title = {Predicting Long-Term Prognosis in Comatose Patients through Brain Network Analysis under Name-Evoked Stimulation.}, journal = {Brain research bulletin}, volume = {}, number = {}, pages = {111794}, doi = {10.1016/j.brainresbull.2026.111794}, pmid = {41759684}, issn = {1873-2747}, abstract = {Accurate prognosis assessment of comatose patients remains a significant challenge in neurocritical care. Growing evidence indicates that brain connectivity is integral to the maintenance of consciousness and may be linked to its recovery. In this study, we recorded bedside electroencephalography (EEG) from comatose patients during an auditory oddball name-calling task to investigate task-related dynamic causal modeling (DCM) connectivity and to examine whether connectivity strengths correlated with patients' functional recovery. Our findings reveal that a bidirectional model, incorporating reciprocal connectivity among the superior frontal gyri, superior parietal lobules, and primary auditory cortices, was significantly associated with the neural processing of name-calling stimuli in comatose patients. Furthermore, the strength of these DCM connections demonstrated a capacity to predict long-term prognostic outcomes, as evaluated via the Glasgow Outcome Scale-Extended scale. Together, these results provide evidence supporting the potential of DCM-derived biomarkers in evaluating functional prognosis in comatose patients. (ChiCTR2000033586).}, }
@article {pmid41759187, year = {2026}, author = {Koseki, S and Hayashibe, M and Owaki, D}, title = {Human-inspired bipedal locomotion: from neuromechanics to mathematical modelling and robotic applications.}, journal = {Journal of the Royal Society, Interface}, volume = {23}, number = {235}, pages = {}, doi = {10.1098/rsif.2025.0662}, pmid = {41759187}, issn = {1742-5662}, support = {//NSK Foundation for the Advancement of Mechatronics/ ; //Japan Society for the Promotion of Science/ ; }, abstract = {Human bipedal locomotion arises from continuous, closed-loop interactions between neural control and biomechanical structure-collectively referred to as neuromechanics. The relationship between human locomotion and robotic locomotion is deeply interconnected through shared principles of neuromechanics, thereby providing a comprehensive framework for understanding human movement and informing robotic system design. In this review, we synthesize insights from neuroscience, biomechanics, computational modelling and robotics to establish a cohesive perspective on human-inspired bipedal locomotion. We begin by outlining essential anatomical and physiological principles, such as spinal circuits, supraspinal coordination and musculoskeletal structure. Next, we analyse mathematical models-ranging from simplified neural oscillators to complex musculoskeletal simulations-that formalize these mechanisms. Finally, we discuss the embodiment of these models in bipedal robots, which promotes reciprocal advancements in both biological understanding and engineering innovation. Rather than offering a comprehensive literature survey, we focus on pivotal developments, emerging trends and unresolved questions that shape this interdisciplinary domain. By integrating diverse fields, this review aims to enhance the design of agile, energy-efficient robots and deepen our understanding of human locomotion.}, }
@article {pmid41758857, year = {2026}, author = {Qiu, L and Hu, Y and Wu, M and Long, B and Chen, T and Pan, J}, title = {A Multi-Scale Attention-based Reconstruction Fusion Network for Motor Imagery Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3668760}, pmid = {41758857}, issn = {2168-2208}, abstract = {Motor imagery (MI) is a widely used cognitive paradigm in brain-computer interface (BCI) systems, where accurate and efficient MI decoding is essential for real-time human-machine interaction. However, the non-stationary nature and pronounced inter-subject variability of electroencephalography (EEG) signals pose significant challenges to reliable decoding. To address these issues, we propose a multi-scale attention-based reconstruction fusion network (MSARFNet) for MI-EEG decoding. The proposed framework employs parallel multi-scale convolutional branches to extract discriminative spatio-temporal features at different temporal resolutions. An attention-based reconstruction fusion module is then introduced to selectively diminish non-dominant information while promoting effective interaction among multi-scale features. Furthermore, a local-global temporal encoding strategy is designed to enhance transient MI-related responses through local temporal context aggregation and subsequently capture long-range temporal dependencies via global temporal modeling. Subject-dependent experiments conducted on the BCI Competition IV 2a and 2b datasets demonstrate that MSARFNet achieves average classification accuracies of 84.64% and 87.96%, respectively, outperforming several state-of-the-art methods. These results indicate that MSARFNet provides an effective and robust solution for EEG-based MI decoding.}, }
@article {pmid41758662, year = {2026}, author = {Senneka, SJ and Dadarlat, MC}, title = {Integration of learned artificial sensation with vision during freely moving navigation.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {123}, number = {9}, pages = {e2521769123}, doi = {10.1073/pnas.2521769123}, pmid = {41758662}, issn = {1091-6490}, abstract = {Humans rely on both proprioceptive and visual feedback during reaching, integrating these two sensory streams to improve movement accuracy and precision. Patients using a brain-computer interface will similarly require artificial proprioceptive feedback in addition to vision to finely control a prosthesis. Intracortical microstimulation (ICMS) elicits sensory perceptions that could replace the lost proprioceptive signal. However, some learning may be required for encoding artificial sensation, as current technology does not give access to neurons with all of the desired encoding properties. We developed a freely moving mouse behavioral task in which to test learning and integration of artificial sensory information with natural vision. Mice implanted with a 16-channel microwire array in the primary somatosensory cortex were trained to navigate to randomly selected targets upon the floor of a custom behavioral training chamber. Target location was encoded with visual and/or patterned multichannel ICMS feedback. Mice received multimodal feedback from the beginning of training of the behavioral task, achieving 75% on multimodal trials after approximately 1,000 training trials. Mice also quickly learned to use the ICMS signal to locate invisible targets, achieving 75% proficiency on ICMS-only trials when tested. Critically, we found that performance with ICMS was as good or better than performance with natural vision, and that performance on multimodal trials significantly exceeded unimodal performance (vision or ICMS), demonstrating that animals rapidly learned to integrate natural vision with artificial sensation.}, }
@article {pmid41757508, year = {2026}, author = {Guo, B and Yan, K and Deng, Y and Zhao, W and Chen, X and Xue, C and Chai, Y and Quan, P and Goel, N and Basner, M and Mao, T and Rao, H}, title = {Domain-Specific Circadian Rescue following Sleep Deprivation.}, journal = {Sleep}, volume = {}, number = {}, pages = {}, doi = {10.1093/sleep/zsag054}, pmid = {41757508}, issn = {1550-9109}, abstract = {STUDY OBJECTIVES: Circadian rhythms regulate sleep-wake cycles and modulate cognitive functions over a 24-hour period. Following sleep loss, certain cognitive performance partially rebounds in the early evening, a phenomenon known as circadian rescue. Yet, the magnitude and domain specificity of circadian rescue remain poorly understood. Here, we integrate experimental and meta-analytic approaches to differential contributions of circadian and homeostatic processes to cognitive rescue following sleep deprivation.<br><br>METHODS: In Study 1, 54 healthy adults remained awake for 35 consecutive hours while repeatedly completing the Psychomotor Vigilance Task (PVT), the Digit Symbol Substitution Test (DSST), and the Karolinska Sleepiness Scale (KSS). Performance dynamics were modeled using the two-process framework of sleep regulation. In Study 2, a meta-analysis of published data contextualized these findings across protocols.<br><br>RESULTS: Results reveal domain-specific circadian recovery rates of 33.0%-52.1% for PVT, 45.7% for DSST, and 23.5% for KSS, indicating that subjective sleepiness is predominantly driven by homeostatic load, whereas objective cognitive performance retains significant circadian modulation under conditions of acute homeostatic pressure.<br><br>CONCLUSIONS: These findings clarify how circadian and homeostatic drives interact to shape cognitive task performance and subjective sleepiness outcomes under sleep loss, with practical implications for optimizing performance in fatigue-prone environments.}, }
@article {pmid41757349, year = {2026}, author = {Wang, Z and Han, Y and Yang, P and Jia, C and Li, C and Yuan, S and Wei, P and Hu, R}, title = {Liquid-liquid phase separation couples MKRN2-mediated ubiquitination of CSDE1 with neurodevelopmental disorders.}, journal = {Frontiers in cellular neuroscience}, volume = {20}, number = {}, pages = {1757304}, pmid = {41757349}, issn = {1662-5102}, abstract = {BACKGROUND: Makorin-2 (MKRN2) is an E3 ubiquitin ligase involved in multiple biological processes, yet its role in neurological disorders remains poorly understood. This study aims to elucidate how MKRN2 regulates the RNA-binding protein CSDE1-a molecule linked to autism-related genes-and to explore the functional implications of this interaction in neurodevelopment.<br><br>METHODS: Using mass-spectrometry screening, we identified CSDE1 as a direct substrate of MKRN2. Ubiquitination sites were validated through mutagenesis of conserved lysine residues. Liquid-liquid phase separation (LLPS) assays were performed in HEK293 and SH-SY5Y cells, and behavioral phenotypes were assessed in Mkrn2-knockout mice. Statistical analyses included appropriate tests for comparing ubiquitination levels, condensate formation, and social behavior outcomes.<br><br>RESULTS: MKRN2 mediates CSDE1 ubiquitination at four lysine residues (K81, K91, K208, K727). Deletion of MKRN2 or mutation of these sites abolished ubiquitination. MKRN2 and CSDE1 formed co-localized condensates via LLPS, which was disrupted by functional impairment of either protein. Mkrn2-knockout mice exhibited sex-specific social abnormalities-increased sociability in males and social withdrawal in females-recapitulating autism-spectrum disorder (ASD) heterogeneity. We further identified MARK1 and HNRNPUL2, ASD-associated mRNAs, as ubiquitination-dependent targets of CSDE1, linking aberrant condensate dynamics to synaptic plasticity deficits.<br><br>CONCLUSION: Our study reveals an LLPS-coupled ubiquitination mechanism by which MKRN2 regulates CSDE1, providing a novel molecular pathway underlying neurodevelopmental disorders. These findings offer new insights for understanding and treating neurological diseases such as ASD.}, }
@article {pmid41756006, year = {2026}, author = {Zhang, H and Deng, H and Zhai, Y and Zhang, J and Zhao, Z and Gong, L}, title = {Subtyping insomnia disorder with a population graph attention autoencoder: revealing two distinct biotypes.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1766155}, pmid = {41756006}, issn = {1662-4548}, abstract = {Insomnia disorder (ID) is neurobiologically heterogeneous and often eludes characterization by traditional group-level neuroimaging. Subtyping based on neuroimaging and clinical data offers a promising strategy for identifying biologically and clinically meaningful ID subgroups. To address this need, we developed a Gray Matter Population Graph Attention Autoencoder (GM-PGAAE) to subtype insomnia disorder in a cohort comprising 140 patients diagnosed with ID and 57 matched healthy controls. Each subject was represented as a node defined by atlas-based gray matter (GM) volumes. Population edges combined imaging-derived intersubject correlations with clinical similarity via a Hadamard product, generating an adjacency matrix that jointly encodes structural and phenotypic relationships. A Graph Attention Autoencoder learned low-dimensional embeddings that adaptively weighted informative intersubject connections, and clustering these embeddings identified distinct subtypes. Regional and network-level differences were further assessed using Voxel-Based Morphometry (VBM) and individualized differential structural covariance networks (IDSCNs). Through this framework, two ID subtypes were identified. Compared with Subtype 2, Subtype 1 showed higher symptom severity and greater GM reductions-particularly in the cerebellar vermis, thalamus, middle occipital cortex, fusiform gyrus, and paracentral lobule-alongside negative associations between GM volume and clinical scores. IDSCNs further revealed reduced thalamocortical and subcortical Z-scores in Subtype 1, indicating subtype-specific network alterations. Overall, GM-PGAAE integrates structural MRI and clinical measures to derive individualized embeddings and delineate biologically distinct ID subtypes.}, }
@article {pmid41755857, year = {2026}, author = {Yu, X and Yin, C and Liu, X and Liu, J and Zhu, Y and Li, D and Zhang, D and Lee, HJ and Ji, B and Tian, L}, title = {Competitive Mg[2+] Regulation of Biomolecular Condensate Microenvironments Enables Diverse Macrophage Response.}, journal = {JACS Au}, volume = {6}, number = {2}, pages = {1308-1318}, pmid = {41755857}, issn = {2691-3704}, abstract = {The intrinsic microenvironments of biomolecular condensates play decisive roles in applications spanning synthetic cell construction, targeted drug delivery systems, cell engineering, bioreactor development, and precision disease interventions. Recent studies highlight that divalent cations play a central role in modulating the internal condensate microenvironments. However, the complex multivalent interaction networks within condensates create significant challenges in unraveling the molecular mechanisms. This study employs model systems of cationic peptides (arginine decamer (R10), lysine decamer (K10)) and polyanionic polymers (polyadenylic acid (PolyA), polyinosinic acid (PolyI), polyglutamic acid (PolyE), polyaspartic acid (PolyD)) to systematically investigate Mg[2+]-mediated modulation of condensate properties. Mg[2+] enrichment dynamically controls ionic microenvironments through competitive interactions with polyelectrolytes. When interpolyelectrolyte affinity dominates (e.g., R10/PolyA), weakly bound Mg[2+] enhances the surface potential, promoting small-molecule enrichment and ribozyme catalytic efficiency. Conversely, when Mg[2+]-polyelectrolyte binding prevails (e.g., R10/PolyE), stable ion-polyelectrolyte complexes reduce the system polarity and amplify dye accumulation but compromise phase stability. Macrophage coculture experiments demonstrate that R10/PolyA@Mg condensates enable targeted magnesium delivery, significantly boosting TNF-α secretion and immune regulation. These findings establish a mechanistic framework for ion-mediated control of condensate microenvironments, offering theoretical insights into the intracellular ionic regulation of phase separation. This work suggests a Mg[2+]-responsive condensate design strategy for modulating macrophage responses, providing a foundation for the design of biomaterials with a tunable immunostimulatory potential.}, }
@article {pmid41755195, year = {2026}, author = {Sztyler, B and Królak, A and Strumiłło, P}, title = {Influence of EEG Signal Augmentation Methods on Classification Accuracy of Motor Imagery Events.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {4}, pages = {}, doi = {10.3390/s26041258}, pmid = {41755195}, issn = {1424-8220}, abstract = {This study investigates the impact of various data-augmentation techniques on the performance of neural networks in EEG-based motor imagery three-class event classification. EEG data were obtained from a publicly available open-source database, and a subset of 25 patients was selected for analysis. The classification task focused on detecting two types of motor events: imagined movements of the left hand and imagined movements of the right hand. EEGNet, a convolutional neural network architecture optimized for EEG signal processing, was employed for classification. A comprehensive set of augmentation techniques was evaluated, including five time-domain transformations, three frequency-domain transformations, two spatial-domain transformations and two generative approaches. Each method was tested individually, as well as in selected two- and three-method cascade combinations. The augmentation strategies were tested using three data-splitting methodologies and applying four ratios of original-to-generated data: 1:0.25, 1:0.5, 1:0.75 and 1:1. Our results demonstrate that the augmentation strategies we used significantly influence classification accuracy, particularly when used in combination. These findings underscore the importance of selecting appropriate augmentation techniques to enhance generalization in EEG-based brain-computer interface applications.}, }
@article {pmid41755176, year = {2026}, author = {Fraternali, M and Magosso, E and Borra, D}, title = {Inferring Arm Movement Direction from EEG Signals Using Explainable Deep Learning.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {4}, pages = {}, doi = {10.3390/s26041235}, pmid = {41755176}, issn = {1424-8220}, support = {Project MNESYS (PE0000006, DN. 1553 11.10.2022)//Ministero dell'Universit&#xe0; e della Ricerca/ ; Project DARE (PNC0000002, CUP: B53C22006450001, D.D. 931 of 06/06/2022)//Ministero dell'Universit&#xe0; e della Ricerca/ ; }, abstract = {Decoding reaching movements from non-invasive brain signals is a key challenge for the development of naturalistic brain-computer interfaces (BCIs). While this decoding problem has been addressed via traditional machine learning, the exploitation of deep learning is still limited. Here, we evaluate a convolutional neural network (CNN) for decoding movement direction during a delayed center-out reaching task from the EEG. Signals were collected from twenty healthy participants and analyzed using EEGNet to discriminate reaching endpoints in three scenarios: fine-direction (five endpoints), coarse-direction (three endpoints), and proximity (two endpoints) classifications. To interpret the decoding process, the CNN was coupled with explanation techniques, including DeepLIFT and occlusion tests, enabling a data-driven analysis of spatio-temporal EEG features. The proposed approach achieved accuracies well above chance, with accuracies of 0.45 (five endpoints), 0.64 (three endpoints) and 0.70 (two endpoints) on average across subjects. Explainability analyses revealed that directional information is predominantly encoded during movement preparation, particularly in parietal and parietal-occipital regions, consistent with known visuomotor planning mechanisms and with EEG analysis based on event-related spectral perturbations. These results demonstrate the feasibility and interpretability of CNN-based EEG decoding for reaching movements, providing insights relevant for both neuroscience and the prospective development of non-invasive BCIs.}, }
@article {pmid41755073, year = {2026}, author = {Ji, Y and Kim, DH and Hong, J}, title = {Enhanced EEG Emotion Recognition Using MIMO-Based Denoising and Band-Wise Attention Graph Neural Network.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {4}, pages = {}, doi = {10.3390/s26041133}, pmid = {41755073}, issn = {1424-8220}, support = {none//Changwon National University Samsung Changwon Hospital Joint Collaboration Research Support Project&#xff1b;the Academic Award from the Lee Won Yong Brain Research Fund/ ; }, abstract = {Electroencephalogram (EEG) signals serve as a primary input for brain-computer interface (BCI) systems, and extensive research has been conducted on EEG-based emotion recognition. However, because EEG signals are inherently contaminated with various types of noise, the performance of emotion recognition is often degraded. Furthermore, the use of a Band Feature Extraction Neural Network (BFE-Net), a state-of-the-art (SOTA) method in this field, has limitations with respect to independent band-wise feature extraction and a simplistic band aggregation process to obtain final classification results. To address these problems, this study proposes the noise-robust band-attention BFE-Net framework, aiming to improve the conventional BFE-Net from two perspectives. First, we implement multiple-input, multiple-output (MIMO)-based preprocessing. Specifically, we utilize multichannel minima-controlled recursive averaging for precise non-stationary noise covariance estimation and generalized eigenvalue decomposition for subspace filtering to enhance the signal-to-noise ratio. Second, we propose an attention-based band aggregation mechanism. By integrating a band-wise self-attention mechanism, the model learns dynamic inter-band dependencies for more sophisticated feature fusion for classification. Experimental results on the SEED and SEED-IV datasets under a subject-independent protocol show that our model outperforms the SOTA BFE-Net by 3.27% and 3.34%, respectively. This confirms that rigorous MIMO noise reduction, combined with frequency-centric attention, significantly enhances the reliability and generalization of BCI systems.}, }
@article {pmid41755066, year = {2026}, author = {Li, H and Xu, G and Feng, S and Du, C and Han, C and Kuang, J and Zhang, S}, title = {Improving Individual-Specific SSVEP-BCI with Adaptive Channel and Subspace Selection in TRCA.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {4}, pages = {}, doi = {10.3390/s26041123}, pmid = {41755066}, issn = {1424-8220}, support = {2025PT-ZCK-06//the Key Research and Development Project in Shaanxi Province/ ; }, abstract = {The individual-specific steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is characterized by individual calibration data, resulting in satisfactory performance. However, existing individual-specific SSVEP-BCIs employ generalized channels and task-related subspaces, which seriously limit their potential advantages and lead to suboptimal solutions. In this study, AS-TRCA was proposed to develop a purely individual-specific SSVEP-BCI by fully exploiting individual-specific knowledge. AS-TRCA involves optimal channel learning and selection (OCLS) as well as optimal subspace selection (OSS). OCLS aims to pick the optimal subject-specific channels by employing sparse learning with spatial distance constraints. Meanwhile, OSS adaptively determines the appropriate number of optimal subject-specific task-related subspaces by maximizing profile likelihood. The extensive experimental results demonstrate that AS-TRCA can acquire meaningful channels and determine the proper number of task-related subspaces for each subject compared to traditional methods. Furthermore, combining AS-TRCA with existing advanced calibration-based SSVEP decoding methods, including deep learning methods, to establish a purely individual-specific SSVEP-BCI can further enhance the decoding performance of these methods. Specifically, AS-TRCA improved the average accuracy as follows: TRCA 7.21%, SSCOR 7.61%, TRCA-R 6.58%, msTRCA 7.70%, scTRCA 4.47%, TDCA 2.91%, and bi-SiamCA 3.23%. AS-TRCA is promising for further advancing the performance of SSVEP-BCI and promoting its practical applications.}, }
@article {pmid41751235, year = {2026}, author = {Fu, B and Lin, K and Chen, Y and Zhang, J and Jin, Z and Li, L}, title = {The Right PPC Plays an Important Role in the Interaction of Temporal Attention and Expectation: Evidence from a tACS-EEG Study.}, journal = {Biomedicines}, volume = {14}, number = {2}, pages = {}, doi = {10.3390/biomedicines14020336}, pmid = {41751235}, issn = {2227-9059}, support = {62571096//National&#xa0;Natural&#xa0;Science&#xa0;Foundation&#xa0;of&#xa0;China/ ; 62176045//National&#xa0;Natural&#xa0;Science&#xa0;Foundation&#xa0;of&#xa0;China/ ; 2025ZNSFSC0453//Sichuan Science and Technology Program/ ; BX202402//Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows/ ; }, abstract = {Background/Objectives: Temporal attention and temporal expectation are two key mechanisms that facilitate perception by prioritizing information at specific moments and by leveraging temporal predictability, respectively. While their behavioral interaction is established, the underlying neural mechanisms remain poorly understood. Building on functional magnetic resonance imaging (fMRI) evidence linking temporal attention to parietal cortex activity and the role of alpha oscillations in temporal prediction, we investigated whether the right posterior parietal cortex (rPPC) may be involved in integrating these two processes. Methods: Experiment 1 used a behavioral paradigm to dissociate temporal expectation from attention across 600 ms and 1400 ms intervals. Experiment 2 retained only the 600 ms interval, combining behavioral assessments with electroencephalography (EEG), recording following transcranial alternating current stimulation (tACS) applied to the rPPC to probe neural mechanisms. Results: Experiment 1 showed an attention/expectation interaction exclusively at 600 ms: enhanced expectation improved response times under attended, not unattended, conditions. Experiment 2 replicated these behavioral and event-related potential (ERP) findings. Temporal attention modulated N1 amplitude: in attended conditions, the N1 was significantly more negative under high versus low expectation, while no difference was observed in unattended contexts. Anodal tACS over the rPPC reduced this N1 amplitude difference between high and low attentional expectation conditions to non-significance. Restricting analyses to attended conditions, paired-samples t-tests revealed that alpha-band power differed between high and low expectation under sham tACS, but this difference was absent under anodal tACS, which also attenuated the corresponding behavioral attention/expectation interaction effects. Conclusions: These findings provide suggestive evidence that the rPPC may be key to integrating temporal attention and expectation, occurring in early processing stages and specific to brief intervals.}, }
@article {pmid41750613, year = {2026}, author = {Li, N and Wang, J and Chen, S and Li, T}, title = {Integrative Multi-Omics Mendelian Randomization Reveals Oxidative Stress Mechanisms in Major Depressive Disorder, Bipolar Disorder, and Schizophrenia.}, journal = {Antioxidants (Basel, Switzerland)}, volume = {15}, number = {2}, pages = {}, doi = {10.3390/antiox15020233}, pmid = {41750613}, issn = {2076-3921}, support = {2021ZD0200404//China Brain Project (STI 2030)/ ; 2021ZD0200800//China Brain Project (STI 2030)/ ; 82230046//National Natural Science Foundation of China/ ; 20241203A14//Key R&amp;D by Hangzhou Science and Technology Bureau/ ; CXTD202501053//Zhejiang Clinovation Pride/ ; }, abstract = {BACKGROUND: Oxidative stress (OS) has been widely implicated in pathophysiology of major psychiatric disorder. However, establishing robust causal links and delineating the specific molecular mechanisms involved continue to pose significant research challenges.<br><br>METHODS: We performed a multi-omics analysis focusing on 817 oxidative stress-related genes (OSGs) in major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). We applied summary data-based Mendelian randomization (SMR), integrating large-scale genome-wide association studies for MDD, BD, and SCZ with quantitative trait loci datasets from both blood and brain tissues, including measures of DNA methylation, gene expression, and protein abundance.<br><br>RESULTS: Multi-omics integration yielded supportive evidence across blood and brain tissues implicating ACE and ACADVL in SCZ, where genetically predicted increases in their methylation, expression, and protein abundance were associated with reduced disease risk. IGF1R was associated with bipolar disorder (BD) risk in blood-specific analyses. Brain-specific analyses further nominated ENDOG as a candidate gene for SCZ. Single-cell SMR indicated that increased ENDOG expression was associated with higher SCZ risk in astrocytes, CD4[+] na&#xef;ve T cells, CD8[+] effector T cells, and natural killer cells, suggesting a potential immune-brain interaction.<br><br>CONCLUSIONS: This study provides multi-level genetic evidence supportive of a potential causal role for specific OSGs in major psychiatric disorders. We identify ACE, ACADVL, IGF1R, and ENDOG as candidate genes for further investigation, offering insights into epigenetic and transcriptional mechanisms that could inform future research on therapeutic targets.}, }
@article {pmid41750230, year = {2026}, author = {Xu, Z and Yu, Z}, title = {Maximizing Single-Feature Separability for Improving Transfer Learning in Motor Imagery EEG Decoding.}, journal = {Brain sciences}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/brainsci16020230}, pmid = {41750230}, issn = {2076-3425}, support = {2022ZD0211700//The Technology Innovation 2030/ ; }, abstract = {BACKGROUND/OBJECTIVES: Motor imagery (MI) EEG-based brain-computer interfaces (BCIs) are promising for neurorehabilitation, but practical use is often hindered by time-consuming per-user calibration and performance instability across sessions/users.<br><br>METHODS: To mitigate this issue, we aim to improve subject-dependent MI classification by leveraging labeled training data from other subjects within the same dataset via transfer learning. We propose Maximizing Single-Feature Separability (MSFS), a lightweight plug-in regularization applied during target-subject fine-tuning. MSFS operates on the network feature layer and constructs batch-wise target positions by maximizing a silhouette-based separability criterion for each feature dimension. The target position computation is implemented in a fully vectorized GPU-friendly manner.<br><br>RESULTS: We evaluate MSFS on BCI Competition IV-2a and IV-2b datasets using three representative backbone networks (EEGNet, ShallowConvNet, ATCNet). MSFS consistently improves standard transfer learning across both datasets and all backbones. When compared against representative transfer learning algorithms from the literature, MSFS remains competitive against the literature baselines. Ablation analysis confirms the effectiveness of each algorithm component. Few-shot experiments further indicate that MSFS is still beneficial when the target subject provides limited labeled data.<br><br>CONCLUSIONS: MSFS provides a within-dataset transfer learning enhancement for MI EEG decoding, improving target-subject accuracy under limited calibration data without relying on external datasets, and can be readily integrated into common deep MI classification pipelines.}, }
@article {pmid41750203, year = {2026}, author = {Duan, X and Xie, S and Cui, Y and Ji, T and Yan, H}, title = {A Neurophysiological Stratification Framework for Intermediate Motor Imagery-BCI Users Based on Independent Event-Related Brain Dynamics.}, journal = {Brain sciences}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/brainsci16020202}, pmid = {41750203}, issn = {2076-3425}, support = {62220106007//Key International (Regional) Cooperation Research Project of National Natural Science Foundation of China/ ; 62407034//National Natural Science Foundation of China/ ; 24YJCZH046//The Ministry of education of Humanities and Social Science project/ ; 2024JC-YBQN-0704//Natural Science Basic Research Program of Shaanxi/ ; 2024K005//Social Science Fund Project of Shaanxi/ ; 24JS042//Scientific Research Program Funded by Shaanxi Provincial Education Department/ ; }, abstract = {Background: Motor imagery-based brain-computer interfaces (MI-BCIs) enable individuals who are unable to perform physical movements to interact with the external world by imagining movements. Users are typically classified as good performers or BCI-illiterate based on the classification accuracy of distinct EEG patterns (e.g., 60% or 70%). Yet, studies show that approximately 70% of users fall within intermediate accuracies between 60% and 80%, and although exceed the chance level, they often fail to achieve reliable MI-BCI control. Intermediate users often exhibit asymmetric motor imagery abilities between left and right hands, highlighting the need for refined early assessment and stratified training approaches. Methods: We employed ICA to decompose each participant's EEG data and extract independent ERD/ERS components as indicators using a rule-based automated framework. This framework integrated dipole localization, ERD/ERS characteristics, and frequency-band power features of ICs. Importantly, we applied a power spectral parameterization approach to remove the 1/f-like background activity in power estimation and used statistical methods to precisely estimate the latency and duration of ERD. The extracted indicators were subsequently subjected to clustering analysis to categorize participants into four groups. Results: In addition to good performers (24.8%) and poor performers (35.8%), two groups were identified: LgoodRpoor (27.5%), who performed well in left-hand MI but poorly in right-hand MI, and LpoorRgood (11.9%), who showed the opposite pattern. Notably, these unilateral performers did not show significant differences in contralateral ERD but exhibited substantial differences in ipsilateral ERS. Conclusions: The proposed independent event-related brain dynamics model enables more refined stratification of MI-BCI users. Findings from this characterization study may inform the design of graded training protocols, especially for users demonstrating unilateral motor imagery proficiency.}, }
@article {pmid41750196, year = {2026}, author = {Öztürk, MK and Göksel Duru, D}, title = {Leveraging Cross-Subject Transfer Learning and Signal Augmentation for Enhanced RGB Color Decoding from EEG Data.}, journal = {Brain sciences}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/brainsci16020195}, pmid = {41750196}, issn = {2076-3425}, abstract = {OBJECTIVES: Decoding neural patterns for RGB colors from electroencephalography (EEG) signals is an important step towards advancing the use of visual features as input for brain-computer interfaces (BCIs). This study aims to overcome challenges such as inter-subject variability and limited data availability by investigating whether transfer learning and signal augmentation can improve decoding performance.<br><br>METHODS: This research introduces an approach that combines transfer learning for cross-subject information transfer and data augmentation to increase representational diversity in order to improve RGB color classification from EEG data. Deep learning models, including CNN-based DeepConvNet (DCN) and Adaptive Temporal Convolutional Network (ATCNet) using the attention mechanism, were pre-trained on subjects with representative brain responses and fine-tuned on target subjects to parse individual differences. Signal augmentation techniques such as frequency slice recombination and Gaussian noise addition improved model generalization by enriching the training dataset.<br><br>RESULTS: The combined methodology yielded a classification accuracy of 83.5% for all subjects on the EEG dataset of 31 previously studied subjects.<br><br>CONCLUSIONS: The improved accuracy and reduced variability underscore the effectiveness of transfer learning and signal augmentation in addressing data sparsity and variability, offering promising implications for EEG-based classification and BCI applications.}, }
@article {pmid41750174, year = {2026}, author = {Sorkin, GC and Caffes, NM and Shank, JP and Hershey, JL and Knaub, DE and Krebs, JC and Niazi, MH}, title = {Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.}, journal = {Brain sciences}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/brainsci16020173}, pmid = {41750174}, issn = {2076-3425}, abstract = {BACKGROUND: Artificial intelligence (AI) has emerged as a transformative tool in medicine, leveraging rapid analysis of large datasets to accelerate diagnosis, enhance clinical decision-making, and improve clinical workflows. This is highly relevant in stroke care given the time-sensitive nature of the disease process. This review evaluates the current landscape of evidence-based medicine utilizing AI in stroke, with emphasis on its use in phases of clinical care across the stroke continuum, including pre-hospital, acute, and recovery phases. This offers a comprehensive understanding of the current state of AI in both practice and literature.<br><br>METHODS: A review of major databases was conducted, identifying peer-reviewed literature evaluating the use of AI and its level of evidence across the stroke continuum. Given the heterogeneity of study designs, interventions, and outcome metrics spanning multiple disciplines, findings were synthesized narratively.<br><br>RESULTS: Across all phases of care, there remain no randomized controlled trials (RCTs) evaluating patient-level outcome data using AI (Level A). In the pre-hospital phase of care, AI has been used to identify stroke symptoms and assist EMS routing/training but presently remains limited to research. AI is most studied in the acute phase of care, representing the only phase to achieve commercial application in imaging detection and telestroke assistance, supported by non-randomized evidence (Level B-NR). In the recovery phase, AI may enhance wearable technologies, tele-rehabilitation, and robotics/brain-computer interfaces, with early RCTs (Level B-R) supporting the latter two, representing the strongest evidence for AI in stroke care to date.<br><br>CONCLUSIONS: Despite the potential for AI to transform all phases of care across the stroke continuum, major challenges remain, including transparency, generalizability, equity, and the need for externally validated clinical studies.}, }
@article {pmid41750145, year = {2026}, author = {He, B and Liu, C and Qi, Z and Xue, N and Yao, L}, title = {NeuroGator: A Low-Power Gating System for Asynchronous BCI Based on LFP Brain State Estimation.}, journal = {Brain sciences}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/brainsci16020141}, pmid = {41750145}, issn = {2076-3425}, support = {LG-GG202402-05//Lingang Laboratory/ ; }, abstract = {The continuous handling of the large amount of raw data generated by implantable brain-computer interface (BCI) devices requires a large amount of hardware resources and is becoming a bottleneck for implantable BCI systems, particularly for power-constrained wireless systems. To overcome this bottleneck, we present NeuroGator, an asynchronous gating system using Local Field Potential (LFP) for the implantable BCI system. Unlike a conventional continuous data decoding approach, NeuroGator uses hierarchical state classification to efficiently allocate hardware resources to reduce the data size before handling or transmission. The proposed NeuroGator operates in two stages: Firstly, a low-power hardware silence detector filters out background noise and non-active signals, effectively reducing the data size by approximately 69.4%. Secondly, a Dual-Resolution Gate Recurrent Unit (GRU) model controls the main data processing procedure on the edge side, using a first-level model to scan low-precision LFP data for potential activity and a second-level model to analyze high-precision LFP data for confirmation of an active state. The experiment shows that NeuroGator reduces overall data throughput by 82% while maintaining an F1-Score of 0.95. This architecture allows the Implantable BCI system to stay in an ultra-low-power state for over 85% of its entire operation period. The proposed NeuroGator has been implemented in an Application-Specific Integrated Circuit (ASIC) with a standard 180 nm Complementary Metal Oxide Semiconductor (CMOS) process, occupying a silicon area of 0.006mm2 and consuming 51 nW power. NeuroGator effectively resolves the resource efficiency dilemma for implantable BCI devices, offering a robust paradigm for next-generation asynchronous implantable BCI systems.}, }
@article {pmid41750143, year = {2026}, author = {Nan, J and Bai, Y and Jiang, H and Zhao, Y and Xiao, Y and Ni, G}, title = {Delta-Band EEG Microstate Dynamics as Promising Candidate Markers of Central Vertigo Severity.}, journal = {Brain sciences}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/brainsci16020143}, pmid = {41750143}, issn = {2076-3425}, support = {2023YFF1203503//Yanru Bai/ ; 2024XPD-0028//Yanru Bai/ ; TJYXZDXK-3-021C//Yanru Bai/ ; }, abstract = {Background/Objectives: Central vertigo (CV) lacks objective biomarkers for severity assessment. This study examined whether resting-state EEG microstate dynamics across frequency bands can distinguish CV severity. Methods: Resting-state EEG was recorded from 50 patients with stroke-related CV and 31 healthy controls. Patients were classified as moderate (MD, n = 31) or severe (SV, n = 19) based on Dizziness Handicap Inventory scores. Microstate analysis was performed in the delta, theta, alpha, and beta bands to assess microstate topographies, temporal parameters, and transition probabilities. Correlations with clinical measures and receiver operating characteristic analyses were conducted. Results: CV patients showed severity-dependent alterations in EEG microstate dynamics, most pronounced in the delta band. Delta-band microstate transition probabilities correlated significantly with symptom severity and balance confidence. The delta-band transition from microstate C to microstate B accurately differentiated MD from SV patients (AUC = 0.983). Conclusions: Delta-band EEG microstate transition dynamics reflect network dysfunction in CV and may serve as promising candidate biomarkers for CV severity stratification.}, }
@article {pmid41750125, year = {2026}, author = {Costa, A and Schmalzried, E and Tong, J and Khanyan, B and Wang, W and Jin, Z and Bergese, SD}, title = {Stroke Rehabilitation, Novel Technology and the Internet of Medical Things.}, journal = {Brain sciences}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/brainsci16020124}, pmid = {41750125}, issn = {2076-3425}, abstract = {Stroke continues to impose an enormous morbidity and mortality burden worldwide. Stroke survivors often incur debilitating consequences that impair motor function, independence in activities of daily living and quality of life. Rehabilitation is a pivotal intervention to minimize disability and promote functional recovery following a stroke. The Internet of Medical Things, a network of connected medical devices, software and health systems that collect, store and analyze health data over the internet, is an emerging resource in neurorehabilitation for stroke survivors. Technologies such as asynchronous transmission to handle intermittent connectivity, edge computing to conserve bandwidth and lengthen device life, functional interoperability across platforms, security mechanisms scalable to resource constraints, and hybrid architectures that combine local processing with cloud synchronization help bridge the digital divide and infrastructure limitations in low-resource environments. This manuscript reviews emerging rehabilitation technologies such as robotic devices, virtual reality, brain-computer interfaces and telerehabilitation in the setting of neurorehabilitation for stroke patients.}, }
@article {pmid41748535, year = {2026}, author = {Liu, R and Wang, Z and Zhong, C and Chen, Y and Sun, B and Jian, J and Ma, H and Gao, D and Yang, J and Li, L and Liu, K and Hu, X and Lin, H}, title = {Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks.}, journal = {Light, science &amp; applications}, volume = {15}, number = {1}, pages = {}, pmid = {41748535}, issn = {2047-7538}, support = {61975179//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12104375//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52025023//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91950204//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Achieving optical computing with thousands of tera-operations per second per watt per square millimeter (TOPs/W/mm[2]) is the key to surpassing electrical computing. This realization requires a breakthrough in the design of a new optical computing architecture and nonlinear activation functions. By leveraging the Kerr effect of silicon and the saturable absorption of graphene, we designed an all-optical nonlinear activator based on a graphene-silicon integrated photonic crystal cavity. The ultralow-threshold, high-speed, compact, and reconfigurable all-optical nonlinear activator could achieve a saturable absorption energy threshold of 4 fJ and a response time of 1.05 ps, a reconfigurable nonlinear activation threshold of 30 fJ and a response time of 4 ps, and an ultrasmall size of 15 μm × 10 μm. This device provides foundation blocks for the picosecond pulsed optical neural network chip to achieve 10[6] TOPs/W/mm[2] level optical computing.}, }
@article {pmid41748031, year = {2026}, author = {Zhan, X and Chen, X and Zhu, L and Kong, W}, title = {Exploration of the mental attention mechanisms in motor imagery-based EEG decoding.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110721}, doi = {10.1016/j.jneumeth.2026.110721}, pmid = {41748031}, issn = {1872-678X}, abstract = {BACKGROUND: Brain-Computer Interface (BCI) systems enable direct communication between the brain and external devices, with motor imagery (MI)-based BCIs as a key paradigm. Although decoding neural signals has advanced via machine learning and deep learning, the influence of human factors,especially mental attention on performance remains underexplored.<br><br>NEW METHOD: This study quantitatively investigates how mental attention modulates MI decoding. Specifically, it examines the enhancement of Common Spatial Pattern (CSP) features under high attention and evaluates attention-based data selection as a decoding criterion.<br><br>RESULTS: Experimental results demonstrate that applying mental attention as a trial selection strategy (Strategy 2) markedly improves MI decoding performance, yielding an 11.6% increase relative to the baseline accuracy of 61.3% observed without attention. These findings highlight that integrating real-time mental attention monitoring into BCI systems can enhance decoding robustness and stability, paving the way for personalized and context-aware brain-computer interactions in neurorehabilitation, cognitive training, and intelligent assistive technologies.<br><br>Prior studies focused largely on algorithmic innovations. In contrast, this work adopts a user-centric perspective, showing that attention-informed trial selection significantly improves performance even within standard CSP-based pipelines.<br><br>CONCLUSIONS: Incorporating mental attention into decoding frameworks enhances MI-BCI performance. This approach may improve the robustness and user-adaptability of online BCI systems, contributing to more effective and user-friendly neurotechnology.}, }
@article {pmid41747352, year = {2026}, author = {Xu, P and Zhang, X and Fang, Y and Zhou, Y and Li, Z and Pei, C}, title = {Effect of bacterial cellulose crystal form on its oil-water separation.}, journal = {Carbohydrate research}, volume = {563}, number = {}, pages = {109866}, doi = {10.1016/j.carres.2026.109866}, pmid = {41747352}, issn = {1873-426X}, abstract = {Cellulose hydrogels have demonstrated outstanding performance in separating oil-in-water emulsions, particularly notable for efficient "water-removing" behavior. However, the strong intrinsic hydration ability of cellulose often limits separation flux, and the influence of cellulose crystalline forms on separation performance remains largely unexplored. In this study, bacterial cellulose (BC) hydrogel was used as the starting material. The crystal structure was converted to cellulose II via alkali treatment and to cellulose III through ethylenediamine treatment. The structure, wettability, and separation performance of the three crystalline cellulose hydrogels (BC-I, BC-II, and BC-III) were systematically investigated for various oil-in-water emulsions. The results showed that all three hydrogels exhibit superhydrophilicity and underwater superoleophobicity, achieving separation efficiencies exceeding 98.1% for all emulsions. However, a significant difference in separation flux was observed, in the order: BC-III > BC-I > BC-II. Notably, the BC-III hydrogel attained a maximum flux of 2806.5 L m[-2] h[-1] MPa[-1] for a cyclohexane-in-water emulsion. The performance differences are mainly attributed to the microstructural and hydration state changes induced by crystalline transformation: BC-II exhibited the lowest flux due to its dense fibrous network and high bound water content, whereas BC-III, while retaining a porous network, optimized water transport channels through its specific crystalline arrangement, resulting in the highest separation flux. This work reveals that the crystalline form of cellulose is a critical factor in regulating its oil-water separation performance, providing a novel strategy for designing high-flux cellulose-based separation membranes.}, }
@article {pmid41744701, year = {2026}, author = {Gracia, DI and Iáñez, E and Ortiz, M and Azorín, JM}, title = {Denoising Non-Invasive Electroespinography Signals by Different Cardiac Artifact Removal Algorithms.}, journal = {Biosensors}, volume = {16}, number = {2}, pages = {}, pmid = {41744701}, issn = {2079-6374}, support = {PID2021-124111OB-C31//MICIU/AEI/10.13039/501100011033 and by ERDF, EU/ ; PID2024-156759OB-C31//MICIU/AEI/10.13039/501100011033 and by ERDF, EU/ ; CIACIF/2022/108//"Conseller&#xed;a de Educaci&#xf3;n, Universidades y Empleo (Generalitat Valenciana)" and the European Social Fund, and grant PRE2022-103336 funded by MICIU/AEI/10.13039/501100011033 and by ESF+/ ; }, abstract = {The non-invasive recording of spinal cord neuronal activity, also known as electrospinography (ESG), using high-density surface electromyography (HD-sEMG) is a promising emerging biosensing modality. However, these recordings often contain electrocardiographic (ECG) artifacts that must be removed for accurate analysis. Given the emerging nature of ESG and the lack of dedicated signal processing methods, this study assesses the performance of seven established EMG denoising algorithms for their ability to preserve the broad spectral bandwidth needed for future ESG characterization: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS combined with HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD). Performance was quantified using six metrics: Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), and Kurtosis Ratio (KR2) and its variation (ΔKR2). ESG data were recorded from nine healthy participants at brachial and lumbar plexus sites with various electrode configurations. ATS consistently outperformed all other methods in suppressing cardiac artifacts of varying shapes. Although it did not fully preserve low-frequency content, ATS achieved the best balance between artifact removal and signal integrity. Algorithm performance improved when ECG contamination was lower, especially in brachial plexus recordings with closer reference electrodes.}, }
@article {pmid41744603, year = {2026}, author = {Wang, Y and Ge, M and Xu, S}, title = {Advances in Brain-Computer Interfaces (BCI): Challenges and Opportunities.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {11}, number = {2}, pages = {}, doi = {10.3390/biomimetics11020157}, pmid = {41744603}, issn = {2313-7673}, abstract = {It appears that the frontier of neural engineering is rapidly advancing towards seamless integration between biological neural networks and digital systems [...].}, }
@article {pmid41743846, year = {2026}, author = {Jin, J and Li, J and Pan, X and Xu, R and Cichocki, A and Du, W and Qian, F}, title = {A Domain Generalization Method for EEG Based on Domain-Invariant Feature and Data Augmentation.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {7}, number = {}, pages = {0508}, pmid = {41743846}, issn = {2692-7632}, abstract = {Brain-computer interface (BCI) technology, which controls external devices by directly decoding brain activities, has made important progress and practical applications in recent years in many fields. However, the domain bias issue in cross-domain applications remains a significant challenge in the practical implementation of BCI technology. This is particularly acute in scenarios where target data are unavailable, largely because of the noise sensitivity and acquisition limitations inherent in electroencephalography (EEG) signal data. When processing nonstationary EEG signals, existing domain generalization methods face limitations: Adversarial training may compromise model stability, while global feature alignment approaches struggle to sufficiently decouple category-dependent and category-independent features, thereby constraining generalization performance. Therefore, in this paper, we propose a hybrid approach based on domain-invariant feature learning and data enhancement. We introduce a "fixed" structure enhancement method that combines domain-invariant feature learning with data enhancement strategies, decouples domain-invariant features from other features, optimizes cross-domain feature extraction, and reduces the effect of noise in data. Through extensive experimental validation on multiple publicly available datasets, the model proposed in this paper outperforms the existing state-of-the-art methods, providing a novel and effective solution to the domain bias problem in BCI.}, }
@article {pmid41743816, year = {2026}, author = {Hons, M and Kober, SE and Wriessnegger, SC and Wood, G}, title = {Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.}, journal = {Frontiers in neuroergonomics}, volume = {7}, number = {}, pages = {1696865}, pmid = {41743816}, issn = {2673-6195}, abstract = {INTRODUCTION: Individuals affected by severe motor impairments often have no means of communicating with others. To build an intuitive speech prosthesis, imagined speech brain-computer interface research began to prosper with numerous studies attempting to classify imagined speech from brain signals. While unimodal neuroimaging techniques, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been widely used, multimodal approaches combining two or more of them remain scarce.<br><br>METHODS: In this study offline phoneme decoding based on hybrid EEG-fNIRS data was performed. Twenty-two right-handed participants performed imagined and perceived speech trials encompassing four phonemes /a/,/i/,/b/ and /k/. Features in the form of power spectral densities and mean hemoglobin concentration changes were extracted from EEG and fNIRS data, respectively. Features were ranked according to the mutual information criterion relative to the target vector, and the optimal number of features to include was determined through optimization via 10-fold cross-validation.<br><br>RESULTS: Hybrid classification yielded accuracy scores of 77.29% and 76.05% regarding imagined and perceived speech, respectively. In both conditions, hybrid and EEG-based classification performances did not differ significantly, while fNIRS based phoneme discrimination produced lower accuracies.<br><br>DISCUSSION: This study represents an innovative phoneme decoding attempt based on multimodal EEG-fNIRS data, both in terms of imagined speech and perception. Four-class imagined speech classification was primarily driven by EEG features yet outperformed comparable previous studies.}, }
@article {pmid41743674, year = {2026}, author = {Hu, X and Wei, Z and Liu, M and Geng, H and Zhang, H}, title = {Digital therapeutics into geriatric cardiovascular emergency care.}, journal = {Frontiers in digital health}, volume = {8}, number = {}, pages = {1673080}, pmid = {41743674}, issn = {2673-253X}, abstract = {This mini review investigates the applications of digital therapeutics (DTx) and artificial intelligence (AI) in geriatric cardiovascular emergency care. Key elements include AI-driven biosensing for real-time risk stratification, blockchain-based secure data interoperability, tele-rehabilitation frameworks, and emerging technologies such as digital twins and brain-computer interfaces. Clinical validations shows that AI-enhanced portable ultrasound systems integrated with virtual reality (VR) optimizes diagnostic protocols and resuscitation workflows, while machine learning models achieve superior accuracy in predicting readmission risks and improving medication adherence. Notable research advances included: (1) Compared with conventional monitoring, AI biosensing improved arrhythmia detection sensitivity; (2) Deep learning models were superior to traditional methods in predicting cardiovascular events; (3) VR-assisted cardiac rehabilitation reduced anxiety scores; (4) The predictive readmission algorithm achieved high accuracy through frailty-comorbidity integration; (5) chatbot guided intervention improved medication adherence. However, limitations remain in this field, particularly in addressing age-related data biases and ethical challenges surrounding algorithmic transparency. Future researches should prioritize developing adaptive interfaces for elderly users, and advancing biocybernetic human-machine interfaces capable of stabilizing autonomic dysregulation. Importantly, these innovations must be validated in conjunction with geriatrics to ensure equitable implementation across diverse older populations.}, }
@article {pmid41743427, year = {2026}, author = {Luo, Y and Liu, X and Yang, M}, title = {Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.}, journal = {Frontiers in rehabilitation sciences}, volume = {7}, number = {}, pages = {1666530}, pmid = {41743427}, issn = {2673-6861}, abstract = {Neurological disorders represent a significant category of diseases that profoundly affect human health, accounting for the second leading cause of global mortality. This group of conditions includes stroke, multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), spinal cord injury, Parkinson's disease, and cerebral palsy, among others. These disorders are highly susceptible to sequelae and profoundly impact individuals' daily lives. In this context, Brain-Computer Interface (BCI) technology has demonstrated considerable potential in the domain of neurorehabilitation, although numerous challenges remain. The manuscript provides a comprehensive review of recent advancements in research and clinical applications, highlighting current limitations and outlining future directions. It elucidates the applicability and constraints of Brain-Computer Interface (BCI) technology across various diseases and patient populations. To facilitate insights across different conditions, comparative tables are presented, aligning BCI strategies with therapeutic targets, outcomes, advantages, limitations, and existing evidence gaps. The scope extends beyond motor restoration to include under-explored domains, such as neuropathic pain, with a focus on real-world translation, including home and community feasibility and the distinction between assistive and rehabilitative applications. The review distills overarching limitations within the field, such as small sample sizes, protocol heterogeneity, and limited longitudinal evidence, while synthesizing the most recent studies. An actionable research and development roadmap is proposed to guide next-generation BCI rehabilitation, incorporating individualized cortical-network simulators, self-architecting decoders, adaptive therapy approaches akin to game seasons, and proprioceptive "write-back" mechanisms via peripheral interfaces. Moreover, the review reveals significant research focal points and critical issues that warrant further investigation in the context of neurological rehabilitation utilizing BCI technology.}, }
@article {pmid41742930, year = {2026}, author = {Saha, S and Karlsson, P and Anderson, C and Kavehei, O and McEwan, A}, title = {Individualized brain-computer interface for people with disabilities: a review.}, journal = {Frontiers in human neuroscience}, volume = {20}, number = {}, pages = {1738876}, pmid = {41742930}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) facilitate functional interaction between the brain and external devices, enabling users to bypass their typical peripheral motor actions to control assistive and rehabilitative technologies (ARTs). This review critically evaluates the state-of-the-art BCI-based ARTs by integrating the psychosocial and health-related factors impacting user needs, highlighting the influence of brain changes during development and aging on the design and ethical use of BCI technologies. As direct human-computer interfaces, BCI-based ARTs offer extended degrees of freedom via augmented mobility, cognition and communication, especially to people with disabilities. However, the innovation in BCI-based ARTs is guided by the complexity of disability types and levels of function across users that define individual needs. Therefore, an adaptable design is essential for tailoring a BCI-based ART that can fulfill user-specific requirements, which may hinder the scalability of BCIs for their widespread adoption across users with disabilities. The trade-offs between implantable and non-implantable BCIs are explored along with complex decisions around informed consent for people with communication or cognitive disabilities and pediatric settings. Non-implantable BCIs offer broader accessibility and transferability across users due to wider standardized signal acquisition and algorithm generalization, making them suited for a more comprehensive user group. This review contributes to the field by providing individualized user needs-informed discussion of BCI-based ARTs, emphasizing the need for adaptable designs that align the evolving functional and developmental needs of users with disabilities.}, }
@article {pmid41742408, year = {2026}, author = {Chen, S and Zhang, B and Qin, T and Zhu, M and Chen, Q and Xia, L and Pan, H and Yang, Q and Guo, S and Gong, R and Jiang, Q and Li, H and Zhang, X and Cheng, P and Qi, X and Chen, W and Mo, W}, title = {Endogenous retrovirus-derived RNA-DNA hybrids induce microglial synaptic pruning in autism models.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2026.01.011}, pmid = {41742408}, issn = {1097-4199}, abstract = {Microglia-mediated neuroinflammation is increasingly recognized as a key pathological component in autism spectrum disorders (ASDs), though the mechanisms driving microglial activation remain largely elusive. Our study reveals that deficiency in the high-risk ASD gene SETDB1, as well as maternal immune activation (MIA), elevates complement protein C4b expression specifically in prefrontal cortex (PFC) neurons. This upregulation triggers excessive microglial synaptic pruning, leading to autistic-like behaviors. Furthermore, we found that microglia elimination improved synaptic density, while complete C4b knockout rescued all observed autistic-like phenotypes in mice. C4b expression is driven by RNA-DNA hybrids formed through the reactivation of endogenous retroviruses (ERVs). Notably, we identify that existing FDA-approved HIV medications, which inhibit retrotranscriptional activity, substantially reduce C4b levels and alleviate ASD symptoms. These findings underscore the crucial role of C4b in microglia-mediated synaptic pruning in ASD and highlight the therapeutic potential of targeting ERV reactivation with existing HIV medications.}, }
@article {pmid41742172, year = {2026}, author = {Marcos-Martínez, D and Santamaría-Vázquez, E and Pérez-Velasco, S and Ruiz-Gálvez, CR and Martín-Fernández, A and Pascual-Roa, B and Martínez-Velasco, R and Martínez-Cagigal, V and Hornero, R}, title = {Motor imagery-based neurofeedback in older adults: neural signatures and feasibility in a randomized controlled trial targeting age-related cognitive decline.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01912-z}, pmid = {41742172}, issn = {1743-0003}, support = {0124 EUROAGE MAS 4 E//European Union/ ; }, }
@article {pmid41741724, year = {2026}, author = {Chen, H and Zhang, Y and Cui, L and Fan, J and Zhu, H and Wu, S and Zhou, H and Zhang, Y and Song, G and Jiang, N and Zhu, M and Lou, C and Chen, W and Lou, J}, title = {Mechanical force regulates the inhibitory function of PD-1.}, journal = {EMBO reports}, volume = {}, number = {}, pages = {}, pmid = {41741724}, issn = {1469-3178}, support = {T2394512//MOST | National Natural Science Foundation of China (NSFC)/ ; 32200549//MOST | National Natural Science Foundation of China (NSFC)/ ; 32090044//MOST | National Natural Science Foundation of China (NSFC)/ ; 11672317//MOST | National Natural Science Foundation of China (NSFC)/ ; T2394511//MOST | National Natural Science Foundation of China (NSFC)/ ; 12172371//MOST | National Natural Science Foundation of China (NSFC)/ ; 32301035//MOST | National Natural Science Foundation of China (NSFC)/ ; XDB37020102//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; YZTZ-2022-0080-0015//Beijing Medical Award Foundation ()/ ; 242102310348//| Henan Provincial Science and Technology Research Project ()/ ; }, abstract = {The immune checkpoint molecule, programmed cell death 1 (PD-1), critically regulates T-cell activation upon binding PD-L1 or PD-L2, making it a key target in cancer immunotherapy. Although extensively studied, the molecular mechanism of the inhibitory function of PD-1 remains incompletely understood. Using the biomembrane force probe (BFP), we measure catch-slip bond behavior between PD-1 and PD-L1/PD-L2 under force. Steered molecular dynamics (SMD) simulation reveals a force-induced bound state distinct from the force-free state observed in solved complex structures. Disrupting interactions that stabilize either state weakens the catch bond, and diminishes the inhibitory function of PD-1. Interestingly, soluble forms of PD-L1/PD-L2 compete with their surface-bound counterparts and attenuate PD-1-mediated T-cell inhibition, suggesting that soluble PD-1 ligands could potentially serve as anti-PD-1 drugs. Tumor growth studies using a gain of function mutant based on the catch-bond mechanism confirm the anti-cancer activity of soluble PD-L1. Our findings highlight that mechanical force governs the inhibitory function of PD-1 and suggest that PD-1 acts as a mechanical sensor in T-cell suppression. Thus, mechanical regulation should be considered when designing PD-1 blocking therapies.}, }
@article {pmid41741650, year = {2026}, author = {Francioni, V and Tang, VD and Toloza, EHS and Ding, Z and Brown, NJ and Harnett, MT}, title = {Vectorized instructive signals in cortical dendrites.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {41741650}, issn = {1476-4687}, abstract = {Vectorization of teaching signals is a key element of almost all modern machine learning algorithms, including backpropagation, target propagation and reinforcement learning. Vectorization allows a scalable and computationally efficient solution to the credit assignment problem by tailoring instructive signals to individual neurons. Recent theoretical models have suggested that neural circuits could implement single-phase vectorized learning at the cellular level by processing feedforward and feedback information streams in separate dendritic compartments[1-5]. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. Here we used a neurofeedback brain-computer interface task with an experimenter-defined reward function to test for vectorized instructive signals in dendrites. We trained mice to modulate the activity of two spatially intermingled populations (four or five neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic and dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. Furthermore, targeted optogenetic perturbation of these signals disrupted learning. These results demonstrate a vectorized instructive signal in the brain, implemented via semi-independent computation in cortical dendrites, unveiling a potential mechanism for solving credit assignment in the brain.}, }
@article {pmid41741014, year = {2026}, author = {Du, M and Shi, P and Liu, Z and Lu, X and Cao, L and Liu, B and Liu, X and Liu, W and Liu, S and Ming, D}, title = {Multidimensional Acoustic-Prosodic Quantification Framework Using Unscripted Speech for Autism Spectrum Disorder Identification.}, journal = {Autism research : official journal of the International Society for Autism Research}, volume = {}, number = {}, pages = {e70206}, doi = {10.1002/aur.70206}, pmid = {41741014}, issn = {1939-3806}, support = {23JCZDJC01030//the Natural Science Foundation of Tianjin/ ; 24ZXZSSS00330//the Natural Science Foundation of Tianjin/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 25HHNJSS00015//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; }, abstract = {Although clinical observations have noted early speech abnormalities in children with autism spectrum disorder (ASD), automatic speech-based detection remains challenging. This is primarily due to the reliance on scripted tasks, which younger children often struggle to complete and which are not generalizable to large-scale, non-clinical screening. To address this, we developed an unscripted speech-based framework to quantify atypical acoustic-prosodic patterns for automatic ASD identification in naturalistic interactions. It processes free-flowing conversations, extracts multidimensional acoustic features from the time and frequency domains, and models ASD-related prosodic patterns for classification. For evaluation, we collected spontaneous speech from 88 children with ASD (3-10 years) and 82 typically developing (TD) children (3-9 years) during naturalistic interactions on daily topics (e.g., toys, animated movies, storybook reading). Group comparisons revealed atypical prosodic patterns in ASD, including reduced speech continuity, speech rate, and Formant 3, alongside increased zero-crossing rate, pitch, pitch variability, and Formant 1 (all p < 0.01). Using these features, a linear discriminant analysis classifier achieved robust performance (accuracy = 0.85 ± 0.07, F1 = 0.86 ± 0.07). Further analyses indicated no significant gender interaction (p > 0.05), but a pronounced effect of speech context (p < 0.01), with atypical patterns being more evident in open-ended dialogues than in text-guided settings. Moreover, these patterns correlated with clinical scores (p < 0.05), particularly language ability, demonstrating the framework's utility for assessing ASD severity. These findings underscore the importance of analyzing unscripted speech to capture atypical prosodic patterns and provide a basis for large-scale ASD screening outside clinical settings.}, }
@article {pmid41737820, year = {2026}, author = {Khalili, MD and Abootalebi, V and Saeedi-Sourck, H}, title = {A Dimensionality Reduction Approach for Motor Imagery Brain-Computer Interface Using Functional Clustering and Graph Signal Processing.}, journal = {Journal of medical signals and sensors}, volume = {16}, number = {}, pages = {6}, pmid = {41737820}, issn = {2228-7477}, abstract = {BACKGROUND: This paper introduces an approach for dimensionality reduction and classification of electroencephalogram signals in motor imagery brain-computer interface (MI-BCI) systems.<br><br>MATERIALS AND METHODS: The proposed Kron-reduced generic learning regularization with differential evolution (K-GLR-DE) framework leverages graph signal processing (GSP) with a meta-heuristic optimizer, integrating functional clustering, Kron reduction, regularized common spatial patterns with generic learning (GLRCSP), and differential evolution (DE). Brain graphs are constructed within a structural-functional framework, where edge weights are defined based on geometric distances and correlations. Graph's dimensionality reduction is achieved by applying physiological regions of interest (ROIs) and Kron reduction to preserve essential topological-spectral features. Feature extraction is performed using graph total variation and GLRCSP, followed by DE-based feature selection.<br><br>RESULTS: The approach was evaluated on BCI Competition III Dataset IVa and the PhysioNet eegmmidb dataset. The support vector machine with a radial basis function (SVM-RBF) classifier achieved superior performance, yielding a mean accuracy of 96.46% &#xb1; 0.81% on BCIC III-IVa.<br><br>CONCLUSIONS: The proposed K-GLR-DE method demonstrates significant performance in MI-BCI classification across various training conditions, including scenarios with small and limited training sets.}, }
@article {pmid41735747, year = {2026}, author = {Zhang, SX and Yang, J and Lou, Y and Xu, SC and Guo, R and Xu, ZZ}, title = {Distinct Role of Specialized Cutaneous Schwann Cell Network in Acute and Chronic Pain Sensation.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {41735747}, issn = {1995-8218}, abstract = {Specialized cutaneous Schwann cells (scSCs) are a recently identified glial class implicated in cutaneous pain modulation, yet their three-dimensional architecture and role in chronic pain remain unclear. Using tissue optical clearing, we reconstructed the 3D morphology of scSCs, revealing an intricate mesh-like network, with extensive branching penetrating the epidermal layer and establishing close associations with A- and C-fiber primary sensory nerve terminals. Optogenetic activation of scSCs elicited nociceptive reflex behaviors, dependent on concurrent A- and C-fiber activation, but not affective-motivational responses. We further investigated the morphological and functional alterations of scSCs in chronic inflammatory pain and neuropathic pain models. Interestingly, scSCs were found to play a partial role in modulating nociceptive behaviors but not aversions in chronic pain. Together, these findings provide new insights into the functional dynamics of scSCs in nociceptive signal processing and their limited contribution to chronic pain states.}, }
@article {pmid41734829, year = {2026}, author = {Zhang, J and Zeng, S and Wang, B and He, J and Jin, Z and Li, L}, title = {The Erlangen Program in Lateral Occipital Cortex: Hierarchical Encoding of Emergent Features.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121827}, doi = {10.1016/j.neuroimage.2026.121827}, pmid = {41734829}, issn = {1095-9572}, abstract = {Emergent features are fundamental concepts in Gestalt psychology, yet the neural encoding of these features, particularly a quantitative understanding of their relative superiority, remains elusive. This study bridges this gap by conceptualizing emergent features through geometric transformations within the Erlangen Program, which provides a principled framework to quantify their hierarchical relationships. We propose that the lateral occipital cortex (LOC) encodes these emergent features in accordance with the geometric hierarchies defined by this program. Using fMRI and multivariate pattern analysis, we demonstrate that LOC reliably discriminates between distinct geometric transformations (Euclidean, affine, projective, and topology). Critically, representational similarity analysis reveals that neural dissimilarities in LOC align with the relative stability of geometries predicted by the Erlangen Program. However, the LOC exhibits similar representational structures for lower-order transformations like Euclidean and affine geometries, suggesting a potential collapse of these distinctions in the region's global geometric hierarchy. Furthermore, transfer learning confirms hierarchical nesting relationships among the geometries: classifiers trained on specific geometric distinctions generalize to others in a manner consistent with the Erlangen hierarchy. These findings establish LOC as the neural substrate where emergent features are organized hierarchically by geometric stability, revealing how the visual system prioritizes invariant global structures to optimize perceptual efficiency.}, }
@article {pmid41734759, year = {2026}, author = {Cao, K and Cheng, W and Qiu, L and Wang, Z and Zhao, Y and Yuan, Y and Wu, W and Xue, J and Zeng, L and Wu, ZY and Ma, H and Hou, T and Hume, DA and Ye, C and Duan, S and Gao, Z}, title = {More than microglial depletion: PLX5622 activates the hepatic constitutive androstane receptor to alter anesthesia and addiction.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2025.12.044}, pmid = {41734759}, issn = {1097-4199}, abstract = {The colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX5622 has been widely used to deplete microglia for functional characterization and therapeutic support. Although diverse outcomes have been described after PLX5622 treatment, whether these phenotypes solely reflect microglial functions remains to be determined. Here, we show that transgenic microglial depletion did not mimic the accelerated anesthetic arousal or the alleviated nicotine addiction withdrawal symptoms observed after PLX5622 treatment in mice. We further identify that PLX5622 potently activates the mouse constitutive androstane receptor (CAR), leading to prominent induction of hepatic enzymes. The induced enzymatic activity enhances the metabolism and clearance of anesthetics and nicotine, thereby contributing to anesthetic insensitivity and addiction relief. Inactivation of CAR abolished these effects of PLX5622, indicating that the impact of PLX5622 treatment cannot be attributed exclusively to microglial depletion. Our findings raise awareness in evaluating consequences of PLX5622 treatment and provide insights into the design of specific CSF1R inhibitors.}, }
@article {pmid41733115, year = {2026}, author = {Shetty, KS and Ravichandran, H and Rafiq, S and Achar, S}, title = {Brain Entropy and Complexity as Biomarkers of Neuroplasticity in Neurorehabilitation-A Scoping Review.}, journal = {Physiotherapy research international : the journal for researchers and clinicians in physical therapy}, volume = {31}, number = {2}, pages = {e70174}, doi = {10.1002/pri.70174}, pmid = {41733115}, issn = {1471-2865}, mesh = {Humans ; *Neuronal Plasticity/physiology ; Biomarkers ; Electroencephalography ; Entropy ; *Neurological Rehabilitation/methods ; *Brain/physiopathology ; Neuroimaging ; Stroke Rehabilitation ; Physical Therapy Modalities ; }, abstract = {BACKGROUND: Neurorehabilitation in physiotherapy depends on experience-dependent neuroplasticity; however, conventional clinical outcomes may lack sensitivity to capture dynamic neural adaptations underlying recovery. Brain entropy and complexity measures derived from EEG and neuroimaging have emerged as potential biomarkers of neural adaptability.<br><br>OBJECTIVE: To map and synthesize evidence on brain entropy and complexity as biomarkers of neuroplasticity in neurorehabilitation, with relevance to physiotherapy practice.<br><br>METHODS: A scoping review was conducted following PRISMA-ScR guidelines. PubMed, Scopus, and Web of Science were searched up to August 2025 for studies reporting quantitative entropy or complexity measures in neurological populations undergoing rehabilitation or task-based assessment.<br><br>RESULTS: Eight studies were included. Interventional studies in stroke and brain injury populations reported moderate to large within-group neural effects, with increases in entropy or complexity accompanying functional improvement following task-oriented, robotic, or brain-computer interface-based rehabilitation. Studies of higher methodological quality demonstrated more consistent entropy-outcome associations, whereas lower-quality observational studies showed greater variability. Degenerative neurological conditions are characterized by reduced neural complexity.<br><br>DISCUSSION: Brain entropy and complexity measures are sensitive indicators of neuroplastic change and may complement clinical outcomes in physiotherapy. Although not yet ready for routine clinical decision-making, these biomarkers show promise for monitoring intervention response and guiding personalized rehabilitation, pending methodological standardization and longitudinal validation.}, }
@article {pmid41723170, year = {2026}, author = {Günaydın, G and Moran, JK and Rohe, T and Senkowski, D}, title = {Causal inference shapes crossmodal postdiction in multisensory integration.}, journal = {Scientific reports}, volume = {16}, number = {1}, pages = {}, pmid = {41723170}, issn = {2045-2322}, abstract = {UNLABELLED: In our environment, stimuli from different sensory modalities are initially processed within a temporal window of multisensory integration spanning several hundred milliseconds. During this window, stimulus processing is influenced not only by preceding and current information, but also by input that follows the stimulus. The computational mechanisms underlying crossmodal backward processing, which we refer to as crossmodal postdiction, are not well understood. We examined crossmodal postdiction in the Illusory Audiovisual (AV) Rabbit and Invisible AV Rabbit Illusions, in which postdiction occurs when flash-beep pairs are presented shortly before and shortly after a single flash or a single beep. We collected behavioral data from 32 participants and fitted four competing models: Bayesian Causal Inference (BCI), forced-fusion, forced-segregation, and non-postdictive BCI. The BCI model fit the data well and outperformed all other models. Building on previous findings that demonstrate causal inference during non-postdictive multisensory integration, our results show that the BCI framework can also explain crossmodal postdiction phenomena. Our findings suggest that the brain performs causal inference not only across concurrent sensory inputs but also across temporal windows, integrating information from past, present, and subsequent events across modalities to construct a unified percept.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1038/s41598-026-36884-6.}, }
@article {pmid41731569, year = {2026}, author = {Tan, F and Qing, W and Ip, WC and Guo, Z and Li, Z and Zhang, S and Hu, X}, title = {Guided corticomuscular neuroplasticity for restoration of wrist-hand function post-stroke.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01915-w}, pmid = {41731569}, issn = {1743-0003}, support = {15218324//University Grants Committee/ ; ITS/011/23 &amp; ITT/012/23GP//Innovation and Technology Commission/ ; }, }
@article {pmid41730036, year = {2026}, author = {Su, X and Pang, H and Zhang, H and Ma, Y}, title = {Stent-Based Electrode for Long-Term Intracranial EEG Recording in Sheep: A Preliminary Study.}, journal = {Stroke}, volume = {57}, number = {3}, pages = {e78-e80}, doi = {10.1161/STROKEAHA.125.054298}, pmid = {41730036}, issn = {1524-4628}, }
@article {pmid41728710, year = {2025}, author = {Amrani, H and Micucci, D and Napoletano, P}, title = {Decoding EEG Signals for Brain-Computer Interfaces.}, journal = {Studies in health technology and informatics}, volume = {330}, number = {}, pages = {551-567}, doi = {10.3233/SHTI251450}, pmid = {41728710}, issn = {1879-8365}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Machine Learning ; *Signal Processing, Computer-Assisted ; }, abstract = {Electroencephalography (EEG) is a non-invasive technique that records brain electrical activity, providing critical insights into neural processes. In recent years, EEG has become integral to brain-computer interface (BCI) research. BCIs enhance human-computer interaction, support assistive solutions for people with disabilities, and enable novel clinical applications. Research in EEG-based BCIs involves several key components: signal acquisition, preprocessing, feature extraction, and classification. Advanced machine learning models, especially those that emphasize personalized and incremental learning approaches, are used to effectively decode EEG signals. This personalization accounts for individual variability and significantly improves model accuracy and robustness. Applications of EEG-based BCIs include emotion recognition, motor imagery for robot control, and EEG-to-text decoding. These applications use EEG signals to make significant advances in their respective fields. Emotion recognition improves human-computer interaction and mental health monitoring; motor imagery enables intuitive robotic control that assists individuals with motor impairments; and EEG-to-text decoding provides new communication pathways for people with severe disabilities. Despite promising advances, challenges such as signal variability, noise, and the need for sophisticated preprocessing techniques remain. Future research should prioritize interdisciplinary collaboration and technological advancements to overcome these challenges, thereby enabling EEG-based BCIs to achieve broader applicability and significantly impact various aspects of human life.}, }
@article {pmid41726112, year = {2026}, author = {Nazeer, H and Noori, FM and Khan, RA}, title = {Editorial: Integrative approaches with BCI and robotics for improved human interaction.}, journal = {Frontiers in robotics and AI}, volume = {13}, number = {}, pages = {1785247}, pmid = {41726112}, issn = {2296-9144}, }
@article {pmid41723634, year = {2026}, author = {Comaduran Marquez, D and Vaandering, K and Babwani, A and Redquest, B and Nikitovic, D and Kelly, D and Kinney-Lang, E and Kirton, A}, title = {BCI sports: exploring the potential of BCI-leveraged sport participation for children with quadriplegic cerebral palsy.}, journal = {Disability and rehabilitation}, volume = {}, number = {}, pages = {1-15}, doi = {10.1080/09638288.2026.2630787}, pmid = {41723634}, issn = {1464-5165}, abstract = {PURPOSE: Children with severe disabilities often face barriers to sport participation, limiting their fundamental human rights. Boccia is a Paralympic sport that offers inclusion for individuals with limited mobility, it does not fully accommodate those with severe motor disabilities and communication difficulties. Our group designed an assistive Boccia ramp controlled via brain-computer interface (BCI), potentially allowing individuals with severe motor disability who are non-speaking to participate. This study aimed to gain insight from caregivers and children with quadriplegic cerebral palsy (QCP) toward how BCI-leveraged Boccia might impact their opportunities for sport participation.<br><br>MATERIALS AND METHODS: We used a mixed-methods approach to gather insights from children and their families. We conducted semi-structured interviews to explore caregiver insights and experiences of their child using BCI (n&#x2009;=&#x2009;6). Additionally, we developed a new 21-item survey to get the feedback of the children (n&#x2009;=&#x2009;6).<br><br>RESULTS: Current participation challenges and facilitators to sport were identified, along with future possibilities and the foreseen benefits of implementing BCI technology. Children expressed keen interest in using a BCI system to access Boccia.<br><br>CONCLUSIONS: BCI-leveraged sport shows promise for caregivers and children with QCP. Successful implementation requires addressing barriers and facilitators to enable access to previously unattainable activities.}, }
@article {pmid41722497, year = {2026}, author = {Iacomi, F and Moroni, M and Mainardi, L and Barbieri, R}, title = {Novel EEG-based signatures of brain connectivity for imagined speech.}, journal = {Computers in biology and medicine}, volume = {205}, number = {}, pages = {111555}, doi = {10.1016/j.compbiomed.2026.111555}, pmid = {41722497}, issn = {1879-0534}, abstract = {Developing effective Brain-Computer Interfaces (BCIs) based on Imagined Speech (IS) is a significant challenge, largely due to high inter-subject variability in neural patterns. This study introduces a novel analytical framework to address this issue by integrating functional, effective, and complex network analyses with a more naturalistic sentence-level experimental protocol. Our findings confirm that while IS connectivity networks are characterized by considerable variability across individuals, our methodology successfully identifies a core set of stable pathways that persist across subjects. Specifically, we identified three principal pathways: a motor-language network in the left hemisphere driven by delta-band activity (CL→FR,CR consistent in 60% of subjects), a right-hemisphere network relayed to motor planning areas via gamma-band activity (TR→CL in 40% of subjects), and a top-down visual-spatial network involving parietal regions (POL→CR in 60% of subjects). In parallel, complex network analysis reveals the gamma frequency band to be the most functionally integrated and robust spectral signature, exhibiting significantly higher mean connectivity strength compared to all other bands (e.g., p=0.0015 vs. beta) and appearing consistently in 6/10 subjects. By distinguishing these stable neural markers from subject-specific activity, this work provides more reliable EEG-based signatures for the future development of advanced speech BCIs.}, }
@article {pmid41719718, year = {2026}, author = {Hwaidi, J and Ghanem, MC}, title = {Motor imagery EEG signal classification using minimally random convolutional kernel transform and hybrid deep learning.}, journal = {NeuroImage}, volume = {328}, number = {}, pages = {121816}, doi = {10.1016/j.neuroimage.2026.121816}, pmid = {41719718}, issn = {1095-9572}, abstract = {The brain-computer interface (BCI) establishes a non-muscle channel that enables direct communication between the human body and an external device. Electroencephalography (EEG) is a popular non-invasive technique for recording brain signals. It is critical to process and comprehend the hidden patterns linked to a specific cognitive or motor task, for instance, measured through the motor imagery brain-computer interface (MI-BCI). A significant challenge is presented by classifying motor imagery-based electroencephalogram (MI-EEG) tasks, given that EEG signals exhibit nonstationarity, time-variance, and individual diversity. Achieving good classification accuracy is also challenging due to the increasing number of classes and the inherent variability among individuals. To overcome these issues, this paper proposes a novel method for classifying EEG motor imagery signals that efficiently extracts features using the Minimally Random Convolutional Kernel Transform (MiniRocket). A linear classifier then utilises the extracted features for activity recognition. Furthermore, a novel deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture was proposed and demonstrated to serve as a baseline. The classification via MiniRocket's features achieved higher performance than the best deep learning models at a lower computational cost. PhysioNet and BCI Comp IV 2a datasets were used to evaluate the performance of the proposed approaches. Using PhysioNet, the proposed models achieved mean accuracy values of 98.63% and 98.06%, respectively, for the MiniRocket and CNN-LSTM. With the BCI-CompIV-2a dataset, proposed models achieved mean accuracy values of 92.57% and 92.32%, respectively. The findings demonstrate that the proposed approach can significantly enhance motor imagery EEG accuracy and provide new insights into the feature extraction and classification of MI-EEG. An additional future direction is non-additive electrode-source fusion (Choquet-integral/coalition formulations) to improve robustness under low-SNR EEG and inter-subject variability.}, }
@article {pmid41719578, year = {2026}, author = {Crell, MR and Kostoglou, K and Suwandjieff, P and Da Cruz, JR and Muller-Putz, GR}, title = {A non-invasive, MRCP-based BCI for online communication.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3666564}, pmid = {41719578}, issn = {1558-0210}, abstract = {Patients with severely impaired motor functions require a stable form of communication for their daily life. Restoring this ability can be achieved with spelling applications controlled by brain-computer interfaces (BCIs). To achieve intuitive control of the application, we propose a BCI system to asynchronously detect single movement intent from EEG. By emulating a button press, we develop a task-agnostic framework applicable to a wide range of interfaces. The system utilizes a model based on movement-related cortical potentials (MRCPs) to detect self-initiated movements without the need for external cues. Twenty participants utilized the developed system to control a spelling interface implemented as a row-column scanner (3-by-3 and 5-by-5 size layouts) to type five-letter words. Participants achieved an overall true positive rate (TPR) of 54.4±27.9% (up to 98.6% in single participants) with an average of 2.0 ± 1.9 false positives per minute (FP/min). 60.9 ± 28.5% of the target characters were correctly selected and participants were able to successfully spell a five-letter word in 41.7 ± 42.7% of all attempts. The analysis of the EEG showed that the MRCP-based classifier maintained consistent detection performance across interface configurations, underscoring its robustness and adaptability to changing applications. These findings demonstrate the potential of the approach as a non-invasive communication aid and establish a foundation for future development of home-use BCIs that offer intuitive, voluntary control with minimal calibration requirements.}, }
@article {pmid41719216, year = {2026}, author = {Jiang, M and Qu, D and Luo, Q and Wang, X}, title = {The aging effect in the processing of Chinese interoceptive- and exteroceptive- reaction affective verbs.}, journal = {Applied neuropsychology. Adult}, volume = {}, number = {}, pages = {1-9}, doi = {10.1080/23279095.2026.2628131}, pmid = {41719216}, issn = {2327-9109}, abstract = {Great uncertainty exists with whether or not old adulthood experiences an age-related decline in affective words' processing capacity. By recruiting two age groups, taking advantage of two types of affective verbs, namely, interoceptive-reaction affective verbs and the exteroceptive-reaction affective verbs, and by manipulating the factor of affective valence, based on a valence judgment task, the present study made a meticulous scrutiny of this issue. It was found that older adults did undergo an age-related decline in processing affective words. The factor affective valence did have a role in modulating the aging effect.}, }
@article {pmid41717813, year = {2026}, author = {Zhu, J and Bao, X and Huang, Q and Wang, T and Huang, L and Han, Y and Huang, H and Zhu, J and Qu, J and Li, K and Chen, D and Jiang, Y and Xu, K and Wang, Z and Wu, W and Li, Y}, title = {A Wearable Brain-Computer Interface for Mitigating Car Sickness via Attention Shifting.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e13040}, doi = {10.1002/advs.202513040}, pmid = {41717813}, issn = {2198-3844}, support = {2022ZD0208900//Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project/ ; 2018B030339001//the Key Research and Development Program of Guangdong Province/ ; 2024D02J0008//Guangzhou Talent Plan/ ; 2024A1515011690//Guangdong Natural Science Foundation General Program/ ; 2023QN100110//Guangdong Talent Program/ ; 62306120//National Natural Science Foundation of China/ ; }, abstract = {Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention-shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyzes electroencephalography (EEG) data via a convolutional neural network to assess the user's mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the user's attention from physiological discomfort toward the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposes a wearable, nonpharmacological intervention for car sickness, validated in a relatively large-scale study involving over 100 participants in real-world car riding. These findings, derived from a between-cohort comparison, support the potential of this approach to improve the travel experience for car sickness sufferers and represent a novel practical application of BCI technology.}, }
@article {pmid41717404, year = {2026}, author = {Do, M and Tyler, WJ}, title = {Transcutaneous vagus nerve stimulation in breast cancer: a neuroimmune model to improve quality of life.}, journal = {Frontiers in oncology}, volume = {16}, number = {}, pages = {1731999}, pmid = {41717404}, issn = {2234-943X}, abstract = {Breast cancer care has shifted beyond remission toward optimizing long-term physiological, emotional, and functional recovery. Many survivors continue, however, to experience persistent symptom clusters, such as insomnia, fatigue, anxiety, pain, depression, and cognitive impairment. These poor quality of life outcomes reflect underlying dysregulation of autonomic, neuroendocrine, and immune systems. Autonomic imbalance characterized by vagal withdrawal and sympathetic hyperactivation is linked to increased inflammatory load, impaired stress regulation, disrupted sleep, and poorer survival outcomes. Given the role of the vagus nerve in coordinating brain-body signaling and immune modulation, transcutaneous vagus nerve stimulation (tVNS) has emerged as a promising intervention to restore autonomic balance and attenuate psychophysiological burdens. Evidence suggests that tVNS modulates locus coeruleus-norepinephrine activity, regulates arousal and sleep, reduces fatigue and anxiety, enhances cognitive function, and activates the cholinergic anti-inflammatory pathways. Supported by mechanistic and clinical evidence, we propose tVNS as a precision-guided bioelectronic strategy for improving survivorship outcomes in breast cancer.}, }
@article {pmid41716657, year = {2026}, author = {Hong, B and Xu, Z and Zhang, T and Zhang, T}, title = {Bidirectional cross-day alignment of neural spikes and behavior using a hybrid SNN-ANN algorithm.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1772958}, pmid = {41716657}, issn = {1662-4548}, abstract = {Recent advances in deep learning have enabled effective interpretation of neural activity patterns from electroencephalogram signals; however, challenges persist in invasive brain signals for cross-day neural decoding and simulation tasks. The inherent non-stationarity of neural dynamics and representational drift across recording sessions fundamentally limit the generalization capabilities of existing approaches. We present AlignNet, a novel framework that establishes cross-modal alignment between spiking patterns and behavioral semantics through U-based representation learning. Our architecture employs hybrid SNN-ANN autoencoders to encode neural spikes and behavior into a shared latent space, where the neural spike autoencoder incorporates multiple neuron nodes following convolution layers, and the behavior autoencoder comprises standard convolution layers. These two representations are optimized through contrastive objectives to achieve session-invariant feature learning. To address cross-day adaptation challenges, we introduce a pretraining strategy leveraging multi-session single monkey experiment data, followed by task-specific fine-tuning for neural decoding and simulation. Comprehensive evaluations demonstrate that AlignNet achieves superior performance under both single-day and cross-day conditions; meanwhile, our pretrained model effectively executes decoding and simulation tasks after fine-tuning. The hybrid SNN-ANN representations exhibit temporal consistency across multi-day recording spikes while retaining behavioral semantics, thereby advancing cross-day neural interface applications.}, }
@article {pmid41716573, year = {2026}, author = {Wang, B and Zhang, H and Kong, XZ}, title = {Growing up with siblings in the age of one child: the potentially confounding role of socioeconomic background.}, journal = {Psychoradiology}, volume = {6}, number = {}, pages = {kkaf035}, pmid = {41716573}, issn = {2634-4416}, }
@article {pmid41716118, year = {2026}, author = {Mattioli, F and Porcaro, C and Baldassarre, G}, title = {RETRACTION: a 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface (2021J. Neural Eng. 18 066053).}, journal = {Journal of neural engineering}, volume = {23}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ae41ab}, pmid = {41716118}, issn = {1741-2552}, }
@article {pmid41714142, year = {2026}, author = {Shang, Z and Zhang, J and Li, M and Li, S and Wang, Y and Yang, L}, title = {Dynamic encoding of reward prediction error signals in the pigeon ventral tegmental area during reinforcement learning.}, journal = {eNeuro}, volume = {}, number = {}, pages = {}, doi = {10.1523/ENEURO.0355-25.2026}, pmid = {41714142}, issn = {2373-2822}, abstract = {Reward prediction errors (RPEs) guide learning by comparing expected and obtained outcomes. In mammals, ventral tegmental area (VTA) activity is closely linked to RPE-like signaling, yet how avian VTA dynamics evolve during reinforcement learning remains less well characterized. Here we recorded VTA spiking in pigeons (2 female and 1 male) performing a cue-guided operant task in which a green cue (Cue+) predicted reward contingent on a key peck, whereas a red cue (Cue-) was unrewarded. Using a 16-channel microwire array, we analyzed pooled channel-level multi-unit activity (MUA) aligned to task events. Across sessions, Cue+ trials showed a learning-related redistribution of event-locked modulation: outcome-locked activity was more prominent early in training, while cue-locked modulation became stronger as performance stabilized, consistent with a temporal-difference-like shift of prediction-related signals. Cue- trials were sparse after early learning and showed limited cue-locked modulation in the available dataset. Together, these results provide initial evidence that pigeon VTA pooled MUA exhibits learning-related dynamics consistent with RPE-like processing and support cross-species comparisons of dopaminergic learning signals.Significance Statement This study provides initial evidence that neurons in the pigeon ventral tegmental area (VTA) may encode reward prediction error (RPE) signals during reinforcement learning. The results show that neural activity related to reward gradually shifts toward the predictive cue as learning progresses, consistent with established models in mammals. These findings suggest that the basic neural processes underlying reward-based learning may be shared across vertebrate species and contribute to a broader understanding of comparative learning mechanisms.}, }
@article {pmid41710300, year = {2026}, author = {Asgher, U}, title = {Editorial: The convergence of AI, LLMs, and industry 4.0: enhancing BCI, HMI, and neuroscience research.}, journal = {Frontiers in computational neuroscience}, volume = {20}, number = {}, pages = {1780276}, doi = {10.3389/fncom.2026.1780276}, pmid = {41710300}, issn = {1662-5188}, }
@article {pmid41709919, year = {2026}, author = {Zhao, Y and Sun, C and Bi, Y and Zhang, Y}, title = {Effects of visually induced motor imagery-based brain-computer interface training on motor function in patients with incomplete spinal cord injury: a small-sample exploratory trial.}, journal = {Frontiers in neurology}, volume = {17}, number = {}, pages = {1700249}, pmid = {41709919}, issn = {1664-2295}, abstract = {OBJECTIVE: This study aimed to investigate the effects of visually induced motor imagery (MI)-based brain-computer interface (BCI) training on the neurological recovery of patients with incomplete spinal cord injury (iSCI), and to preliminarily explore the underlying neural mechanisms.<br><br>METHODS: A single-center, single-blind, small-sample exploratory trial was conducted, enrolling 11 patients with iSCI who were randomly assigned to either the experimental or control group. The experimental group received visually induced BCI training based on a MI paradigm, while the control group received visually guided MI training combined with passive lower limb movements. Both groups underwent interventions five times per week for 4 weeks. Clinical assessments, including the American Spinal Injury Association (ASIA) motor/sensory scores, Berg Balance Scale (BBS), and Functional Ambulation Category (FAC), were conducted before and after the intervention. Simultaneously, electroencephalography (EEG) data were collected to analyze brain engagement, functional connectivity, and time-frequency characteristics, aiming to elucidate the neuromodulatory effects of BCI training.<br><br>RESULTS: After the intervention, both groups showed significant improvements in brain engagement, with the experimental group demonstrating greater enhancement. Compared with before rehabilitation training, the levels of &#x3b8; waves in both groups significantly increased after rehabilitation training, while the levels of &#x3b2; waves significantly decreased (p < 0.05), especially in areas related to exercise planning and sensory integration. The connections between brain regions in the delta and theta frequency bands were significantly enhanced, and the density of brain network connections was significantly increased (p < 0.05) particularly in regions associated with motor planning and sensory integration. Clinically, all functional scores improved significantly in both groups (p < 0.05), and the experimental group showed superior improvement in ASIA motor and sensory scores, BBS, and FAC levels compared to the control group (p < 0.05).<br><br>CONCLUSION: Visually induced MI-based BCI training effectively promotes neurological recovery in patients with iSCI, as evidenced by enhanced brain network reorganization, modulation of cortical excitability, and activation of motor-related neural rhythms. This study confirms the feasibility and safety of this intervention strategy and offers a novel direction for iSCI rehabilitation.<br><br>CLINICAL TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR), identifier: ChiCTR2400095010.}, }
@article {pmid41708167, year = {2026}, author = {Catherine Chan, KL and Yan, C and Wang, X and Huang, S and Dai, W and Luo, Y and Cheng, Y and Xu, B and Zhang, W and Shen, Y}, title = {Efficacy and neural mechanisms of a vibrotactile-enhanced, brain-controlled soft robotic glove for upper limb rehabilitation after stroke: a multicentre randomised controlled trial protocol.}, journal = {BMJ open}, volume = {16}, number = {2}, pages = {e110321}, pmid = {41708167}, issn = {2044-6055}, mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; Single-Blind Method ; *Robotics ; *Brain-Computer Interfaces ; *Upper Extremity/physiopathology ; Vibration ; Multicenter Studies as Topic ; Randomized Controlled Trials as Topic ; Female ; *Stroke/physiopathology ; Male ; Adult ; Middle Aged ; Feedback, Sensory ; Recovery of Function ; }, abstract = {INTRODUCTION: Soft robotic gloves (SRGs) integrated with brain-computer interfaces (BCIs) have demonstrated potential in facilitating motor recovery after stroke by enabling active, intention-driven rehabilitation. Emerging evidence suggests that incorporating vibrotactile stimulation (VTS) into SRG-BCI systems may further enhance sensorimotor feedback. The objective of this study is to evaluate the therapeutic efficacy and underlying neural mechanisms of BCI-driven, intention-based glove activation compared with automated glove-assisted training, with VTS applied identically in both groups.<br><br>METHODS AND ANALYSIS: This multicentre, single-blind, randomised controlled trial will involve 48 post-stroke patients within 1&#x2009;week to 3 months after stroke onset, with stratification by time since stroke during randomisation. Participants will be randomly assigned to either the BCI-SRG group (n=24) or SRG group (n=24). Both groups will receive identical VTS. Patients in the BCI-SRG group will actively initiate movements of the SRG through motor imagery, while those in the SRG group will receive automated glove-assisted training without BCI control. The intervention will be administered 5&#x2009;days per week for 4 weeks. The primary outcome measure is the Fugl-Meyer Assessment of Upper Extremity. Secondary outcome measures include Wolf Motor Function Test, International Classification of Functioning, Disability and Health Generic Set, Barthel Index, Modified Ashworth Scale, Semmes-Weinstein Monofilament Test, as well as event-related spectral perturbation and event-related desynchronisation. All assessments will be conducted at both baseline and post-intervention.<br><br>ETHICS AND DISSEMINATION: Ethics approval of this study protocol has been obtained from the Ethics Committee of the First Affiliated Hospital with Nanjing Medical University (2025-SR-508). The findings will be disseminated through peer-reviewed journals, conference presentations and communication with scientific, professional and general public audiences.<br><br>TRIAL REGISTRATION NUMBER: ChiCTR2500106951.}, }
@article {pmid41707762, year = {2026}, author = {Li, T and Wang, L and Zhao, Y and Su, C and Eickhoff, SB and Olsson, A and Feng, C and Pan, Y}, title = {Neural commonalities and dissociations of human social and experiential learning.}, journal = {Neuroscience and biobehavioral reviews}, volume = {}, number = {}, pages = {106611}, doi = {10.1016/j.neubiorev.2026.106611}, pmid = {41707762}, issn = {1873-7528}, abstract = {Humans navigate the world by learning from both social interactions and direct experiences. Although these two learning strategies are essential for adaptive survival, a systematic neural comparison between them has been lacking. Here, we combined quantitative meta‑analysis with large‑scale network mapping to identify the shared and distinct brain systems underlying social and experiential learning (as represented by Pavlovian conditioning) in healthy humans. Both learning modes engaged common regions involved in value computation, such as the ventral striatum and anterior insula. However, they showed largely dissociable network patterns across the brain: social learning was primarily linked to networks involved in social cognition, whereas experiential learning was predominantly associated with reward and cognitive control. These distinct connectivity profiles reliably differentiated the two learning modes at both aggregate and individual levels. Additionally, we found that appetitive and aversive forms of social learning were supported by separate brain networks. Taken together, our findings provide convergent evidence for how the human brain flexibly reuses core value-processing circuits while engaging specialized networks tailored to distinct learning demands.}, }
@article {pmid41706793, year = {2026}, author = {Fu, B and Li, F and Li, J and Ji, Y and Li, Y and Quan, Y and Zhang, L and Shi, G}, title = {Improved Spontaneous EEG Signal Decoding Efficiency by Function Predefined Convolutional Neural Network.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2026.3652882}, pmid = {41706793}, issn = {2162-2388}, abstract = {A spontaneous electroencephalogram (EEG)-based brain-computer interface (BCI) is an ideal form of brain-computer interaction. The classical decoding methods can achieve classification by using meaningful manual features, but their performance is poor. The neural network (NN) methods have significantly improved the performance, but their interpretability and computational efficiency are much lower than those of the classical methods. This is because NN abandons the strong a priori knowledge of neuroscience and completely relies on training to extract EEG features. How to integrate the characteristics of neural signals into the design of the basic operator of the NNs while retaining its learning ability is the focus of this work. In this work, we proposed a function predefined convolutional NN (FPCNN) to search for the best frequency points and channel weights to decode spontaneous EEG signals. Among the FPCNN, a novel function predefined convolutional (FPC) layer adopts a learnable way to search for the key spatial-frequency parameters of spontaneous EEG, making its parameters have clear physical meanings. Furthermore, a trainable quadrature detector (TQD) based on FPC was constructed, and the quadrature characteristic was utilized to ensure the capture of complex phase change signals. The core contribution of our method lies in the proposal of a novel NN operator for decoding spontaneous EEG, and a quadrature scheme for handling the phase changes of signals. The experimental results show that the proposed FPCNN significantly improves the performance by 2.09% (${}^{\ast } $), 3.08% (${}^{\ast } $), and 3.41% (${}^{\ast \ast }$), respectively, compared with the state-of-the-art (SOTA) methods on three spontaneous EEG datasets. Moreover, the training and testing time cost of FPCNN in a non-GPU environment only takes 67.96 and 19.36 s per epoch. Its savings in computing resources and time are very beneficial for EEG processing in diverse environments. In addition, visualization experiments demonstrated the interpretability and stability of the proposed FPCNN. The experimental results show that our method is efficient, stable, and interpretable. This work has effectively improved the decoding efficiency of spontaneous EEG signals and demonstrated the power of combining traditional signal processing methods with NNs.}, }
@article {pmid41703350, year = {2026}, author = {Zhu, H and Zhang, Y and Beierholm, U and Shams, L}, title = {Crossmodal interaction of flashes and beeps across time and number follows Bayesian causal inference.}, journal = {Psychonomic bulletin &amp; review}, volume = {33}, number = {3}, pages = {58}, pmid = {41703350}, issn = {1531-5320}, mesh = {Humans ; Bayes Theorem ; *Auditory Perception/physiology ; *Visual Perception/physiology ; *Illusions/physiology ; Adult ; Young Adult ; Female ; Male ; }, abstract = {Multisensory perception requires the brain to dynamically infer causal relationships between sensory inputs across various dimensions, such as temporal and spatial attributes. Traditionally, Bayesian Causal Inference (BCI) models have generally provided a robust framework for understanding sensory processing in unidimensional settings where stimuli across sensory modalities vary along one dimension such as spatial location, or numerosity (Samad et al., PloS one, 10 (2), e0117178, 2015). However, real-world sensory processing involves multidimensional cues, where the alignment of information across multiple dimensions influences whether the brain perceives a unified or segregated source. In an effort to investigate sensory processing in more realistic conditions, this study introduces an expanded BCI model that incorporates multidimensional information, specifically numerosity and temporal discrepancies. Using a modified sound-induced flash illusion (SiFI) paradigm with manipulated audiovisual disparities, we tested the performance of the enhanced BCI model. Results showed that integration probability decreased with increasing temporal discrepancies, and our proposed multidimensional BCI model accurately predicts multisensory perception outcomes under the entire range of stimulus conditions. This multidimensional framework extends the BCI model's applicability, providing deeper insights into the computational mechanisms underlying multisensory processing and offering a foundation for future quantitative studies on naturalistic sensory processing.}, }
@article {pmid41336488, year = {2025}, author = {Ivucic, G and Pahuja, S and Li, H and Schultz, T}, title = {Geo-GCN: Geometric-Graphical Convolutional Network for EEG-based Auditory Attention Detection.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11251825}, pmid = {41336488}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; *Convolutional Neural Networks ; *Graph Neural Networks ; *Attention ; Speech ; Hearing ; *Brain-Computer Interfaces ; *Auditory Perception ; Humans ; Datasets as Topic ; Male ; Female ; Hearing Loss, Sensorineural ; }, abstract = {Auditory attention detection (AAD) reveals listeners' attention to a speech stimulus based on their elicited electroencephalography (EEG) signals. We propose a geometric graph convolutional network (Geo-GCN) that uses the physical layout of EEG sensors to construct a distance-based adjacency matrix. This enables Geo-GCN to perform more biologically informed feature learning than standard GCNs. Using data from participants with normal hearing (NH) and hearing-impaired (HI), our method outperforms traditional GCNs. Geo-GCN also demonstrates lower performance variability among participants. Analysis of separate NH and HI groups shows consistent gains over standard GCN, underlining the benefit of explicit modeling of scalp geometry. These findings highlight the potential of geometry-aware graph neural networks to improve EEG-based auditory attention detection, particularly in heterogeneous populations with varied hearing capabilities.}, }
@article {pmid41703299, year = {2026}, author = {Xu, Z and Wang, H and Yu, J and Deng, Y and Tian, X and Ni, R and Xia, F and Yang, L and Xu, C and Zhang, L and Luo, R and Chen, P and Zhang, X and Liu, Y and Hou, J and Zhang, M and Chen, S and Su, L and Sun, H and He, Y and Chen, D and Chen, X and Miao, Z and Xie, J and Liu, X and Zhao, J and Ke, B and Tian, X and Zeng, L and Zhang, L and Tang, X and Yang, S and Liu, J and Wang, X and Yan, W and Shao, Z}, title = {Publisher Correction: Psychedelics elicit their effects by 5-HT2A receptor-mediated Gi signalling.}, journal = {Nature}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41586-026-10249-5}, pmid = {41703299}, issn = {1476-4687}, }
@article {pmid41702101, year = {2026}, author = {Pan, CX and Sokol-Hessner, P}, title = {Trajectories of learning about others: Liking and affiliation follow similar but distinct paths.}, journal = {Acta psychologica}, volume = {264}, number = {}, pages = {106477}, doi = {10.1016/j.actpsy.2026.106477}, pmid = {41702101}, issn = {1873-6297}, abstract = {People quickly form stable impressions of others, but impressions are just the beginning of social interaction. Surprisingly little is known about how impressions may relate to the desire to connect with others, or how they update over time in the presence of complex and changing information. In an online task, participants learned about 12 targets' actions and the contexts of those actions through a series of ten two-sentence vignettes, and rated targets on likeability and desire to connect after each vignette. Actions were positive or negative, and contexts provided dispositional or situational explanations for actions. For some targets, information type in the first five vignettes (e.g., positive dispositional) differed from the last five vignettes (e.g., negative situational). Participants updated impressions and affiliative desires quickly, and for some trajectories, the order of information learned mattered. Most importantly, liking and the desire to connect followed similar but different paths through these trajectories of information, establishing that impressions and affiliative desires are related but distinct constructs.}, }
@article {pmid41701987, year = {2026}, author = {Li, X and Yang, H and Hu, K and Wu, R and Chen, R and Ni, G and Liu, L and Su, R}, title = {CDI-DTI: A Strong Cross-Domain Interpretable Drug-Target Interaction Prediction Framework Based on Multi-Strategy Fusion.}, journal = {Journal of chemical information and modeling}, volume = {}, number = {}, pages = {}, doi = {10.1021/acs.jcim.5c02908}, pmid = {41701987}, issn = {1549-960X}, abstract = {Accurate prediction of drug-target interaction (DTI) is pivotal for drug discovery, yet existing methods often fail to address challenges like cross-domain generalization, cold-start prediction, and interpretability. In this work, we propose CDI-DTI, a novel cross-domain interpretable framework for DTI prediction, designed to overcome these limitations. By integrating multimodal features-textual, structural, and functional-through a multistrategy fusion approach, CDI-DTI ensures robust performance across different domains and in cold-start scenarios. A multisource cross-attention mechanism is introduced to align and fuse features early, while a bidirectional cross-attention layer captures fine-grained intramodal drug-target interaction. At the late fusion stage, we incorporate Gram Loss for feature alignment and a deep orthogonal fusion module to eliminate redundancy. Experimental results on several benchmark data sets demonstrate that CDI-DTI significantly outperforms existing methods, particularly in cross-domain and cold-start tasks, while maintaining high interpretability for practical applications in drug-target interaction prediction.}, }
@article {pmid41480673, year = {2026}, author = {Singh, N and Cohen, DJ and Chen, S and Shah, MA and Stebbins, A and Kosinski, AS and Brothers, L and Vemulapalli, S and Kirtane, AJ and Dizon, JM and George, I and Leon, MB and Nazif, TM}, title = {Outcomes of Patients With New Left Bundle Branch Block After TAVR: TVT Registry Insights.}, journal = {Circulation. Cardiovascular interventions}, volume = {19}, number = {2}, pages = {e015441}, doi = {10.1161/CIRCINTERVENTIONS.125.015441}, pmid = {41480673}, issn = {1941-7632}, mesh = {Humans ; *Transcatheter Aortic Valve Replacement/adverse effects/mortality ; *Bundle-Branch Block/mortality/physiopathology/therapy/diagnosis/epidemiology ; Male ; Female ; Registries ; Aged, 80 and over ; Treatment Outcome ; Aged ; *Aortic Valve Stenosis/surgery/mortality/diagnostic imaging/physiopathology ; Risk Factors ; Time Factors ; Risk Assessment ; Incidence ; United States/epidemiology ; Patient Readmission ; Action Potentials ; }, abstract = {BACKGROUND: Cardiac conduction disturbances remain the most frequent complication of transcatheter aortic valve replacement (TAVR), but the clinical implications of new left bundle branch block (LBBB) after TAVR remain controversial. Here, we aim to assess the impact of new LBBB after TAVR on patient outcomes in a large, real-world registry.<br><br>METHODS: The study population consisted of patients in the TVT registry (Society of Thoracic Surgery and American College of Cardiology Transcatheter Valve Therapy Registry) who underwent TAVR for aortic stenosis between 2016 and 2022 and were discharged alive from the index hospitalization. Key exclusion criteria included preexisting conduction defects and a permanent pacemaker before TAVR or during the index hospitalization. Clinical outcomes were compared between patients with and without new LBBB using Cox proportional hazards models adjusted for baseline demographic, clinical, and echocardiographic variables.<br><br>RESULTS: Among 202&#x2009;533 TAVR recipients, 32&#x2009;933 (16.3%) developed new LBBB after TAVR. Over the study period, there was a significant decrease in the incidence of new LBBB from 19.9% in the first quarter of 2016 to 14.4% in the third quarter of 2022. Patients with new LBBB after TAVR, compared with those without LBBB, had significantly greater 1-year all-cause mortality (adjusted hazard ratio, 1.19 [95% CI, 1.13-1.25]; P<0.001), hospital readmission (adjusted hazard ratio, 1.23 [95% CI, 1.19-1.28]; P<0.001), and new pacemaker requirement (adjusted hazard ratio, 3.50 [95% CI, 3.26-3.76]; P<0.001). Patients with new LBBB also had lower Kansas City Cardiomyopathy Questionnaire Overall Summary scores (adjusted difference, -1.7 points [95% CI, -2.1 to -1.3]; P<0.001) and left ventricular ejection fraction (adjusted difference, -2.8% [95% CI, -3.4% to -2.2%]; P<0.001).<br><br>CONCLUSIONS: New LBBB after TAVR is associated with worse 1-year outcomes, including death, rehospitalization, and permanent pacemaker, as well as worse health status and lower left ventricular ejection fraction. These findings suggest that continued efforts to limit the development of conduction disturbance after TAVR are warranted.}, }
@article {pmid41701745, year = {2026}, author = {Heiney, K and Józsa, M and Rule, ME and Sprekeler, H and Nichele, S and O'Leary, T}, title = {Information theoretic measures of neural and behavioural coupling predict representational drift.}, journal = {PLoS computational biology}, volume = {22}, number = {2}, pages = {e1013130}, doi = {10.1371/journal.pcbi.1013130}, pmid = {41701745}, issn = {1553-7358}, abstract = {In many parts of the brain, population tuning to stimuli and behaviour gradually changes over the course of days to weeks in a phenomenon known as representational drift. The tuning stability of individual cells varies over the population, and it remains unclear what drives this heterogeneity. We investigate how a neuron's tuning stability relates to its shared variability with other neurons in the population using two published datasets from posterior parietal cortex and visual cortex. We quantified the contribution of pairwise interactions to behaviour or stimulus encoding by partial information decomposition, which breaks down the mutual information between the pairwise neural activity and the external variable into components uniquely provided by each neuron and by their interactions. Information shared by the two neurons is termed 'redundant', and information requiring knowledge of the state of both neurons is termed 'synergistic'. We found that a neuron's tuning stability is positively correlated with the strength of its average pairwise redundancy with the population. We hypothesize that subpopulations of neurons show greater stability because they are tuned to salient features common across multiple tasks. Regardless of the mechanistic implications of our work, the stability-redundancy relationship may support improved longitudinal neural decoding in technology that has to track population dynamics over time, such as brain-machine interfaces.}, }
@article {pmid41700523, year = {2026}, author = {Xu, D and Hong, J and Park, K and Ahn, JH}, title = {Flexible Surface Electrodes for Electrocorticography in Neurological Diseases and Brain-Computer Interface Applications.}, journal = {Small (Weinheim an der Bergstrasse, Germany)}, volume = {}, number = {}, pages = {e14286}, doi = {10.1002/smll.202514286}, pmid = {41700523}, issn = {1613-6829}, support = {20012355//Ministry of Trade, Industry and Energy (MOTIE)/ ; }, abstract = {Flexible electrocorticography (ECoG) surface electrode arrays have broadened their application scope from clinical neural recording tools to integral components of brain-computer interface (BCI) systems. Currently used ECoG arrays are typically fabricated with metal contacts embedded in silicone carriers, offering limited mechanical flexibility. This restricts their ability to achieve optimal conformal contact with the brain cortex. Moreover, their channel count is constrained by bulky and cumbersome cabling systems. The recent integration of flexible nanomaterials and advanced patterning techniques into surface electrodes has enabled the development of ultrathin, high-density arrays that conform intimately to the cortical surface. These arrays incorporate on-site amplification and multiplexing capabilities while maintaining stable impedance over extended implantation periods. This review article highlights recent technological advancements in ECoG surface electrode arrays, as well as emerging strategies for their application in the diagnosis and treatment of neurological disorders. In addition, it presents current efforts to incorporate surface electrodes into BCI systems through the utilization of neural signals.}, }
@article {pmid41699180, year = {2026}, author = {Cheng, S and Guo, J and Zhou, YL and Luo, X and Zhang, G and Zhang, YZ and Yang, Y and Xie, J and Xu, P and Shen, DD and Zang, S and Yang, H and Zhen, X and Zhang, M and Zhang, Y}, title = {De novo design of GPCR exoframe modulators.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {41699180}, issn = {1476-4687}, abstract = {G-protein-coupled receptors (GPCRs) are important therapeutic targets and have been targeted mainly through their orthosteric site, where the endogenous agonist binds[1]. However, allosteric modulation has emerged as a promising and innovative strategy in the realm of GPCR drug discovery[1]. Here, drawing inspiration from the natural regulation of GPCRs by transmembrane proteins, we have developed GPCR exoframe modulators (GEMs), de novo designed proteins that specifically target the transmembrane domain of GPCRs. Utilizing a hallucination-like design approach, we crafted GEMs with three strategic structural prompts to achieve the desired binding modes. We selected the dopamine D1 receptor as a prototypical model and systematically investigated four GEMs. Structural studies and functional assays revealed that these GEMs bind to the transmembrane domains and function as diverse allosteric modulators, including agonist-positive allosteric modulator, negative allosteric modulator and biased allosteric modulator. The ago-PAM GEM restores the activity of various D1 receptor loss-of-function mutants, suggesting a promising therapeutic target for GPCR-related disorders. Our work introduces GEMs that target the transmembrane domain as potent agents for allosteric GPCR modulation and highlights the potential of deep learning-based approaches in the design of function-oriented membrane proteins.}, }
@article {pmid41698950, year = {2026}, author = {Hong, J and Wang, W and Najafizadeh, L}, title = {ChatBCI, a P300 speller BCI with context-driven word prediction leveraging large language models, from concept to evaluation.}, journal = {Scientific reports}, volume = {16}, number = {1}, pages = {6379}, pmid = {41698950}, issn = {2045-2322}, mesh = {*Brain-Computer Interfaces ; Humans ; *Event-Related Potentials, P300/physiology ; Electroencephalography ; *Language ; Male ; Female ; Adult ; User-Computer Interface ; Large Language Models ; }, abstract = {P300 speller brain computer interfaces (BCIs) allow users to compose sentences by selecting target keys on a graphical user interface (GUI) through the detection of P300 component in their electroencephalogram (EEG) signals following visual stimuli. Most existing P300 speller BCIs require users to spell all or the first few initial letters of the intended word, letter by letter. Consequently, a large number of keystrokes could be required to write an intended sentence, thereby, increasing user's time and cognitive load. There is a need for more efficient and user-friendly methods for faster, and practical sentence composition. In this work, we introduce ChatBCI, a P300 speller BCI that leverages the zero-shot learning capabilities of large language models (LLMs) to suggest words from user-spelled initial letters or predict the subsequent word(s), reducing keystrokes and accelerating sentence composition. ChatBCI retrieves word suggestions through remote queries to the GPT-3.5 API. A modified GUI, displaying GPT-3.5 word suggestions as extra keys is designed. Stepwise linear discriminant analysis (SWLDA) is used for the P300 classification. Seven subjects completed two online spelling tasks: 1) copy-spelling a self-composed sentence using ChatBCI, and 2) improvising a sentence using ChatBCI's word suggestions. Results demonstrate that for the copy-spelling task, on average, ChatBCI outperforms letter-by-letter BCI spellers, reducing time and keystrokes by [Formula: see text] and [Formula: see text], respectively, and increasing information transfer rate by [Formula: see text]. For the improvised sessions, ChatBCI achieves [Formula: see text] keystroke savings across subjects. Overall, ChatBCI, by employing remote LLM queries outperforms traditional spellers without requiring local model training or storage. ChatBCI's (multi-)word prediction capability paves the way for developing next-generation speller BCIs that are efficient and effective for real-time communication, specially for users with communication and motor disabilities.}, }
@article {pmid41698889, year = {2026}, author = {Du, W and Zhang, J and Wang, Y and Li, M and Cao, J and Yang, B and He, Q and Shao, X and Ying, M}, title = {Palmitic acid activates c-Myc via dual palmitoylation-dependent pathways to promote colon cancer.}, journal = {Cell discovery}, volume = {12}, number = {1}, pages = {12}, pmid = {41698889}, issn = {2056-5968}, support = {U23A20534//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {c-Myc is broadly hyperactivated in colon cancer, yet the mechanisms sustaining its transcriptional activation remain elusive. Here we identify palmitic acid (PA) as a metabolite cue that activates c-Myc via dual palmitoylation-dependent pathways operating across tumor initiation and progression. In colitis models, PA-rich diets exacerbate inflammation and enrich MYC target programs without increasing Myc mRNA. Mechanistically, the palmitoyltransferase ZDHHC9, upregulated by IL-1β, directly palmitoylates c-Myc at C171, enhancing c-Myc/MAX dimerization and transcriptional activity; genetic or pharmacologic inhibition diminishes c-Myc palmitoylation and target gene expression. During tumor progression, c-Myc transactivates FATP2, increasing PA uptake and reinforcing c-Myc palmitoylation, thereby establishing a feedforward loop and metabolic addiction to PA. Functionally, PA accelerates xenograft growth, whereas targeting ZDHHC9 and FATP2 inhibits c-Myc function to suppress tumor burden. These findings uncover metabolite-driven control of c-Myc through palmitoylation and highlight ZDHHC9/FATP2 as actionable vulnerabilities for colon cancer treatment.}, }
@article {pmid41698423, year = {2026}, author = {Wu, W and Daly, I and Chen, W and Liu, L and Liang, W and Chen, Y and Wang, X and Cichocki, A and Jin, J}, title = {HCFNet: A Heterogeneous Frequency Bands Coupling CNN for Enhanced Short-Time Fast Response in Motor Imagery Decoding.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110717}, doi = {10.1016/j.jneumeth.2026.110717}, pmid = {41698423}, issn = {1872-678X}, abstract = {BACKGROUND: Motor imagery signals encompass a broad range of frequency components, and frequency band decomposition can improve the precision of frequency-domain features, helping the model focus on task-relevant information. However, existing methods often treat signals from different frequency bands uniformly, overlooking their heterogeneity and coupling, which leads to redundant features and loss of cooperative information.<br><br>NEW METHOD: We propose a HCFNet that explores heterogeneous feature extraction and coupling across frequency bands. HCFNet first separates the raw signal into high and low-frequency bands, extracting spatiotemporal features through specialized modules. A cross-frequency coupling module then fuses these features, using data augmentation for regularization to capture robust spectral-spatiotemporal features and high-low frequency coupling.<br><br>RESULTS: We evaluated our model on the BCIC-IV-2a and OpenBMI benchmark datasets, and our model achieves average accuracies of 82.41% and 76.52%. Notably, HCFNet maintains excellent performance even with shorter time windows.<br><br>HCFNet outperforms all the state-of-the-art methods we benchmark against. Compared with traditional multi-band isomorphic methods, frequency-band heterogeneous coupling performs better in capturing task-related features and significantly reduces redundancy during feature fusion.<br><br>CONCLUSIONS: This study significantly advances the decoding technology of motor imagery signals through an innovative frequency-band heterogeneous coupling method. Its substantial potential for rapid responses brings tangible improvements to brain-computer interface systems and is expected to be further applied in domain adaptation, cross-domain alignment, and cross-subject contexts in the future.}, }
@article {pmid41643315, year = {2026}, author = {Xie, X and Fan, Z and Mou, H and Lan, Y and Wang, Y and Wang, M and Pan, Y and Chen, G and Chen, W and Zhang, S}, title = {AutoSimTTF: a fully automatic pipeline for personalized electric field simulation and treatment planning of tumor treating fields.}, journal = {Physics in medicine and biology}, volume = {71}, number = {4}, pages = {}, doi = {10.1088/1361-6560/ae4288}, pmid = {41643315}, issn = {1361-6560}, mesh = {Humans ; *Radiotherapy Planning, Computer-Assisted/methods ; *Precision Medicine/methods ; Automation ; *Neoplasms/radiotherapy/diagnostic imaging/therapy ; *Electricity ; }, abstract = {Objective. Tumor treating fields (TTFields) is an emerging cancer therapy whose efficacy is closely linked to the electric field (EF) intensity delivered to the tumor. However, current computational workflows for simulating the EF and planning treatment rely on time-consuming manual segmentation and proprietary software, hindering efficiency, reproducibility, and accessibility.Approach. We introduce AutoSimTTF, a fully automatic pipeline for personalized EF simulation and optimized treatment planning for TTFields. The end-to-end workflow utilizes advanced deep learning model for automated tumor segmentation, conducts finite element method-based EF simulation, and determines a computationally optimized treatment plan via a novel, physics-based parameter optimization method.Main results. The automated segmentation module achieved high precision, yielding a Dice similarity coefficient of 0.91 for the whole tumor. In terms of efficiency, the active planning workflow was completed in approximately 12 min, significantly outperforming conventional multi-day manual processes. The pipeline's simulation accuracy was validated against a conventional semi-automated workflow, demonstrating deviations of less than 14.1% for most tissues. Critically, the parameter optimization generated personalized transducer montages that produced a significantly higher EF intensity at the tumor site (up to 111.9% higher) and substantially improved field focality (19.4% improvement) compared to traditional fixed-array configurations.Significance. AutoSimTTF addresses major challenges in efficiency and reproducibility, paving the way for data-driven personalized TTFields therapy and large-scale computational research.}, }
@article {pmid41697859, year = {2026}, author = {Liu, H and Li, M and Yang, Y and Li, Z}, title = {A simple deep transfer learning model with feature alignment block for motor imagery decoding.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-17}, doi = {10.1080/10255842.2026.2627492}, pmid = {41697859}, issn = {1476-8259}, abstract = {To address data scarcity and distribution shifts in motor imagery electroencephalogram (MI-EEG) based brain computer interface, we propose a 1-dimensional convolution-based deep transfer learning model with embedded Feature Alignment block (1DC-DTL-FA) in this article. It integrates multi-stage feature extraction, classification, and FA block. Unlike complex models, it utilizes Neural Architecture Search (NAS) to automatically locate the optimal FA position in Euclidean space Evaluated on BCI 2000 and BCI IV2a datasets, 1DC-DTL-FA achieved superior accuracies of 89.80% and 82.96%. The results demonstrate that this simple architecture effectively handles complex feature extraction and online alignment, outperforming state-of-the-art models in MI-EEG decoding.}, }
@article {pmid41697833, year = {2026}, author = {Li, L and Chen, W}, title = {EEG-Based Emotion Recognition Using Spatial-Temporal Graph-Aware Network with Channel Selection.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3665517}, pmid = {41697833}, issn = {2168-2208}, abstract = {Electroencephalogram (EEG)-based emotion recognition holds great potential in intelligent human computer interaction and brain-computer interface systems, as the brain generates distinct electrical activity patterns under different emotional states. However, EEG information often contains data from numerous channels, leading to high computational cost and potential redundancy. Existing channel selection methods often rely on uniform rules, lacking frequency-specific adaptability and inter-channel modeling, which can cause information loss and reduced performance during dimensionality reduction. To address this issue, we propose a novel framework that combines discriminative channel selection with hierarchical spatial-temporal modeling to enhance both per formance and efficiency. In preprocessing, wavelet coherence and mutual information are used to adaptively select informative channels across multiple frequency bands. The selected signals are then processed by a Spatial Temporal Graph-aware Network (STG-Net), which models spatial relationships between channels through graph convolution, extracting spatial features from each time frame. Coupled with a temporal modeling module, the network further captures the evolving temporal patterns of emotional states across consecutive frames. Finally, frequency spatial-temporal features are fused for emotion classification. Compared to the state-of-the-art methods, our approach achieves superior performance in both recognition accuracy and model efficiency.}, }
@article {pmid41694587, year = {2026}, author = {Wu, W and Du, J and Li, J and Zhang, S and Kang, X and Cao, Y and Chen, J and Pan, Z and Huang, X and Xu, Z and Yang, B and He, Q and Yang, X and Yan, H and Luo, P}, title = {Inhibition of Cathepsin B protects against vandetanib-induced hepato-cardiotoxicity by restoring lysosomal damage.}, journal = {International journal of biological sciences}, volume = {22}, number = {4}, pages = {1752-1774}, pmid = {41694587}, issn = {1449-2288}, mesh = {*Piperidines/adverse effects/toxicity ; *Quinazolines/adverse effects/toxicity ; Animals ; *Cathepsin B/antagonists &amp; inhibitors/metabolism ; *Lysosomes/metabolism/drug effects ; Humans ; Mice ; *Cardiotoxicity/prevention &amp; control/metabolism ; Apoptosis/drug effects ; }, abstract = {Vandetanib, a critical therapy for advanced thyroid and RET-driven cancers, is limited by life-threatening hepato-cardiotoxicity. This study identifies lysosomal protease cathepsin B (CTSB) as the central mediator of vandetanib-induced organ damage through STAT3-driven transcriptional activation. CTSB triggers mitochondrial apoptosis by cleaving the lysosomal calcium channel mucolipin TRP cation channel 1 (MCOLN1), disrupting calcium/AMP-activated protein kinase (AMPK) signaling and autophagy flux. Crucially, the natural compound tannic acid directly binds and inhibits CTSB, completely protecting against hepato-cardiotoxicity without compromising vandetanib's antitumor efficacy in preclinical models. Overall, our findings establish CTSB-mediated lysosomal dysfunction and MCOLN1-calcium-AMPK axis disruption as the core mechanism of vandetanib-induced hepato-cardiotoxicity, and identify tannic acid as a readily translatable adjuvant strategy to prevent this toxicity. These findings redefine CTSB as a druggable target for kinase inhibitor toxicities and position tannic acid as a clinically translatable adjuvant to enhance vandetanib's safety profile. By preserving lysosomal function and calcium homeostasis, this strategy addresses a critical unmet need in precision oncology, enabling prolonged, safer use of vandetanib and related tyrosine kinase inhibitors. The discovery of shared lysosomal injury mechanisms across organs also opens avenues for preventing multi-organ toxicities in broader cancer therapies.}, }
@article {pmid41694008, year = {2026}, author = {Yang, S and He, S and Shi, B}, title = {fNIRS cortical activation in Tai Chi observational learning.}, journal = {Frontiers in psychology}, volume = {17}, number = {}, pages = {1710673}, pmid = {41694008}, issn = {1664-1078}, abstract = {INTRODUCTION: Observational learning plays a critical role in motor skill acquisition. Investigating the neural substrates involved in this process is of great significance for optimizing teaching methodologies and advancing brain-computer interface technologies.<br><br>METHODS: An experimental design combining functional near-infrared spectroscopy (fNIRS) and behavioral analysis was employed. The fNIRS protocol utilized a 2&#xd7;3&#xd7;2 factorial design.<br><br>RESULTS: Behavioral findings: The RSVD group (Regular-Speed Videos Demonstration) exhibited significantly higher movement accuracy scores compared to the SMVD group (Slow-Motion Video Demonstration). Cognitive load assessments revealed that the SMVD group experienced significantly higher cognitive load than the RSVD group.<br><br>FNIRS FINDINGS: During the observational learning phase, significant activation increases were observed in the Frontal Eye Fields (FEF, BA8) and the Pre-Motor/Superior Motor Cortex (SMA/Pre-SMA, BA6) compared to the demonstration phase. The Frontopolar Cortex (FPC) showed reduced activation during the observational learning phase relative to the demonstration phase. In the Right Frontopolar Area (RFPC, BA10), activation was significantly greater in the simple task condition compared to moderate and difficult task conditions.<br><br>CONCLUSION: In the early stages of instruction, SMVD may impede the effectiveness of observational learning for Tai Chi. Both the action demonstration and observational learning phases demand greater neural resources and broader brain network connectivity, requiring coordinated engagement of cognitive and motor systems.}, }
@article {pmid41693701, year = {2026}, author = {Feng, Y and Wang, W and Zhang, G and Zhou, F and Li, B}, title = {Polymer Brushes in Nanoelectronics: Nanotribology Insights from Fundamentals to Cutting-Edge Applications.}, journal = {Nano letters}, volume = {}, number = {}, pages = {}, doi = {10.1021/acs.nanolett.5c05456}, pmid = {41693701}, issn = {1530-6992}, abstract = {Polymer brushes have gained significant attention in nanoelectronics due to their capability in surface modifications, interfacial physicochemical properties control, nanoscale patterning, and unique dielectric properties. The past few decades have witnessed significant progress made in this field, including the emergence of new concepts in synthetic strategy and molecular structure design and the latest attempts in nanoelectronics. Looking ahead, polymer brushes will continuously play roles in brain-computer interfaces, miniaturization in AI hardware, and next-generation flexible electronics. This Mini-Review summarizes and comments on recent developments and applications of polymer brushes in nanoelectronics, with particular emphasis on their interfacial frictional properties from a nanotribological perspective, which will be helpful for bridging interdisciplinary knowledge, refining existing techniques, and uncovering new applications.}, }
@article {pmid41693496, year = {2026}, author = {Nightingale, R and Vickers, D and Mahon, M}, title = {Being in flux: the experiences of everyday listening among children and young people with cochlear implants.}, journal = {International journal of audiology}, volume = {}, number = {}, pages = {1-12}, doi = {10.1080/14992027.2026.2614519}, pmid = {41693496}, issn = {1708-8186}, abstract = {OBJECTIVE: Although children and young people (CYP) with bilateral cochlear implants (BCI) can have difficulties perceiving speech-in-noise and sound localisation, little is known about their experiences of listening in daily life. This study explored CYP's perspectives of everyday listening.<br><br>DESIGN: Embedded within a randomised controlled trial, a qualitative study using repeat interviews, was conducted. Data were analysed using the Framework approach.<br><br>STUDY SAMPLE: 81 interviews were carried out with 46 CYP with BCI, aged 8-16.<br><br>RESULTS: Two themes were identified: (1) "Being in flux" highlights how CYP's listening experiences changed rapidly depending on contextual factors including the environment, speaker, sound and activity (2) "Managing everyday listening," explains how CYP used various strategies to either alter, accept or avoid each context-specific listening situation. Although CYP's experiences and management of situations changed as they got older, the relationship between age, listening experiences and coping strategies was individualised and complex.<br><br>CONCLUSIONS: Decision-making around how to cope with situational demands was influenced by CYP's agency and choice of strategy impacted on CYP's participation and inclusion. Further research is needed to understand how experiences change over time and how CYP can be supported to develop agency, self-advocacy and resilience to maximise their hearing.}, }
@article {pmid41691347, year = {2026}, author = {He, T and Hou, Y}, title = {The relationship between role stress and compassion fatigue of medical workers: the mediating role of emotional labor and the moderating role of positive psychological capital.}, journal = {BMC psychology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s40359-026-04190-5}, pmid = {41691347}, issn = {2050-7283}, support = {CSXL-22233//Foundation of Sichuan Research Center of Applied Psychology/ ; 24WSXT043//Sichuan Provincial Health Commission Science and Technology Project Youth Nursery - City Collaborative Project/ ; 2025M783477//78th batch of general funding from the China Postdoctoral Science Foundation/ ; GZB20240658//Postdoctoral Fellowship Program (Grade B) of China Postdoctoral Science Foundation/ ; 2024NSFSC1573//Sichuan Natural Science Foundation (Youth Fund) of Science and Technology Department of Sichuan Province/ ; }, abstract = {BACKGROUND: Compassion fatigue has numerous adverse effects on the mental health of medical workers and the diagnosis, treatment of patients. The increase in role stress among medical workers is closely related to compassion fatigue. However, few studies have explored whether their relationship is mediated by emotional labor and moderated by positive psychological capital.<br><br>METHODS: We investigated 1456 medical workers and assessed their role stress, compassion fatigue, emotional labor and positive psychological capital using the Role Stressors Scale, Compassion Fatigue Scale, Emotional Labor Scale and Positive Psychological Capital Questionnaire. The moderated mediation model was tested by SPSS software and PROCESS macro program.<br><br>RESULTS: Role stress was positively associated with compassion fatigue of medical workers, and the surface acting and deep acting of emotional labor play a partial mediating role in the relationship between role stress and compassion fatigue, respectively. Positive psychological capital has a moderating effect on the second half of the path with surface acting as the mediating variable, and has a moderating effect on both the first half and second half of the path with deep acting as the mediating variable, the association of role stress and deep acting and the association of emotional labor and compassion fatigue will gradually weaken with the improvement of the level of positive psychological capital.<br><br>CONCLUSION: The role stress of medical workers can be associated through emotional labor and compassion fatigue, in which positive psychological capital has a certain moderating effect. High surface acting and low positive psychological capital may be important risk factors for compassion fatigue when medical workers face role stress, while high deep acting and high positive psychological capital may be protective factors for medical workers to resist compassion fatigue caused by role stress. Therefore, reducing the surface acting, improving the deep acting and enhancing the positive psychological capital of medical workers will help to alleviate their compassion fatigue and maintain their mental health.}, }
@article {pmid41691330, year = {2026}, author = {Guo, X and Jiang, H and Zheng, Z and Zhang, J and Cao, L and Jiang, H}, title = {Mechanisms and clinical potential of combined tDCS and virtual reality in psychiatric disorders: a systematic review.}, journal = {Annals of general psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12991-025-00621-6}, pmid = {41691330}, issn = {1744-859X}, support = {2022ZD0212400//STI2030-Major Projects/ ; 82371453//National Natural Science Foundation of China/ ; 2024C03006, 2024SSYS0017//Key R&amp;D Program of Zhejiang/ ; 2021WJCY240//Hangzhou Biomedical and Health Industry Special Projects for Science and Technology/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2023ZFJH01-01, 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; }, abstract = {BACKGROUND: Transcranial direct current stimulation (tDCS) and virtual reality (VR) have emerged as promising non-invasive interventions in treating psychiatric disorders. Despite their individual efficacy in improving symptoms of various psychiatric conditions, the understanding of the combined use of tDCS and VR is limited. This review aims to evaluate the clinical effects and mechanisms of combined tDCS and VR in treating psychiatric disorders.<br><br>METHODS: We conducted a PRISMA 2020-compliant systematic review, searching major databases (PubMed, Web of Science, Scopus, PsycINFO, ScienceDirect, Cochrane Library, Google Scholar, medRxiv and ClinicalTrials.gov) for studies from January 2000 to July 2025 that evaluated combined tDCS-VR in psychiatric populations. Eligible clinical trials were screened, with tDCS/VR parameters and clinical outcomes extracted, and randomized controlled trials appraised using the Cochrane Risk of Bias 2 tool.<br><br>RESULTS: Fourteen studies met inclusion criteria: seven reviews and seven empirical trials (five randomized controlled trials, two pilot/feasibility studies) using mainly 1-2&#xa0;mA prefrontal tDCS paired with disorder-congruent VR. In post-traumatic stress disorder (PTSD) and specific phobias showed short-term symptom reductions, with some PTSD benefits maintained up to 12 months. Evidence for social anxiety and mild cognitive impairment-related depression was limited to single small RCTs with transient or inconsistent improvements. Overall confidence in the evidence is limited by small sample sizes, variable protocols, and risk&#x2011;of&#x2011;bias concerns.<br><br>CONCLUSION: Although seven small, heterogeneous studies indicate that combined tDCS-VR is feasible and shows preliminary therapeutic promise-most consistently in PTSD and, to a lesser extent, in specific phobias-the overall evidence base remains limited. Mechanistic findings suggesting modulation of medial and ventromedial prefrontal-amygdala circuits are still exploratory. Given substantial methodological heterogeneity, small sample sizes, and risk of bias, tDCS-VR should be regarded as experimental. The larger, well&#x2011;designed, disorder&#x2011;tailored randomized controlled trials using standardized stimulation/VR protocols, mechanistic outcome measures, and efforts to identify predictors of response are required before routine clinical implementation.}, }
@article {pmid41690337, year = {2026}, author = {Candia-Rivera, D and Chavez, M and Fallani, FV and Corsi, MC}, title = {Imagined movement modulates cardiac-cortico-cortical and cardiac-cortico-cerebellar oscillatory networks.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121804}, doi = {10.1016/j.neuroimage.2026.121804}, pmid = {41690337}, issn = {1095-9572}, abstract = {Understanding the mechanisms of motor imagery, the mental simulation of movement without execution, is key for the development of neurotechnologies, including understanding inter-individual variability in motor imagery performance. For instance, for detecting covert motor intent in noncommunicative patients or refining motor commands through brain-computer interfaces. While motor imagery engages motor-related brain regions, its precise mechanisms remain unclear, particularly in relation to cardiac dynamics. Evidence suggests heart-rate variability features have potential to enhance tasks' classifications, yet the brain-heart relationship is not well understood. In this study, we examined motor imagery learning using a task involving right-hand grasping imagery. We found that motor imagery is correlated with a task-dependent modulation of cardiac sympathetic activity and its relation with directed functional connectivity from the supplementary motor area to premotor and primary motor cortices. Additionally, cerebellar-supplementary motor area segregation, in relation to cardiac parasympathetic activity, indexed longitudinal motor learning. These results suggest that dynamic reconfiguration of brain-heart interactions contributes to sensorimotor function and learning-related physiology during motor imagery, supporting the brain-heart axis as a functional component of motor imagery rather than a passive correlate.}, }
@article {pmid41688540, year = {2026}, author = {Takasaki, K and Iwama, S and Liu, F and Ogura-Hiramoto, M and Okuyama, K and Kawakami, M and Mizuno, K and Kasuga, S and Noda, T and Morimoto, J and Liu, M and Ushiba, J}, title = {Rapid functional reorganization of the targeted contralesional hemisphere induced by one week of noninvasive closed-loop neurofeedback guides motor recovery in post-stroke patients with chronic motor impairment: a phase I trial.}, journal = {Communications medicine}, volume = {}, number = {}, pages = {}, doi = {10.1038/s43856-026-01423-x}, pmid = {41688540}, issn = {2730-664X}, abstract = {BACKGROUND: Post-stroke hemiplegia of the upper extremities continues to pose a significant therapeutic hurdle. Contralesional uncrossed corticospinal pathways (CST) are involved in the recovery processes.<br><br>METHODS: We test the safety, and preliminary efficacy of targeted upregulation of uncrossed CST excitability through self-modulation of cortical activities via noninvasive brain-machine interaction training (Registered with the University Hospital Medical Information Network: UMIN000017525). In this single-arm prospective trial, eight individuals with persistent severe post-stroke motor disability voluntarily actuated their affected shoulder using a brain-computer interface (BCI) bridging the contralesional motor cortex (M1) and an exoskeleton robot. While patients attempted to elevate the affected arm, scalp electroencephalogram (EEG) signals over the contralesional M1 were processed online to provide them with feedback on M1 excitability.<br><br>RESULTS: Here we show that the BCI reconstructs neural pathways, allowing arm elevation without any adverse effects. As evidenced by an increase in primary outcome measure (Fugl- Meyer Assessment, p&#x2009;<&#x2009;0.05, d&#x2009;=&#x2009;1.24), seven days of consecutive system use results in rapid, sustained, and clinically significant improvement in motor function when removed from the system and promotes contralesional M1 functional remodeling.<br><br>CONCLUSIONS: This closed-loop system is safe, feasible, and a promising intervention that recruits intact neural resources to allow patients to recover upper-extremity motor abilities.}, }
@article {pmid41688457, year = {2026}, author = {Medvedeva, A and Syrov, N and Yakovlev, L and Alieva, Y and Berkmush-Antipova, A and Ivanova, G and Shusharina, N and Kaplan, A}, title = {Multisession fNIRS-EEG data of Post-Stroke Motor Recovery. Recordings During Intact and Paretic Hand Movements.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-026-06803-5}, pmid = {41688457}, issn = {2052-4463}, support = {FZWM-2024-0013//Ministry of Education and Science of the Russian Federation (Minobrnauka)/ ; FZWM-2024-0013//Ministry of Education and Science of the Russian Federation (Minobrnauka)/ ; FZWM-2024-0013//Ministry of Education and Science of the Russian Federation (Minobrnauka)/ ; FZWM-2024-0013//Ministry of Education and Science of the Russian Federation (Minobrnauka)/ ; FZWM-2024-0013//Ministry of Education and Science of the Russian Federation (Minobrnauka)/ ; FZWM-2024-0013//Ministry of Education and Science of the Russian Federation (Minobrnauka)/ ; }, abstract = {Accurate diagnosis and monitoring of recovery after stroke are critical for effective motor rehabilitation. As stroke is inherently associated with impaired cerebral blood flow, functional near-infrared spectroscopy (fNIRS) provides a valuable tool for assessing hemodynamic changes in the brain. When combined with electroencephalography (EEG), this multimodal approach can provide complementary insights into neural and vascular responses during recovery. However, longitudinal datasets combining fNIRS and EEG in stroke populations remain limited. The current article presents an open access dataset with simultaneous fNIRS and EEG recordings from 16 post-stroke patients over 84 rehabilitation sessions. Participants performed motor tasks with both paretic and intact hands. The dataset includes raw and processed signals, clinical scores (ARAT, Fugl-Meyer) and patient demographics. This resource supports research into stroke recovery, development of neurorehabilitation strategies and fNIRS-based brain computer interfaces (BCI).}, }
@article {pmid41686905, year = {2026}, author = {Ramesh, R and Azgomi, HF and Louie, KH and Balakid, JP and Marks, JH and Wang, DD}, title = {At-home movement state classification using totally implantable cortical-basal ganglia neural interface.}, journal = {Science advances}, volume = {12}, number = {7}, pages = {eadz4733}, pmid = {41686905}, issn = {2375-2548}, mesh = {Humans ; Male ; Female ; *Parkinson Disease/physiopathology ; *Brain-Computer Interfaces ; *Motor Cortex/physiology/physiopathology ; *Basal Ganglia/physiology/physiopathology ; Middle Aged ; Movement/physiology ; Aged ; Gait/physiology ; Implantable Neurostimulators ; }, abstract = {Decoding human movement from invasive neural signals has traditionally relied on complex machine learning algorithms using data collected from short-term laboratory tasks, limiting understanding of brain function during natural behavior and hindering development of clinically viable closed-loop neuromodulation. Here, we demonstrate the first in-human, at-home classification of a specific movement state-walking-using a fully implantable, bidirectional neurostimulator. In four individuals with Parkinson's disease, we recorded chronic motor cortex and globus pallidus activity synchronized with wearable kinematic data across over 80 hours of unsupervised daily activity. We identified highly predictive personalized spectral biomarkers of gait and validated their performance. Critically, we showed that these biomarkers could drive real-time movement state classification using the neurostimulator's embedded linear discriminant classifier, satisfying device-level constraints for closed-loop stimulation. Our results establish a previously unidentified pipeline for real-world neural decoding and scalable framework for personalized adaptive neuromodulation, expanding the translational reach of implantable brain-computer interfaces.}, }
@article {pmid41686387, year = {2026}, author = {Shah, J and Pathuri, S and Ong, J and Greenbaum, R and Melkumyan, N and Lee, R and Rezaei, K and Parsons, AD and Zheng, J and Golnik, K and Lee, AG}, title = {Neural Vision Restoration in Ophthalmology.}, journal = {Annals of biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {41686387}, issn = {1573-9686}, abstract = {Neural vision restoration is a rapidly advancing discipline at the intersection of neuroscience, bioengineering, and ophthalmology. This review synthesizes emerging strategies to restore visual perception through retinal prostheses, optic nerve and thalamic implants, cortical brain-computer interfaces (BCIs), optogenetics, and non-invasive stimulation. Although initial experiments have demonstrated primitive visual abilities such as light perception and motion detection, artificial vision remains cognitively demanding and fundamentally different from natural vision. Advances in artificial intelligence and machine learning may enable adaptive, closed-loop systems that optimize stimulation, enhance low-light vision, and integrate environmental inputs for more intelligible percepts. At the same time, a growing understanding of neural plasticity, cortical remapping, and perceptual learning highlights the need for multidisciplinary strategies in visual rehabilitation. Ethical and regulatory concerns, including informed consent, data protection, neural enhancement, and equitable access, remain central to responsible implementation. The potential of BCIs to bypass the eye entirely, and of neuroprosthetics to be used in spaceflight, disaster response, or military medicine, expands the applications of vision restoration beyond blindness alone. Bridging technological, clinical, and ethical strategies in this review outlines the challenges and opportunities that define the future of neural ophthalmology. Ultimately, restoring sight will require not only functioning hardware, but systems compatible with the reorganized brain and the lived experience of visual loss.}, }
@article {pmid41685356, year = {2026}, author = {Di Caterina, G and Zhang, M and Liu, J}, title = {Editorial: Theoretical advances and practical applications of spiking neural networks, volume II.}, journal = {Frontiers in neuroscience}, volume = {20}, number = {}, pages = {1771268}, doi = {10.3389/fnins.2026.1771268}, pmid = {41685356}, issn = {1662-4548}, }
@article {pmid41178530, year = {2026}, author = {Bryant, JM and Shotbolt, M and Stimphil, E and Andre, V and Zhang, E and Estrella, V and Husain, K and Weygand, J and Marchion, D and Lopez, AS and Abrahams, D and Chen, S and Abdel-Mottaleb, M and Conlan, S and Oraiqat, I and Khatri, V and Guevara, JA and Pilon-Thomas, S and Redler, G and Latifi, K and Raghunand, N and Yamoah, K and Hoffe, S and Costello, J and Frakes, JM and Liang, P and Gatenby, RA and Malafa, M and Khizroev, S}, title = {Magnetoelectric Nanotherapy Achieves Complete Tumor Ablation and Prolonged Survival in Pancreatic Cancer Murine Models.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {13}, number = {9}, pages = {e17228}, pmid = {41178530}, issn = {2198-3844}, support = {RR232//Radiological Society of North America (RSNA)/ ; N66001-19-C-4019//Defense Advanced Research Projects Agency/ ; ECCS-211082//National Science Foundation/ ; 5P30 240139-02/NH/NIH HHS/United States ; 5P30 240139-02/NH/NIH HHS/United States ; }, mesh = {Animals ; *Pancreatic Neoplasms/therapy/pathology ; Mice ; *Carcinoma, Pancreatic Ductal/therapy ; Disease Models, Animal ; Magnetic Resonance Imaging/methods ; Cell Line, Tumor ; Humans ; Female ; Theranostic Nanomedicine/methods ; }, abstract = {Magnetoelectric nanoparticles (MENPs), when activated by a magnetic field, are shown to provide a minimally invasive, drug-free, theranostic approach to pancreatic ductal adenocarcinoma (PDAC) treatment. The magnetoelectric effect allows intravenously administered MENPs to be magnetically guided to PDAC tumors and remotely activated with a 7T-MRI field to induce targeted, electrode-free tumor ablation with real-time imaging feedback. A single MENP treatment achieved a threefold median reduction in tumor volume and complete tumor responses in 33.3% of mice at 300 and 600 µg doses (N = 17) and significantly longer mean overall survival as compared to the control cohorts (54.1 vs 28.8 days, χ[2] = 40.14, p = 0.045), without evident toxicity in any imaged organ. In contrast, mice receiving subtherapeutic doses, non-activated MENPs, or saline controls showed no significant response. MRI T2* relaxation time decreases closely correlated with tumor reduction (ρ = -0.73, p < 0.001), supporting MENPs as both a therapeutic and imaging biomarker. Mechanistically, MENPs preferentially target cancer cells via magnetic-field-driven electrostatic interactions specific to tumor cell membranes, in agreement with multiphysics numerical simulations. Flow cytometry confirmed that MENP activation primarily induces apoptosis, with minimal necrosis, and time-course studies showed a progressive apoptotic response over 3-hour post-treatment. The findings establish MENPs as a versatile, image-guided, theranostic platform with translational promise for minimally invasive oncology.}, }
@article {pmid41684749, year = {2019}, author = {Huggins, JE and Slutzky, MW}, title = {Articles from the Seventh International Brain-Computer Interface Meeting.}, journal = {Brain computer interfaces (Abingdon, England)}, volume = {6}, number = {4}, pages = {103-105}, pmid = {41684749}, issn = {2326-263X}, support = {R13 DC016830/DC/NIDCD NIH HHS/United States ; }, }
@article {pmid41684733, year = {2026}, author = {Ritzmann, R and De Pauw, K and Wollesen, B}, title = {Editorial: Neuro-cognition in human movement: from fundamental experiments to bio-inspired innovation.}, journal = {Frontiers in neurology}, volume = {17}, number = {}, pages = {1625712}, pmid = {41684733}, issn = {1664-2295}, }
@article {pmid41682716, year = {2026}, author = {Gouret, A and Delaux, A and Le Bars, S and Chokron, S}, title = {Rewiring Attention: Virtual Reality and Brain-Computer Interfaces in the Rehabilitation of Unilateral Spatial Neglect.}, journal = {Journal of clinical medicine}, volume = {15}, number = {3}, pages = {}, doi = {10.3390/jcm15031036}, pmid = {41682716}, issn = {2077-0383}, support = {CIFRE 2022/1439//Association Nationale de la Recherche et de la Technol-722 ogie/ ; }, abstract = {Unilateral spatial neglect (USN) is a complex cognitive syndrome frequently observed after stroke. Characterized by a failure to attend, respond and orient to stimuli on the side opposite the brain lesion, USN significantly impairs patients' functional independence and presents significant challenges for rehabilitation. Current rehabilitation strategies often fall short in addressing the heterogenous manifestations of USN across perceptual modalities due to limited ecological validity, patient engagement and adaptability to individual needs. Recent advances in neurotechnologies such as virtual reality (VR) and brain-computer interfaces (BCIs) offer promising avenues for overcoming these limitations. These tools enable top-down rehabilitation strategies that directly engage cognitive recovery mechanisms to promote neuroplasticity, and support adaptive interventions tailored to individual profiles. This narrative review explores recent developments and future prospects of VR and BCI technologies in the rehabilitation of USN, both individually and in combination. After outlining key features of USN to frame rehabilitation challenges, it examines VR, BCI, and their integrated applications in this context. While there is growing evidence supporting VR interventions efficacy in enhancing conventional strategies and alleviating USN symptoms, research on BCI applications in this context is still emerging. Nevertheless, insights from broader neurorehabilitation research suggest that combining VR and BCI holds significant promise for advancing cognitive rehabilitation and addressing USN-specific challenges. To illustrate the transformative value of advanced USN interventions, we present a concrete example of a VR-BCI integrated rehabilitation framework in the making, designed to provide a comprehensive and personalized therapeutic approach, bridging technological potential with clinical rehabilitation needs.}, }
@article {pmid41682533, year = {2026}, author = {Wolfers, J and Hurst, W and Krampe, C}, title = {Integrating EEG Sensors with Virtual Reality to Support Students with ADHD.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {3}, pages = {}, doi = {10.3390/s26031017}, pmid = {41682533}, issn = {1424-8220}, mesh = {Humans ; *Attention Deficit Disorder with Hyperactivity/physiopathology ; *Electroencephalography/methods ; *Virtual Reality ; Male ; Students ; Female ; Brain-Computer Interfaces ; Adolescent ; Young Adult ; Attention/physiology ; Adult ; Brain/physiopathology ; }, abstract = {Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality (VR) is one such example, and has the potential to address attention-span difficulties among ADHD students. Accordingly, this study presents an EEG-based multimodal sensing pipeline as a methodological contribution, focusing on sensor-based data acquisition, signal processing, and neurophysiological interpretation to assess attention in VR-based environments, simulating a university supply chain educational topic. Thus, in this paper, a sequential exploratory approach investigated how 35 participants experienced an interactive VR-learning-driven supply chain game. A Brain-Computer Interaction (BCI) sensor generated insights by quantitatively analysing electroencephalogram (EEG) data that were processed through the proposed pipeline and integrated with subjective measures to validate participant's subjective feelings. These insights originated from questions during the experiment that followed the Spatial Presence and Technology Acceptance Model to form a multimodal assessment framework. Findings demonstrated that the experimental group experienced a higher improved attention, concentration, engagement, and focus levels compared to the control group. BCI results from the experimental group showed more dominant voltage potentials in the right frontal and prefrontal cortex of the brain in areas responsible for attention, memory, and decision-making. A high acceptance of the VR technology among neurodiverse students highlights the added benefits of multimodal learning assessment methods in an educational setting.}, }
@article {pmid41682467, year = {2026}, author = {Hassanloo, M and Zareh, A and Özdemir, MK}, title = {High-Accuracy Detection of Odor Presence from Olfactory Bulb Local Field Potentials via Deep Neural Networks.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {3}, pages = {}, doi = {10.3390/s26030951}, pmid = {41682467}, issn = {1424-8220}, support = {123E520//Scientific and Technological Research Council of Turkey/ ; }, mesh = {*Odorants/analysis ; *Olfactory Bulb/physiology ; Animals ; Mice ; *Neural Networks, Computer ; Deep Learning ; Smell/physiology ; }, abstract = {Odor detection underpins food safety, environmental monitoring, medical diagnostics, and many more fields. Current artificial sensors developed for odor detection struggle with complex mixtures, while non-invasive recordings lack reliable single-trial fidelity. To develop a general system for odor detection, in this study we present preliminary work where we test two hypotheses: (i) that spectral features of local field potentials (LFPs) are sufficient for robust single-trial odor detection and (ii) that signals from the olfactory bulb alone are adequate. To test these hypotheses, we propose an ensemble of complementary one-dimensional convolutional networks (ResCNN and AttentionCNN) that decodes the presence of odor from multichannel olfactory bulb LFPs. Tested on 2349 trials from seven awake mice, our final ensemble model supports both hypotheses, achieving a mean accuracy of 86.2%, an F1-score of 85.3%, and an AUC of 0.942, substantially outperforming previous benchmarks. The t-SNE visualization confirms that our framework captures biologically significant signatures. These findings establish the feasibility of robust single-trial detection of odor presence from extracellular LFPs and demonstrate the potential of deep learning models to provide deeper understanding of olfactory representations.}, }
@article {pmid41682433, year = {2026}, author = {Kołodziej, M and Majkowski, A and Wiszniewski, P}, title = {Improved SSVEP Classification Through EEG Artifact Reduction Using Auxiliary Sensors.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {3}, pages = {}, doi = {10.3390/s26030917}, pmid = {41682433}, issn = {1424-8220}, support = {Research was funded by Warsaw University of Technology within the Excellence Initiative: Research University (IDUB) program.//Warsaw University of Technology/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Artifacts ; Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; Algorithms ; Signal Processing, Computer-Assisted ; Young Adult ; Brain/physiology ; Electrooculography ; }, abstract = {Steady-state visual evoked potentials (SSVEPs) are one of the key paradigms used in brain-computer interface (BCI) systems. Their performance, however, is substantially degraded by EEG artifacts of muscular, motion-related, and ocular origin. This issue is particularly pronounced in individuals exhibiting increased facial muscle tension or involuntary eye movements. The aim of this study was to develop and evaluate an EEG artifact reduction method based on auxiliary channels, including central (Cz), frontal (Fp1), electrooculographic (HEOG), and muscular electrodes (neck, cheek, jaw). Signals from these channels were used to model the physical sources of interference recorded concurrently with occipital brain activity (O1, O2, Oz). EEG signal cleaning was performed using linear regression in 1-s windows, followed by frequency-domain analysis to extract features related to stimulation frequencies and SSVEP classification using SVM and CNN algorithms. The experiment involved three visual stimulation frequencies (7, 8, and 9 Hz) generated by LEDs and the recording of controlled facial and jaw-related artifacts. Experiments conducted on 12 participants demonstrated a 9% increase in classification accuracy after artifact removal. Further analysis indicated that the Cz and jaw channels contributed most significantly to effective artifact suppression. The results confirm that the use of auxiliary channels substantially improves EEG signal quality and enhances the reliability of BCI systems under real-world conditions.}, }
@article {pmid41682282, year = {2026}, author = {Angrisani, L and De Benedetto, E and D'Iorio, M and Duraccio, L and Lo Regio, F and Tedesco, A}, title = {Online Compensation of Systematic Effects in Stimuli Generation for XR-Based SSVEP BCIs.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {3}, pages = {}, doi = {10.3390/s26030766}, pmid = {41682282}, issn = {1424-8220}, support = {E63C22002040007//Ministero dell'universit&#xe0; e della ricerca/ ; E63C22002130007//Ministero dell'universit&#xe0; e della ricerca/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; Adult ; Male ; Female ; Algorithms ; Young Adult ; }, abstract = {Background: Brain-Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) and Extended Reality (XR) offer promising solutions for highly wearable applications, but their classification performance can be affected by systematic effects in stimulus presentation. Novelty: This study introduces a novel online compensation method to compensate for systematic effects in the Refresh Rate (RR) of XR displays, enhancing SSVEP classification without requiring additional training or invasive measurements. Methods: A non-invasive monitoring module was incorporated into the developed BCI pipeline to measure frame rate variations in the XR display, allowing deviations between nominal RR and measured values to be automatically detected and compensated for. Classification performance was evaluated using Filter Bank Canonical Correlation Analysis (FBCCA). Statistical significance was assessed using Student's t-test. Materials: Two datasets were used: a dataset based on Moverio BT-350, including 9 subjects, and a dataset based on HoloLens 2, including 30 subjects, all collected by the authors. Results: The proposed compensation method led to significant improvements in SSVEP classification accuracy, proportional to the magnitude of fps deviations. In some cases, classification accuracy increased by up to 300% relative to its original value. Statistical analyses confirmed the reliability of the results across subjects and datasets. Conclusions: These findings show that the proposed method effectively enhances SSVEP-based BCIs in XR environments and provides a robust foundation for practical applications requiring high reliability.}, }
@article {pmid41678839, year = {2026}, author = {Abreu, EA and Giarusso de Vazquez, PF and Castellano, G}, title = {Decoding Inner Speech with functional connectivity.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae451b}, pmid = {41678839}, issn = {2057-1976}, abstract = {Inner-Speech (IS) based Brain-Computer Interfaces (BCIs) offer potential communication solutions for individuals with disabilities by decoding brain signals generated during speech imagination. While most IS-BCI systems rely on time-frequency EEG features, this study investigates functional connectivity-specifically, motif synchronization (MS)-to determine whether interactions between brain regions improve the discrimination of imagined words. Methods: We analyzed EEG data from the "Thinking Out Loud" dataset by Results: The model achieved an average classification accuracy of 45.8%, outperforming two of three prior studies using the same dataset while offering greater generalizability than the third (which reported higher accuracy). Conclusions: Functional connectivity features, particularly motif synchronization, show promise in IS-BCI applications by leveraging cross-regional brain interactions. This approach advances neurophysiological signal analysis and can enhance assistive technology and cognitive research. However, larger datasets are required to improve the robustness and validate scalability. .}, }
@article {pmid41677796, year = {2026}, author = {Zhai, X and Hao, Z and Wang, X and Li, C and Cao, Y and Peng, B and Qi, X and Ni, X and Xie, R and Dou, W and Pan, Y}, title = {Effect of Brain-Computer Interface-Controlled Ankle Robot Training on Post-Stroke Motor Rehabilitation and Resting QEEG Neuroplasticity: A Randomized Controlled Trial.}, journal = {Neurorehabilitation and neural repair}, volume = {}, number = {}, pages = {15459683251412286}, doi = {10.1177/15459683251412286}, pmid = {41677796}, issn = {1552-6844}, abstract = {BACKGROUND: Persistent post-stroke ankle impairment hinders functional recovery. Brain-computer interface (BCI)-controlled ankle robot show rehabilitation potential, but their efficacy and underlying neuroplasticity remain unclear.<br><br>OBJECTIVE: To assess BCI-controlled ankle robot training on post-stroke lower-limb motor recovery and neuroplasticity using quantitative EEG (qEEG).<br><br>METHODS: Thirty-two stroke patients were randomized to BCI (n&#x2009;=&#x2009;16, 40-minute BCI-robot training) or control (n&#x2009;=&#x2009;16, 40-minute ankle-robot training) groups, receiving 5 sessions/week for 2&#x2009;weeks. Outcomes included Fugl-Meyer Assessment-Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Functional Ambulatory Category (FAC), Modified Ashworth Scale (MAS), active range of motion (AROM), and muscle strength. QEEG assessed the relative power of the delta (r&#x3b4;), theta (r&#x3b8;), alpha (r&#x3b1;), beta (r&#x3b2;) bands, spectral power ratios, pairwise-derived Brain Symmetry Index (pdBSI), and functional connectivity.<br><br>RESULTS: Both groups showed significant within-group improvements in dorsiflexion AROM, dorsiflexor strength, FMA-LE, BBS, and FAC (P&#x2009;<&#x2009;.05). The BCI group demonstrated significantly greater FMA-LE improvement than controls (&#x2206;FMA-LE, P&#x2009;=&#x2009;.007) and reduced calf spasticity (MAS; P&#x2009;=&#x2009;.038). QEEG analysis in the BCI group revealed decreased r&#x3b4; (P&#x2009;=&#x2009;.005), increased r&#x3b1; (P&#x2009;=&#x2009;.017), reduced DAR and DTABR (P&#x2009;<&#x2009;.05), reduced interhemispheric asymmetry (pdBSI-&#x3b4;; P&#x2009;=&#x2009;.018), and enhanced Cz-parietal connectivity in &#x3b1; and &#x3b2; bands (P&#x2009;<&#x2009;.05).<br><br>CONCLUSION: BCI-controlled ankle robot training significantly improved lower-limb motor function and reduced spasticity post-stroke. Associated neurophysiological changes, characterized by reduced slow-wave power and asymmetry, increased alpha power, and functional connectivity, indicated beneficial neuroplastic reorganization.Clinical trial registration number: China Clinical Trail Registry (ChiCTR2300074381; URL: http://www.chictr.org.cn).}, }
@article {pmid41676631, year = {2026}, author = {Blumenthal, GH and Dekleva, BM and Gontier, C and Gonzalez, IC and Gonzalez-Martinez, JA and Yu, BM and Batista, AP and Sobinov, AR and Miller, LE and Gaunt, RA and Boninger, ML and Chase, SM and Collinger, JL}, title = {Distinct neural modes carry information about grasp force and phase in the sensorimotor cortex.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.02.01.702680}, pmid = {41676631}, issn = {2692-8205}, abstract = {UNLABELLED: Humans perform a variety of complex hand movements to manipulate objects, requiring precise control of changing forces. Understanding the role of sensorimotor cortex and the cortical dynamics underlying these actions is crucial for developing interventions that restore dexterous hand function after injury or disease. In this study, two individuals with tetraplegia resulting from cervical spinal cord injury attempted a series of isometric grasps. Neural activity was recorded from the motor and somatosensory cortices using intracortical microelectrode arrays while participants attempted to exert a static force or to ramp force up and down. Despite their inability to execute movement, and with limited afferent input, the spiking activity in motor and somatosensory cortex was modulated with the task. Within the neural response we identified independent neural modes - distinct patterns of population-level neural activity - that were informative about both the timing and magnitude of the force. Moreover, distinct neural modes were observed during static and dynamic grasping conditions, suggesting independent control schemes for maintaining and changing forces. These modes were related to phases of the task, including the onset, offset, holding periods, as well as phases of increasing and decreasing force. These results will inform the design of intracortical brain-computer interface (iBCI) systems that can leverage these naturally occurring patterns of grasp and force control to restore dexterous hand function.<br><br>SIGNIFICANCE STATEMENT: Restoring dexterous hand function after injury remains a major challenge, partly due to an incomplete understanding of the cortical dynamics underlying grasping and force control. In this study, we investigated neural activity within the motor and somatosensory cortices of individuals with tetraplegia attempting to perform grasps to different target forces with varying temporal profiles. We identified distinct neural modes modulated during specific phases of grasp that encode force information throughout the task. These findings suggest that brain-computer interfaces could leverage these native neural modes to restore grasping and force modulation.}, }
@article {pmid41676620, year = {2026}, author = {Muralidharan, S and Leng, C and Orts, L and Trepka, E and Zhu, S and Panichello, M and Jonikaitis, D and Pennington, J and Pachitariu, M and Moore, T}, title = {A System for Live Sorting of Neuronal Spiking Activity from Large-scale Recordings.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2025.12.29.696938}, pmid = {41676620}, issn = {2692-8205}, abstract = {Online monitoring and quantification of neural signals has tremendous value both for neurofeedback experiments and for brain-computer interfaces. Unfortunately, established methods of online monitoring primarily involve the use of thresholded neural activity rather than sorted single-neuron spikes. The recent introduction of large-scale, high-density electrophysiology has enabled the recording of activity from hundreds of neurons simultaneously in both model organisms and human participants. This development highlights the need for a robust and easily implementable system for sorting spikes during data collection for live analyses of neuronal signals. Here, we describe a system for live sorting of neuronal activity (LSS) based on the widely used Kilosort platform. The LSS workflow utilizes an initial period of recorded neural data to identify waveform templates using Kilosort. LSS then interfaces with the SpikeGLX API to retrieve small batches (e.g. 50 ms) of data and for processing online. We measured the similarity of single-neuron activity sorted live by LSS to that sorted offline in neurophysiological recordings from macaque visual cortex using Neuropixels probes. We show that LSS closely replicates the post-stimulus time histograms and visual response tuning curves of single-neurons obtained using offline sorting. Furthermore, we show that decoding neural signals online with LSS consistently outperforms online decoding of thresholded activity, and that LSS can achieve the same performance as that obtained with offline sorting.}, }
@article {pmid41675914, year = {2026}, author = {Qibin, Z and Lin, L and Yibiao, C and Peng, L and Huiqing, W and Daoqing, S and Lianghong, Y}, title = {RNA networks of lysosomal-related biomarkers in Parkinson's disease and their correlations with freezing of gait-associated genes.}, journal = {Frontiers in genetics}, volume = {17}, number = {}, pages = {1632163}, pmid = {41675914}, issn = {1664-8021}, abstract = {BACKGROUND: Parkinson's disease (PD) is influenced by various factors, with lysosome function playing a critical role. However, the specific involvement of lysosome-related genes (LRGs) in PD remains unclear.<br><br>OBJECTIVE: This study aims to identify biomarkers specific to PD that exhibit robust disease prediction capabilities.<br><br>METHODS: Datasets for patients with PD, LRGs, and inflammation-related genes (IRGs) were retrieved from online databases. miRNAs and mRNAs within key modules were selected through Weighted Gene Co-expression Network Analysis (WGCNA), revealing strong associations with PD. A miRNA-mRNA network was constructed based on highly correlated PD-related LRGs (PD-LRGs) and miRNAs within these modules. Candidate genes were identified by intersecting target genes, differentially expressed genes (DEGs), PD-LRGs, and module-associated mRNAs. Machine learning and expression validation were employed to confirm these biomarkers. A nomogram was established, and its diagnostic performance was evaluated using a confusion matrix. Drug predictions were conducted based on these biomarkers. Spearman's correlation analyses were performed to assess the relationship between IRGs, freezing of gait (FOG)-related genes, and biomarkers. Molecular regulatory networks were constructed using datasets and online resources. Finally, clinical samples were collected for quantitative PCR (qPCR) validation of biomarker expression.<br><br>RESULTS: Key modules related to PD were identified, comprising 190 miRNAs and 7,633 mRNAs. A miRNA-mRNA network was constructed based on 55 PD-LRGs and 181 miRNAs, resulting in the identification of 26 candidate genes strongly linked to lysosomal function. FGD4 and MAN2B1 were selected as biomarkers, and a gene expression-based risk prediction table was created. These biomarkers were significantly correlated with IRGs and several FOG-related genes. Gene localization analysis revealed that FGD4 and LRRK2, both critical to the FOG pathway, are located on chromosome 12. Drug prediction revealed that Tetrachlorodibenzodioxin and bisphenol A target both FGD4 and MAN2B1. qPCR analysis confirmed that FGD4 and MAN2B1 expression levels were significantly higher in patients with PD compared to healthy controls (p < 0.05).<br><br>CONCLUSION: FGD4 and MAN2B1 act as lysosomal biomarkers associated with PD and exhibit strong correlations with genes involved in PD-related freezing of gait. This study offers novel insights into PD diagnosis.}, }
@article {pmid41674222, year = {2026}, author = {Ye, L and Fu, Y and Yang, X and Ning, H and Dong, Y and Zhao, Y and Zhang, L and Li, Y and Zhao, B and Guo, X and Gao, Y and Sun, K and Wang, K and Wang, J and Geng, H and Li, J and Ma, H and He, D}, title = {Nanoconfined Water Manipulated Selective Proton Storage in Layered Tungsten Oxides for Versatile Supercapacitor Diodes.}, journal = {ACS nano}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsnano.6c00028}, pmid = {41674222}, issn = {1936-086X}, abstract = {Supercapacitor diode (CAPode) is an emerging type of electrochemical logic device that integrates ions and electrons as the coinformation carriers, thus being a promising building block for constructing new-type iontronic circuits and achieving seamless brain-computer interaction. However, the lack of understanding on its basic process, i.e., nanoconfined ion transport, greatly blocks the further enhancement of its ion rectification capability and ion transport kinetics. Herein, on the basis of in-depth analysis of the host-guest interactions in the nanoconfined space, a nanoconfined water mediated strategy is proposed to manipulate the ion transport behaviors in typical layered materials, i.e., tungsten oxides (WO3·nH2O, n = 0, 1, 2). The results reveal that WO3·H2O presents an optimal ion rectification capability and superior ion transport kinetics, much outperforming those of WO3·2H2O or WO3 with more or less structural water. Consequently, the WO3·H2O-based CAPode delivers a record-high rectification ratio of 253, an ultrahigh response frequency of 549 Hz, and an excellent cycling stability of up to 5000 cycles, enabling it to handle various complex ion/electron-coupling logic operations. More attractively, WO3·H2O is demonstrated to possess superior biocompatibility, endowing the as-built CAPode with great potential in the cutting-edge field of brain-computer interactions.}, }
@article {pmid41672620, year = {2026}, author = {Wang, Y and Mi, Y and Zhang, X and Zhao, B and Li, H and Liu, X}, title = {[Review of Progress of Optical Brain-Computer Interface Technology and Prospects for Medical Device Testing Technology].}, journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation}, volume = {50}, number = {1}, pages = {54-63}, doi = {10.12455/j.issn.1671-7104.250262}, pmid = {41672620}, issn = {1671-7104}, mesh = {*Brain-Computer Interfaces ; Spectroscopy, Near-Infrared ; Humans ; Optogenetics ; }, abstract = {Optical brain-computer interface (OBCI) technology is a new cross-cutting frontier technology that achieves information interaction between the human brain and external devices through neuroengineering based on optical methods. Among them, brain-computer interface (BCI) devices based on functional near-infrared spectroscopy (fNIRS) have been successively developed and applied, and this type of technology has become one of the main development directions of non-invasive optical brain-computer interface medical device technology. At the same time, OBCI technology based on optogenetics and optical calcium imaging technology has also been proposed as a research hotspot in recent years, and will serve as a new direction for the future development of OBCI medical device testing technology. This paper mainly reviews the basic principles and research progress of functional near-infrared spectroscopy brain-computer interface (fNIRS-BCI), and introduces the research frontiers of BCI based on optogenetics and optical calcium imaging technology. Finally, it presents prospects for the further development of OBCI medical device testing technology, providing certain guidance and ideas for the development, testing, and supervision of the BCI industry.}, }
@article {pmid41672352, year = {2026}, author = {Wang, S and Xu, M and Qian, L and Gao, L and Sun, Y}, title = {Divergent effects of high-frequency rTMS on cognitive performance in sleep-deprived nurses: An EEG brain network study.}, journal = {Brain research bulletin}, volume = {}, number = {}, pages = {111772}, doi = {10.1016/j.brainresbull.2026.111772}, pmid = {41672352}, issn = {1873-2747}, abstract = {BACKGROUND: Sleep deprivation (SD) is a common occupational hazard, particularly for shift workers like nurses, leading to significant impairments in cognitive functions such as sustained attention and working memory. High-frequency repetitive transcranial magnetic stimulation (rTMS) is a promising neuromodulation technique for cognitive enhancement, but its effects in sleep-deprived individuals and the underlying neural mechanisms remain poorly understood. This study aimed to investigate the efficacy of high-frequency rTMS over the left dorsolateral prefrontal cortex (DLPFC) in modulating sustained attention and working memory after a night shift and to explore the associated changes in brain network topology.<br><br>METHODS: In a within-subject design, 28 healthy female night-shift nurses participated in two experimental sessions after a night of work: one with real 5 Hz rTMS and one with sham rTMS applied to the left DLPFC. Following stimulation, participants performed a psychomotor vigilance task (PVT) and a 2-back task while their electroencephalography (EEG) data were recorded. Behavioral performance (reaction time and accuracy) and subjective fatigue were assessed. Graph theory analysis was applied to the EEG data to evaluate changes in functional brain network topology at both global and nodal levels.<br><br>RESULTS: Real rTMS significantly reduced subjective mental fatigue compared to sham stimulation. However, the behavioral effects were task-dependent. For the 2-back task, real rTMS led to a significant impairment in performance, characterized by slower reaction times and lower accuracy. For the PVT, there was a non-significant trend towards improved performance. These behavioral outcomes were mirrored by distinct patterns of network reorganization. During the PVT, real rTMS induced decreased functional segregation (lower clustering coefficient and local efficiency) in the alpha band. Conversely, during the 2-back task, it resulted in increased functional segregation and small-worldness in the theta band.<br><br>CONCLUSION: High-frequency rTMS over the left DLPFC exerts differential, task-specific effects on cognitive function in a sleep-deprived state. The impairment in working memory, despite a network configuration theoretically supportive of local processing, likely results from an inverted-U effect, where the rTMS pushed an already strained and compensating brain system past its optimal level of cortical excitability. The findings highlight the critical role of both baseline brain state and specific cognitive demands in determining the outcomes of neuromodulation, providing crucial insights for the targeted application of rTMS to mitigate cognitive deficits from sleep deprivation.}, }
@article {pmid41671134, year = {2026}, author = {Wang, C and Yang, L and Yuan, B and Zhang, J and Jin, C and Li, R and Bu, J}, title = {Subject-Adaptive EEG Decoding via Filter-Bank Neural Architecture Search for BCI Applications.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2026.3663725}, pmid = {41671134}, issn = {2168-2208}, abstract = {Individual differences pose a significant challenge in brain-computer interface (BCI) research. Designing a universally applicable network architecture is impractical due to the variability in human brain structure and function. We propose Filter-Bank Neural Architecture Search (FBNAS), an EEG decoding framework that automates network architecture design for individuals. FBNAS uses three temporal cells to process different frequency EEG signals, with dilated convolution kernels in their search spaces. A multi-path NAS algorithm determines optimal architectures for multi-scale feature extraction. We benchmarked FBNAS on three EEG datasets across two BCI paradigms, comparing it to six state-of-the-art deep learning algorithms. FBNAS achieved cross-session decoding accuracies of 79.78%, 70.66%, and 68.38% on the BCIC-IV-2a, OpenBMI, and SEED datasets, respectively, outperforming other methods. Our results show that FBNAS customizes decoding models to address individual differences, enhancing decoding performance and shifting model design from expert-driven to machine-aided. The source code can be found at https://github.com/wang1239435478/FBNAS-master.}, }
@article {pmid41670873, year = {2026}, author = {Silva, L and Lima, J and Delisle-Rodriguez, D and Bastos-Filho, T}, title = {A lower-limb motor imagery BCI using virtual reality and novel calibration strategy in post-stroke patients.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {41670873}, issn = {1741-0444}, }
@article {pmid41669281, year = {2026}, author = {Todoroki, S and Phunruangsakao, C and Goto, K and Kutsuzawa, K and Owaki, D and Hayashibe, M}, title = {Deep Learning-Based Decoding and Feature Visualization of Motor Imagery Speeds From EEG Signals.}, journal = {IEEE open journal of engineering in medicine and biology}, volume = {7}, number = {}, pages = {27-34}, pmid = {41669281}, issn = {2644-1276}, abstract = {Objective: This study investigates the neurodynamics of motor imagery speed decoding using deep learning. Methods: The EEGConformer model was employed to analyze EEG signals and decode different speeds of imagined movements. Explainable artificial intelligence techniques were used to identify the temporal and spatial patterns within the EEG data related to imagined speeds, focusing on the role of specific frequency bands and cortical regions. Results: The model successfully decoded and extracted EEG patterns associated with different motor imagery speeds; however, the classification accuracy was limited and high only for a few participants. The analysis highlighted the importance of alpha and beta oscillations and identified key cortical areas, including the frontal, motor, and occipital cortices, in speed decoding. Additionally, repeated motor imagery elicited steady-state movement-related potentials at the fundamental frequency, with the strongest responses observed at the second harmonic. Conclusions: Motor imagery speed is decodable, though classification performance remains limited. The results highlight the involvement of specific frequency bands and cortical regions, as well as steady-state responses, in encoding MI speed.}, }
@article {pmid41668698, year = {2026}, author = {Xu, T and Luo, F and Cui, Y and Zhou, Y}, title = {Editorial: Integrating multimodal approaches to unravel neural mechanisms of learning and cognition.}, journal = {Frontiers in neurology}, volume = {17}, number = {}, pages = {1753883}, pmid = {41668698}, issn = {1664-2295}, }
@article {pmid41668512, year = {2026}, author = {Sun, S and Liu, D and Zhou, S and Wang, Y and Wang, H and Zheng, Z and Zhang, XD}, title = {Atomically Precise Clusterzymes: A Programmable Optoelectronic Platform for Neuroscience.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e19438}, doi = {10.1002/advs.202519438}, pmid = {41668512}, issn = {2198-3844}, support = {2021FJ-0009//Outstanding Youth Funds of Tianjin/ ; U24A6012//National Natural Science Foundation of China/ ; 82302381//National Natural Science Foundation of China/ ; 82302361//National Natural Science Foundation of China/ ; 82372028//National Natural Science Foundation of China/ ; 82102124//National Natural Science Foundation of China/ ; 82525035//National Science Fund for Distinguished Young Scholars/ ; YDZJSX20231A054//Central Guiding Local Science and Technology Development Fund Projects/ ; }, abstract = {Atomically precise metal clusters, characterized by their well-defined structures, have emerged as a versatile platform for energy, catalysis, and biomedicine. Building upon this foundation, the biocatalytic clusterzymes, a class of artificial enzymes with atomic-level programmable activity and renal-excreted properties, have successfully overcome the stability limitations of natural enzymes and biosafety concerns of conventional nanomaterials. This review systematically examines the synthesis, engineering principles, and applications of this programmable platform. First an in-depth analysis of the strategies is provided for programming biocatalytic or enzyme-like activity of metal clusters via atomic and ligand engineering. Meanwhile, infrared emissive metal clusters with tunable electronic structure and optical properties at the atomic level allow to achieve the pathological progression and clinical 3D visualization in deep tissue. Furthermore, semiconductor gold clusters with rich electron carriers can enhance the interface charge transfer between the metal electrode and surface molecular clusters, achieving highly sensitive neuron recording for an efficient brain computer interface. The clusters demonstrate great potential in neuroscience, including neuroinflammation, bioimaging, and neuromodulation. Finally, future challenges are outlined for the rational design and translational development of this programmable platform, poised to address complex challenges in biomedicine.}, }
@article {pmid41667798, year = {2026}, author = {Zhao, C and Liao, Z and Jiang, D and Zhao, X and Yuan, B and Lin, R and Tang, J and Gong, B and Liao, J and Lin, L and Hu, Z}, title = {Parameter-efficient convolutional neural network for drug treatment outcome studies of pediatric epilepsy.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-39728-5}, pmid = {41667798}, issn = {2045-2322}, support = {SZGMTD2025001//Sanming Project of Medicine in Shenzhen Guangming/ ; 62271474//National Natural Science Foundation of China/ ; 321GJHZ2023246GC//International Partnership Program of the Chinese Academy of Sciences/ ; 2023B1515120007 and 2024A1515012138//Natural Science Foundation of Guangdong Province/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; KJZD20230923113259001 and JCYJ20220530160005012//Shenzhen Science and Technology Program/ ; }, }
@article {pmid41667722, year = {2026}, author = {De Schrijver, S and Garcia Ramirez, J and Iregui, S and Aertbeliën, E and De Schutter, J and Theys, T and Decramer, T and Janssen, P}, title = {An intracortical brain-machine interface based on macaque ventral premotor activity.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-38536-1}, pmid = {41667722}, issn = {2045-2322}, support = {C14/22/134//Onderzoeksraad, KU Leuven/ ; C14/22/134//Onderzoeksraad, KU Leuven/ ; C14/22/134//Onderzoeksraad, KU Leuven/ ; G.097422N//Fonds voor wetenschappelijk onderzoek Vlaanderen/ ; }, }
@article {pmid41666799, year = {2026}, author = {Zhou, W and Zeng, T and Liu, D and Pang, R and Gong, L}, title = {Association of FIB-4 with orthostatic hypotension in Parkinson's disease.}, journal = {Autonomic neuroscience : basic &amp; clinical}, volume = {264}, number = {}, pages = {103390}, doi = {10.1016/j.autneu.2026.103390}, pmid = {41666799}, issn = {1872-7484}, abstract = {BACKGROUND: Orthostatic hypotension (OH) is a common complication in Parkinson's disease (PD) patients, significantly impacting their quality of life. Recent evidence suggests a potential link between liver fibrosis, indicated by the Fibrosis-4 (FIB-4) index, and autonomic dysfunction. However, its relationship with OH in PD remains unexplored.<br><br>METHODS: A cross-sectional analysis was conducted using data from 1268 PD patients. The FIB-4 index was calculated based on age, AST, ALT, and platelet count. The association between FIB-4 and OH was assessed using multivariate logistic regression, with further curve fitting and subgroup analyses to test robustness.<br><br>RESULTS: The FIB-4 index was significantly associated with OH. For each 0.2-unit increase in FIB-4, the odds ratio (OR) for OH was 1.11 (95% CI: 1.05-1.17, p&#xa0;<&#xa0;0.001). Tertile analysis showed ORs of 2.05 (95% CI: 1.27-3.31, p&#xa0;=&#xa0;0.003) for T2 and 2.61 (95% CI: 1.64-4.17, p&#xa0;<&#xa0;0.001) for T3, compared to T1. Curve fitting indicated a linear relationship, with no evidence of non-linearity. Sensitivity and subgroup analyses confirmed robustness.<br><br>CONCLUSIONS: Higher FIB-4 index values are independently associated with an increased risk of OH in PD patients, suggesting that liver fibrosis may contribute to OH development. Further longitudinal studies are needed to explore the underlying mechanisms.}, }
@article {pmid41666485, year = {2026}, author = {Wang, H and Zhang, J and Ye, P and Yang, K and Xiong, J and Liu, X and Chen, T and Song, L}, title = {A knowledge-driven self-supervised learning method for enhancing EEG-based emotion recognition.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {199}, number = {}, pages = {108676}, doi = {10.1016/j.neunet.2026.108676}, pmid = {41666485}, issn = {1879-2782}, abstract = {Emotion recognition brain-computer interface (BCI) using electroencephalography (EEG) is crucial for human-computer interaction, medicine, and neuroscience. However, the scarcity of labeled EEG data limits progress in this field. To address this, self-supervised learning has gained attention as a promising approach. Despite its potential, self-supervised methods face two key challenges: (1) ensuring emotion-related information is effectively preserved, as its loss can degrade emotion recognition performance, and (2) overcoming inter-subject variability in EEG signals, which hinders generalization across subjects. To tackle these issues, we propose a novel knowledge-driven self-supervised learning framework for EEG emotion recognition. Our method incorporates domain knowledge to approximate the extraction of statistical feature differential entropy (DE), aiming to preserve emotion-related and generalizable information. The framework consists of two cascaded components as hard and soft alignments: a multi-branch convolutional differential entropy learning (MCDEL) module that simulates the DE extraction process, and a contrastive entropy alignment (CEA) module that exposes complex emotional semantics in high-dimensional space. Experiment results show that our method exhibits superior performance over existing self-supervised methods. The subject-independent mean accuracy and standard deviation of our method reached 84.48% ± 5.79 on SEED and 67.64% ± 6.35 and 68.63% ± 7.77 on the Arousal and Valence dimensions of DREAMER, respectively. We conduct an ablation study to demonstrate the contribution of each proposed component. Moreover, the t-SNE visualization intuitively presents the effect of our method on reducing inter-subject variability and discriminating emotional states.}, }
@article {pmid41666062, year = {2026}, author = {Jin, J and Wang, H and Daly, I and Zhao, X and Li, S and Cichocki, A}, title = {A Fully Unsupervised Online Classification Algorithm for Event-Related Potential based Brain-Computer Interfaces.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3663323}, pmid = {41666062}, issn = {1558-2531}, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on event-related potentials (ERPs) are among the most accurate and reliable BCIs. However, current mainstream classification algorithms struggle to eliminate the need for calibration and rely on expensive labeled data, limiting the practical usability of ERP based BCIs. The development of fully unsupervised algorithms is essential for the advancement of practical applications of BCI systems.<br><br>METHODS: In this study, we propose a novel unsupervised classification method called sliding-window distribution distance maximization (sDDM). This algorithm utilizes sliding windows to highlight important temporal features and transforms the metric of inter-class differences from absolute distances to relative distribution distances in Mahalanobis space, while incorporating information on target event similarity from the BCI paradigm. Additionally, our proposed spatial dimensionality reduction strategy ensures smaller spatial dimensions and more prominent spatial features.<br><br>RESULTS: We compare our proposed method to other state of-the-art unsupervised classification methods and evaluate it offline on our self-collected dataset, a public dataset recorded during the use of a P300 Speller by patients with ALS, and the BCI Competition III Dataset II. Our results demonstrate that our proposed method achieves the best spelling accuracy across all datasets, surpassing other unsupervised algorithms. We further explore its improvement effectiveness through ablation experiments.<br><br>CONCLUSION: Our proposed method enhances the performance of unsupervised classification in ERP-based BCIs.}, }
@article {pmid41666013, year = {2026}, author = {Colque, CA and Lolle, S and La Rosa, R and Bendixen, MP and Rudjord-Levann, AM and Simões, FB and Aanæs, K and Molin, S and Johansen, HK}, title = {Cell-type-independent infection dynamics of clinical Pseudomonas aeruginosa isolates in human airway epithelial models.}, journal = {Cell reports}, volume = {45}, number = {2}, pages = {116951}, doi = {10.1016/j.celrep.2026.116951}, pmid = {41666013}, issn = {2211-1247}, abstract = {Persistent bacterial infections pose major clinical challenges, particularly in people with cystic fibrosis (pwCF), in whom Pseudomonas aeruginosa can persist for decades despite antibiotic treatment. To investigate the host-pathogen interactions influencing infection outcomes, we modeled P. aeruginosa infection using human airway epithelial cells cultured at the air-liquid interface from both CF and non-CF donors and the BCi-NS1.1 cell line. Infection assays with reference and clinical strains revealed four distinct infection clusters based on virulence, epithelial damage, and localization, independent of model type or strain lineage. Correction of CF transmembrane conductance regulator protein (CFTR) channel function did not alter infection outcomes. Dual RNA sequencing showed conserved host inflammatory responses across models, while bacterial transcriptional profiles varied by host context, particularly in CF models. These findings demonstrate that while infection outcomes and host transcriptional responses are consistent across models, bacterial adaptation in the CF airways drives transcriptional reprogramming linked to persistence in pwCF.}, }
@article {pmid41664905, year = {2026}, author = {Lin, D and Sun, H and Shen, Q and Jiang, X and Fu, S and Xiao, Q and Wu, S and Klucharev, V and Shestakova, A and Wang, Y}, title = {Decision for self and other modulates risk attitude and electrophysiological processing: evidence from a behavioral and electrophysiological experiment.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {36}, number = {2}, pages = {}, doi = {10.1093/cercor/bhag001}, pmid = {41664905}, issn = {1460-2199}, support = {72371165//Natural Science Foundation of China/ ; 71971199//Natural Science Foundation of China/ ; ZD20240026//Key Project of Hangzhou Health Science and Technology Plan/ ; 2023KFKT006//Open Research Fund of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; }, mesh = {Humans ; Male ; Female ; *Risk-Taking ; *Decision Making/physiology ; Electroencephalography ; Young Adult ; Adult ; *Event-Related Potentials, P300/physiology ; *Brain/physiology ; Gambling/psychology ; *Attitude ; Adolescent ; Evoked Potentials/physiology ; }, abstract = {Understanding how people make risky decisions for others compared to themselves is central to decision neuroscience. However, the cognitive and neural underpinnings of such self-other shifts in risk preference-and the mechanisms driving individual differences-remain unclear. To address this, we employed a mixed gambling task with feedback in which participants made risky decisions for themselves and for others while electroencephalography was recorded. Although individuals generally exhibited similar patterns across agents, decisions made for others were associated with a higher degree of risk-taking compared to those made for oneself. In terms of individual heterogeneity, predispositions and decision weights derived from the drift-diffusion model accounted for individual differences and agent-specific shifts in risk preferences. The event-related potential (ERP) component P300 was significantly modulated by the agent, valence, and risk attitude. Critically, risk-averse individuals showed larger P300 deflections and greater amplitude differences between the self and other conditions, whereas risk-seeking individuals exhibited smaller and more uniform P300 responses across agents. Together, these findings highlight both shared and distinct behavioral and neural mechanisms underlying risky decision-making for self and others and underscore the potential of ERP components as neural markers of decision-making under risk in social contexts.}, }
@article {pmid41664110, year = {2026}, author = {Zhang, J and Han, S and Shen, Y and Wu, X and Zhao, Y and Wu, Z and Luo, N and Yang, Z and Li, D and Song, M and Wu, P and Tao, DD and Liu, J and Li, Y and Jiang, T}, title = {Digital twin brain reveals state-specific stimulation targets for abnormal brain dynamics in tinnitus.}, journal = {BMC medicine}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12916-026-04687-1}, pmid = {41664110}, issn = {1741-7015}, support = {K2024079//Jiangsu Commission of Health/ ; SSD2024025//Suzhou Basic Research Pilot Project/ ; 82171159//National Natural Science Foundation of China/ ; K2023027//Key Program of Jiangsu Commission of Health/ ; ML12203423//Medicine Plus X Project from Suzhou Medical School of Soochow University/ ; 2021ZD0200201//STI2030-Major Projects/ ; 82151307//National Science Foundation of China/ ; 2022ND0AN01//Scientific Project of Zhejiang Lab/ ; }, abstract = {BACKGROUND: Tinnitus affects 10-15% of adults globally, yet there are still no effective treatments for this major health condition. Repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuromodulation technique, allows modulation of pathologically altered functional activities to promote symptom remission. However, its efficacy critically depends on the selection of stimulation targets, and substantial interindividual variability has been observed in clinical trials. Here, we aimed to identify potential target regions that are causally involved in alleviating distinct functional abnormalities using the digital twin brain (DTB).<br><br>METHODS: A cohort of 89 participants was used to characterize whole-brain neural activity patterns. Multimodal neuroimaging data were used to develop the tinnitus-specific DTB and to generate causal response maps based on more than 1.64 million virtual stimulations. Whole-brain gene expression data were further integrated to examine the neurobiological plausibility of the DTB-derived causal response maps. Finally, we validated the predictive capacity of such response maps using an independent rTMS dataset.<br><br>RESULTS: We identified two aberrant brain states that emerged sequentially with disease progression, predominantly overlapping with the somatomotor and default mode networks, respectively. DTB-derived causal response maps revealed that the modulation of sensory and cognitive states requires stimulation of distinct, functionally specialized regions. Specifically, parieto-occipital regions play a crucial role in sensory modulation, while the dorsolateral prefrontal cortex exerts a causal influence on cognitive modulation. Moreover, these causal response maps correlate with the expression of tinnitus risk genes. By incorporating individual connectivity profiles of target regions, DTB-derived causal response maps accurately predicted rTMS effects on both sensory state (r&#x2009;>&#x2009;0.85, Ppermutation&#x2009;<&#x2009;0.01) and cognitive state (r&#x2009;>&#x2009;0.78, Ppermutation&#x2009;<&#x2009;0.05). Particularly, the predictive capacity exhibited a state-specific nature.<br><br>CONCLUSIONS: This work suggests that brain functional alterations in tinnitus evolve with disease progression, and DTB has the potential to predict rTMS effects on distinct brain states, thereby informing more precise and targeted noninvasive brain stimulation interventions for tinnitus.<br><br>TRIAL REGISTRATION: Trial registered with https://www.chictr.org.cn/indexEN.html, Explore the mechanism of repetitive transcranial magnetic stimulation intervention in tinnitus based on multi-modal functional magnetic resonance imaging (ChiCTR2100047989), Submitted June 2021, First Patient Enrolled July 2021.}, }
@article {pmid41663680, year = {2026}, author = {Valente, M and Branco, D and Bermúdez I Badia, S and Fernandes, JC and Figueiredo, P and Vourvopoulos, A}, title = {EEG-based predictors of motor recovery during immersive VR-BCI rehabilitation.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-39106-1}, pmid = {41663680}, issn = {2045-2322}, support = {10.54499/2022.02283.PTDC//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; }, abstract = {Motor impairment following stroke frequently leads to long-term disability, limiting independence and quality of life. Brain-Computer Interface (BCI) systems integrating motor imagery (MI) with virtual reality (VR) offer promising avenues for enhancing neuroplasticity and engagement through immersive, real-time, and proprioceptive feedback. Yet, identifying reliable electroencephalography (EEG)-based biomarkers that reflect or predict recovery remains challenging. This study investigated the relationship between event-related desynchronization (ERD) dynamics during MI-VR training and motor recovery in individuals with chronic stroke. Fourteen participants with stroke (9 experimental, 5 control) completed a 4-week VR-BCI intervention and were compared with a non-stroke reference cohort (N = 35). Linear mixed-effects models assessed ERD modulation across sessions and groups, and a two-stage regression evaluated the predictive value of ERD features for Fugl-Meyer Assessment (FMA) gains. Results showed no significant ERD change across sessions, but stroke participants exhibited significantly reduced ERD compared to controls. Baseline ERD amplitude predicted motor improvement, whereas ERD progression did not. Ipsilateral ERD showed a compensatory trend in ischemic stroke. These findings indicate that baseline ERD may serve as a stronger prognostic biomarker than short-term ERD dynamics, supporting the development of personalized VR-BCI rehabilitation strategies for chronic stroke recovery.}, }
@article {pmid41662697, year = {2026}, author = {Cummins, DD and Barth, K and Ho, E and Fink Skular, A and Dister, J and Rapoport, B and Saez, I and Bederson, JB}, title = {High-resolution cortical mapping within and across the central sulcus using 1024-electrode micro-electrocorticography arrays: illustrative case.}, journal = {Journal of neurosurgery. Case lessons}, volume = {11}, number = {6}, pages = {}, pmid = {41662697}, issn = {2694-1902}, abstract = {BACKGROUND: Central sulcus identification using phase reversal on electrocorticography (ECoG) is a critical tool for neurosurgical intervention around the primary motor and somatosensory cortices. This mapping is typically performed using cortical arrays with a resolution of several millimeters.<br><br>OBSERVATIONS: A 30-year-old female underwent a right frontoparietal craniotomy for resection of a 4-cm contrast-enhancing lesion within the central sulcus. Central sulcus localization was performed using a standard ECoG array. High-resolution micro-ECoG (&#xb5;ECoG) arrays were then placed over the pre- and postcentral gyri, giving 2048-electrode recordings across the central sulcus. Combining this high-resolution &#xb5;ECoG with an augmented reality imaging overlay to identify the tumor, the central sulcus was split, revealing the underlying tumor. A safe, gross-total resection was obtained with no postoperative complications. Through the use of &#xb5;ECoG arrays spanning into the central sulcus, a high-resolution phase-reversal contour was identified across the central sulcus.<br><br>LESSONS: The authors demonstrate the feasibility and utility of &#xb5;ECoG for sensorimotor mapping within the central sulcus, revealing a phase reversal at a resolution of approximately 400 microns. Compared to standard mapping, which records gyral surface electrophysiology, they further demonstrate phase-reversal electrophysiology within a dissected central sulcus. High-resolution cortical mapping from &#xb5;ECoG may foster several neurosurgical advancements, from tumor resection to brain-computer interfaces. https://thejns.org/doi/10.3171/CASE25534.}, }
@article {pmid41657963, year = {2026}, author = {Cao, Y and Yang, H and Xue, Y and Wang, F and Li, T and Zhao, L and Fu, Y}, title = {Ethical risks and considerations of the integration of Brain-Computer Interfaces with Artificial Intelligence.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {46}, pmid = {41657963}, issn = {1871-4080}, abstract = {In recent years, with the rapid development of Brain-Computer Interface (BCI) and Artificial Intelligence (AI) technologies, a trend of increasing integration between the two has emerged. In some practical applications, BCI has been combined with AI to enhance the overall performance of systems, including usability, user experience, and satisfaction, especially in terms of intelligent capabilities. However, this technological integration also introduces new or exacerbates existing ethical risks, such as neural privacy breaches, cross-domain misuse, and unclear system responsibility attribution. This paper discusses the novel or more severe ethical challenges arising from the fusion of BCI and AI technologies, as well as measures and strategies to address these ethical issues, calling for the establishment of more comprehensive ethical guidelines and governance frameworks. It is hoped that this paper will contribute to a deeper understanding and reflection on the ethical risks and corresponding regulations related to the integration of BCI and AI technologies.}, }
@article {pmid41657959, year = {2025}, author = {Bennett, P and Barr, N}, title = {Neurorehabilitation technologies and functional recovery after brain injury: influence of sex, an integrative review.}, journal = {Frontiers in digital health}, volume = {7}, number = {}, pages = {1677873}, pmid = {41657959}, issn = {2673-253X}, abstract = {BACKGROUND: Acquired brain injury (ABI), which includes traumatic brain injury (TBI) and stroke, is a leading cause of disability. Evidence shows that sex may influence functional recovery post-acquired brain injury, potentially due to biological (e.g., hormones) and social factors (e.g., caregiver availability). Meanwhile, new neurorehabilitation technologies-such as virtual reality, robotic-assistance, and brain-computer interfaces-offer promising avenues for improving functional outcomes. Understanding how these technologies interact with sex differences could advance equitable and personalized healthcare.<br><br>RESEARCH QUESTION: Does evidence support a rationale for studying, developing, or employing neurorehabilitation technologies differently in males and females to improve functional outcomes post-ABI?<br><br>METHODOLOGY: An empirical integrative narrative review was conducted. Searches were performed in PubMed, Cochrane Library, and OVID, focusing on adult populations with ABI. Key terms encompassed "acquired brain injury," "sex differences," and "neurorehabilitation technologies." Fifty-nine studies met inclusion criteria, spanning diverse methodologies, settings, and cultural contexts. Data were synthesized to compare functional outcomes impacted by sex and by neurorehabilitation technologies.<br><br>RESULTS: Findings indicate that the effect of sex on neurorehabilitation outcomes is multifaceted. Studies using functional independence measures often reported no significant sex differences, whereas more specific measures (e.g., those measuring cognitive or social functions) identified notable sex effects. Neurorehabilitation technologies showed positive outcomes in various functional domains (e.g., upper extremity motor function, gait, cognition), but most studies focused on stroke.<br><br>DISCUSSION: Current research does not support the use of sex-differentiated technology interventions to target upper extremity motor function or global functional independence post-stroke. Sex-differentiated treatment may be relevant for other functional domains such as cognitive recovery, psychological well-being and social outcomes, but this requires further research, particularly for non-stroke ABI.<br><br>CONCLUSION: These findings suggest that some neurorehabilitation technologies can be applied without sex-specific modification, whereas others may benefit from sex-specific considerations. Owing to methodological limitations and sparse data, especially for TBI, additional investigations are warranted. As novel neurorehabilitation technologies evolve, accounting for sex differences may enhance personalized care and optimize long-term outcomes.}, }
@article {pmid41657359, year = {2025}, author = {Lyu, R}, title = {Deep learning approaches for EEG-based healthcare applications: a comprehensive review.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1689073}, pmid = {41657359}, issn = {1662-5161}, abstract = {Electroencephalography (EEG) is a longstanding means of non-invasively recording brain signals and has become highly valuable for the study of neurological and cognitive processes. Recent progress in deep learning has also greatly improved both EEG signal analysis and interpretation, making more accurate, reliable and scalable solutions in various healthcare applications. In this review, we present a comprehensive summary of the convergence of EEG and deep learning, with an emphasis on diagnostic of neurological disorders, brain recovery, mental health conditions, and brain-computer interface (BCI) applications. We methodically investigate the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, transformer models and hybrid architectures for EEG-based tasks. Key challenges that have been hampering emerging solutions are critically covered, namely signal-related variability, the lack of data, and deep learning model limited interpretability. Finally, we highlight emerging trends, open issues and promising research directions, with the aim of laying a solid ground toward the improvement of EEG-based healthcare applications and to drive future research in this fast-growing research area.}, }
@article {pmid41656193, year = {2026}, author = {Yan, S and Li, Q and Li, R and Zhang, L and Zhang, R and Chen, M and Li, M and Li, R and Zhang, H and Shi, L and Hu, Y}, title = {Prognosis prediction of patients with disorders of consciousness based on digital twin brain models.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01905-y}, pmid = {41656193}, issn = {1743-0003}, support = {2022ZD0208500//the STI 2030-Major Project/ ; 62173310//the National Natural Science Foundation of China/ ; }, abstract = {BACKGROUND: The accurate prediction of prognosis in patients with disorders of consciousness (DOC) is a significant challenge in clinical practice. Some studies based on traditional electroencephalography (EEG) features have shown potential for DOC prognosis. However, the underlying mechanisms behind the recovery of patients with DOC still lack in-depth research.<br><br>METHODS: In this study, we used mathematical tools to construct digital twin brain models (DTBM) for DOC patients with different outcomes. Then, we trained a support vector machine classifier using model parameters and modal controllability features to distinguish between DOC patients with different outcomes, and assessed the importance of these features. Finally, we used a support vector machine regressor to predict the Coma Recovery Scale-Revised (CRS-R) score at 6-month follow-up.<br><br>RESULTS: The results showed that the prognosis model based on local model parameters and modal controllability features achieved better performance (AUC&#x2009;=&#x2009;90.22%, F-score&#x2009;=&#x2009;86.00%, SEN&#x2009;=&#x2009;84.31%, SPE&#x2009;=&#x2009;91.43%) than the prognosis models based on some traditional EEG features. Additionally, a positive prognosis is associated with lower levels of inhibitory gain, higher levels of excitatory gain and modal controllability, particularly in brain regions within the frontoparietal network. In 74% and 70% of UWS and MCS patients, the MAE between the predicted CRS-R score and the actual CRS-R score was less than 5.<br><br>CONCLUSIONS: Overall, our study contributes to enriching the neuromarkers associated with DOC prognosis and further elucidates the neural mechanisms of consciousness recovery.}, }
@article {pmid41655642, year = {2026}, author = {Wang, J and Li, M}, title = {TPCNet: A Temporal Periodicity Convolutional Network for motor imagery EEG decoding in stroke patients.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110707}, doi = {10.1016/j.jneumeth.2026.110707}, pmid = {41655642}, issn = {1872-678X}, abstract = {BACKGROUND: Stroke caused by vascular rupture or blockage has high incidence and leads to significant disability. Motor imagery (MI) electroencephalogram (EEG) is a promising approach to understanding and addressing stroke-related motor impairments. However, the practical application of EEG-based rehabilitation is hindered by an insufficient understanding of the task-specific features and complex temporal patterns inherent in the EEG signals of stroke patients.<br><br>NEW METHOD: In this study, we collected EEG signals from 24 stroke patients performing four unilateral upper limb MI tasks. Among them, 12 subjects performed forward arm raising and lowering, while the remaining 12 performed lateral arm raising and lowering. Moreover, we propose a Temporal Periodicity Convolutional Network (TPCNet) for EEG-based MI classification. TPCNet consists of a convolutional block for extracting shallow spatiotemporal features, a sliding window structure that ensures consistent action initiation across samples, and a temporal periodicity block for capturing variations in periodic patterns associated with MI tasks.<br><br>RESULTS: TPCNet achieved a classification accuracy of 86.53% on the stroke patient MI dataset and 82.21% on the BCI Competition IV 2a dataset (left hand, right hand, feet, and tongue). Gradient-weighted Class Activation Mapping (Grad-CAM) analysis suggests that stroke patients may exhibit longer task-specific MI periodicity than healthy subjects.<br><br>The proposed method achieves superior performance on stroke patient MI tasks and competitive results on public MI datasets involving healthy subjects.<br><br>CONCLUSIONS: The proposed TPCNet model effectively captures the spatiotemporal features and periodic patterns of EEG signals, leading to enhanced classification accuracy.}, }
@article {pmid41654951, year = {2026}, author = {Xu, C and Zhu, L and Lai, J and Luo, Z and Ying, J and Hu, S and Song, P and Yang, J}, title = {Global and regional quality of care index in major depressive disorder: the global burden of disease study 2021.}, journal = {International journal for equity in health}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12939-026-02775-5}, pmid = {41654951}, issn = {1475-9276}, support = {2023YFC2506200//National Key Research and Development Program of China/ ; 2024C03098, 2025C02109//Key Research and Development Program of Zhejiang Province/ ; 2025C01104//Key Research and Development Program of Zhejiang Province/ ; }, abstract = {BACKGROUND: Major depressive disorder (MDD) is a leading cause of global disability, yet systematic evaluations of quality of care disparities across regions are sparse. Leveraging data from the Global Burden of Disease (GBD) Study 2021, this study quantified the quality of care for MDD from 1990 to 2021 and examined socio-demographic inequities by age and sex.<br><br>METHODS: Data on MDD were extracted from the GBD 2021 study for the globe, 5 socio-demographic index (SDI) regions and 21 GBD regions. The quality of care index (QCI) is a composite, dimensionless index scaling from 0 to 100, with higher values indicating better quality of care. The age-standardized QCI was calculated using the Principal Component Analysis (PCA) method and further stratified by sex, age, and region. The gender disparity ratio (GDR) was used to characterize the sex disparities. The temporal trend of QCI and GDR by sex and age across SDI regions was further calculated.<br><br>RESULTS: Globally, the QCI of MDD increased from 56.26 (1990) to 62.95 (2021), with low SDI regions consistently exhibiting the highest QCI (71.90 in 1990; 71.19 in 2021) and high SDI regions the lowest (40.28 to 51.55). Sex disparities widened as female QCI rose by 14.0% (vs. 7.6% in males) and GDR increased from 1.02 to 1.08. The highest GDR (1.27) persisted in Oceania, while Tropical Latin America had the lowest (0.94 in 2021). Age-specific QCI peaked in adolescents (10-14 years) and declined with age, with notable improvements post-2019. Older adults (>&#x2009;80 years) in high SDI regions saw higher QCI versus low-middle SDI regions. Trend analysis revealed that high and high-middle SDI regions maintained a lower QCI of MDD than the global average level but narrower sex gaps (GDR 1.04 in 2021) compared to low SDI regions (GDR 1.15).<br><br>CONCLUSIONS: While global quality of care for MDD improved, socioeconomic development inversely correlated with QCI, potentially reflecting systemic under-reporting in low-resource settings and overburdened systems in high-income regions. Persistent gender and age disparities necessitate targeted and equal policies, including sex-sensitive care models and geriatric mental health integration.}, }
@article {pmid41653675, year = {2026}, author = {Li, Q and Liu, Y and Zhao, N and Yuan, Y and He, R}, title = {A novel ECG QRS complex detection algorithm based on dynamic Bayesian network.}, journal = {Artificial intelligence in medicine}, volume = {174}, number = {}, pages = {103370}, doi = {10.1016/j.artmed.2026.103370}, pmid = {41653675}, issn = {1873-2860}, abstract = {Accurate detection of the QRS complex, a crucial reference for heartbeat localization in electrocardiogram (ECG) signals, remains inadequate in wearable ECG devices due to complex noise interference. In this study, we propose a novel QRS complex detection method based on dynamic Bayesian network (DBN), integrating the probability distribution of RR intervals. Unlike methods focusing solely on ECG waveforms, our approach explicitly integrates ECG waveform and heart rhythm information into a unified probability model, enhancing noise robustness. Additionally, an unsupervised parameter optimization using expectation maximization (EM) adapts to individual differences of patients. Furthermore, several simplification strategies improve reasoning efficiency, and an online detection mode enables real-time applications. Our method outperforms other state-of-the-art QRS detection methods, including deep learning (DL) methods, on noisy datasets. In conclusion, the proposed DBN-based QRS detection algorithm demonstrates outstanding accuracy, noise robustness, generalization ability, real-time capability, and strong scalability, indicating its potential application in wearable ECG devices.}, }
@article {pmid41401084, year = {2026}, author = {Ying, W and Yu, J and Wang, X and Liu, J and Deng, B and Shao, X and Wang, J and Tao, T and Cao, J and He, Q and Yang, B and Chen, Y and Ying, M}, title = {Therapeutic targeting of YOD1 disrupts the PAX-FOXO1/N-Myc feedback loop in rhabdomyosarcoma.}, journal = {JCI insight}, volume = {11}, number = {3}, pages = {}, doi = {10.1172/jci.insight.193221}, pmid = {41401084}, issn = {2379-3708}, mesh = {Humans ; Animals ; Mice ; *Rhabdomyosarcoma/genetics/pathology/metabolism/drug therapy ; Cell Line, Tumor ; Forkhead Box Protein O1/metabolism/genetics ; *N-Myc Proto-Oncogene Protein/metabolism/genetics ; Oncogene Proteins, Fusion/metabolism/genetics ; Gene Expression Regulation, Neoplastic ; Signal Transduction ; Paired Box Transcription Factors/metabolism ; Feedback, Physiological ; Xenograft Model Antitumor Assays ; Proto-Oncogene Proteins c-myc ; }, abstract = {Fusion-positive rhabdomyosarcoma (FP-RMS), driven by PAX-FOXO1 fusion oncoproteins, represents the subtype of RMS with the poorest prognosis. However, the oncogenic mechanisms and therapeutic strategies of PAX-FOXO1 remain incompletely understood. Here, we discovered that N-Myc, in addition to being a classic downstream target of PAX-FOXO1, can also activate its expression and form a transcriptional complex with PAX-FOXO1, thereby markedly amplifying oncogenic signaling. The reciprocal transcriptional activation of PAX3-FOXO1 and N-Myc is critical for FP-RMS malignancy. We further identified YOD1 as a deubiquitinating enzyme that stabilizes both PAX-FOXO1 and N-Myc. Knocking down YOD1 or inhibiting it with G5 could suppress FP-RMS growth both in vitro and in vivo, through promoting the degradation of both PAX-FOXO1 and N-Myc. Collectively, our results identify that YOD1 promotes RMS progression by regulating the PAX3-FOXO1/N-Myc positive feedback loop, and highlight YOD1 inhibition as a promising therapeutic strategy that concurrently reduces the levels of both oncogenic proteins.}, }
@article {pmid40031285, year = {2026}, author = {Candia-Rivera, D and Faes, L and De Vico Fallani, F and Chavez, M}, title = {Measures and Models of Brain-Heart Interactions.}, journal = {IEEE reviews in biomedical engineering}, volume = {19}, number = {}, pages = {24-40}, doi = {10.1109/RBME.2025.3529363}, pmid = {40031285}, issn = {1941-1189}, mesh = {*Brain-Computer Interfaces ; Biomarkers/analysis ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; *Heart/physiology ; Humans ; Animals ; *Models, Neurological ; }, abstract = {Exploring brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, has demonstrated enormous potential in biomarker development and neuroscientific research. A range of techniques, from molecular to behavioral approaches, has been proposed to measure these interactions. Different frameworks use signal processing techniques, from estimating brain responses to individual heartbeats to interactions linking the heart to changes in brain organization. This review provides an overview of the most notable signal processing strategies currently used for measuring and modeling brain-heart interactions. It discusses their usability and highlights the main challenges that need to be addressed for future methodological developments. Current methodologies have deepened our understanding of the impact of physiological disruptions on brain-heart interactions, solidifying it as a biomarker. The vast outlook of these methods could provide tools for disease stratification in neurological and psychiatric disorders. As we tackle new methodological challenges, gaining a more profound understanding of how these interactions operate, we anticipate further insights into the role of peripheral neurons and the environmental input from the rest of the body in shaping brain functioning.}, }
@article {pmid41651814, year = {2026}, author = {Zhang, Y and Zhao, M and Song, S and Chen, Q and Meng, Y and Yu, X and Wei, W and Deng, W and Guo, W and Li, T and Qi, X}, title = {Blood circulating cell-free mitochondrial DNA as a potential biomarker for major depressive disorder: a meta-analysis.}, journal = {Translational psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41398-026-03865-2}, pmid = {41651814}, issn = {2158-3188}, support = {82371524//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82301709//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81920108018, 82230046//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {BACKGROUND: Mitochondrial dysfunction has been implicated in major depressive disorder (MDD), but reliable, measurable biomarkers remain elusive. As a minimally invasive and quantifiable biomarker, circulating cell-free mitochondrial DNA (ccf-mtDNA) in blood offers potential for objective assessment of mitochondrial stress in MDD. However, evidence linking regarding the association between ccf-mtDNA levels and MDD is limited and inconsistent.<br><br>METHODS: We systematically searched eight databases, including PubMed, EMBASE, and major Chinese repositories. Thirteen studies with 1370 participants (837 individuals with MDD and 533 controls) were included per PRISMA guidelines. P-values were synthesized using the Lipt&#xe1;k-Stouffer Z-score method. Sensitivity and fail-safe N analyses assessed the robustness of the findings and publication bias, and stratified analyses examined the effects of age, antidepressant use, and geographic region.<br><br>RESULTS: Across studies, elevated blood ccf-mtDNA levels were significantly associated with MDD (p&#x2009;=&#x2009;0.013). Stratified analyses revealed stronger associations in older adults (&#x2265;60 years old; p&#x2009;=&#x2009;0.0009), unmedicated patients (p&#x2009;=&#x2009;4.99&#x2009;&#xd7;&#x2009;10&#x207b;&#x2076;), and North American cohorts (p&#x2009;=&#x2009;4.29&#x2009;&#xd7;&#x2009;10&#x207b;&#xb9;&#xb9;), but not in younger individuals (p&#x2009;=&#x2009;0.83), medicated patients (p&#x2009;=&#x2009;0.97), and Asian/European samples (p&#x2009;=&#x2009;0.72, p&#x2009;=&#x2009;0.99). Sensitivity analyses indicated moderate instability overall but confirmed data robustness in key subgroups.<br><br>CONCLUSIONS: This is the first meta-analysis to establish a significant link between elevated blood ccf-mtDNA and MDD, highlighting age and antidepressant exposure as critical modulators. These findings support the potential of blood ccf-mtDNA to serve as a biomarker for late-life and drug-na&#xef;ve depression, with implications for objective diagnosis and personalized treatment.}, }
@article {pmid41649534, year = {2026}, author = {Li, H and Liu, J and Li, J}, title = {Cross-subject motor imagery EEG signal classification based on meta-transfer learning.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-12}, doi = {10.1080/10255842.2026.2626477}, pmid = {41649534}, issn = {1476-8259}, abstract = {Motor imagery (MI) EEG classification, a core BCI task, faces challenges due to EEG's low signal-to-noise ratio and non-stationarity. Traditional supervised learning methods perform poorly in cross-subject and small-sample scenarios, limiting practical use. We propose CMHA-Net, a MI-EEG-optimized CNN integrating depthwise separable convolution, deep convolution and multi-head attention, combined with a Meta-SGD-based meta-transfer learning framework. Experiments on BCI-IV-2a and High Gamma datasets show 81.61% and 88.15% accuracy, outperforming existing models by 4-15% and excelling in small-sample cases, advancing clinical and real-world BCI applications.}, }
@article {pmid41649064, year = {2026}, author = {Murphy, A and Schaly, S and Chiu, D and Mitchell, A and Shen, C and Maddirala, G and Singh, T and McEwan, A and Karlsson, P}, title = {Access technologies for people with significant motor impairment with potential to impact speed and/or accuracy of communication: a scoping review.}, journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/07434618.2026.2619160}, pmid = {41649064}, issn = {1477-3848}, abstract = {Individuals with significant communication and physical impairments often rely on augmentative and alternative communication (AAC) to facilitate independent communication across a range of communication partners and settings. Due to physical impairments, many individuals require alternate methods to access AAC systems, known as access technologies. While access technologies have advanced, they remain considerably slower than verbal communication. This scoping review explored recent advances in access technologies published between 2019 and 2024, focusing on technologies that facilitate communication speed and/or accuracy for individuals of any age with physical disabilities. Forty-six studies met inclusion criteria, covering a range of technologies such as brain-computer interfaces (BCIs), eye-tracking technology, and novel applications, such as a mixed reality AAC environment and multimodal access approaches (e.g., integrated eye-tracking with switch scanning, hybrid BCI eye-tracking). Despite technological progress, fewer than one-third of studies addressed the role of communication partners in setup and support, highlighting a gap in user-centred design. Findings are discussed in terms of practical applications and emerging directions for technology development. Implications for clinical practice and future research include the need for inclusive design, improved usability, and greater consideration of communication partner involvement in AAC access solutions.}, }
@article {pmid41648134, year = {2026}, author = {Levin, AD and Avansino, DT and Kamdar, FB and Card, NS and Wairagkar, M and Jacques, BG and Jude, JJ and Iacobacci, C and Lacayo, BE and Bechefsky, PH and Nason-Tomaszewski, SR and Deo, DR and Hochberg, LR and Rubin, DB and Williams, ZM and Brandman, DM and Stavisky, SD and AuYong, N and Pandarinath, C and Linderman, SW and Henderson, JM and Willett, FR}, title = {Cross-brain transfer of high-performance intracortical speech and handwriting BCIs.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.64898/2026.01.12.699110}, pmid = {41648134}, issn = {2692-8205}, abstract = {Intracortical brain-computer interfaces (BCIs) that decode complex movements, such as handwriting and speech, can require substantial training data to achieve high performance. We investigated whether leveraging the neural activity recordings of previous users could reduce this initial data collection burden for new BCI users (an approach we call "cross-brain transfer"). Using intracortical recordings from five BrainGate2 clinical trial participants, we tested cross-brain transfer for both speech and handwriting neural decoders trained and evaluated on general, unconstrained corpora of spoken and written English. We found that cross-brain transfer improved decoding performance when training data from the target user was limited (< 200 sentences), and that dataset-specific input layers to the decoder were critical for combining data across users. Without trainable input layers, transfer failed and performed worse than training from scratch on target user data only. Finally, we measured the effectiveness of cross-brain transfer relative to training with (1) more data from the same user and (2) more electrode-permuted data from the same user, which simulates sampling from another brain with identical neural latent structure. In some cases (T16 speech, T12 handwriting), cross-brain transfer appeared as effective as additional permuted data from the same user, while in others (T12 speech, T15 speech) electrode-permuted data was more beneficial. Our results successfully demonstrate and characterize cross-brain transfer learning between multiple intracortical BCI users, for both speech and handwriting, using a general open-ended dataset not restricted to small sets of words or phrases. This work highlights a promising path towards addressing a key barrier to the clinical translation of BCIs, while clarifying when cross-brain transfer may be most beneficial and the decoder design choices needed to realize those gains.}, }
@article {pmid41647146, year = {2026}, author = {Zhu, L and Jiang, P and Huang, A and Zhang, J and Yuan, P}, title = {M3T-attention: a multi-level multi-scale temporal attention transformer for EEG hand movement trajectory decoding.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {33}, pmid = {41647146}, issn = {1871-4080}, abstract = {In recent years, brain-computer interface (BCI) technology has made significant progress in the fields of neural engineering and human-computer interaction. Among these advances, decoding upper-limb movements from electroencephalography (EEG) signals has become a key research focus. However, most existing studies concentrate on discrete classification tasks (e.g., motor imagery recognition), while the prediction of three-dimensional continuous movement trajectories still faces several major challenges. These include the low signal-to-noise ratio of EEG signals, substantial inter-subject variability that limits generalizability, and the high degrees of freedom in 3D trajectories, which increase decoding complexity. To address these challenges and improve the accuracy of decoding continuous 3D hand movement trajectories from EEG signals, this study proposes a Multi-level Multi-scale Temporal Attention Transformer framework (M3T-Attention). The model is designed to extract temporal features across multiple time scales from EEG signals and integrate them via cross-scale attention mechanisms, enabling a nonlinear mapping from 0.5 to 12 Hz EEG signals to 3D kinematic parameters (position, velocity, and acceleration). The model was trained using EEG and wrist kinematic data from the WAY-EEG-GAL dataset. Experimental results show that the proposed method achieves Pearson correlation coefficients (PCCs) of 0.8816, 0.8841, and 0.8711 on the X, Y, and Z axes, respectively, demonstrating robust prediction performance across all subjects and outperforming existing state-of-the-art approaches. In summary, through comparative experiments, statistical significance analysis, and ablation studies, we have fully verified its ability to capture neural coding patterns. It significantly enhances the decoding performance from EEG signals to movement trajectories, offering new approaches for BCI applications in complex motor control scenarios. We have made the model's source code publicly available on GitHub repository URL: https://github.com/jjspp/M3T_Attention.}, }
@article {pmid41647142, year = {2026}, author = {Pan, H and Teng, B and Liu, Z and Tong, S and Yu, X and Li, Z}, title = {Five-class motor imagery BCI classification and its application to brain-controlled wheelchairs.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {38}, pmid = {41647142}, issn = {1871-4080}, abstract = {Brain-controlled wheelchair (BCW) technology enables direct wheelchair control via brain-computer interfaces (BCIs), eliminating the need for physical limb interaction. Motor imagery-based BCIs (MI-BCIs) are widely used in non-invasive BCIs due to their ability to provide intuitive neural control without external stimuli. However, developing a BCW system based on MI-BCIs remains challenging, particularly in achieving reliable multi-class classification accuracy.To address this challenge, this study proposes an advanced feature extraction algorithm to enhance MI-BCI performance using a custom-built five-class MI-EEG dataset. The proposed method, EHT-CSP, integrates Ensemble Empirical Mode Decomposition Hilbert-Huang Transform (EEMD-HHT) with Time-Frequency Common Spatial Pattern (TFCSP). Specifically, it extracts marginal spectrum entropy and energy spectrum entropy via EEMD-HHT. It then combines these features with TFCSP-derived feature vectors to improve feature discrimination. The Light Gradient Boosting Machine is then employed for classification. The proposed MI-BCI system is evaluated through both offline analysis and real-world BCW obstacle avoidance experiments. Results demonstrate that the algorithm achieves an average classification accuracy of 78.45%, with all participants successfully completing BCW navigation tasks. In this study, LightGBM and EHT-CSP are compared with other algorithms respectively, and it is verified that the proposed model is superior to the existing models.}, }
@article {pmid41647137, year = {2026}, author = {Gong, A and Man, H and Shi, X and Li, S and Hu, X and Gong, B and Shi, T and Fu, Y}, title = {Trading time for space: a new approach to investigate the EEG neural mechanisms of fine motor brain based on ICA-optimized traceability network analysis.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {35}, pmid = {41647137}, issn = {1871-4080}, abstract = {UNLABELLED: Although electroencephalography (EEG) offers significant advantages in terms of high temporal resolution and cost-effectiveness, its application is often constrained by limited spatial resolution. This limitation makes it challenging to accurately localize and characterize activity within specific target regions of the brain. To address this, we propose a computational model for brain-network analysis based on independent component analysis (ICA) and source-space clustering. First, repetitive ICA decomposition is performed on a trial-by-trial basis, followed by clustering to extract stable independent components and their corresponding spatial mapping vectors. Subsequently, standardized low-resolution brain electromagnetic tomography (sLORETA) is employed for source localization. The resulting source locations are then clustered across trials to define network nodes, which are utilized to construct a source-level brain network for the investigation of neural mechanisms. The efficacy of this algorithm was validated using two datasets: the international Brain-Computer Interface (BCI) competition dataset involving motor imagery, and a self-collected dataset recorded during the preparatory phase of pistol shooting. Analysis of the motor-imagery dataset demonstrated that the proposed method identified active brain regions consistent with those observed in previous functional magnetic resonance imaging (fMRI) studies. Regarding the pistol-shooting preparation dataset, the method revealed heightened activity in the frontal, occipital, and bilateral temporal lobes. Furthermore, the intensity of information interaction among multiple brain regions exhibited a significant correlation with shooting performance. These findings not only corroborate prior research but also uncover novel features regarding source-level functional connectivity. Consequently, this novel framework achieves precise source localization and network analysis using EEG, significantly enhancing spatial resolution and providing a more accurate elucidation of target brain activities and information-interaction mechanisms during motor tasks.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10405-z.}, }
@article {pmid41647136, year = {2026}, author = {Guo, K and Meng, K and Yu, R and Zhang, L and Hu, Y and Zhang, R and Yao, D and Chen, M}, title = {State-dependent alterations of network characteristics between seizure and non-seizure onset zones in drug-resistant epilepsy.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {31}, pmid = {41647136}, issn = {1871-4080}, abstract = {UNLABELLED: Accurate localization of the seizure onset zone (SOZ) is critical for successful surgery in drug-resistant epilepsy (DRE). To investigate the alterations of network characteristics between the SOZ and non-seizure onset zones (NSOZ) across different seizure stages, the intracranial electroencephalogram (iEEG) data based brain networks from 29 DRE patients have been constructed using the weighted phase lag index (WPLI) and phase transfer entropy (PTE), respectively. Then, graph theory metrics, such as eigenvector centrality, betweenness centrality, in-degree and out-degree, are calculated to compare network characteristics of SOZ and NSOZ nodes across interictal, pre-ictal, early-ictal and post-ictal periods in multiple frequency bands. Statistical analyses demonstrate that the SOZ exhibits significantly higher eigenvector centrality and betweenness centrality in the beta and gamma frequency bands, serving as network hubs and primary sources of information outflow. By contrast, the NSOZ shows elevated centrality only in the theta and alpha frequency bands during non-ictal states. Moreover, during the pre-ictal to early-ictal transition, the SOZ progressively evolves into hub nodes with enhanced outflow and reduced inflow, whereas the NSOZ shifts toward non-hub status with increased inflow. Importantly, the random forest model utilizing out-degree features of early-ictal gamma frequency band can effectively identify the SOZ, and achieve an area under the curve (AUC) of 0.82. Overall, these findings offer a novel network-based perspective on the state-dependent alterations of epileptic seizures in DRE and contribute to the treatment of epilepsy.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10400-4.}, }
@article {pmid41647134, year = {2026}, author = {Yang, Z and Wang, K and Ming, Y and Yang, H and Chen, Q and Peng, Y and Kong, W}, title = {Uncertainty-aware human-machine collaboration in Camouflaged Object Detection.}, journal = {Cognitive neurodynamics}, volume = {20}, number = {1}, pages = {45}, pmid = {41647134}, issn = {1871-4080}, abstract = {Camouflaged Object Detection (COD), the task of identifying objects concealed within their environments, has seen rapid growth due to its wide range of practical applications. We propose a human-machine collaboration framework for COD, leveraging the complementary strengths of computer vision (CV) models and noninvasive brain-computer interfaces (BCIs). Our approach introduces a multiview backbone to estimate uncertainty in CV predictions, utilizes this uncertainty during training to improve efficiency, and defers low-confidence cases to human evaluation via RSVP-based BCIs during testing for more reliable decision-making. Evaluated on the CAMO dataset, our framework achieves state-of-the-art results with an average improvement of 4.56% in balanced accuracy (BA) and 3.66% in the F1 score. For the best-performing participants, improvements reached 7.6% in BA and 6.66% in the F1 score. Training analysis showed a strong correlation between confidence and precision, while ablation studies confirmed the effectiveness of our training policy and human-machine collaboration strategy. This work reduces human cognitive load, improves system reliability, and provides a foundation for advancements in real-world COD applications and human-computer interaction. Our code and data are available at: https://github.com/ziyuey/Uncertainty-aware-human-machine-collaboration-in-camouflaged-object-identification.}, }
@article {pmid41646629, year = {2026}, author = {Colli, A and Zilla, P and Calafiore, AM and Padalino, M and Naso, F and George, I}, title = {Quantification of Alpha-Gal Expression in Commercial BioProsthetic Heart Valves and Its Potential Mitigation.}, journal = {Structural heart : the journal of the Heart Team}, volume = {10}, number = {3}, pages = {100739}, pmid = {41646629}, issn = {2474-8714}, abstract = {BACKGROUND AND AIMS: Bioprosthetic heart valves (BHVs) are inherently susceptible to structural degeneration, driven by a combination of mechanical stress, lipid infiltration, glutaraldehyde-induced crosslinking instability, and progressive calcification. Recent evidence has implicated the αGal antigen (galactose-α-1,3-galactose) as an additional contributor to BHV deterioration through activation of innate immune pathways. The present study aims to: 1) perform a quantitative assessment of the residual presence of xenoantigens, specifically αGal, in a range of commercial BHV models; 2) evaluate the efficacy of an experimental polyphenol-based treatment in neutralizing these antigenic determinants; and 3) investigate the long-term stability of glutaraldehyde fixation concerning the potential re-exposure of αGal epitopes.<br><br>METHODS: Twelve distinct BHV models were subjected to in vitro analysis for &#x3b1;Gal antigen quantification both before and following application of an experimental polyphenol treatment. Additionally, glutaraldehyde-fixed bovine pericardial tissues were incubated in a physiologically mimetic, blood-like environment for up to 9 years in real-time to simulate the long-term behavior of BHV materials and assess antigen unmasking associated with glutaraldehyde degradation.<br><br>RESULTS: The average count of the &#x3b1;Gal epitope in original pericardial valve models was 4.18 &#xb1; 0.72 &#xd7; 10[11]/10 mg of tissue, whereas porcine valve-derived prostheses exhibited a higher mean value of 8.51 &#xb1; 2.17 &#xd7; 10[11]/10 mg. Treatment with the polyphenol formulation resulted in a marked reduction (approximately 99%) in detectable &#x3b1;Gal epitopes. Furthermore, glutaraldehyde fixed pericardial tissues subjected to prolonged incubation demonstrated up to 60% re-exposure of previously masked &#x3b1;Gal antigens after 9 years, consistent with a progressive compromise of glutaraldehyde crosslinking integrity.<br><br>CONCLUSION: The data confirm that commercially available BHVs retain a substantial immunogenic burden attributable to &#x3b1;Gal xenoantigens. Importantly, the overtime degradation of glutaraldehyde crosslinks facilitates the gradual re-exhibition of these epitopes, potentially undermining long-term valve performance. The pronounced efficacy of polyphenol-based treatment in inhibiting &#x3b1;Gal antigens highlights its promise as a biocompatibility-enhancing pretreatment strategy for next-generation BHVs.}, }
@article {pmid41643590, year = {2026}, author = {Sehnan, M and Li, H and Li, X and Grebogi, C and Gao, Z and Dang, W}, title = {Multiscale spatiotemporal neural network with multi-attention mechanism using brain partitioning for motor imagery recognition.}, journal = {Journal of neuroscience methods}, volume = {429}, number = {}, pages = {110704}, doi = {10.1016/j.jneumeth.2026.110704}, pmid = {41643590}, issn = {1872-678X}, abstract = {BACKGROUND: Motor imagery (MI)-based electroencephalogram (EEG) brain-computer interfaces (BCIs) facilitate communication for motor-impaired patients by leveraging artificial intelligence to accurately interpret brain signals. However, EEG signal classification remains challenging due to low signal-to-noise ratio (SNR) and individual variability in brain activity.<br><br>NEW METHOD: We propose a novel parallel multi-depth spatial-temporal neural network aimed at enhancing the integration of spatial and temporal features from multichannel EEG signals by leveraging brain functional topography. To improve cortical representations associated with motor imagery, the model incorporates two parallel branches. One branch focuses on inter-channel differences corresponding to contralateral electrode pairs, emphasizing hemispheric disparities, while the other targets the frontal and parietal brain regions. These region-specific enhanced signal representations are then fed into the multi-depth spatial-temporal network for feature extraction and subsequent motor imagery classification. The architecture of the feature extraction network integrates four specialized blocks, ensuring the comprehensive capture of discriminative features that are particularly sensitive to task-relevant frequencies for each MI class. A multi-loss design further optimizes feature integration across networks.<br><br>RESULTS: Cross-validation results on the BCI Competition IV 2a dataset and High Gamma dataset achieve accuracies of 82.14% and 95.61%, respectively, with kappa values of 0.76 and 0.93, surpassing state-of-the-art methods.<br><br>CONCLUSION: These experimental results highlight the significance of parallel spatial-temporal networks based on brain partitioning for MI classification in rehabilitation engineering and real-world BCI applications.}, }
@article {pmid41642225, year = {2026}, author = {R, V and Robinson, N and Reddy, MR}, title = {Enhancing the performance of a deep convolutional neural network model for motor imagery classification using EEG channel-wise attention module.}, journal = {Medical engineering &amp; physics}, volume = {147}, number = {1}, pages = {}, doi = {10.1088/1873-4030/ae1afe}, pmid = {41642225}, issn = {1873-4030}, mesh = {*Electroencephalography ; Humans ; *Neural Networks, Computer ; *Attention ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; *Imagination ; Convolutional Neural Networks ; }, abstract = {The classification of motor imagery-electroencephalography (MI-EEG) is a growing research field in brain-computer interface, which allows people with motor disabilities to communicate with the outside world through assistive devices. Although deep learning-based models have revolutionized MI-EEG decoding, dealing with the MI-EEG signals remains challenging due to the signals being non-stationary, containing noisy signals, and having a low signal-to-noise-ratio. This study proposes to employ a novel EEG channel-wise attention module (ECWAM) in a deep convolutional neural network (deep CNN) to enhance the accuracy of MI-EEG decoding. The proposed method calculates the channel score for each mu band EEG channel and amplifies the prominent EEG channels based on their channel scores. The proposed method is evaluated on 54 subjects, binary class MI dataset from the Korea University EEG dataset. Additionally, the proposed method is compared with the conventional channel-wise attention module mentioned in the literature. The results for the hold-out analysis outcomes suggest that the proposed deep CNN with ECWAM has statistically improved the average classification accuracy of the baseline deep CNN model from 63.96% to 68.98%, withp-value = 0.02 for the subject-specific MI classification. Further, the scalp map of the EEG channel ranking obtained by the proposed method and the conventional channel-wise attention module mentioned in the literature is also compared. The results of the comparison show that the proposed method yields a higher channel ranking in the brain's motor cortex region, which is the primary contributing area for MI activity.}, }
@article {pmid41641333, year = {2026}, author = {Lin, Y and Yuan, Y and Chen, J and Lin, X}, title = {Motor imagery combined with brain-computer interface for stroke patients: a meta-analysis.}, journal = {Frontiers in neurology}, volume = {17}, number = {}, pages = {1672882}, pmid = {41641333}, issn = {1664-2295}, abstract = {OBJECTIVE: To systematically evaluate the effects of motor imagery combined with brain-computer interface (MI-BCI) on stroke patients.<br><br>METHODS: Randomized controlled trials (RCTs) on MI-BCI for stroke patients were retrieved from CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, Embase, and Web of Science databases from inception to June 2025. Data were analyzed using RevMan 5.2 software.<br><br>RESULTS: Eight RCTs involving 357 stroke patients were included. The meta-analysis showed that MI-BCI was associated with an improvement in upper limb motor function, although this did not reach conventional statistical significance (SMD&#x202f;=&#x202f;0.86, 95% CI&#x202f;=&#x202f;-0.04 to 1.75, p&#x202f;=&#x202f;0.06). In contrast, a statistically significant, moderate-to-large improvement was found in activities of daily living (SMD&#x202f;=&#x202f;1.47, 95% CI&#x202f;=&#x202f;0.51 to 2.44, p&#x202f;=&#x202f;0.003). Subgroup analyses indicated that the efficacy in motor function was primarily evident when MI-BCI was administered as an adjunct to conventional rehabilitation or with an intervention duration of &#x2265;4&#x202f;weeks.<br><br>CONCLUSION: The efficacy of MI-BCI is contingent upon its therapeutic context. When used as an adjunct to conventional rehabilitation, MI-BCI can significantly improve both upper limb motor function and activities of daily living in stroke patients. However, current evidence does not support its superiority over motor imagery alone when applied as a standalone therapy. An intervention duration of &#x2265;4&#x202f;weeks is recommended to achieve significant functional gains.}, }
@article {pmid41640392, year = {2026}, author = {E, M and Li, X and Zhang, Y and Yin, J and Mayo, KH and Wang, H}, title = {Simple Sequence Repeat Gene Polymorphisms in Yellow-Rumped Flycatcher With Gender-Specific Associations and Personality Variations.}, journal = {Ecology and evolution}, volume = {16}, number = {2}, pages = {e72991}, pmid = {41640392}, issn = {2045-7758}, abstract = {This study explores the genetic and physiological facets of personality variations in the yellow-rumped flycatcher (Ficedula zanthopygia), with a focus on potential sex-specific associations between simple sequence repeat (SSR) polymorphisms, body condition index (BCI) and behavioral traits. During the 2020 breeding seasons at Zuojia Nature Reserve, northeast China, we conducted field investigations using several stress tests to quantify personality as reflected in breathing rates. This metric demonstrated significant reproducibility between life stages, thereby validating its use as a reliable association with individual boldness. We further examined the influence of genetic diversity by genotyping 10 highly polymorphic SSR loci and calculating individual heterozygosity. As a reflection of stronger personalities, we found significant associations between individual heterozygosity and breathing rates in female adults, with greater heterozygosity correlated with lower breathing rates. The opposite pattern was observed in male nestlings, and no significant correlations were observed in male adults or male chicks. In addition, the BCI tended to be negatively correlated with breathing rates in female adults, suggesting that individuals with better body conditions were less fearful. These findings underscore the importance of genetic diversity and body condition in modulating personality traits, particularly in females. Overall, our results highlight the likelihood that the sex of these birds underlies their behavioral variations. Moreover, this study provides insight into the genetic basis of personality in cavity-nesting birds and emphasizes the need for further research to elucidate specific genetic pathways that influence these traits.}, }
@article {pmid41639858, year = {2026}, author = {Schopp, L and Starke, G and Ienca, M}, title = {Explainability in AI-enabled medical neurotechnology: a scoping review.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01892-0}, pmid = {41639858}, issn = {1743-0003}, }
@article {pmid41638413, year = {2026}, author = {Niu, R and Li, Y and Liu, L and Pan, Y and Liu, Y}, title = {Hierarchical neurobehavioral model reveals that shared flexibility, not individual stability, supports rhythmic coordination.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121773}, doi = {10.1016/j.neuroimage.2026.121773}, pmid = {41638413}, issn = {1095-9572}, abstract = {Interpersonal coordination requires balancing individual control with interaction-derived synergy, yet it remains unclear when neural coupling contributes beyond behavior. Using an fNIRS hyperscanning paradigm, we examined dyadic rhythmic coordination and jointly modeled behavioral stability, dispositional structure, and interbrain synchrony within a hierarchical neurobehavioral framework. Across models, mean individual stability was negatively associated with dyadic performance, whereas interaction-derived behavioral synergy was the most robust positive predictor. Incorporating dispositional structure showed that larger within-dyad differences in figure-embedding performance impaired coordination, whereas higher dyad-level self-esteem facilitated coordination. The neural coupling index (NCI) showed no reliable main effect after accounting for behavioral and trait factors, but moderation analyses indicated a conditional contribution: interbrain synchrony compensated when behavioral synergy was low, with diminishing benefit as synergy increased. Together, these findings support a hierarchical neurobehavioral architecture in which behavioral synergy provides the primary foundation of coordination, dispositional structure shapes the conditions for synergy, and interbrain synchrony contributes in a context-dependent manner.}, }
@article {pmid41637791, year = {2026}, author = {O'Regan, RM and Ren, Y and Zhang, Y and Fleming, GF and Francis, PA and Pagani, O and Walley, BA and Kammler, R and Dell'Orto, P and Viale, G and Loi, S and Colleoni, M and Treuner, K and Regan, MM}, title = {Identifying premenopausal patients with early-stage hormone receptor-positive breast cancer at minimal risk of distant recurrence by breast cancer index.}, journal = {Breast (Edinburgh, Scotland)}, volume = {86}, number = {}, pages = {104714}, doi = {10.1016/j.breast.2026.104714}, pmid = {41637791}, issn = {1532-3080}, abstract = {BACKGROUND: An adjusted Breast Cancer Index (BCI) model with an additional cutpoint identified postmenopausal women with hormone-receptor-positive node-negative disease at minimal (<5%) risk of distant recurrence (DR) within 10 years.<br><br>METHODS: 2025 premenopausal patients with hormone-receptor-positive node-negative breast cancer, randomized to adjuvant endocrine therapy in SOFT and TEXT (35.6% and 40.4% received adjuvant chemotherapy, respectively), previously had BCI assessed. The additional BCI cutpoint re-classified a subset of the low-risk group into minimal-risk; those in intermediate- or high-risk groups were unchanged. The 10-year DR was estimated by Kaplan-Meier method.<br><br>RESULTS: The adjusted BCI model re-classified 17.8 % and 19.6 % of node-negative disease in SOFT and TEXT into BCI minimal-risk groups; 43.2 % and 38.3 % remained classified in low-risk groups, respectively. In SOFT, the estimated 10-year DR was 2.3 % (95 %CI 0.9-6.0 %) and 4.1 % (95 %CI 2.6-6.5 %) in the minimal-risk and revised low-risk groups, respectively. In TEXT, the estimated 10-year DR was 2.0 % (95 %CI 0.7-6.2 %) and 4.6 % (95 %CI 2.8-7.7 %) in the minimal- and low-risk groups, respectively.<br><br>CONCLUSIONS: This study confirmed prognostic ability of the minimal-risk BCI cutpoint to classify patients estimated to have minimal-risk of distant recurrence within 10 years among premenopausal patients treated for hormone-receptor-positive node-negative breast cancer, providing relevant information for personalizing adjuvant endocrine therapy. SOFT: (clinicaltrials.gov NCT00066690) TEXT: (clinicaltrials.gov NCT00066703).}, }
@article {pmid41636698, year = {2026}, author = {Zhang, B and Yu, Z and Yan, F and Sun, Y and Ye, J and Liu, X and Qi, S and Wei, X and Liu, S and Ming, D}, title = {Altered Salience-Default Mode Network Dynamics in Subclinical Depression: A Preclustering-Based Co-Activation Pattern Analysis.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {32}, number = {2}, pages = {e70736}, pmid = {41636698}, issn = {1755-5949}, support = {2023YFF1203700//National Key Research and Development Program of China/ ; 62376187//National Natural Science Foundation of China/ ; 24HHNJSS00016//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; }, mesh = {Humans ; Male ; Female ; Magnetic Resonance Imaging/methods ; Adult ; *Default Mode Network/diagnostic imaging/physiopathology ; *Depression/diagnostic imaging/physiopathology ; Young Adult ; *Brain/physiopathology/diagnostic imaging ; Machine Learning ; Middle Aged ; *Nerve Net/diagnostic imaging/physiopathology ; Brain Mapping ; }, abstract = {BACKGROUND: Neuroimaging studies frequently report aberrant spontaneous brain activity and functional connectivity within core functional networks, including the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in subclinical depression (SD). However, the dynamic coordination among these networks remains poorly understood, impeding comprehensive elucidation of the underlying neuropathology of SD.<br><br>METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were collected from subjects with SD (n&#x2009;=&#x2009;26) and healthy controls (HCs, n&#x2009;=&#x2009;33). A preclustering-based co-activation pattern method was developed to investigate the dynamic patterns of network coordination. Finally, machine learning analysis was conducted to evaluate the potential of network dynamics for clinical diagnosis.<br><br>RESULTS: Subjects with SD exhibited decreased dwell time in the SN and increased transition frequency from the SN to DMN, which was positively correlated with depressive severity. Furthermore, an ensemble learning model based on SN-DMN dynamic features achieved a classification accuracy of 96.44% in distinguishing SD from HC.<br><br>CONCLUSION: These findings underscore the potential of altered SN-DMN dynamics as candidates for future neuroimaging markers of SD and support a neurocognitive model whereby altered SN-DMN dynamic coordination makes subjects with SD more prone to internal directed attention biases, thereby contributing to self-related depressive symptoms like rumination.}, }
@article {pmid41635520, year = {2025}, author = {Liu, Y and Xu, P and Hu, S}, title = {Resting-state gamma power in schizophrenia: a systematic review and meta-analysis.}, journal = {Frontiers in psychiatry}, volume = {16}, number = {}, pages = {1731645}, pmid = {41635520}, issn = {1664-0640}, abstract = {Gamma-band oscillations, generated by excitatory-inhibitory circuit interactions, are strongly implicated in schizophrenia, yet evidence on resting-state abnormalities remains inconsistent. We conducted a systematic review and meta-analysis of EEG and MEG studies comparing resting-state gamma activity in patients with schizophrenia and healthy controls, following PRISMA guidelines and assessing study quality with the Newcastle-Ottawa Scale. Twenty studies (n = 998 patients; n = 952 controls) were included. Standardized mean differences (Hedges' g) were calculated and pooled using random-effects models. Results demonstrated a significant elevation of whole-brain gamma power in schizophrenia (g=0.371; 95% CI = 0.119-0.622; P < 0.001; I[2] = 78.2%). Region-specific analyses showed increases in frontal and temporal cortices, with smaller or inconsistent effects in parietal, occipital, and default mode network (DMN) regions. Meta-regression revealed illness duration (β=1.13) and medication status (β=0.43) as positive predictors, while eyes-open resting conditions attenuated effects (β=-0.70), indicating that both clinical chronicity and methodological factors contribute to heterogeneity. Publication bias was not evident by Egger's test, although trim-and-fill suggested five potentially missing small-effect studies, reducing the pooled estimate to g=0.130. Sensitivity analyses confirmed that findings were not driven by outliers, and GRADE assessments rated the certainty of evidence as moderate for whole-brain gamma and low for regional outcomes. Taken together, these findings suggest that resting-state gamma power differences in schizophrenia represent a small and heterogeneous group-level effect, shaped by illness duration, medication status, and recording conditions. Rather than indicating a uniform abnormality, the results underscore substantial variability across studies and highlight the need for cautious interpretation. Future large-scale, longitudinal, and multimodal investigations-particularly in unmedicated and first-episode patients-are warranted to clarify the temporal dynamics, causal mechanisms, and potential translational relevance of resting-state gamma activity in schizophrenia.}, }
@article {pmid41632971, year = {2026}, author = {Li, YY and Hu, AQ and Yi, LL and Mao, ZX and Lü, QY and Wang, J and Wei, W and Huang, YQ and Huang, S and Dai, WJ and Qiao, MX and Xu, JJ and Wang, Q and Li, XJ and Luo, FG and Deng, W and Hu, YZ and Li, T and Guo, WJ}, title = {Comparing the Associations of Internet Addiction and Internet Gaming Disorder With Psychopathological Symptoms: Cross-Sectional Study of Three Independent Adolescent Samples.}, journal = {Journal of medical Internet research}, volume = {28}, number = {}, pages = {e82414}, doi = {10.2196/82414}, pmid = {41632971}, issn = {1438-8871}, mesh = {Humans ; Cross-Sectional Studies ; Adolescent ; *Internet Addiction Disorder/psychology/epidemiology ; Male ; Female ; Young Adult ; *Video Games/psychology ; *Internet ; Surveys and Questionnaires ; *Behavior, Addictive/psychology/epidemiology ; China/epidemiology ; }, abstract = {BACKGROUND: Both internet gaming disorder (IGD) and internet addiction (IA) have been associated with diverse psychopathological symptoms. However, how the 2 conditions relate to each other and which is more strongly associated with psychopathology remain unclear.<br><br>OBJECTIVE: This study aimed to examine the association between IGD and IA and compare the strength of their associations with various types of psychopathological symptoms.<br><br>METHODS: This cross-sectional study surveyed 3 independent samples of Chinese adolescents: the first sample (S1) comprised 8194 first-year undergraduates at a comprehensive university in Chengdu, the second sample (S2) comprised 1720 students from a high school in Hangzhou, and the third sample (S3) comprised 551 inpatients aged 13 to 19 years recruited from 2 tertiary psychiatric hospitals in Hangzhou and Chengdu. IGD was defined as a score of 22 or more on the Internet Gaming Disorder Scale-Short Form (IGDS9-SF), whereas IA was defined as a score of 50 or more on Young's 20-item Internet Addiction Test (IAT-20). Symptoms of depression, anxiety, psychoticism, paranoid ideation, and attention-deficit or hyperactivity were assessed using internationally validated scales including 9-item the Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, psychoticism and paranoid ideation subscales of the Symptom Checklist 90 (absent for S2), and Adult ADHD Self-Report Scale (absent for S1), through online surveys in S1 (October 2020) and S3 (January 2022 to February 2025) and via an offline survey in S2 (March 2024).<br><br>RESULTS: The prevalence estimates (95% CI) of IGD were 4.8% (4.3%-5.2%) in S1, 15.8% (14.0%-17.5%) in S2, and 32.3% (28.4%-36.2%) in S3, whereas prevalence estimates (95% CI) of IA were consistently higher across samples, ranging from 7.3% (6.8%-7.9%) in S1 and 18.8% (17.0%-20.6%) in S2 to 45.9% (41.8%-50.1%) in S3. The IGDS9-SF and the IAT-20 were moderately correlated (Pearson r=0.51-0.57; all P<.001) and were associated with the severity of most psychopathological symptom domains, with consistently stronger associations observed for IAT-20 scores. In multivariate models including all psychopathological symptoms as independent variables, the coefficients of determination (R&#xb2;, 95% CIs) were consistently higher for the IAT-20 than for the IGDS9-SF in S1 (0.33, 0.30-0.35 vs 0.13, 0.11-0.16) and S2 (0.44, 0.39-0.49 vs 0.23, 0.18-0.27), with a similar but nonsignificant pattern observed in S3 (0.13, 0.06-0.26 vs 0.06, 0.03-0.16). Post hoc analyses indicated that psychopathological symptoms were generally more severe in individuals with IA, either alone or comorbid with IGD, than in those with IGD only.<br><br>CONCLUSIONS: This study provides additional evidence that IGD and IA are distinct yet interrelated constructs, and further demonstrates that IA consistently exhibits stronger associations with the severity of psychopathological symptoms than IGD. These findings underscore the importance of recognizing and addressing compulsive and problematic online behaviors that extend beyond gaming, highlighting the need to refine diagnostic frameworks and prioritize targeted clinical interventions.}, }
@article {pmid41632672, year = {2026}, author = {Xu, Y and Vong, CM and Xu, Z and Fu, J and Li, J and Chen, C}, title = {Disentangled Multimodal Spatiotemporal Learning for Hybrid EEG-fNIRS Brain-Computer Interface.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3660692}, pmid = {41632672}, issn = {1558-2531}, abstract = {The hybrid EEG-fNIRS Brain-computer interface (BCI) combines the high temporal resolution of electroencephalography (EEG) with the high spatial resolution of functional near-infrared spectroscopy (fNIRS) to enable comprehensive brain activity detection. However, integrating these modalities to obtain highly discriminative features remains challenging. Most existing methods fail to effectively capture the spatiotemporal coupling features and correlations between EEG and fNIRS signals. Furthermore, these methods adopt a holistic learning paradigm for the representation of each modality, leading to unrefined and redundant multimodal representations. To address these challenges, we propose a disentangled multimodal spatiotemporal learning (DMSL) method for hybrid EEG-fNIRS BCI systems, which simultaneously performs multimodal spatiotemporal coupling and disentangled representation learning within a unified framework. Specifically, DMSL utilizes a compact convolutional module with one-dimensional temporal and spatial convolution layers to extract complex spatiotemporal patterns from each modality and introduces a multimodal attention interaction module to comprehensively capture the inter-modality correlations, enhancing the representations for each modality. Subsequently, DMSL designs an adaptive multi-branch graph convolutional module based on reconstructed channels to effectively capture the spatiotemporal coupling features, incorporating modality consistency and disparity constraints to disentangle common and modality-specific representations for each modality. These disentangled representations are finally adaptively fused to perform different task predictions. The proposed DMSL demonstrates state-of-the-art performance on publicly available datasets for mental arithmetic, motor imagery, and emotion recognition tasks, exceeding the best baselines by 2.34%, 0.59%, and 1.47%, respectively. These results demonstrate the effectiveness of DMSL in improving EEG-fNIRS decoding and its strong generalization ability in BCI applications.}, }
@article {pmid41631479, year = {2026}, author = {Leung, J and Holanda, LJ and Wheeler, L and Chau, T}, title = {Wireless in-ear EEG system for auditory brain-computer interface applications in adolescents.}, journal = {Biomedical physics &amp; engineering express}, volume = {12}, number = {1}, pages = {}, doi = {10.1088/2057-1976/ae3b45}, pmid = {41631479}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/instrumentation/methods ; Adolescent ; Male ; Female ; *Wireless Technology/instrumentation ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Artifacts ; *Ear ; Acoustic Stimulation ; }, abstract = {In-ear electroencephalography (EEG) systems offer several practical advantages over scalp-based EEG systems for non-invasive brain-computer interface (BCI) applications. However, the difficulty in fabricating in-ear EEG systems can limit their accessibility for BCI use cases. In this study, we developed a portable, low-cost wireless in-ear EEG device using commercially available components. In-ear EEG signals (referenced to left mastoid) from 5 adolescent participants were compared to scalp-EEG collected simultaneously during an alpha modulation task, various artifact induction tasks, and an auditory word-streaming BCI paradigm. Spectral analysis confirmed that the proposed in-ear EEG system could capture significantly increased alpha activity during eyes-closed relaxation in 3 of 5 participants, with a signal-to-noise ratio of 2.34 across all participants. In-ear EEG signals were most susceptible to horizontal head movement, coughing and vocalization artifacts but were relatively insensitive to ocular artifacts such as blinking. For the auditory streaming paradigm, the classifier decoded the presented stimuli from in-ear EEG signals only in 1 of 5 participants. Classification of the attended stream did not exceed chance levels. Contrast plots showing the difference between attended and unattended streams revealed reduced amplitudes of in-ear EEG responses relative to scalp-EEG responses. Hardware modifications are needed to amplify in-ear signals and measure electrode-skin impedances to improve the viability of in-ear EEG for BCI applications.}, }
@article {pmid41629357, year = {2026}, author = {Wang, F and Chen, Y and Wang, P and Gong, A and Xu, J and Fu, Y}, title = {An EEG dataset for handwriting imagery decoding of Chinese character strokes and Pinyin single vowels.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-026-06708-3}, pmid = {41629357}, issn = {2052-4463}, support = {Grant Nos. 62376112, 82172058, 81771926, 61763022, 62366026, and 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; Grant No. 2023M734315//China Postdoctoral Science Foundation/ ; }, abstract = {Non-invasive EEG-based brain-computer interfaces (BCI) for handwriting imagery can support the restoration of fine writing abilities in individuals with motor impairments. However, the development of high-performance decoding algorithms is constrained by scarce training datasets. To address this, we present the first open EEG dataset dedicated to handwriting imagery. The dataset comprises 32-channel EEG recordings (sampled at 1000 Hz) from 21 healthy participants across two sessions separated by at least 24 hours. A dual-paradigm design captures multidimensional neural features: a Chinese character stroke imagery task (five basic strokes, 200 trials per session) and a Pinyin single-vowel imagery task (six vowels, 240 trials per session). After rigorous quality screening, 18,480 standardized trials are provided, ensuring completeness, reliability, and adherence to the Brain Imaging Data Structure (BIDS) standard. This dataset enables the development and evaluation of algorithms for non-invasive BCI and supports research on restoring writing-based communication in individuals with motor impairments.}, }
@article {pmid41626718, year = {2026}, author = {Saeed, S and Luo, Z and Wang, H and Xu, L and Zhang, X and Tang, A and Ma, X and Lai, J and Song, P and Hu, S}, title = {Mapping the Global Burden and Inequalities of Bipolar Disorder, 1990-2021, With Projections to 2050: A Systematic Analysis.}, journal = {Bipolar disorders}, volume = {28}, number = {1}, pages = {e70074}, doi = {10.1111/bdi.70074}, pmid = {41626718}, issn = {1399-5618}, support = {2023YFC2506200//National Key Research and Development Program of China/ ; JNL-2023001B//The Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021C03107//The Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//The Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//The Innovation team for precision diagnosis and treatment of major brain diseases/ ; 2022KTZ004//Chinese Medical Education Association/ ; }, mesh = {Humans ; *Bipolar Disorder/epidemiology ; Male ; Female ; *Global Health/statistics &amp; numerical data ; Adult ; Middle Aged ; *Global Burden of Disease/trends ; Incidence ; Prevalence ; Young Adult ; Socioeconomic Factors ; Adolescent ; Aged ; Sex Factors ; }, abstract = {BACKGROUND: Bipolar disorder is a severe mental disorder affecting millions worldwide, necessitating comprehensive policies and interventions.<br><br>AIMS: To provide assessment of global inequalities in the burden of bipolar disorder and their projected trajectories to 2050.<br><br>METHODS: Global Burden of Disease 2021 data from 204 countries and territories were analyzed, stratified by age, gender, and Socio-demographic Index (SDI) quintiles. Age-standardized prevalence (ASPR), incidence (ASIR), and years lived with disability (ASR YLD) per 100,000 population were calculated. Inequalities were assessed using the slope index of inequality (SII) and concentration index (CI), and ARIMA models were applied to project trends to 2050.<br><br>RESULTS: From 1990 to 2021, global incidence of BD&#xa0;increased, while prevalence and years lived with disability (YLDs) remained relatively stable (ASPR: 453.7 [95% UI: 381.6-540.8] to 454.6 [95% UI: 377.9-545.8]). Females consistently had higher prevalence than males (474.2 vs. 435.0 per 100,000 in 2021). High-SDI regions reported the highest rates, with Australasia reaching 1110.8 (95% UI: 940.3-1305.9). The SII for incidence rose slightly (10.87-11.38), while the CI declined (0.096-0.012), indicating increasing absolute but decreasing relative inequalities. Projections suggest a rising global burden, with female prevalence remaining higher and incidence rates converging between genders (global ASIR: 33.8 per 100,000).<br><br>CONCLUSION: Global inequalities in bipolar disorder persist, disproportionately affecting females and high-SDI regions. Projected trends indicate an increasing burden with a narrowing gender gap in incidence, emphasizing the need for targeted interventions and further research on long-term impacts, including the effects of COVID-19.}, }
@article {pmid41625625, year = {2025}, author = {Muhsin, SM and Akbar, MA and Mustari, S and Alashaikh, MH and Chin, AHB}, title = {Human cognitive enhancement and reprogenetic technologies in Malaysia - A survey study of local Muslim undergraduate students' viewpoints.}, journal = {Frontiers in sociology}, volume = {10}, number = {}, pages = {1701007}, pmid = {41625625}, issn = {2297-7775}, abstract = {INTRODUCTION: Newly emerging human enhancement technologies such as brain chip implants, CRISPR-Cas9-based gene editing, and polygenic embryo screening (PES) alongside preimplantation genetic testing (PGT-P) are highly controversial in Islam. However, the prevailing sociocultural dynamics encourage their uptake. In the current era of declining fertility rates, increased parental investment in fewer children has resulted in a flourishing tuition industry, accompanied by heightened academic pressure on students and widespread parental anxiety. These emerging technologies can be employed for cognitive enhancement, thereby providing an expedient solution for parents and students navigating a highly competitive educational environment.<br><br>MATERIALS AND METHODS: To inform and facilitate future policy decision-making, an online survey was conducted among 575 undergraduate Muslim students at the International Islamic University Malaysia (IIUM) to assess their perspectives and opinions regarding these newly emerging technologies.<br><br>RESULTS: The findings indicated a significant level of opposition among respondents to the uptake of human enhancement technologies, with 54.8% opposing polygenic embryo screening, 69.2% opposing gene editing, and 75.3% opposing brain chip implants, reflecting substantial concerns about altering natural human attributes. The results also indicate that numerous Muslim respondents believe that Allah created humans flawlessly and purposefully, asserting that humanity lacks the authority to alter or amend this creation.<br><br>DISCUSSION/CONCLUSION: A three-pronged governance approach for human enhancement technologies is thus proposed, which encompasses (i) bioethical safeguards, (ii) public engagement and education, and (iii) economic accessibility. It is suggested that the Malaysian government should actively consult relevant stakeholders and various segments of the public before enacting future legislation on these technologies.}, }
@article {pmid41626035, year = {2025}, author = {Jackson, MC and Azarraga, RB and Fraix, MP and Agrawal, DK}, title = {Stage-Based Communication Rehabilitation in Amyotrophic Lateral Sclerosis (ALS): A Review of Strategies for Enhancing Quality of Life.}, journal = {Archives of internal medicine research}, volume = {8}, number = {4}, pages = {359-371}, pmid = {41626035}, issn = {2688-5654}, support = {R25 AI179582/AI/NIAID NIH HHS/United States ; }, abstract = {Amyotrophic Lateral Sclerosis (ALS) is an incurable progressive degenerative neuromuscular disease. One way ALS affects patients is through dysarthria significantly impacting a patient's quality of life by affecting their ability to communicate. This makes maintaining relationships, identity and autonomy difficult, all of which affect psychological wellbeing - a determinant of the quality of life. Dysarthria makes communication difficult, and because the regions affected by ALS first are different for each patient, creating strategies for rehabilitating communication can be challenging. In this review we explore the different communication rehabilitation options available and organize them based on if they are usable based on the onset of intelligibility and locked in state. Interventions before the onset of intelligibility in the early stage are proactive measures such as voice banking and education which empower patient autonomy and a sense of control. Interventions between onset of intelligibility and the locked-in state in the middle stage are alternative and augmentative communication strategies varied in accessibility and usability in patients based on their preferences and functional ability. Late-stage interventions which work after a patient with ALS has entered a locked-in state, are the most technologically advanced alternative and augmentative communication devices and rehabilitate function inaccessible by other methods in this disease stage. While assessing patient values and recommending interventions which meet patient needs is most important in rehabilitation of communication in patient with ALS, using a stage-based approach to evaluate and recommend the treatment of dysarthria and communication rehabilitation will optimize quality of life throughout the progression of disease.}, }
@article {pmid41625473, year = {2025}, author = {Jin, C and Yang, J and Liang, Z and Qiu, J and Zha, R and Yuan, Z and Shen, Y and Zhang, X}, title = {Navigating online emotion: affective patterns and depressive traits in youth digital engagement.}, journal = {Frontiers in psychology}, volume = {16}, number = {}, pages = {1736426}, pmid = {41625473}, issn = {1664-1078}, abstract = {INTRODUCTION: Youth digital engagement serves as a notable avenue for the expression of emotion and the construction of self among today's youth. This study aims to examine the patterns of youth online emotional expression and their association with individual psychological traits, particularly depressive tendencies.<br><br>METHODS: 23,966 Weibo posts published by 103 active youth users were sampled and analyzed. An integrative framework combining Russell's Circumplex Model with multi-level thematic analysis was applied to code each post for valence, arousal, trigger type and coping strategy. Youths also completed a standard depression-screening scale; scores were used to contrast high- versus low-depressive trait sub-groups.<br><br>RESULTS: The findings reveal that youth online emotional expression overall is characterized by a self-focused nature, high pleasure, and high arousal. The study also found that individual psychological traits influence emotional expression patterns. Individuals with depressive tendencies showed a significant propensity for higher emotional arousal expression and more no-trigger expression. Furthermore, no-trigger expression plays a mediating role in their emotional expression mechanism.<br><br>DISCUSSION: The study provides an integrative framework for youth digital engagement and highlights "no-trigger" expression as a mediator in the framework. These findings can guide early detection efforts and contribute to designing targeted digital mental health supports, as well as informing guidance for families and platform managers.}, }
@article {pmid41624131, year = {2025}, author = {Guo, X and Li, P and Liu, H and Ding, S}, title = {A systematic review of the effects of brain-computer interface on lower limb motor function, balance function, and activities of daily living in stroke patients.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1641843}, pmid = {41624131}, issn = {1662-4548}, abstract = {OBJECTIVE: To systematically evaluate the effects of brain-computer interface (BCI) technology on lower limb motor function, balance function, and activities of daily living in stroke patients.<br><br>METHODS: This study followed the PRISMA guidelines and searched PubMed, Web of Science, EMbase, The Cochrane Library, CNKI, Wanfang, and VIP databases, with an additional manual search. The search period was from database inception to March 2024. The PEDro scale was used to assess the quality of the studies, the GRADE system was applied to evaluate the evidence quality for outcome measures, and Meta-analysis was conducted using Stata 17.0 software.<br><br>RESULTS: The systematic review included nine studies. The methodological quality, assessed using the PEDro scale, yielded an average score of 6.9, which corresponds to a moderate-to-low certainty of evidence. The Meta-analysis showed that BCI technology significantly improved lower limb motor function (MD&#x202f;=&#x202f;3.52, 95% CI [2.03, 5.00], p&#x202f;<&#x202f;0.001) and activities of daily living (MD&#x202f;=&#x202f;6.08, 95% CI [1.81, 10.35], p&#x202f;=&#x202f;0.01), but had no significant effect on balance function (MD&#x202f;=&#x202f;4.82, 95% CI [-1.53, 11.16], p&#x202f;=&#x202f;0.14). Subgroup analysis showed that the effect size in the acute and subacute phases was 3.89, and in the recovery phase, it was 3.12, both of which were statistically significant. In terms of intervention methods, the effect size for MI-BCI was 2.73, and for BCI-Robot, it was 4.60, both statistically significant. Regarding intervention dosage, the effect size for 2.5-10&#x202f;h was 2.60, and for 12-20&#x202f;h, it was 5.46, both statistically significant.<br><br>CONCLUSION: Current evidence suggests that BCI-based interventions have a beneficial effect on lower limb motor function and activities of daily living in stroke patients. Interventions initiated during the acute or subacute phase, with a total dose exceeding 12&#x202f;h, appear to be associated with superior outcomes. However, the certainty of this evidence is moderate to low, necessitating further validation. Future research should prioritize large-scale, high-quality randomized controlled trials to definitively establish the efficacy of BCI technology and elucidate its optimal implementation protocols.}, }
@article {pmid41623459, year = {2026}, author = {Eyvazpour, R and Farrokhi, B and Erfanian, A}, title = {A general model based on Riemannian manifold for stable decoding movement trajectory from ECoG signals.}, journal = {iScience}, volume = {29}, number = {2}, pages = {114521}, pmid = {41623459}, issn = {2589-0042}, abstract = {Decoding continuous 3D hand trajectories from electrocorticographic (ECoG) signals holds potential for brain-computer interface (BCI) applications. However, inter-session variability poses a major challenge for generalization. In this study, we propose a framework that leverages Riemannian-based feature extraction combined with stacked long short-term memory (LSTM) network to enable transfer learning across multiple sessions. ECoG recordings from five monkeys performing reaching tasks are considered. Spatial cross-frequency covariance matrices are computed over the brain area for each of 10 frequency band power and projected onto a Riemannian manifold to extract features which are invariant to session variability. These features and spectral feature are then used to train staked LSTM network. The results show that the proposed method achieves a stable cross-session performance and outperforms baseline models which are trained on frequency features. These findings highlight the potential of combining geometric features with temporal deep learning models for generalized decoding in translational BCI systems.}, }
@article {pmid41623142, year = {2026}, author = {de Borman, A and Dyck, BV and Rooy, KV and Carrette, E and Meurs, A and Roost, DV and Van Hulle, MM}, title = {Word classification across speech modes from low-density electrocorticography signals.}, journal = {Journal of neural engineering}, volume = {23}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ae3a1b}, pmid = {41623142}, issn = {1741-2552}, mesh = {Humans ; *Electrocorticography/methods ; Male ; *Brain-Computer Interfaces ; Female ; *Speech/physiology ; Adult ; Middle Aged ; Young Adult ; Imagination/physiology ; Speech Perception/physiology ; }, abstract = {Objective.Speech brain-computer interfaces (BCIs) aim to provide an alternative means of communication for individuals who are not able to speak. Remarkable progress has been achieved to decode attempted speech in individuals with severe anarthria. In contrast, imagined speech remains challenging to decode. The underlying neural mechanisms and relations to other speech modes are still elusive.Approach.In this study, we collected low-density electrocorticography signals from ten participants during a word repetition task. Electrodes were implanted for presurgical epilepsy evaluation in participants with preserved speech abilities. Models were developed using linear discriminant analysis to classify five words in response to different speech modes. We compared models trained during speaking, listening, imagining speaking, mouthing and reading. The relations between speech modes were investigated by transferring and augmenting models across speech modes.Main results.As expected, performed speech achieved the highest word classification accuracy followed by listening, mouthing, imagining and reading. While the accuracies obtained were not high enough for practical application, model transfer and augmentation could be investigated across speech modes. Transferring or augmenting models from one speech mode to another mode could significantly improve model performance. In particular, patterns learned from performed and perceived speech could generalize to imagined speech, leading to significantly improved imagined speech performance in seven participants. For four participants, imagined speech could be decoded above chance exclusively when models were transferred or augmented with performed or perceived speech.Significance.Imagined speech is often preferred by speech BCI users over attempted speech, as it requires less effort and can be produced more quickly. Transferring models across speech modes has the potential to facilitate and boost the development of imagined speech decoders.}, }
@article {pmid41621526, year = {2026}, author = {Zhao, Y and Zhang, Y and Li, T}, title = {Causal relationships between ADHD, ASD and brain structure: A mendelian randomization study.}, journal = {Progress in neuro-psychopharmacology &amp; biological psychiatry}, volume = {}, number = {}, pages = {111631}, doi = {10.1016/j.pnpbp.2026.111631}, pmid = {41621526}, issn = {1878-4216}, abstract = {Neurodevelopmental disorders (NDDs) are debilitating conditions that impose significant burdens on individuals, families, and society. Despite evidence demonstrated altered brain structure in NDDs, definitive conclusions remain elusive. Using two-sample mendelian randomization (MR) and the latest GWAS findings, the current study aimed to elucidate the causal relationships between grey matter (GM), white matter (WM), subcortical regions, and two prevalent NDDs: attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Our findings identified two frontal regions as key neural substrates in NDDs. Specifically, an increased surface area (SA) of the superior frontal gyrus (SFG) was significantly associated with an enhanced risk of ADHD (P = 2.04E-13, β = 4.28E-02, SE = 5.82E-03), while a larger SA of the orbital frontal gyrus (OFG) was associated with a reduced risk of ASD (P = 1.98E-42, β = -9.8E-02; SE = 0.007). Regarding WM tracts, the mode of anisotropy (MO) in the inferior fronto-occipital fasciculus (IFO) emerged as a causal factor for ADHD (P = 3.36E-70, β = -18.35; SE = 1.04), whereas the MO in the retro-lenticular part of the internal capsule (RLIC) was implicated in ASD (P = 1.37E-04, β = -12.73, SE = 3.34). No reverse causal link, i.e., brain alteration caused by NDDs was identified. Further mediation analyses using functional MRI (fMRI) GWAS data revealed that brain functional activities mediated the relationship between structural brain changes and NDDs risk. In conclusion, our findings underscored the critical role of the frontal lobe and association and projection fibers in the pathophysiology of NDDs, provide novel insights into the neural mechanisms underlying ADHD and ASD.}, }
@article {pmid41621181, year = {2026}, author = {Spinelli, R and Sanchis, I and de Orellana, M and Humpola, MV and Rietmann, &#xc1; and Siano, &#xc1;S}, title = {A nature-inspired peptide from the Boana cordobae frog as a potent and reversible AChE inhibitor with anti-amyloid and neuroprotective activities.}, journal = {Bioorganic chemistry}, volume = {171}, number = {}, pages = {109566}, doi = {10.1016/j.bioorg.2026.109566}, pmid = {41621181}, issn = {1090-2120}, abstract = {Alzheimer's disease (AD) is a multifactorial and progressive neurodegenerative disorder for which no effective treatment currently exists. The development of multitarget-directed ligands (MTDLs) capable of simultaneously modulating several pathological pathways represents a rational strategy to address its complex etiology. In this study, we report the isolation, chemical synthesis, and functional characterization of BcI-4, a short cationic peptide identified from the skin secretion of the Argentinean frog Boana cordobae. The peptide exhibited potent and reversible inhibitory activity against acetylcholinesterase (AChE), with IC50 values of 1.10 and 0.9 μM for recombinant human and Electrophorus electricus AChE, respectively, acting through a non-competitive mechanism involving the peripheral anionic site (PAS). BcI-4 also inhibited AChE-induced β-amyloid (Aβ) aggregation, showed modest monoamine oxidase B (MAO-B) inhibition, and displayed both antioxidant and metal-chelating activities, including inhibition of lipid peroxidation. The peptide retained the multifuctional pharmacological profile previously observed for the crude extract of B. cordobae, with significantly enhanced potency and selectivity toward AChE. Moreover, BcI-4 was non-toxic in vitro (hemolysis and HeLa cell assays) and in vivo (Artemia salina test) even at the highest concentrations tested. Altogether, these findings position BcI-4 as a nature-inspired multitarget peptide with neuroprotective potential, combining reversible AChE inhibition, anti-amyloid, antioxidant, and MAO-B modulatory activities. BcI-4 represents a promising lead compound for the development of peptide-based therapeutics against AD.}, }
@article {pmid41621106, year = {2026}, author = {Alhourani, A and Pouratian, N}, title = {Editorial. Defining value and function in miniaturized cortical arrays for human brain-computer interface applications.}, journal = {Neurosurgical focus}, volume = {60}, number = {2}, pages = {E4}, doi = {10.3171/2025.11.FOCUS251041}, pmid = {41621106}, issn = {1092-0684}, }
@article {pmid41621105, year = {2026}, author = {Vattipally, VN and Kramer, P and Troumouchi, K and Shiino, S and Abouelseoud, N and Joshi, K and Xu, R and Theodore, N and Brem, H and Bettegowda, C and Jantzie, LL and Robinson, S and Azad, TD and Kathuria, A}, title = {Engineered neuroglial organoids as living neural interfaces for restorative neurosurgery.}, journal = {Neurosurgical focus}, volume = {60}, number = {2}, pages = {E5}, doi = {10.3171/2025.11.FOCUS25911}, pmid = {41621105}, issn = {1092-0684}, mesh = {*Organoids/transplantation/physiology ; Humans ; *Neuroglia/physiology/transplantation ; Animals ; *Brain-Computer Interfaces ; *Tissue Engineering/methods ; *Neurosurgical Procedures/methods ; Pluripotent Stem Cells ; Neurons/physiology ; }, abstract = {Acute and chronic CNS pathologies that result in tissue loss remain among the most intractable problems in neurosurgery, with current treatments focused on stabilization and neuroprotection rather than structural repair. Neural interfaces such as recording, stimulating, or replacing neural activity have demonstrated value in restoring function via prostheses and brain-computer interfaces, yet these approaches are constrained by electrode design, bandwidth, and limited biological integration. Engineered neuroglial organoids offer a complementary, biologically based interface strategy. Derived from pluripotent stem cells, neuroglial organoids arrive as 3D constructs containing neurons and glia in intrinsic architecture, capable of vascularization, synaptic connectivity, and integration with host tissue. Building on dissociated stem cell suspensions, organoids act not only as reservoirs of cells but also as living neural interfaces, receiving inputs from host circuits and generating functional outputs. Preclinical studies have demonstrated that transplanted organoids can couple to host sensory pathways, respond to stimulation, and support recovery of motor and cognitive functions. Moreover, emerging work coupling organoid grafts to brain-computer interfaces highlights the potential for closed-loop biological electronic systems, in which engineered devices provide precise recording and stimulation while organoids contribute adaptive, active biological circuits. This combination allows real-time bidirectional communication, allowing the graft to be both monitored and adapted to structurally and functionally integrate into host tissue. In this review, the authors examine neuroglial organoid transplantation through the lens of neural interfacing. They outline lessons from non-CNS organoid transplantation, summarize neurotrauma studies where grafts engage host circuits, and highlight opportunities to integrate organoids with electrodes, stimulation paradigms, and computational models. They also discuss challenges, namely vascularization, immune tolerance, surgical delivery, and manufacturing standards, that parallel those in neural device translation. For neurosurgeons, the appeal of neuroglial organoids lies not only in tissue replacement but in establishing a new class of biological neural interfaces, extending the reach of restorative neurosurgery. By merging living constructs with engineered devices, organoid-based strategies may enable hybrid restorative systems that restore function after neurological injury and disease.}, }
@article {pmid41621104, year = {2026}, author = {Johnson, TR and Moralle, S and Luo, Z and Taylor, DM}, title = {Implanting microelectrode arrays in the bottom of the central sulcus targeting somatosensory area 3a for restoration of proprioception.}, journal = {Neurosurgical focus}, volume = {60}, number = {2}, pages = {E8}, doi = {10.3171/2025.11.FOCUS25916}, pmid = {41621104}, issn = {1092-0684}, mesh = {Animals ; Macaca mulatta ; Microelectrodes ; *Electrodes, Implanted ; *Somatosensory Cortex/surgery/physiology/diagnostic imaging ; *Proprioception/physiology ; *Brain-Computer Interfaces ; Male ; Stereotaxic Techniques ; Magnetic Resonance Imaging ; }, abstract = {OBJECTIVE: The long-term goal of this work is to develop a sensorimotor brain-machine interface (BMI) in which intended movements are decoded from the motor cortex and proprioceptive feedback is delivered via intracortical microstimulation of Brodmann's area 3a. A vital step toward this goal is to demonstrate in rhesus macaques a novel surgical approach for the precise and safe implantation of custom-length microelectrode arrays into area 3a at the bottom of the central sulcus.<br><br>METHODS: Preoperative planning combined high-resolution 7-T MR and CT imaging to generate 3D models of the cortices of 2 subjects. These models were used to fabricate 3D-printed skull replicas and to define a stereotactic trajectory that provided the shortest perpendicular path to the base of the central sulcus, where Brodmann's area 3a resides. Custom variable-length microwire electrode arrays were designed to span this target region. The flexibility of the microwires precluded the standard impact-insertion approach used with stiffer electrodes. Therefore, a custom vacuum-powered microdrive holder that moved with the pulsating brain was developed to maintain electrode orientation and to allow slow, controlled insertion along the planned trajectory. After implantation, the craniotomy was closed, and a skull-mounted recording chamber was secured. Postoperative verification of array placement was performed using CT imaging and neural recordings.<br><br>RESULTS: In both animals, imaging revealed that the base of the central sulcus was positioned anterior to its dorsal opening, making a precentral implant trajectory the shortest and most direct path to the bottom of the central sulcus. The integrated imaging and 3D modeling approach enabled accurate stereotactic placement of custom microelectrode arrays using the novel vacuum-assisted microdrive, as confirmed by postoperative CT imaging. Both surgical procedures were completed without complication, and isolatable neuronal spikes were recorded from multiple channels in each subject. In both animals, neural activity was modulated by passive movements of the arm.<br><br>CONCLUSIONS: Intracortical microelectrode implants for BMI applications have traditionally been limited to short (1.5-mm) electrodes targeting cortical sites exposed on the brain surface. The surgical methodology described here enables safe and accurate implantation of custom-length arrays into deep sulcal targets such as Brodmann's area 3a. By expanding access to previously inaccessible cortical regions, this approach broadens the potential neural information available for future BMI applications.}, }
@article {pmid41621103, year = {2026}, author = {Lehner, KR and Luo, S and Greene, B and Angrick, M and Candrea, D and Husari, KS and Barth, K and Dister, J and Anushiravani, R and Miller, JS and Ho, E and Rincon-Torroella, J and Rapoport, B and Comair, Y and Crone, NE}, title = {Initial experience with the precision neuroscience Layer 7 micro-electrocorticography interface for real-time intraoperative neural decoding.}, journal = {Neurosurgical focus}, volume = {60}, number = {2}, pages = {E3}, doi = {10.3171/2025.11.FOCUS25908}, pmid = {41621103}, issn = {1092-0684}, mesh = {Humans ; Adult ; *Electrocorticography/methods/instrumentation ; *Brain-Computer Interfaces ; Male ; Female ; Young Adult ; *Brain Neoplasms/surgery ; *Intraoperative Neurophysiological Monitoring/methods ; Craniotomy/methods ; Speech/physiology ; Brain Mapping/methods ; *Motor Cortex/physiology/surgery ; }, abstract = {OBJECTIVE: The aim of this study was to evaluate the feasibility of using the Layer 7 Cortical Interface, a high-density micro-electrocorticography (μECoG) array, for intraoperative neural recordings and real-time brain-computer interface (BCI) applications, including speech decoding and cursor control.<br><br>METHODS: Four patients (age range 23-43 years) who underwent awake craniotomy for tumor resection near the eloquent cortex were enrolled. The Layer 7 &#xb5;ECoG device (1024 channels, approximately 1.5-cm2 coverage) was placed on the motor cortex following standard cortical mapping. Intraoperative tasks included a joystick-controlled center-out movement paradigm (n = 3) and an auditory-cued speech repetition task (n = 1). Neural data were recorded at 20 kHz, preprocessed, and used to train decoders intraoperatively. A transformer-based model was applied for real-time speech synthesis and a convolutional neural network was trained for speech classification, while a convolutional recurrent neural network was trained to classify 2D cursor direction.<br><br>RESULTS: All 4 patients tolerated the procedure without device-related adverse events. The mean electrode impedances across 6 arrays (6144 channels) ranged from 1.21 to 1.99 M&#x3a9;, with 954-990 channels per array retained for analysis. In the speech task, a 4-word classification model achieved 77.5% accuracy, and a real-time synthesis model was able to distinguish speech and silence during approximately 20 minutes of data recording in the operating room. In the motor task, a 4-direction classification model achieved 78%-84% accuracy. Recordings remained stable during tumor resection.<br><br>CONCLUSIONS: The Layer 7 Cortical Interface device enabled high-resolution nonpenetrating cortical recordings that supported real-time speech classification and cursor control within the limited timeframe of an intraoperative session. These findings highlight the potential clinical applications of high-density &#xb5;ECoG for functional mapping, diagnostic assessment, and future chronic BCI systems for patients with motor and communication impairments.}, }
@article {pmid41621102, year = {2026}, author = {Mortezaei, A and Al-Saidi, N and Taghlabi, KM and Hussein, A and Hallak, H and Pouratian, N and Faraji, AH}, title = {Brain-computer interfaces in poststroke rehabilitation: a meta-analysis of randomized clinical trials.}, journal = {Neurosurgical focus}, volume = {60}, number = {2}, pages = {E7}, doi = {10.3171/2025.11.FOCUS25913}, pmid = {41621102}, issn = {1092-0684}, mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Randomized Controlled Trials as Topic/methods ; *Stroke/therapy/physiopathology ; Recovery of Function/physiology ; }, abstract = {OBJECTIVE: Stroke is a leading cause of long-term disability, with conventional rehabilitation often failing to achieve substantial motor recovery, particularly in patients with severe paresis or in chronic stages. Brain-computer interfaces (BCIs) offer a novel rehabilitation approach by translating neural signals into real-time external feedback. The authors performed a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy and safety of noninvasive BCIs for poststroke motor rehabilitation.<br><br>METHODS: A systematic literature review was performed based on the PRISMA guidelines using 3 databases. Eligible RCTs enrolled stroke patients receiving noninvasive BCI-assisted motor rehabilitation compared with conventional therapies. The primary outcome was the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) improvement. Secondary outcomes included the Action Research Arm Test (ARAT), Motor Activity Log (MAL), Modified Barthel Index (MBI), and Modified Ashworth Scale (MAS). Effect sizes were pooled using random-effects models and expressed as mean differences (MDs), standardized MDs (SMDs), or odds ratios, each with corresponding 95% confidence intervals (CIs).<br><br>RESULTS: Thirty-two RCTs comprising 1187 patients were included with no heterogeneity or significant imbalances in baseline characteristics across groups. A BCI was significantly superior in FMA-UE score improvement compared with controls (MD 3.85, 95% CI 2.84-4.86; p < 0.01), with benefits sustained at follow-up. Within-group analyses revealed greater improvement in the BCI arm from follow-up to baseline (MD 8.18, 95% CI 5.77-10.60; p < 0.01). A BCI was also associated with higher ARAT (MD 7.18, 95% CI 2.4-12.0; p < 0.01) and MAL (SMD 0.59, 95% CI 0.34-0.85; p < 0.01) scores, although between-group differences for these endpoints were not statistically significant. For the MBI, a subgroup analysis did not demonstrate significant differences, but a sensitivity analysis revealed a significant improvement in the BCI group (p = 0.042). There were no significant differences in the within- and between-group analyses of the MAS. A subgroup analysis suggested a synergistic benefit with the BCI combined with neuromuscular electrical stimulation. Adverse events were infrequent and generally mild; 2 withdrawals in the BCI group were reported due to seizure and electrode allergy. Notably, all heterogeneity was successfully resolved through sensitivity analyses, supporting the robustness of the findings.<br><br>CONCLUSIONS: Noninvasive BCI-assisted rehabilitation is a safe and effective adjunct to conventional therapy, enhancing motor recovery after stroke. While all included RCTs evaluated noninvasive systems, the potential value and efficacy of invasive and minimally invasive BCIs may require further consideration.}, }
@article {pmid41620439, year = {2026}, author = {Gong, Q and Fu, X and Feng, D and Rao, S and Pütz, B and Müller-Myhsok, B and Wei, L and Shen, C and Zhang, Y and Xu, L and Chen, W and Yang, K and Chen, D and Lv, X and Yan, Z and Luo, D and Wei, P and Jiang, H and Chen, W}, title = {Randomized, double-blind, sham-controlled pilot trial of theta-band transcranial alternating current stimulation during cognitive training in mild Alzheimer's disease.}, journal = {Translational psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41398-026-03822-z}, pmid = {41620439}, issn = {2158-3188}, support = {82071181//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82101581//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82371453//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Cognitive deficits are a hallmark of Alzheimer's disease (AD), and effective treatments remain elusive. Transcranial alternating current stimulation (tACS), a non-invasive technique, has shown potential in improving cognitive function across various populations, but further research is needed to investigate its efficacy in AD. In a randomized, double-blind, sham-controlled pilot trial, 36 mild AD patients received active or sham theta-tACS (8 Hz, 1.6 mA, 20-min daily) during n-back task for two weeks, followed by a 10-week follow-up. Cognitive assessments and resting-state EEG were analyzed at baseline, after-treatment, and follow-up. The results showed that the active group demonstrated significant cognitive improvements after treatment (MMSE: t (15) =-3.273, p = 0.005, Cohen's d = 0.82), particularly in short-term memory (MMSE-recall: Z = -2.11, p = 0.035, r = 0.53), with maintained benefits after 10 weeks. In contrast, the sham group exhibited long-term cognitive decline (MMSE: t (4) = 3.586, p = 0.023, Cohen's d = -1.60). EEG analysis revealed reduced gamma power (t (23) = 2.689, p = 0.013, Cohen's d = 1.077) and theta connectivity in active group, particularly in the frontotemporal regions (F4/F7: t (23) = 2.467, p = 0.021, Cohen's d = 0.988; F4/T3: t (23) = 2.465, p = 0.022, Cohen's d = 0.987), which was correlated with cognitive improvements (R = -0.57, p = 0.043). In conclusion, tACS combining cognitive training may offer cognitive benefits in mild AD by modulating neural activity, though further studies are needed to clarify its mechanisms.}, }
@article {pmid41620194, year = {2026}, author = {Graham, F and Hutchinson, DW and Moon, TJ and Wang, J and Flores-Jimenez, H and Druschel, L and Ogunnaike, L and Gao, Y and Smith, T and DeTillio, S and Goelz, C and Bhalotia, A and Newman, L and Hess-Dunning, A and Capadona, JR and Karathanasis, E}, title = {Lipid Nanoparticle-Mediated Cd14 siRNA Delivery Ameliorates the Acute Inflammatory Response to Intracortical Microelectrode Implantation.}, journal = {Acta biomaterialia}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.actbio.2026.01.055}, pmid = {41620194}, issn = {1878-7568}, abstract = {Intracortical microelectrodes (IMEs) are an integral component of brain computer interfaces (BCIs) designed to study and treat neurological disorders. Unfortunately, IMEs tend to fail prematurely due in part to the macrophage-mediated inflammation in response to implantation injury and the persistent foreign body reaction. Previous work has established that cluster of differentiation 14 (CD14) is implicated in the neuroinflammatory response to IME implants. CD14 is a conserved damage-associated coreceptor that facilitates immune activation in the presence of inflammatory damage-associated stimuli. We sought to mitigate the inflammatory response to IME implantation by suppressing CD14 expression on macrophages using a lipid nanoparticle (LNP) loaded with Cd14-specific siRNA. We tested the efficacy of the LNP-mediated gene delivery in cultured murine macrophages and in an in vivo mouse model with IME implants. Our in vitro findings indicated that the LNPs suppress inflammatory cytokine secretion. The in vivo studies showed efficient targeting of the LNPs to the desired cell populations with the majority of LNPs found in blood-circulating macrophages and infiltrating macrophages at the intracortical implant site. Our results show that the LNPs efficiently silence expression of the targeted Cd14 gene. Suppression of the CD14 protein led to reduced infiltration of immune cells to the brain parenchyma, as well as a significant decrease of the inflammatory response to implantation within the first 24 hours after implantation, as determined by flow cytometry and transcriptomics. Together our results suggest that LNP-mediated gene therapy can specifically regulate one of the dominant drivers of the innate immune response to IME implantation. STATEMENT OF SIGNIFICANCE: Brain-computer interfaces rely on implanted electrodes to record and stimulate neural activity, but these devices often fail early because the body mounts an inflammatory immune response against them. Here, we focused on a central immune receptor, CD14, as a key driver of the inflammatory response to implants. Using lipid nanoparticles to deliver gene-silencing RNA, we were able to suppress CD14 expression in macrophages both in culture and in a mouse model with implanted electrodes. This targeted approach reduced immune cell infiltration and inflammation around implants. Our findings demonstrate that lipid nanoparticle-mediated gene therapy can selectively weaken the brain's innate immune response to implants, offering a promising strategy to improve the longevity and performance of neural interfaces.}, }
@article {pmid41619890, year = {2026}, author = {Zhou, W and Chen, Y and Cen, K and Li, Z and Huang, C and Su, W and Li, P}, title = {Calcium carboxymethyl cellulose/quaternary ammonium chitosan self-gelling powder with good biocompatibility for wound hemostasis.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {150610}, doi = {10.1016/j.ijbiomac.2026.150610}, pmid = {41619890}, issn = {1879-0003}, abstract = {In this study, a multifunctional self-gelling hemostatic powder (CQA) was designed using natural biomaterials by integrating the antioxidant and biocompatible properties of Aloe vera gel (AV) with the hemostatic efficacy of calcium carboxymethylcellulose (Ca-CMC) and the antibacterial activity of quaternary ammonium chitosan (QCS). The CQA powder rapidly absorbs moisture upon contact with blood, forming a physically sealing hydrogel network through electrostatic and hydrogen bonding interactions. In vitro evaluations revealed that the optimized formulation, CQA0.3, exhibits outstanding adsorption capacity, antioxidant activity, and biocompatibility. Compared to commercial chitosan-based hemostatic powder (CS), CQA0.3 demonstrated significantly enhanced procoagulant performance, with a blood clotting index (BCI) of 8.48% versus 56.65% for CS, and promoted accelerated blood cell adhesion. In whole-blood coagulation assays, the CQA0.3 group achieved rapid clotting within 180 s, while bleeding persisted in the CS group beyond 210 s. In practical hemorrhage models, CQA0.3 reduced blood loss to 94.0 ± 8.7 mg, substantially lower than both the CQ group (225.7 ± 6.03 mg) and the CS group (292.7 ± 14.46 mg). These findings highlight the potential of CQA0.3 as a safe, efficient, and adaptable hemostatic agent for emergency and clinical applications, combining rapid gelation, high biocompatibility, and excellent wound adaptability.}, }
@article {pmid41618548, year = {2026}, author = {Ma, Y and Li, H and Li, W and Jiang, D and Li, H and Xuan, X and Li, M}, title = {Noninvasive Graphene Brain-Computer Interface Integrating EEG Recording and Acoustic-Optical Stimulation for Rhythm Intervention.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e05327}, doi = {10.1002/adhm.202505327}, pmid = {41618548}, issn = {2192-2659}, support = {52271341//National Nature Science Foundation of China/ ; 62271350//National Nature Science Foundation of China/ ; }, abstract = {Noninvasive wearable stimulation-acquisition integrated brain-computer interfaces (BCIs) have significant application value in neurological rehabilitation and health monitoring. However, their widespread adoption depends on the development of long-term, stable dry/semi-dry electrodes and lightweight hardware. In this study, a sodium-doped vertical graphene (Na-VG) electrode that utilized sweat and tissue fluids as electrolytes was developed. When applied with ultrapure water, an extremely low electrode-skin impedance of 4.22 ± 0.50 kΩ was detected at 10 Hz. The 20-channel EEG cap assembled with the Na-VG electrodes maintained a high α-rhythm response of 5.06-14.22 dB in the signal-to-noise ratio of whole-brain EEG signals during a 36-day stability evaluation. Furthermore, a wearable Na-VG headband BCI combining sound-light stimulation and EEG acquisition was developed. Healthy individuals wearing this system, under the coordinated intervention of 40 Hz differential-frequency sound stimulation and 10 Hz light stimulation, showed changes in the frequency and amplitude of the α-rhythm. This improvement increased the proportion of moderate-levels of the vigilance index, neural activity, heart rate, emotion, and arousal index to 84-100%, with a precision of 98.73%. These results provide novel long-term, lightweight strategies and matching software and hardware for the monitoring and noninvasive intervention of emotional and cognitive-related diseases.}, }
@article {pmid41618424, year = {2026}, author = {Campion, S and Navarro-Suné, X and Rivals, I and Morélot-Panzini, C and Serresse, L and Chavez, M and Demoule, A and Niérat, MC and Raux, M and Similowski, T}, title = {SSVEP-based brain-computer interface enabling graded dyspnoea self-report: proof-of-concept study in healthy volunteers.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-025-01846-y}, pmid = {41618424}, issn = {1743-0003}, abstract = {BACKGROUND: Mechanically ventilated patients may experience respiratory suffering, which is difficult to assess when verbal communication is impaired. We evaluated the performance of a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) designed to enable self-reporting of dyspnoea in this context.<br><br>METHODS: Forty-nine healthy volunteers were studied under five respiratory conditions: normal breathing (NB), inspiratory resistive loading (IRL), inspiratory threshold loading (ITL), CO&#x2082; inhalation (CO&#x2082;), and a return to NB as wash-out (NBWO). Respiratory discomfort was evaluated using a visual analogue scale (VAS). Two BCIs models were tested: a detection BCI (D-BCI), designed to discriminate between 'breathing is OK' and 'breathing is difficult', and a quantification BCI in the form of a LED-based analogue scale (LAS), composed of five light-emitting diodes. Visual stimuli were delivered at different frequency sets: 12-15&#xa0;Hz, 15-20&#xa0;Hz, and 20-30&#xa0;Hz for the D-BCI; low frequencies (13-17-19-23-29&#xa0;Hz) and high frequencies (41-43-47-53-59&#xa0;Hz) for the LAS. Performance was assessed using receiver operating characteristic (ROC) curves; the area under the ROC curve (AUC) was the primary outcome.<br><br>RESULTS: Participants reported significant respiratory discomfort during IRL, ITL, and CO&#x2082; conditions in the D-BCI groups, and during ITL and CO&#x2082; in the LAS groups, as reflected by higher dyspnoea VAS scores compared to NB. The best-performing frequency sets were 20-30&#xa0;Hz for the D-BCI (AUC 0.89 [0.89-0.90]) and low frequencies for the LAS (AUC 0.84 [0.83-0.85]).<br><br>CONCLUSIONS: This study demonstrates that an SSVEP-based BCI can sucessfully detect and quantify experimentally induced dyspnoea in healthy individuals. Further research is needed to evaluate its clinical applicability for assessing dyspnoea in non-communicative patients.}, }
@article {pmid41617748, year = {2026}, author = {Samuel, J and Murugan, TK and Govindaraj, L and Balaji, M and SenthilKumar, V and Sundararajan, S}, title = {Adversarial robust EEG-based brain-computer interfaces using a hierarchical convolutional neural network.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-34024-0}, pmid = {41617748}, issn = {2045-2322}, abstract = {Brain-Computer Interfaces (BCIs) based on electroencephalography (EEG) are widely used in motor rehabilitation, assistive communication, and neurofeedback due to their non-invasive nature and ability to decode movement-related neural activity. Recent advances in deep learning, particularly convolutional neural networks, have improved the accuracy of motor imagery (MI) and motor execution (ME) classification. However, EEG-based BCIs remain vulnerable to adversarial attacks, in which small, imperceptible perturbations can alter classifier predictions, posing risks in safety-critical applications such as rehabilitation therapy and assistive device control. To address this issue, this study proposes a three-level Hierarchical Convolutional Neural Network (HCNN) designed to improve both classification performance and adversarial robustness. The framework decodes motor intention through a structured hierarchy: Level 1 distinguishes MI from ME, Level 2 differentiates unilateral and bilateral motor tasks, and Level 3 performs fine-grained movement classification. The model is evaluated on the publicly available BCI Competition IV-2a dataset, which contains multi-class MI EEG recordings from nine healthy subjects. Robustness is assessed under gradient-based adversarial attacks, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and DeepFool, across varying perturbation strengths, with adversarial training incorporated during learning. Experimental results show that the proposed HCNN achieves a clean-data accuracy of 91.2% and exhibits reduced performance degradation under adversarial attacks compared with conventional CNN baselines. These results indicate that hierarchical architectures offer a viable approach for improving the reliability of EEG-based BCIs. All experiments were conducted exclusively on the BCI Competition IV-2a dataset using EEG data from healthy subjects.}, }
@article {pmid41616114, year = {2026}, author = {Yang, J and Huo, J and Liu, M and Feng, C and Zhang, Y and Pan, G and Meng, W and Han, R}, title = {vEMINR: Ultra-Fast Isotropic Reconstruction for Volume Electron Microscopy With Implicit Neural Representation.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e11922}, doi = {10.1002/advs.202511922}, pmid = {41616114}, issn = {2198-3844}, support = {2021YF F0704300//National Key Research and Development Program of China/ ; 62206158//National Natural Science Foundation of China/ ; 32371248//National Natural Science Foundation of China/ ; 2024CANAD-MES-061//Dubai Future Foundation/ ; //Fundamental Research Funds for the Central Universities/ ; 2025A1515010342//Natural Science Foundation of Guangdong Province/ ; ts20230204//Instrument Improvement Funds of Shandong University Public Technology Platform/ ; ZR2023YQ057//Natural Science Foundation of Shandong Province/ ; ZR2022QF097//Natural Science Foundation of Shandong Province/ ; //Shandong University Young Scholar Future Plan to W.M./ ; }, abstract = {Volume electron microscopy (vEM) is a powerful technique that enables 3D visualization of biological structures at the nanometer scale. However, vEM imaging relies on sequential scanning of 2D images, and due to section thickness limitations, the axial resolution is significantly lower than the lateral resolution. In this paper, we propose the vEMINR, an ultra-fast isotropic reconstruction method based on implicit neural representation (INR). This method enhances the reconstruction quality of vEM images by learning the true degradation patterns of low-resolution images, and significantly accelerates the reconstruction process by utilizing the efficient parameterization and a continuous function representation of INR. In experiments on 11 public datasets, vEMINR outperforms mainstream methods with over tenfold faster reconstruction and higher accuracy. vEMINR substantially improved the accuracy of organelle and neuron reconstruction from vEM. Overall, the excellent reconstruction time efficiency of vEMINR enables high-throughput processing of terabyte-scale vEM datasets while maintaining reconstruction accuracy. We believe that it will play a significant role in large-scale vEM image reconstruction and related research fields.}, }
@article {pmid41615974, year = {2026}, author = {Ding, W and Liu, A and Wu, L and Cui, H and Fang, B and Chen, X}, title = {Data Augmentation for Subject-Independent SSVEP-BCIs via Simultaneous Spatial-Energy Representation.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3659606}, pmid = {41615974}, issn = {1558-2531}, abstract = {OBJECTIVE: Data augmentation is important for enhancing subject-independent classification in deep learning (DL) approaches for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) using electroencephalography (EEG). However, current augmentation techniques often inadequately exploit individual-specific style characteristics, limiting the model's robustness against inter-subject style variability. To tackle this problem, this study proposes a novel data augmentation method called Simultaneous Spatial-Energy Representation (SSER).<br><br>METHODS: SSER employs singular value decomposition (SVD) to extract spatial and energy representations from EEG signals, effectively capturing style characteristics. These representations are independently mixed across source domains during signal reconstruction, generating novel domains that cover a broader range of styles. This strategy promotes the learning of domain-invariant features and enhances the model's robustness to style variability.<br><br>RESULTS: Comprehensive experiments on public datasets demonstrate that SSER outperforms state-of-the-art data augmentation techniques and generalizes well across various DL models. Furthermore, self-collected offline and online experiments involving 30 subjects provide additional evidence of the method's effectiveness.<br><br>CONCLUSION: By simultaneously manipulating spatial and energy representations, SSER offers a richer characterization of EEG signal style variability, leading to superior performance.<br><br>SIGNIFICANCE: The proposed innovative data augmentation method advances subject-independent classification, facilitating the broader application of EEG-based BCIs in real-world scenarios.}, }
@article {pmid41554845, year = {2026}, author = {Carević, I and Bajto, J&#x160; and Grubor, M and Štirmer, N}, title = {Wood biomass ash as a clinker substitute in advancing next-generation blended cement: Croatian case study.}, journal = {Scientific reports}, volume = {16}, number = {1}, pages = {3932}, pmid = {41554845}, issn = {2045-2322}, support = {Project No. 101058162//HORIZON-CL4-2021-TWIN-TRANSITION-01/ ; }, abstract = {This research investigates the use of wood biomass ash (WBA) as a supplementary cementitious material (SCM) in blended cement formulations containing 6 and 12 wt% of bottom WBA. Motivated by the need to advance low-carbon cement production, reduce reliance on imported materials, and incorporate waste management strategies, the study explores sustainable pathways for cement manufacturing. Experimental results show that the 6 wt% WBA blend (BLEND BC-II) achieves a compressive strength of 59.3 MPa after 28 days, surpassing the reference CEM II, whereas the 12 wt% WBA blend (BLEND BC-I) also delivers favourable mechanical and durability performance, including a chloride diffusion coefficient of 15.85 × 10[-12] m[2]/s, capillary absorption of 0.68 g/m[2]·h[1]/[2], and gas permeability of 0.50 × 10[-16] m[2]. Volume stability tests of the 12 wt% WBA blend confirm that autogenous deformations remain below − 0.017 mm/m after 90 days, indicating effective mitigation of shrinkage and reliable dimensional stability. When combined with other SCMs, WBA further improves long-term mechanical performance. Despite challenges related to compositional variability and infrastructure requirements, WBA incorporation can reduce environmental impact and support low-carbon cement production. Achieving net-zero emissions extends beyond quantitative targets, requiring the restoration of balance between resource use, material efficiency, and environmental sustainability. These findings demonstrate that WBA is a viable SCM, advancing sustainable and resilient cement manufacturing.}, }
@article {pmid41613455, year = {2025}, author = {Sun, Y and Wang, S and Gong, Y}, title = {Terahertz's silent revolution in physics, engineering, and life science: Beyond the spectrum.}, journal = {Fundamental research}, volume = {5}, number = {5}, pages = {1930-1932}, pmid = {41613455}, issn = {2667-3258}, abstract = {Terahertz technology is revolutionizing photonics, biomedicine, and communications by merging non-ionizing radiation with molecular sensitivity and material penetration. Advances in metamaterials, adaptive antennas, and AI-driven systems address historical limitations in emission efficiency and atmospheric attenuation, enabling secure high-capacity networks and precision biomedical applications. Reconfigurable beamforming and hybrid channel models enhance wireless reliability, while ultra-sensitive biosensors and neuromodulation techniques pioneer non-invasive diagnostics and therapies for neurodegenerative and psychiatric disorders. Terahertz's dual role in molecular sensing and neural modulation establishes closed-loop "detect-treat" paradigms, bridging material science and neuroscience. Challenges remain in optimizing clinical application and hybrid system scalability, yet its capacity to probe carrier dynamics, protein interactions, and neural circuits positions Terahertz as a universal platform for 6G networks, personalized medicine, and brain-machine interfaces. By unifying physics-aware engineering with biological insights, terahertz technology transcends traditional boundaries, offering transformative solutions for healthcare, secure connectivity, and industrial innovation.}, }
@article {pmid41613156, year = {2025}, author = {Mohammadpour, H and Power, SD}, title = {Investigating singing imagery as an additional or alternative control task for EEG-based Brain-Computer Interfaces.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1736711}, pmid = {41613156}, issn = {1662-5161}, abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) provide a movement-free means of communication and control, typically based on motor imagery (MI) tasks of hand, foot, or tongue movements. Most BCI studies focus on classifying up to four such tasks, which limits the number of available commands and restricts overall system functionality. Expanding the range of reliable mental tasks would directly increase the number of possible commands and thereby enhance the practical utility of BCIs. Singing imagery (SI) may offer an intuitive alternative or additional task to complement conventional MI paradigms.<br><br>METHODS: EEG data were recorded from 14 participants performing right-hand, left-hand, foot, and tongue MI, SI, and rest. Features were extracted using filter bank common spatial patterns (FBCSP), and tasks were classified with a random forest algorithm across 2-, 4-, 5-, and 6-class scenarios. Subjective data regarding participants' perceived task difficulty and general task preferences was also collected.<br><br>RESULTS: Classification accuracies with SI included were comparable to subsets of conventional MI tasks in 2-, 4-, and 5-class scenarios. In the 6-class scenario, average accuracy was approximately 60%, with six participants exceeding 70%, the level often cited as being necessary for effective BCI control. It is reasonable to expect performance to improve further with more advanced analysis methods and participant training.<br><br>CONCLUSION: These promising results suggest that singing imagery can serve as both an additional and an alternative task in MI-BCIs. In lower-class systems, SI may provide a valuable option for generating commands, particularly for users who may find some conventional MI tasks less intuitive. When combined with the established MI tasks, SI could increase the number of possible commands, thereby extending the functional capacity of BCI systems. Overall, this work demonstrates the potential of SI to broaden the repertoire of mental tasks available for BCI control and to advance the development of more flexible, powerful, and user-centered BCI applications.}, }
@article {pmid41612341, year = {2026}, author = {Powell, J and Zhou, A}, title = {Brain-computer interface commercialization.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {23}, number = {1}, pages = {45}, pmid = {41612341}, issn = {1743-0003}, }
@article {pmid41611752, year = {2026}, author = {Aars, J and Ieno, EN and Andersen, M and Derocher, AE and Wiig, &#xd8; and Zuur, AF}, title = {Body condition among Svalbard Polar bears Ursus maritimus during a period of rapid loss of sea ice.}, journal = {Scientific reports}, volume = {16}, number = {1}, pages = {2182}, pmid = {41611752}, issn = {2045-2322}, mesh = {Animals ; *Ursidae/physiology ; *Ice Cover ; Female ; Male ; Arctic Regions ; Svalbard ; Ecosystem ; Climate Change ; }, abstract = {Polar bears are only found in Arctic areas with sufficient access to sea ice and seals on which they prey. Studies have highlighted negative effects on condition and demographics in areas where sea ice cover is declining due to warmer climate, but condition of the Barents Sea polar bear population have not been examined yet. Loss of sea ice rate has been considerably higher here than in other areas with polar bears. We investigated variation in body condition index (BCI) among 770 adult bears, 1188 captures, in March-May 1995-2019, in Svalbard, Norway (western part of the Barents Sea). We assessed how intrinsic (female reproductive state, age) and both males and females, BCI declined until 2000, but increased afterwards, during a period with rapid loss of sea ice. In models including sea ice metrics and climate (Arctic Oscillation), there was no support for the predicted negative effect of warmer weather and habitat loss. This indicates a complex relationship between habitat, ecosystem structure, energy intake, and energy expenditure. Increases in some prey species, including harbour seals, reindeer, and walrus, may partly offset reduced access to seals. Our findings underline the importance not to extrapolate findings across populations.}, }
@article {pmid41611285, year = {2026}, author = {Zan, T and Gao, YS}, title = {[Reconstruction of superficial organs: a leap from structural restoration to functional rehabilitation].}, journal = {Zhonghua shao shang yu chuang mian xiu fu za zhi}, volume = {42}, number = {1}, pages = {26-33}, pmid = {41611285}, issn = {2097-1109}, support = {T2024076//National High-Level Talents Program/ ; 82272264, 82472557//General Program of National Natural Science Foundation of China/ ; 2023ZZ02023//Shanghai Plastic Surgery Research Center of Shanghai Priority Research Center/ ; }, mesh = {Humans ; *Tissue Engineering/methods ; *Regenerative Medicine/methods ; *Plastic Surgery Procedures/methods ; Printing, Three-Dimensional ; Artificial Intelligence ; Tissue Scaffolds ; }, abstract = {The core objective of superficial organ reconstruction is to perfectly restore the organ's morphological structure and biological function. Currently, significant progress has been achieved in structural construction, blood supply assurance, and morphological and functional reconstruction of superficial organ reconstruction, primarily relying on approaches including surgical techniques, tissue engineering, and regenerative medicine. In the future, with the integration and application of cutting-edge technologies such as gene editing, artificial intelligence, three-dimensional printing, and brain-computer interfaces, superficial organ reconstruction is poised to enter a new historical stage characterized by high intelligence, precision, and comprehensive functional restoration. This article focuses on superficial organ reconstruction, systematically outlines its concept, challenges, and current development status, and proposes future perspectives for this field.}, }
@article {pmid41610453, year = {2026}, author = {Eguinoa, R and San Martín, R and Luna, P and Herrojo-Ruiz, M and Vidaurre, C}, title = {An EEG correlation framework to study state anxiety and learning under uncertainty.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae3f58}, pmid = {41610453}, issn = {1741-2552}, abstract = {Objective.Recent developments in computational neuroscience have shed light on the neural processes underlying altered decision-making under uncertainty in anxiety. These disruptions are partly attributed to impaired encoding of precision-weighted prediction errors (pwPEs), which guide belief updating during learning and decision-making, as described by hierarchical Bayesian models. In this paper, we introduce a gamified paradigm for collecting decision-making data, together with a framework for extracting EEG features linked to computationally relevant variables, drawing on principles from neurofeedback and brain-computer interface research. This approach aims to develop tools that target functionally meaningful brain networks involved in decision-making, with the potential to inform future neurofeedback interactions.Approach.Forty healthy participants performed a volatile decision-making task in a game-based, immersive environment. EEG data were analysed to identify spatial filters whose theta- and alpha-band power correlated with pwPEs and state anxiety scores. Both intra-subject (trial-wise pwPEs) and intersubject (state anxiety) analyses were conducted to uncover distinct neural signatures.Main results.The intra-subject analysis revealed that pwPEs were significantly and positively correlated with theta power, and significantly and negatively correlated with alpha power - supporting the hypothesis that these oscillatory patterns underlie belief updating. In contrast, the inter-subject analysis showed that higher state anxiety was associated with reduced theta and increased alpha power, consistent with attenuated learning and impaired adaptation in anxious individuals. These findings align with theoretical models of hierarchical Bayesian inference and prior evidence of anxiety-related disruptions in uncertainty processing.Significance.The findings validate the proposed EEG framework for identifying neural markers related to belief updating and anxiety-related learning impairments. This approach lays the foundation for personalized neurofeedback procedures that target maladaptive decision-making in anxiety, with the added benefit of using immersive task paradigms for better engagement and translational potential for real-world applications.}, }
@article {pmid41609332, year = {2026}, author = {Cicciarella, R and Willems, EP and Markham, B and Bizzozzero, MR and Phillips, W and Allen, SJ and Krützen, M and Christiansen, F}, title = {Validation of aerial photogrammetry methods to measure body size, condition and mass in small cetaceans.}, journal = {The Journal of physiology}, volume = {}, number = {}, pages = {}, doi = {10.1113/JP290419}, pmid = {41609332}, issn = {1469-7793}, support = {//A.H. Schultz Foundation/ ; 310030_204974/SNSF_/Swiss National Science Foundation/Switzerland ; }, abstract = {Accurate morphometric measurements are essential for estimating body size and condition in animals. These characteristics are, in turn, key to eco-physiological studies, wildlife management and conservation. For free-ranging cetaceans, however, collecting non-invasive morphometric data is challenging. Unoccupied aerial vehicle (UAV) photogrammetry offers a promising solution but requires ground-truthing to assess accuracy and precision. Similarly, morphometric-based indices of body condition must be validated against the animals' true body condition. Here we validated UAV-derived estimates of body size and condition in bottlenose dolphins (Tursiops spp.) under human care by comparing photogrammetry-based measurements of body length, width, height and girth from both stationary and swimming individuals with manual measurements. The two methods showed negligible differences, with UAV-based data yielding lower variability, confirming both high measurement accuracy and precision. Using UAV-derived measurements we calculated a volume-based body condition index (BCI) and compared it with a mass-based BCI, a standard metric in ecological research. The two indices showed a near-perfect fit, demonstrating that volume-based metrics reliably reflect true body condition in small cetaceans. Body density decreased with increasing body condition, consistent with higher fat-to-muscle ratios. By combining UAV-derived body volume with predicted density, based on their body condition, we accurately estimated individual body mass (mean error = 6.4%). This study provides a comprehensive validation of UAV-based photogrammetry to estimate body size, condition and mass in small cetaceans, highlighting its value as a non-invasive and cost-effective tool for ecological and conservation research. KEY POINTS: Measuring body size and condition in free-ranging dolphins is difficult, yet essential to understand their physiology, energy reserves and health. We used unoccupied aerial vehicles (UAV) to obtain accurate, non-invasive body measurements of bottlenose dolphins and compared them with direct manual measurements. UAV-based photogrammetry produced highly precise and accurate estimates of body length, girth and overall body volume, even for freely swimming animals. A UAV-derived, volume-based body condition index matched traditional mass-based indices and enabled accurate estimation of body mass. These results validate UAV photogrammetry as a reliable, ethical and cost-effective method for assessing body size, condition and mass in small cetaceans, thereby advancing ecological and physiological research in the wild.}, }
@article {pmid41606732, year = {2026}, author = {Hu, L and Ye, L and Ye, H and Liu, X and Xiong, K and Zhang, Y and Zheng, Z and Jiang, H and Chen, C and Shen, C and Wang, Z and Zhou, J and Wu, Y and Huang, K and Zhu, J and Chen, Z and Ding, M and Weiss, S and Yang, D and Wang, S}, title = {Harmonic patterns embedded in ictal EEG signals in focal epilepsy: new insight into the epileptogenic zone.}, journal = {BMC medicine}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12916-026-04665-7}, pmid = {41606732}, issn = {1741-7015}, support = {82171437//The National Natural Science Foundation of China/ ; LD24H090003//The Natural Science Foundation of Zhejiang Province/ ; }, abstract = {BACKGROUND: Localization of the epileptogenic zone (EZ) requires further refinement. We identified a unique ictal spectral structure, the "harmonic pattern" (H pattern), which potentially serves as a novel biomarker for localizing the EZ. This study aimed to analyze the clinical significance of the H pattern and to explore its underlying waveform features.<br><br>METHODS: Seventy patients with drug-resistant focal epilepsy, undergoing stereo-EEG (SEEG) evaluation and surgery, were included. Time-frequency maps (TFM) were generated using Morlet wavelet transform analysis. The H pattern was defined as multiple equidistant, high-density bands with varying frequencies on TFM. The upper quartile was employed to confirm contacts expressing dominant H pattern (dH pattern). Bispectral analysis and transfer function modeling were employed to assess nonlinear properties and signal propagation, respectively. The performance of the dH pattern in evaluating the EZ was compared with other ictal biomarkers.<br><br>RESULTS: Regardless of seizure onset patterns, the H pattern commonly occurred during early or late seizure propagation among 57 patients (81.4%). It harbored within specific EEG segments characterized by fast activity and irregular polyspikes. The H pattern often appeared simultaneously across different brain regions at a consistent fundamental frequency, highlighting a crucial stage in seizure propagation characterized by inter-regional synchronization. The dH pattern demonstrated greater nonlinearity compared to the non-dH pattern, as evidenced by bispectral analysis. The waveforms associated with the dH pattern were more stereotyped and showed increased skewness and/or asymmetry. Notably, the complete removal of areas exhibiting the dH pattern, but not high epileptogenicity index (&#x2265;&#x2009;0.3) or seizure onset zone, was independently associated with seizure freedom after surgery.<br><br>CONCLUSIONS: The H pattern provides unique insights into ictal neural dynamics. Additionally, it is a novel and alternative approach for measuring the EZ over an extended ictal time window.}, }
@article {pmid41525560, year = {2026}, author = {Siviero, I and Vale, N and Menegaz, G and Ramos-Murguialday, A and Francesca Storti, S}, title = {Artificial Intelligence and Wearable Technologies for Upper Limb Neurorehabilitation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {34}, number = {}, pages = {732-749}, doi = {10.1109/TNSRE.2026.3651949}, pmid = {41525560}, issn = {1558-0210}, mesh = {Humans ; *Wearable Electronic Devices ; *Artificial Intelligence ; *Neurological Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Electromyography ; Electroencephalography ; Brain-Computer Interfaces ; }, abstract = {Non-invasive neural interfaces (NIs) are increasingly investigated in upper limb neurorehabilitation, where they exploit biosignals, such as electroencephalography (EEG) and electromyography (EMG), to decode motor intentions using artificial intelligence (AI). Yet, traditional systems are complex and difficult to use outside the clinic. Wearable devices have the potential for innovative neurorehabilitation solutions thanks to their comfort, easy-to-use and long-term monitoring. However, current AI approaches require adaptation to the technical constraints of wearable devices, and the related state-of-the-art is not clearly explained and summarized. In this work, a systematic literature review on 51 studies was conducted analyzing them according to five important concepts: biosignals, wearable devices, AI-driven methods, upper limb, and clinical applications. The review highlights methodological heterogeneity, a variety of wearable sensor configurations, and open challenges related to accuracy, robustness, and clinical validation. Finally, we discuss how explainable AI (XAI) and generative AI (GenAI) may contribute to improve the interpretability and personalization of future neurorehabilitation systems.}, }
@article {pmid41462297, year = {2025}, author = {Zhao, Z and Duan, X and Luo, J and He, Z and Zhang, Y and Wang, M and Qin, J and Lin, S and Chen, H}, title = {Spatiotemporal dynamics of neuronal subtypes and their interactions with glia following intracortical electrode implantation.}, journal = {Biology direct}, volume = {21}, number = {1}, pages = {13}, pmid = {41462297}, issn = {1745-6150}, support = {32201095//National Natural Science Foundation of China/ ; }, abstract = {BACKGROUND: Chronically implanted electrodes offer a promising approach for treating neurological disorders via brain-computer interfaces, yet their long-term efficacy is compromised by the neuroinflammatory foreign body response. While neurons are central to both electrode function and inflammatory regulation, their specific responses post-implantation remain poorly characterized. Here, we combined single-nucleus RNA sequencing (snRNA-seq) and immunofluorescence to delineate the spatiotemporal dynamics of neuronal subtypes in the rat motor cortex at 3, 25, and 50 days after electrode implantation.<br><br>RESULTS: We identified 22 distinct neuronal subpopulations, among which clusters 5, 6, and 8 emerged as injury-responsive subtypes during the acute phase (3 days), exhibiting a specific upregulation of Tmsb4x, a key regulator of neuronal plasticity and repair. Furthermore, our analysis revealed activated signaling pathways mediating neuron-glia communication, most notably the Ptn-Sdc4 and Il34/Csf1-Csf1R axes between neurons and astrocytes.<br><br>CONCLUSIONS: These findings provide a high-resolution map of neuronal adaptation to intracortical implants, uncovering previously unknown repair-associated neuronal subtypes and specific ligand-receptor pairs that coordinate the neuroinflammatory microenvironment, which offers novel insights and potential therapeutic targets for improving the biocompatibility and long-term stability of neural electrodes.<br><br>GRAPHICAL ABSTRACT: [Image: see text]<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13062-025-00719-7.}, }
@article {pmid41606342, year = {2026}, author = {Xu, Z and Wang, H and Yu, J and Deng, Y and Tian, X and Ni, R and Xia, F and Yang, L and Xu, C and Zhang, L and Luo, R and Chen, P and Zhang, X and Liu, Y and Hou, J and Zhang, M and Chen, S and Su, L and Sun, H and He, Y and Chen, D and Chen, X and Miao, Z and Xie, J and Liu, X and Zhao, J and Ke, B and Tian, X and Zeng, L and Zhang, L and Tang, X and Yang, S and Liu, J and Wang, X and Yan, W and Shao, Z}, title = {Psychedelics elicit their effects by 5-HT2A receptor-mediated Gi signalling.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {41606342}, issn = {1476-4687}, abstract = {Psychedelics are undergoing a renaissance as potential therapy for psychiatric disorders, with more than 200 clinical trials being studied across several countries[1-3]. However, the precise mechanisms by which these drugs bring about benefits and the potential clinical risks are not yet fully understood. The serotonin 2A receptor (5-HT2AR) was reported to be a Gq-coupled receptor and the primary interoceptive target of psychedelics[4,5]. Here we compared psychedelics and their non-hallucinogenic analogues (nHAs) using in vitro and in vivo approaches, finding that 5-HT2AR-mediated non-canonical Gi signalling is essential for hallucinogenic effect. We further presented five cryo-electron microscopy structures of 5-HT2AR-Gi/Gq in complex with psychedelics or nHAs. Structural analysis and pharmacological investigation revealed that a special contact between nHAs with 5-HT2AR mediated the signalling bias. Building on this insight, we identified a 2,5-dimethoxy-4-iodoamphetamine derivative, DOI-NBOMe, which exhibits potent and selective Gq-biased activity, and demonstrates promising therapeutic effects in mouse models without hallucinogenic effect. Our finding uncovers the functional mechanisms underlying the Gi signalling mediated by 5-HT2AR and provides valuable insights for designing psychedelic-based drugs with minimized risk from hallucinogenic effects.}, }
@article {pmid41605490, year = {2026}, author = {Chen, H and Yun, G}, title = {Efficacy of Brain-Computer Interface Therapy for Upper Limb Rehabilitation in Chronic Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials.}, journal = {Journal of medical Internet research}, volume = {28}, number = {}, pages = {e79132}, doi = {10.2196/79132}, pmid = {41605490}, issn = {1438-8871}, mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Randomized Controlled Trials as Topic ; *Upper Extremity/physiopathology ; Activities of Daily Living ; Chronic Disease ; *Stroke/physiopathology ; }, abstract = {BACKGROUND: Over 50% of people with chronic stroke experience persistent upper limb dysfunction. Brain-computer interface (BCI) therapy, creating a sensorimotor loop via neural feedback, is a promising alternative; yet, its optimal application remains unclear.<br><br>OBJECTIVE: This meta-analysis evaluates BCI's efficacy on motor function, tone, and activities of daily living (ADL) in chronic stroke and identifies optimal feedback modalities and intervention parameters.<br><br>METHODS: We systematically searched Cochrane Library, Embase, PubMed, Scopus, Web of Science, and Wanfang Data from inception to October 2025 for randomized controlled trials (RCTs) comparing BCI-based training to control interventions in adults with chronic stroke. Primary outcomes were upper limb motor function (Fugl-Meyer Assessment for upper extremity [FMA-UE], Action Research Arm Test [ARAT]), muscle tone (Modified Ashworth Scale [MAS]), and ADL (Modified Barthel Index [MBI], Motor Activity Log [MAL]). Screening, data extraction, and risk-of-bias assessment were performed independently. Meta-analysis used a random-effects model with Hartung-Knapp-Sidik-Jonkman adjustment. Pooled mean differences (MDs) with 95% CIs and 95% prediction intervals (PIs) were calculated. Subgroup analyses examined feedback modalities, intervention intensity, and follow-up effects. Sensitivity analysis was also conducted.<br><br>RESULTS: From 3529 records, 21 RCTs (650 participants) were included. BCI training significantly improved motor function (FMA-UE: MD 2.50, 95% CI 0.60-4.40; P=.01; 95% PI -2.52 to 7.22) and ADL performance (MBI: MD 8.38, 95% CI 2.23-14.53; P=.02; 95% PI -3.92 to 20.53; MAL: MD 2.09, 95% CI 0.42-3.76; P=.03; 95% PI -0.69 to 4.54). No significant effects were observed for fine motor skills (ARAT: MD 0.18, 95% CI -0.27 to 0.62; P=.30; 95% PI -3.64 to 3.99) or muscle tone (MAS: MD -0.48, 95% CI -1 to 0.03; P=.06; 95% PI -1.27 to 0.35). Subgroup analyses revealed that BCI-functional electrical stimulation (FES) yielded the greatest improvement in motor recovery (FMA-UE: MD 5, 95% CI 1.86-8.13; P=.01). The optimal intervention protocol was identified as 30-minute sessions, administered 4-5 times per week over 2 weeks (total of 10-12 sessions). However, benefits were not sustained at follow-up.<br><br>CONCLUSIONS: Low- to moderate-certainty evidence suggests that BCI training, particularly the BCI-FES paradigm, can improve upper limb motor function and ADL in people with chronic stroke on average. However, wide prediction intervals indicate the effect may vary substantially across settings, ranging from negligible to beneficial. Subgroup analyses suggested a potential optimal protocol of 30-minute sessions, 4-5 times per week for 2 weeks, but these findings are limited by the small number of studies in each subgroup and the high risk of bias in several included trials. Therefore, this proposed protocol should be viewed as preliminary and requires validation in future, high-quality RCTs. Future research should also focus on identifying patient subgroups most likely to benefit and on strategies to sustain long-term gains.<br><br>TRIAL REGISTRATION: PROSPERO CRD420251063808; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251063808.}, }
@article {pmid41605384, year = {2026}, author = {Jiang, H and Fu, H and Wei, Q and Wang, Y}, title = {A hierarchical bilayer sponge dressing based on QCMCS@GO/PLA for synergistic wound healing via hemostasis and anti-adhesion.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {150565}, doi = {10.1016/j.ijbiomac.2026.150565}, pmid = {41605384}, issn = {1879-0003}, abstract = {To address the challenges of inefficient hemostasis, high risk of bacterial infection, and biofilm formation in wound management, this study developed a bilayered sponge dressing composed of quaternized carboxymethyl chitosan@ graphene oxide/polylactic acid (QCMCS@GO/PLLA) with triple functionalities: coagulation, antibacterial activity, and anti-adhesion. A hierarchical structure was constructed using freeze-drying and electrospinning techniques: the bottom layer is a QCMCS@GO composite sponge, where graphene oxide (GO) enhances mechanical strength and enriches coagulation factors, while the quaternized carboxymethyl chitosan (QCMCS) promotes platelet activation and intrinsic coagulation pathway via its cationic properties; the top layer consists of electrospun polylactic acid (PLLA) nanofibers that serve as a superhydrophobic physical barrier to effectively inhibit bacterial adhesion. The material exhibits high porosity (>92%) and rapid liquid absorption (≥95% within 40 ms). In vitro experiments demonstrated that the dressing significantly accelerated whole blood coagulation (time reduced by 52.3%), optimized the blood clotting index (BCI = 4.7%), and enhanced thrombus formation through FXII contact activation. It achieved bacterial eradication rates of 99.94% against Staphylococcus aureus and 99.61% against Escherichia coli, while reducing bacterial adhesion on the surface by 91.8%. The dressing showed excellent biocompatibility (hemolysis rate 2.3%, cell proliferation rate 138%). In a rat liver injury model, it shortened hemostatic time by 63.2% and reduced blood loss by 76.5% compared to commercial gelatin sponges. This study provides a novel strategy for developing multifunctional wound dressings.}, }
@article {pmid41605144, year = {2026}, author = {Wei, S and Yu, H and Huang, Y and Meng, J and Xu, R and Jung, TP and Xu, M and Ming, D}, title = {Predicting Attention Decline: An Integrated Beta-Band and SSVEP Approach for Visual Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3658740}, pmid = {41605144}, issn = {1558-0210}, abstract = {Real-time monitoring of sustained attention fluctuations during continuous complex tasks is vital for enhancing task performance and preventing accidents. Attention modulates neurons in the visual cortex in various ways to improve the visual sensitivity at an attended location. EEG-based brain-computer interfaces (BCIs) offer one of the most effective approaches for monitoring the state of human individuals. Whether transient responses evoked by brief stimuli, steady-state responses elicited by prolonged stimuli, or spontaneous neural oscillations, researchers can extract recognized electrophysiological features that reflect attention levels. However, unimodal features face inherent limitations, such as the low signal-to-noise ratio of transient responses and susceptibility of spontaneous rhythms to electrophysiological interference. Nevertheless, few studies have explored multimodal feature fusion for attention state monitoring. Here, we developed an innovative continuous go/no-go task to concurrently evoke both event-related potential (ERP) and steady-state visual evoked potential (SSVEP), while modulating spontaneous oscillatory activities through attentional engagement. To maximize the attentional modulation effect, we integrated the contrast-response functions of the modulation effect of attention on SSVEP and implemented 12 stimulus contrast levels to identify optimal visual stimulation intensity. Results from 25 subjects demonstrated that the decline in sustained attention during a continuous task was predictable before behavioral mistakes. Classification performance peaked at 31.60% stimulus contrast condition using the fused features combining spontaneous beta-band oscillations and SSVEP responses (average: 74.48%; best: 90.83%). These findings advance the development of more robust real-time attention monitoring systems based on BCI technology.}, }
@article {pmid41605140, year = {2026}, author = {Ravi, A and Jiang, N and Tung, J}, title = {EEG-Based Gait Phase Decoding from Combined Action Observation and Motor Imagery.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3659011}, pmid = {41605140}, issn = {1558-0210}, abstract = {Gait recovery is a crucial component of stroke rehabilitation. While Brain-Computer Interfaces (BCIs) decoding motor intent from motor imagery (MI) have shown success, their application in the area of gait phase decoding remains limited. Combining Action Observation (AO) and MI paradigms have demonstrated enhanced motor cortex activation compared to AO or MI alone. This study investigated the feasibility of decoding swing and stance phass of gait from electroencephalogaphy (EEG), via a proposed feature extraction and classification method. A novel dataset, utilizing the Combined AO, MI, and Steady-State Motion Visual Evoked Potential (SSMVEP) (CAMS-BCI) paradigm, was collected from twenty healthy volunteers. Employing an innovative labelling technique, three different classification methods were compared. Among them, broad band EEG features with a linear classifier achieved the highest average f1-score of 0.77 in gait phase classification. Additionally, the methods achieved an overall accuracy of 70% in classifying individual Swing and Stance phases based on the CAMS stimulus responses. These findings provide valuable insights for the development of novel BCI feedback mechanisms specifically targeting different phases of gait. Implementing them in future designs can potentially enhance gait recovery outcomes in post-stroke rehabilitation.}, }
@article {pmid41604716, year = {2026}, author = {Lu, J and Liu, Y and Zhang, X and Han, J and Fan, Z and Yu, N}, title = {A principal brain-region analysis framework based on evolutionary decomposition for fNIRS brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae3eb7}, pmid = {41604716}, issn = {1741-2552}, abstract = {Functional near-infrared spectroscopy (fNIRS) is an emerging technique for brain-computer interfaces (BCIs) due to its advantages in spatial resolution, robustness to artifacts, portability and usability for long-term monitoring, etc. Existing BCI methods take a holistic approach to all signal-collecting channels and corresponding brain regions, while the task-related brain regions and their interactions have not been well explored. Approach. This paper proposes a principal brain-region analysis (PBA) framework to incorporate the functional contribution as well as collaboration of task-specific brain regions (TSBRs) to boost BCI performance. Firstly, the identification of TSBRs is formulated as an optimization problem by maximizing classification accuracy under spatial constraints on brain regions of interest. Then, an evolutionary decomposition algorithm is constructed by combining spatial nondominated operators and genetic iterative computation, identifying TSBRs from the whole brain regions. Afterwards, classifiers are trained by neuroimaging features in the decomposed TSBRs in combination with stacking to generate the final predictions. Results. The proposed PBA method was evaluated on two public datasets for fNIRSbased BCIs, significantly enhancing the classification accuracy for the sliding slopebased method by 8.91% and 6.03% and the sliding mean concentration change method by 13.62% and 6.15%, respectively. Significance. Principal brain-region analysis establishes a pivotal framework to fundamentally advance the accuracy and explainability of BCIs.}, }
@article {pmid41604633, year = {2026}, author = {Bialostocki, LS and Adhia, DB and Mudiyanselage, DR and Smith, ML and Cakmak, YO and De Ridder, D and Mani, R and Mathew, J}, title = {Authors' Reply: Bridging Neurofeedback and Structural Connectivity in Chronic Pain.}, journal = {JMIR research protocols}, volume = {15}, number = {}, pages = {e89007}, doi = {10.2196/89007}, pmid = {41604633}, issn = {1929-0748}, }
@article {pmid41604568, year = {2026}, author = {Acar, A and Yahya, D and Tekirdaş, E}, title = {Bridging Neurofeedback and Structural Connectivity in Chronic Pain.}, journal = {JMIR research protocols}, volume = {15}, number = {}, pages = {e87420}, doi = {10.2196/87420}, pmid = {41604568}, issn = {1929-0748}, }
@article {pmid41603967, year = {2026}, author = {Schumacher, X and Frazzini, V and Adam, C and Dupont, S and Bielle, F and Guesdon, A and Mere, M and Nguyen-Michel, VH and Navarro, V and Mathon, B}, title = {Safety and efficacy of sEEG-guided resective surgery in patients with MRI-negative drug-resistant epilepsy.}, journal = {Neurosurgical review}, volume = {49}, number = {1}, pages = {166}, pmid = {41603967}, issn = {1437-2320}, }
@article {pmid41603128, year = {2026}, author = {Shimizu, S and Osawa, T and Sato, M and Yamada, S and Harabayashi, T and Miki, J and Kobayashi, T and Hashine, K and Kawashima, A and Matsumoto, T and Mochizuki, T and Taoka, R and Urabe, F and Tatarano, S and Sawada, A and Kojima, T and Takahashi, A and Yokomizo, A and Suekane, S and Hashimoto, K and Hashimoto, Y and Yatsuda, J and Morita, K and Kobayashi, K and Satake, Y and Sazawa, A and Matsui, Y and Ito, YM and Nishiyama, H and Kitamura, H and Shinohara, N and Fukuhara, S and , }, title = {Validation of the 7-Item Quality of Life Disease-Specific Impact Scale in Patients Undergoing Radical Cystectomy for Bladder Cancer: A Cross-Sectional Study.}, journal = {International journal of urology : official journal of the Japanese Urological Association}, volume = {33}, number = {2}, pages = {e70364}, doi = {10.1111/iju.70364}, pmid = {41603128}, issn = {1442-2042}, mesh = {Humans ; Cross-Sectional Studies ; *Quality of Life ; Male ; *Urinary Bladder Neoplasms/surgery/psychology ; *Cystectomy/psychology/adverse effects ; Female ; Aged ; Middle Aged ; Reproducibility of Results ; Surveys and Questionnaires ; Psychometrics ; Body Image ; Aged, 80 and over ; Factor Analysis, Statistical ; Self Report ; }, abstract = {OBJECTIVES: To validate, for the first time in patients with bladder cancer who underwent radical cystectomy, the recently developed 7-item Quality of Life Disease-specific Impact Scale (QDIS-7), a brief, unidimensional instrument designed for cross-condition comparisons.<br><br>METHODS: In this cross-sectional study conducted at 24 facilities, patients aged &#x2265;&#x2009;20&#x2009;years who were 3&#x2009;months post-radical cystectomy for bladder cancer completed self-reported questionnaires. The enrollment period was from January 2020 to October 2022. Quality of life measures included the QDIS-7, the Bladder Cancer Index (BCI), and the Body Image Scale (BIS). Confirmatory factor analysis was performed to test the hypothesized one-factor structure of the QDIS-7. Internal consistency reliability was assessed using Cronbach's alpha coefficient. Criterion-based validity was evaluated using Spearman's correlation coefficients (&#x3c1;) between the QDIS-7 scores and the BCI bother subdomains and BIS scores.<br><br>RESULTS: In total, 205 patients (median age, 71&#x2009;years; 78.5% male) were included. The QDIS-7 score showed no floor or ceiling effects. Confirmatory factor analysis supported the one-factor model (factor loadings, 0.71-0.94). Internal consistency reliability was high (Cronbach's alpha, 0.94). The QDIS-7 score showed moderate correlations with the BIS and the BCI urinary and bowel bother subdomain scores (&#x3c1;&#x2009;=&#x2009;0.654, -0.560, and -0.475, respectively).<br><br>CONCLUSIONS: The QDIS-7 effectively captured urinary and bowel symptom burden and body image impairment in patients undergoing radical cystectomy for bladder cancer. Its brevity, strong psychometric properties, and capacity for comparisons across conditions support its use in patient-centered research.<br><br>TRAIL REGISTRATION: UMIN-CTR (UMIN000039538).}, }
@article {pmid41600429, year = {2026}, author = {Falk, M and Shleev, S}, title = {Advances in (Bio)Sensors for Physiological Monitoring: A Special Issue Review.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {2}, pages = {}, pmid = {41600429}, issn = {1424-8220}, mesh = {Humans ; Monitoring, Physiologic/methods/instrumentation ; Wearable Electronic Devices ; *Biosensing Techniques/methods ; Brain-Computer Interfaces ; }, abstract = {Physiological monitoring has become an inherently interdisciplinary field, merging advances in engineering, chemistry, biology, medicine, and data analytics to create sensors that continuously track the vital signals of the body. These developments are enabling more personalized and preventive healthcare, as wearable (bio)sensors and intelligent algorithms can detect subtle physiological changes in real-time. In the Special Issue 'Advances in (Bio)Sensors for Physiological Monitoring', researchers from diverse domains contributed 18 papers showcasing cutting-edge sensor technologies and applications for health and performance monitoring. In this review, we summarize these contributions by grouping them into logical themes based on their focus: (1) cardiovascular and autonomic monitoring, (2) glucose and metabolic monitoring, (3) wearable sensors for movement and musculoskeletal health, (4) neurophysiological monitoring and brain-computer interfaces, and (5) innovations in sensor technology and methods. This thematic organization highlights the breadth of the research, spanning from fundamental sensor hardware to data-driven analytics, and underscores how modern (bio)sensors are breaking traditional boundaries in healthcare.}, }
@article {pmid41600372, year = {2026}, author = {Zhang, B and You, X and Liu, Y and Xu, J and Xu, S}, title = {Multi-Level Perception Systems in Fusion of Lifeforms: Classification, Challenges and Future Conceptions.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {2}, pages = {}, doi = {10.3390/s26020576}, pmid = {41600372}, issn = {1424-8220}, support = {2024YFC3406302//the National Key R&amp;D Program of China/ ; 12204273//the National Natural Science Foundation of China/ ; ZR2024MF107//the Natural Science Foundation of Shandong Province, China/ ; 2017YFA0701302//the National Key R&amp;D Program of China/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Perception/physiology ; }, abstract = {The emerging paradigm of "fusion of lifeforms" represents a transformative shift from conventional human-machine interfaces toward deeply integrated symbiotic systems, where biological and artificial components co-adapt structurally, energetically, informationally, and cognitively. This review systematically classifies multi-level perception systems within fusion of lifeforms into four functional categories: sensory and functional restoration, beyond-natural sensing, endogenous state sensing, and cognitive enhancement. We survey recent advances in neuroprosthetics, sensory augmentation, closed-loop physiological monitoring, and brain-computer interfaces, highlighting the transition from substitution to fusion. Despite significant progress, critical challenges remain, including multi-source heterogeneous integration, bandwidth and latency limitations, power and thermal constraints, biocompatibility, and system-level safety. We propose future directions such as layered in-body communication networks, sustainable energy strategies, advanced biointerfaces, and robust safety frameworks. Ethical considerations regarding self-identity, neural privacy, and legal responsibility are also discussed. This work aims to provide a comprehensive reference and roadmap for the development of next-generation fusion of lifeforms, ultimately steering human-machine integration from episodic functional repair toward sustained, multi-level symbiosis between biological and artificial systems.}, }
@article {pmid41600345, year = {2026}, author = {Dalgaard, KS and Lavesen, ER and Sulkjær, CS and Stevenson, AJT and Jochumsen, M}, title = {Specificity of Pairing Afferent and Efferent Activity for Inducing Neural Plasticity with an Associative Brain-Computer Interface.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {2}, pages = {}, doi = {10.3390/s26020549}, pmid = {41600345}, issn = {1424-8220}, mesh = {Humans ; *Neuronal Plasticity/physiology ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Evoked Potentials, Motor/physiology ; Transcranial Magnetic Stimulation ; Wrist/physiology ; Electric Stimulation ; Movement/physiology ; Young Adult ; }, abstract = {Brain-computer interface-based (BCI) training induces neural plasticity and promotes motor recovery in stroke patients by pairing movement intentions with congruent electrical stimulation of the affected limb, eliciting somatosensory afferent feedback. However, this training can potentially be refined further to enhance rehabilitation outcomes. It is not known how specific the afferent feedback needs to be with respect to the efferent activity from the brain. This study investigated how corticospinal excitability, a marker of neural plasticity, was modulated by four types of BCI-like interventions that varied in the specificity of afferent feedback relative to the efferent activity. Fifteen able-bodied participants performed four interventions: (1) wrist extensions paired with radial nerve peripheral electrical stimulation (PES) (matching feedback), (2) wrist extensions paired with ulnar nerve PES (non-matching feedback), (3) wrist extensions paired with sham radial nerve PES (no feedback), and (4) palmar grasps paired with radial nerve PES (partially matching feedback). Each intervention consisted of 100 pairings between visually cued movements and PES. The PES was triggered based on the peak of maximal negativity of the movement-related cortical potential associated with the visually cued movement. Before, immediately after, and 30 min after the intervention, transcranial magnetic stimulation-elicited motor-evoked potentials were recorded to assess corticospinal excitability. Only wrist extensions paired with radial nerve PES significantly increased the corticospinal excitability with 57 ± 49% and 65 ± 52% immediately and 30 min after the intervention, respectively, compared to the pre-intervention measurement. In conclusion, maximizing the induction of neural plasticity with an associative BCI requires that the afferent feedback be precisely matched to the efferent brain activity.}, }
@article {pmid41600232, year = {2026}, author = {Wu, W and Liu, L and Chen, W and Chen, Y and Wang, X and Cichocki, A and Lu, Y and Jin, J}, title = {MS-TSEFNet: Multi-Scale Spatiotemporal Efficient Feature Fusion Network.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {2}, pages = {}, doi = {10.3390/s26020437}, pmid = {41600232}, issn = {1424-8220}, support = {2022ZD0208900//STI 2030-major projects/ ; 62176090//National Natural Science Foundation of China/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; BE2022064-1//Project of Jiangsu Province Science and Technology Plan Special Fund in 2022/ ; }, mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Deep Learning ; Brain/physiology ; }, abstract = {Motor imagery signal decoding is an important research direction in the field of brain-computer interfaces, which aim to judge the motor imagery state of an individual by analyzing electroencephalogram (EEG) signals. Deep learning technology has been gradually applied to EEG classification, which can automatically extract features. However, when processing complex EEG signals, the existing decoding models cannot effectively fuse features at different levels, resulting in limited classification performance. This study proposes a multi-scale spatiotemporal efficient feature fusion network (MS-TSEFNet), which learns the dynamic changes in EEG signals at different time scales through multi-scale convolution modules and combines the spatial attention mechanism to efficiently capture the spatial correlation between electrodes in EEG signals. In addition, the network adopts an efficient feature fusion strategy to deeply fuse features at different levels, thereby improving the expression ability of the model. In the task of motor imagery signal decoding, MS-TSEFNet shows higher accuracy and robustness. We use the public BCIC-IV2a, BCIC-IV2b and ECUST datasets for evaluation. The experimental results show that the average classification accuracy of MS-TSEFNet reaches 80.31%, 86.69% and 71.14%, respectively, which is better than the current state-of-the-art algorithms. We conducted an ablation experiment to further verify the effectiveness of the model. The experimental results showed that each module played an important role in improving the final performance. In particular, the combination of the multi-scale convolution module and the feature fusion module significantly improved the model's ability to extract the spatiotemporal features of EEG signals.}, }
@article {pmid41598333, year = {2026}, author = {Wankner, MC and Visser-Vandewalle, V and Andrade, P and Heiden, P}, title = {Cervical Spinal Cord Stimulation for Functional Rehabilitation After Spinal Cord Injury: A Systematic Review of Preclinical and Clinical Studies.}, journal = {Life (Basel, Switzerland)}, volume = {16}, number = {1}, pages = {}, doi = {10.3390/life16010179}, pmid = {41598333}, issn = {2075-1729}, abstract = {Cervical spinal cord injury causes severe functional impairment with limited spontaneous recovery, and while spinal cord stimulation has emerged as a promising neuromodulatory strategy, evidence for cervical applications remains fragmented. To address this gap, we conducted a systematic review synthesizing preclinical and clinical evidence on cervical spinal cord stimulation for functional rehabilitation following spinal cord injury. The review was registered on PROSPERO (CRD420251088804) and conducted in accordance with PRISMA guidelines, with PubMed, Embase, IEEE Xplore, and Web of Science searched from inception to July 2025 for animal and human studies of cervical spinal cord stimulation, including epidural, intraspinal, and transcutaneous approaches, reporting functional neurological outcomes. Risk of bias was assessed using the Cochrane RoB 2 and ROBINS-I tools, and due to substantial heterogeneity, results were synthesized narratively. Thirty-one studies comprising 119 animals and 156 human participants, met inclusion criteria. Across studies, outcome measures such as GRASSP, ISNCSCI, and dynamometry consistently demonstrated improvements in hand strength, dexterity, and voluntary motor activation. Several studies also reported gains in sensory and autonomic function, whereas respiratory outcomes were infrequently assessed. Adjunctive interventions, including cortical stimulation, brain-computer interface priming, and task-specific training frequently augmented recovery. Adverse events were generally mild, although overall risk of bias was predominantly serious. Overall, cervical spinal cord stimulation demonstrates preliminary assistive and therapeutic effects on motor recovery, with additional sensory, autonomic, and potential respiratory benefits.}, }
@article {pmid41597853, year = {2026}, author = {Ding, Y and Ding, J and Yang, Z and Fan, X and Chen, W}, title = {A Surface-Mount Substrate-Integrated Waveguide Bandpass Filter Based on MEMS Process and PCB Artwork for Robotic Radar Applications.}, journal = {Micromachines}, volume = {17}, number = {1}, pages = {}, doi = {10.3390/mi17010072}, pmid = {41597853}, issn = {2072-666X}, support = {F2025508015//Natural Science Foundation of Hebei Province/ ; 3142025013//Fundamental Research Funds for the Central Universities/ ; 2025011037//Science and Technology Support Project of Langfang/ ; 3142023023//Fundamental Research Funds for the Central Universities/ ; }, abstract = {To address the pressing need for compact and highly reliable perception systems in autonomous mobile robots, a compact bandpass filter (BPF) integrating slot-line resonator with substrate-integrated waveguide (SIW) technology for robotic millimeter-wave radar front ends was proposed. By integrating slot-line resonators between adjacent SIW cavities, the proposed design effectively increases the filtering order without increasing the layout area. This approach not only generates extra transmission poles but also creates a sharp transmission zero at the upper stopband, thereby significantly enhancing out-of-band rejection. This characteristic is crucial for robotic radar operating in complex and dynamic environments, as it effectively suppresses out-of-band interference and improves the system signal-to-noise ratio and detection reliability. To validate the performance, a prototype filter operating in the 24.25-27.5 GHz passband was fabricated. The measured results show good agreement with simulations, demonstrating low insertion loss, compact size, and wide stopband. Finally, to validate its compatibility with robotic radar modules, the chip was assembled onto a PCB using surface-mount technology. The responses of the bare die and the packaged module were then compared to evaluate the impact of integration on the overall RF performance. The proposed design offers a key filtering solution for next-generation high-performance, miniaturized robotic perception platforms.}, }
@article {pmid41596724, year = {2026}, author = {Yen, C and Chiang, MC}, title = {Neuroimaging-Guided Insights into the Molecular and Network Mechanisms of Chronic Pain and Neuromodulation.}, journal = {International journal of molecular sciences}, volume = {27}, number = {2}, pages = {}, doi = {10.3390/ijms27021080}, pmid = {41596724}, issn = {1422-0067}, support = {NSTC 113-2314-B-030-007, NSTC 113-2515-S-030-001, and NSTC 114-2918-I-030-001//National Science and Technology Council/ ; }, mesh = {Humans ; *Chronic Pain/diagnostic imaging/metabolism/therapy/physiopathology ; *Neuroimaging/methods ; Brain/diagnostic imaging/metabolism/physiopathology ; Transcranial Magnetic Stimulation ; Deep Brain Stimulation ; Animals ; Positron-Emission Tomography ; }, abstract = {Chronic pain is a pervasive and debilitating condition that affects millions of individuals worldwide. Unlike acute pain, which serves a protective physiological role, chronic pain persists beyond routine tissue healing and often arises without a discernible peripheral cause. Accumulating evidence indicates that chronic pain is not merely a symptom but a disorder of the central nervous system, underpinned by interacting molecular, neurochemical, and network-level alterations. Molecular neuroimaging using PET and MR spectroscopy has revealed dysregulated excitatory-inhibitory balance (glutamate/GABA), altered monoaminergic and opioidergic signaling, and neuroimmune activation (e.g., TSPO-indexed glial activation) in key pain-related regions such as the insula, anterior cingulate cortex, thalamus, and prefrontal cortex. Converging multimodal imaging-including functional MRI, diffusion MRI, and EEG/MEG-demonstrates aberrant activity and connectivity across the default mode, salience, and sensorimotor networks, alongside structural remodeling in cortical and subcortical circuits. Parallel advances in neuromodulation, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), deep brain stimulation (DBS), and emerging biomarker-guided closed-loop approaches, provide tools to perturb these maladaptive circuits and to test mechanistic hypotheses in vivo. This review integrates neuroimaging findings with molecular and systems-level mechanistic insights into chronic pain and its modulation, highlighting how imaging markers can link biochemical signatures to neural dynamics and guide precision pain management and individualized therapeutic strategies.}, }
@article {pmid41596014, year = {2026}, author = {Liang, J and Zhou, Y and Ma, K and Jia, Y and Zhang, Y and Han, B and Xiang, M}, title = {Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/bioengineering13010084}, pmid = {41596014}, issn = {2306-5354}, support = {U23A20485//National Natural Science Foundation of China/ ; 62501039//National Natural Science Foundation of China/ ; 2021ZD0300503//Innovation Program for Quantum Science and Technology/ ; BX20240465//China National Postdoctoral Program for Innovative Talents/ ; LMS25H180004//Zhejiang Provincial Natural Science Foundation Exploration Project/ ; }, abstract = {Bio-electric fields-manifested as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG), and Electrooculogram (EOG)-are fundamental to modern medical diagnostics but often suffer from severe data imbalance, scarcity, and environmental noise. Generative Adversarial Networks (GANs) offer a powerful, nonlinear solution to these modeling hurdles. This review presents a comprehensive survey of GAN methodologies specifically tailored for bio-electric signal processing. We first establish a theoretical foundation by detailing GAN principles, training mechanisms, and critical structural variants, including advancements in loss functions and conditional architectures. Subsequently, the paper extensively analyzes applications ranging from high-fidelity signal synthesis and noise reduction to multi-class classification. Special attention is given to clinical anomaly detection, specifically covering epilepsy, arrhythmia, depression, and sleep apnea. Furthermore, we explore emerging applications such as modal transformation, Brain-Computer Interfaces (BCI), de-identification for privacy, and signal reconstruction. Finally, we critically evaluate the computational trade-offs and stability issues inherent in current models. The study concludes by delineating prospective research avenues, emphasizing the necessity of interdisciplinary synergy to advance personalized medicine and intelligent diagnostic systems.}, }
@article {pmid41594981, year = {2025}, author = {Jiao, M and Ding, Z and Huang, C and Xu, Y and Zhong, B and Chen, H}, title = {The Effects of Computerized Cognitive Training via Tablet and Computer Platforms on Cognitive Function in Patients with Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.}, journal = {Behavioral sciences (Basel, Switzerland)}, volume = {16}, number = {1}, pages = {}, doi = {10.3390/bs16010040}, pmid = {41594981}, issn = {2076-328X}, support = {No.2022ZD0210800//the Science and Technology Innovation 2030-"Brain Science and Brain-like Research" Major Project/ ; No. 20&amp;ZD045//the Emerging Enhancement Technology under Perspective of Humanistic Philosophy, supported by the National Office for Philosophy and Social Science/ ; No.32171046//the Emerging Enhancement Technology under Perspective of Humanistic Philosophy, supported by the National Office for Philosophy and Social Science/ ; 226-2024-00207, 226-2024-00118//the Fundamental Research Funds for the Central Universities/ ; WX23A99//the Wuhan Medical Research Project 2023 (Healthy Development)/ ; 2024020801020405//the 2024 Wuhan Natural Science Foundation Exploration Plan Municipal Medical Institutions Clinical Research Key Project/ ; }, abstract = {Background: Mild cognitive impairment (MCI) is a high-risk prodromal stage of dementia. While tablet/computer-based computerized cognitive training (CCT) is widely used, its efficacy and gamification's role need clarification. Objective: This study aimed to evaluate the effect of tablet/computer-based CCT on global cognition in older adults with MCI and explore the impact of gamification. Methods: We systematically searched five databases for RCTs (through October 2025) involving individuals aged ≥55 with MCI. The intervention was task-based CCT via tablets/computers. Primary outcome was global cognition. We used random-effects meta-analysis and subgroup analyses. Results: Nineteen RCTs (1013 participants) were included. CCT demonstrated a significant, moderate positive effect on global cognition (Hedges' g = 0.57, 95% CI [0.36, 0.78]). A trend suggesting greater benefits with higher gamification was observed: high (g = 0.71), medium (g = 0.46), and low (g = 0.45) degrees. However, subgroup differences were not statistically significant (p = 0.4333). Results were robust in sensitivity analyses. Conclusions: Tablet/computer-based CCT effectively improves global cognition in MCI. The potential additive value of gamification highlights its promise for enhancing engagement and effects, warranting further investigation in larger trials. This systematic review was registered with PROSPERO (CRD420251231618).}, }
@article {pmid41594816, year = {2026}, author = {Li, M and Xia, J and Pan, J and Zhao, S and Zhang, X and Jin, H and Dong, S}, title = {SleepMFormer: An Efficient Attention Framework with Contrastive Learning for Single-Channel EEG Sleep Staging.}, journal = {Brain sciences}, volume = {16}, number = {1}, pages = {}, doi = {10.3390/brainsci16010095}, pmid = {41594816}, issn = {2076-3425}, support = {2021ZD0200401//STI2030-Major projects/ ; 2024C03001//Zhejiang Province Key R &amp; D programs/ ; 2025C01137//Zhejiang Province Key R &amp; D programs/ ; 2022R52042//Zhejiang Province high level talent special support plan/ ; 2025ZFJH01//Fundamental Research Funds for the Central Universities/ ; }, abstract = {BACKGROUND/OBJECTIVES: Sleep stage classification is crucial for assessing sleep quality and diagnosing related disorders. Electroencephalography (EEG) is currently recognized as a primary method for sleep stage classification. High-performance automatic sleep staging methods based on EEG leverage the powerful contextual modeling capabilities of Transformer Encoder architectures. However, the global self-attention mechanism in Transformers incurs significant computational overhead, substantially hindering the training and inference efficiency of automatic sleep staging algorithms.<br><br>METHODS: To address these issues, we introduce an end-to-end framework for automatic sleep stage classification using single-channel EEG: SleepMFormer. At the algorithmic level, SleepMFormer adopts a task-driven simplification of the Transformer encoder to improve attention efficiency while preserving sequence modeling capability. At the training level, supervised contrastive learning is incorporated as an auxiliary strategy to enhance representation robustness. From an engineering perspective, these design choices enable efficient training and inference under resource-constrained settings.<br><br>RESULTS: When integrated with the SleePyCo backbone, the proposed framework achieves competitive performance on three widely used public datasets: Sleep-EDF, PhysioNet, and SHHS. Notably, SleepMFormer reduces training and inference time by up to 33% compared to conventional self-attention-based models. To further validate the generalizability of MaxFormer, we conduct additional experiments using DeepSleepNet and TinySleepNet as alternative feature extractors. Experimental results demonstrate that MaxFormer consistently maintains performance across different model architectures.<br><br>CONCLUSIONS: Overall, SleepMFormer introduces an efficient and practical framework for automatic sleep staging, demonstrating strong potential for related clinical applications.}, }
@article {pmid41594762, year = {2025}, author = {Liu, Y and Xue, W and Yang, L and Li, M}, title = {Deep Learning-Based EEG Emotion Recognition: A Review.}, journal = {Brain sciences}, volume = {16}, number = {1}, pages = {}, doi = {10.3390/brainsci16010041}, pmid = {41594762}, issn = {2076-3425}, support = {252102311095//Key Scientific and Technological Projects of Henan Province/ ; 2025T180781//China Postdoctoral Science Foundation/ ; 62301496//National Natural Science Foundation of China/ ; GZC20232447//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; }, abstract = {Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human-computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education, psychology, and military domains. Electroencephalographic (EEG) signals have gained widespread application in emotion recognition due to their inherent characteristics of being non-concealable and directly reflecting brain activity. In recent years, with the establishment of open datasets and advancements in deep learning, an increasing number of researchers have integrated EEG with deep learning methods for emotion recognition studies. This review summarizes commonly used deep learning models in EEG-based emotion recognition along with their applications in this field, including the design of different network architectures, optimization strategies, and model designs based on EEG signal features. We also discuss limitations from the perspectives of commonality-individuality (C-I) and suggest improvements. The review outlines future research directions and provided a minimal C-I framework to assess models. Through this review, we aim to provide researchers in this field with a comprehensive reference and approach to balance universality and personalization to promote the development of deep learning-based EEG emotion recognition methods.}, }
@article {pmid41594729, year = {2025}, author = {Tyler, WJ}, title = {Transcutaneous Auricular Vagus Nerve Stimulation for Treating Emotional Dysregulation and Inflammation in Common Neuropsychiatric Disorders.}, journal = {Brain sciences}, volume = {16}, number = {1}, pages = {}, doi = {10.3390/brainsci16010008}, pmid = {41594729}, issn = {2076-3425}, support = {FA8650-18-2-5402//United States Air Force Research Laboratory/ ; }, abstract = {Development of new therapeutic approaches and strategies for common neuropsychiatric disorders, including Major Depressive Disorder, anxiety disorders, and Post-Traumatic Stress Disorder, represent a significant global health challenge. Recent research indicates that emotional dysregulation and persistent inflammation are closely linked and serve as key pathophysiological features of these conditions. Emotional dysregulation is mechanistically coupled to locus coeruleus and norepinephrine (LC-NE) or noradrenergic system activity. Stemming from chronic stress, persistently elevated activity of the LC-NE system leads to hypervigilance, anxious states, and depressed mood. Concurrently, these symptoms are marked by systemic inflammation as indicated by elevated pro-inflammatory cytokines, and central neuroinflammation indicated by microglial activation in brain regions and networks involved in mood regulation and emotional control. In turn, chronic inflammation increases sympathetic tone and LC-NE activity resulting in a vortex of psychoneuroimmunological dysfunction that worsens mental health. Transcutaneous auricular vagus nerve stimulation (taVNS) in a non-invasive neuromodulation method uniquely positioned to address both noradrenergic dysfunction and chronic inflammation in neuropsychiatric applications. Evidence spanning the past decade demonstrates taVNS works via two complementary mechanisms. An ascending pathway engages vagal afferents projecting to the LC-NE system in the brain stem, which has been shown to modulate cortical arousal, cognitive function, mood, and stress responses. Through descending circuits, taVNS also modulates the cholinergic anti-inflammatory pathway to suppress the production of pro-inflammatory cytokines like TNF-α and IL-6 mitigating poor health outcomes caused by inflammation. By enhancing both central brain function and peripheral immune responses, taVNS has shown significant potential for recalibrating perturbed affective-cognitive processing. The present article describes and discusses recent evidence suggesting that taVNS offers a promising network-based paradigm for restoring psychoneuroimmunological homeostasis in common neuropsychiatric conditions.}, }
@article {pmid41594269, year = {2026}, author = {Wang, H and Xu, S and Guo, R and Han, J and Huang, MC}, title = {Neurosense: Bridging Neural Dynamics and Mental Health Through Deep Learning for Brain Health Assessment via Reaction Time and p-Factor Prediction.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/diagnostics16020293}, pmid = {41594269}, issn = {2075-4418}, support = {N/A//Kunshan Municipal Government research funding/ ; }, abstract = {Background/Objectives: Cognitive decline and compromised attention control serve as early indicators of neurodysfunction that manifest as broader psychopathological symptoms, yet conventional mental health assessment relies predominantly on subjective self-report measures lacking objectivity and temporal granularity. We propose Neurosense, an AI-driven brain health assessment framework using electroencephalography (EEG) to non-invasively capture neural dynamics. Methods: Our Dual-path Spatio-Temporal Adaptive Gated Encoder (D-STAGE) architecture processes temporal and spatial EEG features in parallel through Transformer-based and graph convolutional pathways, integrating them via adaptive gating mechanisms. We introduce a two-stage paradigm: first training on cognitive task EEG for reaction time prediction to acquire cognitive performance-related representations, then featuring parameter-efficient adapter-based transfer learning to estimate p-factor-a transdiagnostic psychopathology dimension. The adapter-based transfer achieves competitive performance using only 1.7% of parameters required for full fine-tuning. Results: The model achieves effective reaction time prediction from EEG signals. Transfer learning from cognitive tasks to mental health assessment demonstrates that cognitive efficiency representations can be adapted for p-factor prediction, outperforming direct training approaches while maintaining parameter efficiency. Conclusions: The Neurosense framework reveals hierarchical relationships between neural dynamics, cognitive efficiency, and mental health dimensions, establishing foundations for a promising computational framework for mental health assessment applications.}, }
@article {pmid41593817, year = {2026}, author = {Bahadir, S and Robinson, JT and Morse, LR and Yoo, PB and Kern, R and Makowski, NS and Kozai, TDY and Fudim, M and Barbe, MF and Lim, HH and Chang, EH and Zanos, S}, title = {The sixth bioelectronic medicine summit: Neurotechnologies for individuals and communities.}, journal = {Bioelectronic medicine}, volume = {12}, number = {1}, pages = {3}, pmid = {41593817}, issn = {2332-8886}, support = {NIH SPARC U41 - NS129436/NH/NIH HHS/United States ; NIH-NIGMS R01GM143362/NH/NIH HHS/United States ; NIH-NINDS 1R01NS136685/NH/NIH HHS/United States ; }, }
@article {pmid41592346, year = {2026}, author = {Suo, X and Li, W and Liao, X and Wu, Y and Zhang, H}, title = {A study of cortical activation and corticomuscular coupling enhancement following pre-task electrical stimulation in motor imagery.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae3e17}, pmid = {41592346}, issn = {1741-2552}, abstract = {OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been extensively studied. However, their widespread application is limited by the difficulty in extracting motor intentions from electroencephalography (EEG) signals, leading to low recognition rates. Additionally, the phenomenon of motor imagery blindnes s in some individuals further limits its applicability. Previous studies have attempted to improve motor imagery ability through electrical stimulation. However, applying electrical stimulation during motor imagery may introduce EEG artifacts and interfere with participants' concentration. The goal of this study is to investigate a new experimental paradigm. The new experimental paradigm improves motor imagery ability through pre-task electrical stimulation while preventing participant distraction or EEG artifacts.<br><br>APPROACH: This study implemented two paradigms: motor imagery with pre-task electrical stimulation (MI-ES) and motor imagery-only (MI-Only). Electrical stimulation was applied over hand muscle groups. Electromyography (EMG) and 64-channel EEG signals were simultaneously recorded under two experimental conditions.<br><br>MAIN RESULTS: We analyzed cortical activities and correlations between different brain regions under the two experimental conditions. Participants in the MI-ES condition exhibited a higher level of brain activation compared to the MI-Only condition. Additionally, in the MI-ES condition, the correlation between participants' EEG and EMG signals increased after electrical stimulation, indicating that the activation level of the motor-related cortex increased. A novel convolutional spiking neural network was applied to classify motor intentions, with participants achieving higher accuracy under the MI-ES condition, demonstrating enhanced motor imagery ability through pre-task electrical stimulation.<br><br>SIGNIFICANCE: This research demonstrates that pre-task electrical stimulation significantly enhances motor imagery ability, while also increasing cortical activation and corticomuscular coupling without introducing EEG artifacts or attentional interference.}, }
@article {pmid41590267, year = {2025}, author = {Mehmood, F and Rehman, SU and Mehmood, A and Kim, YJ}, title = {Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review.}, journal = {Biosensors}, volume = {16}, number = {1}, pages = {}, doi = {10.3390/bios16010015}, pmid = {41590267}, issn = {2079-6374}, support = {20022793//KEIT/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Nervous System Diseases/diagnosis ; *Artificial Intelligence ; *Ocular Motility Disorders/diagnosis ; Machine Learning ; Deep Learning ; }, abstract = {Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis for the diagnosis, classification, and monitoring of neurological conditions, including oculomotor-related disorders. Following the PRISMA guidelines, a structured literature search was conducted across major scientific databases, resulting in the inclusion of 15 peer-reviewed studies published over the last decade. The reviewed works encompass a range of neurological and ocular-related disorders and employ diverse AI models, from conventional ML algorithms to advanced DL architectures capable of learning complex spatiotemporal representations of neural signals. Key trends in feature extraction, signal representation, model design, and validation strategies are synthesized here to highlight methodological advancements and common challenges. While the reviewed studies demonstrate the growing potential of AI-enhanced EEG analysis for supporting clinical decision-making, limitations such as small sample sizes, heterogeneous datasets, and limited external validation remain prevalent. Addressing these challenges through standardized methodologies, larger multi-center datasets, and robust validation frameworks will be essential for translating EEG-driven AI approaches into reliable clinical applications. Overall, this review provides a comprehensive overview of current methodologies and future directions for AI-driven EEG analysis in neurological and oculomotor disorder assessment.}, }
@article {pmid41590101, year = {2026}, author = {He, M and Huang, Y and Cui, Z and Cheng, Z and Cao, W and Wang, G and Yao, W and Feng, M}, title = {Construction of Flexible Kaolin/Chitin Composite Aerogels and Their Properties.}, journal = {Gels (Basel, Switzerland)}, volume = {12}, number = {1}, pages = {}, doi = {10.3390/gels12010076}, pmid = {41590101}, issn = {2310-2861}, support = {253A7631D//Hebei Science and Technology Department Project/ ; 51503177//National Natural Science Foundation of China/ ; No. CNP-C-240204//Sichuan Province Engineering Technology Research Center of Novel CN Polymeric Materials/ ; }, abstract = {In this work, kaolin/chitin (K/Ch) composite aerogels with different mass ratios were successfully fabricated via a freeze-drying approach. The influence of kaolin content on the microstructure, properties and hemostatic performance of the composite aerogels was systematically investigated. The results demonstrated that the incorporation of kaolin endowed the chitin-based aerogels with tunable porous structures, excellent water absorption capacity (up to 4282% for K0.25/Ch2), and enhanced water retention (73.7% for K2/Ch2 at 60 min). Moreover, the K/Ch composite aerogels exhibited good biodegradability, no cytotoxicity (cell viability > 91.9%), and no hemolysis (hemolysis rate < 1.5% at all test concentrations). In vitro hemostatic evaluations revealed that the composite aerogels exhibited rapid blood coagulation (blood clotting time of 16 s for K2/Ch2) with a blood coagulation index (BCI) as low as 0.5%, which was attributed to the synergistic effect of the physical adsorption of chitin and the coagulation cascade activation by kaolin. These findings indicated that the K/Ch composite aerogels could be used as novel natural hemostatic materials for potential effective and rapid hemostasis.}, }
@article {pmid41587494, year = {2026}, author = {Jia, J and Zhang, R and Yuan, D and Yu, D and Li, P}, title = {Theoretical and applied research on spatio-temporal graph attention networks for single-trial P300 detection.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae3d68}, pmid = {41587494}, issn = {1741-2552}, abstract = {Accurate detection of single-trial P300 ERPs (event-related potentials) is crucial for developing high-performance non-invasive BCIs (brain-computer interfaces). However, this task remains challenging because of the low SNR (signal-to-noise ratio) of EEG (electroencephalography) and the limited ability of existing models to concurrently capture the complex non-Euclidean spatiotemporal dynamics of brain signals. Approach: We propose a novel ST-GraphTRNet (spatiotemporal graph transformer network). This architecture synergis-tically integrates temporal convolutions for local feature extraction, graph convolutions to explicitly model the neurophysio-logical spatial relationships between EEG electrodes, and a temporal transformer with a self-attention mechanism to capture global, long-range temporal dependencies across the entire signal. Main Results: Extensive evaluation of four public P300 datasets demonstrates that ST-GraphTRNet significantly outper-forms SOTA (state-of-the-art) benchmarks under both within-subject and cross-subject paradigms. Crucially, interpretability analyses via t-SNE (T-distributed Stochastic Neighbor Embedding) and Grad-CAM (Gradient-weighted Class Activation Mapping) revealed that the model's decisions aligned with established neurophysiological priors, focusing on parietal elec-trodes approximately 300 ms post-stimulus. Significance: This study provides a powerful and interpretable framework for single-trial ERPs decoding. By effectively integrating the strengths of CNNs (Convolutional Neural Networks), GNNs (Craph Neural Networks), and Transformers, a new benchmark for building high-accuracy, generalizable, and clinically viable BCIs is established, moving closer to the goal of plug-and-play systems that require minimal user-specific calibration. .}, }
@article {pmid41586177, year = {2026}, author = {Chin, AHB and Roslan, R and Alsomali, N and Al-Balas, Q and Salhab, BBS and Muhsin, SM}, title = {Islamic Bioethics Viewpoint on Elective Brain Chip Implants and Brain-Computer Interfaces for Enhancing Academic Performance in Competitive Examinations.}, journal = {Asian bioethics review}, volume = {18}, number = {1}, pages = {79-92}, pmid = {41586177}, issn = {1793-9453}, abstract = {The first implantation of a brain chip into a human paralysis patient by Neuralink demonstrated much potential for treating debilitating neurological diseases and injuries. Nevertheless, brain chips can also be implanted in healthy people to provide an interface between the human brain with computers, robotic machines, and novel artificial intelligence platforms, which generates new ethical issues. The focus here is on the development of brain chip implants that can significantly improve memory, intelligence, and cognition, thereby boosting performance in national examinations for university admissions and securing civil service jobs, thus providing a "game-changer" and "shortcut" for many students and parents. Given that Islam is a major world religion, constituting a significant portion of the global population, it is crucial for the biomedical industry to comprehend Islamic perspectives on emerging medical technologies, which will enable it to more effectively cater to a substantial and growing demographic. We thus critically examine whether the application of brain chip technology to enhance academic performance in highly competitive examinations is consistent with Islamic principles. Based on the Islamic jurisprudential framework, such an application for intellectual enhancement of normal and healthy people without any mental impairment may conflict with the injunction to preserve intellect (Hifz al-Aql) and "consideration of consequences" (murā'āt al-ma'ālāt) in Islam. It may also be viewed as tampering with Allah's creation (Taghyir Khalq Allah). Gaining such unfair advantages in competitive examinations will likely be viewed as unethical, by transgressing the core Islamic precepts of Amanah (trustworthiness), Al-'Adl (justice), Ikhlas (sincerity), and Mujahadah (striving).}, }
@article {pmid41585948, year = {2025}, author = {Wang, S and Wang, R and Chang, L and Wu, J and Hu, L}, title = {AMANet: a data-augmented multi-scale temporal attention convolutional network for motor imagery classification.}, journal = {Frontiers in neurorobotics}, volume = {19}, number = {}, pages = {1704111}, pmid = {41585948}, issn = {1662-5218}, abstract = {Motor imagery brain-computer interface (MI-BCI) has garnered considerable attention due to its potential for neural plasticity. However, the limited number of MI-EEG samples per subject and the susceptibility of features to noise and artifacts posed significant challenges for achieving high decoding performance. To address this problem, a Data-Augmented Multi-Scale Temporal Attention Convolutional Network (AMANet) was proposed. The network mainly consisted of four modules. First, the data augmentation module comprises three steps: sliding-window segmentation to increase sample size, Common Spatial Pattern (CSP) extraction of discriminative spatial features, and linear scaling to enhance network robustness. Then, multi-scale temporal convolution was incorporated to dynamically extract temporal and spatial features. Subsequently, the ECA attention mechanism was integrated to realize the adaptive adjustment of the weights of different channels. Finally, depthwise separable convolution was utilized to fully integrate and classify the deep extraction of temporal and spatial features. In 10-fold cross-validation, the results show that AMANet achieves classification accuracies of 84.06 and 85.09% on the BCI Competition IV Datasets 2a and 2b, respectively, significantly outperforming baseline models such as Incep-EEGNet. On the High-Gamma dataset, AMANet attains a classification accuracy of 95.48%. These results demonstrate the excellent performance of AMANet in motor imagery decoding tasks.}, }
@article {pmid41585347, year = {2025}, author = {Zhang, M and Wang, T and Zhu, Z}, title = {Bridging neuromorphic computing and deep learning for next-generation neural data interpretation.}, journal = {Frontiers in computational neuroscience}, volume = {19}, number = {}, pages = {1737839}, pmid = {41585347}, issn = {1662-5188}, }
@article {pmid41584816, year = {2026}, author = {Kim, DU and Yoo, MA and Choi, SI and Kim, MY and Kim, SP}, title = {Toward zero-calibration MEG brain-computer interfaces based on event-related fields.}, journal = {Biomedical engineering letters}, volume = {16}, number = {1}, pages = {67-76}, pmid = {41584816}, issn = {2093-985X}, abstract = {Magnetoencephalography (MEG) offers high spatiotemporal resolution, but its application in practical brain-computer interface (BCI) systems remains limited partially due to the need for user-specific calibration and inter-subject variability. We present a zero-calibration MEG-based BCI based on event-related fields (ERFs) by leveraging spatial filters and deep learning techniques. First, we developed an on-line ERF-based MEG BCI with a visual oddball paradigm, achieving the mean classification accuracy of 94.29% and an information transfer rate (ITR) of 20.47 bits/min. Using the resulting multi-subject dataset, we applied xDAWN spatial filtering and trained a deep convolutional neural network (DeepConvNet) to classify target versus non-target responses. To simulate real-world plug-and-play use, zero-calibration performance was evaluated using a leave-one-subject-out (LOSO) cross-validation approach. The combination of xDAWN and DeepConvNet achieved the average accuracy of 80.32% and ITR of 12.75 bits/min, respectively, demonstrating cross-subject generalization. These results underscore the feasibility of zero-calibration MEG BCIs for more practical use.}, }
@article {pmid41582168, year = {2026}, author = {Mannan, MMN and Palipana, DB and Mulholland, K and Jurd, E and Lloyd, ECR and Quinn, ARJ and Crossley, CB and Rabbi, MF and Lloyd, DG and Teng, YD and Pizzolato, C}, title = {Virtual reality mediated brain-computer interface training improves sensorimotor neuromodulation in unimpaired and post spinal cord injury individuals.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-026-36431-3}, pmid = {41582168}, issn = {2045-2322}, abstract = {Real-time brain-computer interfaces (BCIs) that decode electroencephalograms (EEG) during motor imagery (MI) are powerful adjuncts to rehabilitation after neurotrauma. Further, immersive virtual reality (VR) could complement BCIs by delivering visual and auditory sensory feedback (VR biofeedback) congruent to user's MI, enabling task-oriented therapies. Yet, therapeutic outcomes rely on user's proficiency in evoking MI to attain volitional BCI-commanded VR interaction. While previous studies have explored multi-session BCIs, we investigated the impact of longitudinal training on sensorimotor neuromodulation using BCI combined with VR-mediated externally-cued and self-paced lower-limb MI tasks. The EEG-based BCI was coupled with real-time VR biofeedback congruent with the MI task. Over multiple training sessions in laboratory conditions, five unimpaired individuals progressively learnt to improve control over their EEG during MI virtual walking, corresponding with increased BCI classification accuracy. Further, similar improvements were found with four individuals with chronic complete spinal cord injury (SCI) using the system in real-world neurorehabilitation settings. These findings demonstrate that unimpaired and SCI impaired individuals learnt to control their sensorimotor EEG associated with MI tasks through VR-mediated BCI training, which was associated with improved BCI classification accuracy. Our findings highlight the potential of VR-mediated BCIs in enhancing neuromodulation, providing a foundation for future rehabilitation therapies.}, }
@article {pmid41581018, year = {2026}, author = {Liao, X and Gao, G}, title = {Strategies for improving recovery of consciousness after acute brain injury.}, journal = {Current opinion in critical care}, volume = {}, number = {}, pages = {}, doi = {10.1097/MCC.0000000000001365}, pmid = {41581018}, issn = {1531-7072}, abstract = {PURPOSE OF REVIEW: Advances in critical care have improved survival rates after severe brain injuries, yet many patients experience prolonged disorders of consciousness, resulting in significant care burdens and ethical challenges. Therefore, a systematic review of current treatment strategies for these disorders following acute brain injury is essential to provide evidence-based guidance for clinicians, ultimately aiming to enhance patient prognosis and quality of life.<br><br>RECENT FINDINGS: Research has rapidly evolved beyond traditional drugs like amantadine and zolpidem, with significant breakthroughs in neuromodulation techniques such as spinal cord stimulation, transcranial direct current stimulation, and brain-computer interfaces. These innovations are reshaping clinical practice by transitioning from theoretical concepts to validated interventions, enabling more precise, individualized treatment protocols. This shift moves clinical management from empirical medication toward targeted neural circuit modulation, while technologies detecting covert consciousness are helping redefine diagnostic standards. The differential effects of these interventions are also advancing fundamental research, deepening understanding of consciousness networks and shifting focus from single targets to whole brain dynamic regulation.<br><br>SUMMARY: These developments collectively highlight the need for integrated multimodal assessment and multilevel interventions, pointing toward a future of personalized, precision medicine for arousal promotion that offers tangible hope for improving patient recovery outcomes and quality of life.}, }
@article {pmid41580794, year = {2026}, author = {Niu, X and Yuan, M and Zhang, J and Yang, J and Yu, Q and Wang, D}, title = {Noninvasive BCI-based cognitive rehabilitation in poststroke cognitive impairment: study protocol for a randomized controlled trial.}, journal = {Trials}, volume = {}, number = {}, pages = {}, doi = {10.1186/s13063-026-09449-1}, pmid = {41580794}, issn = {1745-6215}, abstract = {BACKGROUND: Poststroke cognitive impairment (PSCI) significantly reduces quality of life and survival rates. Current interventions face challenges in efficacy and accessibility. Noninvasive brain-computer interface (BCI) technology may enhance neural plasticity and cognitive recovery through real-time neurofeedback, offering a safe and accessible approach for poststroke cognitive rehabilitation. This trial aims to evaluate the efficacy of BCI-based cognitive training and explore its neural mechanisms.<br><br>METHODS: A prospective, randomized, double-blind, controlled, single-center trial will enroll 66 PSCI patients. Participants will be randomized into the intervention group or control group. Interventions will be administered 5 days/week for 4 weeks. Primary outcome is as follows: The 4-week post-intervention MoCA scores; secondary outcomes are as follows: 3-month follow-up MoCA scores, attention, memory, executive function, neurophysiological markers, and daily living function. Assessments will be conducted at baseline (T0W), post-intervention (T4W), and 3-month follow-up (T16W).<br><br>DISCUSSION: Results will provide evidence for BCI's clinical utility and neuroplasticity mechanisms, guiding personalized neurorehabilitation strategies.<br><br>TRIAL STATUS: The protocol version used for this study is Version 3.0, dated May 8, 2025. Recruitment is scheduled to begin on June 10, 2025, and is expected to be completed by May 8, 2026.<br><br>TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2500107318. Registered on 8 August 2025.}, }
@article {pmid41243805, year = {2026}, author = {Colucci, A and Vermehren, M and Angerhöfer, C and Peekhaus, N and Kim, WS and Chang, WK and Hömberg, V and Paik, NJ and Soekadar, SR}, title = {Hybrid Brain/Neural Exoskeleton Enables Bimanual ADL Training in Routine Stroke Rehabilitation.}, journal = {Stroke}, volume = {57}, number = {2}, pages = {505-510}, pmid = {41243805}, issn = {1524-4628}, mesh = {Humans ; Middle Aged ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Aged ; Female ; *Activities of Daily Living ; Adult ; Aged, 80 and over ; *Exoskeleton Device ; *Stroke/physiopathology/complications ; Pilot Projects ; Electroencephalography ; Young Adult ; Electrooculography ; *Brain/physiopathology ; Adolescent ; }, abstract = {BACKGROUND: Severe upper limb motor impairment is one of the most disabling consequences of stroke. Although brain-controlled rehabilitation technologies, such as brain/neural exoskeletons (B/NE), have been shown to be effective in promoting motor recovery, their clinical adoption remains limited because of insufficient integration of B/NE into existing clinical workflows. Here, we introduce and validate a fully portable B/NE system that overcomes this limitation by relying on brain (electroencephalography) and ocular (electrooculography) signals to restore bimanual activities of daily living within a novel therapeutic framework.<br><br>METHODS: In this pilot study, we tested the feasibility of the novel approach in 5 stroke survivors (mean age, 51 years; SD=14.8) undergoing inpatient neurorehabilitation. Stroke survivors aged 18 to 80 years, who exhibited hemiparesis and sufficient cognitive ability to understand and follow instructions, were invited to participate in a 1-hour training session. This session included system setup and calibration, followed by performing B/NE-supported, self-paced bimanual activities of daily living. As primary outcome measures, we assessed control accuracy, the ability to reliably modulate electroencephalography and electrooculography signals, and time to initialize, defined as the time required to react to cues and initiate the task, serving as a measure of control intuitiveness. In addition, participants' B/NE control performance during assisted training of bimanual activities of daily living, as well as setup preparation time, were assessed via direct observation.<br><br>RESULTS: Participants demonstrated reliable control accuracy in using both brain (mean, 83%; SD=15.36) and ocular (mean=100%) signals, as well as intuitive control (time to initialize <2 s). All participants reliably controlled the B/NE performing a battery of 10 bimanual activities of daily living. Moreover, setup and calibration times remained below 20 minutes.<br><br>CONCLUSIONS: These findings highlight the compatibility of the novel B/NE with existing clinical workflows and its feasibility to enable B/NE-supported stroke neurorehabilitation by facilitating seamless integration into clinical practice.}, }
@article {pmid41585048, year = {2023}, author = {Rodriguez-Calienes, A and Oliver, M and Hassan, AE and Vivanco-Suarez, J and Divani, AA and Ribo, M and Petersen, N and Abraham, M and Fifi, J and Guerrero, WR and Malik, AM and Siegler, JE and Nguyen, T and Sheth, S and Yoo, A and Linares, G and Janjua, N and Quispe-Orozco, D and Galecio-Castillo, M and Alhajala, H and Malaga, M and Farooqui, M and Jovin, T and Jumaa, M and Ortega-Gutierrez, S}, title = {Safety of Intravenous Cangrelor Versus Dual Oral Antiplatelet Loading Therapy in Endovascular Treatment of Tandem Lesions: An Observational Cohort Study.}, journal = {Stroke (Hoboken, N.J.)}, volume = {3}, number = {6}, pages = {e001020}, pmid = {41585048}, issn = {2694-5746}, abstract = {BACKGROUND: Procedural intravenous cangrelor has been proposed as an effective platelet inhibition strategy for stenting in acute ischemic stroke. We aimed to compare the safety profile of low-dose intravenous cangrelor versus dual oral antiplatelet therapy (DAPT) loading in patients with acute cervical tandem lesions.<br><br>METHODS: We retrospectively identified cases from an international multicenter cohort who underwent intraprocedural administration of intravenous cangrelor (15 &#x3bc;g/kg followed by an infusion of 2&#xa0;&#x3bc;g/kg per min) or DAPT loading during acute tandem lesions intervention. Safety outcomes included rates of symptomatic intracranial hemorrhage, parenchymal hematoma type 2, petechial hemorrhage, and in-stent thrombosis. Inverse probability of treatment weighting matching was used to reduce confounding.<br><br>RESULTS: From 691 patients, we included 195 patients, 30 of whom received intravenous cangrelor and 165 DAPT. The DAPT regimens were aspirin+clopidogrel (93.3%) or aspirin+ticagrelor (6.6%). After inverse probability of treatment weighting, the patients treated with cangrelor were not at greater odds of symptomatic intracranial hemorrhage (odds ratio [OR], 1.30 [95% CI, 0.09-17.3]; P=0.837), symptomatic intracranial hemorrhage-parenchymal hematoma type 2 (OR, 0.54 [95% CI, 0.05-4.98]; P=0.589), or petechial hemorrhage (OR, 1.11 [95% CI, 0.38-3.28]; P=0.836). Similarly, the rate of in-stent thrombosis was not significantly different between the 2 groups (1.8% versus 0%; P=0.911).<br><br>CONCLUSION: Cangrelor at the half dose of the myocardial infarction protocol showed a similar safety profile compared with the commonly used DAPT loading protocols in patients with acute tandem lesions. Further studies with larger samples are warranted to elucidate the safety of antiplatelet therapy in tandem lesions.}, }
@article {pmid41580462, year = {2026}, author = {Huang, Q and Chen, D and Pereira, AC and Leonard, A and Ellis, CL and Velthuis, H and Dimitrov, M and Ponteduro, FM and Wong, NML and Kowalewski, L and Pretzsch, CM and Daly, E and Murphy, DGM and McAlonan, GM}, title = {Differential GABA dynamics across brain functional networks in autism.}, journal = {Communications biology}, volume = {}, number = {}, pages = {}, doi = {10.1038/s42003-026-09563-5}, pmid = {41580462}, issn = {2399-3642}, support = {2024A1515011690//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Differences in the ϒ-aminobutyric acid (GABA) system contribute to an excitatory-inhibitory imbalance in autism, particularly affecting sensory processing. However, the brain's broader response to interventions targeting GABA pathways in individuals with autism remains poorly understood. This study tested the hypothesis that GABAergic control of information transfer across large-scale brain functional networks is altered in autism. We conducted a phase-amplitude coupling (PAC) analysis of resting-state EEG signals within and between these networks. Responses were compared after double-blind, randomized oral administration of either a placebo or 15/30 mg of arbaclofen, a GABAB receptor agonist. Twenty-four non-autistic (9 males; 19-53 years) and 15 autistic participants (9 males; 20-51 years) completed 93 study visits. Autistic participants exhibited significantly higher theta-beta PAC, especially within the limbic network. High-dose arbaclofen shifted PAC metrics in visual and somatomotor networks towards non-autistic levels, but had minimal effects on networks related to higher cognitive functions. Interestingly, altered PAC within and between networks in the limbic system of autistic participants was normalized by low-dose arbaclofen, yet reemerged after high-dose administration. These findings provide compelling evidence for altered GABAergic responsivity in autism, helping explain some of the challenges in prescribing medications for autistic individuals, such as paradoxical reactions and dose sensitivity.}, }
@article {pmid41579859, year = {2026}, author = {Huang, C and Tao, H and Zhou, Y and Wu, Q and Li, M and Liu, A and Zhu, T and Yu, C and Li, P and Huang, S and Guo, H and Hu, J and Wang, G}, title = {Pregnenolone promotes immune evasion through blocking endogenous retrovirus expression.}, journal = {Cell metabolism}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cmet.2025.12.020}, pmid = {41579859}, issn = {1932-7420}, abstract = {Research into steroid hormones shaping tumor biology has gained increasing attention. Using multiple mouse tumor models, we show that pregnenolone promoted tumor progression and reduced sensitivity to immunotherapy. Pregnenolone levels were markedly elevated in maternal mice experiencing mating deficiency. Mechanistically, pregnenolone directly binds Kap1 and inhibits Trim39-mediated ubiquitination at K750, leading to Kap1 stabilization and repression of endogenous retrovirus (ERV) expression and type-I interferon production. Furthermore, pharmacological antagonism of pregnenolone effectively suppressed tumor growth and enhanced immunotherapy efficacy. These findings reveal a previously unrecognized link between mating-associated steroid metabolism and tumor immune regulation and identify pregnenolone signaling as a potential therapeutic target in cancer.}, }
@article {pmid41576955, year = {2026}, author = {Ning, C and Fu, G and Zhang, YY and Meyniel, F and Wang, L}, title = {Macaque prefrontal cortex integrates multiple components for metacognitive judgments of working memory.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2025.11.014}, pmid = {41576955}, issn = {1097-4199}, abstract = {The ability to evaluate one's own memory is known as metamemory. Whether metamemory is inherent to memory strength or requires additional computation in the brain remains largely unknown. We investigated the metacognitive mechanism of working memory (WM) using two-photon calcium imaging in the prefrontal cortex (PFC) of macaque monkeys, memorizing spatial sequences of varying difficulties. In some trials, after viewing the sequence, monkeys could opt out of retrieval for a smaller reward, reflecting their confidence in WM (meta-WM). We discovered that PFC neurons encoded WM strength by jointly representing the remembered locations and their associated uncertainties. Additional factors-trial history and arousal-encoded in baseline activity also predicted opt-out decisions, serving as cues for meta-WM. We further identified a code for meta-WM itself that integrated WM strength with these cues. Thus, the PFC neural geometry implements metacognitive computations, integrating WM strength with cues into a meta-WM signal to guide behavior.}, }
@article {pmid41574639, year = {2026}, author = {Guo, Z and Ye, R and Guan, L and He, W and Qiu, S and Shao, X and Fang, J and Fang, J and Du, J}, title = {Differential roles of EA-TRAPed cells in the anterior cingulate cortex across various intervention times in inflammatory pain.}, journal = {Animal models and experimental medicine}, volume = {}, number = {}, pages = {}, doi = {10.1002/ame2.70118}, pmid = {41574639}, issn = {2576-2095}, support = {//The National Natural Science Fund of China (82374561, 82174490, 81873360), the Research Project of Zhejiang Chinese Medical University (2022JKZKTS44, 2022FSYYZZ07), and the Zhejiang Medical and Health Science and Technology Program (2021RC098)/ ; }, abstract = {BACKGROUND: The analgesic effects of multiple electroacupuncture (EA) sessions and single EA sessions differ significantly in pain management. Area 24b (A24b) of the anterior cingulate cortex (ACC) is crucial in pain processing. EA relieves pain by targeting and modulating the neuronal activity within this subregion. However, whether the cumulative effect of EA antinociception is connected to A24b mechanisms has remained unclear.<br><br>METHODS: In our study, we used the Complete Freund's Adjuvant (CFA) model to induce inflammatory pain and the Spared Nerve Injury (SNI) model to induce neuropathic pain, and adult male C57BL/6, FosTRAP, and FosTRAP:Ai9 mice were used as experimental subjects to investigate the cumulative effect of EA antinociception and whether multiple EA sessions and a single EA session regulate different neuronal populations in the A24b.<br><br>RESULTS: We observed that EA effectively alleviated pain in mice, with three EA sessions yielding superior analgesic effects compared to a single session. Using chemical genetics combined with FosCreER technology to activate EA-TRAPed cells in the A24b, we found that pain relief was more pronounced with three EA sessions. Moreover, chemogenetic inhibition of EA-TRAPed cells in the A24b reversed the analgesic effects of a single EA session but not those of three EA sessions. Fluorescent in&#xa0;situ hybridization results indicated that three EA sessions significantly increased the number of GABAergic neurons in the A24b compared with a single session. Additionally, retrograde tracing revealed that the A24b circuit that monosynaptically innervates EA-TRAPed cells included projections from the central lateral nucleus (CL), lateral mediodorsal thalamic nucleus (MDL), lateral habenula (LHb), dorsal raphe nucleus (DR), caudal linear nucleus of the raphe (CLi), dorsal tuberomamillary nucleus (DTM), periventricular hypothalamic nucleus (Pe) and hippocampal fields CA1, CA2, and CA3. These findings suggest that multiple EA sessions and single EA sessions activated different neuronal populations in the A24b. The enhanced analgesic effect of multiple EA sessions may be attributed to an increase in the proportion of GABAergic neurons within the A24b.<br><br>CONCLUSIONS: Multiple and single EA sessions recruit distinct neuronal populations in A24b, with the stronger analgesic effect of repeated EA linked to a higher proportion of GABAergic neurons in this region.}, }
@article {pmid41573300, year = {2025}, author = {Nieves-Méndez, C}, title = {From neurotechnology to the classroom: the promise of brain-computer interfaces for training systems engineers.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1733768}, pmid = {41573300}, issn = {1662-5161}, abstract = {This perspective article explores the transformative potential of brain-Computer Interfaces (BCI) in undergraduate systems engineering programs, a domain characterized by high attrition and a widening gap between rapid technological innovation and slower pedagogical change. I argue that BCI, by enabling real-time detection of cognitive states such as mental workload, attention, and frustration, can evolve from laboratory tools to central pedagogical instruments for adaptive, student-centered education. I review the state-of-the-art methods, which demonstrate the technical feasibility of low-cost electroencephalography (EEG) devices and machine learning algorithms that classify cognitive states with high accuracy in controlled settings. Building on this evidence, I outline concrete applications in three dimensions: formative assessment, dynamic curricular adaptation, and cognitive inclusion, with a specific emphasis on preventing dropout in foundational courses such as algorithms. I also examine ethical, technical, and pedagogical challenges, and propose a scalable, ethically grounded pilot model tailored for universities, particularly in Latin America. This study reports no empirical results. It synthesizes the existing evidence and proposes a roadmap for research and educational action.}, }
@article {pmid41571161, year = {2026}, author = {Artigas, R and Ruiz, S and Montalba, C and Peñafiel, C and Figueroa, R and Irarrazaval, P}, title = {Decreased levels of N-Acetylaspartyglutamate, myo-inositol, and syllo-inositol, in cortical brain regions of women exposed to adverse childhood experiences.}, journal = {Magnetic resonance imaging}, volume = {128}, number = {}, pages = {110621}, doi = {10.1016/j.mri.2026.110621}, pmid = {41571161}, issn = {1873-5894}, abstract = {Adverse Childhood Experiences (ACE), including abuse and neglect, can have lasting negative effects on health, decreasing lifespan and increasing the risk of chronic diseases. While research on ACE's impact on brain biochemistry is limited, Magnetic Resonance Spectroscopy (MRS) provides a non-invasive way to study these alterations. This study aims to identify neurochemical patterns linked to ACE exposure using J-edited MRS methods. 43 female participants (18 Low-ACE and 25 High-ACE), aged 19 to 31, were recruited. ACE exposure was assessed using the Maltreatment and Abuse Chronology of Exposure (MACE) test. MRS was conducted on a 3.0 T scanner, with J-edited single-voxel 1H-MRS from the Anterior Cingulate Cortex (ACC), Pre-Frontal Cortex (PFC), and hippocampus. Metabolite quantification was carried out using the Osprey pipeline and analyzed using univariate and multivariate methods. Univariate analysis showed reduced N-Acetylaspartyglutamate (NAAG) and syllo-Inositol (sI) levels in the ACC (p = 0.06) and PFC (p = 0.057), respectively, among High-ACE participants. Logistic Regression identified lower NAAG, GABA, glutathione (GSH), and myo-Inositol (mI) in the ACC, and differences in sI, lactate, NAAG, and GSH in the PFC, within the High-ACE group. Random Forest and Support Vector Machines confirmed NAAG, mI, and sI as possible ACE biomarkers. Throughout this study, cortical regions consistently showed reduced levels of NAAG, mI, and sI in the High-ACE group, suggesting a potential link to ACE. These findings improve our understanding of neurochemical changes associated with ACE, aiding in the identification of at-risk individuals and in the development of strategies to prevent long-term health effects.}, }
@article {pmid41570674, year = {2026}, author = {Fu, X and Jiang, W and Liu, R and Müller-Putz, GR and Guan, C}, title = {EEG-to-gait decoding via phase-aware representation learning.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {198}, number = {}, pages = {108608}, doi = {10.1016/j.neunet.2026.108608}, pmid = {41570674}, issn = {1879-2782}, abstract = {Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This study presents NeuroDyGait, a two-stage, phase-aware EEG-to-gait decoding framework that explicitly models temporal continuity and domain relationships. To address challenges of causal, phase-consistent prediction and cross-subject variability, Stage I learns semantically aligned EEG-motion embeddings via relative contrastive learning with a cross-attention-based metric, while Stage II performs domain relation-aware decoding through dynamic fusion of session-specific heads. Comprehensive experiments on two benchmark datasets (GED and FMD) show substantial gains over baselines, including a recent 2025 model EEG2GAIT. The framework generalizes to unseen subjects and maintains inference latency below 5 ms per window, satisfying real-time BCI requirements. Visualization of learned attention and phase-specific cortical saliency maps further reveals interpretable neural correlates of gait phases. Future extensions will target rehabilitation populations and multimodal integration.}, }
@article {pmid41570350, year = {2026}, author = {Sun, J and Xie, R and Yu, J and Ji, L and Jia, T and Pan, Y and Li, C}, title = {Dynamic modulation of corticomuscular coherence during ankle dorsiflexion after stroke: towards hybrid BCI for lower-limb rehabilitation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae3c41}, pmid = {41570350}, issn = {1741-2552}, abstract = {OBJECTIVE: Hybrid brain-computer interface (BCI) systems incorporate electroencephalography (EEG) and electromyography (EMG) signals to extract corticomuscular coherence (CMC) features, enabling self-modulation of neural communication. While promising for stroke rehabilitation, the neurophysiological mechanism underlying hybrid BCI therapy remains poorly understood. To address this gap, we characterized post-stroke CMC dynamics during ankle dorsiflexion and further established their relationship with functional motor recovery.<br><br>APPROACH: We acquired synchronous EEG and high-density EMG (HD-EMG) recordings from 13 subacute stroke patients (with their affected limb) before and after three-week rehabilitation, and 9 age-matched healthy controls (using their dominant limb) during isometric ankle dorsiflexion. Using multivariate coupling analysis, we computed EEG and EMG projection vectors to identify optimal coupling patterns. Subsequently, we derived CMC spectra and topographies through coherence analysis to characterize corticomuscular interactions at spatial and spectral scales.<br><br>MAIN RESULTS: Compared to healthy controls, stroke patients demonstrated reduced beta-band CMC patterns, particularly within the sensorimotor areas involved in the foot movement. No significant differences in CMC patterns were observed between stroke patients before and after rehabilitation training. Further analysis revealed significant correlation between betaband CMC changes and clinical improvements measured by the Berg Balance Scale (BBS).<br><br>SIGNIFICANCE: Beta-band CMC is a potential neurophysiological biomarker of motor recovery following stroke. These findings provide novel insights into the disrupted corticomuscular communication underlying post-stroke motor dysfunction, while offering mechanistic evidence to guide the design and implementation of hybrid BCI systems that target these specific biomarkers for therapeutic intervention.}, }
@article {pmid41529302, year = {2026}, author = {Yue, H and Ruan, H and Zhao, Y}, title = {LMSA-net: a lightweight multi-scale attention network for eeg-based emotion recognition.}, journal = {Biomedical physics &amp; engineering express}, volume = {12}, number = {1}, pages = {}, doi = {10.1088/2057-1976/ae3763}, pmid = {41529302}, issn = {2057-1976}, mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; Algorithms ; *Signal Processing, Computer-Assisted ; *Attention ; *Neural Networks, Computer ; }, abstract = {Electroencephalogram (EEG)-based emotion recognition holds great potential in affective computing, mental health assessment, and human-computer interaction. However, EEG signals are non-stationary, noisy, and composed of multiple frequency bands, making direct feature learning from raw data particularly challenging. While end-to-end models alleviate the need for manual feature engineering, advancing the performance frontier of lightweight architectures remains a crucial and complex challenge for practical deployment. To address these issues, we propose LMSA-Net (Lightweight Multi-Scale Attention Network), a lightweight, interpretable, and end-to-end model that directly learns spatio-temporal features from raw EEG signals. The architecture integrates learnable channel weighting for adaptive spatial encoding, multi-scale temporal separable convolution for rhythm-specific feature extraction, and Sim Attention Module for parameter-free saliency enhancement. Our proposed LMSA-Net is evaluated on three benchmark datasets, SEED, SEED-IV, and DEAP, under subject-dependent protocols. It achieves top performance on SEED (65.53% accuracy), competitive results on SEED-IV (48.52% accuracy), and strong performance in arousal classification on DEAP, demonstrating good generalization. Ablation studies confirm the critical role of each proposed module. Frequency analysis reveals that our multi-scale temporal kernels inherently specialize in distinct EEG rhythms, validating their neurophysiological alignment. Lightweight design is evidenced by minimal parameters (7.64K) and low latency, ideal for edge deployment. Interpretability analysis further shows the model's focus on emotion-related brain regions. LMSA-Net thus delivers an efficient, interpretable, and high-performing solution. The code is available athttps://github.com/rhr0411/LMSA-Net.git.}, }
@article {pmid41566808, year = {2026}, author = {Huang, C and Bai, J and Lin, K and Li, X and Guo, D and Song, J and Wang, J and Chen, Z and Wang, C}, title = {Exploring Back Muscle Activities in Chronic Low Back Pain Patients Using a Large-Area Stretchable Electrode Array With Full-Back Coverage.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e04815}, doi = {10.1002/adhm.202504815}, pmid = {41566808}, issn = {2192-2659}, support = {12302223//National Natural Science Foundation of China/ ; 2024E10108//Zhejiang Key Laboratory of Intelligent Rehabilitation and Translational Neuroelectronics/ ; 2022C03038//Key R&amp;D Program of Zhejiang Province/ ; BLB19J014//Key R&amp;D Program of Zhejiang Province/ ; 2022M710126//China Postdoctoral Science Foundation/ ; LGF20H170004//Zhejiang Province Basic Public Welfare Research Project/ ; BX20220268//National Postdoctoral Program for Innovative Talents/ ; }, abstract = {Exploring the back muscle activity in chronic low back pain (CLBP) patients is crucial for the quantitative assessment of their neuromuscular function. However, hindered by the lack of stretchable, large-area electrode arrays capable of spanning the entire back, existing studies have primarily focused on the erector spinae and multifidus muscles in the lower back. Here, we report a large-area, stretchable high-density surface electromyography (HD-sEMG) electrode array designed to cover both lower and upper back regions, enabling comprehensive characterization of back muscle activity in CLBP patients. The array comprises 64 channels arranged in a half-dumbbell configuration, inspired by the anatomical distribution of the erector spinae and multifidus muscles. Notably, this array demonstrates unprecedented operability, scalability for mass production, and reliable data acquisition capabilities. 128-channel HD-sEMG signals were acquired from both healthy controls and CLBP patients during the Biering-Sørensen test, a standardized lumbar endurance protocol. Statistical analyses of sEMG-derived metrics and topographic maps revealed significant intergroup differences in muscle activity, as well as regional variations between the lower back, upper back, and full-back segments, particularly in contraction time and fatigue-related metric changes. These findings offer novel insights into the neuromuscular dysfunction in CLBP, potentially illuminating the underlying physiological adaptations associated with CLBP.}, }
@article {pmid41566501, year = {2026}, author = {Prasad, NK and Perry, NJ and Goldring, AL and Fleisher, LA and Petrossian, L and Leuthardt, EC and Souders, L and Wilk, SJ}, title = {A retrospective analysis of post-stroke rehabilitation with real world use of brain-computer interface.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-026-01880-4}, pmid = {41566501}, issn = {1743-0003}, }
@article {pmid41565807, year = {2026}, author = {Wang, Z and Chen, K and Shi, X and Du, Q and Ai, Y and Li, P and Yong, L and Sun, X and Wang, N and Hu, X and Lu, C and Tang, C and Wang, L and Zheng, Y and Zhang, Y and Guo, H and Pu, Z and Wang, X and Zhang, Y and Jiang, H and Liu, Y and Tang, Z and You, L and Deng, J and Che, R and Gao, Y and Zhang, S and Wang, B and Sun, X and Qin, J and Huang, Y and Shen, L and Ge, J and Zeng, X and Chen, L and Chen, P and Peng, H}, title = {Fibre integrated circuits by a multilayered spiral architecture.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {41565807}, issn = {1476-4687}, abstract = {Fibre electronic devices are transforming traditional fibres and garments into new-generation wearables that can actively interact with human bodies and the environment to shape future life[1-5]. Fibre electronic devices have achieved almost all of the desired functions, such as powering[6,7], sensing[8,9] and display[10,11] functions. However, viable information-processing fibres, which lie at the heart of building intelligent interactive fibre systems similar to any electronic product, remain the missing piece of the puzzle[12-15]. Here we fill this gap by creating a fibre integrated circuit (FIC) with unprecedented microdevice density and multimodal processing capacity. The integration density reaches 100,000 transistors per centimetre, which effectively satisfies the requirements for interactive fibre systems. The FICs can not only process digital and analogue signals similar to typical commercial arithmetic chips but also achieve high-recognition-accuracy neural computing similar to that of the state-of-the-art in-memory image processors. The FICs are stable under harsh service conditions that bulky and planar counterparts have difficulty withstanding, such as repeated bending and abrasion for 10,000 cycles, stretching to 30%, twisting at an angle of 180° cm[-1] and even crushing by a container truck weighing 15.6 tons. The realization of FICs enables closed-loop systems in a single fibre, without the need for any external rigid and bulky information processors. We demonstrate that this fully flexible fibre system paves the way for the interaction pattern desired in many cutting-edge applications, for example, brain-computer interfaces, smart textiles and virtual-reality wearables. This work presents new insights that can promote the development of fibre devices towards intelligent systems.}, }
@article {pmid41565681, year = {2026}, author = {Zhu, L and Li, R and Qian, M and Lv, F and Hong, H and Li, Y and Zhou, Y and Li, Z and Lei, J and Zou, W and Guan, MX and Zhang, Y and Zhao, G and Ma, H and Gong, J and Kang, L}, title = {A Glial Hub-and-Spoke Circuitry in C. elegans orchestrates bidirectional thermosensation.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-026-68766-w}, pmid = {41565681}, issn = {2041-1723}, support = {2021ZD0203303//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; }, abstract = {Thermosensation is evolutionarily conserved for survival, yet the roles of glia in temperature coding and circuit dynamics remain poorly understood. Here, we identify C. elegans AMsh glia as dual-mode thermosensory hubs that autonomously detect temperature fluctuations by co-expressing the heat receptor GCY-28 (guanylate cyclase) and cold receptor GLR-3 (ionotropic glutamate receptor). Thermal changes induce spatiotemporal calcium dynamics in glia, driving GABA release to bidirectionally modulate neural circuits: enhancing AFD-mediated warmth detection through the excitatory receptor EXP-1 and suppressing ASH-dependent cold avoidance via the inhibitory receptor LGC-38. This GABAergic hub-and-spoke architecture regulates a broad range of thermal behaviors, including thermal avoidance, thermal resistance, and thermal preference. These findings establish glia as critical interpreters of environmental cues, highlighting their dual roles as sensors and modulators in sensory processing and providing a paradigm for understanding conserved glial mechanisms in neural circuit dynamics and behavioral plasticity.}, }
@article {pmid41565527, year = {2026}, author = {Sun, Y and Liu, W and Zhang, H and Du, Z and Liu, H and Ma, K and Li, D and Wang, S and Fan, S and Li, L and Zheng, D and Shen, G}, title = {An ultrasoft, breathable, and multichannel ear-computer interface patch.}, journal = {Science bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.scib.2025.12.042}, pmid = {41565527}, issn = {2095-9281}, abstract = {Brain-computer interface (BCI) presented by the non-invasive electroencephalography (EEG) cap/band or implantable chips enabling people to fast and reliable control computers or mobile devices with thoughts has redefined the boundaries of human capabilities. However, the existing cap/band-adhered sticky gel usually needs to be tightly fixed on the scalp through the hair to ensure intimate contact, which inconveniences the user. And the implantable chips represented by Neuralink gave a living example of how BCI can make quadriplegic live better, but the destructive unacceptable for healthy people. Here we proposed a multichannel wearable ear-computer interface (ECI) patch worn behind the ears for direct communication and control via brain activity. The 8-channel ECI patch based on MXene electrode was prepared by a facile direct inject print approach on the soft, thin, and breathable medical film that enables superior adherence. The fatigue induction experiments tested by the ECI patch offer an average classification accuracy of 90.5%, showing effective monitoring of the fatigue state. Participants wearing the ECI patch also perform the 4-target steady state visual evoked potential (SSVEP) BCI classification offline and online experiment, the online 4-route tasks reap a comparable average accuracy of 93.5% to the commercial cap. Moreover, the complex route task relied on the subjects who gave commands while observing the unmanned vehicle completed 3 times, demonstrating the reliability and possibility of the ECI patch.}, }
@article {pmid41564094, year = {2025}, author = {Zafar, R and Abdulrab, H}, title = {Deep Learning Unveils Health Predictions From EEG and MRI Data.}, journal = {IEEE pulse}, volume = {16}, number = {5}, pages = {27-34}, doi = {10.1109/MPULS.2025.3618430}, pmid = {41564094}, issn = {2154-2317}, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiology ; Neuroimaging/methods ; Brain-Computer Interfaces ; }, abstract = {The field of neuroscience and neuroimaging has been revolutionized with the use of artificial intelligence (AI), as it helps in enhancing the detection of brain activities and accurately diagnosing neurological disorders using various modalities. There are different modalities that help in measuring brain activities, but the most common and widely used are functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The advanced AI approaches, like deep learning (DL) models, give a new opportunity to various fields, including brain research. This research investigates various AI-driven techniques used for the detection and exploration of the human brain using fMRI and EEG. The AI methods include different machine learning (ML) and DL techniques used to interpret neural activities. Basically, the AI-based models, which also include ML and DL, identify the patterns and detect the abnormalities with higher accuracy, which is helpful in many applications, including brain decoding, monitoring cognitive states, brain-computer interface (BCI), and diagnosis of various diseases. This research provides a comprehensive overview of AI applications in neuroimaging, highlights key applications in cognitive neuroscience and medical imaging, along with a discussion of challenges and future directions. The AI impact of the transformation of neuroimaging research is comprehensively discussed with examples to enhance comprehension.}, }
@article {pmid41561967, year = {2025}, author = {Sebastián-Romagosa, M and Cho, W and Ortner, R and Sieghartsleitner, S and Guger, M and von Oertzen, TJ and Meuth, SG and Laureys, S and Allison, BZ and Guger, C}, title = {Toward Brain-Computer Interface motor rehabilitation for people with Multiple Sclerosis.}, journal = {Frontiers in medicine}, volume = {12}, number = {}, pages = {1661972}, pmid = {41561967}, issn = {2296-858X}, abstract = {BACKGROUND: Multiple Sclerosis (MS) is a chronic neurodegenerative disease in which the immune system attacks the myelin sheaths around nerves. People with MS (pwMS) often experience pain, fatigue, cognitive dysfunction, and reduced mobility. Today, MS is incurable, and treatments can at best slow the progression of the disease and manage symptoms. We conducted a preliminary, single-arm study using a motor-imagery brain-computer interface (MI-BCI) with functional electrical stimulation (FES) and virtual reality avatar targeting gait in pwMS.<br><br>METHODS: Twenty-six pwMS were enrolled; 24 completed 30 BCI sessions. Outcomes were assessed at Baseline, immediately post-treatment (Post1, week 13) and during follow-up (Post2, week 17; Post3, week 37). Change from baseline was analyzed using mixed models for repeated measures (with log-ratio models for skewed measures) and multiplicity control. This uncontrolled study is hypothesis-generating.<br><br>RESULTS: Patients treated with the BCI-based intervention obtained significant improvements that were largely maintained to 6 months after the therapy. The walking endurance, assessed by the 6-minute walking test (6MWT), increased by 37.3 m (95% CI 21.50-53.10) after the treatment (p < 0.001), exceeding the minimal clinically important difference (MCID). This improvement in the walking endurance was maintained during the following 6 months after the intervention. Mobility/speed improved: TUG and T25FW times decreased by -15.5% and -16.4% after the last BCI session (both p < 0.001), with benefits persisting after 6 months. Spasticity (MAS) declined by about 1 point, and patient-reported outcomes improved statistically and clinically (MSIS-29 10.18 points, MFIS 7.29 points). Pairwise post-visit contrasts were not significant, consistent with maintenance. Exploratory models found no consistent MS-subtype effect on 6MWT change and suggested larger gains with higher baseline EDSS. Two discontinuations were due to participant availability, not concerns with fatigue or safety.<br><br>CONCLUSION: In this preliminary, single-arm study, a MI-BCI + FES system was associated with statistically significant, clinically meaningful gains in gait endurance, mobility/speed, spasticity, and patient-reported outcomes, sustained up to 6 months after the intervention.}, }
@article {pmid41560829, year = {2026}, author = {Li, Z and Li, T and Ge, R and Chen, F and Du, C and Wang, D and Wang, L}, title = {Thermo-responsive, on-demand adhesive and tissue-conformal hydrogel electrodes for organ repair and brain-computer interfaces.}, journal = {Materials today. Bio}, volume = {36}, number = {}, pages = {102705}, pmid = {41560829}, issn = {2590-0064}, abstract = {Implantable bioelectronic devices, such as brain-computer interfaces (BCIs), face persistent challenges in achieving stable, rapid, and reversible adhesion on wet tissues due to hydration layers and mechanical mismatch, which can cause interfacial failure and unstable signals. Here, we report a conductive hydrogel interface with tissue-adaptive, temperature-controllable adhesion. The material is synthesized via dynamic co-entanglement of poly(acrylic acid) and poly(lipoic acid) with LA-NHS, establishing a dual physico-chemical anchoring mechanism that enables efficient tissue integration in aqueous environments. The hydrogel penetrates tissue microstructures within 5 s, withstands burst pressures >213 mmHg, exhibits <10 % swelling, ∼2784 % extensibility, and a low modulus of 41 kPa, thereby conforming to soft, irregular surfaces and reducing interfacial mismatch. Its temperature-triggered adhesion allows safe detachment and repositioning without apparent tissue damage, supporting repeated applications. In vivo and ex vivo tests confirm rapid hemostasis in mouse liver and tail injury models, effective sealing of porcine gastric, bladder, and intestinal defects, and stable electrocorticography and electrocardiography recordings. Moreover, the hydrogel demonstrates high cytocompatibility (>90 %), <5 % hemolysis, reactive oxygen species scavenging, and ∼90 % antibacterial efficiency. By integrating rapid wet adhesion, mechanical compliance, electrical functionality, and bioprotective features, this hydrogel provides a versatile platform for next-generation bioelectronic interfaces and soft therapeutic devices.}, }
@article {pmid41559987, year = {2026}, author = {Xu, Q and Shao, Z and Ma, D and Zhai, X and Wang, Y and Dou, W and Pan, Y}, title = {Predicting rehabilitation outcomes of unilateral stroke after brain-computer interface training based on magnetic resonance imaging data.}, journal = {Medicine}, volume = {105}, number = {3}, pages = {e46280}, doi = {10.1097/MD.0000000000046280}, pmid = {41559987}, issn = {1536-5964}, support = {2022YFC3601100//National Key R&amp;D Program of China/ ; 2022YFC3601105//National Key R&amp;D Program of China/ ; Z221100003522016//Beijing Science and Technology Program/ ; 2022-2Z-2242//Capital&#xe2;&#x20ac;&#x2122;s Funds for Health Improvement and Research/ ; 1001020124//Tsinghua University Precision Medicine Research Program/ ; }, mesh = {Humans ; *Stroke Rehabilitation/methods ; Male ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging/methods ; Female ; Middle Aged ; Aged ; *Stroke/diagnostic imaging/complications/physiopathology ; Prognosis ; Recovery of Function ; Adult ; Treatment Outcome ; China ; *Hemiplegia/rehabilitation/etiology ; }, abstract = {Stroke remains a significant cause of disability globally, with a noticeable prevalence in China. Post-stroke rehabilitation, particularly through brain-computer interface (BCI) methods, plays a vital role in enhancing motor function recovery. However, the efficacy of BCI rehabilitation might be hindered by challenges in individualized program of prognosis prediction. This study aimed to develop prognostic prediction models for unilateral hemiplegia after BCI rehabilitation, utilizing both clinical and functional magnetic resonance imaging (fMRI) data, in order to enhance treatment efficiency and optimize patient outcomes. The study included 40 stroke patients (22 left hemisphere affected and 18 right hemisphere affected) who underwent BCI rehabilitation training at the Beijing Tsinghua Changgung Hospital (Beijing, China). Data related to patients' demographics, disease duration, and assessment scores were collected. Based on the improvement in the Fugl-Meyer assessment of the upper extremity (FMA-UE) rating scale, patients were categorized into responder and non-responder groups. Linear regression and its variants, including multivariate logistic regression and optimal subset regression, were utilized to predict the post-treatment scores based on both fMRI and clinical data. The accuracy and R-squared value of the models were assessed using leave-one-out cross-validation (LOOCV). The linear regression model using imaging data exhibited a remarkable performance with a classification accuracy of 100% and R2 (LOOCV) exceeding 0.94. In contrast, the model relying solely on clinical data achieved a classification accuracy of <80%. These results clearly demonstrated the potential of employing imaging data and machine learning methods to effectively predict the effectiveness of BCI rehabilitation. This study assessed the effectiveness of neuroimaging in predicting the efficacy of BCI rehabilitation for unilateral stroke patients. The developed model could serve as a foundation for enhancing our comprehension of rehabilitation outcomes, especially in uniqueness of left and right stroke, and ultimately improving patient well-being. The findings underscored the potential of neuroimaging data in optimizing BCI rehabilitation, leading to the enhanced recovery of motor function in unilateral stroke patients.}, }
@article {pmid41559146, year = {2026}, author = {Du, M and Shi, P and Liu, Z and Xu, Y and Liu, X and Liu, W and Liu, S and Ming, D}, title = {Naturalistic facial dynamics enable quantitative clinical assessment of atypical expression phenotypes in children with autism spectrum disorder.}, journal = {NPJ digital medicine}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41746-026-02375-1}, pmid = {41559146}, issn = {2398-6352}, support = {23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; }, abstract = {Existing facial-expression studies in children with autism spectrum disorder (ASD) rely mainly on discrete, task-driven measures that overlook the sustained emotional fluctuations and ambiguous expressions in naturalistic interactions. This study quantified atypical facial expression patterns in ASD during spontaneous, unscripted interactions. We analyzed 184 naturalistic video sessions from 99 children with ASD and 85 typically developing (TD) peers and extracted three features capturing spontaneous dynamics: emotion variation (temporal stability of emotional states), expression intensity (magnitude of facial muscle activation), and facial coordination (synchrony across facial muscles). These features integrated holistic and processual representations across coarse- and fine-grained levels, enabling detailed quantification of facial patterns. Compared with TD peers, the ASD group exhibited increased prominence of anger, altered emotion transition probabilities, heightened activation in non-core facial muscles, and atypical facial coordination (p < 0.05). These findings reveal subtle facial dynamics inaccessible to traditional approaches and provide a quantitative explanation for the hard-to-describe atypical expressions. Using the fused feature set, ASD classification reached 92.4% accuracy and 0.977 AUC. Regression analyses further predicted symptom severity with mean absolute errors of 13.94 on the ABC scale and 3.84 on the CABS scale. These quantitative and interpretable markers show promise for large-scale ASD screening in naturalistic settings.}, }
@article {pmid41559106, year = {2026}, author = {Qamar, WUR and Abibullaev, B}, title = {Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.}, journal = {Scientific reports}, volume = {16}, number = {1}, pages = {2503}, pmid = {41559106}, issn = {2045-2322}, support = {040225FD4727//Nazarbayev University under Faculty development competitive research grants program/ ; 040225FD4727//Nazarbayev University under Faculty development competitive research grants program/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; }, abstract = {High inter-subject variability and the non-stationary nature of EEG signals pose significant challenges for subject-independent Brain-Computer Interfaces (BCIs) leading to poor model generalization. Differences in neural activity patterns, electrode placements, and external noise further degrade performance making it difficult to develop BCIs that remain reliable across users without extensive recalibration. This study presents a Compact Convolutional Swin Transformer (CCST) to address this issue by using hierarchical window based self-attention combined with convolutional feature extraction to efficiently capture both local electrode interactions and global temporal dependencies. This multi-scale feature representation enhances generalization across subjects, a critical factor for real world BCI deployment. We evaluated CCST on the BCI Competition IV (2a, 2b) and PhysioNet MI datasets using Leave-One-Subject-Out (LOSO) cross-validation achieving state-of-the-art classification accuracies of 68.27%, 76.61%, and 71.70% respectively. Our statistical analysis using the Wilcoxon signed-rank test with Bonferroni correction confirms significant performance improvements over benchmark models. Additionally, CCST achieves a reduction in parameters and a decrease in FLOPs compared to full self-attention models making it more efficient for real-time BCI applications. These results establish CCST as a scalable and efficient framework for adaptive subject-independent BCIs with promising applications in neurorehabilitation, assistive technology, and cognitive training.}, }
@article {pmid41557503, year = {2026}, author = {Deng, GC and Liu, L and Liu, BY and Jing, R and Wu, C and Li, YY and Feng, X and Li, KY and Wang, JH and Liu, YJ and Yu, YQ and Chen, JD and Yang, HB and Li, XY and Duan, S and Sun, L}, title = {Transthyretin-mediated regulation of neuropathic pain and anxiety-like behavior in the lateral parabrachial nucleus.}, journal = {Cell reports}, volume = {45}, number = {1}, pages = {116860}, doi = {10.1016/j.celrep.2025.116860}, pmid = {41557503}, issn = {2211-1247}, abstract = {Neuropathic pain presents a complex challenge in clinical treatment due to its multifaceted etiology and frequent comorbidities with anxiety. Despite its prevalence, the underlying molecular, cellular, and circuit mechanisms remain poorly understood. The lateral parabrachial nucleus (LPBN) is a critical center that regulates both pain perception and the emotional aspects. In this study, single-cell sequencing shows upregulated transthyretin (TTR) in neuropathic pain models. Through bidirectional conditional knockout (cKO) and overexpression of TTR in LPBN neurons of mice, we confirm that TTR in LPBN glutamatergic neurons serves as a necessary and sufficient regulator of pain-anxiety comorbidity. Furthermore, TTR plays a pivotal role in pain regulation by binding to its receptor, receptor for advanced glycation end products (RAGE), thereby influencing neuroinflammation and neuronal excitability through the NF-κB signaling pathway. These results highlight potential molecular targets for the treatment of neuropathic pain.}, }
@article {pmid40768444, year = {2026}, author = {Scholten, K and Xu, H and Lu, Z and Jiang, W and Jin, Z and Ortigoza-Diaz, J and Petrossians, A and Orler, S and Gallonio, R and Liu, X and Song, D and Meng, E}, title = {A Comprehensive Research Dissemination Model for Polymer-Based Neural Interfaces.}, journal = {IEEE transactions on bio-medical engineering}, volume = {73}, number = {2}, pages = {934-944}, doi = {10.1109/TBME.2025.3596222}, pmid = {40768444}, issn = {1558-2531}, support = {U24 NS113647/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; Rats ; *Polymers/chemistry ; Microelectrodes ; *Electrodes, Implanted ; Electroencephalography/instrumentation ; Equipment Design ; Rats, Sprague-Dawley ; *Brain-Computer Interfaces ; Hippocampus/physiology ; }, abstract = {OBJECTIVE: Implantable polymer microelectrode arrays (pMEAs) offer stable integration with neural tissue but are not widely available. An academic resource model is explored as a means of standardizing and disseminating pMEAs.<br><br>METHODS: The resource is based on a multi-project wafer model, originally developed in the semiconductor industry, allowing the simultaneous microfabrication of pMEAs with arbitrary designs. This model leverages innovations in design, manufacturing, and packaging to produce custom penetrating, surface, and cuff-type form-factors in batch and at low cost. Device quality is verified through benchtop testing and chronic electrophysiological recording in rats.<br><br>RESULTS: To date, over 1000 pMEAs (more than 50 designs) were provided to 45 academic labs. Implanted penetrating arrays in the hippocampus achieved high quality, chronic recordings from freely moving rats. Surface arrays reliably recorded electroencephalogram signals from the cortex and evoked potentials from the somatosensory cortex in awake rats.<br><br>CONCLUSION: Efficient production of custom pMEAs for research is possible through a unique resource model inspired by the semiconductor industry.<br><br>SIGNIFICANCE: Greater access to pMEAs enables researchers to conduct new experiments across different regions of the nervous system, accelerating discoveries.}, }
@article {pmid41557476, year = {2026}, author = {Kristen, R and Lenarz, T and Keintzel, T and Sprinzl, G and Köstler, C and Knoelke, N and Busch, S and Raffelsberger, T and Magele, A and Schörg, P and Wiek, RJ}, title = {Lifetime Real-World Evidence on Safety and Performance of the First Active Transcutaneous Bone Conduction Implant (BCI), the Bonebridge Covering Conductive to Mixed Hearing Loss (CMHL), and Single-Sided Deafness (SSD): Results From a Long-Term Retrospective Analysis.}, journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology}, volume = {}, number = {}, pages = {}, doi = {10.1097/MAO.0000000000004837}, pmid = {41557476}, issn = {1537-4505}, abstract = {OBJECTIVE: Confirm the safety and performance of the first partially implantable active transcutaneous Bone Conduction Implant (tBCI) in patients who have been implanted for a minimum of 5 years before 2023.<br><br>SETTING: Otolaryngology departments of 4 German and Austrian hospitals.<br><br>STUDY DESIGN: Retrospective, multicenter, longitudinal, open-label case series study. Patients: 186 ears treated for conductive and mixed hearing loss (CMHL), or single-sided deafness (SSD) implanted for 5 years (151 aged 18&#xa0;y or older, 35 aged 5 to 17&#xa0;y) at the time of implantation.<br><br>INTERVENTION: Implantation of the Bonebridge (BB) BCI 601, a partially implantable active middle ear implant (AMEI).<br><br>MAIN OUTCOME MEASURES: Patients' audiometric pure-tone average (PTA4) (0.5, 1, 2, 4 kHz) thresholds (bone conduction, sound field) and speech perception (word recognition scores) were retrospectively collected up to 10 years 10 months postoperatively. Complications were recorded with focus on revision surgery and explantations. Subgroups were adults and children.<br><br>RESULTS: Safety was established by stable bone conduction (BC) thresholds 5 years after implantation or later with mean paired differences of -5.33 dB for adults and -8.05 dB for children and underscored by a low number of technical failures and high survival rates 10 years after implantation. Paired mean sound field PTA4 thresholds and word recognition scores significantly improved as tested by post hoc analysis 5 years or later after implantation, with functional gains for CMHL of 23.44 dB (adults), 27.69 dB (children), and word recognition scores of 58.22% (adults), 80.00% (children). Furthermore, mean sound field PTA4 thresholds and word recognition scores remain significantly improved over time at 36.37 dB HL and 68.75% 5 years or later after implantation as tested with linear mixed-effects model.<br><br>CONCLUSIONS: The findings of this study demonstrate that this tBCI remains safe and effective for up to 10 years.}, }
@article {pmid41555272, year = {2026}, author = {An, Q and Cao, M and Zhang, J and Liu, D and Jialin, A and Chen, D and Wang, J and Wang, C and Zhao, X and Wang, D and Li, K and Zhang, D and Deng, W}, title = {Spatiotemporal disruption of prefrontal dynamics during affective association in depression: an fNIRS case-control study.}, journal = {BMC psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12888-026-07800-z}, pmid = {41555272}, issn = {1471-244X}, }
@article {pmid41554257, year = {2026}, author = {Althoff, J and Nogueira, W}, title = {Selective auditory attention decoding in bilateral cochlear implant users to music instruments.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae3a1a}, pmid = {41554257}, issn = {1741-2552}, abstract = {Electroencephalography (EEG) data can be used to decode an attended sound source in normal-hearing (NH) listeners, even for music stimuli. This information could steer the sound processing strategy for cochlear implants (CIs) users, potentially improving their music listening experience. The aim of this study was to investigate whether selective auditory attention decoding (SAAD) could be performed in CI users for music stimuli. Approach: High-density EEG was recorded from 8 NH and 8 CI users. Duets containing a clarinet and cello were dichotically presented. A linear decoder was trained to reconstruct audio features of the attended instrument from EEG data. The estimated attended instrument was selected based on which of the two instruments had a higher correlation to the reconstructed instrument. EEG recordings are challenging in CI users, as these devices introduce strong electrical artifacts. We also propose a new artifact rejection technique that employs ICA calculating ICs and automating their selection for removal, which we termed ASICA. Main results: We showed that it was possible to perform SAAD for music in CI users. The decoding accuracies were 59.4 \% for NH listeners and 60 \% for CI users with the proposed algorithm. Using the proposed algorithm, the correlation coefficients between the reconstructed audio feature and the attended audio feature were improved in conditions where artifact was dominating. Significance: Results indicate that selective auditory attention to musical instruments can be effectively decoded, and that this decoding is enhanced by the new artifact reduction algorithm, particularly in scenarios where the cochlear implant's electrical artifact has greater influence. Moreover, these results could be relevant as an objective measure of music perception or for a brain computer interface that improves music enjoyment. Additionally we showed that the stimulation artifact can be suppressed. The ethic's committee of the MHH approved this study (8874_BO_K_2020).}, }
@article {pmid41553892, year = {2026}, author = {Lim, H and Choi, H and Ahmed, B and Park, Y and Ku, J}, title = {Attention-Adaptive BCI-AOT System Enhances Motor Recovery and Neural Engagement After Stroke.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3654935}, pmid = {41553892}, issn = {1558-0210}, abstract = {Stroke frequently results in long-term motor deficits that impair quality of life. Action observation therapy (AOT) has shown promise for motor recovery through engagement of the mirror neuron system (MNS), yet its passive nature and lack of attentional tracking limit its neuroplasticity efficacy. To address these limitations, we developed a closed-loop Brain-Computer Interface-integrated AOT (BCI-AOT) system employing real-time Steady-State Visual Evoked Potential (SSVEP)-based attention monitoring to dynamically control therapy delivery. In a within-subject crossover study, 22 individuals with hemiplegic stroke completed both BCI-AOT and conventional AOT conditions, each consisting of five daily sessions and separated by a one-week washout. In BCI-AOT, video playback depended on sustained attentional engagement detected via SSVEPs. Behavioral outcomes (Box and Block Test [BBT], Action Research Arm Test [ARAT]) and physiological measures (Motor Evoked Potential [MEP] amplitude and latency, EEG power) were assessed pre- and post-intervention. Significant Condition × Day interactions were found for both BBT and ARAT, indicating greater functional gains over time in the BCI-AOT condition. Both conditions showed increased corticospinal excitability over time, while BCI-AOT additionally exhibited distinct mu and theta modulation over time. Participants also reported greater motivation and attention after BCI-AOT compared to conventional AOT. These results suggest that BCI-AOT elicits stronger neuroplasticity responses and user engagement than standard AOT. This study supports the feasibility and clinical potential of closed-loop, attention-adaptive neurorehabilitation for stroke recovery.}, }
@article {pmid41553833, year = {2026}, author = {Yang, Y and Su, Z and Liu, X and Song, J and Li, C and Xu, Y and Ye, J and Lin, LL}, title = {A flexible plasmonic SERS hydrogel patch for metabolite sensing on bio-interfaces.}, journal = {Nanoscale}, volume = {}, number = {}, pages = {}, doi = {10.1039/d5nr04403k}, pmid = {41553833}, issn = {2040-3372}, abstract = {The growing demand for real-time, non-invasive monitoring of biochemical molecules has driven the development of advanced, flexible sensing materials. Surface-enhanced Raman spectroscopy (SERS) offers high molecular specificity and ultralow detection limits. While rigid SERS substrates based on plasmonic nanoparticle arrays provide strong signal enhancements, they lack the mechanical compatibility and conformal adhesion required for dynamic biological surfaces, such as human skin or neural tissues. Here, we present a flexible SERS hydrogel patch for the label-free detection of metabolites at bio-interfaces. The patch integrates a self-assembled silver nanoparticle film with an ultrathin polyvinyl alcohol (PVA) hydrogel layer to achieve good plasmonic enhancement, mechanical durability, conformity and reliable SERS stability. The SERS patch allows the detection of metabolites within 6 min upon analyte exposure, enabling the label-free detection of key metabolites, such as glucose, uric acid and urea with concentrations down to 1 μM, 50 μM and 1 mM, respectively. We demonstrate the versatility of this platform by performing ex vivo experiments on porcine brain and muscle tissues to simulate real-world application scenarios in brain-machine interfaces and implantable sensors. This work demonstrates the feasibility of SERS hydrogel-based flexible platforms for the in situ monitoring of metabolites at bio-interfaces.}, }
@article {pmid41553800, year = {2026}, author = {Jafar, R}, title = {Dimensions of Transparency: How Dys-Appearance Affects BCI Embodiment.}, journal = {AJOB neuroscience}, volume = {17}, number = {1}, pages = {25-27}, doi = {10.1080/21507740.2025.2606297}, pmid = {41553800}, issn = {2150-7759}, }
@article {pmid41553799, year = {2026}, author = {Zilio, F}, title = {A Multi-Criteria Framework for Transparency in the Design and Use of Brain-Computer Interfaces.}, journal = {AJOB neuroscience}, volume = {17}, number = {1}, pages = {22-25}, doi = {10.1080/21507740.2025.2606296}, pmid = {41553799}, issn = {2150-7759}, }
@article {pmid41553797, year = {2026}, author = {Barnhart, AJ}, title = {A Phenomenological Photo Finish: Testing Transparency at the Cybathlon Brain-Computer Interface Race.}, journal = {AJOB neuroscience}, volume = {17}, number = {1}, pages = {20-22}, doi = {10.1080/21507740.2025.2606294}, pmid = {41553797}, issn = {2150-7759}, }
@article {pmid41553197, year = {2026}, author = {Bhargava, EK and Arvaneh, M}, title = {Expanding the olfactory implant paradigm through recent advances in brain-computer interface technology.}, journal = {Rhinology}, volume = {}, number = {}, pages = {}, doi = {10.4193/Rhin25.554}, pmid = {41553197}, issn = {0300-0729}, abstract = {The international opinion paper by Whitcroft et al. provides invaluable guidance for the emerging field of olfactory implants (1). While the authors thoroughly address clinical considerations and current technological approaches, we would like to expand upon Statements 9.1 and 9.3 regarding electrode technology limitations and highlight recent advances in brain-computer interface (BCI) technology that could address key technological challenges around electrode longevity and biocompatibility.}, }
@article {pmid41552876, year = {2026}, author = {Ding, Y and Lu, Y and Zhao, G and Gong, Z}, title = {Drosophila Larvae Generate Force to Counteract External Mechanical Pressures.}, journal = {The Journal of experimental biology}, volume = {}, number = {}, pages = {}, doi = {10.1242/jeb.250849}, pmid = {41552876}, issn = {1477-9145}, support = {T2293720/ T2293721, 32271041//National Natural Science Foundation of China/ ; 2021ZD0200405//National Major Science and Technology Projects of China/ ; 2022C01022//Key Research and Development Program of Zhejiang Province/ ; }, abstract = {To counteract or to retreat presents a fundamental dilemma for biological organisms when facing adverse abiotic environmental conditions. In many cases, the predominant strategy animals adopt is to retreat. However, if counteraction is possible, and how the choice between counteraction and retreat is decided, are not clear. Here, we report that Drosophila larvae can actively counteract external mechanical pressure, inspired by Drosophila larval cleft-squeezing behaviour. We developed a behavioural paradigm to investigate the counteracting force of larvae in response to external pressures. Instead of retreating by crawling backward, a portion of Drosophila larvae could crawl forward and counteract against the external physical pressure. Under externally applied pressing forces of 25mN, 93.9% of forward peristaltic movements increased the counterforce, while 88.2% of backward peristaltic movements decreased it. The activeness in counteraction force was reflected by the longer inter-wave delay, more oscillation work and longer force wave period during consecutive forward peristaltic waves. As the external pressing force was increased from 25mN to 50mN, 75mN and 100mN, counteraction by forward peristalsis was less frequent, while retreat by backward peristalsis was more frequent when pressure is high. A reduction of the external pressure immediately following the counteracting forward peristalsis, which might serve as rewarding signal, could reinforce the counteraction and induce more ensuing forward peristalsis. The rewarding effect of reducing external pressure by forward crawling was much more than that by backward crawling. Our study sheds light on the intricate mechanisms underlying animal proactive responses to adverse abiotic environmental conditions.}, }
@article {pmid41551232, year = {2025}, author = {Lin, Z and Choi, J and Mao, R and Zhao, B and Kang, J}, title = {Spatial Adaptive Selection using Binary Conditional Autoregressive Model with Application to Brain-Computer Interface.}, journal = {Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America}, volume = {}, number = {}, pages = {}, pmid = {41551232}, issn = {1061-8600}, abstract = {In medical imaging studies, scalar-on-image regression presents significant challenges due to limited sample sizes and the high-dimensionality of datasets. Additionally, imaging predictors often exhibit spatially heterogeneous activation patterns and complex nonlinear associations with the response variable. To address these issues, we propose a novel Bayesian scalar-on-image regression model with the Spatial Adaptive Selection using Binary Conditional Autoregressive Model (SAS-BCAR) prior. The proposed approach leverages a binary conditional autoregressive model to capture spatial dependencies among feature selection indicators, effectively identifying spatially structured sparsity patterns within image data, while addressing nonlinear relationships between image predictors and the response variable. Furthermore, our SAS-BCAR incorporates an adaptive feature selection mechanism that adjusts to varying spatial dependencies across different image regions, ensuring a more precise and robust feature selection process. Through extensive numerical simulations on benchmark computer vision datasets and analysis of electroencephalography data in brain-computer interface applications, we demonstrate that the SAS-BCAR model achieves superior predictive performance compared to state-of-the-art alternatives, particularly in scenarios with limited training data. Supplementary materials including computer code, R packages, datasets, and additional figures are available online.}, }
@article {pmid41551041, year = {2025}, author = {Otarbay, Z and Kyzyrkanov, A}, title = {Transfer learning for subject-independent motor imagery EEG classification using convolutional relational networks.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1691929}, pmid = {41551041}, issn = {1662-4548}, abstract = {Motor imagery (MI) based electroencephalography (EEG) classification is central to brain-computer interface (BCI) research but practical deployment remains challenging due to poor generalization across subjects. Inter-individual variability in neural activity patterns significantly limits the development of subject-independent BCIs for healthcare and assistive technologies. To address this limitation, we present a transfer learning framework based on Convolutional Relational Networks (ConvoReleNet) designed to extract subject-invariant neural representations while minimizing the risk of catastrophic forgetting. The method integrates convolutional feature extraction, relational modeling, and lightweight recurrent processing, combined with pretraining on a diverse subject pool followed by conservative fine-tuning. Validation was conducted on two widely used benchmarks, BNCI IV-2a (four-class motor imagery) and BNCI IV-2b (binary motor imagery), to evaluate subject-independent classification performance. Results demonstrate clear improvements over training from scratch: accuracy on BNCI IV-2a increased from 72.22 (±20.49) to 79.44% (±11.09), while BNCI IV-2b improved from 75.10 (±17.17) to 83.85% (±10.30). The best-case performance reached 87.55% on BNCI IV-2a with Tanh activation and 83.85% on BNCI IV-2b with ELU activation, accompanied by reductions in inter-subject variance of 45.9 and 40.0%, respectively. These findings establish transfer learning as an effective strategy for subject-independent MI-EEG classification. By enhancing accuracy, reducing variability, and maintaining computational efficiency, the proposed framework strengthens the feasibility of robust and user-friendly BCIs for rehabilitation, clinical use, and assistive applications.}, }
@article {pmid41548826, year = {2026}, author = {Gwon, Y and Chung, CK}, title = {Distinct Post-Sentence Neural Patterns Representing Lexical Items vs. Sentence Integration.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121703}, doi = {10.1016/j.neuroimage.2026.121703}, pmid = {41548826}, issn = {1095-9572}, abstract = {While comprehension marks the starting point in daily communication, the process is only fulfilled when suitable responses or inferences are followed. Listeners retain sentence information after initial comprehension. Although comprehension during listening has been widely studied, comparatively little is understood about how and where the brain retains linguistic information beyond the end of a sentence (EOS). A key question is whether the brain retains not only a holistic, sentence-level representation but also independent traces of individual lexical items-and, if so, how and where these dissociable signals are encoded in the brain. By analyzing the high gamma envelope in electrocorticography (ECoG) data from 15 patients with epilepsy, we directly investigated how neural signals encode and retain information about individual lexical items as well as the integrated sentence representation after the EOS. To this end, we employed a question-and-answer paradigm in which participants heard one of four sentences ("Is it alive?", "Is it not alive?", "Is it a part of body?" or "Is it not a part of the body?"), followed by a response prompt. To answer correctly, subjects must retain the relevant linguistic information, so we could trace retained neural representations in post-question periods, that respond either to each lexical item independently-content ("alive" vs. "part of the body") and negation ("positive" vs. "negative")-or to sentence-specific representations integrating both lexical items. Label-based encoding models were fit to predict neural responses from each label, and encoding strength was quantified by the correlation between predicted and observed signals. We found that channels selectively encoding lexical information were distributed across widespread cortical areas. In contrast, sentence-specific encoding was highly localized in the left posterior superior temporal gyrus (pSTG). Furthermore, by applying the same encoding model to neural signals recorded during the subsequent response-preparation period, we found that both lexical-item and integrated sentence information can persist significantly while participants prepared their responses. These findings provide direct evidence for the distinct spatial organization of lexical and sentence-level representations in the human brain after the end of a sentence.}, }
@article {pmid41548791, year = {2026}, author = {Liu, Y and Wang, S and Zhang, Y and Wan, L and Wang, H and Zhang, L}, title = {Oxidized alginate-based interpenetrated dual-network antibacterial hydrogel for enhanced diabetic wound healing.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {150335}, doi = {10.1016/j.ijbiomac.2026.150335}, pmid = {41548791}, issn = {1879-0003}, abstract = {Plagued by a prolonged healing process and recurrent bacterial infections, diabetic wounds pose a significant clinical challenge. This underscores the urgent need to develop advanced dressings to address microbial resistance and dysfunctional healing processes. Herein, we present a self-healing double-network hydrogel that integrates antibacterial activity with enhanced tissue regenerative potential, offering a promising strategy to accelerate diabetic wound repair. The hydrogel was constructed by interpenetrating a stable polyacrylamide (PAM) network into a dynamically crosslinked oxidized alginate-polydopamine (OSPB) network. Owing to the multiple dynamic interactions, including ionic chelation, Schiff base coordination, and hydrogen bonding, the hydrogel exhibits intrinsic self-healing behavior. The compact crosslinked double-network architecture imparted reduced swelling and enhanced mechanical strength while maintaining tissue conformity. Its high stretchability, toughness, and rapid recovery under repetitive stress ensured the hydrogel for dynamic wound protection and long-term wound management. To maximize antibacterial potency, the hydrogel incorporates the antimicrobial Jelleine-1 peptide (J-1), which was deposited at the tissue-adhesive interfaces, imparting strong antibacterial activity. Besides, the enhanced transdermal penetration was confirmed using bovine serum albumin - fluorescein isothiocyanate (BSA-FITC) as the macromolecular model. In vivo studies demonstrated an accelerated wound closure with promoted cell proliferation, migration, and angiogenesis, which consequently improves granulation tissue formation and collagen deposition. Collectively, our work presents a multifunctional hydrogel system for promising clinical treatment of diabetic wounds.}, }
@article {pmid41548584, year = {2026}, author = {Lim, JH and Kuo, PC}, title = {Enhancing Brain-Computer interface performance through source-level attention mechanism: An EEG motor imagery study.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110666}, doi = {10.1016/j.jneumeth.2025.110666}, pmid = {41548584}, issn = {1872-678X}, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) enable direct communication between humans and machines by translating brain signals into control commands. Electroencephalography (EEG) is a commonly used modality in BCI systems due to its non-invasiveness and high temporal resolution. However, EEG-based BCIs often suffer from low signal-to-noise ratios and limited spatial resolution, primarily due to the small number of recording electrodes. Although source estimation techniques can improve spatial specificity, they typically require subject-specific information such as individual brain anatomy or electrode positions, which may not always be available. This study aims to address these challenges by proposing a framework that enhances task-relevant EEG signals using an attention-guided source estimation approach based on coarse predefined brain regions.<br><br>NEW METHOD: We developed an attention-guided neural network that estimates source-level activity most relevant to the BCI task, without requiring subject-specific structural data. The model uses predefined regions of interest to guide attention mechanisms toward informative spatial features.<br><br>RESULTS: The framework was validated using publicly available motor imagery EEG datasets, achieving strong performance.<br><br>Comparative analyses were conducted against baseline models using traditional EEG signals and standard feature extraction methods. This study presents an effective approach for improving EEG-based BCI performance by integrating an attention-guided source estimation network into the decoding pipeline. The method does not rely on subject-specific anatomical information, making it broadly applicable.<br><br>CONCLUSION: By emphasizing task-relevant source activity, the framework enhances signal quality and classification accuracy, thereby advancing the potential of BCIs for precise and practical applications.}, }
@article {pmid41547354, year = {2026}, author = {Gao, X and Liu, X and Wang, N and Cui, C and Liu, W and Yang, M and Li, Q and Ou, Y and Ning, A and Wei, X and Zhang, M and Qiu, S and Lei, Y and Fu, D and Li, H and Sun, L and Lu, M and Zhang, M and Wang, Y}, title = {Nanoparticles hijack calvarial immune cells for CNS drug delivery and stroke therapy.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2025.12.008}, pmid = {41547354}, issn = {1097-4172}, abstract = {The rapid accessibility of calvarial immune cells to the brain, in principle, may offer transformative opportunities for overcoming drug delivery barriers in central nervous system (CNS) disorders. Here, we hijacked calvarial immune cells using drug-loaded nanoparticles (NPs) and leveraged their unique migration mechanism through skull-meninges microchannels to bypass the blood-brain barrier (BBB) for CNS drug delivery. We constructed NP-loaded immune cells in situ via intracalvariosseous (ICO) injection, validated their prompt migration in response to CNS perturbation, and targeted therapeutic delivery to CNS lesions. Compared with conventional delivery approaches, this strategy achieved promising therapeutic efficacy in improving both short- and long-term outcomes in preclinical stroke models. Our prospective clinical trial further supports the translational feasibility of ICO immune access in treating malignant stroke. These findings establish skull-based delivery as a promising, clinically translatable route for CNS drug delivery and highlight immune-assisted transport as a potentially transformative strategy for improving therapeutic outcomes in neurological disorders.}, }
@article {pmid41546096, year = {2026}, author = {Zhang, H and Song, X and Huang, N and Xiong, K and Shao, N and Su, Y and Bian, S and Sawan, M}, title = {A programmable peptide interface for on-demand neural culturing platforms.}, journal = {Journal of nanobiotechnology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12951-026-04032-x}, pmid = {41546096}, issn = {1477-3155}, support = {W2431058//the National Natural Science Foundation of China/ ; 2024C03002//the "Pioneer" and "Leading Goose" Research and Development Program of Zhejiang/ ; 2023GD004//Project of Westlake Institute for Optoelectronics/ ; }, abstract = {The precise spatial organization of neural cells into two-dimensional networks or three-dimensional spheroids is crucial for advancing neuroscience research and drug discoveries, yet remains challenging with conventional, single-function coatings. Here, we propose a programmable bifunctional peptide that integrates a silica-binding domain with a tunable cell-adhesive Arginine-Glycine-Aspartate (RGD) tripeptide. By systematically improving the RGD variant and linker rigidity, we introduced a single coating material that enables on-demand switching between two distinct functions: guiding the patterned growth of functional neural circuits on glass and facilitating the high-throughput formation of uniform neural spheroids. The latter exhibited high viability, extensive neurite outgrowth, and spontaneous electrophysiological activity, which validates their functional maturity. We establish by this work a versatile and reliable platform for advanced neural interface research, with significant potential for drug discovery and disease modeling.}, }
@article {pmid41545509, year = {2026}, author = {Li, Y and Li, W and Liu, Y and Chen, Q and Guo, X and Tan, M and Yang, R and Xu, X and Qin, H and Chen, L}, title = {HRV features as potential biomarkers for auxiliary diagnosis in epilepsy.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-34682-0}, pmid = {41545509}, issn = {2045-2322}, support = {2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; }, abstract = {Epilepsy affects around 70 million people worldwide, and diagnosis is often difficult and delayed, exposing patients to avoidable morbidity and psychosocial burden. Heart rate variability (HRV) is a non-invasive marker of autonomic nervous system function that may be altered in epilepsy and may support clinical decision-making. In this single-center case-control study, we recorded short-term HRV during a standardized cardiovascular autonomic reflex test including supine resting, deep-breathing and three challenges (active standing, Valsalva manoeuvre and sustained handgrip) in 200 adults with epilepsy and 200 age- and sex-matched healthy controls. Patients with epilepsy showed consistently lower HRV than controls. Using HRV and demographic features, we developed logistic regression models to distinguish epilepsy from health in an independent test set. A model integrating rest and sustained handgrip achieved the highest performance, although still only moderate (area under the curve 0.68; sensitivity 0.821; specificity 0.484). Standardized multi-paradigm HRV assessment may therefore provide a feasible, low-cost adjunct to support, but not replace, conventional diagnostic evaluation. However, the single-center design, relatively short recordings and inclusion of only healthy controls limit generalizability, and larger multicenter studies including patients with paroxysmal conditions that mimic epilepsy are needed to determine clinical utility.}, }
@article {pmid41544906, year = {2026}, author = {Hong, T and Su, C and Zhou, H and Geng, F and Hu, Y}, title = {Brain activity inhibition during Short Video Viewing: neurochemical insights.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121722}, doi = {10.1016/j.neuroimage.2026.121722}, pmid = {41544906}, issn = {1095-9572}, abstract = {Cognitive control enables individuals to adapt to the ever-changing environmental demands. The dorsal anterior cingulate cortex (dACC) and the dorsolateral prefrontal cortex (dlPFC) are key regions of the cognitive control network, activated during cognitively demanding tasks. In contrast, the entertaining and habitual nature of short-video consumption for leisure shifts neural processing toward emotional engagement and immediate gratification, contributing to excessive use and diminished self-control in some individuals. This raises a critical question: Does short-video viewing suppress cognitive control regions, and what neurochemical factors may underlie individual differences in this process? To address this question, this preregistered study used proton magnetic resonance spectroscopy ([1]H-MRS) to measure glutamate and γ-aminobutyric acid (GABA) concentrations in the dACC at rest, and employed functional magnetic resonance imaging (fMRI) to examine dACC and dlPFC activity during free viewing of short videos in 56 young adults. We found that both the dACC and the dlPFC exhibited significant deactivation in response to preferred videos that were watched to completion, compared to less-preferred videos that were terminated early. Moreover, resting-state glutamate levels in the dACC were associated with the magnitude of this deactivation, with higher glutamate concentrations associated with less suppression of both dACC and dlPFC activity. Additionally, functional connectivity between the dACC and dlPFC increased during video viewing, particularly for preferred videos. By integrating fMRI with [1]H-MRS, our study provides novel evidence that immersive viewing of preferred short videos deactivates the cognitive control network and that individual differences in this deactivation are linked to glutamate metabolism. These findings enhance our understanding of how digital media consumption interacts with neurochemical processes to influence self-regulation. Our study offers new insights into the neural mechanisms underlying short-video engagement and has implications for understanding excessive digital media use.}, }
@article {pmid32413878, year = {2020}, author = {Del Campo-Vera, RM and Gogia, AS and Chen, KH and Sebastian, R and Kramer, DR and Lee, MB and Peng, T and Tafreshi, A and Barbaro, MF and Liu, CY and Kellis, S and Lee, B}, title = {Beta-band power modulation in the human hippocampus during a reaching task.}, journal = {Journal of neural engineering}, volume = {17}, number = {3}, pages = {036022}, pmid = {32413878}, issn = {1741-2552}, support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; R25 NS099008/NS/NINDS NIH HHS/United States ; }, mesh = {*Beta Rhythm ; Cerebral Cortex ; Electroencephalography ; Hippocampus ; Humans ; *Movement ; }, abstract = {OBJECTIVE: Characterize the role of the beta-band (13-30 Hz) in the human hippocampus during the execution of voluntary movement.<br><br>APPROACH: We recorded electrophysiological activity in human hippocampus during a reach task using stereotactic electroencephalography (SEEG). SEEG has previously been utilized to study the theta band (3-8 Hz) in conflict processing and spatial navigation, but most studies of hippocampal activity during movement have used noninvasive measures such as fMRI. We analyzed modulation in the beta band (13-30 Hz), which is known to play a prominent role throughout the motor system including the cerebral cortex and basal ganglia. We conducted the classic 'center-out' direct-reach experiment with nine patients undergoing surgical treatment for medically refractory epilepsy.<br><br>MAIN RESULTS: In seven of the nine patients, power spectral analysis showed a statistically significant decrease in power within the beta band (13-30 Hz) during the response phase, compared to the fixation phase, of the center-out direct-reach task using the Wilcoxon signed-rank hypothesis test (p < 0.05).<br><br>SIGNIFICANCE: This finding is consistent with previous literature suggesting that the hippocampus may be involved in the execution of movement, and it is the first time that changes in beta-band power have been demonstrated in the hippocampus using human electrophysiology. Our findings suggest that beta-band modulation in the human hippocampus may play a role in the execution of voluntary movement.}, }
@article {pmid41544497, year = {2026}, author = {Luo, H and Ran, X and Li, Z and Xue, H and Jiang, T and Shen, J and Kärkkäinen, T and Xu, Q and Cong, F}, title = {Key-value pair-free continual learner via task-specific prompt-prototype.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {198}, number = {}, pages = {108576}, doi = {10.1016/j.neunet.2026.108576}, pmid = {41544497}, issn = {1879-2782}, abstract = {Continual learning aims to enable models to acquire new knowledge while retaining previously learned information. Prompt-based methods have shown remarkable performance in this domain; however, they typically rely on key-value pairing, which can introduce inter-task interference and hinder scalability. To overcome these limitations, we propose a novel approach employing task-specific Prompt-Prototype (ProP), thereby eliminating the need for key-value pairs. In our method, task-specific prompts facilitate more effective feature learning for the current task, while corresponding prototypes capture the representative features of the input. During inference, predictions are generated by binding each task-specific prompt with its associated prototype. Additionally, we introduce regularization constraints during prompt initialization to penalize excessively large values, thereby enhancing stability. Experiments on several widely used datasets demonstrate the effectiveness of the proposed method. In contrast to mainstream prompt-based approaches, our framework removes the dependency on key-value pairs, offering a fresh perspective for future continual learning research.}, }
@article {pmid41544454, year = {2026}, author = {Zheng, L and Lu, Y and Lyu, H and Li, T and Cui, S and Xu, Y and Cai, Z and Hou, Y and Li, Y and Yang, Q and Ye, Z and Yang, G and Xu, K}, title = {Laser fabrication of flexible electrodes for bioelectronics.}, journal = {Biosensors &amp; bioelectronics}, volume = {298}, number = {}, pages = {118386}, doi = {10.1016/j.bios.2026.118386}, pmid = {41544454}, issn = {1873-4235}, abstract = {Bioelectronics lies at the intersection of electronics and biology, enabling real-time signal exchange between living systems and machines. As next-generation applications such as wearable diagnostics, brain-computer interfaces, and closed-loop therapeutic systems desire for soft, miniaturized, and biocompatible platforms, the role of bioelectrodes becomes even more critical. Direct laser writing (DLW) has emerged as a powerful microscale fabrication approach, capable of directly patterning functional electrodes with high spatial resolution on diverse materials. In addition, DLW uniquely offers localized material processing and property modulation, enabling controlled synthesis, phase transition, and surface functionalization. This review presents a comprehensive overview of the underlying mechanisms and advanced material systems that enable DLW. We highlight how DLW enables structural design that impart stretchability and tissue conformity, and how such electrodes are integrated into wearable and implantable bioelectronic systems. Finally, we discuss key challenges and future opportunities for DLW-based bioelectrodes, which are poised to become foundational components of intelligent and adaptive biomedical interfaces.}, }
@article {pmid41544325, year = {2026}, author = {Gong, C and Zou, L and Li, P and Wu, X and Qiao, Y and Hu, Z and Wang, X and Zhou, Y and Wang, K and Hu, Y and Wang, H}, title = {Rapid spatio-temporal MR fingerprinting using physics-informed implicit neural representation.}, journal = {Medical image analysis}, volume = {109}, number = {}, pages = {103935}, doi = {10.1016/j.media.2026.103935}, pmid = {41544325}, issn = {1361-8423}, abstract = {The potential of Magnetic Resonance Fingerprinting (MRF), which allows for rapid and simultaneous multi-parametric quantitative MRI, is often limited by severe aliasing artifacts caused by aggressive undersampling. Conventional MRF approaches typically treat these artifacts as detrimental noise and focus on their removal, often at the cost of either reduced reconstruction speed or increased reliance on large training datasets. Building on the insight that structured aliasing can be leveraged as an informative spatial encoding mechanism, we propose to extend MRF's encoding capacity to the global spatio-temporal domain by introducing a novel Physics-informed implicit neural MRF (πMRF) framework. πMRF integrates physics-informed spatio-temporal fingerprint modeling with implicit neural representations (INRs), enabling unsupervised, gradient-driven joint estimation of quantitative tissue parameters and coil sensitivity maps (CSMs) with enhanced accuracy and robustness. Specifically, πMRF leverages a scalable component based on physics-informed neural networks (PINNs) to facilitate accurate high-dimensional signal modeling and memory-efficient optimization. In addition, a subspace-guided sensitivity regularization is developed to improve the robustness of CSM estimation in highly undersampled scenarios. Experimental results on simulated, phantom, and in vivo datasets demonstrate that πMRF achieves improved quantitative accuracy and robustness even under highly accelerated acquisitions, outperforming state-of-the-art MRF methods.}, }
@article {pmid41543945, year = {2026}, author = {Ju, J and Li, H}, title = {Neural Signatures and Multi-Cognitive Decoding of EEGSignals Induced by Shared Stimulus: A Paradigm Study.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3654639}, pmid = {41543945}, issn = {1558-2531}, abstract = {Multi-task decoding from electroencephalogram (EEG) signals is valuable for brain-computer interface (BCI) applications in naturalistic settings. Most existing studies focus on decoding distinctly different tasks, leaving the diversity of cognitive responses elicited by a single stimulus underexplored. We introduced a novel experimental paradigm where a common visual stimulus elicits five distinct cognitive processes: single reach, interception reach, sequence reach, attention reach, and inhibition reach. EEG signatures were analyzed using temporal and spectral methods. A regularized linear discriminant analysis (RLDA) classifier was employed for decoding, utilizing both temporal and event-related spectral perturbation (ERSP) features. Significant neural activation differences (p < 0.05) were observed across tasks and brain regions. The RLDA classifier achieved high decoding accuracy: 91.72% ± 6.10% for classifying the five cognitive states using ERSP features. Furthermore, for the sequence reach task, temporal features enabled classification of normal versus catch trials with 77.96% ± 7.03% accuracy. All these results demonstrate the potential for EEG-based BCI applications to distinguish diverse cognitive states elicited by identical stimuli, offering new insights for improving the naturalness and intelligence of BCI systems. Future work will focus on enhancing decoding performance and extending this research to online applications.}, }
@article {pmid41539931, year = {2026}, author = {Beste, C and Slagter, HA and Herff, C and Kamitani, Y and Coninx, S and van Wezel, R and Frings, C}, title = {Moving intentions from brains to machines.}, journal = {Trends in cognitive sciences}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tics.2025.12.003}, pmid = {41539931}, issn = {1879-307X}, abstract = {Brain-computer interface (BCI) research has achieved remarkable technical progress but remains limited in scope, typically relying on motor and visual cortex signals in limited patient populations. We propose a paradigm shift in BCI design rooted in ideomotor theory, which conceptualizes voluntary action as driven by internally represented sensory outcomes. This underused framework offers a principled basis for next-generation BCIs that align closely with the brain's natural intentional and action-planning architecture. We suggest a more intuitive, generalizable, and scalable path by reorienting BCIs around the 'what for' of action-user goals and anticipated effects. This shift is timely and feasible, enabled by advances in neural recording and artificial intelligence-based decoding of sensory representations. It may help resolve challenges of usability and generalizability in BCI design.}, }
@article {pmid41537311, year = {2026}, author = {Shu, L and Tang, J and Guan, X and Zhang, D}, title = {A comprehensive survey of genome language models in bioinformatics.}, journal = {Briefings in bioinformatics}, volume = {27}, number = {1}, pages = {}, pmid = {41537311}, issn = {1477-4054}, support = {62136004//National Natural Science Foundation of China/ ; 62276130//National Natural Science Foundation of China/ ; 2023YFF1204803//National Key R&amp;D Program of China/ ; BE2022842//Key Research and Development Plan of Jiangsu Province/ ; }, mesh = {*Computational Biology/methods ; *Natural Language Processing ; Humans ; *Genomics/methods ; Deep Learning ; *Genome ; }, abstract = {Large language models have revolutionized natural language processing by effectively modeling complex semantics and capturing long-range contextual relationships. Inspired by these advancements, genome language models (gLMs) have recently emerged, conceptualizing DNA and RNA sequences as biological texts and enabling the identification of intricate genomic grammar and distant regulatory interactions. This review examines the need for gLMs, emphasizing their capacity to overcome the limitations of traditional deep learning approaches in genomic sequence characterization. We comprehensively survey contemporary gLM architectures, including Transformer models, Hyena convolutions, and state space models, as well as various sequence tokenization strategies, assessing their applicability, and effectiveness across diverse genomic applications. Additionally, we discuss foundational pretraining strategies and provide an overview of genomic pretraining datasets spanning multiple species and functional domains. We critically analyze evaluation methodologies, including supervised, zero-shot, and few-shot learning paradigms, as well as fine-tuning approaches. An extensive taxonomy of downstream tasks is presented, alongside a summary of existing benchmarks and emerging trends. Finally, we contemplate key challenges such as data scarcity, interpretability, and the computational demands of genomic modeling, and propose a roadmap to guide future advances in genome language modeling.}, }
@article {pmid41532318, year = {2026}, author = {Sun, X and Wang, T and Gong, H and Qiu, Y and Zhang, Y and Chen, M and Xue, J and Ye, G and Mou, R and Teng, P and Li, W and Chen, T and Zhang, L and Guo, X and Mao, W and Zhao, H and Ma, L and Xu, Q}, title = {Circulating CD34[+] Fibroblast Progenitors Engaged in Heart Fibrosis of Allograft.}, journal = {Circulation research}, volume = {}, number = {}, pages = {}, doi = {10.1161/CIRCRESAHA.125.326558}, pmid = {41532318}, issn = {1524-4571}, abstract = {BACKGROUND: Fibrosis is one of the major causes of cardiac allograft malfunction and is mainly driven by fibroblasts. However, the role of recipient-derived cells in generating allograft fibroblasts and the underlying mechanisms remain to be explored.<br><br>METHODS: We analyzed human heart allograft samples and used murine transplant models (C57BL/6J, Cd34-CreER[T2]; R26-tdTomato, mRFP mice, Rosa26-iDTR, Postn-CreER[T2]; R26-tdTomato, double-tdTomato, and immunodeficient mice with BALB/c donors). Human progenitor cells were cultivated from blood. Single-cell RNA sequencing, Western blotting, quantitative polymerase chain reaction, and immunohistochemistry, whole-mount staining with 3-dimensional reconstruction, and in vivo/in vitro experiments were applied to characterize allograft cellular composition and communication.<br><br>RESULTS: Single-cell RNA sequencing was introduced to delineate the allograft cell atlas of patients and mice. Y chromosome analysis identified that recipient-derived cells contributed to allograft fibroblasts in both patients and murine models. Combining the genetic cell lineage tracing technique, we found that recipient-derived CD34[+] cells could give rise to activated fibroblasts. Bone marrow transplantation and parabiosis models revealed that the recipient's circulating non-bone marrow Cd34[+] cells could generate allograft fibroblasts. Human CD34[+] cells could differentiate into fibroblasts both in vivo and in vitro. CD34[+] fibroblast progenitors were recruited by CXCL12-ACKR3 and MIF-ACKR3 interactions and differentiated via the TGF&#x3b2; (transforming growth factor beta)/GFPT2 (glutamine-fructose-6-phosphate transaminase 2)/SMAD2/4 axis. Ablation of recipient Cd34[+] cells reduced activated fibroblasts and alleviated allograft fibrosis.<br><br>CONCLUSIONS: We identify circulating CD34[+] cells as a novel source of fibroblast progenitors that contribute to cardiac allograft fibrosis, suggesting that targeting recipient CD34[+] cells could be a novel therapeutic potential for treating cardiac fibrosis after heart transplantation.}, }
@article {pmid41531435, year = {2026}, author = {Lu, X and Chen, Y and Li, Z and Zhao, J and Zhou, Y and Wu, D and Zhang, M}, title = {Electroencephalography Enables Continuous Decoding of Hand Motion Angles in Polar Coordinates.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {7}, number = {}, pages = {0469}, pmid = {41531435}, issn = {2692-7632}, abstract = {Hand movements in task space are typically represented using either Cartesian or polar coordinate systems. While Cartesian coordinates are commonly used in electroencephalography (EEG)-based brain-computer interface (BCI) studies, polar coordinates offer a more natural representation for circular motion by directly encoding angular information. This study investigates the feasibility of continuous decoding of hand motion angles in polar coordinates using EEG signals. In the paradigm, human participants engaged in bimanual circular tracing with a fixed radius while their EEG signals were recorded. To evaluate the feasibility of this approach, 6 deep learning models, including commonly used EEGNet, DeepConvNet, and ShallowConvNet, and their variants incorporating long short-term memory (LSTM) layers, were employed. Performance was assessed using mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (CC) between decoded and actual angles. Across 8 participants, all 6 models significantly outperformed the chance level (P < 0.01), with the best model achieving an MSE of 1.012 rad[2], an MAE of 0.627 rad, and a CC of 0.895. These results demonstrate the feasibility of continuous angular decoding of circular hand motion in polar coordinates using EEG signals. This approach offers a promising alternative to traditional Cartesian-based decoding methods, particularly for applications involving circular or rotational movements.}, }
@article {pmid41520740, year = {2026}, author = {Liu, Q and Zhang, X and Niu, J and Chen, K and Xia, J and He, Y and Xu, S and Li, W and Chen, H and Zhang, D and Liao, W and Li, J}, title = {Uniformity in happiness and uniqueness in sadness: Naturalistic emotional representation in major depression.}, journal = {NeuroImage}, volume = {326}, number = {}, pages = {121712}, doi = {10.1016/j.neuroimage.2026.121712}, pmid = {41520740}, issn = {1095-9572}, abstract = {Humans develop shared concepts of others' emotions to support adaptive social functioning, yet how these concepts are dynamically represented in major depressive disorder (MDD) during naturalistic movie viewing is not yet fully established. Using functional MRI, we examined patients with MDD (n = 55) and healthy controls (HCs; n = 62) as they freely viewed movie clips depicting happy and sad emotions. Neural similarity was quantified with inter-subject correlation at whole-brain, network, and regional levels, and its association with emotional traits was assessed using inter-subject representational similarity analysis. Compared with HCs, patients with MDD showed significantly reduced whole-brain similarity, particularly during sad contexts. Network analyses revealed that HCs exhibited increased similarity in the limbic network during sadness, reflecting a shared "sadness resonance," whereas patients with higher depressive severity showed widespread disruptions across visual, limbic, dorsal attention, and default mode networks. At the regional level, similarity in the inferior temporal gyrus and lateral occipital cortex was closely linked to individual differences in emotional awareness, with pronounced context- and region-specificity. These findings highlight neural decoupling and heterogeneity as core features of MDD and provide new evidence for potential biomarkers to inform risk assessment and personalized interventions.}, }
@article {pmid41398114, year = {2025}, author = {Evans, NG and L Gross, M and Shandler, R}, title = {Enhancing Soldiers for Future Warfare: Good Science; Bad Ethics?.}, journal = {Science and engineering ethics}, volume = {32}, number = {1}, pages = {5}, pmid = {41398114}, issn = {1471-5546}, support = {FA9550-21-1-0142//Air Force Office of Scientific Research/ ; W911NF-24-1-0361//Army Research Office/ ; }, abstract = {UNLABELLED: Ethical concerns dog emerging technologies designed to enhance warfighter performance. Brain-computer interfaces, exoskeletons, and mind- or body-altering drugs raise fears about risky, invasive, and experimental medical procedures that offer armies physically and cognitively superior soldiers that will dictate and disrupt the course of future war. What counts as enhancement, however, has been subject to longstanding and passionate debate. This study aims to put an end to this dispute by employing a conjoint experimental design to survey a group of military and professional experts from across the world to explore how definitional perceptions of enhancement influence ethical acceptability. Two main findings emerge. First, we find that there already exists a broad agreement about what constitutes enhancement, and this consensus spans countries, discipline, political orientation, and age. Future policy may now be able to accommodate a definition of enhancement that is widely shared among members of the international community. Second, across the board, ethical acceptability diminishes as medical technologies aim for transhuman warfighting capabilities. Enhancement research and development for military purposes must navigate the conflicting ethical demands of medical experimentation and lawful war. Human enhancement is not morally unacceptable but ethically precarious, requiring regulation, oversight, and transparency.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11948-025-00573-w.}, }
@article {pmid41529886, year = {2026}, author = {Zhu, H and Gan, Y and Ye, J and Li, Y and Yu, JZ and Li, X}, title = {Effectiveness of brain-computer interface interventions in autism spectrum disorder rehabilitation: a systematic review and meta-analysis protocol.}, journal = {BMJ open}, volume = {16}, number = {1}, pages = {e102277}, doi = {10.1136/bmjopen-2025-102277}, pmid = {41529886}, issn = {2044-6055}, mesh = {Humans ; *Autism Spectrum Disorder/rehabilitation ; *Brain-Computer Interfaces ; Systematic Reviews as Topic ; Meta-Analysis as Topic ; Research Design ; }, abstract = {BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterised by impairments in social interaction, communication and the presence of repetitive behaviours. Recent advancements in brain-computer interface (BCI) technologies have demonstrated potential benefits in enhancing cognitive, social and communication skills in individuals with ASD. However, the effectiveness of BCI-based interventions in ASD rehabilitation remains inconsistent across studies. Therefore, this protocol outlines a systematic review and meta-analysis to synthesise the evidence on the effectiveness of BCI-based interventions for ASD rehabilitation.<br><br>METHODS: We will conduct a comprehensive literature search across multiple databases, including MEDLINE Ovid, Embase Ovid, Cochrane Central Register of Controlled Trials (CENTRAL), Conference Proceedings Citation Index-Science (CPCI-S), Science Citation Index Expanded (SCI-EXPANDED) and so on, to identify relevant studies published from inception to the present. The search will be supplemented by screening the reference lists of included studies and relevant systematic reviews. Two independent reviewers will screen the titles, abstracts and full texts of identified studies for eligibility based on predefined criteria. Data extraction will be performed using a standardised form, and the risk of bias (RoB) will be assessed using the Cochrane RoB tool. Heterogeneity will be evaluated using the I&#xb2; statistic, and a random-effects or fixed-effects model will be selected for meta-analysis based on the degree of heterogeneity. Subgroup analyses will be conducted to explore potential sources of heterogeneity, including participant age, ASD severity, type of BCI intervention and duration of the intervention. The review will be conducted from January 2026 to April 2026.<br><br>ETHICS AND DISSEMINATION: Ethical approval is not required for this study, as it does not involve the collection of primary data from individual patients. Findings will be disseminated through peer-reviewed publication and conference presentations.<br><br>PROSPERO REGISTRATION NUMBER: CRD420251010496.}, }
@article {pmid41528907, year = {2026}, author = {Zhao, Y and Cao, D and Yu, H and Liang, G and Chen, Z}, title = {MSHANet: A Multiscale Hybrid Attention Network for Motor Imagery EEG Decoding.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3653824}, pmid = {41528907}, issn = {1558-2531}, abstract = {Brain-computer interface (BCI) technology has significant applications in neuro rehabilitation and motor function restoration, especially for patients with stroke or spinal cord injury. Motor imagery electroencephalog-raphy (MI-EEG) is widely used in BCIs, but its nonlinear dynamics and inter-subject variability limit decoding accuracy. In this paper, a multiscale hybrid attention network (MSHANet) for MI-EEG decoding, which consists of spatiotemporal feature extraction (STFE), talking head self-attention (THSA), dynamic squeeze-and-excitation attention (DSEA), and a temporal convolutional network (TCN), is proposed. MSHANet was evaluated via within-subject experiments using BCI Competition IV Datasets 2a and 2b, as well as EEGMMID, achieving decoding accuracies of 83.56%, 89.75%, and 75.66%, respectively. In cross-subject experiments on the three datasets, the mode lattained accuracies of 69.93% on BCI-2a, 81.85% on BCI-2b, and 79.67% on EEGMMID. In addition, we propose an electrode spatial structure-aware encoder. This technique encodes the spatial positions of electrodes in the original data, enabling the model to obtain richer spatial electrode information at the input stage. In within-subject and cross-subject tasks on BCI-2a, this encoding improved the decoding performance by 2.83% and 2.91%, respectively. Visualization was also employed to elucidate feature distributions and the effec tiveness of its attention mechanisms. Experimental results demonstrate that MSHANet performs exceptionally well in MI-EEG decoding tasks and has high potential for clinical applications, particularly in neurorehabilitation and motor function reconstruction.}, }
@article {pmid41528455, year = {2026}, author = {Becker, L and Krüger, L and Wolf, M and Alfen, K and Theysohn, J and Lefering, R and Dudda, M and Kamp, O and , }, title = {The necessity of CT scans on pediatric carotid injury after blunt trauma - An analysis of the traumaregister DGU[®].}, journal = {European journal of trauma and emergency surgery : official publication of the European Trauma Society}, volume = {52}, number = {1}, pages = {13}, pmid = {41528455}, issn = {1863-9941}, mesh = {Humans ; Child ; *Wounds, Nonpenetrating/diagnostic imaging/epidemiology ; Child, Preschool ; Adolescent ; Male ; Registries ; Female ; Infant ; Germany/epidemiology ; *Carotid Artery Injuries/diagnostic imaging/epidemiology ; *Tomography, X-Ray Computed ; Injury Severity Score ; Infant, Newborn ; Prevalence ; Risk Factors ; }, abstract = {PURPOSE: Blunt carotid injuries (BCI) in pediatric trauma patients are rare. Using data from the TraumaRegister DGU[®][,] this study aims to identify screening parameters and calculate the prevalence of pediatric BCI. By proposing potential risk factors for a BCI, this research seeks to reduce unnecessary radiation exposure in pediatric trauma cases. These findings may enhance understanding of pediatric BCI and highlight the necessity of cautious diagnostic approaches that balance clinical needs with radiation risks.<br><br>METHODS: The TraumaRegister DGU[&#xae;] is a multicenter database established in 1993 to document the treatment of severely injured patients from initial injury to hospital discharge. Data are collected in four phases: demographics, injury patterns, treatments, and outcomes. Almost 700 hospitals, primarily from Germany, contribute to the registry annually. Statistical analysis was conducted using SPSS. For analysis, the dataset was divided into two groups: trauma patients diagnosed with BCI and trauma patients without BCI. The complete dataset from the TraumaRegister DGU[&#xae;] for 2006-2020 was screened for relevant cases. The dataset was limited to patients between 0 and 15 years old.<br><br>RESULTS: Out of 9070 severely injured pediatric trauma patients analysed, 50 cases of pediatric BCI were identified, representing a prevalence of 0.6%. Patients with BCI presented with higher injury severity scores (ISS), lower Glasgow Coma Scale (GCS) scores, and a greater prevalence of head injuries, as well as thoracic, abdominal, and extremity injuries. These patients also experienced higher in-hospital mortality rates (34%) and required more frequent blood transfusions. Full-body CT scans were more commonly performed in patients with BCI.<br><br>CONCLUSION: This study highlights the rarity and severity of BCI in pediatric trauma patients, with a prevalence of 0.6%. Significant risk factors for a BCI include high injury severity, head trauma, neurological deficits, and pre-hospital hypotension. The findings emphasise the importance of early diagnosis and targeted diagnostic strategies to balance the need for prompt intervention with reducing unnecessary radiation exposure.}, }
@article {pmid41527472, year = {2026}, author = {Niu, J and Xia, J and He, Y and Li, W and Chen, K and Liu, Q and Li, W and Qiu, J and Chen, H and Li, J and Liao, W}, title = {Controllability of morphometric network colocalize with underlying neurobiology in major depression.}, journal = {Psychological medicine}, volume = {56}, number = {}, pages = {e15}, doi = {10.1017/S0033291725103140}, pmid = {41527472}, issn = {1469-8978}, support = {62473082, 62571105, 82121003, 62036003, 62333003//National Natural Science Foundation of China/ ; ZYGX2022YGRH008, ZYGX2024XJ054//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Depressive Disorder, Major/diagnostic imaging/physiopathology/metabolism/pathology ; Female ; Adult ; Male ; Middle Aged ; Case-Control Studies ; *Brain/diagnostic imaging/metabolism/pathology ; *Nerve Net/diagnostic imaging ; Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Young Adult ; }, abstract = {BACKGROUND: Cognitive and behavioral symptoms of major depressive disorder (MDD) are linked to aberrant changes in the controllability of brain networks. However, previous studies examined network controllability using white matter tractography, neglecting the contributions of gray matter. We aimed to examine differences in the controllability of morphometric networks between patients with MDD and demographic-matched healthy controls and identify the associated neurobiological signatures.<br><br>METHODS: Based on the structural and diffusion MRI data from two independent cohorts, we calculated the controllability of morphometric similarity networks for each participant. A generalized additive model was used to investigate the case-control differences in regional controllability and their cognitive and behavioral associations. We investigated the associations between imaging-derived controllability and neurotransmitters, brain metabolism, and gene transcription profiles using multivariate linear regression and partial least squares regression analyses.<br><br>RESULTS: In both cohorts, depression-related abnormalities of morphometric network controllability were primarily located in the prefrontal, cingulate, and visual cortices, contributing to memory, sensation, and perception processes. These abnormalities in network controllability were spatially aligned with the distributions of serotonergic transmission pathways as well as with altered oxygen and glucose metabolism. In addition, these abnormalities spatially overlapped with differentially expressed genes enriched in annotations related to protein catabolism and mitochondria in neuronal cells and were disproportionately located on chromosome 22.<br><br>CONCLUSIONS: Collectively, neuroimaging evidence revealed aberrant morphometric network controllability underlying MDD-related cognitive and behavioral deficits, and the associated genetic and molecular signatures may help identify the neurobiological mechanisms underlying MDD and provide feasible therapeutic targets.}, }
@article {pmid41526383, year = {2026}, author = {Wang, D and Shi, Y and Pang, J and Zhu, X and Meng, L and Ming, D}, title = {Data-driven subtyping of early Parkinson's disease via mutual cross-attention fusion of EEG and dual-task gait features.}, journal = {NPJ Parkinson's disease}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41531-026-01258-2}, pmid = {41526383}, issn = {2373-8057}, support = {82372083//National Natural Science Foundation of China/ ; }, abstract = {Parkinson's disease (PD) exhibits marked clinical heterogeneity, which poses challenges for diagnosis, prognosis, and therapeutic precision, especially for early-stage PD patients. Existing subtyping approaches often rely on subjective clinical scales and single-modality data, which limits their sensitivity in capturing subtle but clinically relevant differences across patients. To reveal clinically meaningful PD subtypes, we propose a data-driven multimodal framework that integrates resting-state electroencephalography (EEG) and dual-task gait features using mutual cross-attention (MCA) fusion. Forty idiopathic early-stage PD patients were enrolled in a prospective study. EEG biomarkers were encoded via a convolutional neural network for the prediction of motor severity (MDS-UPDRS-III), while dual-task gait features were derived to capture subtle motor dysfunctions. The MCA enabled bidirectional attention-guided integration of EEG and gait features, which were then clustered using an unsupervised method. The analysis revealed three distinct subtypes, with dual-task-based fusion providing superior clinical separation. Subtype I was characterized by pronounced motor deficits; Subtype II showed moderate symptoms with relatively preserved quality of life; and Subtype III presented mild motor impairments but exhibited poorer cognitive and psychosocial outcomes. Feature contribution analyses highlighted central beta and theta EEG activity, along with dual-task gait metrics (e.g., stride length during turning), as key drivers of subtype differentiation. Longitudinal follow-up demonstrated subtype-specific rehabilitation responses, with Subtype II showing an insufficient response compared to other subtypes. In conclusion, this study enables digital phenotyping of PD with prognostic implications for personalized rehabilitation strategies and accelerates precision medicine.}, }
@article {pmid41525762, year = {2026}, author = {Ding, W and Chen, X and Liu, A}, title = {Breaking the performance barrier in deep learning-based SSVEP-BCIs: A joint frequency-phase training strategy.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae36f6}, pmid = {41525762}, issn = {1741-2552}, abstract = {OBJECTIVE: Deep learning exhibits considerable potential for steady-state visual evoked potential (SSVEP) classification in electroencephalography (EEG)-based brain-computer interfaces (BCIs). SSVEP signals contain both frequency and phase characteristics that correspond to the visual stimuli. However, existing deep learning training strategies typically focus on either frequency or phase information alone, thus failing to fully exploit these dual inherent properties and substantially limiting classification accuracy.<br><br>APPROACH: To tackle this limitation, this study proposes a Joint Frequency-Phase Training Strategy (JFPTS), which comprises two complementary stages with distinct time-window sampling schemes. The first stage adopts a frequency prior-driven sampling scheme to improve frequency component utilization, whereas the second stage employs a phase-locked sampling scheme to enhance intra-category phase consistency. This design enables JFPTS to effectively leverage both frequency and phase properties of SSVEP signals.<br><br>MAIN RESULTS: Comprehensive experiments on two well-established public datasets validate the effectiveness of JFPTS. The results demonstrate that the JFPTS-enhanced model achieves a marked superiority over the current state-of-the-art classification approaches, notably surpassing the long-standing performance benchmark set by task discriminative component analysis (TDCA).<br><br>SIGNIFICANCE: Overall, JFPTS establishes a new training paradigm that advances deep learning approaches for SSVEP classification and promotes the broader adoption of SSVEP-BCIs.}, }
@article {pmid41525614, year = {2026}, author = {Jin, J and Wang, C and Xu, R and He, X and Wu, X and Li, J and Chen, W and Wang, X and Cichocki, A}, title = {RUNet: A Zero-Calibration Framework for Cross-Domain EEG Decoding via Riemannian and Unsupervised Representation Learning.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2026.3653024}, pmid = {41525614}, issn = {1558-2531}, abstract = {OBJECTIVE: Inter-session and inter-subject variability in electroencephalography (EEG) signals, resulting from individual differences and environmental factors, poses a major challenge for neural decoding in brain-computer interface (BCI) applications.<br><br>METHODS: To address this issue, we propose RUNet, a zero-calibration motor imagery EEG decoding framework based on Riemannian manifold learning and unsupervised representation learning. RUNet incorporates a multi-scale spatiotemporal convolutional module that jointly captures local global spatial and multi-resolution temporal dynamics features. To enhance the robustness of EEG features against non stationarity, a polysynergistic covariance optimization module is employed, which strengthens the covariance matrix representation through multiple regularizations and adaptive fusion. In addition, RUNet integrates the Riemannian Affine Log Mapping layer, based on Affine-Invariant Transformation and Log-Euclidean Mapping, in an end-to-end manner to mitigate cross-domain covariance drift and enhance domain-invariant feature learning. A transfer learning framework is further integrated into RUNet: during pre-training, an unsupervised contrastive loss is applied to resting-state EEG data to learn domain-invariant spatiotemporal features; during retraining, task-specific data are used to enhance discriminability and feature disentanglement.<br><br>CONCLUSION: Experimental results on the BCI Competition IV 2a, 2b datasets and a self-collected laboratory dataset show that RUNet achieves average cross-session accuracies of 87.19%, 88.03% and 85.45%, and cross-subject accuracies of 68.09%, 78.29% and 87.25%, respectively. On the PhysioNet dataset, a cross-subject accuracy of 78.14% is achieved. These results demonstrate the effectiveness of RUNet's unified pipeline and its robust cross-domain generalization.}, }
@article {pmid41525559, year = {2026}, author = {Guan, S and Li, Y and Gao, Y and Yin, R and Luo, Y and Liang, J and Zhang, J and Zhang, Y and Li, R}, title = {Enhanced Mapping of Finger Movement Representations Using Diffuse Optical Tomography: A Systematic Comparison with fNIRS.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3652812}, pmid = {41525559}, issn = {1558-0210}, abstract = {Advancing neuroimaging modalities for motor cortex analysis is critical for understanding the neural mechanisms underlying fine motor tasks and for expanding clinical applications. Functional Near-Infrared Spectroscopy (fNIRS) is widely used for measuring cortical hemodynamic activity due to its portability and accessibility, but its inherent limitations in spatial resolution and noise sensitivity reduce its utility for precise neural mapping. Diffuse Optical Tomography (DOT) has emerged as a promising alternative with superior spatial resolution and sensitivity. In this study, we performed a systematic comparison of DOT and fNIRS in detecting task-evoked neural activation during a finger-tapping paradigm including four conditions varying by finger type (thumb vs. little finger) and frequency (high vs. low). Our results demonstrated that DOT consistently captured robust activation in motor-related brain regions, even during less demanding conditions, while fNIRS exhibited limited sensitivity. Temporal trace analyses revealed that DOT achieved higher contrast-to-noise ratio (CNR) and contrast-to-background ratio (CBR), validating its enhanced signal quality and ability to distinguish subtle hemodynamic responses. Furthermore, statistical comparisons highlighted significant differences in task-related activations detected by the two modalities, particularly in low-effort conditions. These findings underscore the advantages of DOT over fNIRS, particularly in applications requiring high spatial resolution and sensitivity to subtle neural processes. The results contribute to ongoing efforts to refine optical imaging techniques for motor neuroscience and reinforce DOT's potential for clinical translation in motor deficit diagnosis, rehabilitation monitoring, and brain-computer interface development.}, }
@article {pmid41525552, year = {2026}, author = {Zhu, J and Li, K and Chen, S and Huang, H and Zhang, Y and Hu, L and Li, Y}, title = {Smart Ward Control Based on a Wearable Multimodal Brain-Computer Interface Mouse.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3653138}, pmid = {41525552}, issn = {1558-0210}, abstract = {For patients with severe extremity motor function impairment, traditional smart ward control methods, such as those using joysticks and touchscreens, are frequently unsuitable due to their limited physical abilities. Consequently, developing an effective brain-computer interface (BCI) suitable for their operation has become an immediate concern. This paper presents a wearable multimodal BCI system for smart ward control, which employs a self-designed wearable headband to capture head rotation and blinking movement. By wearing the headband, users can control a computer cursor on the screen only with head rotation and blinking, and further control devices in a smart ward with self-designed graphical user interfaces (GUIs). The system decodes signals from an inertial measurement unit (IMU) to map the head posture to the position of the cursor on the screen and decodes electrooculography (EOG) and electroencephalography (EEG) signals to detect valid blinks for selecting and activating function buttons. Ten participants were recruited to perform two experimental tasks that simulate the daily needs of patients with extremity motor function issues. To our satisfaction, all the participants fully accomplished the simulated tasks, and an average accuracy of 97.0±3.9 % and an average response time of 2.39±0.53 s were achieved. Different from traditional step-controlled BCI nursing beds, we designed a continuous-controlled nursing bed and achieved satisfactory results. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate the effectiveness of our proposed system, indicating significant potential for practical applications.}, }
@article {pmid41525550, year = {2026}, author = {Padmaja, GKR and Bhagat, NA and Balasubramani, PP}, title = {Assessing the utility of Fronto-Parietal and Cingulo-Opercular networks in predicting the trial success of brain-machine interfaces for upper extremity stroke rehabilitation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3653049}, pmid = {41525550}, issn = {1558-0210}, abstract = {Brain-machine interfaces (BMIs) have the potential to improve stroke rehabilitation by actively facilitating sensory-cognitive-motor connections to restore movement. However, individuals with cognitive impairments are often excluded from BMI-based neurorehabilitation due to concerns about impaired cognition, specifically reduced attention and executive control. We propose leveraging the trial-wise dynamics of large-scale cognitive control networks-specifically, the frontoparietal (FPN) and cingulo-opercular (CON) networks-to build neural markers of cognitive control. Using existing BMI datasets, we demonstrate that trial-wise activity within these networks predicts motor task performance, suggesting that cognitive control signals in these networks could serve as adaptive modulations for BMI-based rehabilitation. Our system is able to predict unsuccessful BMI trials at the population level about 84.2% of the time on average, with an overall mean accuracy of 72.2% in a 3-fold cross-validation. Additionally, in a leave-one-subject-out validation, our system achieved 71% specificity on average, with an overall mean accuracy of 68.3%. Notably, model performance varies across subjects, with some individuals showing up to 92% specificity and 100% sensitivity. Unlike previous studies that primarily focus on resting-state data, our findings point toward the untapped potential of incorporating cognitive network state monitoring into BMI systems to optimize online performance through trials. Specifically, we suggest that our pre-trained models can be fine-tuned with subject-specific information to design more targeted rehabilitation programs that enhance motor performance by identifying precise attention and learning tasks to improve the successful response of the network model in patients with significant cognitive impairment.}, }
@article {pmid41525004, year = {2026}, author = {Yan, Y and Zhang, Y and Zhao, X and Chen, R and Fang, S and Zhou, Y and Huang, J and Wang, F and Chen, C and Lin, Z and Xu, X}, title = {Life-course body shape trajectories and cerebral oxygen metabolism in community-dwelling older adults.}, journal = {GeroScience}, volume = {}, number = {}, pages = {}, pmid = {41525004}, issn = {2509-2723}, support = {NSFC/72274170//Natural Science Foundation of China/ ; NSFC/82201733//Natural Science Foundation of China/ ; }, abstract = {Obesity and lifelong body-shape fluctuation are associated with late-life structural brain damage, suggesting the involvement of metabolic pathways. The cerebral metabolic rate of oxygen (CMRO2) reflects hemodynamic and oxidative stress and precedes structural atrophy, but its role in adiposity-related brain change remains unclear. We examined whether current and life-course adiposity relate to CMRO2 and to structural change. A total of 303 community-dwelling adults aged 50 years and older were included. Body shape was assessed using Body Mass Index (BMI) and Body Roundness Index (BRI). Global CMRO2 was derived from TRUST and phase-contrast MRI. T1-weighted MPRAGE provided volumetry, and medial temporal atrophy (MTA) grading. General linear models estimated associations of BMI and BRI with CMRO2, including age interactions. Age-stratified mediation tested CMRO2 as a mediator of adiposity to MTA associations. Body-shape trajectories at ages 25, 40, 60, and current age were modeled and related to CMRO2 and metabolism-related regions. Adiposity was associated with lower CMRO2: with overweight (β = -1.12 μmol/100 g/min, 95%CI = (-1.96, -0.28)) and higher BRI (β = -1.31, 95%CI = (-2.36, -0.27)) showing stronger effects with advancing age. Among participants aged 70 years, CMRO2 mediated the association between BMI and MTA (indirect β = 0.06, 95%CI = (0.01, 0.14)). Three adulthood body-shape patterns emerged, and CMRO2 was lower in moderate increasing (β = -11.40; 95%CI = (-20.90, -1.90)) and high-rising (β = - 12.23; 95%CI = (-23.56, -0.90)) groups. Metabolism-related regions were larger in higher-risk patterns, particularly the left hypothalamus. Greater and prolonged adiposity is linked to reduced CMRO2 and related structural differences in older adults.}, }
@article {pmid41523970, year = {2025}, author = {Xu, C and Kong, L and Mou, T and Tang, A and Hu, S and Lai, J}, title = {Vitamin B12 and Affective Disorders: A Focus on the Gut-Brain Axis.}, journal = {Alpha psychiatry}, volume = {26}, number = {6}, pages = {49138}, pmid = {41523970}, issn = {2757-8038}, abstract = {Accumulating evidence highlights the role of Vitamin B12 (VitB12) in the pathophysiology of affective disorders. However, its influence on brain function and the underlying mechanisms remain incompletely understood. In humans, VitB12 is obtained solely from dietary sources, primarily animal-based foods. VitB12 deficiency leads to the accumulation of homocysteine, a known contributor to emotional and behavioral dysregulation. VitB12 plays a critical role in maintaining neuron stability, synapsis plasticity, and regulating neuroinflammation by modulating key bioactive factors. These processes help to alleviate hippocampal damage, mitigate blood-brain barrier disruption, reduce oxidative stress, and enhance both structural and functional connectivity-collectively contributing to resilience against affective disorders. VitB12 from both diet and microbial sources is essential to gut homeostasis. Within the gut lumen, it stabilizes gut microbial communities, promotes short-chain fatty acid (SCFA) production, and supports neurotransmitter metabolism (e.g., serotonin and dopamine) via its role in S-adenosyl-l-methionine biosynthesis. Crucially, VitB12, gut microbiota, SCFAs, intestinal mucosa, and vagal nerve signaling interact synergistically within the gut-brain axis (GBA) to maintain gut microenvironment stability, protect the gut-blood barrier, and suppress neuroinflammatory cascades, eventually reducing the susceptibility to affective disorders. This review synthesizes current evidence on the involvement of VitB12 in the GBA, its association with mood regulation, and its potential as a nutritional adjunct in managing affective disorders. By elucidating this integrative mechanism, we provide new insights into targeting the GBA to improve clinical outcomes in affective disorders.}, }
@article {pmid41523966, year = {2025}, author = {Wang, R and Hou, X and Li, R and Cheng, B and Zhou, C and Xue, C and Li, K and Deng, W}, title = {Maintenance of Noninvasive Brain Stimulation for Preventing Relapse in Depression: A Systematic Review and Meta-Analysis.}, journal = {Alpha psychiatry}, volume = {26}, number = {6}, pages = {49140}, pmid = {41523966}, issn = {2757-8038}, abstract = {BACKGROUND: Depression relapse rates remain high after acute treatment; this study evaluates the efficacy of maintenance noninvasive brain stimulation in preventing relapse and identifies optimal treatment parameters.<br><br>METHODS: This meta-analysis was conducted following PRISMA guidelines. We conducted a systematic search of PubMed, Embase, Web of Science, Cochrane Library, and PsycINFO databases up to January 5, 2025. The primary outcome was relapse rate.<br><br>RESULTS: A total of nine randomized controlled trials with 837 participants were included, six studies used electroconvulsive therapy (ECT) and three studies used repetitive transcranial magnetic stimulation (rTMS). Our findings indicate that ECT combined with pharmacotherapy or rTMS alone demonstrated superiority over pharmacotherapy alone in reducing the relapse of depression during 6, 9, 12-month maintenance treatment periods. Interestingly, ECT alone did not show significant results. In terms of stimulation parameters, the ECT combined with pharmacotherapy group mainly received right unilateral stimulation, while the ECT alone group had bitemporal stimulation. The stimulation frequency was similar between the two groups. In contrast, the rTMS-alone group had significantly higher stimulation frequencies than the ECT groups. We did not find any eligible studies on transcranial direct current stimulation, transcranial alternating current stimulation or magnetic seizure therapy, but they also showed potential in the maintenance treatment of depression, which warrants further investigation.<br><br>CONCLUSIONS: ECT combined with pharmacotherapy, or rTMS alone, is more effective than pharmacotherapy alone in preventing relapse of depression during 6 to 12 months of maintenance treatment. Future research should prioritize identifying the optimal treatment regimen and exploring the potential of combination therapies.<br><br>THE PROSPERO REGISTRATION: CRD42023490546, https://www.crd.york.ac.uk/PROSPERO/view/CRD42023490546.}, }
@article {pmid41523191, year = {2026}, author = {van Balen, B and Ramsey, NF and Vansteensel, MJ}, title = {Relational personhood: the missing link for evaluating clinical impact of brain-computer interfaces.}, journal = {Brain communications}, volume = {8}, number = {1}, pages = {fcaf470}, pmid = {41523191}, issn = {2632-1297}, }
@article {pmid41521389, year = {2026}, author = {Yilmaz Kars, M and Akkar, I and Dogan, MH and Turgut, ZI and Cicek, O and Dikmeer, A and Kollu, K and Cakir Ozden, EC and Kizilarslanoglu, MC}, title = {EXPRESS: The CRP/Albumin Ratio (CAR) may be more strongly linked to delirium than other indices derived from laboratory parameters in older patients in an intensive care unit.}, journal = {Journal of investigative medicine : the official publication of the American Federation for Clinical Research}, volume = {}, number = {}, pages = {10815589261415891}, doi = {10.1177/10815589261415891}, pmid = {41521389}, issn = {1708-8267}, abstract = {The aim of this study is to investigate the association of delirium with laboratory-derived indices and ratios in patients staying in an intensive care unit (ICU). Delirium was diagnosed according to DSM-5 criteria, and laboratory data obtained at the time of diagnosis were retrospectively analyzed. The following indices were calculated: C-reactive protein(CRP)/albumin ratio(CAR), CRP-albumin-lymphocyte(CALLY), B12-CRP(BCI), Systemic Immune-Inflammation(SII), Prognostic Nutritional Index(PNI), Advanced Lung Cancer Inflammation (ALI), Systemic Inflammation Response indices (SIRI) and Glasgow Prognostic Score (GPS). In addition, inflammation markers derived from the complete blood count were also analyzed. They were compared between patients with and without delirium. The study included 215 patients, of whom 104 had delirium (median age 76 years, 51.6% female). Patients with delirium were older than those without delirium(p=0.008). The median CAR index was higher in patients with delirium (3.4 mg/g, 0.02-28.23) compared to those without delirium (2.19 mg/g,0.02-16.74), with borderline statistical significance(p=0.071). No statistically significant differences were found in other indices and laboratory parameters between patients with delirium and those without it (p>0.05 for all). When patients were stratified into tertiles based on CAR levels, the occurrence of delirium was significantly higher in the third tertile than in the other two tertiles (p=0.020). Even after adjusting for all significant confounding factors, CAR remained independently associated with delirium [Odds ratio(OR):1.099, 95% confidence interval(CI):1.002-1.205, p=0.046]. The findings of this study suggest that the CAR index may serve as an independent associated factor for delirium compared to other laboratory-derived markers in critically ill patients.}, }
@article {pmid41521257, year = {2026}, author = {Wang, S and Song, X and Song, X and Gu, Y and Cong, Z and Shen, Y and Yu, L}, title = {Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.}, journal = {Nano-micro letters}, volume = {18}, number = {1}, pages = {193}, pmid = {41521257}, issn = {2150-5551}, abstract = {The development of non-invasive brain-computer interfaces (BCIs) relies on multidisciplinary integration across neuroscience, artificial intelligence, flexible electronics, and systems engineering. Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding. Parallel progress in electrode design-particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies-has enhanced wearability and operational stability. Nevertheless, key challenges persist, including individual variability, biocompatibility limitations, and susceptibility to interference in complex environments. Further validation and optimization are needed to address gaps in generalization capability, long-term reliability, and real-world operational robustness. This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade, highlighting key design principles, material innovations, and integration strategies that are poised to advance non-invasive BCI capabilities. It also discusses the importance of multimodal data fusion, hardware-software co-optimization, and closed-loop control strategies. Furthermore, the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation, aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.}, }
@article {pmid41521019, year = {2026}, author = {Lv, Z and Li, X and Zhang, X}, title = {Commentary on He et al.: From static association to dynamic causation - a methodological leap in understanding and addressing addiction.}, journal = {Addiction (Abingdon, England)}, volume = {}, number = {}, pages = {}, doi = {10.1111/add.70312}, pmid = {41521019}, issn = {1360-0443}, support = {2024YFF0507600//National Key R&amp;D Program of China/ ; 2021ZD0202101//Chinese National Programs for Brain Science and Brain-like Intelligence Technology/ ; 32571266//National Natural Science Foundation of China/ ; 32171080//National Natural Science Foundation of China/ ; 32400919//National Natural Science Foundation of China/ ; 32200914//National Natural Science Foundation of China/ ; ZSYS(2024)001//Project of Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing QianKeHe Platform/ ; 2408085QC081//Natural Science Foundation of Anhui Province/ ; 24YJCZH014//Ministry of Education of China/ ; //Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; }, }
@article {pmid41518865, year = {2026}, author = {Hu, W and Xiao, J and Li, L and Zhao, W and Feng, Y and Shan, X and Chen, H and Duan, X}, title = {Developmental organization of neural dynamics supporting social processing: Evidence from naturalistic fMRI in children and adults.}, journal = {Developmental cognitive neuroscience}, volume = {78}, number = {}, pages = {101670}, doi = {10.1016/j.dcn.2026.101670}, pmid = {41518865}, issn = {1878-9307}, abstract = {The development of social cognition underpins significant implications for diagnosing and treating neurodevelopmental disorders such as autism spectrum disorder. This study investigates the dynamic neural organization of social cognition in children (n = 60, ages 3-10) and adults (n = 55) using a naturalistic fMRI paradigm that tracks continuous brain activity during real-world social interactions. We identify four distinct co-activation patterns (CAP) that reflect a functional hierarchy, ranging from basic sensory processing to complex social-cognitive integration. These brain state dynamics reveal significant developmental differences: children exhibit immature transitions, often bypassing intermediate states (e.g., salience-driven filtering, State 3) and prematurely shifting from early sensory encoding (State 1) to internally-directed integration (State 2). Moreover, during mentalizing and pain events, children show reduced modulation of sensory and perceptual brain states, indicating limited cognitive flexibility that is essential for social interaction. Structural equation modeling reveals a developmental cascade linking the maturation of sensory (State 1), perceptual filtering (State 3), and social-cognitive (State 2) processing states. This pathway is mediated by individual differences in Theory of Mind (ToM) development and further predicts empathic abilities. These findings advance our understanding of how brain state reorganization supports social cognitive maturation and offer new insights into neurodevelopmental disorders.}, }
@article {pmid41518463, year = {2026}, author = {Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Vizcaíno-Martín, FJ and Ron-Angevin, R}, title = {Evaluation of video background and stimulus transparency in a visual ERP-based BCI under RSVP.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {41518463}, issn = {1741-0444}, abstract = {Rapid serial visual presentation (RSVP) is a promising paradigm for visual brain-computer interfaces (BCIs) based on event-related potentials (ERPs) for patients with limited muscle and eye movement. This study explores the impact of video background and stimulus transparency on BCI control, factors that have not been previously examined together under RSVP. Two experimental sessions were conducted with 12 participants each. Four BCI conditions were tested: opaque pictograms, and white background (A255W); opaque pictograms, and video background (A255V); intermediate transparent pictograms, and video background (A085); and highly transparent pictograms, and video background (A028V). The results indicated that the video background had a negative impact on BCI performance. In addition, the intermediate transparent pictograms (A085V) proved to be balanced, as it did not show significant performance differences compared to opaque pictograms (A255V) but was rated significantly better by users on subjective measures related to attending to the video background. Therefore, in applications where users must shift attention between BCI control and their surroundings, balancing stimulus transparency is a suitable option for enhancing system usability. These findings are particularly relevant for designing asynchronous ERP-BCIs using RSVP for patients with impaired oculomotor control.}, }
@article {pmid41518099, year = {2026}, author = {Berwal, U and Kumar, V}, title = {Exploring assistive technology in adaptive sports: a bibliometric analysis.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/17483107.2025.2612557}, pmid = {41518099}, issn = {1748-3115}, abstract = {Assistive technology in adaptive sports has become a transformative force for individuals with disabilities. It helps disabled athletes to overcome physical and cognitive barriers to participate in sports. This study presents a bibliometric analysis of assistive technology in adaptive sports to examine its development, key themes, and emerging trends. The analysis used data from 8,660 documents across 2,137 sources retrieved from the Scopus database from 1987 to 2025. The result shows that due to advancements in technology and increased awareness of inclusivity in sports, the research output grows exponentially after 2010. Among these research outputs, the most used theme was rehabilitation. The other emerging topics incorporated into adaptive sports are virtual reality, brain-computer interfaces, wearable technologies. Further, the co-occurrence network analysis reveals that there are strong interdisciplinary connections between rehabilitation, assistive technology, and physical activity. However, several areas remain unexplored such as digital health and telehealth applications in adaptive sports. Thus, bibliometric analysis provides a roadmap for future research by identifying critical trends and gaps. This study highlights the interdisciplinary collaboration and technological innovation in advancing accessibility and inclusivity for athletes with disabilities.}, }
@article {pmid41516662, year = {2025}, author = {Gomez-Rivera, A and Collazos-Huertas, DF and Cárdenas-Peña, D and Álvarez-Meza, AM and Castellanos-Dominguez, G}, title = {Gaussian Connectivity-Driven EEG Imaging for Deep Learning-Based Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {1}, pages = {}, doi = {10.3390/s26010227}, pmid = {41516662}, issn = {1424-8220}, support = {91908//Ministerio de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n/ ; 57661//Universidad Nacional de Colombia/ ; }, mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Deep Learning ; Normal Distribution ; Neural Networks, Computer ; Brain/physiology ; *Imagination/physiology ; Algorithms ; }, abstract = {Electroencephalography (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) hold considerable potential for applications in neuro-rehabilitation and assistive technologies. Yet, their development remains constrained by challenges such as low spatial resolution, vulnerability to noise and artifacts, and pronounced inter-subject variability. Conventional approaches, including common spatial patterns (CSP) and convolutional neural networks (CNNs), often exhibit limited robustness, weak generalization, and reduced interpretability. To overcome these limitations, we introduce EEG-GCIRNet, a Gaussian connectivity-driven EEG imaging representation network coupled with a regularized LeNet architecture for MI classification. Our method integrates raw EEG signals with topographic maps derived from functional connectivity into a unified variational autoencoder framework. The network is trained with a multi-objective loss that jointly optimizes reconstruction fidelity, classification accuracy, and latent space regularization. The model's interpretability is enhanced through its variational autoencoder design, allowing for qualitative validation of its learned representations. Experimental evaluations demonstrate that EEG-GCIRNet outperforms state-of-the-art methods, achieving the highest average accuracy (81.82%) and lowest variability (±10.15) in binary classification. Most notably, it effectively mitigates BCI illiteracy by completely eliminating the "Bad" performance group (<60% accuracy), yielding substantial gains of ∼22% for these challenging users. Furthermore, the framework demonstrates good scalability in complex 5-class scenarios, performing competitive classification accuracy (75.20% ± 4.63) with notable statistical superiority (p = 0.002) against advanced baselines. Extensive interpretability analyses, including analysis of the reconstructed connectivity maps, latent space visualizations, Grad-CAM++ and functional connectivity patterns, confirm that the model captures genuine neurophysiological mechanisms, correctly identifying integrated fronto-centro-parietal networks in high performers and compensatory midline circuits in mid-performers. These findings suggest that EEG-GCIRNet provides a robust and interpretable end-to-end framework for EEG-based BCIs, advancing the development of reliable neurotechnology for rehabilitation and assistive applications.}, }
@article {pmid41516650, year = {2025}, author = {Li, J and Yang, H and Xu, M and Wu, Y and Shou, X and Huang, Z and Hao, Y and Wu, F and Ruan, W and Zhang, Y and Cui, Z and Wei, Y}, title = {Task-Dependent Cortical Oscillatory Dynamics in Functional Constipation.}, journal = {Sensors (Basel, Switzerland)}, volume = {26}, number = {1}, pages = {}, doi = {10.3390/s26010211}, pmid = {41516650}, issn = {1424-8220}, support = {62373326//National Natural Science Foundation of China/ ; 32471148//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Constipation/physiopathology ; Male ; Electroencephalography/methods ; Female ; Adult ; Middle Aged ; *Cerebral Cortex/physiopathology ; Cognition/physiology ; Defecation/physiology ; Brain/physiopathology ; }, abstract = {Functional constipation (FC) is a common functional gastrointestinal disorder thought to arise from the brain-gut axis dysfunction, yet direct human neurophysiological evidence is lacking. We recorded high-density electroencephalography (EEG) data in 21 FC patients and 37 healthy controls across resting, cognitive, and defecation-related tasks. We observed that FC patients displayed a consistent, task-dependent signature compared with healthy controls. At the regional level, FC patients exhibited increased alpha during both resting and defecation-related tasks, reduced temporal gamma during defecation-related tasks, as well as elevated temporal theta during the cognitive task. At the global level, we found altered network properties, such as global efficiency in the delta and beta band networks during resting and defecation-related tasks. These findings establish a direct neurophysiological link between specific, condition-dependent perturbations in cortical rhythm activity and FC pathophysiology. Our work implicates the brain-gut axis in symptom generation and opens a path toward EEG-based biomarkers and targeted neuromodulatory therapies.}, }
@article {pmid41514692, year = {2025}, author = {Marques, L and Rodrigues, DP and Duarte, RC and Calado, R}, title = {Thermal Limits of the Estuarine Amphipod Melita palmata Under Different Salinities and Its Relevance for Aquaculture Production.}, journal = {Animals : an open access journal from MDPI}, volume = {16}, number = {1}, pages = {}, doi = {10.3390/ani16010004}, pmid = {41514692}, issn = {2076-2615}, support = {10.54499/2022.01620.PTDC//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; 10.54499/UID/50017/2025//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; 10.54499/LA/P/0094/2020//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; }, abstract = {Estuarine organisms experience frequent fluctuations in salinity and temperature, facing major challenges to their physiological homeostasis. Such variability can promote high energetic costs for osmoregulation, potentially reducing tolerance to additional stressors. We investigated the effect of salinity on the thermal tolerance of the estuarine amphipod Melita palmata (Montagu, 1804), a species of growing interest for aquaculture, either as live feed or as a potential source for essential fatty acids in formulated diets. The critical thermal maximum (CTmax) was determined for males and females collected from three sites within a temperate coastal lagoon (Ria de Aveiro, Portugal) characterized by different salinity regimes (15, 20, and 30). Individuals from lower-salinity environments exhibited significantly lower CTmax values than those from higher salinities, indicating that osmoregulatory costs may restrict thermal resistance. No significant sex-based differences in CTmax were detected. However, thermal safety margins (TSMs) increased with salinity, indicating greater thermal tolerance under higher salinity conditions, and differences in body condition index (BCI) between sites suggest salinity-related effects on growth performance. These results highlight that the elevated energetic demands of osmoregulation under hypo-osmotic conditions can constrain the thermal limits of M. palmata, underscoring the complex trade-offs between environmental variability and physiological performance in estuarine habitats. Beyond its ecological implications, understanding the physiological responses of M. palmata to salinity and temperature is key, optimising its use in aquaculture. The species' physiological plasticity under such variable conditions reinforces its suitability for aquaculture production, particularly in earthen ponds in estuarine environments.}, }
@article {pmid41512946, year = {2026}, author = {Xia, S and Zhao, X and Lv, B and Gan, Y and Kang, Y and Long, J and Liu, F and Hu, X and He, G and Xing, H and Cheng, B}, title = {Functional Gradient Alteration and Structural Remodeling in Postpartum Women.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121702}, doi = {10.1016/j.neuroimage.2026.121702}, pmid = {41512946}, issn = {1095-9572}, abstract = {Postpartum women (PW) undergo profound brain functional and structural reorganization to support maternal adaptation. However, the specific large-scale neural adaptation mechanisms remain unclear. The current study employed a multimodal MRI approach integrating functional gradient analysis, graph-theoretical network metrics, and morphometry to explore the brain connectome reorganization across the postpartum period and its clinical correlates in 209 participants (134 PW and 75 healthy nulliparous women (HNW)). Compared to HNW, PW exhibited a significant contraction of the first two principal functional gradients, reduced local network segregation and less efficient information processing, accompanied by matter volume (GMV) reductions. Mediation analysis revealed that GMV alterations in PW modulate functional gradient reorganization by influencing network integration and segregation. These neural changes were closely linked to clinical symptoms including sleep quality and anxiety. Our findings revealed a large-scale network reconfiguration in PW, simultaneously elucidating neurobiological mechanisms of adaptive plasticity in postpartum period.}, }
@article {pmid41512029, year = {2026}, author = {Liu, P and Zhou, L and Xu, D and An, D and Lu, Y and Hu, B and Shao, Y and Huang, N and Guo, C and Chen, L and Li, J and Li, J and Liang, F and Liu, J and Huang, G and Mei, Y and Li, R and Song, E}, title = {A self-wrapping, bioresorbable neural interface for wireless multimodal therapy of localized peripheral nerve injury.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {123}, number = {2}, pages = {e2521817123}, doi = {10.1073/pnas.2521817123}, pmid = {41512029}, issn = {1091-6490}, support = {2022ZD0209900//Ministry of Science and Technology of the People's Republic of China (MOST)/ ; 62204057 62304044//MOST | National Natural Science Foundation of China (NSFC)/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality (STCSM)/ ; }, mesh = {Animals ; *Peripheral Nerve Injuries/therapy ; Rats ; *Wireless Technology/instrumentation ; Rats, Sprague-Dawley ; Combined Modality Therapy ; *Absorbable Implants ; Photothermal Therapy/methods ; Drug Delivery Systems/methods ; }, abstract = {High-precision in vivo therapeutic technologies that establish three-dimensional (3D), multimodal neural interfaces with targeted biotissues offer significant clinical potential for the timely treatments of localized peripheral nerve injury (PNI). Current approaches for this purpose such as implantable devices face challenges in terms of percutaneous wires and/or nondegradable designs, and support only single-mode operation that lack microscale spatial resolution. Here, we develop a miniaturized, self-wrapping system that yields wireless, multimodal neural interfaces with 3D adaptation across localized peripheral nerves at scales ranging from tens of micrometers (15 μm) to millimeters. Such platform integrates multilayer architectures that include SiNx layers as the mechanically triggered substrate for 3D wrapping, with multimodal treatments via MXene and drug-loaded layers for photothermal stimulation and pharmacological release. Experimental and computational studies establish operational principle as the basis for the combination of long-term photothermal therapy and transient drug delivery at high spatiotemporal resolution. In vivo tests on living rat models demonstrate that the implantable neural interface can roll up across the localized, dynamic surface of injured nerves, providing sustained treatments over 1 mo in a fully bioresorbable design after the healing process. These findings create future opportunities of such wireless, multimodal system with 3D self-wrapping techniques for precise PNI therapeutic strategies.}, }
@article {pmid41510853, year = {2026}, author = {Zhang, X and Liu, X and Liu, M and Li, Y and Yan, X and Zhang, X and Xu, J}, title = {The Integrated Application and Future Trends of Multimodal Neuromodulation Techniques in Spinal Cord Injury Rehabilitation.}, journal = {Neurology India}, volume = {74}, number = {1}, pages = {3-11}, pmid = {41510853}, issn = {1998-4022}, mesh = {Humans ; *Spinal Cord Injuries/rehabilitation ; *Transcranial Magnetic Stimulation/methods ; *Transcranial Direct Current Stimulation/methods ; Brain-Computer Interfaces ; *Spinal Cord Stimulation/methods ; Recovery of Function/physiology ; Neuronal Plasticity/physiology ; Combined Modality Therapy ; }, abstract = {Spinal cord injury (SCI) remains a severe condition that leads to permanent motor and sensory impairments, significantly affecting patients' quality of life. In recent years, neuromodulation techniques such as spinal cord stimulation (SCS), transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS) have shown promising results in promoting neural plasticity and functional recovery. However, the limitations of single-modality approaches have spurred the development of multimodal neuromodulation strategies. This review systematically analyzes the integrated application of multimodal neuromodulation techniques in SCI rehabilitation. We first provide an overview of current neuromodulation methods, including SCS, TMS, tDCS, and brain-computer interface (BCI), highlighting their individual mechanisms and clinical outcomes. Next, we discuss the synergistic effects of combining these techniques, such as SCS with TMS or BCI, which act on multiple levels of the nervous system to enhance neuroplasticity, reconstruct neural networks, and modulate neurotransmitter release. Additionally, we explore the mechanisms underlying multimodal neuromodulation, emphasizing its role in promoting axonal regeneration, synaptic reconnection, and adaptive functional recovery. Despite the promising advancements, challenges remain, including technical complexity, safety concerns, and the heterogeneity of SCI patients. Addressing these limitations requires standardized treatment protocols and further clinical validation. Future trends, such as the development of closed-loop systems, artificial intelligence-driven precision rehabilitation, and personalized therapies, will likely drive innovations in this field. In conclusion, multimodal neuromodulation techniques offer a synergistic and integrative approach for SCI rehabilitation, providing new avenues for clinical intervention. This review underscores the importance of combining complementary techniques to optimize neural recovery and highlights the potential for future breakthroughs in neurorehabilitation.}, }
@article {pmid41506085, year = {2026}, author = {Wang, Y and Xu, S}, title = {Relationship between artificial intelligence tool usage experience and academic stress among college students: Mediating role of loneliness and moderating role of academic self-efficacy.}, journal = {Acta psychologica}, volume = {263}, number = {}, pages = {106220}, doi = {10.1016/j.actpsy.2026.106220}, pmid = {41506085}, issn = {1873-6297}, abstract = {As artificial intelligence (AI) rapidly integrates into higher education, AI tools are increasingly being utilized to support student learning. Although these tools offer efficiency and convenience, their psychological implications-particularly vis-à-vis academic stress-remain unclear. This study investigated the relationship between AI tool usage experience and academic stress among college students, focusing on the potential mediating role of loneliness and the moderating role of academic self-efficacy. Overall, 624 university students were surveyed using the AI Tool Usage Experience Scale, UCLA Loneliness Scale, Academic Stress Scale, and Academic Self-Efficacy Scale. The following three key findings were observed: (1) AI tool usage experience significantly positively predicted students' academic stress. (2) Loneliness partially mediated this relationship. (3) Academic self-efficacy significantly moderated the mediation pathway's first stage. Specifically, AI usage's positive predictive effect on loneliness was stronger (weaker) for students with higher (lower) academic self-efficacy levels. These findings suggest that AI tool usage not only directly influences academic stress but also contributes indirectly through heightened feelings of loneliness, particularly among students with strong self-efficacy beliefs. This study underscores the complex psychological mechanisms underlying students' interactions with AI in educational settings.}, }
@article {pmid41505837, year = {2026}, author = {Zhang, K and Dong, S and Shi, P and Hu, D and Gao, G and Yang, J and Gan, T and Rao, N}, title = {GenoPath-MCA: Multimodal masked cross-attention between genomics and pathology for survival prediction.}, journal = {Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society}, volume = {128}, number = {}, pages = {102699}, doi = {10.1016/j.compmedimag.2026.102699}, pmid = {41505837}, issn = {1879-0771}, abstract = {Survival prediction using whole slide images (WSIs) and bulk genes is a key task in computational pathology, essential for automated risk assessment and personalized treatment planning. While integrating WSIs with genomic features presents challenges due to inconsistent modality granularity, semantic disparity, and the lack of personalized fusion. We propose GenoPath-MCA, a novel multimodal framework that models dense cross-modal interactions between histopathology and gene expression data. A masked co-attention mechanism aligns features across modalities, and the Multimodal Masked Cross-Attention Module (M2CAM) jointly captures high-order image-gene and gene-gene relationships for enhanced semantic fusion. To address patient-level heterogeneity, we develop a Dynamic Modality Weight Adjustment Strategy (DMWAS) that adaptively modulates fusion weights based on the discriminative relevance of each modality. Additionally, an importance-guided patch selection strategy effectively filters redundant visual inputs, reducing computational cost while preserving critical context. Experiments on public multimodal cancer survival datasets demonstrate that GenoPath-MCA significantly outperforms existing methods in terms of concordance index and robustness. Visualizations of multimodal attention maps validate the biological interpretability and clinical potential of our approach.}, }
@article {pmid41501731, year = {2026}, author = {Zhang, W and Xiong, B and Shen, D and Wang, W}, title = {Characteristics of resting-state EEG after deep brain stimulation in nucleus accumbens and anterior limb of internal capsule: a pilot study.}, journal = {BMC psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12888-025-07681-8}, pmid = {41501731}, issn = {1471-244X}, }
@article {pmid41103211, year = {2026}, author = {Martín, I and Zamora-López, G and Fousek, J and Schirner, M and Ritter, P and Jirsa, V and Deco, G and Patow, G}, title = {TVB C++: A Fast and Flexible Back-End for The Virtual Brain.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {13}, number = {2}, pages = {e06440}, doi = {10.1002/advs.202406440}, pmid = {41103211}, issn = {2198-3844}, support = {PID2021-122136OB-C22//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; PID2022-136216NB-I00//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; 785907 (HBP SGA2)//H2020 Excellent Science/ ; Horizon EBRAINS2.0 (101147319)//HORIZON EUROPE Framework Programme/ ; Virtual Brain Twin (101137289)//HORIZON EUROPE Framework Programme/ ; EBRAINS-PREP 101079717//HORIZON EUROPE Framework Programme/ ; AISN - 101057655//HORIZON EUROPE Framework Programme/ ; EBRAIN-Health 101058516//HORIZON EUROPE Framework Programme/ ; EICgrantPHRASE(101058240)//HORIZON EUROPE Framework Programme/ ; DigitalEuropeTEF-Health(101100700)//HORIZON EUROPE Framework Programme/ ; BRIDGE(101219311)//HORIZON EUROPE Framework Programme/ ; SHAIPED(101195135)//HORIZON EUROPE Framework Programme/ ; CoordinaTEF(101168074)//HORIZON EUROPE Framework Programme/ ; SFB 1436 (project ID 425899996)//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; SFB 1315 (project ID 327654276)//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; SFB 936 (project ID 178316478//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; SFB-TRR 295 (project ID 424778381)//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; SPP Computational Connectomics RI 2073/6-1//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; RI 2073/10-2//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; RI 2073/9-1//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; BECAUSE-Y 504745852//DFG Clinical Research Group/ ; CZ.02.01.01/00/22\_008/0004643//ERDF/ ; 2021SGR00917//AGAUR/ ; 2021SGR01035//AGAUR/ ; 945539(HBPSGA3)//Horizon 2020/ ; 425899996//German Research Foundation/ ; 327654276//German Research Foundation/ ; 178316478//German Research Foundation/ ; 424778381//German Research Foundation/ ; RI2073/6-1//SPP Computational Connectomics/ ; RI2073/10-2//SPP Computational Connectomics/ ; RI2073/9-1//SPP Computational Connectomics/ ; ANR-22-PESN-0012//Agence Nationale de la Recherche/ ; NEMESIS(101071900)/ERC_/European Research Council/International ; }, mesh = {Humans ; *Brain/physiology ; *Computer Simulation ; *Brain-Computer Interfaces ; *User-Computer Interface ; *Software ; }, abstract = {This study introduces TVB C++, a streamlined and fast C++ Back-End for The Virtual Brain (TVB), a renowned platform and a benchmark tool for full-brain simulation. TVB C++ is engineered with speed as a primary focus while retaining the flexibility and ease of use characteristic of the original TVB platform. Positioned as a complementary tool, TVB serves as a prototyping platform, whereas TVB C++ becomes indispensable when performance is paramount, particularly for large-scale simulations and leveraging advanced computation facilities like supercomputers. Developed as a TVB-compatible Back-End, TVB C++ seamlessly integrates with the original TVB implementation, facilitating effortless usage. Users can easily configure TVB C++ to execute the same code as in TVB but with enhanced performance and parallelism capabilities. As a consequence, TVB C++ will enable the widespread use of individualized models that will open the possibility of designed tailored solutions at the individual patient level.}, }
@article {pmid41499961, year = {2026}, author = {Pan, L and Wang, K and Yi, W and Zhang, Y and Xu, M and Ming, D}, title = {CTSSP: A temporal-spectral-spatial joint optimization algorithm for motor imagery EEG decoding.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae34ea}, pmid = {41499961}, issn = {1741-2552}, abstract = {OBJECTIVE: Motor imagery brain-computer interfaces (MI-BCIs) hold significant promise for neurorehabilitation, yet their performance is often compromised by EEG non-stationarity, low signal-to-noise ratios, and severe cross-session variability. Current decoding methods typically suffer from fragmented optimization, treating temporal, spectral, and spatial features in isolation.<br><br>APPROACH: We propose common temporal-spectral-spatial patterns (CTSSP), a unified framework that jointly optimizes filters across all three domains. The algorithm integrates: 1) multi-scale temporal segmentation to capture dynamic neural evolution, 2) channel-adaptive finite impulse response (FIR) filters to enhance task-relevant rhythms, and 3) low-rank regularization to improve generalization.<br><br>MAIN RESULTS: Evaluated across five public datasets, CTSSP achieves state-of-the-art performance. It yielded mean accuracies of 76.9% (within-subject), 68.8% (cross-session), and 69.8% (cross-subject). In within-subject and cross-session scenarios, CTSSP significantly outperformed competing baselines by margins of 2.6-14.6% (p < 0.001) and 2.3-13.8% (p < 0.05), respectively. In cross-subject tasks, it achieved the highest average accuracy, proving competitive against deep learning models. Neurophysiological visualization confirms that the learned filters align closely with motor cortex activation mechanisms.<br><br>SIGNIFICANCE: CTSSP effectively overcomes the limitations of decoupled feature extraction by extracting robust, interpretable, and coupled temporal-spectral-spatial patterns. It offers a powerful, data-efficient solution for decoding MI EEG in noisy, non-stationary environments. The code is available at https://github.com/PLC-TJU/CTSSP.}, }
@article {pmid41499837, year = {2026}, author = {Ge, H and Feng, T and Wu, H and Hu, H and Li, J and Wu, X}, title = {Uncovering the Cognitive Mechanisms of Risk Decision-Making among ICU Nurses in Complex Clinical Contexts.}, journal = {Intensive &amp; critical care nursing}, volume = {93}, number = {}, pages = {104329}, doi = {10.1016/j.iccn.2025.104329}, pmid = {41499837}, issn = {1532-4036}, abstract = {OBJECTIVES: The intensive care unit is a high-stakes, information-intensive environment requiring nurses to make rapid and accurate decisions. This study aimed to elucidate the cognitive and neural mechanisms underlying nurses' risk decision-making under time pressure and complex clinical demands.<br><br>METHODS: Thirty ICU nurses participated in a computer-based multitasking experiment simulating concurrent medical multitasking scenarios, with twenty-one valid datasets analyzed. Participants performed priority judgments under high- and low-risk conditions while EEG signals were continuously recorded. Event-related potential components and oscillatory activities across &#x3b4;, &#x3b8;, &#x3b1;, and &#x3b2; frequency bands were analyzed. Gaussian Hidden Markov Models were used to characterize cognitive state transition dynamics aligned to task events.<br><br>RESULTS: Risk decision-making emerged as a multi-stage, dynamically coordinated process involving four distinct cognitive patterns: monolithic stability progression, compulsory path lock-in, multi-path flexible convergence, and flow separation and premature convergence. Correct decisions were associated with enhanced low-frequency oscillations (&#x3b4;, &#x3b8;) and stable HMM transitions, reflecting efficient integration and adaptive cognitive control. In contrast, incorrect decisions exhibited early perceptual inefficiency, unstable state transitions, and premature cognitive closure under high-risk conditions.<br><br>CONCLUSIONS: This study is the first to identify four distinct dynamic cognitive patterns of risk decision-making in a simulated ICU multitasking context. The findings indicate that decision accuracy is closely linked to coordinated state-transition dynamics rather than isolated neural activations, highlighting the importance of adaptive cognitive control in clinical judgment.<br><br>Although the present findings are exploratory, they may provide a preliminary reference for future research on brain-machine collaboration in clinical nursing contexts. In particular, future work could examine how EEG-decoded cognitive states might be incorporated as input information for robot-assisted systems to characterize nurses' cognitive intentions during risk tasks. Further studies with larger samples and in more realistic clinical settings are needed to validate the model's robustness and generalizability.}, }
@article {pmid41499809, year = {2026}, author = {Aktaş, FA and Eken, A and Erogul, O}, title = {Explainable AI for Pain Perception: Subject-Independent EEG Decoding Using DeepSHAP and CNNs.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae34b4}, pmid = {41499809}, issn = {2057-1976}, abstract = {Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learning. Approach: EEG signals from 50 subjects exposed to low and high pain stimuli were analyzed. A 1D convolutional neural network (CNN) was trained using leave-one-subject-out (LOSO) cross-validation. To enhance interpretability, DeepSHAP was applied to identify frequency-specific contributions of EEG features to the model's decisions. Main Results: The CNN achieved a classification accuracy of 95.85%, outperforming traditional classifiers (SVM, LDA, RF, etc.) on the same dataset. Explainability analysis showed that increased beta activity (14-15 Hz) was associated with high pain, while alpha (11-12 Hz) theta and delta bands correlated with lower pain states. Significance: This work demonstrates the potential of explainable deep learning in real-time, subject-independent pain decoding. The results support the integration of XAI techniques into EEG-based brain-computer interface (BCI) systems for objective pain monitoring.}, }
@article {pmid41499216, year = {2026}, author = {Zhai, W and Sun, L and Fang, W and Dong, Y and Cheng, C and Liu, Y and Zhou, Y and Ji, J and Wu, L and Pan, A and Gamazon, ER and Pan, XF and Zhou, D}, title = {Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification.}, journal = {Briefings in bioinformatics}, volume = {27}, number = {1}, pages = {}, doi = {10.1093/bib/bbaf707}, pmid = {41499216}, issn = {1477-4054}, support = {82204118//National Natural Sciences Foundation of China/ ; K-20230085//Healthy Zhejiang One Million People Cohort/ ; SN-ZJU-SIAS-0021//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; 82473646//National Natural Science Foundation of China/ ; 2024NSFSC0578//Sichuan Provincial Natural Science Foundation/ ; 2024YFC2707602//National Key Research and Development Program of China/ ; YJ202346//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Genome-Wide Association Study ; *Proteomics/methods ; *Proteome/genetics ; *Quantitative Trait Loci ; }, abstract = {Genetics-informed proteome-wide association studies (PWASs) provide an effective way to uncover proteomic mechanisms underlying complex diseases. PWAS relies on an ancestry-matched reference panel to model the impact of genetically determined protein expression on phenotype. However, reference panels from underrepresented populations remain relatively limited. We developed a multi-ancestry framework to enhance protein prediction in these populations by integrating diverse information-sharing strategies into a Multi-Ancestry Best-performing Model (MABM). Results indicated that MABM increased the prediction performance with higher performance observed in both cross-validation and an external dataset. Leveraging the Biobank Japan, we identified three times as many significant PWAS associations using MABM as using Lasso model. Notably, 47.5% of the MABM specific associations were reproduced in independent East Asian datasets with concordant effect sizes. Furthermore, MABM enhanced decision-making in gene/protein prioritization for functional validation for complex traits by validating well-established associations and uncovering novel trait-related candidates. The benefits of MABM were further validated in additional ancestries and demonstrated in brain tissue-based PWAS, underscoring its broad applicability. Our findings close critical gaps in multi-omics research among underrepresented populations and facilitate trait-relevant protein discovery in underrepresented populations.}, }
@article {pmid41497491, year = {2026}, author = {Huang, Y and Ding, Q and Chen, Z and Chen, J and Li, Y and Chen, L and Yao, S and Lan, Y and Xu, G}, title = {Brain-Computer Interface Training Enhances Attention Function via Modulating Frontoparietal Connectivity: Evidence From Functional Near-Infrared Spectroscopy.}, journal = {Neural plasticity}, volume = {2026}, number = {}, pages = {8133428}, pmid = {41497491}, issn = {1687-5443}, mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Male ; Female ; *Parietal Lobe/physiology/diagnostic imaging ; Young Adult ; *Attention/physiology ; Adult ; *Frontal Lobe/physiology ; Prefrontal Cortex/physiology ; Executive Function/physiology ; *Nerve Net/physiology ; }, abstract = {OBJECTIVE: Attention is a critical cognitive function impaired in various neurological disorders, and brain-computer interface (BCI) training shows potential for cognitive improvement. However, the neural mechanisms of BCI training on attention networks remain unclear. This study investigated the effects of BCI training on attention and the underlying neural mechanisms in healthy young adults.<br><br>METHODS: Thirty healthy young adults participated in this study. Attention function was assessed using the attention network test (ANT), while brain activation and connectivity were measured using functional near-infrared spectroscopy (fNIRS). Participants underwent the ANT and fNIRS assessments before and after BCI training.<br><br>RESULTS: BCI training significantly improved the efficiency of the executive control network (p = 0.016). Nodal efficiency in the right posterior parietal cortex (PPC) was decreased (p&#x2009; = 0.044). In the resting state, effective connectivity (EC) analysis showed decreased connectivity from the right PPC to the left PPC in the resting state (p&#x2009; = 0.047). In the task state, the EC from the right prefrontal cortex (PFC) to the right PPC was significantly increased (p&#x2009; = 0.016), and the connectivity from the left PFC to the right PFC was significantly decreased (p&#x2009; = 0.023).<br><br>CONCLUSION: BCI training optimized connectivity within frontoparietal networks (FPNs), leading to enhanced executive control function. These findings suggest that BCI training could be an effective cognitive intervention for improving the function of FPNs. Future studies should explore the long-term effects of BCI training and its potential application in clinical populations, such as patients with attention deficit hyperactivity disorder and stroke.}, }
@article {pmid41495620, year = {2026}, author = {Li, Y and Feng, Y and Liu, X and Yuan, R and Chen, S and Wang, J and Pan, C and Li, G and Tang, Z}, title = {Functional near-infrared spectroscopy: Systematic mapping of abnormal brain function features in neurological disorders.}, journal = {Neural regeneration research}, volume = {}, number = {}, pages = {}, doi = {10.4103/NRR.NRR-D-25-00595}, pmid = {41495620}, issn = {1673-5374}, abstract = {Functional near-infrared spectroscopy quantifies cerebral hemodynamic signals by capturing oxygenation-dependent changes in hemoglobin in a noninvasive, portable, and ecologically valid manner, providing a unique insight into neurovascular coupling. However, functional imaging biomarkers with high ecological validity for neurological disorders such as stroke, Parkinson's disease, dementia, amyotrophic lateral sclerosis, epilepsy, spinal cord injury, and traumatic brain injury are lacking, limiting the mechanistic understanding, treatment evaluations, and individualized interventions. The aim of this review is to systematically summarize evidence from the past decade on the use of functional near-infrared spectroscopy under the aforementioned conditions, synthesize its value for revealing neural mechanisms and assessing therapeutic responses, and identify current technical bottlenecks and future directions for advancement. Collectively, the findings demonstrate that functional near-infrared spectroscopy possesses substantial and far-reaching potential for uncovering the neural mechanisms underlying disease and for evaluating treatment-induced changes in brain function. Equipped with wearable probes, functional near-infrared spectroscopy can continuously and noninvasively monitor brain activity in naturalistic environments for extended periods, thereby overcoming the limitations of conventional imaging modalities that can only acquire data under restricted settings. This capability can furnish unprecedented objective neuroimaging evidence for neuroregenerative therapy research. Moreover, the portability of functional near-infrared spectroscopy allows it to be integrated into neurofeedback training systems: hemoglobin signals can be fed back to participants within milliseconds, enabling targeted, individualized, closed-loop modulation of brain function and considerably expanding the scope of hemodynamics-based neurofeedback. When combined with other brain function assays (such as electroencephalography) and intervention techniques (such as transcranial magnetic stimulation and transcranial direct current stimulation), functional near-infrared spectroscopy also supplies high-temporal-resolution hemodynamic information, laying a critical foundation for the construction of high-precision noninvasive brain-computer interfaces, real-time cognitive-state decoding, and adaptive neuromodulation. Admittedly, almost all existing functional near-infrared spectroscopy studies are still observational and have small sample sizes, short follow-ups, and insufficient controls-shortcomings that together produce low-grade evidence. Therefore, there is still a significant gap before clinical translation can be achieved. Technically, the limited penetration depth of functional near-infrared spectroscopy restricts sampling to the superficial cortex, leaving deep nuclei largely unreachable. In addition, no consensus exists across devices regarding optode layout, light-source choice, motion-artifact correction, or analytical pipelines, creating pronounced heterogeneity that undermines reproducibility. With artificial intelligence and big data analytics advancing rapidly, functional near-infrared spectroscopy embedded within multimodal fusion frameworks is now poised to systematically map aberrant brain function signatures of neurological disorders, identify pathological regions suitable for targeted intervention, and provide real-time assessments of functional changes produced by neuroregenerative therapies.}, }
@article {pmid41495200, year = {2026}, author = {Li, D and Cui, G and Yang, K and Lu, C and Jiang, Y and Zhang, L and Wu, Q and Dixit, D and Zhu, Z and Gimple, RC and Gu, D and Gao, J and Lin, Q and Yu, H and Shi, Z and Chen, Y and Wang, Q and Jin, G and Lin, F and Shao, J and Zhou, Q and Liu, C and Li, C and You, Y and Zhang, N and Zhang, J and Qian, X and Zhang, Q and Rich, JN and Wang, X}, title = {Inhibiting macrophage-derived lactate transport restores cGAS-STING signalling and enhances antitumour immunity in glioblastoma.}, journal = {Nature cell biology}, volume = {}, number = {}, pages = {}, pmid = {41495200}, issn = {1476-4679}, support = {82525047//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82573312//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Glioblastoma (GBM) is a malignancy with a complex tumour microenvironment (TME) dominated by GBM stem cells (GSCs) and infiltrated by tumour-associated macrophages (TAMs) and exhibits aberrant metabolic pathways. Lactate is a critical glycolytic metabolite that promotes tumour progression; however, the mechanisms of lactate transport and lactylation in the TME of GBM remain elusive. Here we show that lactate is transported from TAMs to GSCs via MCT4-MCT1. TAMs provide lactate to GSCs, promoting GSC proliferation and inducing lactylation of the non-homologous end joining protein KU70 at lysine 317 (K317), which inhibits cGAS-STING signalling and remodels the immunosuppressive TME. Inhibition of lactate transport or targeting the lactylation of KU70, in combination with the immune checkpoint blockade, demonstrates additive therapeutic benefits in immunocompetent xenograft models. This study unveils TAM-derived lactate and lactylation as critical regulators in GSCs to enforce an immunosuppressive microenvironment, opening avenues for developing combinatorial therapy for GBM.}, }
@article {pmid41494647, year = {2026}, author = {Luckie, DB and Green, MA and Hami, DW and Zawisa, HL}, title = {CURE lecture too: MCAT, BCI &amp; tracking data show students who regularly discussed research data in lecture learned more than peers using traditional textbooks.}, journal = {Advances in physiology education}, volume = {}, number = {}, pages = {}, doi = {10.1152/advan.00002.2025}, pmid = {41494647}, issn = {1522-1229}, support = {//Cystic Fibrosis Foundation (CFF)/ ; //Pennsylvania Cystic Fibrosis Inc./ ; //National Science Foundation (NSF)/ ; }, abstract = {The purpose of this study was to examine the impact of an intervention, a "CURE lecture" approach, which introduced course-based undergraduate research experience (CURE) strategies into the lecture setting. Rather than learning biological explanations from a traditional textbook, instead students studied primary literature curated in a reformed research-focused textbook and had discussions of data and experimental design. In control cohorts, reformed active and cooperative pedagogies were used in lecture to engage students in learning traditional textbook content. In experimental cohorts, "lecture" format was replaced with active and cooperative "journal club" discussions of published experiments. Prior studies examined use of research-focused Integrating Concepts in Biology (ICB) textbook readings in two sequential introductory biology courses. In this study assessments focused on student learning gains after a single semester. Klymkowsky's Biology Concept Inventory with known misconceptions as distractors, and Loznak's MCAT instrument used for over a decade prior, joined longitudinal tracking to evaluate impact of intervention. The ICB student cohort had higher scores (46.3% versus 34.3%) than controls on the Concept Inventory, and on the MCAT questions performed comparably in the range achieved by peer controls since the year 2000. Longitudinal tracking revealed ICB students immediately outperformed peers in their next biology course the following semester. The literature suggested a two-semester ICB experience helped students better succeed, and these findings support even a shorter exposure, of just a single semester, to the "CURE Lecture" strategy is impactful to students.}, }
@article {pmid41494544, year = {2026}, author = {Jiang, H and He, J and Zhou, B and Guo, Y and Gan, X and Fan, X and Wang, X and Ferraro, S and Vatansever, D and Kendrick, KM and Li, L and Becker, B}, title = {Adolescents with non-suicidal self-injury exhibit increased pain empathic neural reactivity and personal distress to physical but not affective pain.}, journal = {Journal of affective disorders}, volume = {}, number = {}, pages = {121145}, doi = {10.1016/j.jad.2025.121145}, pmid = {41494544}, issn = {1573-2517}, abstract = {BACKGROUND: Non-suicidal self-injury (NSSI) in adolescents represents a critical public health issue. While symptomatic links between NSSI and alterations in pain and social processing have been established, changes in neural responses and everyday reactivity to others' pain remain unknown.<br><br>METHODS: This pre-registered study examined pain empathic processing in unmedicated adolescents with NSSI (n&#x202f;=&#x202f;29) and healthy controls (n&#x202f;=&#x202f;33) using functional magnetic resonance imaging (fMRI). A validated paradigm assessed neural responses to physical pain versus affective pain observation and was combined with both univariate and machine learning analytic approaches.<br><br>RESULTS: NSSI participants exhibited significantly increased neural reactivity during physical pain empathy in lateral prefrontal, insular, temporal, and the somatomotor network regions (all p&#x202f;<&#x202f;0.05, FDR-corrected), while affective pain processing remained intact. Machine learning analysis revealed distinguishable whole-brain signatures, with a physical pain empathic pattern achieving superior discrimination in NSSI. NSSI participants reported elevated personal distress to others' negative experiences in everyday life, which was associated with enhanced limbic reactivity during physical pain empathy.<br><br>CONCLUSIONS: Findings identify domain-specific neural hyperreactivity to others' physical pain in NSSI adolescents and elevated personal distress in daily life. These characteristics may represent predisposing alterations that facilitate engagement in self-harm or consequences of repeated engagement in NSSI that impact everyday social behavior.}, }
@article {pmid41494206, year = {2026}, author = {Qi, W and Wang, X and Yang, W and Wang, W}, title = {ACFSENet: an adaptive cross-frequency global sparse encoding network for end-to-end EEG emotion recognition.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae33c7}, pmid = {41494206}, issn = {2057-1976}, abstract = {End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in human-computer interaction, mental health, and affective brain-computer interfaces (aBCIs). However, most existing methods overlook cross-frequency interactions in neural oscillations and suffer from high computational complexity, limiting their applicability in real-time or resource-constrained scenarios. To this end, we propose ACFSENet, a novel end-to-end neural architecture that integrates adaptive cross-frequency modeling with global sparse encoding. ACFSENet employs an adaptive frequency-aware mechanism to dynamically focus on subject- and task-specific local brain dynamics, thereby enhancing the flexibility of emotional representation. In parallel, it incorporates a sparse attention mechanism with a temporal distillation structure to reduce computational complexity while preserving the ability to model long-range temporal dependencies. We evaluate ACFSENet using cross-block validation on three benchmark datasets: DEAP, SEED, and SEED-IV. Results demonstrate that ACFSENet outperforms state-of-the-art methods and achieves a favorable balance between recognition performance and computational efficiency.}, }
@article {pmid41494035, year = {2026}, author = {Chia, R and Lin, CT}, title = {Biologically-constrained spiking neural network for neuromodulation in locomotor recovery after spinal cord injury.}, journal = {PLoS computational biology}, volume = {22}, number = {1}, pages = {e1013866}, doi = {10.1371/journal.pcbi.1013866}, pmid = {41494035}, issn = {1553-7358}, abstract = {Presynaptic inhibition after spinal cord injury (SCI) has been hypothesised to disproportionately affect flexion reflex loops in locomotor spinal circuitry. Reducing gamma-aminobutyric acid (GABA) inhibitory activity increases the excitation of flexion circuits, restoring muscle activation and stepping ability. Conversely, nociceptive sensitisation and muscular spasticity can emerge from insufficient GABAergic inhibition. To investigate the effects of neuromodulation and proprioceptive sensory afferents in the spinal cord, a biologically constrained spiking neural network (SNN) was developed. The network describes the flexor motoneuron (MN) reflex loop with inputs from ipsilateral Ia- and II-fibres and tonically firing interneurons. The model was tuned to a Baseline level of locomotive activity before simulating an inhibitory-dominant and body-weight supported (BWS) SCI state. Electrical stimulation (ES) and serotonergic agonists were simulated by the excitation of dorsal fibres and reduced conductance in excitatory neurons. ES was applied across all afferent fibres without phase- or muscle-specific protocols. The present computational findings suggest that reducing stance-phase GABAergic inhibition on flexor motoneurons could facilitate more physiological flexor activation during locomotion. The model further predicts that neuromodulatory therapy, together with body-weight support, modulates the balance of synaptic excitation and inhibition in ankle flexor motoneurons to mitigate excessive inhibitory drive in the ankle flexor circuitry.}, }
@article {pmid41493973, year = {2026}, author = {Gao, M and Zang, S and Zhu, Y and Xi, K and Du, Y and Cheng, S and Miao, L and Lu, Y and Mao, C and Zhang, Y and Ma, X}, title = {Structural insights into the activation mechanism of the human metabolite receptor HCAR1.}, journal = {Science signaling}, volume = {19}, number = {919}, pages = {eadw1483}, doi = {10.1126/scisignal.adw1483}, pmid = {41493973}, issn = {1937-9145}, mesh = {Humans ; Cryoelectron Microscopy ; *Receptors, G-Protein-Coupled/chemistry/metabolism/genetics/agonists ; *Lactic Acid/metabolism/chemistry ; Ligands ; Binding Sites ; Protein Binding ; Signal Transduction ; }, abstract = {Hydroxycarboxylic acid receptor 1 (HCAR1) is a class A G protein-coupled receptor (GPCR) that is activated by the endogenous metabolite l-lactate and that plays an important role in various metabolic and inflammatory disorders. HCAR1 uses distinct ligand recognition and self-activation mechanisms to mediate specific pathophysiological functions through Gαi/o and β-arrestin signaling pathways. To support effective drug development targeting HCAR1, we investigated ligand recognition and activation mechanisms through cryo-electron microscopy (cryo-EM) structures of the HCAR1-Gαi1 complex in the apo state or with l-lactate or with the synthetic agonist CHBA. Compared with other HCARs, HCAR1 has a more compact binding pocket, which is stabilized by three unique disulfide bonds. l-lactate exhibited a flexible binding mode and relatively weak intermolecular interactions, thus requiring millimolar concentrations for receptor activation. In contrast, the binding of CHBA was more stable because of its chlorinated benzene ring, thus resulting in improved agonist potency. Structural comparisons with HCAR2 identified critical residues that restrict the size of the binding pocket of HCAR1 and influence ligand selectivity. Self-activation of HCAR1 is driven by conformational rearrangements within extracellular loop 2, with Phe168[ECL2] playing a pivotal role as the key agonist. Together, these results clarify the mechanisms underlying HCAR1 activation, self-activation, and ligand selectivity, providing a structural framework for the design of high-affinity, selective agonists and inverse agonists with minimized off-target effects.}, }
@article {pmid41493559, year = {2026}, author = {Andrade, P and Mercado, R and Jimenez, F and Visser-Vandewalle, V}, title = {[Neuroprosthetics].}, journal = {Chirurgie (Heidelberg, Germany)}, volume = {}, number = {}, pages = {}, pmid = {41493559}, issn = {2731-698X}, abstract = {Neuroprosthetics represents a dynamic field at the interface of neurosciences, engineering and neurosurgery that is based on implanted devices for restoration or extension of neurological functions. Important advances involve brain-computer and brain-spine interfaces that enable communication, motor and sensory feedback in paralyzed or anarthric patients. Intracortical arrays, subdural electrocorticographic lattices and endovascular electrodes provide different access routes, supplemented by strategies, such as spinal neuromodulation and functional electrostimulation. Recent studies confirmed the restoration of grasping movements, standing and walking as well as fluid speech and text communication, sometimes via avatars. Bidirectional systems with sensory feedback enhance the naturalness and precision. There are challenges in signal stability, longevity and minimally invasive access routes. With interdisciplinary cooperation and technical maturity neuroprostheses can enrich the routine neurosurgical care in the future.}, }
@article {pmid41490776, year = {2026}, author = {Bao, M and Feng, S and Wang, J and Ye, J and Wang, J and Li, W and Jiang, K and Yao, L}, title = {Efficacy and Safety of a Video Game-Like Digital Therapy Intervention for Chinese Children With Attention-Deficit/Hyperactivity Disorder: Single-Arm, Open-Label Pre-Post Study.}, journal = {JMIR serious games}, volume = {14}, number = {}, pages = {e76114}, doi = {10.2196/76114}, pmid = {41490776}, issn = {2291-9279}, abstract = {BACKGROUND: The digital therapy of attention-deficit/hyperactivity disorder (ADHD) based on a "self-adaptive multitasking training paradigm" has been developed to improve the cognitive functional impairments and attention deficits of children with ADHD. However, the efficacy and safety of such treatment for Chinese patients remain untested.<br><br>OBJECTIVE: This study aimed to preliminarily evaluate the actual intervention effects of a video game-like training software (ADHD-DTx) for children with ADHD aged 6-12 years as the first nationally certified digital therapeutics medical device for ADHD in China. We performed a single-arm, open-label efficacy and safety study.<br><br>METHODS: This is a single-arm, open-label, pre-post efficacy and safety study. A total of 97 participants were included in the analysis. Participants received digital therapy (ADHD-DTx) and basic behavioral parent training for 4 weeks (25 min/day, &#x2265;5 times/week) without medication. The efficacy outcomes included the Test of Variables of Attention (TOVA), Swanson, Nolan, and Pelham Questionnaire, version 4 (SNAP-IV), Weiss Functional Impairment Rating Scale (WFIRS), and Conner's Parent Symptom Questionnaire (PSQ). Safety-related events were monitored during and after the trial.<br><br>RESULTS: From day 0 (baseline) to day 28, the population TOVA Attention Performance Index exhibited statistically significant improvement (from mean -4.15, SE of the mean [SEM] 0.32 to mean -1.70, SEM 0.30; t94=-8.78; n=95; P<.001); the population total, inattention (AD), hyperactivity/impulsivity (HD), and oppositional defiant disorder (ODD) scores of SNAP-IV all significantly improved (total: from mean 1.33, SEM 0.05 to mean 1.09, SEM 0.05; t96=5.32; P<.001; AD: from mean 1.71, SEM 0.06 to mean 1.44, SEM 0.06; t96=4.44; P<.001; HD: from mean 1.38, SEM 0.07 to mean 1.05, SEM 0.06; t96=5.96; P<.001; ODD: mean 0.84, SEM 0.05 to mean 0.75, SEM 0.05; Z=2.47; P=.03; n=97); for WFIRS results, domains of "family" and "social activities" showed significant population improvement (family: from mean 0.75, SEM 0.05 to mean 0.65, SEM 0.04; Z=2.80; P=.01; social activities: from mean 0.56, SEM 0.05 to mean 0.45, SEM 0.05; Z=2.91; P=.01; n=97); for PSQ results, domains of "learning problem," "psychosomatic problem," "impulsivity-hyperactivity," and "hyperactivity index" showed significant improvement (learning problem: from mean 1.72, SEM 0.06 to mean 1.57, SEM 0.06; Z=2.42; P=.03; psychosomatic problem: from mean 0.40, SEM 0.03 to mean 0.32, SEM 0.03; Z=2.66; P=.02; impulsivity-hyperactivity: from mean 0.94, SEM 0.06 to mean 0.80, SEM 0.06; Z=2.49; P=.03; hyperactivity index: from mean 1.06, SEM 0.05 to mean 0.92, SEM 0.05; Z=2.90; P=.01; n=97). No device-related adverse event or severe adverse event was observed or reported during or after the intervention.<br><br>CONCLUSIONS: This study preliminarily suggested the significant improvements of ADHD symptoms and attention function after 4 weeks of ADHD-DTx digital therapy combining basic behavioral parent training with satisfying safety outcomes.}, }
@article {pmid41489950, year = {2026}, author = {Chung, CM and Tsai, CH and Chu, YL and Hsu, CH and Lu, JB and Hsu, YC and Su, YJ and Wu, Y and Hung, CF and Wang, YT}, title = {3D printed watermill-like semi-dry electrodes for BCI applications.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2026.3650950}, pmid = {41489950}, issn = {1558-0210}, abstract = {Wet electrodes with conductive gel are widely applied as the gold standard for recording EEG signals due to their low impedance between the scalp and the electrode. However, their extensive preparation time before data collection and the required cleaning afterward make them impractical for real-world Brain-Computer Interface (BCI) applications. Recent advancements in semi-dry electrodes, which use a minimal amount of conductive material and achieve a comparable signal-to-noise quality to wet electrodes, present an alternative approach for continuous EEG monitoring when comparing to dry electrodes. Our prior study introduced a potential solution for overcoming challenges related to hair-layer penetration and dose control through 3D-printed, watermill-shaped EEG electrodes. Based on those promising results, this study prototypes three designs of watermill-shaped EEG electrodes and refines the fabrication process to scale production and accommodate diverse hairstyles in real-world scenarios. Eight different wig styles which were made of either human or synthetic hair were tested in offline experiments to evaluate hair-layer penetration performance and gel-applying application efficiency. In the real-world experiment, 15 participants with varying hairstyles were recruited in neurophysiological experiments. Statistical analysis revealed that the watermill electrodes consumed significantly less gel than wet electrodes (p<0.001), with the star electrode requiring the fewest mean rolls to achieve target impedance (1.94 rolls). The results demonstrate that the watermill-shaped electrode effectively works across different hairstyles, ensuring consistent hair-layer penetration and controlled application of conductive material. These findings establish the proposed electrode as a viable semi-dry solution for real-world BCI applications.}, }
@article {pmid41489217, year = {2026}, author = {Lu, J and Zhan, G and Jia, J and Zhang, L and Kang, X}, title = {Automated source domain EEG analysis based on graph theory for healthy controls and stroke patients in different tasks.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-19}, doi = {10.1080/10255842.2025.2609653}, pmid = {41489217}, issn = {1476-8259}, abstract = {This study aimed to compare functional brain networks and identify recovery markers in 12 stroke patients (SG) and 14 healthy controls (HG) using EEG during three fist-task paradigms. Analyzing clustering coefficient (CC), characteristic path length (CPL), small-world index (SWI), and frontal node strength across frequency bands, passive task revealed significant alpha band differences in CC/CPL/SWI between groups. Lower SG strength in alpha/mu vs. controls predicted better recovery. An automated source imaging pipeline reduced volume conduction effects, providing new insights into stroke rehabilitation outcomes. Large-scale source imaging shows promise for broader disease applications.}, }
@article {pmid41488688, year = {2025}, author = {Cao, Y and Ding, J and Zhao, Z and He, Y and Fu, M and Liu, X and Lyv, X}, title = {Improved filter bank common spatial pattern algorithm based on the sparrow search algorithm.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1679329}, pmid = {41488688}, issn = {1662-5161}, abstract = {INTRODUCTION: The application of motor imagery in human-computer interaction and rehabilitative medicine has attracted growing attention due to recent advances in brain-computer interface technologies. However, traditional EEG decoding paradigms based on fixed frequency-band segmentation often exhibit limited performance because they fail to capture individual variability in brain rhythms.<br><br>METHODS: This work proposes an adaptive method that integrates the sparrow search algorithm (SSA) with Filter Bank Common Spatial Pattern (FBCSP) to optimize sub-band segmentation for motor imagery EEG decoding. SSA adaptively searches for optimal sub-band boundaries, enabling individualized frequency-band selection.<br><br>RESULTS: Experiments on the BCI Competition IV 2a dataset under a cross-session evaluation protocol (training on session T, testing on session E) demonstrated that SSA-FBCSP effectively improves frequency-band adaptability. The SSA-FBCSP approach was further combined with Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor (KNN) classifiers to evaluate the influence of different downstream classifiers.<br><br>CONCLUSION: Among them, SSA-FBCSP-LDA achieved the best performance, outperforming the conventional uniform sub-band approach by 21.76% and reaching an average accuracy of 89.92%. The adaptively selected sub-bands closely matched the ERD/ERS distribution, confirming the method's effectiveness in frequency-band optimization. Compared with recent deep-learning-based MI-EEG models, the proposed technique offers a balance of accuracy, interpretability, and computational efficiency, providing a promising direction for personalized brain-computer interface systems.}, }
@article {pmid41486740, year = {2026}, author = {Zhang, T and Ngetich, RK and Zhang, J and Jin, Z and Li, L}, title = {The role of emotion in economic decision making: behavioral and neurophysiological evidence from the Wheel of Fortune Gambling Task.}, journal = {Reviews in the neurosciences}, volume = {}, number = {}, pages = {}, pmid = {41486740}, issn = {2191-0200}, abstract = {Decision making is frequently influenced by factors such as an individual's emotional state, cognitive biases, social influences, and environmental constraints. Understanding how these factors influence the way decisions are made is essential for optimizing and improving this cognitive process. Therefore, this review examines the theoretical basis of emotion-influenced decision making. Here, we integrate insights from eye-tracking, electroencephalography (EEG), and magnetic resonance imaging (MRI) evidence, as well as behavioral findings. We specifically review evidence from studies applying the Wheel of Fortune Gambling Task paradigm. Through critical and reflective synthesis, we (1) present suggestions for distinguishing between emotion types in decision-making theoretical models, (2) identify key research gaps, and (3) explore innovative applications of emerging technologies. In essence, our review highlights the role of diverse emotions in decision making across theoretical models and neural mechanisms, utilizing the Wheel of Fortune Gambling Task paradigm to link clinical disorders with decision-making impairments. This knowledge may have implications for predicting and intervening in behavioral addictions and cognitive disorders through strategies such as the neuromodulation. Additionally, by synthesizing existing knowledge and proposing new avenues for research, this review aims to deepen understanding of emotion-driven decision making and inspire further exploration into this vital area of cognitive science.}, }
@article {pmid41486339, year = {2026}, author = {Li, S and Wang, X and Zheng, J and Xu, H}, title = {Subparafascicular Thalamic Nucleus: An Integration Center for Sexual Motivation and Physical Contact in Mating Behaviour.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {41486339}, issn = {1995-8218}, }
@article {pmid41486068, year = {2026}, author = {Yamada, S and Sato, M and Osawa, T and Harabayashi, T and Miki, J and Kobayashi, T and Hashine, K and Kawashima, A and Matsumoto, T and Mochizuki, T and Taoka, R and Urabe, F and Tatarano, S and Sawada, A and Kojima, T and Takahashi, A and Yokomizo, A and Suekane, S and Hashimoto, K and Hashimoto, Y and Yatsuda, J and Morita, K and Kobayashi, K and Satake, Y and Sazawa, A and Matsui, Y and Ito, YM and Shimizu, S and Fukuhara, S and Nishiyama, H and Kitamura, H and Shinohara, N and , }, title = {Longitudinal Impact of Urinary Diversion on Health-Related Quality of Life After Radical Cystectomy: A Multicenter Study in Japan.}, journal = {Cancer science}, volume = {}, number = {}, pages = {}, doi = {10.1111/cas.70289}, pmid = {41486068}, issn = {1349-7006}, support = {2019-67//Japan Urological Association, Young Researcher Promotion Grant/ ; }, abstract = {This multicenter longitudinal study was conducted across 24 institutions in Japan to examine the impact of urinary diversion on health-related quality of life (HRQOL) among bladder cancer patients who underwent radical cystectomy (RC). We evaluated bladder cancer-specific HRQOL and general HRQOL via the bladder cancer index (BCI) and the QOL General (QGEN-8), respectively, before the operation and at 3, 6, and 12 months postoperatively. The scores were compared across urinary diversion groups as well as across different time points within each urinary diversion group with linear mixed-effects models. Data from 227 patients were analyzed (151 with ileal conduits, 45 with ureterostomy, and 31 with neobladders). Neobladder patients were more likely to experience longitudinal impacts of their urinary diversion on urinary function than ileal conduit or ureterostomy patients were. Compared with that at baseline, the bowel function of neobladder patients remained impaired 12 months after surgery. All urinary diversion groups had worse sexual function scores at 3 and 6 months than at baseline, and the ileal conduit and neobladder groups had significantly worse sexual function scores at 12 months than at baseline. On the other hand, there was no significant difference in bother scores in the urinary, bowel, or sexual domain. The generic HRQOL was maintained from the preoperative to the postoperative period in all urinary diversion groups. This study explored longitudinal changes in HRQOL after RC, and the findings may help inform patient counseling regarding possible QOL trajectories.}, }
@article {pmid41485025, year = {2026}, author = {Ying, W and Wang, X and Yu, J and Wang, J and He, Q and Yang, B and Chen, Y and Ying, M}, title = {Fusion oncoproteins orchestrate tumorigenesis and sustain malignant progression via a positive feedback mechanism.}, journal = {Cell &amp; bioscience}, volume = {}, number = {}, pages = {}, doi = {10.1186/s13578-025-01523-6}, pmid = {41485025}, issn = {2045-3701}, support = {No. 82272677//National Natural Science Foundation of China/ ; No. LR23H310001//Natural Science Fund for Distinguished Young Scholars of Zhejiang Province/ ; No. GZC20232321//Postdoctoral Fellowship Program of CPSF/ ; No. 2024C03181//Pioneer and Leading Goose R&amp;D Program of Zhejiang Province/ ; No. 226-2025-00136//Fundamental Research Funds for the Central Universities/ ; }, abstract = {Chromosomal translocations are prevalent genetic events across multiple pediatric cancers, notably in CNS tumors, solid tumors, and leukemias. For decades, Fusion oncoproteins resulting from chromosomal translocations have been proposed as a hallmark of cancers, some of which can drive the process of cancers as the initial event of the disease. In addition, studies have shown that some tumor cells become addicted to the activity of fusion proteins, and cell death occurs when the fusion proteins are depleted. These researches suggest that fusion oncoproteins are one of the most promising targets for cancer treatment. Although fusion proteins are already recognized as critical oncogenic drivers, increasing evidence suggests that they can also form positive feedback loops with other proteins. In cancer patients, positive feedback loops have been shown to activate various oncogenic signals to drive tumor development, and influencing tumor cells' sensitivity to different therapies. Therefore, these loops not only amplify the functions of the fusion proteins but also render single-agent targeting of the fusion protein insufficient to suppress tumor growth, highlighting the therapeutic potential of combination strategies in treating fusion-positive tumors. This review highlights the oncogenic roles of fusion protein-driven positive feedback loops in tumor initiation and progression, outline the molecular mechanisms underlying their formation and function, and summarize emerging therapeutic strategies targeting these circuits, offering new insights into the treatment of fusion-positive cancers.}, }
@article {pmid41484139, year = {2026}, author = {Gao, J and Liu, Y and Li, Z and Huang, K and Wang, F and Xu, J and Zhao, L and Li, T and Fu, Y}, title = {An EEG Dataset for Visual Imagery-Based Brain-Computer Interface.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-025-06512-5}, pmid = {41484139}, issn = {2052-4463}, support = {No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, and No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, and No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, and No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {With the advancement of non-invasive brain-computer interface (BCI) technologies, decoding high-level cognitive activity has become pivotal for expanding human-machine interaction. Visual imagery-based BCI (VI-BCI) enable voluntary activation of specific brain regions without external cue, offering novel pathways for immersive applications. However, research on the neural representation of such complex cognitive tasks is still limited, and most existing electroencephalogram (EEG) datasets primarily target motor imagery, hindering the development of robust VI decoding models. Here we present an EEG dataset recorded from 22 participants performing visual imagery tasks involving ten commonly recognized images across three categories: figures, animals, and objects. Each participant completed two sessions, with EEG recorded from 32-channels at 1000 Hz. This resource helps overcome data homogeneity issues in VI studies and provides a foundation for exploring neuroplasticity, adaptive decoding algorithms, and cross-subject generalization, facilitating the transition from controlled experiments to real-world applications.}, }
@article {pmid41483149, year = {2026}, author = {Zhu, L and Hong, H and Qian, M and Cao, W and Luo, Z and Gong, J and Zou, W and Kang, L}, title = {Hierarchical Channel System Drives Stimulus Specificity and Polymodal Encoding in A Mechano-Cold Sensory Neuron.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {41483149}, issn = {1995-8218}, abstract = {Polymodal sensory neurons integrate diverse stimuli for environmental perception, but their modality discrimination mechanisms remain unclear. We focused on Caenorhabditis elegans inner labial type 1 (IL1) neurons, key polymodal neurons mediating mechanical and cold responses, and identified a hierarchical channel system supporting their multimodal function. Specifically, DEG-1 sodium channels are dedicated mechanotransduction receptors; GLR-3 glutamate receptors are the main rapid cold sensors, driving cold-induced calcium signals and behaviors; TRPA-1 bidirectionally modulates mechanical adaptation via calcium signaling and promotes cold-related longevity. This framework reveals a polymodal design logic: dedicated channels (DEG-1/GLR-3) process discrete modalities in parallel for specificity, while TRPA-1 regulates both. Our work provides a molecular blueprint for IL1's precise stimulus processing, offering insights into conserved multimodal integration mechanisms across lineages.}, }
@article {pmid41482611, year = {2026}, author = {Nochalabadi, A and Khazaei, M and Kadivarian, S and Rezakhani, L}, title = {Innovative Herbal-Based Decellularization of Pericardium for Advanced Polymeric Skin Substitutes.}, journal = {Artificial organs}, volume = {}, number = {}, pages = {}, doi = {10.1111/aor.70087}, pmid = {41482611}, issn = {1525-1594}, support = {//Kermanshah University of Medical Sciences/ ; }, abstract = {INTRODUCTION: Tissue engineering has opened new horizons with the introduction of biological scaffolds obtained by decellularization techniques as novel tools in regenerative medicine. Chemical agents such as SDS, although effective in cell removal, can cause cytotoxicity. Herbal agents can be a safer and more biocompatible alternative. This study aimed to investigate the efficacy of Acanthophelium extract (ACP) as a herbal agent in decellularization of sheep pericardium and compare it with SDS for use in skin engineering.<br><br>METHODS: Pericardial tissues were decellularized with different concentrations of ACP (5, 7.5% and 10%) and SDS (1%), as well as the combination of ACP&#x2009;+&#x2009;SDS. Tissue staining, biocompatibility (MTT), hemolysis, blood clotting index (BCI), scanning electron microscopy (SEM), ATR-FTIR spectroscopy, mechanical testing, contact angle, and antibacterial activity were performed.<br><br>RESULTS: Complete cell removal was observed in the ACP&#x2009;+&#x2009;SDS combination groups, while the ECM structure was preserved. Biocompatibility was more than 90% in all groups. ACP-based scaffolds had less hemolysis, a more favorable coagulation index, preserved protein structure, higher porosity, and higher hydrophilicity. Although the mechanical properties were slightly reduced, they remained acceptable. The 10% ACP&#x2009;+&#x2009;0.1% SDS group reported the highest antibacterial effect.<br><br>CONCLUSIONS: ACP extract, as a plant agent in pericardial decellularization, has an effective and biocompatible function, and in combination with a small amount of SDS, it can provide a balanced scaffold with desirable properties for skin engineering.}, }
@article {pmid41481676, year = {2026}, author = {Proverbio, AM and Zanetti, A}, title = {Reinstating motivational states: Electrical signatures of craving and neural mind reading.}, journal = {PloS one}, volume = {21}, number = {1}, pages = {e0315068}, pmid = {41481676}, issn = {1932-6203}, mesh = {Humans ; Male ; Female ; Electroencephalography ; Adult ; *Motivation/physiology ; Young Adult ; Evoked Potentials/physiology ; *Craving/physiology ; *Brain/physiology ; Brain Mapping ; }, abstract = {The aim of this electroencephalogram (EEG) study was to identify electrical neuro-markers of 12 different motivational and physiological states such as visceral craves, affective and somatosensory states, and secondary needs. Event-related potentials (ERPs) were recorded in 30 right-handed participants while recalling a specific state upon the presentation of an auditory verbal command incorporating an evocative sound background consistent with that state (e.g., the chirping of cicadas associated with the verbal complaint about feeling hot). ERP data showed larger amplitude N400 responses in the affective and somatosensory states, while the P400 component displayed greater amplitudes for the secondary and visceral states. Furthermore, the two components were also discernibly responsive to the 12 micro-categories (e.g., joy vs. pain or hunger), by providing a distinctive electric pattern for mostly all microstates. The reconstruction of the intracranial generators of surface signals revealed common imagery-related activations, including the middle and superior frontal gyri, the fusiform and lingual gyri, supramarginal, and middle occipital regions, as well as the middle temporal region. Additionally, specific regions were identified that were active for distinct mentally represented content, such as that visceral needs were associated with activations in the medial and inferior frontal gyri, uncus, precuneus, and cingulate gyrus. Affective states were associated with activations in the medial frontal, superior temporal, and middle temporal gyri. Somatosensory states (e.g., pain or cold) activated regions in the parietal cortex and the crave for music was linked to activations in the auditory and motor regions. These findings support the use of ERP markers for BCI applications.}, }
@article {pmid41480666, year = {2026}, author = {Hu, X and Li, N and Pang, M and Bai, S and Mo, J and Yao, S and Lu, Y and Huang, M and Di, J and Kang, Y and Tang, J and Zhang, H and Zhao, T and He, J and He, L and Xie, R and Liu, B and Xu, G and Hu, X and Rong, L}, title = {Brain-Computer Interface-Controlled Exoskeleton Training for Lower-Limb Rehabilitation in Spinal Cord Injury: A Pilot Randomized Clinical Trial.}, journal = {Annals of neurology}, volume = {}, number = {}, pages = {}, doi = {10.1002/ana.78144}, pmid = {41480666}, issn = {1531-8249}, support = {U22A20297//National Natural Science Foundation of China/ ; 202206060003//Key Research and Development Program of Guangzhou/ ; GZC20251372//Postdoctoral Fellowship Program and China Postdoctoral Science Foundation/ ; }, abstract = {OBJECTIVE: This study aimed to evaluate the efficacy of brain-computer interface (BCI)-controlled exoskeleton training on lower-limb functional recovery, psychological outcomes, and neural plasticity in patients with spinal cord injury (SCI).<br><br>METHODS: We conducted a single-center, prospective, randomized, single-blind pilot trial (ChiCTR2300074503) including 21 patients with SCI. Participants were randomized to a BCI-exoskeleton group (B&#x2009;+&#x2009;E, n&#x2009;=&#x2009;10) or an exoskeleton-only group (E, n&#x2009;=&#x2009;11) for lower-limb training. Both groups received conventional rehabilitation plus 30&#x2009;minutes of training, 6&#x2009;days per week, for 4&#x2009;weeks. The primary outcomes were Walking Index for Spinal Cord Injury II (WISCI II) scoring. Secondary outcomes included Lambert-Eaton myasthenic syndrome (LEMS), Spinal Cord Independence Measure version III (SCIM III), International Association of Neurorestoratology Spinal Cord Injury Functional Rating Scale (IANR-SCIFRS), 10-Meter Walk Test (10MWT), 6-Minute Walk Test (6MWT), and Hospital Anxiety and Depression Scale (HADS). Cortical plasticity was assessed by electroencephalography (EEG) and magnetic resonance imaging (MRI).<br><br>RESULTS: The B&#x2009;+&#x2009;E group showed a significant improvement in LEMS (p&#x2009;=&#x2009;0.003), whereas both groups improved in IANR-SCIFRS (p&#x2009;<&#x2009;0.05). The B&#x2009;+&#x2009;E group demonstrated significant within-group gains in walking speed (10MWT, p&#x2009;<&#x2009;0.001) and endurance (6MWT, p&#x2009;=&#x2009;0.031), although between-group differences were not significant. Compared with the E group, the B&#x2009;+&#x2009;E group had larger reductions in HADS scores (p&#x2009;=&#x2009;0.003). EEG analyses revealed stronger &#x3bc;/&#x3b2; desynchronization and increased network efficiency, whereas MRI showed no structural changes.<br><br>INTERPRETATION: BCI-controlled exoskeleton training enhanced motor function, walking performance, and depressive symptoms more than exoskeleton training alone, likely through cortical reorganization. Extended training may further consolidate these benefits, supporting BCI-exoskeleton integration as a promising rehabilitation strategy for SCI. ANN NEUROL 2026.}, }
@article {pmid41476655, year = {2025}, author = {Li, Y and Miao, Y and Wei, L and Li, W and Shan, M and Jiang, Q and Wang, F and Wang, L and Zhang, Z and Song, J and Zhu, Y and Mao, J}, title = {An Anisotropic and Stable-Conductance Patch for Mechanical-Electrical Coupling With Infarcted Myocardium.}, journal = {Exploration (Beijing, China)}, volume = {5}, number = {6}, pages = {20250021}, pmid = {41476655}, issn = {2766-2098}, abstract = {Polymeric conductive patches have conventionally been employed to facilitate the repair of infarcted myocardium by enhancing myocardial electrical conduction and providing mechanical support. However, it remains a challenge to restore the electrical conduction and diastolic-systolic functions with stable and anisotropic mechanical and electrical cues in the dynamic physiological environment. Herein, inspired by the hierarchical myocardial fiber microscopic striated structure, we established a weaving-based processing method to compound a striated polypyrrole conductive coating on the surface of highly oriented elastic fiber bundles. This unique design endows the patch with exceptional stretchability (elongation at break > 400%), stable conductance (ΔR/R 0 = 0.04 within 20% strain), and excellent fatigue resistance (ΔR/R 0 = 0.01 after 1,000,000 cycles). In addition, the precision process grounded on woven molding accomplished the tunable mechanical and electrical properties of the patch, which facilitates the achievement of long-term, stable, and anisotropic mechanical-electrical coupling with the infarcted myocardium. The rat MI model experiments demonstrated that this anisotropic conductive patch can not only improve cardiac function and electrical activity over an extended period, but also effectively inhibit myocardial inflammation and fibrosis and promote angiogenesis. This study proposes a promising MI-treatment patch and highlights the potential of woven technology in processing biomaterials composed of both rigid and elastic materials.}, }
@article {pmid41474622, year = {2025}, author = {Chen, J and Xu, T and Xiong, X and Yang, X and Wang, Y and Qi, Y}, title = {Surrogate deep neural networks reveal hierarchical handwriting encoding in the human motor cortex.}, journal = {Cell reports}, volume = {45}, number = {1}, pages = {116837}, doi = {10.1016/j.celrep.2025.116837}, pmid = {41474622}, issn = {2211-1247}, abstract = {Skilled fine movements are essential for daily life. Although prior work has identified motor cortical tuning to low-level kinematic features like velocity and position, these findings fall short of explaining the precision underlying complex motor behaviors. Critically, it remains unclear whether and how the motor cortex (MC) represents higher-level features of movement. Using single-unit recordings from the human MC during handwriting, we employed surrogate deep neural networks (DNNs) as a tool to investigate these mechanisms. We found that surrogate DNNs capture key aspects of neural activity at both single-unit and population levels. Through this approach, we demonstrate that the MC encodes hierarchical information of movement, including both low-level kinematics and high-level features related to the written content. These results uncover neural encoding behind dexterous motor execution and provide a framework for studying the neural basis of complex behavior.}, }
@article {pmid41472918, year = {2025}, author = {Sedi Nzakuna, P and D'Auria, E and Paciello, V and Gallo, V and Kamavuako, EN and Lay-Ekuakille, A and Kyamakya, K}, title = {Real-world evaluation of deep learning decoders for motor imagery EEG-based BCIs.}, journal = {Frontiers in systems neuroscience}, volume = {19}, number = {}, pages = {1718390}, pmid = {41472918}, issn = {1662-5137}, abstract = {INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG)-based control in online Brain-Computer Interfaces requires decisions to be made within short temporal windows. However, the majority of published Deep Learning (DL) EEG decoders are developed and validated offline on public datasets using longer window lengths, leaving their real-time applicability unclear.<br><br>METHODS: To address this gap, we evaluate 10 representative DL decoders, including convolutional neural networks (CNNs), filter-bank CNNs, temporal convolutional networks (TCNs), and attention- and Transformer-based hybrids-under a soft real-time protocol using 2-s windows. We quantify performance using accuracy, sensitivity, precision, miss-as-neutral rate (MANR), false-alarm rate (FAR), information-transfer rate (ITR), and workload. To relate decoder behavior to physiological markers, we examine lateralization indices, mu-band power at C3 vs. C4, and topographical contrasts between MI and neutral conditions.<br><br>RESULTS: Results show shifts in performance ranking between offline and online BCI settings, along with a pronounced increase in inter-subject variability. Best online means were FBLight ConvNet 71.7% (&#xb1;2.1) and EEG-TCNet 70.0% (&#xb1;5.3), with attention/Transformer designs less stable. Errors were mainly Left-Right swaps while Neutral was comparatively stable. Lateralization indices/topomaps revealed subject-specific &#x3bc;/&#x3b2; patterns consistent with class-wise precision/sensitivity.<br><br>DISCUSSION: Compact spectro-temporal CNN backbones combined with lightweight temporal context (such as TCNs or dilated convolutions) deliver more stable performance under short-time windows, whereas deeper attention and Transformer architectures are more susceptible to variation across subjects and sessions. This study establishes a reproducible benchmark and provides actionable guidance for designing and calibrating online-first EEG decoders that remain robust under real-world, short-time constraints.}, }
@article {pmid41471452, year = {2025}, author = {Gao, D and Zhao, Y and Zhou, J and Zhang, H and Li, H}, title = {MCRBM-CNN: A Hybrid Deep Learning Framework for Robust SSVEP Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {24}, pages = {}, pmid = {41471452}, issn = {1424-8220}, abstract = {The steady-state visual evoked potential (SSVEP), a non-invasive EEG modality, is a prominent approach for brain-computer interfaces (BCIs) due to its high signal-to-noise ratio and minimal user training. However, its practical utility is often hampered by susceptibility to noise, artifacts, and concurrent brain activities, complicating signal decoding. To address this, we propose a novel hybrid deep learning model that integrates a multi-channel restricted Boltzmann machine (RBM) with a convolutional neural network (CNN). The framework comprises two main modules: a feature extraction module and a classification module. The former employs a multi-channel RBM to unsupervisedly learn latent feature representations from multi-channel EEG data, effectively capturing inter-channel correlations to enhance feature discriminability. The latter leverages convolutional operations to further extract spatiotemporal features, constructing a deep discriminative model for the automatic recognition of SSVEP signals. Comprehensive evaluations on multiple public datasets demonstrate that our proposed method achieves competitive performance compared to various benchmarks, particularly exhibiting superior effectiveness and robustness in short-time window scenarios.}, }
@article {pmid41471422, year = {2025}, author = {Ammar, S and Triki, N and Karray, M and Ksantini, M}, title = {A Multidimensional Benchmark of Public EEG Datasets for Driver State Monitoring in Brain-Computer Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {24}, pages = {}, doi = {10.3390/s25247426}, pmid = {41471422}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Automobile Driving ; Benchmarking ; Male ; Female ; Adult ; }, abstract = {Electroencephalography (EEG)-based brain-computer interfaces (BCIs) hold significant potential for enhancing driver safety through real-time monitoring of cognitive and affective states. However, the development of reliable BCI systems for Advanced Driver Assistance Systems (ADAS) depends on the availability of high-quality, publicly accessible EEG datasets collected during driving tasks. Existing datasets lack standardized parameters and contain demographic biases, which undermine their reliability and prevent the development of robust systems. This study presents a multidimensional benchmark analysis of seven publicly available EEG driving datasets. We compare these datasets across multiple dimensions, including task design, modality integration, demographic representation, accessibility, and reported model performance. This benchmark synthesizes existing literature without conducting new experiments. Our analysis reveals critical gaps, including significant age and gender biases, overreliance on simulated environments, insufficient affective monitoring, and restricted data accessibility. These limitations hinder real-world applicability and reduce ADAS performance. To address these gaps and facilitate the development of generalizable BCI systems, this study provides a structured, quantitative benchmark analysis of publicly available driving EEG datasets, suggesting criteria and recommendations for future dataset design and use. Additionally, we emphasize the need for balanced participant distributions, standardized emotional annotation, and open data practices.}, }
@article {pmid41471369, year = {2025}, author = {Ga, YJ and Yeh, JY}, title = {Does Coxsackievirus B3 Require Autophagosome Formation for Replication? Evidence for an Autophagosome-Independent Mechanism: Insights into Its Limited Potential as a Therapeutic Target.}, journal = {Pharmaceuticals (Basel, Switzerland)}, volume = {18}, number = {12}, pages = {}, doi = {10.3390/ph18121880}, pmid = {41471369}, issn = {1424-8247}, support = {RS-2025-02304897//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries/ ; }, abstract = {Background/Objectives: Coxsackievirus B3 (CVB3), a neurotropic enterovirus, is a major causative agent of viral encephalitis and myocarditis, yet no protective vaccine or effective antiviral therapy is currently available. Autophagy plays a dual role in viral infections, acting as both an antiviral defense and a process that can be exploited by certain viruses. Although CVB3 has been proposed to utilize autophagosomes as replication platforms, the underlying mechanisms remain controversial. Methods: In this study, we investigated the relationship between CVB3 replication and autophagosome formation under starvation-induced conditions and in ATG5 knockout cells. Results: While nutrient deprivation robustly induced autophagy, CVB3 infection did not trigger autophagosome formation. Moreover, viral replication proceeded efficiently in ATG5-deficient cells lacking autophagosomes. Pharmacological modulation of autophagy using rapamycin, a potent autophagy inducer, did not alter intracellular viral titers or protein expression, although extracellular viral release was modestly reduced. These results indicate that CVB3 replication occurs independently of autophagosome formation, suggesting that pharmacological targeting of autophagy provides limited therapeutic benefit. Conclusions: This study refines our understanding of autophagy as an antiviral target and highlights the need to identify alternative host-directed pathways for antiviral drug development.}, }
@article {pmid41470530, year = {2025}, author = {Wang, C and Cheng, B and Tang, Q and Wu, R and Li, H}, title = {Design and Validation of a Brain-Controlled Hip Exoskeleton for Assisted Gait Rehabilitation Training.}, journal = {Micromachines}, volume = {16}, number = {12}, pages = {}, pmid = {41470530}, issn = {2072-666X}, support = {GXXT2022053//the Collaborative Innovation Program for Universities in Anhui Province/ ; 2023A3112//Huainan City Science and Technology Plan Project/ ; 2023AHIMB05//Base for Innovative Methods Promotion Application and Demonstration of Anhui Province/ ; }, abstract = {This study presents an integrated micro-system solution to address the challenges of gait instability in patients with impaired hip motor function. We developed a novel wearable hip exoskeleton, where a flexible support unit and a parallel drive mechanism achieve self-alignment with the biological hip joint to minimize parasitic forces. The system is driven by an active brain-computer interface (BCI) that synergizes an augmented reality visual stimulation (AR-VS) paradigm for enhanced motor intent recognition with a high-performance decoding algorithm, all implemented on a real-time embedded processor. This integration of micro-sensors, control algorithms, and actuation enables the establishment of a gait phase-dependent hybrid controller that optimizes assistance. Online experiments demonstrated that the system assisted subjects in completing 10 gait cycles with an average task time of 37.94 s, a correlated instantaneous rate of 0.0428, and an effective output ratio of 82.17%. Compared to traditional models, the system achieved an 18.64% reduction in task time, a 28.31% decrease in instantaneous rate, and a 7.36% improvement in output ratio. This work demonstrates a significant advancement in intelligent micro-system platforms for human-centric rehabilitation robotics.}, }
@article {pmid41469392, year = {2025}, author = {Jilderda, MF and Bartlett, JMS and Liefers, GJ and Zhang, Y and Dunn-Davies, H and Rebattu, V and Salunga, R and Meershoek-Klein Kranenbarg, E and de Munck, L and Hasenburg, A and Markopoulos, C and Dirix, L and van de Velde, CJH and Rea, D and Anderson, AKL and Bastiaannet, E and Treuner, K and Taylor, KJ}, title = {Validation of minimal risk of recurrence classification by the Breast Cancer Index in early stage breast cancer.}, journal = {NPJ breast cancer}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41523-025-00885-x}, pmid = {41469392}, issn = {2374-4677}, abstract = {The Breast Cancer Index (BCI) was previously shown to identify ~20% of postmenopausal patients with early stage, hormone receptor positive (HR+), node negative (N0) breast cancer with minimal (<5%) risk of 10-year distant recurrence (DR) even without receiving adjuvant endocrine therapy (ET). This prospective-retrospective study further validated the BCI minimal risk classification in postmenopausal patients with early-stage, HR + HER2- N0 breast cancer from the Netherlands Cancer Registry (NCR) and the Tamoxifen and Exemestane Adjuvant Multinational (TEAM, NCT00279448, NCT00032136) randomized trial who received 5 years of primary adjuvant ET. BCI classified approximately 15% of patients as minimal risk. In the NCR cohort (n = 1264 out of 15,053 HR+ patients in the registry), risks of DR in the minimal, low, intermediate, and high groups were 4.8%, 3.3%, 8.0%, and 12.4%, respectively (P < 0.001). In the TEAM cohort (n = 978 out of 3544 in the BCI study), DR risks were 3.8%, 8.3%, 12.6% and 22.7% (P < 0.001). In multivariate analyses, BCI risk scores provided independent information over standard prognostic factors (P < 0.001). This study confirmed the ability of the adjusted BCI model to identify postmenopausal women with HR + HER2- N0 breast cancer who are at minimal risk of DR and may consider de-escalating adjuvant ET.}, }
@article {pmid41469234, year = {2026}, author = {Ge, H and Gu, X and Wang, Z and Tan, S and Jiao, B and Zhang, L and Yang, Y and Li, W and Xie, J and Bai, R}, title = {Anesthetics Modulate Cerebrospinal Fluid Efflux Pathways in Mice by Altering Perineural and Perivascular Spaces.}, journal = {NMR in biomedicine}, volume = {39}, number = {2}, pages = {e70222}, doi = {10.1002/nbm.70222}, pmid = {41469234}, issn = {1099-1492}, support = {2022ZD0206000//STI2030-Major Projects of China/ ; 92359303//National Natural Science Foundation of China/ ; 82222032//National Natural Science Foundation of China/ ; 2025ZD0215000//Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project/ ; }, mesh = {Animals ; *Cerebrospinal Fluid/drug effects/metabolism ; *Anesthetics/pharmacology ; Male ; Mice ; Magnetic Resonance Imaging ; *Glymphatic System/drug effects ; Mice, Inbred C57BL ; }, abstract = {The brain-wide glymphatic transport system facilitates cerebrospinal fluid (CSF) circulation and the clearance of metabolic waste, processes largely influenced by sleep and sleep-like anesthesia. Recent research indicates that different anesthetic agents modulate CSF dynamics in distinct ways; however, their effects on CSF efflux pathways remain unclear. This study utilized dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and structural MRI to investigate CSF efflux pathways in mice under three anesthesia protocols (n = 6 per group): isoflurane alone (ISO), isoflurane combined with dexmedetomidine (DEXI), and ketamine/xylazine (K/X). Additionally, blood vessel diameters and CSF volume fractions were quantified. Our results demonstrate that ISO induced vasodilation in the anterior brain, slowing CSF flow to the dorsal brain while substantially accelerating CSF efflux across the cribriform plate and nasal mucosa toward the nasopharyngeal lymphatic plexus compared with DEXI and K/X (p < 0.001). However, ISO reduced CSF outflow through the spinal subarachnoid space primarily due to a decreased spinal subarachnoid CSF volume (ISO vs. DEXI, p = 0.0373; ISO vs. K/X, p = 0.0436). K/X considerably impaired CSF efflux via the cervical ganglia relative to DEXI and ISO, likely resulting from a lower CSF volume fraction within the peri-cranial nerve space (ISO vs. K/X, p = 0.0328, K/X vs. DEXI, p = 0.023). In conclusion, different anesthesia protocols modulate CSF efflux pathways by altering perineural and perivascular CSF spaces. These findings suggest that anesthetic agents influence glymphatic function by modulating distinct CSF efflux routes.}, }
@article {pmid41468722, year = {2025}, author = {Ye, QY and Zhang, SY and He, XL and Yang, YQ and Ni, K and Yang, HX and Wei, W and Preece, DA and Chan, RCK and Li, BM and Cai, XL}, title = {Interrelationships between childhood trauma, alexithymia, and depressive symptoms: A network analysis and replication.}, journal = {Child abuse &amp; neglect}, volume = {172}, number = {}, pages = {107877}, doi = {10.1016/j.chiabu.2025.107877}, pmid = {41468722}, issn = {1873-7757}, abstract = {BACKGROUND: Childhood trauma has been found to increase the risk of developing alexithymia and depressive symptoms. However, the complex interplay between childhood trauma, alexithymia, and depressive symptoms remains unclear.<br><br>OBJECTIVE: To understand how different facets of childhood trauma, alexithymia across positive and negative emotions, and depressive symptoms interact with each other, this study adopted the network analysis approaches to examine this complex relationship.<br><br>PARTICIPANTS AND SETTING: An initial sample of 2918 Chinese college students completed a set of psychometric questionnaires measuring childhood trauma, alexithymia and depressive symptoms. Another independent sample (n&#xa0;=&#xa0;858) was used to investigate the replicability of our results.<br><br>METHODS: Undirected networks were estimated to explore the most relevant connections between the above variables. Bayesian network analysis was further used to explore the potential causal directions between the variables.<br><br>RESULTS: Findings from the initial dataset showed that childhood trauma was positively correlated with both alexithymia and depressive symptoms in the undirected networks. Physical abuse was the most central node. The Bayesian network analysis indicated that externally orientated thinking and depressed mood may be key drivers for activating other symptoms. Physical abuse might affect suicide ideation through difficulties in describing negative emotions. The replication dataset showed similar network structures as the initial dataset.<br><br>CONCLUSIONS: The findings suggest that childhood trauma, especially physical abuse, plays an important role in developing later depressive symptoms via valenced components of alexithymia. This study clarifies how early adversities link to depressive symptoms through emotional functioning and informs clinical interventions targeting influential symptoms in trauma-exposed populations.}, }
@article {pmid41467724, year = {2025}, author = {Wang, C and Allison, BZ and Wu, X and Li, J and Zhao, R and Chen, W and Wang, X and Cichocki, A and Jin, J}, title = {Multi-Domain Dynamic Weighting Network for Motor Imagery Decoding.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2650005}, doi = {10.1142/S012906572650005X}, pmid = {41467724}, issn = {1793-6462}, abstract = {In motor imagery (MI)-based brain-computer interfaces (BCIs), convolutional neural networks (CNNs) are widely employed to decode electroencephalogram (EEG) signals. However, due to their fixed kernel sizes and uniform attention to features, CNNs struggle to fully capture the time-frequency features of EEG signals. To address this limitation, this paper proposes the Multi-Domain Dynamic Weighted Network (MD-DWNet), which integrates multimodal complementary feature information across time, frequency, and spatial domains through a branch structure to enhance decoding performance. Specifically, MD-DWNet combines multi-band filtering, spatial convolution, and temporal variance calculation to extract spatial-spectral features, while a dual-scale CNN captures local spatiotemporal features at different time scales. A dynamic global filter is designed to optimize fused features, improving the adaptive modeling capability for dynamic changes in frequency band energy. A lightweight mixed attention mechanism selectively enhances salient channel and spatial features. The dual-branch joint loss function adaptively balances contributions through a task uncertainty mechanism, thereby enhancing optimization efficiency and generalization capability. Experimental results on the BCI Competition IV 2a, IV 2b, OpenBMI, and a self-collected laboratory dataset demonstrate that MD-DWNet achieves classification accuracies of 83.86%, 88.67%, 75.25% and 84.85%, respectively, outperforming several advanced methods and validating its superior performance in MI signal decoding.}, }
@article {pmid41467584, year = {2026}, author = {Wang, M and He, Q and Zhu, S and Cao, T and Wang, N and Jia, Y and Wu, H and Liang, J and Niu, H and Xu, Z and Cui, Z and Yang, Y and Zhao, J}, title = {Global White Matter Damage in Focal Brainstem Injury Patients With Disorders of Consciousness: A Diffusion Tensor Tractography Study.}, journal = {European journal of neurology}, volume = {33}, number = {1}, pages = {e70476}, doi = {10.1111/ene.70476}, pmid = {41467584}, issn = {1468-1331}, support = {2022ZD0205300//Science and Technology Innovation 2030/ ; Z221100002722014//International (Hong Kong, Macao, and Taiwan) Science and Technology Cooperation Project/ ; 2022-NKX-XM-02//Chinese Institute for Brain Research Youth Scholar Program/ ; 82371197//National Natural Science Foundation of China/ ; 7232049//Natural Science Foundation of Beijing Municipality/ ; }, mesh = {Humans ; Diffusion Tensor Imaging ; Female ; Male ; *White Matter/pathology/diagnostic imaging ; Adult ; *Consciousness Disorders/etiology/pathology/diagnostic imaging ; *Brain Stem/pathology/diagnostic imaging/injuries ; Middle Aged ; Retrospective Studies ; Young Adult ; Neural Pathways/pathology/diagnostic imaging ; Aged ; }, abstract = {BACKGROUND: Disorders of consciousness (DoC) pose significant challenges in clinical diagnosis and treatment. This study aims to investigate the relationship between consciousness levels and the brainstem-cortical white matter tracts in DoC patients resulting from focal brainstem injury using diffusion tensor imaging (DTI).<br><br>METHODS: DTI data of DoC patients with focal brainstem injury and healthy volunteers were retrospectively collected. White matter tractography was performed to reconstruct brainstem-cortical projections. The number of streamlines, total volume, and fractional anisotropy (FA) were analyzed from the perspective of global brain, physiological pathways, and functional networks. The relationship between these measurements and consciousness levels was investigated.<br><br>RESULTS: A cohort of 28 DoC patients and 32 healthy controls were included in the analysis. DoC patients exhibited significant reductions in the number of streamlines in global brainstem-cortical projections compared to controls. However, the total volume and FA of these fibers were relatively preserved. Specific pathways such as the corticospinal tract and frontoparietal tract showed marked reductions in streamline counts. Significant reductions in streamline counts were also observed in the somatomotor and frontoparietal networks. No significant changes in mean FA were observed across different physiological pathways and brain networks. Correlation analyses revealed significant associations between consciousness levels and structural connections in the frontoparietal tract and frontoparietal network.<br><br>CONCLUSION: This study highlights the impact of focal brainstem injury on global brain structural connectivity in DoC patients. Despite significant reductions in streamline counts, the preservation of FA suggests maintained microstructural integrity in surviving fibers.}, }
@article {pmid41466537, year = {2025}, author = {Selcuk, C and Boulgouris, NV}, title = {Dynamic graph representation of EEG signals for speech imagery recognition.}, journal = {Journal of neural engineering}, volume = {22}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ae2ccb}, pmid = {41466537}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Speech/physiology ; Algorithms ; *Pattern Recognition, Automated/methods ; Male ; Female ; }, abstract = {Objective. Speech imagery recognition from electroencephalography (EEG) signals is an emerging challenge in brain-computer interfaces, and has important applications, such as in the interaction with locked-in patients. In this work, we use graph signal processing for developing a more effective representation of EEG signals in speech imagery recognition.Approach. We propose a dynamic graph representation that uses multiple graphs constructed based on the time-varying correlations between EEG channels. Our methodology is particularly suitable for signals that exhibit fluctuating correlations, which cannot be adequately modeled through a static (single graph) model. The resultant representation provides graph frequency features that compactly capture the spatial patterns of the underlying multidimensional EEG signal as well as the evolution of spatial relationships over time. These dynamic graph features are fed into an attention-based long short-term memory network for speech imagery recognition. A novel EEG data augmentation method is also proposed for improving training robustness.Main results. Experimental evaluation using a range of experiments shows that the proposed dynamic graph features are more effective than conventional time-frequency features for speech imagery recognition. The overall system outperforms current state-of-the-art approaches, yielding accuracy gains of up to 10%.Significance. The dynamic graph representation captures time-varying spatial relationships in EEG signals, overcoming limitations of static graph models and conventional feature extraction. Combined with data augmentation and attention-based classification, it demonstrates substantial improvements over existing methods in speech imagery recognition.}, }
@article {pmid41464697, year = {2025}, author = {Kimmeyer, M and Buijze, GA and Soares, MN and Rab, P and Colombini, AG and Diot, R and Macken, A and Lafosse, T}, title = {Arthroscopic Bioinductive Collagen Scaffold Augmentation in High-Risk Posterosuperior Rotator Cuff Tears: Clinical and Radiological Outcomes.}, journal = {Journal of clinical medicine}, volume = {14}, number = {24}, pages = {}, doi = {10.3390/jcm14248797}, pmid = {41464697}, issn = {2077-0383}, abstract = {Background/Objectives: Bioinductive bovine collagen implants (BCI) have been introduced to enhance tendon biology and promote tissue regeneration in rotator cuff (RC) repairs. This study aimed to assess the clinical and radiological outcomes of arthroscopic posterosuperior rotator cuff (psRC) repair with BCI augmentation in full-thickness tears at increased risk of retear. Methods: This case series analyzed 30 patients with psRC tears who were classified as being at high risk of failure according to a predefined set of parameters, including patient history, radiological findings and intraoperative assessments, and the presence of psRC retears. All patients subsequently underwent arthroscopic psRC repair with BCI augmentation, compromising 21 primary and 9 secondary repairs. Clinical outcomes were assessed using Subjective Shoulder Value (SSV), American Shoulder and Elbow Surgeons (ASES) shoulder score, and Constant score at 6 and 12 months postoperatively. Tendon integrity was assessed using the Sugaya classification. Results: At 12 months, magnetic resonance imaging revealed complete tendon healing in 56.7%, partial healing in 16.7%, and insufficient healing in 26.7%. Significant improvements in SSV (45.3 to 83.5), ASES (40.6 to 77.8), and Constant score (36.6 to 71.7) were observed at 12 months postoperatively, with all outcome measures exceeding their respective minimally clinically important differences. Two patients (6.7%) developed secondary shoulder stiffness, and 1 patient (3.3%) required revision surgery for bicipital groove pain. Conclusions: Augmentation with a BCI in arthroscopic repair of high-risk psRC tears demonstrate promising short-term results. Patients achieve significant improvements in pain and shoulder function, accompanied by satisfactory tendon healing on MRI.}, }
@article {pmid41461597, year = {2025}, author = {Di Nicola, MR and Colla, L and Mulder, KP and Storniolo, F and Verbrugghe, E and Esposito, G and Grasso, DA and Pasmans, F and Martel, A}, title = {Ophidiomycosis Prevalence and Disease Ecology in a Natrix tessellata (Laurenti, 1768) Population From Northern Italy.}, journal = {Journal of experimental zoology. Part A, Ecological and integrative physiology}, volume = {}, number = {}, pages = {}, doi = {10.1002/jez.70061}, pmid = {41461597}, issn = {2471-5646}, abstract = {Fungal pathogens pose a growing threat to vertebrate biodiversity. In snakes, Ophidiomyces ophidiicola (Oo) has garnered particular concern, although its impact in Europe remains poorly understood. We conducted a season-long, standardized survey of dice snakes (Natrix tessellata) along the northern shore of Lake Como (Italy) to quantify Oo and ophidiomycosis prevalence, identify the circulating strain, and explore the association with environmental, morphological and behavioral traits. Between March and October 2024, we collected 96 N. tessellata samples (23 sheds and swabs from 73 live individuals; scale clips were also collected from 60 out of the 73 live individuals). These samples were analyzed through qPCR, histopathology, and direct field observations. After excluding four recaptures, the dataset comprised 92 N. tessellata samples (23 sheds and swabs from 69 individuals), of which 49 tested positive for Oo (53.3%). Among live individuals, 26 tested positive (37.7%). Of these, 21 showed clinical signs (i.e., skin lesions; 80.8%), and histology confirmed ophidiomycosis in 10 of 20 tested Oo-positive samples (47.6%). Among the five Oo-positive snakes without skin lesions, only one showed histological evidence of ophidiomycosis. This resulted in "at least apparent" ophidiomycosis (i.e., pooling the case-classification categories "Apparent ophidiomycosis", "Ophidiomycosis" and "Ophidiomycosis and Oo shedder") being confirmed in 22 out of 69 live snakes (31.9%), corresponding to an overall disease prevalence of 23.9% (22 out of 92) across the full sample set. All sequenced samples belonged to clade II. Bayesian models revealed that skin lesions predicted both Oo detection and ophidiomycosis, while snout-vent length was inversely related to both pathogen presence and disease, suggesting age-linked susceptibility. Both Oo-positive and diseased snakes had lower body temperatures but showed no clear preference for warmer substrates, suggesting limited or absent behavioral fever. Body-condition index (BCI) did not differ between Oo/disease-positive and Oo/disease-negative snakes, suggesting possible host tolerance. An assessment of antipredator behavior revealed a marked reduction in musking among Oo-positive snakes, potentially compromising antipredator defenses. Our findings identify N. tessellata as a possible model for European ophidiomycosis research and highlight the need for multi-season capture-recapture studies.}, }
@article {pmid41460615, year = {2025}, author = {Liu, H and Bai, Y and Guo, M and Zhao, R and Zhu, J and Ni, G}, title = {Dynamic brain functional connectivity in age-related hearing loss during auditory selective spatial attention.}, journal = {GeroScience}, volume = {}, number = {}, pages = {}, pmid = {41460615}, issn = {2509-2723}, support = {824B2056//National Natural Science Foundation of China/ ; 2023YFF1203500//Key Technologies Research and Development Program/ ; 2025XJ1-0006//Seed Foundation of Tianjin University/ ; }, abstract = {Age-related hearing loss (ARHL) is a common health problem that impairs auditory perception. However, the dynamic patterns of brain functional connectivity in ARHL during auditory spatial selective attention have not been thoroughly investigated. In this study, 32 older adults were recruited to investigate the dynamic brain functional connectivity in ARHL. First, an experimental paradigm for auditory spatial selective attention was designed, and neural electrical signals were recorded using electroencephalography. Then, a multilayer time-varying brain network was constructed based on multiple time windows, equally dividing each epoch signal to capture dynamic functional connectivity across time scales. Finally, the core layer brain network was identified by the multilayer time-varying brain network properties to investigate the changing patterns of network topology. Behavioral analysis revealed a significant negative correlation between the severity of hearing loss and auditory spatial selective attention performance. Multilayer time-varying brain network analysis revealed that worsening hearing loss was found to lead to increased inter-layer connectivity strength, decreased multilayer modularity and a higher participation coefficient. This suggests that the brain compensates by weakening the independence of local functional modules and enhancing cross-interaction. Core layer analysis further highlighted the critical role of the right parietal lobe in auditory spatial selective attention. It also suggested that connectivity between the right prefrontal and frontal lobes may play a compensatory role in ARHL. In conclusion, these findings provide important neuroscientific insights into the dynamic brain functional connectivity of ARHL, and potential biomarkers and time windows for the development of precision auditory rehabilitation strategies.}, }
@article {pmid41459740, year = {2026}, author = {Kwon, J and Shin, Y}, title = {Foundation Models for Neural Signal Decoding: EEG-Centered Perspectives Toward Unified Representations.}, journal = {The European journal of neuroscience}, volume = {63}, number = {1}, pages = {e70376}, doi = {10.1111/ejn.70376}, pmid = {41459740}, issn = {1460-9568}, mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Models, Neurological ; Machine Learning ; Animals ; }, abstract = {Neural signals such as EEG, ECoG, and intracortical recordings offer a valuable window into brain dynamics but remain difficult to decode due to high dimensionality, nonstationarity, and substantial interindividual variability. Traditional machine learning and deep learning models often show limited generalizability and insufficient interpretability in these settings. Foundation models (FMs)-large-scale architectures pretrained on diverse datasets-have recently emerged as a promising paradigm for building robust, transferable, and physiologically grounded neural representations. Among these modalities, EEG currently serves as the most practical and representative platform for FM development due to its large-scale open datasets, standardized protocols, and broad clinical applicability, while the same conceptual framework remains generalizable to other neural recording types. This review synthesizes emerging FM approaches for neural decoding and critically examines representative EEG-based architectures. We highlight three essential design principles: physiology-aware representation learning that captures oscillatory and dynamic structure, structure-aware architectures that incorporate spatial and anatomical priors, and interpretability mechanisms that ensure neuroscientific and clinical validity. Although models such as the Patched Brain Transformer, CBraMod, and BrainGPT demonstrate encouraging adaptability, many still inherit objectives from non-neural domains and underutilize spatial priors such as electrode topology or functional connectivity. While this review focuses on EEG as the most data-rich and scalable testbed, the same framework can extend to ECoG and intracortical recordings to support unified neural representations across spatial and temporal scales. Fully realizing the potential of neural FMs will require biologically informed objectives, structure-aware architectures, interpretable representations, and standardized data ecosystems.}, }
@article {pmid41467019, year = {2025}, author = {Yang, L and Zhen, H and Li, L and Li, Y and Zhang, H and Xie, X and Zhang, RY}, title = {Functional diversity of visual cortex improves constraint-free natural image reconstruction from human brain activity.}, journal = {Fundamental research}, volume = {5}, number = {6}, pages = {2639-2648}, pmid = {41467019}, issn = {2667-3258}, abstract = {Previous brain decoding studies using functional magnetic resonance imaging (fMRI) have greatly advanced our understanding of human visual coding and non-invasive brain-machine interfaces. However, most of these studies focus on classifying a limited number of image categories or reconstructing visual images with additional information, e.g., semantic categories and textual cues. Constraint-free visual reconstruction remains scarce. Here, we propose a generative network based on the functional diversity of the human visual cortex (FDGen) that takes multivariate brain activity as input and directly reconstructs natural images perceived by observers without any additional cues (semantic categories or textual description). Our FDGen is augmented by two bio-inspired computational modules. Based on the functional specializations of the human visual cortex, we propose a new function-based input module (FIM) that projects responses from different brain regions into separate feature spaces. Second, inspired by human attention, we construct a computational module to derive attentive feature weights at the function level to refine the feature map. These function-selection modules (FSMs) allow the network to dynamically select multiscale visual information during the generation process. We test FDGen on the popular fMRI datasets of natural images and achieve highly robust performance. Our work represents an important step forward in the development of fMRI-based brain decoding algorithms and highlights the utility of neuroscience theories in the design of deep learning models.}, }
@article {pmid41459561, year = {2025}, author = {Pham, DT and Titkanlou, MK and Mouček, R}, title = {A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1716204}, pmid = {41459561}, issn = {1662-4548}, abstract = {INTRODUCTION: Motor imagery (MI) classification and sleep apnea (SA) detection are two critical tasks in brain-computer interface (BCI) and biomedical signal analysis. Traditional deep learning models have shown promise in these domains, but often struggle with temporal sparsity and energy efficiency, especially in real-time or embedded applications.<br><br>METHODS: In this study, we propose SpiTranNet, a novel architecture that deeply integrates Spiking Neural Networks (SNNs) with Transformers through Spiking Multi-Head Attention (SMHA), where spiking neurons replace standard activation functions within the attention mechanism. This integration enables biologically plausible temporal processing and energy-efficient computations while maintaining global contextual modeling capabilities. The model is evaluated across three physiological datasets, including one electroencephalography (EEG) dataset for MI classification and two electrocardiography (ECG) datasets for SA detection.<br><br>RESULTS: Experimental results demonstrate that the hybrid SNN-Transformer model achieves competitive accuracy compared to conventional machine learning and deep learning models.<br><br>DISCUSSION: This work highlights the potential of neuromorphic-inspired architectures for robust and efficient biomedical signal processing across diverse physiological tasks.}, }
@article {pmid41459239, year = {2025}, author = {Radu, R}, title = {Cognitive frontiers: neurotechnology and global internet governance.}, journal = {Frontiers in digital health}, volume = {7}, number = {}, pages = {1690489}, pmid = {41459239}, issn = {2673-253X}, abstract = {This article explores the largely uncharted intersection of neurotechnology and Internet governance on the international policy agenda. Neurotechnologies encompass a broad spectrum of functions and applications, from the direct recording or alteration of brain activity to the analysis of emotions and mental states through data collected from wearable devices, applications, and AI-based tools. Innovations such as cochlear implants, sleep optimisation technologies, and immersive educational tools are already available, and significant investments are made in the next generation of devices that blur the lines between mind, machine, and action, posing unprecedented challenges. While some international organisations have begun addressing the ethical and human rights implications of neurotechnology, there remains significant fragmentation and a lack of clarity regarding its integration into Internet governance. Critical issues related to neural infrastructure, standards, access to technologies, and protections for neural data have been overlooked in the 2024 Global Digital Compact and might remain off the agenda for the upcoming 20th review of the World Summit on the Information Society. This contribution underscores the urgent need to analyse the profound implications of neurotechnology, advocating for proactive measures that align with progress made across Internet governance fora, with respect to legal safeguards, multistakeholder consultations and institutional pillars.}, }
@article {pmid41457672, year = {2025}, author = {Cai, Z and Zhang, S and Wang, J and Luo, Y and Zhu, M and Lv, Z and Li, X and Chen, Y and Song, Y and Gao, X and Guan, C and Chen, X}, title = {Bioinspired Heat-Induced Viscoelasticity-Switchable Electrodes for Conformal Brain-Computer Interfaces.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e17936}, doi = {10.1002/adma.202517936}, pmid = {41457672}, issn = {1521-4095}, support = {//Prime Minister's Office/ ; //Campus of Research Excellence and Technological Enterprise (CREATE) programme/ ; //Agency for Science, Technology and Research (A*STAR)/ ; //Scent Digitalization and Computation (SDC) programme/ ; U2241208//National Natural Science Foundation of China/ ; M23L8b0049//Agency for Science, Technology and Research/ ; SHINE//National Research Foundation Singapore/ ; CREATE-SGSR//National Research Foundation Singapore/ ; }, abstract = {Electroencephalography is a promising noninvasive modality for brain-computer interfaces (BCIs), yet its widespread adoption is constrained by electrode limitations: dry electrodes yield unstable signals, whereas wet electrodes require laborious setup and are ill-suited to wearable devices. Inspired by honeybees that locally heat beeswax to reversibly switch it between rigid and moldable states for comb construction, this work introduces a heat-induced viscoelasticity-switchable electrode (HIVE) that enables conformal contact on hairy scalps and user-friendly operation in wearable systems. HIVE integrates a thermoresponsive gelatin gel confined in a sponge matrix with an on-electrode microheater. Its temperature is actively modulated on demand, enabling autonomous switching between the gel and sol states. As a flowable sol, it permeates hair, conforms to the skin. At body temperature, it remains in a viscoelastic state, providing strong adhesion. Moreover, heating duration is closed-loop controlled using real-time electrode-skin impedance. In steady-state visual evoked potential paradigm, HIVE delivers high classification accuracy comparable to gold-standard wet electrodes while supporting wearable BCI devices for vision-based wheelchair navigation and high-speed text entry. By translating honeybee viscoelasticity-modulation strategy into bioelectronic interfaces, this work provides a practical solution for wearable BCI devices and a new design paradigm for conformal biointerfaces on hairy or piliferous surfaces.}, }
@article {pmid41456250, year = {2025}, author = {Vincenzo, R and Marianna, C and Rossella, C and Gianluca, DF and Andrea, G and Daniele, G and Gianluca, B and Fabio, B and Pietro, A}, title = {Beyond the lab: real-world benchmarking of wearable EEGs for passive brain-computer interfaces.}, journal = {Brain informatics}, volume = {}, number = {}, pages = {}, doi = {10.1186/s40708-025-00290-x}, pmid = {41456250}, issn = {2198-4018}, support = {SAP_RICERCA_2024_TCI_ARIC&#xd2;_P_01//Sapienza Universit&#xe0; di Roma/ ; B83C24006240005//Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro/ ; }, abstract = {PURPOSE: Wearable EEG systems are increasingly used for brain-computer interface (BCI) applications beyond controlled laboratory environments. However, there is still limited evidence on their reliability in real-world cognitive monitoring, especially for deriving robust mental-state indicators. This study investigates the signal quality, computational stability, and neurometric consistency of two widely used consumer-grade EEG devices (Emotiv EPOC X and Muse S) compared to a validated research-grade system (Mindtooth Touch) during naturalistic tasks relevant to passive BCIs and brain-machine intelligence.<br><br>METHOD: Twenty-four participants completed a multimodal protocol including video observation, multitasking under varying cognitive loads, and a simulated driving task. Each participant used all three EEG systems in a counterbalanced order to avoid any bias induced by the order. Signal quality was assessed through artefact analysis and Power Spectral Density (PSD) stability. Neurometrics, i.e., metrics related to specific mental and emotional states that can be extracted from EEG signal processing (workload, attention, vigilance, and approach-withdrawal) were extracted and compared across devices, conditions, and subjective reports of effort and comfort.<br><br>FINDING: The research grade system demonstrated higher signal stability, fewer artefacts, and more consistent neurometric responses to cognitive variations, with high significant correlation with subjective measures. Post-processing improved data continuity in consumer devices, but neurometrics remained less sensitive to task demands and less aligned with subjective ratings. Each device reflected different trade-offs between data quality, usability, and cost.<br><br>CONCLUSION: Research-grade systems remain more reliable for passive BCI applications requiring high-resolution cognitive state monitoring. Nevertheless, consumer-grade headsets may still be appropriate for exploratory studies or non-critical applications. This work highlights key trade-offs between signal quality, usability, and application goals, contributing to the broader integration of wearable neurotechnologies into brain-machine intelligence frameworks.}, }
@article {pmid41456194, year = {2025}, author = {Kotov, SV and Isakova, EV and Borisova, VA}, title = {[Spectrum of tolerability and safety of the use of brain-computer interfaces with biofeedback in cognitive rehabilitation after a stroke].}, journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova}, volume = {125}, number = {12. Vyp. 2}, pages = {86-93}, doi = {10.17116/jnevro202512512286}, pmid = {41456194}, issn = {1997-7298}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke Rehabilitation/methods ; *Biofeedback, Psychology ; *Stroke/complications/psychology ; Aged ; Electroencephalography ; Adult ; Event-Related Potentials, P300 ; Cognitive Training ; }, abstract = {OBJECTIVE: To assess the tolerability and safety of using high-tech software complexes with biofeedback (BF) via a brain-computer interface (BCI) in the recovery of patients after a stroke, based on an analysis of neuropsychological examination data.<br><br>MATERIAL AND METHODS: The study included 100 stroke patients: 40 patients in the main group, 40 patients in the comparison group, and 20 patients in the control group. The Hospital Anxiety and Depression Scale (HADS), the Beck Depression Inventory (BDI), the Hamilton Anxiety Rating Scale (HARS), the Hamilton Depression Rating Scale (HDRS), the Montreal Cognitive Assessment (MoCA), and the Mini-Mental State Examination (MMSE) were used. In the main group, sessions were conducted using BCI-BF1 based on the P300 potential; in the comparison group, sessions were conducted using BCI-BF2 based on the mu-rhythm of electroencephalography (EEG); control group patients received standard of care.<br><br>RESULTS: Improvement of the symptoms was reported; no &#xab;aggravation/increase&#xbb; of the existing symptoms or the occurrence of new symptoms was observed, which indicated good tolerance of using BCI-BF1 and BCI-BF2. The results of the assessment on the BDI, HARS, and HDRS scales showed a statistically significant improvement, indicating the regression of existing affective disorders corresponding to the level of minor disorders, namely &#xab;subclinical anxiety/depression&#xbb; (p<0.001). When assessing the BDI and HDRS scales, a statistically significant decrease in the scores for the subscale of affective-cognitive disorders was found in the main group (p=0.002) and in the comparison group (p<0.001). MoCA score showed no decrease from the baseline score of 25 or more: in the main group, there was an increase in the median total score (p=0.014); in the comparison group, there was no change (p=0.683).<br><br>CONCLUSION: Treatment with BCI-BF1 based on P300 and BCI-BF2 based on the EEG mu-rhythm was safe in patients in the recovery period of stroke, showed good tolerance, did not cause the occurrence or increase of affective disorders, and did not reduce the MoCA score.}, }
@article {pmid41455765, year = {2025}, author = {Huang, X and Zhou, W and Hou, W and Zhou, Y and Li, M and Zhang, Y and Zhang, Q and Yan, W and Zhang, D and Lee, HJ}, title = {Tumor-secreted factors induce aberrant accumulation of vitamin A-enriched lipid droplets in the liver.}, journal = {Communications biology}, volume = {}, number = {}, pages = {}, doi = {10.1038/s42003-025-09404-x}, pmid = {41455765}, issn = {2399-3642}, support = {LZ25H180001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {Cancer is increasingly recognized as a systemic disease, extending beyond local alterations to systemic alterations in distant organs through the release of various factors that promote tumor progression and metastasis. Here, we applied hyperspectral stimulated Raman scattering (hSRS) microscopy to study metabolic alterations in the liver driven by distant tumors, revealing unprecedented accumulation of vitamin A-enriched lipid droplets. Quantitative spectral analysis uncovered increased unsaturation levels and abnormal vitamin A ester. Notably, inhibition of secretory pathways in remote tumors effectively abrogated these metabolic alterations, with FABP5 in tumor-derived extracellular vesicles identified as a key mediator. These findings uncover a unique aspect of cancer progression mechanisms, implicating tumor-driven systemic lipid metabolic remodeling and vitamin A dysregulation in metastatic progression and therapeutic response.}, }
@article {pmid41455146, year = {2025}, author = {Zhang, H and Che, J}, title = {From Hierarchical Decoding to State Dependent Computation: "Comment on Neural decoding in brain computer interfaces Hierarchical representations, complexity measures, and dynamical perspectives" by Li et al.}, journal = {Physics of life reviews}, volume = {56}, number = {}, pages = {202-203}, doi = {10.1016/j.plrev.2025.12.014}, pmid = {41455146}, issn = {1873-1457}, }
@article {pmid41454830, year = {2025}, author = {Wen, X and Xue, P and Ma, S and Zhu, M and Liu, Y and Liu, P and Jing, B and Ge, R and Yang, M and Mo, X and Zhang, D}, title = {Sex effects on cortical alterations in infants with complex congenital heart disease.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {35}, number = {12}, pages = {}, doi = {10.1093/cercor/bhaf339}, pmid = {41454830}, issn = {1460-2199}, support = {62476129//National Natural Science Foundation of China/ ; 81970265//National Natural Science Foundation of China/ ; 82270310//National Natural Science Foundation of China/ ; NZ2024040//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; Male ; Female ; *Heart Defects, Congenital/diagnostic imaging/complications/physiopathology ; Infant ; Magnetic Resonance Imaging ; *Cerebral Cortex/diagnostic imaging/growth &amp; development/pathology ; *Sex Characteristics ; Child, Preschool ; Gray Matter/diagnostic imaging/growth &amp; development ; }, abstract = {Congenital heart disease is linked to substantial variability in neurodevelopmental outcomes, with sex being a key contributing factor. Compared with females, male congenital heart disease infants often show greater impairments in motor, cognitive, and language development. However, studies on sex differences in early brain development among congenital heart disease patients remain limited. To fill these gaps, this study included 79 infants with complex congenital heart disease (42 males, 37 females) and 87 healthy controls (47 males, 40 females), collecting magnetic resonance imaging data, clinical information, and neurodevelopmental assessments. We examined sex-specific effects on global and regional brain development in congenital heart disease infants aged 1 to 2 yr using imaging and statistical analysis. Male congenital heart disease infants showed global brain volume reduction and regional cortical delays, including increased cortical thickness and gray matter volume. In contrast, female congenital heart disease infants had no significant global volume change but exhibited localized structural abnormalities, such as reduced surface area and increased cortical thickness. Notably, reduced global brain volume in congenital heart disease males was associated with poorer gross motor skills. Distinct sex differences in brain development exist among congenital heart disease infants during early life. Recognizing these differences is critical for developing sex-specific treatment and neuroprotective strategies.}, }
@article {pmid41454419, year = {2025}, author = {Stump, T and Baker, B and Caldwell, R and Sharma, R and Negi, S and Rieth, L}, title = {Improved electrode stimulation stability of Utah arrays.}, journal = {Bioelectronic medicine}, volume = {11}, number = {1}, pages = {30}, pmid = {41454419}, issn = {2332-8886}, support = {UG3NS107688//BRAIN Initiative/ ; N66001-15-C-4017//DARPA HAPTIX/ ; }, }
@article {pmid41453502, year = {2025}, author = {Niu, S and Han, X and Cao, L and Tian, Y and Yuan, D and Cheng, L}, title = {Fusion framework: Conditional-aware one-stage nested event extraction model.}, journal = {Journal of biomedical informatics}, volume = {}, number = {}, pages = {104972}, doi = {10.1016/j.jbi.2025.104972}, pmid = {41453502}, issn = {1532-0480}, abstract = {We present CA-NEE, a Conditional-Aware one-stage model for overlapping and nested biomedical event extraction. CA-NEE integrates an event-type-aware conditioning mechanism with token-pair relation modeling to jointly identify triggers, argument spans, and roles. A Conditional Layer Normalization (CLN) dynamically adapts token representations to candidate event types, and a parallel word-pair scorer predicts span boundaries and roles in a single pass. Evaluations on GENIA11 and GENIA13 show consistent gains in Trigger Classification (TC) and Argument Classification (AC) over strong baselines, particularly on complex overlapping and nested structures. These results demonstrate that CA-NEE offers an effective and efficient solution for biomedical event extraction.}, }
@article {pmid41452697, year = {2025}, author = {Zhang, L and Luo, X and Cao, P and Cheng, K and Liu, H and Zhao, R and Zan, X and Ma, J and Cheng, R and Wang, R and Hou, X and Chou, X and He, J}, title = {A Novel Rat Robot: Multi Degree of Freedom Motion Control.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3648651}, pmid = {41452697}, issn = {1558-2531}, abstract = {OBJECTIVE: The development of brain-computer interface (BCI) technology has enabled animals to execute movements in accordance with human intent. The rat robot represents a novel robotic system based on BCI technology. However, due to limitations in electrode fabrication techniques and the use of simplistic control strategies, current rat robots are restricted to limited movement patterns, which hinder their applicability in real-world scenarios. To address these challenges, we have developed a portable wireless neural stimulator and a novel 3D integrated stimulating electrode. By refining the locomotion control strategy, we aim to achieve complex, high-degree-of-freedom movement in rat robot systems.<br><br>METHODS: 3D integrated electrodes were implanted into the rats' head, with no reward-based training required. By utilizing a wearable wireless stimulation backpack to connect the electrodes and deliver electrical stimulation to multiple brain regions, thereby enabling the rat to perform forward movement, turning, and stopping behaviors.<br><br>RESULTS: The experimental results demonstrate that under optimized stimulation parameters, the forward speed of the rat robot can be controlled to achieve 31.06 &#xb1; 1.21 m/min, the turning angle can reach up to 150 &#xb1; 1.22&#xb0;, and the stopping duration can be flexibly adjusted. Furthermore, we presented a practical scenario in which the rat robot successfully executed a predefined navigation task in a real-world environment, thereby validating its high degree of movement flexibility and control precision.<br><br>CONCLUSION: This study achieved high-degree-of-freedom motion control of rat robots without the need for reward-based training, which was previously unattainable.<br><br>SIGNIFICANCE: This research establishes a crucial foundation and provides valuable technical references for the application of animal robots in fields such as information reconnaissance and wreckage search and rescue operations.}, }
@article {pmid41451424, year = {2025}, author = {Huang, X and Zhang, Y and Xiao, H and Chen, J and Jiang, Y}, title = {The evolution of cervical spine trauma classification: a paradigm shift from morphological description to clinical decision-making.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1728720}, pmid = {41451424}, issn = {1664-2295}, abstract = {OBJECTIVE: This review systematically traces the evolution of subaxial cervical spine classification, highlighting the paradigm shift from morphological description to decision-oriented functional assessment and exploring future technological directions.<br><br>METHODS: A comprehensive narrative literature review was conducted, analyzing key classification systems, their underlying principles, and the technological advancements shaping the field.<br><br>RESULTS: Early mechanistic classifications were limited by poor interobserver reliability. The Subaxial Injury Classification (SLIC) system was a pivotal advance, integrating morphology, disco-ligamentous complex (DLC) integrity, and neurological status into a treatment-guiding score. However, its inconsistent reliability, particularly in DLC assessment, limited its adoption. The subsequent AO spine classification resolved these issues by introducing a more rigorous, hierarchical framework that achieved excellent, validated interobserver reliability. Crucially, the AO spine system also provides significant prognostic value by correlating morphological subtypes with long-term neurological recovery.<br><br>CONCLUSION: The classification of cervical trauma has transitioned from a descriptive to an applied science. Future developments promise to resolve remaining challenges: artificial intelligence (AI) offers a definitive solution to interobserver variability, advanced imaging like diffusion tensor imaging (DTI) will refine prognostication, and brain-computer interfaces (BCI) provide new hope for functional reconstruction in severe injuries, heralding an era of precision medicine.}, }
@article {pmid41450957, year = {2025}, author = {Sondh, I and Johnson, LA and Ghose, GM and Loveland, A and Larson, L and Lim, HH and Adams, ME}, title = {Development of a non-human primate model for preclinical research of a novel auditory nerve implant.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1669116}, pmid = {41450957}, issn = {1662-4548}, abstract = {The cochlear implant is a widely available hearing restoration technology that can provide speech understanding in quiet environments. This technology struggles however, in noisy settings or situations involving multiple speakers. The primary cause of these performance limitations is a poor neural interface, in which the bony wall of the cochlea separates the electrode surface from the auditory nerve fibers, thus causing unwanted current spread and non-specific frequency activation. This study utilizes an alternative auditory prosthetic technology (auditory nerve implant, ANI) that enables direct auditory nerve stimulation, which provides a potentially superior neural interface and enables more precise targeting of auditory nerve fibers than traditional cochlear implants. As auditory nerve implants progress towards clinical translation, new implant designs and stimulation strategies will be created. Animal models to efficiently test and iterate through these new designs will be useful for the continued development of ANI technology. We present a viable surgical approach in the non-human primate (rhesus macaque) along with electrophysiological results that demonstrate robust activation of the auditory system at low current levels via intraneural stimulation. Our findings indicate that the rhesus macaque, which possesses an inner ear anatomy more similar to the human compared to other animal models used in the hearing field (e.g., rodents, felines and ferrets), has strong potential as a useful preclinical testbed involving an upright head model for future ANI prototypes and stimulation strategy development.}, }
@article {pmid41450855, year = {2025}, author = {Cui, J}, title = {An adaptive hand exoskeleton rehabilitation training system integrating virtual reality and an AI-based assessment engine.}, journal = {Frontiers in sports and active living}, volume = {7}, number = {}, pages = {1724021}, pmid = {41450855}, issn = {2624-9367}, abstract = {INTRODUCTION: Post-stroke hand motor impairment is a major cause of long-term functional disability and reduced quality of life, with approximately 70% of stroke survivors experiencing persistent limitations in fine motor control. Conventional rehabilitation is constrained by low adherence, subjective assessment, and insufficient individualization, which limits exploitation of the neuroplasticity window for motor relearning. To address these challenges, we propose a bio-AI-VR integrated hand rehabilitation system that fuses biosignal sensing (bio), AI-based analysis, and virtual reality (VR) interaction to realize an efficient, adaptive, and quantifiable closed-loop training process. The integration rationale is grounded in three theoretical pillars: (i) multimodal data fusion theory-combining heterogeneous biosignal and behavioral data through AI to overcome single-modality limitations; (ii) closed-loop adaptive control theory-dynamically balancing challenge and capability via real-time feedback; (iii) neuroplasticity multisensory enhancement theory-coordinating visual, proprioceptive, and motor pathways to strengthen cortical reorganization. This work addresses three testable hypotheses: (RQ1) Can multimodal biosignal fusion achieve real-time assessment with R 2 ≥ 0.65 and latency < 50 ms? (RQ2) Does bio-AI-VR integration yield FMA-UE improvement ≥ 6 points (minimal clinically important difference) with effect size d ≥ 0.8 ? (RQ3) Are all three components (bio, AI, VR) necessary, with ablation causing ≥ 15 % performance degradation?<br><br>METHODS: A lightweight hand exoskeleton (< 400 g, 3 DoF/finger) integrates a 6-axis IMU (100 Hz) and 16-channel sEMG (1 kHz) to synchronously acquire kinematics and muscle activation. Extended Kalman filtering fuses sensor streams before AI processing. Features include range of motion (ROM), smoothness metrics (SPARC, LDLJ), sEMG root-mean-square (RMS), median frequency (MDF), and co-contraction index (CCI). A hybrid model combining random forests (200 trees, depth 8) and support vector regression (RBF kernel, &#x3b3; = 0.01 , C = 10) outputs a real-time composite score S t &#x2208; [ 0 , 1 ] via multi-task learning with GroupKFold cross-validation, mapped to clinical scales through Sigmoid normalization. FMA-UE proxy labels for window-level training were constructed via linear interpolation (80%), biomechanical anchoring (15%), and expert annotation (5%, inter-rater &#x3ba; = 0.78). A cloud AI engine communicates bidirectionally with Unity-based VR over MQTT to close the perception-assessment-assistance loop. The assistance-as-needed (AAN) algorithm adjusts exoskeleton torque (u t) and VR difficulty (d t) using S t as control input with hysteresis, dead zone, and rate limiting to ensure smooth adaptation. Twenty-four stroke survivors (3-12 months post-stroke, FMA-UE 15-50) underwent 4-week training (5 sessions/week, 20 min/session). Outcomes included FMA-UE (primary), ARAT, grip strength, normalized ROM, task success rate, and System Usability Scale (SUS). Statistical analysis employed paired t -tests with Hedges' correction for effect sizes, Bonferroni adjustment for multiple comparisons, and leave-one-subject-out cross-validation (LOSOCV) to assess model generalization.<br><br>RESULTS: All 24 participants completed the study with one missed session (479 of 480 scheduled sessions, 8,946 annotated segments); end-to-end latency median 38 ms (IQR 33-42 ms), decomposed as: sampling 8 &#xb1; 2 ms, preprocessing 4 &#xb1; 1 ms, network 12 &#xb1; 3 ms, AI inference 5 &#xb1; 1 ms, control 2 &#xb1; 0.5 ms, command return 7 &#xb1; 2 ms. Offline model performance: R 2 = 0.72 (GroupKFold), MAE = 3.2 points, Spearman &#x3c1; = 0.68 with FMA-UE (p < 0.001); LOSOCV: R 2 = 0.68 &#xb1; 0.09 ; test-retest ICC(2,1) = 0.84 [0.76, 0.91]. AAN algorithm reduced assist torque 62 % &#x2192; 45 % (- 27.4 %), increased VR difficulty 0.42 &#x2192; 0.69 (+ 64.3 %), improved task success 61.3 % &#x2192; 82.1 % (+ 20.8 pp). Clinical outcomes (paired t -test): FMA-UE + 9.1 [6.7, 11.5], d = 0.98 ; ARAT + 7.6 [5.2, 10.0], d = 0.93 ; grip + 4.1 kg [2.5, 5.7], d = 0.72 ; ROM n + 0.14 ; SPARC - 0.16 . Subgroup analysis: moderate-to-severe (n = 13) showed greater FMA-UE gain (+ 10.7 vs + 7.2 in mild-to-moderate, p < 0.05). Ablation experiments confirmed synergistic necessity: Bio only (R 2 = 0.45 , FMA-UE + 4.3), VR only (R 2 = 0.38 , + 3.9), Bio-AI (R 2 = 0.70 , + 7.2 , compliance 68%), complete system (R 2 = 0.72 , + 9.1 , compliance 88%). SUS 84 &#xb1; 6 ; no serious adverse events.<br><br>DISCUSSION: Results validate all three hypotheses: (i) multimodal fusion exceeded technical targets (R 2 = 0.72 > 0.65 , latency 38 ms < 50 ms); (ii) clinical efficacy surpassed MCID with large effect sizes (FMA-UE + 9.1 > 6 points, d = 0.98 > 0.8), exceeding published spontaneous recovery rates (2-4 points); (iii) ablation experiments demonstrated &#x2265; 15 % degradation when removing any component, confirming non-additive synergistic effects of bio-AI-VR integration. Compared to recent brain-computer interface systems using EEG-based motor imagery, this approach achieves paradigm shift toward execution-based rehabilitation with direct motor intent capture and real-time physical feedback. The AAN control law elevates from low-level motion control to high-level rehabilitation strategy, spanning multiple temporal scales (window to course-level) and dual channels (physical assistance + cognitive challenge). Limitations include single-arm design limiting causal inference, small sample size (n = 24), short intervention period (4 weeks), FMA-UE proxy construction via linear interpolation, and controlled clinical setting vs. real-world deployment. Future work requires larger RCTs with active control arms, extended follow-up (3-6 months), dense longitudinal assessments, exploration of deep learning architectures for temporal modeling, and validation in home-based telerehabilitation settings. The bio-AI-VR system demonstrates feasibility of data-driven, multimodal closed-loop rehabilitation, offering a wearable, low-latency, and personalized solution for post-stroke hand recovery that bridges the gap between laboratory innovation and clinical translation.}, }
@article {pmid41450502, year = {2025}, author = {Li, J and Hu, M and Wang, T and Xie, Y and Liu, Y and Yao, J and Hua, G and Yan, X and Fan, H}, title = {Temporal trends in chronic diseases among offshore oil workers and the interaction effect of age with body mass index.}, journal = {Frontiers in public health}, volume = {13}, number = {}, pages = {1738126}, pmid = {41450502}, issn = {2296-2565}, mesh = {Humans ; *Body Mass Index ; Middle Aged ; Male ; Adult ; Female ; *Hypertension/epidemiology ; Chronic Disease/epidemiology ; Prevalence ; China/epidemiology ; Age Factors ; *Diabetes Mellitus/epidemiology ; *Dyslipidemias/epidemiology ; Risk Factors ; *Oil and Gas Industry ; Aged ; }, abstract = {OBJECTIVE: To analyze trends in hypertension, diabetes, and dyslipidemia prevalence among Chinese offshore oil workers and explore the independent and interaction effects of age and BMI.<br><br>METHODS: Using health examination data of this population (2014-2024), we calculated the crude prevalence rate (CPR, the prevalence rate without age-structure adjustment) and the age-standardized prevalence rate (ASPR, adjusted to a standard population structure). Joinpoint regression assessed ASPR trends, and multivariable logistic regression analyzed age and BMI effects.<br><br>RESULTS: The overall mean CPR for hypertension, diabetes, and dyslipidemia from 2014 to 2024 were 22.41, 2.53, and 29.64%, respectively. Trend analysis revealed that ASPR for diabetes [Annual Percent Change (APC): 24.08, 95% CI: 12.93-35.23] and Dyslipidemia (APC: 21.83, 95% CI: 10.45-33.21) increased significantly (both p&#x202f;<&#x202f;0.001), while hypertension trend was non-significant. In the risk factor analysis, both age (OR for hypertension&#x202f;=&#x202f;1.03, 95% CI: 1.03-1.04; OR for diabetes&#x202f;=&#x202f;1.11, 95% CI: 1.08-1.13; OR for Dyslipidemia&#x202f;=&#x202f;1.02, 95% CI: 1.01-1.03) and BMI (OR for hypertension&#x202f;=&#x202f;1.13, 95% CI: 1.11-1.15; OR for diabetes&#x202f;=&#x202f;1.18, 95% CI: 1.12-1.22; OR for Dyslipidemia&#x202f;=&#x202f;1.18, 95% CI: 1.16-1.20) were independent risk factors for all three conditions (all p&#x202f;<&#x202f;0.001). A significant multiplicative interaction effect was observed among age and BMI, the group "Age >40&#x202f;years and BMI&#x202f;&#x2265;&#x202f;24&#x202f;kg/m[2]" had the highest risk for hypertension (OR&#x202f;=&#x202f;2.98, 95% CI: 2.37-3.74, p&#x202f;<&#x202f;0.05), diabetes (OR&#x202f;=&#x202f;16.11, 95% CI: 6.95-37.31), and Dyslipidemia (OR&#x202f;=&#x202f;4.01, 95% CI: 3.30-4.88).<br><br>CONCLUSION: The prevalence of chronic diseases among Chinese offshore oil workers is high, with diabetes and Dyslipidemia showing significant upward trends. Age and BMI are important influencing factors and exhibit an interaction effect. This population should be prioritized in occupational health surveillance, and comprehensive interventions focusing on weight management and metabolic screening should be implemented, particularly targeting middle-aged individuals with elevated BMI.}, }
@article {pmid41266142, year = {2025}, author = {Chu, JP and Coulter, ME and Denovellis, EL and Nguyen, TTK and Liu, DF and Deng, X and Eden, UT and Kemere, CT and Frank, LM}, title = {RealtimeDecoder: A Fast Software Module for Online Clusterless Decoding.}, journal = {eNeuro}, volume = {12}, number = {12}, pages = {}, doi = {10.1523/ENEURO.0252-24.2025}, pmid = {41266142}, issn = {2373-2822}, mesh = {Animals ; *Software ; *Hippocampus/physiology ; Algorithms ; Rats ; Action Potentials/physiology ; *Neurons/physiology ; Male ; *Signal Processing, Computer-Assisted ; }, abstract = {Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing neural activity. That, in turn, allows experimenters to test hypotheses about the role of that content in driving downstream activity patterns and behaviors. While multiple real-time systems have been developed, they are typically implemented with a compiled programming language, making them more difficult for users to quickly adapt for new experiments. Here we present a software system written in the widely used Python programming language to facilitate rapid experimentation. Our solution implements the state space based clusterless decoding algorithm for an online, real-time environment. The parallelized application processes neural data with temporal resolution of 6 ms and median computational latency <50 ms for medium- to large-scale (32+ tetrodes) rodent hippocampus recordings without the need for spike sorting. It also executes auxiliary functions such as detecting sharp wave ripples from local field potential data. Even with an interpreted language, the performance is similar to state-of-the-art solutions that use compiled programming languages. We demonstrate this real-time decoder in a rat behavior experiment in which the decoder allowed closed-loop neurofeedback based on decoded hippocampal spatial representations. Overall this system provides a powerful and easy-to-modify tool for real-time feedback experiments.}, }
@article {pmid41448755, year = {2025}, author = {Li, A and Mei, J and Chen, W and Tao, L and Xu, M and Ming, D}, title = {[Brain-controlled unmanned aerial vehicle system based on meta brain computer interface open-source software platform].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {6}, pages = {1139-1147}, doi = {10.7507/1001-5515.202506031}, pmid = {41448755}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; Humans ; *Software ; *Unmanned Aerial Devices ; Electroencephalography ; Signal Processing, Computer-Assisted ; *Brain/physiology ; }, abstract = {Brain computer interface (BCI) system includes multiple links such as stimulus presentation, data acquisition, signal processing, external device control and command feedback. As an open-source software platform which covers all links of BCI chain, meta brain computer interface (MetaBCI) has provided flexible solutions for effectively encoding, decoding and feeding back brain activities, but has not yet provided an integrated tool that can support the implementation of a complete BCI system. In view of the above shortcoming, this paper designed and constructed a brain-controlled unmanned aerial vehicle system by using MetaBCI, which realized the online control of the physical unmanned aerial vehicle. The results of the experiment involving 10 subjects indicated that the average online classification accuracy and information transfer rate (ITR) of this system could reach 93.83% and 38.57 bits/min, respectively, which verified the feasibility of constructing a practical BCI system for external device control by using MetaBCI. Meanwhile, this paper elaborated the design idea, implementation process and the usage logic of MetaBCI toolkit involved in this brain-controlled unmanned aerial vehicle system in detail, hoping to provide guidance for subsequent developers to design and construct BCI systems that can meet individual needs by using MetaBCI independently.}, }
@article {pmid41448752, year = {2025}, author = {Dong, Z and Bao, X and Yang, Y and Wu, J}, title = {[A motor imagery decoding study integrating differential attention with a multi-scale adaptive temporal convolutional network].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {6}, pages = {1115-1122}, doi = {10.7507/1001-5515.202507012}, pmid = {41448752}, issn = {1001-5515}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; *Attention/physiology ; *Neural Networks, Computer ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; }, abstract = {Motor imagery electroencephalogram (MI-EEG) decoding algorithms face multiple challenges. These include incomplete feature extraction, susceptibility of attention mechanisms to distraction under low signal-to-noise ratios, and limited capture of long-range temporal dependencies. To address these issues, this paper proposes a multi-branch differential attention temporal network (MDAT-Net). First, the method constructed a multi-branch feature fusion module to extract and fuse diverse spatio-temporal features from different scales. Next, to suppress noise and stabilize attention, a novel multi-head differential attention mechanism was introduced to enhance key signal dynamics by calculating the difference between attention maps. Finally, an adaptive residual separable temporal convolutional network was designed to efficiently capture long-range dependencies within the feature sequence for precise classification. Experimental results showed that the proposed method achieved average classification accuracies of 85.73%, 90.04%, and 96.30% on the public datasets BCI-IV-2a, BCI-IV-2b, and HGD, respectively, significantly outperforming several baseline models. This research provides an effective new solution for developing high-precision motor imagery brain-computer interface systems.}, }
@article {pmid41448732, year = {2026}, author = {Wei, Z and Zhang, X}, title = {Refining Accelerated Intermittent Theta Burst Stimulation for Depression.}, journal = {Biological psychiatry}, volume = {99}, number = {3}, pages = {182-183}, doi = {10.1016/j.biopsych.2025.10.027}, pmid = {41448732}, issn = {1873-2402}, }
@article {pmid41448222, year = {2025}, author = {Bourhis, J and Aupérin, A and Borel, C and Lefebvre, G and Racadot, S and Geoffrois, L and Sun, XS and Saada, E and Cirauqui, B and Rutkowski, T and Henry, S and Modesto, A and Johnson, A and Chapet, S and Calderon, B and Sire, C and Malard, O and Bainaud, M and Da Silva Motta, A and Thureau, S and Pointreau, Y and Blanchard, P and Buiret, G and Bozec, L and Lopez, S and Vanbockstael, J and Bosset, M and Greilsamer, C and Daste, A and Bruna, A and N'Guyen, F and Plana, M and Iruarrizaga, E and Temam, S and Even, C and Ruiz, EP and Bert, M and Karamouza, E and Thariat, J and Kazmierska, J and Psyrri, A and Mesia, R and Tao, Y}, title = {Nivolumab added to cisplatin and radiotherapy versus cisplatin and radiotherapy alone after surgery for people with squamous cell carcinoma of the head and neck at a high risk of relapse (GORTEC 2018-01 NIVOPOST-OP): a randomised, open-label, phase 3 trial.}, journal = {Lancet (London, England)}, volume = {}, number = {}, pages = {}, doi = {10.1016/S0140-6736(25)01850-1}, pmid = {41448222}, issn = {1474-547X}, abstract = {BACKGROUND: Postoperative cisplatin and radiotherapy is the standard of care for high-risk resected locally advanced squamous cell carcinoma of the head and neck (LA-SCCHN). The NIVOPOST-OP trial aimed to assess the efficacy and safety of programmed death 1 blockade by nivolumab added to cisplatin and radiotherapy in this setting.<br><br>METHODS: This open-label, phase 3 trial evaluated adding nivolumab to cisplatin and radiotherapy after surgery for LA-SCCHN with high-risk pathological features. The main inclusion criteria were age 19-74 years, an Eastern Cooperative Oncology Group performance status 0-1, squamous cell carcinoma of the oral cavity, oropharynx, larynx, or hypopharynx resected with macroscopic complete resection, and at least one high-risk pathological feature: nodal extracapsular extension, microscopically positive margins, four or more cervical nodal involvements without extracapsular extension, and multiple perineural invasions. 680 participants recruited in 82 sites across six countries (France, Spain, Poland, Belgium, Greece, and Switzerland) were randomly assigned 1:1 to receive cisplatin and radiotherapy (66 Gy, cisplatin 100 mg/m[2] intravenously once every 3 weeks, for three cycles); or nivolumab 240 mg intravenously, followed by cisplatin and radiotherapy with three cycles of concomitant nivolumab 360 mg once every 3 weeks, and six cycles of adjuvant nivolumab 480 mg once every 4 weeks. The primary endpoint was disease-free survival as per investigator assessment in the intention-to-treat population. 230 disease-free survival events (relapses or deaths) were required to detect a hazard ratio of 0&#xb7;65 with 0&#xb7;05 two-sided &#x3b1; error, with 90% power. The trial is registered at ClinicalTrials.gov (NCT03576417) and is active, but not recruiting.<br><br>FINDINGS: The 680 patients were recruited from Oct 15, 2018, to July 3, 2024. The analysis was based on 666 participants randomly assigned until the cutoff date (April 30, 2024), at which point the required number of events was reached (median follow-up 30&#xb7;3 months). Disease-free survival was significantly improved with nivolumab, cisplatin, and radiotherapy versus cisplatin and radiotherapy alone, irrespective of programmed death ligand 1 expression (HR 0&#xb7;76; 95% CI 0&#xb7;60-0&#xb7;98; stratified log-rank test p value=0&#xb7;034). There was an increase in the rate of participants with treatment-related grade 4 adverse events with nivolumab, cisplatin, and radiotherapy compared with cisplatin and radiotherapy (30 [10%] of 312 vs 16 [5%] of 306). Treatment-related deaths occurred in two participants in each group.<br><br>INTERPRETATION: Nivolumab added to cisplatin and radiotherapy in high-risk resected LA-SCCHN improves disease-free survival with moderate toxic effect increase, and can be proposed as a new standard treatment.<br><br>FUNDING: Groupe Oncologie Radiotherapie Tete Et Cou (GORTEC) and Bristol Myers Squibb.}, }
@article {pmid41447233, year = {2025}, author = {Wahid, SR and Khan, AA}, title = {Basic Science and Pathogenesis.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {21 Suppl 1}, number = {}, pages = {e106434}, doi = {10.1002/alz70855_106434}, pmid = {41447233}, issn = {1552-5279}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Alzheimer Disease/diagnosis/physiopathology ; *Dementia/diagnosis/physiopathology ; *Cognitive Dysfunction/diagnosis/physiopathology ; *Brain/physiopathology ; Biomarkers ; }, abstract = {BACKGROUND: Dementia, a syndrome characterized by progressive cognitive decline, affects over 55 million people globally, with Alzheimer's disease accounting for 60-70% of cases. Traditional interventions, such as medication and cognitive therapies, have limited success in slowing down dementia progress. In this abstract, we propose Brain-Machine Interfaces (BMIs), which enable direct communication between the brain and external devices, present a novel opportunity to detect early stage dementia and help in rehabilitation of dementia patients. BMIs can detect early neural biomarkers of cognitive decline, such as altered theta-gamma oscillations or diminished functional connectivity, while offering real-time therapeutic interventions like neurofeedback. This exploratory study investigates the feasibility of non-invasive BMIs as tools for monitoring cognitive health and enhancing resilience in dementia patients, focusing on usability, neural correlates of decline, and patient engagement.<br><br>METHODS: Proposed Framework for Detection: BMIs could use non-invasive sensors (e.g., EEG, fNIRS) to monitor biomarkers linked to early dementia, such as: Theta-gamma phase-amplitude coupling: Reduced coupling correlates with memory deficits. Event-related potentials (ERPs): Delayed P300 responses during attention tasks could indicate processing speed decline. Machine learning algorithms can be employed to analyze these patterns to classify dementia risk before clinical symptoms show. Passive Monitoring:Wearable BMIs could track neural activity during daily activities (e.g., reading, social interactions) to detect anomalies in real time. For example, erratic frontal alpha oscillations during problem-solving might signal executive dysfunction. Proposed Framework for Rehabilitation: BMIs could provide real-time feedback during cognitive exercises. For instance: A memory game adjusts difficulty based on hippocampal theta activity. Personalized Engagement: Gamified interfaces, tailored to user preferences (e.g., music, visual themes), could improve adherence.<br><br>RESULTS: Detecting dementia using EEG and machine learning classifiers have shown promising results. For example, Joshi et al. used EEG signals and a BiLSTM model which achieved an accuracy of 97.27%.<br><br>CONCLUSION: This exploratory study proposes that BMIs hold transformative potential for dementia care, bridging early detection with dynamic, personalized rehabilitation. By utilizing neural biomarkers, BMIs could identify at-risk individuals years before symptom onset, while adaptive neurofeedback systems might slow decline by strengthening cognitive reserve.}, }
@article {pmid41446876, year = {2025}, author = {Sun, Y and Chen, D and Ye, Q and You, Z and Zhao, Z and Shi, J and Sun, H and Li, S and Xu, X and Xu, Y and Zhang, P and Tang, Z}, title = {Applications of Endovascular Brain-Computer Interface in Patients with Alzheimer's Disease.}, journal = {Research (Washington, D.C.)}, volume = {8}, number = {}, pages = {1049}, pmid = {41446876}, issn = {2639-5274}, abstract = {Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the elderly, leading to important impairments in cognitive function and the ability to live independently. This results in substantial disability and places an increasing burden on families and society. Currently, the therapeutic approaches adopted in clinical practice predominantly hinge upon cholinesterase inhibitors and the N-methyl-d-aspartate (NMDA) receptor antagonist memantine. Nevertheless, these medications merely alleviate symptoms and fail to tackle the pathological characteristics of AD. In recent years, monoclonal antibodies such as lecanemab and donanemab against β-amyloid (Aβ) have shown good efficacy in clinical practice for early-stage AD patients. However, the early diagnosis of AD remains a challenge. Against this backdrop, endovascular brain-computer interface (EBCI) offers an integrated solution for the early diagnosis and neuroregulatory treatment of AD patients, with minimal invasiveness. This review comprehensively examines the safety and feasibility of EBCI for AD patients, focusing on 3 major application areas: early diagnosis, deep brain stimulation targeting specific brain regions, such as the fornix and the basal nuclei of Meynert, and the use of external neurofeedback devices. Furthermore, we explore future development trends in this field, including miniaturization, integration, and the exploration of deep brain regions.}, }
@article {pmid41446611, year = {2025}, author = {Duarte-Mendes, P and Ramalho, A and Bertollo, M and Neiva, HP and Marinho, DA}, title = {To move without moving: a perspective article on motor imagery.}, journal = {Frontiers in psychology}, volume = {16}, number = {}, pages = {1697086}, pmid = {41446611}, issn = {1664-1078}, abstract = {Motor imagery - the mental simulation of movement without execution - activates motor networks with near-physical fidelity. Once considered ancillary, it is now central to neuroplasticity, enhancing skill acquisition, accelerating rehabilitation, and sustaining motor function across the lifespan. From stroke recovery to elite performance, motor imagery demonstrates that movement begins in cognition. As neurofeedback, brain-computer interfaces and virtual reality integrate with mental rehearsal, the boundary between thought and action becomes narrower. This perspective argues that motor imagery is not a cognitive accessory but the neurocognitive foundation of movement - a rehearsal mechanism through which the brain reshapes the body. In doing so, it supports the view that action is cognitively prepared before it is expressed.}, }
@article {pmid41444995, year = {2025}, author = {Zhu, Z and Wang, X and Xu, Y and Chen, W and Zheng, J and Chen, S and Chen, H}, title = {A heart rate variability-driven framework for depression screening leveraging emotion-elicited autonomic divergence.}, journal = {Journal of physiological anthropology}, volume = {44}, number = {1}, pages = {33}, pmid = {41444995}, issn = {1880-6805}, mesh = {Humans ; *Heart Rate/physiology ; Male ; *Depression/diagnosis/physiopathology ; Female ; Adult ; *Emotions/physiology ; Young Adult ; *Autonomic Nervous System/physiopathology/physiology ; Middle Aged ; Machine Learning ; }, abstract = {OBJECTIVE: Depression manifests significant emotional dysregulation, characterized by heightened sadness susceptibility and attenuated happiness responsiveness in individuals with depression (IWD). This study employs structured emotion induction protocols to analyze physiological response disparities between IWD and healthy controls (HC) across multiple affective states, establishing empirical foundations for optimizing affective computing-based depression screening.<br><br>METHODS: Dual-phase statistical identification was conducted using Mann-Whitney U tests: initially verifying emotion elicitation validity by comparing HRV features between emotional states and resting conditions, subsequently detecting IWD/HC response differences within each emotion. Machine learning frameworks were then constructed leveraging HRV features and intergroup differential response patterns.<br><br>RESULTS: Comparative analysis revealed generally consistent directional patterns and response magnitudes across groups for most features, while critical divergences emerged characterized by heightened sadness reactivity in IWD alongside attenuated happiness responsiveness. Implemented models achieved 76.8% accuracy (AUC&#x2009;=&#x2009;0.772, 95% CI 0.699-0.841) under sadness-specific conditions, outperforming anger/happiness-induced models (&#x2248;&#xa0;70% accuracy) and substantially surpassing resting-state baselines.<br><br>CONCLUSION: Systematic investigation of HRV-mediated elicitation patterns through discrete emotion induction confirms clinically significant differential responsiveness between groups, empirically validating heightened sadness susceptibility in IWDs.<br><br>SIGNIFICANCE: These findings offer valuable guidance for refining affective computing-based depression screening algorithms, while contributing to the mechanistic understanding of disorder-specific physiological responses to emotional stimuli.}, }
@article {pmid41444014, year = {2025}, author = {Knopman, J and Davies, JM and Mokin, M and Hassan, AE and Harbaugh, RE and Khalessi, A and Fiehler, J and Levy, EI and Gross, BA and Grandhi, R and Tarpley, J and Sivakumar, W and Bain, M and Crowley, RW and Link, TW and Fraser, JF and Levitt, MR and Chen, PR and Hanel, RA and Bernard, JD and Jumaa, M and Youssef, PP and Cress, MC and Chaudry, MI and Shakir, HJ and Lesley, WS and Billingsley, J and Jones, J and Koch, MJ and Paul, AR and Mack, WJ and Osbun, JW and Dlouhy, KM and Grossberg, JA and Kellner, CP and Sahlein, DH and Santarelli, J and Schirmer, CM and Mazaris, P and Liu, JJ and Majjhoo, AQ and Wolfe, T and Patel, NV and Roark, CD and Siddiqui, AH and , }, title = {EMBOLISE randomized surgical trial for subdural hematoma: clinical benefits beyond reoperation with middle meningeal artery embolization.}, journal = {Journal of neurointerventional surgery}, volume = {}, number = {}, pages = {}, doi = {10.1136/jnis-2025-024587}, pmid = {41444014}, issn = {1759-8486}, abstract = {BACKGROUND: Randomized clinical trials have demonstrated that middle meningeal artery embolization (MMAe) reduces reoperation rates in surgically treated patients with subacute/chronic subdural hematoma (SDH). The effect of embolization on outcomes beyond reoperation remains to be determined. We analyzed the impact of reoperation and healthcare encounters among patients enrolled in the EMBOLISE trial.<br><br>METHODS: Symptomatic subacute/chronic SDH patients were randomized to surgical evacuation alone (control) or surgical evacuation plus Onyx MMAe (treatment). Changes in modified Rankin Scale (mRS) scores, frequency of unscheduled follow-up visits, and radiographic evolution of hematomas in patients with versus without reoperation were analyzed.<br><br>RESULTS: A total of 197 patients were randomly assigned to the treatment group and 203 to the control group. Patients who required reoperation compared with those who did not exhibited a ~threefold higher incidence of mRS >2 (37.0% vs 12.9%, P=0.0025) and an ~2.5&#x2009;fold increase in mRS worsening (22.2% vs 9.5%, P=0.0503) at 180 days. In patients who did not receive MMAe, there was a ~threefold&#x2009;fold increase in rate of SDH recurrence/progression even among those who did not require reoperation (14.3% vs 5.3%, P=0.0045) and a ~twofold&#x2009;increase in unscheduled physician follow-up visits (27.1% vs 14.7%, P=0.0031).<br><br>CONCLUSION: Among patients with symptomatic subacute/chronic SDH, reoperation was associated with increased rates of mRS worsening and higher mRS scores at follow-up. Adjunctive Onyx MMAe resulted in lower rates of hematoma recurrence/progression and fewer unscheduled physician follow-up visits. Thus, in addition to reducing surgical reoperation rates, adjunctive MMAe led to improved clinical outcomes and reduced healthcare encounters.}, }
@article {pmid41443766, year = {2025}, author = {Zhang, H and Liao, Y and Wen, H and Pang, T and Zhao, X and Zhang, W and Lou, X and Chen, C and Liu, Z and Hu, S and Xu, X}, title = {Clinical Manifestations.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {21 Suppl 3}, number = {}, pages = {e097364}, doi = {10.1002/alz70857_097364}, pmid = {41443766}, issn = {1552-5279}, mesh = {Humans ; Male ; Female ; *Dementia/epidemiology ; Middle Aged ; United Kingdom/epidemiology ; Aged ; *Bipolar Disorder/epidemiology ; Incidence ; Comorbidity ; Adult ; *Mood Disorders/epidemiology ; Depression/epidemiology ; }, abstract = {BACKGROUND: Mood disorders including depression and bipolar disorders have been linked to dementia. However, early manifestation of bipolar disorder, especially manic symptom, were easily overlooked. The present study aimed to investigate the association of midlife and late-life mood symptoms, especially their comorbidity, with long-term dementia incidence among multi-regional and ethnic adults.<br><br>METHOD: The study used UK Biobank as a discovery dataset and three Asian studies as validation datasets. Participants aged > 35 were included in the analysis. Individuals with diagnosed mood disorders and dementia were excluded at baseline. Baseline mood symptoms were classified as: normal, manic symptoms, depressive symptoms, and comorbidity of depressive and manic symptoms. Long-term (12 years) incident mood disorders (depression, mania and bipolar) and dementia were diagnosed and recorded. Primary outcome was dementia incidence. Secondary outcomes were domain-specific cognitive function and metabolomics. Fine-Gray sub-distribution hazard models and linear regression were used to estimate the associations of mood symptoms with dementia risk, cognitive function and selected metabolites.<br><br>RESULT: The study included 142,670 UK and 1,610 Asian participants (mean [SD] age, 57.2 [8.2] and 70.5 [7.3] years, respectively). Mood symptoms were prevalent (11.4% and 31.2%) among 1462 (1.0%) and 74 (19.4%) who developed dementia during a mean follow-up of 11.0 and 4.4 years in community and clinical settings, respectively. The average durations from mood symptoms and disorders to dementia onset were 7.5 and 1.7 years, respectively. Comorbidity of depressive and manic symptoms was associated with an earlier onset and a higher risk of developing dementia (sub-distribution hazard ratios [sHR]=9.46, 95% confidence interval [CI]=4.07-21.97; and sHR=4.32, 95%CI=2.10-8.88; respectively), as compared to single symptom or none (on average 0.9 and 1.6 year earlier). Comorbidity of symptoms were associated with worse cognition (B=-0.32; 95% CI=-0.38--0.25), especially in reasoning and numeric memory, and an exacerbation of metabolic dysfunction, especially in fatty acids, lipoproteins and triglycerides.<br><br>CONCLUSION: Mood symptoms were prevalent among incident dementia patients. Comorbidity of mood symptoms in midlife and late-life could lead to a higher cumulative risk of dementia. Future studies warrant in-depth investigation of distinct pathophysiological mechanisms.}, }
@article {pmid41443376, year = {2025}, author = {Xia, XY and Huang, ZQ and Lin, HH and Liu, ZY and Zhang, L and Li, MC and Tu, YQ and Chen, NP and Ni, J and Chen, QL and Hu, JP and Gan, SR and Chen, XY}, title = {Diffusion along perivascular spaces as a marker for Glymphatic system impairment in spinocerebellar Ataxia type 3.}, journal = {Neurobiology of disease}, volume = {}, number = {}, pages = {107232}, doi = {10.1016/j.nbd.2025.107232}, pmid = {41443376}, issn = {1095-953X}, abstract = {Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder characterized by the accumulation of polyglutamylated ATXN3 protein within neurons, which can potentially compromise the integrity of the brain's glymphatic system. Our objective is to investigate whether glymphatic function is impaired in patients with SCA3 and its clinical relevance. This study recruited 129 SCA3 subjects, including 98 symptomatic (ataxic SCA3) and 31 presymptomatic (preataxic SCA3) individuals, along with 67 healthy controls (HCs). We calculated the index for diffusion tensor image analysis along the perivascular space (DTI-ALPS) across groups and examined its correlation with SCA3 clinical features. Except for the left cerebral hemisphere DTI-ALPS index showing no statistically significant difference between HC and preataxic SCA3, statistically significant differences in ALPS index were observed among the remaining three groups. The DTI-ALPS index decreased in the order HC group > preataxic SCA3 group > ataxic SCA3 group. The Ataxic SCA3 group exhibited a significantly lower DTI-ALPS index than the HC group. The mean DTI-ALPS index showed negative correlations with the Scale for the Assessment and Rating of Ataxia (SARA) scores and International Cooperative Ataxia Rating Scale (ICARS) scores. In this study, we demonstrate that glymphatic waste clearance is impaired in SCA3 and that the magnitude of ALPS-detected dysfunction parallels clinical burden. DTI-ALPS may serve as a potential indicator for evaluating glymphatic system alterations and disease.}, }
@article {pmid41442763, year = {2025}, author = {Okoye, C and Cuffaro, L and Pozzi, FE and Ferrara, MC and Noale, M and Calciolari, S and Chicco, D and Cincotti, F and Daini, R and Finazzi, A and Francioso, L and Gasparini, F and Pagan, E and Ribino, P and Romeo, Z and Sala, G and Solfrizzi, V and Zambon, A and Maggi, S and Bellelli, G and Ferrarese, C and , }, title = {Multicomponent interventions and technologies to reduce the burden of frailty, functional, and cognitive decline: insights from the Age-It Research Program.}, journal = {The journals of gerontology. Series B, Psychological sciences and social sciences}, volume = {80}, number = {Supplement_2}, pages = {S180-S188}, doi = {10.1093/geronb/gbaf186}, pmid = {41442763}, issn = {1758-5368}, support = {DM 1557//Next Generation EU/ ; //National Recovery and Resilience Plan/ ; //Ageing Well in an Ageing Society/ ; }, mesh = {Humans ; *Frailty/prevention &amp; control ; *Cognitive Dysfunction/prevention &amp; control ; Aged ; Aged, 80 and over ; *Frail Elderly ; Aging ; Cost-Benefit Analysis ; Male ; }, abstract = {OBJECTIVES: Preventing age-related complications is a critical priority for health systems. Within the Age-It program, Spoke 8 aims to evaluate scalable, multicomponent, technology-assisted interventions to prevent frailty and mitigate functional and cognitive decline in older adults across different care settings.<br><br>METHODS: Spoke 8 includes three clinical studies conducted in community, hospital, and long-term care settings, supported by cross-cutting work packages on digital infrastructure, technology development, and economic evaluation. The intervention model integrates physical, cognitive, nutritional, and psychosocial components, supported by digital tools, biomarkers of aging, and a centralized data platform.<br><br>RESULTS: The project is expected to generate evidence on the effectiveness, feasibility, and cost-effectiveness of multidomain interventions implemented across diverse real-world settings, including community, hospital, and long-term care. Technology-assisted strategies-such as wearable sensors and digital cognitive tools-may enhance adherence and enable remote monitoring, while also supporting more personalized care delivery. The integration of artificial intelligence will facilitate the interpretation of complex clinical and biological data, improving risk stratification and the early identification of individuals most likely to benefit from targeted interventions. Together, these approaches may help reduce hospitalizations, delay functional decline, and promote aging in place.<br><br>DISCUSSION: This initiative supports the transition toward more integrated and equitable care models for older adults. Through the implementation of scalable, person-centered interventions within routine services, the project offers policy-relevant strategies to address frailty and functional decline-contributing to the redesign of aging care in Italy and providing insights applicable across diverse health systems facing the challenges of population aging countries.}, }
@article {pmid41442279, year = {2025}, author = {Zhao, Y and Yang, Z and Shi, S and Hao, H and Li, X and Ma, D and Su, N and Zhao, W and Shao, J and An, Y and Wang, K and Liu, Y and Zou, L and Qi, J and Zhang, H and Guo, J and Du, X}, title = {Structure basis for the activation of KCNQ2 by endogenous and exogenous ligands.}, journal = {Cell reports}, volume = {45}, number = {1}, pages = {116771}, doi = {10.1016/j.celrep.2025.116771}, pmid = {41442279}, issn = {2211-1247}, abstract = {The voltage-gated potassium channel KCNQ2 is crucial for stabilizing neuronal membrane potential, and its mutations can cause various epilepsies. KCNQ2 is activated by endogenous ligand phosphatidylinositol-4,5-bisphosphate (PIP2) and exogenous ligands, yet the structural mechanisms underlying these activations remain unclear. Here, we report the cryo-electron microscopy structures of human KCNQ2 in complex with exogenous ligands QO-58 and QO-83 in the absence or presence of PIP2 in either closed or open conformation. While QO-83 binds in the classical fenestration pocket of the pore domain, QO-58 mainly binds at the flank of S4 in the voltage-sensing domain. These structures, along with electrophysiological assays and computational studies, provide mechanistic insights into the ligand activation of KCNQ2 and may guide the development of anti-epileptic drugs targeting KCNQ2.}, }
@article {pmid41441860, year = {2025}, author = {He, C and Ding, Y and Rabczuk, T and Ding, C}, title = {Reliable AI Platform for Monitoring BCI Caused Brain Injury and Providing Real-Time Protection.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e06747}, doi = {10.1002/advs.202506747}, pmid = {41441860}, issn = {2198-3844}, support = {TZ2025006//Science Challenge Project/ ; 2023YFA1008902//National Key R and D Program of China/ ; 12472191//National Natural Science Foundation of China/ ; //Fundamental Research Funds for the Central Universities, Peking University/ ; }, abstract = {Invasive brain-computer interface (BCI) holds great promise for restoring motor, sensory, and cognitive functions in patients with disabilities, yet chronic implantation induces neuroinflammation and degeneration at the electrode-tissue interface, impairing neural connectivity and device long-term stability. Current brain injury assessment approaches cannot simultaneously meet the requirements of efficiency and interpretability in healthcare with high-risk diagnoses and treatment. Meanwhile, limited and expensive biomechanics data pose significant challenges in AI training. Herein, feature-based Gaussian process emulators are proposed to enable interpretable data-driven modeling with limited biomechanics data under noise. Furthermore, a reliable AI platform, BrainGuard is developed, for efficiently providing a reliable and quantitative patient-specific basis and real-time monitoring of BCI caused brain injury. These results demonstrate exceptional performance of BrainGuard in rapidly and accurately predicting and monitoring the full-field von Mises strain revealing the brain injury even under challenging noise conditions. By constructing interpretable digital brain twins to offer reliable digital healthcare solutions, the platform enhances real-time patient protection and improves the security and durability of long-term BCI-based measurement and treatment strategies.}, }
@article {pmid41440053, year = {2025}, author = {Zhang, N and Jian, H and Li, X and Jiang, G and Tang, X}, title = {LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.}, journal = {Brain sciences}, volume = {15}, number = {12}, pages = {}, doi = {10.3390/brainsci15121257}, pmid = {41440053}, issn = {2076-3425}, abstract = {Objectives: Existing motor imagery electroencephalography (MI-EEG) decoding approaches are constrained by their reliance on sole representations of brain connectivity graphs, insufficient utilization of multi-scale information, and lack of adaptability. Methods: To address these constraints, we propose a novel Local-Partition-Global Graph learning Network (LPGGNet). The Local Learning module first constructs functional adjacency matrices using partial directed coherence (PDC), effectively capturing causal dynamic interactions among electrodes. It then employs two layers of temporal convolutions to capture high-level temporal features, followed by Graph Convolutional Networks (GCNs) to capture local topological features. In the Partition Learning module, EEG electrodes are divided into four partitions through a task-driven strategy. For each partition, a novel Gaussian median distance is used to construct adjacency matrices, and Gaussian graph filtering is applied to enhance feature consistency within each partition. After merging the local and partitioned features, the model proceeds to the Global Learning module. In this module, a global adjacency matrix is dynamically computed based on cosine similarity, and residual graph convolutions are then applied to extract highly task-relevant global representations. Finally, two fully connected layers perform the classification. Results: Experiments were conducted on both the BCI Competition IV-2a dataset and a laboratory-recorded dataset, achieving classification accuracies of 82.9% and 87.5%, respectively, which surpass several state-of-the-art models. The contribution of each module was further validated through ablation studies. Conclusions: This study demonstrates the superiority of integrating multi-view brain connectivities with dynamically constructed graph structures for MI-EEG decoding. Moreover, the proposed model offers a novel and efficient solution for EEG signal decoding.}, }
@article {pmid41439901, year = {2025}, author = {Lee, DG and Lee, SB}, title = {Robust Motor Imagery-Brain-Computer Interface Classification in Signal Degradation: A Multi-Window Ensemble Approach.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {12}, pages = {}, doi = {10.3390/biomimetics10120832}, pmid = {41439901}, issn = {2313-7673}, support = {Bisa Research Grant: project number 20240421//Keimyung University/ ; }, abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) mimics the brain's intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. However, this biomimetic interaction is highly vulnerable to signal degradation, particularly in mobile or low-resource environments where low sampling frequencies obscure these MI-related oscillations. To address this limitation, we propose a robust MI classification framework that integrates spatial, spectral, and temporal dynamics through a filter bank common spatial pattern with time segmentation (FBCSP-TS). This framework classifies motor imagery tasks into four classes (left hand, right hand, foot, and tongue), segments EEG signals into overlapping time domains, and extracts frequency-specific spatial features across multiple subbands. Segment-level predictions are combined via soft voting, reflecting the brain's distributed integration of information and enhancing resilience to transient noise and localized artifacts. Experiments performed on BCI Competition IV datasets 2a (250 Hz) and 1 (100 Hz) demonstrate that FBCSP-TS outperforms CSP and FBCSP. A paired t-test confirms that accuracy at 110 Hz is not significantly different from that at 250 Hz (p < 0.05), supporting the robustness of the proposed framework. Optimal temporal parameters (window length = 3.5 s, moving length = 0.5 s) further stabilize transient-signal capture and improve SNR. External validation yielded a mean accuracy of 0.809 ± 0.092 and Cohen's kappa of 0.619 ± 0.184, confirming strong generalizability. By preserving MI-relevant neural patterns under degraded conditions, this framework advances practical, biomimetic BCI suitable for wearable and real-world deployment.}, }
@article {pmid41439390, year = {2025}, author = {Gupta, D and Brangaccio, JA and Hill, NJ}, title = {Methodological optimization for eliciting robust median nerve somatosensory evoked potentials for realtime single trial applications.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae30ac}, pmid = {41439390}, issn = {1741-2552}, abstract = {OBJECTIVE: Single-trial measurement of median nerve Somatosensory Evoked Potentials (SEPs) with noninvasive electroencephalography (EEG) is challenging due to low signal-to-noise ratio (SNR), limiting its use in real-time neurorehabilitation applications. We describe and evaluate methodological optimizations for eliciting reliable median nerve SEPs measurable in real time, with reduced reliance on post-processing.<br><br>METHODS: In twelve healthy participants, two sessions each, SEPs were assessed at three pulse widths (0.1, 0.5, 1 msec), at a low-frequency stimulation (0.5 Hz &#xb1; 10%), and at an intensity sufficient to evoke consistent and robust sensory nerve action potentials (SNAPs) and compound muscle action potentials (CMAPs). The Evoked Potential Operant Conditioning System platform was used to monitor responses in real time. Feasibility was also evaluated in a participant with incomplete spinal cord injury (iSCI).<br><br>RESULTS: SEP P50 and N70 were reliably elicited in healthy participants, and in individual with iSCI, across all tested pulse widths with minimal discomfort. N70 amplitude increased significantly with pulse width (&#x3c7;[2]= 17.64, p= 0.0001, w= 0.80), while P50 amplitude remained unchanged. SNR showed a significant pulse width-dependent increase (&#x3c7;[2]= 7.82, p= 0.02, w= 0.35) with improvements of 40% and 52% at 0.5 and 1 msec, respectively. N70 single-trial separability significantly improved at 1 msec (AUC of 0.83, &#x3c7;[2]= 8.17, p= 0.017), including the iSCI participant (0.84-less impaired hand, 0.79-more impaired hand). Test-retest reliability (ICC= 0.70-0.84, p< 0.05) was highest at 0.5 msec, indicating more consistent N70 and P50 measurements across sessions at a longer pulse width.<br><br>SIGNIFICANCE: Robust median nerve SEPs can be measured at single trials with methodological optimizations such as a longer pulse width (0.5-1ms), low frequency (0.5 Hz), a consistent afferent excitation guided by nerve and muscle responses, and a robust EEG acquisition system. This setup can be useful for real time SEP-based brain computer interface applications for rehabilitation.}, }
@article {pmid41438920, year = {2025}, author = {Kundu, B and Pleitez, J}, title = {Brain Implants in the Age of Artificial Intelligence.}, journal = {Missouri medicine}, volume = {122}, number = {6}, pages = {517-524}, pmid = {41438920}, issn = {0026-6620}, mesh = {Humans ; *Artificial Intelligence/trends ; *Brain/physiology ; *Brain-Computer Interfaces ; }, abstract = {Brain implants are routinely used to treat movement disorders and other network disorders such as obsessive-compulsive disorder. Closed-loop intracranial brain stimulation systems can now detect neural biomarkers of disease in real-time and therapeutically stimulate the brain based on these signals. Research devices can measure neural data on the order of single neurons and transform these data, via machine learning algorithms, into cursor movements and keyboard clicks, so that a quadriplegic patient can control a robotic arm. It is still a challenge to find the important brain signals of interest, that encode a patient's intentions or needs. Furthermore, the ethics of developing devices that allow for human cognitive and physical enhancement should be a part of societal discussion. The hope is that artificial intelligence (AI) will continue to advance neurotechnology's role in human health.}, }
@article {pmid41438236, year = {2025}, author = {Li, T and Gao, Y and Zhou, J and Chen, Y and Zhang, S and Gong, X and Liu, Y}, title = {Advancements in the application of brain-computer interfaces based on different paradigms in amyotrophic lateral sclerosis.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1658315}, pmid = {41438236}, issn = {1662-4548}, abstract = {Amyotrophic lateral sclerosis (ALS) is a progressive neurological condition that leads to the gradual loss of movement and communicative abilities, significantly diminishing the quality of life for affected individuals. Recent advancements in neuroscience and engineering have propelled the swift evolution of brain-computer interfaces (BCIs), which are now extensively utilised in medical rehabilitation, military applications, assistive technologies, and various other domains. As a communication medium facilitating direct interaction between the brain and the external world independent of the peripheral nervous system, BCI provides ALS patients with an innovative method for communication and control, offering unparalleled prospects for improving their quality of life. Recent collaborative endeavours among several specialists have markedly enhanced the precision and velocity of diverse BCI paradigms, signifying a breakthrough in BCI applications for ALS. Nonetheless, obstacles and constraints remain. This study methodically extracted pertinent literature from the Web of Science and PubMed databases in accordance with PRISMA guidelines. Following stringent inclusion and exclusion criteria, 23 studies were identified. This data allows us to summarise the application results and current limitations of several BCI paradigms in motor control and communication, while delineating prospects in multimodal fusion and adaptive calibration. This review presents evidence-based references for the effective translation and application of BCI technology in ALS rehabilitation.}, }
@article {pmid41437397, year = {2025}, author = {Qian, MB and Wang, L and Huang, JL and Zhou, CH and Zhu, TJ and Zhou, XN and Lai, YS and Li, SZ}, title = {Disability-adjusted life years of clonorchiasis in China: a high-resolution spatial analysis.}, journal = {Infectious diseases of poverty}, volume = {14}, number = {1}, pages = {126}, pmid = {41437397}, issn = {2049-9957}, support = {82373645//National Natural Science Foundation of China/ ; 82073665//National Natural Science Foundation of China/ ; 2021YFC2300800//National Key Research and Development Program of China/ ; }, mesh = {Humans ; China/epidemiology ; *Clonorchiasis/epidemiology/parasitology ; Male ; Female ; *Disability-Adjusted Life Years ; Clonorchis sinensis/isolation &amp; purification/physiology ; Spatial Analysis ; Middle Aged ; Adult ; Animals ; Child ; Aged ; Young Adult ; Bayes Theorem ; Adolescent ; Prevalence ; Child, Preschool ; Incidence ; Quality-Adjusted Life Years ; }, abstract = {BACKGROUND: Clonorchiasis is caused by the ingestion of raw freshwater fish containing infective metacercariae of Clonorchis sinensis. This study aimed to fully evaluate disease burden in terms of disability-adjusted life years (DALYs) for clonorchiasis in China.<br><br>METHODS: Following our previous study which established the fine-scale prevalence distribution of C. sinensis infection in China, we further adopted Bayesian geostatistical models to estimate the infection intensity in terms of eggs per gram of feces (EPG) in infected individuals based on the national surveillance data of clonorchiasis between 2016 and 2021. Disability weight was then captured through its quantitative association with EPG, and used to estimate years of life living with a disability (YLDs). Incidence of cholangiocarcinoma attributed to C. sinensis infection was employed to calculate years of life lost (YLLs). DALYs was then estimated at 5&#x2009;&#xd7;&#x2009;5&#xa0;km[2] resolution, and aggregated by areas and populations.<br><br>RESULTS: In 2020, 431,009 [95% Bayesian credible interval (BCI): 370,427 to 500,553] DALYs were exerted due to clonorchiasis in China, of which 372,918 (95% BCI: 318,775-435,727) was due to YLDs and 57,998 (95% BCI: 50,816-66,069) due to YLLs. The DALYs, YLDs and YLLs per 1000 were 0.31 (95% BCI: 0.26-0.35), 0.26 (95% BCI: 0.23-0.31), and 0.04 (95% BCI: 0.04-0.05), respectively. The DALYs predominantly distributed in southern areas including Guangxi (201,029, 95% BCI: 157,589-248,287) and Guangdong (161,958, 95% BCI: 128,326-211,358). The DALYs was over doubled in male (302,678, 95% BCI: 262,028-348,300) than in female (127,970, 95% BCI: 106,834-151,699), and high in middle aged population.<br><br>CONCLUSIONS: Clonorchiasis causes significant disease burden in China especially in southern areas including Guangxi and Guangdong. Urgent control is needed for clonorchiasis in the endemic areas with high burden, and adult males need to be prioritized.}, }
@article {pmid41437118, year = {2025}, author = {Chen, H and Dai, H and Zhang, L and Deng, Y and Zhang, K and Yu, J and Peng, G and Guo, Z and Zhang, J and Yuan, C and Xie, F and Luo, B}, title = {The biomarker and clinical changes across the Alzheimer's continuum study (BCAS): rationale, design, and baseline characteristics of the first 1,013 participants.}, journal = {Alzheimer's research &amp; therapy}, volume = {}, number = {}, pages = {}, doi = {10.1186/s13195-025-01937-x}, pmid = {41437118}, issn = {1758-9193}, support = {2022C03064//Key R&amp;D Program of Zhejiang/ ; }, abstract = {INTRODUCTION: Alzheimer's disease (AD) is the leading cause of dementia in China, but deeply phenotyped clinical cohorts remain limited. The Biomarker and Clinical changes across the Alzheimer's continuum Study (BCAS) was established at the First Affiliated Hospital, Zhejiang University School of Medicine to capture biological and clinical changes across the AD spectrum.<br><br>METHODS: BCAS is an ongoing, longitudinal memory clinic-based cohort initiated in 2016 in Zhejiang, one of China's most economically vigorous and rapidly aging regions. Individuals aged&#x2009;&#x2265;&#x2009;40&#xa0;years with cognitive concerns are recruited and undergo standardized clinical evaluation, comprehensive neuropsychological testing, biospecimen collection, and multimodal neuroimaging including MRI and amyloid and tau PET in subsets. Participants are followed every 1-2&#xa0;years with repeat assessments. This paper reports baseline characteristics and preliminary findings from the first 1,013 participants enrolled up to January 2025.<br><br>RESULTS: Participants had a mean age of 66.5&#xa0;years (SD 9.6), with 49.8% women and an average of 9.7&#xa0;years of education. Hypertension (41.4%), diabetes (14.6%), and hypercholesterolemia (12.0%) were the most prevalent comorbidities. The mean MoCA score was 19.2 (SD 6.1). Mean cognitive scores showed gradient decline across diagnostic groups from cognitively unimpaired, mild cognitive impairment to dementia, consistent with expected disease severity. Tau PET positivity showed a numerically larger cognitive z-score difference (-0.973 for T&#x2009;+&#x2009;vs. T-) compared with amyloid PET positivity (-0.530 for A&#x2009;+&#x2009;vs. A-). Among risk factors, higher age and diabetes were linked to lower scores, whereas higher education, tea consumption, and higher BMI were associated with better cognitive performance.<br><br>CONCLUSIONS: The BCAS served as a biomarker-rich and multimodal resource to study the clinical and biological progression of AD in China. Preliminary analyses demonstrate expected associations and support the data quality. BCAS will act as a platform for biomarker validation and precision approaches to AD diagnosis and intervention.}, }
@article {pmid41434442, year = {2025}, author = {Zhao, X and Lin, Z and Zhang, H and Chen, C and Ji, H and Liu, Z and Hu, S and Xu, X}, title = {Public Health.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {21 Suppl 6}, number = {}, pages = {e097185}, doi = {10.1002/alz70860_097185}, pmid = {41434442}, issn = {1552-5279}, mesh = {Humans ; Male ; Female ; Aged ; Middle Aged ; *Dementia/epidemiology ; *Mental Disorders/epidemiology ; *Public Health ; United Kingdom/epidemiology ; *Cardiovascular Diseases/epidemiology ; Risk Factors ; *Renal Insufficiency, Chronic/epidemiology ; }, abstract = {BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome describes pathological interactions among metabolic risk factors, chronic kidney disease, and cardiovascular dysfunction. These conditions are shared risk factors for psychiatric disorders and dementia. This study examined the associations of CKM syndrome with psychiatric disorders and dementia in middle-aged and older adults.<br><br>METHOD: Using data from the UK Biobank, we included participants free of psychiatric disorders and dementia at baseline. CKM syndrome was categorized into five stages (0 to 4) based on AHA definitions. Psychiatric disorders (psychotic, bipolar, depressive, and anxiety disorders) and dementia (Alzheimer's and vascular dementia) were identified using ICD-10 codes. Multi-state models analyzed the impact of CKM on transitions from healthy status to psychiatric disorders and dementia. Competing risk (death) models assessed the associations of CKM with specific psychiatric disorders and dementia. Additionally, Cox regression models and XGBoost classifiers were employed to identify key metabolomics associated with CKM stage-related outcomes.<br><br>RESULT: Among 389,314 participants, CKM stages were distributed as follows: stage 0 (10.0%), stage 1 (8.0%), stage 2 (57.6%), stage 3 (17.9%), and stage 4 (6.5%). Multi-state model results indicated that each one-stage increment in CKM stage was associated with higher hazards of psychiatric disorders (Healthy &#x2192; Psychiatric Disorder: HR=1.26, 95% CI: [1.24, 1.29]) and subsequent transition to dementia (Psychiatric Disorder &#x2192; Dementia: HR=1.30, 95% CI: [1.41, 1.49]). However, each CKM stage increment increased the hazards of directly developing dementia (Healthy &#x2192; Dementia: HR=1.26, 95% CI: [1.31, 1.49]) but was not linked to subsequent psychiatric disorders. Competing risk analyses revealed that worsening CKM stages were associated with greater hazards of developing pre-dementia psychiatric disorders, including bipolar disorder, depressive disorder, and anxiety disorder whilst only advanced CKM stages (CKM stage 3/4) were associated with all-cause, Alzheimer's and vascular dementia. We identified several key predictors of pre-dementia psychiatric disorders at different CKM stages (e.g., citrate at CKM stages 1 and 2; degree of unsaturation at CKM stages 3 and 4).<br><br>CONCLUSION: CKM syndrome is associated with pre-dementia psychiatric disorders and dementia, emphasizing the need for regular monitoring and early intervention to manage CKM progression and reduce geriatric neuropsychiatric disturbances.}, }
@article {pmid41433160, year = {2025}, author = {Hamdan, E and Luo, Y and Forelli, R and Liufu, M and Zhou, N and Shridhar, S and Quattrocchi, E and Leveroni, Z and Ogrenci, S and Tran, N and Cetin, AE and Yu, JY}, title = {Real-time Instantaneous Phase Estimation Using a Deep Dual-Branch Complex Neural Network.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3647598}, pmid = {41433160}, issn = {1558-2531}, abstract = {Estimating the instantaneous phase of neural oscillations is crucial for technology that interfaces with the brain, such as brain-computer interfaces (BCIs) and neuromodulation systems. In these systems, phase information from the oscillating neural signal can be used to guide subsequent decisions to apply experimental perturbation. Traditional methods for phase estimation rely on the Hilbert transform computed using the Discrete Fourier Transform (DFT), which introduces a phase lag due to dependency on past and present signal values. This paper proposes a deep learning algorithm utilizing a dual-branch structure similar to the complex wavelet transform to generate a pseudo-complex valued signal for instantaneous phase estimation. The network has Discrete Cosine Transform (DCT) layers, which help to extract latent space representations for the real and imaginary signal components, respectively. An additional design goal was to make this Deep Learning (DL)-based algorithm suitable for deployment on portable edge devices with limited computing resources such as field-programmable gate arrays (FPGAs). This work demonstrates a proof-of-principle for real-time instantaneous phase estimation in neuromodulation applications. Our generalized model achieves an improvement of 40.3% in phase estimation accuracy over the endpoint-corrected Hilbert Transform (ecHT) method and an improvement of 9.2% over conventional deep learning model architectures.}, }
@article {pmid41432054, year = {2025}, author = {Chen, Q and Wu, H and Xie, S and Zhu, F and Xu, F and Xu, Q and Sun, L and Yang, Y and Xie, L and Xie, J and Li, H and Dai, A and Zhang, W and Wang, L and Jiao, C and Zhang, H and Zhou, X and Xu, ZZ and Chen, X}, title = {GPR30 in spinal cholecystokinin-positive neurons modulates neuropathic pain.}, journal = {eLife}, volume = {13}, number = {}, pages = {}, doi = {10.7554/eLife.102874}, pmid = {41432054}, issn = {2050-084X}, support = {82371220//National Natural Science Foundation of China/ ; 82171206//National Natural Science Foundation of China/ ; ZDFY2022-4XA102//4+X Clinical Research Project of Women's Hospital, School of Medicine, Zhejiang University/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 226-2022-00227//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Animals ; *Receptors, G-Protein-Coupled/metabolism/genetics ; *Neuralgia/physiopathology/metabolism ; *Cholecystokinin/metabolism ; Mice ; *Neurons/metabolism ; *Receptors, Estrogen/metabolism/genetics ; Male ; Mice, Inbred C57BL ; Disease Models, Animal ; *Spinal Cord ; }, abstract = {Neuropathic pain, a major health problem affecting 7-10% of the global population, lacks effective treatment due to its elusive mechanisms. Cholecystokinin-positive (CCK[+]) neurons in the spinal dorsal horn (SDH) are critical for neuropathic pain, yet the underlying molecular mechanisms remain unclear. Here, we show that the membrane estrogen receptor G-protein coupled estrogen receptor (GPER/GPR30) in spinal neurons was significantly upregulated in chronic constriction injury (CCI) mice and that inhibition of GPR30 in CCK[+] neurons reversed CCI-induced neuropathic pain. Furthermore, GPR30 in spinal CCK[+] neurons was essential for the enhancement of AMPA-mediated excitatory synaptic transmission in CCI mice. Moreover, GPR30 was expressed in spinal CCK[+] neurons that received direct projection from the primary sensory cortex (S1-SDH). Chemogenetic inhibition of S1-SDH post-synaptic neurons alleviated CCI-induced neuropathic pain. Conversely, chemogenetic activation of these neurons mimicked neuropathic pain symptoms, which were attenuated by spinal inhibition of GPR30. Finally, we confirmed that GPR30 in S1-SDH post-synaptic neurons was required for CCI-induced neuropathic pain. Taken together, our findings suggest that GPR30 in spinal CCK[+] neurons and S1-SDH post-synaptic neurons is pivotal for neuropathic pain, thereby representing a promising therapeutic target for neuropathic pain.}, }
@article {pmid41431688, year = {2024}, author = {Lee, J and Letner, JG and Lim, J and Atzeni, G and Liao, J and Kamboj, A and Mani, B and Jeong, S and Kim, Y and Sun, Y and Koo, B and Richie, J and Valle, ED and Patel, PR and Sylvester, D and Kim, HS and Jang, T and Phillips, JD and Chestek, CA and Weiland, J and Blaauw, D}, title = {A Sub-mm[3] Wireless Neural Stimulator IC for Visual Cortical Prosthesis With Optical Power Harvesting and 7.5-kb/s Data Telemetry.}, journal = {IEEE journal of solid-state circuits}, volume = {59}, number = {4}, pages = {1110-1122}, pmid = {41431688}, issn = {0018-9200}, abstract = {This article proposes StiMote, an untethered, free-floating and individually addressable stimulator mote designed for visual cortex stimulation, aimed at vision restoration. The system is optically powered by a custom photovoltaic (PV) layer. In addition, the photodiode (PD) layer captures the light modulation and forwards it to the optical receiver (ORX) including a tranimpedance amplifier. Translated instructions can assign a unique slot, up to 1024 available, to each mote within the time-division multiple access (TDMA) framework. In this work, we propose an automatic charge balance (CB) technique that monitors the injected charge to balance in bi-phasic switched-capacitor stimulation (SCS). The chip was confirmed fully functional when operated completely wirelessly using harvested light. Measurement results revealed a power consumption of 4.48 μ W with a 7.5-kb/s optical downlink data rate, corresponding to continuous updates at 2.5 Hz of 1024 motes to their individual 3-b stimulation intensity levels. The dc-dc converter, responsible for providing high voltage for stimulation, demonstrated 4.3-V output voltage when unloaded, with a maximum efficiency of 67.4%. The proposed CB circuit exhibited linear controllability of stimulation charge up to 16 nC, with a charge imbalance of less than 0.2 nC. Furthermore, in vitro testing confirmed the absence of chemical reactions at electrodes, and in vivo experiments conducted on live rats verified the effectiveness of the stimulation through StiMote.}, }
@article {pmid41429310, year = {2025}, author = {Rizzuto, DS and Herrema, HG and Hu, Z and Utin, D and Kahn, J and Ho, C and Smiles, A and Gross, RE and Lega, BC and Das, SR and Kahana, MJ}, title = {A wireless, 60-channel, AI-enabled neurostimulation platform.}, journal = {Brain stimulation}, volume = {}, number = {}, pages = {103013}, doi = {10.1016/j.brs.2025.103013}, pmid = {41429310}, issn = {1876-4754}, abstract = {OBJECTIVE: Closed-loop neuromodulatory therapies require devices that can decode ongoing brain states and deliver multi-site stimulation.<br><br>METHODS: We describe the Smart Neurostimulation System (SNS), a cranially mounted implant with 60 configurable recording/stimulation channels, inductive power, and onboard spectral-feature classification. In three freely-moving sheep, we streamed local-field potentials and conducted two parameter-sweep experiments.<br><br>RESULTS: Cross-validated movement classifiers achieved an average AUC exceeding 0.95. Increasing stimulation amplitude and frequency produced post-stimulation elevations in &#x3b1;-band (8-12 Hz) and &#x3b3;-band (78-82 Hz) power at most target locations.<br><br>CONCLUSION: The SNS unifies high-density sensing, real-time brain state decoding, and programmable closed-loop stimulation in a single device, demonstrating behavioral-state prediction and parameter-dependent neuromodulation in vivo.<br><br>SIGNIFICANCE: These findings establish a preclinical foundation for biomarker-guided stimulation targeting distributed cortical networks underlying memory and cognition.}, }
@article {pmid41429054, year = {2025}, author = {Radman, M and Podmore, JJ and Poli, R and Paulmann, S and Daly, I}, title = {Decoding semantic categories: Insights from an fMRI ALE meta analysis.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae302b}, pmid = {41429054}, issn = {1741-2552}, abstract = {The human brain organizes conceptual knowledge into semantic categories; however, the extent to which these categories share common or distinct neural representations remains unclear. This study aims to clarify this organizational structure by identifying consistent, modality-controlled activation patterns across several widely used and frequently investigated semantic domains in fMRI research. By quantifying the distinctiveness and overlap among these patterns, we provide a more precise foundation for understanding the brain's semantic architecture, as well as for applications such as semantic brain-computer interfaces (BCI). Approach: Following PRISMA guidelines, we conducted a systematic review and meta-analysis of 75 fMRI studies covering six semantic categories: animals, tools, food, music, body parts, and pain. Using Activation Likelihood Estimation (ALE), we identified convergent activation patterns for each category while controlling for stimulus modality (visual, auditory, tactile, and written). Subsequently, Jaccard-based overlap analyses were performed to quantify the degree of neural commonality and separability across concept-modality pairs, thereby revealing the underlying structure of representational similarity. Main Results: Distinct yet partially overlapping activation networks were identified for each semantic category. Tools and animals showed shared activity in the lateral occipital and ventral temporal regions, reflecting common object-based visual processing. In contrast, food-related stimuli primarily recruited limbic and subcortical structures associated with affective and motivational processing. Music and animal sounds overlapped within the superior temporal and insular cortices, whereas body parts and pain engaged occipito-parietal and cingulo-insular networks, respectively. Together, these findings reveal a hierarchically organized and modality-dependent semantic architecture in the human brain. Significance: This meta-analysis offers a quantitative and integrative characterization of how semantic knowledge is distributed and differentiated across cortical systems. By demonstrating how conceptual content and sensory modality jointly shape neural organization, the study refines theoretical models of semantic cognition and provides a methodological basis for evaluating conceptual separability. These insights have direct implications for semantic neural decoding and for the development of BCI systems grounded in meaning-based neural representations. .}, }
@article {pmid41428932, year = {2025}, author = {Lu, R and Deng, W and Gao, T and Huang, S and Zhang, Z and Liu, Y and Zhong, SH}, title = {Mutual Generation for Cross-domain Challenge in Stroke Patients' Motor Imagery Classification and Functional Recovery Prediction.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3646871}, pmid = {41428932}, issn = {2168-2208}, abstract = {The accumulating body of research indicates that Motor Imagery (MI)-BCIs have the potential to enhance the quality of life for individuals with disabilities and to advance our understanding of brain function and rehabilitation strategies. Among these diseases, stroke is the leading cause of long-term motor disability across the globe, thereby underscoring the need for innovative rehabilitation strategies, such as MI-BCI technologies. In contrast with these expectations, the majority of existing research is built upon data obtained from healthy subjects. The construction of effective classification models for Motor Imagery tasks in patients with brain diseases, particularly stroke, remains a significant challenge. The lateralization of the left and right hemispheres is more pronounced in patients who have suffered a stroke than in healthy individuals. Moreover, the specific locations of lesions and the regions of influence result in significant variations in the electroencephalogram (EEG) data of patients with different hemiplegic sides. This paper explores the potential of generative models in addressing the issue of domain differences arising from different hemiplegic sides EEG data. Furthermore, this paper circumvents the potential adverse effects of rigorous optimization of low-quality samples on model performance through the utilization of label softening algorithm. Two MI-EEG datasets of stroke patients performing Motor Imagery tasks are used to validate our method. In comparison to both classical machine learning methods and those state-of-the-art models for MI classification, the classification model in this paper achieves a noticeable performance improvement in different data partitioning strategies, including subject-dependent and subject-independent scenarios. Each sub-module, and each designed loss function, contributes to the final performance growth. In addition, this paper also investigates the potential of the proposed framework for predicting a patient's level of functional recovery. Our findings indicate that the addition of a prediction layer to the proposed model enables the accurate prediction of functional recovery level in stroke patients. The source code is available at https://github.com/arrogant-R/MutualGeneration.}, }
@article {pmid41428911, year = {2025}, author = {Fu, X and Liu, R and Wai, AAP and Pulferer, H and Robinson, N and Muller-Putz, GR and Guan, C}, title = {EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3647101}, pmid = {41428911}, issn = {1558-0210}, abstract = {Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for accurate temporal and spectral feature extraction, and the scarcity of high-quality gait EEG datasets. To address these issues, we propose EEG2GAIT, a novel hierarchical graph-based model that captures multi-level spatial embeddings of EEG channels using a Hierarchical Graph Convolutional Network (GCN) Pyramid. To further improve decoding performance, we introduce a Hybrid Temporal-Spectral Reward (HTSR) loss function, which integrates time-domain, frequency-domain, and reward-based loss components. In addition, we contribute a new Gait-EEG Dataset (GED), consisting of synchronized EEG and lower-limb joint angle data collected from 50 participants across two laboratory visits. Extensive experiments demonstrate that EEG2GAIT with HTSR achieves superior performance on the GED dataset, reaching a Pearson correlation coefficient (r) of 0.959, a coefficient of determination (R[2]) of 0.914, and a Mean Absolute Error (MAE) of 0.193. On the MoBI dataset, EEG2GAIT likewise consistently outperforms existing methods, achieving an r of 0.779, an R[2] of 0.597, and an MAE of 4.384. Statistical analyses confirm that these improvements are significant compared to all prior models. Ablation studies further validate the contributions of the hierarchical GCN modules and the proposed HTSR loss, while saliency analysis highlights the involvement of motor-related brain regions in decoding tasks. Collectively, these findings underscore EEG2GAIT's potential for advancing brain-computer interface applications, particularly in lower-limb rehabilitation and assistive technologies.}, }
@article {pmid41426299, year = {2025}, author = {Huang, S and Chen, C and Mo, Y and Zhao, Y and Zhu, Y and Dong, K and Xu, T}, title = {Exploring the n-back task: insights, applications, and future directions.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1721330}, pmid = {41426299}, issn = {1662-5161}, abstract = {The n-back task has become a central paradigm for investigating the mechanisms of working memory (WM) and related executive functions. This review provides an integrative analysis of the n-back experiment, covering its cognitive operations, task variants, neuroimaging findings, and practical applications across multiple domains. We first delineate three core cognitive components-updating, maintenance, and attentional control-and summarize converging evidence that these functions rely on overlapping fronto-striatal and fronto-parietal networks. We then examine major task variants and review applications in: (1) cognitive training and transfer effects, particularly the proposed association between WM and fluid intelligence; (2) clinical contexts including attention deficit hyperactivity disorder (ADHD), depression, and neurological rehabilitation; (3) developmental and educational settings; and (4) emerging research on social cognition, stress, and emotional regulation. Critically, this review evaluates ongoing inconsistencies in how the n-back task is interpreted as a measure of WM and highlights methodological factors, such as task heterogeneity, multi-process interference, and mental fatigue, that complicate both behavioral and neural inferences. To address these issues, we outline methodological recommendations including adaptive task design, multimodal physiological monitoring, and standardized experimental protocols. We further discuss future directions involving virtual reality (VR), mobile platforms, and brain-computer interface (BCI) integration to improve ecological validity and translational relevance. By synthesizing behavioral and neural evidence, this review underscores the n-back task's versatility while emphasizing the need for improved construct clarity and methodological rigor.}, }
@article {pmid41426186, year = {2025}, author = {Khan, H and Nazeer, H and Minhas, HS and Naseer, N and Mirtaheri, P}, title = {Open-access fNIRS dataset for motor imagery of lower-limb knee and ankle joint tasks.}, journal = {Frontiers in robotics and AI}, volume = {12}, number = {}, pages = {1695169}, pmid = {41426186}, issn = {2296-9144}, }
@article {pmid41424861, year = {2026}, author = {Li, X and Zheng, C and Tian, Y and Ming, D}, title = {Channel-specific differential effects of bacterial mechanosensitive channels for ultrasound neuromodulation in precision sonogenetics.}, journal = {Theranostics}, volume = {16}, number = {5}, pages = {2447-2465}, pmid = {41424861}, issn = {1838-7640}, mesh = {Animals ; Rats ; *Ion Channels/genetics/metabolism ; *Hippocampus/metabolism/physiology/radiation effects ; Male ; Rats, Sprague-Dawley ; Ultrasonic Waves ; *Escherichia coli Proteins/genetics/metabolism ; Mechanotransduction, Cellular ; Dependovirus/genetics ; }, abstract = {Rationale: Ultrasound neuromodulation offers promising therapeutic potential, but its effectiveness is limited by imprecise targeting of neural circuits. Engineering mechanosensitive ion channels can enhance ultrasound sensitivity, providing a more precise approach for targeted neuromodulation. This study aimed to compare three bacterial mechanosensitive channels (MscL-G22S, MscL-G22N, and MscS) for mediating ultrasound-responsive hippocampal activity to identify optimal candidates for precision sonogenetics applications. Methods: We expressed MscL-G22S, MscL-G22N, and MscS in the rat hippocampus using AAV vectors and applied focused ultrasound stimulation at various intensities while recording local field potentials. Neural oscillatory patterns, ultrasound-evoked potentials, behavioral outcomes, immunohistology, and transcriptomic analyses were conducted to assess response consistency, efficacy, and biosafety. Results: Each channel conferred distinct neuromodulatory signatures: MscL-G22S exhibited remarkable ultrasound sensitivity with non-monotonic intensity-response amplification of evoked potentials (2.3-fold increase at maximum intensity), and accelerated response timing (latency reduction). Notably, MscL-G22N showed weaker ultrasound responses despite having a lower mechanical threshold than G22S, suggesting ultrasound sensitivity depends on factors beyond mechanical gating thresholds. Conversely, MscS displayed diminished responses at higher intensities. No statistically significant differences were detected in behavior assessments and histology evaluations. All channels maintained normal anxiety indices, spatial memory, and neuronal morphology, though MscS selectively increased depressive-like behaviors. Transcriptomic analysis revealed that MscS demonstrated exceptional genomic compatibility with minimal off-target gene alterations (9 vs. >400 in MscL variants). Conclusion: This characterization provides insights for potential precision sonogenetics applications: MscS offers a biosafety-optimized option with minimal genomic footprint, whereas MscL-G22S enables modulation of neural oscillations. These findings contribute to the development of customized neuromodulation approaches for targeting pathological circuits in neurological disorders.}, }
@article {pmid41357970, year = {2025}, author = {Wei, Y and Wang, Y and Wei, T and Lu, X and Li, D and Sherwood, CC and Zhang, Y and Cheng, C and Jiang, T and Fan, L and Cheng, L}, title = {Cross-Species Cortical Geometry Reveals Conserved Gradients Across Primates and Human-Specific Expansion.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {41357970}, issn = {2692-8205}, support = {R24 NS092988/NS/NINDS NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; R01 AG067419/AG/NIA NIH HHS/United States ; R01 AG087945/AG/NIA NIH HHS/United States ; R01 HG011641/HG/NHGRI NIH HHS/United States ; }, abstract = {The primate cerebral cortex, characterized by its complex structural geometry, underlies advanced cognitive functions and represents a defining feature distinguishing primates from other mammals. However, cross-species patterns of cortical geometry and the links between human cortical geometry and transcriptional architecture remain poorly understood. We developed a geometry-based cross-species cortical alignment framework to systematically investigate the similarities and differences in structural connectivity and cortical expansion characteristics among macaques, chimpanzees, and humans, and additionally explored the transcriptional underpinnings of human cortical geometry. Our analysis revealed conserved spatial patterns of cortical geometric features across species, providing the foundation for constructing a cross-species structural common space to support the alignment framework. We found that primary sensory, somatomotor, and face-selective regions exhibited high structural connectivity similarity across species, whereas prefrontal and parietal association cortices displayed significant divergence. We also identified disproportionate cortical expansion in the default mode network, with a consistent expansion trend across different evolutionary lineages in primates. Furthermore, neuroimage-transcription analysis indicated that cortical geometric features were correlated with transcriptional profiles enriched in neurodevelopmental and connectivity-related pathways. These results highlight a conserved yet hierarchically differentiated organization of the cerebral cortex in primates, providing new insights into the biological basis of human brain evolution.}, }
@article {pmid41329575, year = {2026}, author = {Zhang, W and Lai, J and Xu, B and Zeng, H and Wu, T and Hu, H and Song, A}, title = {The Role of Vibrotactile Stimulation in Soft Rehabilitation Glove-Assisted Hand Rehabilitation Training: A Pilot Study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {34}, number = {}, pages = {149-162}, doi = {10.1109/TNSRE.2025.3639490}, pmid = {41329575}, issn = {1558-0210}, mesh = {Humans ; Male ; Pilot Projects ; Female ; *Vibration ; *Stroke Rehabilitation/methods/instrumentation ; *Hand/physiopathology ; Electroencephalography ; Adult ; Middle Aged ; Robotics/instrumentation ; Spectroscopy, Near-Infrared ; Young Adult ; Motor Cortex/physiology/physiopathology ; Aged ; Somatosensory Cortex/physiology ; Hand Strength ; Brain-Computer Interfaces ; Stroke/physiopathology ; }, abstract = {Brain-controlled robotic hand rehabilitation systems based on motor intention recognition have been used to promote recovery of hand function in stroke patients. However, the low decoding accuracy of motor imagery (MI) and unclear neural response mechanisms limit its widespread application. This study introduces a novel vibrotactile-assisted brain-controlled soft robotic hand rehabilitation system to validate its effectiveness in activating the motor sensory areas of the brain and to explore the neural response mechanisms of vibration stimulation in hand rehabilitation training. A total of 23 healthy subjects and 5 stroke patients were recruited to perform EEG and fNIRS-based experiments. Healthy subjects performed an EEG-based active rehabilitation task and an fNIRS-based passive rehabilitation task driven by the soft glove. Stroke patients only completed an EEG-based passive rehabilitation task. All experiments were conducted under two conditions: with and without vibrotactile stimulation. EEG results revealed that vibration stimulation significantly enhanced motor-sensory cortex activation during MI, and improved the online decoding performance of subjects with poor training outcomes. Grasping and stretching movements driven by the soft glove effectively activated the subjects' motorsensory cortex. Vibration stimulation boosted the event-related desynchronization (ERD) phenomenon in the contralateral somatosensory cortex of the healthy subjects, but was not significant in the motor cortex. Meanwhile, it strengthened bilateral sensorimotor activation in stroke patients. Moreover, fNIRS results indicated that vibration stimulation increased the concentration of HbO in the motor-sensory areas during passive movement and enhanced the bidirectional functional connectivity between the left and right hemispheres. These findings suggest that the proposed tactile-assisted hand rehabilitation system can effectively enhance neural activation in the motor-sensory cortex, potentially leading to improved hand function recovery in stroke patients.}, }
@article {pmid41325123, year = {2026}, author = {Wei, R and Hua, C and Chen, J and Mu, D and Zhao, J}, title = {Improving Generalization in Federated Learning for Steady-State Visual Evoked Potential Classification and Its Application in Soft Gripper.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {34}, number = {}, pages = {126-136}, doi = {10.1109/TNSRE.2025.3639091}, pmid = {41325123}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Male ; *Hand Strength/physiology ; Adult ; Algorithms ; Female ; Brain-Computer Interfaces ; *Machine Learning ; Young Adult ; Signal Processing, Computer-Assisted ; Databases, Factual ; Federated Learning ; }, abstract = {Conventional cross-subject electroencephalogram (EEG) signal identification frameworks require centralized aggregation of all subjects' data for feature extraction, which inherently poses substantial risks of data privacy breaches. In response to this critical issue, the present study delves into the classification of steady-state visual evoked potential (SSVEP) signals with an emphasis on data privacy preservation. First, we design a federated learning framework (FedGF) consisting of a central server and multiple clients, where the server generates global features and coordinates distributed training across clients, while retaining subject-specific raw data locally to ensure privacy protection. Then, to enhance model generalizability, FedGF employs data-free knowledge distillation (DFKD) to achieve knowledge transfer across clients through global feature learning. Extensive experiments on two public datasets (Dataset 1 'session01' and 2 'session02') and one private dataset (Dataset 3) demonstrate the superiority of the proposed method over baseline approaches, achieving performance improvements of 0.52%, 0.65%, and 0.53%, respectively. Finally, we develop a novel smart soft gripper with thermochromic capabilities and seamlessly integrate it with the trained network, demonstrating robust performance in daily grasping tasks. The source code is available at https://github.com/raow923/FedGF.}, }
@article {pmid41423674, year = {2025}, author = {Van Den Kerchove, A and Meunier, J and de Moura, M and Willemssens, A and Geeurickx, D and Schiettecatte, E and Van Damme, P and Si-Mohammed, H and Cabestaing, F and Allart, E and Van Hulle, MM}, title = {Visual ERP-based brain-computer interface use with severe physical, speech and eye movement impairments: case studies.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-025-01836-0}, pmid = {41423674}, issn = {1743-0003}, support = {G0A4321N//Fonds Wetenschappelijk Onderzoek/ ; G0A4118N//Fonds Wetenschappelijk Onderzoek/ ; GPUDL/20/031//KU Leuven Special Research Fund/ ; C24/18/098//KU Leuven Special Research Fund/ ; RITMEA//European Regional Development Fund/ ; RITMEA//European Regional Development Fund/ ; 101118964//HORIZON EUROPE Marie Sklodowska-Curie Actions/ ; 857375//Horizon 2020/ ; AKUL 043//Herculesstichting/ ; }, abstract = {BACKGROUND: Individuals who experience severe speech and physical impairment face significant challenges in communication and daily interaction. Visual brain-computer interfaces (BCIs) offer a potential assistive solution, but their usability is limited when facing restrictions in eye motor control, gaze redirection and fixation. This study investigates the feasibility of a gaze-independent visual oddball BCI for use as an augmentative and alternative communication (AAC) device in the presence of limited eye motor control.<br><br>METHODS: Seven participants with varying degrees of eye motor control were recruited and their conditions were thoroughly assessed. Visual oddball BCI decoding accuracy was evaluated with multiple decoders in three visuospatial attention (VSA) conditions: overt VSA, with fixation cued on the target, covert VSA, with fixation cued on the center of the screen, and free VSA without gaze cue.<br><br>RESULTS: covert VSA with central fixation leads to decreased accuracy, whereas free VSA is comparable to overt VSA for some participants. Furthermore, cross-condition decoder training and evaluation suggests that training with overt VSA may improve performance in BCI users experiencing gaze control difficulties.<br><br>CONCLUSIONS: These findings highlight the need for adaptive decoding strategies and further validation in applied settings in the presence of gaze impairment.}, }
@article {pmid41421050, year = {2025}, author = {Ming, W and Zheng, Y and Lian, Q and Shen, C and Zhang, Y and Wang, Z and Wang, S and Li, F and Zheng, Z and Qi, Y and Zhu, J and Wu, H}, title = {Brain connectivity predict surgical outcomes of low-grade epilepsy-associated neuroepithelial tumors.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {183}, number = {}, pages = {2111478}, doi = {10.1016/j.clinph.2025.2111478}, pmid = {41421050}, issn = {1872-8952}, abstract = {OBJECTIVE: Low-grade epilepsy-associated neuroepithelial tumors (LEATs) often cause drug-resistant epilepsy. Despite complete resection of these lesions, approximately 20% of patients continue to experience suboptimal seizure control. This study aims to investigate the predictive value of quantitative features in determining the surgical outcomes for LEAT patients.<br><br>METHODS: We retrospectively analyzed 44 temporal LEAT patients who underwent gross-total lesionectomy. EEG features, including power spectral density (PSD) and weighted phase lag index (wPLI), were compared between patients with good (Engel I) and poor (Engel II-IV) outcomes. Significant EEG features were identified through these analyses. Domain Adversarial Neural Network (DANN) was employed to assess the predictive value of these features for surgical outcomes.<br><br>RESULTS: No significant PSD differences were found, but patients with good outcomes had higher alpha-band wPLI (p&#xa0;=&#xa0;0.008). LEATnet, predicted outcomes with an AUC of 0.81and correctly classified 8 of 11 patients in the independent validation cohort.<br><br>CONCLUSIONS: Alpha-band functional connectivity is a key predictor of surgical outcomes in LEAT patients.<br><br>SIGNIFICANCE: EEG-based connectivity analysis may improve prognostic accuracy and aid clinical decision-making in LEAT epilepsy.}, }
@article {pmid41418936, year = {2025}, author = {Zhou, Y and Jiang, R and Zhang, J}, title = {A multi-scale deep CNN based on attention mechanism for EEG emotion recognition.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110662}, doi = {10.1016/j.jneumeth.2025.110662}, pmid = {41418936}, issn = {1872-678X}, abstract = {BACKGROUND: Recognizing emotion is a crucial project within the domain of brain-computer interface technology. Recently, researchers have found that deep learning have been proven to be superior to machine learning, but how to obtain more discriminative features still faces great challenges.<br><br>NEW METHOD: We propose a multi-scale convolutional neural network (MSCNN) based on channel attention and spatial attention (CSA-MSCNN) for EEG emotion recognition. The channel attention enhances the feature extraction ability of critical channels by generating channel weights, while suppressing noise or interference from redundant channels. The spatial attention helps the model to more precisely locate key areas related to emotion by generating a spatial weight matrix. To extract more comprehensive features, CSA-MSCNN uses MSCNN for feature extraction, with smaller convolutional kernels capturing the local details of the signals, and larger convolutional kernels with a broader receptive field to obtain deeper signal information.<br><br>RESULTS: CSA-MSCNN achieves average accuracies of 95.75% and 95.39% for three-class classification of valence and arousal on DEAP, respectively, while achieving an average three-class classification accuracy of 90.48% on SEED.<br><br>The classification accuracy of CSA-MSCNN is not only significantly better than traditional machine learning models, but also shows strong competitiveness compared with mainstream deep learning models such as graph convolutional neural network (GCNN).<br><br>CONCLUSIONS: CSA-MSCNN addresses the issues of multiple EEG signal channels and complex regional information.}, }
@article {pmid41418897, year = {2025}, author = {Zhang, T and Zhang, Q and Xiong, R and Zhang, J and Jin, Z and Li, L}, title = {Grey Matter Volume Predicts Decision Speed and Reveals Stage-Specific Contributions of Large-Scale Brain Networks in Gambling Tasks.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121659}, doi = {10.1016/j.neuroimage.2025.121659}, pmid = {41418897}, issn = {1095-9572}, abstract = {Large-scale brain networks are well-established in resting-state research and are increasingly being used in task-based functional magnetic resonance imaging (fMRI) studies. However, the mechanisms by which brain networks dynamically reorganize across the various stages of decision-making remain unclear. Here, we investigated the neural basis of decision-making by integrating voxel-based morphometry and fMRI within a modified "Wheel of Fortune" gambling task. Stage-specific brain activation was characterized using the Yeo-7 network atlas to delineate large-scale network dynamics across task stages. We found that: (1) Reaction time (RTs) were significantly longer during choose conditions compared to follow conditions; (2) Gray matter volume correlated with individual variability in RT and predicted RT during choose conditions using multivariate pattern analysis with a Kernel Ridge Regression model, effects absent during follow conditions; (3) A negative correlation was observed between RT and activation in the right superior temporal gyrus and left mid-cingulate cortex; (4) Choice stage involved more extensive network engagement than the result and rating stages, with the rating stage showing the lowest overall activation. Network-specific fractional contributions revealed dominant engagement of the ventral attention network, default mode network, and somato-motor network during the choice stage; the frontoparietal network (FPN), dorsal attention network (DAN), and visual network during the result stage; and the DAN and FPN during the rating stage. These findings provide structural and functional explanations for individual differences in decision speed within a gambling paradigm, revealing the distinct and dynamic roles of brain networks across decision stages and offering mechanistic insights into the neural architecture of this process.}, }
@article {pmid41417240, year = {2025}, author = {Yakovlev, L and Miroshnikov, A and Syrov, N and Berkmush-Antipova, A and Kaplan, A}, title = {Sensorimotor event-related desynchronization and hemodynamic responses during motor and tactile imagery.}, journal = {Brain structure &amp; function}, volume = {231}, number = {1}, pages = {4}, pmid = {41417240}, issn = {1863-2661}, support = {24-75-00163//Russian Science Foundation/ ; }, mesh = {Humans ; Male ; *Imagination/physiology ; Female ; Adult ; Electroencephalography ; Young Adult ; *Hemodynamics/physiology ; Spectroscopy, Near-Infrared ; *Touch Perception/physiology ; *Touch/physiology ; Brain Mapping ; *Cortical Synchronization/physiology ; *Sensorimotor Cortex/physiology ; }, abstract = {Mental imagery is widely used in cognitive neuroscience and rehabilitation studies, yet their neural mechanisms remain not fully understood. In this study, we investigated neural correlates of motor and tactile imagery using simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recordings. A total of 16 healthy participants performed motor and tactile imagery tasks while brain activity was assessed. We analyzed event-related desynchronization (ERD) of the mu-rhythm and hemodynamic responses in sensory-motor regions. Similar spatio-temporal EEG patterns were observed for both motor and tactile imagery conditions (e.g., prominent contralateral ERD at C3). Hemodynamic responses differed: motor imagery elicited activation in both precentral and postcentral regions (p = 0.433), whereas tactile imagery predominantly engaged postcentral regions. The latter effect reached significance only in the functional channels of interest (fCOI) analysis (p = 0.003) and showed a non-significant trend across the full anatomical channel groups (p = 0.101). Correlation analysis revealed a strong across-subject correlation (r = 0.84; p < 0.001) between ERD values in motor and tactile imagery, but no correlation between ERD and hemodynamic responses. Linear mixed model analysis revealed significant (p < 0.001) associations between precentral and postcentral HRs for both MI and TI. These findings suggest that although motor and tactile imagery share common sensorimotor engagement at the electrophysiological level, their hemodynamic signatures are distinct. The absence of linear associations between modalities highlights the complexity of brain dynamics and the importance of multimodal assessments. The findings have implications for the design of brain-computer interfaces and rehabilitation protocols using mental imagery.}, }
@article {pmid41416623, year = {2025}, author = {Adhikary, S and Dutta, S and Bose, A and Ranjan, R}, title = {Brain computer interface to recognize hand movements by magnification of subtle electroencephalogram patterns.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-15}, doi = {10.1080/10255842.2025.2602830}, pmid = {41416623}, issn = {1476-8259}, abstract = {Brain-computer interfacing facilitates usage of medical devices such as Electroencephalograms to study brain activities using signal processing techniques. Hand movements are motor activities which cause signature electrical signals in the electroencephalogram recordings. Signal processing and machine learning can be used to remove artefact contamination, amplify subtle features associated with hand movement and classify them. This paper experiments to utilize mathematical models to extract features and classify hand movement from electroencephalogram data up to 98% accuracy based on tests performed on an open-sourced dataset. The study, after further tests, can be used to build prosthetic limbs and mind-controlled robotic arms.}, }
@article {pmid41414721, year = {2025}, author = {Zhang, Y and Huang, HF and Xie, JJ and Ni, W and Yu, H and Wu, ZY}, title = {Genetic and Clinical Characteristics of Chinese Adult Patients With Krabbe Disease.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {31}, number = {12}, pages = {e70708}, doi = {10.1002/cns.70708}, pmid = {41414721}, issn = {1755-5949}, support = {82230062//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholar of Zhejiang University/ ; }, mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; China ; Exome Sequencing ; *Galactosylceramidase/genetics ; *Leukodystrophy, Globoid Cell/genetics/diagnosis ; East Asian People/genetics ; }, abstract = {AIM: This study aims to expand the clinical and genetic spectrum of Krabbe disease (KD) in Chinese adult patients and to improve diagnosis and understanding of its phenotypic diversity.<br><br>METHODS: Patients clinically suspected of leukodystrophy were recruited between 2015 and 2025. Clinical features were collected, and whole-exome sequencing (WES) was performed to identify potential variants. The pathogenicity of detected variants was classified according to the American College of Medical Genetics and Genomics (ACMG) standards and guidelines. Functional assays assessing protein expression, processing, secretion, subcellular localization, and enzymatic activity were conducted to further validate variant pathogenicity.<br><br>RESULTS: Fourteen unrelated patients were genetically diagnosed with KD, and their genetic and clinical features were summarized. Eleven variants in GALC were identified, including a novel missense variant c.1019C>T (p.P340L) which is not reported in the Human Gene Mutation Database (HGMD). Unlike most adult patients who typically present with spastic paraplegia, the patient carrying this variant exhibited initial symptoms of peripheral neuropathy. Functional experiments demonstrated that the variant led to impaired protein processing and localization, as well as reduced GALC enzymatic activity. Other variants including p.D56H, p.L377X, p.L441X, and p.L634S also affected GALC functions to varying degrees.<br><br>CONCLUSION: This study enhances the genotypic and phenotypic characterization of KD in China, aiding in differential diagnosis and genetic counseling. Functional data reinforce the pathogenicity of identified variants.}, }
@article {pmid41413923, year = {2025}, author = {Xu, Y and Wei, Y and Xu, M and Zhou, H and Zheng, J and Chen, H and Chen, S and Chen, W}, title = {The relationship between heart rate variability and baseline state anxiety during stress and recovery.}, journal = {BMC psychology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s40359-025-03823-5}, pmid = {41413923}, issn = {2050-7283}, support = {No. QD2025017//Scientific Research Foundation of Hang Zhou City University/ ; }, }
@article {pmid41413226, year = {2025}, author = {Sun, Y and Si, X and He, R and Hu, X and Smielewski, P and Wang, W and Tong, X and Yue, W and Pang, M and Zhang, K and Song, X and Ming, D and Liu, X}, title = {An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework.}, journal = {NPJ digital medicine}, volume = {8}, number = {1}, pages = {768}, pmid = {41413226}, issn = {2398-6352}, support = {ZYGXQNJSKYCXNLZCXM-H15//Scientific Research Innovation Capability Support Project for Young Faculty/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; 82472098, 32300704//National Natural Science Foundation of China/ ; 24JCJQJC00250//Tianjin Natural Science Foundation-Outstanding Youth Project/ ; 24ZXZSSS00510//Major Science and Technology Special Projects and Engineering-Major Project of National Key Laboratories/ ; 2021YFF1200602//Key Technologies Research and Development Program/ ; 2024-JKCS-16//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; }, abstract = {Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited in application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial intelligence models. In this study, we describe an automated classifier, VIPEEGNet, which leverages the advantage of transfer learning from ImageNet-pretrained models to distinguish six types of brain activities. For the development cohort, the recall of VIPEEGNet ranges from 36.8% to 88.2%, and the precision ranges from 55.6% to 80.4%, with performance comparable to that of human experts. Notably, the external testing showed Kullback-Leibler divergence (KLD) values of 0.223 (public) and 0.273 (private), ranking second among the existing 2767 competing algorithms, while using only 0.7% of the parameters of the top-ranked algorithm. Its minimal parameter requirements and modular design offer a deployable solution for real-time brain monitoring, potentially expanding access to expert-level EEG interpretation in resource-limited settings.}, }
@article {pmid41412372, year = {2025}, author = {Zhang, L and Li, B and Cao, M and Peng, C and Wang, H}, title = {Classification of EEG-fNIRS bimodal brain signals for motor imagery tasks based on wavelet transform and spatio-temporal domain processing.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2025.12.036}, pmid = {41412372}, issn = {1873-7544}, abstract = {The fusion of Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provides richer neural information for brain-computer interface decoding. However, due to their distinct physiological mechanisms and heterogeneous temporal and statistical properties, EEG and fNIRS are difficult to temporally align and to project into a shared latent representation. To address this challenge, we propose BiCAT, a lightweight bimodal decoding framework that integrates wavelet-based preprocessing, artifact-aware time-domain refinement, and early feature-level fusion with a compact Transformer encoder. Wavelet transform is first applied to separate signal and noise components across frequency bands, after which spatio-temporal domain processing suppresses motion and physiological artifacts while preserving task-relevant patterns. The cleaned EEG and fNIRS features are concatenated and fed into a single-encoder Transformer, where joint self-attention captures salient temporal cues within each segment.BiCAT is evaluated on two publicly available EEG-fNIRS datasets covering motor imagery (MI), mental arithmetic (MA), and word generation (WG) tasks. The model achieves 93.41 % accuracy on MI, outperforming the strongest unimodal baseline (fNIRS) by 4.39 percentage points. On MA and WG, BiCAT attains 96.47 % and 96.41 % accuracy, corresponding to gains of 10.39 and 3.86 points over the best unimodal fNIRS and HbR baselines, respectively. Despite having only 111 k parameters, BiCAT performs competitively with representative multimodal fusion methods on the same benchmarks. These results demonstrate that BiCAT provides effective bimodal feature integration and robust performance across multiple EEG-fNIRS tasks while maintaining low computational complexity.}, }
@article {pmid41410819, year = {2025}, author = {Hickman, J and Tsai, A and Fullard, M and Korsmo, M and Forbes, E and Aslam, S and Baumgartner, AJ and Feuerstein, JS and Bayram, E}, title = {Early-Onset Parkinson's Disease: Unique Features and Management Approaches.}, journal = {Current neurology and neuroscience reports}, volume = {26}, number = {1}, pages = {3}, pmid = {41410819}, issn = {1534-6293}, support = {R01NS120850/NS/NINDS NIH HHS/United States ; R00AG073453/AG/NIA NIH HHS/United States ; }, mesh = {Humans ; *Parkinson Disease/therapy/diagnosis/epidemiology ; Age of Onset ; *Disease Management ; Risk Factors ; Disease Progression ; }, abstract = {PURPOSE OF REVIEW: To highlight the unique clinical features, risk factors, and management strategies associated with early-onset Parkinson's disease (EOPD), and contrast these with late-onset Parkinson's disease (LOPD). We outline how these differences influence diagnostic and therapeutic approaches and identify key knowledge gaps critical to improving clinical care.<br><br>RECENT FINDINGS: Compared to LOPD, EOPD (onset age 21-50) has a higher prevalence of monogenic risk factors, focal dystonia, depression, anxiety; slower motor progression; lower rates of cognitive decline; higher risk for delayed diagnosis. Treatment is complicated by earlier and more frequent dyskinesias, motor fluctuations, and unique considerations such as pregnancy and career impact. Risk factors, clinical presentation, progression, and management needs of EOPD can differ from LOPD. Despite advances in characterizing and diagnosing EOPD, most research remains focused on LOPD. There is a critical need to tailor research and clinical trials to address the distinct needs of people with EOPD.}, }
@article {pmid41408409, year = {2025}, author = {Zhen, X and Yu, Z and Shi, Y and Zhao, Y}, title = {Fusing LandTrendr BCI and machine learning for spoil dump mapping.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-32957-0}, pmid = {41408409}, issn = {2045-2322}, }
@article {pmid41408286, year = {2025}, author = {Zhang, Y and Li, M and Guo, M and Xu, G and Wang, A}, title = {Decoding preparatory movement state-based motor imagery with multi layer energy decoder.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-025-01837-z}, pmid = {41408286}, issn = {1743-0003}, support = {F2024202019//Natural Science Foundation of Hebei Province/ ; 52320105008//International Cooperation and Exchange of the National Natural Science Foundation of China/ ; 2022YFC2402203//National Key Research and Development Program of China/ ; }, abstract = {BACKGROUND: Motor imagery (MI) is a widely used paradigm in brain-computer interface (BCI) research due to its potential applications in areas such as motor rehabilitation. As a purely cognitive process, MI produces low-amplitude, non-stationary EEG. Despite improving accuracies, cross-subject variability and limited generalization continue to motivate approaches that strengthen MI representations and enhance system robustness.<br><br>METHODS: We designed a task-guided preparatory movement state-based motor imagery (PMS-MI) paradigm that elicits a brief motor preparatory state before MI and captures EEG features from both the preparation and imagery phases. To decode the features effectively, we introduced a multilayer energy decoder (MLED) that integrates graph signal processing (GSP): EEG is modeled as intra- and cross-frequency multilayer brain networks, and a graph Fourier transform (GFT) projects the signals into network energy features before classification. We benchmarked the PMS-MI paradigm and the MLED method across multiple time window lengths using a panel of classical and deep-learning classifiers.<br><br>RESULTS: The PMS-MI paradigm elicited significant energy variations during the movement preparation phase and induced earlier event-related desynchronization (ERD) with broader frequency band activation during MI, compared to traditional MI paradigms. Classification performance using CSP in the PMS-MI paradigm surpassed that of the traditional paradigm at all time windows. Further accuracy improvements were achieved with the MLED method. Brain network analysis revealed distinct neural representations between the preparation and MI phases, and MLED effectively captured these differences. Feature fusion of preparation and MI stages resulted in classification accuracies exceeding 85% for both 1&#xa0;s and 4&#xa0;s windows. The results demonstrate that both algorithmic design and paradigm choice play important roles in MI EEG decoding, with their relative contributions varying across temporal windows and experimental conditions.<br><br>CONCLUSIONS: Integration of preparatory movement states into the movement imagery process can generate distinguishable features at different stages and improve the classification performance of BCI systems. The proposed PMS-MI paradigm, combined with the MLED decoding method, provides a promising direction for developing more accurate and robust BCIs, particularly in the context of neurorehabilitation.}, }
@article {pmid41407306, year = {2025}, author = {Wang, J and Liu, H and Wu, W and Hu, X and Wu, Z and Zhu, S and Ma, G and Wan, H and Feng, C and Wang, H}, title = {Structure-Property Modulation in Pyrolytic Photoresist Films Enabled Size-Dependent Electrochemical Performance of Neural Interfaces.}, journal = {ACS applied materials &amp; interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.5c20142}, pmid = {41407306}, issn = {1944-8252}, abstract = {Neural probes are critical devices used to monitor and record brain activity, usually connected to neurons to measure neural activity. However, traditional metal electrodes face numerous challenges, including high Young's modulus, susceptibility to electromagnetic interference, insufficient biocompatibility, and the risk of corrosion and delamination. In this study, we explore a highly biocompatible carbon material, a pyrolytic photoresist film (PPF), developed through a photoresist pyrolysis process. The effects of the pyrolysis temperature and hold time on material properties were systematically studied. The optimal pyrolysis condition was identified as 1000 °C for 2 h. Furthermore, a quantitative model was established to link the electrode's geometric area with electrochemical performance and optimize the performance of PPF neural probes. Ultimately, we successfully fabricated a multichannel flexible neural probe with superior electrochemical performance.}, }
@article {pmid41406614, year = {2025}, author = {Sultana, M and Matran-Fernandez, A and Halder, S and Nawaz, R and Jain, O and Scherer, R and Chavarriaga, R and Millan, JDR and Perdikis, S}, title = {An out-of-the-lab evaluation of dry EEG technology on a large-scale motor imagery brain-computer interface dataset.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2e8a}, pmid = {41406614}, issn = {1741-2552}, abstract = {OBJECTIVE: This study assesses the signal quality of state-of-the-art dry electroencephalography (EEG) under highly challenging, uncontrolled, real-world conditions and compares it to conventional wet EEG.<br><br>APPROACH: EEG data from 530 participants recorded during a public exhibition were benchmarked against several established signal quality metrics, including spiking activity, kurtosis, Auto-Mutual Information (AMI), spectral entropy, gamma-band power, and parameters extracted using the Fitting Oscillations and One-Over F (FOOF) model. Additionally, ICLabel decomposition was applied to quantify artifact influences across EEG channels. Dry electrode results were compared with their equivalents extracted on two control datasets comprising 71 and 80 participants, respectively, recorded with wet EEG systems in laboratory, home, or clinical surroundings. Main Results The analysis revealed condition-specific susceptibility to artifacts for both EEG modalities. The dry EEG system exhibited substantial robustness in moderate-noise scenarios, with artifact profiles comparable to controlled wet EEG recordings. However, recordings obtained in highly dynamic conditions showed increased muscle artifacts and broadband activity, notably in frontal and temporal regions. Wet EEG systems, under controlled conditions, were overall less inflicted by artifacts, yet, fronto-central ocular and muscular artifacts were consistently present. ICLabel analysis further confirmed these findings, indicating similar proportions of brain-related activity across systems (approximately 31-49.5%), but highlighted increased vulnerability to muscular and environmental artifacts in dry EEG during dynamic tasks.<br><br>SIGNIFICANCE: In agreement with recent similar investigations, our findings demonstrate that dry EEG caps have significantly matured, achieving signal quality comparable to wet EEG systems even in challenging real-world conditions, provided appropriate artifact mitigation strategies are employed. These results affirm the practical readiness and broad feasibility of dry EEG technologies for diverse Brain-Computer Interface (BCI) applications in naturalistic environments.}, }
@article {pmid41406276, year = {2025}, author = {Wang, F and Cao, F and Gao, J and An, N and Yang, J and Wang, Y and Yu, D and Ma, X and Xiang, M and Ning, X}, title = {Exploring the Potential of SSVER-BCI Based on Contactless Measurement Using Optically Pumped Magnetometers.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3644887}, pmid = {41406276}, issn = {2168-2208}, abstract = {Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been widely applied in health monitoring and neurorehabilitation. However, EEG signals are often attenuated and distorted by tissues like the scalp and skull, limiting EEG-based BCI performance. In contrast, magnetoencephalography (MEG) with contactless measurement offers higher spatial resolution and immunity to volume conduction effects. Traditional MEG systems, based on superconducting quantum interference devices (SQUIDs), are hindered by their size and cost, while optically pumped magnetometers (OPMs) have made OPM-MEG-based BCIs more practical and accessible. Nevertheless, the performance potential of OPM-MEG in BCI applications remains underexplored. To address this, we developed an OPM-MEG BCI system based on steady-state visual evoked response (SSVER) and conducted a systematic evaluation of its performance, highlighting the practical advantages of OPM-MEG in this context. Furthermore, we proposed a fusion framework for OPM-MEG and EEG to further enhance system performance. Offline experiments conducted with 13 participants showed that the developed EEG-BCI achieved an average accuracy of 94.30% and an information transfer rate (ITR) of 122.76 bits/min, the developed OPM-MEG BCI achieved an average accuracy of 98.68% and an ITR of 138.20 bits/min, while the hybrid BCI achieved an average accuracy of 99.72% and an ITR of 159.4 bits/min. The findings highlight the advantages of OPM-MEG for BCI applications and validate the proposed fusion framework as a viable means to enhance decoding performance, thereby extending the potential use cases of OPM-MEG-based systems.}, }
@article {pmid41406275, year = {2025}, author = {Carlino, MF and Gielen, G}, title = {An artifact-free 290$μ$m[2]/ch 610nW/ch neural readout frontend with hybrid EDO compensation for high-channel-count closed-loop neuromodulation.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2025.3644137}, pmid = {41406275}, issn = {1940-9990}, abstract = {Next-generation neurorehabilitation implants demand high-channel-count closed-loop systems with ultra-low area and ultra-low-power readout and classification. This is essential in applications such as multi-type epileptic seizure detection, brain machine interfaces or brain-to-text conversion. Although recent designs achieve compactness and low power, they often cannot record neural signals during stimulation due to large, saturating artifacts. Conversely, artifact-tolerant solutions typically incur excessive area and power overhead to avoid saturation. We introduce a paradigm shift: enabling an ultra-compact, artifact-tolerant readout frontend by permitting brief saturation during stimulation pulses and applying backend interpolation to reconstruct the signals. High-fidelity neural features can thus be extracted with minimal error. To minimize the readout area footprint and to facilitate the routing from many electrodes, we reuse the whole frontend to read-out 64 inputs in a time-multiplexed fashion. Implemented in a 40nm CMOS process, our chip leverages the first published secondorder fully time-based incremental analog-to-digital converter, achieving a state-of-the-art 290-$μ$m[2]/ch area occupation and only 610-nW/ch of power consumption. The proposed hybrid electrode offset compensation further minimizes the area overhead without significantly compromising the noise or common-mode/power rejection across the full cancellation range. Artifact tolerance is validated in saline using an external stimulator chip. We demonstrate that the error on a broad set of features extracted from interpolated local-field-potential data remains below ±10%, even under harsh stimulation conditions.}, }
@article {pmid41404976, year = {2025}, author = {Garg, M and Kaur, J and Prakash, NR}, title = {Ocular artifact from electroencephalogram - a comparative analysis of feature extraction, selection and classification.}, journal = {Journal of medical engineering &amp; technology}, volume = {}, number = {}, pages = {1-8}, doi = {10.1080/03091902.2025.2600336}, pmid = {41404976}, issn = {1464-522X}, abstract = {An electroencephalogram (EEG) is a record of signals that represent surface potentials varying whenever the brain performs any task and can be recorded by placing an arrangement of electrodes at the scalp of the brain. These recordings are often contaminated by unwanted movement near these electrodes, resulting in non-cerebral signals called artefacts. The presence of artefacts makes the study of EEG signals difficult. This work focuses on a comparative analysis of classification of ocular artefacts from EEG signal that mainly comprise of eye blinks. Various feature extraction, feature selection and classification techniques are used to compare the prediction performance of the system. Three different methods were used to extract features from the EEG recording done on eight subjects, performing two different tasks. Then the diagnostic performance of three feature selection and 30 classification methods were evaluated using 5-fold cross-validation. Performance of the system on various combinations has been calculated in terms of accuracy and results have been discussed. The maximum accuracy of 93.8% was yielded by classifiers: Kernel Naïve Bayes, Linear Support Vector Machine (SVM) and Ensemble Bagged Trees using wavelet-based features, principal component analysis as feature selection algorithm. By methodically assessing 360 feature-classifier combinations, this study is innovative and provides one of the most thorough benchmarks for ocular artefact identification with exceptional accuracy. It also has great potential for real-time EEG preprocessing in clinical and BCI applications.}, }
@article {pmid41402805, year = {2025}, author = {Wu, Y and Zhao, X and Jiang, Y and Chen, C and Liu, L and Hou, X and Xian, Q and Guo, J and Sun, L}, title = {Microbubble-enhanced ultrasound stimulation of β-cells improves insulin release and glycemic control in mice.}, journal = {Journal of nanobiotechnology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12951-025-03926-6}, pmid = {41402805}, issn = {1477-3155}, support = {C5053-22 GF//Hong Kong Research Grants Council Collaborative Research Fund/ ; 15126524//General Research Fund/ ; 2023YFC2410900//National Key Research and Development Program of Ministry of Science and Technology of China/ ; G-SACD//Hong Kong Polytechnic University/ ; 1-CE0M//Research Center for Non-invasive Brain Computer Interface/ ; 1-CDJM//Research Institute of Smart Ageing/ ; }, abstract = {Diabetes poses a significant global health burden, with complications such as cardiovascular disease, stroke, and kidney failure. While insulin therapy is central to type 2 diabetes (T2D) management, its limitations-including rapid degradation and the need for frequent injections-highlight the demand for non-invasive alternatives. Here, we present an ultrasound (US)-mediated approach to enhance insulin release by selectively stimulating pancreatic β-cells via targeted microbubbles (MBs). In vitro experiments using RINm5F β-cells demonstrated that US-MB stimulation induces significant calcium influx and subsequent insulin release. In addition, this method effectively decreased blood glucose levels in mice by promoting insulin release. Mechanistic studies revealed that mechanosensitive ion channels play a pivotal role, as their inhibition (via GdCl3) abolished the ultrasonic effect. Importantly, the approach exhibited high biosafety, with no detectable cell death or tissue damage. Our findings establish ultrasound-stimulated β-cell targeting as a promising non-invasive strategy for diabetes treatment, offering a potential alternative to conventional insulin therapy.}, }
@article {pmid41399831, year = {2025}, author = {Sorokin N, I and Nesterova O, Y and Khokhlov M, A and Kamalov D, M and Dzitiev V, K and Strigunov A, A and Tereshina A, D and Veriaskina A, E and Kamalov A, A and Pshikhachev A, M and Mikhalchenko A, V}, title = {[Urodynamic risk factors for transient urinary incontinence after endoscopic enucleation of prostate hyperplasia].}, journal = {Urologiia (Moscow, Russia : 1999)}, volume = {}, number = {5}, pages = {104-112}, pmid = {41399831}, issn = {1728-2985}, mesh = {Humans ; Male ; *Prostatic Hyperplasia/surgery/physiopathology ; *Urinary Incontinence/etiology/physiopathology ; Aged ; *Urodynamics ; Risk Factors ; Middle Aged ; Prospective Studies ; *Postoperative Complications/etiology/physiopathology ; *Endoscopy/adverse effects ; *Prostatectomy/adverse effects/methods ; }, abstract = {INTRODUCTION: Urinary incontinence in men after endoscopic enucleation of benign prostate hyperplasia (BPH) can reach 55% and significantly impairing the quality of life and social rehabilitation of patients. A large number of individual patient parameters and features of surgical treatment are considered as potential risk factors. At the same time, the influence of urodynamic factors, including the external urethral sphincter function at the preoperative stage, fades into the background, and research on this issue is extremely limited.<br><br>OBJECTIVE: comprehensive assessment of urodynamic risk factors for urinary incontinence after endoscopic enucleation of BHP.<br><br>MATERIALS AND METHODS: This prospective study included 69 patients who underwent endoscopic enucleation of BPH (thulium fiber enucleation - 62 patients, bipolar enucleation - 7 patients) performed by single surgeon between October 2023 and August 2024. All patients underwent an invasive urodynamic study 1 day before the planned surgical treatment, including uroflowmetry, cystometry, flow/pressure study and profilometry performed by single urologist. In the postoperative period, the presence and duration of urinary incontinence were recorded in accordance with the definition of the International Continence Society. Statistical data processing was carried out using RStudio software in the R programming language.<br><br>RESULTS: Transient urinary incontinence after endoscopic enucleation was detected in 36.2% patients. In 100% cases, the duration of incontinence did not exceed a 3-month period. The independent urodynamic predictors of urinary incontinence were the bladder outlet obstruction index (BOOI), the bladder contractility index (BCI) and maximum intraurethral pressure (Pura max). Thus, with an increase in BOOI for 1 unit, the chance of urinary incontinence increased by 1,027 times or 2.7% (OR=1,027; 95%CI=1,003-1,052; p=0,027). With an increase in BCI for every 1, the chance of urinary incontinence increased by 1,020 times or 2.0% (OR=1,020; 95%CI=1,001-1,039; p=0,043). Large values of Pura max, on the contrary, led to a decrease in the chance of urinary incontinence, thereby acting as a protective factor. With an increase in Pura max for every 1 cm of H2O, the chance of urinary incontinence decreased by 1,087 times or by 8% (OR=0,920; 95%CI=0,876-0,966). The overall accuracy of the proposed model was 88,1% with sensitivity and specificity of 90,5 and 86,8% (ROC-AUC=0,897). The only independent intraoperative factor associated with urinary incontinence was the operation time: with an increase in the operation time for every 1 minute, the chance of urinary incontinence increased by 1,022 times or by 2,2%, regardless of the type of energy used and the early sphincter release (OR=1,022; 95%CI=1,005-1.040; p=0,011; ROC-AUC=0,721).<br><br>CONCLUSION: The chance of urinary incontinence at longer endoscopic enucleation, higher BOOI and BCI and low Pura max increases, which, thereby, can be used in predicting the functional results of endoscopic enucleation, taking into account individual urodynamic risk factors.}, }
@article {pmid41399276, year = {2025}, author = {Wang, Z and Du, Y and Guo, D and Jiang, H and Li, Z and Wu, J and Yang, J and Li, H and Li, L and Fei, J and Li, Z}, title = {Brain-computer interface and functional electrical stimulation: a novel approach to motor rehabilitation in CNS injury patients.}, journal = {International journal of surgery (London, England)}, volume = {}, number = {}, pages = {}, doi = {10.1097/JS9.0000000000004392}, pmid = {41399276}, issn = {1743-9159}, abstract = {Central nervous system (CNS) injuries, such as stroke and spinal cord injury, often result in persistent motor impairments that conventional rehabilitation can only partially alleviate. Recent developments in brain-computer interfaces (BCIs) combined with functional electrical stimulation (FES) have introduced a novel approach to motor rehabilitation by directly linking cortical signals with specific muscle activation. This closed-loop system compensates for disrupted neural transmission and simultaneously promotes activity-dependent plasticity, thereby supporting functional reorganisation within the CNS. Findings from pilot trials and preclinical studies indicate that BCI-FES enhances motor recovery in both upper and lower limbs, increases patient engagement, and facilitates long-term cortical reorganisation. However, significant limitations persist, such as inconsistent neural decoding, stimulation-related fatigue, and the lack of standardised treatment protocols. Moreover, ethical challenges such as informed consent, neural data privacy, and equitable access must be resolved before broad clinical adoption can be achieved. Future research should focus on rigorous multicentre trials, tailored intervention strategies, and integration with emerging digital health technologies. This review synthesises current evidence on BCI-FES paradigms, stimulation parameters, underlying mechanisms, and ethical considerations, and outlines future directions to accelerate its clinical translation in CNS rehabilitation.}, }
@article {pmid41399243, year = {2025}, author = {Lawrence, D and Avraham, G and Yao, J and Li, L and Shi, C and Starr, PA and Little, SJ}, title = {Cortico-basal oscillations index naturalistic movements during deep brain stimulation.}, journal = {Brain : a journal of neurology}, volume = {}, number = {}, pages = {}, doi = {10.1093/brain/awaf466}, pmid = {41399243}, issn = {1460-2156}, abstract = {The basal ganglia and sensorimotor cortex are essential nodes of a network that supports motor control. In Parkinson's disease, disruptions in this network lead to rigidity and slowness during movement execution. Deep brain stimulation (DBS) of the basal ganglia has proven effective in alleviating Parkinson's disease-related hypokinetic symptoms, and sensing-enabled neurostimulators now afford the opportunity to detect cortico-basal oscillations during motion. However, the specific contributions of these motor network nodes to chronic, naturalistic movement and the effects of DBS on circuit dynamics are not well understood. To address these gaps, we recorded over 530 hours of cortical and subcortical signals from 15 Parkinson's disease patients (27 hemispheres) during unsupervised, unconstrained daily activities and subthalamic or pallidal DBS. Synchronized wrist-worn accelerometers tracked forearm speeds, supporting the evaluation of neural biomarkers related to motion. Our study validated and extended the known relationship between cortical and subcortical beta power (13-30 Hz) and movement. We show that cortical low (13-20 Hz) and high (21-30 Hz) beta movement-related desynchronization (MRD) effectively distinguished between mobile and stationary states. In the subthalamic nucleus (STN) and globus pallidus interna (GPi), high beta MRD and gamma (40-80 Hz) movement-related synchronization (MRS) exhibited significant group-level correlations with movement kinematics. When stimulated at 130 Hz, cortical stimulation-entrained gamma oscillations at the half-harmonic (∼65 Hz) were observed. Further, cortical entrained gamma MRS was a stronger predictor of motion than broadband gamma MRS. We developed machine learning (ML) models to predict naturalistic movement over extended periods using spectral features from brief neural recordings (0.5-8 s epochs). Cortical models outperformed subcortical models, although combining cortico-basal signals yielded the highest model performance (AUC > 0.85 for binary movement state classifiers; Pearson r statistic > 0.68 for continuous forearm speed regressors). Higher DBS current amplitudes were associated with reduced beta MRD and low gamma (40-60 Hz) MRS in the STN/GPi. This negatively impacted the accuracy of the subcortical models, whereas cortical and cortico-basal model performance remained stable across stimulation amplitudes. Our study demonstrates that cortico-basal nodes of the motor network encode complementary kinematic information, which can be integrated to enhance the accuracy and stability of chronic, naturalistic movement decoding during deep brain stimulation. These insights support the development and integration of therapeutic brain-computer interfaces (BCIs) with closed-loop, adaptive DBS (aDBS) to leverage rapid and precise movement-predictive models for the treatment of motor network disorders.}, }
@article {pmid41398930, year = {2025}, author = {Chen, Y and Ge, H and Deng, C}, title = {A novel method for EEG-based motor imagery classification using feature fusion.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-15}, doi = {10.1080/10255842.2025.2568700}, pmid = {41398930}, issn = {1476-8259}, abstract = {This paper introduces a multi-scale feature fusion framework for EEG-based motor imagery (MI) classification, designed to leverage the spectral-temporal-spatial structure of EEG data, its nonlinear intrinsic characteristics, and convolutional features. Several proposed feature fusion models surpass current state-of-the-art classification systems for MI tasks. A support vector machine (SVM) model achieves an accuracy of 86.92% on the BCIC-IV-2a dataset. To mitigate redundancy, the proposed models incorporate dimensionality reduction via factor analysis (FA) and channel selection using common spatial pattern (CSP). Selecting 12 channels yields superior classification performance compared to using all 22or only 8 selected channels, achieving an accuracy of 88.17%.}, }
@article {pmid41398422, year = {2025}, author = {Qu, B and Tan, X and Tang, Z and Wang, H and Lan, L and Schriver, KE and Pan, G and Friedman, RM and Lai, HY}, title = {Unveiling interactions of spatial-temporal information in tactile motion perception.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {43838}, pmid = {41398422}, issn = {2045-2322}, support = {2021ZD0200401//STI 2030-Major/ ; 2021ZD0200401//STI 2030-Major Projects/ ; 2021ZD0200401//STI 2030-Major Projects/ ; 2021YFF0702200//National Key R&amp;D Program of China/ ; 82101323//National Natural Science Foundation of China/ ; 2021C03001//Key R&amp;D Program of Zhejiang Province/ ; 2019XZZX003-20//Fundamental Research Funds for the Central Universities/ ; }, abstract = {Tactile perception is inherently dynamic, relying on active manual exploration to extract information about motion and surface properties. Spatiotemporal inputs facilitate tactile motion perception by conveying information both direction and speed perception. Although previous studies have examined these features separately, the interactions between spatial and temporal features in shaping perceptual outcomes remain poorly understood. To address this gap, we conducted two psychophysical experiments in which tactile motion stimuli, varying in direction, speed and spatial frequency (wavelength), were delivered to the distal fingerpad of healthy participants, and then requested the participants to report their feedback directly. In Experiment I, we found that the anisotropic distortion of directional perceptual bias is quadrant-dependent, while variations in speed did not alter this general pattern. Experiment II revealed a dissociation between spatial and temporal contributions to perception. Spatial frequency primarily determined the overall pattern of perceptual bias, reflecting the structural properties of the stimulus. In contrast, speed modulates its dynamic expression by influencing the amplitude and phase of deviations. Additional psychometric function analyses indicated that tactile speed perception arises from a combination of linear and nonlinear processes. Collectively, these findings elucidate how the brain integrates spatiotemporal cues to construct a coherent tactile motion representation, thereby accounting for the systematic directional distortions and nonlinear speed estimation.}, }
@article {pmid41397373, year = {2025}, author = {Jia, Y and Lian, Q and Wang, L and Wang, Y and Qi, Y}, title = {Learning discrete neural latent spaces for high-performance speech decoding.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2ccd}, pmid = {41397373}, issn = {1741-2552}, abstract = {OBJECTIVE: Speech brain-computer interfaces (BCIs), which directly transform neural signals into intelligible voices, offer a promising avenue for people with aphasia. To decode speech information from brain signals, neural representation learning plays an important role.<br><br>APPROACH: Existing studies mainly explored continuous neural latent spaces for speech decoding and ignored the intrinsic discrete property in speech production. Here, we propose to learn a discrete neural latent spaces by constructing a quantized representation learning network for speech decoding.<br><br>MAIN RESULTS: Experiments with intracranial stereotactic EEG (sEEG) signals from 11 subjects demonstrated that our approach significantly improved the precision and robustness of speech decoding.<br><br>SIGNIFICANCE: These results underscore the potential of our method to improve the functionality and usability of speech BCIs for people with aphasia.}, }
@article {pmid41397350, year = {2025}, author = {Xia, Y and Wei, Y and Li, S and Mai, X and Luo, R and Zhu, X and Meng, J}, title = {A potential field shared control approach for wheelchair navigation via brain‑computer interface.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2ccc}, pmid = {41397350}, issn = {1741-2552}, abstract = {OBJECTIVE: Electroencephalography (EEG) -based brain-computer interfaces (BCIs) can help patients with disabilities control external devices directly without peripheral pathways. Due to the limitations in EEG signal quality, the performance of EEG-based BCIs may not be satisfactory. Shared control has become an important research direction in the field of brain-controlled wheelchairs (BCWs). However, most existing studies do not achieve the flexible movement of BCW in environments with narrow spaces. This study proposes a shared controller based on the potential field method to integrate environmental information and user commands intelligently.<br><br>APPROACH: Considering the flexibility of wheelchair movement, we incorporated EEG decoding results obtained through the motor imagery paradigm and fused them with environmental information to create a fusion field. We then used these components separately to construct the BCI and obstacle fields. Twelve subjects participated in the virtual wheelchair navigation experiment, while five subjects took part in the real-world wheelchair navigation experiment, aiming to evaluate the control performance in different scenarios under three control modes (keyboard, BCI-only, and shared control).<br><br>MAIN RESULTS: The experimental results show that the proposed shared controller: 1) significantly enhances navigation performance in both general and narrow environments compared with BCI-only control; 2) improves the total success rate from 8.33% to 83.33% in virtual complex environments and from 23.33% to 66.67% in real-world two-way navigation; 3) achieves success rates that are statistically comparable to keyboard control (p > 0.05). Moreover, the shared control reduced the average navigation time by nearly 100 seconds compared with BCI-only control in real-world experiments.<br><br>SIGNIFICANCE: This new shared control method improves the ability of BCWs to move flexibly in challenging, narrow environments.}, }
@article {pmid41397305, year = {2025}, author = {Zhang, L and Li, B and Shi, X and Peng, C}, title = {Hybrid BCI-Based Instruction Set for Dual Robotic Arm Control Using EEG and Eye Movement Signals.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae2c8f}, pmid = {41397305}, issn = {2057-1976}, abstract = {A brain-computer interface (BCI) establishes a pathway for information transmission between a human (or animal) and an external device. It can be used to control devices such as prosthetic limbs and robotic arms, which in turn assist, rehabilitate, and enhance human limb function. At present, although most studies focus on brain signal acquisition, feature extraction and recognition, and further explore the use of brain signals to control external devices, the features obtained via noninvasive approaches are fewer and less robust, which makes it difficult to directly control devices with more degrees of freedom such as robotic arms. To address these issues, we propose an extended instruction set based on motor imagery that fuses eyemovement signals and electroencephalogram (EEG) signals for motion control of a dual collaborative robotic arm. The method incorporates spatio-temporal convolution and attention mechanisms for brain-signal classification. Starting from a small base of control commands, the hybrid BCI combining eye-movement signals and EEG expands the command set, enabling motion control of the dual cooperative manipulator. On the Webots simulation platform, we carried out kinematic control and three-dimensional motion simulation of a dual 6-degree-of-freedom collaborative robotic arm (UR3e). The experimental results demonstrate the feasibility of the proposed method. Our algorithm achieves an average accuracy of 83.8% with only 8.8k parameters, and the simulation results are within the expected range. The results demonstrate that the proposed extended instruction set based on motor imagery is effective not only for controlling dual collaborative robotic arms to perform grasping tasks in complex scenarios, but also for operating other multi-degree-of-freedom peripheral devices.}, }
@article {pmid41397035, year = {2025}, author = {Zhang, L and Shi, W and Zhao, Z and Wang, Z and Chu, C and Zhao, B and Zhang, J and Liu, Q and Lan, Y and Jiang, T}, title = {Lysergic acid diethylamide-derived excitatory/inhibitory ratio change enhances global synchrony in functional brain dynamics.}, journal = {PLoS computational biology}, volume = {21}, number = {12}, pages = {e1013822}, doi = {10.1371/journal.pcbi.1013822}, pmid = {41397035}, issn = {1553-7358}, abstract = {Lysergic acid diethylamide (LSD) has shown remarkable potential in modulating brain functional organization and dynamics. However, the exact mechanisms underlying its effects remain unclear. In this study, we employed a data-driven approach to analyze recurrent functional connectivity patterns in resting-state fMRI data and developed a parameterized feedback inhibition model to characterize excitatory/inhibitory (E/I) balance. The findings demonstrate that LSD enhances global brain synchrony and dynamic complexity. This enhanced synchrony likely stems from LSD's preferential stabilization of a globally synchronized yet functionally non-modular brain state - a pattern showing higher occurrence probability and acts as an "attractor" that recruits transitions from cognitive control networks. Crucially, these phenomena appear underpinned by LSD-induced convergence of excitatory/inhibitory balance across cortical hierarchies, particularly through Sensorimotor (SOM) suppression coupled with transmodal potentiation, where the Sensorimotor cortices emerge as potential regulatory hubs driving this neurochemical rebalancing. These convergent effects are consistent with the emergence of a brain state characterized by weakened sensory anchoring and enhanced cognitive flexibility, where the typical separation between concrete perception and abstract cognition becomes blurred. This neurophysiological remodeling therefore suggests a potential mechanism that could contribute to LSD's hallucinatory effects and its therapeutic potential in mental disorders characterized by rigid thought patterns.}, }
@article {pmid41397031, year = {2025}, author = {Xu, K and Li, W and Yin, Y and Li, F and Wang, H and Sui, H and Zou, J and Mu, J and Wang, S}, title = {Hemi-obturator Nerve Innervated Latissimus Dorsi Muscle for Restoring Voluntary Voiding: Anatomic Study and Clinical Application.}, journal = {Plastic and reconstructive surgery}, volume = {}, number = {}, pages = {}, doi = {10.1097/PRS.0000000000012721}, pmid = {41397031}, issn = {1529-4242}, abstract = {This study presents a modified latissimus dorsi detrusor myoplasty (LDDM) technique using the hemi-obturator nerve for neurogenic underactive bladder (NUAB) reconstruction. Anatomical studies (n=22 hemipelves) revealed that the diameters of the anterior (mean: 0.209 cm) and posterior branches (mean: 0.199 cm) matched the thoracodorsal nerve's diameter (one-way ANOVA, p = 0.557), confirming their ideal donor potential. LDDM by using posterior branch of intrapelvic obturator nerve as the donor nerve was performed in five patients with NUAB. 4/5 (80%) patients restored voluntary voiding postoperatively, with post-void residual volume (PVR) decreasing significantly from 308.5(187.5) mL to 62.0 (58.8) mL (P=0.042) and bladder contractility index (BCI) improving significantly from 12.8(5.7) to 151.9(46.5) (P=0.007). These results demonstrate that LDDM using the hemi-obturator nerve is an effective surgical approach for functional detrusor reconstruction in NUAB patients.}, }
@article {pmid41396750, year = {2025}, author = {Chen, W and Mei, J and Xiao, X and Li, A and Tao, L and Wang, K and Xu, M and Ming, D}, title = {An Online Adaptation Framework for Enhancing Calibration-Free SSVEP-Based BCI Performance.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3644250}, pmid = {41396750}, issn = {2168-2208}, abstract = {Accomplishing a plug-and-play steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a critical challenge, due to the unsatisfying performance of calibration-free decoding algorithms. A current method called online adaptive canonical correlation analysis (OACCA) has proved efficient in enhancing calibration-free performance by self-adaptation merely with online data. However, OACCA only concerns the adaptation of spatial filters and excludes other useful adaptive procedures like individual template estimation, hindering fully exploitable model decoding and adaptation. This study proposes a new online adaptation framework termed online adaptive extended correlation analysis (OAECA) to augment the calibration-free online adaptation loop. OAECA first recalls and cleans the online trials for reliable data learning, then tunes individual templates and spatial filters for complete model updating, and finally adopts extended feature matching to improve target recognition. The simulation results on two public SSVEP datasets revealed that OAECA significantly outperformed OACCA for almost all 105 subjects, and both offline and online experiments further confirmed the effectiveness of OAECA. Particularly, OAECA achieved the highest average information transfer rate (ITR) of 202.17 bits/min in the online experiment, significantly exceeding the state-of-the-art OACCA of 177.02 bits/min. This study enhanced the calibration-free performance through comprehensive online adaptation, hopefully advancing SSVEP-based BCIs toward practical plug-and-play real-world applications.}, }
@article {pmid41394963, year = {2025}, author = {Wang, N and Si, J and He, Y and Song, J and Chai, X and Liu, D and Li, J and Zhang, T and Cao, T and He, Q and Zhu, S and Jia, Y and Ma, W and Yang, Y and Zhao, J}, title = {Cerebral Neurovascular Networks May Serve as Potential Targets for Identifying Disorders of Consciousness: A Synchronous Electroencephalography and Functional Near-Infrared Spectroscopy Study.}, journal = {MedComm}, volume = {6}, number = {12}, pages = {e70530}, pmid = {41394963}, issn = {2688-2663}, abstract = {The diagnosis and management of disorders of consciousness (DoC) remain a critical challenge in clinical medicine and neuroscience. The key bottleneck is the lack of reliable biomarkers and an incomplete understanding of the pathophysiological mechanisms that underlie DoC. In view of this, a bedside-compatible, multimodal technique based on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was utilized to simultaneously capture neuronal oscillations and accompanying hemodynamics, so as to explore neurovascular biomarkers that can effectively discriminate different states of DoC. Resting-state EEG-fNIRS data from 13 regions of interest (ROIs) were acquired and compared across healthy controls (HC), minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS) groups. Hemodynamics-based functional connectivity and the spectral power of neuronal activity were quantified and subsequently employed to interrogate neurovascular coupling. The results demonstrated significantly stronger neurovascular coupling and beta-band power in premotor and Broca's areas of the MCS group. A multimodal classifier achieved an accuracy of 87.9% in distinguishing between MCS and UWS. The noninvasive, bedside-suitable nature of this tool underscores its potential for routine monitoring and prognostic assessment in DoC, addressing a critical need for accessible and reliable biomarkers in both neurology and intensive-care practice.}, }
@article {pmid41394940, year = {2025}, author = {Liu, P and Ge, Q and Dong, L and Jiao, L and Han, S and Kang, X and Wang, H and He, J and Zhang, H}, title = {Motor imagery-based brain-computer interface for differential diagnosis in prolonged disorders of consciousness.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1695730}, pmid = {41394940}, issn = {1662-5161}, abstract = {INTRODUCTION: Patients with prolonged disorders of consciousness (pDoC) present significant challenges to the assessment of consciousness. This study investigated the clinical utility of motor imagery-based brain-computer interface (MI-BCI) for discriminating consciousness levels in patients with pDoC.<br><br>METHODS: Thirty-one pDoC patients [12 with unresponsive wakefulness syndrome (UWS) and 19 in a minimally conscious state (MCS)] underwent EEG recordings during resting state and MI-BCI training. The analysis focused on relative power spectral density across five frequency bands (delta, theta, alpha, beta, gamma) in motor imagery-related regions (frontal and parietal cortices), along with BCI performance metrics (classification accuracy and attention indices).<br><br>RESULTS: We found that MCS patients exhibited multiband neural oscillation modulation during MI-BCI tasks, including slow-wave enhancement [(delta in frontal lobes (p = 0.003); theta in frontal (p = 0.026) and parietal lobes (p < 0.001)) and fast-wave suppression (alpha in frontal (p < 0.001) and parietal lobes (p = 0.049); beta in frontal (p = 0.014) and parietal lobes (p = 0.001); gamma in parietal lobes (p = 0.023)]. In contrast, UWS patients only showed localized parietal gamma enhancement (p = 0.042). Notably, the MCS group achieved significantly higher classification accuracy (55% vs. 38%, p = 0.02), and attention indices correlated moderately with CRS-R scores across all patients (Spearman's &#x3c1; = 0.43, p = 0.02).<br><br>CONCLUSION: The findings suggest that MI-BCI classification accuracy and attention indices may serve as auxiliary discriminators between UWS and MCS patients, with MCS patients demonstrating superior responsiveness to MI-BCI training.}, }
@article {pmid41293018, year = {2025}, author = {Bougou, V and Gamez, J and Rosario, ER and Liu, C and Pejsa, K and Bari, A and Andersen, RA}, title = {Hierarchical and Context-Dependent Encoding of Actions in Human Posterior Parietal and Motor Cortex.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {41293018}, issn = {2692-8205}, support = {UG1 EY032039/EY/NEI NIH HHS/United States ; }, abstract = {Action understanding requires internal models that link vision to motor goals. In monkeys, mirror neurons demonstrate motor resonance during observation, but single-unit evidence in humans is limited, leaving open whether such representations rely solely on motor resonance. We recorded neural activity from motor cortex (MC) and superior parietal lobule (SPL) in two tetraplegic participants implanted with Utah arrays while they intended or observed hand actions. MC strongly encoded intention but showed only weak, feature-specific overlap during observation, evident primarily at the population level. SPL, in contrast, supported shared models across intended movement and observation formats at both single-unit and population levels. In variants with incongruent instructed and observed actions, SPL encoded observed actions only when behaviorally relevant, whereas MC remained intention-dominant. Our results identify a context-dependent gating mechanism in SPL and suggest a hierarchical organization in which MC maintains intention-specific codes while SPL flexibly links observed input with internal goals to support action understanding.}, }
@article {pmid41392156, year = {2025}, author = {Xia, J and Zhang, L and Wang, S and Yu, Y and Ding, L and Zhang, F and Zhang, S and Luo, J and Huang, YYS and Occhipinti, L and Pan, G and Cao, Z and Ding, G and Dong, S}, title = {Implantable neural probes with monolithically integrated CNTFET arrays for multimodal monitoring.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-025-67535-5}, pmid = {41392156}, issn = {2041-1723}, abstract = {The implantable neural probe for simultaneous recording of various brain signals is one of the key technologies for neurological science and clinics that is yet to be broken through. Here, we introduce an implantable neural probe with integrated carbon nanotube field-effect transistors which is able to perform multimodal recording of electrical and chemical signals of the brain under magnetic resonance imaging (MRI). We demonstrate here a simultaneous measurement of an electrophysiological signal with high signal-to-noise ratio up to 40.34 dB and calcium concentration with a detection limit down to 0.47 nM. We use our neural probes to detect neural activity in rats and results reveal that changes in Ca[2+] concentration occur concurrently with the epileptiform local field potential events, providing an alternative method for accurate detection of epilepsy. Our work may provide a powerful means for the future studies of brain and holds great potential for practical diagnostic applications.}, }
@article {pmid41391167, year = {2025}, author = {Pang, J and Sun, Y and Cheng, T and Wang, J and He, X and Xiang, Y and Zhu, W and Cao, Y and Wu, M and Pei, W and Pei, R and Cao, Y}, title = {Multifunctional Composite Coating-Enhanced Flexible Microelectrodes for Chronic, High-Fidelity Neural Signal Recording.}, journal = {Analytical chemistry}, volume = {}, number = {}, pages = {}, doi = {10.1021/acs.analchem.5c04799}, pmid = {41391167}, issn = {1520-6882}, abstract = {Implantable flexible neuroelectrodes are critical for brain-computer interface (BCI) applications. However, conventional flexible electrodes often face challenges such as increased electrochemical impedance upon miniaturization, mechanical mismatch with brain tissue, and implantation-induced damage, all of which compromise long-term signal stability and recording quality. Here, we present a multifunctional surface modification strategy to address these limitations. By integrating polycaprolactone/silk fibroin-methacrylate (PCL-SFMA) nanofibers loaded with anti-inflammatory minocycline hydrochloride (MH), nanostructured poly(3,4-ethylenedioxythiophene) (PEDOT) for impedance reduction, and a bioactive SFMA hydrogel layer for seamless neural integration, we developed a composite-coated flexible microelectrode (Au-PCLSFMA-PEDOT-GEL). Comprehensive in vitro and in vivo evaluations demonstrated that the modified electrode exhibited low impedance, enhanced biocompatibility, improved biointegration, and effective mitigation of both acute and chronic inflammation. Long-term electrophysiological recordings in freely moving mice revealed stable, high-fidelity neural signal acquisition for up to 8 months, maintaining a signal-to-noise ratio of approximately 20. This work establishes a durable and functionally stable neural interface, offering a promising platform for long-term neuroscience research and the development of next-generation BCIs.}, }
@article {pmid41389568, year = {2025}, author = {Pfeffer, MA and Wong, JKW and Ling, SH}, title = {Transformer-based hybrid systems to combat BCI illiteracy.}, journal = {Computers in biology and medicine}, volume = {200}, number = {}, pages = {111378}, doi = {10.1016/j.compbiomed.2025.111378}, pmid = {41389568}, issn = {1879-0534}, abstract = {This study addresses the challenge of enhancing Brain-Computer Interfaces (BCIs), focusing on low Signal-to-Noise Ratios and "BCI illiteracy" often affecting up to 20% of users. Transformer-based models show promise but remain underexplored. Three experiments were conducted. Experiment A assessed the performance of architectures combining Convolutional and Transformer Blocks for binary Motor Imagery (MI) classification. Experiment B introduced a hybrid system, refining both block types and adding a Noise Focus Block to infuse Stochastic Noise, enhancing multi-class classification robustness. Experiment C evaluated the emerging architectures on 106 subjects, focusing on robustness across weak and strong learners. In Experiment A, the best networks achieved a validation accuracy of 0.914 and a loss of 0.146 (p=0.000967, F=12.675). In Experiment B, the proposed architecture improved multi-class MI classification to 84.5% on Dataset II, significantly improving performance for BCI-illiterate users. Experiment C showed a Kappa >83%, reduced standard deviation, and a highest validation accuracy of 88.69% across all individuals. The hybrid integration of Transformers, CNNs, and Noise-Resonance-based layers significantly enhances classification performance, particularly for weak BCI learners. Further research is recommended to optimize hybrid system architectures and hyperparameter settings to overcome current limitations in BCI performance.}, }
@article {pmid41389307, year = {2025}, author = {Li, T and Zhao, ZH and Tang, HB and Chen, Z and Lu, ZW and Yang, XL and Zhao, LL and Li, Y and Dang, MJ and Chen, ZY and Zhang, GL and Liu, L and Fan, H}, title = {Advances in Bionic Therapies for Targeting Neural Circuit Reconstruction and Integration for Spinal Cord Injury.}, journal = {Cellular and molecular neurobiology}, volume = {}, number = {}, pages = {}, doi = {10.1007/s10571-025-01647-w}, pmid = {41389307}, issn = {1573-6830}, support = {82101551//National Natural Science Foundation of China/ ; 82471333//National Natural Science Foundation of China/ ; 82171361//National Natural Science Foundation of China/ ; 82171471//National Natural Science Foundation of China/ ; }, abstract = {Spinal cord injury (SCI) is one of the most common critical illnesses, which can cause neurological deficits and disabilities of motor, sensory and autonomic nervous system in mild cases, and lead to paralysis or even death following severe trauma. Although there are currently no effective and satisfactory clinical treatments, the efforts for repair SCI never stop. Besides the traditional strategies such as drugs, surgical interventions and rehabilitative care, the bionic therapies have attracted significant attention due to its considerable promise. The bionic therapies for SCI mainly included engineered biomaterials-based approaches aiming for reconstruction of internal neural circuit and brain machine interfaces (BMI)-based technologies to integrate extrinsic control and intrinsic circuit. This review provides an extensive overview of SCI research and bionic therapies, with focus on reconstruction and integration of neural circuit, which might provide promising insights on clinical treatment.}, }
@article {pmid41389026, year = {2025}, author = {Zhou, T and Shang, K and Liu, C and Cui, Z and Liang, D}, title = {Deep equilibrium-adversarial robust unfolding network for MRI reconstruction.}, journal = {Medical physics}, volume = {52}, number = {12}, pages = {e70185}, doi = {10.1002/mp.70185}, pmid = {41389026}, issn = {2473-4209}, support = {JCYJ20240813155840052//Shenzhen Science and Technology&#xa0;Program/ ; 2022YFA1004203//National Key R&amp;D Program of China/ ; 2021YFF0501503//National Key R&amp;D Program of China/ ; 62125111//National Natural Science Foundation of China/ ; 62331028//National Natural Science Foundation of China/ ; 62476268//National Natural Science Foundation of China/ ; 62206273//National Natural Science Foundation of China/ ; }, mesh = {*Magnetic Resonance Imaging ; *Image Processing, Computer-Assisted/methods ; Artifacts ; Brain/diagnostic imaging ; Humans ; *Deep Learning ; Signal-To-Noise Ratio ; *Neural Networks, Computer ; Knee/diagnostic imaging ; }, abstract = {BACKGROUND: Deep unfolding neural networks have shown significant promise in magnetic resonance imaging (MRI) reconstruction by replacing traditional iterative prior modeling with more efficient and flexible network architectures. However, the iterative optimization process makes these methods susceptible to signal perturbations caused by noticeable artifacts in the reconstructed images.<br><br>PURPOSE: To develop a general framework that enhances the robustness of the reconstruction process against prominent artifacts and noise in k-space, while also improving the stability of the&#xa0;reconstruction.<br><br>METHODS: This paper proposes a deep equilibrium-adversarial robust unfolding network (DEAR-net), a novel framework that integrates adversarial learning with deep equilibrium architectures. In this design, adversarial learning enhances the capability of network to suppress perturbations during the reconstruction process, effectively addressing the issue of noise and artifacts amplification in deep equilibrium architectures. However, the modification of the learned mapping from clean k-space to MR images by adversarial learning may compromise the stability of the reconstruction. Fortunately, this problem can be mitigated through the application of deep equilibrium&#xa0;architectures.<br><br>RESULTS: Experimental results demonstrate that DEAR-net achieves superior reconstruction performance, delivering higher image quality and greater robustness to varying levels of noise and artifacts in k-space, as evidenced by tests on the fastMRI knee dataset and our private brain&#xa0;dataset.<br><br>CONCLUSIONS: DEAR-net enhances the robustness of the reconstruction process in the presence of mild noise and artifacts in under-sampled k-space. Furthermore, we provide a mathematical analysis of the reconstruction&#xa0;error.}, }
@article {pmid41388263, year = {2025}, author = {Zhang, Y and Wang, Y and Guo, J and Fang, T and Wang, R and Liu, W and Zhao, X and Fan, Q and Chen, Y and Peng, Y}, title = {Age-dependent recovery of white matter integrity after surgical correction in children with infantile esotropia.}, journal = {BMC neurology}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12883-025-04578-7}, pmid = {41388263}, issn = {1471-2377}, support = {82071994//National Natural Science Foundation of China/ ; 82202249//National Natural Science Foundation of China/ ; 12171330//National Natural Science Foundation of China/ ; DFL20221002//the Beijing Hospitals Authority's Ascent Plan/ ; 2021ZD0200508//the STI 2030-Major Projects/ ; }, abstract = {BACKGROUND: Infantile esotropia may interfere with white matter maturation during early childhood, a critical period of brain development. Surgical correction not only restores ocular alignment but may also influence neurodevelopmental trajectories. However, the role of age in modulating white matter recovery after surgery remains unclear. This study aimed to investigate the effects of age on white matter rehabilitation following surgical intervention in children with infantile esotropia, with the goal of identifying the optimal therapeutic window to maximize both neurodevelopmental and clinical outcomes.<br><br>METHODS: We included 29 typically developing children (F/M&#x2009;=&#x2009;14/15) and 30 children with IE (F/M&#x2009;=&#x2009;13/17), 17 of whom provided longitudinal data following surgical intervention. All participants underwent MRI scanning and clinical assessments. Diffusion tensor imaging (DTI) was performed to quantify white matter integrity using fractional anisotropy (FA) and mean diffusivity (MD). Automated fiber quantification was applied to analyze microstructural properties across 20 major white matter tracts. Cross-sectional and longitudinal analyses were conducted to evaluate developmental trajectories in patients versus controls.<br><br>RESULTS: Preoperatively, IE patients exhibited significantly elevated MD across multiple tracts, including the thalamic radiation and forceps minor. Following surgery, MD values decreased significantly in most tracts. FA alterations were less pronounced, with preoperative reductions and postoperative improvement limited to only a few tracts. In controls, age was negatively correlated with MD and FA changes. Longitudinal analysis revealed that surgical intervention was associated with accelerated growth in white matter microstructure compared to typical development, particularly in younger children.<br><br>CONCLUSIONS: Surgical correction of IE facilitates white matter restoration through mechanisms that operate independently of, and synergistically with, typical neurodevelopment. Earlier intervention is associated with faster rates of microstructural recovery, suggesting a higher sensitive period during which surgery can maximize white matter repair and optimize functional outcomes.}, }
@article {pmid41387563, year = {2025}, author = {Peplow, M}, title = {Brain-computer interfaces race to the clinic.}, journal = {Nature nanotechnology}, volume = {}, number = {}, pages = {}, pmid = {41387563}, issn = {1748-3395}, }
@article {pmid41387461, year = {2025}, author = {Zhou, L and Liu, P and Liu, J and Yuan, W and Wu, Z and Xu, D and Hu, B and Shao, Y and Lu, Y and Huang, N and Li, J and Li, Z and Liang, F and Wu, X and Ma, L and Wang, M and Di, Z and Li, R and Bi, Y and Xu, F and Mei, Y and Song, E}, title = {Wireless battery-free ultrathin lithium-niobate resonator as wearable and implantable electronics for continuous monitoring of mechanical vital signs.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-025-67413-0}, pmid = {41387461}, issn = {2041-1723}, support = {62204057//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Continuous monitoring of physiological parameters associated with dynamic biomechanics, such as intracranial pressure (ICP) and vital signs, is important for clinical diagnosis of brain diseases and timely medical intervention. Current skin-interfaced and implant technologies face challenges in terms of bulky tethers and/or percutaneous wires with high infection risks. Here, we report the wireless, battery-free, and lightweight devices for both wearable and fully implantable applications. The devices incorporate an ultrathin piezoelectric resonator with suspended lithium niobate thin film (LNTF, 3 μm thick), enabling the wireless tracking of mechanophysiological signals by detecting variations in resonance frequency. We experimentally and computationally establish the operational principles of the resonator sensor and assess the device performance as wearables for dynamically monitoring artery pulse and apnea events during respiration. Implantable wireless pressure sensors adapted from this scheme allow for untethered, minimally invasive ICP sensing with a low detection limit of 0.15 mmHg over a wide range up to 240 mmHg. In vivo experiments performed on rat models validate the device capabilities of accurately capturing clinically relevant ICP variations and elevated levels of ICP under pathophysiological conditions of hydrocephalus, with excellent biocompatibility after long-term implantation periods. These findings create the clinical significance of such battery-less and wireless devices for precise characterization of dynamic biomechanics of living tissues.}, }
@article {pmid41386385, year = {2025}, author = {Schippers, A and Freudenburg, ZV and Vansteensel, MJ and Raemaekers, M and Ramsey, NF}, title = {High-density electrocorticography reveals sensorimotor cortex engagement in two distinct sites with different roles during audiovisual, audio, and visual speech perception.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121645}, doi = {10.1016/j.neuroimage.2025.121645}, pmid = {41386385}, issn = {1095-9572}, abstract = {Recent neuroimaging studies have shown the involvement of the speech motor system in the sensorimotor cortex (SMC) in speech perception, but knowledge on the relative contributions of visual and auditory speech information on SMC engagement and the cortical representation thereof remains scarce. To further elucidate the representation of different components of perceived speech on the SMC, we recorded high-density ECoG during a passive speech perception task. We found that audiovisual, visual-only, and auditory-only speech perception increased high frequency band activity in the SMC. We discovered two distinct regions of the SMC that are differentially engaged depending on the perceptual input modality, being a dorsally located cluster of activity associated with both unimodal and bimodal perception of auditory and visual information and a ventral cluster that is involved specifically in auditory speech perception. Together, these results shine a new light on the engagement of the sensorimotor cortex during speech perception and suggest that auditory and visual information play different roles.}, }
@article {pmid41385954, year = {2025}, author = {Kripalal, A and Sekar, C}, title = {Intelligent electroencephalogram feature engineering for rapid mental health diagnosis.}, journal = {Psychiatry research. Neuroimaging}, volume = {356}, number = {}, pages = {112103}, doi = {10.1016/j.pscychresns.2025.112103}, pmid = {41385954}, issn = {1872-7506}, abstract = {Schizophrenia is one of the serious disorders and, if left untreated, can result in a range of problems with cognition, behavior, and emotions that affect every area of life. Diagnosis based on behavioral and clinical investigations remains difficult with schizophrenia symptoms which are complex and heterogenic. Early detection of schizophrenia is essential for the timely treatment leading to betterment of the life of patients. In this study based on machine learning algorithms, we have identified the relevant set of features from the electroencephalogram (EEG) signal to improve the classification accuracy of patients with schizophrenia and healthy controls. Combinations of these identified relevant features have been used to diagnose schizophrenia.Furthermore, we validated this same feature set as the high performing feature subset on an independent dataset, confirming its robustness and generalizability. The results show that the selected features from the EEG signal achieve the highest accuracy of 94.7% and 96.4% for Logistic Regression (LR) and Support Vector Machines (SVM) respectively with reduced data. Reduction in training data with this feature selection enhances the performance of edge devices that are optimized for applications such as brain computer interfaces, neurological disorder detection, cognitive state monitoring, and neurofeedback training.}, }
@article {pmid41385812, year = {2025}, author = {Tian, X and Zhang, X and Zhou, C and Jiang, Y and Ren, X and Li, T and Ni, P}, title = {Generation and characterization of a human-derived iPSC line (HZSMHCi003-A) from a male child with fragile X syndrome.}, journal = {Stem cell research}, volume = {90}, number = {}, pages = {103880}, doi = {10.1016/j.scr.2025.103880}, pmid = {41385812}, issn = {1876-7753}, abstract = {This study reports the successful establishment of induced pluripotent stem cells (iPSCs) derived from a pediatric patient with Fragile X Syndrome (FXS), representing a valuable cellular model for studying the most prevalent hereditary form of intellectual disability. Blood samples were collected from an 8-year-old Han Chinese male presenting with intellectual disability and carrying a full FMR1 gene mutation (>200 CGG repeat expansion). A stable iPSC line designated HZSMHCi003-A was generated using episomal vector-mediated reprogramming with seven transcription factors (OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT). Comprehensive characterization confirmed normal chromosomal integrity, robust expression of pluripotency-associated markers, and tri-lineage differentiation potential as evidenced by teratoma formation assays. This FXS patient-derived iPSC line provides a unique platform for investigating neurodevelopmental pathophysiology and screening potential therapeutic interventions for intellectual disability associated with FMR1 dysfunction.}, }
@article {pmid41385417, year = {2025}, author = {Meng, M and Yu, P and She, Q and Xi, X and Kong, W}, title = {ASA-STGCN: Adaptive Sparse Awareness-Spatiotemporal Graph Convolutional Network for Multi-Class Motor Imagery EEG Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3643173}, pmid = {41385417}, issn = {2168-2208}, abstract = {Graph Convolutional Networks (GCNs) have shown promise in motor imagery electroencephalogram (EEG) signals classification by modeling spatial dynamics and brain connectivity. However, over-smoothing remains a challenge, leading to homogenized node features and reduced discrimination. To address this, we propose an Adaptive Sparse Awareness-Spatiotemporal Graph Convolutional Network (ASA-STGCN) that combines adaptive sparse graph convolution with attention mechanisms. Notably, a Graph Sparse Convolutional Network (GSCN) in the Adaptive Sparse Awareness Spatial Module (ASAM) enhances brain region feature selection, while the Graph Node Neighborhood Awareness Layer (GNNAL) applies self-attention to reinforce critical topological relationships. The Multi-scale Temporal Convolution Module (MTCM) captures both transient and sustained temporal dependencies. Experimental results achieve accuracies of 97.2%±3.4% (binary) and 83.6%±4.9% (four-class) on BCIC-IV-2a, 96.6%±3.1% (binary) on BCIC-III IVa, and 83.41%±4.3 (binary) on OpenBMI. Discussion confirms the model's effectiveness and its potential to support EEG-based neurorehabilitation and clinical brain computer interface applications.}, }
@article {pmid41385296, year = {2025}, author = {Sha, L and Li, H and Peng, A and Yang, H and Liu, X and Zhao, H and Ma, W and Hong, Q and Tang, Y and Zhang, M and Chen, L}, title = {Diagnostic value of saccades in mild cognitive impairment (MCI): a community-based study.}, journal = {The journals of gerontology. Series A, Biological sciences and medical sciences}, volume = {}, number = {}, pages = {}, doi = {10.1093/gerona/glaf264}, pmid = {41385296}, issn = {1758-535X}, abstract = {BACKGROUND: Accurate diagnosis and assessment of mild cognitive impairment (MCI) are essential. The efficacy of saccades in the detection of MCI lacks validation through large-scale clinical trials.<br><br>METHODS: All eligible participants underwent saccadic assessment in four tasks and cognitive assessment. MCI diagnoses were made on the basis of clinical indicators and MRI by experienced physicians. The physicians were blinded to the saccade experiments and the operators of saccade experiments were blind to the diagnosis of physicians. The classification models based on machine learning was constructed for assessing the diagnostic accuracy of MCI based on saccadic parameters.<br><br>RESULTS: Of the 559 residents who consented to participate, 383 (153 with MCI and 230 controls) were completely assessed. The classification model trained by saccadic parameters achieved high accuracy in dissociating MCI and control with AUROC of 0.945 (95% CI, 0.924-0.964), sensitivity of 0.824 (95% CI, 0.769-0.886) and specificity of 0.904 (95% CI, 0.867-0.935). The parameters of the memory-guided and antisaccade tasks demonstrated better diagnostic efficacy. The saccade model also exhibited a good diagnostic value in patients with borderline cognition being defined by the score of MoCA. When the borderline cognition was defined as 23-27 of MoCA score, the diagnosing accuracy of MCI based on saccadic parameters resulted with AUROC of 0.911 (95% CI: 0.836-0.972), sensitivity of 0.929 (95% CI, 0.762-1.000) and specificity of 0.796 (95% CI, 0.718-0.863).<br><br>CONCLUSIONS: Saccades can distinguish MCI from controls with great accuracy, offering a sensitive and objective diagnostic aid of MCI, especially in participants with borderline cognition.}, }
@article {pmid41385039, year = {2025}, author = {Wu, YH and Chen, SF and Kuo, HC}, title = {Therapeutic outcomes and predictive factors of intradetrusor onabotulinumtoxinA for neurogenic detrusor overactivity (NDO) associated with spinal cord lesion.}, journal = {International urology and nephrology}, volume = {}, number = {}, pages = {}, pmid = {41385039}, issn = {1573-2584}, support = {TCMF-MP-114-02-01//Buddhist Tzu Chi Medical Foundation/ ; }, abstract = {PURPOSE: Intradetrusor onabotulinumtoxinA (Botox) is an established treatment for neurogenic detrusor overactivity (NDO), although predictors of success remain unclear. This study evaluated the therapeutic efficacy of Botox and identified predictors of response in patients with spinal cord lesion (SCL)-related NDO.<br><br>METHODS: We retrospectively reviewed 167 patients with SCL-related NDO who received intradetrusor 200 U Botox at a single center between January 1, 2002, and December 31, 2024. Treatment response was classified using the Global Response Assessment (GRA) as excellent (GRA&#x2009;=&#x2009;3), moderately improved (GRA&#x2009;=&#x2009;2), mildly improved (GRA&#x2009;=&#x2009;1), or no change (GRA&#x2009;=&#x2009;0). Success was defined as GRA&#x2009;=&#x2009;3 or 2. Baseline demographics, neurological level, and videourodynamic (VUDS) parameters, including detrusor pressure at maximum flow (Pdet), maximum flow rate, voided volume, post-void residual, voiding efficiency (VE), bladder outlet obstruction index (BOOI), and bladder contractility index (BCI), were analyzed as predictors.<br><br>RESULTS: VUDS confirmed detrusor overactivity in 92.8%. Overall, 51.5% (86/167) achieved an excellent response, 43.1% (72/167) improved, and 5.4% (9/167) showed no change. Outcomes did not differ by neurological level (P&#x2009;=&#x2009;0.665). Patients with successful outcomes had higher baseline Pdet (41.9&#x2009;&#xb1;&#x2009;20.3 vs 22.9&#x2009;&#xb1;&#x2009;13.4 cmH&#x2082;O, P&#x2009;<&#x2009;0.001), BOOI (33.2&#x2009;&#xb1;&#x2009;21.9 vs 12.7&#x2009;&#xb1;&#x2009;15.2, P&#x2009;<&#x2009;0.001), and BCI (63.6&#x2009;&#xb1;&#x2009;33.0 vs 48.4&#x2009;&#xb1;&#x2009;27.9, P&#x2009;=&#x2009;0.010), but lower VE (0.28&#x2009;&#xb1;&#x2009;0.31 vs 0.40&#x2009;&#xb1;&#x2009;0.35, P&#x2009;=&#x2009;0.045). Logistic regression analysis showed that higher Pdet, BOOI, and BCI predicted treatment success, while higher VE predicted nonresponse. Sex distribution differed across GRA groups (P&#x2009;=&#x2009;0.004), with more men in the failure group. Acute adverse events were similar among groups.<br><br>CONCLUSIONS: Intradetrusor Botox produced good efficacy in SCL-related NDO. Higher baseline detrusor pressure, bladder contractility, and bladder outlet resistance predicted better outcomes, whereas greater VE was associated with nonresponse. Neurological level did not influence treatment success.}, }
@article {pmid41382998, year = {2025}, author = {Ju, Y and Liu, J and Li, Z and Liu, Y and He, H and Liu, J and Liu, B and Wang, M and Zhang, Y}, title = {[Prospects and technical challenges of non-invasive brain-computer interfaces in manned space missions].}, journal = {Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences}, volume = {50}, number = {8}, pages = {1363-1370}, doi = {10.11817/j.issn.1672-7347.2025.250389}, pmid = {41382998}, issn = {1672-7347}, mesh = {*Brain-Computer Interfaces ; Humans ; *Space Flight ; *Astronauts/psychology ; Neurofeedback ; Cognition ; Electroencephalography ; Man-Machine Systems ; }, abstract = {During long-duration manned space missions, the complex and extreme space environment exerts significant impacts on astronauts' physiological, psychological, and cognitive functions, thereby posing direct risks to mission safety and operational efficiency. As a key bridge between the brain and external devices, brain-computer interface (BCI) technology enables precise acquisition and interpretation of neural signals, offering a novel paradigm for human-machine collaboration in manned spaceflight. Non-invasive BCI technology shows broad application prospects across astronaut selection, mission training, in-orbit task execution, and post-mission rehabilitation. During mission preparation, multimodal signal assessment and neurofeedback training based on BCI can effectively enhance cognitive performance and psychological resilience. During mission execution, BCI can provide real-time monitoring of physiological and psychological states and enable intention-based device control, thereby improving operational efficiency and safety. In the post-mission rehabilitation phase, non-invasive BCI combined with neuromodulation may improve emotional and cognitive functions, support motor and cognitive recovery, and contribute to long-term health management. However, the application of BCI in space still faces challenges, including insufficient signal robustness, limited system adaptability, and suboptimal data processing efficiency. Looking forward, integrating multimodal physiological sensors with deep learning algorithms to achieve accurate monitoring and individualized intervention, and combining BCI with virtual reality and robotics to develop intelligent human-machine collaboration models, will provide more efficient support for space missions.}, }
@article {pmid41382997, year = {2025}, author = {Li, Z and Liu, J and Liu, B and Wang, M and Ju, Y and Zhang, Y}, title = {[Potential biological mechanisms underlying spaceflight-induced depression symptoms in astronauts].}, journal = {Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences}, volume = {50}, number = {8}, pages = {1355-1362}, doi = {10.11817/j.issn.1672-7347.2025.250380}, pmid = {41382997}, issn = {1672-7347}, mesh = {*Space Flight ; Humans ; *Astronauts/psychology ; *Depression/etiology/physiopathology ; Gastrointestinal Microbiome ; *Weightlessness/adverse effects ; Oxidative Stress ; Brain/physiopathology ; Hypothalamo-Hypophyseal System ; Neuronal Plasticity ; Pituitary-Adrenal System ; }, abstract = {Long-term spaceflight exposes astronauts to multiple extreme environmental factors, such as cosmic radiation, microgravity, social isolation, and circadian rhythm disruption, that markedly increase the risk of depressive symptoms, posing a direct threat to mental health and mission safety. However, the underlying biological mechanisms remain complex and incompletely understood. The potential mechanisms of spaceflight-induced depressive symptoms involve multiple domains, including alterations in brain structure and function, dysregulation of neurotransmitters and neurotrophic factors, oxidative stress, neuroinflammation, neuroendocrine system imbalance, and gut microbiota disturbances. Collectively, these changes may constitute the biological foundation of depressive in astronauts during spaceflight. Space-related stressors may increase the risk of depressive symptoms through several pathways: impairing hippocampal neuroplasticity, suppressing dopaminergic and serotonergic system function, reducing neurotrophic factor expression, triggering oxidative stress and inflammatory responses, activating the hypothalamic-pituitary-adrenal axis, and disrupting gut microbiota homeostasis. Future research should integrate advanced technologies such as brain-computer interfaces to develop individualized monitoring and intervention strategies, enabling real-time detection and effective prevention of depressive symptoms to safeguard astronauts' psychological well-being and mission safety.}, }
@article {pmid41382980, year = {2025}, author = {Kasper-Jędrzejewska, M and Ptaszkowski, K and Rutkowski, T and Halski, T}, title = {Surface Electromyography Characteristics of Pelvic Floor Muscles in Healthy Women, Pelvic Floor Dyssynergia, and Urinary Incontinence: A Retrospective Comparative Study.}, journal = {Medical science monitor : international medical journal of experimental and clinical research}, volume = {31}, number = {}, pages = {e950086}, doi = {10.12659/MSM.950086}, pmid = {41382980}, issn = {1643-3750}, mesh = {Humans ; Female ; *Electromyography/methods ; *Pelvic Floor/physiopathology/physiology ; Retrospective Studies ; *Urinary Incontinence/physiopathology ; Adult ; Middle Aged ; Muscle Contraction/physiology ; *Pelvic Floor Disorders/physiopathology ; *Ataxia/physiopathology ; }, abstract = {BACKGROUND Surface electromyography (sEMG) of pelvic floor muscles (PFM) offers insights into neuromuscular control but lacks standardized normative values. This study aimed to evaluate baseline and contractile sEMG signal characteristics - including root mean square (RMS) amplitude in microvolts and normalized to maximum voluntary contraction (%MVC) - in a healthy control (H) group, pelvic floor dyssynergia (DS) group, and urinary incontinence (UI) group. MATERIAL AND METHODS A retrospective analysis included 68 women (H=28, UI=22, DS=18). UI was confirmed by the International Consultation on Incontinence Questionnaire-Short Form, and DS diagnosed via anorectal manometry. sEMG was recorded with a intravaginal probe using the Glazer protocol. RMS and %MVC were analyzed using Bayesian multivariate regression adjusted for age and BMI. RESULTS No significant differences were found at baseline rest or rapid contractions (P>0.05). The DS group showed higher RMS during tonic contractions vs H group (Δ=4.20, 95% BCI [0.99, 7.29], P<0.05) and UI (Δ=3.44, 95% BCI [0.48, 6.20], P<0.05), and impaired post-tonic relaxation vs H group (Δ=1.13, 95% BCI [0.10, 2.15], P<0.05). Normalized to %MVC, DS group showed lower rapid contraction activity than H group (Δ=-10.49, 95% BCI [-19.46, -1.86], P<0.05). H group outperformed UI group in tonic contraction (P<0.05). CONCLUSIONS DS showed higher RMS amplitudes during tonic contractions, impaired relaxation, and reduced %MVC efficiency, indicating paradoxical activity. UI patterns were heterogeneous, highlighting its multifactorial nature. Reliance on raw RMS alone may misclassify dysfunctions; multiparametric assessment and validation in larger cohorts are needed.}, }
@article {pmid41381932, year = {2025}, author = {Qin, X and Li, H and Zhao, H and Wang, X}, title = {Photobiomodulation and Addiction: Exploring Mechanisms, Therapeutic Potential, and Future Directions in Substance Use Disorders.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {41381932}, issn = {1995-8218}, }
@article {pmid41381864, year = {2025}, author = {Tang, A and Chen, Y and Si, K and Lai, J and Gong, W and Hu, S}, title = {Gut microbiota modulates synaptic plasticity, connectivity, and dopamine transmission in the VTA-mPFC pathway in bipolar depression.}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {41381864}, issn = {1476-5578}, support = {LR20F050002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; LR22F050007//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82201676//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82471542//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Adequate evidence has shown that gut microbial dysbiosis is an emerging disease phenotype of bipolar disorder (BD), and is closely related to clinical symptoms of this intractable disease. However, how gut microbiota affects the nervous system in BD remains largely unclear. In this study, we constructed a BD depression-like mouse model via fecal microbiota transplantation, and explored the changes of synaptic plasticity and connectivity in the medial prefrontal cortex (mPFC) of BD mice. We found that bipolar depression-like mice presented with a decrease in the density of dendritic spines in medial prefrontal neurons, and "Translation at postsynapse" as a key contributor to the changes in synaptic plasticity. In addition, analysis of synaptic connectivity in the mPFC revealed that compared to control mice, less connections were observed between ventral tegmental area and mPFC glutamate neurons and dopamine response was decreased in BD mice. These findings suggest that gut microbiota from BD depression patients induces the development of bipolar depression possibly by modulating aberrant synaptic connectivity and dopamine transmission in the VTA-mPFC pathway, which sheds light on the microbiota-gut-brain mechanisms underlying BD.}, }
@article {pmid41381863, year = {2025}, author = {Liang, R and Meng, L and Liu, X and Zhu, J and Wang, L and Ren, J and Zhao, M and Liu, J and Zheng, C and Yang, J and Ming, D}, title = {Syn III participated in rTMS-modulated emotional rescue in the prefrontal cortex under simulated space composite environment.}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {41381863}, issn = {1476-5578}, abstract = {Emotional state is a critical indicator of astronaut performance during long-duration space missions, significantly impacting both mission efficiency and post-mission adaptation to life on Earth. In this context, transcranial magnetic stimulation (TMS) may serve as a valuable tool for studying the psychological changes induced by the space environment. By combining whole-brain imaging, finite element model, cerebral blood flow imaging, genomics, and molecular validation, we tried to identify potential regulatory targets and their cofactors involved in rTMS-mediated improvement of emotional abnormalities under simulated spaceflight conditions. We identified the activation patterns of brain-wide neurons in simulated space composite environment (SSCE), particularly the reduced neuronal activity in the prefrontal cortex (PFC). The rTMS could activate PFC neurons and, on a macro scale, alleviate abnormal cortical hemodynamics. Importantly, synapsin III (Syn III) is a key candidate for rTMS-mediated improvement of emotional abnormalities under SSCE, working together with proteins such as MAPK, PSD95, and NR2B. Our work not only advances the understanding of spaceflight-associated neuropsychiatric risks but also establishes a molecular framework for developing targeted neuromodulation strategies in stress-related psychiatric disorders.}, }
@article {pmid41381618, year = {2025}, author = {Yang, H and Fukuma, R and Namima, T and Okuda, K and Nishi, A and Iwata, T and Reza, A and Sasaki, KS and Kaiju, T and Gill, G and Kishima, H and Nishimoto, S and Yanagisawa, T}, title = {Longitudinal multitask wireless electrocorticography data from two fully implanted nonhuman primates.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-025-06359-w}, pmid = {41381618}, issn = {2052-4463}, support = {JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012 (TY)//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJCR24U2 (TY)//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJCR18A5 (TY)//MEXT | JST | Core Research for Evolutional Science and Technology (CREST)/ ; }, abstract = {We present a unique dataset of chronic wireless electrocorticography (ECoG) recordings obtained from two fully implanted nonhuman primates (adult Japanese macaques, Macaca fuscata) spanning hundreds of days post implantation. Each animal was equipped with bilateral subdural ECoG arrays targeting the sensorimotor cortices and a fully implantable wireless transmission unit. The dataset involves multiple tasks, including resting-state measurements, auditory listening paradigms, voluntary button presses, reaching movements, and somatosensory evoked potentials, providing a broad range of behavioural and stimulus conditions. All raw signals, event annotations, and metadata are organized according to the Brain Imaging Data Structure (BIDS) extension for intracranial electrophysiology, ensuring ease of reuse and interoperability with common neurophysiological software. We verified the data quality and stability through impedance monitoring, power spectral analyses, and task-specific event-related measures across the recording period, confirming the reliability and consistency of the ECoG signals. By offering open access to these longitudinal wireless ECoG data, we aim to facilitate the acquisition of new insights into long-term cortical dynamics and advance brain-computer interface (BCI) research.}, }
@article {pmid41381487, year = {2025}, author = {Mao, L and Liu, P and Li, J and Wang, X and Su, H and Zhang, X and Sun, J and Li, T}, title = {Tactile-evoked EEG Dataset for Natural Perception Using an Integrated Stimulation-Recording Framework.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-025-06250-8}, pmid = {41381487}, issn = {2052-4463}, abstract = {The increasing demand for assistive living and medical technologies in aging societies has driven advancements in tactile-evoked Brian Computer Interface (BCI) systems, offering an alternative to traditional visual and auditory-based BCI systems. However, the development of such systems is constrained by challenges in quantifying tactile sensations and a lack of diverse datasets. This study presents an integrated system enabling natural tactile perception during dynamic touch experience while simultaneously recording electroencephalographic (EEG) responses. EEG signals were collected from 10 healthy participants (64 channels, 1000 Hz) in natural tactile perception tasks involving contact with three distinct materials. Preliminary analysis revealed significant differences in the P300 peak latency and amplitude between tactile conditions, highlighting the unique characteristics of tactile-evoked EEG signals. A three-class classification using Common Spatial Pattern (CSP) and Support Vector Machine (SVM) models demonstrated above-chance accuracy. This tactile-evoked EEG dataset provides a valuable resource for seeking tactile-related neural mechanisms and driving the practical application of BCI systems, offering a pathway to improved user experiences and functionality in real-world scenarios.}, }
@article {pmid41380689, year = {2025}, author = {Wei, Y and Ma, Z and Zhang, B and Fu, L and Sun, X and Li, K and Wang, Z and Wang, Y and Yu, Q and Yang, H and Tan, C and Duan, S and Ni, JD}, title = {Sympathetic functional units encoded by genetically defined postganglionic neurons.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2025.10.028}, pmid = {41380689}, issn = {1097-4199}, abstract = {The sympathetic system connects the brain with internal organs through distinct functional pathways; however, our understanding of their organization is limited. Here, we employed genetic labeling and single-cell transcriptomic analysis and identified two molecularly defined subpopulations of celiac-superior mesenteric ganglia (CG-SMG) neurons that implement different sympathetic functional pathways. Calb2-positive CG-SMG neurons project exclusively to the muscular layer of the gastrointestinal tract, forming endings associated with myenteric ganglia. Conversely, Nxph4-positive neurons innervate blood vessels within multiple organs, creating perivascular endings. Functional manipulations demonstrated that Calb2-labeled sympathetic neurons regulate gut motility without affecting blood flow, whereas Nxph4-positive neurons act as visceral vasoconstrictors, regulating blood flow independently of gut motility. The selectively induced autonomic responses by these two transcriptionally distinct subsets of postganglionic neurons suggest that the sympathetic nervous system uses a labeled line logic to control organ physiology.}, }
@article {pmid41379999, year = {2025}, author = {Lyu, B and Qin, L and Wang, X and Ou, J and Nour, MM and Ding, N and Gao, JH and Liu, Y}, title = {Building hierarchically nested structure by rapid neural sequences.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {50}, pages = {e2507417122}, doi = {10.1073/pnas.2507417122}, pmid = {41379999}, issn = {1091-6490}, support = {82327806 W2431053//MOST | National Natural Science Foundation of China (NSFC)/ ; 32271093//MOST | National Natural Science Foundation of China (NSFC)/ ; 2021ZD0200500 2021ZD0200506//National science and technology innovation 2030 major program/ ; 2022ZD0205500//National science and technology innovation 2030 major program/ ; n/a//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; 7100604651//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; }, mesh = {Humans ; Magnetoencephalography ; *Cognition/physiology ; Male ; Female ; *Brain/physiology ; Adult ; Young Adult ; }, abstract = {Hierarchically nested structures are fundamental to human cognition, enabling complex behaviors across domains including language, planning, and mathematics. However, the neural mechanisms that enable the flexible construction of these hierarchical structures are poorly understood. Here, we designed a task where participants mentally built sequences with nested, multidepth structures by recursively applying a fixed set of rules. Using magnetoencephalography, we find that the brain constructs nested hierarchies through rapid neural sequences that perform two recurring generative operations. The first operation identifies the hierarchy depth of a symbol and is associated with increased ripple-band power; while the second arranges the symbol into its correct order at that level, a process that scales with the number of depths, also positively correlated with planning time. These results reveal a fundamental neural computation for transforming sensory information into structured representations, which is essential for higher-order cognition.}, }
@article {pmid41379898, year = {2025}, author = {Chen, X and Li, Z and Shen, Y and Mahmud, M and Pham, H and Ng, MK and Pun, CM and Wang, S}, title = {High-Fidelity Functional Ultrasound Reconstruction via a Visual Auto-Regressive Framework.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3623196}, pmid = {41379898}, issn = {2168-2208}, abstract = {Functional ultrasound (fUS) imaging provides exceptional spatiotemporal resolution for neurovascular mapping, yet its practical application is significantly hampered by critical challenges. Foremost among these is data scarcity, arising from ethical considerations and signal degradation through the cranium, which collectively limit dataset diversity and compromise the fairness of downstream machine learning models. To address these limitations, we introduce UltraVAR (Ultrasound Visual Auto-Regressive model), the first data augmentation framework designed for fUS imaging that leverages a pre-trained visual auto-regressive generative model. UltraVAR is designed not only to mitigate data scarcity but also to enhance model fairness through the reconstruction of diverse and physiologically plausible fUS samples. The generated samples preserve essential neurovascular coupling features-specifically, the dynamic interplay between neural activity and microvascular hemodynamics. This capability distinguishes UltraVAR from conventional augmentation techniques, which often disrupt these vital physiological correlations and consequently fail to improve, or even degrade, downstream task performance. The proposed UltraVAR employs a scale-by-scale reconstruction mechanism that meticulously preserves the spatial topological relationships within vascular networks. The framework's fidelity is further enhanced by two integrated modules: the Smooth Scaling Layer, which ensures the preservation of critical image information during multi-scale feature propagation, and the Perception Enhancement Module, which actively suppresses artifact generation via a dynamic residual compensation mechanism. Comprehensive experimental validation demonstrates that datasets augmented with UltraVAR yield statistically significant improvements in downstream classification accuracy. This work establishes a robust foundation for advancing ultrasound-based neuromodulation techniques and brain-computer interface technologies by enabling the reconstruction of high-fidelity, diverse fUS data.}, }
@article {pmid41379894, year = {2025}, author = {Ke, Y and Fu, Z and Yang, J and Shang, H and Basu, A}, title = {A 1024-Channel 0.8V 23.9-nW/Channel Event-based Compute In-memory Neural Spike Detector.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2025.3642865}, pmid = {41379894}, issn = {1940-9990}, abstract = {The increasing data rate has become a major issue confronting next-generation intracortical brain-machine interfaces (iBMIs). The scaling number of recording sites requires complex analog wiring and lead to huge digitization power consumption. Compressive event-based neural frontends have been used in high-density neural implants to support the simultaneous recording of more channels. Event-based frontends (EBF) convert recorded signals into asynchronous digital events via delta modulation and can inherently achieve considerable compression. But EBFs are prone to false events that do not correspond to neural and may affect the output firing rate, which is the key feature for neural decoding. Spike detection (SPD) is a key process in the iBMI pipeline to detect neural spikes and further reduce the data rate. However, conventional digital SPD suffers from the increasing buffer size and frequent memory access power, and conventional spike emphasizers are not compatible with EBFs. In this work we introduced an event-based spike detection (Ev-SPD) algorithm for scalable compressive EBFs. To implement the algorithm effectively, we proposed a novel low-power 10-T eDRAM-SRAM hybrid random-access memory (HRAM) in-memory computing (IMC) bitcell for event processing. We fabricated the proposed 1024-channel IMC SPD macro in a 65nm process and tested the macro with both synthetic dataset and Neuropixel recordings. The proposed macro achieved a high spike detection accuracy of 96.06% on a synthetic dataset and 95.08% similarity and 0.05 firing pattern MAE on Neuropixel recordings. Our event-based IMC SPD macro achieved a high per channel spike detection energy efficiency of 23.9 nW per channel and an area efficiency of 375 μm[2] per channel. Our work presented a SPD scheme compatible with compressive EBFs for high-density iBMIs, achieving ultra-low power consumption with an IMC architecture while maintaining considerable accuracy.}, }
@article {pmid41377313, year = {2025}, author = {Ali, MR and Talpur, Y and Irshad, NUN and Imran, SB}, title = {Brain-computer interfaces: a new horizon in communication for locked-in syndrome.}, journal = {Annals of medicine and surgery (2012)}, volume = {87}, number = {12}, pages = {9159-9160}, pmid = {41377313}, issn = {2049-0801}, }
@article {pmid41374720, year = {2025}, author = {Jochumsen, M and Sulkjær, CS and Dalgaard, KS}, title = {Comparison of Classifier Calibration Schemes for Movement Intention Detection in Individuals with Cerebral Palsy for Inducing Plasticity with Brain-Computer Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {23}, pages = {}, doi = {10.3390/s25237347}, pmid = {41374720}, issn = {1424-8220}, support = {22-B01-1432//Elsass Foundation/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Cerebral Palsy/physiopathology ; Electroencephalography/methods ; Movement/physiology ; Male ; Female ; Adult ; Calibration ; Intention ; Young Adult ; }, abstract = {Brain-computer interfaces (BCIs) have successfully been used for stroke rehabilitation by pairing movement intentions with, e.g., functional electrical stimulation. It has also been proposed that BCI training is beneficial for people with cerebral palsy (CP). To develop BCI training for CP patients, movement intentions must be detected from single-trial EEG. The study aim was to detect movement intentions in CP patients and able-bodied participants using different classification scenarios to show the technical feasibility of BCI training in CP patients. Five CP patients and fifteen able-bodied participants performed wrist extensions and ankle dorsiflexions while EEG was recorded. All but one participant repeated the experiment on 1-2 additional days. The EEG was divided into movement intention and idle epochs that were classified with a random forest classifier using temporal, spectral, and template matching features to estimate movement intention detection performance. When calibrating the classifier on data from the same day and participant, 75% and 85% classification accuracies were obtained for CP- and able-bodied participants, respectively. The performance dropped by 5-15 percentage points when training the classifier on data from other days and other participants. In conclusion, movement intentions can be detected from single-trial EEG, indicating the technical feasibility of using BCIs for motor training in people with CP.}, }
@article {pmid41374637, year = {2025}, author = {Paredes Ocaranza, CR and Yun, B and Paredes Ocaranza, ED}, title = {Traditional Machine Learning Outperforms EEGNet for Consumer-Grade EEG Emotion Recognition: A Comprehensive Evaluation with Cross-Dataset Validation.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {23}, pages = {}, doi = {10.3390/s25237262}, pmid = {41374637}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Machine Learning ; Brain-Computer Interfaces ; Deep Learning ; Signal Processing, Computer-Assisted ; Adult ; }, abstract = {OBJECTIVE: Consumer-grade EEG devices have the potential for widespread brain-computer interface deployment but pose significant challenges for emotion recognition due to reduced spatial coverage and the variable signal quality encountered in uncontrolled deployment environments. While deep learning approaches have employed increasingly complex architectures, their efficacy in noisy consumer-grade signals and cross-system generalizability remains unexplored. We present a comprehensive systematic comparison of EEGNet architecture, which has become a benchmark model for consumer-grade EEG analysis versus traditional machine learning, examining when and why domain-specific feature engineering outperforms end-to-end learning in resource constrained scenarios.<br><br>APPROACH: We conducted comprehensive within-dataset evaluation using the DREAMER dataset (23 subjects, Emotiv EPOC 14-channel) and challenging cross-dataset validation (DREAMER&#x2192;SEED-VII transfer). Traditional ML employed domain-specific feature engineering (statistical, frequency-domain, and connectivity features) with random forest classification. Deep learning employed both optimized and enhanced EEGNet architectures, specifically designed for low channel consumer EEG systems. For cross-dataset validation, we implemented progressive domain adaptation combining anatomical channel mapping, CORAL adaptation, and TCA subspace learning. Statistical validation included 345 comprehensive evaluations with fivefold cross-validation &#xd7; 3 seeds &#xd7; 23 subjects, Wilcoxon signed-rank tests, and Cohen's d effect size calculations.<br><br>MAIN RESULTS: Traditional ML achieved superior within-dataset performance (F1 = 0.945 &#xb1; 0.034 versus 0.567 for EEGNet architectures, p < 0.000001, Cohen's d = 3.863, 67% improvement) across 345 evaluations. Cross-dataset validation demonstrated good performance (F1 = 0.619 versus 0.007) through systematic domain adaptation. Progressive improvements included anatomical channel mapping (5.8&#xd7; improvement), CORAL domain adaptation (2.7&#xd7; improvement), and TCA subspace learning (4.5&#xd7; improvement). Feature analysis revealed inter-channel connectivity patterns contributed 61% of the discriminative power. Traditional ML demonstrated superior computational efficiency (95% faster training, 10&#xd7; faster inference) and excellent stability (CV = 0.036). Fairness validation experiments supported the advantage of traditional ML in its ability to persist even with minimal feature engineering (F1 = 0.842 vs. 0.646 for enhanced EEGNet), and robustness analysis revealed that deep learning degrades more under consumer-grade noise conditions (17% vs. <1% degradation).<br><br>SIGNIFICANCE: These findings challenge the assumption that architectural complexity universally improves biosignal processing performance in consumer-grade applications. Through the comparison of traditional ML against the EEGNet consumer-grade architecture, we highlight the potential that domain-specific feature engineering and lightweight adaptation techniques can provide superior accuracy, stability, and practical deployment capabilities for consumer-grade EEG emotion recognition. While our empirical comparison focused on EEGNet, the underlying principles regarding data efficiency, noise robustness, and the value of domain expertise could extend to comparisons with other complex architectures facing similar constraints in further research. This comprehensive domain adaptation framework enables robust cross-system deployment, addressing critical gaps in real-world BCI applications.}, }
@article {pmid41370926, year = {2025}, author = {Qiu, X and Wang, ZY and Jiang, XH and Zhao, H and Yan, ZP and Li, KH and Zhang, L and Chen, L and Meng, L and Ni, J}, title = {Neural correlation between swallowing motor imagery and execution: An EEG analysis.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2b37}, pmid = {41370926}, issn = {1741-2552}, abstract = {OBJECTIVE: The relationship between swallowing motor imagery and actual swallowing remains unclear, leading to a lack of physiological basis for the application of swallowing imagery-based brain-computer interface (BCI) paradigms in rehabilitation. This research explored the link between swallowing execution and imagery, aiming to optimize brain-computer interface applications for swallowing rehabilitation in patients with dysphagia.<br><br>APPROACH: Thirty healthy participants performed swallowing motor imagery and saliva swallowing tasks under video cues, and Electroencephalography (EEG) signals from 64 channels and electromyographic (EMG) signals from the suprahyoid muscles were recorded. This study investigates swallowing onset detection using EMG, and explores neural dynamics during swallowing imagery and execution through EEG-based time-frequency analysis, functional connectivity analysis, and nonlinear dynamic analysis (Sample Entropy).<br><br>MAIN RESULTS: The results revealed event-related desynchronization (ERD) in the central region (CPz, CP1-CP4) and parietal region (Pz, P1-P4) for both swallowing motor imagery and actual swallowing. Pearson's correlation analysis indicated a weak but significant correlation (P = 0.0102). The ERD phenomenon during swallowing imagery was more similar to that during the pharyngeal stage, with a weak but significant correlation (P = 0.0139). Functional connectivity analysis revealed greater activation of the central region during swallowing imagery than during actual swallowing. In terms of sample entropy, swallowing motor execution exhibited higher signal complexity and dynamic characteristics compared to imagery.<br><br>SIGNIFICANCE: This study highlights the similarity in neural activation between swallowing imagery and execution, particularly in the central and parietal regions, supporting the application of the swallowing imagery paradigm in these regions for rehabilitation. Further research is required to enhance BCI applications in swallowing disorders.}, }
@article {pmid41370855, year = {2025}, author = {Karaiskou, AI and Varon, C and Musluoglu, CA and Alaerts, K and De Vos, M}, title = {EEG-Based meditation decoding: Tackling subject variability with spatial and temporal alignment.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2b0f}, pmid = {41370855}, issn = {1741-2552}, abstract = {Objective. Meditation and mindfulness are increasingly recognized as important in improving mental well-being. However, Electroencephalography (EEG)-based neurofeedback systems supporting these practices typically fail to generalize to unseen subjects. This study investigates the application of both spatial and spectral alignment to EEG to improve the classification of meditation and rest states for new subjects without any model retraining.Approach. Two unsupervised domain adaptation techniques are employed to reduce differences between subjects in their EEG recordings. The first, Riemannian Space Data Alignment (RSDA), adjusts and brings together patterns of brain activity across electrodes (spatial domain). The second, Convolutional Monge Mapping Normalization (CMMN), aligns the distribution of brain rhythms across frequencies (spectral domain). Each method is evaluated separately, in combination, and in interaction with z-score normalization. Classification between meditation and rest is performed on the aligned time series using EEGNet, a compact convolutional neural network architecture, with leave-one-subject-out (LOSO) cross-validation to assess generalization across subjects. All experiments are based on a publicly available dataset of meditation EEG recordings from 53 subjects, including both novice and expert meditators.Main Results. The combined RSDA+CMMN approach significantly improved LOSO classification accuracy (66.6%) compared to non-aligned (55.7%) and z-score normalized (59.6%) baselines, even though it did not improve overall harmonization. Spectral analysis identified consistent classification contributions from the Theta (4-8 Hz), Alpha (8-14 Hz), and Beta (14-30 Hz) bands, while spatial analysis highlighted Frontopolar and Temporal regions as critical for distinguishing the mental states of meditation and rest.Significance. This work is the first to explore both spatial and spectral alignment in subject-independent meditation decoding for improved cross-subject generalization. Aligning EEG time series without retraining provides a practical solution for real-time neurofeedback, thereby reducing subject variability and paving the way toward calibration-free neurotechnology that supports mental well-being. .}, }
@article {pmid41369883, year = {2025}, author = {Tan, Y and Li, B and Sun, Z and Duan, F and Solé-Casals, J}, title = {Multi-source self-guided domain adaptation framework for EEG-based emotion recognition.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {41369883}, issn = {1741-0444}, support = {No. 2025YFE0107700//National Key R&amp;D Program of China/ ; No. 24ZXYXSY00140//Tianjin Science and Technology Plan Project/ ; }, }
@article {pmid41369868, year = {2025}, author = {Lin, C and Lai, Q and Fang, E and Luo, B and Chen, Z and Huang, J and Hu, F and Yao, E}, title = {Serum urate, cardiovascular mediators, and atrial fibrillation: genetic evidence for URAT1-targeted therapy.}, journal = {Clinical rheumatology}, volume = {}, number = {}, pages = {}, pmid = {41369868}, issn = {1434-9949}, support = {2018-CX-20//Medical Innovation Project of Fujian Province/ ; }, abstract = {BACKGROUND: Current evidence indicates that high serum urate levels are associated with an increased occurrence of atrial fibrillation (AF), and urate-lowering drugs could potentially reduce this risk. Nonetheless, the processes driving this relationship remain unclear.<br><br>OBJECTIVE: To identify key mediators linking urate to AF and assess the direct effects of potential drug targets on AF risk.<br><br>METHODS: Genetic variants associated with serum urate levels, potential mediators, and urate-lowering drug targets were identified from genome-wide association studies (GWAS). Univariable Mendelian randomization, multivariable Mendelian randomization, and two-step-cis-MR were conducted. The Bayesian horseshoe prior MR approach was used as the primary method, and Genomic SEM was employed to support the mediation model.<br><br>RESULTS: The study identified a genetic and causal relationship between serum urate levels and AF onset. Key mediators included systolic blood pressure (proportion mediated 56.23%), diastolic blood pressure (25.27%), hypertension (49.46%), hypercholesterolemia (4.83%), coronary atherosclerosis (12.24%), myocardial infarction (30.32%), coronary artery disease (29.74%), and heart failure (47.66%). Drug target MR analysis found strong evidence for URAT1 inhibition reducing AF risk (odds ratio [OR]&#x2009;=&#x2009;0.91, 95% Bayesian credible interval [BCI] 0.85 to 0.97; Bayesian posterior probability [BPP]&#x2009;=&#x2009;0.997), which persisted after mediator adjustment. Under stricter flanking regions, evidence weakened after adjustment for heart failure (OR&#x2009;=&#x2009;0.93, 95% BCI 0.84 to 1.04; BPP&#x2009;=&#x2009;0.907) but remained robust for other mediators.<br><br>CONCLUSION: This study highlights several cardiovascular conditions (hypertension, hypercholesterolemia, heart failure, coronary artery diseases) as key mediators between serum urate and AF and supports URAT1 inhibition as a potential therapeutic strategy. Key points &#x2022;Elevated serum urate increases the risk of atrial fibrillation, potentially through cardiovascular mediators such as hypertension, heart failure, and coronary artery diseases. &#x2022;Genetic evidence from drug-target Mendelian randomization supports URAT1 inhibition as a potential therapeutic strategy for reducing atrial fibrillation risk. &#x2022;The protective effect of URAT1 inhibition against atrial fibrillation persists after adjusting for key cardiovascular mediators, suggesting additional therapeutic pathways beyond those identified.}, }
@article {pmid41368697, year = {2025}, author = {Huang, T and Yin, X and Jiang, E}, title = {EEG motor imagery classification through a two-dimensional CNN-LSTM deep architecture and fuzzy decision-making.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-16}, doi = {10.1080/10255842.2025.2554256}, pmid = {41368697}, issn = {1476-8259}, abstract = {This study presents a robust deep learning framework for automatic motor imagery detection from raw EEG signals. Six band-power features were extracted using STFT, and dedicated 2D CNN-LSTM models were trained for each band. Their outputs were fused using a Choquet fuzzy integral to enhance decision reliability under noisy EEG conditions. Alpha- and sigma-band models achieved 88% and 87.1% accuracy, respectively. The fused architecture reached 90.4% on BCI IV-2a and 92.21% on BCI IV-1, outperforming existing methods in motor imagery classification.}, }
@article {pmid41368519, year = {2025}, author = {Wang, P and Xie, T and Zhou, Y and Gong, P and Chan, RHM}, title = {TCPL: task-conditioned prompt learning for few-shot cross-subject motor imagery EEG decoding.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1689286}, pmid = {41368519}, issn = {1662-4548}, abstract = {Motor imagery (MI) electroencephalogram (EEG) decoding plays a critical role in brain-computer interfaces but remains challenging due to large inter-subject variability and limited training data. Existing approaches often struggle with few-shot cross-subject adaptation, as they require either extensive fine-tuning or fail to capture individualized neural dynamics. To address this issue, we propose a Task-Conditioned Prompt Learning (TCPL), which integrates a Task-Conditioned Prompt (TCP) module with a hybrid Temporal Convolutional Network (TCN) and Transformer backbone under a meta-learning framework. Specifically, TCP encodes subject-specific variability as prompt tokens, TCN extracts local temporal patterns, Transformer captures global dependencies, and meta-learning enables rapid adaptation with minimal samples. The proposed TCPL model is validated on three widely used public datasets, GigaScience, Physionet, and BCI Competition IV 2a, demonstrating strong generalization and efficient adaptation across unseen subjects. These results highlight the feasibility of TCPL for practical few-shot EEG decoding and its potential to advance the development of personalized brain-computer interface systems.}, }
@article {pmid41367898, year = {2025}, author = {Paveliev, M and Melnikova, A and Samigullin, DV and Egorchev, AA and Titova, AA and Kiyasov, AP and Popova, IY and Parpura, V and Aganov, AV}, title = {Second harmonic generation for brain imaging: pathology-related studies.}, journal = {Biophysical reviews}, volume = {}, number = {}, pages = {}, pmid = {41367898}, issn = {1867-2450}, abstract = {Microscopy of the brain has been facing problems of contrast and thick tissue imaging. Second harmonic generation (SHG) is a non-linear effect of the light interaction with the imaged material, resulting in photon emission at half the wavelength of the absorbed light. SHG microscopy provides an unprecedented opportunity for imaging collagen and other noncentrosymmetric protein fibrils in unstained thick tissue samples and in the live brain via a regular multiphoton setup. This opens a remarkable methodological window for imaging pathological processes of high importance, including brain trauma, fibrosis, tumorigenesis, and neuroimplant-induced foreign body response. Moreover, SHG is a valuable tool for imaging astrocytes and nerve fiber microtubules. Third harmonic generation enhanced by three-photon resonance with the Soret band of hemoglobin is combined with SHG to resolve the microstructure of blood vessel walls and astrocyte-process endfeet on gliovascular interfaces. Here, we review current state-of-the-art methods in the field of brain imaging applications of SHG, including research on brain and spinal cord injury, glioma, ischemia, Alzheimer's disease, neuroimplantation, and brain meninges. We then address the method development perspective in the broader context of other tissue pathologies. Finally, we account for recent progress in artificial intelligence applications for SHG microscopy data analysis.}, }
@article {pmid41365192, year = {2025}, author = {Li, Y and Ye, M and He, Q and Yang, B and Luo, P and Yang, X}, title = {Novel dual AMPK/NRF2 activation by leucocyanidin from Hawthorn (Crataegus) for mitochondria repair-Targeted therapy of hepatic steatosis.}, journal = {Phytomedicine : international journal of phytotherapy and phytopharmacology}, volume = {150}, number = {}, pages = {157614}, doi = {10.1016/j.phymed.2025.157614}, pmid = {41365192}, issn = {1618-095X}, abstract = {BACKGROUND AND PURPOSE: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a global health challenge with limited therapeutic options. This study identified leucocyanidin (Leuc), a bioactive flavonoid from the traditional herb Crataegus pinnatifida (hawthorn), as a novel dual-target therapeutic agent against MASLD.<br><br>METHODS AND RESULTS: We evaluated the effects of Leuc on a mouse model induced by a 60% high-fat diet and a cell model induced by free fatty acids (FFA). Compared to the model group, Leuc treatment dose-dependently significantly reduced liver weight, serum levels of TG and TC, hepatic inflammation markers (IL-6 and TNF-&#x3b1;), as well as cellular TG content. Histological and fluorescence analyses revealed a significant reduction in lipid droplet accumulation. Mechanistically, Leuc exerted its protective effects through two major pathways: (1) By activating the NRF2 antioxidant axis, Leuc attenuated oxidative stress-induced mitochondrial dysfunction and restored fatty acid &#x3b2;-oxidation capacity; (2) Through direct allosteric binding to AMPK, Leuc suppressed fatty acid uptake, inhibited lipogenesis, and enhanced mitochondrial fatty acid transport.<br><br>CONCLUSION: These coordinated mechanisms reestablished hepatic lipid homeostasis, positioning Leuc as a promising dual-target natural compound for MASLD intervention through simultaneous AMPK/NRF2 activation.}, }
@article {pmid41364937, year = {2025}, author = {Patrick-Krueger, KM and Pavlidis, I and Contreras-Vidal, JL}, title = {The state of science convergence in implantable brain-computer interface clinical trials.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2a6f}, pmid = {41364937}, issn = {1741-2552}, abstract = {Advances in implantable brain-computer interfaces (iBCI) have rapidly accelerated in the last decade that promises to improve the quality of life of patients with communications, sensory, and motor control disabilities (CSM). In this Perspective, we quantify the extent and nature of scientific convergence across 21 research groups conducting iBCI clinical trials worldwide. Using Medical Subject Headers (MeSH) and Classification of Instructional Programs (CIP) taxonomies, we analyze topical and disciplinary integration within 161 publications from 1998-2023 to assess how deeply team composition aligns with research themes and translational impact. Our findings indicate uneven patterns of convergence, with many teams combining engineering and clinical expertise yet omitting ethical, legal, and social dimensions. This represents what we term short-cut convergence. We propose an operational definition of this phenomenon and identify practical steps for researchers and funders to strengthen full convergence to accelerate iBCI translation and implementation.}, }
@article {pmid41363017, year = {2025}, author = {Rayson, H and Moreau, Q and Gailhard, S and Szul, MJ and Bonaiuto, JJ}, title = {Beta Burst Waveform Diversity: A Window onto Cortical Computation.}, journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry}, volume = {}, number = {}, pages = {10738584251390779}, doi = {10.1177/10738584251390779}, pmid = {41363017}, issn = {1089-4098}, abstract = {Neural activity in the beta band is increasingly recognized to occur not as sustained oscillations but as transient burst-like events. These beta bursts are diverse in shape, timing, and spatial distribution, but their precise functional significance remains unclear. Here, we review emerging evidence on beta burst properties, functional roles, and developmental trajectories and propose a new framework in which beta bursts are not homogeneous events but reflect distinct patterns of synaptic input from different brain regions targeting different cortical layers. We argue that burst waveform shape carries mechanistic and computational significance, offering a window into the dynamic integration of specific combinations of cortical and subcortical signals. This perspective repositions beta bursts as transient computational primitives, rather than generic inhibitory signals or averaged rhythms. We conclude by outlining key open questions and research priorities, including the need for improved detection methods, investigation into developmental and clinical biomarkers, and translational applications in neuromodulation and brain-computer interfaces.}, }
@article {pmid41362972, year = {2025}, author = {Labor, VV and Mokienko, OA and Cherkasova, AN and Ikonnikova, ES and Lyukmanov, RK and Suponeva, NA}, title = {[Movement image training and brain-computer interface in cognitive rehabilitation].}, journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova}, volume = {125}, number = {11}, pages = {27-35}, doi = {10.17116/jnevro202512511127}, pmid = {41362972}, issn = {1997-7298}, mesh = {Humans ; *Brain-Computer Interfaces ; Parkinson Disease/rehabilitation ; Cognition ; Movement ; Stroke Rehabilitation ; Multiple Sclerosis/rehabilitation ; *Neurological Rehabilitation/methods ; Cognitive Training ; }, abstract = {The paper provides an overview of studies on the use of movement image training and brain-computer interfaces (BCIs) for cognitive rehabilitation in patients with neurological diseases. Based on the analysis of studies published from 2004 to 2025, the effectiveness of these methods in recovering cognitive functions in patients with stroke (13 studies), Parkinson's disease (4 studies), and multiple sclerosis (2 studies) was evaluated. Most studies demonstrated a positive effect of movement image training on the cognitive functions of patients with neurological diseases and moderate cognitive deficits. The effectiveness of this approach is comparable to that of specialized cognitive training. In studies using BCI to control movement image training, an improvement in cognitive functions was also reported. Some studies showed a positive correlation between changes in cognitive indicators and the degree of motor recovery. In groups of patients with normal or near-normal baseline MoCA scores, no significant improvement in cognitive function was reported after a training course. The heterogeneity of the analyzed studies makes direct comparison between them difficult. The results of the analysis of published studies indicate the prospect of using the movement image training with BCI control in the cognitive rehabilitation of neurological patients. However, well-designed randomized controlled trials are necessary to study the mechanisms of the ideomotor training effects on cognitive functions and to develop standardized protocols for assessing their effectiveness.}, }
@article {pmid41362341, year = {2025}, author = {Simistira Liwicki, F and Saini, R and Chakladar, DD and Rakesh, S and Gupta, V and Liwicki, M and Eriksson, J}, title = {Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data during an inner speech task.}, journal = {Data in brief}, volume = {63}, number = {}, pages = {112258}, pmid = {41362341}, issn = {2352-3409}, abstract = {Inner speech, or covert speech, refers to the internal generation of language without overt articulation. Decoding inner speech has significant implications for brain-computer interfaces (BCIs), particularly for assistive communication in individuals with speech and motor impairments. To facilitate research in this area, we introduce a publicly available dataset comprising simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data during inner speech production. Data were collected from three healthy, right-handed participants performing an inner speech task. The task involved silent repetition of visually presented words belonging to either a social or numerical category. The experiment consisted of 40 trials per word, with eight unique words and starts with a fixation period of two seconds. Stimuli were displayed for two seconds at the beginning of each session, followed by a 12-second rest period to allow hemodynamic responses to return to baseline. Participants were instructed to remain still and avoid movements to minimize artifacts. EEG was recorded using a 64-channel MR-compatible cap (BrainCap MR, EasyCap GmbH) at a 5 kHz sampling rate. Electrocardiogram (ECG) signals were simultaneously acquired using an additional electrode placed on the trapezius muscle to facilitate cardioballistic artifact correction. Gradient and cardioballistic artifacts were corrected using BrainVision Analyzer software. Functional MRI data were acquired using a 3T scanner with a 48-channel headcoil, and an echo-planar imaging (EPI) sequence optimized for whole-brain coverage. The repetition time (TR) was 2 s. High-resolution anatomical T1-weighted images were also acquired for structural reference. The dataset is publicly available in the OpenNeuro repository. The aim of this dataset is to provide a resource for studying inner speech processing, multimodal neuroimaging, EEG-fMRI fusion techniques, and BCI-driven speech prosthesis development.}, }
@article {pmid41361966, year = {2025}, author = {Song, Y and An, S and Choi, Y and Shin, R and Song, KH and Doh, J}, title = {Jammed Foamed Microgel-based Bioprinting for Ex Vivo Reconstruction of 3D T Cell-Cancer Cell Interactions.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e05696}, doi = {10.1002/adhm.202505696}, pmid = {41361966}, issn = {2192-2659}, support = {//Korea Health Industry Development Institute (KHIDI)/ ; RS-2023-00208359//National Research Foundation of Korea (NRF)/ ; RS-2023-00218543//National Research Foundation of Korea (NRF)/ ; RS-2023-00217061//National Research Foundation of Korea (NRF)/ ; RS-2024-00512240//Korea Health Industry Development Institute/Republic of Korea ; RS-2024-00406325//Korea Health Industry Development Institute/Republic of Korea ; }, abstract = {T cells in solid tumors migrate through the tumor tissues to find cancer cells and eliminate them. Ex vivo reconstruction of T cell-cancer cell interactions is key for the rational design of cancer immunotherapy. Porous 3D structures essential for optimal T cell motility are challenging to fabricate by 3D printing using conventional bioinks: at high ink concentration, rheological properties are suitable for printing, but T cells are trapped in dense polymer networks, and vice versa. To overcome this limitation, a new bioink based on foamed microgels (FMGs) that facilitates T cell motility is devised, without compromising printability in extrusion 3D printing. Norbornene-functionalized gelatin is synthesized, foamed, cross-linked, and ground to generate FMGs. The FMGs exhibited rougher surfaces than non-foamed microgels (NFMGs), and generated finer pores when jammed. T cell motility is significantly higher in JFMGs than in JNFMGs. Using the JFMG, two compartment structures containing T cells in one compartment and cancer cells in the other compartment are printed. T cells rapidly migrated to the cancer cell compartment and killed the cancer cells. This new bioink enables the ex vivo fabrication of various tissues where immune cell migration is critical.}, }
@article {pmid41361599, year = {2025}, author = {Wilson, GH and Stein, EA and Kamdar, F and Avansino, DT and Pun, TK and Gross, R and Hosman, T and Singer-Clark, T and Kapitonava, A and Hochberg, LR and Simeral, JD and Shenoy, KV and Druckmann, S and Henderson, JM and Willett, FR}, title = {Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {41361599}, issn = {2157-846X}, support = {U01DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01-NS098968//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01-NS098968//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01-EB028171//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; 542969//Simons Foundation/ ; }, abstract = {Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time, which result in periods when users cannot use their device. Here we introduce a hidden Markov model to infer which targets users are moving towards during iBCI use and we retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms distribution alignment methods in large-scale, closed-loop simulations over two months, as well as in a closed loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we show how target inference recalibration methods appear capable of long-term unsupervised recalibration, whereas recently proposed data-distribution-matching approaches appear to accumulate compounding errors over time. We show offline that our approach performs well on freeform datasets of a person using a home computer with an iBCI. Our results demonstrate the use of task structure to bootstrap a noisy decoder into a highly performant one, thereby overcoming one of the major barriers to clinically translating BCIs.}, }
@article {pmid41361479, year = {2025}, author = {Vermehren, M and Colucci, A and Angerhöfer, C and Peekhaus, N and Kim, WS and Chang, WK and Kim, H and Hömberg, V and Paik, NJ and Soekadar, SR}, title = {The Berlin bimanual test for stroke survivors (BeBiT-S): evaluating exoskeleton-assisted bimanual motor function after stroke.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-025-01822-6}, pmid = {41361479}, issn = {1743-0003}, abstract = {BACKGROUND: Brain/neural hand exoskeletons (B/NHEs) can restore motor function after severe stroke, enabling bimanual tasks critical for various activities of daily living. Yet, reliable clinical tests for assessing bimanual function compatible with B/NHEs are lacking. Here, we introduce the Berlin Bimanual Test for Stroke (BeBiT-S), a 10-task assessment focused on everyday bimanual activities, and evaluate its psychometric properties as well as compatibility with assistive technologies such as B/NHEs.<br><br>METHODS: BeBiT-S tasks were selected based on their relevance to daily activities, representation of various grasp types, and compatibility with current (neuro-)prosthetic devices. A scoring system was developed to assess key aspects of bimanual function-including reaching, grasping, stabilizing, manipulating, and lifting-based on video recordings of task performance. The BeBiT-S was administered without support of assistive technology (unassisted condition) to 24 stroke survivors (mean age&#x2009;=&#x2009;56.5&#xa0;years; 9 female) with upper-limb hemiparesis. We evaluated interrater reliability through the intraclass correlation coefficient (ICC) and construct validity through correlations with the Chedoke Arm and Hand Activity Inventory (CAHAI), and Stroke Impact Scale (SIS). A subgroup of 15 stroke survivors (mean age 50.3&#xa0;years, 5 female) completed a second session supported by a B/NHE (B/NHE-assisted condition) to assess the BeBiT-S' sensitivity to change related to B/NHE-application.<br><br>RESULTS: The BeBiT-S demonstrated high interrater reliability in both the unassisted (ICC&#x2009;=&#x2009;0.985, P&#x2009;<&#x2009;.001) and B/NHE-assisted (ICC&#x2009;=&#x2009;0.862, P&#x2009;<&#x2009;.001) conditions. Unassisted BeBiT-S scores correlated with the CAHAI-8 (r(22)&#x2009;=&#x2009;0.95, P&#x2009;<&#x2009;.001) and the SIS subscales "strength" (r(20)&#x2009;=&#x2009;0.53, P&#x2009;=&#x2009;.012) and "hand function" (r(20)&#x2009;=&#x2009;0.50, P&#x2009;=&#x2009;.018), indicating construct validity. BeBiT-S scores improved significantly with B/NHE assistance (Mdn&#x2009;=&#x2009;60, P&#x2009;<&#x2009;.05), compared to when no assistance was provided (Mdn&#x2009;=&#x2009;38, P&#x2009;<&#x2009;.05), demonstrating the test's sensitivity to change following the application of a B/NHE.<br><br>CONCLUSIONS: The findings support that the BeBiT-S is a reliable and valid tool for evaluating bimanual task performance in stroke survivors and is compatible with the use of assistive technology such as B/NHEs. Trial registration NCT04440709, submitted June 18th, 2020.}, }
@article {pmid41361196, year = {2025}, author = {Zhao, R and Bai, Y and Zhang, S and Zhu, J and Liu, H and Ni, G}, title = {An open dataset of multidimensional signals based on different speech patterns in pragmatic Mandarin.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1934}, pmid = {41361196}, issn = {2052-4463}, mesh = {Humans ; *Speech ; Electroencephalography ; *Language ; Brain-Computer Interfaces ; Electromyography ; China ; Brain/physiology ; }, abstract = {Speech is essential for human communication, but millions of people lose the ability to speak due to conditions such as amyotrophic lateral sclerosis (ALS) or stroke. Assistive technologies like brain-computer interfaces (BCIs), can convert brain signals into speech. However, these technologies still face challenges in decoding accuracy. This issue is especially challenging for tonal languages like Mandarin Chinese. Furthermore, most existing speech datasets are based on Indo-European languages, which hinders our understanding of how tonal information is encoded in the brain. To address this, we introduce a comprehensive open dataset, which includes multimodal signals from 30 subjects using Mandarin Chinese across overt, silent, and imagined speech modes, covering electroencephalogram (EEG), surface electromyogram (sEMG), and speech recordings. This dataset lays a valuable groundwork for exploring the neural encoding of tonal languages, investigating tone-related brain dynamics, and improving assistive communication strategies. It supports cross-linguistic speech processing research and contributes to data-driven neural speech decoding technology innovations.}, }
@article {pmid41361138, year = {2025}, author = {Wu, M and Yang, Y and Zhang, J and Efimov, AI and Li, X and Zhang, K and Wang, Y and Bodkin, KL and Riahi, M and Gu, J and Wang, G and Kim, M and Zeng, L and Liu, J and Yoon, LH and Zhang, H and Freda, SN and Lee, M and Kang, J and Ciatti, JL and Ting, K and Cheng, S and Zhang, X and Sun, H and Zhang, W and Zhang, Y and Banks, A and Good, CH and Cox, JM and Pinto, L and Vázquez-Guardado, A and Huang, Y and Kozorovitskiy, Y and Rogers, JA}, title = {Patterned wireless transcranial optogenetics generates artificial perception.}, journal = {Nature neuroscience}, volume = {}, number = {}, pages = {}, pmid = {41361138}, issn = {1546-1726}, support = {U01NS131406//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS107539//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01MH117111//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; 2T32MH06756//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; R00MH120047//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; SP-2022-19027//Alfred P. Sloan Foundation/ ; 872599SPI//Simons Foundation/ ; }, abstract = {Synthesizing perceivable artificial neural inputs independent of typical sensory channels remains a fundamental challenge in developing next-generation brain-machine interfaces. Establishing a minimally invasive, wirelessly effective and miniaturized platform with long-term stability is crucial for creating research methods and clinically meaningful biointerfaces capable of mediating artificial perceptual feedback. Here we demonstrate a miniaturized, fully implantable transcranial optogenetic neural stimulator designed to generate artificial perceptions by patterning large cortical ensembles wirelessly in real time. Experimentally validated numerical simulations characterized light and heat propagation, whereas neuronal responses were assessed by in vivo electrophysiology and molecular methods. Cue discrimination during operant learning demonstrated the wireless genesis of artificial percepts sensed by mice, where spatial distance across large cortical networks and sequential order-based analyses of discrimination predicted performance. These conceptual and technical advances expand understanding of artificially patterned neural activity and its perception by the brain to guide the evolution of next-generation all-optical brain-machine communication.}, }
@article {pmid41360829, year = {2025}, author = {Matran-Fernandez, A and Halder, S}, title = {An EEG dataset to study neural correlates of audiovisual long-term memory retrieval.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1933}, pmid = {41360829}, issn = {2052-4463}, mesh = {Humans ; *Electroencephalography ; *Memory, Long-Term ; *Mental Recall ; }, abstract = {Memory retrieval is a fundamental cognitive process that plays a critical role in our lives. Studying the neural correlates of this process has significant implications for numerous fields, such as education and health care. Advances in neuroimaging technologies have facilitated the use of neural data, such as electroencephalography (EEG), to decode cognitive states associated with memory tasks. However, most memory research is still conducted using simple stimuli, such as lists of words, and it is unclear how much the discoveries made with such stimuli generalise to more naturalistic scenarios. We introduce a dataset of EEG signals from 27 participants acquired while they watched 10-second long clips of movies (some of which they had previously seen), together with annotations that reflect whether they recognised or remembered the scenes and the time points of recognition. This dataset allows the study of neural correlates of long-term memory recall in a naturalistic task.}, }
@article {pmid41360823, year = {2025}, author = {Ma, X and Jiang, Y and Jiang, N}, title = {3M-CPSEED, An EEG-based Dataset for Chinese Pinyin Production in Overt, Mouthed, and Imagined Speech.}, journal = {Scientific data}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41597-025-06346-1}, pmid = {41360823}, issn = {2052-4463}, abstract = {Speech brain-computer interfaces (BCIs) enable communication with the external world by decoding neural signals. However, language function as a higher-order brain function, the neural mechanisms underlying speech production remain incompletely understood. Currently most existing Chinese EEG datasets use sentences as stimuli, overlooking that Pinyin constitutes the phonetic foundation of Chinese characters, which limits research on decoding individual Chinese character components. Moreover, most datasets employ only one speech production paradigm, preventing exploration of the brain's diverse speech production modes. This study aims to construct the 3M-CPSEED Chinese Pinyin dataset for exploring neural activity during three distinct speech modes (overt speech, silently articulated speech, imagined speech)of syllables from distinct articulatory positions. The dataset comprises EEG recordings from 20 participants completing four experimental blocks within one day, yielding 1,800 validated trials. 3M-CPSEED holds significant implications for speech neurophysiology research, not only facilitating exploration of neural activity differences across pinyin articulations but also enabling robust transfer learning studies for other alphabetic languages.}, }
@article {pmid41360014, year = {2025}, author = {Xiong, H and Chang, S and Liu, J}, title = {Dual-Channel TRCA-net based on cross-subject positive transfer for SSVEP-BCI.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae291c}, pmid = {41360014}, issn = {2057-1976}, abstract = {To enhance the decoding accuracy and information transfer rate of steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems and to reduce inter-subject variability for broader SSVEP-BCI applications, a dual-channel TRCA-net (DC-TRCA-net) method is proposed, based on cross-subject positive transfer. The proposed method incorporates an innovative Transfer-Accuracy-based Subject Selection (T-ASS) strategy and a deep learning network integrated with the SSVEP Domain Adaptation Network (SSVEP-DAN) to enhance SSVEP-BCI decoding performance. The T-ASS strategy constructs contribution scores by computing each subject's self-accuracy and transfer accuracy, and enables effective source subject selection while mitigating negative transfer risks. DC-TRCA-net is further developed to improve model generalization through cross-subject data augmentation. The effectiveness of the proposed method is validated on two large-scale public benchmark datasets. Experimental results demonstrate that DC-TRCA-net outperforms existing networks across both datasets, with particularly substantial performance gains observed in complex experimental scenarios.}, }
@article {pmid41360010, year = {2025}, author = {Jensen, MA and Schalk, G and Ince, NF and Hermes, D and Worrell, GA and Brunner, P and Staff, NP and Miller, KJ}, title = {sEEG-Based brain-computer interfacing in a large adult and pediatric cohort.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2955}, pmid = {41360010}, issn = {1741-2552}, abstract = {OBJECTIVE: Stereoelectroencephalography (sEEG) is a mesoscale intracranial monitoring technique that records from the brain volumetrically with depth electrodes. sEEG is typically used for monitoring of epileptic foci, but can also serve as a useful tool to study distributed brain dynamics. Herein, we detail the implementation of sEEG-based brain-computer interfacing (BCI) across a diverse and large patient cohort.<br><br>APPROACH: Across 27 subjects (15 female, 31 total feedback experiments), we identified channels with increases in broadband power during hand, tongue, or foot movements using a simple block-design screening task. Subsequently, broadband power in these channels was coupled to continuous movement of a cursor on a screen during both overt movement and kinesthetic imagery.<br><br>MAIN RESULTS: 26 subjects (29 out of 31 feedback conditions) established successful control, defined as more than 80 percent accuracy, during the overt movement BCI task, while only 12 (of the same 31 conditions) achieved control during the motor imagery BCI task. In successful imagery BCI, broadband power in the reinforced control channel(s) in the two target conditions separated into distinct subpopulations. Outside of the control channel(s), we demonstrate that imagery BCI engages unique subnetworks of the motor system compared to cued movement or kinesthetic imagery alone.<br><br>SIGNIFICANCE: Pericentral sEEG-based motor BCI utilizing overt movement and kinesthetic imagery is robust across a diverse patient cohort with inconsistent accuracy during imagined movement. Cued movement, kinesthetic imagery, and feedback engage the motor network uniquely, providing the opportunity to understand the network dynamics underlying BCI control and improve future BCIs.}, }
@article {pmid41360009, year = {2025}, author = {Faisal, M and Sahoo, S and Hazarika, J}, title = {STeCANet: spatio-temporal cross attention network for brain computer interface systems using EEG-fNIRS signals.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2954}, pmid = {41360009}, issn = {1741-2552}, abstract = {Background- Multimodal neuroimaging fusion has shown promise in enhancing brain-computer interface (BCI) performance by capturing complementary neural dynamics. However, most existing fusion frameworks inadequately model the temporal asynchrony and adaptive fusion between EEG and fNIRS, thereby limiting their ability to generalize across sessions and subjects. Objective- This work aims to develop an adaptive fusion framework that effectively aligns and integrates EEG and fNIRS representations to improve cross-session and cross-subject generalization in BCI applications. Approach- To address this, we propose STeCANet, a novel Spatiotemporal Cross-Attention Network that integrates EEG and fNIRS signals through hierarchical attention-based alignment. The model leverages fNIRS-guided spatial attention, EEG-fNIRS temporal alignment, adaptive fusion, and adversarial training to ensure robust cross-modal interaction and spatiotemporal consistency. Main results- Evaluations across three cognitive paradigms, namely motor imagery (MI), mental arithmetic (MA), and word generation (WG), demonstrate that STeCANet significantly outperforms unimodal and recent multimodal baselines under both session-independent and subject-independent settings. Ablation studies confirm the contribution of each sub-module and loss function, including the domain adaptation component, in boosting classification accuracy and robustness. Significance- These results suggest that STeCANet offers a robust and interpretable solution for next-generation BCI applications.}, }
@article {pmid41359836, year = {2025}, author = {Wang, Y and Liu, F and Shan, Q and Wang, X and Liu, W and Chen, X and Teng, C and Lv, Y and Gu, X and Wang, X and Yu, B}, title = {Functional recovery induced by KCC2-enabled relay pathways in completely injured spinal cords in adult rats.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {50}, pages = {e2421823122}, doi = {10.1073/pnas.2421823122}, pmid = {41359836}, issn = {1091-6490}, mesh = {Animals ; *Spinal Cord Injuries/physiopathology/therapy/metabolism ; *Symporters/metabolism/agonists ; K Cl- Cotransporters ; Rats ; *Recovery of Function/drug effects/physiology ; Neural Stem Cells/transplantation/metabolism ; Rats, Sprague-Dawley ; Female ; Nerve Regeneration/drug effects/physiology ; Axons/physiology ; Locomotion/drug effects ; Insulin-Like Growth Factor I/metabolism ; Osteopontin/metabolism ; }, abstract = {Despite tremendous progress in promoting endogenous axon regeneration and engineering relay pathways by cell transplantation, the obtained functional recovery is still limited. We reason that these regenerated connections might not be able to integrate into the functional circuits in injured spinal cord. In this study, we tested whether modulating the neuronal excitability by pharmacological potassium-chloride cotransporter (KCC2) activation could enhance the functional outcomes of these regenerative treatments in a complete spinal cord injury (SCI) in adult rats. We found that while osteopontin/insulin-like growth factor 1 overexpression (to enhance axon regeneration) or neural stem cell (NSC) transplantation (to build a relay) alone failed to restore the interrupted spinal circuitry, the double treatment facilitated the integration of NSCs into the host spinal network, significantly promoting axonal regeneration and synapse formation. Behavioral assessments demonstrated that the addition of CLP290, a KCC2 agonist, to the combined treatment markedly improved hindlimb locomotion, as evidenced by higher Basso, Beattie and Bresnahan (BBB) scores and enhanced joint oscillation in fine locomotion analysis. Consistently, electrophysiological evaluations indicated partial restoration of electrical transmission through the reconstructed spinal network. Our findings highlight the synergistic effects of KCC2-mediated neuronal modulation on promoting functional recovery after complete SCI.}, }
@article {pmid41359725, year = {2025}, author = {Sun, Y and Chahine, D and Wen, Q and Liu, T and Li, X and Yuan, Y and Calamante, F and Lv, J}, title = {Voxel-Level Brain States Prediction Using Swin Transformer.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {12}, pages = {8719-8726}, doi = {10.1109/JBHI.2025.3613793}, pmid = {41359725}, issn = {2168-2208}, mesh = {Humans ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiology ; *Connectome/methods ; Male ; Female ; Adult ; *Image Processing, Computer-Assisted/methods ; *Signal Processing, Computer-Assisted ; Young Adult ; }, abstract = {Understanding brain dynamics is important for neuroscience and mental health. Functional magnetic resonance imaging (fMRI) enables the measurement of neural activities through blood-oxygen-level-dependent (BOLD) signals, which represent brain states. In this study, we aim to predict future human resting brain states with fMRI. Due to the 3D voxel-wise spatial organization and temporal dependencies of the fMRI data, we propose a novel architecture which employs a 4D Shifted Window (Swin) Transformer as encoder to efficiently learn spatio-temporal information and a convolutional decoder to enable brain state prediction at the same spatial and temporal resolution as the input fMRI data. We used 100 unrelated subjects from the Human Connectome Project (HCP) for model training and testing. Our novel model has shown high accuracy when predicting 7.2s resting-state brain activities based on the prior 23.04s fMRI time series. The predicted brain states highly resemble BOLD contrast and dynamics. This work shows promising evidence that the spatiotemporal organization of the human brain can be learned by a Swin Transformer model, at high resolution, which provides a potential for reducing the fMRI scan time and the development of brain-computer interfaces in the future.}, }
@article {pmid41358304, year = {2025}, author = {Hsieh, TH and Song, H and Shallal, C and Levine, DV and Yeon, SH and Qiao, J and Shu, T and Carty, MJ and McCullough, J and Herr, HM}, title = {Continuous neural control of a 2-DOF ankle-foot prosthesis enables dynamic obstacle maneuvers after transtibial amputation.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.11.25.25340897}, pmid = {41358304}, abstract = {UNLABELLED: Bionic reconstruction techniques that employ surgical neuroprosthetic interfaces, biomimetic control systems, and powered mechatronics have enabled versatile and biomimetic legged gait without reliance on intrinsic gait controllers. However, relative emphasis has been placed on the emulation of sagittal plane biomechanics while neglecting to provide control over frontal plane mechanics critical for terrain adaptation. Here, we present a 2-degree-of-freedom (DOF) bionic reconstruction at the transtibial amputation level that enables continuous neural control of both sagittal and frontal ankle and subtalar joint mechanics. To demonstrate its capabilities in a case study design, we integrated a 2-DOF robotic ankle-foot device via surface electromyographic electrodes to an individual provisioned with a surgical neuroprosthetic interface that augments residual muscle afferents. The subject was able to neurally control both degrees of freedom to regain nominal gait mechanics during both level-ground walking and continuous cross-slope navigation. Furthermore, the subject strategically traversed an obstacle course by dynamically hopping between a series of discrete cross-slope blocks, adapting to the slopes, and responding to rapid foot slips. These preliminary findings suggest that bionic reconstruction techniques can restore continuous neural control over multi-DOF prostheses to achieve agile locomotion over complex terrain.<br><br>ONE-SENTENCE SUMMARY: A multi-DOF ankle-foot prosthesis under continuous neural control enables agile locomotion over complex terrain.}, }
@article {pmid41358277, year = {2025}, author = {Baniasad, A and Chao, S and Nguyen, JA and Tian, E and Modongo, C and Minin, VM and Sebastian, JN and Shin, SS}, title = {HIV Remains a Risk Factor for Unfavorable Tuberculosis Treatment Outcomes in the Era of Universal Access to Antiretroviral Therapy in Botswana.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.11.26.25340699}, pmid = {41358277}, abstract = {Botswana implemented its universal "Treat All" antiretroviral therapy (ART) policy in 2016, expanding treatment eligibility to all people living with HIV (PLHIV). HIV has been known to be a leading risk factor for tuberculosis (TB) and poor TB treatment outcomes. The primary goal of this study is to assess whether HIV infection and HIV-associated immunosuppression remain risk factors for unfavorable TB treatment outcomes in the Post-Treat All era. We analyzed 636 TB patients treated in Gaborone (2017-2023), of whom 54.4% were HIV-positive. Unfavorable outcomes (death, failure, or loss to follow-up) occurred in 19.7% of HIV-positive and 8.5% of HIV-negative patients. We used logistic regression to estimate unadjusted and covariate-adjusted associations between TB treatment outcome and HIV status and between TB treatment outcome and CD4+ T-cell count. HIV-positive patients had 2.5-fold higher odds of unfavorable outcomes compared with HIV-negative patients [adjusted OR: 2.51, 95% CI: (1.48, 4.38)], controlling for age, sex, TB history, distance to clinic, substance use, and occupational status. PLHIV with CD4+ T-cell < 200 cells/ µ L was associated with approximately three-fold higher odds of unfavorable outcomes compared with HIV-negative participants [OR: 3.12, 95% CI: (1.65, 5.97)]. The secondary goal was to test whether the HIV effect changed following Treat All implementation. We combined the data from 2017-2023 with a Pre-Treat All cohort (2012-2016, n= 233, HIV prevalence 60.8%) and fit a frequentist logistic regression and Bayesian mixed-effects models with an interaction term that allows treatment era (Pre- vs. Post-Treat All) to modify the effect of HIV on TB treatment outcome. The estimated change in the HIV effect was uncertain [relative OR: 0.41; 95% CI: (0.11, 1.55)]. Combining the two Botswana data sets with 12 Pre- and Post-Treat All studies from neighboring Ethiopia showed that the pooled effect of HIV infection on unfavorable TB outcome has increased in the Post-Treat All period [relative OR: 2.39; 95% BCI: (1.36, 3.34)].}, }
@article {pmid41357676, year = {2025}, author = {Akhoundi, A and Yan, P and Landbrug, Y and Hays, M and Murmann, B and Chichilnisky, EJ and Muratore, DG}, title = {A Scalable 1024-Channel Ultra-Low-Power Spike Sorting Chip with Event-Driven Detection and Spatial Clustering.}, journal = {IEEE journal of solid-state circuits}, volume = {60}, number = {11}, pages = {3985-4001}, pmid = {41357676}, issn = {0018-9200}, abstract = {This paper presents a 1024-channel ultra-low-power spike sorting chip featuring event-driven spike detection and spatial clustering for large-scale neural recording. To address power and scalability constraints in brain-computer interfaces, the design integrates a compressive ADC with a two-stage spike detector that significantly reduces memory and processing activity. Spatial features derived from high-density microelectrode array (MEA) enhance cluster separability, enabling robust performance even under neural signal distortion or probe drift, particularly when recordings are obtained using planar MEAs. A modified self-organizing map algorithm clusters spikes in the spatial domain with minimal memory access, supporting on-chip training and real-time operation with low latency. Fabricated in 40 nm CMOS, the chip achieves 0.00029 mm[2]/channel area and 74 nW/channel power consumption, with over 1000× data compression. Performance is validated across synthetic and ex vivo datasets containing up to 500 neurons, demonstrating competitive accuracy and robust drift tracking compared to state-of-the-art solutions with much lower data bandwidth, processing, and power demands.}, }
@article {pmid41356598, year = {2025}, author = {Chen, S and Xie, N and Tang, Y and Ji, Y and He, Z and Wang, Y and Huang, X and Fu, J and Ge, M and Liu, Q and Li, M and Xiao, Q and Xu, Y and Wang, J and Jia, J and Xu, S}, title = {Long-Term Brain-Computer Interface Functional Electrical Stimulation Enhances Neuroplasticity and Functional Recovery in Elderly Stroke: A 4.5-Year Longitudinal Study Integrating Electroencephalography Biomarkers and Clinical Assessments.}, journal = {Research (Washington, D.C.)}, volume = {8}, number = {}, pages = {0984}, pmid = {41356598}, issn = {2639-5274}, abstract = {Stroke-induced motor and cognitive impairments substantially reduce the quality of life in elderly populations, driving the need for rehabilitation strategies that integrate neural plasticity and functional recovery. In this 4.5-year longitudinal study, we evaluated the efficacy of brain-computer interface combined with functional electrical stimulation (BCI-FES) versus FES only and conventional care (control) in 100 stroke survivors (60 to 90 years; 4,172 total screened, with 24 chronic-stage patients [>1 year post-onset] completing long-term follow-up). We integrated clinical metrics (Fugl-Meyer assessment [FMA], modified Barthel index [MBI], and Montreal Cognitive Assessment [MoCA]) with electroencephalography-based neurophysiological profiling to dissect recovery mechanisms. BCI-FES yielded superior and sustained improvements across all domains: motor function (FMA Δ = 4.5 ± 1.2 points, Cohen's d = 1.2) versus FES (Δ = 1.7 ± 0.8, d = 0.4) and control (Δ = 0.9 ± 0.6, d = 0.2), functional independence (MBI Δ = 5.4 ± 1.5, d = 1.1) exceeding FES (Δ = 2.2 ± 1.1, d = 0.4) and control (Δ = 1.3 ± 0.5, d = 0.5), and cognitive function (MoCA Δ = 1.6 ± 0.5, d = 0.8 at 4 months), although cognitive gains declined to near baseline by 4.5 years. Hemorrhagic stroke patients showed exceptional BCI-FES responses, while ischemic patients exhibited higher variability. Neurophysiologically, BCI-FES induced theta (Cz and C4) and alpha (FC3 and CP3) power increases, with theta power at Cz strongly predicting FMA gains (r = 0.68), and enhanced theta/alpha band functional connectivity (clustering coefficient +22%, local efficiency +18%, and small-world index +15%). Predictive modeling identified that an optimal treatment window (3 to 12 months post-onset with 10 to 15 weeks of therapy) maximizes recovery via peak neuroplasticity, and a responder profile (stroke duration <23 months) includes patients with residual plasticity (age <70, baseline MBI >40), predicting 76% of favorable outcomes. These findings establish BCI-FES as a transformative rehabilitation tool, driving dual-phase recovery via early cortical plasticity and sustained network coherence while highlighting the need for age-tailored cognitive maintenance strategies. This work redefines precision stroke care by merging clinical outcomes with mechanistic insights, positioning BCI-FES as the standard of care for diverse stroke subtypes.}, }
@article {pmid41356332, year = {2025}, author = {Coutray, K and Barbel, W and Groth, Z and LaViola, JJ}, title = {NeuroGaze: a hybrid EEG and eye-tracking brain-computer interface for hands-free interaction in virtual reality.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1695446}, pmid = {41356332}, issn = {1662-5161}, abstract = {Brain-Computer Interfaces (BCIs) have traditionally been studied in clinical and laboratory contexts, but the rise of consumer-grade devices now allows exploration of their use in daily activities. Virtual reality (VR) provides a particularly relevant domain, where existing input methods often force trade-offs between speed, accuracy, and physical effort. This study introduces NeuroGaze, a hybrid interface combining electroencephalography (EEG) with eye tracking to enable hands-free interaction in immersive VR. Twenty participants completed a 360° cube-selection task using three different input methods: VR controllers, gaze combined with a pinch gesture, and NeuroGaze. Performance was measured by task completion time and error rate, while workload was evaluated using the NASA Task Load Index (NASA-TLX). NeuroGaze successfully supported target selection with off-the-shelf hardware, producing fewer errors than the alternative methods but requiring longer completion times, reflecting a classic speed-accuracy tradeoff. Workload analysis indicated reduced physical demand for NeuroGaze compared to controllers, though overall ratings and user preferences were mixed. While the differing confirmation pipelines limit direct comparison of throughput metrics, NeuroGaze is positioned as a feasibility study illustrating trade-offs between speed, accuracy, and accessibility. It highlights the potential of consumer-grade BCIs for long-duration use and emphasizes the need for improved EEG signal processing and adaptive multimodal integration to enhance future performance.}, }
@article {pmid41356065, year = {2025}, author = {Nair, K and Cecotti, H}, title = {Deep Learning Architectures for Code-Modulated Visual Evoked Potentials Detection.}, journal = {ArXiv}, volume = {}, number = {}, pages = {}, pmid = {41356065}, issn = {2331-8422}, abstract = {Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (C-VEPs) require highly robust decoding methods to address temporal variability and session-dependent noise in EEG signals. This study proposes and evaluates several deep learning architectures, including convolutional neural networks (CNNs) for 63-bit m-sequence reconstruction and classification, and Siamese networks for similarity-based decoding, alongside canonical correlation analysis (CCA) baselines. EEG data were recorded from 13 healthy adults under single-target flicker stimulation. The proposed deep models significantly outperformed traditional approaches, with distance-based decoding using Earth Mover's Distance (EMD) and constrained EMD showing greater robustness to latency variations than Euclidean and Mahalanobis metrics. Temporal data augmentation with small shifts further improved generalization across sessions. Among all models, the multi-class Siamese network achieved the best overall performance with an average accuracy of 96.89%, demonstrating the potential of data-driven deep architectures for reliable, single-trial C-VEP decoding in adaptive non-invasive BCI systems.}, }
@article {pmid41355286, year = {2025}, author = {King, SE and Waddell, JT and Jan, I and McDonald, A and Raymond, C and Corbin, WR}, title = {Solitary drinking as a day-level risk factor for unique negative consequences among college students.}, journal = {Alcohol, clinical &amp; experimental research}, volume = {}, number = {}, pages = {}, doi = {10.1111/acer.70216}, pmid = {41355286}, issn = {2993-7175}, support = {T32-DA039772/DA/NIDA NIH HHS/United States ; }, abstract = {BACKGROUND: Solitary drinking represents a high-risk pattern of drinking across individuals but when examined within individuals, solitary moments are associated with less risk. One possibility is that solitary drinking confers risk for specific negative consequences at the day level, but aggregate measures of negative consequences mask such relations. Thus, this study examined the extent to which solitary drinking increased the likelihood of reporting specific negative consequences, controlling for drinking quantity.<br><br>METHOD: College students (N&#x2009;=&#x2009;1043; 51.8% female) completed a 30-day Timeline Followback Interview in which they reported day-level drinking context, drinking quantity, and negative consequences. A total of 7340 drinking days were reported. Two-level multilevel probit regressions with Bayesian estimation tested whether drinking context (i.e., solitary vs. social) was associated with an increased likelihood of experiencing each of eight unique negative consequences (i.e., social/interpersonal, risky behavior, blackouts, occupational, impaired control, physical dependence, self-care, and self-perception), controlling for drinking quantity.<br><br>RESULTS: When controlling for drinking quantity, solitary (vs. social) drinking days were associated with a higher likelihood of occupational consequences [&#x3b2;&#x2009;=&#x2009;0.05, 95% BCI&#x2009;=&#x2009;(0.01, 0.08)] and diminished self-perception [&#x3b2;&#x2009;=&#x2009;0.06, 95% BCI&#x2009;=&#x2009;(0.03, 0.10)]. Solitary drinking days were also associated with a lower likelihood of interpersonal consequences (&#x3b2;&#x2009;=&#x2009;-0.06, 95% BCI&#x2009;=&#x2009;[-0.11, -0.03]) and blackout drinking (&#x3b2;&#x2009;=&#x2009;-0.06, 95% BCI&#x2009;=&#x2009;[-0.09, -0.03]). Person-level results suggest that those who more often drink alone experience greater blackout drinking, impaired control, dependence, occupational consequences, and diminished self-perception (all p's&#x2009;<&#x2009;0.001). When consequences were summed, solitary drinking days (vs. social) were associated with fewer negative consequences (&#x3b2;&#x2009;=&#x2009;-0.023, 95% BCI&#x2009;=&#x2009;[-0.049, -0.005]), whereas at the person level, those who more frequently drink alone experienced more negative consequences (&#x3b2;&#x2009;=&#x2009;0.10, 95% BCI&#x2009;=&#x2009;[0.04, 0.17]).<br><br>CONCLUSIONS: Results suggest that social and solitary drinking contexts confer risk for specific consequences and that risk for consequences differs if consequences are aggregated. Findings may inform future interventions by emphasizing certain protective behavioral strategies in specific drinking contexts to reduce the likelihood of negative outcomes.}, }
@article {pmid41353180, year = {2025}, author = {Soriano-Segura, P and Ortiz, M and Polo-Hortigüela, C and Iáñez, E and Azorín, JM}, title = {Characterization of error-related potentials during the command of a lower-limb exoskeleton based on deep learning.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-025-01833-3}, pmid = {41353180}, issn = {1743-0003}, support = {PID2021-124111OB-C31//MICIU/AEI/10.13039/501100011033 and by ERDF, EU/ ; PRE2022-103336//MICIU/AEI/10.13039/501100011033 and by ERDF, EU/ ; }, }
@article {pmid41352637, year = {2025}, author = {Roc, A and Kolodzienski, L and Dreyer, P and Appriou, A and Monseigne, T and Lotte, F}, title = {Evolution of users' subjective experience over three training sessions with an EEG Motor-Imagery Brain-Computer Interface (MI-BCI).}, journal = {Brain research}, volume = {}, number = {}, pages = {150085}, doi = {10.1016/j.brainres.2025.150085}, pmid = {41352637}, issn = {1872-6240}, abstract = {Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) have been shown to be promising for numerous applications, including sport training and entertainment for healthy users, but also for improving or restoring functions in neurological and neuropsychiatric disorders, e.g., for motor rehabilitation post-stroke or for attention training in attention deficits. Reliable interactions with such MI-BCIs require a heavy training process for both the machine and the user. Yet, how User eXperience (UX) evolves during standard training is still largely unclear, both within and between sessions/days. Through an exploratory study, we investigated the variations of users' answers to a UX questionnaire when training with a standard left vs. right-hand MI-BCI. 24 healthy novice users engaged in 3 training sessions (with 12 runs each) on different days. Each short run was followed by six questions on screen measuring UX factors on scales from 1 to 10: mental demand, performance, mental effort, frustration, mental fatigue and anxiety. Interestingly, BCI performances did not correlate with any subjective UX measure in this study. However, a time effect was observed. Within session, the results suggested that mental demand, effort, and fatigue significantly augmented during BCI operation, and that frustration significantly fluctuated but did not differ pre- vs. post-session. Between sessions, the first session was rated significantly more challenging than the other two regarding frustration, anxiety, mental demand, mental effort and mental fatigue. This highlights the importance of conducting studies across sessions and of considering the users' mental states during BCI use, for improving UX and thus possibly BCI treatment outcome.}, }
@article {pmid41351188, year = {2025}, author = {Wang, N and Chai, X and Song, J and He, Y and He, Q and Zhang, T and Liu, D and Li, J and Cao, T and Zhu, S and Jia, Y and Si, J and Ma, W and Yang, Y and Zhao, J}, title = {Motor Intention Quantization for Patients With Disorders of Consciousness by Multimodal BCI Combining Electroencephalography and Functional Near-Infrared Spectroscopy.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {31}, number = {12}, pages = {e70679}, doi = {10.1002/cns.70679}, pmid = {41351188}, issn = {1755-5949}, support = {7232049//Natural Science Foundation of Beijing Municipality/ ; 7252004//Natural Science Foundation of Beijing Municipality/ ; 2019-I2M-5-021//CAMS Innovation Fund for Medical Sciences&#xff08;CIFMS&#xff09;/ ; Z221100002722014//International (Hong Kong, Macao, and Taiwan) Science and Technology Cooperation Project/ ; 2022GKZS0003//2022 Open Project of Key Laboratory and Engineering Technology Research Center in the Rehabilitation Field of the Ministry of Civil Affairs/ ; 2022ZD0205300//Science and Technology Innovation 2030/ ; 82371197//National Natural Science Foundation of China/ ; 82501457//National Natural Science Foundation of China/ ; 2025-PUMCH-D-004//National High Level Hospital Clinical Research Funding/ ; }, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Electroencephalography/methods ; Female ; Male ; *Brain-Computer Interfaces ; Middle Aged ; *Consciousness Disorders/physiopathology/diagnosis ; Adult ; *Intention ; Aged ; Young Adult ; }, abstract = {OBJECTIVE: The current application of single-modality electroencephalography (EEG) or functional near-infrared spectroscopy (fNIRS) to assess consciousness levels in patients with disorders of consciousness (DoC) has garnered significant attention. However, the diagnostic accuracy of unimodal approaches remains suboptimal. Therefore, this study aims to apply the multimodal fusion technology of EEG and fNIRS to the clinical diagnosis of DoC patients.<br><br>METHODS: Eleven patients with DoC (six with a minimally conscious state [MCS] and five with a vegetative state [VS]) were enrolled. The motor intention-based brain-computer interface (MI-BCI) paradigm was adopted. EEG and fNIRS were recorded simultaneously. The synchronous states of EEG and fNIRS were analyzed, including time-frequency analysis, event-related desynchronization (ERD), and changes in oxy-hemoglobin (HbO)/de-oxygenated (HbR)/total hemoglobin (HbT) content. A multimodal method combining EEG and fNIRS was used to classify DoC patients.<br><br>RESULTS: The machine-learning results of the MI-BCI model showed that the EEG-fNIRS multimodal approach was superior to single-modality techniques in the diagnosis of healthy controls (HC), MCS, and VS. The multimodal model achieved a mean AUC of 0.69&#x2009;&#xb1;&#x2009;0.10, significantly outperforming both unimodal EEG (0.43&#x2009;&#xb1;&#x2009;0.19; p&#x2009;<&#x2009;0.01) and standalone fNIRS (0.63&#x2009;&#xb1;&#x2009;0.10; p&#x2009;<&#x2009;0.05). The EEG_ERD index of left-handed MI-BCI significantly differentiated the MCS and VS groups. Meanwhile, for the classification tasks of HC, MCS, and VS, the importance ranking of the indicators was as follows: fNIRS_ACC, EEG_ACC, fNIRS_slope, fNIRS_centroid, EEG_ERD, fNIRS_integral, and fNIRS_mean.<br><br>CONCLUSION: The integration of multimodal MI-BCI paradigms demonstrates clinical potential in evaluating consciousness levels, while the synergistic combination of neurophysiological and hemodynamic biomarkers provides a robust framework for enhancing the precision of bedside diagnostic protocols.<br><br>TRIAL REGISTRATION: Clinical Trial Registry: ChiCTR2400085830.}, }
@article {pmid41351118, year = {2025}, author = {Liu, Q and Zhang, X and Zhang, H and Chen, K and He, Y and Niu, J and Li, W and Chen, H and Zhang, D and Li, J and Liao, W}, title = {Same movies, different stories: aberrant brain state dynamics during naturalistic emotional stimuli in depression.}, journal = {Journal of translational medicine}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12967-025-07512-0}, pmid = {41351118}, issn = {1479-5876}, }
@article {pmid41350592, year = {2025}, author = {Gao, X and Lin, H and Wu, X and Zhang, D}, title = {Integrating active brain-computer interfaces (aBCIs) with passive BCIs (pBCIs) under different frustration levels.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-30168-1}, pmid = {41350592}, issn = {2045-2322}, abstract = {The mental state of the users can significantly affect the performance of active brain-computer interfaces (aBCIs). In this work, we aim to adopt passive BCIs (pBCIs) to measure a typical mental state, frustration, which is much relevant to aBCIs. A novel paradigm has been developed that combines both aBCIs and pBCIs under different frustration levels of users. The aBCI in this work is based on classic binary motor imagery (MI). In experiments, a new strategy was implemented that uses visual feedback to induce different levels of frustration. The electroencephalography (EEG) data collected were used for both aBCIs and pBCIs. The pBCI was utilized to assess the frustration level during the aBCI tasks, and the aBCI classification models for different levels of frustration were trained. For pBCI, the filter bank common spatial pattern (FBCSP) feature extraction and support vector machine (SVM) classification were utilized to classify three (i.e., low, moderate, high) frustration levels. For aBCI, the same method (FBCSP+SVM) was used to classify left versus right MI. We also aim to improve the performance of aBCIs in such conditions, so we developed two new methods to incorporate the pBCI results to adapt three MI classifiers to the varying states of frustration. Compared to the conventional approach of directly classifying MI tasks without considering frustration, the two proposed methods increased the mean classification accuracy by 7.40% and 8.62%, respectively. (Compared with the commonly used non-emotional discrimination data, the results are improved by 4.56% and 5.87% respectively.) Within the scope of non-invasive EEG and MI-based aBCI, this study provides, to our knowledge, an initial integrated demonstration in which a frustration-level classifier (pBCI) is trained and then used to adapt MI decoding (aBCI). It should not be taken as a claim of originality beyond this context. Starting from "user subjective perception", this paper rises to the engineering level of "objective frustration recognition and classification model adaptation", and makes a contribution to the depth of EEG data analysis and methodological integrity.}, }
@article {pmid41350471, year = {2025}, author = {Yamaguchi, T and Hashimoto, RI and Sato, H}, title = {Cortical Representation of Auditory Selective Attention in a Dichotic Listening Task: A Functional Near-Infrared Spectroscopy Study.}, journal = {Brain topography}, volume = {39}, number = {1}, pages = {8}, pmid = {41350471}, issn = {1573-6792}, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Attention/physiology ; Male ; Female ; Dichotic Listening Tests ; Young Adult ; *Auditory Perception/physiology ; Adult ; Acoustic Stimulation ; Brain-Computer Interfaces ; Brain Mapping ; Music ; Reading ; *Cerebral Cortex/physiology ; }, abstract = {To advance the application of functional near-infrared spectroscopy (fNIRS) in brain-computer interface (BCI) technology, we investigated cortical activation patterns associated with auditory selective attention. Using a dichotic listening paradigm, participants were presented with simultaneous music and reading sounds to the left or right ear. During fNIRS recordings, they were instructed to selectively attend to the sound attribute (music vs. reading) or the spatial location (left vs. right ear). Cortical activity differences related to attentional targets were analyzed using a two-way analysis of variance (ANOVA), with sound attribute and spatial information as factors. Our results revealed a significant main effect of the sound attribute factor across multiple measurement channels. Notably, the right parietal region exhibited consistently greater activation when attention was directed toward music compared to reading sounds. Conversely, bilateral dorsolateral prefrontal cortex (DLPFC) channels showed higher activation when participants attended to reading sounds than to music. These findings indicate that cortical activation patterns are modulated by auditory attentional states based on sound attributes. Furthermore, preliminary classification analyses achieved an accuracy of 73.7% in discriminating attentional targets (music vs. reading sounds), demonstrating the feasibility of fNIRS-based BCI applications.}, }
@article {pmid41350343, year = {2025}, author = {Houmani, N and Yabouri, R and Garcia-Salicetti, S and Bedoin, M and Medani, T and Andrade, K}, title = {Individual neural dynamics of successful Gamma neuromodulation through EEG-neurofeedback in the aging brain.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-30212-0}, pmid = {41350343}, issn = {2045-2322}, abstract = {Gamma-band synchronization is a key mechanism for healthy cognitive function, yet it tends to decrease with age. EEG-based Neurofeedback (EEG-NF) is a promising tool enabling subjects to modulate their brain activity. However, its efficacy at the individual level remains unclear, which may partly explain the heterogeneity of neurofeedback outcomes. The primary objective of this study was to investigate individual neural dynamics of Gamma-band synchronization through EEG-NF training. We analyzed data from a double-blind, placebo-controlled trial using an EEG-based brain-computer interface, involving healthy older adults with subjective memory complaints, randomly assigned to a neurofeedback or a sham feedback group. Specifically, we employed a two-step unsupervised machine learning framework: first, epoch-based Agglomerative Hierarchical Clustering to identify individual-level response patterns, then Spectral Bi-Clustering to uncover higher-order structure at the population level. Results revealed a subgroup of individuals within the real neurofeedback condition who successfully enhanced their Gamma-band synchronization, with effects extending across the broader frequency spectrum. In contrast, the remaining participants in the neurofeedback group exhibited neural responses comparable to those observed in the sham group. This randomized controlled trial offers novel insights into the individual neural dynamics underlying successful Gamma EEG-NF training, highlighting its potential to promote healthy brain aging.}, }
@article {pmid41349820, year = {2025}, author = {Solano-Suarez, KG and Arango-Sabogal, JC and Roy, JP and Molgat, E and Bédard, C and Gagnon, CA and Buczinski, S and Dufour, S}, title = {Bayesian diagnostic accuracy estimation of milk enzyme-linked immunosorbent assay, blood polymerase chain reaction, and peripheral blood lymphocyte count tests to determine bovine leukosis virus status in dairy cows.}, journal = {Journal of dairy science}, volume = {}, number = {}, pages = {}, doi = {10.3168/jds.2025-27485}, pmid = {41349820}, issn = {1525-3198}, abstract = {We assessed the diagnostic accuracy of an adapted antibody ELISA (ELISA-Ab) test, originally designed for bulk milk samples but applied on individual DHI-collected milk samples, to identify the bovine leukosis virus infection status of individual cows. Blood real-time PCR (qPCR) and blood lymphocyte count (LC) tests were used for comparison. For the milk ELISA-Ab, secondary objectives included identifying a fit-for-purpose threshold for result interpretation and evaluating whether the test's specificity could be influenced by the sampling technique (i.e., DHI-collected milk samples). Additionally, we evaluated whether the accuracy of each test varied with cow age, categorizing cows as young (2 to 4 yr old) or older (>4 yr old). In 2023, 8 dairy herds in Québec, Canada, were selected based on their historical within-herd leukosis prevalence, which was estimated to range from 10% to 75%. From all milking cows within these herds (n = 637), milk samples were collected during regular DHI, and blood samples were collected by the research team within one week of the DHI sampling. The indirect IDEXX Leukosis Milk Screening ELISA test was adapted to accommodate individual cow milk samples (as opposed to bulk tank milk samples), whereas an in-house qPCR assay targeting gag-pro-pol gene regions and LC determination were applied to blood samples. Bayesian latent class models were used to estimate the diagnostic accuracy of the tests. An optical density threshold of ≥0.5 for the ELISA-Ab provided an optimal control of the misclassification cost across various leukosis prevalence and, to a lesser extent, false negative to false positive cost ratio scenarios. With this threshold, the sensitivity and specificity estimates (95% Bayesian credible interval [BCI]) were 92% (BCI: 88%, 95%) and 99% (BCI: 96%, 100%), respectively. Sensitivity was higher in cows >4 yr old (99%, BCI: 96%, 100%) compared with cows 2 to 4 yr old (88%, BCI: 80%, 94%). We observed lower ELISA-Ab specificity in cows milked immediately after a positive cow (median: 82%, BCI: 72%, 97%) compared with those milked after a negative cow (median: 91%, BCI: 85%, 99%), suggesting a milk carryover effect due to the sampling technique. This carryover effect had a more pronounced impact on the false positive rate in herds with 30% to 50% leukosis prevalence, with the largest differences observed at the 30% prevalence scenario. However, the overall influence of the carryover effect remained limited. The qPCR test showed a sensitivity of 81% (BCI: 75%, 86%) and a specificity of 100% (98%, 100), whereas the LC test had a sensitivity of 55% (49%, 61%) and a specificity of 96% (93%, 98%). Both the qPCR and LC test accuracy parameters remained similar across age groups. In conclusion, the adapted ELISA-Ab test appears suitable for individual cow testing using DHI-collected milk samples, with higher sensitivity in cows >4 yr old. Its integration into existing milk recording programs provides a practical opportunity for herd-level leukosis monitoring.}, }
@article {pmid41349431, year = {2025}, author = {Zou, T and Wang, X and Hu, X and Gao, Q and Han, H and Chen, H and Li, R}, title = {Distinct cortical morphometric inverse divergence changes in Parkinson's disease correlate with transcriptional expression patterns.}, journal = {NeuroImage. Clinical}, volume = {48}, number = {}, pages = {103916}, doi = {10.1016/j.nicl.2025.103916}, pmid = {41349431}, issn = {2213-1582}, abstract = {Growing evidence shows that parkinson's disease (PD) is a heterogeneous neurodegenerative disorder associated with region-specific changes in brain anatomy. However, the genetic mechanisms underlining these abnormalities are unclear. We aim to investigate PD neuroanatomical subtypes and uncover the specific brain-wide gene expression associated with morphometric abnormalities in each PD subtype. The morphometric inverse divergence (MIND) algorithm was used to quantify the morphological similarity based on multiple MRI features in 127 patients with PD and 101 healthy controls (HC). Then, heterogeneity through discriminant analysis (HYDRA) was employed to investigate the PD subtypes based on the MIND strength. Intergroup comparisons were conducted to assess MIND strength and clinical behavioral differences among PD subtypes and HC. Finally, we explored the associations between MIND network changes and gene expression in each PD subtype through partial least squares (PLS) regression, functional enrichment of PLS-weighted genes and transcriptional signature assessment of cell types. We identified two distinct subtypes of PD-related MIND changes, indicating that MIND decreased mainly in the frontal and cingulate cortices in subtype 1, and increased mainly in the occipital cortex and postcentral gyrus in subtype 2 (Bonferroni correction, p < 0.05). Both PD subtypes exhibited impaired cognitive function compared to HC, with subtype 2 showing lower Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) and Hoehn and Yahr (H&amp;Y) scores than subtype 1. Moreover, genetic commonalities analysis were identified 5 shared negative genes in the PD subtypes. Subtype 1 PLS1 genes were functionally enriched in biological processes related to synaptic function, neurodevelopment and degeneration. In addition, subtype 2 PLS1 genes showed additional involvement of metabolic pathways alongside synaptic function. Moreover, we identified MIND-related genes involved in inhibitory and excitatory neurons in subtype 1. In subtype 2, MIND-related genes were involved in astrocytes besides excitatory and inhibitory neurons. Our findings suggest two distinct neuroanatomical subtypes in PD, deepening the understanding of the heterogeneity of PD by bridging the gap between the transcriptome and neuroimaging.}, }
@article {pmid41348969, year = {2025}, author = {Liu, X and Li, F and Czosnyka, M and Czosnyka, Z and Yu, H and Tong, X and Xing, Y and Li, H and Pu, K and Feng, K and Zhang, K and Pang, M and Ming, D}, title = {Multi-Omics and High-Spatial-Resolution Omics: Deciphering Complexity in Neurological Disorders.}, journal = {GigaScience}, volume = {}, number = {}, pages = {}, doi = {10.1093/gigascience/giaf137}, pmid = {41348969}, issn = {2047-217X}, abstract = {BACKGROUND: The world has witnessed a steady rise in neurological diseases, which represent a heterogeneous group of disorders characterized by complex pathogenesis involving disruptions at multiple molecular levels, including genomic, transcriptomic, proteomic, and metabolomic levels. These disorders, often caused by genetic mutations, metabolic imbalances, immune dysregulation, and environmental factors, pose significant challenges to global public health due to their high prevalence, mortality, and disability burden.<br><br>RESULTS: The advent of high-throughput technologies, such as next-generation sequencing and mass spectrometry, has provided valuable insights into the underlying mechanisms of disease, especially the development of multi- and high-spatial-resolution omics technologies, enabling the interaction of multiple levels of biology and analysis of the complex molecular networks and pathophysiological processes.<br><br>CONCLUSIONS: This review provides a comprehensive analysis of the latest advancements in multi- and high-spatial-resolution omics, with a focus on their applications in precision diagnostics, biomarker discovery, and therapeutic target identification in brain diseases. The study also highlights the current challenges in the clinical implementation and discusses the future directions, with artificial intelligence being anticipated to enhance clinical translation and diagnostic accuracy significantly.}, }
@article {pmid41348794, year = {2025}, author = {Fu, Z and Zhang, P and He, X and Wang, H and Guo, Y and Chen, X and Huang, J}, title = {Deep Transfer Learning in Intra-subject and Inter-subjects for Intracortical Brain Machine Interface Decoding.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3640764}, pmid = {41348794}, issn = {1558-2531}, abstract = {OBJECTIVE: This study proposes an Improved Deep Transfer Network (IDTN) to enhance decoding accuracy, calibration efficiency, and adaptability of intracortical brain machine interface (iBMI) systems while reducing the reliance on new labeled samples.<br><br>METHODS: IDTN integrates two core components: Structural Joint Discriminative Maximum Mean Discrepancy (SJDMMD) and Kernel Norm Improved Multi-Gaussian Kernel (KNK). SJDMMD extends the standard MMD framework by incorporating a structure-enhanced soft label weighting mechanism that simultaneously minimizes intra-class distributional shifts and maximizes inter-class margins for precise cross-domain alignment. KNK employs multi-Gaussian kernels with kernel norm regularization to enhance high-dimensional feature representations and sharpen inter-class boundaries, thereby improving the effectiveness of SJDMMD.<br><br>RESULTS: Evaluated on neural datasets from two rhesus macaques, IDTN achieved superior performance in both intra- subject and inter-subject transfer scenarios, consistently outperforming state-of-the-art methods in decoding accuracy. IDTN also exhibited consistent decoding stability across daily recording sessions. Ablation studies further confirm that SJDMMD improves inter-class separability and intra-class coherence, while KNK contributes to more effective kernel mapping in complex feature spaces.<br><br>CONCLUSION: These findings underscore the effectiveness of structure-aware transfer learning for neural decoding.<br><br>SIGNIFICANCE: They also highlight the potential of IDTN for deployment in real-world iBMI applications, particularly in data-limited or cross-subject environments.}, }
@article {pmid41346965, year = {2025}, author = {Mariscal, DM and Driscoll, B and Huang, H and Fisher, LE}, title = {Somatosensory restoration and neural control strategies in lower-limb prostheses.}, journal = {npj biomedical innovations}, volume = {2}, number = {1}, pages = {44}, pmid = {41346965}, issn = {3005-1444}, abstract = {People with lower-limb amputation cannot directly control or receive feedback from existing prostheses, but emerging technologies aim to address this gap. Some approaches focus on restoring somatosensation in the missing limb, while others record signals from residual muscles for prosthetic control. This review provides an overview of the current state of neuroprosthetics for somatosensory restoration and prosthetic control in lower-limb amputation, offering perspectives on integrating these technologies for bidirectional neuroprostheses.}, }
@article {pmid41346464, year = {2025}, author = {Guragai, B and Jin, Z and Amos, TJ and Zhang, Q and Zhang, J and Li, L}, title = {Genetic contribution to intrinsic functional connectivity underlying general intelligence: evidence from adult twin study.}, journal = {Brain communications}, volume = {7}, number = {6}, pages = {fcaf461}, pmid = {41346464}, issn = {2632-1297}, abstract = {Resting-state functional connectivity has been linked to intelligence, and twin studies suggest that these associations may be influenced by genetic factors. To investigate this relationship, we analysed behavioural and resting-state functional magnetic resonance imaging data from young adult twins in the Human Connectome Project. General intelligence was assessed based on ten cognitive task performances. The results showed a positive correlation in both identical and fraternal twins, indicating a similarity of general intelligence among twin pairs. For the resting-state functional connectivity analysis, we conducted two approaches. In the first approach, twins were randomly assigned to two separate groups, ensuring that each pair was split between the groups. We then applied a connectome-based predictive method separately for identical and fraternal twins to predict general intelligence. Specifically, a predictive model was trained using one group's functional connectivity and then applied to its co-twin group to predict their general intelligence. Significant prediction was recorded in identical twins but not in fraternal twins, suggesting a high level of similarity of intelligence-related functional connectivity among identical twins. In the second approach, we aimed to quantify the intelligence similarity using the resting-state functional connectivity. To implement this, we generated models to predict the difference in general intelligence in twin pairs, where a smaller difference indicates a greater degree of similarity. The results showed that only the intelligence difference in identical twins was successfully predicted, where the default mode network showed a significant contribution, suggesting a higher neural basis for intelligence similarity in identical twins. Together, these findings demonstrate that functional connectivity patterns associated with intelligence extend across genetically identical twins. More broadly, they highlight the default mode network role in intelligence similarity and illustrate the utility of predictive modelling as a complementary framework to classical twin analyses.}, }
@article {pmid41282873, year = {2025}, author = {Chen, H and Wang, J and Lai, S and Peng, G and Zong, G and Yuan, C and Luo, B}, title = {Smoking Cessation, Weight Change, and Risk of Dementia: A Prospective Cohort Study.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {41282873}, abstract = {OBJECTIVES: To assess the associations of smoking cessation and post-cessation weight gain with the risk of dementia and cognitive trajectories.<br><br>DESIGN: Prospective cohort study.<br><br>SETTING: The U.S. Health and Retirement Study (1995-2020).<br><br>PARTICIPANTS: A total of 32,802 dementia-free participants were included, with a mean age of 60.5 years (SD 10.7) and 57.1% female.<br><br>EXPOSURE: Smoking status and body weight were collected biennially via structural interviews.<br><br>MAIN OUTCOME MEASURES: Dementia was identified using the Langa-Weir algorithm. Cognitive function was assessed using a 27-unit scale. Cox proportional hazard models estimated hazard ratio (HR) of dementia by smoking cessation status, subsequent weight change, and duration of cessation. Among participants who quit during follow-up, linear mixed models assessed cognitive trajectories before and after cessation.<br><br>RESULTS: Over 25 years of follow-up, 5,868 dementia cases were documented. Compared with current smokers, individuals who quit during follow-up had a lower dementia risk after quitting (HR: 0.82, 95% confidence interval: 0.72-0.93), similar to those who had quit before baseline (0.76, 0.69-0.83) and to never smokers (0.72, 0.66-0.79). The benefits of cessation were largely limited to participants with no or modest weight gain (&#x2264;5 kg). By contrast, quitting accompanied by >10 kg weight gain was marginally associated with higher dementia risk (1.31, 0.95-1.80). Dementia risk declined steadily with increasing cessation duration, reaching the level of never smokers after approximately 5-7 years. Cognitive trajectory analyses showed that quitting was associated with long-term slower cognitive decline but no transient change, especially among those with no or modest weight gain.<br><br>CONCLUSIONS: Smoking cessation was associated with a sustained lower dementia risk and slower cognitive decline, comparable to benefits observed in never smokers and without evidence of a short-term risk increase. However, substantial post-cessation weight gain may attenuate these advantages. Smoking cessation programs should incorporate weight-management strategies to optimize long-term brain health.}, }
@article {pmid41345782, year = {2025}, author = {Gebeyehu, TF and Sabbaghalvani, MA and Failla, G and Kabani, AS and Shah, Y and Kharichev, A and Dian, JA and Matsoukas, S and Vaccaro, AR and Schroeder, GD and Prasad, SK and Jallo, J and Heller, JE and Fehlings, MG and Harrop, JS}, title = {The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review.}, journal = {Spinal cord}, volume = {}, number = {}, pages = {}, pmid = {41345782}, issn = {1476-5624}, abstract = {STUDY DESIGN: Systematic Review.<br><br>OBJECTIVE: To describe applications of AI for traumatic SCI management with focus on diagnostics, prognostication, and therapeutic interventions.<br><br>METHODS: PubMed, Scopus and Cochrane libraries were searched (March 2025). Studies published in English between January 1[st], 2020, and March 18, 2025, dealing with clinical aspects in the acute, post-injury rehabilitative and first year phases of SCI were included. Studies on brain computer interface, robotics and non-neurologic aspects of SCI were excluded. Extracted were country of study, study design, focus of study, total participants, American Spinal Injury Association (ASIA) Impairment Scale (AIS), machine learning (ML) models, inputs, outcomes and performance metrices.<br><br>RESULTS: A total of 23 studies with 120,931 individuals were identified. Classical Machine Learning Models, Ensemble Learning Models and Deep Learning Models were the most used ML families. Age, AIS, neurologic level of injury, sex, mechanism of injury and motor score were the most common inputs. Predictions of neurologic status, functionality status, Hospital/ICU utilizations, complications, survival, discharge destination and results of image segmentation and patient grouping were the outputs of interest. The performance metrices were satisfactory in most and higher than humans in some studies.<br><br>CONCLUSION: AI can facilitate personalized approach to diagnosis of SCI, prediction of outcomes like neurological improvement, complications, functionality indicators like walking, selfcare and independence, re-admissions, prolonged length of stays, discharge destination and mortality after injury. It was also useful to suggest specific MAP goals and time of surgical intervention. These functions complement clinical judgement.}, }
@article {pmid41345432, year = {2025}, author = {Francis, N and Vadivu, G}, title = {ReHA-Net: a ReVIN-hybrid attention network with multiscale convolution for robust EEG artifact removal in brain-computer interfaces.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-28855-0}, pmid = {41345432}, issn = {2045-2322}, abstract = {Electroencephalography (EEG) is a non-invasive technique for monitoring brain activity, but its signal quality is frequently degraded by artifacts from ocular movements, muscle activity, and environmental noise. ReHA-Net is a deep learning framework for robust EEG denoising, combining a U-Net-based encoder-decoder with three core modules. (1) Hybrid Attention integrates temporal, spatial, and frequency attention to emphasize neural patterns while suppressing structured noise. (2) The Multiscale Separable Convolution (MSC) block employs dilated and parallel depth-wise separable convolutions with varying kernel sizes to capture both short-term and long-term temporal dependencies. (3) Reversible Instance Normalization (ReVIN) enhances cross-subject generalization while retaining subject-specific features. The model trains on an enhanced EEGdenoiseNet dataset with a wider signal-to-noise ratio range, combined artifact conditions, and tailored normalization strategies. ReHA-Net achieved strong denoising performance, with a PSNR of 27.10 dB, an SNR of about 17.06 dB, and a correlation coefficient of 0.976 with clean signals and a relative root mean square error (RRMSE) of 0.165. These outcomes demonstrate effective artifact reduction while maintaining neural activity, highlighting its suitability as a preprocessing step for tasks such as seizure detection, imagined speech decoding, and cognitive state monitoring.}, }
@article {pmid41345285, year = {2025}, author = {Miao, T and Sha, L and Huang, K and Li, Y and Liu, B}, title = {SATrans-Net: Sparse Attention Transformer for EEG-based motor imagery decoding.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-30806-8}, pmid = {41345285}, issn = {2045-2322}, support = {SJC2022011//Basic Research Program of Suzhou/ ; SJC2022011//Basic Research Program of Suzhou/ ; SJC2022011//Basic Research Program of Suzhou/ ; SJC2022011//Basic Research Program of Suzhou/ ; SJC2022011//Basic Research Program of Suzhou/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; }, abstract = {Brain-computer interface (BCI) technology decodes electroencephalography (EEG) signals to identify motor intentions associated with motor imagery (MI), offering assistive solutions for individuals with motor impairments. However, current deep learning methods often overlook the long-sequence nature of EEG-MI signals, leading to limited feature extraction and reduced decoding accuracy. To address this, we propose SATrans-Net, an end-to-end framework that models long-range dependencies in EEG-MI signals to enhance decoding performance. SATrans-Net uses two-dimensional depthwise separable convolution (2D-DSC) to extract spatiotemporal features and incorporates a Top-K Sparse Attention (TKSA) mechanism into the Transformer architecture, improving long-range modeling while reducing computational cost. By fusing local and global features, the model achieves accurate classification via a fully connected layer. For interpretability, Grad-CAM is applied to generate Class Activation Topography (CAT) maps, visualizing spatial attention over cortical regions. Cross-session evaluations show that SATrans-Net achieves average accuracies of 84.72%, 89.76%, and 96.79% on the BCI IV-2a, BCI IV-2b, and High-Gamma datasets, respectively, outperforming existing methods. Ablation studies further verify the critical role of the TKSA module. Overall, SATrans-Net demonstrates high decoding accuracy and strong interpretability, paving the way for the application of computational techniques in biomedical signal processing. Source Code:https://github.com/Jasmin-Tianhua/EEG-research_SATrans-Net.}, }
@article {pmid41345210, year = {2025}, author = {Do, M and Evancho, A and Tyler, WJ}, title = {Bilateral transcutaneous auricular vagus nerve stimulation for the treatment of insomnia in breast cancer.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-30600-6}, pmid = {41345210}, issn = {2045-2322}, support = {PREP Award//University of Alabama at Birmingham/ ; }, abstract = {Substantial diagnostic and therapeutic advances have been made in medicine to address breast cancer. There remain unmet needs to translate solutions for addressing insomnia and mental health concerns in breast cancer patients. In this open-label, pilot clinical trial, we evaluated the safety and efficacy of nightly, bilateral, transcutaneous auricular vagus nerve stimulation (taVNS) on insomnia and mental health outcomes in breast cancer patients across a two-week treatment period. Our results demonstrate that noninvasive vagus nerve stimulation can significantly reduce insomnia severity, improve sleep quality, decrease sleep onset latency, and enhance sleep efficiency. Treatment with taVNS also significantly reduced the number of nightly awakenings, cancer-related fatigue, and depression scores while increasing heart rate variability. These observations demonstrate that auricular vagus nerve stimulation holds promise for improving sleep quality and mental health in patients diagnosed with breast cancer. Future investigations aimed at more thoroughly investigating the safety profile and clinical impacts of taVNS on the quality of life in patients with breast cancer are warranted.ClinicalTrials.gov Identifier: NCT06006299 23/08/2023.}, }
@article {pmid41345143, year = {2025}, author = {Zhang, P and Yao, L and Yang, T and Lou, Y and Xu, W and Jiang, W and Li, W and Ji, X and Gao, F and Qian, Z}, title = {Revealing neural resonance in neuronal ensembles through frequency response tests.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-21252-7}, pmid = {41345143}, issn = {2045-2322}, support = {2024ZB661//Jiangsu Funding Program for Excellent Postdoctoral Talent/ ; ZKX24043//Key Project of Nanjing Health Science and Technology Development Special Fund/ ; BK20210531//Natural Science Foundation of Jiangsu Province/ ; NZ2024032//Fundamental Research Funds for the Central Universities/ ; 82151311//National Natural Science Foundation of China/ ; }, abstract = {Photobiomodulation emerges as a novel method to boost neuronal activities and brain function, with notable implications for treating brain disorders. Yet, the mechanisms and optimal frequency parameters of transcranial photobiomodulation are still unclear, which highlights a research gap in understanding how different stimulation frequencies affect neural responses. This study proposes a hypothesis that the nervous system exhibits resonance phenomena, suggesting that external stimuli near the system's resonant frequency trigger the strongest responses. We tested this by performing frequency response tests with pulsed transcranial near-infrared light (10-200 Hz) on mouse brains, monitoring neural responses across frequencies by analyzing cerebral blood flow, concentration of oxygenated hemoglobin, and neurophysiological activity in both cortical and deep brain regions. Our results reveal pronounced neural responses in cortical and deep brain areas at 60-80 Hz and 120-140 Hz, suggesting the potential existence of neural system resonance. Conceptually, the neural system appears to be modulatable by external stimuli, reaching maximal neural response when the stimulation frequency aligns with the system's resonant frequency, leading to neural resonance. These findings will expect to become guide new theoretical frameworks and strategies for neural modulation and therapeutic interventions.}, }
@article {pmid41344323, year = {2025}, author = {Che, X and Zhao, H and Ye, X and Ye, S and Zhen, Z and Huang, Z and Li, Y and Zhang, S and Xu, P and Chen, X and Jiang, C and Pan, F and Luan, H and Chen, J and Shang, D and Hu, S and Tu, Y and Hu, L and Fitzgibbon, BM and Fitzgerald, PB and Cash, RFH and Huang, M}, title = {Frontoparietal network mediates the antidepressant effects of accelerated iTBS and cTBS: TMS-EEG study.}, journal = {Cell reports. Medicine}, volume = {}, number = {}, pages = {102470}, doi = {10.1016/j.xcrm.2025.102470}, pmid = {41344323}, issn = {2666-3791}, abstract = {Accelerated intermittent and continuous theta burst stimulation (a-iTBS and a-cTBS) show strong efficacy for treatment-resistant depression (TRD), yet their neural mechanisms remain unclear. This study uses concurrent transcranial magnetic stimulation (TMS) and electroencephalography (TMS-EEG) to examine these mechanisms in 40 TRD patients and 40 healthy controls (HCs). TRD individuals demonstrate abnormal local cortical excitability at baseline, characterized by left hypoactivity and right disinhibition. A-iTBS increases left excitability, and a-cTBS increases right inhibition, and both normalize it to the level of HCs. Network analyses reveal that a-iTBS improves current propagation to the left inferior parietal lobule (IPL), correlating with a better antidepressant effect. Contrastingly, a-cTBS induces a widespread inhibition as indicated by current propagation over parietal cortices, with the left IPL being most prominent, and this also correlates with a better antidepressant effect. These findings outline the frontoparietal circuitry in TMS antidepressant effects and provide insights for optimizing treatment efficacy. This study was registered at the Chinese Clinical Trial Registry (ChiCTR2200055320).}, }
@article {pmid41344290, year = {2025}, author = {Liu, YJ and Wang, XD}, title = {Parallel supramammillary-hippocampal routes: Organization, dysregulation, and restoration.}, journal = {Neuron}, volume = {113}, number = {23}, pages = {3879-3881}, doi = {10.1016/j.neuron.2025.10.020}, pmid = {41344290}, issn = {1097-4199}, mesh = {Animals ; *Hippocampus/physiology ; Alzheimer Disease/physiopathology/pathology ; Neural Pathways/physiology ; Mice ; Humans ; }, abstract = {In this issue of Neuron, Luo et al.[1] report two supramammillary neuronal populations with segregated projections to the dorsal and ventral dentate gyrus that selectively modulate cognitive and emotional processes, respectively. Targeted activation of each pathway alleviates domain-specific behavioral deficits in an Alzheimer's disease mouse model.}, }
@article {pmid41341607, year = {2025}, author = {Mahrouk, A}, title = {Symbolic feedback for transparent fault anticipation in neuroergonomic brain-machine interfaces.}, journal = {Frontiers in robotics and AI}, volume = {12}, number = {}, pages = {1656642}, pmid = {41341607}, issn = {2296-9144}, abstract = {BACKGROUND: Brain-Machine Interfaces (BMIs) increasingly mediate human interaction with assistive systems, yet remain sensitive to internal cognitive divergence. Subtle shifts in user intention-due to fatigue, overload, or schema conflict-may affect system reliability. While decoding accuracy has improved, most systems still lack mechanisms to communicate internal uncertainty or reasoning dynamics in real time.<br><br>OBJECTIVE: We present NECAP-Interaction, a neuro-symbolic architecture that explores the potential of symbolic feedback to support real-time human-AI alignment. The framework aims to improve neuroergonomic transparency by integrating symbolic trace generation into the BMI control pipeline.<br><br>METHODS: All evaluations were conducted using high-fidelity synthetic agents across three simulation tasks (motor control, visual attention, cognitive inhibition). NECAP-Interaction generates symbolic descriptors of epistemic shifts, supporting co-adaptive human-system communication. We report trace clarity, response latency, and symbolic coverage using structured replay analysis and interpretability metrics.<br><br>RESULTS: NECAP-Interaction anticipated behavioral divergence up to 2.3 &#xb1; 0.4 s before error onset and maintained over 90% symbolic trace interpretability across uncertainty tiers. In simulated overlays, symbolic feedback improved user comprehension of system states and reduced latency to trust collapse compared to baseline architectures (CNN, RNN).<br><br>CONCLUSION: Cognitive interpretability is not merely a technical concern-it is a design priority. By embedding symbolic introspection into BMI workflows, NECAP-Interaction supports user transparency and co-regulated interaction in cognitively demanding contexts. These findings contribute to the development of human-centered neurotechnologies where explainability is experienced in real time.}, }
@article {pmid41341241, year = {2024}, author = {Kubben, P}, title = {Invasive Brain-Computer Interfaces: A Critical Assessment of Current Developments and Future Prospects.}, journal = {JMIR neurotechnology}, volume = {3}, number = {}, pages = {e60151}, pmid = {41341241}, issn = {2817-092X}, abstract = {Invasive brain-computer interfaces (BCIs) are gaining attention for their transformative potential in human-machine interaction. These devices, which connect directly to the brain, could revolutionize medical therapies and augmentative technologies. This viewpoint examines recent advancements, weighs benefits against risks, and explores ethical and regulatory considerations for the future of invasive BCIs.}, }
@article {pmid41338361, year = {2025}, author = {Li, Y and Chen, S and Liu, YJ}, title = {Microglial phagoptosis in development, health, and disease.}, journal = {Neurobiology of disease}, volume = {}, number = {}, pages = {107211}, doi = {10.1016/j.nbd.2025.107211}, pmid = {41338361}, issn = {1095-953X}, abstract = {Microglial phagoptosis, defined as the phagocytosis of a viable cell by microglia that ultimately causes the death of the engulfed cell, has emerged as a pivotal process in sculpting neural circuits within the central nervous system (CNS). Essential for neurodevelopmental circuit refinement and ongoing tissue homeostasis, this process relies on dynamic molecular cues that direct microglia to specific cellular substrates. Physiologically, phagoptosis contributes to neural circuit refinement and cell number regulation during development; however, its dysregulation can drive neurodevelopmental and neurodegenerative disorders via aberrant cell removal. Recent advances have elucidated the distinct signaling pathways involved in target recognition and engulfment, revealing the dual roles of microglial phagoptosis in both CNS health and disease. Deeper mechanistic insight into this process offers new therapeutic opportunities for conditions characterized by defective or excessive cell clearance. This review summarizes current progress, highlights unresolved challenges, and discusses future perspectives on targeting microglial phagoptosis for intervention in CNS disorders.}, }
@article {pmid41337436, year = {2025}, author = {Ding, Y and Wang, L and Wang, X and Chen, F}, title = {Developing Lightweight Models with Data Optimization for Attending Speaker Identity from EEG without Spatial Information.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11253106}, pmid = {41337436}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; *Attention/physiology ; Signal Processing, Computer-Assisted ; Male ; Adult ; Artifacts ; }, abstract = {Spatial auditory attention decoding (Sp-AAD) holds great promise for brain-computer interfaces (BCIs). However, studies have shown that the high performance of Sp-AAD relies heavily on eye gaze artifacts rather than actual auditory attention features. For this reason, this study focuses on verifying whether EEG signals contain sufficient discriminative features for attending target speaker identity without eye gaze artifacts. In this study, we proposed an EEG-Mixup data optimization method to suppress trial-specific features in EEG data by adjusting the data distribution and generating soft labels through linear interpolation. In addition, a lightweight EEG-MLP model containing only 2.5k parameters was designed, which showed significant advantages over the latest SOTA model (DenseNet-3D) in cross-trial scenarios. It is shown that the model's generalization ability can be significantly improved by optimizing the data without increasing the data volume; meanwhile, the lightweight model demonstrates higher computational efficiency and inference speed in specific tasks. This study provides important theoretical and practical references for future optimization applications of BCI systems.Clinical Relevance- This study demonstrates the potential of lightweight EEG-based methods for attending target speaker identity without relying on eye gaze artifacts, providing a foundation for future auditory brain-computer interface systems.}, }
@article {pmid41337381, year = {2025}, author = {Haqiqat, A and Karimi, N and Mirmahboub, B and Sobhaninia, Z and Shirani, S and Samavi, S}, title = {Tri-Model Integration: Advancing Breast Cancer Immunohistochemical Image Generation through Multi-Method Fusion.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11252716}, pmid = {41337381}, issn = {2694-0604}, mesh = {Humans ; *Breast Neoplasms/diagnostic imaging/metabolism/diagnosis ; Female ; *Immunohistochemistry/methods ; *Image Processing, Computer-Assisted/methods ; Neural Networks, Computer ; Algorithms ; Reproducibility of Results ; }, abstract = {Immunohistochemical (IHC) staining is a crucial technique for diagnosing and formulating treatment plans for breast cancer, particularly by evaluating the expression of biomarkers like human epidermal growth factor receptor-2. However, the high cost and complexity of IHC staining procedures have driven research toward generating IHC-stained images directly from more readily available Hematoxylin and Eosin-stained images using image-to-image (I2I) translation methods. In this work, we propose a novel approach that combines the predictive capabilities of three state-of-the-art I2I models to enhance the quality and reliability of synthetic IHC images. Specifically, we designed a Convolutional Neural Network that takes as input a four-dimensional input comprising the outputs of three distinct models (each contributing an IHC prediction, which is an RGB three-dimensional output for each) and produces a final consensus image through a fusion mechanism. This ensemble method leverages the strengths of each model, leading to more robust and accurate IHC image generation. Extensive experiments on the BCI dataset demonstrate that our approach outperforms existing single-model methods, achieving superior Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. All of our code is available at: https://github.com/arshamhaq/BCI-fusion.Clinical RelevanceImproving the quality of synthetic IHC images can potentially reduce costs and streamline the diagnostic process, ultimately benefiting patient outcomes.}, }
@article {pmid41337376, year = {2025}, author = {Kim, H and Ahn, M and Jun, SC}, title = {A Brain Switch for SSVEP-Based BCI Speller Using an RNN-Based Detection Approach.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11252734}, pmid = {41337376}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Adult ; *Neural Networks, Computer ; Female ; }, abstract = {Steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) systems are used commonly as spellers because they have high information transfer rate and high accuracy relative to other BCI paradigms. Asynchronous BCI systems allow users to input commands whenever they wish to use them, which may make these systems more realistic and practical than synchronous systems. In contrast, asynchronous BCIs, known as the Brain Switch, require robust mechanisms to detect users' intentions accurately while maintaining classification performance. This highlights the need for a BCI system that distinguishes users' intentions reliably. SSVEP paradigms often show variability in their frequency designs. In this study, we propose a two-stage asynchronous BCI system that combines a robust brain switch model that uses autocorrelation and Long Short-Term Memory (LSTM)) for detection and an EEGNet-based classifier. Our proposed system was evaluated using a 40-class SSVEP dataset involving 40 subjects. It achieved an impressive detection performance with a sensitivity (SEN) of 98.24 ± 2.21% and specificity (SPC) of 82.28 ± 11.63% for even 1-second epochs. Further, the system attained a classification accuracy (ACC) of 77.05 ± 14.95%. This model demonstrates significant potential to help develop more realistic and practical asynchronous BCI systems.}, }
@article {pmid41337322, year = {2025}, author = {Zhao, R and Zhang, S and Bai, Y and Ni, G}, title = {Neural Dynamics in Imagined Speech: A Spatiotemporal Analysis Based on EEG Source Localization and Functional Connectivity.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11254701}, pmid = {41337322}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Speech/physiology ; Male ; *Imagination/physiology ; Brain-Computer Interfaces ; Female ; Adult ; Spatio-Temporal Analysis ; *Brain/physiology ; Brain Mapping/methods ; Young Adult ; }, abstract = {Communication is a crucial part of daily life. However, patients with speech disorders may have difficulty communicating with the outside world and, in severe cases, may even completely lose the ability to speak. Imagined speech is an intrinsic speech activity that does not explicitly move any vocal organs, which has emerged as a promising avenue for brain-computer interface (BCI) research. In this study, we developed a novel experimental paradigm tailored to imagined speech tasks based on Chinese characters and collected participants' high-temporal-resolution electroencephalogram (EEG) data. Using dynamic statistical parametric mapping (dSPM), we delineated the spatial distribution of neural activation, while functional connectivity was quantified through phase-locking value (PLV) analysis to capture the temporal interplay between distinct brain regions. We introduced a novel spatiotemporal feature representation, termed information flow (IF), by segmenting the imagined speech process into 10 continuous temporal windows, we systematically analyzed the evolution of global and local information flow dynamics. The results revealed distinct spatiotemporal patterns of neural activation and functional connectivity, underscoring the coordinated interaction of critical brain regions involved in the process of imagined speech, which help to elucidate the spatiotemporal dynamics of imagined speech and provide valuable insights into its underlying neural mechanisms. This work provides a foundation for advancing speech BCI applications and contributes to understanding the cognitive and neural bases of imagined speech in Chinese.}, }
@article {pmid41337318, year = {2025}, author = {Yadav, A and Garcia, FC and Gonzalez, A and Trevisan, BE and Xu, A and Ugur, M and Bhattacharjee, A and Pothukuchi, RP}, title = {Foresee: A Modular and Open Framework to Explore Integrated Processing on Brain-Computer Interfaces.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254710}, pmid = {41337318}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; Algorithms ; *Signal Processing, Computer-Assisted ; Electroencephalography ; }, abstract = {Brain-computer interfaces (BCIs) with processing integrated on the device enable fast and autonomous closed-loop interaction with the brain. While such BCIs are rapidly gaining traction, they are also difficult to design due to the tight and conflicting power and performance needs of on-device processing. Meeting these specifications often requires the BCI processors to be co-designed with applications and algorithms, with processor designers and computational neuroscientists working closely to converge on the target hardware platform. But, this process has traditionally been cumbersome and ad hoc, due to the lack of systematic design space exploration frameworks. In response, we present Foresee, a new framework for fast exploration of BCI processors. Foresee offers a unified and modular interface for iteratively co-optimizing BCI processors with their algorithms, without sacrificing accuracy, speed, or ease of use. Foresee is publicly available, and comes with a library of hardware blocks for common signal processing functions that the community could contribute and build on. We demonstrate Foresee's utility and capability by analyzing on-device processing for two seizure detection methods from prior work, and validating our analysis on real hardware. We expect Foresee to be vital in designing next-generation BCIs.}, }
@article {pmid41337309, year = {2025}, author = {Thapa, BR and Bae, J}, title = {A Window Analysis for the Decoding of Premovement and Movement Intentions in Freewill EEG.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11253481}, pmid = {41337309}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; Movement/physiology ; Male ; Female ; Support Vector Machine ; *Brain-Computer Interfaces ; Adult ; *Intention ; Young Adult ; Signal Processing, Computer-Assisted ; }, abstract = {Decoding movement-related intentions from electroencephalogram (EEG) is important for developing real-time brain machine interfaces (BMIs). While most studies focus on cue-based tasks in EEG-based BMIs, freewill reaching and grasping tasks allow subjects to initiate movements of their own will, making them relevant to practical EEG-based BMIs. However, the investigation of EEG window size for decoding freewill movements remains unexplored. This study systematically analyzes the effect of different window sizes on decoding EEG premovement (prior to the movement onset) and movement (after movement initiation) intentions in freewill reaching and grasping tasks. We used 49 EEG recordings from 23 subjects, and EEG windows of 0.1-1s in 0.1s increments were analyzed within the range of -3 to 3s relative to the movement onset at 0. Decoding was performed using regularized linear support vector machine (LSVM) and regularized linear discriminant analysis (RLDA), and performance was evaluated in terms of accuracy. Larger window sizes consistently outperformed smaller ones, with peak accuracy occurring between 0-1s relative to the movement onset. LSVM outperformed RLDA across all 10 window sizes, with peak accuracy ranging from 86.98% with 0.1s window to 90.94% with 1s window. Using LSVM, the earliest peak accuracy (90.03%) was achieved with a 0.7s window starting at 0.35s after the movement onset. Notably, a 0.5s window provided a peak accuracy of 89.5% which is not statistically significant compared to the 0.7s window (p = 0.05). The start point of the 0.5s window was 0.5s after the onset. With LSVM, considering the trade-off between decoding accuracy and latency, the 0.5s window offers the optimal choice for decoding movement intention in freewill EEG.Clinical relevance- Identifying the optimal window size to decode movement-related intentions in freewill EEG can help improve strategies to develop real-time BMIs for individuals with motor impairments.}, }
@article {pmid41337275, year = {2025}, author = {Rutkowski, TM and Kasprzak, H and Otake-Matsuura, M and Komendzinski, T}, title = {Classifying Awareness with a Lightweight CNN in an Olfactory Oddball Passive BCI.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11253457}, pmid = {41337275}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Neural Networks, Computer ; *Awareness/physiology ; *Smell/physiology ; Algorithms ; Male ; Signal Processing, Computer-Assisted ; Adult ; Female ; }, abstract = {Olfaction, or the sense of smell, presents a promising avenue for enhancing brain-computer interface (BCI) usability and enabling passive cognitive state monitoring. In reactive BCI paradigms, odor cues can be associated with specific commands, facilitating more intuitive interaction. Furthermore, passive BCI applications can leverage olfactory stimuli to monitor cognitive processes. Despite this potential, challenges remain, notably the requirement for precise odor delivery mechanisms and robust algorithms capable of detecting and interpreting associated brain activity. This work proposes a novel approach, combining electroencephalography (EEG) and electrobulbogram (EBG) within an olfactory modality oddball paradigm, for predicting user awareness levels. A pilot study is presented, demonstrating improved user awareness classification performance with a newly developed multiclass, lightweight convolutional neural network (CNN) for this passive olfactory BCI modality, surpassing previously reported results.Clinical relevance- This research demonstrates the feasibility of inferring user awareness levels from concurrently acquired electroencephalographic (EEG) and electrobulbogram (EBG) neurophysiological data.}, }
@article {pmid41337269, year = {2025}, author = {Dijkema, EB and Pennartz, CMA and Olcese, U}, title = {A Proof-of-Concept Spike Based Neuromorphic Brain-Computer Interface.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253485}, pmid = {41337269}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Animals ; Mice ; Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; *Action Potentials/physiology ; Visual Cortex/physiology ; }, abstract = {Closed-loop brain-computer interfaces (BCIs) hold promise for restoring function after neurological damage by dynamically processing neural signals and delivering targeted brain stimulation. To achieve clinically meaningful outcomes, such systems must operate with high spatiotemporal precision. This work aims to demonstrate a proof-of-concept neuromorphic BCI that processes neural spike events in near-real time, without necessitating preprocessing besides signal filtering and spike detection. Methods - We developed a system that acquires neural signals and streams spike events into a spiking neural network (SNN) running on SpiNNaker neuromorphic hardware. We evaluated the system's performance using both in vivo recordings from mouse visual cortex and simulated neural waveforms. We measured the roundtrip latency, defined as the time from spike detection to an output spike generated by the SNN. Results - Under baseline conditions with no hidden SNN layers, mean roundtrip latency was 4.69 ms (±1.70 ms). Adding hidden layers increased latency by approximately 3.65 ms per layer, reflecting the computational overhead of deeper networks. The system successfully detected and processed spikes in near real-time, demonstrating that neuromorphic hardware can manage spike-based input at speeds suitable for closed-loop intervention. Discussion - These findings indicate that neuromorphic SNNs can rapidly process neural signals, providing a foundation for closed-loop BCIs capable of bypassing damaged neural pathways. Future efforts will involve implementing stimulation protocols and functional SNNs. Such developments may ultimately facilitate more effective, flexible, and power-efficient neuroprosthetic devices.}, }
@article {pmid41337259, year = {2025}, author = {Daling, MH and Alonzo, J and Lee, J and Lee, AH and Durfee, D and Larson, L and Nurmikko, A and Leung, VW}, title = {Shielded Relay Coil design to Optimize WPT and SAR for Distributed Wireless Brain Implants.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11253961}, pmid = {41337259}, issn = {2694-0604}, mesh = {*Wireless Technology/instrumentation ; Humans ; *Brain-Computer Interfaces ; Equipment Design ; *Brain/physiology ; *Prostheses and Implants ; }, abstract = {This paper presents a shielded relay antenna to simultaneously enhance Wireless Power Transfer (WPT) and reduce Specific Absorption Rate (SAR) for a network of distributed brain microimplants. Through strategic placement of conductive features, Eddy currents are created to oppose high magnetic fields. This design advantageously equalizes and increases the field strength over the cortical surface area. This work has the potential to address the WPT/ SAR co-optimization challenges for biomedical implants in general. When applied to the target 2 × 2 cm[2] wireless brain-machine interface (BMI) system operating at 915 MHz, HFSS simulations show it provides 1.2 dB WPT enhancement and a 29% SAR reduction.}, }
@article {pmid41337212, year = {2025}, author = {Arjona, L and Rosenthal, J and Azkarate, M}, title = {Wireless Communication Protocol for backscatter-based Neural Implants.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253936}, pmid = {41337212}, issn = {2694-0604}, mesh = {*Wireless Technology/instrumentation ; Humans ; *Brain-Computer Interfaces ; *Prostheses and Implants ; }, abstract = {This work presents a novel protocol for bidirectional wireless communication with neural implants that contributes to the growing field of closed-loop brain-computer interfaces (BCIs). BCIs are an emerging technology for studying and treating neurological disorders, such as spinal cord injuries. Furthermore, BCI heavily rely on neural implants as a crucial element, because they hold the potential to restore functionality of paralyzed limbs. The proposed protocol presents an open configuration to enable neural implants to communicate wirelessly with an external reader. Because computation to extract movement intention is performed externally, computing power is nearly unlimited and the energy consumption of the implant is reduced drastically. To validate the proposed protocol, the downlink (reader to implant) was implemented on a software defined radio running GNU-Radio toolkit with custom communication blocks. The uplink (implant to reader) was implemented on an FPGA. Finally, to validate the movement intention decoding, pre-recorded neural data was backscattered from an FPGA-based implant and the decoding was executed successfully.}, }
@article {pmid41337189, year = {2025}, author = {Bleuze, A and Martel, F and Aksenova, T and Struber, L}, title = {Modification of cortical activation pattern after long-term BCI training and its impact on decoding model performances.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253801}, pmid = {41337189}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Models, Neurological ; Male ; *Cerebral Cortex/physiology ; }, abstract = {In brain-computer-interfaces (BCIs) variability usually appears in brain signals from one session to another. This inter-session-variability is of major importance for two reasons. On the one hand it poses an issue for a model learned on previous session, that does not always perform correctly on new sessions. On the other hand, it can also be a marker of long-term adaptation in the brain of patients, which may reflect learning or even rehabilitation. This study investigates the phenomenon of physiological drift in BCIs, focusing on the evolution of brain activity over sessions. In order to do so, we analyzed the spatial patterns of synchronization and desynchronization in a wide range of frequencies. A linear regression model was proposed to quantify drift and residual variability. In this article, we study the inter-session variability both physiologically and from the point of view of the decoder performance and compute the correlation between them to examine their coherence. This study provides valuable insights on the physiological drift and its impact on BCI performance, contributing to the development of more stable and reliable BCI systems for rehabilitation medicine.(p)(p)Clinical Relevance-The long-term modifications in the activation patterns after BCI training studied in this article is an additional evidence of potential for rehabilitation using BCI.}, }
@article {pmid41337178, year = {2025}, author = {Wang, M and Wang, J and Zhao, J and Yao, L and Wang, Y}, title = {EIMNet: An EEG and iEEG-Fused Interactive Modality Network for Accurate Memory State Prediction during Working Memory Task.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11253846}, pmid = {41337178}, issn = {2694-0604}, mesh = {Humans ; *Memory, Short-Term/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; }, abstract = {Recent advancements in Brain-Computer Interface (BCI) research have increasingly highlighted the significance of multimodal integration for effectively extracting task-discriminative features. In the context of working memory (WM) task, we introduce EIMNet, a cross-modality fusion model inspired by the phase-amplitude coupling phenomenon. By enabling interaction between electroencephalography (EEG) and intracranial electroencephalography (iEEG), EIMNet enhances the representation of task-related features, improving the prediction of memory-related effects. Our ablation experiments demonstrate that EIMNet enhances decoding performance, with factors such as interaction factor selection, frequency band splitting, and data augmentation playing vital roles. We demonstrate the effectiveness of EIMNet in improving decoding accuracy by integrating EEG and iEEG for working memory task, with promising applications in memory and attention-related cognitive research.}, }
@article {pmid41337165, year = {2025}, author = {Xu, Y and Otsuka, S and Nakagawa, S}, title = {Enhancing EEG-Based Emotion Classification by Refining the Spatial Precision of Brain Activity.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11253823}, pmid = {41337165}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology/classification ; *Brain/physiology ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain-Computer Interfaces ; Algorithms ; }, abstract = {Advancements in neuroscience and deep learning have significantly enhanced bio-signal-based emotion recognition, a critical component in Brain-Machine Interface (BMI) applications for healthcare, human-computer interaction, and human-AI assistant communication. Former studies have proposed Manual Mapping electrode matrices and employing Convolutional Neural Networks (CNNs) to recognize spatial EEG activities. However, this Manual Mapping of EEG electrodes onto matrix grids limits spatial precision and introduces inefficiencies. This study proposes automated channel mapping methods of Orthographic Projection and Stereographic Projection to address these challenges, using Differential Entropy and Power Spectral Density with Linear Dynamical Systems as features. A 3-branch multiscale CNN was trained on open-source dataset, employing a 5-fold cross-classification approach. Experimental results demonstrate that higher-resolution grids (16×16, 24×24) with automated projections significantly outperform Manual Mappings, achieving up to a 4.06% improvement in classification accuracy (p < 0.05). This result indicates that enhancing spatial precision of EEG data improves emotion classification, establishing automated spatial mapping as an advancement in EEG-based emotion recognition.Clinical Relevance-Advancement in emotion classification accuracy can facilitate more reliable diagnostic tools and personalized therapeutic interventions for mental health disorders, such as depression and anxiety.}, }
@article {pmid41337115, year = {2025}, author = {Rivelli, F and Popov, M and Kouzinopoulos, CS and Tang, G}, title = {Adaptively Pruned Spiking Neural Networks for Energy-Efficient Intracortical Neural Decoding.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254088}, pmid = {41337115}, issn = {2694-0604}, mesh = {Animals ; *Brain-Computer Interfaces ; Algorithms ; *Neural Networks, Computer ; Humans ; *Neurons/physiology ; *Action Potentials/physiology ; *Nerve Net/physiology ; }, abstract = {Intracortical brain-machine interfaces demand low-latency, energy-efficient solutions for neural decoding. Spiking Neural Networks (SNNs) deployed on neuromorphic hardware have demonstrated remarkable efficiency in neural decoding by leveraging sparse binary activations and efficient spatiotemporal processing. However, reducing the computational cost of SNNs remains a critical challenge for developing ultra-efficient intracortical neural implants. In this work, we introduce a novel adaptive pruning algorithm specifically designed for SNNs with high activation sparsity, targeting intracortical neural decoding. Our method dynamically adjusts pruning decisions and employs a rollback mechanism to selectively eliminate redundant synaptic connections without compromising decoding accuracy. Experimental evaluation on the NeuroBench Non-Human Primate (NHP) Motor Prediction benchmark shows that our pruned network achieves performance comparable to dense networks, with a maximum tenfold improvement in efficiency. Moreover, hardware simulation on the neuromorphic processor reveals that the pruned network operates at sub-μW power levels, underscoring its potential for energy-constrained neural implants. These results underscore the promise of our approach for advancing energy-efficient intracortical brain-machine interfaces with low-overhead on-device intelligence.}, }
@article {pmid41337108, year = {2025}, author = {Song, Q and Kang, G}, title = {A Multi-Band Self-Attention Network for Motor Imagery Classification.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254113}, pmid = {41337108}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; }, abstract = {Brain-computer interface (BCI) systems create a novel communication method between humans and machines by translating human thoughts into actionable commands to control external devices. Motor imagery (MI) electroencephalogram (EEG) signals have significant applicability in various medical and non-medical industries, including stroke rehabilitation, wheelchair control, and drone operation. However, the practical application of EEG remains limited by the decoding performance and generalization ability of MI signalsThis study introduces a multi-branch self-attention network for motor imagery (MI) signal classification. Each branch independently processes EEG signals decomposed into distinct frequency bands through convolutional neural networks (CNNs) and multi-head self-attention (MHA) mechanisms, enabling the extraction of both fundamental and discriminative spatial-temporal features. To further capture dynamic temporal dependencies, long short-term memory (LSTM) networks are integrated. We systematically evaluate three signal decomposition ensemble empirical mode decomposition (EEMD), wavelet packet decomposition (WPD), and brain rhythm-based decomposition-to optimize feature representation. Extensive experiments on the BCI Competition IV 2a dataset demonstrate state-of-the-art performance, with subject-dependent and subject-independent accuracies of 84.04% and 71.67%, respectively. Comparative analyses against benchmark models (EEGNet, EEGTCNet, ShallowConvNet, etc.) validate the superiority of our approach in classification accuracy and generalization capabilityClinical relevance- This study investigates the methods for decoding motor imagery EEG signals and establishes the positive role of each module in classification. The improvement in accuracy can lead to better outcomes in medical applications such as controlling prosthetics, wheelchairs, and stroke rehabilitation.}, }
@article {pmid41337106, year = {2025}, author = {Zhong, Y and Wen, H and Assam, M and Yao, L and Wang, Y}, title = {Motor-Sensory Coupled Learning for Motor Imagery Decoding.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11254055}, pmid = {41337106}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Imagination/physiology ; Stroke Rehabilitation ; Signal Processing, Computer-Assisted ; *Learning ; Male ; }, abstract = {Brain-Computer Interface (BCI) technology has significant potential for advancing stroke rehabilitation by promoting motor recovery by decoding motor intentions from electroencephalogram (EEG) signals. However, the practical application of BCI in rehabilitation faces several challenges, particularly in decoding accuracy. This limitation often stems from an overemphasis on motor imagery signals, while sensory components, which are crucial for effective motor function recovery, are frequently overlooked. In this paper, we propose a novel framework to enhance BCI performance by integrating both sensory and motor modalities through a motor-sensory coupled learning approach. The model leverages EEG data induced by both motor imagery (MI) and tactile sensation (TS), using adversarial training to capture the coupled features of these two domains. By incorporating reliable sensory signals, the proposed approach aims to improve the robustness and accuracy of motor imagery decoding, offering particular benefits for stroke patients with impaired motor rhythms. Experimental results from BCI-naive subjects show a significant improvement in classification accuracy compared to traditional motor imagery-only models, suggesting that this approach holds promise as a potential solution for stroke rehabilitation. These findings indicate that integrating sensory signals into BCI systems could lead to more effective rehabilitation strategies, paving the way for the development of more robust and adaptive BCI technologies in the future.}, }
@article {pmid41337085, year = {2025}, author = {Ong, JX and Premchand, B and Lim, RY and Chew, E and Jiang, M and Tang, N and Ang, KK}, title = {Inhibitory Effects of Individualized Transcranial Alternating Current Stimulation on Motor Imagery and Interhemispheric Symmetry: Implications for Stroke Rehabilitation.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11254631}, pmid = {41337085}, issn = {2694-0604}, mesh = {Humans ; *Stroke Rehabilitation ; *Transcranial Direct Current Stimulation/methods ; Male ; Female ; Stroke/physiopathology ; *Imagination/physiology ; Adult ; Brain-Computer Interfaces ; }, abstract = {Transcranial alternating current stimulation (tACS) holds potential in stroke rehabilitation, but its effects when delivered at an individual's peak motor imagery (MI) frequency remain unclear. This study investigated the impact of tACS delivered at subject-specific peak MI frequencies on MI performance accuracy, quantified in terms of classification accuracy, and interhemispheric symmetry, measured via the brain symmetry index (BSI). Using a brain-computer-brain closed-loop system, each subject's peak MI performance frequency was first identified during the Pre-stimulation phase, after which tACS was delivered at this determined frequency. Our findings show that active individualized tACS decreased MI performance and increased BSI, suggesting inhibitory effects on motor-related neural processes.Clinical Relevance- The observed inhibitory effects of tACS highlight its potential for targeted neuromodulation in stroke recovery. Future research should explore how inhibitory effects can be harnessed therapeutically and investigate stimulation parameters that could optimize outcomes for functional recovery. The demonstrated ability of tACS to modulate brain activity, evidenced by increased BSI, underscores its promise as a neuromodulatory tool in clinical applications.}, }
@article {pmid41337074, year = {2025}, author = {Carvallo, A and Struber, L and Costecalde, T and Souriau, R and Charvet, G and Aksenova, T}, title = {Decoding of Individual Fingers Attempted Movement from Epidural ECoG in a Patient with Tetraplegia.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254592}, pmid = {41337074}, issn = {2694-0604}, mesh = {Humans ; *Quadriplegia/physiopathology ; *Brain-Computer Interfaces ; *Fingers/physiopathology/physiology ; Movement/physiology ; Algorithms ; *Electrocorticography/methods ; Male ; }, abstract = {Brain-Computer interfaces (BCIs) enable direct communication between the brain and external devices. This technology holds significant potential for restoring motor function in individuals with severe neurological impairments. Among others, restoration of fine hand motor functions allowing grasping and objects manipulation is a priority for enhancing patients' lifestyle. Decoding finger movements is crucial for the precise control of hand neuroprosthetics. In this article, we analyzed neural activity of a tetraplegic patient implanted with two WIMAGINE ECoG recording devices in front of the sensorimotor cortex of both hemispheres. ECoG was recorded over three sessions while the patient attempted to move individual fingers on the right hand. The attempted finger movements was decoded using a Hidden Markov Model, integrating Recursive Sample Weighted - N-Ways Partial Least Square algorithm addressing class imbalance. In the offline study, we obtained balanced accuracy 0.6603 ± 0.0087 in average for decoding activation of five individual fingers. Our results shows that decoding individual fingers movements attempts is possible in ECoG, paving the way for fine movement restoration using BCI.Clinical Relevance- Efficient decoding of individual fingers attempted movements using chronic ECoG recording devices in a tetraplegic patient, suggesting the feasibility of hand neuroprosthesis aimed at fine hand motor restoration in impaired individuals.}, }
@article {pmid41337062, year = {2025}, author = {Zhu, Z and Han, J and Zhang, Z and Wannawas, N and Faisal, AA}, title = {Identifying the Nature of Grip Force Signals in EEG &amp; fNIRS with Multi-Modal Graph Fusion Network.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254624}, pmid = {41337062}, issn = {2694-0604}, mesh = {Humans ; *Hand Strength/physiology ; *Electroencephalography/methods ; Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; }, abstract = {Brain-Computer interfaces can assist motor rehabilitation for people with severe paralysis by directly decoding their brain signals into movement intention and executing with external devices without passing the impaired neural pathways. It is crucial to restore natural and smooth daily movements, and continuous force control is one of the most important kinaesthetic functions. However, the complex continuous force decoding and limited relevant public datasets greatly challenge this field. How the brain coordinates the motor command or sensory feedback during the force control behaviour also remains to be discussed. This work investigated these questions through a novel experimental setup by isolating the motor intention and sensory feedback and combining both components flexibly for hand grip. We applied functional electrical stimulation to induce passive gripping and collected grip force with multi-modal brain signals. Significant neural pattern differences were found in EEG time-frequency representation by comparing the brain responses under different task conditions, including voluntary movement, motor imagery, and passive perception status. Additionally, we present a multi-modal graph fusion model fusing both EEG and fNIRS for continuous bimanual grip force decoding. These contributions are beneficial to developing neural interfaces for rehabilitation and assistive devices that involve force manipulation or operate in isometric schemes.}, }
@article {pmid41337056, year = {2025}, author = {Abdo, EA and Yakovlev, A and Degenaar, P}, title = {Multipolar Hybrid Stimulation for Visual Prostheses: Enhancing Resolution and Specificity.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254606}, pmid = {41337056}, issn = {2694-0604}, mesh = {*Visual Prosthesis ; Humans ; *Electric Stimulation/methods ; Optogenetics ; Brain-Computer Interfaces ; *Visual Cortex/physiology ; Animals ; }, abstract = {Advancements in neural stimulation techniques are essential for improving the precision and efficiency of brain-machine interfaces, particularly in visual cortical prostheses. These prostheses aim to restore vision by stimulating the visual cortex, but current methods face challenges such as limited spatial resolution, high power consumption, and non-specific activation. This work proposes a multipolar hybrid stimulation approach that combines electrical and optical neuromodulation to mitigate these limitations. Unlike traditional monopolar and bipolar methods, which require numerous electrodes or suffer from crosstalk and timing issues, the proposed system employs polarity switching and selective electrode control, enabling customizable electric fields alongside optogenetics for precise neural targeting and enhanced resolution. By utilizing subthreshold electrical and optogenetic stimulation, this approach improves spatial selectivity, minimizes crosstalk, and reduces power consumption. The conceptual design for neural tissue stimulation is presented, with ongoing efforts focused on integrating this system into a microelectronic chip. By addressing key limitations in current prosthetic systems, this work contributes to the development of more efficient and scalable solutions for visual restoration.}, }
@article {pmid41337025, year = {2025}, author = {Liu, G and Yan, Y and He, S and Cai, J and Cheok, AD and Qi Wu, E and Song, A}, title = {A Neuromorphic Approach for Brain-Machine Interface Using Spiking Neural Networks.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11254255}, pmid = {41337025}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Animals ; *Neural Networks, Computer ; Algorithms ; *Action Potentials/physiology ; Motor Cortex/physiology ; Macaca mulatta ; Humans ; }, abstract = {Brain-machine interfaces (BMIs) have emerged as a promising technology for restoring motor function in paralyzed individuals through direct neural control of prosthetic devices. While conventional decoding algorithms have achieved considerable success, they often overlook the fundamental biological properties of neural information processing. This paper presents a novel approach using Spiking Neural Networks (SNNs), a neuromorphic computing paradigm that closely mimics biological neural dynamics through event-driven processing and spike-timing-dependent plasticity. A SNN-based decoder was implemented for offline decoding of intracortical neural recordings from the primary motor cortex (M1) and dorsal premotor cortex (PMd) to continuous 2D cursor movements in a macaque monkey. This approach leverages the temporal processing capabilities of SNNs to capture the complex, time-varying nature of neural representations, potentially enabling more naturalistic and adaptive BMI control.}, }
@article {pmid41337011, year = {2025}, author = {Yao, R and Du, Z and Liang, F and Li, W and Hong, B}, title = {Dual-layer hand gestures decoding with wireless epidural braincomputer interface in a tetraplegia.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11254206}, pmid = {41337011}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Quadriplegia/physiopathology ; *Gestures ; *Hand/physiopathology/physiology ; *Wireless Technology ; Algorithms ; Electroencephalography ; }, abstract = {Spinal cord injury disrupts the neural connections between the brain and limbs, resulting in tetraplegia. Brain-computer interface (BCI) hold promise for enabling voluntary limb movements in tetraplegic patients, yet achieving fine motor control of the hand remains a challenge. Invasive BCI based on intracortical electrode arrays have demonstrated real-time multi-gesture decoding. However, their long-term safety is a major barrier in clinical applications. In this study, a tetraplegic patient was implanted with our recently developed wireless minimally invasive BCI, which records epidural field potential from eight electrodes over the sensorimotor cortex to decode continuous hand movement intentions. Natural hand movements can be decomposed into dual layers: the high level movement states and the low level finger kinematics. Accordingly, we propose a dual-layer decoding algorithm for multi-gesture BCI decoding. The upper layer infers the movement state using a hidden Markov model, while the lower layer decodes finger motion variables through a mixture of experts and filters them with a state specific linear system. This approach enables the real-time decoding of six hand gestures, outperforming classical decoders and recurrent neural networks. The proposed dual-layer framework achieves multi-gesture decoding solely from epidural EEG signals, paving the way for the development of flexible and robust BCI control of hand movement.}, }
@article {pmid41336923, year = {2025}, author = {Chen, X and Peng, Y and Li, C and Pan, Y and Ding, N and Zhang, S}, title = {MI-LTN: A Neurosymbolic Framework for Enhanced EEG Feature Extraction and Model Interpretability in MI-BCI.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11252655}, pmid = {41336923}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Brain-Computer Interface (BCI) is a cutting-edge technology that facilitates human-computer interaction. Motor Imagery Electroencephalogram (MI-EEG) decoding technology has emerged as a significant direction in BCI research. Despite the remarkable advancements in deep learning for EEG signal decoding in recent years, two major challenges persist: the comprehensive representation and extraction of features, and the lack of interpretability. To address these issues, we propose a novel neurosymbolic framework termed MI-LTN (Motor Imagery Logic Tensor Network), incorporate logical constraints into the training model using the Logic Tensor Network (LTN) and employ Shapley values to evaluate and adjust the importance of channels. Our experimental results show that MI-LTN achieves classification accuracies of 86.00% and 88.84% on the BCI IV 2a and BCI IV 2b datasets, respectively. These results demonstrate the great potential of LTN in MI-EEG decoding.}, }
@article {pmid41336899, year = {2025}, author = {Bradshaw Bernacchi, JK and Lopez Valdes, A}, title = {Electrophysiological Characterisation of Commercial Ear-EEG Devices.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11252639}, pmid = {41336899}, issn = {2694-0604}, mesh = {*Electroencephalography/instrumentation ; Humans ; *Ear/physiology ; Adult ; Male ; Female ; Wearable Electronic Devices ; Electrodes ; Brain-Computer Interfaces ; Equipment Design ; Young Adult ; Signal Processing, Computer-Assisted ; }, abstract = {Ear-EEG devices are advanced wearables revolutionizing EEG technology by combining comfort and portability. With the increasing availability of commercial ear-EEG devices, there is a need for an independent characterisation of the electrophysiological performance to guide users and researchers. Here, we evaluate the performance of the IDUN Guardian Earbuds (IGEB, IDUN Technologies AG) by analysing electrophysiological responses to several well-established EEG paradigms, including event-related potentials (ERPs), auditory steady-state response (ASSR), steady-state visually evoked potential (SSVEP), and alpha block, and comparing them to standard scalp-based EEG recordings acquired simultaneously from eight participants utilizing a validation toolkit previously developed in our lab. Results indicate that the in-ear device is capable of detecting SSVEPs. However, we did not observe ERPs, ASSRs, or alpha blocking. Simulating in-ear EEG with electrode T8 referenced to T7 slightly improved the quality of the signal, which was further enhanced with midline reference electrodes.Clinical Relevance- Characterising this technology marks a step forward providing independent assessment of commercially available devices in view of expanding EEG applications, from long-term monitoring and wearable health solutions to advanced brain-machine interfaces (BMI).}, }
@article {pmid41336877, year = {2025}, author = {Torgersen, EL and Ragnarson, I and Molinas, M}, title = {Decoding Attention through EEG: Paving the Way for BCI Applications in Attention-Related Disorders.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11252840}, pmid = {41336877}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Attention Deficit Disorder with Hyperactivity/physiopathology/diagnosis ; *Attention/physiology ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Machine Learning ; Young Adult ; Brain/physiopathology ; }, abstract = {This study investigates attention-related traits in EEG signals to assess the potential of Electroencephalography (EEG) as an objective diagnostic tool for attention-related disorders such as ADHD, anxiety, and learning disabilities. EEG data were collected from 31 participants, including individuals with ADHD, while they performed a Go/No-Go task designed to evaluate attention and impulsivity. The analysis focused on the spectral characteristics of brain activity, examining the relative power of theta, alpha, and beta frequency bands, along with the theta-to-beta ratio (TBR), to identify distinguishing patterns of attention-related brain activity. Results indicate that the ADHD group exhibited higher theta power and consistently elevated TBR, particularly in the Frontal, Temporal, and Occipital brain regions. Machine learning models, such as K-Nearest Neighbors, effectively classified ADHD and Control groups based on TBR with high accuracy. Additionally, the ADHD group demonstrated faster reaction times but made more errors on the Go/No-Go task, highlighting difficulties with sustained attention. These findings suggest that this approach holds promise for developing objective diagnostic tools for attention-related disorders. While some limitations exist, this study underscores the potential of integrating EEG with machine learning to create brain-computer interface (BCI) systems for assessing attention processes.}, }
@article {pmid41336846, year = {2025}, author = {Pahuja, S and Ivucic, G and Cai, S and De Silva, D and Li, H and Schultz, T}, title = {XAGnet: Cross-Attention Graph Network for Detecting Auditory Attention in Ear-EEG Signals.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11252872}, pmid = {41336846}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Attention/physiology ; *Signal Processing, Computer-Assisted ; *Ear/physiology ; Algorithms ; *Neural Networks, Computer ; Brain-Computer Interfaces ; }, abstract = {Auditory Attention Detection (AAD) is essential for developing advanced brain-computer interfaces including neuro-steered hearing technologies capable of functioning in complex auditory environments. In this study, we propose XAGnet, a novel method that leverages ear-centered EEG (ear-EEG) data to model both intra-ear and inter-ear neural dependencies for detection of auditory attention to one of the spatial locations. Specifically, Graph Convolutional Networks (GCNs) are applied separately to left and right ear-EEG signals to extract spatial features from each side for intra-ear interactions. A cross-attention mechanism is then introduced to model inter-ear interactions between the left and right ears. The attended features are combined for multi-class classification, with each class representing a speaker or a speaking location. We evaluate our method on a publicly available ear-EEG dataset, involving AAD tasks with four speakers. Experimental results demonstrate that XAGnet outperforms baseline models, highlighting the effectiveness of modeling both intra-ear and inter-ear dependencies in AAD tasks.}, }
@article {pmid41336840, year = {2025}, author = {Jahanjoo, A and Wei, Y and Haghi, M and Schorpf, P and TaheriNejad, N}, title = {Hybrid CNN-Transformer Model for Accurate Classification of Human Attention Levels Using Workplace EEG Data.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11251604}, pmid = {41336840}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; *Neural Networks, Computer ; *Workplace ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Fourier Analysis ; }, abstract = {Accurately detecting human attention levels is a key challenge in cognitive neuroscience, with broad application value in improving productivity. Although Electroencephalography (EEG) signals are often used to study cognitive states, most studies still rely on data collected in controlled laboratory environments. This paper collects EEG data from employees during their daily work using a commercial single-channel EEG headband, making attention detection closer to real-world applications and increasing its feasibility and promotion value. We propose a new classification method based on a multi-head attention transformer to identify six different attention levels. We first perform a Short-Time Fourier Transform (STFT) on the EEG signal. Subsequently, we constructed a transformer architecture to effectively model long-range dependencies and subtle pattern changes in EEG data using self-attention and stacked encoder layers. Experimental results show that our proposed model achieves 87.37% classification accuracy in the six-level attention classification task, outperforming traditional high-performance methods and demonstrating superior performance compared to existing similar approaches. This achievement not only verifies the potential of the transformer architecture in EEG attention level classification but also provides new possibilities for developing advanced tools in fields such as brain-computer interface (BCI) and cognitive monitoring.}, }
@article {pmid41336806, year = {2025}, author = {Quiles, V and Polo-Hortiguela, C and Soriano-Segura, P and Ortiz, M and Ianez, E and Azorin, JM}, title = {Design of an Asynchronous BMI with Interpretable Neural Networks for Exoskeleton Control: A Proof of Concept on Data Evolution and Scalability Over One Week.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11251541}, pmid = {41336806}, issn = {2694-0604}, mesh = {Humans ; *Exoskeleton Device ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Robotics ; Equipment Design ; }, abstract = {This paper presents a concept study of a week-long experimental protocol for controlling a lower-limb exoskeleton via a brain-machine interface. The system employed a neural network adapted from EEGNet that distinguishes motor imagery and resting states in a two-dimensional space under both static and movement conditions. Each day, the model was fine-tuned with that day's training data as well as data from previous days. Daily closed-loop asynchronous evaluations were carried out to assess real-time exoskeleton control performance. The results indicate steady improvements in system accuracy over the week, likely due to the cumulative integration of additional data, which enhanced the neural network-based approach to cognitive state classification in a multi-day setting.Clinical relevance-Incorporating repetitive robotic therapies in which the patient can actively engage in rehabilitation is a core goal of neurorehabilitation. Developing non-invasive brain-machine interfaces that enable an increasingly effective mind-robot connection is of great importance. This work outlines a protocol for creating a brain-machine interface controlled by motor imagery.}, }
@article {pmid41336805, year = {2025}, author = {Yuan, Z and Li, Y and Zhang, H and Liu, X and Li, S and Zhu, Y and Wang, H and Li, J and Wang, H}, title = {Decoding Hybrid EEG-fNIRS Upper Limb Motor Execution with Capsule Dynamic Graph Convolutional Neural Network.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11251587}, pmid = {41336805}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; Spectroscopy, Near-Infrared/methods ; *Neural Networks, Computer ; *Upper Extremity/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Convolutional Neural Networks ; }, abstract = {In this study, we proposed a capsule dynamic graph convolution network (EF-CapsDGCN) for accurate decoding of upper limb motor execution (ME) based on both electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. In EF-CapsDGCN, EEG/fNIRS features are extracted using the same convolutional architecture but different parameter settings. The extracted features from both modalities are then dynamically routed to capsules. Afterwards, the single-modality capsules are concatenated to form EEG-fNIRS multimodal capsules. Each capsule is treated as a graph node, and hidden feature representations are learned through dynamic graph convolution. Finally, after concatenating the original capsules with the learned hidden features, the combined features are passed through multi-head self-attention and then flattened to feed into a fully connected layer for classification. Compared to current state-of-the-art methods such as ANN, DeepConvNet, DNN, and EF-Net, the proposed method demonstrated superior classification performance on the multimodal EEG-fNIRS dataset HYGRIP. Furthermore, our model achieves at least 8% higher classification accuracy in multimodal EEG-fNIRS compared to single modality EEG/fNIRS. These results demonstrate the potential of capsule dynamic graph convolution for the multimodal fusion of EEG and fNIRS. The proposed model is promising for accurately decoding motor execution-based brain computer interfaces with EEG-fNIRS multiple signals. Overall, this study provides an effective solution for multimodal-BCI decoding.Clinical Relevance- This study demonstrates that integrating EEG and fNIRS signals via a capsule dynamic graph convolution network (EF-CapsDGCN) improves upper limb motor execution decoding accuracy by at least 8% compared to single-modality approaches, offering clinicians a more reliable tool for developing brain-computer interface systems to enhance rehabilitation or assistive device control in patients with motor impairments.}, }
@article {pmid41336758, year = {2025}, author = {Cueva, VM and Lotte, F and Bougrain, L and Rimbert, S}, title = {Quantifying Inter- and Intra-Subject Variability of Sensorimotor Desynchronization Induced by Median Nerve Stimulation and Motor Imagery for BCI.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254356}, pmid = {41336758}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Median Nerve/physiology ; Male ; *Imagination/physiology ; Adult ; Female ; Electric Stimulation ; *Sensorimotor Cortex/physiology ; }, abstract = {Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) enable users to control external devices by interpreting sensorimotor activity recorded via ElectroEncephaloGraphy (EEG). Median Nerve Stimulation (MNS) has recently emerged as a promising alternative motor task for BCI applications. However, intra- and inter-subject EEG variability remains a major challenge, affecting BCI system reliability. While variability is a well-known issue, its precise sources and impact on different EEG patterns remain unclear, with a lack of formal and quantitative studies of BCI variability. Thus, this study quantifies intra- and inter-subject variability in MNS-induced sensorimotor desynchronization (ERD) and compares it with that of MI. Results show that MI elicits stronger ERD with lower intra-subject variability, suggesting more consistent activation patterns, while inter-subject variability is similar between tasks. Additionally, the variability of classification accuracies based on Riemannian geometry exhibits a similar trend. These findings provide insights into EEG variability and its implications for BCI design. Identifying stable neural patterns could improve MI- and MNS-based BCIs, particularly for applications such as intraoperative awareness monitoring.}, }
@article {pmid41336716, year = {2025}, author = {Abid, U and Zulfiqar, O and Nazeer, H and Naseer, N and Bo, APL and Khan, H}, title = {fNIRS Based Comparative Study of Classifiers and Feature Selection Techniques for Finger Tapping.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11254285}, pmid = {41336716}, issn = {2694-0604}, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Fingers/physiology ; Algorithms ; Support Vector Machine ; Male ; Movement/physiology ; Machine Learning ; Adult ; Female ; }, abstract = {This study seeks to classify five-finger movements using machine learning (ML) algorithms. It also examines how feature optimization methods affect classification performance. The signals of functional near-infrared spectroscopy (fNIRS) were acquired from 20 healthy participants as they performed five different finger movements. The recorded signals are represented by a total of 17 spatial features such as kurtosis, variance, mean, skewness and others. The ML classifiers used in the beginning are Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost). Their performance parameters including precision, accuracy, F1-score, recall and processing time are recorded initially for the dataset comprising of all the features. Afterwards, three population-based metaheuristic algorithms Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to determine the top features from the dataset. The same ML classifiers are then applied to the selected feature datasets. Classification performance is significantly improved by optimized features, with GA and PSO outperforming ACO. SVM is beaten by XGBoost, while its accuracy (94.94%) is greatest when adopting GA-optimized features. The study also shows the role played by feature selection in improving the efficiency and accuracy of ML models in neuroimaging applications. It also suggests optimized classification pipelines for brain-computer interface systems.}, }
@article {pmid41336656, year = {2025}, author = {Memar, MO and Ziaei, N and Nazari, B and Yousefi, A}, title = {RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11252788}, pmid = {41336656}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods/instrumentation ; Neural Networks, Computer ; Brain-Computer Interfaces ; *Electrodes, Implanted ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {Intracranial electroencephalography (iEEG) is increasingly used for clinical and brain-computer interface applications due to its high spatial and temporal resolution. However, inter-subject variability in electrode implantation poses a challenge for developing generalized neural decoders. To address this, we introduce a novel decoder model that is robust to inter-subject electrode implantation variability. We call this model RISE-iEEG, which stands for Robust to Inter-Subject Electrode Implantation Variability iEEG Classifier. RISE-iEEG employs a deep neural network structure preceded by a participant-specific projection network. The projection network maps the neural data of individual participants onto a common low-dimensional space, compensating for the implantation variability. In other words, we developed an iEEG decoder model that can be applied across multiple participants' data without requiring the coordinates of electrode for each participant. The performance of RISE-iEEG across multiple datasets, including the Music Reconstruction dataset, and AJILE12 dataset, surpasses that of advanced iEEG decoder models such as HTNet and EEGNet. Our analysis shows that the performance of RISE-iEEG is about 7% higher than that of HTNet and EEGNet in terms of F1 score, with an average F1 score of 0.83, which is the highest result among the evaluation methods defined. Furthermore, Our analysis of the projection network weights reveals that the Superior Temporal and Postcentral lobes are key encoding nodes for the Music Reconstruction and AJILE12 datasets, which aligns with the primary physiological principles governing these regions. This model improves decoding accuracy while maintaining interpretability and generalization.}, }
@article {pmid41336644, year = {2025}, author = {Si, Y and Wang, Z and Zhao, X and Xu, T and Zhou, T and Hu, H}, title = {Sub-Group Partition Strategy for RSVP-based Collaborative Brain-Computer Interfaces.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11252828}, pmid = {41336644}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; Reproducibility of Results ; }, abstract = {Collaborative brain-computer interfaces (cBCIs) have demonstrated significant improvements in single-trial electroencephalogram (EEG) classification performance in rapid serial visual presentation (RSVP) tasks. However, it remains unclear how to effectively organize multiple collaborators into sub-groups to optimize system performance. This study introduces a novel sub-group partition strategy for RSVP-based cBCI systems. We first developed intra-individual and inter-individual neural response reproducibility (IINRR) as a metric to estimate subgroup capability in RSVP tasks. Based on this metric, we propose an IINRR-based partition strategy to optimize sub-group composition. Additionally, we introduce a metric called collaborative information processing rate (CIPR) to evaluate overall system performance. Our experiments verified the effectiveness of the proposed strategy on a public RSVP-based cBCI dataset. The results showed that our strategy consistently outperformed random partitioning in both within-session and cross-session scenarios, achieving higher classification performance and system efficiency. These findings suggest the strategy's potential for optimizing group mode in practical RSVP-based cBCI applications.}, }
@article {pmid41336643, year = {2025}, author = {Merino, EC and Sun, Q and Dauwe, I and Carrette, E and Meurs, A and Van Roost, D and Boon, P and Van Hulle, MM}, title = {Medial Wall's Potential in Enhancing Finger Movement Decoding from Electrocorticography (ECoG): A Single-Subject Pilot Study.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11252768}, pmid = {41336643}, issn = {2694-0604}, mesh = {Humans ; *Electrocorticography/methods ; *Fingers/physiology ; Pilot Projects ; Movement/physiology ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Male ; Adult ; }, abstract = {The next generation of motor brain-computer interfaces (BCIs) will likely benefit from integrating recordings from multiple motor-related brain regions. Among these is the medial wall, yet it remains relatively understudied in the case of finger movement decoding. Using electrocorticographic (ECoG) recordings from a subject implanted both over medial and lateral cortical areas, we first assessed the medial wall's potential for multiclass classification (5 fingers + rest). We achieved a six-class accuracy of 0.46, significantly above chance, with rest trials classified most accurately, followed by thumb movement trials. Several frequency features contributed to decoding, with Local Motor Potentials (LMP) being the most influential one, with distinctive activity already prior to movement onset, and power in the α (8-12 Hz) band aiding in decoding rest trials over finger movement trials. Next, we explored whether combining the best medial wall channel with lateral cortical channels could improve decoding performance. We found a significant accuracy improvement for most lateral channels (from an average of 0.36 to 0.42), except for the channel closest to the finger primary motor region, whose accuracy was already high (0.77). These findings highlight the medial wall's potential for motor decoding and its value as a target region for future motor BCIs, especially for individuals with impaired hand motor areas.}, }
@article {pmid41336630, year = {2025}, author = {Wen, Y and An, Y and Chu, M and Chen, S and Lu, X and Guo, H and Yu, J}, title = {Classification of Functional Near-Infrared Spectroscopy Based on Gramian Angular Difference Field and a Temporal-Spatial Feature Fusion Network.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11254538}, pmid = {41336630}, issn = {2694-0604}, mesh = {Spectroscopy, Near-Infrared/methods ; Humans ; Algorithms ; Deep Learning ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; }, abstract = {Functional near-infrared spectroscopy (fNIRS) is a non-invasive functional neuroimaging technique widely employed in brain-computer interface (BCI) research and diverse clinical applications. The key challenge in fNIRS applications lies in extracting nonlinear structures and complex patterns from one-dimensional time series data. Gramian angular difference field (GADF) transforms one-dimensional time series into two-dimensional images, providing effective feature representation for subsequent signal classification. However, most studies have not explored the combined effects of image features and time series features. In this paper, we propose a deep learning model, VisiTempNet, which integrates both time series and GADF image features in a temporal-spatial fusion approach. The model first performs convolution on time series data based on delayed hemodynamic responses to highlight key features. It then separates the feature extraction process into two parallel modules, and normalizes and fuses these features with learnable weights, assigning greater importance to the most relevant information for classification. Experimental results show that our model achieved an accuracy of 76.65±2.43% on the open access fNIRS2MW dataset, outperforming all baseline models. This validates the effectiveness of combining image and time series features and demonstrates the superiority of the proposed model.}, }
@article {pmid41336626, year = {2025}, author = {Bao, X and Xu, K and Zhu, J and Huang, H and Li, K and Huang, Q and Li, Y}, title = {Seasickness Alleviation based on a Mindfulness Brain-Computer Interface.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11254567}, pmid = {41336626}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; *Mindfulness ; Adult ; *Motion Sickness/therapy/prevention &amp; control/physiopathology ; Female ; Young Adult ; Attention ; }, abstract = {Seasickness is a common condition that negatively affects both the experience of passengers and the operating performance of maritime personnel. Techniques aimed at redirecting attention have been proposed to alleviate motion sickness symptoms; however, their effectiveness has not yet been rigorously verified, especially in maritime environments, which present unique challenges due to the prolonged and severe motion conditions. This research introduces a mindfulness brain-computer interface (BCI) specifically designed to redirect attention and alleviate seasickness. The system employs a single-channel headband to record prefrontal electroencephalography (EEG) signals, which are wirelessly transmitted to computing devices for real-time mindfulness assessments. Participants receive feedback in the form of mindfulness scores and audiovisual cues, facilitating a redirection of attention from physical discomfort. In maritime experiments with 43 participants across three sessions, 81.39% reported the BCI's effectiveness, and a substantial reduction in seasickness severity was observed using the Misery Scale (MISC). Together, our work presents the first wearable and nonpharmacological solution for alleviating seasickness, and opens up a brand-new application domain for BCIs.}, }
@article {pmid41336622, year = {2025}, author = {Ahmadi, K and Dong, L and Kok, RL and Findeisen, R}, title = {Gaussian Process-Based Surrogate Models for Optimizing Electrode Configurations in HD-tDCS.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254512}, pmid = {41336622}, issn = {2694-0604}, mesh = {*Transcranial Direct Current Stimulation/instrumentation/methods ; Humans ; Electrodes ; Normal Distribution ; Brain/physiology ; Computer Simulation ; Algorithms ; }, abstract = {High-definition transcranial direct current stimulation (HD-tDCS) is a promising noninvasive neurostimulation technique used in therapeutic applications and brain-machine interfaces. It delivers direct current via multiple scalp electrodes, generating targeted electrical fields to modulate specific brain areas. In the context of HD-tDCS, optimizing electrode placements is challenging due to the complexity of brain anatomy and the vast number of possible configurations. While simulation models enable model-based optimization, continuous electrode positioning is generally computationally prohibitive. We propose Gaussian Process (GP)-based framework for optimizing HD-tDCS, allowing continuous prediction of electric field distributions. Unlike traditional leadfield-based methods, which restrict electrode placement, our approach expands the search space for greater precision. We employ a Sparse Gaussian Process (SGP) approximation, optimized using Block-Coordinate Descent and Subset of Data techniques, to efficiently handle large datasets. Results demonstrate that the SGP-based model significantly enhanced focality for superficial and mid-brain regions, achieving performance comparable to leadfield-based methods for deep brain targets. Overall, this framework offers enhanced stimulation precision and flexibility, supporting the advancement of tDCS in research and clinical contexts.}, }
@article {pmid41336584, year = {2025}, author = {Caracci, V and Riccio, A and D'Ippolito, M and Galiotta, V and Quattrociocchi, I and Formisano, R and Cincotti, F and Toppi, J and Mattia, D}, title = {Impact of latency jitter correction on offline P300-based classification: a preliminary study for BCI applications in MCS patients.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11253369}, pmid = {41336584}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300 ; Male ; Female ; Adult ; Electroencephalography/methods ; *Persistent Vegetative State/physiopathology ; Algorithms ; Middle Aged ; }, abstract = {Disorders of Consciousness (DoC) are clinical conditions characterized by different levels of arousal and awareness, including coma, Unresponsive Wakefulness Syndrome and Minimally Conscious State (MCS). A Brain-Computer Interface (BCI) employs brain signals to establish a non-muscular outward channel, representing a key frontier in the clinical care of individuals in MCS, with high potential to enhance communication and quality of life. The P300-based BCIs, which use the P300 ERP as a control signal, are the most investigated to emulate communication in MCS. However, a reliable control by MCS patients of these BCIs still remains matter of question. One major challenge could be the across trials variability of P300 characteristics, possibly related to attentional fluctuations in this population. The trial-by-trial instability of the P300 peak latency, known as latency jitter, negatively impacts classification performance, and an approach to mitigating this issue involves template matching algorithms (e.g. the Adaptive Wavelet Filtering, AWF) which detect and realign the P300 latency at the single-trial level. This study investigated the offline classification performance using Stepwise Linear Discriminant Analysis (SWLDA) models trained with progressively larger training sets, to discriminate target from non-target stimuli during an active auditory oddball paradigm. Performance from raw and jitter-corrected data, collected from a control group and a group of patients diagnosed as MCS, were compared. Results highlighted the key role of latency jitter correction in the enhancement of performance and classification speed.Clinical Relevance- The findings suggest that jitter correction could improve real-world applicability of P300-BCI systems for individuals with DoC.}, }
@article {pmid41336583, year = {2025}, author = {Orlandi, M and Rapa, PM and Baracat, F and Benini, L and Donati, E and Benatti, S}, title = {Neural Strategies for Upper Limb Movements: Motor Unit Control during Dynamic Contractions at Increasing Speeds.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253409}, pmid = {41336583}, issn = {2694-0604}, mesh = {Humans ; Electromyography ; *Upper Extremity/physiology ; Male ; Movement/physiology ; *Muscle Contraction/physiology ; Adult ; *Motor Neurons/physiology ; Muscle, Skeletal/physiology ; Female ; Young Adult ; }, abstract = {Understanding motor unit (MU) behavior in dynamic movements remains a critical gap in neuro-rehabilitation, prosthetics, and human-machine interfaces (HMI). While machine learning applied to surface electromyography (sEMG) enables movement classification, it provides little insight into neural control, limiting the development of more precise and adaptive assistive technologies. Recent studies have demonstrated that MU activity can be accurately extracted using high-density sEMG decomposition under isometric conditions. However, extracting and tracking MUs during dynamic tasks remains challenging due to signal non-stationarity caused by changes in muscle length. This study investigates MU control in the forearm flexor muscles across different contraction velocities (5°/s, 10°/s, 20°/s) and force levels (15% and 25% of the maximum voluntary contraction [MVC]). We investigate whether increases in movement velocity are primarily achieved through MU recruitment strategies or by adjusting the discharge rates of already-recruited units. Our findings show that MU control in the upper limb follows a velocity-dependent modulation pattern (p-value < 0.05), favoring discharge rate adjustments over additional MUs recruitment at higher speeds. We also validate the feasibility of MU tracking in dynamic conditions, opening new opportunities for neurotechnology applications such as HMI.}, }
@article {pmid41336567, year = {2025}, author = {Roy Chowdhury, M and Ding, Y and Sen, S}, title = {SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253365}, pmid = {41336567}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Supervised Machine Learning ; *Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Neural Networks, Computer ; }, abstract = {Electroencephalography (EEG) plays a crucial role in brain-computer interfaces (BCIs) and neurological diagnostics, but its real-world deployment faces challenges due to noise artifacts, missing data, and high annotation costs. We introduce SSL-SE-EEG, a framework that integrates Self-Supervised Learning (SSL) with Squeeze-and-Excitation Networks (SE-Nets) to enhance feature extraction, improve noise robustness, and reduce reliance on labeled data. Unlike conventional EEG processing techniques, SSL-SE-EEG transforms EEG signals into structured 2D image representations, suitable for deep learning. Experimental validation on MindBigData, TUH-AB, SEED-IV and BCI-IV datasets demonstrates state-of-the-art accuracy (91% in MindBigData, 85% in TUH-AB), making it well-suited for real-time BCI applications. By enabling low-power, scalable EEG processing, SSL-SE-EEG presents a promising solution for biomedical signal analysis, neural engineering, and next-generation BCIs. The code is available at https://github.com/roycmeghna/SSL_SE_EEG_EMBC25.}, }
@article {pmid41336566, year = {2025}, author = {Guttmann-Flury, E and Wei, Y and Zhao, S}, title = {Automatic Blink-Based Bad EEG channels Detection for BCI Applications.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253420}, pmid = {41336566}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Blinking/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Artifacts ; Adult ; Male ; }, abstract = {In Brain-Computer Interface (BCI) applications, noise presents a persistent challenge, often compromising the quality of EEG signals essential for accurate data interpretation. This paper focuses on optimizing the signal-to-noise ratio (SNR) to improve BCI performance, with channel selection being a key method for achieving this enhancement. The Eye-Bci multimodal dataset is used to address the issue of detecting and eliminating faulty EEG channels caused by non-biological artifacts, such as malfunctioning electrodes and power line interference. The core of this research is the automatic detection of problematic channels through the Adaptive Blink-Correction and DeDrifting (ABCD) algorithm. This method utilizes blink propagation patterns to identify channels affected by artifacts or malfunctions. Additionally, segmented SNR topographies and source localization plots are employed to illustrate the impact of channel removal by comparing Left and Right hand grasp Motor Imagery (MI). Classification accuracy further supports the value of the ABCD algorithm, reaching an average classification accuracy of 93.81% [74.81%; 98.76%] (confidence interval at 95% confidence level) across 31 subjects (63 sessions), significantly surpassing traditional methods such as Independent Component Analysis (ICA) (79.29% [57.41%; 92.89%]) and Artifact Subspace Reconstruction (ASR) (84.05% [62.88%; 95.31%]). These results underscore the critical role of channel selection and the potential of using blink patterns for detecting bad EEG channels, offering valuable insights for improving real-time or offline BCI systems by reducing noise and enhancing signal quality.}, }
@article {pmid41336553, year = {2025}, author = {Sen, O and Khalifa, A and Chatterjee, B}, title = {High-Speed Neural Signal Inferencing for Handwritten Character Recognition on a Portable Hardware Device.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11253375}, pmid = {41336553}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Handwriting ; *Signal Processing, Computer-Assisted/instrumentation ; Algorithms ; }, abstract = {Brain-computer interfaces (BCIs) hold immense potential in assisting individuals with severe motor and communication disabilities by enabling neural signal-based activity recognition, such as handwriting. This study presents the very first implementation of neural signal inference on a portable hardware device, facilitating efficient handwritten character recognition on resource-constrained platforms. Neural signals from a publicly available dataset are processed into neural spike-event data, facilitating the classification of 31 handwritten characters on an NVIDIA Jetson TX2. To enhance model generalization and mitigate overfitting, random noise injection and time-shifting-based data augmentation techniques are applied. The proposed approach utilizes EfficientNetB0 with neural spikes, and achieves 99.17% test accuracy, significantly outperforming previous model results. During high-speed inference, EfficientNetB0 achieved a Word Error Rate (WER) of 0.96% and a Character Error Rate (CER) of 0.2%, with a character decoding latency of 37.5 milliseconds on the Jetson TX2 while processing 100 sentences used in daily life. These results validate the feasibility of accurate high-speed neural decoding on portable edge hardware, highlighting the impact of lightweight machine learning models in BCI applications.}, }
@article {pmid41336530, year = {2025}, author = {Li, S and Yang, M and Sun, J and Sun, J and Yu, G and Lin, L and Meng, J and Xu, M}, title = {EEG features and suitable decoding algorithm of RSVP-based brain-computer interface in continuous scenes.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11251802}, pmid = {41336530}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Algorithms ; Male ; Signal Processing, Computer-Assisted ; Adult ; Female ; Evoked Potentials ; Young Adult ; }, abstract = {Brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) hold significant value for achieving robust target detection through the integration of human and machine. RSVP in continuous scenes presents video materials and is thus much closer to real-world applications, which greatly exceeds traditional discrete-scene RSVP in terms of practicality. However, the similarities and differences in electroencephalography (EEG) features between continuous and discrete scenes have not yet been clearly clarified. And there is a lack of research on decoding algorithms that are more suitable for continuous scenes, which seriously hinders the development of continuous-scene target detection. To solve these problems, this study designed a comparative experiment based on RSVP paradigm in continuous and discrete scenes. Event-related potential (ERP), event-related spectral perturbation (ERSP), and inter-trial coherence (ITC) were used to investigate EEG features induced by distinct scenes. Further, this study used sliding hierarchical discriminant component analysis (sHDCA), shrinkage discriminative canonical pattern matching (SKDCPM) and attention-based temporal convolutional network (ATCNet) to implement target/non-target trial classification. Consequently, continuous scenes exhibited fewer induced ERP components, a shorter latency of P300, and reduced neural oscillation activities in alpha and beta1 bands over the occipital region within 0~0.2s. As for classification, traditional machine learning algorithms obtained significantly lower accuracy in continuous scenes. While ATCNet achieved the best and same level of accuracy in both scenes, indicating its suitability for decoding continuous-scene RSVP. The results contributed to develop more practical RSVP-BCI target detection systems.}, }
@article {pmid41336489, year = {2025}, author = {Song, Z and Wu, S and Zhou, T and Wang, Y}, title = {Extracting Preserved Neural Latent Dynamics Across Tasks using Convolutional Transformer-based Variational Autoendecoder.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11251780}, pmid = {41336489}, issn = {2694-0604}, mesh = {Rats ; Animals ; Algorithms ; *Neurons/physiology ; *Neural Networks, Computer ; Brain-Computer Interfaces ; }, abstract = {Understanding how neural systems drive behavior is a fundamental goal in neuroscience. Numerous studies have demonstrated that the activity of large neural populations is often governed by low-dimensional neural dynamics. While much of the current research has focused on extracting informative and interpretable latent dynamics from individual motor tasks, it remains unclear whether these dynamics are preserved across different motor tasks. This question is particularly critical, as prior experience with a related task can facilitate faster learning in a new task. In this paper, we propose a Convolutional Transformer-based Variational Autoencoder (Conformer-VAE) to extract preserved neural latent dynamics across tasks by leveraging the rich spatiotemporal patterns in neural activity. We validate our approach using neural recordings from a rat, which first performed a one-lever pressing task (old task) and subsequently a two-lever discrimination task (new task). By projecting the inferred latent dynamics from both tasks onto a common 2D PCA plane, our results demonstrate that Conformer-VAE effectively captures preserved neural dynamics across tasks, outperforming baseline methods. Moreover, these preserved dynamics enable faster decoder training for the new task by transferring the neural-to-movement mapping learned from the old task. This capability facilitates seamless real-time task switching, offering promising applications for brain-machine interface systems.Clinical Relevance-This work facilitates faster adaptation in brain-machine interfaces by preserving neural dynamics across tasks, offering potential benefits for neuroprosthetics and motor rehabilitation in patients with motor impairments.}, }
@article {pmid41336487, year = {2025}, author = {Iacomi, F and Tiberio, P and Tonon, T and Perugini, S and Farabbi, A and Barbieri, R and Mainardi, L}, title = {Validation of a Novel Protocol for Whole-Sentence Imagined Speech Acquisition: Advancing Brain-Computer Interface Applications.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11251720}, pmid = {41336487}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; Male ; Female ; *Imagination/physiology ; Young Adult ; Adult ; Electroencephalography/methods ; }, abstract = {This study aims to validate a novel protocol for whole-sentence imagined speech acquisition, building upon and addressing limitations of a previous single-word acquisition protocol. Eight participants (gender-balanced, mean age 21.3±6 years) were recruited for this study. Participant attention indices, and session variations were evaluated across multiple sessions. The protocol successfully maintains participant engagement while effectively stimulating language imagination processes. The neurophysiological findings, particularly the activation patterns in specific frequency bands and cortical regions, align well with established literature on imagined speech processing. The enhanced delta band activation observed during second sessions, associated with memory mechanisms, provides valuable insight into the cognitive processes involved in repeated imagined speech tasks. These findings contribute to the broader field of Brain Computer Interface (BCI) development and suggest potential applications in clinical settings, particularly for individuals with speech impairments.}, }
@article {pmid41336480, year = {2025}, author = {Ramiotis, G and Mania, K}, title = {Enhancing EEG Classification for Motor Imagery Control of a VR Game based on Deep Learning Techniques on Small Datasets.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11251707}, pmid = {41336480}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Algorithms ; *Virtual Reality ; Neural Networks, Computer ; *Video Games ; Signal Processing, Computer-Assisted ; *Imagination ; Movement ; }, abstract = {Motor imagery-based Brain-Computer Interfaces (BCIs) suffer from limited accuracy when the EEG dataset is recorded from naive BCI users due to noisy components. Neural networks capture more robust representations of EEG features, but require large amount of data which is challenging to collect, due to long motor imagery training sessions. On the other hand, linear- and Riemann-based machine learning algorithms achieve above chance-level accuracy on small scale datasets, but, performance degrades on noisy datasets. To address this issue, we implemented a Wasserstein Generative Adversarial Network (WGAN) for data augmentation to prevent overfitting for the deep classifier, while reaching training convergence faster than existing models. For classification, we developed a Convolutional Neural Network (CNN) to eliminate noisy components caused by BCI illiteracy and extract robust temporal representations of EEG features. To evaluate our system, we designed a VR maze game utilizing the proposed BCI system to translate the EEG signal into movement for a playable character. We achieve increased accuracy, compared to conventional machine learning models, with minimal overfitting, on our own dataset, recorded from 16 naive BCI users.}, }
@article {pmid41336461, year = {2025}, author = {Soriano-Segura, P and Quiles, V and Ortiz, M and Ianez, E and Azorin, JM}, title = {Effect of Electrode Reduction on the Error-Related Potential Detection During the Start of the Gait.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11251757}, pmid = {41336461}, issn = {2694-0604}, mesh = {Humans ; *Gait/physiology ; Electrodes ; *Brain-Computer Interfaces ; Male ; Adult ; }, abstract = {Self-correcting Brain-Machine Interfaces based on Motor Imagery (MI-BMIs) using Error-Related Potentials (ErrP) are a promising approach to improve the accuracy of the system and enhancing their feasibility for the neurorehabilitation of patients with spinal cord injuries (SCI). However, these technologies require extensive preparation time, which shortens the therapy session and causes fatigue in the patient even before starting, potentially reducing the therapy's effectiveness. To address this issue, this study evaluates five electrode configurations to determine the impact of electrode reduction on ErrP detection at the beginning of the gait with a lower-limb exoskeleton. The results indicate that reducing the number of electrodes does not significantly affect detection performance but does reduce false positive rates (FPR). Therefore, these findings support the feasibility of using a reduced electrode configuration of 11 electrodes to enhance BMI usability while maintaining detection reliability.Clinical relevance- The long preparation time required for MI-BMI therapies poses a significant challenge. As a result, patients may begin therapy fatigued or experience rapid exhaustion, limiting their engagement in the rehabilitation process. To address this issue, this study explores electrode reduction for ErrP detection as a strategy to minimize preparation time, enhancing the feasibility of MI-BMIs for clinical applications.}, }
@article {pmid41336460, year = {2025}, author = {Wang, X and Lai, YH and Chen, F}, title = {EEG-based Syllable-Level Voice Activity Detection.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11251715}, pmid = {41336460}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; *Voice/physiology ; Adult ; Female ; *Speech/physiology ; Signal Processing, Computer-Assisted ; Young Adult ; }, abstract = {Speech brain-computer interface (BCI), as an ideal means to achieve direct communication between the brain and the outside world, has become a research area of great interest. This work studied syllable-level voice activity detection (VAD) based on electroencephalogram (EEG) signals to help identify the presence or absence of speech-related EEG activity. We utilized EEG signals from 10 participants performing auditory (listening to stimuli) and speech (pronouncing syllables) tasks to measure brain activity. Speech-Based VAD was employed to label the auditory stimuli and voice recordings, generating corresponding brain activity labels, which were then used to classify resting and active (listening or pronouncing) EEG states, respectively. The experimental results showed that the EEG-based VAD model achieved accuracies of 90.93% and 69.57% for the speech production and auditory speech tasks, respectively. The accuracies were lower in the cross-subject classification, with accuracies of 72.63% and 61.15% for the two tasks. Additionally, the experiment further compared the model's performance under different time window conditions, but no significant correlation was found between window length and classification accuracy. This study provided new insights into the application of EEG based speech decoding, particularly in future self-paced speech BCI applications.}, }
@article {pmid41336448, year = {2025}, author = {Liu, G and Yan, Y and Cai, J and Cheok, AD and Qi Wu, E and Song, A}, title = {A More Rational and Efficient Kalman Filter Design for Motor Brain-Machine Interfaces.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11251710}, pmid = {41336448}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; Algorithms ; }, abstract = {The Kalman Filter has long been one of the most widely used models in motor brain-machine interface (BMI) research due to its noise handling capabilities and real-time adaptability. However, as a model originally developed for traditional control systems, its underlying assumptions of Markov property and the designs of observation models may not always hold true in the context of BMI applications, potentially leading to oversimplifications. This paper examines the limitations that arise when applying the Kalman Filter to BMI, and proposes the Dilated Kalman Filter, which performs Gaussian multiplication between state transition distribution and observation-mapped state distribution in state space, thereby combining observation noise with BMI-specific observation model noise, and consequently incorporates historical information from both states and observations. The proposed method improves the accuracy of Kalman Filter while significantly enhancing computational efficiency, particularly when processing data from large numbers of neurons.}, }
@article {pmid41336444, year = {2025}, author = {Lin, L and Lin, J and Pu, Q and Zhou, H and Wang, H and Sun, J and Luo, R and Yu, G and Meng, L and He, F and Meng, J and Xu, M}, title = {Regularization SAME Method can Enhance the Performance of SSVEP-BCI with Very Weak Stimulation.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11251722}, pmid = {41336444}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; Algorithms ; Male ; Signal-To-Noise Ratio ; Photic Stimulation ; Adult ; Signal Processing, Computer-Assisted ; Female ; }, abstract = {The steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) has gained considerable attention due to its high information transfer rate (ITR) and stable performance. However, the comfort of SSVEP-BCI still needs to be improved, as strong flickering stimuli cause users' visual fatigue. Reducing the pixel density of the stimuli has been demonstrated as an effective method to improve its comfort. However, the signal-to-noise rate (SNR) of the SSVEP signal induced by such very weak stimuli is low, posing challenges for their decoding. Therefore, it is necessary to develop suitable strategy for better decoding the SSVEP induced by very weak stimuli. This study employed the source aliasing matrix estimation (SAME) method to enlarge the dataset and improve decoding accuracy for SSVEP induced by low-pixel density stimuli. Additionally, this study further optimized the SAME with a regularization method to achieve much higher decoding performance. A SSVEP experiment was designed with various pixel densities (100%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10% and 1%) and frequencies (low: 7Hz, 11Hz, and 15Hz; mid-to-high: 23Hz, 31Hz, and 39Hz) to verify our methods. The results indicated SAME significantly improved the classification accuracy compared to traditional method without the SAME, especially under very weak stimulation conditions (pixel densities ≤ 50%), with the maximum increase reaching 8.6%. Besides, regularization SAME further yielded a significant enhancement, achieved maximum improvements of 4.29% compared to SAME. The regularization SAME proposed in this study significantly improves SSVEP decoding performance under low-pixel density stimuli, paving the way for the development of comfortable and effective SSVEP-BCI.}, }
@article {pmid41336422, year = {2025}, author = {Li, H and Zhang, M and Karkkainen, T and Meng, Z}, title = {Single Trial Classification of per-stimulus EEG between Different Speed Accuracy Tradeoffs Instruction.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11254002}, pmid = {41336422}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Signal Processing, Computer-Assisted ; Adult ; Deep Learning ; Neural Networks, Computer ; Young Adult ; }, abstract = {The speed-accuracy tradeoff represents a cornerstone concept in cognitive processing, highlighting the inherent trade-off between decision-making speed and accuracy. Patients may have different speed-accuracy strategies during their neurologic consultation due to differences in understanding of instructions or increased diagnostic time. Despite extensive investigations into the neural mechanisms underpinning speed-accuracy trade-off (SAT), the classification of neural data to differentiate between distinct SAT strategies remains largely unexplored. This study bridges this critical gap by implementing a deep learning framework to classify single-trial EEG signals based on participants' instructed response strategies-either prioritizing speed or accuracy and leveraging a dataset from 20 participants engaged in a mirror-image judgment task. The data underwent preprocessing and were subsequently transformed using continuous wavelet transformation to extract time-frequency features. Employing a channel-stacking technique, we organized the EEG data into RGB-like images, which were then input into a RegNet convolutional neural network for classification. Ten-fold cross-validation results demonstrated that the occipital region achieved the highest classification accuracy (85.37%), followed by the parietal (82.97%), frontal (80.46%), and central regions (78.57%). This study not only validates the feasibility of single-trial EEG classification in distinguishing between speed and accuracy strategies but also highlights its potential applications in adaptive brain-computer interfaces and cognitive neuroscience research.Clinical Relevance- This study provides a novel method for real-time identification of cognitive strategies (speed vs. accuracy prioritization) via EEG, offering clinicians a tool to tailor neurofeedback or rehabilitation protocols based on individualized neural signatures.}, }
@article {pmid41336408, year = {2025}, author = {Hu, G and Zeng, F and Tang, H and Zhao, Y and Zhang, X}, title = {A Study of Brain-Computer Interface Recognition Performance Crossing Action Observation Paradigms.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11254040}, pmid = {41336408}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; Male ; Adult ; Female ; Evoked Potentials, Visual/physiology ; Movement/physiology ; }, abstract = {Action observation-based brain-computer interface (AO-BCI) could induce visual motor imagery through biological motion while relying on its movement frequency to stimulate steady-state visual evoked potential. This hybrid BCI with dual-brain-region activation offers significant potential for stroke rehabilitation. Since varying AO paradigms are employed in the rehabilitation of different limb movements, a limited training dataset can compromise recognition performance. Thus, this study tried to investigate the BCI performance crossing different AO paradigms for the first time. Three AO paradigms, each containing four actions, were designed to establish an online BCI system. Task discriminant component analysis was utilized to analyze the online and offline EEG data. Three training schemes were developed to construct spatial filters including target session (TS) data, source session (SS) data, and a combination of both. Results indicated that the paradigm content significantly affected the recognition performance (F=7.65, p=0.0039). The recognition accuracies of the four actions for each AO paradigm were 71.86%, 89.71%, and 82.71%, respectively. Among the three training schemes, the combined TS and SS data approach notably enhanced recognition accuracy for the AO paradigm with poor performance using TS data alone (p=0.0319). This study demonstrated that EEG data from existing AO paradigms can be used to construct training sets for new paradigms. And combining a small amount of data from the new paradigm could improve the recognition performance. Future research should focus on developing data calibration methods specific to cross-AO paradigms to further enhance recognition accuracy. This work will provide valuable insights for advancing AO-BCI applications in rehabilitation.}, }
@article {pmid41336362, year = {2025}, author = {McDorman, RA and Raj Thapa, B and Kim, J and Bae, J}, title = {Transfer Learning in EEG-based Reinforcement Learning Brain Machine Interfaces via Q-learning Kernel Temporal Differences.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11253000}, pmid = {41336362}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Machine Learning ; Male ; Adult ; Algorithms ; Female ; Young Adult ; Movement ; }, abstract = {Reinforcement learning based brain machine interfaces (RLBMIs) is an emerging technology with many possible real-time applications. Transfer learning (TL) has proved beneficial as it can improve performance of machine learning algorithms by reusing learned knowledge from similar tasks. However, its application in BMIs has mainly focused on supervised learning approaches. In this study, we investigate the effect of TL in RLBMIs to decode freewill movement related intentions using multichannel scalp electroencephalogram (EEG). We applied TL strategies to Q-learning Kernel Temporal Difference (Q-KTD), which is an algorithm to estimate the action value function, Q, by a nonlinear function approximator using kernel methods. A publicly available EEG dataset recorded while healthy adult participants conduct a key pressing task was used to decode premovement (before movement onset) and movement intention (after movement onset). Differently from most cue-based tasks, participants had freewill to choose the key being pressed, providing unique neural dynamics for decoding. TL was applied between and within subjects to decode the movement related intentions. Significant increase on success rates (p < 0.01) were observed in 96% cases. The success rate increases in each case ranged from 1.39 to 10.69%. These results support the use of TL as an effective way to improve the efficiency of RL-based neural decoder's learning.Clinical Relevance- The improved performance of the neural decoder using transfer learning provides efficient modeling strategy of RLBMIs that can assist patients with neurological disorders.}, }
@article {pmid41336341, year = {2025}, author = {Germano, D and Ronca, V and Capotorto, R and Di Flumeri, G and Borghini, G and Giorgi, A and Babiloni, F and Arico, P}, title = {Towards the Correction of Covariate Shift in EEG-Based Passive Brain-Computer Interfaces for Out-of-Lab Applications.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11252974}, pmid = {41336341}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {The increasing adoption of wearable EEG technology is enabling the development of passive Brain-Computer Interface (pBCI) systems for real-world applications, in the near future, such as Industry 5.0. However, one major challenge in classifying electroencephalographic (EEG) signals in these settings is covariate shift, which occurs when the distribution of the data changes between training and testing sessions due to variations in EEG headset positioning. This study investigates the effectiveness of a linear transformation approach to mitigate the negative effect of covariate shift. Simulations were conducted by using different shift conditions (i.e. deviation of the headset position from the original one), to evaluate (i) the performance of the transformation function used for mitigating the covariate shift occurrence and (ii) the importance that the change of reference and/or channels has on the classification performance. Results show that normalizing covariate shift-affected data (i.e., target) using shift-free data as a template (i.e., source) helps mitigate the negative impact of covariate shift, leading to improved classification performanceThe accuracy loss drops from 14% to 6% in the worst configuration and from 5% to 4% in the best configuration. This improvement is more pronounced when the shift is larger, i.e., when both the reference and channels change between the control dataset and the test dataset. These findings have significant implications for the development of robust and reliable pBCI models for out-of-the-lab contexts.}, }
@article {pmid41336339, year = {2025}, author = {Hu, C and Liu, Q and Luo, J and Lu, Y and Jiang, N and Li, G and Huai, Y and Li, Y}, title = {Can ICA-Based Artifact Removal Affect Deep Learning Decoding Accuracy? Yes!.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253033}, pmid = {41336339}, issn = {2694-0604}, mesh = {Humans ; *Deep Learning ; *Artifacts ; *Electroencephalography/methods ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Male ; Female ; Adult ; Stroke/physiopathology ; }, abstract = {Regarding brain-computer interfaces (BCIs), the effectiveness of Independent Component Analysis (ICA) for artifact removal in traditional machine learning-based EEG decoding has been widely implemented. However, its utility in deep learning-based EEG decoding remains understudied. This paper investigated the impact of ICA-based artifact removal on the accuracy of deep learning models for decoding motor imagery and motor execution from EEG signals in short time windows. We employed an ICA-based artifact removal approach named ERASE for automatic artifact removal and evaluated the performance of three decoding approaches: CNN, LSTM, and CEBRA. Compared to before artifact removal, The F1-score improved by averages of 27.90% (CNN), 22.06% (LSTM), and 28.38% (CEBRA) after artifacts removal for motor execution tasks in healthy subjects. For motor imagery tasks in stroke patients,The F1-score improved by averages of 18.90% (CNN), 21.04% (LSTM), and 25.84% (CEBRA). Topographic maps and manifold visualizations further confirmed that ICA enhances the spatial specificity and interpretability of neural signals. These findings suggest that ICA-based artifact removal is a valuable preprocessing step for deep learning-based EEG decoding, particularly in scenarios with significant artifact contamination, offering potential benefits for clinical applications such as stroke rehabilitation.}, }
@article {pmid41336297, year = {2025}, author = {Ding, Y and Lee, JH and Zhang, S and Luo, T and Guan, C}, title = {Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11254148}, pmid = {41336297}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Neural Networks, Computer ; ROC Curve ; }, abstract = {Learning the spatial topology of electroencephalogram (EEG) channels and their temporal dynamics is crucial for decoding attention states. This paper introduces EEG-PatchFormer, a transformer-based deep learning framework designed specifically for EEG attention classification in Brain-Computer Interface (BCI) applications. By integrating a Temporal CNN for frequency-based EEG feature extraction, a pointwise CNN for feature enhancement, and Spatial and Temporal Patching modules for organizing features into spatial-temporal patches, EEG-PatchFormer jointly learns spatial-temporal information from EEG data. Leveraging the global learning capabilities of the self-attention mechanism, it captures essential features across brain regions over time, thereby enhancing EEG data decoding performance. Demonstrating superior performance, EEG-PatchFormer surpasses existing benchmarks in accuracy, area under the ROC curve (AUC), and macro-F1 score on a public cognitive attention dataset. The code can be found via: https://github.com/yi-ding-cs/EEG-PatchFormer.}, }
@article {pmid41336280, year = {2025}, author = {Hong, J and Rao, P and Wang, W and Chen, S and Najafizadeh, L}, title = {ChatBCI-4-ALS: A High-Performance, LLM-Driven, Intent-Based BCI Communication System for Individuals with ALS.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11253329}, pmid = {41336280}, issn = {2694-0604}, mesh = {*Amyotrophic Lateral Sclerosis/physiopathology ; Humans ; *Brain-Computer Interfaces ; Algorithms ; *Communication Devices for People with Disabilities ; Electroencephalography ; Event-Related Potentials, P300 ; Language ; }, abstract = {Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that leads to significant motor and speech impairments, increasing the need for alternative means of communication to support quality of life. P300 speller brain computer interfaces (BCIs) have shown promise in facilitating non-muscular communication by detecting P300 event-related potentials (ERPs) in response to visual stimuli. However, these systems are generally slow and can not fully address the communication needs of ALS patients, specially, when the primary goal is to convey intent with minimal cognitive load. In this paper, we present ChatBCI-4-ALS, the first intent-based BCI communication system designed for individuals with ALS. ChatBCI-4-ALS leverages large language models (LLMs) and employs a dynamic flash algorithm to enhance typing speed, and enable efficient communication of the user's intent beyond exact lexical matches. Additionally, we introduce new semantic-based quantitative performance metrics to evaluate the effectiveness of intent-based communication. Results from online experiments suggest that ChatBCI-4-ALS achieves record-breaking average spelling speed of 23.87 char/min (with the best case scenario of 42.16 char/min), and a best information transfer rate (ITR) of 128.85 bits/min, marking an advancement in P300 BCI-based communication systems.}, }
@article {pmid41336226, year = {2025}, author = {Kaseler, RL and Andreasen Struijk, LNS}, title = {Harmonic Component Analysis: A novel training-free and asynchronous BCI classification method.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253522}, pmid = {41336226}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Adult ; Algorithms ; Male ; Evoked Potentials, Visual/physiology ; Signal Processing, Computer-Assisted ; Female ; }, abstract = {Assistive technologies can provide people with locked-in syndrome independence and improve their quality of life. However, existing brain-computer interfaces (BCI) can be unreliable and require excessive training. Therefore, we investigate the possibility of a training-free BCI that can provide asynchronous and online control of assistive robotic technologies. We propose the harmonic component analysis (HCA), a new training-free classifier for signals with known harmonic characteristics, such as steady-state visually evoked potentials. To validate the HCA, it is compared to the well-known canonical correlation analysis (CCA), using an offline data set of 10 healthy participants who performed cue trials with 16 SSVEP-targets. The HCA achieved better performance than a three-component CCA with up to 74% lower computational cost. For asynchronous control, the HCA achieved a detection accuracy of 85% with an average activation time of 1.6s, against 77% after an average of 1.7s for the CCA. For continuous activation, the HCA achieved a true positive rate of 65% with a false positive rate of 0. 59% from 2 s after cue onset until 5 s after, while the CCA achieved a true positive rate of 59% with a false positive rate of 0. 27%. Thus, the HCA is shown to be a well-suited SSVEP-classifier for systems that require asynchronous classification without the need for a calibration or training-session.}, }
@article {pmid41336218, year = {2025}, author = {Ciferri, M and Ferrante, M and Toschi, N}, title = {Optimal Transport and Contrastive Learning for Brain Decoding of Musical Perception.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253498}, pmid = {41336218}, issn = {2694-0604}, mesh = {*Music ; Humans ; Magnetic Resonance Imaging ; *Brain/physiology/diagnostic imaging ; *Auditory Perception/physiology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Algorithms ; Male ; *Learning ; }, abstract = {Brain decoding aims to reconstruct external stimuli from brain activity, providing insights into the neural representation of cognitive experiences. Music decoding from functional magnetic resonance imaging (fMRI) is particularly challenging due to the complexity of auditory processing and the temporal limitations of fMRI signals. In this study, we introduce a novel decoding framework that improves the alignment between fMRI activity and latent musical representations extracted using a pre-trained multimodal model (CLAP). We propose a dual-loss approach combining Optimal Transport and Contrastive Learning to enhance feature mapping and retrieval accuracy. The first loss ensures structural consistency between brain-predicted and true musical embeddings, while the contrastive loss refines the embedding space by maximizing similarities between corresponding pairs and minimizing non-correspondences. Using fMRI data from five subjects listening to music tracks from the GTZAN dataset, our method achieves improved decoding performance, surpassing traditional regression-based approaches from 22.1% top-1 accuracy to 29.3%. These results highlight the potential of integrating Optimal Transport and Contrastive Learning to improve brain decoding performance, paving the way for extending the approach to different sensory domains and applications in Brain-Computer Interfaces (BCI).Clinical relevance- This study could have clinical implications for understanding auditory processing disorders and developing neurorehabilitation strategies. By elucidating how the brain encodes complex auditory stimuli, this approach may contribute to BCI applications for speech and music perception restoration in individuals with hearing impairments or neurological conditions affecting auditory cognition.}, }
@article {pmid41336204, year = {2025}, author = {Padfield, N and Turk, S and Mujahid, K and Camilleri, T and Peng, Y and Camilleri, K}, title = {A Spatio-Spectral Analysis of Decoding Imagined Speech from the Idle State.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253510}, pmid = {41336204}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Speech/physiology ; *Imagination/physiology ; Brain-Computer Interfaces ; Male ; Adult ; Female ; }, abstract = {Studies into speech imagery (SI) classification from electroencephalogram (EEG) data have generally focused on distinguishing imagined words from each other, but accurate discrimination from the idle state, when the user is relaxed, is also necessary for asynchronous brain-computer interfaces (BCIs). In this study, frequency bands and scalp regions most important for distinguishing SI from the idle state were identified and related to underlying neural processes. Power spectral density (PSD) features were extracted from each channel, and a statistical analysis of the features, as well as a classification analysis involving six classifiers, was carried out. The parietal region was identified as the most important scalp region, whilst the delta, theta, and gamma bands were the most important frequency bands. Furthermore, the importance of the alpha band, and of the temporal, frontal-temporal, frontal-central, and parietal regions varied significantly between the SI vs Idle and SI vs SI classification problems, highlighting the importance of including the idle state in SI classification studies.Clinical Relevance-This study identifies frequency bands and scalp regions that are significantly important for the SI vs Idle classification problem, which is important for asynchronous SI BCIs.}, }
@article {pmid41336201, year = {2025}, author = {Wimpff, M and Aristimunha, B and Chevallier, S and Yang, B}, title = {Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253543}, pmid = {41336201}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; Longitudinal Studies ; Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Signal Processing, Computer-Assisted ; Deep Learning ; Adult ; Female ; Algorithms ; Movement ; }, abstract = {This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) decoding within a causal setting involving a large user group and multiple sessions per participant. We are the first to explore such strategies across a large user group, as longitudinal adaptation is typically studied in the single-subject setting with a single adaptation strategy, which limits the ability to generalize findings. First, we examine the impact of different fine-tuning approaches on decoder performance and stability. Building on this, we integrate online test-time adaptation (OTTA) to adapt the model during deployment, complementing the effects of prior fine-tuning. Our findings demonstrate that fine-tuning that successively builds on prior subject-specific information improves both performance and stability, while OTTA effectively adapts the model to evolving data distributions across consecutive sessions, enabling calibration-free operation. These results offer valuable insights and recommendations for future research in longitudinal online MI decoding and highlight the importance of combining domain adaptation strategies for improving BCI performance in real-world applications.Clinical Relevance-Our investigation enables more stable and efficient long-term motor imagery decoding, which is critical for neurorehabilitation and assistive technologies.}, }
@article {pmid41336191, year = {2025}, author = {Gonzalez-Mitjans, A and Salinas-Medina, A and Toussaint, PJ and Valdes-Sosa, P and Evans, A}, title = {AI-Driven Neurodiagnostics: A Scalable Framework for EEG Anomaly Detection Using a Distributed-Delay Neural Mass Model.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253534}, pmid = {41336191}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; Epilepsy/diagnosis/physiopathology ; Machine Learning ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; Neural Networks, Computer ; }, abstract = {The integration of biophysically grounded neural simulations with Artificial Intelligence (AI) has the potential to transform clinical neurodiagnostics by overcoming the inherent challenges of limited pathological EEG datasets. We present a novel AI-driven framework that leverages a Distributed-Delay Neural Mass Model (DD-NMM) to generate synthetic EEG signals replicating both healthy and pathological brain states. Through systematic parameter tuning and domain-specific data augmentation, we enrich the diversity of simulated signals, enabling robust anomaly detection using machine learning techniques. Our approach integrates supervised classification and unsupervised one-class anomaly detection, achieving over 95% accuracy in synthetic tests and over 89% when applied to empirical EEG data from epilepsy patients and healthy volunteers. By providing an engineered solution that bridges computational neuroscience with AI, this framework enhances early seizure detection, adaptive neurofeedback, and brain-computer interface applications. Our results demonstrate that theory-driven simulation, combined with state-of-the-art machine learning, can address critical gaps in medical AI, significantly advancing clinical neuroengineering.Clinical relevance- This study provides a scalable and interpretable AI-driven method for EEG anomaly detection, which can support clinicians in identifying seizure patterns and other neurological disorders with high accuracy. The integration of computational neuroscience with AI-based diagnostics offers a potential pathway for early intervention and personalized neurotherapeutic strategies.}, }
@article {pmid41336140, year = {2025}, author = {Huang, H and Chen, Z and You, Q and Pan, J and Xiao, J}, title = {Emotion Decoding and Consciousness Evaluation in patients with DOC through EEG Microstate analysis.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253041}, pmid = {41336140}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Consciousness Disorders/physiopathology/diagnosis ; Male ; Female ; Adult ; Brain-Computer Interfaces ; *Consciousness ; Middle Aged ; Signal Processing, Computer-Assisted ; }, abstract = {Clinicians commonly employ the Coma Recovery Scale-Revised (CRS-R) as a standard tool for assessing patients with disorders of consciousness (DOC). However, the assessment is easily affected by subjective judgment, and patients with DOC are usually unable to provide adequate behavioral responses. Previous studies have indicated that emotion recognition-based brain-computer interface (BCI) can assist in the assessment of DOC, yet they lack more specific and quantitative indicators. This study is the first to apply electroencephalography (EEG) microstates for emotion recognition in patients with DOC. Specifically, EEG microstates were utilized to capture crucial spatio-temporal features of EEG signals, simplifying the rapidly changing EEG signals into a series of prototype topoplots. In this study, EEG data was recorded from 9 patients with DOC and 11 healthy volunteers. Among healthy participants, our system achieved an average classification accuracy of 94.16%, effectively demonstrating its success in eliciting and recognizing emotions. When applied to patients with DOC, the system yielded an average classification accuracy of 77.94%. The results of this study indicate that EEG microstate dynamics are associated with conscious processing in patients with DOC. However, further validation in a larger patient dataset is required to confirm these preliminary findings.}, }
@article {pmid41336101, year = {2025}, author = {Wang, X and Wang, L and Ding, Y and Chen, F}, title = {EEG-based Auditory Attention Switch Detection with Multi-scale Gated Attention and Multi-task Learning based Hierarchical Spatiotemporal Networks.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11253070}, pmid = {41336101}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; *Attention/physiology ; Humans ; Signal Processing, Computer-Assisted ; Algorithms ; Signal-To-Noise Ratio ; *Auditory Perception/physiology ; }, abstract = {Auditory attention switch detection (AASD) poses significant challenges for adaptive neurotechnologies, particularly under electroencephalogram (EEG) with low signal-to-noise ratios (SNRs). However, the performance of existing methods is limited due to insufficient feature discriminability and high detection delay. To solve the problem, this paper proposes a Hierarchical Spatiotemporal Network (HSTN) for detecting auditory attention switch from EEG signals. The model employs a hierarchical spatiotemporal encoder to extract spatiotemporal features of EEG signals, integrates short-term transient and long-term dependency information through a multi-scale gated attention mechanism, and synchronously optimizes auditory attention switch detection and auditory attention decoding tasks via a multi-task joint training strategy. Experimental results demonstrate that HSTN significantly outperforms baseline models in both auditory attention switch detection (AASD F1=0.89, accuracy 88.6%) and auditory attention decoding tasks (AAD accuracy 89.3%), with superior model parameter efficiency and inference time. Ablation experiments further validate the critical roles of multi-task learning, gated attention, and multi-scale convolutions. This study provides an efficient solution for auditory attention switch detection in complex auditory scenarios.Clinical Relevance-The study confirms that spatiotemporal feature encoding combined with multi-task joint training significantly enhances performance in EEG attention switch detection, providing a practical technical framework for enabling dynamic sound source enhancement in intelligent hearing aids and auditory brain-computer interface systems.}, }
@article {pmid41336083, year = {2025}, author = {Parashiva, PK and Gangadharan K, S and Vinod, AP}, title = {EEGScaler: A Deep Learning Network to Scale EEG Electrode and Samples for Hand Motor Imagery Speed Decoding.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11251649}, pmid = {41336083}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/instrumentation ; *Deep Learning ; Brain-Computer Interfaces ; Electrodes ; *Hand/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Movement ; Algorithms ; Male ; }, abstract = {Motor Imagery (MI)-based Brain-Computer Interface (MI-BCI) systems induce neuroplasticity, promoting rehabilitation in stroke patients. Existing MI-BCI systems decode bilateral MI actions from Electroencephalogram (EEG) data to facilitate motor recovery. However, such systems offer limited degrees of freedom. Decoding kinematics information, such as movement speed can enhance control and provide a more natural interface with the environment. Decoding speed-related information from unilateral MI tasks is challenging due to the significant spatial overlap of neuronal sources and the inherently low spatial resolution of EEG. To address this, we propose EEGScaler, an end-to-end deep learning framework designed to decode slow v/s fast MI tasks by adaptively scaling EEG samples and electrodes with high discriminative value. EEGScaler leverages a Multi-Layer Perceptron (MLP) network to assign scale factors to both samples and electrodes. Spatiotemporal features are subsequently extracted using temporal and depth-wise convolution filters. The model is pre-trained on subject-independent data to learn filter weights, while subject-specific fine-tuning further optimizes the MLP-based scaling mechanism. The EEGScaler model performance is evaluated on 14 healthy subjects' data recorded while performing slow v/s fast unilateral MI tasks. The proposed model achieves an average cross-validated accuracy of 65. 98% for decoding fast v/s slow MI speed tasks, outperforming existing methods by approximately 6%. The subject-specific scaling of samples and electrodes using an end-to-end deep learning model for speed from unilateral MI tasks is novel. By effectively decoding movement speed, EEGScaler enhances the degree of freedom in MI-BCI systems, paving the way for more intuitive and efficient neurorehabilitation applications.Clinical Relevance- This advancement has the potential to improve motor rehabilitation strategies by enabling more precise and adaptive BCI-driven therapy tailored to individual recovery needs.}, }
@article {pmid41336067, year = {2025}, author = {Ahmadi, H and Mesin, L}, title = {Decoding Visual Imagination and Perception from EEG via Topomap Sequences.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11251641}, pmid = {41336067}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Visual Perception/physiology ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Adult ; Male ; }, abstract = {We propose a Topomap-based EEG decoding framework for distinguishing pictorial Imagination from Perception. By converting each trial's EEG signals into dense sequences of scalp voltage maps at short time intervals, our approach captures crucial spatiotemporal patterns that standard methods may overlook. We then apply a CNN with squeeze-and-excitation (SE) blocks to these Topomap "frames," enabling direct learning of both spatial topographies and rapid temporal fluctuations. Despite using only one trial per subject to simulate a data-scarce scenario, our model achieves 95.1% accuracy under a leave-one-subject-out (LOSO) cross-validation scheme. Results indicate clear neural distinctions between Imagination and Perception states, reflecting focused brain-region engagement during visual recall. In addition to confirming the viability of Topomaps as EEG feature representations, this study underscores their potential generalizability. We anticipate future extensions incorporating other modalities (orthographic, audio) and more advanced deep architectures will further expand the utility and robustness of this approach for brain-computer interface (BCI) applications.Clinical relevance- This framework offers a robust method for accurately distinguishing visual Imagination from Perception, even in data-scarce scenarios. It holds potential for enhancing diagnostic tools in cognitive disorders and refining BCI applications in clinical settings.}, }
@article {pmid41336065, year = {2025}, author = {Perley, AS and Coleman, TP}, title = {A Dynamic Mutual Information Measure of Phase Amplitude Coupling.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11251622}, pmid = {41336065}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; Algorithms ; *Signal Processing, Computer-Assisted ; Linear Models ; Sleep/physiology ; }, abstract = {Phase-amplitude coupling (PAC) is a fundamental neural phenomenon in which the phase of a slow oscillation modulates the amplitude of a faster oscillation. PAC has been implicated in various cognitive and clinical conditions, including Parkinson's disease, epilepsy, and depression. Traditional methods for quantifying PAC compute a single summary statistic over an entire time series, limiting their ability to capture dynamic fluctuations. Growing interest in time-varying PAC has led to methods that rely on windowed time-series analysis, but these approaches struggle to track rapid changes in coupling at single-sample resolution. To address this limitation, we propose a novel dynamic mutual information measure of PAC, leveraging a state-space modeling approach based on a Gamma generalized linear model (GLM). By introducing a Gauss-Markov process on the regression weights, our method enables dynamic, interpretable PAC estimation at each time point. We validate our approach using synthetic phase-amplitude coupled signals with time-varying coupling coefficients and demonstrate superior performance in smoothly tracking PAC over time and distinguishing coupled from uncoupled states. Additionally, we apply our technique to sleep EEG data, successfully identifying PAC during sleep spindles, which may serve as a biomarker for neurophysiological conditions such as Alzheimer's disease. Our findings suggest that this dynamic PAC measure is a powerful tool for neuroscientific and clinical research, with potential applications in real-time brain-computer interfaces and neurostimulation protocols.Clinical relevanceThis work demonstrates a new technique for quantifying time-varying electrophysiological coupling. This may allow for understanding transient neural dynamics in disease states and may help more robustly inform electrical stimulation protocols for patients with neurodegenerative disorders.}, }
@article {pmid41335991, year = {2025}, author = {Li, H and Xu, G and Zhang, S and Xie, J and Han, C and Wu, Q and Zhang, S}, title = {Signal extension with SeU-net for boosting the decoding performance of short-time SSVEP-based brain-computer interfaces.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11253264}, pmid = {41335991}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; Electroencephalography/methods ; Algorithms ; }, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (SSVEP-BCIs) have greatly benefited the lives of patients. However, existing SSVEP recognition methods exhibit poor performance on short SSVEP signals. SSVEP recognition accuracy heavily depends on signal length, which increases as the signal length. From a novel data perspective, this study proposes a signal extension method called SeU-net without requiring calibration data from the target subject to improve the recognition performance of calibration-free methods for short-time SSVEP signals. SeU-net employs LSTM and contrastive learning to enhance feature extraction, converting signals from sample space to feature space, and then back to the sample space to realize signal extension. SeU-net is designed to focus only on signal extension in the temporal domain, without subject-specific feature extraction operations, resulting in strong cross-subject signal extension performance. The extensive experiments demonstrate that SeU-net significantly enhances the decoding performance of calibration-free methods for short-time SSVEP signals. By enabling more accurate decoding with shorter SSVEP signals, SeU-net holds the potential to advance the practical application of high-speed SSVEP-BCIs further.}, }
@article {pmid41335988, year = {2025}, author = {Kulwa, F and Sarwatt, DS and Asogbon, MG and Huang, J and Khushaba, RN and Oyemakinde, TT and Li, G and Samuel, OW and Li, H and Li, Y}, title = {A Novel Levant's Differentiator-Based Descriptor for EEG-Based Motor Intent Decoding.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11253249}, pmid = {41335988}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Stroke/physiopathology ; Male ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation ; Algorithms ; Movement ; Female ; }, abstract = {Motor intent (MI)-based brain-computer interfaces (BCIs) have been extensively studied to improve the performance and clinical realization of assistive robots for motor recovery in stroke patients. However, challenges arise in their low decoding performance. This can be attributed to the low spatial resolution and signal-to-noise ratio of electroencephalography (EEG), particularly in accurately deciphering hand movements, which reduces classification performance. Therefore, we have developed a novel feature extraction technique that exploits Levant's differentiators to extract distinct patterns in EEG signals and employs symmetric positive definite matrices (SPD) to effectively leverage the spatial-temporal properties of the EEG signal. Results from nine post-stroke patients and fifteen normal subjects showed an improved decoding accuracy of 99.16±0.64% and 99.30±0.69%, respectively in classifying twenty-four hand motor intents, significantly outperforming existing related methods. Thus, the proposed technique has the potential to greatly enhance the reliability and effectiveness of EEG-based control systems for post-stroke rehabilitation.Clinical Relevance- The outcome of this study can lead to better control of rehabilitation robots and improve the recovery speed of the stroke patients.}, }
@article {pmid41335965, year = {2025}, author = {Thomas, A and Cho, Y and Zhao, H and Carlson, T}, title = {MI-CES: An explainable weak labelling approach to example selection for Motor Imagery BCI classification.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253265}, pmid = {41335965}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; Electroencephalography/methods ; Algorithms ; Movement/physiology ; }, abstract = {Motor Imagery (MI) Brain Computer Interfaces (BCI) can be used to control assistive devices such as wheelchairs. These systems require a training period to get both the user and the machine to learn and adapt to each other, achieving an acceptable control accuracy. Previous systems have discovered that providing a form of feedback to the user about what the system thinks the user is thinking can increase the effect of training and increase both the control accuracy of the user and the classification accuracy of the BCI system. However, if this feedback is 'incorrect' due to the classifier behind the BCI system having a poor accuracy, this may cause the user to 'incorrectly' adapt to the feedback, providing the system with further poor examples of MI. In this paper, we propose MI-CES, an explainable 'example selection' approach based on the neuro-physiological principle of MI. We found that while using 2 classification techniques, we achieved a statistically significant increase in classification accuracy across 3 datasets that were comprised of both multi-participant and multi-session recordings.}, }
@article {pmid41335962, year = {2025}, author = {Buda, C and Gambosi, B and Toschi, N and Astolfi, L}, title = {A Deep Learning Framework for Multi-Source EEG Localization.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253252}, pmid = {41335962}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; Algorithms ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; }, abstract = {Electroencephalography (EEG) provides millisecond-scale resolution of neural activity but struggles to accurately localize multiple concurrent sources, especially in spatially close regions. Classical linear inverse methods, such as MNE, sLORETA, and dSPM, address the ill-posed inverse problem through regularization but often exhibit a "single-source bias", suppressing smaller generators. This paper introduces a deep learning framework designed to robustly identify multiple sources of activity from short EEG segments. Our approach leverages a realistic simulation pipeline that systematically generates EEG recordings from physiologically plausible, distributed current sources. We train a convolutional neural network (ConvNET) on thousands of such simulations, ensuring generalization by using a forward model distinct from that of classical solvers, thereby minimizing the risk of an "inverse crime". We evaluate our ConvNet against nine well-established inverse solvers (MNE, dSPM, sLORETA, eLORETA, LORETA, LAURA, and depth-weighted variants). Benchmarking across multiple synthetic test scenarios demonstrates that our method consistently outperforms traditional solvers, particularly in resolving closely spaced sources, while maintaining or improving accuracy for single-source cases. These results highlight the potential of deep learning to overcome biases in EEG source imaging, offering a more reliable approach for multi-source localization.Clinical relevance- By leveraging deep learning, our approach improves localization accuracy, particularly in closely spaced or deep brain sources, potentially enhancing presurgical planning, brain-computer interfaces, and real-time neurofeed-back applications.}, }
@article {pmid41335905, year = {2025}, author = {Ding, Y and Wang, X and Chen, F}, title = {Enhancing Cross-subject Auditory Attention Detection with Contrastive Learning for EEG Feature Refinement.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11252602}, pmid = {41335905}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Attention/physiology ; Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; *Machine Learning ; }, abstract = {Electroencephalography (EEG)-based auditory attention detection (AAD) plays a crucial role in recent auditory brain-computer interface applications. However, the performance of AAD models in cross-subject tasks tends to be significantly degraded due to the excessive differences in EEG features across subjects. To address this challenge, we proposed a novel framework, AAD-ContrastNet, that incorporated contrastive learning to refine the temporal features from EEG and reduce the variance of EEG features across subjects. AAD-ContrastNet consists of four main components: (a) an attention-based EEG encoder; (b) a contrastive-learning-based EEG encoder; (c) a feature refinement module; and (d) a classifier. T-SNE visualization results show that combining contrastive learning with cross-attention feature refinement significantly improves the generalization of extracted EEG features. By comparing with SOTA models (i.e., DenseNet-3D and DARNet), we validate the significant effect of AAD-ContrastNet in improving cross-subject decoding accuracy, highlighting its potential in enhancing the robustness and generalization of EEG-based AAD systems.Clinical Relevance- This study demonstrates the potential of contrastive learning in mitigating cross-subject performance degradation, providing a solid foundation for applying generalized auditory brain-computer interface systems.}, }
@article {pmid41335889, year = {2025}, author = {Sun, Y and Zhang, Z and Qi, Q and Li, X and Sun, J and Zhang, K and Zhuang, J and Chen, X and Gao, X}, title = {Beyond Frequency: Leveraging Spatial Features in SSVEP-Based Brain-Computer Interfaces with Visual Animations.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11254745}, pmid = {41335889}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; Female ; Electroencephalography/methods ; Adult ; Young Adult ; }, abstract = {Current research on steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) predominantly focuses on utilizing the frequency- and phase-locking characteristics of SSVEP for encoding purposes. In this study, we propose an innovative paradigm wherein SSVEP serves as a marker, integrated with different types of motion animations to identify distinct neural processing pathways associated with these animations. This approach enables the classification of SSVEP-based BCIs without relying on frequency features. We designed six distinct animations corresponding to six behaviors commonly observed in daily life. Each animation was tagged with a uniform 6 Hz stimulus frequency, forming a six-target classification task. Offline testing was conducted with 10 participants. Despite identical frequency components, significant differences in spatial distribution corresponding to the animations were observed, likely due to the behavioral variations in the animations. Classification analysis demonstrated an accuracy of 0.93 within a 6-second window, validating the practical feasibility of this approach. This paradigm offers a novel direction for the advancement of SSVEP-based BCIs, potentially enabling the integration of multi-sensory information.}, }
@article {pmid41335877, year = {2025}, author = {Lee, SH and Lee, SH and Lee, SW}, title = {EEG-Translator: A Cross-Modality Framework for Subject-Specific EEG and Voice Reconstruction from Imagined Speech.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254826}, pmid = {41335877}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Speech/physiology ; *Voice/physiology ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; }, abstract = {Non-invasive brain-computer interfaces (BCIs) offer the potential to enable communication for individuals with speech impairments by decoding neural signals through speech-related electroencephalography (EEG) signals. Beyond domain-specific speech EEG decoding, generative approaches that enable cross-domain reconstruction are needed to enhance the overall system performance. Here, we propose a cross-modal EEG translation framework that reconstructs overt speech EEG from imagined speech EEG, for subject-specific speech synthesis. Our approach integrates a diffusion-based model with GAN training to enhance cross-domain EEG reconstruction by preserving both EEG class information and its time-frequency domain properties. In classification tasks, the reconstructed EEG improves class decoding accuracy by 6.2% over the original imagined EEG. Additionally, EEG reconstruction was trained not only on the EEG signal itself but also by incorporating spectrogram-based features, leveraging a fusion of spatial and spectral losses to preserve EEG properties. Beyond EEG reconstruction, category-wise analysis across a multi-speech paradigm dataset reveals variations in decoding performance, offering linguistic insights crucial for the advancement of speech BCI systems. Our findings highlight the potential of diffusion-driven EEG translation in speech BCIs, emphasizing the importance of integrating deep learning methodologies with linguistic insights for improved neural signal reconstruction and interpretation.}, }
@article {pmid41335876, year = {2025}, author = {Wang, A and Zhang, Y and Zhan, G and Zhang, L and Kang, X}, title = {Flexible-Rigid Bonding of Silicon Based Neural Interface for Deep Brain LFP Recording.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11254814}, pmid = {41335876}, issn = {2694-0604}, mesh = {*Silicon/chemistry ; *Brain-Computer Interfaces ; *Brain/physiology ; Electrodes, Implanted ; Humans ; Equipment Design ; Animals ; }, abstract = {Microfabricated silicon neural probes have become the dominant technology in the field of implantable brain-computer interfaces. Mechanical bonding, electroplating, template printing, flip-chip bonding, and welding are prevalent methods for electrode packaging in preparation; however, these techniques often present challenges such as complex processes, elevated temperatures, or increased electrode thickness. We proposed a novel flexible-rigid bonding method for the silicon based neural interface, which markedly reduced the bonding volume compared with the traditional board to board connector. It simplified the assembly process of silicon probes, increased the electrode integration density and facilitated the assembly of the probe and flexible cable. This approach enables the flexible implantation of silicon electrodes in deep brain regions for recording neural signals.}, }
@article {pmid41335863, year = {2025}, author = {Li, M and Yao, Y and Dong, B and Wang, K and Yu, H and Xu, M and Ming, D}, title = {A Novel Approach to Improve SSVEP-BCI Performance Through Neurofeedback Training.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11254803}, pmid = {41335863}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Neurofeedback/methods ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography ; Female ; }, abstract = {Brain-Computer interface (BCI), which translates neural activities into commands for external devices, holds significant promise for clinical rehabilitation and assisted movement for individuals with motor disabilities. Among various BCI paradigms, the steady-state visual evoked potential (SSVEP) based BCI garnered considerable attention due to its relatively stable and high-speed communication capabilities. However, a notable portion of the population, referred to as BCI illiteracy, struggles to effectively control BCI systems due to their inability to generate or modulate the neural patterns required for interaction. To address this issue, we proposed a user-centered approach using neurofeedback training (NFT) to improve individual's performance on SSVEP-BCI. As a result, after a five-day training period, significant improvements in SSVEP-BCI performance were only observed in the training group rather than the control group without training. Notably, some subjects initially determined as BCI-illiterate also gained effective control of the BCI system after training. Further analysis revealed that the improvement of SSVEP-BCI performance had a close link with increased power and inter-trial phase coherence of the SSVEP response, indicating that NFT successfully strengthened the user's task-related neural responses. These findings highlight the potential of NFT as a user-centered intervention to improve BCI control performance, offering a promising pathway to address BCI illiteracy and promote the broader application of BCI systems.Clinical Relevance- This study proposes an effective approach to enhancing the controllability of SSVEP-BCI systems, addressing the critical issue of individual control limitations. The developed method demonstrates significant clinical potential for promoting SSVEP-BCI applications, particularly in facilitating communication and device control for patients with severe motor impairments, such as amyotrophic lateral sclerosis (ALS) and locked-in syndrome (LIS).}, }
@article {pmid41335841, year = {2025}, author = {Ramos, J and Silva, S and Marques, B and Pais-Vieira, M and Stevenson, A and Bras, S}, title = {Empowering Accessibility: Human-Centered Approach to a BCI Home Control for Impaired People.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11253641}, pmid = {41335841}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; User-Computer Interface ; *Locked-In Syndrome/physiopathology/rehabilitation ; Self-Help Devices ; Male ; }, abstract = {Brain-Computer Interfaces (BCIs) have shown significant potential for individuals with motor impairments, either by improving physiotherapy treatments or by enabling to perform simple tasks, autonomously. However, much of this progress remains confined to controlled laboratory environments. This study aims to develop a BCI-controlled interface, for real-life scenario, tailored to allow individuals with Locked-In Syndrome (LIS) to interact with their home environment. To ensure system usability, a Human-Centered Design (HCD) approach was adopted prioritizing end-user needs. The interface control system was tested using a BITalino for Electroencephalogram (EEG) acquisition. Preliminary results demonstrated that professionals recognize the system's potential, highlighting the importance of real-time feedback, and design simplicity features to minimize user fatigue and improve control accuracy.Clinical Relevance-This interdisciplinary methodology bridges the gap between assistive technologies and the user needs, promoting autonomy and communication with a BCI-controlled interface for real home interaction.}, }
@article {pmid41335820, year = {2025}, author = {Luo, R and Zheng, C and Ding, R and Shi, T and Li, D and Xiao, X and Huang, Y and Xu, M and Ming, D}, title = {Boosting Spatial Properties of Single-Flicker SSVEP via Laplacian Electrodes.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-4}, doi = {10.1109/EMBC58623.2025.11253662}, pmid = {41335820}, issn = {2694-0604}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/instrumentation/methods ; Electrodes ; Male ; Adult ; Female ; Brain-Computer Interfaces ; Photic Stimulation ; }, abstract = {Spatially-encoded steady-state visual evoked potentials (SSVEP) acquired by electroencephalography (EEG) are extensively utilized in brain-computer interface and neuroscience research. However, EEG suffers from low spatial resolution due to volume conduction effects. To tackle this problem, this study developed a bipolar concentric ring electrode (CRE) for collecting high-resolution Laplacian EEG (LEEG), which was validated through a tank simulation experiment and a human experiment. The tank simulation experiment confirmed its high spatial resolution, and the results showed that LEEG acquired by CRE achieved 2.35 times greater spatial attenuation than EEG. Meanwhile, the human experiment designed a single-flicker SSVEP paradigm with stimuli positioned at different visual field orientations. The results revealed that LEEG had lower inter-channel similarity than EEG, with average coefficients of 0.63 for EEG and 0.46 for LEEG (p<0.01). Topographical analysis further demonstrated that CRE sharpened the spatial features of spatially-encoded SSVEPs, and indicated a clear visual hemifield dominance phenomenon. This study effectively enhances the spatial properties of SSVEP and holds promise for advancing high-resolution LEEG.}, }
@article {pmid41335778, year = {2025}, author = {Nguyen, MTD and Zhu, HY and Burnham, M and Sun, H and Zhu, Q and Nguyen, V and Brown, S and Wu, E and Jin, C and Lin, CT}, title = {Auditory Steady-State Responses and the Effects of Interaural Decoherence and Presence of Vision.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254438}, pmid = {41335778}, issn = {2694-0604}, mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; *Vision, Ocular/physiology ; *Auditory Perception/physiology ; Acoustic Stimulation ; *Evoked Potentials, Auditory/physiology ; Electroencephalography/methods ; }, abstract = {The Auditory Steady-State Response (ASSR) is a periodic neural response used to detect speech and hearing loss, and it is also used as a Brain-Computer Interface paradigm. Our paper identifies two key factors that impact the quality and consistency of the ASSR. First is the interaural decoherence, the timing and intensity of sounds arriving at two ears produced by speakers in reverberant environments. Second is the impact of vision on modulating auditory perception and spatial attention, which could potentially influence the neural synchronisation of the response. To demonstrate this, we conducted an experiment on 26 healthy participants to examine the effects of interaural decoherence, by comparing the frequency responses between speakers and earphones, and the presence of vision, by comparing being blindfolded and non-blindfolded, on the ASSR. This study demonstrates that earphones elicit more consistent and reliable ASSRs compared to speakers, emphasising the detrimental effects of interaural decoherence from speaker-based sound delivery on ASSRs. Furthermore, we found that the response is more biased to one side in the absence of vision compared to the presence of vision. This study highlights the importance of using rooms with anechoic properties or less reverberation when using speakers to ensure the consistency and clarity of the response. Future ASSR paradigms should also consider fixating on a target to elicit less bias in ASSR and more accurate spatial features.}, }
@article {pmid41335768, year = {2025}, author = {Morales-Magallon, F and Bojorges-Valdez, E}, title = {Intended and Non-Volitional Knee Joint Movements Elicit Distinct Functional Brain Networks.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-6}, doi = {10.1109/EMBC58623.2025.11254479}, pmid = {41335768}, issn = {2694-0604}, mesh = {Humans ; *Knee Joint/physiology ; Movement/physiology ; Electroencephalography/methods ; *Brain/physiology ; Male ; Adult ; Brain-Computer Interfaces ; *Nerve Net/physiology ; Female ; }, abstract = {Motor execution induces significant alterations in the dynamics of electroencephalography (EEG) signals, which are crucial for assessing rehabilitation, brain plasticity, and brain-computer interface (BCI) applications. While traditional analyses have primarily focused on power spectral changes, recent advancements incorporate non-linear indices to uncover previously undetected characteristics of brain dynamics.Network analysis provides a powerful framework to examine the structural organization and communication within complex systems composed of interconnected neural units. This study investigates the structural properties functional networks formed during both active and resting states under different knee joint flexion tasks. These movements were performed under three physical demand conditions, including an assisted, non-volitional movement.Functional networks were constructed from EEG analysis over 16 electrodes for the μ, β, and γ frequency bands, and key network metrics were estimated, including input and output node degree centrality, clustering coefficient, and betweenness centrality. Results indicate that motor execution leads to a reduction in overall network connectivity while enhancing communication efficiency. Additionally, networks in the γ and μ bands were more involved in voluntary movement, whereas the β band played a predominant role in assisted movements. The spatial distribution of electrodes contributing to these networks differed between voluntary and assisted conditions, suggesting distinct underlying neural mechanisms rather than a simple linear modulation of connectivity.}, }
@article {pmid41335749, year = {2025}, author = {Sun, Y and You, Z and Sun, D and Huang, Y and Wu, Q and Pan, J}, title = {DC-FFNet: Dual Channel Feature Fusion Network for Real-Time Asynchronous Signal Analysis.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254486}, pmid = {41335749}, issn = {2694-0604}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Electroencephalography/methods ; Algorithms ; Computer Systems ; }, abstract = {Steady-state visual evoked potentials (SSVEP) are widely used in brain-computer interface (BCI) systems due to their high accuracy and fast response performance and are commonly used for the control of a variety of external devices. However, existing SSVEP signal classification methods still face the problems of insufficient recognition accuracy and poor real-time performance in complex dynamic scenes. Therefore, this study proposes a new SSVEP signal classification model Dual Channel Feature Fusion Network (DC-FFNet), and constructs a real-time control framework by combining it with an asynchronous control mechanism. DC-FFNet is a novel model for SSVEP signal classification based on a dual channel architecture. It incorporates a multi-head self-attention mechanism to capture global features, enhance local features, and fuse multimodal information, significantly improving classification accuracy. The classification accuracy of DC-FFNet reaches 91.80% on the SSVEP_SANDIEGO Dataset and 90.93% on the Self-recorded Dataset, which both exceed the existing models. In addition, the real-time framework that incorporates an asynchronous control mechanism effectively reduces the response time and improves the information transfer rate of the system (up to 128.66 bits/min). This research is expected to provide an efficient and flexible SSVEP signal processing scheme for multi-device asynchronous collaborative control systems assisting people with disabilities, balancing performance and real-time, which is of great significance for BCI technology.}, }
@article {pmid41335716, year = {2025}, author = {Turk, S and Padfield, N and Mujahid, K and Camilleri, T and Camilleri, K}, title = {Word-specific properties affect classification performance in Brain Computer Interfaces for decoding imagined speech from EEG.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11254906}, pmid = {41335716}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Speech/physiology ; *Imagination/physiology ; Male ; Adult ; Female ; Young Adult ; *Brain/physiology ; }, abstract = {Decoding imagined speech from brain signals has become one of the most significant fields for BCI applications. One of the current challenges that researchers face is an insufficient classification performance for real-world applications. In this study, we investigate for the first time the effect of word-specific properties known to modulate brain signals on classification performance. We chose 16 word prompts that vary in age of acquisition (AoA) and word frequency, two word-specific properties known to modulate speech processing, and investigated their classification performance for speech imagery (SI) trials compared to the idle state using a random forest classifier and 10-fold cross-validation. We found highly significant effects of AoA, word frequency and their interaction on classification performance. Our results yield evidence that the word frequency and AoA of word prompts used in SI paradigms significantly influence the classification accuracy in a BCI application when SI trials are compared to the idle state.Relevance - Choosing word prompts with optimal properties can significantly improve classification performance in BCI applications.}, }
@article {pmid41335707, year = {2025}, author = {Ronca, V and Di Flumeri, G and Lungarini, L and Capotorto, R and Germano, D and Giorgi, A and Borghini, G and Babiloni, F and Arico, P}, title = {A Novel Multi-Stage Algorithm for Real-Time Detection and Correction of Ocular Artifacts in EEG: A Calibration-Free Approach.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-7}, doi = {10.1109/EMBC58623.2025.11254864}, pmid = {41335707}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Artifacts ; *Signal Processing, Computer-Assisted ; Calibration ; Electrooculography/methods ; Male ; Adult ; Female ; }, abstract = {Ocular artifacts, particularly blinks, significantly affect the integrity of electroencephalographic (EEG) signals, posing a challenge for real-time applications. Traditional correction methods often require a calibration phase or additional electrooculogram (EOG) channels, limiting their applicability in mobile and real-world settings. This study presents a novel detection and correction method, designed for online ocular artifact correction without the need for prior calibration: the CFo-CLEAN. The proposed method integrates an Enhanced Adaptive Data-driven Algorithm (eADA) for real-time identification and correction of ocular artifacts directly from EEG signals. Unlike conventional approaches, this implementation adapts dynamically to ongoing EEG variations, enhancing flexibility and performance. The study evaluates the CFo-CLEAN method using EEG data recorded from 38 participants during real-world driving scenarios. Performance comparisons were conducted against established correction techniques, including Independent Component Analysis (ICA), regression-based methods, and subspace reconstruction approaches. The evaluation considered both artifact removal efficiency and EEG signal preservation across different experimental conditions. Results demonstrated that the method effectively reduced ocular artifact contamination while preserving neurophysiological content. Specifically, two implementations of the method, utilizing 60-second and 90-second time windows, were analyzed, revealing that longer windows provided superior EEG signal preservation, particularly in higher frequency bands. These findings validate the effectiveness of the CFo-CLEAN method for real-time applications, making it a valuable tool for brain-computer interfaces (BCIs), neuroergonomics, and cognitive state monitoring. By avoiding the need for a calibration phase and incorporating adaptive processing, this method represents a significant advancement in real-time EEG artifact correction, facilitating its deployment in dynamic, real-world environments.}, }
@article {pmid41335679, year = {2025}, author = {Farabbi, A and Ballabio, F and Rossi, M and Palmisciano, AC and Antonello, N and Trojaniello, D and Ongarello, T and Cerveri, P and Mainardi, L}, title = {A Two-Stage Deep Learning Approach for EEG Artifact Removal and Classification: Towards Reliable Wearable Applications.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2025}, number = {}, pages = {1-5}, doi = {10.1109/EMBC58623.2025.11254976}, pmid = {41335679}, issn = {2694-0604}, mesh = {*Electroencephalography/methods/instrumentation ; Humans ; *Artifacts ; *Deep Learning ; *Wearable Electronic Devices ; *Signal Processing, Computer-Assisted ; Algorithms ; Male ; Blinking/physiology ; }, abstract = {EEG artifact removal remains a critical challenge in neural signal processing. In this paper, we present a novel two-stage approach combining a modified IC-UNet architecture for artifact removal with a modified VGGNet for artifact type identification. The system automatically triggers the classification stage when the difference between original and denoised signals exceeds a learned threshold, enabling the classification of ocular artifacts (eye blinks and saccadic movements) in the original signals. The denoising stage employs parallel encoding paths with channel-specific feature extraction, followed by a shared bottleneck and decoder network. The system was evaluated using EEG data from subjects performing controlled eye blink and saccadic movement tasks. The denoising network achieves high correlation values between predicted and ground truth signals, particularly in temporal and specific frontal regions (T5: 0.86 ± 0.01, T6: 0.85 ± 0.01, F3: 0.83 ± 0.01). The classification network shows excellent performance, achieving 99.35% accuracy on the test set with only four misclassifications out of 620 cases.Clinical relevance- This study demonstrates the feasibility of accurate artifact removal and classification in temporal and behind-the-ear EEG recordings, which is particularly relevant for the development of wearable EEG devices for continuous monitoring and hybrid BCI systems.}, }
@article {pmid41335297, year = {2025}, author = {Belwafi, K and Alsuwaidi, A and Mejri, S and Djemal, R}, title = {Brain-inspired signal processing for detecting stress during mental arithmetic tasks.}, journal = {Brain informatics}, volume = {}, number = {}, pages = {}, doi = {10.1186/s40708-025-00281-y}, pmid = {41335297}, issn = {2198-4018}, abstract = {Brain-Computer Interfaces provide promising alternatives for detecting stress and enhancing emotional resilience. This study introduces a lightweight, subject-independent method for detecting stress during arithmetic tasks, designed for low computational cost and real-time use. Stress detection is performed through ElectroEncephaloGraphy (EEG) signal analysis using a simplified processing pipeline. The method begins with preprocessing the EEG recordings to eliminate artifacts and focus on relevant frequency bands (α, β, and γ). Features are extracted by calculating band power and its deviation from a baseline. A statistical thresholding mechanism classifies stress and no-stress epochs without the need for subject-specific calibration. The approach was validated on a publicly available dataset of 36 subjects and achieved an average accuracy of 88.89%. The method effectively identifies stress-related brainwave patterns while maintaining efficiency, making it suitable for embedded and wearable devices. Unlike many existing systems, it does not require subject-specific training, enhancing its applicability in real-world environments.}, }
@article {pmid41335119, year = {2025}, author = {Hu, K and Wang, Y and Tu, K and Guo, H and Yan, J}, title = {Cross-domain correlation analysis to improve SSVEP signals recognition in brain-computer interfaces.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae2772}, pmid = {41335119}, issn = {2057-1976}, abstract = {The recognition of steady-state visual evoked potential (SSVEP) signals in brain-computer interface (BCI) systems is challenging due to the lack of training data and significant inter-subject variability. To address this, we propose a novel unsupervised transfer learning framework that enhances SSVEP recognition without requiring any subject-specific calibration. Our method employs a three-stage pipeline: (1) preprocessing with similarity-aware subject selection and Euclidean alignment to mitigate domain shifts; (2) hybrid feature extraction combining canonical correlation analysis (CCA) and task-related component analysis (TRCA) to enhance signal-to-noise ratio and phase sensitivity; and (3) weighted correlation fusion for robust classification. Extensive evaluations on the Benchmark and BETA datasets demonstrate that our approach achieves state-of-the-art performance, with average accuracies of 83.20% and 69.08% at 1s data length, respectively-significantly outperforming existing methods like ttCCA and Ensemble-DNN. The highest information transfer rate reaches 157.53 bits/min, underscoring the framework's practical potential for plug-and-play SSVEP-based BCIs.}, }
@article {pmid41332552, year = {2025}, author = {Chan, AYC and Stiles, NRB and Levitan, CA and Wu, DA and Tanguay, AR and Shimojo, S}, title = {Bayesian Causal Inference Accounts for Multisensory Filling-In at the Blind Spot.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.11.15.623713}, pmid = {41332552}, issn = {2692-8205}, abstract = {We asked three questions about multisensory perception across the physiological blind spot: (1) Does audiovisual integration persist without bottom-up visual input? (2) Does the brain adjust its sensory uncertainties and priors accordingly? (3) Are the underlying causal-inference computations preserved? Participants judged flashes and beeps in an audiovisual illusion presented across the blind spot or a matched control location. Responses were fit with a Bayesian Causal Inference (BCI) model, estimating sensory noise, numerosity priors, and causal-inference priors under multiple decision strategies evaluated using BIC. Illusions were robust at both locations, indicating preserved integration. Model fits showed higher visual uncertainty and broader prior expectations at the blind spot, while auditory precision and the causal prior remained stable. Thus, the computational architecture of causal inference is maintained, but its parameters flexibly adapt to local sensory reliability. These findings demonstrate that perceptual inference remains intact even in regions without retinal input, achieved by adjusting internal uncertainty rather than altering core multisensory computations.}, }
@article {pmid41332173, year = {2025}, author = {Uwimbabazi, M and Muhanguzi, G and Eryenyu, D and Arua, P and Tweheyo, M and Patten, MA and Eycott, AE and Babweteera, F}, title = {A link between increased temperature and avian body condition in a logged tropical forest.}, journal = {Conservation biology : the journal of the Society for Conservation Biology}, volume = {}, number = {}, pages = {e70190}, doi = {10.1111/cobi.70190}, pmid = {41332173}, issn = {1523-1739}, support = {//Earthwatch Institute/ ; //Royal Zoological Society of Scotland/ ; }, abstract = {The combined effects of anthropogenic disturbances, such as logging and climate change, remain poorly understood; yet, they are the main threats to tropical biodiversity. Most tropical African countries lack long-term climate data, so climate impacts on biodiversity cannot be assessed. However, individuals experience weather, rather than climate, such that climate effects could be seen as the cumulative effects of weather over time. We used morphometric data collected in 1996-2000 and 2017-2021 on understory birds in the Budongo Forest, Uganda, to assess how logging history and short-term weather variations affected the body condition (body condition index [BCI]) of birds. Birds were captured in mist nets in logged and unlogged sites. We analyzed data with Bayesian mixed-effects models. The BCI values were lower in logged forests and decreased as maximum temperatures increased, irrespective of the sensitivity of the birds to logging. Birds responded quickly to increasing temperatures and precipitation (within 1 week), and the longer a hot period was, the worse the effect on birds in heavily logged forests, suggesting reduced thermal buffering. Contrary to our expectations, BCI values for 2017-2021 were higher than values for 1996-2000, indicating possible forest recovery. Our findings underscore the importance of short-term weather data to predict climate change impacts. Such predictions can inform tropical forest management and restoration measures.}, }
@article {pmid41330936, year = {2025}, author = {Liu, Y and Fu, Y and Tang, E and Wu, H and Han, J and Xie, M and Zhang, Y and Peng, B and Huang, J and Liu, H and Chen, H and Qin, P}, title = {Neural dissociation of attention and working memory through inhibitory control.}, journal = {Nature communications}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41467-025-66553-7}, pmid = {41330936}, issn = {2041-1723}, support = {32171046//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200844//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371098//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31971032//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Attention and working memory (WM) have traditionally been considered closely linked processes with shared neural mechanisms. In information selection, attention is often conceptualized as a gatekeeper to WM, regulating which information is encoded and stored. Here, combining tasks specifically designed to separate attention from WM encoding with a multimodal approach, we provide converging neural and causal evidence that these processes are dissociable. Functional MRI identifies the supramarginal gyrus (SMG) as the key region enabling this dissociation, while dynamic causal modeling reveals the neural circuitry through which the SMG exerts inhibitory control over attentional representations, regulating their integration into WM. Furthermore, neuromodulation via transcranial direct current stimulation (tDCS) demonstrates that enhancing SMG activity strengthens this inhibitory control. A second tDCS experiment using varied stimuli confirms the generalizability of the effect. Finally, a transcranial magnetic stimulation (TMS) experiment provides further causal evidence with greater spatial precision. These findings challenge the long-standing view that attention and WM encoding form a continuous process, demonstrating instead that they constitute two dissociable neural processes of information selection.}, }
@article {pmid41330225, year = {2025}, author = {Yuan, J and Xu, M and Qian, L and Gao, L and Sun, Y}, title = {Task-specific effects of sleep deprivation on cognitive function and EEG brain network in night-shift nurses.}, journal = {Brain research bulletin}, volume = {233}, number = {}, pages = {111661}, doi = {10.1016/j.brainresbull.2025.111661}, pmid = {41330225}, issn = {1873-2747}, abstract = {BACKGROUND: Night-shift nurses experience chronic sleep deprivation, which impairs cognitive functions crucial for patient safety. However, the underlying reorganization of brain functional networks remains poorly understood. This study aimed to investigate the task-specific effects of sleep deprivation on brain network topology during sustained attention and working memory in night-shift female nurses.<br><br>METHODS: In a within-subjects design, electroencephalography (EEG) data from 28 female nurses were recorded during a rested session (R-Session) and a sleep-deprived session (SD-Session) immediately following a night shift. Participants performed the psychomotor vigilance test (PVT) and 2-back tasks. Functional connectivity was estimated using the weighted phase lag index (wPLI), and brain network properties were quantified using graph theoretical analysis at both global and nodal levels.<br><br>RESULTS: Our findings revealed a clear behavioral dissociation: sleep deprivation significantly impaired PVT performance but had no effect on 2-back task performance. This dissociation was mirrored by distinct patterns of neural reorganization. During the PVT, the brain network exhibited a compensatory enhancement of global topology, characterized by a significant increase in clustering coefficient, global efficiency, local efficiency, and small-worldness, alongside a decrease in characteristic path length, particularly in the theta and beta bands. In contrast, the 2-back task showed only a localized increase in the theta-band clustering coefficient. Nodal analysis further revealed a critical topographical distinction: PVT-related efficiency changes were strongly right-lateralized, whereas 2-back changes were bilaterally distributed.<br><br>CONCLUSION: In conclusion, these results demonstrate that sleep deprivation elicits task-specific neurocognitive adaptations. Sustained attention appears highly vulnerable, prompting a broad compensatory reorganization of the right-hemispheric attention network. Conversely, working memory function remains behaviorally stable, underpinned by a more specific network reorganization, primarily involving increased local connectivity. This study deepens our understanding of the neural mechanisms underlying cognitive vulnerability and resilience in nurses group.}, }
@article {pmid41330041, year = {2025}, author = {Li, Y and Chen, E and Xiao, X and Xu, M and Ming, D}, title = {Lightweight deep learning models for EEG decoding: A Review.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2717}, pmid = {41330041}, issn = {1741-2552}, abstract = {Brain-computer interface (BCI) technology enables direct communication between the human brain and external devices by decoding electroencephalogram (EEG) signals into actionable commands. As a noninvasive and portable modality, EEG-based BCIs hold promise for applications ranging from neurorehabilitation to assistive technologies. However, their performance depends critically on the accurate extraction of relevant neural features and the reliable recognition of underlying patterns. Deep learning has transformed this process. By automatically learning complex, task-relevant representations from raw or minimally processed EEG data, deep neural networks have surpassed many traditional handcrafted feature approaches in both accuracy and adaptability. Yet, the substantial computational and memory demands of many deep learning architectures limit their deployment in portable or real-time BCI systems. This challenge has motivated a growing interest in lightweight models-architectures optimized to reduce complexity while preserving or even enhancing performance. This paper provides a systematic review of such lightweight deep learning models for EEG signal classification, with EEGNet serving as a representative baseline. To organize this landscape, existing approaches are categorized into three main strategies: (1) information integration through multi-scale feature fusion, (2) optimization of hidden layer design, and (3) hybrid strategies combining multiple structural enhancements. The review synthesizes recent advances, identifies emerging trends, and outlines potential directions for future research. These insights aim to inform the design of efficient and robust EEG classification architectures capable of meeting the practical demands of real-world BCI applications.}, }
@article {pmid41329786, year = {2025}, author = {Luo, X and Zhang, L and Pan, Y}, title = {Do we advise as one likes? The alignment bias in social advice giving.}, journal = {PLoS computational biology}, volume = {21}, number = {12}, pages = {e1013732}, doi = {10.1371/journal.pcbi.1013732}, pmid = {41329786}, issn = {1553-7358}, abstract = {We often give advice to influence others, but could our own advice also be shaped by the very individuals we aim to influence (i.e., advisees)? This reverse flow of social influence-from those typically seen as being influenced to those who provide the influence-has been largely neglected, limiting our understanding of the reciprocal nature of human communications. Here, we conducted a series of experiments and applied computational modelling to systematically investigate how advisees' opinions shape the advice-giving process. In an investment game, participants (n = 346, across four studies) provided advice either independently or after observing advisees' opinions (Studies 1 &amp; 2), with feedback on their advice (acceptance or rejection) provided by advisees (Studies 3 &amp; 4). Our findings reveal that advisors tend to adjust their advice to align with the advisees' opinions (we refer to this as the alignment bias) (Study 1). This tendency, which reflects normative conformity, persists even when advisors were directly incentivized to provide accurate advice (Study 2). As feedback is introduced, advisors' behavior shifts in ways best captured by a reinforcement learning model, suggesting that advisees' feedback drives adaptations in advice giving that maximize acceptance and minimize rejection (Study 3). This adaptation persisted even when acceptance is rare, as bolstered by the model-based evidence (Study 4). Collectively, our findings highlight advisors' susceptibility to the consequence of giving advice, which can lead to counterproductive impacts on decision-making processes and misinformation exacerbation in social encounters.}, }
@article {pmid41329325, year = {2025}, author = {Luo, S and Fan, Y and Yu, F and Zhou, X and Hu, K and Yi, H and Zhou, H and Li, T and Chen, JF and Zhang, L}, title = {The Secondary Motor Cortex-External Globus Pallidus Pathway Regulates Auditory Feedback of Volitional Control.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {41329325}, issn = {1995-8218}, abstract = {Effective use of brain-computer interfaces (BCIs) requires the ability to suppress a planned action (volitional inhibition) for adaptable control in real-world scenarios, but their mechanisms are unclear. Here, we used fiber photometry to monitor external globus pallidus (GPe) and subthalamic nucleus (STN) neurons' activity in mice during a volitional stop-signal task (67% GO, 33% NO-GO). GPe/STN neurons (receiving M2 projections) responded to auditory cues, feedback, and rewards in both trials. Importantly, chemogenetic activation of the M2-GPe pathway enhanced volitional inhibition by modulating auditory feedback response, yet inhibited GPe neurons' feedback response. Furthermore, time-locked optogenetic inhibition of M2-projecting GPe neurons at auditory feedback also enhanced volitional inhibition via prolonged GO trial response times. Collectively, these findings identified the M2-GPe pathway for auditory biofeedback to improve volitional control, offering novel avenues for the advancement of neural interfaces for biofeedback and enhancement of BCI efficacy.}, }
@article {pmid41328607, year = {2025}, author = {Zhang, H and Liao, Y and Lin, Z and Wen, H and Pang, T and Zhao, X and Zhang, W and Lou, X and Chen, C and Hu, S and Liu, Z and Xu, X}, title = {Comorbidity of undiagnosed mood symptoms with dementia risk in multi-regional multi-ethnic adults: evidence from epidemiological findings and plasma metabolites.}, journal = {Epidemiology and psychiatric sciences}, volume = {34}, number = {}, pages = {e58}, doi = {10.1017/S2045796025100346}, pmid = {41328607}, issn = {2045-7979}, mesh = {Humans ; Female ; Male ; United Kingdom/epidemiology ; *Dementia/epidemiology/ethnology/blood ; Middle Aged ; Aged ; Comorbidity ; Prospective Studies ; *Mood Disorders/epidemiology/ethnology/blood ; *Bipolar Disorder/epidemiology/ethnology ; Risk Factors ; Ethnicity/statistics &amp; numerical data ; Prevalence ; *Depression/epidemiology/ethnology ; }, abstract = {AIMS: To investigate the association of midlife and late-life undiagnosed mood symptoms, especially their comorbidity, with long-term dementia risk among multi-regional and ethnic adults.<br><br>METHODS: The prospective study used data from the UK Biobank (N = 142,670; mean follow-up 11.0 years) and three Asian studies (N = 1,610; mean follow-up 4.4 years). Undiagnosed mood symptoms (manic symptoms, depressive symptoms and comorbidity of depressive and manic symptoms) and diagnosed mood disorders (depression, mania and bipolar disorders) were classified. Plasma levels of 168 metabolites were measured. The association between undiagnosed mood symptoms and 12-year dementia (including subtypes) risk and domain-specific cognitive function was examined. The contribution of metabolites in explaining the association between symptom comorbidity and dementia risk was estimated.<br><br>RESULTS: Undiagnosed mood symptoms were prevalent (11.4% in the UK cohort and 31.2% in Asian cohorts) among 1,462 (1.0%) and 74 (19.4%) participants who developed dementia. Comorbidity of undiagnosed mood symptoms was associated with higher dementia risk (sub-distribution hazard ratios = 9.46; 95% confidence interval = 4.07-21.97), especially Alzheimer's disease, and with worse reasoning ability, poorer numeric memory and metabolic dysfunction. Glucose and total Esterified Cholesterol explained 9.1% of the association between symptom comorbidity and dementia, with most of the contribution being from glucose (6.8%).<br><br>CONCLUSIONS: Comorbidity of undiagnosed mood symptoms was associated with a higher cumulative risk of dementia in the long term. Glucose metabolism could be implicated in the development of mood disorders and dementia. The distinctive pathophysiological mechanism between psychiatric and neurodegenerative disorders warrants further exploration.}, }
@article {pmid41328405, year = {2025}, author = {Zhu, R and Zhao, Y and Li, Y}, title = {Paradigm Shift in Global Governance of Medical Brain-Computer Interface: Addressing Practical Challenges Through Institutional Innovation.}, journal = {Risk management and healthcare policy}, volume = {18}, number = {}, pages = {3755-3768}, pmid = {41328405}, issn = {1179-1594}, abstract = {The rapid advancement of medical brain-computer interface (BCI) technology necessitates the transformation and upgrading of traditional governance paradigms urgently. China, the United States, and the European Union hold prominent positions in the global medical BCI landscape and have developed three highly representative governance models. Existing research on medical BCI primarily focuses on specific countries or regions, but it has failed to conduct a comprehensive comparison of governance frameworks across different jurisdictions from a horizontal perspective. In this study, a horizontal policy text analysis was employed to comprehensively compare the divergent approaches of China, the United States, and the European Union in regulating medical BCI, focusing on regulatory frameworks, approval procedures, neural data governance, and ethical governance. China's medical BCI governance is state-led, prioritizing safety; the United States features innovation-driven flexibility; the European Union uses an empowerment model to strictly mitigate risks. Yet these three models have inherent drawbacks. To ensure the healthy development of medical BCI, we suggest China, the United States, the European Union and other jurisdictions establish a lifecycle regulatory mechanism, introduce the regulatory sandbox, promote collaborative governance among multiple subjects, build hierarchical informed consent rules, endow users with neurorights and refine BCI ethical governance.}, }
@article {pmid41328166, year = {2025}, author = {Sun, H and Wang, Z and Qi, Y and Wang, Y}, title = {Decoding multi-joint hand movements from brain signals by learning a synergy-based neural manifold.}, journal = {Patterns (New York, N.Y.)}, volume = {6}, number = {11}, pages = {101394}, pmid = {41328166}, issn = {2666-3899}, abstract = {Brain-computer interfaces have shown great potential in the reconstruction of motor functions. However, decoding complex and natural movements, such as hand movements, remains challenging. Traditional approaches primarily decode the movement of multiple joints in the hand independently, while the inherent synergies underlying these movements have not been well explored. Here, we demonstrate that complex hand movements can be decomposed into a set of motor primitives, each involving a synergy of multi-joint movements. Motor cortical neural activities recruit the motor synergies through spatiotemporal parameters to accomplish the complex motor targets. By learning a joint neural-motor representation of these motor synergies and decoding spatiotemporal parameters rather than the joint-level kinematics, significant improvement could be obtained in hand movement decoding. We propose a neural decoding framework, SynergyNet, to effectively learn the neural-motor synergies for hand movement control. The proposed approach significantly outperforms benchmark methods and provides high interpretability with the hand movement neural decoding task.}, }
@article {pmid41326740, year = {2025}, author = {Tiawongsuwan, L and Klomchitcharoen, S and Chumanee, W and Tangwattanasirikun, T and Saksittikorn, S and Chawaruechai, S and Jatupornpoonsub, T and Wongsawat, Y}, title = {Autism spectrum disorder disrupts brain network connectivity maturation during childhood development.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-30971-w}, pmid = {41326740}, issn = {2045-2322}, support = {B42G670043//National Higher Education Science Research and Innovation Policy Council (PMU B)/ ; }, abstract = {Understanding the developmental trajectory of autism spectrum disorder (ASD) remains a critical barrier for timely intervention in children. Here, we investigated the deficit brain maturation trajectory during childhood development in 35 ASD level 1 and 35 neurotypical children through an electroencephalography (EEG) approach. An empirical study of the potential EEG biomarkers was demonstrated in a comprehensive view of group difference and age-related group comparison using alpha power, peak alpha frequency and transfer entropy during resting. We found a significant disruption of directional brain network communication between regions in children with ASD compared to neurotypical children. Our results also suggested that the children with ASD had altered occipital alpha power and peak alpha frequency development. The present study revealed promising findings that underpinned the developmental disruption of autism spectrum disorder, which may provide a prevailing insight into the disease pathology mechanisms, paving the way for future intervention advancement.}, }
@article {pmid41326639, year = {2025}, author = {Jo, H and Yang, Y and Han, J and Duan, Y and Xiong, H and Lee, WH}, title = {Evaluating EEG-to-text models through noise-based performance analysis.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-29587-x}, pmid = {41326639}, issn = {2045-2322}, support = {RS-2023-00226263//Korea Creative Content Agency/ ; RS-2024-00509257//Institute for Information and Communications Technology Promotion/ ; }, abstract = {Brain-computer interfaces (BCIs) have the potential to revolutionize communication for individuals with severe disabilities. EEG-to-text models, which translate brain signals into written language, offer a promising avenue for restoring communication abilities. Recent advancements in machine learning have improved the accuracy and speed of these models, but their true capabilities remain unclear due to limitations in evaluation methodologies. This study critically examines the performance of EEG-to-text models, focusing on their ability to learn from EEG signals rather than simply memorizing patterns. We introduce a novel methodology that compares model performance on EEG data with that on noise inputs. Our findings reveal that many EEG-to-text models perform similarly or even better on noise, suggesting that they may be memorizing patterns rather than truly learning from EEG signals. These results highlight the need for more rigorous benchmarking and evaluation practices in the field of EEG-to-text translation. By addressing the limitations of current methodologies, we can develop more reliable and trustworthy systems that truly harness the potential of brain-computer interfaces for communication.}, }
@article {pmid41325805, year = {2025}, author = {Zhang, P and Xu, W and Jiang, W and Jin, X and Lou, Y and Yang, T and Li, W and Gao, K and Gao, F and Qian, Z}, title = {Automated Ladder Rung Test for Evaluating Motor Coordination in Parkinson's Disease Mouse Models.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110642}, doi = {10.1016/j.jneumeth.2025.110642}, pmid = {41325805}, issn = {1872-678X}, abstract = {BACKGROUND: The ladder rung walking test assesses fine motor coordination in Parkinson's disease (PD) mouse models but relies on labor-intensive, subjective manual scoring, necessitating an automated, objective system.<br><br>NEW METHOD: We developed a cost-effective automated ladder rung test system with a ladder featuring regular and irregular rung patterns, array through-beam optical sensors for foot-error detection, and an Arduino microcontroller. Custom Python software enables intuitive control, real-time visualization, dynamic sensor mapping, adjustable debounce, and CSV data export.<br><br>RESULTS: In an MPTP-induced PD mouse model, the system detected increased foot errors on irregular rungs (5.13 &#xb1; 1.04 vs. 1.78 &#xb1; 0.69 in controls, p < 0.0001) and longer traversal times (18.04 &#xb1; 2.64s vs. 13.38 &#xb1; 1.95s, p = 0.001), corroborated by open field and rotarod tests and a 68.7% reduction in substantia nigra neurons.<br><br>Unlike costly camera-based systems requiring complex algorithms, our system uses simple photoelectric sensors and costs approximately 127 USD for all components, achieving 96.4% precision and 99.3% recall, making it accessible and user-friendly.<br><br>CONCLUSIONS: This automated system offers a reproducible, high-throughput tool for objective motor assessment in PD and neurological models, enhancing preclinical research.}, }
@article {pmid41323223, year = {2025}, author = {Zhang, L and Zhang, M and Zhang, Y and Li, N and Hu, J and Peng, X}, title = {Efficacy of brain-computer interface with functional electrical stimulation, transcranial direct current stimulation, and conventional therapy on upper limb recovery after stroke: a systematic review and network meta-analysis.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1643536}, pmid = {41323223}, issn = {1664-2295}, abstract = {OBJECTIVE: To systematically evaluate and rank the efficacy of brain-computer interface-based functional electrical stimulation (BCI-FES), transcranial direct current stimulation (tDCS), functional electrical stimulation (FES), conventional therapy (CT), and their combination (BCI-FES + tDCS) on upper limb functional recovery after stroke, and to compare the advantages of different intervention combinations through network meta-analysis, providing evidence-based medicine for clinical practice.<br><br>METHODS: A network meta-analysis method was used to comprehensively compare the efficacy of BCI-FES, tDCS and conventional motor rehabilitation in upper limb rehabilitation of stroke survivors. Statistical analysis was performed using R and Stata software, including direct meta-analysis and network meta-analysis. The direct meta-analysis used mean difference (MD) and its 95% confidence interval (CI) as effect size indicators. The network meta-analysis was performed within a Bayesian framework using the gemtc package in R.<br><br>RESULTS: A total of 13 relevant studies were finally included, comprising 11 two-arm studies and 2 three-arm studies, with a total of 777 subjects. Direct comparison meta-analysis showed: BCI-FES vs. CT MD&#x202f;=&#x202f;6.01 (95%CI: 2.19, 9.83); BCI-FES vs. FES MD&#x202f;=&#x202f;3.85 (95%CI: 2.17, 5.53); BCI-FES vs. tDCS MD&#x202f;=&#x202f;6.53 (95%CI: 5.57, 7.48); BCI-FES&#x202f;+&#x202f;tDCS vs. BCI-FES MD&#x202f;=&#x202f;3.25 (95%CI: -1.05, 7.55); BCI-FES&#x202f;+&#x202f;tDCS vs. tDCS MD&#x202f;=&#x202f;6.05 (95%CI: -2.72, 14.82). BCI-FES showed significantly better effects than CT, FES and tDCS in improving FMA. Network meta-analysis: The inconsistency model was not significant (p&#x202f;=&#x202f;0.060), so the consistency model was adopted. The efficacy ranking was BCI-FES&#x202f;+&#x202f;tDCS (98.9), BCI-FES (73.4), tDCS (33.3), FES (32.4), CT (12.0). BCI-FES and BCI-FES&#x202f;+&#x202f;tDCS were significantly better than CT, but there was no statistically significant difference compared with FES and tDCS.<br><br>CONCLUSION: The combined application of BCI-FES and tDCS appears promising for upper limb rehabilitation after stroke, with potential therapeutic advantages arising from multimodal promotion of neuroplasticity. However, given the small number of trials, methodological variability, and risk of bias, this conclusion should be considered exploratory and hypothesis-generating rather than definitive guidance. Future studies should further verify its clinical benefits through standardized stimulation protocols, individualized parameter optimization and multicenter long-term follow-up studies, to promote the translational application of brain-computer interface technology in the field of neurorehabilitation.<br><br>INPLASY202550066.}, }
@article {pmid41322937, year = {2025}, author = {Baladaniya, M and Baldania, S and Gandhi, NV and Hait, A}, title = {Impact of Physical Therapy on Empowering Neurological Aging: A Narrative Review.}, journal = {Cureus}, volume = {17}, number = {10}, pages = {e95640}, pmid = {41322937}, issn = {2168-8184}, abstract = {Aging is an inevitable biological process that is frequently accompanied by neurological decline, which profoundly impacts gait, balance, and the ability to perform activities of daily living. Physical therapy (PT) plays a pivotal role in mitigating these deficits by enhancing mobility, strength, and independence through evidence-based interventions like resistance training, balance exercises, and functional mobility programs. This narrative review synthesizes current evidence on PT's effectiveness in managing age-related neurological changes, emphasizing its integration within interdisciplinary teams and the use of innovative technologies such as exoskeletons, telerehabilitation, and brain-computer interfaces. A combination of specific keywords and Boolean operators was utilized to identify peer-reviewed studies on the databases PubMed, Google Scholar, and ScienceDirect, focusing on the impact of PT in empowering neurological aging. PT encourages active engagement and enhances the quality of life for elderly people with neurological disorders. With an aging population, the demand for PT services is expected to continue rising, underscoring the importance of adequate resources and specialized training programs in this profession. Despite robust evidence supporting the benefits of PT, gaps persist in understanding its long-term efficacy, optimal intervention dosing, and the integration of emerging technologies into routine practice. Challenges such as limited access to specialized services and insufficient data on cost-effectiveness and patient adherence further complicate the delivery of care. This review advocates for future research to refine PT strategies, enhance interdisciplinary collaboration, and leverage technological advancements to optimize outcomes for older adults with neurological conditions, ultimately promoting successful aging and sustained independence.}, }
@article {pmid41322339, year = {2026}, author = {Huang, X and Lu, W and Jiang, D and Fang, Z and Feng, B}, title = {DUSP6 inhibitor (E/Z)-BCI hydrochloride stimulates glucose clearance and adipose lipolysis in diet-induced obese mice.}, journal = {Genes &amp; diseases}, volume = {13}, number = {2}, pages = {101671}, pmid = {41322339}, issn = {2352-3042}, }
@article {pmid41321942, year = {2025}, author = {Danso, A and Ehlert, M and Koehler, F and Kirk, R and Natarajan, N and Wright, SE and Timmers, R and Saarikallio, S}, title = {Patterns of pre-sleep music use and sleep quality: exploratory survey findings on state anxiety.}, journal = {PeerJ}, volume = {13}, number = {}, pages = {e20444}, pmid = {41321942}, issn = {2167-8359}, mesh = {Humans ; Female ; Male ; Cross-Sectional Studies ; Adult ; *Sleep Quality ; *Anxiety/psychology ; *Music/psychology ; Surveys and Questionnaires ; Young Adult ; Middle Aged ; Stress, Psychological/psychology ; Self Report ; }, abstract = {Music listening is a widely used self-help approach that may influence psychological and physiological processes associated with sleep. This cross-sectional study explored patterns of pre-sleep music use in relation to psychological distress (state anxiety, mood disturbance, stress) and subjective sleep quality. Adults (N = 269, 52.6% female; M age = 27.7, SD = 9.0) completed validated self-report measures of sleep quality (the Pittsburgh Sleep Quality Index (PSQI)) and psychological distress. Pre-sleep music use was modestly associated with poorer sleep quality (r = 0.23, p < 0.01). A borderline interaction between state anxiety and music use (β = -0.170, p = 0.050) suggested, but did not confirm, a possible buffering pattern in which the anxiety-sleep association appeared weaker among more frequent music users. No moderation effects were observed for mood or stress. These preliminary findings suggest that pre-sleep music use may reflect a coping-oriented effort among individuals experiencing anxiety. However, given the cross-sectional design, self-report measures, and borderline statistical support, the results should be viewed as descriptive and hypothesis-generating.}, }
@article {pmid41320900, year = {2025}, author = {Zhu, H and Zhu, S and Zhao, M and Zhu, Z and Shao, Y and Lu, X and Liu, T and Zhu, H and Shu, N and Lin, H and Cheng, J}, title = {Cerebellar and Brainstem White Matter Geometric Alterations in Multiple System Atrophy: A DFA-Based Biomarker for Disease Staging.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {31}, number = {12}, pages = {e70623}, doi = {10.1111/cns.70623}, pmid = {41320900}, issn = {1755-5949}, support = {4252004//Beijing Natural Science Foundation/ ; L242038//Beijing Natural Science Foundation/ ; CFH2022-2-2014//Capital's Funds for Health Improvement and Research/ ; 2022ZD0213300//the STI2030-Major Projects/ ; 2024YFE0100900//National Key Research and Development Program Intergovernmental Key Project of China/ ; BMI2400001//the Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; CX25YQ11//Chinese Institutes for Medical Research Beijing/ ; BYSYZD2023016//Key Clinical Project of Peking University Third Hospital/ ; 2022YFC2402205//National Key Research and Development Program of China/ ; 82471498//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Multiple System Atrophy/diagnostic imaging/pathology ; Male ; *White Matter/diagnostic imaging/pathology ; Female ; *Brain Stem/diagnostic imaging/pathology ; Middle Aged ; *Cerebellum/diagnostic imaging/pathology ; Aged ; Diffusion Magnetic Resonance Imaging/methods ; Biomarkers ; Disease Progression ; }, abstract = {AIMS: To characterize white matter geometric pathology in cerebellar subtype of multiple system atrophy (MSA-C) using director field analysis (DFA) and identify stage-specific biomarkers.<br><br>METHODS: We analyzed single-shell diffusion MRI (b&#x2009;=&#x2009;1000) in 31 MSA-C patients (15 early-, 16 late-stage) and 33 controls. DFA quantified axonal geometry (splay/bend/twist), complemented by fixel-based analysis (FBA) and brainstem volumetry. Group comparisons used threshold free cluster enhancement (TFCE) (p&#x2009;<&#x2009;0.05 FWE-corrected). DFA-altered regions were correlated with clinical scores. AutoGluon evaluated classification performance using different feature sets.<br><br>RESULTS: MSA-C exhibited distinct geometric degeneration patterns: cerebellar pathways showed reduced splay, bend, and twist (reflecting Wallerian degeneration), whereas brainstem tracts demonstrated dissociated geometry (increased splay/bend but decreased twist). Brainstem twist reduction strongly differentiated early- and late-stage MSA-C (AUC&#x2009;=&#x2009;0.95). Clinically, middle cerebellar peduncle bend correlated with motor progression (UMSARS-II: r&#x2009;=&#x2009;0.48), while cerebellar splay reduction predicted ataxia severity (SARA: r&#x2009;=&#x2009;-0.43).<br><br>CONCLUSION: DFA captures circuit-specific white matter pathology in MSA-C, with brainstem twist emerging as a novel biomarker associated with disease stage. The integration of geometric metrics with automated machine learning provides a robust framework for early diagnosis and disease staging, highlighting distinct neurodegenerative mechanisms in cerebellar versus brainstem pathways.}, }
@article {pmid41320740, year = {2025}, author = {Palanichamy, C and Thirumoorthi, SP and Lakshminarayanan, K and Madathil, D and Rahman, MH}, title = {Multimodal brain-computer interface for robotic control: integration of real-time gaze tracking and EEG-based motor imagery.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {41320740}, issn = {1741-0444}, abstract = {Individuals with upper limb dysfunction face significant challenges in performing everyday tasks, often depending on healthcare professionals, caregivers, or family members. Such reliance places a continuous burden on helpers who must remain available for assistance. To address these challenges, this study investigated a virtual hybrid brain-computer interface (BCI) system that integrates gaze tracking with motor imagery (MI) to control a robotic arm, potentially reducing the dependency on human support. Twenty healthy, right-handed participants took part in a virtual game environment where they controlled a robotic arm using both gaze tracking and MI. During an initial training phase, participants' electroencephalography (EEG) signals were recorded with an EEG cap. These signals were then processed and classified using the common spatial pattern (CSP) algorithm and linear discriminant analysis (LDA). In parallel, a webcam was used for real-time gaze calibration to enable accurate target selection. In the subsequent testing phase, MI commands directed the virtual robot toward predetermined targets in a Unity-based game. Training accuracy consistently outperformed online testing accuracy. The MI signal classification achieved a true positive (TP) rate of approximately 75.5%, while a significant negative correlation (r = - 0.45) was observed between MI classification accuracy and game completion times, suggesting that higher MI accuracy led to quicker task execution. These findings demonstrate the potential of combining gaze tracking with MI-based BCI for robotic control as an assistive technology for upper limb impairments. Despite its promise, technical limitations indicate that further improvements are needed to enhance system robustness, practicality, and usability for everyday activities.}, }
@article {pmid41320142, year = {2025}, author = {Ran, H and Li, Q and Li, Y and Deng, F and Pan, Y}, title = {Online and in-person collaborative writing have similar benefits but different costs.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121629}, doi = {10.1016/j.neuroimage.2025.121629}, pmid = {41320142}, issn = {1095-9572}, abstract = {With the rapid rise of online education, collaborative learning is no longer confined to physical classrooms. Yet, it remains unclear whether online collaboration, especially with or without visual cues, can support the same cognitive and neural processes as in-person collaboration. This study used multimodal learning analytics to compare collaboration processes and inter-brain synchronization (IBS) under three conditions: in-person, online with camera on, and online with camera off. Seventy-seven learner dyads completed a 28-minute collaborative writing task while their brain activity was recorded simultaneously using functional near-infrared spectroscopy (fNIRS). Across all three conditions, collaborative learning significantly improved outcomes. In-person and online (camera on) learners showed comparable IBS in the middle temporal gyrus. However, camera-on learners displayed more frequent higher-order behaviors (e.g., monitoring, questioning, mutual understanding, argument building) and greater dorsolateral prefrontal cortex activation, reflecting increased executive control demands. In contrast, camera-off learners achieved learning gains but engaged in less information exchange, emphasized mutual understanding and collaborative planning, and exhibited markedly lower IBS. Together, these findings indicate that while both in-person and online collaboration can yield similar levels of achievement, their cognitive costs differ: in-person collaboration is more efficient, whereas online collaboration requires additional regulation and cognitive effort. The absence of visual cues further constrains information sharing and social interaction, undermining IBS. These insights help explain the mechanisms that shape collaborative learning across contexts and offer guidance for designing more effective online learning environments.}, }
@article {pmid41319418, year = {2025}, author = {Lin, D and Shen, Q and An, Y and Fu, S and Xiao, Q and Wu, S and Song, X and Jiang, X and Klucharev, V and Cai, D and Wang, Y}, title = {Assessing the roles of subjective value and valence in outcome evaluation for consumer products: evidence from behavioral and electrophysiological experiments.}, journal = {Acta psychologica}, volume = {262}, number = {}, pages = {106011}, doi = {10.1016/j.actpsy.2025.106011}, pmid = {41319418}, issn = {1873-6297}, abstract = {Value-based decision-making is ubiquitous in our daily lives, yet most EEG studies focus on monetary outcomes, with limited attention to how the brain encodes the subjective value and valence of consumer products during outcome evaluation. To address these questions, we set up a novel three-stage task to investigate the behavioral regularities of recall of valence for food products with varying subjective values and their underlying electrophysiological mechanisms for subjective valuation and valence differentiation. With respect to the event-related potential results, we found that not receiving the food products (No-Gain) led to an increase in the FRN. Regarding the P300, we found that both higher subjective values and positive feedback elicited greater deflection of P300 at the outcome stage. Intriguingly, further single-trial analysis of EEG demonstrated that the magnitude of P300, rather than the FRN at the outcome stage, could predict subsequent behavioral performance as represented by memory accuracy. Therefore, these findings highlight the vital and dissociated roles of FRN and P300 in the subjective valuation of consumer products and suggest the possible role of P300 as a biomarker to predict subsequent choice behavior.}, }
@article {pmid41319286, year = {2025}, author = {Li, CY and Huang, H and Shen, XF and Cao, KL and Zheng, D and Zhu, Y and Xie, SZ and Yu, XD and Wang, H and Chen, JD and Shi, J and Li, Y and Yan, M and Li, XM}, title = {Delta Opioid Receptors within the Cortico-Thalamic Circuitry Underlie Hyperactivity Induced by High-Dose Morphine.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e03831}, doi = {10.1002/advs.202503831}, pmid = {41319286}, issn = {2198-3844}, support = {82090031//National Natural Science Foundation of China/ ; 82288101//National Natural Science Foundation of China/ ; 3220081//National Natural Science Foundation of China/ ; 82071227//National Natural Science Foundation of China/ ; 82371217//National Natural Science Foundation of China/ ; U23A20433//National Natural Science Foundation of China/ ; 2021ZD0202700//STI2030-Major Projects/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 010904013//Nanhu Brain-computer Interface Institute/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 226-2024-00133//Fundamental Research Funds for the Central Universities/ ; LR25C090001//Zhejiang Provincial Natural Science Foundation of China/ ; 2024SSYS0017//Key Research and Development Program of Zhejiang Province/ ; 2024C03091//Key Research and Development Program of Zhejiang Province/ ; }, abstract = {Hyperactivity is a well-documented neurobehavioral effect of morphine and other opioid drugs, predominantly observed in rodent models, yet the neural circuits and molecular mechanisms underlying this effect remain elusive. In this study, an excitatory projection from the cingulate cortex (Cg) to the intermediate rostrocaudal division of zona incerta (ZIm) is revealed that is activated by morphine in mice. Chemogenetic inhibition of the Cg-ZIm pathway decreased high-dose (10-15mg kg[-1]) morphine-induced hyperlocomotion without affecting its analgesic effects. Activation of this pathway faithfully reproduced the motor effect of morphine. Furthermore, high-dose morphine-induced hyperlocomotion is quickly attenuated by microinjecting delta-opioid receptor (DOR) antagonists into the ZI, which is not observed following the targeted knockout of the DOR in Cg-projecting ZI neurons, indicating a postsynaptic DOR-mediated mechanism. In summary, these findings identify the critical role of the DOR within the Cg-ZIm circuit in the psychomotor properties of morphine. This work sheds light on potential targets within the Cg-ZIm pathway for mitigating the undesired psychomotor effects of morphine and thereby optimizing its clinical outcomes.}, }
@article {pmid41299012, year = {2025}, author = {Díaz-Pérez, A and de Eulate, NA and Masvidal-Codina, E and Illa, X and Navarro, X and Guimerà-Brunet, A and Jiménez-Altayó, F and Penas, C}, title = {Cortical spreading depolarizations in stroke: Mechanisms, neuroprotective interventions, and monitoring techniques.}, journal = {GeroScience}, volume = {}, number = {}, pages = {}, pmid = {41299012}, issn = {2509-2723}, support = {PID2021-126117NA-I00//Ministerio de Ciencia e Innovaci&#xf3;n/ ; CNS2023-144492//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2022-140655OB-I00//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2020-113634RB-C22//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PDC2023-145866-I0//Ministerio de Ciencia e Innovaci&#xf3;n/ ; 101130650 (META-BRAIN)//European Commission/ ; 101136541 (GphT-BCI)//European Commission/ ; CB06/01/0049//Centro de Investigaci&#xf3;n Biom&#xe9;dica en Red en Bioingenier&#xed;a, Biomateriales y Nanomedicina/ ; CB06/05/1105//Centro de Investigaci&#xf3;n Biom&#xe9;dica en Red sobre Enfermedades Neurodegenerativas/ ; 2021-SGR-00495//Generalitat de Catalunya/ ; 2021-SGR-004488//Generalitat de Catalunya/ ; 2021-SGR-00969//Generalitat de Catalunya/ ; CEX2023-001397-M//Agencia Estatal de Investigaci&#xf3;n/ ; }, abstract = {Cortical spreading depolarization (CSD) is a pathophysiological event critically implicated in ischemic stroke and other brain disorders. It consists of slowly propagating waves of massive neuronal and glial depolarization in cerebral gray matter, accompanied by spreading depression of cortical activity. CSD disrupts ion homeostasis, alters cerebral blood flow, and contributes to neuronal death in vulnerable tissue. This comprehensive review summarizes both classic and recent studies on CSD mechanisms and their role in brain damage progression after stroke. We also review potential neuroprotective strategies to mitigate CSD-induced damage and discuss available technologies for detecting CSD. Advancing our understanding of CSD mechanisms, combined with targeted neuroprotective strategies and improved monitoring techniques, holds promise for reducing stroke-related brain injury and guiding personalized recovery approaches.}, }
@article {pmid40338479, year = {2025}, author = {Moreno-Alcayde, Y and Traver, VJ and Leiva, LA}, title = {Predicting fixations and gaze location from EEG.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {63}, number = {10}, pages = {2969-2981}, pmid = {40338479}, issn = {1741-0444}, support = {CHIST-ERA-20-BCI-001//HORIZON EUROPE Framework Programme/ ; 101071147//HORIZON EUROPE European Innovation Council/ ; PCI2021-122036-2A//Agencia Estatal de Investigaci&#xf3;n/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Fixation, Ocular/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Neural Networks, Computer ; Brain/physiology ; }, abstract = {Brain signals carry cognitive information that can be relevant in downstream tasks, but what about eye-gaze? Although this can be estimated with eye-trackers, it can be very convenient in practice to do it without extra equipment. We consider the challenging tasks of fixation prediction and gaze estimation from electroencephalography (EEG) using deep learning models. We argue that there are three critical design criteria when designing neural architectures for EEG: (1) the spatial and temporal dimensions of the data, (2) the local vs global nature of the data processing, and (3) the overall structure and order with which the steps (1) and (2) are orchestrated. We propose two model architectures, based on Transformers and LSTMs, with different variants in this large design space, and compare them with recent state-of-the-art (SOTA) approaches under two constraints: reduced EEG signal length and reduced set of EEG channels. Our Transformer-based model outperforms the LSTM-only model, but it turns out to be more sensitive with short signal lengths and with less number of channels. Interestingly, our results are similar or slightly better than SOTA, and the models are trained from scratch (i.e., without pre-training or fine-tuning). Our findings provide useful insights for advancing in eye-from-EEG tasks.}, }
@article {pmid40025160, year = {2025}, author = {Ye, Z and Ai, Q and Liu, Y and de Rijke, M and Zhang, M and Lioma, C and Ruotsalo, T}, title = {Generative language reconstruction from brain recordings.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {346}, pmid = {40025160}, issn = {2399-3642}, support = {CHIST-ERA-20-BCI-001//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ ; }, mesh = {Humans ; *Language ; *Brain/physiology/diagnostic imaging ; *Magnetic Resonance Imaging/methods ; *Brain Mapping/methods ; Male ; Female ; Adult ; Young Adult ; }, abstract = {Language reconstruction from non-invasive brain recordings has been a long-standing challenge. Existing research has addressed this challenge with a classification setup, where a set of language candidates are pre-constructed and then matched with the representation decoded from brain recordings. Here, we propose a method that addresses language reconstruction through auto-regressive generation, which directly uses the representation decoded from functional magnetic resonance imaging (fMRI) as the input for a large language model (LLM), mitigating the need for pre-constructed candidates. While an LLM can already generate high-quality content, our approach produces results more closely aligned with the visual or auditory language stimuli in response to which brain recordings are sampled, especially for content deemed "surprising" for the LLM. Furthermore, we show that the proposed approach can be used in an auto-regressive manner to reconstruct a 10 min-long language stimulus. Our method outperforms or is comparable to previous classification-based methods under different task settings, with the added benefit of estimating the likelihood of generating any semantic content. Our findings demonstrate the effectiveness of employing brain language interfaces in a generative setup and delineate a powerful and efficient means for mapping functional representations of language perception in the brain.}, }
@article {pmid38830946, year = {2024}, author = {Dubiel, M and Barghouti, Y and Kudryavtseva, K and Leiva, LA}, title = {On-device query intent prediction with lightweight LLMs to support ubiquitous conversations.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {12731}, pmid = {38830946}, issn = {2045-2322}, support = {101071147 (SYMBIOTIK)//European Innovation Council Pathfinder program/ ; CHIST-ERA-20-BCI-001//Horizon 2020 FET program/ ; }, abstract = {Conversational Agents (CAs) have made their way to providing interactive assistance to users. However, the current dialogue modelling techniques for CAs are predominantly based on hard-coded rules and rigid interaction flows, which negatively affects their flexibility and scalability. Large Language Models (LLMs) can be used as an alternative, but unfortunately they do not always provide good levels of privacy protection for end-users since most of them are running on cloud services. To address these problems, we leverage the potential of transfer learning and study how to best fine-tune lightweight pre-trained LLMs to predict the intent of user queries. Importantly, our LLMs allow for on-device deployment, making them suitable for personalised, ubiquitous, and privacy-preserving scenarios. Our experiments suggest that RoBERTa and XLNet offer the best trade-off considering these constraints. We also show that, after fine-tuning, these models perform on par with ChatGPT. We also discuss the implications of this research for relevant stakeholders, including researchers and practitioners. Taken together, this paper provides insights into LLM suitability for on-device CAs and highlights the middle ground between LLM performance and memory footprint while also considering privacy implications.}, }
@article {pmid41318198, year = {2025}, author = {Fliti, T and Shhaytli, A and Serhal, A and Takesh, Z and Chokor, M}, title = {Technology-assisted interventions for neuropsychiatric disorders.}, journal = {Progress in brain research}, volume = {298}, number = {}, pages = {241-269}, doi = {10.1016/bs.pbr.2025.08.017}, pmid = {41318198}, issn = {1875-7855}, mesh = {Humans ; *Mental Disorders/therapy ; Telemedicine ; *Brain-Computer Interfaces ; }, abstract = {Neuropsychiatric disorders are chronic diseases present in the community and cause both personal and community burdens. Though therapeutically useful and beneficial, standard treatments and managements face some challenges such as social discrimination, concerns about treatments' side effects, and delay in the delivery of the healthcare services. To overcome these barriers, technology-assisted interventions have emerged and are nowadays increasingly used due to their potentials to offer accessible, personalized, and cost-efficient care in neuropsychiatric field. It is believed that these new advancements provide many advantages, such as accessibility, the direct follow-up of the patients, and the development of neuropsychiatric care in the low-income countries. In contrast, technology-assisted interventions in neuropsychiatric disorders encounter certain limitations, especially those related to ethical considerations such as patient privacy, equal access, and data security. This article reviews the role of digital health tools, neurostimulation techniques, and brain-computer interface in neuropsychiatric field. Also, it discusses the advantages and limitations of each technology.}, }
@article {pmid41318089, year = {2025}, author = {Yamashita, T and Sawada, M and Demura, A and Aoyama, S and Yao, Y and Chihara, H and Ikedo, T and Hattori, EY and Sano, N and Takada, S and Tanji, M and Mineharu, Y and Kikuchi, T and Arakawa, Y}, title = {Usefulness of the Scalp EEG over Bone Defect for the Decoding of Muscle Activity.}, journal = {World neurosurgery}, volume = {}, number = {}, pages = {124670}, doi = {10.1016/j.wneu.2025.124670}, pmid = {41318089}, issn = {1878-8769}, abstract = {BACKGROUND: Artificial neural connections (ANCs) included in brain-machine interfaces (BMIs) translate neural activity into control commands for external stimulators to restore motor function in individuals with paralysis. While invasive methods such as stereotactic electroencephalography (sEEG), electrocorticography (ECoG) and intracortical microelectrodes provide high-bandwidth, information-rich signals capable of controlling ANCs, noninvasive techniques like electroencephalography (EEG) are often limited by the skull's attenuation of cortical activity. In this study, we tested whether EEG recorded over a bone defect following decompressive craniectomy for brain injury retains rich neural information sufficient for effective ANCs control.<br><br>METHODS: In this cross-sectional study, we recorded scalp EEG signals from patients who had undergone hemicraniectomy and analyzed neural activity, including high-gamma frequencies (65-300 Hz), during a simple hand-grip task. Using these EEG signals, we predicted hand muscle activity through regression-based decoding models. For comparison, ECoG signals were recorded from patients undergoing awake tumor resection, and muscle activity was similarly decoded. The prediction accuracy of EEG over the bone defect was then compared with that obtained from ECoG.<br><br>RESULTS: EEG over the craniectomy site captured substantial neural activity, enabling decoding of muscle activity with moderate to high accuracy (R = 0.74 &#xb1; 0.21, N = 5). In comparison, decoding accuracy from ECoG signals did not significantly differ (R = 0.55 &#xb1; 0.18, N = 4).<br><br>CONCLUSION: EEG recorded over a bone defect preserves rich cortical signals comparable to invasive ECoG, providing a minimally invasive platform for developing effective ANCs-based therapy in patients with brain injury.}, }
@article {pmid41318041, year = {2025}, author = {Gao, L and Xu, M and Qian, L and Zhang, R and Chen, M and Hu, Y and Li, C and Sun, Y}, title = {Identifying individuals with high susceptibility to mental fatigue: A functional connectivity study.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121623}, doi = {10.1016/j.neuroimage.2025.121623}, pmid = {41318041}, issn = {1095-9572}, abstract = {Substantial inter-individual difference of behavioral performance was repeatedly revealed during prolonged time-on-task (TOT), indicating complex neural mechanisms underlying mental fatigue. In this work, we provided a comprehensive investigation to identify individuals with high susceptibility to mental fatigue and to reveal its influence on brain network reorganization. Specifically, behavioral data and EEG signals were collected from 95 participants when they performed a 20-min psychomotor vigilance task (PVT). A composite index (Findex) was introduced, based upon which the participants were categorized into the fatigue-susceptible (FS, corresponding to top third Findex value) and the fatigue-resistant (FR, corresponding to bottom third Findex value) groups (NFS/NFR = 30/30). Functional connectivity was then estimated and set as input for the following analyses. As expect, significant impairment of behavioral performance was showed in the FS group, while the performance of the FR group remained relatively stable. Following brain network analyses showed frequency-dependent reorganizations in both groups, whereas the FR group exhibited greater stability and higher integrity than the FS group. Further classification analyses revealed satisfactory accuracy for FS identification (95.61%) and the prominent centro-parietal distribution of contributed nodal features. In sum, this study provides further evidence to support the notion of substantial individual differences in fatigue susceptibility and provides a practical approach to identify the individuals whose performance is particularly prone to performance decline.}, }
@article {pmid41317873, year = {2025}, author = {He, Y and Gong, Z}, title = {Muscular Regulation of Strategic Self-righting Behavior in Drosophila Larvae.}, journal = {Behavioural brain research}, volume = {}, number = {}, pages = {115964}, doi = {10.1016/j.bbr.2025.115964}, pmid = {41317873}, issn = {1872-7549}, abstract = {Adjusting posture is crucial for animals. When animals topple over, they attempt to restore their body posture to the default state. In the case of Drosophila larvae, they can restore their posture through self-righting (SR) behavior when placed side-up or ventral-up. However, the mechanisms of muscular regulation underlying SR behavior remains unknown. In this study, we reported that Drosophila larvae achieve postural reorientation through four strategies and their combinations for the first time, while exhibiting strategic bias. Among the four SR strategies, the most frequently used were the asymmetric SR-fwd, followed by the oblique muscle-powered SR-torsion as the second most frequently employed strategy, while SR-bwd and SR-roll exhibit significantly lower utilization frequencies. These findings not only provide a detailed characterization of larval SR behavior and its strategic diversity, but also elucidate critical muscular regulatory mechanisms underlying SR execution and strategy bias modulation. This research offers important implications for motion control system design and biomimetic robotics development, particularly regarding self-posture adjustment mechanisms.}, }
@article {pmid41316368, year = {2025}, author = {Feng, S and Yu, X and Guo, Y and Zheng, B and Peng, J and Wang, P and Wu, W}, title = {Corticomuscular coupling study for post-stroke rehabilitation: a scoping review.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-025-01711-y}, pmid = {41316368}, issn = {1743-0003}, support = {No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; U21A20479//National Natural Science Foundation of China, Joint Fund Project/ ; U21A20479//National Natural Science Foundation of China, Joint Fund Project/ ; U21A20479//National Natural Science Foundation of China, Joint Fund Project/ ; U21A20479//National Natural Science Foundation of China, Joint Fund Project/ ; JCYJ20240813150250045//Shenzhen Municipal Science, Technology and Innovation Commission, Basic Research General Program/ ; JCYJ20240813150250045//Shenzhen Municipal Science, Technology and Innovation Commission, Basic Research General Program/ ; JCYJ20240813150250045//Shenzhen Municipal Science, Technology and Innovation Commission, Basic Research General Program/ ; JCYJ20240813150250045//Shenzhen Municipal Science, Technology and Innovation Commission, Basic Research General Program/ ; 2024010//Sun Yat-sen University, Clinical Medicine 5010 Special Program/ ; 2024010//Sun Yat-sen University, Clinical Medicine 5010 Special Program/ ; 2024010//Sun Yat-sen University, Clinical Medicine 5010 Special Program/ ; 2024010//Sun Yat-sen University, Clinical Medicine 5010 Special Program/ ; KJZD20230923115114028//Shenzhen Municipal Science, Technology and Innovation Commission&#xff0c; Major Science and Technology Project/ ; KJZD20230923115114028//Shenzhen Municipal Science, Technology and Innovation Commission&#xff0c; Major Science and Technology Project/ ; KJZD20230923115114028//Shenzhen Municipal Science, Technology and Innovation Commission&#xff0c; Major Science and Technology Project/ ; KJZD20230923115114028//Shenzhen Municipal Science, Technology and Innovation Commission&#xff0c; Major Science and Technology Project/ ; }, abstract = {The challenge of post-stroke rehabilitation lies in the difficulty of quantifying the dynamic process of neural remodeling using traditional assessment methods. Corticomuscular coupling (CMC), as an emerging neurophysiological index, offers a novel perspective for quantifying this dynamic process of neural remodeling following a stroke and optimizing rehabilitation interventions. This paper systematically reviews the research advancements in CMC within stroke rehabilitation through a scoping review, focusing on four primary areas: mechanisms, analytical methods, experimental paradigms, and interventions. Studies indicate that CMC can assess the neural mechanisms underlying motor dysfunction and guide personalized rehabilitation strategies by analyzing the dynamic information transfer between the brain and muscles. However, current studies encounter challenges such as technical calibration difficulties, insufficient sample sizes, and the heterogeneity of experimental paradigms. Moving forward, it is essential to promote large-sample multicenter studies, standardize the analytical processes, and explore the synergistic application of CMC with brain-computer interfaces and other technologies to facilitate the paradigm shift from experience-driven to data-driven stroke rehabilitation.}, }
@article {pmid41315523, year = {2025}, author = {Kalyuzhner, Z and Agdarov, S and Beiderman, Y and Beiderman, Y and Zalevsky, Z}, title = {Visual cortex speckle imaging for shape recognition.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {42690}, pmid = {41315523}, issn = {2045-2322}, abstract = {This study introduces a non‑invasive approach for neurovisual classification of geometric shapes by capturing and decoding laser‑speckle patterns reflected from the human striate cortex. Using a fast digital camera and deep neural networks (DNN), we demonstrate that each visual stimulus - rectangle, triangle, mixed shapes, or blank screen, arouses a detectably distinct speckle pattern. Our optimized DNN classifier achieved near perfect recall (98%) for rectangles and high recall (91%) for triangles in single‑shape trials and sustained robust performance (82% recall) when multiple shapes appeared simultaneously. Circular stimuli produced subtler and less reliable speckle dynamics and were not classified with consistent accuracy. By leveraging low‑cost optics and scalable AI processing, this technique paves the way for real‑time, portable monitoring of visual cortex activity, offering transformative potential for cognitive neuroscience, brain-machine interfaces, and clinical assessment of visual processing. Future work will expand stimulus complexity, optimize model architectures, and explore multimodal neurophotonic applications.}, }
@article {pmid41315347, year = {2025}, author = {Chang, W and Kong, W and Yan, G and Lv, R and Du, K and Sadiq, MT and Guo, B and Yin, R and Liu, X}, title = {A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1877}, pmid = {41315347}, issn = {2052-4463}, support = {62366028, 62466032, W2421090;//National Natural Science Foundation of China (National Science Foundation of China)/ ; 24JRRA256//Natural Science Foundation of Gansu Province/ ; }, mesh = {Humans ; *Electroencephalography ; *Upper Extremity/physiopathology ; Brain-Computer Interfaces ; *Stroke Rehabilitation ; Adult ; Male ; *Exercise Therapy ; }, abstract = {Most stroke survivors experience persistent upper limb motor dysfunction, and brain-computer interface (BCI) rehabilitation technologies have been widely explored to address this issue. However, systematic comparisons and analyses of differences among rehabilitation paradigms remain challenging due to the lack of multi-paradigm EEG datasets from the same subjects. This study aims to construct an EEG dataset that collects various rehabilitation paradigms for the same subjects. A total of 28 healthy subjects were recruited, and EEG data were collected under six types of upper limb rehabilitation paradigms. Each paradigm involves two or three actions, including grasping and releasing with the left, right, or both hands. The dataset includes both raw EEG signals and preprocessed versions with bandpass filtering and artifact removal. This resource will support studies comparing the neural mechanisms underlying different rehabilitation paradigms and aid in the development of optimized rehabilitation strategies.}, }
@article {pmid41314034, year = {2025}, author = {Brouwer, H}, title = {Mapping meaning in the brain's language.}, journal = {Cortex; a journal devoted to the study of the nervous system and behavior}, volume = {194}, number = {}, pages = {12-21}, doi = {10.1016/j.cortex.2025.10.012}, pmid = {41314034}, issn = {1973-8102}, abstract = {Recent advances in neuroscience and artificial intelligence have pushed the state-of-the-art from being able to decode the meaning of individual words from non-invasive brain recordings, to the reconstruction of the meaning of continuous language. Beyond game changing practical implications of such "mind reading" mapping models, e.g., brain-computer interfaces that restore lost ability to speak, they also hold the promise to be instrumental in addressing a fundamental question in the cognitive sciences: How does the human brain represent the meaning of concepts, phrases, and sentences? In order to fulfil this promise, however, important methodological and theoretical challenges need to be overcome: (1) extant mapping results are inconsistent and difficult to reconcile with neurocognitive theory, (2) extant neural meaning representations do not model the compositional semantics capturing the meaning of multi-word utterances, and (3) extant mapping models fail to take into account the spatiotemporal dynamics of lexical and compositional semantic representation and computation. I argue that in order to overcome these challenges, we should ground mapping models in linguistic and neurocognitive theory, and develop neurocomputational models that explicate the spatiotemporal dynamics of meaning in the brain's language.}, }
@article {pmid41313574, year = {2025}, author = {Lin, H and Qiu, Y and Hu, Z and Chen, L and Dai, Y and Jiang, T and Li, R and Wang, S and Cao, Y and Li, J and Liu, H and Ye, Y and Lin, J and Zheng, Y and Liang, S and Tao, J and Chen, L and Yang, M and Liu, W}, title = {Integrated Aerobic Exercise and Multisensory Environment Training for Age-related Cognitive Decline via Hippocampal-prefrontal Neural Circuit Modulation.}, journal = {Molecular neurobiology}, volume = {63}, number = {1}, pages = {196}, pmid = {41313574}, issn = {1559-1182}, support = {2023ZQNZD015//Health Service Research of Fujian Province/ ; 82274626//National Natural Science Foundation of China/ ; XQB202203//Youth Science and Technology Innovation Talent Cultivation Program of FJTCM/ ; }, mesh = {Animals ; *Hippocampus/physiopathology/metabolism/pathology ; *Prefrontal Cortex/physiopathology/metabolism ; *Physical Conditioning, Animal/physiology ; *Cognitive Dysfunction/physiopathology/therapy ; Male ; Mice, Inbred C57BL ; *Aging ; Mice ; Neuronal Plasticity ; Receptors, N-Methyl-D-Aspartate/metabolism ; *Environment ; Neurons/metabolism ; Neural Pathways/physiopathology ; }, abstract = {Age-associated cognitive decline, characterized by progressive memory and executive function impairment without dementia, poses challenges to elderly health. While aerobic exercise and environmental enrichment training may improve cognitive function, the underlying neural mechanisms remain unclear. In this study, we developed a novel intervention combines aerobic exercise (AE) with multisensory stimulation environment training (MSET). This combined training (CT) was more effective in mitigating cognitive decline in aged mice than either individual component or controls, aligning with increased neuronal activity and synaptic plasticity in the hippocampus (HPC) and prefrontal cortex (PFC). Using neural circuit tracing and chemogenetics, we explored the importance of the HPC-PFC circuit. Inhibiting the HPC-PFC circuit reduced the improvement effect of combined training (CT) on cognitive function, whereas activating this circuit enhanced cognitive function. We found candidate molecules responsive to CT in the HPC and PFC using single-cell sequencing. We identified that AE component modulated the expression levels of proprotein convertase subtilisin/kexin type 1 inhibitor (PCSK1N) and lymphocyte antigen 6 family member H (LY6H) in neurons in the HPC and PFC. At the same time, MSET component influenced the expression levels of dipeptidyl peptidase like 6 (DPP6) and glutamate ionotropic receptor NMDA type subunit associated protein 1 (GRINA) in neurons of the HPC and PFC. CT was linked to the upregulation of these molecular targets, which correlated with its beneficial effects. These findings provide insight into the mechanism underlying cognitive improvement associated with CT, suggesting a potential basis for exploring strategies aimed at mitigating cognitive decline through interventions like CT.}, }
@article {pmid41310746, year = {2025}, author = {Niu, X and Yuan, M and Wang, D}, title = {Influence of age, cognitive function, attention, and mental state on the effectiveness of EEG-based brain-computer interface device use: a systematic review.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1186/s12984-025-01813-7}, pmid = {41310746}, issn = {1743-0003}, support = {No.24QNMP077//Health Commission of Sichuan Province Medical Science and Technology Program/ ; No.Q2024016//Sichuan Medical Association Medical Youth Innovation Project/ ; }, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) are increasingly used to support rehabilitation and assistive communication. Individual traits such as age, cognitive function, attention, and mental state have been linked to variability in BCI performance. However, these factors have not been comprehensively evaluated across paradigms and populations.<br><br>METHODS: A systematic review was conducted following PRISMA 2020 guidelines and registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42024600285). PubMed and Web of Science databases were searched through June 2025 for studies reporting electroencephalography (EEG)-based BCI performance metrics stratified by age, cognition, attention, or psychological state. Twenty-five human studies were included after screening. Risk of bias was assessed using validated appraisal tools.<br><br>RESULTS: Across the 25 included studies, visual paradigms such as&#xa0;P300 event-related potential&#xa0;and&#xa0;steady-state visual evoked potential (SSVEP)&#xa0;showed stable performance across age groups.&#xa0;Motor imagery (MI)-based systems demonstrated higher sensitivity to cognitive and developmental differences. Attention scores and mental rotation were positively associated with EEG signal clarity and classification accuracy. Fatigue, motivation, and training duration influenced user responsiveness.<br><br>CONCLUSION: Age and cognitive traits impact BCI performance and system adaptability. To optimize usability in diverse populations, future BCI applications should integrate individualized training strategies, real-time feedback mechanisms, and standardized evaluation metrics.}, }
@article {pmid41281231, year = {2025}, author = {Patel, D and Tanveer, MS and Gonzalez-Ferrer, J and Loeffler, A and Kagan, BJ and Mostajo-Radji, MA and Wang, G}, title = {A Computational Perspective on NeuroAI and Synthetic Biological Intelligence.}, journal = {ArXiv}, volume = {}, number = {}, pages = {}, pmid = {41281231}, issn = {2331-8422}, abstract = {NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied intelligence. In this review, we organize the NeuroAI landscape into three interacting domains: hardware, software, and wetware. We outline computational frameworks that integrate biological and non-biological systems and highlight recent advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning. These developments collectively point toward a new class of systems that compute through interactions between living neural tissue and digital algorithms.}, }
@article {pmid41309636, year = {2025}, author = {Yang, D and Meng, W and Wang, Z and Yu, T and Li, C and Zhang, Q and Zhang, Z and Li, H and Lin, Y and Xue, F and Lin, P and Sun, L}, title = {Polymorphic functionalization driven by ion displacement-induced antiferroelectric ordering in CuBiP2Se6.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {10666}, pmid = {41309636}, issn = {2041-1723}, abstract = {Antiferroelectric two-dimensional materials, with their unique physical mechanisms, exhibit tunable polarization dynamics and layered structural characteristics, enabling the synergistic implementation of synaptic plasticity, sensory-mimetic functionality, and in-memory computing within a unified device architecture. These capabilities meet the growing polymorphic requirements of neuromorphic systems and position such materials as strong candidates for next-generation neuromorphic computing platforms. Among them, CuBiP2Se6 stands out among 2D antiferroelectric materials due to its intrinsic antiferroelectric properties, featuring a stable interlayer antiparallel Cu[+] dipole configuration. This structure, combined with its relaxor-like behavior, enables a reversible transition between antiferroelectric and ferroelectric states under an applied electric field, along with gradual polarization tuning. This transition mechanism enables continuously tunable conductance states, providing essential physical support for the gradual modulation of synaptic weights and the hardware implementation of complex neural functions, making it particularly suited for high-precision emulation of multilevel synaptic plasticity in neuromorphic applications. In this work, memristor based on two-dimensional antiferroelectric CuBiP2Se6 exhibit stable multilevel conductance states, high endurance, and excellent device uniformity, thus supporting diverse neurosynaptic functions and advanced learning rules. These attributes highlight the immense potential of antiferroelectric 2D materials as a foundation for compact, energy-efficient, and highly integrated neuromorphic hardware.}, }
@article {pmid41308307, year = {2025}, author = {Sharma, D and Krekelberg, B}, title = {Predicting spiking activity from scalp EEG.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2541}, pmid = {41308307}, issn = {1741-2552}, abstract = {OBJECTIVE: Despite decades of electroencephalography (EEG) research, the relationship between EEG and underlying spiking dynamics remains unclear. This limits our ability to infer neural dynamics reflected in intracranial signals from EEG, a critical step to bridge electrophysiological findings across species and to develop non-invasive brain-machine interfaces (BMIs). In this study, we aimed to estimate spiking activity in the visual cortex using non-invasive scalp EEG.<br><br>APPROACH: We recorded spiking activity from a 32-channel floating microarray permanently implanted in parafoveal V1 and scalp-EEG in a male macaque monkey. While the animal fixated, the screen flickered at different temporal frequencies to induce steady-state visual evoked potentials (SSVEP). We analyzed the relationship between the V1 multi-unit spiking activity envelope (MUAe) and EEG frequency bands to predict MUAe at each time point from EEG. We extracted instantaneous spectrotemporal features of the EEG signal, including phase, amplitude, and phase-amplitude coupling of its frequency bands.<br><br>MAIN RESULTS: Although the relationship between these spectrotemporal features and the V1 MUAe was complex and frequency-dependent, they were reliably predictive of the MUAe. Specifically, in a linear regression predicting MUAe from EEG, each EEG feature (phase, amplitude, coupling) contributed to model predictions. In addition, we found that MUAe predictions were better in shallow than deep cortical layers, and that the phase of stimulus frequency further improved MUAe predictions.<br><br>SIGNIFICANCE: Our study shows that a comprehensive account of spectrotemporal features of non-invasive EEG provides information on underlying spiking activity beyond what is available when only the amplitude or phase of the EEG signal is considered. This demonstrates the richness of the EEG signal and its complex relationship with neural spiking activity and suggests that using more comprehensive spectrotemporal signatures could improve BMI applications.}, }
@article {pmid41308205, year = {2025}, author = {Jianqiu, W and Yang, B and Chen, X and Chen, J and Kuang, S}, title = {Research on Combining Motor Imagery and Somatosensory Attentional Orientation to Enhance BCI Performance.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae2512}, pmid = {41308205}, issn = {2057-1976}, abstract = {In this study, we propose a motor imagery(MI) method based on Somatosensory Attentional Orientation(SAO) to enhance the performance of MI based brain-computer interfaces (BCI). In this BCI system, participants perform unilateral hand MI tasks while maintaining attention to the corresponding hand, as if the wrist skin is actually receiving tactile stimulation(TS). A total of 44 participants were recruited and randomly divided into the experimental group(SAO and MI joint group, SMI group) and control group(MI group). The MI group performed right hand MI tasks, and two sessions were conducted, the content of the two experiments was identical. Each session was divided into two stages: the first stage including 1 run was the right hand MI mental task with TS on the right wrist, and the second stage including 6 runs was the right hand MI mental task without TS . For SAO group, first session was the same with the MI group. However, the second stage for SAO group was the right hand MI mental task with SAO. Compared with the first session, the performance in the first session was comparable between the MI group and SMI group, indicating similar MI abilities in both set of participants. For SAO group, A 6.5% performance enhancement was observed in the second session relative to the first session(p<0.05). However, no significant improvement was observed in the MI group(p>0.05), indicating no evidence of learning effect. EEG topographic mapping demonstrated robust bilateral hemispheric engagement when right hand MI mental task was performed for MI group. While in the SAO mental task, EEG exhibited clear hemispheric lateralization. This paradigm combining attention mechanisms with MI restructures the bilateral control modality inherent in conventional MI paradigms. As SAO paradigm engages endogenous cognitive processes, this approach augments corticomotor excitability during MI task, thereby improving BCI control performance.}, }
@article {pmid41308097, year = {2025}, author = {Ke, Y and Wang, Z and Liu, S and Ming, D}, title = {A High-speed 120-target SSVEP-BCI Employing Dual-Frequency and Phase Modulation with Minimal Calibration.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3638253}, pmid = {41308097}, issn = {2168-2208}, abstract = {Large instruction-set brain-computer interfaces (BCIs) allow users to issue many commands through a single interface, greatly expanding their application scope. Increasing the number of targets, however, raises encoding complexity and intensifies the trade-off between calibration time and decoding performance. We introduce a 120-target steady-state visual evoked potential (SSVEP)-BCI that pairs dual-frequency phase modulation (DFPM) with a lightweight global multi stimulus canonical correlation analysis-based spatiotemporal filtering (gmsCCA-st) method. DFPM encodes the 120 targets with only 23 low-frequency carriers by simultaneously flickering two frequency-phase tags in a checkerboard pattern, thereby mitigating the "frequency scarcity" problem and eliciting pronounced harmonic and intermodulation responses. Instead of training a separate filter for each target, gmsCCA-st learns a set of shared spatiotemporal filters from all targets. With just one calibration trial per target in the offline experiment, the system achieved a peak information transfer rate (ITR) of 326.49±55.13 bits/min. During online cue-guided spelling, the system attained 94.69±5.99% accuracy and 251.47±25.47 bits/min ITR; in free-spelling mode, accuracy was 91.72±6.89% at 176.64±23.75 bits/min. These findings demonstrate the feasibility of a high-performance 120 target SSVEP-BCI after only three minutes of calibration, overcoming the compromise among instruction-set size, calibration burden, and performance. This study therefore offers a practical pathway toward high-performance, minimal-calibration large instruction-set BCIs.}, }
@article {pmid41307947, year = {2025}, author = {Yu, H and Chen, Y and Li, D and Liu, W and Dong, B and Pei, G}, title = {Dual Processing of Aberrant Data Perception: Evidence From EEG Oscillations.}, journal = {Annals of the New York Academy of Sciences}, volume = {}, number = {}, pages = {}, doi = {10.1111/nyas.70146}, pmid = {41307947}, issn = {1749-6632}, support = {LGG21G010002//Zhejiang Provincial Natural Science Foundation of China/ ; 72401263//National Natural Science Foundation of China/ ; 2025C25080(SYS)//Soft Science Research Program of Zhejiang Province/ ; ZSKT2402//Research Project of Zhejiang Laboratory of Philosophy and Social Sciences - Laboratory of Intelligent Society and Governance, Zhejiang Lab/ ; 2023KFKT003//Open Research Project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior, Shanghai International Studies University/ ; }, abstract = {The perception of aberrant data (PAD) is an essential cognitive ability in human socialization, yet the underlying dual processing mechanisms remain underexplored. Based on dual processing theory, this study uses electroencephalogram (EEG) time-frequency analysis to investigate the mediating role and representational patterns of neural oscillatory activity in automatic processes (APs) and controlled processes (CPs). The results indicated that during the PAD task, β oscillations in the frontal-parietal regions exhibited clear event-related desynchronization in the AP mode, whereas β oscillations displayed prominent event-related synchronization in the CP mode. The brain network excitation mediated by β oscillations was closely followed by brain network inhibition mediated by α oscillations, allowing for effective separation of the dual processing modes in PAD tasks through the β-kα index (p < 0.001). Moreover, in the PAD task, the AP mode was primarily attributed to the efficient communication mediated by cross-frequency phase coherence between β and α oscillations, as well as information integration mediated by intersite phase coherence in the frontal-parietal regions. This study provides a framework for a comprehensive understanding of the dual processing neural mechanisms behind PAD, with promising applications in the study of pathophysiological mechanisms in neurodegenerative diseases and clinical interventions.}, }
@article {pmid41306427, year = {2025}, author = {Zhang, W and Shi, X and Li, M and Zhang, L and Zhang, R and Wu, X and Xin, M and Li, R and Zhang, H and Hu, Y}, title = {Assess the level of consciousness in patients with disorders of consciousness by combining resting-state and auditory-evoked EEG.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1613356}, pmid = {41306427}, issn = {1662-4548}, abstract = {INTRODUCTION: Electroencephalography (EEG) can provide objective neural marker for assessing the level of consciousness of patients with disorders of consciousness (DoC), but current research mainly focuses on the EEG features of a single modality, such as the resting-state or the evoked state, which results in less than ideal assessment accuracy. To accurately assess the level of consciousness of DoC patients, we proposed a new method by combine with resting-state and auditory-evoked EEG.<br><br>METHODS: The EEG data of resting-state and auditory-evoked potential were collected from 157 DoC patients. Then, nonlinear dynamics feature (NDF) include spatiotemporal correlation entropy and neuromodulation intensity of multimodal EEG were extracted. Next, the multi-form feature selection algorithm (MFFS) was adopted to optimize the extracted EEG features. Finally, a diagnosis model was constructed using support vector machine (SVM).<br><br>RESULTS: Among them, SC-Theta, SC-Alpha, NI-Alpha and ERP features were significantly (p < 0.05) correlated with the patient's level of consciousness, resulting in an average grouping accuracy of 92.4%.<br><br>DISCUSSION: The proposed diagnostic model has demonstrated its distinctive advantages, showcasing remarkable effectiveness and reliability in accurately assessing consciousness states. This method holds promise for improving the reliability of clinical awareness assessments.}, }
@article {pmid41305274, year = {2025}, author = {Gomez-Rivera, A and Álvarez-Meza, AM and Cárdenas-Peña, D and Orozco-Gutierrez, A}, title = {Takens-Based Kernel Transfer Entropy Connectivity Network for Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {22}, pages = {}, doi = {10.3390/s25227067}, pmid = {41305274}, issn = {1424-8220}, support = {111091991908//Ministerio de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n/ ; }, mesh = {Humans ; Electroencephalography/methods ; Brain-Computer Interfaces ; Entropy ; Brain/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; *Imagination/physiology ; Neural Networks, Computer ; }, abstract = {Reliable decoding of motor imagery (MI) from electroencephalographic signals remains a challenging problem due to their nonlinear, noisy, and non-stationary nature. To address this issue, this work proposes an end-to-end deep learning model, termed TEKTE-Net, that integrates time embeddings with a kernelized Transfer Entropy estimator to infer directed functional connectivity in MI-based brain-computer interface (BCI) systems. The proposed model incorporates a customized convolutional module that performs Takens' embedding, enabling the decoding of the underlying EEG activity without requiring explicit preprocessing. Further, the architecture estimates nonlinear and time-delayed interactions between cortical regions using Rational Quadratic kernels within a differentiable framework. Evaluation of TEKTE-Net on semi-synthetic causal benchmarks and the BCI Competition IV 2a dataset demonstrates robustness to low signal-to-noise conditions and interpretability through temporal, spatial, and spectral analyses of learned connectivity patterns. In particular, the model automatically highlights contralateral activations during MI and promotes spectral selectivity for the beta and gamma bands. Overall, TEKTE-Net offers a fully trainable estimator of functional brain connectivity for decoding EEG activity, supporting MI-BCI applications, and promoting interpretability of deep learning models.}, }
@article {pmid41305083, year = {2025}, author = {Liyanagedera, ND and Bareham, CA and Kempton, H and Guesgen, HW}, title = {Machine Learning-Based Comparative Analysis of Subject-Independent EEG Data Classification Across Multiple Meditation and Non-Meditation Sessions.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {22}, pages = {}, doi = {10.3390/s25226876}, pmid = {41305083}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Machine Learning ; *Meditation ; Male ; Algorithms ; Adult ; Female ; Brain-Computer Interfaces ; Brain/physiology ; }, abstract = {In this study, subject-independent (inter-subject), multiple-session electroencephalography (EEG) data classification was tested for loving-kindness meditation (LKM) and non-meditation. This is a novel study that extends our previous work on intra-subject, multiple-session classification. Here, two meditation techniques, LKM-Self and LKM-Other, were independently compared with non-meditation. For each mental task, five sessions of data collected from each of the twelve participants were placed in a common data pool, from which randomly selected session data were used for training and testing the machine learning algorithms. Three previously tested BCI pipelines were used. In each case, feature extraction was performed using common spatial patterns (CSPs), short-time Fourier transform (STFT), or a fusion of CSP and STFT, followed by classification using a neural network structure. This study was further divided into three cases, where two, three, or four session pairs were used to train the algorithms, and the remaining session pair was used for testing. For each individual instance, the test was repeated thirty times to generalize the results. Thus, a total of 9900 independent tests were conducted for the entire dataset. The mean classification accuracies obtained in this study were lower than those reported in our previous intra-subject classification study. For example, in LKM-Self/non-meditation classification using three session pairs with the CSP + STFT pipeline, the mean accuracy for all tests was 62.3%, with the bottom 50% at 46.0% and the top 50% at 78.3%, demonstrating variability across session selections. The corresponding intra-subject classification result for the same instance was 72.1%. For all other instances, a similar pattern was observed. Furthermore, when considering all mean accuracies obtained, in 83.3% of the instances, CSP + STFT produced better classification accuracies than CSP or STFT alone. At the same time, in 75.0% of the instances, increasing the number of training session pairs led to improved classification accuracies. This study demonstrates that the subject-independent, multiple-session EEG classification of meditation and non-meditation is feasible for specific session combinations. Further research is needed to confirm these findings across larger and more diverse participant groups. These findings provide a foundation for developing subject-independent algorithms that can guide long-term meditation practice.}, }
@article {pmid41303071, year = {2025}, author = {Moskiewicz, D and Sarzyńska-Długosz, I}, title = {Modern Technologies Supporting Motor Rehabilitation After Stroke: A Narrative Review.}, journal = {Journal of clinical medicine}, volume = {14}, number = {22}, pages = {}, doi = {10.3390/jcm14228035}, pmid = {41303071}, issn = {2077-0383}, support = {Journal of Clinical Medicine//Invitation letter - *100% discount* for my submission/ ; }, abstract = {Introduction: Stroke remains one of the leading causes of long-term disability worldwide. Post-stroke motor recovery depends on neuroplasticity, which is stimulated by intensive, repetitive, and task-specific training. Modern technologies such as robotic rehabilitation (RR), virtual reality (VR), functional electrical stimulation (FES), brain-computer interfaces (BCIs), and non-invasive brain stimulation (NIBS) offer novel opportunities to enhance rehabilitation. They operate through sensory feedback, neuromodulation, and robotic assistance which promote neural reorganization and motor relearning. Neurobiological Basis of Motor Recovery: Mechanisms such as long-term potentiation, mirror neuron activation, and cerebellar modulation underpin functional reorganization after stroke. Literature Review Methodology: A narrative review was conducted of studies published between 2005 and 2025 using PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar. Randomized controlled trials, cohort studies, and systematic reviews assessing the efficacy of these modern technologies were analyzed. Literature Review: Evidence indicates that RR, VR, FES, BCIs, and NIBS improve upper and lower limb motor function and strength, and enhance activities of daily living, particularly when combined with conventional physiotherapy (CP). Furthermore, integrated rehabilitation technologies (IRT) demonstrate synergistic neuroplastic effects. Discussion: Modern technologies enhance therapy precision, intensity, and motivation but face challenges related to cost, standardization, and methodological heterogeneity. Conclusions: RR, VR, FES, BCIs, NIBS, and IRT are effective complements to CP. Early, individualized, and standardized implementation can optimize neuroplasticity and functional recovery.}, }
@article {pmid41302782, year = {2025}, author = {Aydın, S and Melek, M and Gökrem, L}, title = {Intersession Robust Hybrid Brain-Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism.}, journal = {Micromachines}, volume = {16}, number = {11}, pages = {}, doi = {10.3390/mi16111264}, pmid = {41302782}, issn = {2072-666X}, support = {36126//T&#xfc;rkiye Sa&#x11f;l&#x131;k Enstit&#xfc;leri Ba&#x15f;kanl&#x131;&#x11f;&#x131;/ ; }, abstract = {This study introduces a hybrid Brain-Computer (BCI) system with a robust and secure activation mechanism between sessions, aiming to minimize the negative effects of visual stimulus-based BCI systems on user eye health. The system is based on the integration of Electroencephalography (EEG) signals and Electrooculography (EOG) artefacts, and includes an LED stimulus operating at a frequency of 7 Hz for safe activation and objects moving in different directions. While the LED functions as an activation switch that reduces visual fatigue caused by traditional visual stimuli, moving objects provide command generation depending on the user's intention. In order to evaluate the stability of the system against physiological and psychological conditions, data were collected from 15 participants in two different sessions. The Correlation Alignment (CORAL) method was applied to the data to reduce the variance between sessions and to increase stability. A Bootstrap Aggregating algorithm was used in the classification processes, and with the CORAL method, the system accuracy rate was increased from 81.54% to 94.29%. Compared to similar BCI approaches, the proposed system offers a safe activation mechanism that effectively adapts to users' changing cognitive states throughout the day by reducing visual fatigue, despite using a low number of EEG channels, and demonstrates its practicality and effectiveness by performing on par or superior to other systems in terms of high accuracy and robust stability.}, }
@article {pmid41301176, year = {2025}, author = {Kim, HG and Kim, JY}, title = {EEG-Based Local-Global Dimensional Emotion Recognition Using Electrode Clusters, EEG Deformer, and Temporal Convolutional Network.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {11}, pages = {}, doi = {10.3390/bioengineering12111220}, pmid = {41301176}, issn = {2306-5354}, support = {NRF-2023R1A2C1006756//National Research Foundation of Korea(NRF)/ ; }, abstract = {Emotions are complex phenomena arising from cooperative interactions among multiple brain regions. Electroencephalography (EEG) provides a non-invasive means to observe such neural activity; however, as it captures only electrode-level signals from the scalp, accurately classifying dimensional emotions requires considering both local electrode activity and the global spatial distribution across the scalp. Motivated by this, we propose a brain-inspired EEG electrode-cluster-based framework for dimensional emotion classification. The model organizes EEG electrodes into nine clusters based on spatial and functional proximity, applying an EEG Deformer to each cluster to learn frequency characteristics, temporal dynamics, and local signal patterns. The features extracted from each cluster are then integrated using a bidirectional cross-attention (BCA) mechanism and a temporal convolutional network (TCN), effectively modeling long-term inter-cluster interactions and global signal dependencies. Finally, a multilayer perceptron (MLP) is used to classify valence and arousal levels. Experiments on three public EEG datasets demonstrate that the proposed model significantly outperforms existing EEG-based dimensional emotion recognition methods. Cluster-based learning, reflecting electrode proximity and signal distribution, effectively captures structural patterns at the electrode-cluster level, while inter-cluster information integration further captures global signal interactions, thereby enhancing the interpretability and physiological validity of EEG-based dimensional emotion analysis. This approach provides a scalable framework for future affective computing and brain-computer interface (BCI) applications.}, }
@article {pmid41300224, year = {2025}, author = {Kuipers, JA and Hoffman, NH and Carrick, FR and Jemni, M}, title = {Reconnecting Brain Networks After Stroke: A Scoping Review of Conventional, Neuromodulatory, and Feedback-Driven Rehabilitation Approaches.}, journal = {Brain sciences}, volume = {15}, number = {11}, pages = {}, doi = {10.3390/brainsci15111217}, pmid = {41300224}, issn = {2076-3425}, abstract = {BACKGROUND: Stroke leads to lasting disability by disrupting the connectivity of functional brain networks. Although several rehabilitation methods are promising, our full understanding of how these strategies restore network function is still limited. Here, we map how non-invasive brain stimulation (NIBS), brain-computer interface (BCI)/neurofeedback, virtual reality (VR), and robot-assisted therapy restore connectivity within the sensorimotor network (SMN), default mode network (DMN), and salience network, and we contextualize these effects within the known temporal evolution of post-stroke motor network reorganization.<br><br>METHODS: This scoping review adhered to PRISMA guidelines and searched PubMed, Cochrane, and Medline from January 2015 to January 2025 for clinical trials focused on stroke rehabilitation with functional connectivity outcomes. Included studies used conventional therapy, neuromodulation, or feedback-based interventions.<br><br>RESULTS: Twenty-three studies fulfilled the inclusion criteria, covering interventions like robotic training, transcranial stimulation (tDCS/TMS), brain-computer interfaces, virtual reality, and cognitive training. Motor impairments were linked to disrupted interhemispheric sensorimotor connectivity, while cognitive issues reflected changes in frontoparietal and default mode networks. Combining neuromodulation with feedback-based methods showed better network recovery than standard therapy alone, with clinical improvements closely associated with connectivity alterations.<br><br>CONCLUSIONS: Effective stroke rehabilitation depends on targeting specific disrupted networks through various modalities. Robotic interventions focus on restoring structural motor pathways, feedback-enhanced methods improve temporal synchronization, and cognitive training aims to enhance higher-order network integration. Future research should work toward standardizing connectivity assessment protocols and conducting multicenter trials. This will help develop evidence-based, network-focused rehabilitation guidelines that effectively translate mechanistic insights into personalized clinical treatments.}, }
@article {pmid41300174, year = {2025}, author = {Zhang, L and Zhang, X and Zhang, X and Yu, C and Liu, X}, title = {Objective Emotion Assessment Using a Triple Attention Network for an EEG-Based Brain-Computer Interface.}, journal = {Brain sciences}, volume = {15}, number = {11}, pages = {}, doi = {10.3390/brainsci15111167}, pmid = {41300174}, issn = {2076-3425}, abstract = {Background: The assessment of emotion recognition holds growing significance in research on the brain-computer interface and human-computer interaction. Among diverse physiological signals, electroencephalography (EEG) occupies a pivotal position in affective computing due to its exceptional temporal resolution and non-invasive acquisition. However, EEG signals are inherently complex, characterized by substantial noise contamination and high variability, posing considerable challenges to accurate assessment. Methods: To tackle these challenges, we propose a Triple Attention Network (TANet), a triple-attention EEG emotion recognition framework that integrates Conformer, Convolutional Block Attention Module (CBAM), and Mutual Cross-Modal Attention (MCA). The Conformer component captures temporal feature dependencies, CBAM refines spatial channel representations, and MCA performs cross-modal fusion of differential entropy and power spectral density features. Results: We evaluated TANet on two benchmark EEG emotion datasets, DEAP and SEED. On SEED, using a subject-specific cross-validation protocol, the model reached an average accuracy of 98.51 ± 1.40%. On DEAP, we deliberately adopted a segment-level splitting paradigm-in line with influential state-of-the-art methods-to ensure a direct and fair comparison of model architecture under an identical evaluation protocol. This approach, designed specifically to assess fine-grained within-trial pattern discrimination rather than cross-subject generalization, yielded accuracies of 99.69 ± 0.15% and 99.67 ± 0.13% for the valence and arousal dimensions, respectively. Compared with existing benchmark approaches under similar evaluation protocols, TANet delivers substantially better results, underscoring the strong complementary effects of its attention mechanisms in improving EEG-based emotion recognition performance. Conclusions: This work provides both theoretical insights into multi-dimensional attention for physiological signal processing and practical guidance for developing high-performance, robust EEG emotion assessment systems.}, }
@article {pmid41295260, year = {2025}, author = {Di Liddo, R and Naso, F and Gandaglia, A and Sturaro, G and Spina, M and Melder, RJ}, title = {Enhanced Detection of αGal Using a Novel Monoclonal IgG1 Antibody: Comparative Evaluation with IgM Antibody [Clone M86].}, journal = {Journal of personalized medicine}, volume = {15}, number = {11}, pages = {}, pmid = {41295260}, issn = {2075-4426}, abstract = {Introduction. Over the past two decades, the αGal (Galα1-3Galβ1-4GlcNAc-R) epitope, a carbohydrate found in many non-primate mammals, has gained significant relevance in medicine due to its association with an increasing number of allergic reactions to animal-derived foods, drugs, and medical devices. Due to a mutated gene coding for α1,3-galactosyltransferase (α1-3GT), humans lack αGal and, therefore, naturally produce anti-α-Gal antibodies (IgM, IgA, and IgG), especially in the context of a xenotransplantation, which can lead to extreme immunological reactivity, including hyperacute rejection of the transplant. Recently, these uncontrollable immune reactions have driven demand for more accurate procedures to better detect αGal in animal-derived foods or bioprosthetics. The currently most widely used α-Gal-specific monoclonal antibody is an IgM antibody (clone M86), developed in Ggta1 KO mice and isolated from hybridoma tissue culture. As the IgM isotype has limited purification properties, specificity, and sensitivity, we aimed to produce a novel IgG antibody with high affinity and extensive applicability. Methods. An experimental murine IgG1 anti-αGal antibody (IgG-αGalomab) was developed by immunization of Ggta1 knockout (KO) mice, and its affinity was evaluated using ELISA, Western blot, flow cytometry, and immunohistochemistry/immunofluorescence. Results. Compared to IgM-M86, IgG-αGalomab demonstrated ~1200-fold higher binding potency and lower cross-reactivity with competitive molecules, i.e., bovine serum albumin, galactobiose, and lactose. Unlike IgM-M86, IgG-αGalomab showed an increasing affinity over time in the binding tests performed on xenogeneic tissues. Notably, high-affinity for αGal was detected by Western blot at high dilution [1:200,000] of IgG-αGalomab compared to IgM-M86 [1:1000]. By flow cytometry, specificity and dose-dependent response were confirmed using in vitro cultures of porcine and human fibroblasts. Finally, in immunofluorescence and immunohistochemistry analysis, αGal was demonstrated to be detectable by IgG-αGalomab at a dilution of [1:1000], while IgM-M86 was demonstrated to be detectable at [1:100]. Conclusions. Altogether, our newly developed antibody showed high sensitivity and specificity for α-Gal in various applications. Based on its potential binding capacity, IgG-αGalomab could have important applications in precision medicine for predicting, treating, and preventing immune-mediated phenomena of patients in different medical areas.}, }
@article {pmid41299162, year = {2025}, author = {Meng, L and Song, Z and Lu, J}, title = {Brain-imager: a multimodal framework for image reconstruction and captioning from human brain activity.}, journal = {Brain informatics}, volume = {12}, number = {1}, pages = {32}, pmid = {41299162}, issn = {2198-4018}, support = {62073061//National Natural Science Foundation of China/ ; 2025A1515011602//Guangdong Basic and Applied Basic Research Foundation/ ; }, abstract = {OBJECTIVE: The reconstruction of visual stimuli and captions from brain activity offers a distinctive viewpoint on how perception reconstructs the external world within neural dynamics. Despite considerable advancements in deep generative models in recent years, simultaneously generating images and captions with both detailed accuracy and semantic consistency remains a significant challenge.<br><br>METHODS: We introduce panoptic segmentation and generative semantics for the first time, offering enhanced, multi-level data support and a novel perspective in the domain of brain decoding. Using multi-scale fusion techniques, we integrate pixel features from natural images with structural features from panoptic segmentation, creating a state-of-the-art "initial guess." Building upon the neural paradigm that we discovered, we propose an innovative semantic connection strategy to guide image reconstruction. Additionally, by fine-tuning visual semantics within the encoded space compressed by a language model and further combining our advanced retrieval module with the comprehension capabilities of large language models (LLMs), we generate high-quality brain captions.<br><br>RESULTS: Experimental results demonstrate that we surpass current methods in visual decoding and brain captioning tasks. We offer a webpage to showcase the results: www.neuai4science.cn:5001/brain_visual_decode .<br><br>CONCLUSION: Our proposed Brain-Imager framework, which incorporates multi-level data and semantic guidance, sets a new standard in the domain.<br><br>SIGNIFICANCE: This work provides a novel perspective on the relationship between text and image semantics and the visual pathways of the human brain, with potential applications in downstream tasks such as brain-computer interfaces. Additionally, our code is publicly available at https://github.com/songqianyi01/Brain-Imager .}, }
@article {pmid41298548, year = {2025}, author = {Ciferri, M and Ferrante, M and Toschi, N}, title = {Reconstructing music perception from brain activity using a prior guided diffusion model.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {42108}, pmid = {41298548}, issn = {2045-2322}, support = {101070908//European Union's European Innovation Council/ ; 101017727//European Union's Horizon 2020 Research and Innovation Programme/ ; }, mesh = {Humans ; *Music ; *Auditory Perception/physiology ; *Brain/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; Bayes Theorem ; Male ; Female ; Adult ; Brain Mapping/methods ; Young Adult ; Models, Neurological ; Acoustic Stimulation ; }, abstract = {Reconstructing music directly from brain activity provides insight into the neural representations underlying auditory processing and paves the way for future brain-computer interfaces. We introduce a fully data-driven pipeline that combines cross-subject functional alignment with bayesian decoding in the latent space of a diffusion-based audio generator. Functional alignment projects individual fMRI responses onto a shared representational manifold, increasing the performance of cross-participant accuracy with respect to anatomically normalized baselines. A bayesian search over latent trajectories then selects the most plausible waveform candidate, stabilizing reconstructions against neural noise. Crucially, we bridge CLAP's multi-modal embeddings to music-domain latents through a dedicated aligner, eliminating the need for hand-crafted captions and preserving the intrinsic structure of musical features. Evaluated on ten diverse genres, the model achieves a cross-subject-averaged identification accuracy of [Formula: see text], and produces audio that human listeners recognize above chance in 85.7% of trials. Voxel-wise analyses locate the predictive signal within a bilateral circuit spanning early auditory, inferior-frontal, and premotor cortices, consistent with hierarchical and sensorimotor theories of music perception. The framework establishes a principled bridge between generative audio models and cognitive neuroscience.}, }
@article {pmid41296974, year = {2025}, author = {Zhang, J and Zhang, L and Mu, F and Huang, Z and Zou, C and Huang, R and Wang, C and Cheng, H}, title = {Spatiotemporal Dynamics Modeling of Brain Activity for Human-Robot Cognitive Interaction: ADistributed-Lumped Parameter System Framework.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2025.3631130}, pmid = {41296974}, issn = {2162-2388}, abstract = {This article investigates the system modeling problem for the dynamical process of human brain activity in human-robot cognitive interaction (HRCI). An important novelty of the proposed approaches is to build a computational model of a human-distributed robot-lumped parameter system (HDRLPS) that describes the inherent dynamical principle of human brain activity (with spatiotemporal-varying characteristic) undergoing the interaction between the intrinsic cognitive dynamics and extrinsic robot stimuli. A deterministic learning (DL)-based spatiotemporal dynamics identification scheme is proposed to accurately identify the spatiotemporal dynamics of HDRLS and obtain the associated knowledge as a constant radial basis functional neural network (RBF NN) model. A spatiotemporal dynamics estimator is designed with this model, which can accurately evaluate and monitor the dynamical process of human brain activity in real-time HRCI by the generated dynamics-synchronized state. The effectiveness and practicability of the approaches in the dynamics identification and evaluation for the human brain activity in HRCI are validated by the thorough analysis, including the mathematical proof, the simulation study, and the brain-computer interface (BCI) experiment using publicly available datasets. Our method is compared with state-of-the-art (SOTA) methods, such as LGGNet, EEGNet, Tsception, EEG-Deformer, EEG-Transformer, and EEGViT. The results show that our method can outperform these methods with better recognition accuracy and macro- $F1$ scores. The source code can be found at: https://github.com/alonexing/source_code/tree/master.}, }
@article {pmid41296962, year = {2025}, author = {Wang, J and Bi, L and Wei, Y and Fei, W and Liu, H and Miao, D}, title = {EEG-Based Movement Decoding in Motor-Impaired Patients by Extracting and Aligning Neural Patterns with Healthy Individuals.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3637053}, pmid = {41296962}, issn = {2168-2208}, abstract = {Decoding human movement intentions from electroencephalography (EEG) signals is critical for brain-computer interface (BCI) applications in motor neurorehabilitation, active assistance, and functional augmentation. However, current BCI models face two challenges for motor-impaired patients: 1) prolonged EEG data collection from patients is difficult; 2) differences in brain functional structures and motor behaviors between healthy individuals and patients limit the generalizability of models trained on healthy individuals' EEG data. To address these challenges, this study proposes a transfer learning-based model, TL-ME, to bridge the gap between healthy individuals' and patients' EEG data and improve movement decoding accuracy for patients. TL-ME integrates an attention-based feature extractor, adversarial domain discriminator, multi-source selection, and movement classifier to transfer knowledge from healthy individuals' EEG data (source domain) to patients' EEG data (target domain). Temporal and spectral visualizations are used to inspect brain activation patterns for shared motor tasks between healthy individuals and patients. Experimental results show a 10.8% improvement in upper-limb movement decoding's accuracy using TL-ME, with each module contributing to performance gains. Visualization analyses also demonstrate similar brain activation patterns across domains, validating the transferability of healthy individuals' EEG data to patient-specific models. This work introduces a novel cross-population transfer learning approach that leverages healthy individuals' EEG data to enhance neural decoding for motor-impaired patients, bridging the gap between experimental studies and real-world applications in BCI-based neurorehabilitation.}, }
@article {pmid41295901, year = {2025}, author = {Turner, S and Yadav, P and Morrin, H and Bhat, A}, title = {The future of psychiatry: clinical practice, diagnosis, and treatment.}, journal = {International review of psychiatry (Abingdon, England)}, volume = {}, number = {}, pages = {1-22}, doi = {10.1080/09540261.2025.2594523}, pmid = {41295901}, issn = {1369-1627}, abstract = {This paper overviews the future of clinical practice in psychiatry, covering diagnosis, treatment, and public health. We consider recent advances and new controversies as psychiatry moves from a categorical to a dimensional approach to diagnosing and classifying mental illness; as well as the potential pitfalls of overdiagnosis, underdiagnosis, and misdiagnosis. We also review some of the most exciting new developments in treatment modalities, such as psychedelic treatments, ketamine, and new antipsychotics. The potential of interventional psychiatry using technology, and review techniques including neuromodulation, neurofeedback, brain-computer interfaces, AI-assisted psychotherapy, and virtual reality is also discussed in the context of future of public mental health strategy, including the important issue of online disinformation and how it can influence the public's understanding of mental health. Finally, we consider the evolving understanding of addiction, particularly behavioural and technological addictions. We conclude with a brief discussion of how best to influence the political leadership in using these new advances to develop evidence-based, scientifically-informed healthcare policy.}, }
@article {pmid41294772, year = {2025}, author = {Qi, L and Wang, Y and Liang, X}, title = {Emerging Implantable Sensor Technologies at the Intersection of Engineering and Brain Science.}, journal = {Biosensors}, volume = {15}, number = {11}, pages = {}, doi = {10.3390/bios15110762}, pmid = {41294772}, issn = {2079-6374}, support = {32200882//National Natural Science Foundation of China/ ; 5250071402//National Natural Science Foundation of China/ ; 2024YQB048//Young Doctoral Program of Xinqiao Hspital/ ; 0//Chongqing Brain Science Key Project/ ; }, mesh = {Humans ; *Biosensing Techniques ; *Brain/physiology ; Brain-Computer Interfaces ; *Prostheses and Implants ; Transistors, Electronic ; Animals ; }, abstract = {Advances in implantable sensor technologies are revolutionizing the landscape of brain science by enabling chronic, precise, and multimodal interfacing with neural tissues. With the convergence of material science, electronics, and neurobiology, flexible, wireless, bioresorbable, and multimodal sensors are expanding the frontiers of diagnosis, therapy, and brain-machine interfacing. This review presents the latest breakthroughs in implantable neural sensor technologies, emphasizing bio-integration, signal fidelity, and functional adaptability. We highlight innovations such as CMOS-integrated flexible probes, internal ion-gated organic electrochemical transistors (IGTs), multimodal neurotransmitter-electrophysiology sensors, and wireless energy systems. Finally, we discuss the clinical potential, translational challenges, and future directions for brain science and neuroengineering. We further benchmark transduction and analytical performance in physiological media and outline in vivo calibration, antifouling/packaging, and on-node data-efficient processing for long-term stability.}, }
@article {pmid41294401, year = {2025}, author = {Gkintoni, E and Halkiopoulos, C}, title = {Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {11}, pages = {}, doi = {10.3390/biomimetics10110730}, pmid = {41294401}, issn = {2313-7673}, abstract = {Background: Electroencephalography (EEG) offers millisecond-precision measurement of neural oscillations underlying human cognition and emotion. Despite extensive research, systematic frameworks mapping EEG metrics to psychological constructs remain fragmented. Objective: This interdisciplinary scoping review synthesizes current knowledge linking EEG signatures to affective and cognitive models from a neuroscience perspective. Methods: We examined empirical studies employing diverse EEG methodologies, from traditional spectral analysis to deep learning approaches, across laboratory and naturalistic settings. Results: Affective states manifest through distinct frequency-specific patterns: frontal alpha asymmetry (8-13 Hz) reliably indexes emotional valence with 75-85% classification accuracy, while arousal correlates with widespread beta/gamma power changes. Cognitive processes show characteristic signatures: frontal-midline theta (4-8 Hz) increases linearly with working memory load, alpha suppression marks attentional engagement, and theta/beta ratios provide robust cognitive load indices. Machine learning approaches achieve 85-98% accuracy for subject identification and 70-95% for state classification. However, significant challenges persist: spatial resolution remains limited (2-3 cm), inter-individual variability is substantial (alpha peak frequency: 7-14 Hz range), and overlapping signatures compromise diagnostic specificity across neuropsychiatric conditions. Evidence strongly supports integrated rather than segregated processing, with cross-frequency coupling mechanisms coordinating affective-cognitive interactions. Conclusions: While EEG-based assessment of mental states shows considerable promise for clinical diagnosis, brain-computer interfaces, and adaptive technologies, realizing this potential requires addressing technical limitations, standardizing methodologies, and establishing ethical frameworks for neural data privacy. Progress demands convergent approaches combining technological innovation with theoretical sophistication and ethical consideration.}, }
@article {pmid41293812, year = {2025}, author = {Ivanov, N and Wong, M and Chau, T}, title = {A Multi-Class Intra-Trial Trajectory Analysis Technique to Visualize and Quantify Variability of Mental Imagery EEG Signals.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2550075}, doi = {10.1142/S0129065725500753}, pmid = {41293812}, issn = {1793-6462}, abstract = {High inter- and intra-individual variation is a prominent characteristic of electroencephalography (EEG) signals and a significant inhibitor to the practical implementation of brain-computer interfaces (BCIs) outside of research laboratories. However, a few methods exist to assess EEG signal variability. Here, a novel multi-class intra-trial trajectory (MITT) analysis to study EEG variability for mental imagery BCIs is presented. The methods yield insight into different aspects of signal variation, specifically (i) inter-individual, (ii) inter-task, (iii) inter-trial, and (iv) intra-trial. A novel representation of the time evolution of EEG signals was developed. Task trials were segmented into short temporal windows and represented in a feature space derived from unsupervised clustering of trial covariance matrices. Using this representation, temporal trajectories through the feature space were constructed. Two metrics were defined to assess user performance based on these trajectories: (1) InterTaskDiff, based on time-varying distances between the mean trajectories of different tasks, and (2) InterTrialVar, which measured the inter-trial variation of the temporal trajectories along the feature dimensions. Analysis of three-class BCI data from 14 adolescents revealed both metrics correlated significantly with classification results. Further analysis of intra-trial trajectories suggested the existence of characteristic task- and user-specific temporal dynamics. The participant-specific insights provided by MITT analysis could be used to overcome EEG-variability challenges impeding practical implementation of BCIs by elucidating avenues to improve user training feedback or selection of user-optimal classifiers and hyperparameters.}, }
@article {pmid41293116, year = {2025}, author = {Zheng, M and Qian, Z and Zhao, T}, title = {Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1689647}, pmid = {41293116}, issn = {1662-4548}, abstract = {INTRODUCTION: Motor-imagery (MI) brain-computer interfaces often suffer from limited EEG datasets and redundant channels, hampering both accuracy and clinical usability. We address these bottlenecks by presenting a unified framework that simultaneously boosts classification performance, reduces the number of required sensors, and eliminates the need for extra recordings.<br><br>METHODS: A three-stage pipeline is proposed. (1) Wavelet-packet decomposition (WPD) partitions each MI class into low-variance "stable" and high-variance "variant" trials; sub-band swapping between matched pairs generates synthetic trials that preserve event-related desynchronization/synchronization signatures. (2) Channel selection uses wavelet-packet energy entropy (WPEE) to quantify both spectral-energy complexity and class-separability; the top-ranked leads are retained. (3) A lightweight multi-branch network extracts multi-scale temporal features through parallel dilated convolutions, refines spatial patterns via depth-wise convolutions, and feeds the fused spatiotemporal tensor to a Transformer encoder with multi-head self-attention; soft-voted fully-connected layers deliver robust class labels.<br><br>RESULTS: On BCI Competition IV 2a and PhysioNet MI datasets the proposed method achieves 86.81 and 86.64% mean accuracies, respectively, while removing 27% of sensors. These results outperform the same network trained on all 22 channels, and paired t-tests confirm significant improvements (p&#x202f;<&#x202f;0.01).<br><br>DISCUSSION: Integrating WPD-based augmentation with WPEE-driven channel selection yields higher MI decoding accuracy with fewer channels and without extra recordings. The framework offers a computationally efficient, clinically viable paradigm for enhanced EEG classification in resource-constrained settings.}, }
@article {pmid41292958, year = {2025}, author = {Lichenstein, SD and Weng, Y and Robinson, H and Rodriguez, L and Babaeianjelodar, M and Maynard, J and Metayer, M and Suneja, S and Horien, C and Greene, AS and Constable, RT and Moore, TM and Barzilay, R and Yip, SW and Keller, AS}, title = {Multivariate environmental exposures are reflected in whole-brain functional connectivity and cognition in youth.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.11.13.688261}, pmid = {41292958}, issn = {2692-8205}, abstract = {Each individual's complex, multidimensional environment, known as their 'exposome', plays an essential role in shaping cognitive neurodevelopment. Understanding the mechanisms whereby children's exposome influences their development is crucial to facilitate the design of interventions to foster positive developmental trajectories for all youth. Recent work has identified a general exposome factor associated with socio-economic inequality that is strongly related to cognition and individual differences in the spatial organization of functional brain networks in youth. Building on these findings, the current study explores whether alterations in functional connectivity may represent a potential mechanism linking variation in the exposome to cognitive performance. We apply a data-driven, cross-validated, whole-brain machine learning approach, connectome-based statistical inference, to identify patterns of functional connectivity associated with exposome scores among early adolescents enrolled in the Adolescent Brain Cognitive Development (ABCD) Study using data collected during three cognitive tasks and during rest. Additionally, we investigate whether the identified patterns of functional connectivity relate to individual differences in cognitive performance across three domains: General Cognition, Executive Functioning, and Learning/Memory. Models incorporating 10-fold cross-validation over 100 iterations identified consistent functional connections associated with the exposome across task and rest conditions (model performance: ns = 6,137-8,391, rs = 0.34 - 0.44, ps <.001). Results were robust across data collection sites and functional connections common across all significant models were associated with cognitive performance across domains (ps < 0.0009). Collectively, these findings reveal that multidimensional environmental exposures are reflected in patterns of functional connectivity and relate to cognitive functioning among youth.}, }
@article {pmid41248548, year = {2025}, author = {Lian, K and Liu, H and Fang, Z and Peng, Y and Padfield, N and Yang, B and Kong, W and Cichocki, A}, title = {P[2]CSL: cross-subject EEG classification by subspace class prototype-based progressive confident target sample labeling.}, journal = {Journal of neural engineering}, volume = {22}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ae204c}, pmid = {41248548}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods/classification ; Male ; Adult ; *Brain/physiology ; Female ; Algorithms ; Young Adult ; }, abstract = {Objective.Domain adaptation (DA) has achieved remarkable performance in cross-subject electroencephalogram (EEG) decoding by mitigating the inter-subject data distribution discrepancies. However, when exploring the feature alignment subspace and performing self-supervised pseudo-labeling in an iterative way, two difficulties are often encountered: one is that unreliable target labeling results inevitably mislead the domain-free feature learning process in the early stage and the other is that the contribution of source and target samples should be balanced in the later stage.Approach.To address both issues, this paper proposes prototype-based progressive confident target sample labeling (P[2]CSL) method to use subspace class prototypes to assist in labeling target samples under the unified framework of domain-invariant EEG feature learning and the self-supervised target sample labeling, and progressively incorporate confident target samples into DA model fitting. The underlying rationality is that early-stage pseudo-labels from unconverged models are prone to error propagation, requiring auxiliary mechanisms to ensure their reliability and stabilize training. With the gradual alignment of cross-subject features, the estimated pseudo-label information of target domain will be more reliable, meaning that more target samples should be involved in model training.Main results.Experiments on emotion recognition and inner speech decoding demonstrate the competitive performance of P[2]CSL in cross-subject EEG classification in comparison with SOTA methods.Significance.Our study indicates the effectiveness of jointly considering the reliability of target samples and their contribution to model training in the context of DA. In addition, some fine-grained results including the sample confidence allocation strategy, the DA effects, and the dynamic model optimization process are provided to further illustrate the model execution details.}, }
@article {pmid41289136, year = {2025}, author = {Liu, Y and Wu, W and Gui, Z and Yan, D and Wang, Z and Han, N and Gao, R and Zhang, Z and Cui, L and Wu, J and Ming, D}, title = {The Enhancement Efficacy of Motor Imagery Based on Gait Phase Encoding Sequential Electrical Stimulation in Stroke Patients.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3637128}, pmid = {41289136}, issn = {1558-0210}, abstract = {Motor imagery-based brain-computer interface (MI-BCI) has been widely used to promote stroke rehabilitation. However, the conventional lower limb MI paradigm can only induce weak brain activation in stroke patients and cannot effectively guide patients to generate pronounced features during MI tasks, limiting the widespread application of MI-BCI. In this study, we applied a novel walking MI paradigm based on gait phase encoding sequential sensory threshold electrical stimulation (SES-MI) in stroke patients, and systematically explored the efficacy of SES-MI in enhancing brain response patterns and improving classification accuracy, compared with the MI paradigm only with text cues (Non-MI) and with invariable electrical stimulation (IES-MI). Thirteen stroke patients were recruited for this experiment. Event-related spectral perturbation (ERSP) was utilized to supply details about the event-related desynchronization (ERD) phenomenon. Brain activation region, intensity and functional connectivity were compared among the three paradigms. SES-MI induced stronger and wider-area ERD activation than Non-MI and IES-MI. In the somatosensory cortex, the ERD amplitudes of SES-MI increased by a maximum of 115% in contrast to Non-MI. The enhancement of activation in bilateral sensorimotor cortex and prefrontal cortex was observed in SES-MI. The increased brain excitability only occurred in the alpha frequency band. Compared with Non-MI, decreased functional connectivity between different brain regions was found in SES-MI and IES-MI, especially in SES-MI. In the alpha+beta bands, the 2-class classification accuracy for SES-MI vs. SES-Idle (81.30%) was significantly improved compared with the other two paradigms. This work demonstrates that SES-MI is a more efficient paradigm for the modulation of the brain activation patterns, having the potential to promote the development of MI-BCI in stroke lower limb rehabilitation.}, }
@article {pmid41289134, year = {2025}, author = {Cai, X and Xue, C and Cao, L and Guo, Z and Xu, H and Zhang, S and Fan, C and Jia, J}, title = {A Novel Brain-Computer Interface Application: Precise Decoding of Urination and Defecation Motor Attempts in Spinal Cord Injury Patients.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3637066}, pmid = {41289134}, issn = {1558-0210}, abstract = {Patients with spinal cord injury (SCI) often face urinary and defecation dysfunction, and existing treatments have limited effectiveness. Brain-computer interface (BCI) technology has been shown to have positive effects on the rehabilitation of SCI patients, but its application in promoting the recovery of urinary and defecation functions has not been explored. This study proposes a new BCI application approach and develops an accurate decoding model targeted at urination and defecation motor attempt tasks. Specifically, we designed a Bidirectional Temporal Convolutional Network (UDCNN-BiTCN) to decode both the suppressed urination and defecation (S-UD) task and the urination and defecation (UD) task. Seventy-one participants (including 44 healthy controls and 27 SCI patients) were recruited for the experiment. The results showed that UDCNN-BiTCN achieved an average accuracy of 91.47% on the S-UD task and 91.81% on the UD task. The study also conducted within-subject cross-task transfer learning and cross-subject experiments, further validating the superiority of the model. In addition, we conducted a comprehensive analysis of this new paradigm from the perspective of classification performance. The research approach and findings in this study provide a valuable new perspective for BCI applications in the recovery of urinary and defecation functions.}, }
@article {pmid41288610, year = {2025}, author = {Shu, L and Zhuang, D and Tang, J and Zhao, J and Shao, W and Guan, X and Zhang, D}, title = {DemuxTrans: Transformer and temporal convolution network for accurate barcode demultiplexing in nanopore sequencing.}, journal = {Bioinformatics (Oxford, England)}, volume = {41}, number = {11}, pages = {}, doi = {10.1093/bioinformatics/btaf612}, pmid = {41288610}, issn = {1367-4811}, support = {62136004//National Natural Science Foundation of China/ ; 62276130//National Natural Science Foundation of China/ ; 2023YFF1204803//National Key R&amp;D Program of China/ ; BE2022842//Key Research and Development Plan of Jiangsu Province/ ; }, mesh = {*Nanopore Sequencing/methods ; *Deep Learning ; *Sequence Analysis, RNA/methods ; *Software ; Nanopores ; }, abstract = {MOTIVATION: Oxford Nanopore Technologies (ONT) direct RNA sequencing (dRNA-seq) offers high-resolution, single-molecule analysis but is hindered by the lack of robust multiplex barcoding methods. Existing approaches struggle to accurately demultiplex raw nanopore signals, failing to capture both local patterns and long-range dependencies. This limitation underscores the requirement for advanced solutions to improve accuracy, efficiency, and adaptability in sequencing workflows. We present DemuxTrans, a hybrid deep learning framework that integrates Multi-Layer Feature Fusion, Transformers, and Temporal Convolutional Networks (TCN) for precise barcode demultiplexing.<br><br>RESULTS: DemuxTrans achieves state-of-the-art performance across multiple datasets by effectively balancing local feature extraction, global context modeling, and long-term dependency capture, excelling in metrics such as accuracy, recall and F1-score. These results demonstrate DemuxTrans as a scalable, efficient solution for barcode demultiplexing in nanopore sequencing, enabling precise identification of multiplexed RNA samples and improving throughput in transcriptomic and epigenomic analyses.<br><br>The code and datasets are publicly available on https://github.com/LiyuanShu116/Demuxtrans.}, }
@article {pmid41286920, year = {2025}, author = {Sun, Z and Hu, S and Zhu, J and Ye, Z and Ma, M and Ma, G}, title = {The impact of non-invasive brain-computer interface technology on the therapeutic effect of patients with spinal cord injury: a summary of evidence based on meta-analysis.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {250}, pmid = {41286920}, issn = {1743-0003}, support = {No. YC2024022//Graduate Student Research and Innovation Fund of Jilin Sport University/ ; No. YC2024022//Graduate Student Research and Innovation Fund of Jilin Sport University/ ; No. 2019B122//Social Science Foundation of Jilin Province/ ; }, mesh = {Humans ; *Spinal Cord Injuries/rehabilitation ; *Brain-Computer Interfaces ; Activities of Daily Living ; }, abstract = {BACKGROUND: The objective of this study is to systematically evaluate the effects of non-invasive brain-computer interface technology on motor and sensory functions and daily living abilities of patients with spinal cord injuries. In addition, the study will investigate the related modifying factors. Ultimately, the study will provide evidence-based recommendations for clinical practice.<br><br>METHODS: A systematic search was conducted on PubMed, Web of Science, Scopus, Wiley Online Library, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data Resource System, and VIP Database for relevant literature from database inception to February 2025. The quality of the studies was assessed using Review Manager 5.4, with the risk of bias visually represented. The presence of publication bias was assessed through the utilization of the "metafor" package (version 4.6-0) in R (version 4.4.1). The certainty of the evidence was evaluated using the GRADE framework.<br><br>RESULTS: A total of 9 papers were included, including 4 randomized controlled trials and 5 self-controlled trials with 109 spinal cord injury patients. Compared with the control group, the non-invasive brain-computer interface intervention had a significant impact on patients' motor function (SMD&#x2009;=&#x2009;0.72, 95% CI: [0.35,1.09], P&#x2009;<&#x2009;0.01, I[2]&#x2009;=&#x2009;0%, medium level of evidence), sensory function (SMD&#x2009;=&#x2009;0.95, 95% CI: [0.43,1.48], P&#x2009;<&#x2009;0.01, I[2]&#x2009;=&#x2009;0%, medium level of evidence), activities of daily living (SMD&#x2009;=&#x2009;0.85, 95% CI: [0.46,1.24], P&#x2009;<&#x2009;0.01, I[2]&#x2009;=&#x2009;0%, low level of evidence) reached statistical significance. Subgroup analyses showed that for the current summary of evidence, noninvasive brain-computer interface interventions in patients with subacute stage spinal cord injuries showed statistically stronger effects on motor function, sensory function, and ability to perform activities of daily living than in patients with slow chronic stage spinal cord injuries.<br><br>CONCLUSION: As far as the existing literature is concerned, non-invasive brain-computer interface technology shows the potential to improve motor and sensory functioning as well as the ability to perform activities of daily living in patients with spinal cord injury. However, the conclusions are preliminary and hypothetical, and as the current evidence for non-invasive BCI interventions for people with spinal cord injury remains limited, this paper does not recommend the application of the conclusions to clinical practice until future large-sample RCTs.}, }
@article {pmid41287227, year = {2021}, author = {Glannon, W}, title = {Ethical and social aspects of neural prosthetics.}, journal = {Progress in biomedical engineering (Bristol, England)}, volume = {4}, number = {1}, pages = {}, doi = {10.1088/2516-1091/ac23e6}, pmid = {41287227}, issn = {2516-1091}, abstract = {Neural prosthetics are devices or systems that bypass, modulate, supplement, or replace regions of the brain and its connections to the body that are damaged and dysfunctional from congenital abnormalities, brain and spinal cord injuries, limb loss, and neuropsychiatric disorders. Some prosthetics are implanted in the brain. Others consist of implants and systems outside the brain to which they are connected. Still others are completely external to the brain. But they all send inputs to and receive outputs from neural networks to modulate or improve connections between the brain and body. As artificial systems, neural prosthetics can improve but not completely restore natural sensory, motor and cognitive functions. This review examines the main ethical and social issues generated by experimental and therapeutic uses of seven types of neural prosthetics: auditory and visual prosthetics for deafness and blindness; deep brain stimulation for prolonged disorders of consciousness; brain-computer and brain-to-brain interfaces to restore movement and communication; memory prosthetics to encode and retrieve information; and optogenetics to modulate or restore neural function. The review analyzes and discusses how recipients of neural prosthetics can benefit from them in restoring autonomous agency, how they can be harmed by trying and failing to use or adapt to them, how these systems affect their identities, how to protect people with prosthetics from external interference, and how to ensure fair access to them. The review concludes by emphasizing the control these systems provide for people and a brief exploration of the future of neural prosthetics.}, }
@article {pmid41285817, year = {2025}, author = {Li, S and Chen, J and Zhang, C and Tang, S and Xie, Y and Wang, L}, title = {Flexible Use of Limited Resources for Sequence Working Memory in Macaque Prefrontal Cortex.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {10386}, pmid = {41285817}, issn = {2041-1723}, mesh = {Animals ; *Prefrontal Cortex/physiology ; *Memory, Short-Term/physiology ; Neurons/physiology ; Macaca mulatta/physiology ; Male ; Behavior, Animal/physiology ; }, abstract = {Our brain is remarkably limited in how many items it can hold simultaneously, but it can also represent unbounded novel items through generalization. How the brain rationally uses limited resources in working memory (WM) remains unexplored. We investigated mechanisms of WM resource allocation using calcium imaging and electrophysiological recording in the prefrontal cortex of monkeys performing sequence WM (SWM) tasks. We found that changes in the neural representation of SWM, including geometry, generalizable and separate rank subspaces, reflected WM load. SWM resources, represented by neurons' signal strength and spatial tuning projected onto each rank subspace, were shared flexibly between ranks. Crucially, the prefrontal cortex dynamically utilized shared tuning neurons to ensure generalization, while engaging disjoint and spatially shifted neurons to minimize interference, thus achieving a trade-off between behavioral and neural costs within capacity. The allocated resources can predict monkeys' behavior. Thus, the geometry of compositionality underlies the flexible use of limited resources in SWM.}, }
@article {pmid41285049, year = {2025}, author = {Lampert, F and Baker, MR and Jensen, MA and Ayyoubi, AH and Bentler, C and Bowersock, JL and Esteller, R and Herron, JA and Johnson, GW and Kipke, DR and Kovach, CK and Kremen, V and Mivalt, F and Neimat, JS and Netoff, TI and Opri, E and Rockhill, AP and Rosenow, JM and Sellers, KK and Staff, NP and Swamy, CP and Viswanathan, A and Schalk, G and Denison, TJ and Hermes, D and Ince, NF and Brunner, P and Worrell, GA and Miller, KJ}, title = {Adaptive neuromodulation dialogues: navigating current challenges and emerging innovations in neuromodulation system development.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae2359}, pmid = {41285049}, issn = {1741-2552}, abstract = {Adaptive neuromodulation systems and implantable brain-computer interfaces have made notable strides in recent years, translating experimental prototypes into clinical applications and garnering substantial attention from the public. This surge in interest is accompanied by increased scrutiny related to the safety, efficacy, and ethical implications of these systems, all of which must be directly addressed as we introduce new neurotechnologies. In response, we have synthesized the insights resulting from discussions between groups of experts in the field and summarized them into five key domains essential to therapeutic device development: (1) analyzing current landscape of neuromodulation devices and translational platforms (2) identifying clinical need, (3) understanding neural mechanisms, (4) designing viable technologies, and (5) addressing ethical concerns. The role of translational research platforms that allow rapid, iterative testing of hypotheses in both preclinical and clinical settings is emphasized. These platforms must balance experimental flexibility with patient safety and clear clinical benefit. Furthermore, requirements for interoperability, modularity, and wireless communication protocols are explored to support long-term usability and scalability. The current regulatory processes and funding models are examined alongside the ethical responsibilities of researchers and device manufacturers. Special attention is given to the role of patients as active contributors to research and to the long-term obligations we have to them as the primary burden-bearers of the implanted neurotechnologies. This article represents a synthesis of scientific, engineering, and clinical viewpoints to inform key stakeholders in the neuromodulation and brain-computer interface spaces.}, }
@article {pmid41284455, year = {2025}, author = {Baradaran, Y and Rojas, RF and Goecke, R and Ghahramani, M}, title = {Exploring Prefrontal Cortex Involvement in Postural Control Across Degraded Sensory Conditions Using fNIRS and Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3636169}, pmid = {41284455}, issn = {2168-2208}, abstract = {The prefrontal cortex (PFC) of the brain is involved in processing visual, vestibular, and somatosensory inputs to stabilise postural balance. However, the PFC's activation map for a standing person and different sensory inputs remains unclear. This study aimed to explore the PFC activity map and distinct haemodynamic responses during postural control when sensory inputs change. To this end, functional near-infrared spectroscopy (fNIRS) was employed to capture the haemodynamic responses throughout the PFC from a group of young adults standing in four sensory conditions. The results revealed distinct PFC activation patterns supporting sensory processing, motor planning, and cognitive control to maintain balance under different degraded sensory conditions. Furthermore, by applying machine learning classifiers and multivariate feature selection, the PFC locations and haemodynamic responses indicative of different sensory conditions were identified. The findings of this study offer valuable insights for optimising rehabilitation approaches, enhancing the design of fNIRS studies, and advancing brain-computer interface technologies for balance assessment and training.}, }
@article {pmid41284444, year = {2025}, author = {Liu, J and Li, M and Li, Z and Yang, Y and Qi, Q}, title = {DA-META: A Dual Attention Meta-Learning Framework for Unsupervised Motor Imagery Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3636462}, pmid = {41284444}, issn = {2168-2208}, abstract = {Motor imagery electroencephalography (MI-EEG) decoding demonstrates significant potential for paralysis rehabilitation, and its generalization capability is often compromised by intersubject variability and scarcity of labeled target domain data. Meta-learning has emerged as a promising approach for unsupervised domain adaptation problem. However, existing implementations suffer from two critical limitations: insufficient feature extraction and overlooking the guiding role of unlabeled target data. To overcome these challenges, we propose a dual-attention meta-learning framework (DA-META) with model-agnostic architecture in this paper. The framework comprises three stages: meta-task construction, guided meta-training, and fine-tuning-free meta-testing. In the guided meta-training stage, DA-META incorporates two key attention mechanisms: an enhanced temporal attention module for effective feature extraction, and a cosine similarity-based attention module to leverage the guidance of target domain. Using EEGNet as the backbone network, DA-META achieves mean classification accuracies of 68.04% and 76.61% on self-collected datasets from patients and healthy subjects, and 73.29% and 80.93% on the public BCI Competition IV 2a and 2b datasets, outperforming state-of-the-art methods. When employing EEGNet, DeepConvNet, and EEG Conformer as backbone networks respectively, the framework achieves accuracy improvements of 5.17%, 2.56%, and 0.85% on the 2a dataset, compared to the baseline. These results demonstrate the framework's superior ability to handle inter-subject variability and its significant potential to improve practical applicability.}, }
@article {pmid41282818, year = {2025}, author = {Johnson, TR and Foli, C and Conlan, EC and Koenig, KA and Lowe, MJ and Memberg, WD and Kirsch, RF and Herring, EZ and Bazarek, SF and Graczyk, EL and Taylor, DM and Ajiboye, AB and Sweet, J}, title = {Targeting Optimal Grasp-Related Cortical Areas for Intracortical Brain-Machine Interfaces after Spinal Cord Injury.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.10.10.25337598}, pmid = {41282818}, abstract = {OBJECTIVE: This study aimed to optimize intracortical microelectrode array implantation sites for grasp-related motor decoding by integrating anatomical, functional, and vascular imaging with preoperative 3D modeling.<br><br>METHODS: A participant with C5 tetraplegia underwent anatomical magnetic resonance imaging (MRI), diffusion-weighted imaging, and task-based functional MRI (fMRI) to identify grasp-related cortical regions while avoiding vasculature and speech-critical areas. Quicktome software was used to refine target selection by integrating structural connectivity and functional activation data. A 3D-printed skull and cortical model enabled preoperative planning, including craniotomy and electrode positioning simulations. Electrode placement was validated post-operatively using neural data collected from the implanted arrays during attempted movements of the arm and hand.<br><br>RESULTS: Functional imaging identified distinct grasp-related activation in anterior intraparietal area (AIP), ventral premotor cortex (PMv), and inferior frontal gyrus (IFG). AIP was selected based on its strong connectivity with motor cortex and distinct functional activation. Subregions 6v and 6r of PMv, which exhibited robust grasp-related activity and were surgically accessible, were chosen over the posterior IFG region, which extended into a sulcus making implantation difficult. Post-surgically, the arrays enabled high-fidelity decoding of arm/hand movements, achieving a combined accuracy of 96%.<br><br>CONCLUSION: This study presents a multi-modal approach for optimizing intracortical electrode placement by combining MRI-based anatomical mapping, fMRI-guided functional localization, connectivity information, and 3D surgical modeling. These findings demonstrate an effective method for identifying surgically feasible grasp network implant locations in a paralyzed individual. This is an essential step for brain-machine interface (BMI) systems that use grasp-related brain activity to command devices, such as neuromuscular stimulation systems for restoring upper limb function in individuals with spinal cord injury (SCI).}, }
@article {pmid41281720, year = {2025}, author = {Gan, L and Yuan, S and Guo, M and Wang, Q and Deng, Z and Jia, B}, title = {Triboelectric nanogenerators for neural data interpretation: bridging multi-sensing interfaces with neuromorphic and deep learning paradigms.}, journal = {Frontiers in computational neuroscience}, volume = {19}, number = {}, pages = {1691017}, pmid = {41281720}, issn = {1662-5188}, abstract = {The rapid growth of computational neuroscience and brain-computer interface (BCI) technologies require efficient, scalable, and biologically compatible approaches for neural data acquisition and interpretation. Traditional sensors and signal processing pipelines often struggle with the high dimensionality, temporal variability, and noise inherent in neural signals, particularly in elderly populations where continuous monitoring is essential. Triboelectric nanogenerators (TENGs), as self-powered and flexible multi-sensing devices, offer a promising avenue for capturing neural-related biophysical signals such as electroencephalography (EEG), electromyography (EMG), and cardiorespiratory dynamics. Their low-power and wearable characteristics make them suitable for long-term health and neurocognitive monitoring. When combined with deep learning models-including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and spiking neural networks (SNNs)-TENG-generated signals can be efficiently decoded, enabling insights into neural states, cognitive functions, and disease progression. Furthermore, neuromorphic computing paradigms provide an energy-efficient and biologically inspired framework that naturally aligns with the event-driven characteristics of TENG outputs. This mini review highlights the convergence of TENG-based sensing, deep learning algorithms, and neuromorphic systems for neural data interpretation. We discuss recent progress, challenges, and future perspectives, with an emphasis on applications in computational neuroscience, neurorehabilitation, and elderly health care.}, }
@article {pmid41281344, year = {2025}, author = {Xu, M and He, Z and Zhou, J and Zhao, J and Tian, X and Cheng, Q and Lin, Y and Xin, H and Mou, C and Xue, Q and Luo, B}, title = {Altered oral microbiomes in patients with prolonged disorders of consciousness.}, journal = {Journal of oral microbiology}, volume = {17}, number = {1}, pages = {2577220}, pmid = {41281344}, issn = {2000-2297}, abstract = {BACKGROUND: The host microbiome is increasingly recognized as a key modulator of brain function and disease progression, yet the role of the oral microbiome in patients with prolonged disorders of consciousness remains underexplored.<br><br>METHODS: This study characterized oral microbiota differences among pDoC patients (n&#x2009;=&#x2009;89) in the vegetative state (VS), the minimally conscious state (MCS), and emerging from the MCS (EMCS), with a particular focus on the impact of antibiotic use. We used 16S ribosomal RNA sequencing to profile oral microbiota in patients with different levels of consciousness.<br><br>RESULTS: &#x3b2;-diversity was significantly reduced in the VS group compared to the EMCS group. Differential abundance analysis identified five taxa (i.e., species Streptococcus danieliae, species Corynebacterium durum, family Lachnospiraceae, species Phocaeicola abscessus, and species Campylobacter showae) that exhibited significant differences between VS and EMCS, suggesting they were potentially involved in regulating oral microbial dysbiosis and brain-microbiome interactions. Antibiotic treatment induced pronounced microbial shifts in the VS group, while no such effect was observed in the MCS or EMCS groups. Prognostic models built using differential and dominant microbiota panels demonstrated strong predictive performance, achieving areas under the curve of 0.820 and 0.920, respectively.<br><br>CONCLUSIONS: These findings highlight oral microbiome alterations in pDoC and their potential relevance to disease progression, emphasizing the importance of microbiome-informed clinical strategies.}, }
@article {pmid41280332, year = {2025}, author = {Wu, Z and Yu, S and Tian, D and Cheng, L and Jing, J}, title = {Microglial TREM2 and cognitive impairment: insights from Alzheimer's disease with implications for spinal cord injury and AI-assisted therapeutics.}, journal = {Frontiers in cellular neuroscience}, volume = {19}, number = {}, pages = {1705069}, pmid = {41280332}, issn = {1662-5102}, abstract = {Cognitive impairment is a frequent but underrecognized complication of neurodegenerative and traumatic central nervous system disorders. Although research on Alzheimer's disease (AD) revealed that microglial triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in inhibiting neuroinflammation and improving cognition, its contribution to cognitive impairment following spinal cord injury (SCI) is unclear. Evidence from AD shows that TREM2 drives microglial activation, promotes pathological protein clearance, and disease-associated microglia (DAM) formation. SCI patients also experience declines in attention, memory, and other functions, yet the specific mechanism of these processes remains unclear. In SCI, microglia and TREM2 are involved in inflammation and repair, but their relationship with higher cognitive functions has not been systematically examined. We infer that TREM2 might connect injury-induced neuroinflammation in the SCI with cognitive deficits, providing a new treatment target. Artificial intelligence (AI) offers an opportunity to accelerate this endeavor by incorporating single-cell transcriptomics, neuroimaging, and clinical data for the identification of TREM2-related disorders, prediction of cognitive trajectories, and applications to precision medicine. Novel approaches or modalities of AI-driven drug discovery and personalized rehabilitation (e.g., VR, brain-computer interface) can more precisely steer these interventions. The interface between lessons learned from AD and SCI for generating new hypotheses and opportunities for translation.}, }
@article {pmid41279978, year = {2025}, author = {Canfield, RA and Ouchi, T and Fang, H and Macagno, B and Smith, LI and Scholl, LR and Orsborn, AL}, title = {The spatiotemporal structure of neural activity in motor cortex during reaching.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {41279978}, issn = {2692-8205}, abstract = {UNLABELLED: Intracortical brain-computer interfaces (BCI) leverage knowledge about neural representations to translate movement-related neural activity into actions. BCI implants have targeted broad cortical regions known to have relevant motor representations, but emerging technologies will allow flexible targeting to specific neural populations. The structure of motor representations at this scale, however, has not been well characterized across frontal motor cortices. Here, we investigate how motor representations and population dynamics (temporal coordination) vary across a large expanse of frontal motor cortices. We used high-density, laminar, microelectrode arrays to simultaneously record many neurons and then sampled neural populations across frontal motor cortex in two monkeys while they performed a reaching task. Our experiments allowed us to map neuronal activity across three spatial dimensions and relate them to movement. Target decoding analysis revealed that task information was heterogeneously distributed across the cortical surface and in depth. Similarly, we found that the temporal dynamics of different neural populations were heterogeneous, but that the amount of task information predicted which neural populations had similar dynamics. The neural populations with the most similar dynamics were composed of neurons with high task information regardless of spatial location. Our results highlight the spatiotemporal complexity of motor representations across frontal motor cortex at the level of neurons and neural populations, where well-learned movements consistently recruit a spatially distributed subset of neurons. Further insights into the spatiotemporal structure of neural activity patterns across frontal motor cortex will be critical to guide future implants for improved BCI performance.<br><br>SIGNIFICANCE STATEMENT: Motor brain-computer interfaces (BCI) translate neural activity into movement, but how to target implants within motor cortices to maximize performance remains unclear. We used high-density recordings of neural activity spanning a large cortical area and related them to movement to map the spatial distribution of task information and the evolution of neural population activity over time. Our measurements revealed that neurons with the most task information were heterogeneously distributed across cortex yet also evolved coherently in time, suggesting that spatially distributed neurons coordinate to control movements. Our results provide new links between neuron- and population-level maps of motor representations, and highlight the complex spatiotemporal structure of activity that may need to be considered when designing next-generation BCIs.}, }
@article {pmid41278188, year = {2025}, author = {Li, Z and Kambara, H and Koike, Y}, title = {Neural signatures of engagement in driving: comparing active control and passive observation.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1698625}, pmid = {41278188}, issn = {1662-4548}, abstract = {Understanding how the human brain differentiates between active engagement and passive observation is a fundamental question in cognitive neuroscience. Using a matched-stimulus driving paradigm to isolate engagement from sensory input, we recorded whole-brain EEG while participants performed a manual control task and passively viewed a replay of their own performance. Manual control elicited distinct spectral signatures, including stronger frontal midline theta power and, paradoxically, greater occipital alpha power, consistent with heightened cognitive control and active attentional filtering. While a classifier could distinguish these states with high within-subject accuracy, performance declined in cross-subject validation, highlighting inter-individual variability. These findings delineate the distinct neural signatures of active versus passive engagement under controlled conditions. This work establishes a foundational neurophysiological baseline that can inform research on cognitive state monitoring and the design of neuroadaptive systems in complex human-machine interaction.}, }
@article {pmid40936379, year = {2025}, author = {Shin, CJ and Lee, K and Langford, L and Bai, W}, title = {Conductive and Semiconductive 2D Materials for Neural Interfaces, Biosensors, and Therapeutic Modulation.}, journal = {Small methods}, volume = {9}, number = {11}, pages = {e01330}, doi = {10.1002/smtd.202501330}, pmid = {40936379}, issn = {2366-9608}, support = {CCSS-2443105//Division of Electrical, Communications and Cyber Systems/ ; 1R01EB034332/EB/NIBIB NIH HHS/United States ; 1R01EB034332/EB/NIBIB NIH HHS/United States ; }, mesh = {Humans ; *Biosensing Techniques/methods/instrumentation ; *Semiconductors ; Graphite/chemistry ; Electric Conductivity ; Animals ; *Brain-Computer Interfaces ; }, abstract = {Due to population aging, the surge in chronic diseases, and recent pandemics, healthcare is increasingly shifting from hospital-centered models toward digital care. However, widespread adoption is impeded by signal degradation under physiological motion, biofouling, stringent power and data constraints. Effectively overcoming these challenges will require clinically robust devices providing precise, reliable, and reproducible performance. 2D materials address these demands through high carrier mobility that can improve signal-to-noise ratios, low-defect lattices for uniformity, and mechanical pliability that maintains intimate tissue contact and stable impedance during motion. These traits have fueled the rapid growth of 2D-material bioelectronics for remote care in lightweight, stretchable devices. This review surveys flexible, low-impedance neural electrodes of graphene, transition-metal dichalcogenides, and MXenes that integrate electrophysiological recording with optical imaging to provide high-resolution brain interfaces. It then examines their roles in biosensing and autonomous therapy, including sub-picomolar biomarker detection in complex fluids and photothermal, genetic, and antibacterial interventions. Open questions regarding long-term biocompatibility, scalable manufacturing, and protocol harmonization are highlighted. By aligning recent breakthroughs with persistent challenges, the review outlines the prospects of conductive and semiconductive 2D materials for neural interfacing, biosensing, and therapeutic delivery, and maps a pathway toward practical clinical translation.}, }
@article {pmid41275665, year = {2025}, author = {Li, T and An, X and Di, Y and Wang, H and Yan, Y and Liu, S and Dong, Y and Ming, D}, title = {Fuzzy symbolic convergent cross mapping: A causal coupling measure for EEG signals in disorders of consciousness patients.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {195}, number = {}, pages = {108318}, doi = {10.1016/j.neunet.2025.108318}, pmid = {41275665}, issn = {1879-2782}, abstract = {Accurate and timely diagnosis in disorders of consciousness (DOC) patients remains a core clinical challenge. Electroencephalography (EEG) shows strong potential for detecting physiological biomarkers of consciousness, and brain network analysis serves as an effective technique. Therefore, a robust approach to brain network construction is of great significance. The convergent cross mapping (CCM) is a powerful tool for capturing the coupling relationship between two signals. However, a major drawback of CCM is its sensitivity to noise. To address this problem, we proposed a symbolic method that combines fuzzy membership functions called fuzzy symbolic convergent cross mapping (FuzzSCCM). Through the simulation results, we verified its robustness to noise, sensitivity to coupling, and data length. Building on this coupling measure, we constructed EEG brain networks and validated the approach on real DOC EEG datasets. In patients with DOC, FuzzSCCM identified distinct network features between vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). Specifically, compared with the MCS group, the VS group showed greater asymmetry between the left hemisphere and the right hemisphere in the α band, and was relatively less active in the anterior in the θ band. Moreover, our results demonstrate spontaneous transitions between distinct brain network states, suggesting these dynamic reconfigurations may constitute a fundamental mechanism underlying consciousness modulation. These findings provide novel insights into the dynamic neural signatures of DOC, while establishing a potential diagnostic tool.}, }
@article {pmid41274911, year = {2025}, author = {Hardstone, R and Ostrowski, LM and Dusang, AN and López-Larraz, E and Jesser, J and Cash, SS and Cramer, SC and Hochberg, LR and Ramos-Murguialday, A and Lin, DJ}, title = {Extension of voxel-based lesion mapping to multidimensional neurophysiological data.}, journal = {Scientific reports}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41598-025-17247-z}, pmid = {41274911}, issn = {2045-2322}, support = {FMD clinical research fellowship//MGH ECOR/ ; Clinical research Training Scholar//American Academy of Neurology/ ; 1IK2RX004237//U.S. Department of Veterans Affairs/ ; }, abstract = {Focal brain lesions cause neurophysiological changes in local and distributed neural systems. While electroencephalography (EEG) has a long history in post-stroke neurophysiological assessment, the observed changes have rarely been linked to specific lesion locations, leaving neuroanatomical-neurophysiological relationships after stroke unclear. Current data-driven methods, such as voxel-based lesion symptom mapping (VLSM), relate lesion locations to single-feature "symptoms" but currently cannot associate anatomical injury with multidimensional data such as EEG, with its rich spatiotemporal information. To overcome this limitation, we introduce MD-VLM, an extension of VLSM to multidimensional "symptoms" that identifies relationships between lesion locations and neurophysiology. MD-VLM is data-agnostic, compatible with various lesion (e.g., lesion maps, lesion network maps) and neurophysiological (e.g., channel-level or source-localized EEG) inputs, and uses robust statistics to test for the existence of significant neuroanatomical-neurophysiological relationships. We demonstrate MD-VLM's feasibility by applying it to EEG from chronic stroke patients performing a cued-movement task. MD-VLM revealed significant associations between frontal white-matter lesions and reduced ipsilesional parietal cue-evoked responses, consistent with damage to known fronto-parietal networks. MD-VLM is a novel data-driven extension to VLSM for multidimensional "symptoms". Applying MD-VLM to link lesions to neurophysiological data can improve mechanistic understanding of post-stroke neurological impairments and guide future biomarker development.}, }
@article {pmid41274087, year = {2025}, author = {Xu, H and Lin, N}, title = {Neurovista: A bidirectional masked cross-Modal fusion network for robust EEG-to-Image decoding.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {195}, number = {}, pages = {108297}, doi = {10.1016/j.neunet.2025.108297}, pmid = {41274087}, issn = {1879-2782}, abstract = {Electroencephalography (EEG)-based visual decoding has significant potential in brain-computer interfaces but faces substantial challenges due to noise, inter-subject variability, and limited fine-grained alignment between neural signals and visual representations. Existing approaches predominantly utilize global EEG embeddings and static fusion methods, restricting their capability to capture nuanced cross-modal interactions. To address these limitations, We propose NeuroVista, a novel framework that integrates localized EEG masking with dynamic bidirectional cross-modal attention, achieving state-of-the-art EEG-to-image decoding performance. Specifically, NeuroVista employs a channel-level EEG masking strategy during training, encouraging the model to learn robust, context-sensitive neural features, thus significantly improving generalization and noise resistance. Simultaneously, our bidirectional cross-modal attention module dynamically aligns EEG embeddings with corresponding visual features, enhancing semantic coherence across modalities. Extensive experiments on standard EEG-to-image benchmarks demonstrate that NeuroVista consistently outperforms state-of-the-art methods, achieving up to +16.0 % top-1 accuracy improvement in both subject-dependent and subject-independent settings. Our results validate the effectiveness of combining localized masking and interactive cross-modal attention, establishing NeuroVista as a robust, interpretable, and highly generalizable approach for EEG-based visual decoding tasks.}, }
@article {pmid41273580, year = {2026}, author = {Miloulis, ST and Kakkos, I and Zorzos, I and Karampasi, A and Anastasiou, A and Asvestas, P and Ventouras, EC and Kalatzis, I and Matsopoulos, GK}, title = {Deep Learning Discrimination for BCI Implementation Using 3D Convolutional Neural Network and EEG Topographic Maps.}, journal = {Advances in experimental medicine and biology}, volume = {1487}, number = {}, pages = {405-413}, pmid = {41273580}, issn = {0065-2598}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Deep Learning ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Adult ; Male ; Convolutional Neural Networks ; }, abstract = {The growing interest in improved rehabilitation systems and assistive technologies for individuals with motor impairments necessitates the need for new applications of Deep Learning approaches for Brain-Computer Interface (BCI) implementation. This study investigates the application of Deep Learning techniques, specifically the Hierarchical 3D Convolutional Neural Network (H3DCNN) model, for enhancing classification systems utilizing electroencephalography (EEG) data. As such, topographic maps were extracted from EEG signals in a real motion task experiment integrating 4 different motions. The H3DCNN model was then employed in a step-wise fashion to classify and decode the EEG signals, demonstrating its effectiveness in distinguishing between different movement intentions. Moreover, three different optimizers were implemented, including RMSprop, Adam, and Stochastic Gradient Descent (SGD), to further assess and enhance the model performance. The findings indicate that the integration of advanced deep learning techniques can significantly enhance the accuracy and reliability of BCI systems, with RMSprop and SGD showing superior results in terms of accuracy. Moreover, our results illustrate the possibility of decoding neural mechanisms via deep learning paradigms, paving the way for future developments in BCI applications, thus aiming to improve the quality of life for individuals with motor impairments.}, }
@article {pmid41272829, year = {2025}, author = {Shi, Y and Ma, J and Zhao, X and Zhao, H and Wang, D and Zhang, X and Zhu, X and Meng, L and Ming, D}, title = {Bilateral intermittent theta-burst stimulation as a priming strategy to enhance action observation and imitation training in early parkinson's disease: a proof-of-concept study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {247}, pmid = {41272829}, issn = {1743-0003}, support = {82372083//National Natural Science Foundation of China/ ; 2022YFF1202500//National Key Research and Development Program of China/ ; }, mesh = {Humans ; *Parkinson Disease/rehabilitation/physiopathology ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Double-Blind Method ; Middle Aged ; Aged ; Cross-Over Studies ; Proof of Concept Study ; Evoked Potentials, Motor/physiology ; *Gait Disorders, Neurologic/rehabilitation/physiopathology/etiology ; Motor Cortex/physiopathology ; *Imitative Behavior/physiology ; Postural Balance/physiology ; Theta Rhythm/physiology ; }, abstract = {BACKGROUND: Action observation and imitation training (AOIT) is an evidence-based cognitive-motor rehabilitation strategy for Parkinson's disease (PD), particularly for the postural instability and gait disorder (PIGD) subtype. However, its effectiveness may decline with disease-related impairments in neuroplasticity. Intermittent theta burst stimulation (iTBS), a patterned repetitive transcranial magnetic stimulation protocol, can induce LTP-like plasticity and may enhance responsiveness to rehabilitation. This study investigated whether iTBS priming augments AOIT effects on gait and cognition in early-stage PIGD and explored underlying neurophysiological mechanisms.<br><br>METHODS: Fifteen patients with early-stage PIGD participated in a randomized, double-blind, sham-controlled crossover trial. Each phase included five consecutive days of AOIT preceded by either real or sham iTBS applied over the bilateral leg region of the primary motor cortex, separated by a washout period of more than four weeks. Pre- and post-intervention assessments included dual-task gait analysis, cognitive tests, clinical scales, neurophysiological measures (motor evoked potentials, cortical silent period), and resting-state EEG power spectral density.<br><br>RESULTS: Both conditions improved balance and gait measures. However, real iTBS significantly enhanced dual-task gait automaticity (F&#x2009;=&#x2009;5.558, P&#x2009;=&#x2009;0.026) and global cognition (F&#x2009;=&#x2009;5.294, P&#x2009;=&#x2009;0.026) compared to sham. Real iTBS also increased cortical silent period (F&#x2009;=&#x2009;4.655, P&#x2009;=&#x2009;0.040) and MEP-based cortical plasticity response (F&#x2009;=&#x2009;6.131, P&#x2009;=&#x2009;0.020). Improvements in cortical plasticity were significantly correlated with better gait performance (r = -&#x2009;0.429, P&#x2009;=&#x2009;0.020) and motor scores (r = -&#x2009;0.463, P&#x2009;=&#x2009;0.011). No adverse events were reported.<br><br>CONCLUSIONS: Bilateral iTBS targeting the leg representation of the primary motor cortex can potentiate AOIT effects in early-stage PIGD by enhancing cortical plasticity and motor learning. These findings support the integration of iTBS as a priming strategy within cognitive-motor rehabilitation protocols for PD. Trial registration Chinese Clinical Trial Registry, ChiCTR2300067657. Registered January 17, 2023.}, }
@article {pmid41268354, year = {2025}, author = {Haro, S and Beauchene, C and Quatieri, TF and Smalt, CJ}, title = {A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.}, journal = {IEEE access : practical innovations, open solutions}, volume = {13}, number = {}, pages = {189903-189914}, pmid = {41268354}, issn = {2169-3536}, abstract = {There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement. This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy were used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding. In this study, we found evidence of suppression of (i.e., reduction in) net neural tracking and decoding of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.02, Cohen's d = -1.29, 95% CI [-0.02, -0.01] and p = 0.01, Cohen's d = -1.56, 95% CI [-7.25, -3.44], respectively). We did not find a statistically significant increase in the neural tracking or decoding of the attended talker. These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.}, }
@article {pmid41266624, year = {2025}, author = {Sen, O and Soni, R and Virmani, D and Parekh, A and Lehman, P and Jena, S and Katikhaneni, A and Khalifa, A and Chatterjee, B}, title = {A low-latency neural inference framework for real-time handwriting recognition from EEG signals on an edge device.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {41040}, pmid = {41266624}, issn = {2045-2322}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Handwriting ; Male ; Female ; Adult ; Signal-To-Noise Ratio ; Machine Learning ; Young Adult ; Signal Processing, Computer-Assisted ; }, abstract = {Brain-computer interfaces (BCIs) hold significant promise for restoring communication in individuals with severe motor or speech impairments. Imagined handwriting, as a form of motor imagery, offers an intuitive paradigm for character-level neural decoding. While invasive techniques such as electrocorticography (ECoG) offer high decoding accuracy, their surgical requirements pose clinical risks and hinder scalability. Non-invasive alternatives like electroencephalography (EEG) are safer and more accessible but suffer from low signal-to-noise ratio (SNR) and spatial resolution, limiting their effectiveness in high-resolution decoding. Here, we investigate how advanced machine learning, combined with informative feature extraction, can overcome these limitations, enabling EEG-based decoding performance that approaches invasive methods, while supporting real-time inference on edge devices. We present the first real-time, low-latency, high-accuracy system for decoding imagined handwriting from non-invasive EEG signals on a portable edge device. EEG data were collected from 15 participants using a 32-channel headcap and preprocessed with bandpass filtering and artifact subspace reconstruction (ASR). We extracted 85 time domain, frequency domain, and graphical features, then applied Pearson correlation coefficient-based feature selection to reduce latency while preserving accuracy. We developed a hybrid architecture, EEdGeNet, which integrates a Temporal Convolutional Network (TCN) with a multilayer perceptron (MLP), trained on the extracted features and deployed on the NVIDIA Jetson TX2 for real-time inference. The system achieved [Formula: see text] accuracy with 914.18 ms per-character inference latency. By selecting only ten key features, the model incurred a minimal accuracy loss of [Formula: see text], while achieving a [Formula: see text] reduction in inference latency (202.62 ms) compared to the full 85-feature set. These findings show that non-invasive EEG, combined with efficient feature and model design, can enable accurate, real-time neural decoding on low-power edge devices, paving the way for practical, portable BCIs.}, }
@article {pmid41265571, year = {2025}, author = {Luo, R and Meng, J and Wei, Y and Mai, X and Li, G}, title = {Outcome processing response coupled to feedback-related EEG dynamics during discrete and continuous performance monitoring.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110629}, doi = {10.1016/j.jneumeth.2025.110629}, pmid = {41265571}, issn = {1872-678X}, abstract = {BACKGROUND: Error-related potential (ErrP) reflects the inconsistency between internal expectation and external feedback outcome. Despite the exploration of numerous experimental paradigms, ErrP components exhibit distinct latency and amplitude across different paradigms. However, previous studies have not quantitatively correlated potential influencing factors with this ErrP variability. Additionally, these qualitatively analyzed factors offer limited predictions for ErrP in new paradigms.<br><br>NEW METHOD: We proposed that a neutral condition removing goal-directed outcome expectations reflects cross-paradigm variability in correct and erroneous outcome responses. This neutral condition was designed as a control condition for each paradigm. Three different paradigms were designed to provide discrete and continuous varied feedback outcomes. Correlations were assessed between neutral condition responses and correct and erroneous outcome responses in latency and amplitude. The predictive effectiveness of neutral condition responses for new paradigms was further evaluated through single-trial cross-paradigm classification.<br><br>RESULTS: Correct and erroneous outcome responses were observed to have significant latency and amplitude coupling with these neutral condition responses in the middle frontal and bilateral parietal regions. Results from source reconstruction, pupillometry data, and workload score confirm that the neutral condition serves as the baseline response for outcome processing responses. This baseline relationship explains the cross-paradigm ErrP variability.<br><br>The single-trial decoding results show that utilizing neutral condition responses can significantly increase the accuracy of cross-paradigm classification by up to 7% and 17% with covariance-based and amplitude-based approaches.<br><br>CONCLUSION: These findings provide a quantitative physiological explanation for cross-paradigm ErrP variability and promote transfer learning applications in ErrP-based BCIs.}, }
@article {pmid41264938, year = {2025}, author = {Fang, S and Zhao, X and Wang, Z and Si, Y and Haifeng, L and Hu, H and Xu, T and Zhou, T}, title = {Enhancing SSVEP-BCI performance through multi-stimulus discriminant fusion analysis.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae220d}, pmid = {41264938}, issn = {1741-2552}, abstract = {To enhance frequency recognition in Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interfaces (BCIs), particularly under short data acquisition and complex environmental conditions. Approach. We propose Multi-Stimulus Discriminant Fusion Analysis (MSDFA), a novel method that integrates multi-stimulus strategies with discriminant modeling. MSDFA was evaluated on two public datasets (Benchmark and BETA) and compared with conventional approaches including eCCA, eTRCA, and their variants. Main results. MSDFA consistently outperformed existing methods across different data lengths and training block quantities. It achieved maximum information transfer rates of 247.17±10.15 bpm on the Benchmark dataset and 192.72±9.44 bpm on the BETA dataset, demonstrating superior robustness and efficiency. Significance. By combining complementary algorithmic strengths, MSDFA improves adaptability to individual variability and complex environments, advancing the practical utility and reliability of SSVEP-BCI systems. .}, }
@article {pmid41262557, year = {2025}, author = {Zhang, X and Wang, S and Gao, Y and Wang, Y and Qiu, S and He, H}, title = {Enhancing visual brain-computer interface through V1-targeted RTMS by modulating visual attention.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {3}, number = {}, pages = {}, pmid = {41262557}, issn = {2837-6056}, abstract = {Brain-computer interfaces (BCIs) enable users to control devices directly through brain activity. Despite recent advancements in machine-learning algorithms, the signal-to-noise ratio (SNR) of the brain's responses still limits decoding performance, highlighting the necessity for targeted neuromodulation techniques to overcome this limitation. To evaluate whether 5 Hz repetitive transcranial magnetic stimulation (rTMS) targeting the primary visual cortex (V1) can enhance SSVEP-based BCI performance by improving neural signal SNR and modulating visual network dynamics. Twenty-four healthy subjects underwent both real and sham rTMS in a randomized order. The rTMS was precisely implemented through magnetic resonance imaging (MRI)-guided navigation to stimulate V1 in participants. Electroencephalograms (EEGs) were recorded during SSVEP tasks and resting-state before, immediately after, and 20 min after rTMS. SSVEP tasks were conducted across four frequency bands: low frequency (LF: 8-12 Hz), middle frequency (MF: 18-22 Hz), high frequency (HF: 28-32 Hz), and super high frequency (SHF: 38-42 Hz). The discriminability of BCI commands in the MF (+7.53%) and HF (+11.4%) bands significantly improved (p < 0.001), driven by enhanced prominence of both fundamental and harmonic components (p < 0.01). Quantitative analysis indicated that the improved SNR was due to the suppression of the background activity (p < 0.05). This effect was linked to rTMS-induced enhancements in visual attention, evidenced by increased occurrence and contribution of microstate B during the SSVEP task (p < 0.01). This study highlights the potential of 5 Hz rTMS as an effective neuromodulatory tool for optimizing BCI performance, particularly through facilitating visual attention.}, }
@article {pmid41261122, year = {2025}, author = {Gobert, F and Merida, I and Maby, E and Seguin, P and Jung, J and Morlet, D and André-Obadia, N and Dailler, F and Berthomier, C and Otman, A and Le Bars, D and Scheiber, C and Hammers, A and Bernard, E and Costes, N and Bouet, R and Mattout, J}, title = {Disorder of consciousness rather than complete Locked-In Syndrome for end stage Amyotrophic Lateral Sclerosis: a case series.}, journal = {Communications medicine}, volume = {5}, number = {1}, pages = {482}, pmid = {41261122}, issn = {2730-664X}, abstract = {BACKGROUND: The end-stage of amyotrophic lateral sclerosis (ALS) is commonly regarded as a complete Locked-In Syndrome (cLIS). Shifting the perspective from cLIS (assumed consciousness) to Cognitive Motor Dissociation (potentially demonstrable consciousness), we aimed to assess the preservation of covert awareness (internally preserved but externally inaccessible) using a multimodal battery.<br><br>METHODS: We evaluate two end-stage ALS patients using neurophysiological testing, passive and active auditory oddball paradigms, an auditory Brain-Computer Interface (BCI), functional activation-task imaging, long-term EEG, brain morphology, and resting-state metabolism to characterize underlying brain function.<br><br>RESULTS: Patient 1 initially follows simple commands but fails twice at BCI control. At follow-up, command following is no longer observed and his oddball cognitive responses disappear. Patient 2, at a single evaluation, is unable to follow commands or control the BCI. Both patients exhibit altered wakefulness, brain atrophy, and a global cortico-subcortical hypometabolism pattern consistent with a disorder of consciousness, regarded as an extreme manifestation of ALS-associated fronto-temporal dementia.<br><br>CONCLUSIONS: Although it is not possible to firmly prove the absence of awareness, each independent measure concurred with suggesting that a "degenerative disorder of consciousness" rather than a cLIS may constitute the final stage of ALS. This condition appears pathophysiologically distinct from typical tetraplegia and anarthria, in which behavioural communication and BCI use persist to enhance quality of life. Identifying the neuroimaging signatures of this condition represents a substantial milestone in understanding end-stage ALS. Large-scale longitudinal investigations are warranted to determine the prevalence of this profile among patients whose communication appears impossible.}, }
@article {pmid41260504, year = {2025}, author = {Wang, J and Gan, X and Han, M and Dong, W and He, J and Fu, K and Bore, MC and Xu, T and Klugah-Brown, B and Ferraro, S and Becker, B}, title = {Effects of exogenous oxytocin on human brain function are regulated by oxytocin gene expression: a meta-analysis of 20 years of oxytocin neuroimaging and transcriptomic analyses.}, journal = {Neuroscience and biobehavioral reviews}, volume = {}, number = {}, pages = {106478}, doi = {10.1016/j.neubiorev.2025.106478}, pmid = {41260504}, issn = {1873-7528}, abstract = {Over the past two decades, numerous pharmaco-imaging studies have examined the role of oxytocin (OT) in human cognition and behavior, yet results remain highly heterogeneous and the link between large-scale functional effects and molecular architecture is unclear. To address this, we conducted a comprehensive analysis combining neuroimaging meta-analysis, meta-analytic connectivity modeling, and transcriptomics. Across 75 experiments (n=2,247), consistent, domain-general effects of OT emerged in the left thalamus, pallidum, caudate, and insula. Connectivity modeling showed these regions form an integrated thalamus-striatum-insula circuit directly modulated by OT. Transcriptomic analyses revealed that the expression of three OT pathway genes (CD38, OXT, and OXTR) is enriched in these subcortical regions and associated with the observed neural effects. OT's neural effects were also strongly linked with acetylcholinergic, dopaminergic, and opioidergic gene distributions, potentially reflecting functional interactions with these systems. Findings provide convergent evidence that OT exerts robust effects on human brain function via a biologically-plausible core circuit and can inform effective pharmacotherapeutic targets.}, }
@article {pmid41259181, year = {2025}, author = {Qin, C and Yang, R and Zhu, L and Chen, Z and Huang, M and Alsaadi, FE and Wang, Z}, title = {EEG-Infinity: A Mathematical Modeling-Inspired Architecture for Addressing Cross-Device Challenges in Motor Imagery.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3635018}, pmid = {41259181}, issn = {1558-0210}, abstract = {The distribution of electroencephalogram (EEG) data generally varies across datasets due to the huge difference between the physical structure of brain-computer interface devices, known as cross-device variability. Such variability poses great challenges in EEG decoding and hinders the standardized utilization of EEG datasets. In this study, we explore a new issue concerning the cross-device variability problem, pointing to the gap in the existing studies facing cross-device variability. To tackle this challenge, our paper is the first to model the cross-device variability problem through a "sequentially comprehensive formula" and a "spatial comprehensive formula". Inspired by this modeling, a novel deep domain adaptation network named EEG-Infinity is proposed, incorporating replaceable EEG feature extraction backbones with a novel structure named "alignment head". To show the effectiveness of the proposed EEG-Infinity, systematic experiments are conducted across four different EEG-based motor imagery datasets under 48 cases. The experimental results highlight the superior performance of the proposed EEG-Infinity over commonly used approaches with an average classification accuracy improvement of 1.51% across 34 cases, laying a foundation for research in large-scale EEG models. The code can be assessed at https://github.com/Baizhige/cd-infinity.}, }
@article {pmid41257892, year = {2025}, author = {Xiong, W and Ma, L and Li, H}, title = {Adaptive EEG preprocessing to mitigate electrode shift variability for robust motor imagery classification.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {40808}, pmid = {41257892}, issn = {2045-2322}, mesh = {*Electroencephalography/methods/instrumentation ; Humans ; Electrodes ; Brain-Computer Interfaces ; *Imagination/physiology ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Electrode placement variability poses a critical challenge in EEG-based motor imagery tasks, often resulting in reduced classification robustness. We present the Adaptive Channel Mixing Layer (ACML), a plug-and-play preprocessing module that dynamically adjusts input signal weights through a learnable transformation matrix based on inter-channel correlations. By leveraging the inherent spatial structure of EEG caps, ACML effectively compensates for electrode misalignments and noise, enhancing resilience to signal distortion. Experimental validation on two motor imagery datasets with varying channel counts demonstrated consistent improvements in accuracy (up to 1.4%), kappa scores (up to 0.018), and robust performance across subjects, using five neural network architectures including a state-of-the-art model (ATCNet). Notably, ACML requires minimal computational overhead and no task-specific hyperparameter tuning, ensuring compatibility with diverse applications. This method offers a robust and efficient solution for advancing EEG-based motor imagery classification, with potential applications in real-time brain-computer interface systems and neurorehabilitation.}, }
@article {pmid41257158, year = {2025}, author = {Ali, U and Khan, JA and Ahsan, MT and Altaf, B and Azreen, S and Alamu, OS and Rana, MS}, title = {Brain-Computer Interfaces in the Rehabilitation of Stroke and Spinal Cord Injury: A Systematic Review and Meta-Analysis of Clinical Efficacy.}, journal = {Cureus}, volume = {17}, number = {10}, pages = {e94833}, pmid = {41257158}, issn = {2168-8184}, abstract = {Brain-computer interfaces (BCIs) have emerged as innovative tools for neurorehabilitation, enabling patients with stroke and spinal cord injury (SCI) to engage in task-specific training through direct neural control of external devices. Despite growing evidence, the overall clinical efficacy of BCIs in functional recovery remains debated. This systematic review and meta-analysis evaluated the effectiveness of BCI-based rehabilitation on motor recovery in stroke and SCI, with a focus on upper and lower limb function. We systematically searched PubMed, EMBASE, Web of Science, and Cochrane CENTRAL for clinical trials published between January 2008 and October 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible studies included randomized controlled trials and controlled interventional trials employing BCI interventions for motor rehabilitation. Risk of bias was assessed with RoB-2 and ROBINS-I. Meta-analysis was performed using a random-effects model. Seventeen studies met the inclusion criteria, comprising both stroke (acute, subacute, and chronic phases) and SCI populations. The pooled analysis demonstrated a significant mean difference of 3.26 points on the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) in favour of BCI interventions (95% CI: 2.73-3.78, p < 0.001). Heterogeneity was negligible (I[2] = 0%). Subgroup analyses suggested that combining BCI with functional electrical stimulation or robotics yielded larger gains. BCI-based rehabilitation significantly improves motor function in stroke and SCI populations, with effect sizes exceeding the minimal clinically important difference for FMA-UE. These findings highlight the translational potential of BCIs as adjunctive therapies in neurorehabilitation. Larger, multicenter trials with standardised protocols are warranted to establish long-term efficacy and guide clinical integration.}, }
@article {pmid41256527, year = {2025}, author = {Shah, NP and Krasa, BA and Kunz, E and Hahn, N and Kamdar, F and Avansino, D and Hochberg, LR and Henderson, JM and Sussillo, D}, title = {Improved interpretability in LFADS models using a learned, context-dependent per-trial bias.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.10.03.680303}, pmid = {41256527}, issn = {2692-8205}, abstract = {The computation-through-dynamics perspective argues that biological neural circuits process information via the continuous evolution of their internal states. Inspired by this perspective, Latent Factor Activity using Dynamical systems (LFADS, [1]) identifies a generative model consistent with the neural activity recordings. LFADS models neural dynamics with a recurrent neural network (RNN) generator, which results in excellent fit to the data. However, it has been difficult to understand the dynamics of the LFADS generator. In this work, we show that this poor interpretability arises in part because the generator implements complex, multi-stable dynamics. We introduce a simple modification to LFADS that ameliorates issues with interpretability by providing an inferred per-trial bias (modeled as a constant input) to the RNN generator, enabling it to contextually adapt a simpler dynamical system to individual trials. In both simulated neural recordings from pendulum oscillations and real recordings during arm movements in nonhuman primates, we observed that the standard LFADS learned complex, multi-stable dynamics, whereas the modified LFADS learned easier-to-understand contextual dynamics. This enabled direct analysis of the generator, which reproduced at a single-trial level previous results shown only through more complex analyses at the trial average. Finally, we applied the per-trial inferred bias LFADS model to human intracortical brain computer interface recordings during attempted finger movements and speech. We show that modifying neural dynamics using linear operations of the per-trial bias addresses non-stationarity and identifies the extent of behavioral variability, problems known to plague BCI. We call our modification to LFADS as "contextual LFADS".}, }
@article {pmid41256173, year = {2025}, author = {Candrea, DN and Angrick, M and Luo, S and Ganji, R and Coogan, C and Milsap, GW and Rosenblatt, KR and Uchil, A and Clawson, L and Maragakis, NJ and Vansteensel, MJ and Tenore, FV and Ramsey, NF and Fifer, MS and Crone, NE}, title = {Longitudinal study of gesture decoding in a clinical trial participant with ALS.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.09.26.25335804}, pmid = {41256173}, abstract = {Brain-computer interfaces (BCIs) have the potential to preserve or restore communication and device control in people with paralysis from a variety of causes. For people living with amyotrophic lateral sclerosis (ALS), however, the progressive loss of cortical motor neurons could theoretically pose a challenge to the stability of BCI performance. Here we tested the stability of gesture decoding with a chronic electrocorticographic (ECoG) BCI in a man living with ALS and participating in a clinical trial (ClinicalTrials.gov , NCT03567213). We evaluated offline decoding performance of attempted gestures over two periods: a 5-week period beginning roughly 2 years post-implant and a 6-week period ending roughly 5 months later. Decoder sensitivity was high in both periods (90 - 98%), while classification accuracy was 37 - 68% in the first period and worsened to 23 - 39% in the second. We investigated multiple frequency bands that were used as model features in both periods, and we observed reductions in high gamma band power (70 - 110 Hz) and between-class separation during the second period compared to the first. Over the 5-month period motor function did not appreciably decline. These results, albeit preliminary, suggest that declines in the neural population responses that drive ECoG BCI performance can occur without overt signs of disease progression in people living with ALS, and could serve as a biomarker for disease progression in the future.}, }
@article {pmid41255819, year = {2025}, author = {Vooijs, M and Bassil, K and van den Brink, A and van Stuijvenberg, OC and Ramsey, NF and Jongsma, KR}, title = {Ethical, legal, and sociocultural considerations in neural device explantation: a systematic review.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1568800}, pmid = {41255819}, issn = {1662-4548}, abstract = {INTRODUCTION: Implantable neural devices, including brain-computer interfaces and spinal cord stimulators, hold significant therapeutic promise for conditions such as paralysis and chronic pain. However, the novelty of these technologies introduces unique ethical challenges. While much of the existing literature emphasizes development-related concerns such as device safety, the ethical issues surrounding explantation remain relatively underexplored.<br><br>METHODS: We conducted a systematic review to identify ethical, legal, and sociocultural considerations relevant to the explantation of neural devices. The review applied the IEEE BRAIN Neuroethics framework as a guiding structure for the categorization of the themes. A subsequent thematic analysis was performed to categorize and synthesize findings across studies.<br><br>RESULTS: Thematic analysis revealed that medical motives were the predominant factor in discussions of explantation, with 83% of studies citing medical complications as a central concern. Additional themes identified included changes in cognition and behavior, emotional well-being, lack of therapeutic benefit, identity, financial issues, autonomy, post-trial considerations, and neurorights.<br><br>DISCUSSION: Our findings underscore the multifaceted nature of neural device explantation, extending beyond purely medical considerations to include psychological, financial, legal, and sociocultural dimensions. These results highlight the necessity of interdisciplinary approaches to adequately address the broad spectrum of challenges associated with explantation.}, }
@article {pmid41255549, year = {2025}, author = {Hyung, W and Kim, M and Kim, Y and Im, CH}, title = {DeepAttNet: deep neural network incorporating cross-attention mechanism for subject-independent mental stress detection in passive brain-computer interfaces using bilateral ear-EEG.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1685087}, pmid = {41255549}, issn = {1662-5161}, abstract = {INTRODUCTION: Electroencephalography (EEG)-based mental stress detection has the potential to be applied in diverse real-world scenarios, including workplace safety, mental health monitoring, and human-computer interaction. However, most previous passive brain-computer interface (BCI) studies have employed EEG recorded during the performance of specific tasks, making the classification results susceptible to task engagement effects rather than reflecting stress alone. To address this limitation, we introduce a rest-versus-rest paradigm that compares resting EEG recorded immediately after exposure to a stressor with that recorded after meditation, thereby isolating mental stress from the task-related confounds. EEG recording setups were designed under the assumption of bilateral ear-EEG, a compact and discreet form factor suitable for real-world applications. Furthermore, we developed a novel subject-independent deep learning classifier tailored to model interhemispheric neural dynamics for enhanced mental stress detection performance.<br><br>METHODS: Thirty-two adults participated in the experiment. To classify mental stress status in a subject-independent manner, we proposed DeepAttNet, a deep learning model based on cross-attention and pointwise temporal compression, specifically designed to effectively capture left and right hemispherical interactions. Classification performance was assessed using eight-fold subject-level cross-validation against conventional deep learning models, including EEGNet, ShallowConvNet, DeepConvNet, and TSception. Ablation studies evaluated the impact of the cross-attention and/or pointwise compression modules.<br><br>RESULTS: DeepAttNet achieved the highest average accuracy and macro-F1 values, with performance declining when either the cross-attention or pointwise compression module was removed in the ablation studies. Explainability analyses indicated lower cross-attention entropy with stronger directional ear-to-ear asymmetry under stress, and temporal occlusion identified mid-late windows supporting stress decisions. Moreover, six of seven canonical scalp-EEG markers were FDR-significant for post-stressor vs. post-relaxation rest.<br><br>CONCLUSION: The proposed rest-versus-rest paradigm and DeepAttNet enabled robust, subject-independent mental stress detection with a fairly high accuracy using only two-channel EEG recordings. This approach is expected to offer a practical solution for continuous stress monitoring, potentially advancing passive BCI applications outside laboratory settings.}, }
@article {pmid41253791, year = {2025}, author = {Shi, J and Chen, D and Zhao, X and Zhao, Z and Li, S and Xu, Y and Ding, T and Zhu, Z and Zhang, P and Ye, Q and Tang, Y and Zhang, P and Tao, B and Tang, Z}, title = {HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1816}, pmid = {41253791}, issn = {2052-4463}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Cerebral Hemorrhage/rehabilitation/physiopathology ; Spectroscopy, Near-Infrared ; }, abstract = {This study introduces the first hybrid brain-computer interface dataset specifically designed for research on intracerebral hemorrhage (ICH) rehabilitation. It offers a novel data source through the synchronized acquisition of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The dataset innovatively incorporated neural recordings from 17 normal subjects and 20 patients with ICH under standardized left-right hand motor imagery (MI) paradigms, featuring systematically collected and preprocessed dual-modality neural data. Beyond raw neural signals, the resource provides feature-engineered data optimized for classification algorithms and multidimensional signal decoding. The public availability of this dataset can facilitate the validation and optimization of MI decoding algorithms and advance the development of precision rehabilitation systems based on multimodal neural feedback.}, }
@article {pmid41253750, year = {2025}, author = {Sun, X and Dias, L and Peng, C and Zhang, Z and Ge, H and Wang, Z and Jin, J and Jia, M and Xu, T and Guo, W and Zheng, W and He, Y and Wu, Y and Cai, X and Agostinho, P and Qu, J and Cunha, RA and Zhou, X and Bai, R and Chen, JF}, title = {Author Correction: 40 Hz light flickering facilitates the glymphatic flow via adenosine signaling in mice.}, journal = {Cell discovery}, volume = {11}, number = {1}, pages = {92}, doi = {10.1038/s41421-025-00845-6}, pmid = {41253750}, issn = {2056-5968}, }
@article {pmid41253390, year = {2025}, author = {Ji, X and Deng, S}, title = {Cognitive Change as an Early Warning for Late-Life Depression: Implications for Population Health Screening Strategies.}, journal = {Population health management}, volume = {}, number = {}, pages = {}, doi = {10.1177/19427891251395738}, pmid = {41253390}, issn = {1942-7905}, abstract = {Cognitive decline and late-life depression are intertwined public health challenges for aging populations globally. To inform effective prevention, the current study investigated the dynamic temporal associations between multidimensional cognitive functions and depressive symptoms. Using four waves of longitudinal data (2013-2020) from a large panel study of older adults, the current study employed an integrated framework combining optimized dynamic time warping, cross-lagged panel models, and network analysis to model complex, lagged relationships. Results provided strong empirical support for the "cognition-first" hypothesis, with declines in several cognitive domains-notably temporal orientation, calculation, and immediate recall-acting as significant upstream predictors of subsequent depressive symptoms. A modest but significant protective feedback effect from positive affect to cognitive maintenance was also identified, while negative affect showed no significant predictive role sample of older adults who were cognitively and emotionally healthy at baseline. These findings offer preliminary empirical support for a strategic shift in population health management from reactive treatment toward proactive prevention. Based on these results, the current study discusses a conceptual framework for integrating cognitive screening into primary care to identify at-risk older adults, an approach that warrants further investigation and validation. This proactive approach could enable timely, low-cost interventions aimed at promoting positive affect and cognitive resilience, offering a potentially cost-effective strategy to mitigate the long-term burden of mental illness and advance the goals of healthy aging.}, }
@article {pmid41253019, year = {2025}, author = {Fan, YS and Ye, M and Xu, Y and Xu, Y and Guo, J and Yang, M and Huang, W and Chen, H}, title = {Spatio-temporal information transition abnormalities across brain functional networks in early-onset schizophrenia.}, journal = {Schizophrenia research}, volume = {287}, number = {}, pages = {37-45}, doi = {10.1016/j.schres.2025.11.007}, pmid = {41253019}, issn = {1573-2509}, abstract = {Schizophrenia is a complex neurodevelopmental disorder characterized by widespread functional dysconnectivities across the brain. While disturbed temporal dynamics have been reported in schizophrenia, the information flow involving both temporal and spatial dynamics remains unclear. To capture spatio-temporal transition of brain information and to investigate these processes from a neurodevelopmental perspective, we collected resting-state functional MRI (rs-fMRI) data from 86 early-onset schizophrenia (EOS) patients (onset before age 18) and 91 demographically matched typically developing (TD) controls. We employed a non-homogeneous Markov model (NHMM) on dynamic functional connectivities derived from fMRI data. By means of transition probabilities, we modeled the switching of information flow in brain functional networks over time. Stationary probability vectors were used to describe the information convergence distribution of each network, while optimal reachable steps were used to characterize inter-network transmission efficiency. Compared to controls, EOS patients showed significantly increased stationary transition probabilities in the ventral attention network (VAN) and the dorsal attention network (DAN) but decreased probabilities in the default mode network (DMN). In terms of the dynamic interaction characteristics between networks, patients showed increased optimal reachable steps relative to controls, particularly in the VAN-DMN pathway. By integrating NHMM with neuroimaging data, this study revealed VAN- and DMN-involved information transition abnormalities in the early stage of schizophrenia spatio-temporal dynamics, offering novel insights into the developmental pathophysiology of the disorder. Our approach thus provides a novel analytical framework for quantifying spatio-temporal brain dynamics in neurodevelopmental disorders.}, }
@article {pmid41252716, year = {2025}, author = {Yi, L and Yang, Y and Zeng, BF and Liu, X and Edel, JB and Ivanov, AP and Tang, L}, title = {Single-molecule quantum tunnelling sensors.}, journal = {Chemical Society reviews}, volume = {}, number = {}, pages = {}, doi = {10.1039/d4cs00375f}, pmid = {41252716}, issn = {1460-4744}, abstract = {Single-molecule sensors are pivotal tools for elucidating chemical and biological phenomena. Among these, quantum tunnelling sensors occupy a unique position, due to the exceptional sensitivity of tunnelling currents to sub-ångström variations in molecular structure and electronic states. This capability enables simultaneous sub-nanometre spatial resolution and sub-millisecond temporal resolution, allowing direct observation of dynamic processes that remain concealed in ensemble measurements. This review outlines the fundamental principles of electron tunnelling through molecular junctions and highlights the development of key experimental architectures, including mechanically controllable break junctions and scanning tunnelling microscopy-based approaches. Applications in characterising molecular conformation, supramolecular binding, chemical reactivity, and biomolecular function are critically examined. Furthermore, we discuss recent methodological advances in data interpretation, particularly the integration of statistical learning and machine learning techniques to enhance signal classification and improve throughput. This review highlights the transformative potential of quantum-tunnelling-based single-molecule sensors to advance our understanding of molecular-scale mechanisms and to guide the rational design of functional molecular devices and diagnostic platforms.}, }
@article {pmid41250658, year = {2025}, author = {Gonzalez-Astudillo, J and de Vico Fallani, F}, title = {Feature Interpretability in Motor Imagery Brain Computer Interfaces: A Meta-Analysis Across Connectivity, Spatial Filtering, and Riemannian Methods.}, journal = {Brain connectivity}, volume = {}, number = {}, pages = {}, doi = {10.1177/21580014251392230}, pmid = {41250658}, issn = {2158-0022}, abstract = {Introduction: Brain-computer interfaces (BCIs) translate brain activity into commands, enabling applications in communication, control, and neurorehabilitation. A major challenge in noninvasive BCIs is balancing classification performance with interpretability, as many approaches prioritize accuracy while overlooking the neural mechanisms underlying their predictions. Methods: In this study, we conduct a meta-analysis of feature interpretability across widely used methods in motor imagery (MI)-based BCIs, including power spectral density, common spatial patterns (CSP), Riemannian geometry, and functional connectivity. Specifically, we explore how network topology and spatial organization contribute to MI decoding by investigating brain network lateralization. Results: Through evaluations on multiple EEG-based BCI datasets, our results confirm the superior classification performance of CSP and Riemannian methods. However, network lateralization provides stronger neurophysiological plausibility, revealing robust lateralization patterns in sensorimotor and frontal regions contralateral to imagined movements. Discussion: These findings underscore the potential of connectivity-based features as a complementary tool for enhancing interpretability, supporting the development of more transparent and clinically relevant MI-based BCIs. Impact Statement This study addresses a critical gap in motor imagery-based brain-computer interfaces (BCIs) by systematically evaluating and comparing the interpretability of widely used methods, including power spectral density, common spatial pattern, Riemannian geometry, and functional connectivity. By analyzing these approaches across wide-ranging datasets, we offer valuable insights into the underlying neural mechanisms driving their performance. Our findings contribute to enhancing the transparency and biological relevance of BCI systems, ultimately advancing the development of more clinically meaningful and neurophysiologically interpretable BCIs.}, }
@article {pmid41250191, year = {2025}, author = {Plontke, SK and Lenarz, T and Toner, J and Keintzel, T and Sprinzl, G and Baumgartner, WD and Koitschev, A and Schmutzhard, J and Götze, G and Rahne, T and Knoelke, N and Busch, S and Corkill, S and Raffelsberger, T and Niederwanger, L and Magele, A and Schörg, P and Honeder, C and Liepins, R and Berger, N and Koci, V and Wiek, R}, title = {The Bonebridge BCI 602 Safety and Performance 1 Year Post-Implantation in Adults and Children: A Multicentric Post-Market Study.}, journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology}, volume = {}, number = {}, pages = {}, doi = {10.1097/MAO.0000000000004688}, pmid = {41250191}, issn = {1537-4505}, abstract = {OBJECTIVE: To confirm the long-term safety and performance of the Bonebridge BCI 602 in patients suffering from conductive or mixed hearing loss (CMHL) or single-sided deafness (SSD) over a 12-month period post-implantation.<br><br>STUDY DESIGN: Multicentric, multinational, ambidirectional, observational Post-Market Clinical Follow-Up (PMCF) study.<br><br>SETTING: Eight tertiary referral hospitals.<br><br>PARTICIPANTS: Fifty-two participants in 3 categories: adults CMHL (N=24), children CMHL (N=17), and SSD (N=11; 9 adults and 2 children).<br><br>INTERVENTION: Participants were implanted with the Bonebridge BCI 602 device.<br><br>MAIN OUTCOME MEASURES: Outcome measures focused on sound field thresholds (SF), word recognition scores (WRS), speech reception thresholds (SRT) in both quiet and noise, adverse events, and subjective satisfaction (SSQ and AQoL questionnaires) at initial activation and at 3-month and 12-month post-implantation.<br><br>RESULTS: Safety was established by stable bone conduction (BC) thresholds and a low adverse event rate with no unanticipated events. Safety was underlined by clinically relevant improvements in the health-related assessment of Quality of Life (AQoL) questionnaire of mean+0.1 (adults and children CMHL) and +0.07 (SSD). Hearing significantly improved in sound field thresholds with mean functional gains of 24.05&#xb1;8.68 dB (adults CMHL), 21.34&#xb1;25.43 dB (children CMHL), and 32.89&#xb1;25.87 dB (SSD). Mean word recognition scores improved by 65.83&#xb1;28.62 percent points (PP) for adult CMHL and 65.77&#xb1;27.53 PP for children CMHL and speech reception thresholds (SRT) in quiet by 15.4&#xb1;9.34 dB and 19.96&#xb1;14.66 dB, respectively. SRT in noise improved by -5.57&#xb1;4.23 dB (adults; S0&#xb0;N0&#xb0;), -5.12&#xb1;5.08 dB (children, S0&#xb0;N0&#xb0;), and -3.05&#xb1;3.06 dB (SSD, SSSDNNH). Subjective hearing ability tested with the Speech, Spatial, and Qualities (SSQ) of Hearing questionnaire improved and was clinically relevant for the adult (+2.23) and children (+1.51) CMHL groups.<br><br>CONCLUSIONS: The Bonebridge BCI 602 demonstrates significant enhancements in hearing and speech understanding 12 months postoperatively, showing high user satisfaction and safety.}, }
@article {pmid41248054, year = {2025}, author = {Xie, X and Mou, H and Chen, W and Zhang, S and Xu, Y and Cheng, R and Wang, M}, title = {Noninvasive Temporal Interference Electrical Stimulation for Spinal Cord Rehabilitation.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {224}, pages = {}, doi = {10.3791/68574}, pmid = {41248054}, issn = {1940-087X}, mesh = {*Spinal Cord Injuries/rehabilitation ; Humans ; *Spinal Cord Stimulation/methods/instrumentation ; *Electric Stimulation Therapy/methods/instrumentation ; }, abstract = {Spinal cord injury (SCI) can lead to permanent loss of motor, sensory, and autonomic functions, presenting a significant clinical challenge for rehabilitation. In addition to conventional rehabilitation approaches, epidural spinal cord stimulation (eSCI) is often used to enhance recovery. However, the invasive nature of eSCI limits patient acceptance and widespread application. Compared to traditional spinal cord stimulation, temporal interference (TI) stimulation offers a noninvasive approach to stimulate deep spinal cord regions, making it a promising technique for SCI treatment. A critical factor in achieving effective TI stimulation for SCI rehabilitation is the accurate placement of two electrode pairs on the skin surface to generate a high electric field envelope within the targeted spinal cord area. We propose a unique protocol that utilizes electric field simulations and parameter optimization to determine the optimal electrode placement for specific SCI regions. Additionally, this protocol provides a systematic description of how to efficiently implement the optimized electrode placement strategy in clinical TI stimulation.}, }
@article {pmid41245956, year = {2025}, author = {Lu, Y and Jin, Z and Jian, Y and Kong, D and Zhou, H and Xu, Y and Cao, R and Xia, Z and Yang, F and Wu, Q and Gao, Y and Cui, A and Yang, S and Zheng, N and Bang, J and Yang, G and Ko, SH and Yang, H and Xu, K}, title = {Metal-hydrogel chelation interfaces for ultrasoft and bidirectional bioelectronics.}, journal = {National science review}, volume = {12}, number = {11}, pages = {nwaf399}, doi = {10.1093/nsr/nwaf399}, pmid = {41245956}, issn = {2053-714X}, abstract = {Emerging demand in soft bioelectronic systems poses critical challenges in stiffness control and end-to-end connections due to the huge modulus difference in various components. Here, a bidirectional electrical interface of hydrogel and metal electrodes to bridge soft skin/tissue and data collection circuits is enabled by coordination interactions. The dual-mode chelation including internal chelation and surface chelation effectively configures the cross-linking structure of hydrogel, as well as enhances the binding interface of metal-hydrogel complex surfaces. Internally, strong chelation competes with esterification, yielding tissue-like softness of hydrogel with an ultra-low modulus of ∼339.9 Pa. Externally, the hydrogel passivates the combined metal surfaces, promoting the formation of interlocked structures between metal oxide nanoislands, achieving a high binding strength of ∼1.95 MPa without compromising electrical conductivity. The stable electrical interconnections via hybrid interfacial bonding enable high signal-to-noise ratio signal recordings from the skin, neural surfaces and brain, maintaining reliable performance, even under mechanical disturbances. This work provides an effective strategy for achieving mechanically and electrically robust hybrid bioelectronic interfaces, advancing their applications in capturing both in vitro and in vivo electrical signals.}, }
@article {pmid41245196, year = {2025}, author = {Wang, KJ and Vinjamuri, R and Alimardani, M and Kumar Reddy, T and Mao, ZH}, title = {Editorial: NeuroDesign in human-robot interaction: the making of engaging HRI technology your brain can't resist.}, journal = {Frontiers in robotics and AI}, volume = {12}, number = {}, pages = {1699371}, doi = {10.3389/frobt.2025.1699371}, pmid = {41245196}, issn = {2296-9144}, }
@article {pmid41242443, year = {2025}, author = {Mir, M and Badea, I and Wilson, LD}, title = {Hierarchical chitosan-lignocellulosic duplex system: An in vitro evaluation of controlled release, anti-pathogenic and hemostatic effects.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {149018}, doi = {10.1016/j.ijbiomac.2025.149018}, pmid = {41242443}, issn = {1879-0003}, abstract = {Critical design challenges that affect novel drug delivery systems concern chemical processing and tissue compatibility of source materials, which highlight the need for sustainable, biocompatible materials and versatile manufacturing methods. This investigation leverages the unique surface chemistry of biomass-derived lignocellulose substrates to form biocomposite frameworks with effective antipathogenic and hemostatic properties via noncovalent synthesis. Complementary electrostatic interactions support a highly porous biocomposite framework, according to spectral (IR, Raman and NMR) and microscopy results. Biocomposite complexes with antipathogenic agents (gentamicin and rifamycin salts) are revealed by kinetic release profiles, as noted by an initial burst and sustained release profile under physiological conditions. In vitro biocompatibility was demonstrated by MTT cell viability assays (ca. 90 % after 48 h). Anti-pathogenic effects are revealed by agar diffusion assays with E. coli (up to 16 mm inhibition zones). In vitro blood sorption, cell adhesion and blood clotting index (BCI) results of biocomposites reveal impressive blood absorption capacity (ca. 10-fold; w/w) with good cell adhesion and efficient hemostatic properties with BCI below 2 %. This study challenges the current limits of specialized biomedical applications of lignocellulose fiber-chitosan biocomposites via in-vitro results at bioactive interfaces for anti-infective targeted drug delivery and trauma management.}, }
@article {pmid41242344, year = {2025}, author = {Huang, Y and Lin, Z and Huang, J and Chen, T and Liu, T}, title = {Correlating the Evans index and bicaudate index with ventricle volume at the three kinds of scanning baselines.}, journal = {Brain research bulletin}, volume = {}, number = {}, pages = {111641}, doi = {10.1016/j.brainresbull.2025.111641}, pmid = {41242344}, issn = {1873-2747}, abstract = {PURPOSE: Evans' Index (EI) and Bicaudate Index (BCI), as two-dimensional linear indexes, are commonly used to evaluate ventricle size. This study is investigated the differences in linear measures at the three kinds of scanning baselines and their correlations with ventricle volume.<br><br>METHODS: In 186 healthy volunteers,117 hydrocephalus patients with complete skull and 72 hydrocephalus patients without complete skull, the linear indexes, intracranial volume and ventricle volume were calculated by 3D Slicer. Wilcoxon rank test was used for comparisons of the linear indexes at the scanning baselines respectively. Spearman analysis was applied for the correlations between linear indexes and ventricle volume respectively.<br><br>RESULTS: There were statistical differences in the linear indexes of the three scanning baselines. Comparison of the linear indexes in people from three groups, the difference of linear indexes was minimum at Reid's base line (RBL), but max at supraorbitomeatal line (SML). Compared with the third and the fourth ventricle, the linear indexes had a stronger correlation with lateral ventricle volume or total ventricle volume. On the other hand, EI at RBL had a stronger correlation with ventricle volume, compared with other two kinds of scanning baselines.<br><br>CONCLUSION: For consistent and representative linear measurements, we recommend using the Evans Index at Reid's baseline (RBL). However, the correlations between linear indices and ventricular volume were only modest, underscoring the limitation of 2D indices for precise volumetric assessment.}, }
@article {pmid41241353, year = {2025}, author = {Li, G and Said, FM and Liang, J and Li, Y and Jing, Z}, title = {Fabrication of dual physically cross-linked agarose-based double network composite hydrogels with antibacterial and hemostatic properties for infected wound healing.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {149011}, doi = {10.1016/j.ijbiomac.2025.149011}, pmid = {41241353}, issn = {1879-0003}, abstract = {An agarose-based double network composite hydrogel with good mechanical, antibacterial, and hemostatic properties was synthesized to accelerate the healing of infected wounds. The double network composite hydrogel was fabricated by hydrogen bonding between poly(ACG-co-NBAA) chains generated by free radical polymerization and helical conformation formed by the agarose-graft-gelatin chains in the presence of Zn-MOF. The synthesized hydrogels exhibited a three-dimensional network structure and excellent pH sensitivity. The disintegration of hydrogen bonds in the hydrogel network caused the increase of swelling ratio of the hydrogels as the pH rose. The mechanical and antibacterial properties of agarose-based composite hydrogels can be well adjusted by changing their composition. The special structure of the hydrogels and Zn-MOF embedding endowed them with good antibacterial properties against S. aureus and E. coli. The results of the hemostasis experiment found that the agarose-based composite hydrogels had a lower BCI value, and the mice treated with the hydrogel sample had lower blood loss and shorter hemostasis time, indicating that the synthesized hydrogels had good hemostatic performance. In addition, a full-layer skin wound infection model demonstrated that the agarose-based composite hydrogels can accelerate the healing of infected wounds, and the wound healing rate of mice treated with the hydrogel sample can reach 97.6 ± 0.8 % at 14 days. Therefore, a biocompatible agarose-based double network composite hydrogel with good mechanical, antibacterial, and hemostatic properties, is expected to be used as a medical dressing to promote the healing of infected wounds.}, }
@article {pmid41241070, year = {2025}, author = {Kong, L and Wang, H and Sang, R and Saeed, S and Shen, Y and Lai, J and Hu, S}, title = {Down-regulated expressions of LOC151174, GSTT1, and IFI27L1 in the peripheral blood exhibit the biological and immunological features of major depressive disorder.}, journal = {Journal of affective disorders}, volume = {}, number = {}, pages = {120687}, doi = {10.1016/j.jad.2025.120687}, pmid = {41241070}, issn = {1573-2517}, abstract = {BACKGROUND: Mechanism of major depressive disorder (MDD), especially the associations between genetic and peripheral immune changes remain to be elucidated.<br><br>METHODS: Databases including Gene Expression Omnibus and GWAS Catalog were investigated and analyzed via differential analyses and summary data-based Mendelian randomization to identify feature genes. Functional annotations, gene-gene interaction network were performed, with immune functions and immune infiltration further analyzed.<br><br>RESULTS: Three RNA sequencing datasets and seven genome-wide association study datasets were considered eligible. Genes including LOC151174 (logFC&#x202f;=&#x202f;-0.704, Padjusted&#x202f;=&#x202f;0.024), GSTT1 (logFC&#x202f;=&#x202f;-0.713, Padjusted&#x202f;=&#x202f;0.028), and IFI27L1 were identified (logFC&#x202f;=&#x202f;-0.138, Padjusted&#x202f;=&#x202f;0.043; betaSMR&#x202f;=&#x202f;-0.018, PSMR&#x202f;=&#x202f;6.714e[-13], PHEIDI&#x202f;=&#x202f;0.058), and all showed a down-regulated trend in the background of MDD. Functioning pathways including cytokine receptor interaction, ABC transporters, and Ca[2+] signaling pathways were shared by more than one feature gene. As for immune function, scores of antigen presenting cell co-inhibition, natural killer cells, and T cell co-inhibition were significantly higher in the group with low-expression of GSTT1 (P&#x202f;<&#x202f;0.001), score of T cell co-inhibition was higher in the group with high-expression of IFI27L1 (P&#x202f;<&#x202f;0.01), and score of dendritic cells was higher in the group with high-expression of both LOC151174 (P&#x202f;<&#x202f;0.01) and IFI27L1 (P&#x202f;<&#x202f;0.05). Macrophages M0 showed the highest significance of immune infiltration (P&#x202f;<&#x202f;0.001). Moreover, expression of GSTT1 showed significant correlation with the activity of plasma cells (R&#x202f;=&#x202f;-0.2, P&#x202f;=&#x202f;0.041) and activated memory CD4(+) T cells (R&#x202f;=&#x202f;-0.2, P&#x202f;=&#x202f;0.045).<br><br>CONCLUSION: Our work indicates that peripheral expressions of LOC151174, GSTT1, and IFI27L1 might be correlated with MDD particularly through peripheral immune abnormalities.}, }
@article {pmid41240747, year = {2025}, author = {Rosenblum, D and Karandinos, G and Unick, J and Cauchon, D and Ciccarone, D}, title = {Early evidence of the effects of xylazine-adulterated fentanyl in Ohio.}, journal = {The International journal on drug policy}, volume = {146}, number = {}, pages = {105066}, doi = {10.1016/j.drugpo.2025.105066}, pmid = {41240747}, issn = {1873-4758}, abstract = {BACKGROUND: Xylazine is becoming a prevalent fentanyl adulterant in the US. It has been associated with severe wounds and withdrawal symptoms. However, its impact on fatal overdose rates is poorly understood.<br><br>METHODS: Poisson and ordinary least squares regression analyses are used to estimate the relationship between xylazine prevalence and unintentional overdose death and death rates at the county-month level in Ohio from April through December 2023. Xylazine prevalence is calculated from the Ohio Bureau of Criminal Investigation's (BCI) Crime Lab Data, and mortality data is from the Ohio Department of Health.<br><br>RESULTS: Xylazine prevalence is positively correlated with overdose deaths and death rates in large population counties. Xylazine adulteration is associated with 319 more overdose deaths [95 percent CI: 147-491 deaths], 10 percent of all unintentional overdose deaths in Ohio, over the nine-month period. Our estimates predict that if all fentanyl had been adulterated with xylazine over these nine months, this would have led to an additional 519 deaths.<br><br>DISCUSSION: Although the data covers a limited time period, our estimates provide evidence that xylazine-adulterated fentanyl is likely to lead to additional overdose deaths as it continues to spread across the US, blunting the initial signs of a declining trend in overdose deaths. If the findings can be extrapolated to the rest of the country, it is likely that overdose deaths would have fallen more substantially in 2023 if xylazine had not already been so prevalent in large parts of the US.}, }
@article {pmid41239595, year = {2025}, author = {Deng, X and Lai, K and Huang, W and Liao, F}, title = {A retrospective study on the clinical efficacy of pneumatic hand rehabilitation devices in managing post-stroke chirospasm following ischemic stroke.}, journal = {Medicine}, volume = {104}, number = {46}, pages = {e45389}, doi = {10.1097/MD.0000000000045389}, pmid = {41239595}, issn = {1536-5964}, support = {2022A01146//Research on Wearable Brain-Computer Interface for Robotic Rehabilitation of Hand Function after Stroke/ ; }, mesh = {Humans ; Retrospective Studies ; Male ; Female ; Middle Aged ; Aged ; *Ischemic Stroke/complications ; *Hand/physiopathology ; *Stroke Rehabilitation/methods/instrumentation ; *Muscle Spasticity/etiology/rehabilitation ; Activities of Daily Living ; Treatment Outcome ; Quality of Life ; *Intermittent Pneumatic Compression Devices ; }, abstract = {Chirospasm is a common sequela of ischemic stroke (IS), often resulting in substantial impairment of hand function and quality of life. Although conventional rehabilitation can partially improve motor recovery, it is often insufficient in effectively reducing spasticity and edema, thereby necessitating adjunctive interventions. This retrospective study aimed to evaluate the effectiveness of a pneumatic hand rehabilitation device in improving hand function and alleviating spasticity in IS patients with chirospasm. Clinical data from 76 patients with chirospasm following IS, treated at our institution between March 2022 and March 2024, were retrospectively analyzed. Patients were divided into 2 groups based on treatment modality: a control group receiving standard rehabilitation therapy and an intervention group receiving additional treatment with a pneumatic hand rehabilitation device. Key evaluation indicators included metacarpophalangeal joint circumference, finger swelling volume, hand function scores (STEF, Fugl-Meyer, MFT), spasticity grading (Ashworth and MAS), neurological deficit indices, pain scores (Visual Analogue Scale), and activities of daily living (ADL). Clinical efficacy was assessed at baseline and after 8 weeks of treatment. Both groups demonstrated improvements after treatment; however, the intervention group showed significantly greater reductions in joint circumference, finger swelling, and muscle tone, as well as higher improvements in hand function scores (P < .05). Notably, Visual Analogue Scale scores were lower and ADL scores were higher in the intervention group. Furthermore, the total effective rate in the intervention group (94.74%) was significantly higher than that in the control group (76.32%). This retrospective analysis suggests that pneumatic hand rehabilitation devices, when integrated with conventional therapy, are more effective in reducing spasticity, alleviating hand edema, improving hand motor function, and enhancing quality of life in post-IS patients with chirospasm. These findings support the broader clinical application of such devices in stroke rehabilitation programs.}, }
@article {pmid41238552, year = {2025}, author = {Zou, Q and Zou, G and Wang, S and Wang, Y and Xu, J and Long, Y and Zhou, S and Wu, X and Yang, G and Qin, L and Su, ZH and Cui, Z and Zuo, XN and Tang, X and Rao, H and Gao, JH}, title = {Cortical hierarchy underlying homeostatic sleep pressure alleviation.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {10014}, pmid = {41238552}, issn = {2041-1723}, mesh = {Humans ; Adult ; *Homeostasis/physiology ; Magnetic Resonance Imaging ; Male ; *Sleep/physiology ; Female ; Electroencephalography ; Sleep Deprivation/physiopathology ; *Cerebral Cortex/physiology/diagnostic imaging ; Young Adult ; Wakefulness/physiology ; Oxygen/blood ; Brain Mapping ; Middle Aged ; }, abstract = {Sleep dissipates accumulated sleep pressure and restores brain function, yet how this recovery unfolds across the cortical hierarchy remains unclear. Here, we record simultaneous electroencephalogram (EEG) and blood oxygen level-dependent (BOLD) functional magnetic resonance imaging data from 130 healthy adults to map spatial patterns underlying sleep pressure alleviation. Compared to wakefulness, sleep elicits spatially heterogeneous changes in BOLD fluctuation along a sensory-association cortical gradient. The magnitude of these sleep-wake differences correlates with individual slow-wave activity and is most pronounced during the first hour of sleep. As slow waves dissipates, these hierarchical differences are progressively downscaled, implicating homeostatic regulation in sculpting cortical plasticity. In addition, the homeostatic regulation of BOLD fluctuation amplitude is spatially associated with the regional distribution of glycolysis. Finally, recovery sleep reinstates hierarchical BOLD dynamics after sleep loss in an independent sleep deprivation study. These findings consistently suggest a cortical hierarchy underlying the dynamic changes in sleep homeostasis.}, }
@article {pmid41237236, year = {2025}, author = {Li, F and Wang, G and Genon, S and Eickhoff, SB and He, R and Yi, C and Dong, D and Yao, D and Jiang, L and Wu, W and Xu, P}, title = {Mapping neurophysiological and molecular profiles of heterogeneity and homogeneity in schizophrenia-bipolar disorder.}, journal = {Science advances}, volume = {11}, number = {46}, pages = {eadz0389}, pmid = {41237236}, issn = {2375-2548}, mesh = {Humans ; *Bipolar Disorder/physiopathology/metabolism/diagnosis ; *Schizophrenia/physiopathology/metabolism/diagnosis ; Adult ; Male ; Female ; Electroencephalography ; Machine Learning ; Middle Aged ; Psychotic Disorders/physiopathology ; Brain Mapping ; }, abstract = {The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connectivity (FC) in patients with psychotic bipolar disorder (PBD), schizophrenia (SCZ), and schizoaffective disorder (SAD) into individualized and shared subspaces. The results demonstrated that individualized FCs captured disease heterogeneity and predicted symptom severity more accurately than raw FCs, while shared FCs revealed diagnosis-specific abnormalities and achieved an accuracy of 79.30% in differentiating PBD, SCZ, and SAD. Furthermore, molecular decoding implicated regionally selective serotonin systems and astrocytes in the neurobiological differences among disorders, suggesting disorder-specific pharmacological targets. Critically, these findings were replicated in an independent cohort, confirming the effectiveness of the COBE framework in mining neurophysiological and molecular profiles of schizophrenia-bipolar disorder. These findings advance mechanistic understanding of psychotic disorders and offer a promising avenue toward objective, clinically relevant tools for psychotic evaluation.}, }
@article {pmid41236698, year = {2025}, author = {Cai, M and Liu, H and Shao, C and Li, T and Jin, J and Liang, Y and Wang, J and Cao, J and Yang, B and He, Q and Shao, X and Ying, M}, title = {Metabolomics and metabolites in cancer diagnosis and treatment.}, journal = {Molecular biomedicine}, volume = {6}, number = {1}, pages = {109}, pmid = {41236698}, issn = {2662-8651}, support = {U23A20534//National Natural Science Foundation of China/ ; LR23H310001//Science Fund for Distinguished Young Scholars of Zhejiang Province/ ; LR24H310001//Science Fund for Distinguished Young Scholars of Zhejiang Province/ ; 2024C03181//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang Province/ ; 226-2024-00178//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Metabolomics/methods ; *Neoplasms/diagnosis/metabolism/therapy ; Biomarkers, Tumor/metabolism ; *Metabolome ; Prognosis ; }, abstract = {Cancer is a leading cause of death worldwide. Metabolic reprogramming in cancers plays an important role in tumor initiation, malignant progression and therapeutic response. Based on this, significant progress has been made in the development of the metabolite-based early cancer detection and targeted interventions. Over the past decade, metabolomics has been widely applied to detect metabolic alterations in tumor cells as well as their microenvironment. However, an up-to-date systematic review to summarize the current metabolomic and metabolites in cancer, especially their connections to cancer diagnostics/prognostic biomarkers and therapeutic strategies, is lacking. Here, we first introduced the platforms and analytical processes of metabolomics, as well as their application in different biological matrix of tumor patients. Then, we summarized representative cancer studies in which specific metabolites was found to be act as diagnostic or prognostic/stratification biomarkers. Furthermore, we reviewed the current therapeutic strategies targeting cancer metabolism, particularly the drugs/compounds that are either market-approved or in clinical trials, and also analyzed the potential of metabolites in personalizing precision treatment. Finally, we discussed the key challenges in this field, including the technical limitations of metabolomics and the clinical limitations of therapeutic targeting cancer metabolism, and further explored the future directions such as multi-omics perspective and lifestyle interventions. Taken together, we provides a comprehensive overview from technological platforms of metabolomics to translational applications of metabolites, facilitating the discovery of novel biomarkers and targeting strategies for precision oncology.}, }
@article {pmid41235174, year = {2025}, author = {Ge, J and Wang, J and Zheng, X and Li, M and Wang, F and Xu, G}, title = {A multi-domain graph convolutional network-based prediction model for personalized motor imagery action.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1637018}, pmid = {41235174}, issn = {1662-4548}, abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) offer a novel method to decode action imagination. Our previous study demonstrated that actions play a key role in causing individual differences. Cognitive EEG signals showed a positive correlation with MI, reflecting these differences and providing a foundation for predicting suitable MI actions for each individual. This study aimed to propose a multi-domain graph convolutional network (M-GCN) for predicting personalized MI action using cognitive data. The M-GCN extracts time, frequency, and spatial domain features from cognitive tasks to construct multi-domain brain networks using different EEG quantization methods according to the characteristics of the three domains. Subsequently, the M-GCN utilizes spectral GCN to learn the topology relationship between EEG channels by analyzing functional connection strength. Finally, for each action, the M-GCN can accurately map cognitive data to the corresponding MI action and output a personalized action for each subject. A subject-independent decoding paradigm with leave-one-subject-out cross-validation is adopted to validate the model on ten subjects. Compared to baseline and single-domain models, the M-GCN achieves the highest prediction accuracy of 73.60% (p = 7.1 × 10[-3]), improving by 15.87% (p = 2.0 × 10[-4]) and by 7.2% (p = 4.0 × 10[-4]), respectively. This study proves that the M-GCN can precisely predict personalized MI actions, reflecting the efficiency of the multi-domain feature fusion based on cognitive tasks and GCN and offering a novel method for personalized BCI.}, }
@article {pmid41235025, year = {2025}, author = {Chen, ZJ and Huang, XL and Xia, N and Gu, MH and Xu, J and Lu, M and Chen, H and Xiong, CH and Chen, Y}, title = {Next-Generation Neurotechnologies Inspired by Motor Primitive Model for Restoring Human Natural Movement.}, journal = {Research (Washington, D.C.)}, volume = {8}, number = {}, pages = {0942}, pmid = {41235025}, issn = {2639-5274}, abstract = {Advances in neuroengineering and artificial intelligence are transforming the landscape of motor rehabilitation, aiming to restore human movement as natural as possible. In recent decades, more advanced interventions are increasingly achievable via hybrid robotic systems, neuroprosthetics, and brain-computer interfaces. However, a fundamental gap of these neurotechnologies remains in modeling the complexity of neuromotor control, particularly how the central nervous system coordinates high-dimensional motor outputs in naturalistic behaviors. Rooted in theoretical neuroscience, the motor primitive (MP) model proposes an adaptable framework to deconstruct and reproduce motor tasks through low-dimensional modules. Interestingly, recent studies have indicated that the MP model may reform current-generation neurotechnologies by digitally shaping the course of human-machine interaction. In this narrative review, we will critically examine conventional target settings and identify their limitations in guiding biomimetic control in neurotechnologies. We then introduce the MP model with its machine learning and physiological scaffolds for better understanding and replicating human natural movement. Finally, we will present its potential in facilitating the next-generation neurotechnologies across kinematic, muscular, and neural domains. By modeling motor control in human and neuroengineering, we believe that the MP-inspired paradigms can initiate a new era of intelligent, patient-specific, and naturalistic motor restoration for various neurological and traumatic diseases.}, }
@article {pmid41233249, year = {2025}, author = {Yu, Y and Wang, Z and Kroemer, NB and Zhang, L and Lu, L and Sun, J}, title = {Closed-loop brain-body interface: integrating brain-computer interfaces and peripheral nerve stimulation for adaptive neuromodulation.}, journal = {Science bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.scib.2025.10.037}, pmid = {41233249}, issn = {2095-9281}, }
@article {pmid41232683, year = {2025}, author = {Cai, X and Sun, W and Zheng, X and Ding, N and Luo, M and Tu, Y and Meng, D and Liu, Y and Ding, S and Yuan, B and Long, X}, title = {Safety and efficacy of low intensity transcranial ultrasound stimulation for depression: A single-blind randomized controlled clinical study.}, journal = {Journal of affective disorders}, volume = {}, number = {}, pages = {120666}, doi = {10.1016/j.jad.2025.120666}, pmid = {41232683}, issn = {1573-2517}, abstract = {AIMS: This study aimed to confirm the safety and effectiveness of transcranial ultrasound stimulation (TUS) in treating depression by targeting a subregion of the left dorsolateral prefrontal cortex (dlPFC).<br><br>METHODS: A single-blind, randomized, sham-controlled clinical study was conducted involving 24 patients with depression in the TUS group and 12 in the sham group. Participants underwent psychiatric assessments and functional MRI scans. We employed an MR-compatible transducer, integrating dual navigation through optical guidance and MR acoustic radiation force imaging, to accurately target Brodmann area 46 (BA46) of the left dlPFC. The treatment group received active TUS, while the sham group received identical treatment without energy output, followed by actual TUS treatment.<br><br>RESULTS: Following treatment, the TUS group exhibited significant improvements in depression and anxiety scores, as well as sleep quality, with benefits lasting up to four weeks. The sham group showed minor improvements after sham stimulation, but these became significant after subsequent real TUS treatment. Functional connectivity analysis revealed changes in the TUS group, particularly in connectivity with regions implicated in emotion processing, including the subgenual anterior cingulate cortex, ventral posterior cingulate cortex, and precuneus, all of which correlated with symptom improvements. Adverse effects of TUS were minimal and well-tolerated.<br><br>CONCLUSION: This study underscores the potential of low-intensity TUS as a safe and effective treatment for depression, with the capacity to modulate neural activity in targeted brain areas. Future research should emphasize optimizing TUS parameters and exploring its effects on other brain regions linked to depression.}, }
@article {pmid41232376, year = {2025}, author = {Zhang, H and Xie, J and Liu, K and Liu, Y and Dong, W}, title = {Dual-TTFNet: An end-to-end dual-branch temporal and time-frequency fusion network for auditory attention decoding in steady state motion auditory evoked potential.}, journal = {Computers in biology and medicine}, volume = {199}, number = {}, pages = {111284}, doi = {10.1016/j.compbiomed.2025.111284}, pmid = {41232376}, issn = {1879-0534}, abstract = {Auditory attention decoding based on steady-state motion auditory evoked potential (SSMAEP) offers a promising pathway for developing auditory brain-computer interface (BCI) driven by auditory selective attention. However, achieving high decoding performance with strong interpretability remains a major challenge. To address this issue, we proposed an end-to-end dual-branch neural network that fuses temporal and time-frequency information (Dual-TTFNet) to enhance SSMAEP decoding performance. The model consisted of a temporal convolutional branch and a time-frequency branch with learnable S-transform convolutional kernels for modeling of time-frequency patterns. To further strengthen inter-branch interactions, bidirectional cross-branch EEG channel attention mechanism and attention mechanism-based Transformer was introduced to achieve deep integration of temporal and time-frequency representations. Experiments on two and three-target SSMAEP-BCI datasets demonstrate that Dual-TTFNet consistently outperforms state-of-the-art methods under various tasks, time windows, and EEG channel configurations. It achieved accuracies of 95.08 ± 7.46 % (two-class) and 91.50 ± 4.90 % (three-class) at 5 s, with information transfer rate of 7.94 ± 3.08 bits/min and 11.06 ± 2.35 bits/min, respectively. Ablation studies and visualization analyses further validated the crucial role of the attention mechanisms and S-transform kernels in enhancing feature discriminability and neural interpretability. Dual-TTFNet achieves a synergistic optimization of SSMAEP-BCI decoding performance and interpretability, demonstrating excellent generalization ability and application potential.}, }
@article {pmid41231905, year = {2025}, author = {Ergün, E and Aydemir, &#xd6; and Korkmaz, OE}, title = {A novel scrolling text reading paradigm for improving the performance of multiclass and hybrid brain computer interface systems.}, journal = {PloS one}, volume = {20}, number = {11}, pages = {e0334784}, pmid = {41231905}, issn = {1932-6203}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; Female ; Adult ; *Reading ; Spectroscopy, Near-Infrared ; Young Adult ; Algorithms ; *Brain/physiology ; }, abstract = {A Brain-Computer Interface (BCI) enables direct communication between the brain and external devices, such as computers or prosthetic limbs. This allows the brain to send commands while receiving sensory feedback from the device. Despite their potential, the performance limitations of existing BCI systems have motivated researchers to improve their efficiency and reliability. To address this challenge, the present study introduces a novel BCI paradigm centered on a cognitive task involving the reading of scrolling text in four different directions: right, left, up and down. The primary objective was to explore the electroencephalography (EEG) and near-infrared spectroscopy (NIRS) signals within this framework and assess the potential of hybrid BCI systems based on this innovative paradigm. The experimental protocol involved eight participants performing tasks across four classes of scrolling text. To optimize system accuracy and speed, EEG and NIRS data were segmented into discrete temporal windows. Features were extracted using the Hilbert Transform, while classification was performed via the k-nearest neighbor algorithm. The proposed approach achieved a classification accuracy of 96.28% [Formula: see text] 1.30% for multi-class tasks, demonstrating the effectiveness of hybrid modalities. This study not only introduces a novel paradigm for hybrid BCI systems, but also validates its performance, providing a promising direction for advancing the field.}, }
@article {pmid41231687, year = {2025}, author = {Chang, TY and Wang, JB and Tsai, YH and Tsao, Y and Yang, CH}, title = {A 40-nm 3.9mW, 200words/min Neural Signal Processor in Speech Decoding for Brain-Machine Interface.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2025.3625650}, pmid = {41231687}, issn = {1940-9990}, abstract = {Brain-machine interface (BMI) technology enables the human brain to communicate directly with machines. This work presents a neural signal processor for real-time BMI, supporting translation from user's speech attempt to sentences. By employing speech attempt detection, the energy consumption is reduced by 46% and the number of channels for speech attempt detection can be decreased from 128 to 16. The proposed weight encoding, which leverages both sparse encoding and mixed-precision arithmetic, reduces the off-chip memory size of the neural network by 80%. Computation reordering decreases the processing latency by 55%. For the partial sum caching technique, the number of neural network operations is reduced by 25%. The processing element (PE) array in the neural network engine exploits both input and weight sparsity to lower the processing latency by 95%. By using the proposed mixed-precision multiplier in the PE array, the area is reduced by 27% compared with the PE array with the full precision. In the beam search engine, the proposed approximate top-k selection architecture exhibits 16× fewer comparators. The neural signal processor achieves speech decoding with a phone error rate of 16.6% and a word error rate of 23.5%. Fabricated in 40-nm CMOS, the chip achieves the maximum communication rate of 200 words/min, which is 16.7-to-42.6× faster than the state-of-the-art designs. This work is able to decode up to 125,000 words, which is not achievable by prior works that can only decode up to 31 characters.}, }
@article {pmid41230997, year = {2025}, author = {Tang, R and Sun, C and Chang, J and Ju, Z and Si, Y and Yang, Y and Shi, Y and Wu, J and Ye, Y and Bao, K and Deng, Q and Wu, Y and Jian, J and Chen, Z and Wang, Y and Sun, H and Wang, Y and Ji, B and Lin, H and Li, L}, title = {Ambient-Stable NIR Nanolasing: Monolithic Integration of PbS CQDs on a Silicon Photonic Platform.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e16460}, doi = {10.1002/adma.202516460}, pmid = {41230997}, issn = {1521-4095}, support = {2024SDXHDX0005//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang Province/ ; 62175202//National Natural Science Foundation of China/ ; 62205274//National Natural Science Foundation of China/ ; 2023GD003/110500Y0022303//Key Project of Westlake Institute for Optoelectronics/ ; 210230006022302/002//Research Center for Industries of the Future (RCIF) at Westlake University/ ; }, abstract = {Nanolasers based on colloidal quantum dots (CQDs), while transformative in the visible spectrum, face critical roadblocks in the near-infrared (NIR) regime due to material instability under ambient conditions and ultrafast Auger recombination in large NIR CQDs. Here, these limitations are addressed through zinc-doped PbS CQDs that suppress nonradiative decay, integrated with compact high-Q silicon nanobeam cavities to leverage the Purcell effect for efficiently guiding spontaneous emission into laser modes, thereby significantly reducing the threshold power. This work demonstrates a monolithic CQD-integrated silicon photonic platform that achieves NIR lasing under pulsed optical pumping, featuring a record narrow linewidth of 0.29 nm (0.15 meV) at 1579.20 nm and an ultralow threshold of 127 µJ cm[-2]. Notably, under continuous-wave (CW) pumping, the device exhibits cavity-filtered spontaneous emission with a sub-nanometer linewidth across the 1350-1600 nm spectrum. This emission showcases <6% peak power decay over 15 h at 300 K, robust performance up to 360 K, and negligible degradation after 250 days of ambient storage. By monolithically integrating solution-processed CQDs with CMOS-compatible silicon photonics, this platform establishes a reliable, scalable, and low-cost route toward multiwavelength on-chip nanolaser arrays in the NIR regime, unlocking transformative potential for compact photonic technologies in imaging, sensing, and communications.}, }
@article {pmid41230692, year = {2025}, author = {Shao, X and Xia, Z and Cai, M and Shao, C and Bing, S and Wang, T and Du, W and Liu, J and Shen, D and Cao, J and Yang, B and He, Q and Xu, X and Zhang, J and Ying, M}, title = {Chromosomal rearrangement-enhanced mRNA stability drives the oncogenic potential of fusion genes in pediatric leukemia.}, journal = {Haematologica}, volume = {}, number = {}, pages = {}, doi = {10.3324/haematol.2025.288256}, pmid = {41230692}, issn = {1592-8721}, abstract = {Acute lymphoblastic leukemia (ALL), the most common type of pediatric leukemia, is frequently driven by fusion genes generated by chromosomal rearrangements. Compared with wild-type genes, many oncogenic fusions show increased expression and sustained functional activity that drives tumorigenesis. However, the mechanisms by which chromosomal rearrangements lead to functional enhancement remain largely elusive. In addition, although large-scale sequencing has identified numerous fusion events, the functional significance of most remains unclear. Here, we demonstrate that enhanced mRNA stability represents an important tumorigenic mechanism for oncogenic fusions, including classical PAX5 fusions. Based on this mechanism, we characterize a novel oncogenic fusion, STK38-PXT1, which exhibits upregulated STK38 mRNA levels and drives the development of ALL. Mechanistically, the increased mRNA stability results primarily from enhanced m6A modification of oncogenic fusions, which is attributable to "gene truncation" (as in PAX5 fusions) and "partner collaboration" (as in STK38-PXT1). Furthermore, the m6A reader IGF2BP3 is crucial for maintaining the high mRNA stability of oncogenic fusions. We further propose venetoclax as an innovative and clinically available therapy for ALL driven by these oncogenic fusions characterized by high mRNA stability. Our study not only highlights mRNA stabilization as a crucial mechanism by which oncogenic fusions to drive tumorigenesis, but also presents a promising therapeutic strategy for patients with ALL.}, }
@article {pmid41224809, year = {2025}, author = {Zhang, J and Zhang, ZY and Wang, YL and Zhou, B and Xia, CY and Su, M and Li, CG}, title = {Unveiling the upper-limb functional recovery mechanisms in stroke patients using brain-machine interfaces: a near-infrared functional imaging-based study.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {39704}, pmid = {41224809}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods ; Middle Aged ; Spectroscopy, Near-Infrared/methods ; Aged ; *Recovery of Function/physiology ; *Stroke/physiopathology/diagnostic imaging ; }, abstract = {Upper limb dysfunction is highly prevalent among patients in the chronic stage of stroke. Brain-computer interface (BCI) technology, which creates a direct link between the brain's electrical signals and external devices, stroke patients with motor disabilities are able to perform BCI tasks for clinical rehabilitation. However, traditional BCI applications are often limited in their capacity to monitor the brain function of patients. In this study, functional near-infrared spectroscopy (fNIRS) was employed to observe changes in brain cortex activation patterns before and after BCI use in ischemic stroke patients with upper limb dysfunction. Thirty-four ischemic stroke patients with upper limb dysfunction meeting the inclusion criteria were selected and randomly assigned to either a treatment group or a control group using a random number table, with 17 patients in each group. During the study, 4 participants dropped out, leaving 30 patients for the final statistical analysis, 15 in each group. Both groups received routine upper limb rehabilitation training. Additionally, the treatment group underwent daily BCI training for 30 min, 5 days a week, for 4 consecutive weeks. Upper limb function was evaluated using the Fugl-Meyer assessment for upper extremity (FM), and daily living activities were assessed with the modified barthel index (MBI). The six regions of interest (ROIs) in the cortex for fNIRS measurement were the ipsilesional and contralesional primary motor cortex (PMC), supplementary motor area (SMA), and somatosensory motor cortex (SMC). The three time points of measurement were baseline (prior to any treatment), 2 weeks of treatment, and 4 weeks of treatment. fNIRS was used to detect the oxygenated hemoglobin values (HbO) in six ROIs at each time point. After treatment, both groups exhibited improvements in FM and MBI scores. The treatment group demonstrated significantly greater functional gains than the control group at both 2 and 4 weeks, as reflected in FM (T1T0: 5.867 ± 3.482 vs. 3.200 ± 2.077, P < 0.01, d = 0.93; T2T0: 13.533 ± 5.705 vs. 7.133 ± 2.503, P < 0.05, d = 1.45) and MBI scores (T1T0: 13.400 ± 7.129 vs. 8.133 ± 4.357, P < 0.05, d = 0.89; T2T0: 27.867 ± 10.106 vs. 16.467 ± 7.010, P < 0.05, d = 1.31). fNIRS data revealed that after 4 weeks, the treatment group showed significantly increased oxygenated hemoglobin levels in PMC and SMA compared to baseline (PMC: P < 0.001, d = 0.62; SMA: P < 0.001, d = 0.89), along with a more pronounced PMC activation and higher brain network efficiency relative to the control group (PMC: 0.019 ± 0.017 vs. 0.007 ± 0.005, P < 0.01, d = 1.01; network efficiency: P < 0.05). Moreover, improvements in brain network efficiency were positively correlated with gains in both FM and MBI scores across the cohort. Our study suggests that BCI treatment combined with conventional medical and rehabilitation therapy can effectively enhance motor function and activities of daily living in stroke patients with upper-limb dysfunction. Additionally, it can promote cortical activation in the ipsilesional PMC and SMA regions and improve the network efficiency between brain regions.}, }
@article {pmid41223449, year = {2025}, author = {Jia, Z and Wang, H and Shen, Y and Hu, F and An, J and Shu, K and Wu, D}, title = {Magnetoencephalography (MEG) based non-invasive Chinese speech decoding.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae1ea2}, pmid = {41223449}, issn = {1741-2552}, abstract = {OBJECTIVE: As an emerging paradigm of brain-computer interfaces (BCIs), speech BCI has the potential to directly reflect auditory perception and thoughts, offering a promising communication alternative for patients with aphasia. Chinese is one of the most widely spoken languages in the world, whereas there is very limited research on speech BCIs for Chinese language.<br><br>APPROACH: This paper reports a text-magnetoencephalography (MEG) dataset for non-invasive Chinese speech BCIs. It also proposes a multi-modality assisted speech decoding (MASD) algorithm to capture both text and acoustic information embedded in brain signals during speech activities.<br><br>MAIN RESULTS: Experiment results demonstrated the effectiveness of both our text-MEG dataset and our proposed MASD algorithm.<br><br>SIGNIFICANCE: To our knowledge, this is the first study on multi-modality assisted decoding for non-invasive Chinese speech BCIs.}, }
@article {pmid41223103, year = {2025}, author = {Sun, J and Meng, J and Wang, H and He, F and Jung, TP and Xu, M and Yu, H and Ming, D}, title = {Joint-Shrinkage Pattern Matching for Small-Sample and Imbalanced ERP Decoding in Brain-Computer Interfaces.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3632096}, pmid = {41223103}, issn = {1558-2531}, abstract = {Event-related potential (ERP)-based brain-computer interface (BCI) systems are approaching sub-microvolt-level resolution, enabling detailed decoding of sophisticated cognitive processes. This progress has increased the demand for robust classifiers. Current algorithms encounter two fundamental challenges when decoding ERPs: data scarcity and class imbalance. To address these challenges, we propose a joint-shrinkage pattern matching (JSPM) algorithm consisting of two modules. First, a novel joint-shrinkage spatial filter is constructed by integrating shrinkage-based regularization with the ℓℓ22,pp norm. This regularization approach effectively bridges the gap between complex structured regularization and implementation simplicity, which introduces automated regularization to enhance module robustness under data-scarce conditions. The ℓℓ22,pp-norm provides a flexible feature distance measurement, enabling adaptation to data quality variability. Second, a weighted template matching module mitigates decision boundary shift caused by class imbalance. Using error-related potentials (ErrPs) as representative signals, we validated the algorithm through comprehensive comparisons. JSPM significantly outperformed 14 state-of-the-art classifiers on one self-collected and two public ErrP datasets. With only 40 imbalanced training samples, it achieved up to 14.84% higher average balanced accuracy (bAcc) than competing methods, maintaining a 4.88% average bAcc advantage over its nearest competitor. Notably, JSPM significantly enhanced inter-class discriminability for ErrP features with approximately 1 μV amplitude, achieving a maximum bAcc enhancement of 8.80%compared to deep learning methods. Overall, JSPM effectively addresses small-sample and imbalanced ERP decoding in BCI systems, facilitating the transition from laboratory research to real-world applications.}, }
@article {pmid41222907, year = {2025}, author = {Wen, H and Xu, M and Cui, S}, title = {Global research trends in brain-computer interfaces for Alzheimer's disease: a bibliometric perspective.}, journal = {International journal of surgery (London, England)}, volume = {}, number = {}, pages = {}, doi = {10.1097/JS9.0000000000004000}, pmid = {41222907}, issn = {1743-9159}, }
@article {pmid41222817, year = {2025}, author = {Şahin, E and Özdemir, D}, title = {ThinkSTra: a transformer-driven architecture for decoding imagined speech from EEG with spatial-temporal dynamics.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {41222817}, issn = {1741-0444}, support = {125E067//Scientific and Technological Research Council of T&#xfc;rkiye (TUBITAK)/ ; }, abstract = {Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices without requiring physical movement, offering a transformative solution particularly for individuals with impaired or lost motor functions. By providing an alternative communication pathway, BCIs hold considerable promise for both clinical interventions and cognitive neuroscience research. In this study, we introduce ThinkSTra, a novel Transformer-based framework for classifying inner speech commands from electroencephalography (EEG) signals. Unlike conventional models, ThinkSTra jointly captures the temporal dynamics and spatial distributions of neural activity, thereby enabling a more comprehensive representation of the complex structure inherent in EEG signals. We systematically evaluated ThinkSTra on multiple datasets, including the sentence-level TSEEG dataset and the Kumar EEG datasets encompassing character, digit, and visual object classification. To rigorously examine its robustness and generalizability, we additionally performed region- and channel-wise contribution analyses, conducted pretraining and cross-validation experiments, and visualized the learned feature representations using t-SNE. ThinkSTra consistently surpassed existing state-of-the-art approaches, achieving accuracies of 100% on sentence-level, 98.10% on character recognition, 98.34% on digit classification, and 99.5% on visual object tasks. Overall, this study advances inner speech decoding by introducing a robust Transformer-based framework and uncovering how distinct cortical regions contribute to this process, offering both methodological and neuroscientific insights for future brain-computer interfaces.}, }
@article {pmid41221369, year = {2025}, author = {Lee, JY and Lee, S and Mishra, A and Yan, X and McMahan, B and Gaisford, B and Kobashigawa, C and Qu, M and Xie, C and Kao, JC}, title = {Brain-computer interface control with artificial intelligence copilots.}, journal = {Nature machine intelligence}, volume = {7}, number = {9}, pages = {1510-1523}, pmid = {41221369}, issn = {2522-5839}, support = {DP2 NS122037/NS/NINDS NIH HHS/United States ; R01 NS121097/NS/NINDS NIH HHS/United States ; }, abstract = {Motor brain-computer interfaces (BCIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the past two decades, BCIs face a key obstacle to clinical viability: BCI performance should strongly outweigh costs and risks. To significantly increase the BCI performance, we use shared autonomy, where artificial intelligence (AI) copilots collaborate with BCI users to achieve task goals. We demonstrate this AI-BCI in a non-invasive BCI system decoding electroencephalography signals. We first contribute a hybrid adaptive decoding approach using a convolutional neural network and ReFIT-like Kalman filter, enabling healthy users and a participant with paralysis to control computer cursors and robotic arms via decoded electroencephalography signals. We then design two AI copilots to aid BCI users in a cursor control task and a robotic arm pick-and-place task. We demonstrate AI-BCIs that enable a participant with paralysis to achieve 3.9-times-higher performance in target hit rate during cursor control and control a robotic arm to sequentially move random blocks to random locations, a task they could not do without an AI copilot. As AI copilots improve, BCIs designed with shared autonomy may achieve higher performance.}, }
@article {pmid41219281, year = {2025}, author = {Bhaskara, S and Shabari Girishan, KV and Murugaiyan, S and Dwivedi, AA and Krishnakumaran, R and Pandya, HJ}, title = {An L-shaped flexible neural implant for chronic ECoG signal acquisition in M2 region of control and Parkinsonian rat models.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {39461}, pmid = {41219281}, issn = {2045-2322}, support = {/WT_/Wellcome Trust/United Kingdom ; FG/PTCH-22-2098//Pratiksha Trust BCD-Moonshot project/ ; IA/TSG/23/1/600493//DBT/Wellcome Trust India Alliance (India Alliance) Team Science Grant (TSG)/ ; }, mesh = {Animals ; Rats ; Disease Models, Animal ; *Electrocorticography/methods/instrumentation ; *Electrodes, Implanted ; *Parkinson Disease/physiopathology ; Male ; Rats, Sprague-Dawley ; *Motor Cortex/physiopathology ; }, abstract = {Neural implants help understand neurological disorders and are actively used to study deep and cortical brain surface regions. Dealing with cortical surface regions is less complicated in clinical therapy than deep brain regions. Researchers are interested in identifying cortical surface region/s for a particular neurological disorder. Rodent models are extensively used in preclinical studies. Usually, microwires, screws, and grid-type implants are used for such studies, but they are not designed for specific rodent brain regions. Since the grids are typically standard in size, in some cases, the craniotomy required to implant the grid will be significantly bigger than the region of interest, which may pose challenges for chronic studies due to infection. Additionally, the grids may block the nearby brain regions in multisite studies, posing difficulty for another device to be implanted. In this study, a novel L-shaped surface neural implant with five electrodes (diameter: 400 μm) spanning a 1 mm × 3 mm area is fabricated using biocompatible Polyimide material for cortical surface studies. The overall thickness of the neural implant is around 25 μm. The average impedance of the electrodes is 18.315 kΩ at 1 kHz. A bilateral craniotomy is performed to place the neural implants in the secondary motor area for subdural chronic electrocorticography (ECoG) signal acquisition in control and hemi parkinsonian rats. After the recovery period, the ECoG signals are acquired using the Open BCI Cyton Daisy Biosensing board for two weeks from the rats.}, }
@article {pmid41218505, year = {2025}, author = {Wu, X and Ge, H and Zhao, W and Thummavichai, K and Bi, L and Chen, B}, title = {Multi-functional 3D printed hydrogel electrodes for brain-computer interfaces and wearable sensing.}, journal = {Journal of colloid and interface science}, volume = {704}, number = {Pt 2}, pages = {139418}, doi = {10.1016/j.jcis.2025.139418}, pmid = {41218505}, issn = {1095-7103}, abstract = {In this study, a 3D printing-based polyvinyl alcohol (PVA)/κ-carrageenan (κ-CA)/ carbon nanotubes (CNTs) hydrogel composite (referred to as PCC) was developed for the fabrication of flexible electrodes, targeting applications in brain-computer interfaces (BCIs) and wearable strain sensors. The hydrogel composite exhibited excellent mechanical properties, including a tensile strength of 633 kPa, an elastic modulus of 243 kPa, and a maximum tensile strain of 283 %. In BCI tests, the PCC hydrogel electrode achieved a scalp contact impedance of 76.08 kΩ across five channels, with signal quality comparable to wet electrodes (3.06 μV at 13 Hz stimulation) and significantly higher than dry electrodes (2.16 μV). The decoding accuracy for the PCC hydrogel electrode was 78.2 % with a 1.25 s window length, comparable to the wet electrode, and the information transfer rate (ITR) reached 71.3 bits/min. Furthermore, the hydrogel demonstrated excellent strain sensing performance, with a gauge factor (GF) of 2.7 in the 0-75 % strain range and fast self-recovery, making it a promising material for dynamic wearable sensing devices. This work highlights the successful integration of material optimization and structural design, offering a new approach for development of next-generation flexible bioelectronic devices.}, }
@article {pmid41218224, year = {2025}, author = {Kenyeres, B and Helmeczi, A and Pytel, &#xc1;}, title = {Efficacy of Transurethral Resection of the Prostate in Male Patients With Impaired Detrusor Contractile Function and Urinary Retention.}, journal = {Lower urinary tract symptoms}, volume = {17}, number = {6}, pages = {e70040}, pmid = {41218224}, issn = {1757-5672}, mesh = {Humans ; Male ; *Urinary Retention/surgery/physiopathology/etiology ; *Transurethral Resection of Prostate/methods/adverse effects ; Retrospective Studies ; Aged ; Urodynamics ; *Urinary Bladder, Underactive/surgery/physiopathology/complications ; Treatment Outcome ; Middle Aged ; *Prostatic Hyperplasia/surgery/complications ; Aged, 80 and over ; }, abstract = {OBJECTIVES: Detrusor underactivity (DUA) increasingly affects aging male patients with voiding symptoms, while its management remains challenging, with less favorable surgical outcomes compared to bladder outlet obstruction. Our aim was to evaluate the efficacy of TURP in male patients with urinary retention and unfavorable urodynamic findings.<br><br>MATERIALS AND METHODS: This retrospective, single-center study included 67 male patients undergoing TURP between September 2021 and September 2024 after a failed trial of voiding. Patients were divided into three groups labeled as detrusor acontractility (DA, n&#x2009;=&#x2009;18, voided without detrusor contraction), DUA (n&#x2009;=&#x2009;19, voided with BCI&#x2009;<&#x2009;100 and BOOI&#x2009;<&#x2009;20), or non-voiders (n&#x2009;=&#x2009;30, failed to urinate and lacked measurable detrusor contractions on pressure-flow study). Surgical success was defined as successful voiding with post-void residual (PVR) <&#x2009;150&#x2009;mL at 3&#x2009;months. Baseline parameters (PSA, prostate volume, cystoscopy and urodynamic findings), rate of surgical success, Patient Global Impression of Improvement (PGI-I) score and adverse events (subsequent surgeries and urinary tract infection) were registered and analyzed.<br><br>RESULTS: Overall 37 (55.2%) patients became catheter-free within 3&#x2009;months. The mean follow-up duration was 25.4&#x2009;&#xb1;&#x2009;9.6&#x2009;months. Surgical success was achieved in DA, DUA, and non-voider groups in 6 (33%), 13 (68.4%), and 18 (60%) cases, respectively, and a PGI-I score greater than 4 was reported by 35 (52.2%) patients. Multivariate analysis showed higher prostate volume as an independent predictor for failure (OR: 1.7; 95% CI: 1.010-2.940; p&#x2009;=&#x2009;0.046). Two patients developed stress urinary incontinence, and three required additional surgical intervention due to urethral stricture. Urinary tract infections occurred more frequently in the treatment failure group: Nine patients (30%) were hospitalized, and 16 (53%) required more than two antibiotic prescriptions within a 6-month period. In contrast, among the success group, only two patients (5.4%) were hospitalized, and none required frequent antibiotic therapy.<br><br>CONCLUSION: TURP offers a reasonable chance for catheter discontinuation in case of unfavorable urodynamic parameters. With careful patient selection in mind, surgery remains a viable option even in this patient population.}, }
@article {pmid41217924, year = {2025}, author = {Liu, D and Li, S and Wang, Z and Li, W and Wu, D}, title = {SDDA: Spatial Distillation based Distribution Alignment for Cross-Headset EEG Classification.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3631604}, pmid = {41217924}, issn = {1558-2531}, abstract = {OBJECTIVE: A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. This paper tackles the problem of decoding EEG signals across different headsets, which is challenging due to differences in the number and locations of the electrodes.<br><br>METHODS: We propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains.<br><br>RESULTS: Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms.<br><br>SIGNIFICANCE: Our approach enables effective transfer between heterogenous EEG headsets, improving and expediting BCI calibration.}, }
@article {pmid41217916, year = {2025}, author = {Wu, Z and Chen, Z and He, W and Xie, Q and Pan, J}, title = {Cross-Subject P300-Based Audiovisual BCI System via Continual Learning: A Clinical Application for Disorders of Consciousness.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3631664}, pmid = {41217916}, issn = {1558-0210}, abstract = {This study proposes an advanced cross-subject P300-based audiovisual brain-computer interface (BCI) system to assess consciousness levels and predict clinical outcomes in patients with disorders of consciousness (DOC). The system employs an audiovisual stimulus paradigm, integrating face photos and corresponding name sounds, to enhance the elicitation of P300 signals. It further incorporates a hybrid prototype-based continual learning method (HPC) to improve diagnostic accuracy and robustness. The HPC constructs P300 prototypes for each historical task and selectively integrates both similar and dissimilar prototypes when a new task is introduced. Dissimilar prototypes are hybridized and masked, while similar prototypes are merged via an attention mechanism, effectively preventing catastrophic forgetting. Experimental results demonstrate the efficacy of this approach, with the HPC achieving 98.33% accuracy in a P300 spelling task among healthy subjects and 95.50% accuracy in healthy controls within a clinical setting. Significantly, eight out of ten DOC patients exhibited notable accuracy, underscoring the system's clinical potential. This BCI system thus offers a robust and adaptable solution for assessing consciousness levels and predicting outcomes in DOC patients, contributing to enhanced clinical diagnosis and prognosis.}, }
@article {pmid41217794, year = {2025}, author = {Filippov, MS and Pogonchenkova, IV and Kostenko, EV and Rassulova, MA and Makarova, MR and Egorov, PD}, title = {[Ideomotor training combining the use with integrated application of electromyostimulation and a robotic brain-computer interface in post-stroke upper limb dysfunction: a randomized controlled trial].}, journal = {Voprosy kurortologii, fizioterapii, i lechebnoi fizicheskoi kultury}, volume = {102}, number = {5}, pages = {5-19}, doi = {10.17116/kurort20251020515}, pmid = {41217794}, issn = {0042-8787}, mesh = {Humans ; Middle Aged ; Male ; Female ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; *Robotics ; *Stroke/physiopathology/complications/therapy ; *Electric Stimulation Therapy/methods ; Aged ; }, abstract = {UNLABELLED: One of the leading causes that disrupt human interaction with the environment is upper limb (UL) dysfunction, which develops in 48-77% of cases after a stroke. The combination of electromyostimulation (EMS) with neurocomputer interface (NCI) technology demonstrates the greatest clinical effectiveness among various types of sensorimotor BOS, the study of which seems promising.<br><br>OBJECTIVE: To study the effect of combined use the integrated of EMS and robotic NCI on the functioning of UL in post-stroke spastic paresis in the early recovery period of ischemic stroke (IS).<br><br>MATERIAL AND METHODS: A randomized controlled trial involved 120 patients in the early recovery period of IS with moderate to severe spastic paresis of UL, with an average age of 57.43&#xb1;3.68 years. By simple randomization, the patients were divided into 4 groups of 30 people each, depending on the medical rehabilitation program (MR). All patients received a basic MR program: therapeutic gymnastics for 30 minutes; magnetic field therapy on the neck and collar area for 20 minutes; therapeutic massage for 20 minutes. The patients of the control group (GC) received only the basic program; The main group (MG) - interval complex multi-purpose EMS of the agonist muscles and antagonist muscles of the forearm in combination with the use of NCI with exoskeletons of both hands; comparison group 1 (CG-1) - training using a robotic NCI; comparison group 2 (CG-2) - EMS. The duration of the MR course is 2 weeks, daily, 5 days a week, 10 treatments for each factor. The effectiveness of MR was evaluated at three control points (T): after completion of 5 procedures (T1) and 10 procedures (T2), 3 months after completion of MR (T3). Assessment tools: Medical Research Committee Scale (MRCs), Modified Ashworth Scale (mAs), The Fugl-Meyer Assessment for upper extremity (FMA-UE), The Action Research Arm Test (ARAT).<br><br>RESULTS: Patients with MG demonstrated significant (p<0.05) positive dynamics of recovery of UL function at the end of the MR course and after 3 months. The increase in muscle strength in MG and CG-1 averaged 0.77 and 0.59 points (p<0.05) in the distal muscle group, in CG-2 (0.24 points) and GC (0.21 points), p>0.05 compared with baseline values. Only patients with MG (+7.7 points) achieved a clinically significant difference (&#x394;) in FMA-UE-total at the end of MR, while patients with CG-1 achieved &#x394;=+4.9 points. In patients with GC and CG-2, the values of &#x394; according to FMA-UE-total were comparable (+2.3 and 2.6 points, respectively). According to the ARAT test, only MG patients also achieved a clinically significant difference (+6.2 points). Patients with CG-1 - &#x394;=+3.5 points. In patients with GC and CG-2, &#x394; values were comparable (+1.3 and 2.2 points, respectively).<br><br>CONCLUSION: Ideomotor training with EMS in MR of impaired VC function in patients with IS, combining stimulation of visual, vestibular, and proprioceptive analyzers with training of cognitive functions, promotes regression of sensorimotor disorders of UL and restoration of manipulative activity.}, }
@article {pmid41216611, year = {2025}, author = {Li, J and Chen, H and Liao, W}, title = {Mapping the white-matter functional connectome: a personal perspective.}, journal = {Psychoradiology}, volume = {5}, number = {}, pages = {kkaf028}, pmid = {41216611}, issn = {2634-4416}, abstract = {In contemporary neuroscience, mapping the human brain's functional connectomes is essential to understanding its functional organization. Functional organizations in the brain gray matter have been the subject of previous research, but the functional information in white matter (WM), the other half of the brain, has been relatively underexplored. However, the dynamics of functional magnetic resonance imaging (fMRI) have been reliably identified in the brain WM. This review summarizes current knowledge about task-free (resting-state) fMRI neuroimaging analyses for the WM functional connectome. We present comparative findings of the WM functional connectome, including its mapping, physiological underpinnings, cognitive neuroscience relationships, and clinical applications. Furthermore, we explore the emerging consensus that WM functional networks have valid topological characteristics that can distinguish between individuals with brain diseases and healthy controls, predict general intelligence, and identify inter-subject variabilities. Lastly, we emphasize the need for further studies and the limitations, challenges, and future directions for the WM functional connectome. An overview of these developments could lead to new directions for cognitive neuroscience and clinical neuropsychiatry.}, }
@article {pmid41214430, year = {2025}, author = {Cheng, X and Zhang, R and Chen, P and Song, Z and Cheng, F and Dikker, S and Pan, Y}, title = {Promoting Social Connectedness Through Interbrain Neurofeedback.}, journal = {Annals of the New York Academy of Sciences}, volume = {}, number = {}, pages = {}, doi = {10.1111/nyas.70135}, pmid = {41214430}, issn = {1749-6632}, abstract = {Humans are inherently driven to form meaningful relationships, yet attempts at social connection often fall short or fail. This study investigates whether social connectedness can be improved by modulating interbrain coupling-a neural correlate of successful social interactions-through neurofeedback. Using a multibrain computer interface that visualized, in real time, the degree to which dyad members' electroencephalography (EEG) signals synchronized, dyads were randomly assigned to receive either neurofeedback or sham feedback generated from random signals. Compared with the sham group, dyads receiving neurofeedback showed greater interbrain coupling, and increases in coupling were associated with stronger feelings of social connectedness. A chain-mediation analysis suggested that the experience of enhanced social connectedness was driven by a sense of joint control and shared intentionality. Together, these findings demonstrate the potential of interbrain neurofeedback to modulate interbrain coupling and support key components of social connectedness.}, }
@article {pmid41214065, year = {2025}, author = {He, Y and Jan, YH and Yang, F and Ma, Y and Chen, XY and Pei, C}, title = {The fatigue status feature of bicycle movement based on deep learning and signal processing technology.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {39328}, pmid = {41214065}, issn = {2045-2322}, support = {2020J01653//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 2020J01653//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 2023J01323//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 2020J01653//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 22SCZZX009//Fujian Special Financial Project for Research/ ; XRCZX2022010//Start-up funds for scientific research of high-level talents, Fujian Medical University/ ; 2023CDPFAT-02//China Disabled Persons' Federation Research Project on Assistive Devices for Persons with Disabilities/ ; }, mesh = {Humans ; Adult ; Male ; *Bicycling/physiology ; Female ; Middle Aged ; *Deep Learning ; *Fatigue/physiopathology ; Young Adult ; *Signal Processing, Computer-Assisted ; Movement/physiology ; Adolescent ; Algorithms ; Aged ; }, abstract = {Cycling is a common and effective home-based rehabilitation exercise. Accurate and accessible assessment of the onset of fatigue is essential to achieving optimal exercise benefits and preventing overuse injuries. To obtain fatigue-related parameters in different age groups, we applied deep learning algorithms and signal processing technology to analyze cycling movement features for the people aged over 45. 20 healthy adults aged over 45 and 20 aged 18-30 were recruited. Participants were asked to ride a stationary exercise bike at their self-regulated pedaling speeds for 10 min and wear a COSMED K5 device to collect physiological signals. The Keypoint RCNN (KR) algorithm and three signal processing methods (Fourier transform, short-time Fourier transform, and multiscale entropy analysis were used to analyze the cycling movement data. Based on time-frequency analysis, subjects' movement status change points were identified when fatigue occurred. Four movement status parameters were calculated, including the peak frequency before/after the movement status change point and the complexity index average (CIA) before/after the movement status change point. Inter-group and intra-group movement features, movement status, and physiological data were compared to determine fatigue-related features. Results showed that the peak frequency (p = 0.005), the peak frequency before/after the change point (p = 0.008/0.019), the CIA after the change point (p = 0.014), the maximum heart rate, maximal oxygen consumption, metabolic equivalents, and energy efficiency exhibited significant inter-group differences. The KR algorithm demonstrated outstanding performance in keypoint detection, achieving an accuracy of 86.5%, significantly outperforming OpenPose. With an inference speed of 30 FPS, it fulfills the demands for real-time monitoring. In addition, CIA valuses before and after change pointsshowed significant differences in the the middle-aged and elderly people group. After the change point, the CIA canidentify movement status changes in inter-group and intra-group comparisons, suggesting it can be used as a indicator of fatigue status, especially for people aged over 45.}, }
@article {pmid41213447, year = {2025}, author = {Calabrò, RS and Calderone, A and Simoncini, L and Naro, A and Haughton, LOS and Quartarone, A and Leochico, CFD}, title = {The potential of robotics: A systematic review of neuroplastic changes following advanced lower limb rehabilitation in neurological disorders.}, journal = {Neuroscience and biobehavioral reviews}, volume = {180}, number = {}, pages = {106459}, doi = {10.1016/j.neubiorev.2025.106459}, pmid = {41213447}, issn = {1873-7528}, abstract = {BACKGROUND: Neurological diseases are among the most common pathologies that strongly influence a person's ability to walk and move, affecting the lower extremities. They disrupt motor brain networks that enable precise movement, leading to deficits in gait, balance, and coordination; while conventional therapies remain essential, advances in robotic technologies show growing promise for rehabilitation.<br><br>AIM OF REVIEW: This systematic review aims to investigate the role of robotic rehabilitation in improving neuroplasticity and motor outcomes for individuals with neurological disorders, with a particular focus on studies incorporating neurophysiological or neuroimaging techniques to assess neuroplastic changes and their long-term impact on recovery.<br><br>A systematic review was carried out utilizing an online search of articles from 2014 to 2025 on the PubMed, Web of Science, Cochrane Library, Embase, EBSCOhost, and Scopus databases in accordance with PRISMA guidelines. Studies were chosen based on predetermined inclusion criteria, with an emphasis on robotic rehabilitation therapies targeted at improving neuroplasticity in lower limb rehabilitation for people with neurological conditions. This review has been registered on Prospero with the following number: CRD42025640347. The search identified 12,769 records; after screening and eligibility assessment, 25 studies met inclusion criteria. Studies demonstrate that robot-assisted gait training (RAGT) and exoskeleton-based therapies improve motor function, gait, balance, and neuroplasticity across stroke, spinal cord injury, cerebral palsy, and brain injury populations. Adjunctive approaches such as brain-computer interface (BCI) integration, virtual reality feedback, and neuromodulation further enhance outcomes, with increases in cortical activation and improvements in functional connectivity supported by convergent neurophysiological and neuroimaging data; changes in corticospinal excitability are also reported. Taken together, robotic interventions, often combined with neuromodulation or virtual reality (VR), appear to catalyze neuroplasticity in ways that align with clinically meaningful gains. These findings underscore their transformative potential for tailored, multimodal rehabilitation strategies in neurological recovery.}, }
@article {pmid41212978, year = {2025}, author = {Miller, KJ and Abosch, A}, title = {A Moment of Reckoning for Implanted Brain-Computer Interface Studies.}, journal = {Neurosurgery}, volume = {97}, number = {2}, pages = {277-280}, doi = {10.1227/neu.0000000000003585}, pmid = {41212978}, issn = {1524-4040}, }
@article {pmid41212707, year = {2025}, author = {Fitriah, N and Zakaria, H and Budikayanti, A and Suksmono, AB and Mengko, TLER}, title = {Decoding Speech Imagery: A Spectro-spatial Approach to Electroencephalography Band Power Analysis.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3631062}, pmid = {41212707}, issn = {2168-2208}, abstract = {Decoding speech imagery from brain signals potentially assists individuals with speech impairments. However, limited data and complex brain activity in recent studies have made accurate decoding challenging. We analyzed both the spectral (frequency) and spatial (location) aspects of brain activity to enhance decoding accuracy in small datasets. To our knowledge, previous studies have mainly used generated features without adequately considering spectro-spatial aspects. We trained machine learning with time-frequency representation (TFR) features using a public dataset from the Brain-Computer Interface (BCI) competition (BCI-DB) and our own recordings (PrimAudio-DB). The results showed prominent feature patterns of speech imagery in the frontal region and Gamma band, achieving accuracies of 98.6% in BCI-DB (exceeding benchmarks) and 81.7% in PrimAudio-DB. Moreover, our analysis revealed insights regarding speech differences (language and semantics). This study contributed to non-invasive speech imagery decoding and offered valuable insights for future speech rehabilitation and assistive technologies.}, }
@article {pmid41212618, year = {2025}, author = {Liu, X and Cao, L and Du, Z and Cui, Y and Saleem, KS and Zhang, Y and Lu, Y and Zhang, B and Liu, Y and Hou, X and Cheng, L and Li, K and Fan, L and Yang, Z and Jiang, T}, title = {Proteomic insights into the macaque insular parcellation based on structural connectivity gradients.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {35}, number = {11}, pages = {}, doi = {10.1093/cercor/bhaf307}, pmid = {41212618}, issn = {1460-2199}, support = {2021ZD0200200//Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project of China/ ; 62327805//National Natural Science Foundation of China/ ; 62336007//National Natural Science Foundation of China/ ; 82151307//National Natural Science Foundation of China/ ; }, mesh = {Animals ; *Proteomics/methods ; Male ; *Insular Cortex/metabolism/diagnostic imaging/anatomy &amp; histology ; Diffusion Magnetic Resonance Imaging/methods ; Neural Pathways/metabolism/diagnostic imaging ; Macaca mulatta ; Macaca ; }, abstract = {Gradients across microstructure, macro-connectivity, and gene expression scales have been identified in the primate brain, offering a continuous perspective to explore regional heterogeneity. The macaque insula, with its extensive connections with other cortical regions and involvement in diverse functions, exhibits gradient transitions at the microstructural level. However, the gradients of macroscopic structural connectivity (SC) and its relationship with gene expression in the macaque insula remain unclear. We hypothesized that SC gradients are closely associated with gene expression, driving insular parcellation. To test this, we analyzed high-resolution diffusion-weighted MR imaging alongside spatially aligned proteomic data. Our findings revealed a rostrocaudal organization of the dominant SC gradient in the macaque insula, leading to the identification of a four-subregion pattern within the insula based on the first two SC gradients. Proteomic profiles strongly correlated with the dominant SC gradient and the clustering of proteomic similarity aligned with the four-subregion pattern. Notably, the dominant SC gradient more effectively captured spatial protein expression variations than T1w/T2w and cortical thickness maps. Overall, this study demonstrated that the SC gradient analysis revealed a four-subregion pattern of parcellation aligned with the spatial distribution of proteomic profiles along the rostro-caudal axis.}, }
@article {pmid41211047, year = {2025}, author = {Zhang, N and Hu, BW and Li, XM and Huang, H}, title = {Rethinking parvalbumin: From passive marker to active modulator of hippocampal circuits.}, journal = {IBRO neuroscience reports}, volume = {19}, number = {}, pages = {760-773}, pmid = {41211047}, issn = {2667-2421}, abstract = {Parvalbumin (PV)-expressing interneurons are critical regulators of neural circuit dynamics, and for decades, the PV protein has served as their definitive molecular marker. This review confronts a central, yet underappreciated, paradox: the incongruity of a kinetically slow Ca[2+] buffer (PV) being the defining feature of the brain's fastest-spiking neurons. We synthesize evidence from molecular biophysics, genetics, in vivo circuit analysis, and disease modeling to dissect the dual role of PV as both a cellular marker and an active functional regulator. We argue that PV's slow kinetics are not a coincidence but a crucial adaptation that shapes short-term synaptic plasticity, protects against metabolic stress during high-frequency firing, and allows the circuit to shift between states of plasticity and stability. This reframing resolves the paradox by demonstrating how a "slow" molecule is essential for "fast" neuronal function. Furthermore, we highlight that dysfunction of the PV system is a convergent hub of pathology in numerous neurological and psychiatric disorders, including schizophrenia, epilepsy, and Alzheimer's disease. By moving beyond its identity as a passive marker, we establish PV as an active modulator of neural computation and a potential therapeutic target for restoring network function in disease.}, }
@article {pmid41209581, year = {2025}, author = {Zeng, YY and Saeed, S and Hu, SH}, title = {Non-Suicidal Self-Injury: Pain Addiction Mechanisms, Neurophysiological Signatures, and Therapeutic Advances.}, journal = {Journal of clinical medicine research}, volume = {17}, number = {10}, pages = {537-549}, pmid = {41209581}, issn = {1918-3003}, abstract = {The aim of this study was to review the neurobiological mechanisms, epidemiology, and therapeutic interventions for non-suicidal self-injury (NSSI), emphasizing the pain addiction model and electroencephalographic biomarkers as frameworks for precision intervention. A narrative review of the literature was conducted using PubMed, Web of Science, CNKI, and Wanfang Data up to October 2025. Search strategy employed the terms "non-suicidal self-injury," "pain addiction," "electroencephalography," "endogenous opioid system," and "HPA axis." Selection criteria prioritized original human studies, high-quality systematic reviews, and mechanistic investigations. Pain addiction and electroencephalography (EEG) were selected as focal variables based on their explanatory power: pain addiction elucidates NSSI perpetuation through endogenous opioid-mediated reward sensitization and dopaminergic reinforcement, while event-related potentials (ERPs) provide temporal precision in mapping cognitive-affective dysregulation underlying emotional impulsivity and regulatory deficits. Global adolescent NSSI prevalence averages 17.2%, with Chinese rates reaching 24.7% and trends toward earlier onset. Neurobiological substrates include fronto-limbic dysregulation, hypoactive hypothalamic-pituitary-adrenal (HPA) axis function with blunted cortisol reactivity, and endogenous opioid system alterations producing widespread hypoalgesia. EEG/ERP studies demonstrate increased N2 amplitude with decreased P3 amplitude and prolonged latency during negative stimuli processing, reflecting impaired conflict monitoring and attentional resource allocation. Dialectical behavior therapy shows established efficacy, while repetitive transcranial magnetic stimulation and opioid antagonists demonstrate therapeutic potential. NSSI emerges from neurobiological vulnerability within pain-reward-emotion circuits interacting with psychosocial factors. The pain addiction framework and EEG signatures provide translatable targets for biomarker development and personalized intervention. Future research requires multimodal neuroimaging, longitudinal designs, and genetic integration to establish predictive algorithms and precision therapeutics.}, }
@article {pmid41209401, year = {2025}, author = {Lu, Y and Yang, W and Wu, S and Li, Y and Wei, J and Li, M and Li, Y and Huai, Y}, title = {Exploring neural activity changes during motor imagery-based brain-computer interface training with robotic hand for upper limb rehabilitation in ischemic stroke patients: a pilot study.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1626000}, pmid = {41209401}, issn = {1662-5161}, abstract = {OBJECTIVE: This pilot study aimed to evaluate the feasibility and tolerability of motor imagery (MI)-based brain-computer interface (BCI) training with robotic hand assistance for upper limb rehabilitation, and to explore preliminary neural markers in ischemic stroke patients.<br><br>METHODS: Three post-stroke participants performed MI tasks combined with exoskeleton-assisted movements to facilitate rehabilitation training. Electroencephalography (EEG) signals were recorded to assess the neural correlates of MI. Functional outcomes were evaluated using standard assessment tools.<br><br>RESULTS: Our results demonstrated significant improvements in motor function across all participants. Additionally, EEG analysis revealed event-related desynchronization (ERD) in the high-alpha band power at motor cortex locations, with individual differences in both the frequency and power of neural activity. However, no significant trends in neural activity were observed across the training sessions.<br><br>CONCLUSION: These findings suggest that MI-based BCI training, combined with robotic assistance, offer a promising approach for enhancing upper limb function in ischemic stroke patients.}, }
@article {pmid41209398, year = {2025}, author = {Milyani, AH and Attar, ET}, title = {Deep learning for inner speech recognition: a pilot comparative study of EEGNet and a spectro-temporal Transformer on bimodal EEG-fMRI data.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1668935}, pmid = {41209398}, issn = {1662-5161}, abstract = {BACKGROUND: Inner speech-the covert articulation of words in one's mind-is a fundamental phenomenon in human cognition with growing interest across BCI. This pilot study evaluates and compares deep learning models for inner-speech classification using non-invasive EEG derived from a bimodal EEG-fMRI dataset (4 participants, 8 words). The study assesses a compact CNN (EEGNet) and a spectro-temporal Transformer using leave-one-subject-out validation, reporting accuracy. Macro-F1, precision, and recall.<br><br>OBJECTIVE: This study aims to evaluate and compare deep learning models for inner speech classification using non-invasive electroencephalography (EEG) data, derived from a bimodal EEG-fMRI dataset. The goal is to assess the performance and generalizability of two architectures: the compact convolutional EEGNet and a novel spectro-temporal Transformer.<br><br>METHODS: Data were obtained from four healthy participants who performed structured inner speech tasks involving eight target words. EEG signals were preprocessed and segmented into epochs for each imagined word. EEGNet and Transformer models were trained using a leave-one-subject-out (LOSO) cross-validation strategy. Performance metrics included accuracy, macro-averaged F1 score, precision, and recall. An ablation study examined the contribution of Transformer components, including wavelet decomposition and self-attention mechanisms.<br><br>RESULTS: The spectro-temporal Transformer achieved the highest classification accuracy (82.4%) and macro-F1 score (0.70), outperforming both the standard and improved EEGNet models. Discriminative power was also substantially improved by using wavelet-based time-frequency features and attention mechanisms. Results showed that confusion patterns of social word categories outperformed those of number concepts, corresponding to different mental processing strategies.<br><br>CONCLUSION: Deep learning models, in particular attention-based Transformers, demonstrate great promise in decoding internal speech from EEG. These findings lay the groundwork for non-invasive, real-time BCIs for communication rehabilitation in severely disabled patients. Future work will take into account vocabulary expansion, wider participant variety, and real-time validation in clinical settings.}, }
@article {pmid41207999, year = {2025}, author = {Yang, J and Xia, F and Jin, H and Mahanand, C and Lin, H and Cao, Y and Bian, J and Wei, D and Nevo, E and Du, J and Duan, S and Guo, F and Zhao, Y and Chen, X}, title = {Variations of Corticotropin-Releasing Factor Receptor 1α Contribute to the Blunted HPA Axis Responses to Hypoxia in Plateau Mammals.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {41207999}, issn = {1995-8218}, abstract = {Corticotropin-releasing factor (CRF) and its receptor (CRFR1) are critical components of the hypothalamic-pituitary-adrenocortical (HPA) axis. Ochotona curzoniae (O. curzoniae), Myospalax baileyi (M. baileyi), and Microtus oeconomus (M. oeconomus) have diversely evolved adaptive strategies to the extreme environment at high altitude. Here, we found blunted HPA axis responsiveness in native Tibetan mammals. CRF was 100% conserved, three amino-acid variations were in M. oeconomus-urocortin (UCN), and unique amino-acid variations in ligand-receptor binding domains of O. curzoniae-, M. baileyi-, and M. oeconomus-CRFR1αs. The native mammals' binding affinity and cAMP production varied depending on different doses of ligand-CRF/UCN treatment. Variations in M. oeconomus-UCN and O. curzoniae-, M. baileyi-, M. oeconomus-CRFR1α were responsible for weaker CRF-CRFR1α binding and higher EC50. They had the same HPA response pattern as that of CRF-CRFR1α binding affinity, cAMP production, and cell permeability. AlphaFold3.0 predicted altered structural interactions for both CRF-CRFR1α and UCN-CRFR1α complexes corroborate our findings. This study reveals that the variations of UCN/CRFR1α contribute to the different responsiveness of the HPA axis to extreme environments.}, }
@article {pmid41207468, year = {2025}, author = {Upadhyay, PK and Chandra, KA}, title = {Quantum enhanced EEG classifier towards brain-controlled wheelchair navigation.}, journal = {Neuroscience}, volume = {591}, number = {}, pages = {1-20}, doi = {10.1016/j.neuroscience.2025.10.047}, pmid = {41207468}, issn = {1873-7544}, abstract = {Brain-computer interfaces (BCIs) provide a pathway to assistive technologies such as brain-controlled wheelchairs, yet accurate motor imagery (MI) classification from electroencephalography (EEG) remains challenging due to noise and subject variability. In this work, we propose a hybrid Quantum Enhanced CNN-LSTM model EEG Classifier (HQeCL), incorporating a simulated quantum pooling layer for richer feature abstraction. The framework integrates power spectral density (PSD) from the frequency domain, common spatial patterns (CSP) from the spatial domain, and quantum entropy from the non-linear domain to capture complementary EEG characteristics. The model was evaluated using leave-one-subject-out (LOSO) cross-validation on the 8-channel motor imagery dataset, achieving 92.1%±5.9 accuracy, 93.1%±6.2 precision, 91.9%±1.3 recall, 92.5%±1.3 F1-score, and Cohen's κ=0.89±0.02. Compared to existing methods, HQeCL outperformed CSP-LDA (74.5%±1.4), ShallowConvNet (83.3%±1.6), and CNN-LSTM (88.8%±1.2), while remaining competitive with QuEEGNet (91.4%±1.3). Ablation analysis confirmed the contribution of quantum pooling, which provided a +0.7% gain over average pooling, and UMAP, which improved performance by +14.8% over PCA and +29.7% over t-SNE. Complexity analysis further demonstrated the efficiency of HQeCL with only 0.12M parameters, 270.2M FLOPs, and an inference latency of 77.6ms. While these results demonstrate near real-time feasibility in simulation, translation to hardware remains a challenge, positioning HQeCL as a quantum-inspired, Pareto-efficient EEG classifier advancing motor imagery decoding for brain-controlled wheelchair navigation.}, }
@article {pmid41207160, year = {2025}, author = {Shirodkar, VR and Reddy Edla, D and Kumari, A and Afonso, MM}, title = {Multi-domain feature extraction and Sand Cat Swarm Optimized Broad Learning System for EEG-based Motor Imagery decoding in stroke patients.}, journal = {Computers in biology and medicine}, volume = {199}, number = {}, pages = {111285}, doi = {10.1016/j.compbiomed.2025.111285}, pmid = {41207160}, issn = {1879-0534}, abstract = {Brain-Computer Interfaces (BCIs) enable the translation of brain activity into executable commands, with Motor Imagery (MI)- based systems gaining prominence for their intuitive and non-invasive control. Electroencephalography is widely used due to its portability and time resolution, though its non-stationary and subject-specific nature poses major challenges for reliable classification. This research proposes a lightweight and efficient classification architecture that first selects discriminative filter bands based on Event-Related Desynchronization (ERD) scores. It then integrates Empirical Mode Decomposition (EMD), the Hilbert-Huang Transform (HHT), Riemannian Geometry (RG), and Common Spatial Pattern (CSP)-based feature extraction with a Broad Learning System (BLS) classifier. The BLS parameters are optimized using the Sand Cat Swarm Optimization (SCSO) algorithm to enhance convergence speed, avoid local minima, and improve generalization. EMD separates the EEG signal into a set of Intrinsic Mode Functions, while HHT extracts instantaneous amplitude and frequency features, effectively modeling the nonlinear and dynamic properties of EEG signals. Performance assessment was done on two datasets: the BCI IV 2a dataset and a clinical stroke EEG dataset. It achieved classification accuracies of 90.78% on BCI-IV 2a and 96.41% on the stroke dataset. The proposed approach also showed competitive generalization performance in All-subjects and Leave-One-Subject-Out (LOSO) validation settings. Analysis reveals that the proposed pipeline effectively extracts discriminative features and handles inter-subject variability, illustrating its applicability to real-world BCI systems.}, }
@article {pmid41206890, year = {2025}, author = {Zapata-Catzin, GA and Vargas-Coronado, RF and Ceballos-Gongora, E and Arana-Argáez, VE and Rodríguez-Velázquez, E and Alatorre-Meda, M and Molina-Salinas, GM and Uc-Cachon, A and Gallardo, A and Copes, F and Mantovani, D and Cauich-Rodríguez, JV}, title = {Effect of Polyurethane Structure on the Physicochemical, Mechanical, and Biological Properties on their Copper Complexes Composites.}, journal = {Macromolecular bioscience}, volume = {}, number = {}, pages = {e00419}, doi = {10.1002/mabi.202500419}, pmid = {41206890}, issn = {1616-5195}, support = {AtencionaProblemasNacionales(248378)//Consejo Nacional de Humanidades, Ciencia y Tecnolog&#xed;a/ ; FronterasdelaCiencia(1360)//Consejo Nacional de Humanidades, Ciencia y Tecnolog&#xed;a/ ; 1360//Atenci&#xf3;n a Problemas Nacionales/ ; //Fronteras de la Ciencia/ ; }, abstract = {Polyurethanes and their composites are versatile materials widely used in numerous medical applications. However, limited information is available regarding their copper composites. Copper is a trace element in the human body that functions as an enzyme cofactor in both normal and pathological angiogenesis, as well as in muscle and brain formation. Considering this, copper complexes of D-penicillamine (DP), L-cysteine (LC), and dopamine (DOP) were incorporated into segmented polyurethanes (SPU) synthesized with either a semi-crystalline (poly-ε-caprolactone, PCL) or an amorphous (polytetramethylene ether glycol, PTMEG) soft segment. FTIR and Raman revealed new absorptions and peak shifts, confirming the presence of the complexes within the matrix of all composites. XPS further corroborated the presence of copper and sulfur. The crystallinity of the PCL-based polyurethanes was influenced by the addition of the filler, as observed through DSC and DRX. Furthermore, TGA analysis indicated the emergence of new decomposition temperatures following the incorporation of copper complexes. In general, no significant reduction in Young's modulus was observed, except for certain composites containing DPENCUII as filler, which exhibited a slight increase compared to pristine SPU´s. Finally, the composites demonstrated neither hemolytic nor procoagulating behavior (hemolysis < 5% and BCI > 20), although they exhibited some degree of impairment in cytocompatibility compared to their respective pristine SPUs. Collectively, these findings suggest that some composites possess promising properties for potential cardiovascular applications.}, }
@article {pmid41206869, year = {2025}, author = {Tian, Y and Jiang, R and Guo, F}, title = {Protocol for in vivo two-photon calcium imaging of the Drosophila brain.}, journal = {STAR protocols}, volume = {6}, number = {4}, pages = {104194}, doi = {10.1016/j.xpro.2025.104194}, pmid = {41206869}, issn = {2666-1667}, abstract = {Two-photon calcium imaging facilitates the real-time observation of neuronal activity. Here, we present a protocol for conducting in vivo two-photon calcium imaging of the Drosophila melanogaster brain. We describe steps for fly preparation, recording chamber construction, and preparation of the buffer solution. We then detail procedures for fly brain surgery, execution of the recording, and data analysis. This protocol enables the monitoring and assessment of neuronal responses to external stimuli and the mapping of functional connectivity coupled with optogenetics. For complete details on the use and execution of this protocol, please refer to Jiang et al.[1].}, }
@article {pmid41205898, year = {2025}, author = {Taranath, JR}, title = {On questions of predictability and control of an intelligent system using probabilistic state-transitions.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2025.10.062}, pmid = {41205898}, issn = {1873-7544}, abstract = {One of the central aims of neuroscience is to reliably predict the behavioral response of an organism using its neural activity. If possible, this implies we can causally manipulate the neural response and design brain-computer-interface systems to alter behavior, and vice-versa. Hence, predictions play an important role in both fundamental neuroscience and its applications. Can we predict the neural and behavioral states of an organism at any given time? Can we predict behavioral states using neural states, and vice-versa, and is there a memory-component required to reliably predict such states? Are the predictions computable within a given timescale to meaningfully stimulate and make the system reach the desired states? Through a series of mathematical treatments, such conjectures and questions are discussed. Answering them might be key for future developments in understanding intelligence and designing brain-computer-interfaces.}, }
@article {pmid41205562, year = {2025}, author = {Sun, Z and Sun, Y and Zeng, Y}, title = {BACNet: A multi-attention network for cross-subject and cross-task EEG-based pilot operational intent recognition.}, journal = {Computer methods and programs in biomedicine}, volume = {274}, number = {}, pages = {109134}, doi = {10.1016/j.cmpb.2025.109134}, pmid = {41205562}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Recognizing pilot operational intent is crucial for enhancing flight safety and improving the efficiency of human-machine interaction. Electroencephalography (EEG), known for its high temporal resolution and non-invasive acquisition, has become a prominent modality for this task. However, current approaches often suffer from high model complexity and limited accuracy in EEG feature extraction. This study aims to address these limitations by proposing efficient and accurate neural network architecture for pilot intent recognition based on EEG signals.<br><br>METHODS: We introduce a novel framework, the Balanced Attention Convolutional Network (BACNet), designed to enhance EEG-based intent recognition through collaborative optimization in both channel and spatial dimensions. BACNet features: (1) a three-branch parallel convolutional structure that extracts multi-scale time-frequency features; and (2) dynamic feature modulation mechanisms to adaptively highlight salient channels and spatial locations. EEG data were collected from 15 participants across various simulated flight phases, forming a labeled dataset for model training and evaluation. Five-fold cross-validation was conducted to ensure the robustness of the performance assessment.<br><br>RESULTS: BACNet achieved an average classification accuracy of 96.07 % in a three-class EEG-based intent recognition task, outperforming five state-of-the-art baseline methods. The model also demonstrated a significant reduction in computational complexity. Ablation experiments validated the individual and combined contributions of the multi-scale attention modules, highlighting the effectiveness of the collaborative attention design.<br><br>CONCLUSION: With its lightweight architecture and high accuracy, BACNet not only provides a novel solution for pilot operational intent recognition but also demonstrates broad applicability in brain-computer interface (BCI) systems.}, }
@article {pmid41205408, year = {2025}, author = {Bao, C and Ma, Y and Li, M and Li, Y and Zhang, C and Liu, X and Fan, R and Cui, W and Fan, X and Zheng, F and Duan, F and Liu, J}, title = {Assessment of glymphatic dysfunction in ulcerative colitis using DKI-ALPS: An innovative imaging biomarker.}, journal = {Journal of neuroradiology = Journal de neuroradiologie}, volume = {53}, number = {1}, pages = {101402}, doi = {10.1016/j.neurad.2025.101402}, pmid = {41205408}, issn = {0150-9861}, abstract = {PURPOSE: Ulcerative colitis (UC) is associated with higher anxiety, depression, and cognitive disorders linked to brain glymphatic dysfunction. In this study, we used along-the-perivascular-space (ALPS) index (based on DTI and DKI) to determine if UC relates to glymphatic dysfunction and explore how microbiota dysbiosis and inflammation affect brain glymphatic function.<br><br>MATERIALS AND METHODS: In this study, 63 patients with UC and 68 healthy controls underwent 3-Tesla MRI scans to evaluate DTI-ALPS and DKI-ALPS index. The protocol included diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) sequences to calculate the ALPS index, which quantifies glymphatic system function. All participants completed cognitive (MMSE) and depression (SAS/SDS) assessments (SAS/SDS). Patients with UC also underwent assessment for inflammation and gut microbiota (based on metagenomic analysis). Data analysis was performed using correlation analysis and linear regression.<br><br>RESULTS: Patients with UC showed lower DTI-ALPS index (1.25) and DKI-ALPS index (1.40) compared to controls (1.40 vs. 1.69; P < 0.001). In multi-adjusted linear regression models, UC was associated with lower DTI-ALPS index and DKI-ALPS index (&#x3b2; =-0.142 vs.-0.284), with DKI-ALPS showing higher sensitivity. The results remained significant even after stratification by age and sex. The Mayo score correlated negatively with DTI and DKI-ALPS index. The ALPS index correlates with gut microbiota, particularly those involved in butyrate and short-chain fatty acid (SCFA) production. DTI-ALPS index was significantly correlated with ESR (&#x3b2; =-0.003), CRP (&#x3b2; =-0.035), SII (&#x3b2; =-0.062), INFLA (&#x3b2; =-0.010), and SIRI (&#x3b2; =-0.058). We also observed significant correlations between DKI ALPS index and ESR (&#x3b2; =-0.006), CRP (&#x3b2; =-0.051), SII (&#x3b2; =-0.130), INFLA (&#x3b2; =-0.017), SIRI (&#x3b2; =-0.095), IL-6 (&#x3b2; =-0.081) and NLR (&#x3b2; =-0.108).<br><br>CONCLUSIONS: UC is associated with brain glymphatic dysfunction, correlating with inflammation level. DKI-ALPS serves as a more sensitive method than DTI-ALPS, offering a new approach for managing ulcerative colitis through glymphatic dysfunction.}, }
@article {pmid41204711, year = {2025}, author = {Scherer, J and Finke, A and Everding, V and Lindenbaum, L and Kayser, C and Kissler, J}, title = {NeuroCommTrainer: Toward an Adaptive and Wearable Multimodal Brain-Computer Interface.}, journal = {Brain connectivity}, volume = {}, number = {}, pages = {}, doi = {10.1177/21580014251393151}, pmid = {41204711}, issn = {2158-0022}, abstract = {Introduction: To date, brain-computer interfaces (BCIs) have not achieved reliable real-time communication through auditory or tactile modalities. Such interfaces would be crucial for brain-injured patients with severe motor impairments who are also blind or deaf. This study validates the functionality of the NeuroCommTrainer, a mobile and easy-to-use multimodal BCI with flex-printed electrode strips that does not require vision and adapts to users' attentiveness levels to initiate stimulation. Methods: In a study of 20 healthy participants, we evaluated auditory and vibrotactile oddball paradigms to train the system to differentiate rare and frequent event-related potentials (ERPs). In real-time online sessions, the system detected participants' mental focus to adaptively initiate stimulation through attentiveness monitoring. Results: The NeuroCommTrainer successfully captured auditory and tactile ERPs, achieving a classification accuracy of 75% for stimuli in the calibration session, which is not yet reflected in the online session with 34% of found targets (chance level = 16.7%). Discussion: The presented early-stage prototype of the NeuroCommTrainer requires several improvements before clinical application in brain-damaged patients, which include refined algorithms to reduce classification variance across participants, and enhanced attentiveness detection specifically tuned to brain activity of the targeted patient group. The present study makes a critical step in this direction and shows that a transition into a practicable communication system for brain-damaged patients may be achievable in the future.}, }
@article {pmid41204680, year = {2025}, author = {Ehrlich, SK and Tougas, G and Bernstein, J and Buie, N and Rumbach, AF and Simonyan, K}, title = {Brain-Computer Interface Improves Symptoms of Isolated Focal Laryngeal Dystonia: A Single-Blind Study.}, journal = {Movement disorders : official journal of the Movement Disorder Society}, volume = {}, number = {}, pages = {}, doi = {10.1002/mds.70114}, pmid = {41204680}, issn = {1531-8257}, support = {R01DC019353/DC/NIDCD NIH HHS/United States ; }, abstract = {BACKGROUND AND OBJECTIVE: Laryngeal dystonia (LD) is a focal task-specific dystonia, affecting speaking but not whispering or emotional vocalizations. Therapeutic options for LD are limited. We developed and tested a non-invasive, closed-loop, neurofeedback, brain-computer interface (BCI) intervention for LD treatment.<br><br>METHODS: Ten patients with isolated focal LD participated in the study. The personalized BCI system included visual neurofeedback of individual real-time electroencephalographic (EEG) activity during symptomatic speaking compared to&#xa0;asymptomatic whispering, presented in the virtual reality (VR) environment of real-life scenarios. During five consecutive days of intervention, patients used the BCI to learn to modulate their abnormally increased brain activity during speaking and match it to near-normal activity of asymptomatic whispering. Changes in voice symptoms and EEG activity were quantified for the evaluation of BCI effects.<br><br>RESULTS: Compared to baseline, LD patients had a statistically significant reduction of their voice symptoms on Days 1-5 of BCI intervention. Thi&#xa0;was paralleled by improved controllability of the visual neurofeedback and a significant reduction of left frontal delta power, including superior and middle frontal gyri, on Day 1 and left central gamma power, including premotor, primary sensorimotor, and inferior parietal areas, on Days 3 and 5. The majority of patients (70%) reported sustained positive effects of the BCI intervention on their voice quality 1 week after the study participation.<br><br>CONCLUSION: The closed-loop BCI neurofeedback intervention specifically targeting disorder pathophysiology shows significant potential as a novel treatment option for patients with LD and likely other forms of task-specific focal dystonia. &#xa9; 2025 International Parkinson and Movement Disorder Society.}, }
@article {pmid41204389, year = {2025}, author = {Zhang, H and Chen, WJ and Chao, YG and Su, N and Robba, C and Czosnyka, M and Smielewski, P and Czosnyka, Z and He, W and Hu, X and Yao, DZ and Hu, CG and Zhou, M and Wang, YJ and Ma, XC and Liu, XY and Ming, D}, title = {Neurogenic organ dysfunction syndrome after acute brain injury.}, journal = {Military Medical Research}, volume = {12}, number = {1}, pages = {77}, pmid = {41204389}, issn = {2054-9369}, support = {ZYGXQNJSKYCXNLZCXM-H15//Scientific Research Innovation Capability Support Project for Young Faculty/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; 82472098//Innovative Research Group Project of the National Natural Science Foundation of China/ ; 32300704//National Natural Science Foundation of China/ ; 24JCJQJC00250//National Outstanding Youth Science Fund Project of National Natural Science Foundation of China/ ; 24ZXZSSS00510//Major Science and Technology Special Projects and Engineering - Major Project of National Key Laboratories/ ; 2021YFF1200602//National Key Technologies Research and Development Program/ ; 2024-JKCS-16//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; }, mesh = {Humans ; *Brain Injuries/complications/physiopathology ; *Multiple Organ Failure/etiology/physiopathology ; }, abstract = {Systemic complications are common after acute brain injury (ABI) and may trigger coagulation cascades, systemic inflammation, as well as dysfunction of the cardiovascular, respiratory, and gastrointestinal systems, etc. The pathogenesis of these systemic manifestations is multifactorial but not yet fully elucidated. This paper introduces the novel term neurogenic organ dysfunction syndrome (NODS) to characterize systemic instability arising from internal and external perturbations of the neuronal center following ABI. Elucidating the central neurogenic mechanisms of NODS is critical for early detection and prevention of complications, thereby reducing mortality and improving patient outcomes following ABI. In this paper, we explore the potential central neurogenic mechanisms of NODS from the perspective of complex brain network theory, focusing on the structural network of the central autonomic system (CAS) that maintains systemic stability, and the functional network governed by the central stress system (CSS). The CAS can be divided into the cortical autonomic network, which involves higher cortical regions, and the subcortical autonomic network, which is relatively conserved, with its main connections located in deep brain structures. The CSS is a large-scale complex network characterized by hierarchy, hubs, and modularity, which together enable the competitive optimization of functional segregation and integration. Under physiological conditions, modules (mediating functional segregation) and hubs (functional integration) within the CSS dynamically trade-off with each other to maintain the overall homeostasis. However, this balance is disrupted following pathological insults or injury, resulting in weakened functional integrity of the CSS following ABI, impaired module activity, and disturbed hub integration. This paper also demonstrates the distinct pathological manifestations arising from disturbances at different levels of the homeostatic system. Finally, this study proposes potential clinical interventions, including analgesia and sedation, neuromodulation, and receptor regulation, for early interventions and potential treatment of NODS, aiming to improve patient outcomes.}, }
@article {pmid41203630, year = {2025}, author = {Thapa, BR and Boggess, J and Bae, J}, title = {A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1760}, pmid = {41203630}, issn = {2052-4463}, mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Male ; Female ; Young Adult ; Adolescent ; *Hand Strength ; }, abstract = {Brain machine interfaces (BMIs) offer great potential to improve the quality of life for individuals with neurological disorders or severe motor impairments. Among various neural recording modalities, electroencephalogram (EEG) is particularly favorable for BMIs due to its noninvasive nature, portability, and high temporal resolution. Existing EEG datasets for BMIs are often limited to experimental settings that fail to address subjects' freewill in decision making. We present a large EEG dataset, containing a total of 6808 trials, recorded from 23 healthy young adults (eight females and 15 males with an age range from 18 to 24 years) while performing reaching and grasping tasks, where the target object is freely chosen at their desired pace according to their own will. This EEG dataset provides a realistic representation of reaching and grasping movement, making it useful for developing practical BMIs.}, }
@article {pmid41201930, year = {2025}, author = {Yang, Y and Wang, Z and Jia, Z and Wang, B and Zhang, S and Wong, CM and Gao, X and Jung, TP and Wan, F}, title = {Dual-Branch Attention-based Frequency Domain Network for Cross-subject SSVEP-BCIs.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3630249}, pmid = {41201930}, issn = {2168-2208}, abstract = {Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) hold significant promise for enabling high-speed human-computer interaction in real-world scenarios. However, existing frequency-domain decoding methods treat frequency spectrum features (the real and imaginary spectrum features) as a single feature without considering their unique spatial and spectral characteristics, resulting in insufficient generalizable features and limited classification accuracy in cross-subject scenarios. To address this issue, we propose a Dual-Branch Attention-Based Frequency Domain Network (DB-AFDNet) to independently decode real and imaginary spectral components, aiming to acquire more discriminative and generalizable features for cross-subject applications. Specifically, we construct inter-branch attention similarity constraints to encourage the two branches to have similar attention properties, promoting to learn the consensus characteristics in the dual branches. Furthermore, we propose intra-branch orthogonality constraints to explore branch-specific discriminative features to learn generalizable features. Experimental studies on two public datasets, the Benchmark and Beta datasets, demonstrate that DB-AFDNet outperforms state-of-the-art methods in cross-subject classification, achieving a relative improvement of 1.36$\%$ and 1.45$\%$, respectively. The code is available at https://github.com/YYingDL/DBAFDNet.}, }
@article {pmid41199758, year = {2025}, author = {Wang, Y and Yu, H and Zhao, X and Yin, X and Li, H and Wang, C}, title = {A dual-branch neural network and attention mechanism for decoding EEG-based motor imagery.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {177}, pmid = {41199758}, issn = {1871-4080}, abstract = {Motor imagery (MI) is a fundamental paradigm in brain-computer interfaces (BCIs), extensively employed to assist individuals with disabilities to operate external devices. Accurate decoding of MI signals is essential for effective interaction. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. To address this issue, we propose an innovative Dual-Branch Multi-Attention Temporal Convolutional Network (DBMATCN) to improve the performance of MI-EEG signal classification. First, the dual-branch structure extracts rich spatial-temporal features. Then, the channel attention enhances local channel feature extraction and calibrate feature mapping. Next, by combining a sliding window technique and multi-head locality self-attention improves the feature representation of MI-EEG signals by emphasizing the most relevant features. Finally, the temporal convolution fusion network decoding module is used to extensively capture comprehensive temporal features from MI data and carry out the classification task. DBMATCN achieves average accuracies of 88.08%, 96.83%, and 89.71% in inter-session validation on the BCI-IV-2a, HGD, and BCI-IV-2b datasets, respectively. In cross-validation, the model reaches an accuracy of 85.14%, and in the subject-independent scenario, it attains 71.78%. DBMATCN outperforms all baseline models in these cases. These results suggest that our model is effective in decoding MI signals.}, }
@article {pmid41199757, year = {2025}, author = {Zhu, L and Ding, Y and Hung, A and Tan, X and Zhang, J}, title = {SFT-HN: a novel spatial-frequency-temporal hybrid network for EEG-based emotion recognition.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {176}, pmid = {41199757}, issn = {1871-4080}, abstract = {Electroencephalograph (EEG) emotion recognition is a key task in the brain-computer interface(BCI) field. A mounting quantity of studies have shown that deep learning methods for emotion recognition exhibit superior performance compared to traditional techniques. However, it is still challenging to fuse the EEG's Spatial, Frequency and Temporal information, as well as how to make full use of discriminative local patterns among the features for different emotions. To address these issues, a novel hybrid model called Spatial-Frequency-Temporal Hybrid Network(SFT-HN) is proposed. This model includes three Spatial Frequency Residual Modules (SFRM) and an attention-based Bidirectional Long Short-Term Memory (ATBI-LSTM). The former module extracts spatial-frequency features, while the latter learns temporal contexts. SFT-HN is trained to seize the complementarity among the spatial-frequency-temporal information and adaptively explore discriminative local patterns. Specifically, 4D representations are created from raw EEG signals to preserve spatial, frequency, and temporal information. The SFRM module then adopts split-convert-merge techniques, residual and attention mechanisms to enhance its spatial-frequency feature extraction ability for each input 4D representation tensor time slice. Moreover, an attention-enhanced mechanism is incorporated into a bidirectional LSTM module to capture the crucial temporal dependencies among the extracted features, thereby enhancing the discriminative power of the EEG features. The proposed method attains average accuracies of 97.61% and 97.57% for arousal-based and valence-based classification on the DEAP dataset, respectively. On SEED dataset, the method achieves average accuracy of 97.44%. Furthermore, we validate the robust generalization of our proposed model on a novel dataset, FACED, achieving an average accuracy of 96.24%. The model code is available at: https://github.com/AllGGI/SFT-HN-model.}, }
@article {pmid41199756, year = {2025}, author = {Wu, X and Long, D and Yang, J}, title = {Generative motor imagery dynamic networks: EEG-controlled grasping via individualized model training.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {174}, pmid = {41199756}, issn = {1871-4080}, abstract = {Improving the accuracy of non-invasive brain-computer interface (BCI) and promoting their daily use can be achieved by developing an individualized model training framework, where individual training means that the model is based on small-sample learning from individual data. In the process of data augmentation through synthetic data, the criteria for data generation needs to be further specified according to the requirements. Therefore, in this study, the proposed BCI model utilizes dynamic networks to describe electroencephalogram (EEG) activity during the motor imagery (MI) task, innovatively generates individualized dynamic networks from individual data, and ultimately achieves EEG-controlled grasping through model training. Specifically, this study involves the EEG signals of the right-hand grasping movements of eight subjects and proposes using morphological pattern spectrum (MPS) to encode EEG potentials during MI processes. The MI condition representation was achieved by combining the dynamic networks with MPS encoding, and more dynamic network EEG encoding samples were synthesized through generative adversarial network (GAN) or variational autoencoder (VAE). The AUCs based on the long short-term memory (LSTM) architecture for generating and classifying can be improved by 0.003-0.07. The optimal BCI model based on the Wasserstein GAN and Granger causality (GC) dynamic network encoded by MPS achieved a mean true/false positive rate (TPR/FPR) of 90.0%/0.0%, far better than the 52.9%/4.4% achieved without individualized modeling. Moreover, the BCI establishment of handling multi-task and complex command outputs further demonstrates the reliability of MPS encoding of the GC dynamic network in BCI modeling. The advantage of this "generative-individual" approach is that it not only reduces the sample size requirement while ensuring accuracy but also avoids building models that are applicable to all individuals, which leads to difficult convergence.}, }
@article {pmid41199478, year = {2025}, author = {Bobby J, S and V Francis, S and Ramya V, S and C L, A}, title = {Preliminary Findings on a Deep Learning Model Using Electroencephalogram for Multi-Level Neuropathic Pain Detection in Post-Stroke Patients.}, journal = {The International journal of neuroscience}, volume = {}, number = {}, pages = {1-10}, doi = {10.1080/00207454.2025.2584081}, pmid = {41199478}, issn = {1563-5279}, abstract = {AIM: Neuropathic pain occurs commonly after stroke and represents a major source of disability for affected patients. This study aims to develop an accurate and computationally efficient framework for multi-level neuropathic pain detection using electroencephalography signals.<br><br>METHODS: A Quantum-Inspired Pyramid Depthwise Separable Residual Network is proposed, which integrates three innovations: a depthwise separable Residual Network to reduce computational complexity, a pyramid attention mechanism to capture multi-scale patterns, and a quantum-inspired transformation layer to model complex nonlinear dependencies among Electroencephalogram features.<br><br>RESULTS: Experiments conducted on benchmark electroencephalography datasets confirm that the proposed model gains a accuracy of 99.65%, with a recall of 98.00%.<br><br>CONCLUSION: The proposed model provides a reliable solution for objective neuropathic pain detection in post-stroke patients. The framework demonstrates potential for integration into intelligent clinical decision-support and brain-computer interface-based rehabilitation systems.}, }
@article {pmid41199005, year = {2025}, author = {Zhang, H and Wang, X and Xi, K and Shen, Q and Xue, J and Zhu, Y and Zang, SK and Yu, T and Shen, DD and Guo, J and Chen, LN and Ji, SY and Qin, J and Dong, Y and Zhao, M and Yang, M and Wu, H and Yang, G and Zhang, Y}, title = {The molecular basis of μ-opioid receptor signaling plasticity.}, journal = {Cell research}, volume = {}, number = {}, pages = {}, pmid = {41199005}, issn = {1748-7838}, support = {2019YFA0508800//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&amp;T for Social Development)/ ; 32430051//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92353303//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32141004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82271001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2024M752856//China Postdoctoral Science Foundation/ ; }, abstract = {Activation of the μ-opioid receptor (μOR) alleviates pain but also elicits adverse effects through diverse G proteins and β-arrestins. The structural details of μOR complexes with Gz and β-arrestins have not been determined, impeding a comprehensive understanding of μOR signaling plasticity. Here, we present the cryo-EM structures of the μOR-Gz and μOR-βarr1 complexes, revealing selective conformational preferences of μOR when engaged with specific downstream signaling transducers. Integrated receptor pharmacology, including high-resolution structural analysis, cell signaling assays, and molecular dynamics simulations, demonstrated that transmembrane helix 1 (TM1) acts as an allosteric regulator of μOR signaling bias through differential stabilization of the Gi-, Gz-, and βarr1-bound states. Mechanistically, outward TM1 displacement confers structural flexibility that promotes G protein recruitment, whereas inward TM1 retraction facilitates βarr1 recruitment by stabilizing the intracellular binding pocket through coordinated interactions with TM2, TM7, and helix8. Structural comparisons between the Gi-, Gz-, and βarr1-bound complexes identified a TM1-fusion pocket with significant implications for downstream signaling regulation. Overall, we demonstrate that the conformational and thermodynamic heterogeneity of TM1 allosterically drives the downstream signaling specificity and plasticity of μOR, thereby expanding the understanding of μOR signal transduction mechanisms and providing new avenues for the rational design of analgesics.}, }
@article {pmid41198855, year = {2025}, author = {Heerspink, HJL and Collier, WH and Chaudhari, J and Miao, S and Tighiouart, H and Appel, GB and Caravaca-Fontán, F and Floege, J and Hannedouche, T and Imai, E and Jafar, TH and Lewis, JB and Li, PKT and Locatelli, F and Maes, BD and Neuen, BL and Perkovic, V and Perrone, RD and Remuzzi, G and Schena, FP and Wanner, C and Greene, T and Inker, LA}, title = {A meta-analysis of albuminuria as a surrogate endpoint for kidney failure.}, journal = {Nature medicine}, volume = {}, number = {}, pages = {}, pmid = {41198855}, issn = {1546-170X}, abstract = {Albuminuria is a central biomarker in chronic kidney disease (CKD), used for the detection and prognosis of the disease. In clinical trials assessing CKD progression, change in the level of albuminuria is a candidate surrogate endpoint for kidney failure. Evaluation of the validity of this surrogate endpoint across a diverse range of interventions and populations is required to support its further acceptance. Here, in an individual participant data analysis of 48 randomized controlled trials (studies) involving 85,681 participants, we assessed the association between treatment effects on 6-month urinary albumin:creatinine ratio (UACR) change and the established clinical endpoint of kidney failure or doubling of serum creatinine concentrations. Across all trials, each 30% reduction in the geometric mean of the UACR in the treatment group relative to the control group was associated with an average of 19% lower hazard for the clinical endpoint (95% Bayesian credible interval (BCI): 5-30%); median coefficient of determination (R[2]) = 0.66 (95% BCI: 0.06-0.98). There was no clear evidence that this association varied by CKD etiology. These results provide further support for use of albuminuria change as a surrogate endpoint in CKD clinical trials.}, }
@article {pmid41197164, year = {2025}, author = {Cinquetti, E and Menegaz, G and Storti, SF}, title = {Toward in-silico data assessment for passive BCIs: Generating EEG rhythms with GANs.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae1c6f}, pmid = {41197164}, issn = {1741-2552}, abstract = {Passive brain-computer interface based on electroencephalography (EEG) has gained traction as reliable method for monitoring human vigilance in attention-demanding critical contexts. Unfortunately, the lack of extensive public datasets compromises artificial intelligence (AI) research. Proposing a solution to this issue, we augmented two EEG datasets using generative adversarial networks (GAN) and defined a quality-assessment pipeline to overcome the absence of a univocal method to test synthetic data. Approach. Using GAN, we augmented a publicly resting-state EEG dataset (SPIS) and a custom one simulating activity during repetitive tasks. After extracting relevant time-variant rhythms via the continuous wavelet transform, we quantitatively compared synthetic data with the real one using L2 distance and cross-correlation function. To evaluate the impact of data augmentation, we trained six forecasting models, three on the original and three on the augmented datasets, over the whole, half and a quarter of total available data, and compared improvements in MAE and SMAPE. To study the forecaster's embeddings, we computed a metric inspired by the Fréchet Inception Distance (FID) between latent values of real and synthetic data. Finally, to offer a baseline comparison, we extended the performance and embeddings analysis to data generated by a simple linear interpolation method. Main Results. The integration of GAN-produced synthetic data improved signal prediction, as evidenced by a 29.0%, 46.4%, 37.4% reduction in mean absolute error (MAE) for splits of the resting-state dataset, and an average MAE reduction of 15.4%, 21.2% for 100% and 50% splits, and a ∽-2.5\% increase for the 25% split). Conversely, training on interpolated data manifest worse performance and denotes extremely small FID distances w.r.t real signals, a sign of overspecialization. Significance. This study contributes a reproducible and complete framework for EEG signal generation and evaluation, addressing one of the main barriers to scalable AI application in BCI.}, }
@article {pmid41196745, year = {2025}, author = {Zhang, S and Chen, W and Chang, S and Zhou, LF and Ding, X}, title = {How visual imagery representations are formed: Through suppression, not activation.}, journal = {Journal of experimental psychology. General}, volume = {}, number = {}, pages = {}, doi = {10.1037/xge0001863}, pmid = {41196745}, issn = {1939-2222}, support = {//National Natural Science Foundation of China/ ; //Natural Science Foundation of Guangdong Province/ ; //Guangzhou Science and Technology Plan Project-Leading Elite Program/ ; //Fundamental Research Funds for the Central Universities of China/ ; //Major Project Cultivation and Emerging Interdisciplinary Cultivation Plan/ ; //Shanghai Science and Technology Development Foundation/ ; }, abstract = {Voluntary imagery is described as "weak perception" and is thought to be represented through activating the neurons corresponding to imagined features, that is, activation hypothesis. However, direct evidence for this hypothesis is lacking. Inspired by Pace et al. (2023), we examine an alternative suppression hypothesis, which states imagery involves suppression of neurons favoring nearby nonimagined features. While the activation hypothesis predicts a bell-shaped tuning curve of the neural representation for the imagined feature, the suppression hypothesis predicts a W-shaped tuning curve. To test these two hypotheses, we combined an imagery task with a discrimination task following the logic that different imagery-induced tuning curves would differently bias the perceived difference in the discrimination task. We probed the bias pattern by systematically manipulating the physical orientation difference and the discrimination-imagery relation condition. A series of psychophysical experiments were conducted. Results showed that after an imagery prior, bias pattern in the discrimination task followed the prediction of suppression hypothesis (Experiment 1a). By contrast, when substituting the imagery prior with a strong/weak perceptual prior, bias pattern was consistent with the prediction of activation hypothesis (Experiments 2a and 2b). Confounding effects of visual attention and perceptual imagery cue were excluded (Experiments 1b and 1c). We further constructed mathematical models and again validated our findings. In conclusion, behavioral and modeling results coherently suggested that the suppression hypothesis was a better explanation for imagery than the activation hypothesis. Our study challenges the traditional activation theory and provides novel empirical evidence for the suppressive representation of voluntary visual imagery. (PsycInfo Database Record (c) 2025 APA, all rights reserved).}, }
@article {pmid41193512, year = {2025}, author = {Kim, H and Won, K and Ahn, M and Jun, SC}, title = {A 40-Class SSVEP Speller Dataset: Beta Range Stimulation for Low-Fatigue BCI Applications.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1751}, pmid = {41193512}, issn = {2052-4463}, support = {RS-2024-00361688//National Research Foundation of Korea (NRF)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; *Fatigue ; Adult ; Photic Stimulation ; Male ; Female ; }, abstract = {The inherent non-stationarity of electroencephalography (EEG) signals necessitates large, consistent datasets for reliable brain-computer interface (BCI) research. In steady-state visual evoked potential (SSVEP) paradigms, prolonged exposure to visual stimuli can induce visual fatigue, leading to alterations in EEG patterns that degrade BCI performance. To mitigate fatigue-induced variability, this study employs visual stimulation in the beta frequency range (14-22 Hz), a range that appears less susceptible to the effects of fatigue. We present a comprehensive 40-class SSVEP speller dataset acquired from 40 participants, with EEG data recorded from 31 central-to-occipital channels. Each subject completed six sessions of the SSVEP speller task in addition to pre- and post-experiment resting-state recordings under both eyes-open and eyes-closed conditions. Subjective fatigue ratings combined with EEG band power analyses confirm that beta-range stimulation minimizes fatigue effects. Moreover, the high classification accuracy achieved by calibration-based algorithms indicates that the dataset is well-suited for training advanced SSVEP-based BCI systems.}, }
@article {pmid41193466, year = {2025}, author = {Chiti, E and Micera, S and Palmerini, E}, title = {Making the case for sandboxes in implantable neurotechnologies.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {9783}, pmid = {41193466}, issn = {2041-1723}, abstract = {Regulatory sandboxes could be fruitfully used to boost Invasive Brain-Computer Interfaces, but they should be carefully designed. We highlight five elements are essential: they concern the entry criteria, the participated, adaptive and supervised design of decision-making process, and long-term risk management.}, }
@article {pmid41192731, year = {2025}, author = {Dai, C and Lin, M and Xu, N and Fu, Y and Li, X and Shi, Y and Wu, M and Li, Y and Xie, J and Hu, S and Zhao, Q}, title = {The impact of CYP3A4 rs2242480 on oral lurasidone: A population pharmacokinetic model and exposure-efficacy analysis in Chinese bipolar depression patients.}, journal = {Journal of affective disorders}, volume = {394}, number = {Pt B}, pages = {120588}, doi = {10.1016/j.jad.2025.120588}, pmid = {41192731}, issn = {1573-2517}, abstract = {OBJECTIVE: This study aims to develop a population pharmacokinetic (PPK) model and perform an exposure-efficacy analysis for lurasidone in patients with bipolar depression, thus interpretating the inter-individual variability in its pharmacokinetics and optimizing dosing regimens.<br><br>METHODS: A PPK model and exposure-efficacy analysis were established in Chinese patients with bipolar depression. 241 lurasidone concentration measurments from 133 patients were included. Demographic information was collected and genotypes for CYP3A4 and HTR1A alleles were determined. Treatment efficacy was defined as the reduction in the Montgomery-Asberg Depression Rating Scale (MADRS) score at week 4.<br><br>RESULTS: A one-compartment model with first-order kinetics for lurasidone was fitted. The apparent clearance (CL/F) of lurasidone was significantly lower in CYP3A4 rs2242480 CC carriers (330&#xa0;L/h) than in TC (385&#xa0;L/h) and TT (441&#xa0;L/h) carriers, representing reductions of 14.3&#xa0;% and 25.2&#xa0;%, respectively. Additionally, CL/F was positively correlated with ideal body weight (IBW). Incorporating these covariates reduced the interindividual variability in CL/F from 40.5&#xa0;% to 37.1&#xa0;%. The exposure-efficacy analysis demonstrated a dose-denpedent increase in area under the curve (AUC), and MADRS score improved with an increasing AUC and reached a plateau at an AUC of approximately 167&#xa0;mg&#xb7;h&#xb7;L[-1], corresponding to an optimal daily dose range of 45-55&#xa0;mg.<br><br>CONCLUSION: The pharmacokinetics of lurasidone in patients with bipolar depression are significantly influenced by IBW and the rs2242480 genotype, enabling a practical framework for precision dosing.}, }
@article {pmid41192010, year = {2025}, author = {Yang, M and Wang, Z and Zhou, Q and Zhang, Q and Li, Y and Wang, Z}, title = {The adjunctive efficacy of repetitive transcranial magnetic stimulation with non-pharmacological interventions in cognitive disorders: A meta-analysis of randomized sham-controlled trials.}, journal = {Asian journal of psychiatry}, volume = {114}, number = {}, pages = {104758}, doi = {10.1016/j.ajp.2025.104758}, pmid = {41192010}, issn = {1876-2026}, abstract = {OBJECTIVE: This meta-analysis aimed to systematically evaluate the specific, adjunctive efficacy of repetitive transcranial magnetic stimulation (rTMS) when combined with non-pharmacological interventions-namely, transcranial direct current stimulation (tDCS), Tai Chi, or cognitive training (CT)-in patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI). The goal is to isolate the net therapeutic contribution of rTMS beyond the effects of the base interventions alone.<br><br>METHODS: A comprehensive search of Chinese and English databases was conducted from their inception until April 26, 2025. Randomized controlled trials (RCTs) that compared "a non-pharmacological intervention plus active rTMS" versus "the same non-pharmacological intervention plus sham rTMS".This "add-on" study design was selected to precisely isolate the effect of rTMS. The risk of bias was assessed using the PEDro scale and Cochrane tools. Statistical analyses were performed using Review Manager 5.4 software.<br><br>RESULTS: 9 studies involving 391 participants were included. The pooled analysis revealed that the adjunctive use of rTMS was significantly superior to the sham control in improving global cognitive function at the immediate post-treatment assessment (SMD=0.38, 95&#x202f;%CI[0.20,0.56], P&#x202f;<&#x202f;.001,&#xa0;n&#x202f;=&#x202f;9). This benefit was consistent across the MMSE (SMD=0.38,&#xa0;n&#x202f;=&#x202f;6), MoCA (SMD=0.37,&#xa0;n&#x202f;=&#x202f;2), and ADAS-cog (SMD=0.39,&#xa0;n&#x202f;=&#x202f;3) scores. Subgroup analysis suggested that the rTMS-tDCS combination might offer a short-term advantage in improving MMSE scores (MD=4.67,&#xa0;P&#x202f;=&#x202f;.008). Furthermore, the adjunctive effect of rTMS was sustained, as particularly evidenced by the ADAS-cog at follow-up (SMD=0.74, P&#x202f;=&#x202f;.02). The pooled analysis indicated that rTMS combined with non-pharmacological therapy demonstrated a short-term, sustained (4-8weeks) improvement in global cognitive function (SMD=0.34, 95&#x202f;% CI[0.07, 0.60], P&#x202f;=&#x202f;.01). Subgroup analysis revealed that this sustained benefit reached statistical significance on the ADAS-cog scale (SMD = 0.41, 95&#x202f;%CI[0.01, 0.81], P&#x202f;=&#x202f;.04) but showed a non-significant positive trend on the MMSE (SMD=0.26,&#xa0;95&#x202f;%CI[-0.19, 0.72], P&#x202f;=&#x202f;.26). However, a key limitation was that most studies did not systematically report outcomes related to activities of daily living or behavioral function.<br><br>CONCLUSION: The evidence indicates that rTMS as an adjunct to non-pharmacological interventions provides a significant specific effect on global cognitive function in patients with AD and MCI shortly after treatment, which may be sustained in the short-term. However, long-term follow-up data are extremely limited, and the effect on activities of daily living remains to be validated. The combination of rTMS and tDCS shows promise,but conclusions are constrained by the small number of studies,limited sample sizes,and heterogeneity in intervention protocols. Future large-scale studies incorporating long-term, standardized follow-up and assessments of daily living abilities are warranted to confirm the specific clinical value of rTMS as an augmentative therapy.}, }
@article {pmid41191990, year = {2025}, author = {Xing, Y and He, Y and Gong, Z and Zhou, J and Sun, Y and Zhong, Z}, title = {A study on the microstructure and micromechanical properties of Drosophila larval cuticle using scanning probe microscopy and viscoelastic modeling.}, journal = {Journal of biomechanics}, volume = {194}, number = {}, pages = {113051}, doi = {10.1016/j.jbiomech.2025.113051}, pmid = {41191990}, issn = {1873-2380}, abstract = {The Drosophila larval cuticle exhibits compliant yet resilient viscoelasticity, serving as a soft exoskeleton that enables effective locomotion while maintaining structural integrity. Investigating its microstructure and micromechanical properties not only advances our understanding of soft-bodied biomechanics but also guides the design of biomimetic materials and soft robotic systems. In this study, we employed scanning probe microscopy (SPM)-based stress relaxation tests to characterize viscoelastic properties across the denticle and smooth skin bands in three larval instars. Four viscoelastic models were evaluated, and the five-element Maxwell (MX5) model provided the best fit, enabling the extraction of mechanical parameters and plotting of relaxation modulus functions. Results showed that the larval instar stage had minimal influence on viscoelasticity, while the denticle and smooth skin bands exhibited distinct mechanical behaviors. Across all instars, the denticle bands showed higher moduli throughout the relaxation process, and notably, exhibited a greater degree and faster rate of relaxation compared to the smooth skin bands. These findings reveal region-specific viscoelastic adaptations that enable rapid stress dissipation while maintaining stiffness, supporting effective deformation during locomotion. This study provides essential quantitative foundations for bioinspired stretchable electronics, soft robotic materials, and broader understanding of soft exoskeleton mechanics.}, }
@article {pmid41191976, year = {2025}, author = {Forrest, A and Kunigk, NG and Collinger, J and Gaunt, RA and Vande Geest, JP and Chen, X and Kozai, TDY}, title = {Finite element model predicts micromotion-induced strain profiles that correlate with the functional performance of Utah arrays in humans and non-human primates.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae1bda}, pmid = {41191976}, issn = {1741-2552}, abstract = {OBJECTIVE: Utah arrays are widely used in both humans and non-human primates (NHPs) for intracortical brain-computer interfaces (BCIs), primarily for detecting electrical signals from cortical tissue to decode motor commands. Recently, these arrays have also been applied to deliver electrical stimulation aimed at restoring sensory functions. A key challenge limiting their longevity is the micromotion between the array and cortical tissue, which may induce mechanical strain in surrounding tissue and contribute to performance decline. This strain, due to mechanical mismatch, can exacerbate glial scarring around the implant, reducing the efficacy of Utah arrays in recording neuronal activity and delivering electrical stimulation.<br><br>APPROACH: To investigate this, we employed a finite element model (FEM) to predict tissue strains resulting from micromotion.<br><br>MAIN RESULTS: Our findings indicated that strain profiles around edge and corner electrodes were greater than those around interior shanks, affecting both maximum and average strains within 50 &#xb5;m of the electrode tip. We then correlated these predicted tissue strains with in-vivo electrode performance metrics. We found negative correlations between 1 kHz impedance and tissue strains in human motor arrays and NHP area V4 arrays at 1-mo, 1-yr, and 2-yrs post-implantation. In human motor arrays, the peak-to-peak waveform voltage (PTPV) and signal-to-noise ratio (SNR) of spontaneous activity were also negatively correlated with strain. Conversely, we observed a positive correlation between the evoked SNR of multi-unit activity and strain in NHP area V4 arrays.<br><br>SIGNIFICANCE: This study establishes a spatial dependence of electrode performance in Utah arrays that correlates with tissue strain.}, }
@article {pmid41191971, year = {2025}, author = {Bjanes, D and Bashford, L and Pejsa, K and Lee, B and Liu, CY and Andersen, RA}, title = {Charge density of multi-channel intra-cortical micro-stimulation modulates intensity and naturalness of evoked somatosensations.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae1bd8}, pmid = {41191971}, issn = {1741-2552}, abstract = {Human patients with somatosensory loss often experience severe motor deficits, causing profound challenges to independently accomplish typical tasks of daily life. Brain-machine Interfaces (BMIs) offer the potential to restore lost functionality through direct electrical stimulation of the somatosensory cortex via intra-cortical micro-stimulation (ICMS). By modulating temporal patterns of stimulation, our group has previously shown single-channel ICMS can evoke both naturalistic cutaneous and proprioceptive sensory feedback. However, accurate modulation of the sensory feedback's qualia (somatotopic location, intensity and description) will be critical for fluid, dexterous motor control. In nonhuman primate studies, multi-channel ICMS has shown promise in improving quantifiable metrics such as reaction time. In recent human work, multi-channel ICMS has improved discrimination performance; however, evoked qualia has not been well characterized. We hypothesized multi-channel ICMS could evoke unique qualia compared to single-channel. A human participant with tetraplegia and chronically implanted microelectrode arrays in primary somatosensory cortex, reported perceptual thresholds, sensation descriptions, intensity and somatotopic locations of single- and multi-channel ICMS patterns. We found multi-channel ICMS patterns evoked unique qualia compared to single-channel ICMS. To investigate the role of charge in producing these unique evoked sensory percepts, we delivered equal amounts of charge with differing spatial patterns across multiple electrodes. Multi-channel ICMS substantially reduced the minimum stimulation amplitude required to evoked somatosensations, lowering the charge per electrode detection threshold, while increasing the total charge injected. Delivered charge across multiple electrodes, positively modulated the sensation's perceived intensity; providing early evidence of spatial integration of ICMS in the target network. Multi-channel ICMS resulted in more frequent verbal reports of "natural" sensation descriptors (100% vs 85% for single-channel ICMS, p-val<0.05) and robustly evoked sensations with high repeatability in stable somatotopic locations. Multi-channel ICMS patterns demonstrated improvements in reliability, somatotopic coverage and "natural-ness" of the evoked sensations, marking significant advances towards state-of-the-art somatosensory brain-machine-interfaces (BMIs). By better understanding of the input/output relationship for somatosensory feedback BMIs, we can expect to improve movement accuracy and increase embodiment for human users. .}, }
@article {pmid41191851, year = {2025}, author = {Williams, C and Anik, FI and Hasan, MM and Rodriguez-Cardenas, J and Chowdhury, A and Tian, S and He, S and Sakib, N}, title = {Advancing Brain-Computer Interface Closed-Loop Systems for Neurorehabilitation: Systematic Review of AI and Machine Learning Innovations in Biomedical Engineering.}, journal = {JMIR biomedical engineering}, volume = {10}, number = {}, pages = {e72218}, pmid = {41191851}, issn = {2561-3278}, abstract = {BACKGROUND: Brain-computer interface (BCI) closed-loop systems have emerged as a promising tool in health care and wellness monitoring, particularly in neurorehabilitation and cognitive assessment. With the increasing burden of neurological disorders, including Alzheimer disease and related dementias (AD/ADRD), there is a critical need for real-time, noninvasive monitoring technologies. BCIs enable direct communication between the brain and external devices, leveraging artificial intelligence (AI) and machine learning (ML) to interpret neural signals. However, challenges such as signal noise, data processing limitations, and privacy concerns hinder widespread implementation.<br><br>OBJECTIVE: The primary objective of this study is to investigate the role of ML and AI in enhancing BCI closed-loop systems for health care applications. Specifically, we aim to analyze the methods and parameters used in these systems, assess the effectiveness of different AI and ML techniques, identify key challenges in their development and implementation, and propose a framework for using BCIs in the longitudinal monitoring of AD/ADRD patients. By addressing these aspects, this study seeks to provide a comprehensive overview of the potential and limitations of AI-driven BCIs in neurological health care.<br><br>METHODS: A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, focusing on studies published between 2019 and 2024. We sourced research articles from PubMed, IEEE, ACM, and Scopus using predefined keywords related to BCIs, AI, and AD/ADRD. A total of 220 papers were initially identified, with 18 meeting the final inclusion criteria. Data extraction followed a structured matrix approach, categorizing studies based on methods, ML algorithms, limitations, and proposed solutions. A comparative analysis was performed to synthesize key findings and trends in AI-enhanced BCI systems for neurorehabilitation and cognitive monitoring.<br><br>RESULTS: The review identified several ML techniques, including transfer learning (TL), support vector machines (SVMs), and convolutional neural networks (CNNs), that enhance BCI closed-loop performance. These methods improve signal classification, feature extraction, and real-time adaptability, enabling accurate monitoring of cognitive states. However, challenges such as long calibration sessions, computational costs, data security risks, and variability in neural signals were also highlighted. To address these issues, emerging solutions such as improved sensor technology, efficient calibration protocols, and advanced AI-driven decoding models are being explored. In addition, BCIs show potential for real-time alert systems that support caregivers in managing AD/ADRD patients.<br><br>CONCLUSIONS: BCI closed-loop systems, when integrated with AI and ML, offer significant advancements in neurological health care, particularly in AD/ADRD monitoring and neurorehabilitation. Despite their potential, challenges related to data accuracy, security, and scalability must be addressed for widespread clinical adoption. Future research should focus on refining AI models, improving real-time data processing, and enhancing user accessibility. With continued advancements, AI-powered BCIs can revolutionize personalized health care by providing continuous, adaptive monitoring and intervention for patients with neurological disorders.}, }
@article {pmid41191764, year = {2025}, author = {Qian, Y and Liu, C and Yu, P and Ran, X and Li, S and Yang, Q and Liu, Y and Xia, L and Wang, Y and Qi, J and Zhou, E and Lu, J and Li, Y and Tao, TH and Zhou, Z and Wu, J}, title = {Real-time decoding of full-spectrum Chinese using brain-computer interface.}, journal = {Science advances}, volume = {11}, number = {45}, pages = {eadz9968}, pmid = {41191764}, issn = {2375-2548}, mesh = {*Brain-Computer Interfaces ; Humans ; Language ; Male ; *Speech/physiology ; Female ; Adult ; Electroencephalography ; China ; East Asian People ; }, abstract = {Speech brain-computer interfaces (BCIs) offer a promising means to provide functional communication capacity for patients with anarthria caused by neurological conditions such as amyotrophic lateral sclerosis (ALS) or brainstem stroke. Current speech decoding research has predominantly focused on English using phoneme-driven architectures, whereas real-time decoding of tonal monosyllabic languages such as Mandarin Chinese remains a major challenge. This study demonstrates a real-time Mandarin speech BCI that decodes monosyllabic units directly from neural signals. Using the 256-channel microelectrocorticographic BCI, we achieved robust decoding of a comprehensive set of 394 distinct syllables based purely on neural signals, yielding median syllable identification accuracy of 71.2% in a single-character reading task. Leveraging this high-performing syllable decoder, we further demonstrated real-time sentence decoding. Our findings demonstrate the efficacy of a tonally integrated, direct syllable neural decoding approach for Mandarin Chinese, paving the way for full-coverage systems in tonal monosyllabic languages.}, }
@article {pmid41187598, year = {2025}, author = {Jui, JJ and Hettiarachchi, IT and Bhatti, A and Creighton, D}, title = {PLVNet: EEG-based trust classification using Phase Locking Value connectivity and deep neural networks.}, journal = {Computers in biology and medicine}, volume = {198}, number = {Pt B}, pages = {111269}, doi = {10.1016/j.compbiomed.2025.111269}, pmid = {41187598}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Trust ; Male ; *Neural Networks, Computer ; Female ; Adult ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; *Brain/physiology ; Young Adult ; }, abstract = {Trust in automation is critical for effective human-automation interaction, yet traditional subjective measures are limited in capturing rapid and dynamic changes in user trust. This study introduces PLVNet, a novel deep neural network architecture designed to classify trust versus distrust states from EEG functional connectivity features derived using Phase Locking Value (PLV). PLV features were extracted across six canonical EEG frequency bands (Delta, Theta, Alpha, Beta, Low Gamma, High Gamma) from 30-channel EEG recordings. The PLVNet model was evaluated using three complementary approaches: aggregated analysis (5× 5 stratified cross-validation), participant-wise analysis, and leave-one-subject-out (LOSO) cross-validation. PLVNet significantly outperformed convolutional neural network (CNN), support vector machine (SVM) and k-nearest neighbours (KNN) classifiers across all evaluation schemes. Beta and Low Gamma bands provided the highest discriminative power, while functional connectivity analysis revealed that trust is associated with enhanced fronto-parietal and fronto-occipital synchronisation, reflecting global network integration, whereas distrust shows fragmented connectivity patterns. PLVNet's ability to capture non-linear inter-dependencies in connectivity patterns highlights its advantages over conventional methods. These findings demonstrate that PLV-based connectivity robustly reflects trust-related neural dynamics, underscoring the potential of PLVNet for real-time, objective monitoring of trust in human-automation systems, which paves the way for adaptive and neuro-aware interfaces.}, }
@article {pmid41187597, year = {2025}, author = {Liu, J and Deng, X and Li, H and Kazemi, A and Grashei, C and Wilkens, G and You, X and Groll, T and Navab, N and Mogler, C and Schüffler, PJ}, title = {From pixels to pathology: Restoration diffusion for diagnostic-consistent virtual IHC.}, journal = {Computers in biology and medicine}, volume = {198}, number = {Pt B}, pages = {111264}, doi = {10.1016/j.compbiomed.2025.111264}, pmid = {41187597}, issn = {1879-0534}, mesh = {Humans ; *Immunohistochemistry/methods ; *Breast Neoplasms/metabolism/pathology/diagnosis/diagnostic imaging ; Female ; *Image Processing, Computer-Assisted/methods ; *Image Interpretation, Computer-Assisted/methods ; Biomarkers, Tumor/metabolism ; }, abstract = {Hematoxylin and eosin (H&amp;E) staining is the clinical standard for assessing tissue morphology, but it lacks molecular-level diagnostic information. In contrast, immunohistochemistry (IHC) provides crucial insights into biomarker expression, such as HER2 status for breast cancer grading, but remains costly and time-consuming, limiting its use in time-sensitive clinical workflows. To address this gap, virtual staining from H&amp;E to IHC has emerged as a promising alternative, yet faces two core challenges: (1) Lack of fair evaluation of synthetic images against misaligned IHC ground truths, and (2) preserving structural integrity and biological variability during translation. To this end, we present an end-to-end framework encompassing both generation and evaluation in this work. We introduce Star-Diff, a structure-aware staining restoration diffusion model that reformulates virtual staining as an image restoration task. By combining residual and noise-based generation pathways, Star-Diff maintains tissue structure while modeling realistic biomarker variability. To evaluate the diagnostic consistency of the generated IHC patches, we propose the Semantic Fidelity Score (SFS), a clinical-grading-task-driven metric that quantifies class-wise semantic degradation based on biomarker classification accuracy. Unlike pixel-level metrics such as SSIM and PSNR, SFS remains robust under spatial misalignment and classifier uncertainty. Experiments on the BCI dataset demonstrate that Star-Diff achieves state-of-the-art (SOTA) performance in both visual fidelity and diagnostic relevance. With rapid inference and strong clinical alignment, it presents a practical solution for applications such as intraoperative virtual IHC synthesis.}, }
@article {pmid41187327, year = {2025}, author = {Bialostocki, LS and Adhia, DB and Mudiyanselage, DR and Smith, ML and Cakmak, YO and De Ridder, D and Mani, R and Mathew, J}, title = {Neurofeedback Training for Managing Neuropathic Pain-Like Features in Chronic Musculoskeletal Pain: Protocol for an Open-Label Pilot Feasibility Clinical Trial.}, journal = {JMIR research protocols}, volume = {14}, number = {}, pages = {e78806}, doi = {10.2196/78806}, pmid = {41187327}, issn = {1929-0748}, mesh = {Humans ; *Neurofeedback/methods ; Pilot Projects ; *Neuralgia/therapy/physiopathology ; Feasibility Studies ; *Musculoskeletal Pain/therapy/physiopathology ; Electroencephalography/methods ; *Chronic Pain/therapy ; Adult ; Male ; Female ; Middle Aged ; Pain Measurement ; *Pain Management/methods ; }, abstract = {BACKGROUND: Neuropathic pain (NP) is characterized as pain arising from lesions of the somatosensory nervous system. However, NP-like features have been found in several chronic secondary musculoskeletal (MSK) pain conditions in the absence of detectable lesion or damage to the somatosensory pathways. Emerging evidence has demonstrated associations between NP-like symptoms and altered neural activity within brain regions implicated in sensory perception and affective-emotional processing of pain with consistent findings of abnormal activity in the right insula (RIns) cortex and dorsal anterior cingulate cortex (dACC). Electroencephalography neurofeedback (EEG-NF) is a brain-computer interface biofeedback technique that allows individuals to self-regulate the real-time cortical brain activities of the regions of interest.<br><br>OBJECTIVE: The primary objective of this study is to investigate the feasibility and safety of a novel EEG-NF intervention designed to simultaneously downtrain activity in the RIns and dACC in individuals with a chronic secondary MSK pain condition exhibiting NP-like features. In addition, this study will conduct secondary exploratory analyses to investigate EEG-derived neuronal changes and their associations with clinical and experimental pain outcomes following the EEG-NF training.<br><br>METHODS: We will design a single-arm, open-label, pilot-feasibility trial. We will recruit adults aged 35-75 years with a score of &#x2265;19 using the PainDETECT questionnaire and an average pain score of &#x2265;4 on the 11-point Numeric Pain Rating Scale over the last 3 months, with a minimum pain duration of 3 months, to receive active EEG-NF training. Participants will receive auditory feedback as a reward for achieving a predetermined activity threshold of the RIns and dACC. Primary outcomes will evaluate feasibility, acceptability, and safety using both self-reported questionnaires and monitoring data. Collected data will be summarized descriptively, with mean (SD) reported where appropriate. Secondary outcomes will include EEG parameters, self-reported measures, heart rate variability, and quantitative sensory testing. An exploratory within-group pre-post statistical comparison will be conducted for all secondary outcome measures, and correlation analysis will be performed to explore relationships between EEG measures, self-reported outcomes, heart rate variability, and quantitative sensory testing measures.<br><br>RESULTS: This study has received approval from the Health and Disability Ethics Committee and is registered with the Australian New Zealand Clinical Trials Registry. Participant recruitment began in April 2025 and is ongoing. As of October 2025, data collection has been completed, with a total of 5 participants enrolled, all of whom have completed the study to date. We expect to complete the study in March 2026. This study will generate data on feasibility, safety, acceptability, and preliminary data to inform a fully powered effectiveness clinical trial.<br><br>CONCLUSIONS: The results and data generated will inform the design and sample size calculation for a fully powered randomized controlled trial aimed at evaluating the effectiveness of EEG-NF in targeting NP-like features in individuals with chronic MSK pain.<br><br>TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12625000706471; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=389568&amp;isReview=true.<br><br>DERR1-10.2196/78806.}, }
@article {pmid41183389, year = {2025}, author = {Yang, T and Cai, S and Xu, D and Hu, N}, title = {End-to-End EEG Artifact Removal Method via Nested Generative Adversarial Network.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ae1a8c}, pmid = {41183389}, issn = {2057-1976}, abstract = {As physiological artifacts commonly overlap with EEG signals in both time and frequency domains, developing an effective end-to-end EEG artifact removal method is essential for a brain-computer interface (BCI) system. Approach. An end-to-end artifact removal method based on nested generative adversarial network (GAN) is proposed, to recover the EEG signals from artifact-contaminated ones. The nested GAN consists of two components: an inner GAN operating in time-frequency domain and an outer GAN functioning in time domain. A light-weighted complex-valued restormer, designed in time-frequency domain, is employed as the generator to reconstruct the denoised EEG signal. Two metric discriminators in the inner GAN and two multi-resolution discriminators in the outer GAN are used, and gradient balance is used to address the partial learning issue during training. Main results. The performance of the nested GAN has been evaluated in the realistic EEG dataset and semi-synthetic dataset. Compared to the benchmark methods, the proposed one achieved best average performance evaluation metrics, including mean square error (MSE) = 0.098, Pearson correlation coefficient (PCC) = 0.892, relative root MSE (RRMSE) = 0.065, the percentage reduction of time domain artifacts () = 71.6%, and the percentage reduction of frequency domain artifacts () = 76.9%. The performance of artifact removal also showed robustness across a wide range of signal-to-noise ratio (SNR) levels. Significance. The superior performance of the proposed end-to-end artifact removal method is expected to contribute to the advancement of BCI system development. .}, }
@article {pmid41183383, year = {2025}, author = {Sharafkhani, N and Zhang, H}, title = {Deployable electrode arrays for brain interfaces: structural reconfiguration strategies for long-term stability and high-fidelity recording - a review.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae1ab3}, pmid = {41183383}, issn = {1741-2552}, abstract = {Neural electrodes, as essential tools for recording and stimulating neural tissues, significantly impact therapeutic strategies for neurological disorders through deep brain stimulation, responsive neurostimulation, and brain-computer interfaces. Despite considerable advancements, the efficiency and longevity of neural electrodes are compromised by brain micromotion, induced by physiological activities such as cardiac pulsation and respiration. The mechanical mismatch between rigid electrodes and soft neural tissue generates persistent stresses at the electrode-tissue interface, triggering tissue damage, inflammatory responses, encapsulation, and ultimately electrode failure. Deployable neural electrodes, characterized by structural reconfiguration after implantation, have emerged to address these challenges. Deployment mechanisms, including unfolding, expanding, unrolling, or ejecting electrode arms from an initially compact configuration, reduce insertion trauma, maximize spatial coverage, and mitigate brain micromotion effects, thereby enhancing long-term stability and recording fidelity. Approach. This review provides the first comprehensive analysis of deployable intracortical and electrocorticography electrodes, emphasizing their design principles, deployment mechanisms, mechanical performance, advantages, and limitations. This review fills a critical gap in the existing neural electrode literature by transitioning the focus from traditional geometric and material considerations to advanced structural reconfiguration strategies. Significance. An understanding of the advantages and disadvantages of these deployment strategies provides essential insights and future directions for optimizing neural electrode technologies. .}, }
@article {pmid41182766, year = {2025}, author = {O'Regan, RM and Ren, Y and Zhang, Y and Siuliukina, N and Schnabel, CA and Kammler, R and Viale, G and Dell'Orto, P and Munzone, E and Láng, I and Tondini, C and Gomez, HL and Chini, C and Nicoletti, SVL and Puglisi, F and Zaman, K and Goetz, MP and Stearns, V and Martino, S and Salim, M and Loibl, S and Geyer, CE and Bonnefoi, HR and Ciruelos, EM and Loi, S and Colleoni, M and Fleming, GF and Francis, PA and Walley, BA and Pagani, O and Treuner, K and Regan, MM}, title = {Assessment of Adjuvant Endocrine Therapy With Ovarian Function Suppression by Breast Cancer Index.}, journal = {JAMA network open}, volume = {8}, number = {11}, pages = {e2540931}, pmid = {41182766}, issn = {2574-3805}, mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy ; *Tamoxifen/therapeutic use ; Chemotherapy, Adjuvant/methods ; Adult ; Middle Aged ; Androstadienes/therapeutic use ; Prospective Studies ; *Antineoplastic Agents, Hormonal/therapeutic use ; Prognosis ; Retrospective Studies ; *Ovary/drug effects ; Premenopause ; Biomarkers, Tumor ; Homeodomain Proteins ; Receptors, Interleukin-17 ; }, abstract = {IMPORTANCE: The Breast Cancer Index (BCI) previously identified premenopausal patients with tumors in which the ratio of expression of HOXB13 relative to IL17BR (hereafter, BCI [H/I]-low tumors) as likely to derive greatest benefit from ovarian function suppression (OFS)-containing adjuvant therapy in the Suppression of Ovarian Function Trial (SOFT) trial.<br><br>OBJECTIVES: To assess BCI as a predictive biomarker of benefit from exemestane plus OFS vs tamoxifen plus OFS and to validate BCI as a prognostic biomarker for premenopausal patients.<br><br>This prognostic study used a prospective-retrospective translational design within the Tamoxifen and Exemestane (TEXT) and SOFT trials (enrolled November 2003 to April 2011). Blinded BCI testing in all available tumor samples was completed in March 2024. Premenopausal women with hormone receptor-positive breast cancer randomized to tamoxifen plus OFS or exemestane plus OFS who had BCI assessed were included. Analysis occurred from March to August 2024.<br><br>EXPOSURE: 5 years of adjuvant tamoxifen plus OFS or exemestane plus OFS.<br><br>MAIN OUTCOMES AND MEASURES: The primary outcomes were breast cancer-free interval (BCFI) for predictive analyses and distant recurrence-free interval (DRFI) for prognostic analyses after a median follow-up of 13 years in the TEXT cohort. Secondary objectives examined the predictive performance of BCI (H/I) in the combined TEXT and SOFT cohort overall and in prespecified clinical subgroups.<br><br>RESULTS: Of 1782 patients in the TEXT study, 1034 (58.0%) had BCI (H/I)-low tumors; 915 (51.3%) of patients had N0 disease and 1077 (60.4%) were younger than 45 years. Patients with BCI (H/I)-low tumors had a 6.6% absolute benefit in 12-year BCFI (HR, 0.61; 95% CI, 0.44-0.85) for exemestane plus OFS vs tamoxifen plus OFS, while those with BCI (H/I)-high tumors had a 6.3% absolute benefit (HR, 0.78; 95% CI, 0.57-1.07; P for interaction&#x2009;=&#x2009;.29). Results were consistent in the combined TEXT plus SOFT cohort (2896 patients) and adjusting for clinicopathological variables. Clinical subgroup analyses consistently showed benefit of exemestane plus OFS vs tamoxifen plus OFS for BCI (H/I)-low tumors, and more variable relative treatment effects among BCI (H/I)-high tumors, including by age. Post hoc exploratory time-varying estimates suggested the treatment&#x2009;&#xd7;&#x2009;BCI associations may differ in years 0 to 5 vs greater than 5 years. BCI and BCI N+ as continuous indices were prognostic for distant recurrence in N0 (HR, 1.27; 95% CI, 1.11-1.44; P&#x2009;<&#x2009;.001) and N1 (HR, 1.58; 95% CI, 1.21-2.05; P&#x2009;<&#x2009;.001) cancers. The 12-year DRFI was 96.3%, 90.3%, and 84.9% for BCI low-, intermediate-, and high-risk N0 cancers, respectively.<br><br>CONCLUSIONS AND RELEVANCE: In this study of premenopausal women with hormone receptor-positive breast cancer, BCI (H/I) status did not clearly predict greater benefit of adjuvant exemestane plus OFS vs tamoxifen plus OFS for women with BCI (H/I)-low tumors than for those with BCI (H/I)-high tumors; BCI continuous indices were reconfirmed as prognostic for premenopausal women. These findings support prior results of SOFT, which compared tamoxifen-alone vs OFS with either exemestane or tamoxifen, indicating premenopausal patients with BCI (H/I)-low tumors may benefit from more intensive endocrine therapy.}, }
@article {pmid41182326, year = {2025}, author = {Yuan, Z and Chen, F and Huang, X and Huang, K and Song, Z and Ding, Y and Gong, Z and Gu, G}, title = {Soft Tubular-Surface Rolling Robots.}, journal = {Soft robotics}, volume = {}, number = {}, pages = {}, doi = {10.1177/21695172251387190}, pmid = {41182326}, issn = {2169-5180}, abstract = {Soft creatures like Drosophila larvae can quickly ascend tubular surfaces via rolling, a capability not yet replicated by soft robots. Here, we present a single-piece soft robot capable of rolling along tubular structures by sequentially actuating its built-in axial muscles. We reveal that the sequential actuation generates distributed spinning torques along the robot's curved axis, enabling continuous non-coaxial rolling-distinct from current gravity-dependent rolling solutions. This non-coaxial rolling mechanism allows the robot to swiftly navigate tubular surfaces while conforming to their shapes and maintaining a stable grip. The robot's deformation and gripping force are actively adjusted to enhance its adaptability to various surfaces. We demonstrate that our robot can ascend pipes with varying geometries (e.g., varying-diameter, spiral-shaped, or non-cylindrical), traverse diverse terrains, pass through confined tunnels, and transition smoothly between planar rolling and pipe climbing. The robot's great adaptability and rapid movement underscore its potential for navigating scenarios with intricate surface geometries.}, }
@article {pmid41180699, year = {2025}, author = {Ali, E and Kamran, S and Cheema, AAA}, title = {Brain-computer interfaces in post-stroke rehabilitation: a neurotechnological leap toward functional recovery.}, journal = {Annals of medicine and surgery (2012)}, volume = {87}, number = {11}, pages = {7784-7785}, pmid = {41180699}, issn = {2049-0801}, }
@article {pmid41180117, year = {2025}, author = {Hall, R and Jackson, M and Maleki, M and Crogman, HT}, title = {Modeling cognition through adaptive neural synchronization: a multimodal framework using EEG, fMRI, and reinforcement learning.}, journal = {Frontiers in computational neuroscience}, volume = {19}, number = {}, pages = {1616472}, pmid = {41180117}, issn = {1662-5188}, abstract = {INTRODUCTION: Understanding the cognitive process of thinking as a neural phenomenon remains a central challenge in neuroscience and computational modeling. This study addresses this challenge by presenting a biologically grounded framework that simulates adaptive decision making across cognitive states.<br><br>METHODS: The model integrates neuronal synchronization, metabolic energy consumption, and reinforcement learning. Neural synchronization is simulated using Kuramoto oscillators, while energy dynamics are constrained by multimodal activity profiles. Reinforcement learning agents-Q-learning and Deep Q-Network (DQN)-modulate external inputs to maintain optimal synchrony with minimal energy cost. The model is validated using real EEG and fMRI data, comparing simulated and empirical outputs across spectral power, phase synchrony, and BOLD activity.<br><br>RESULTS: The DQN agent achieved rapid convergence, stabilizing cumulative rewards within 200 episodes and reducing mean synchronization error by over 40%, outperforming Q-learning in speed and generalization. The model successfully reproduced canonical brain states-focused attention, multitasking, and rest. Simulated EEG showed dominant alpha-band power (3.2&#x202f;&#xd7;&#x202f;10[-4]&#x202f;a.u.), while real EEG exhibited beta-dominance (3.2&#x202f;&#xd7;&#x202f;10[-4]&#x202f;a.u.), indicating accurate modeling of resting states and tunability for active tasks. Phase Locking Value (PLV) ranged from 0.9806 to 0.9926, with the focused condition yielding the lowest circular variance (0.0456) and a near significant phase shift compared to rest (t&#x202f;=&#x202f;-2.15, p&#x202f;=&#x202f;0.075). Cross-modal validation revealed moderate correlation between simulated and real BOLD signals (r&#x202f;=&#x202f;0.30, resting condition), with delayed inputs improving temporal alignment. General Linear Model (GLM) analysis of simulated BOLD data showed high region-specific prediction accuracy (R [2]&#x202f;=&#x202f;0.973-0.993, p&#x202f;<&#x202f;0.001), particularly in prefrontal, parietal, and anterior cingulate cortices. Voxel-wise correlation and ICA decomposition confirmed structured network dynamics.<br><br>DISCUSSION: These findings demonstrate that the framework captures both electrophysiological and spatial aspects of brain activity, respects neuroenergetic constraints, and adaptively regulates brain-like states through reinforcement learning. The model offers a scalable platform for simulating cognition and developing biologically inspired neuroadaptive systems.<br><br>CONCLUSION: This work provides a novel and testable approach to modeling thinking as a biologically constrained control problem and lays the groundwork for future applications in cognitive modeling and brain-computer interfaces.}, }
@article {pmid41179991, year = {2025}, author = {Yuan, L and Wei, J and Liu, Y}, title = {Spiking neural networks for EEG signal analysis using wavelet transform.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1652274}, pmid = {41179991}, issn = {1662-4548}, abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) leverage EEG signal processing to enable human-machine communication and have broad application potential. However, existing deep learning-based BCI methods face two critical limitations that hinder their practical deployment: reliance on manual EEG feature extraction, which constrains their ability to adaptively capture complex neural patterns, and high energy consumption characteristics that make them unsuitable for resource-constrained portable BCI devices requiring edge deployment.<br><br>METHODS: To address these limitations, this work combines wavelet transform for automatic feature extraction with spiking neural networks for energy-efficient computation. Specifically, we present a novel spiking transformer that integrates a spiking self-attention mechanism with discrete wavelet transform, termed SpikeWavformer. SpikeWavformer enables automatic EEG signal time-frequency decomposition, eliminates manual feature extraction, and provides energy-efficient classification decision-making, thereby enhancing the model's cross-scene generalization while meeting the constraints of portable BCI applications.<br><br>RESULTS: Experimental results demonstrate the effectiveness and efficiency of SpikeWavformer in emotion recognition and auditory attention decoding tasks.<br><br>DISCUSSION: These findings indicate that SpikeWavformer can address the key limitations of existing BCI methods and holds promise for practical deployment in portable, resource-constrained scenarios.}, }
@article {pmid41179694, year = {2025}, author = {Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Vizcaíno-Martín, FJ and Ron-Angevin, R}, title = {Impact of stimulus presentation speed in a visual ERP-based BCI under RSVP.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {171}, pmid = {41179694}, issn = {1871-4080}, abstract = {Rapid serial visual presentation (RSVP) is one of the most effective gaze-independent paradigms for event-related potential (ERP)-based brain-computer interfaces (BCIs), particularly for individuals with limited muscle and eye movement control. The speed of visual stimulus presentation is a critical factor influencing system performance and warrants thorough investigation. This study evaluates the impact of different stimulus presentation speeds on the performance of an ERP-BCI used for pictogram selection under RSVP. Thirteen participants tested the ERP-BCI across three experimental conditions, each with a different stimulus onset asynchrony (SOA): 80 ms (C080), 160 ms (C160), and 320 ms (C320). In addition to performance metrics such as accuracy, information transfer rate (ITR), and pictograms per minute (PPM), a subjective evaluation of the user experience was conducted for each condition. The results indicate that C160 outperformed both C080 and C320 across all performance metrics, achieving an ITR of 26.49 bit/min (81.28% accuracy in 4.8 s). Subjective evaluations also revealed a preference for C160 and C320 over C080. Therefore, among the SOAs evaluated, 160 ms appears to be the most suitable for enhancing system usability. These findings underscore the crucial role of stimulus presentation speed in the usability of ERP-BCIs for pictogram selection under RSVP, emphasizing its importance in future gaze-independent ERP-BCI designs for communication purposes.}, }
@article {pmid41178032, year = {2025}, author = {Li, Q and Choi, EPH and Gou, M and Tian, Y and Baptiste, D}, title = {Brain-Computer Interface: Bring Care Into a Future Phase? Challenges and Opportunities for Nursing in the Era of Emerging Technologies.}, journal = {Nursing open}, volume = {12}, number = {11}, pages = {e70345}, pmid = {41178032}, issn = {2054-1058}, }
@article {pmid41177816, year = {2025}, author = {Fu, R and Liu, Y and Wang, Z and Liang, Z}, title = {Virtual Reality (VR) Paradigm-Agnostic Motor Imagery Decoding Using Lightweight Network With Adaptive Attention Mechanism.}, journal = {Journal of medical systems}, volume = {49}, number = {1}, pages = {152}, pmid = {41177816}, issn = {1573-689X}, support = {62073282//National Natural Science Foundation of China/ ; F2022203092//Natural Science Foundation of Hebei Province/ ; 202302B015//the S&amp;T Program of Qinhuangdao City/ ; KFKT2025B88//Project of State Key Laboratory for Novel Software Technology/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; Algorithms ; *Imagination/physiology ; Electroencephalography ; Movement/physiology ; Attention ; Male ; Adult ; }, abstract = {Motor imagery (MI) is widely used in brain-computer interfaces (BCIs) due to its simplicity and reproducibility, enabling individuals with motor impairments to perform non-muscular limb training for the rehabilitation of motor-related neurons. While MI-based BCIs have shown promise for neurorehabilitation, current 2D paradigms fail to engage critical sensorimotor networks. To address this limitation, we designed an immersive MI paradigm in a virtual reality (VR) environment, where participants imagined limb movements in response to continuous three-dimensional (3D) palm motion stimuli. Furthermore, we proposed a novel decoding algorithm that integrates depthwise separable convolution with multi-head self-attention mechanisms. The proposed method was evaluated against existing approaches, demonstrating superior classification accuracy while reducing the temporal and spatial complexity associated with attention mechanisms. To assess the generalizability and robustness of the algorithm across different scenarios, we conducted experiments on two publicly available datasets: BCI Competition IV-2a and the PhysioNet MI dataset. Results showed that our method achieved an average increase of nearly 8% in kappa score over EEGNet in decoding four-class MI tasks in 2D paradigms. Consistent performance across both VR and 2D paradigms confirmed the algorithm's effectiveness and applicability in multi-scenario MI decoding. This study introduces a novel immersive MI paradigm and decoding framework, offering a promising approach for enhancing user engagement in neurorehabilitation and advancing EEG-based intention recognition in VR environments.}, }
@article {pmid41177674, year = {2025}, author = {Almufareh, MF and Kausar, S and Humayun, M and Tehsin, S and Farooq, A}, title = {Inner Speech Decoding: A Comprehensive Review.}, journal = {Wiley interdisciplinary reviews. Cognitive science}, volume = {16}, number = {6}, pages = {e70016}, doi = {10.1002/wcs.70016}, pmid = {41177674}, issn = {1939-5086}, support = {KSRG-2024-063//King Salman Center for Disability Research/ ; }, mesh = {Humans ; *Speech/physiology ; Electroencephalography ; Machine Learning ; Magnetic Resonance Imaging ; *Brain/physiology ; *Speech Perception/physiology ; Brain-Computer Interfaces ; }, abstract = {Inner speech decoding is the process of identifying silently generated speech from neural signals. In recent years, this candidate technology has gained momentum as a possible way to support communication in severely impaired populations. Specifically, this approach promises hope for people with a variety of physical or neurological disabilities who need alternative means of verbal expression. This review covers recording modalities that range from the noninvasive EEG to the high-density electrocorticography and discusses how linear discriminant analysis, deep convolutional networks, and hybrid fusion of EEG with fMRI are integrated into machine learning strategies to infer covert speech. This review synthesizes evidence to suggest that small vocabularies, under controlled conditions, can yield relatively reasonable accuracy while further refining the decoding outcome via context-based approaches. The impact of sensor quality, training data size, and domain adaptation is illustrated by focusing on public datasets of imagined or articulated speech. Throughout the article, the methodological standards emerging across laboratories will be discussed, emphasizing that effective inner speech recognition involves high-quality preprocessing, subject calibration, and informed modeling choices balanced against computational power for interpretability. In addition to technical advancements, this review also examines the ethical, societal, and regulatory challenges surrounding inner speech decoding, including brain data privacy, neural rights, informed consent, and user trust. Addressing these interdisciplinary issues is critical for the responsible development and real-world adoption of such technologies. This article is categorized under: Neuroscience > Computation Computer Science and Robotics > Machine Learning.}, }
@article {pmid41177306, year = {2025}, author = {Zhong, X and Li, G and Xu, C and Luo, R and Meng, J and Schalk, G}, title = {Detection of eye movements and eye blinks using a portable two-channel EEG platform.}, journal = {Journal of neuroscience methods}, volume = {425}, number = {}, pages = {110616}, doi = {10.1016/j.jneumeth.2025.110616}, pmid = {41177306}, issn = {1872-678X}, abstract = {BACKGROUND: The ability to detect eye movements can facilitate human-computer interaction (HCI) and may complement brain-computer interfaces (BCIs). Recent studies have shown that multi-channel EEG systems can provide information about eye movements, but these systems can be bulky and/or require complex setup.<br><br>NEW METHOD: We introduce a portable, two-channel EEG platform that can be placed in seconds and detect eye blinks/movements and gaze trajectories. Forty adults performed cued blinks and horizontal/vertical gaze shifts; 21 EEG features were extracted, and machine learning models were evaluated with leave-one-subject-out validation.<br><br>RESULTS: Our system effectively identified eye blinks (avg. detection accuracy of 95%, 50% chance) and horizontal eye movements (avg. accuracy of 94%, 33% chance), and showed decreased performance detecting vertical eye movements (avg. accuracy of 60%, 33% chance). It was also able to predict horizontal and vertical eye movement trajectories (r = 0.79 and r = 0.14, respectively).<br><br>Classification accuracies for eye blinks and horizontal eye movements using our system with only two electrodes are comparable to those previously reported only for complex multi-channel EEG/EOG setups.<br><br>CONCLUSION: This study provides evidence, for the first time, that a wearable EEG device can give substantial information about eye blinks and eye movements. With further refinements, this approach may enable portable solutions for real-world HCI and BCI applications.}, }
@article {pmid41176895, year = {2025}, author = {Cavallé Garrido, L and de Paula Vernetta, C and Guzmán Calvete, A and Álvarez Arocas, J and Gonçalves, C and Armengot Carceller, M}, title = {Bonebridge active transcutaneous bone conduction hearing implant: Results in the pediatric population.}, journal = {International journal of pediatric otorhinolaryngology}, volume = {199}, number = {}, pages = {112610}, doi = {10.1016/j.ijporl.2025.112610}, pmid = {41176895}, issn = {1872-8464}, abstract = {PURPOSE: This study provides prospective and retrospective data on safety and performance results with the Bonebridge BCI 602 (MED-EL) active transcutaneous bone conduction implant in children.<br><br>METHODS: Audiological data were collected at 3 intervals (preoperative, initial activation and 3 months postoperative). Quality of life was assessed with the Speech, Spatial, and Qualities of Hearing (SSQ12/P), KID KINDL and Audio Processor Satisfaction Questionnaire (APSQ) as well as a postoperative questionnaire specifically designed for this study.<br><br>RESULTS: 22 pediatric patients (20 conductive/mixed hearing loss (CHL/MHL) and 2 single-sided deafness (SSD)) aged 4-17 received a BCI 602. Three-month post-op pure-tone average (PTA4) functional gain (FG) was 31.9&#xa0;dB HL for the CHL/MHL group and 11.3&#xa0;dB HL in the SSD patients. CHL/MHL patients had a mean word recognition score (WRS) improvement of 80.6&#xa0;&#xb1;&#xa0;23.9&#xa0;% at initial activation and 83&#xa0;&#xb1;&#xa0;20.3&#xa0;% at 3 months post-op. Speech recognition in noise at +5&#xa0;dB SNR in the CHL/MHL group improved from 24.6&#xa0;&#xb1;&#xa0;28.3&#xa0;% unaided to 74.9&#xa0;&#xb1;&#xa0;26&#xa0;% aided at 3 months post-op. The mean post-op total scores were 5.5&#xa0;&#xb1;&#xa0;1.8 on the SSQ12/P and 8.87&#xa0;&#xb1;&#xa0;0.93 on the APSQ questionnaires. No major complications were noted on the postoperative questionnaire; minor complications were resolved by the end of the study. Stable bone and air conduction thresholds confirmed device safety.<br><br>CONCLUSION: The Bonebridge BCI 602 is safe and effective for use in the pediatric population.}, }
@article {pmid41174212, year = {2025}, author = {Zhou, H and Iramina, K}, title = {Discovery of EEG effective connectivity during visual motor imagery with multi-scale symbolic transfer entropy.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {38200}, pmid = {41174212}, issn = {2045-2322}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; *Imagination/physiology ; Brain-Computer Interfaces ; Entropy ; Young Adult ; Parietal Lobe/physiology ; *Brain/physiology ; Brain Mapping ; Occipital Lobe/physiology ; }, abstract = {Visual motor imagery (VMI) is an important component of motor imagery, with potential applications in brain-computer interfaces and motor rehabilitation due to its lower training cost compared to kinesthetic motor imagery (KMI). However, the neural mechanisms underlying VMI, particularly the effects of imagery hand and imagery perspective (first-person perspective, 1pp, vs. third-person perspective, 3pp) remain unclear. This study examines the effective connectivity of VMI EEG using multi-scale symbolic transfer entropy. Time-frequency analysis revealed prominent event-related synchronization (ERS) in the alpha and high-beta bands, while connectivity analysis emphasized strong information flow within the parieto-occipital network. Notably, hand effect dominant information flows were found between the motor and posterior parietal-occipital regions, while perspective suggested a more remarkable effect. 1pp imagery significantly enhanced top-down modulation of the occipital cortex, whereas 3pp imagery engaged the right posterior parietal region, suggesting stronger spatial localization processing. These findings provide novel insights into the distinct neural mechanisms of VMI and its potential applications in cognitive neuroscience and brain-machine engineering.}, }
@article {pmid41173359, year = {2025}, author = {Chen, Z and Lu, Y and Xu, X}, title = {EEG-SGENet: A lightweight convolutional network integrating SGE for motor imagery brain-computer interfaces.}, journal = {Neuroscience}, volume = {589}, number = {}, pages = {300-307}, doi = {10.1016/j.neuroscience.2025.09.040}, pmid = {41173359}, issn = {1873-7544}, abstract = {In recent years, there has been a significant increase in research activity on electroencephalography (EEG)-based motor imagery brain-computer interfaces (MI-BCI) in the field of deep learning. However, despite achieving high accuracy, the size of models is increasing, requiring significant memory and computational resources. Therefore, finding a balance between accuracy and computational cost has always been a challenge in MI classification research. Convolutional Neural Networks (CNNs) generate feature representations of objects by collecting semantic sub-features. The activation of subfeatures is susceptible to noisy backgrounds. The Spatial Group-wise Enhance (SGE) module adjusts the importance of each sub-feature by generating an attention factor for the spatial location of each semantic group, thus enhancing useful features and suppressing noise. The design of the SGE module is lightweight, with few parameters and computations. Therefore, we introduce the SGE module to improve accuracy and minimize model parameters. In this paper, we propose EEG-SGENet, a novel end-to-end convolutional neural network model that considers both the lightweight model and accuracy. Experimental results on the BCI IV 2a dataset show that EEG-SGENet achieves an accuracy of 80.98% in the four categories of MI. The average classification accuracy for the two-category task of BCI IV 2b is 76.17%. Comparisons with other lightweight models in terms of classification accuracy and other aspects have shown that this model achieves a good balance between decoding performance and computational cost. Overall, experimental results demonstrate that the proposed model is expected to become a new method for decoding EEG signals.}, }
@article {pmid41171945, year = {2025}, author = {Khanam, T and Siuly, S and Ahmad, K and Wang, H}, title = {A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems.}, journal = {PloS one}, volume = {20}, number = {10}, pages = {e0335511}, pmid = {41171945}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Neural Networks, Computer ; Male ; Adult ; *Imagination/physiology ; Female ; Movement/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redundant information, and performance degradation. Irrelevant channels introduce noise, which reduces accuracy and slows system performance. To address these issues, this study aims to develop a novel channel selection method to enhance EEG-based MI task performance in BCI applications. Our proposed hybrid approach combines statistical t-tests with a Bonferroni correction-based channel reduction technique, followed by the application of a Deep Learning Regularized Common Spatial Pattern with Neural Network (DLRCSPNN) framework. This framework employs DLRCSP for feature extraction and neural network (NN) algorithm for classification. Our developed method excluded channels with correlation coefficients below 0.5, retaining only significant, non-redundant channels and tested on three real-time EEG-based BCI datasets. This study produces the highest accuracy score in the case of every subjects above 90% for all the applied datasets. In the first dataset, our method achieved the highest accuracy, improving by 3.27% to 42.53% in terms of individual subject compared to seven existing machine learning algorithms. In the second and third dataset, it outperformed existing approaches, with accuracy gains of 5% to 45% and 1% to 17.47% respectively. Comparisons with a CSP and NN framework confirmed DLRCSPNN's algorithms superior performance. These results demonstrate the effectiveness of the approach, offering a new perspective on the identification of MI task performance in EEG based BCI technology. This proposed technique will enable rapid identification of motor-disabled individuals' intentions, supporting patient rehabilitation and improving daily living.}, }
@article {pmid41171651, year = {2025}, author = {Liu, H and Wang, Z and Li, R and Zhao, X and Xu, T and Zhou, T and Hu, H}, title = {A Novel Binocular-Encoded SSVEP Framework for Efficient VR-Based Brain-Computer Interface.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3626332}, pmid = {41171651}, issn = {2168-2208}, abstract = {This paper presents a novel binocular-encoded SSVEP (beSSVEP) method, leveraging binocular vision in virtual reality (VR) to enhance brain-computer interface (BCI) applications. We introduce the Binocular Periodically Repeated Component Analysis (bPRCA) algorithm, designed to address the unique characteristics of binocular-encoded targets, which include combinations of monocular single-frequency SSVEP units or void units, with frequency units being reused multiple times in the encoded interface. To further optimize performance, we propose the Fusion Component Analysis (FusionCA) framework, which integrates bPRCA with Task-related Component Analysis (TRCA), effectively utilizing both steady-state periodic components and cross-trial aperiodic components. Experimental results demonstrate that ensemble-FusionCA achieves the highest information transfer rate (ITR) with an average accuracy of $71.39\%$ and an ITR of 138.50 bits/min at 0.4 seconds, among the comparison with ensemble-bPRCA and ensemble-TRCA. Compared to traditional SSVEP approaches, beSSVEP significantly enhances frequency utilization, making VR-BCI systems more efficient and practical. This study highlights the application of physiological mechanisms of binocular vision to improve BCI systems, offering a new perspective for developing fast and scalable brain-computer interactions in VR environments.}, }
@article {pmid41170547, year = {2025}, author = {Ren, J and Mo, WY and Wang, L and Ni, GJ and Yang, JJ}, title = {[Research progress on the role of dopamine system in regulating hippocampal related brain functions].}, journal = {Sheng li xue bao : [Acta physiologica Sinica]}, volume = {77}, number = {5}, pages = {893-904}, doi = {10.13294/j.aps.2025.0055}, pmid = {41170547}, issn = {0371-0874}, mesh = {*Hippocampus/physiology ; *Dopamine/physiology ; Humans ; Animals ; Receptors, Dopamine D2/physiology ; Memory/physiology ; Signal Transduction/physiology ; Neurodegenerative Diseases/physiopathology ; }, abstract = {Dopamine, as a catecholamine neurotransmitter widely distributed in the central nervous system, is involved in physiological functions such as motivation, arousal, reinforcement, and movement through various dopamine signaling pathways. The hippocampus receives dopaminergic neuron projections from regions such as the ventral tegmental area, locus coeruleus, and substantia nigra. Through D1-like and D2-like receptors, dopamine exerts significant regulatory effects such as spatial navigation, episodic memory, fear, anxiety, and reward. This review mainly summarizes the research progress on the functions of dopamine in the hippocampus from aspects including the sources of dopamine, receptor distribution and function, and the association of hippocampal dopamine system dysregulation with neurodegenerative diseases. The aim is to provide insights into the involvement of the dopamine system in hippocampal functions and the diagnosis and treatment of related diseases.}, }
@article {pmid41170533, year = {2025}, author = {Gherman, DE and Zander, TO}, title = {Towards neuroadaptive chatbots: a feasibility study.}, journal = {Frontiers in neuroergonomics}, volume = {6}, number = {}, pages = {1589734}, pmid = {41170533}, issn = {2673-6195}, abstract = {INTRODUCTION: Large-language models (LLMs) are transforming most industries today and are set to become a cornerstone of the human digital experience. While integrating explicit human feedback into the training and development of LLM-based chatbots has been integral to the progress we see nowadays, more work is needed to understand how to best align them with human values. Implicit human feedback enabled by passive brain-computer interfaces (pBCIs) could potentially help unlock the hidden nuance of users' cognitive and affective states during interaction with chatbots. This study proposes an investigation on the feasibility of using pBCIs to decode mental states in reaction to text stimuli, to lay the groundwork for neuroadaptive chatbots.<br><br>METHODS: Two paradigms were created to elicit moral judgment and error-processing with text stimuli. Electroencephalography (EEG) data was recorded with 64 gel electrodes while participants completed reading tasks. Mental state classifiers were obtained in an offline manner with a windowed-means approach and linear discriminant analysis (LDA) for full-component and brain-component data. The corresponding event-related potentials (ERPs) were visually inspected.<br><br>RESULTS: Moral salience was successfully decoded at a single-trial level, with an average calibration accuracy of 78% on the basis of a data window of 600 ms. Subsequent classifiers were not able to distinguish moral judgment congruence (i.e., moral agreement) and incongruence (i.e., moral disagreement). Error processing in reaction to factual inaccuracy was decoded with an average calibration accuracy of 66%. The identified ERPs for the investigated mental states partly aligned with other findings.<br><br>DISCUSSION: With this study, we demonstrate the feasibility of using pBCIs to distinguish mental states from readers' brain data at a single-trial level. More work is needed to transition from offline to online investigations and to understand if reliable pBCI classifiers can also be obtained in less controlled language tasks and more realistic chatbot interactions. Our work marks preliminary steps for understanding and making use of neural-based implicit human feedback for LLM alignment.}, }
@article {pmid41169537, year = {2025}, author = {Li, X and Ji, X and Wang, Y and Chen, X}, title = {The influence of different visual eccentricity on SSVEPs elicited by ultra-low frequency visual stimulation in the lower peripheral visual field.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {170}, pmid = {41169537}, issn = {1871-4080}, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been widely explored due to their high information transfer rate (ITR) and minimal training requirements. Traditional SSVEP-based BCIs typically use low- and medium-frequency visual stimuli from the central visual field to induce SSVEPs, but these can easily lead to visual fatigue. In order to improve system's comfort, some studies have attempted to use visual stimuli from the peripheral visual field to elicit SSVEPs. However, few studies have investigated the effects of different visual eccentricities on induced SSVEPs. In this study, we used ultra-low frequency (i.e., 2.00-3.32 Hz) visual stimulation in the lower peripheral visual field to induce SSVEPs. Furthermore, we further explored the effects of different visual eccentricities (i.e., 2.1°, 3.1°, and 4.1°) on induced SSVEPs. Experimental results obtained from twelve participants revealed that all three eccentricity conditions were capable of eliciting SSVEP responses. Moreover, SSVEP amplitude gradually decreased as eccentricity increased. These results provide new parametric references for optimizing the spatial layout of visual stimuli in peripheral SSVEP-based BCI systems.}, }
@article {pmid41167038, year = {2025}, author = {Schippers, A and Berezutskaya, J and Vansteensel, MJ and Freudenburg, ZV and Crone, NE and Ramsey, NF}, title = {The effect of perceived auditory feedback on speech Brain-Computer Interface decoding performance.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {180}, number = {}, pages = {2111403}, doi = {10.1016/j.clinph.2025.2111403}, pmid = {41167038}, issn = {1872-8952}, abstract = {OBJECTIVE: Brain-Computer Interfaces (BCI) provide alternative means of communication for individuals with severe motor impairment. Implantable speech BCIs have shown great potential, particularly in individuals who could still produce some speech-related movements and/or sounds. As perception of auditory feedback is important for correct speech sound production in able-bodied people, it is conceivable that a complete absence of such feedback in individuals who lost all ability to produce audible speech affects BCI performance. The current study therefore set out to investigate to what extent perception of auditory feedback of self-produced speech contributes to speech decoding performance.<br><br>METHODS: In three able-bodied participants, patterns of 65-95&#xa0;Hz power over sensorimotor cortex were compared between normal speech and speech in which auditory feedback was masked by noise. In addition, decoding accuracy was compared between feedback situations.<br><br>RESULTS &amp; CONCLUSIONS: We found subtle differences in brain activity patterns associated with speech production between situations in which participants could versus could not perceive their produced speech. Importantly, absence of such auditory feedback led to lower speech decoding performance in all participants.<br><br>SIGNIFICANCE: These results underline the need to validate speech BCI efficacy with fully paralyzed individuals, as perceived feedback can influence the attainable speech decoding accuracy.}, }
@article {pmid41165717, year = {2025}, author = {Lydiatt, WB}, title = {Mind, Machine, and Medicine-Challenges and Opportunities.}, journal = {JAMA otolaryngology-- head &amp; neck surgery}, volume = {}, number = {}, pages = {}, doi = {10.1001/jamaoto.2025.4108}, pmid = {41165717}, issn = {2168-619X}, }
@article {pmid41163348, year = {2025}, author = {Song, K and Liu, Y and Xu, P}, title = {Acute Effects of Portable Dry-EEG Neurofeedback on Classical Chinese Learning: A Three-Arm Repeated-Measures Study.}, journal = {Brain and behavior}, volume = {15}, number = {11}, pages = {e70977}, pmid = {41163348}, issn = {2162-3279}, support = {2025ZSD017//Shanghai Municipal Education Science Research Project "Special Program for Philosophy and Social Sciences Research in Shanghai Higher Education Institutions"/ ; }, mesh = {Humans ; *Neurofeedback/methods ; Male ; Female ; Young Adult ; *Electroencephalography/methods ; Adult ; *Learning/physiology ; Attention/physiology ; Cognition/physiology ; Comprehension/physiology ; Language ; Adolescent ; East Asian People ; }, abstract = {OBJECTIVE: Dry-electrode electroencephalography (dry-EEG) systems offer promising opportunities for real-time neurofeedback in naturalistic educational settings, yet their effectiveness in supporting complex language learning remains underexplored. This study investigated the acute effects of portable dry-EEG neurofeedback on students' cognitive performance and attentional states during classical Chinese learning, using a repeated-measures design to compare neurofeedback, sham feedback, and device control conditions.<br><br>METHODS: A total of 20 undergraduate participants completed three sessions involving a customized semantic disambiguation task after passive reading. EEG signals were acquired using a dry-sensor OpenBCI system from four frontal sites (Fp1, Fp2, F3, F4). Real-time attention indices were computed based on the beta/(alpha+theta) ratio and fed back visually in the neurofeedback condition. Cognitive outcomes included comprehension test scores and semantic conflict resolution performance (RT, accuracy, cognitive load).<br><br>RESULTS: Compared to sham and control conditions, neurofeedback significantly improved comprehension accuracy (p < 0.001), reduced reaction times in the interference task (p&#xa0;<&#xa0;0.05), and lowered subjective cognitive load (p&#xa0;=&#xa0;0.002). EEG indices of attention were significantly elevated during neurofeedback (p&#xa0;<&#xa0;0.001) and positively correlated with behavioral gains (r&#xa0;=&#xa0;0.63, p&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: Portable dry-electrode EEG systems can reliably support real-time neurofeedback to enhance attention and cognitive control in complex language learning contexts. This study provides empirical validation for deploying dry-EEG sensors in adaptive educational technologies and contributes to the broader integration of wearable brain-computer interfaces in cognitive augmentation applications.}, }
@article {pmid41161815, year = {2025}, author = {Yang, Y and Liu, C and Liu, S and Ding, P and Bai, R and Chen, G and Li, S and Song, X and Cheng, Y and Xu, J}, title = {Role of combination immunotherapy in restoring brain synergistic functional connectivity in patients with systemic lupus erythematosus without overt neuropsychiatric manifestations.}, journal = {Lupus science &amp; medicine}, volume = {12}, number = {2}, pages = {}, pmid = {41161815}, issn = {2053-8790}, mesh = {Humans ; Female ; *Lupus Erythematosus, Systemic/drug therapy/physiopathology ; Adult ; Male ; Cyclophosphamide/therapeutic use/administration &amp; dosage ; Magnetic Resonance Imaging ; *Immunosuppressive Agents/therapeutic use/administration &amp; dosage ; Hydroxychloroquine/therapeutic use/administration &amp; dosage ; *Brain/physiopathology/diagnostic imaging/drug effects ; Glucocorticoids/therapeutic use/administration &amp; dosage ; Middle Aged ; Case-Control Studies ; Drug Therapy, Combination ; Young Adult ; *Immunotherapy/methods ; }, abstract = {OBJECTIVE: To determine whether subclinical brain dysfunction in SLE can be detected by disrupted interhemispheric connectivity and assess its modulation by immunosuppressive regimens.<br><br>METHODS: 234 subjects (140 patients with SLE and 94 healthy controls (HCs)) were included. Through stratified analysis, patients with SLE were divided into treatment-na&#xef;ve group (n=22), glucocorticoid monotherapy group (GC group, n=30) and GC combined with cyclophosphamide (CTX) and/or hydroxychloroquine (HCQ) treatment group (n=50) to assess the differences in voxel-mirrored homotopic connectivity (VMHC) between groups.<br><br>RESULTS: SLE group showed lower VMHC than the HC group in bilateral superior temporal gyrus, medial superior frontal gyrus, calcarine fissure and surrounding cortex and middle occipital cortices (Gaussian random field corrected: voxel p<0.005, cluster p<0.01). The VMHC in the bilateral superior temporal gyrus (rs=-0.250, p=0.024) and medial superior frontal gyrus (rs=-0.246, p=0.026) was negatively correlated with the depression score, while the VMHC in the medial superior frontal gyrus was negatively correlated with the anxiety score (rs=-0.239, p=0.031). Three SLE subgroups and HCs had different VMHC in the postcentral/precentral gyrus (F=8.942) and anterior cingulate/paracingulate gyrus (F=9.868). Post hoc analysis found that compared with the HC group, VMHC in the treatment-na&#xef;ve group was decreased in the bilateral posterior central gyrus (t=-2.953), while in the GC monotherapy group, it decreased in the posterior central gyrus (t=-2.999) and anterior cingulate/paracingulate gyrus (t=-2.999). Compared with GC combined with CTX and/or HCQ group, VMHC in GC monotherapy group was decreased in the postcentral gyrus (t=-2.999).<br><br>CONCLUSION: Even without overt neuropsychiatric symptoms, patients with SLE exhibit impaired interhemispheric functional synergy that is partially reversed by combination immunosuppression, suggesting an early targetable brain pathway.}, }
@article {pmid41160913, year = {2025}, author = {Russo, JS and Colebatch, JG and Lin, CS and John, SE and Grayden, DB and Todd, NPM}, title = {Feasibility of decoding cerebellar movement-related potentials for brain-computer interface applications.}, journal = {Journal of neural engineering}, volume = {22}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ae18fa}, pmid = {41160913}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Cerebellum/physiology ; Adult ; Movement/physiology ; *Electroencephalography/methods ; Female ; Feasibility Studies ; Young Adult ; *Evoked Potentials, Motor/physiology ; Electromyography/methods ; }, abstract = {Objective.In brain-computer interface (BCI) applications, signals are conventionally acquired from the cerebrum, and only a subset of the complex interactions that occur in several areas of the brain are collected. One area that has not been investigated for BCI application is the cerebellum, despite its involvement in movement and executive function. The present study aimed to determine the features of movement-related cerebellar electrocerebellography (ECeG) that are most useful for decoding, and how performance compares with conventional electroencephalography (EEG) recordings from the cerebrum.Approach.ECeG and EEG data were collected from six healthy adults to identify useful movement-related features from both cerebrum and cerebellum. Electromyography was used to capture the movements from the muscles. Decoding was conducted in binary movement vs. rest and movement vs. movement systems using support vector machines. Decoding performance was compared between cerebral, cerebellar, a combination of both, and temporal groups. Re-referencing techniques were applied to compensate for possible common reference artefacts or volume conduction effects.Main results. Movement-related features were decoded from over the cerebellum and the cerebrum. Classification accuracies were similar in both the cerebrum and cerebellum, when classifying movement vs. rest (cerebrum: 0.78 ± 0.02, cerebellum: 0.70 ± 0.01) and movement vs. movement states (cerebrum: 0.76 ± 0.02, cerebellum: 0.71 ± 0.02). The delta band (1-3 Hz) was the most useful feature for decoding.Significance.This study demonstrated, for the first time, that ECeG is a feasible source of movement related signals for implementing a BCI. The present study also demonstrated that the ECeG closely resembled the EEG signals and represents an alternate approach for BCI where the signal from the cerebrum is unreliable either due to disease or injury.}, }
@article {pmid41160812, year = {2025}, author = {Li, J and Lu, Y and Li, Z and Jin, L and Zhou, L and Ding, K and Liu, J and Hu, B and Liu, P and An, D and Liang, F and Hu, Y and Shao, Y and Ding, Y and Ma, L and Li, R and Mei, Y and Zhang, R and Song, E}, title = {An Active, Multimodal Neural Interface for Real-Time Monitoring of Cortical Electrical, Thermal, and Optical Dynamics.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e12114}, doi = {10.1002/advs.202512114}, pmid = {41160812}, issn = {2198-3844}, support = {2022ZD0209900//STI 2030-Major Project/ ; 62204057//National Natural Science Foundation of China/ ; 62304044//National Natural Science Foundation of China/ ; 82304124//National Natural Science Foundation of China/ ; U2230108//National Natural Science Foundation of China/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality/ ; LGL-8998-09//Lingang Laboratory/ ; SKLICS-K202515//State Key Laboratory of Integrated Chips and Systems/ ; }, abstract = {Chronic neurophysiological monitoring devices facilitate the timely diagnosis and treatment of episodic or recurrent neurological disorders. Compared with passive electrodes, silicon-based active transistors provide intrinsic signal amplification and, when combined with capacitive-coupling measurement mechanisms, enable high-density, high-fidelity recordings. However, most existing systems remain limited to single-modality electrical sensing and fail to address the growing demands of contemporary neurodynamic research. Here, a chronically implantable, large-area cortical interface capable of real-time multimodal monitoring of electrical, thermal, and photodynamic signals is presented. Building upon a silicon-transistor array for neural electrical detection, the device integrates thin-film metal resistors for temperature sensing while preserving mechanical flexibility sufficient for stable, long-term tissue contact. By leveraging the photoelectric effect of silicon transistors and functional multiplexing of active elements, the interface also achieves precise photodynamic measurement. In vitro experiments confirm long-term stability and channel isolation. In vivo evaluation in Sprague-Dawley rats, together with biocompatibility assessments, demonstrates reliable performance under physiological conditions. The technology used in this multifunctional platform has universal applicability in neural interfaces, offering continuous multimodal neurodynamic data acquisition with potential utility in monitoring, diagnosing, and treating chronic neurological conditions such as epilepsy and brain tumors.}, }
@article {pmid41160441, year = {2025}, author = {Jeong, SY and Lee, JW and Kim, TG}, title = {Comparative analysis across diverse plant species reveals superior antibiofilm efficacy and dose-dependency of root extracts compared to leaf extracts.}, journal = {FEMS microbiology letters}, volume = {372}, number = {}, pages = {}, doi = {10.1093/femsle/fnaf116}, pmid = {41160441}, issn = {1574-6968}, support = {RS-2023-00273372//Ministry of Education/ ; }, mesh = {*Biofilms/drug effects/growth &amp; development ; *Plant Roots/chemistry ; *Plant Extracts/pharmacology ; *Plant Leaves/chemistry ; *Anti-Bacterial Agents/pharmacology ; Microbial Sensitivity Tests ; Dose-Response Relationship, Drug ; }, abstract = {Although both root- and leaf-derived plant extracts hold potential as antibiofilm agents, research has predominantly focused on leaf tissues. In this study, we systematically compared the antibiofilm efficacy of 158 root and 248 leaf extracts from 360 plant species across five concentrations (0.1, 0.25, 0.5, 1.0, and 2.0 g/l). As concentration increased, the biofilm control incidence (BCI) of root extracts rose from 68.4% to 94.3%, while leaf extracts showed a smaller increase, from 52.2% to 71.7%. Similarly, the biofilm control efficacy (BCE) of root extracts increased from 27.6% to 54.2%, whereas leaf extracts ranged from -2.7% to 16.2%. Bootstrapping analysis (10 000 iterations) confirmed significantly higher antibiofilm activity of root extracts at concentrations ≥ 0.5 g/l (P < 0.05). Paired comparisons of species with both extract types further demonstrated the consistent superiority of root extracts across all concentrations (bootstrapped, P < 0.05), despite interspecific variation at higher doses. Linear regression revealed a significantly steeper dose-response slope for root extracts (29.2 ± 2.4) than for leaf extracts (8.1 ± 2.8) (bootstrapped, P < 0.05), indicating a stronger concentration-dependent effect of root extracts. These results suggest that plant roots typically harbor more potent and/or diverse antibiofilm compounds than leaves, underscoring their untapped potential for biofilm control applications.}, }
@article {pmid41160433, year = {2025}, author = {Chen, Y and Liu, T and Jia, K and Theeuwes, J and Gong, M}, title = {Dual-format attentional template during preparation in human visual cortex.}, journal = {eLife}, volume = {13}, number = {}, pages = {}, pmid = {41160433}, issn = {2050-084X}, support = {Major Project 2021ZD0200409//National Science and Technology Innovation 2030/ ; 32371087//National Natural Science Foundation of China/ ; 32300855//National Natural Science Foundation of China/ ; 3200784//National Natural Science Foundation of China/ ; 226-2024-00118//Fundamental Research Funds for the Central University/ ; Non-profit Central Research Institute Fund 2023-PT310-01//Chinese Academy of Medical Sciences/ ; }, mesh = {Humans ; *Attention/physiology ; *Visual Cortex/physiology ; Magnetic Resonance Imaging ; Male ; Female ; Adult ; Young Adult ; *Visual Perception/physiology ; Brain Mapping ; Cues ; Photic Stimulation ; }, abstract = {Goal-directed attention relies on forming internal templates of key information relevant for guiding behavior, particularly when preparing for upcoming sensory inputs. However, evidence on how these attentional templates are represented during preparation remains controversial. Here, we combine functional magnetic resonance imaging with an orientation cueing task to isolate preparatory activity from stimulus-evoked responses. Using multivariate pattern analysis, we found decodable information about the to-be-attended orientation during preparation; yet preparatory activity patterns were different from those evoked when actual orientations were perceived. When perturbing the neural activity by means of a visual impulse ('pinging' technique), the preparatory activity patterns in visual cortex resembled those associated with perceiving these orientations. The observed differential patterns with and without the impulse perturbation suggest a predominantly non-sensory format and a latent, sensory-like format of representation during preparation. Furthermore, the emergence of the sensory-like template coincided with enhanced information connectivity between V1 and frontoparietal areas and was associated with improved behavioral performance. By engaging this dual-format mechanism during preparation, the brain is able to encode both abstract, non-sensory information and more detailed, sensory information, potentially providing advantages for adaptive attentional control. For example, consistent with recent theories of visual search, a predominantly non-sensory template can support the initial guidance and a latent sensory-like format can support prospective stimulus processing.}, }
@article {pmid41159356, year = {2025}, author = {Atan, Y and Doğan, M and Karayel, F and Üzün, &#x130;}, title = {Fatal Isolated Right Ventricular Rupture Without External Chest Injury in a Young Driver: Forensic Autopsy Findings After a One-Sided Vehicle Collision.}, journal = {Archives of Iranian medicine}, volume = {28}, number = {9}, pages = {530-535}, pmid = {41159356}, issn = {1735-3947}, mesh = {Humans ; Male ; *Accidents, Traffic ; Young Adult ; *Heart Ventricles/injuries/pathology ; Fatal Outcome ; Autopsy ; *Heart Injuries/pathology/etiology ; *Wounds, Nonpenetrating/pathology ; Forensic Pathology ; }, abstract = {Traumatic deaths are common, with cardiac trauma affecting 7‒12% of patients with thoracic injuries. Blunt cardiac injury (BCI), although rare, is associated with a high mortality rate. This report presents a case of blunt cardiac rupture (BCR) observed at autopsy despite the absence of external chest trauma, suggesting the presence of severe internal injuries. A 19-year-old male was found dead in his vehicle which had collided with a wall. At the crime scene investigation, external examination revealed no substantial chest wall injuries in the individual despite significant damage to the vehicle. Autopsy revealed a 2-cm rupture of the right ventricle (heart), accompanied by 400 cc of partially coagulated blood in the pericardial cavity, consistent with cardiac tamponade. Pregabalin was detected in the toxicology analysis, but not in lethal concentrations. Traffic accidents are a major cause of BCI, typically resulting from compression of the heart between the thoracic structures during high-energy impacts. BCR is particularly fatal and often results in rapid death before arrival to the hospital. The absence of external trauma in the current case underscores the need for thorough internal examination in trauma-related deaths.}, }
@article {pmid41157441, year = {2025}, author = {Moreno-Castelblanco, SR and Vélez-Guerrero, MA and Callejas-Cuervo, M}, title = {Lower-Limb Motor Imagery Recognition Prototype Based on EEG Acquisition, Filtering, and Machine Learning-Based Pattern Detection.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {20}, pages = {}, pmid = {41157441}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Machine Learning ; Brain-Computer Interfaces ; *Lower Extremity/physiology ; Signal Processing, Computer-Assisted ; Male ; Algorithms ; Adult ; *Imagination/physiology ; Movement/physiology ; *Pattern Recognition, Automated/methods ; Female ; Young Adult ; }, abstract = {Advances in brain-computer interface (BCI) research have explored various strategies for acquiring and processing electroencephalographic (EEG) signals to detect motor imagery (MI) activities. However, the complexity of multichannel clinical systems and processing techniques can limit their accessibility outside specialized centers, where complex setups are not feasible. This paper presents a proof-of-concept prototype of a single-channel EEG acquisition and processing system designed to identify lower-limb motor imagery. The proposed proof-of-concept prototype enables the wireless acquisition of raw EEG values, signal processing using digital filters, and the detection of MI patterns using machine learning algorithms. Experimental validation in a controlled laboratory with participants performing resting, MI, and movement tasks showed that the best performance was obtained by combining Savitzky-Golay filtering with a Random Forest classifier, reaching 87.36% ± 4% accuracy and an F1-score of 87.18% ± 3.8% under five-fold cross-validation. These findings confirm that, despite limited spatial resolution, MI patterns can be detected using appropriate AI-based filtering and classification. The novelty of this work lies in demonstrating that a single-channel, portable EEG prototype can be effectively used for lower-limb MI recognition. The portability and noise resilience achieved with the prototype highlight its potential for research, clinical rehabilitation, and assistive device control in non-specialized environments.}, }
@article {pmid41157340, year = {2025}, author = {Iadarola, G and Mengarelli, A and Iarlori, S and Monteriù, A and Spinsante, S}, title = {RGB-D Cameras and Brain-Computer Interfaces for Human Activity Recognition: An Overview.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {20}, pages = {}, pmid = {41157340}, issn = {1424-8220}, support = {CUP I33C2200133000//Vitality Project/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Human Activities ; Electroencephalography ; Brain/physiology ; }, abstract = {This paper provides a perspective on the use of RGB-D cameras and non-invasive brain-computer interfaces (BCIs) for human activity recognition (HAR). Then, it explores the potential of integrating both the technologies for active and assisted living. RGB-D cameras can offer monitoring of users in their living environments, preserving their privacy in human activity recognition through depth images and skeleton tracking. Concurrently, non-invasive BCIs can provide access to intent and control of users by decoding neural signals. The synergy between these technologies may allow holistic understanding of both physical context and cognitive state of users, to enhance personalized assistance inside smart homes. The successful deployment in integrating the two technologies needs addressing critical technical hurdles, including computational demands for real-time multi-modal data processing, and user acceptance challenges related to data privacy, security, and BCI illiteracy. Continued interdisciplinary research is essential to realize the full potential of RGB-D cameras and BCIs as AAL solutions, in order to improve the quality of life for independent or impaired people.}, }
@article {pmid41156422, year = {2025}, author = {He, J and Xu, J and Wang, Y}, title = {Non-Linear Modeling and Precision Analysis Approach for Implantable Multi-Channel Neural Recording Systems.}, journal = {Micromachines}, volume = {16}, number = {10}, pages = {}, pmid = {41156422}, issn = {2072-666X}, support = {2021ZD0200401//STI 2030-Major Project/ ; 2025C01187,2024C03001//Pioneer R&amp;D Program of Zhejiang/ ; 62176232,62336007//National Natural Science Foundation of China/ ; SNZJU-SIAS-002//Starry Night Science Fund of Zhe- 406 jiang University Shanghai Institute for Advanced Study/ ; 2025ZFJH01,226-2024-00127//Fundamental Research Funds for the Central Universities/ ; }, abstract = {High-precision implantable multi-channel neural recording systems are considered as having a crucial role in the diagnosis and treatment of neurological disorders. However, it is a significant design challenge to achieve an optimal trade-off among linear parameters, signal fidelity, power consumption, and circuit area. To address this challenge, a Simulink-based modeling approach has been proposed to incorporate adjustable non-linear parameters across the front-end circuits and analog-to-digital converter (ADC) stages. The model evaluates non-linearity impacts on system performance through both quantitative spike detection accuracy analysis and a neural decoding paradigm based on Chinese handwriting reconstruction. Simulated results show that total harmonic distortion (THD) can be set to -34.32 dB for the low-noise amplifier (LNA), -33.73 dB for the programmable gain amplifier (PGA), and -57.95 dB for the ADC in order to achieve reliable detection accuracy with minimal design cost. Moreover, ADC non-linearity has a greater influence on system performance than that of the LNA and PGA. The proposed approach offers quantitative and systematic hardware design guidance to balance signal fidelity and resource efficiency for future low-power, high-accuracy neural recording systems.}, }
@article {pmid41155027, year = {2025}, author = {Yao, Y and Wang, X and Hao, X and Sun, H and Dong, R and Li, Y}, title = {Trans-cVAE-GAN: Transformer-Based cVAE-GAN for High-Fidelity EEG Signal Generation.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {10}, pages = {}, pmid = {41155027}, issn = {2306-5354}, abstract = {Electroencephalography signal generation remains a challenging task due to its non-stationarity, multi-scale oscillations, and strong spatiotemporal coupling. Conventional generative models, including VAEs and GAN variants such as DCGAN, WGAN, and WGAN-GP, often yield blurred waveforms, unstable spectral distributions, or lack semantic controllability, limiting their effectiveness in emotion-related applications. To address these challenges, this research proposes a Transformer-based conditional variational autoencoder-generative adversarial network (Trans-cVAE-GAN) that combines Transformer-driven temporal modeling, label-conditioned latent inference, and adversarial learning. A multi-dimensional structural loss further constrains generation by preserving temporal correlation, frequency-domain consistency, and statistical distribution. Experiments on three SEED-family datasets-SEED, SEED-FRA, and SEED-GER-demonstrate high similarity to real EEG, with representative mean ± SD correlations of Pearson ≈ 0.84 ± 0.08/0.74 ± 0.12/0.84 ± 0.07 and Spearman ≈ 0.82 ± 0.07/0.72 ± 0.12/0.83 ± 0.08, together with low spectral divergence (KL ≈ 0.39 ± 0.15/0.41 ± 0.20/0.37 ± 0.18). Comparative analyses show consistent gains over classical GAN baselines, while ablations verify the indispensable roles of the Transformer encoder, label conditioning, and cVAE module. In downstream emotion recognition, augmentation with generated EEG raises accuracy from 86.9% to 91.8% on SEED (with analogous gains on SEED-FRA and SEED-GER), underscoring enhanced generalization and robustness. These results confirm that the proposed approach simultaneously ensures fidelity, stability, and controllability across cohorts, offering a scalable solution for affective computing and brain-computer interface applications.}, }
@article {pmid41154635, year = {2025}, author = {Tabish, M and Malik, I and Akhtar, A and Afzal, M}, title = {A Review on Low-Dimensional Nanoarchitectonics for Neurochemical Sensing and Modulation in Responsive Neurological Outcomes.}, journal = {Biomolecules}, volume = {15}, number = {10}, pages = {}, pmid = {41154635}, issn = {2218-273X}, support = {KSRG-2024-41//King Salman Center for Disability Research/ ; }, mesh = {Humans ; Brain-Computer Interfaces ; Animals ; Artificial Intelligence ; *Biosensing Techniques/methods ; *Nanostructures/chemistry ; Nanotechnology/methods ; Brain ; }, abstract = {Low-Dimensional Nanohybrids (LDNHs) have emerged as potent multifunctional platforms for neurosensing and neuromodulation, providing elevated spatial-temporal precision, versatility, and biocompatibility. This review examines the intersection of LDNHs with artificial intelligence, brain-computer interfaces (BCIs), and closed-loop neurotechnologies, highlighting their transformative potential in personalized neuro-nano-medicine. Utilizing stimuli-responsive characteristics, optical, thermal, magnetic, and electrochemical LDNHs provide real-time feedback-controlled manipulation of brain circuits. Their pliable and adaptable structures surpass the constraints of inflexible bioelectronics, improving the neuronal interface and reducing tissue damage. We also examined their use in less invasive neurological diagnostics, targeted therapy, and adaptive intervention systems. This review delineates recent breakthroughs, integration methodologies, and fundamental mechanisms, while addressing significant challenges such as long-term biocompatibility, deep-tissue accessibility, and scalable manufacturing. A strategic plan is provided to direct future research toward clinical use. Ultimately, LDNHs signify a transformative advancement in intelligent, tailored, and closed-loop neurotechnologies, integrating materials science, neurology, and artificial intelligence to facilitate the next era of precision medicine.}, }
@article {pmid41154223, year = {2025}, author = {Du, A and Huang, M and Wang, Z and Zhou, H and Duan, H and Hu, S and Zheng, Y}, title = {Using Low-Intensity Focused Ultrasound to Treat Depression and Anxiety Disorders: A Review of Current Evidence.}, journal = {Brain sciences}, volume = {15}, number = {10}, pages = {}, pmid = {41154223}, issn = {2076-3425}, support = {2023YFC2506200//National Key Research and Development Program of China/ ; 2023YFC2506203//National Key Research and Development Program of China/ ; 2022YFB3204300//National Key Research and Development Program of China/ ; 2022C01002//Zhejiang Provincial Key Research and Development Program of China/ ; }, abstract = {Background: Depression and anxiety disorders impact millions globally. In recent years, low-intensity focused ultrasound (LIFU), characterized by its high precision, deep penetration, and non-invasive nature, has garnered significant interest in neuroscience and clinical practice. To enhance understanding of its effects on mood, therapeutic availability in treatment of depression/anxiety disorders, and potential mechanisms, a systematic review of studies investigating the emotional impact of LIFU on depressive/anxious-like animal models, healthy volunteers, and patients with depression or anxiety disorders has been undertaken. Methods: Relevant papers published before 15 July 2025 were searched across four databases: Web of Science, PubMed, Science Direct, and Embase. A total of 28 papers which met the inclusion and exclusion criteria are included in this review. Results: Our findings indicate that LIFU reversed the depressive/anxious-like behaviors in the animal models and showed antidepressant/anti-anxiety effects among the state-of-art clinical studies. For example, immobility time in FST or TST is reduced in depressive animal models, and HRSD/BAI scales are improved in human studies. Key molecules such as BDNF/5-HT are found restored in animal models, and FC between key brain areas related to depression/anxiety is modulated after LIFU treatment. Notably, no brain tissue damage was observed in animal studies, and only mild adverse effects (such as dizziness and vomiting) were noted in a few human studies. Conclusions: The studies using LIFU to treat depression and anxiety remain in the preliminary stage. The mechanisms underlying LIFU's mood effects-such as activation or inhibition of specific brain regions or neural circuits, anti-inflammatory effects, alterations in functional connectivity, synaptic plasticity, neurotransmitter levels, and BDNF-remain incompletely understood and warrant further investigation. Nevertheless, the LIFU technique holds promise for regulating both cortical and subcortical brain areas implicated in depression/anxiety disorders as a precise neuromodulation tool.}, }
@article {pmid41154218, year = {2025}, author = {Tan, L and Fang, H and Ding, P and Wang, F and Wei, Y and Fu, Y}, title = {P300 Spatiotemporal Prior-Based Transformer-CNN for Auxiliary Diagnosis of PTSD.}, journal = {Brain sciences}, volume = {15}, number = {10}, pages = {}, pmid = {41154218}, issn = {2076-3425}, support = {82172058, 62376112, 81771926, 61763022, 62366026, 62006246//The National Natural Science Foundation of China under Grant Nos/ ; }, abstract = {Objectives: To address the challenges of subjectivity, misdiagnosis and underdiagnosis in post-traumatic stress disorder (PTSD), this study proposes an objective auxiliary diagnostic method based on P300 signals. Existing studies largely rely on conventional P300 features, lacking the systematic integration of event-related potential (ERP) priors and facing limitations in spatiotemporal feature modeling. Methods: Using common spatiotemporal pattern (CSTP) analysis and quantitative evaluation, we revealed significant spatiotemporal differences in P300 signals between PTSD patients and healthy controls. ERP prior information was then extracted and integrated into a hybrid architecture combining transformer encoders and a convolutional neural network (CNN), enabling joint modeling of long-range temporal dependencies and local spatial patterns. Results: The proposed P300 spatiotemporal transformer-CNN (P300-STTCNet) achieved a classification accuracy of 93.37% in distinguishing PTSD from healthy controls, markedly outperforming traditional approaches. Conclusions: Significant spatiotemporal differences in P300 signals exist between PTSD and healthy control groups. The P300-STTCNet model effectively captures PTSD-related spatiotemporal features, demonstrating strong potential for electroencephalogram-based objective auxiliary diagnosis.}, }
@article {pmid41152182, year = {2025}, author = {Cao, Y and Xue, Y and Yang, H and Wang, F and Li, T and Zhao, L and Fu, Y}, title = {[Ethical considerations for artificial intelligence-enhanced brain-computer interface].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {5}, pages = {1085-1091}, pmid = {41152182}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces/ethics ; *Artificial Intelligence/ethics ; Humans ; Deep Learning ; User-Computer Interface ; Electroencephalography ; }, abstract = {Artificial intelligence-enhanced brain-computer interfaces (BCI) are expected to significantly improve the performance of traditional BCIs in multiple aspects, including usability, user experience, and user satisfaction, particularly in terms of intelligence. However, such AI-integrated or AI-based BCI systems may introduce new ethical issues. This paper first evaluated the potential of AI technology, especially deep learning, in enhancing the performance of BCI systems, including improving decoding accuracy, information transfer rate, real-time performance, and adaptability. Building on this, it was considered that AI-enhanced BCI systems might introduce new or more severe ethical issues compared to traditional BCI systems. These include the possibility of making users' intentions and behaviors more predictable and manipulable, as well as the increased likelihood of technological abuse. The discussion also addressed measures to mitigate the ethical risks associated with these issues. It is hoped that this paper will promote a deeper understanding and reflection on the ethical risks and corresponding regulations of AI-enhanced BCIs.}, }
@article {pmid41152174, year = {2025}, author = {Wang, P and Ji, X and Wang, J and Yu, X}, title = {[Brain computer interface nursing bed control system based on deep learning and dual visual feedback].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {5}, pages = {1021-1028}, pmid = {41152174}, issn = {1001-5515}, mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; *Feedback, Sensory ; Neural Networks, Computer ; Beds ; }, abstract = {In order to meet the need of autonomous control of patients with severe limb disorders, this paper designs a nursing bed control system based on motor imagery-brain computer interface (MI-BCI). In view of the low decoding performance of cross-subjects and the dynamic fluctuation of cognitive state in the existing MI-BCI technology, the neural network structure optimization and user interaction feedback enhancement are improved. Firstly, the optimized dual-branch graph convolution multi-scale neural network integrates dynamic graph convolution and multi-scale convolution. The average classification accuracy is higher than that of multi-scale attention temporal convolution network, Gram angle field combined with convolution long short term memory hybrid network, Transformer-based graph convolution network and other existing methods. Secondly, a dual visual feedback mechanism is constructed, in which electroencephalogram (EEG) topographic map feedback can improve the discrimination of spatial patterns, and attention state feedback can enhance the temporal stability of signals. Compared with the single EEG topographic map feedback and non-feedback system, the average classification accuracy of the proposed method is also greatly improved. Finally, in the four classification control task of nursing bed, the average control accuracy of the system is 90.84%, and the information transmission rate is 84.78 bits/min. In summary, this paper provides a reliable technical solution for improving the autonomous interaction ability of patients with severe limb disorders, which has important theoretical significance and application value.}, }
@article {pmid41152144, year = {2025}, author = {Bek, J and Aziz, A and Brady, N}, title = {Transcranial Direct Current Stimulation to Augment Motor Imagery Training: A Systematic Review.}, journal = {The European journal of neuroscience}, volume = {62}, number = {8}, pages = {e70280}, pmid = {41152144}, issn = {1460-9568}, support = {101034345//H2020 Marie Sk&#x142;odowska-Curie Actions/ ; }, mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; *Motor Cortex/physiology ; *Imagination/physiology ; Brain-Computer Interfaces ; *Imagery, Psychotherapy/methods ; Neuronal Plasticity ; *Stroke Rehabilitation/methods ; }, abstract = {Motor imagery training (MIT) is a widely used technique for motor learning and recovery. To optimize training outcomes, researchers have explored the integration of MIT with complementary approaches. One such approach is transcranial direct current stimulation (tDCS), which also shows promise as a method to enhance motor performance and neuroplasticity. This systematic review aimed to synthesize the current evidence on the synergistic effects of MIT combined with tDCS, with a specific focus on behavioral outcomes. Heterogeneous methods across 16 studies with 432 participants in total, including both healthy and clinical populations, yielded mixed results. Nonetheless, the potential of anodal tDCS applied over the primary motor cortex to augment the beneficial effects of MIT for motor performance in healthy participants is suggested by the current literature. The benefits of combining tDCS with MIT in brain-computer interface (BCI) protocols with stroke patients were less clear, which may relate to population differences, timing of stimulation, or the similarity between outcome measures and trained tasks. Overall, small samples and heterogeneous methods limit interpretation of the findings of combined intervention studies, and further research should aim to measure both behavioral and neurophysiological outcomes in larger samples as well as examining longer-term synergistic effects.}, }
@article {pmid41151221, year = {2025}, author = {Liu, S and Su, L and He, Q and Qiu, M and Liang, R}, title = {Comparative evaluation of ChatGPT and Gemini in brain-computer interfaces patient education: A multi-dimensional analysis of reliability, accuracy, comprehensibility, and readability.}, journal = {International journal of medical informatics}, volume = {206}, number = {}, pages = {106164}, doi = {10.1016/j.ijmedinf.2025.106164}, pmid = {41151221}, issn = {1872-8243}, abstract = {BACKGROUND: Brain-Computer Interfaces (BCI) are a type of life-altering neurotechnology, but their inherent complexity poses significant challenges to patient education. Large Language Models (LLMs), such as ChatGPT and Gemini, offer new possibilities to address this challenge. This study aims to conduct a multi-dimensional, rigorous comparative analysis of the performance of these two mainstream AI models in responding to common patient questions related to BCI.<br><br>METHODS: Through a structured process combining clinical expert consensus, literature review, and online patient community analysis, we identified 13 key patient questions covering the entire BCI treatment cycle. We then obtained responses to these questions from ChatGPT and Gemini on September 1, 2025. An evaluation panel, composed of clinical experts and non-medical professionals, conducted a blinded assessment of the response quality using standardized Likert scales across three dimensions: reliability, accuracy, and comprehensibility. Concurrently, we performed an objective, quantitative analysis of the response texts using the Flesch-Kincaid readability tests.<br><br>RESULTS: On core quality metrics such as reliability, accuracy, and comprehensibility, the performance of the two models was generally comparable, both demonstrating a high level of proficiency with only sporadic statistical differences on a few technical questions. However, a clear significant disparity emerged in the dimension of readability: for 12 of the 13 questions, the text generated by Gemini required a significantly lower reading grade level than that of ChatGPT (p&#xa0;<&#xa0;0.05) and had significantly higher reading ease scores. This difference stemmed from Gemini's tendency to use shorter sentences and simpler vocabulary.<br><br>CONCLUSION: AI chatbots possess immense potential in the field of BCI patient education. Although both ChatGPT and Gemini can provide high-quality information, Gemini demonstrates a clear advantage in the accessibility and approachability of information, making it a potentially more suitable tool for initial application across diverse patient populations. Nevertheless, the limitations of AI in handling highly specialized and dynamically changing knowledge underscore the indispensable role of human expert supervision and validation in any clinical application.}, }
@article {pmid41149457, year = {2025}, author = {Lee, HH and Siu-Li, N and Pagano, I and Fukui, JA}, title = {Examining a Genomic Test in Predicting Extended Endocrine Benefit and Recurrence Risk in a Diverse Breast Cancer Population.}, journal = {Current oncology (Toronto, Ont.)}, volume = {32}, number = {10}, pages = {}, pmid = {41149457}, issn = {1718-7729}, support = {P30 CA071789/CA/NCI NIH HHS/United States ; P30CA071789-17S2/CA/NCI NIH HHS/United States ; }, mesh = {Humans ; *Breast Neoplasms/genetics/drug therapy/pathology ; Female ; Middle Aged ; *Neoplasm Recurrence, Local/genetics ; Aged ; Retrospective Studies ; Adult ; *Genomics/methods ; *Antineoplastic Agents, Hormonal/therapeutic use ; }, abstract = {(1) Background: Extended endocrine therapy (EET) beyond five years can reduce distant recurrence in early-stage hormone receptor-positive (HR+) breast cancer. The Breast Cancer Index (BCI) predicts recurrence risk and EET benefits, yet racial/ethnic differences in its results remain unexplored. This study evaluates such differences in a diverse early-stage HR+ breast cancer population. (2) Methods: We retrospectively analyzed demographics, tumor characteristics and BCI scores of 159 women in Hawaii with early-stage HR+ breast cancer, self-identifying as Caucasian, Filipino, Japanese, Native Hawaiian, Other Asian/Pacific Islander, or Other. Tumor characteristics included size, grade, histology, lymph node/receptor status, Oncotype DX score, and laterality. Logistic regression used demographics and tumor features as predictor variables, with BCI's benefit prediction and recurrence risk as outcome variables. (3) Results: Japanese and other Asian/Pacific Islander patients had significantly lower odds of high recurrence risk compared to Caucasian patients. Higher recurrence risk was associated with greater odds of predicted EET. Racial/ethnic differences in EET benefit prediction were not statistically significant. (4) Conclusions: No racial/ethnic differences in EET benefit prediction suggest BCI's applicability in racially and ethnically diverse populations. Findings among Japanese and other Asian/Pacific Islanders point to potential biological or socioeconomic variation. Limitations include sample size and underrepresentation of certain groups. Future studies should address these gaps and adjust for known risk factors to further clarify BCI's racial and ethnic implications.}, }
@article {pmid41149344, year = {2025}, author = {Kucukselbes, H and Sayilgan, E}, title = {Real-Time EEG Decoding of Motor Imagery via Nonlinear Dimensionality Reduction (Manifold Learning) and Shallow Classifiers.}, journal = {Biosensors}, volume = {15}, number = {10}, pages = {}, pmid = {41149344}, issn = {2079-6374}, mesh = {Humans ; *Electroencephalography/methods ; Adult ; Male ; Signal Processing, Computer-Assisted ; Female ; Machine Learning ; Brain-Computer Interfaces ; Young Adult ; Dimensionality Reduction ; }, abstract = {This study introduces a real-time processing framework for decoding motor imagery EEG signals by integrating manifold learning techniques with shallow classifiers. EEG recordings were obtained from six healthy participants performing five distinct wrist and hand motor imagery tasks. To address the challenges of high dimensionality and inherent nonlinearity in EEG data, five nonlinear dimensionality reduction methods, t-SNE, ISOMAP, LLE, Spectral Embedding, and MDS, were comparatively evaluated. Each method was combined with three shallow classifiers (k-NN, Naive Bayes, and SVM) to investigate performance across binary, ternary, and five-class classification settings. Among all tested configurations, the t-SNE + k-NN pairing achieved the highest accuracies, reaching 99.7% (two-class), 99.3% (three-class), and 89.0% (five-class). ISOMAP and MDS also delivered competitive results, particularly in multi-class scenarios. The presented approach builds upon our previous work involving EEG datasets from individuals with spinal cord injury (SCI), where the same manifold techniques were examined extensively. Comparative findings between healthy and SCI groups reveal consistent advantages of t-SNE and ISOMAP in preserving class separability, despite higher overall accuracies in healthy subjects due to improved signal quality. The proposed pipeline demonstrates low-latency performance, completing signal processing and classification in approximately 150 ms per trial, thereby meeting real-time requirements for responsive BCI applications. These results highlight the potential of nonlinear dimensionality reduction to enhance real-time EEG decoding, offering a low-complexity yet high-accuracy solution applicable to both healthy users and neurologically impaired individuals in neurorehabilitation and assistive technology contexts.}, }
@article {pmid41146476, year = {2025}, author = {Yue, X and Lu, L and Liu, H and Zang, Y}, title = {LRR-UNet: A Deep Unfolding Network With Low-Rank Recovery for EEG Signal Denoising.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {31}, number = {10}, pages = {e70632}, pmid = {41146476}, issn = {1755-5949}, support = {2023YFF1204200//National Key Research and Development Program of China/ ; 62476197//National Natural Science Foundation of China/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Artifacts ; Algorithms ; *Brain/physiology ; }, abstract = {BACKGROUND: Electroencephalogram (EEG) signals are crucial for brain-computer interface research but are highly susceptible to noise contamination, necessitating effective denoising. While deep learning has been widely applied, its "black-box" nature limits interpretability. In contrast, traditional model-based methods like Low-Rank Recovery (LRR) offer strong interpretability by decomposing signals into low-rank and sparse components.<br><br>OBJECTIVE: This paper aims to develop an interpretable deep-learning model for EEG denoising that combines the performance of deep learning with the interpretability of traditional LRR methods.<br><br>METHODS: We propose LRR-Unet, a deep unfolding network that transforms the traditional iterative LRR algorithm into a neural network architecture. Specifically, the time-consuming Singular Value Decomposition (SVD) and sparse optimization processes in LRR are replaced with learnable neural network modules.<br><br>RESULTS: Extensive experiments demonstrate that LRR-Unet outperforms other state-of-the-art models in removing ocular and electromyographic artifacts, achieving superior performance on both quantitative and qualitative metrics. Furthermore, in downstream classification tasks, EEG signals preprocessed with LRR-Unet yield better results across various evaluation indicators.<br><br>CONCLUSION: The proposed LRR-Unet provides an effective and interpretable solution for EEG denoising. Its superiority in denoising performance and practical utility in enhancing downstream application performance is validated through comprehensive experiments.}, }
@article {pmid41146424, year = {2025}, author = {Yang, C and Wang, X and Ye, X and Shen, Y and Tong, J and Zhang, X and Zhou, Y}, title = {Spatiotemporal Immune Dynamics in Experimental Retinal Ganglion Cell Injury Models.}, journal = {Immunity, inflammation and disease}, volume = {13}, number = {10}, pages = {e70284}, pmid = {41146424}, issn = {2050-4527}, support = {//This study was supported by a National Natural Science Foundation of China (NSFC) Key Program grant 82430038, Key R&amp;D Program of Zhejiang Province 2025C02109, NSFC grants 82371455 and 82371084, a National Key Research and Development Program of China grant 2023YFC2506200, a China Postdoctoral Science Foundation grant 2024M752831, and the Open Project Program of Shaanxi Provincial Key Laboratory of Biological Psychiatry XJJSHTS-2504./ ; }, mesh = {*Retinal Ganglion Cells/immunology/pathology ; Animals ; *Optic Nerve Injuries/immunology/pathology ; Disease Models, Animal ; *Glaucoma/immunology/pathology ; Humans ; }, abstract = {BACKGROUND: The damage and regeneration of retinal ganglion cells (RGCs) have been extensively studied. Among them, immune cells in different parts of the visual pathway play an important role in injury, regeneration and repair, but a comprehensive analysis of their spatial and temporal distribution is lacking.<br><br>PURPOSE: This review emphasizes the unique characteristics of immune cells within the visual input pathway, focusing on their spatiotemporal dynamics in the retina, optic nerve head (ONH), and optic nerve during glaucoma and traumatic optic nerve injury.<br><br>METHODS: A comprehensive search was conducted across PubMed and Web of Science up to April 2025. Studies were included if they reported immune cells under glaucoma or optic nerve crush (ONC) animal models.<br><br>FINDINGS: Each region of the visual input pathway displays a distinct immune cell composition, including M&#xfc;ller cells, microglia, astrocytes, T cells, and oligodendrocytes, all of which work together to maintain homeostasis and respond to injury. Some immune cells are specific to certain regions, while others are shared across areas. Furthermore, even within a single glial cell type, there are different subtypes with unique developmental origins or marker profiles, reflecting a range of functions. In both glaucoma and traumatic optic nerve injury, retinal immune cells are rapidly activated, regardless of whether the initial impairment occurs in the soma or axon of RGCs, in the subacute or chronic course. The early stages of injury also see the presence of adaptive immune cells, such as T cells and neutrophils. Macrophages and microglia typically play complementary roles, while astrocytes show prolonged activation compared to microglia in the optic nerve, though this pattern does not hold in the retina following ONC.<br><br>CONCLUSIONS: Understanding the spatiotemporal dynamics of these immune responses in glaucoma and traumatic optic nerve injury is crucial for developing targeted therapies that can reduce RGC loss, mitigate neurotoxicity, and promote functional recovery, ultimately preventing vision impairment. Targeting specific immune cell subsets may provide new strategies for delaying RGC damage.}, }
@article {pmid41146245, year = {2025}, author = {López-Larraz, E and Sarasola-Sanz, A and Birbaumer, N and Ramos-Murguialday, A}, title = {Uncovering attempted movements of the paralyzed upper limb after stroke through EEG and EMG.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {221}, pmid = {41146245}, issn = {1743-0003}, support = {2422-0-1//Fort&#xfc;ne program - University of T&#xfc;bingen/ ; E! 113928 SubliminalHomeRehab//Eureka-Eurostars/ ; MAIA 951910//European Union H2020-FETPROACT-EIC-2018-2020/ ; NanoNeuro//Basque IKUR initiative/ ; 01QE2023//Bundesministerium f&#xfc;r Bildung und Forschung/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Male ; *Stroke/physiopathology/complications ; Female ; *Electromyography/methods ; Middle Aged ; *Upper Extremity/physiopathology ; Aged ; *Paralysis/physiopathology/etiology/rehabilitation ; Adult ; Movement/physiology ; *Stroke Rehabilitation ; }, abstract = {Detecting attempted movements of a paralyzed limb is a key step for neural interfaces for motor rehabilitation and restoration after a stroke. In this paper, we present a systematic evaluation of electroencephalographic (EEG) and electromyographic (EMG) activity to decode when stroke patients with severe upper-limb paralysis attempt to move their affected arm. EEG and EMG recordings of 35 chronic stroke patients were analyzed. We trained classifiers to discriminate between rest and movement attempt states relying on brain, muscle, or both types of signals combined. Our results reveal that: (i) EEG and residual EMG activity provide complementary information to detect attempted movements, obtaining significantly higher decoding accuracy when both sources of activity are combined; (ii) EMG-based, but not EEG-based, decoding accuracy correlates with the degree of impairment of the patient; and (iii) the percentage of patients that achieve decoding accuracy above the chance level strongly depends on the type of features considered, and can be as low as 50% of them if only ipsilesional EEG is used. These results offer new perspectives to develop improved neurotechnologies that establish a more accurate contingent link between the central and peripheral nervous system after a stroke, leveraging Hebbian learning and facilitating functional plasticity and recovery.}, }
@article {pmid41145802, year = {2025}, author = {Hazrati, H and Daliri, MR}, title = {Decoding covert visual attention of electroencephalography signals using continuous wavelet transform and deep learning approach.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {37503}, pmid = {41145802}, issn = {2045-2322}, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Attention/physiology ; *Wavelet Analysis ; Male ; Adult ; Female ; Young Adult ; Brain-Computer Interfaces ; *Visual Perception/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Covert visual attention decoding from EEG signals is a key challenge in cognitive neuroscience and brain-computer interface applications. Traditional approaches often rely on manual feature extraction and handcrafted pipelines, which limit scalability and generalization. In this study, we propose a deep learning-based framework that leverages time-frequency representations, specifically Continuous Wavelet Transform (CWT), to enable end-to-end classification of covert attention states without manual feature engineering. EEG data were recorded from ten healthy participants performing spatial and feature-based attention tasks. Among the tested models, ShallowConvNet achieved 100% accuracy in binary classification and over 90% in four-class conditions. EEGNet also performed competitively, exceeding 97% and 88% accuracy in two- and four-class scenarios, respectively. These findings demonstrate that integrating CWT with deep neural architectures significantly enhances decoding performance compared to conventional raw-signal approaches, offering a scalable and efficient solution for real-time attention monitoring.}, }
@article {pmid41145516, year = {2025}, author = {Wei, Z and Lin, X and Zhang, L and Guo, L and Liu, J and Hu, L and Liu, Y and Kong, Y}, title = {CoSpine open access simultaneous cortico-spinal fMRI database of thermal pain and motor tasks.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1696}, pmid = {41145516}, issn = {2052-4463}, support = {82072010//National Natural Science Foundation of China (National Science Foundation of China)/ ; IS23108//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; }, mesh = {*Magnetic Resonance Imaging ; Humans ; *Pain/physiopathology/diagnostic imaging ; Brain/diagnostic imaging ; *Spinal Cord/diagnostic imaging ; }, abstract = {Simultaneous cortico-spinal functional magnetic resonance imaging (fMRI) enables non-invasive investigation of integrated central nervous system function, but acquisition challenges have restricted the availability of public datasets and slowed the development of advanced analytic methods. Here, we introduce the CoSpine database, the first open-access, BIDS-compliant cortico-spinal task-based fMRI resource (N = 61), acquired using a novel single-field-of-view (FOV) imaging protocol covering the whole brain (including cortical, subcortical, brainstem, and cerebellar regions) and cervical spinal cord. The dataset contains raw images, field maps, physiological recordings, and BIDS event files from thermal pain and voluntary motor tasks. An optimized acquisition and preprocessing framework is provided, validated by quality-control metrics such as temporal signal-to-noise ratio and alignment precision. Spanning a broad age range and standardized paradigms, CoSpine serves as a reference for neuroimaging methods development (e.g., hyperalignment) and for artificial intelligence (AI) model benchmarking. Potential applications include sensorimotor phenotyping, studies of age-related neurodegeneration, and exploratory work in neurorehabilitation, while also supporting early-stage development of brain-computer interface (BCI) systems involving spinal activity and personalized neuromodulation strategies.}, }
@article {pmid41145005, year = {2025}, author = {Yao, Y and Wang, H and Chen, L and Peng, Y and Luo, J}, title = {Foundation models for EEG decoding: current progress and prospective research.}, journal = {Journal of neural engineering}, volume = {22}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ae17e9}, pmid = {41145005}, issn = {1741-2552}, mesh = {*Electroencephalography/methods/trends ; Humans ; *Brain/physiology ; Prospective Studies ; Deep Learning ; Brain-Computer Interfaces ; }, abstract = {Objective.Electroencephalography (EEG) records the spontaneous electrical activity in the brain. Despite the growing application of deep learning in EEG decoding, traditional methods still rely heavily on supervised learning, which is often limited by task specificity and dataset dependency, restricting model performance and generalization. Inspired by the success of large language models, EEG foundation models (EEG FMs) are attracting increasing attention as a unified paradigm for EEG decoding. In this study, we review a selection of representative studies on EEG FMs, aiming to extract trends and provide recommendations for future research.Approach.We provide a comprehensive analysis of recent advances in EEG FMs, with a focus on downstream tasks, benchmark datasets, model architectures, and pre-training techniques. We analyze and synthesize core FMs components, and systematically compare their performances and generalizabilities.Main results.Our review reveals that EEG FMs are pre-trained on large-scale datasets, typically involving several hundred subjects. The number of subjects can reach up to 14 987, with a maximum total duration of 27 062 h. Current EEG FMs most adopt mask-based reconstruction pre-training strategy and employ efficient transformer-based architectures. Our comparative analysis shows that EEG FMs demonstrate significant potential in advancing EEG decoding tasks, particularly in seizure detection. However, their performance in complex scenarios such as motor imagery decoding remains limited.Significance.This review summarizes the existing approaches and performance outcomes of EEG FM, offers valuable insights into their current limitations and delineates prospective avenues for future research.}, }
@article {pmid41144819, year = {2025}, author = {Liu, H and Cao, X and Li, J and Zheng, L and Li, J and Li, Q and Xie, M and Li, H and Wang, X and Wu, Y and Zhang, X and Wang, Y and Gao, X and Sheng, T and Du, N and Xu, C and Zhou, K and Xu, J and Yan, C and Liu, L and Gao, L and Li, X and Zhang, M}, title = {Deciphering Neural Mechanisms Underlying Marmoset Dynamic Natural Behaviors Using a Miniaturized Wireless Large-Scale Coverage Neural Recorder.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e07110}, doi = {10.1002/advs.202507110}, pmid = {41144819}, issn = {2198-3844}, support = {2023YFB4705500//National Key Research and Development Program of China/ ; 62350710211//National Natural Science Foundation of China/ ; }, abstract = {Deciphering neural mechanisms underlying dynamic natural behaviors of freely moving species requires long-term recordings of large-scale brain activities. However, most conventional neural recorders are limited by their weights and measures, electrode coverage, and signal throughput, hindering the dissection of underlying neural mechanisms. This study reports real-time large-scale recordings and deciphering of brain activities from frontal and temporal cortices of freely moving marmoset across various natural behavioral repertoire using a miniaturized wireless neural recorder comprising a custom-designed 120-channel flexible µECoG array. Behavior-specific highly resolved spatiotemporal neural dynamics are observed, including alpha-band activations during drinking, anticipatory responses before vocalization, and transient high-gamma increase during vigilance to human intruders. Three phases of drinking behavior are identified using multi-area neural features captured by the recorder with an accuracy exceeding 87%. After over 16 months (March 13, 2024-August 1, 2025, remaining actively recording) of recordings, the neural signals acquired using the recorder maintain high fidelity and low attenuation during both the resting and drinking states, enabling potential long-term dissection of the neural mechanisms of natural behaviors in freely moving marmosets.}, }
@article {pmid41144414, year = {2025}, author = {Lu, B and Chen, J and Wang, F and Wen, G and Fu, R and Hua, C}, title = {Causality-Driven Convolutional Manifold Attention Network for Electroencephalogram Signal Decoding.}, journal = {IEEE transactions on pattern analysis and machine intelligence}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TPAMI.2025.3625631}, pmid = {41144414}, issn = {1939-3539}, abstract = {Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology.}, }
@article {pmid41143908, year = {2025}, author = {Xie, X and Hu, F and Yuan, S and Wen, D and Duan, D}, title = {MS-CANet: lightweight multi-scale channel attention network with depthwise residual blocks for EEG-based spatial cognition evaluation.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {41143908}, issn = {1741-0444}, support = {62276022//National Natural Science Foundation of China/ ; 2023YFF1203702//National Key Research and Development Program of China/ ; 62206014//National Key Research and Development Program of China/ ; FRFBD-25-052//Fundamental Research Funds for the Central Universities/ ; }, abstract = {Objective assessment of spatial cognitive ability is crucial for screening cognitive impairment and in neurorehabilitation. While deep learning methods for electroencephalogram (EEG) analysis show great promise, they increasingly rely on complex, parameter-heavy architectures. This complexity often leads to poor generalization due to overfitting on small datasets and hinders deployment on mobile healthcare devices. To overcome these limitations, we propose a novel lightweight multi-scale channel attention network with depthwise residual blocks. The model incorporates multi-scale convolutional layers to capture diverse temporal and spatial patterns in EEG signals. It then leverages channel attention mechanisms to dynamically prioritize informative channels, focusing on task-critical features. Furthermore, a novel depthwise separable residual block is introduced to significantly reduce computational complexity and maintain stable model performance. Evaluations on a spatial cognition EEG dataset demonstrate that our network achieves higher accuracy than baselines with only 8.453M parameters, making it an efficient and practical solution for mobile deployment. It also holds strong potential for extension to early screening and intervention in a wider range of cognitive disorders.}, }
@article {pmid41142108, year = {2025}, author = {Peng, Q and Huang, J and Li, C and Jiang, M and Huang, C and Luo, J and Li, H and Yin, T and Cai, M and Fu, S and Ma, G and Liu, Z and Xu, T}, title = {Magnetically Actuated Soft Electrodes for Multisite Bioelectrical Monitoring of Ex Vivo Tissues.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {6}, number = {}, pages = {0434}, pmid = {41142108}, issn = {2692-7632}, abstract = {Multisite electrophysiological monitoring of ex vivo tissues and organ models is essential for basic research and drug toxicity evaluation. However, conventional microelectrode arrays with fixed positions and rigid structures are insufficient for dynamic, curved tissue surfaces. Here, we present a magnetically actuated soft electrode (MSE) with precise navigation, adaptive attachment, and high-fidelity signal acquisition. Operating in a "locate-adhere-record-detach" cycle, the MSE enabled continuous multisite detection on beating ex vivo tissues. In isolated rat heart experiments, the MSE demonstrated millimeter-level navigation accuracy, stable contact, and high signal-to-noise ratio (average 28 dB). By integrating magnetic locomotion with electrophysiological sensing, this work establishes a programmable, actively addressable platform for multisite electrophysiological monitoring of organ models, tissue slices, and engineered constructs, offering broad potential for cardiotoxicity screening and cardiovascular research.}, }
@article {pmid41141194, year = {2025}, author = {Jayalaksshme Srinivasan, K and Periasamy, P and Gunasekaran, S}, title = {Motor Imagery and Motor Execution: A Narrative Review of Electroencephalographic (EEG) Signatures, Methodological Consistency, and Translational Applications.}, journal = {Cureus}, volume = {17}, number = {9}, pages = {e93011}, pmid = {41141194}, issn = {2168-8184}, abstract = {This narrative review evaluates when electroencephalography (EEG) signatures elicited by kinesthetic motor imagery (MI) genuinely approximate those of motor execution (ME), appraises methodological consistency across studies, and outlines pragmatic routes to translation in brain-computer interfaces (BCIs) and neurorehabilitation. A keyword-driven search of Web of Science, Scopus, PubMed, and conference repositories was used to extract empirical, English-language EEG studies reporting sensorimotor rhythm (mu 8-13 Hz; beta 13-30 Hz) event-related desynchronization/synchronization (ERD/ERS) metrics and/or decoding performance for MI and/or ME, with structured extraction of task/sample features, imagery protocol, EEG methods/signatures, MI-ME overlap, translational readouts, and limitations. Across convergent datasets, MI reliably evokes contralateral mu/beta ERD with timing and topography akin to ME, typically with smaller amplitudes and broader fields; realistic decoding benchmarks cluster around the mid-70% for MI versus low-80% for ME, with ≈70% a usability threshold and 15%-30% of naïve users below it. Convergence and performance improve with first-person kinesthetic instructions, higher imagery vividness, synchronised action observation, object-oriented tasks, EMG monitoring, and contingent neurofeedback; source-space modelling and synergy-aware features can lift MI accuracy into the ~82%-95% range in constrained settings, though offline gains often overestimate online control. In stroke cohorts, most patients exhibit clear ERD/ERS, and a meaningful subset exceeds operational thresholds; however, calibration-to-online drops (e.g., ~80% to ~70%) are common and partially recover with adaptive retraining. The principal barriers to translation are heterogeneous protocols (band definitions, referencing, validation), small and selective samples, sparse EMG to exclude covert movement, non-stationarity across sessions, and persistent non-responders. To move from plausibility to practice, future studies should standardise mu/beta windows and baselines, report closed-loop outcomes, personalise training with vividness assessment and synchronised action observation, anticipate drift with adaptive algorithms and periodic recalibration, and integrate MI with robotics, functional electrical stimulation, or virtual reality in multisite trials that track durable functional gains.}, }
@article {pmid41140580, year = {2025}, author = {Boonstra, JT}, title = {Ethical imperatives in the commercialization of brain-computer interfaces.}, journal = {IBRO neuroscience reports}, volume = {19}, number = {}, pages = {718-724}, pmid = {41140580}, issn = {2667-2421}, abstract = {The rapid commercialization of brain-computer interfaces (BCIs) raises urgent ethical and scientific challenges for human research oversight. While BCIs hold transformative potential for treating neurological disorders, their premature translation into consumer markets risks outpacing neuroscientific understanding and ethical frameworks. This essay critically examines the mismatch between commercial claims and the technical limitations of current BCI systems, decoding accuracy and biocompatibility, unresolved ethical dilemmas posed by neural data commodification and procedural risks, and the inadequacy of existing governance to address vulnerabilities in consent, privacy, and long-term safety. Responsible innovation demands proactive measures and robust public engagement to align development with societal values. Without such safeguards, the rush to commercialize BCIs risks prioritizing market interests over patient welfare and eroding public trust in neurotechnology.}, }
@article {pmid41139722, year = {2025}, author = {Li, J and Chen, T and Yan, X and Luo, L}, title = {The effect of device-based neuromodulation on the motor recovery of patients with spinal cord injury.}, journal = {Spinal cord}, volume = {}, number = {}, pages = {}, pmid = {41139722}, issn = {1476-5624}, abstract = {STUDY DESIGN: This paper systematically analyzes literature from PubMed, MEDLINE, Embase, and Cochrane databases over the past 10 years (up to May 25, 2025). It employs defined search terms, inclusion/exclusion criteria, and a documented search flow to evaluate mechanisms, efficacy, challenges, and future directions of neuromodulation technologies for spinal cord injury rehabilitation. The results synthesize findings from clinical trials, and representative papers.<br><br>OBJECTIVE: This review aims to evaluate the mechanisms and clinical applications of device-based neuromodulation technologies in spinal cord injury (SCI) rehabilitation, focusing on their efficacy, challenges, and future directions.<br><br>SETTING: The countries and regions worldwide participating in neuromodulation.<br><br>METHODS: We systematically analyzed advancements in neuromodulation over the past decade, including brain-spinal interfaces (BSI), brain-computer interfaces (BCI), cranial stimulation techniques (DBS, TMS, tDCS), spinal cord stimulation (SCS), robotic exoskeletons. The review integrates findings from clinical trials.<br><br>RESULTS: Neuromodulation technologies demonstrate significant potential in restoring motor and sensory function post-SCI. BSI and BCI improve mobility but face infection and cybersecurity risks. Cranial stimulation techniques enhance neuroplasticity, with DBS and TMS showing efficacy, while tDCS requires further validation. Epidural SCS enables motor recovery in complete paralysis but has high infection rates. Robotic exoskeletons benefit younger patients.<br><br>CONCLUSION: Neuromodulation technologies represent promising interventions for SCI, yet challenges remain in precision, safety, and efficacy. Future research should prioritize AI-driven parameter optimization, wearable device development, and multicenter randomized trials to establish these methods as core treatments, ultimately improving patient outcomes and quality of life.}, }
@article {pmid41138930, year = {2025}, author = {Wang, J and Wang, X and Qiao, S and La, H and Yu, Y and An, X}, title = {Investigation of fatigue mechanisms and detection methods for anesthesiologists based on multimodal physiological signals.}, journal = {Brain research bulletin}, volume = {232}, number = {}, pages = {111597}, doi = {10.1016/j.brainresbull.2025.111597}, pmid = {41138930}, issn = {1873-2747}, mesh = {Humans ; Electroencephalography/methods ; Male ; *Fatigue/physiopathology/diagnosis ; Female ; Adult ; Electrocardiography/methods ; *Anesthesiologists/psychology ; Attention/physiology ; Memory, Short-Term/physiology ; Cognition/physiology ; Young Adult ; }, abstract = {Anesthesiologists are highly susceptible to fatigue due to the demanding intensity and critical responsibility of their work, which poses substantial risks to both clinician health and patient safety. To elucidate fatigue mechanisms, this study systematically assessed cognitive and physiological alterations before and after prolonged high-intensity work. Cognitive performance was evaluated with paradigms targeting attention (0-back), working memory (2-back), and visuospatial processing, complemented by multimodal physiological monitoring with electroencephalogram (EEG) and electrocardiogram (ECG) recordings. Prolonged work was associated with significant declines in n-back accuracy, reflecting impaired attention and working memory, while visuospatial performance showed marked increases in both error rate and reaction time, indicating deterioration of spatial cognition and executive control. Concurrently, physiological analyses revealed enhanced EEG alpha-band connectivity, shortened RR intervals, a reduced LF/HF ratio, and elevated multiscale entropy, collectively indicating autonomic imbalance and central-autonomic dysregulation under fatigue. Building on these mechanistic findings, we applied transfer learning algorithms to statistically significant multimodal physiological features, achieving 99.4 % cross-subject classification accuracy. This integration of mechanistic insights with computational modeling underscores the reliability of the proposed strategy and its translational potential for real-world clinical fatigue monitoring.}, }
@article {pmid41137585, year = {2025}, author = {Zadeh Makouei, ST and Uyulan, C and Erguzel, TT and Tarhan, N}, title = {Advanced Facial Expression Recognition Using Model Averaging Ensembles of Convolutional Neural Networks and CAM Analysis.}, journal = {Clinical EEG and neuroscience}, volume = {}, number = {}, pages = {15500594251366792}, doi = {10.1177/15500594251366792}, pmid = {41137585}, issn = {2169-5202}, abstract = {Facial expressions play a vital role in non-verbal communication, conveying a wide range of emotions and messages. Although prior research achieved notable advances through architecture design or dataset-specific optimization, few studies have integrated multiple advanced techniques into a unified facial expression recognition (FER) pipeline. Addressing this gap, we propose a comprehensive approach that combines (i) multiple pre-trained CNNs, (ii) MTCNN-based face detection for improved facial region localization, and (iii) Grad-CAM-based interpretability. While MTCNN enhances the quality of face localization, it may slightly affect classification accuracy by focusing on cleaner yet more challenging samples. We evaluate four pre-trained models - DenseNet121, ResNet-50, ResNet18, and MobileNetV2 - on two datasets: Raf-DB and Cleaned-FER2013. The proposed pipeline demonstrates consistent improvements in interpretability and overall system robustness. The results emphasize the strength of integrating face detection, transfer learning, and interpretability techniques within a single framework can significantly enhance the transparency and reliability of FER systems. Combining FER with EEG-based systems significantly enhances the emotional intelligence of brain-computer interfaces, enabling more adaptive and personalized user experiences. With this approach the paper bridges the gap between affective computing and cognitive neuroscience, aligning closely EEG-centered interaction methodologies. Besides understanding the relationship between facial expressions of emotions and EEG signals will be an important study for literature.}, }
@article {pmid41136747, year = {2025}, author = {Zuo, H and Zhang, W and Wang, L and Wu, Y and Zheng, Y and Hao, S and Chen, QY and Cao, P and Ouyang, M and Huang, S and Zhou, W and Xue, YX and Pan, Y and Wei, W and Zhuo, M and Yuan, T and Zha, R and Zhang, Z and Zhang, X}, title = {Transcranial direct current stimulation restores addictive behavior via prefrontal-striatal circuit.}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {41136747}, issn = {1476-5578}, support = {32171080//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32400919//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200914//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Dependence on methamphetamine (METH) is a severe brain disorder characterized by high relapse rates and cognitive decline following detoxification. Recent research suggests that transcranial direct current stimulation (tDCS) may treat addiction, but the underlying neural mechanisms remain unknown. Here, we employed METH-conditioned place preference (CPP) paradigm integrated with fMRI, electrophysiology, chemogenetics, in vivo fiber photometry recordings and a novel rodent tDCS model to examine the neural circuit underlying tDCS modulation on METH-induced addictive behavior. We demonstrated that tDCS targeted at the medial prefrontal cortex (mPFC) prevents relapse. Specifically, tDCS enhanced the activity of neurons in both the infralimbic cortex (IL) and the nucleus accumbens shell (NAcSh) simultaneously. Furthermore, chemogenetic inhibition of the IL-NAcSh circuit eliminated the modulatory effects of tDCS, while activation of the IL-NAcSh circuit was sufficient to suppress the relapse. These findings reveal that the IL-NAcSh pathway functions as a descending regulatory circuit mediating the therapeutic outcomes of tDCS in the treatment of substance use disorder, offering new insights into circuit-based neuro-modulatory treatments for addiction.}, }
@article {pmid41135743, year = {2025}, author = {Wang, Y and Chen, HJ and Cheng, Y and Xie, Y and Cheng, Y and Zhao, S and Jiang, Y and Bai, T and Huo, Y and Wang, K and Zhang, M and Huang, W and Feng, G and Han, Y and Shu, N}, title = {Multimodal integration of plasma biomarkers, MRI, and genetic risk to predict cerebral amyloid burden in Alzheimer's disease.}, journal = {NeuroImage}, volume = {322}, number = {}, pages = {121550}, doi = {10.1016/j.neuroimage.2025.121550}, pmid = {41135743}, issn = {1095-9572}, mesh = {Humans ; *Alzheimer Disease/genetics/pathology/diagnostic imaging/blood ; Male ; Female ; *Magnetic Resonance Imaging/methods ; Aged ; Biomarkers/blood ; *Amyloid beta-Peptides/blood/metabolism ; Machine Learning ; Aged, 80 and over ; Positron-Emission Tomography ; Genetic Predisposition to Disease ; Neuroimaging/methods ; Middle Aged ; Cognitive Dysfunction ; *Brain/pathology/diagnostic imaging ; Longitudinal Studies ; }, abstract = {Alzheimer's disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ positron emission tomography (Aβ-PET) remains the gold standard for assessing cerebral Aβ burden, its clinical utility is hindered by cost, radiation exposure, and limited availability. Plasma biomarkers have emerged as promising, non‑invasive indicators of Aβ pathology, yet they do not incorporate individual genetic risk or neuroanatomical context. To address this gap, we developed a multimodal machine‑learning framework that integrates plasma biomarkers, MRI‑derived brain structural features (regional volumes, cortical thickness, cortical area and structural connectivity), and genetic risk profiles to predict cerebral Aβ burden. This approach was evaluated in 150 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 101 participants from a domestic Chinese Sino Longitudinal Study of Cognitive Decline (SILCODE). Incorporating multimodal features substantially improved predictive performance: the baseline model using plasma and clinical variables alone achieved an R[2] of 0.56, whereas integrating neuroimaging and genetic information increased accuracy (R[2] = 0.63 with apolipoprotein E genotypes and R[2] = 0.64 with polygenic risk scores). Furthermore, a multiclass classifier trained on the same multimodal features achieved robust discrimination of cognitive status, with area‑under‑the‑curve values of 0.87 for normal controls, 0.76 for mild cognitive impairment, and 0.95 for AD dementia. These findings highlight the value of combining plasma, imaging, and genetic data to non-invasively estimate cerebral Aβ burden, offering a potential alternative to PET imaging for early AD risk assessment.}, }
@article {pmid41135661, year = {2025}, author = {Pan, Y and Yang, X and Wu, M and Hu, S}, title = {Latent profile analysis of childhood trauma in Chinese individuals with bipolar disorder: Differential associations with suicidality and clinical symptomatology.}, journal = {Journal of affective disorders}, volume = {394}, number = {Pt A}, pages = {120490}, doi = {10.1016/j.jad.2025.120490}, pmid = {41135661}, issn = {1573-2517}, abstract = {BACKGROUND: Childhood trauma is a well-established risk factor for poor clinical outcomes in bipolar disorder (BD), yet most studies have relied on cumulative trauma scores, potentially overlooking heterogeneity in trauma exposure and its differential impact on psychopathology.<br><br>METHODS: This study employed latent profile analysis (LPA) to identify distinct subtypes of childhood trauma based on the Childhood Trauma Questionnaire (CTQ) among 725 individuals with BD in a Chinese clinical sample. Differences across trauma profiles were examined in relation to demographic features, psychiatric symptoms (anxiety, depression, mania), and suicidal ideation (Beck Scale for Suicide Ideation, BSSI).<br><br>RESULTS: A four-class solution was identified, and the relationship with mental health outcomes was analyzed. Class 4 group, characterized by the most severe emotional abuse and physical neglect, along with the lowest emotional neglect, reported the highest levels of anxiety (HAMA), depression (HAMD), and suicidal ideation (BSSI). In contrast, manic symptoms (YMRS) were present across all groups but did not differ significantly between trauma profiles. Logistic regression indicated that emotional abuse was the strongest predictor of trauma class membership.<br><br>CONCLUSIONS: Distinct trauma profiles in BD are differentially associated with symptom severity and suicide risk. These findings highlight the clinical value of moving beyond cumulative trauma scores to identify trauma-specific subtypes. Early identification of high-risk trauma configurations may inform personalized assessment and intervention strategies for individuals with BD.}, }
@article {pmid41135523, year = {2025}, author = {Wang, Z and Tang, Q and Li, K and Mou, J and Chen, Y and Kuang, W and Sun, L and Ma, Z and Wei, Y and Bao, R and Sun, X and Wang, S and Lu, W and Xu, GY and Tang, YQ and Duan, S and Ni, JD}, title = {An enteric-DRG pathway for interoception and visceral pain in mice.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2025.09.035}, pmid = {41135523}, issn = {1097-4199}, abstract = {Sensory afferents are major interoceptive pathways for organ-brain communication. Within the distal colon, dorsal root ganglia (DRGs) afferents regulate key gut physiology. Inflammation causes hypersensitivity of DRG pathways, leading to visceral pain. However, whether enteric neurons contribute to interoception and visceral pain remains unclear. Here, we surveyed the DRG innervation along the gastrointestinal tract in mice and found extensive associations between DRG terminals and enteric neurons. Optogenetic activation of different DRG terminals in the distal colon elicited variable degrees of behavioral responses, but only designated subpopulations induced aversion. Notably, optogenetic activation of colon cholinergic, but not nitrergic, enteric neurons signaled through the DRG-spinal pathway to evoke a non-aversive nociceptive-like reflex. Acetylcholine is part of the enteric-DRG signaling. Remarkably, inflammation shifted the nature of the enteric-DRG pathway from non-aversive to aversive. These findings expand the previous understanding of DRG-mediated visceral sensation, highlighting the contribution of enteric neuron-DRG communication to inflammation-induced visceral pain.}, }
@article {pmid41134961, year = {2025}, author = {Jin, J and Qin, K and Allison, BZ and Li, S and Zhang, Y and Wang, X and Cichocki, A}, title = {A Transfer Learning SSVEP Decoding Algorithm Calibrated With Single-Trial Data.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2025.3617508}, pmid = {41134961}, issn = {2162-2388}, abstract = {Training-based algorithms significantly outperform training-free methods in terms of recognition performance for steady-state visual-evoked potential (SSVEP)-based brain-computer Interfaces (BCIs). However, collecting training data requires calibration experiments that are effort-intensive and often costly. These calibration demands limit the practicality of BCI, as users (and even system operators) may experience fatigue or lose interest in continued use. Transfer learning (TL) offers an effective solution, but it typically relies on either a certain amount of target domain data or extensive source domain data. To address this limitation, we introduce the concept of cross-dataset TL in SSVEP for the first time to extract transfer knowledge from other datasets. During this process, we identified a data mismatch problem that severely compromises the generalizability of transfer knowledge. To overcome this challenge, we propose a TL-SSVEP decoding algorithm calibrated with single-trial data (TL-CSTD). Specifically, we use 2 s of 8 Hz single-trial calibration data from the target domain to obtain matched transfer templates from the source domain. These templates are then corrected to extract holistic and single-period transfer knowledge, which are subsequently employed to construct an efficient TL-SSVEP decoding model for the target subject. Experimental results on three large SSVEP datasets demonstrate that TL-CSTD effectively addresses the data mismatch problem and achieves excellent SSVEP recognition performance using only 2 s of single-trial calibration data, showing its significant application potential and practicality.}, }
@article {pmid41134945, year = {2025}, author = {Kim, DS and Lee, SH and Yin, K and Lee, SW}, title = {Reconstructing Unseen Sentences From Speech-Related Biosignals for Open-Vocabulary Neural Communication.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {4338-4348}, doi = {10.1109/TNSRE.2025.3625219}, pmid = {41134945}, issn = {1558-0210}, mesh = {Humans ; Electroencephalography ; *Speech/physiology ; Male ; Female ; Adult ; Electromyography ; Young Adult ; Algorithms ; Brain-Computer Interfaces ; Phonetics ; Communication ; Brain/physiology ; Signal Processing, Computer-Assisted ; Speech Perception/physiology ; }, abstract = {Brain-to-speech (BTS) systems represent a groundbreaking approach to human communication by enabling the direct transformation of neural activity into linguistic expressions. While recent non-invasive BTS studies have largely focused on decoding predefined words or sentences, achieving open-vocabulary neural communication comparable to natural human interaction requires decoding unconstrained speech. Additionally, effectively integrating diverse signals derived from speech is crucial for developing personalized and adaptive neural communication and rehabilitation solutions for patients. This study investigates the potential of speech synthesis for previously unseen sentences across various speech modes by leveraging phoneme-level information extracted from high-density electroencephalography (EEG) signals, both independently and in conjunction with electromyography (EMG) signals. Furthermore, we examine the properties affecting phoneme decoding accuracy during sentence reconstruction and offer neurophysiological insights to further enhance EEG decoding for more effective neural communication solutions. Our findings underscore the feasibility of biosignal-based sentence-level speech synthesis for reconstructing unseen sentences, highlighting a significant step toward developing open-vocabulary neural communication systems adapted to diverse patient needs and conditions. Additionally, this study provides meaningful insights into the development of communication and rehabilitation solutions utilizing EEG-based decoding technologies.}, }
@article {pmid41134943, year = {2025}, author = {Sun, J and Lin, PJ and Zhai, X and Wang, W and Jia, T and Li, Z and Pan, Y and Ji, L and Zhou, B and Li, C}, title = {Multimodal behavioral data predict stroke patient's response to BCI treatment through explainable AI.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3625222}, pmid = {41134943}, issn = {1558-0210}, abstract = {Brain-computer interface (BCI)-based neurorehabilitation holds promise in enhancing motor recovery after stroke. However, recent research has reported heterogeneous results, indicating both responders and non-responders to BCI therapy. Using explainable artificial intelligence (XAI) methods, this study aims to investigate the independent and combined importance of multimodal behavioral data to predict patients' response to BCI therapy. Forty-two subacute stroke patients with lower-limb motor impairment underwent behavioral assessments, and received two-week BCI rehabilitation training. Linear regression, elastic net and artificial neural network models were developed to predict response to BCI therapy. Two XAI techniques, the stepwise method and Shapley additive explanation, were used to interpret model outcomes. The multivariate model (R[2]=0.852, P<0.001) that combines an optimal subset of multimodal behavioral data outperformed the univariate model (R[2]=0.758, P<0.001) trained on a single variable. Elastic net and artificial neural network models both demonstrated high prediction performance, as indicated by classification accuracies of 0.810 and 0.762, and areas under the receiver operating characteristic curve of 0.782 and 0.771. Our results revealed that multimodal behavioral data, including demographic, clinical, and biomechanical characteristics, provided unique and complementary information for interpreting the response of subacute patients to BCI therapy. Particularly, baseline motor impairment, muscle spasticity and balance function were primary predictors. Our findings highlight the core role of XAI methods towards precision medicine, which can help clinicians to identify individual recovery potentials and plan optimal treatment strategies.}, }
@article {pmid41132843, year = {2025}, author = {Jin, S and Lin, C and Li, P and Wang, X and Wang, Y and Zhang, C and Wang, X and Peng, Y and Li, H and Lu, Y and Wang, X}, title = {Cannabidiol alleviates methamphetamine addiction via targeting ATP5A1 and modulating the ATP-ADO-A1R signaling pathway.}, journal = {Acta pharmaceutica Sinica. B}, volume = {15}, number = {10}, pages = {5261-5276}, pmid = {41132843}, issn = {2211-3835}, abstract = {Cannabidiol (CBD), a non-psychoactive cannabinoid, shows great promise in treating methamphetamine (METH) addiction. Nonetheless, the molecular target and the mechanism through which CBD treats METH addiction remain unexplored. Herein, CBD was shown to counteract METH-induced locomotor sensitization and conditioned place preference. Additionally, CBD mitigated the adverse effects of METH, such as cristae loss, a decline in ATP content, and a reduction in membrane potential. Employing an activity-based protein profiling approach, a target fishing strategy was used to uncover CBD's direct target. ATP5A1, a subunit of ATP synthase, was identified and validated as a CBD target. Moreover, CBD demonstrated the ability to ameliorate METH-induced ubiquitination of ATP5A1 via the D376 residue, thereby reversing the METH-induced reduction of ATP5A1 and promoting the assembly of ATP synthase. Pharmacological inhibition of the ATP efflux channel pannexin 1, blockade of ATP hydrolysis by a CD39 inhibitor, and blocking the adenosine A1 receptor (A1R) all attenuated the therapeutic benefits of CBD in mitigating METH-induced behavioral sensitization and CPP. Moreover, the RNA interference of ATP5A1 in the ventral tegmental area resulted in the reversal of CBD's therapeutic efficacy against METH addiction. Collectively, these data show that ATP5A1 is a target for CBD to inhibit METH-induced addiction behaviors through the ADO-A1R signaling pathway.}, }
@article {pmid41132728, year = {2025}, author = {Berlet, R and Azapagic, A and Jha, NK and Aksenov, D and Bookwalter, J and Ullah, A and Bobustuc, G and Lee, J and Sant, H and McDaid, J and Walker, M and Shea, J and Graff, D and Barlow, AK and Frigerio, R and Aliee, D and Bailes, C and Gale, BK and Bailes, JE}, title = {An implantable, intracerebral osmotic pump for convection-enhanced drug delivery in glioblastoma multiforme.}, journal = {Frontiers in oncology}, volume = {15}, number = {}, pages = {1676691}, pmid = {41132728}, issn = {2234-943X}, abstract = {BACKGROUND: Glioblastoma multiforme (GBM; WHO Grade 4) is an aggressive brain tumor that invariably recurs after surgical resection, chemoradiation, and adjuvant chemotherapy. Treatment is limited, in part, because the blood-brain barrier (BBB) restricts entry of chemotherapeutic agents to the brain. Introducing drugs directly into the brain circumvents the BBB, but diffusion of these typically large drug molecules within brain parenchyma is limited. Convection-enhanced delivery (CED), based on the principles of bulk flow, can achieve drug distribution over a wider area to target residual cancer cells and thus remains a promising technique for treating GBM and other neuro-oncologic pathologies. Here, we propose a new method that combines direct brain delivery and CED using a fully implantable, microfluidic pump placed at the time of initial resection surgery.<br><br>METHODS: In this initial proof-of-concept study, we evaluated the function of a 3D-printed pump in an in vitro system and in vivo in a rat C6 glioma model.<br><br>RESULTS: In vitro osmosis-driven distribution of a high molecular-weight marker dye extended up to 18 mm from the pump with minimal reflux, including under simulations of increased intracranial pressure. In vivo, MRI imaging demonstrated wide distribution of superparamagnetic iron oxide particles from a pump implanted after the resection of a C6 glioma. Histological staining indicated that pump implantation did not cause additional inflammatory changes compared to controls.<br><br>CONCLUSION: This preliminary study demonstrated the feasibility of using an implantable, osmosis-driven pump to bypass the BBB and provide targeted delivery for treatment of GBM.}, }
@article {pmid41129590, year = {2025}, author = {Cao, D and Yu, Z and Wang, J and Wu, Y}, title = {SMMTM: Motor imagery EEG decoding algorithm using a hybrid multi-branch separable convolutional self-attention temporal convolutional network.}, journal = {PloS one}, volume = {20}, number = {10}, pages = {e0333805}, pmid = {41129590}, issn = {1932-6203}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Algorithms ; Neural Networks, Computer ; *Imagination/physiology ; }, abstract = {Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI signals decoding remains a significant barrier to the broader growth of the BCI applications. In this study, we propose the SMMTM model, which combines spatiotemporal convolution (SC), multi-branch separable convolution (MSC), multi-head self-attention (MSA), temporal convolution network (TCN), and multimodal feature fusion (MFF). Specifically, we use the SC module to capture both temporal and spatial features. We design a MSC to capture temporal features at multiple scales. In addition, MSA is designed to extract valuable global features with long-term dependence. The TCN is employed to capture higher-level temporal features. The MFF consists of feature fusion and decision fusion, using the features output from the SMMTM to improve robustness. The SMMTM was evaluated on the public benchmark BCI Comparison IV 2a and 2b datasets, the results showed that the within-subject classification accuracies for the datasets were 84.96% and 89.26% respectively, with kappa values of 0.797 and 0.756. The cross-subject classification accuracy for the 2a dataset was 69.21%, with a kappa value of 0.584. These results indicate that the SMMTM significantly enhances decoding performance, providing a strong foundation for advancing practical BCI implementations.}, }
@article {pmid41129446, year = {2025}, author = {Dang, W and Ren, Z and Sun, J and Lv, D and Xiong, Z and Guo, W and Gao, Z and Yu, H}, title = {ML-TGNet: A Multi-Level Topology Guidance Network for Motor Imagery Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3624298}, pmid = {41129446}, issn = {2168-2208}, abstract = {Brain-computer interfaces (BCIs) based on motor imagery electroencephalogram (MI-EEG) signals have been extensively applied in various neural rehabilitation scenarios. However, existing methods primarily focus on designing complex architectures to extract spatio-temporal features from MI-EEG signals, often neglecting the brain dynamics information embedded within them. This oversight leads to the extraction of redundant information, ultimately reducing decoding performance. To address these challenges, we design a multi-level topology-guidance network (ML-TGNet) that leverages topological brain synchronization information to more effectively extract features related to MI tasks. ML-TGNet specifically comprises a multi-level topology guidance module, a feature pool module, and a multi-branch decoding module. To evaluate its performance, extensive experiments are conducted on three publicly available MI datasets: the BCI Competition IV-2a dataset, the High Gamma dataset, and the OpenBMI dataset. ML-TGNet achieves classification accuracies of 82.33%, 96.42%, and 85.26% on these three datasets, respectively, outperforming current state-of-the-art models. These findings confirm the efficacy of using brain synchronization information to guide MI decoding, thereby opening a novel approach for EEG-based brain state decoding by integrating brain dynamics into deep learning.}, }
@article {pmid41129442, year = {2025}, author = {Wang, Z and Wang, H and Jia, T and He, X and Li, S and Wu, D}, title = {DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3622725}, pmid = {41129442}, issn = {2168-2208}, abstract = {Electroencephalography (EEG)-based brain computer interfaces (BCIs) transform spontaneous/evoked neural activity into control commands for external communication. While convolutional neural networks (CNNs) remain the mainstream backbone for EEG decoding, their inherently short receptive field makes it difficult to capture long-range temporal dependencies and global inter-channel relationships. Recent CNN-Transformer (Con former) hybrids partially address this issue, but most adopt a serial design, resulting in suboptimal integration of local and global features, and often overlook explicit channel-wise modeling. To address these limitations, we propose DBConformer, a dual-branch convolutionalTrans former network tailored for EEG decoding. It integrates a temporal Conformer to model long-range temporal dependencies and a spatial Conformer to extract inter-channel interactions, capturing both temporal dynamics and spatial patterns in EEG signals. A lightweight channel attention module further refines spatial representations by assigning data-driven importance to EEG channels. Extensive experiments under four evaluation settings on three paradigms, including motor imagery, seizure detection, and steady state visual evoked potential, demonstrated that DBCon former consistently outperformed 13 competitive baseline models, with over an eight-fold reduction in parameters than current high-capacity EEG Conformer architecture. Furthermore, the visualization results confirmed that the features extracted by DBConformer are physiologically in terpretable and aligned with prior knowledge. The superior performance and interpretability of DBConformer make it reliable for accurate, robust, and explainable EEG decoding. Code is publicized at https://github.com/wzwvv/ DBConformer.}, }
@article {pmid41129430, year = {2025}, author = {Bai, Y and Zhang, S and Zhao, R and Han, X and Ni, G and Ming, D}, title = {Cross-Hemispheric Spatial-Temporal Attention Network for Decoding Silent Speech From EEG.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3624878}, pmid = {41129430}, issn = {1558-2531}, abstract = {OBJECTIVE: Speech, as the core of advanced human cognition, is fundamental to social interaction and daily life. Electroencephalogram (EEG)-based speech brain-computer interface (BCI) offers a novel communication pathway for patients with speech disorders, where deep learning has demonstrated significant advantages. Given the established dominance of the left hemisphere in speech processing, exploring methods to extract speech related neural features fully is crucial for enhancing decoding per formance.<br><br>APPROACH: In this study, EEG signals were recorded during a silent speech task involving the articulation of 10 distinct Chinese characters. Leveraging the principle of language function lateralization, we proposed a novel deep learning model, the cross hemispheric spatial-temporal attention network (CHSTAN), for EEG-based silent speech recognition. A multiscale temporal con volution block was employed to extract the temporal dynamics of EEG signals. A hemispheric spatial convolutional block was designed to independently process spatial information from the left and right hemispheres. Furthermore, the cross-attention mechanism was introduced to enhance inter-hemispheric feature inter action and specifically reinforce left-hemispheric feature representation for the final classification.<br><br>RESULTS: We compared CHSTAN with other existing methods using 5-fold cross-validation on the collected dataset. CHSTAN achieved an average classification accuracy of 49.88% and an average F1-score of 48.75% in decoding the 10 Chinese characters, significantly outperforming other methods.<br><br>CONCLUSION: The results indicate that the CHSTAN performs effectively in silent speech EEG classification tasks. Notably, the feature patterns learned through its innovative architecture correspond to neural speech processing mechanism.<br><br>SIGNIFICANCE: CHSTAN provides valuable insights and practical solutions for improving the performance of EEG-based speech decoding.}, }
@article {pmid41127543, year = {2025}, author = {Sun, J and Li, H and Wang, J and Yang, W}, title = {Application of biomimetic approaches in the treatment of neurological disorders.}, journal = {Materials today. Bio}, volume = {35}, number = {}, pages = {102334}, pmid = {41127543}, issn = {2590-0064}, abstract = {Neurological disorders usually involve nerve cell damage or death, and traditional treatments have significant limitations in neural repair. Biomimetic approaches mimic the structure and function of biological systems, providing an important approach to neural repair and regeneration. This paper first summarizes the current challenges in treating neurological disorders. It then explores the applications of bioinspired strategies in drug delivery systems (DDS), neural repair, three-dimensional (3D) printed neural scaffolds, and brain-machine interfaces (BMIs) with neuromodulation. Additionally, it discusses the challenges, strategies, advantages, and prospects of bioinspired methods in neurological disease treatment. The aim is to provide a comprehensive perspective on the potential of biomimicry-based methods in this field.}, }
@article {pmid41127304, year = {2025}, author = {He, B and Guo, Y and Yang, G}, title = {Integrated Piezoelectric Vibration and In Situ Force Sensing for Low-Trauma Tissue Penetration.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {6}, number = {}, pages = {0417}, pmid = {41127304}, issn = {2692-7632}, abstract = {Precision-controlled microscale manipulation tasks-including neural probe implantation, ophthalmic surgery, and cell membrane puncture-often involve minimally invasive membrane penetration techniques with real-time force feedback to minimize tissue trauma. This imposes rigorous design requirements on the corresponding miniaturized instruments with robotic assistance. This paper proposes an integrated piezoelectric module (IPEM) that combines high-frequency vibration-assisted penetration with real-time in situ force sensing. The IPEM features a compact piezoelectric actuator integrated with a central tungsten probe, generating axial micro-vibration (4,652 Hz) to enable smooth tissue penetration while simultaneously measuring contact and penetration forces via the piezoelectric effect. Extensive experiments were conducted to validate the effectiveness and efficacy of the proposed IPEM. Both static and dynamic force-sensing tests demonstrate the linearity, sensitivity (9.3 mV/mN), and accuracy (mean absolute error < 0.3 mN, mean absolute percentage error < 1%) of the embedded sensing unit. In gelatin phantom tests, the module reduced puncture and insertion forces upon activation of vibration. In vivo experiments in mouse brains further confirmed that the system could reduce penetration resistance (from an average of 11.67 mN without vibration to 7.8 mN with vibration, decreased by 33%) through the pia mater and accurately mimic the electrode implantation-detachment sequence, leaving a flexible electrode embedded with minimal trauma. This work establishes a new paradigm for smart surgical instruments by integrating a compact actuator-sensor design with real-time in situ force feedback capabilities, with immediate applications in brain-machine interfaces and microsurgical robotics.}, }
@article {pmid41121568, year = {2025}, author = {Skarzynski, PH and Cywka, KB and Czaplicka, EA and Skarzynski, H}, title = {The Bonebridge Active Bone Conduction Hearing Implant: Safety, Effectiveness and Outcomes Based on 355 Patients.}, journal = {Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology &amp; Cervico-Facial Surgery}, volume = {}, number = {}, pages = {}, doi = {10.1111/coa.70050}, pmid = {41121568}, issn = {1749-4486}, abstract = {OBJECTIVES: This study evaluates the safety and efficacy of the Bonebridge BCI 601 and 602 bone conduction implants in our largest cohort to date of 355 patients. The patients had a wide age range and exhibited conductive, mixed, or single-sided deafness (SSD).<br><br>DESIGN: All patients underwent Bonebridge implantation. Pre- and post-implantation evaluations included pure-tone audiometry, speech recognition tests, and free-field audiometry. Word recognition was measured using the Polish Monosyllabic Word Test, while speech reception in noise was assessed using the Polish Sentence Matrix Test. Subjective benefit was assessed using the APHAB questionnaire. Follow-up tests were performed 3-6&#x2009;months after activation.<br><br>RESULTS: Revision surgery was required in 17 patients (4.8%) due to complications, including implant removal in 5 cases. Reimplantation was successful in 4 of these. The APHAB questionnaire showed improved hearing function and all hearing tests also showed significant improvement.<br><br>CONCLUSION: Active bone conduction implantation is an effective method for the rehabilitation of conductive hearing loss, mixed hearing loss, and unilateral deafness. This large cohort study confirms significant hearing improvement and subjective benefits. The low complication rate supports the reliability of the Bonebridge system.}, }
@article {pmid41121378, year = {2025}, author = {Chen, HJ and Dong, X and Wang, Y and Wang, K and Feng, G and Bai, T and Zhang, M and Gan, K and Peng, JJ and Huang, W and Zhang, Z and Shu, N and Ma, G}, title = {Polygenic risk for Alzheimer's disease in healthy aging: age-related and APOE-driven effects on brain structures and cognition.}, journal = {Genome medicine}, volume = {17}, number = {1}, pages = {126}, pmid = {41121378}, issn = {1756-994X}, support = {2022ZD0213300//STI2030-Major Projects/ ; }, mesh = {Humans ; *Alzheimer Disease/genetics/pathology ; *Multifactorial Inheritance ; Male ; Aged ; Female ; *Cognition ; Middle Aged ; *Apolipoproteins E/genetics ; *Brain/pathology ; *Healthy Aging/genetics ; *Genetic Predisposition to Disease ; Aged, 80 and over ; Risk Factors ; Gray Matter/pathology ; White Matter/pathology ; Magnetic Resonance Imaging ; }, abstract = {BACKGROUND: Alzheimer's disease (AD) is characterized by progressive neurodegeneration and cognitive decline with age. The genetic architecture of AD involves multiple loci, including the apolipoprotein E gene (APOE). The polygenic risk scores for AD (AD-PRS) provide a comprehensive genome-wide assessment of AD risk, yet their age-related effects on brain structures and cognitive function in cognitively unimpaired individuals remain largely undefined.<br><br>METHODS: We analyzed cognitively unimpaired, genetically unrelated Caucasians from the UK Biobank (N&#x2009;=&#x2009;21,236, 64.5&#x2009;&#xb1;&#x2009;7.6 years). AD-PRS was derived using a Bayesian approach incorporating approximately 5 million genetic variants (UK Biobank's standard PRS). Brain structures were measured with regional gray matter (GM) volumes and tract-wise microstructural white matter (WM) integrity. Cognitive performance was evaluated with executive function, visuospatial function, reasoning, and memory. Sliding window analyses were performed to investigate age-related polygenic effects on brain structures, and mediation analyses tested whether structural changes mediated the gene-cognition relationship across different age groups. Analyses were replicated using two custom PRSs-one including APOE and the other excluding APOE regions-calculated with the clumping-and-thresholding approach.<br><br>RESULTS: High AD-PRS was associated with accelerated GM atrophy (particularly in the hippocampus, thalamus, and parahippocampus), increased cerebral ventricular volume, and reduced&#xa0;WM integrity (especially in the fornix, cingulum, and superior fronto-occipital fasciculus). These polygenic effects demonstrated significant age-related amplification (pBonf&#x2009;<&#x2009;0.05), with the strongest effects in individuals aged&#x2009;&#x2265;&#x2009;75. Elevated AD-PRS was linked to lower cognitive performance across aging, especially in executive function, reasoning, and memory, which were significantly mediated by structural brain changes in subcortical and posterior limbic regions and their WM connections, predominantly in late aging (p&#x2009;<&#x2009;0.05). Sensitivity analyses confirmed the robustness of these findings, emphasizing the dominant contribution of APOE, while also identifying age-specific effects from non-APOE variants.<br><br>CONCLUSIONS: High polygenic risk for AD may be associated with accelerated cognitive decline in healthy aging, mediated by structural changes within hippocampal-thalamic regions and their connecting WM tracts. We provide insights into the early pathogenesis of AD and support the potential for age-targeted screening and early intervention for individuals at high genetic risk.}, }
@article {pmid41120372, year = {2025}, author = {He, S and Li, Z and Dang, J and Luo, Y and Zhang, G}, title = {CIRE: A Chinese EEG Dataset for decoding speech intention modulated by prosodic emotion.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1664}, pmid = {41120372}, issn = {2052-4463}, support = {No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Electroencephalography ; *Emotions ; *Speech ; Brain-Computer Interfaces ; China ; Machine Learning ; Speech Perception ; Intention ; Female ; Male ; Adult ; East Asian People ; }, abstract = {Neural decoding of speech intention could advance the development and application of brain-computer interface (BCI) technology. Currently, lack of dataset limited the research on decoding the true speech intention, especially the diverse intentions expressed by the same text when no context is given. This study provides an EEG dataset, CIRE, on spoken language interaction intention featuring aligned textual expressions with divergent intentional meanings due to the differences in prosodic emotion. The dataset comprises preprocessed high-density (128-channel) EEG recordings from 38 participants engaged in comprehension of attitude-conveying speech stimuli, accompanied by Wav2vec2-derived acoustic embeddings of the listening materials. To validate our dataset through cognitive neuroscience studies and binary intent classification, we applied signal processing pipelines, cognitive analysis frameworks, and machine learning (ML) approaches. Our baseline model achieved a cross-subject classification accuracy of 68.2%, with differences exhibiting interpretable neurophysiological correlates. The high-density and high temporal resolution EEG data offer broader application areas, both in cognitive neuroscience and speech BCI, and can also contribute to the brain-inspired algorithms.}, }
@article {pmid41117243, year = {2025}, author = {Mou, T and Lai, J and Kong, L}, title = {Effects of Paliperidone on Serum D-dimer Levels: Clinical and Experimental Findings.}, journal = {Actas espanolas de psiquiatria}, volume = {53}, number = {5}, pages = {959-966}, pmid = {41117243}, issn = {1578-2735}, mesh = {*Paliperidone Palmitate/therapeutic use/pharmacology ; Humans ; *Antipsychotic Agents/therapeutic use/pharmacology ; *Fibrin Fibrinogen Degradation Products/analysis ; Animals ; Male ; Adult ; *Schizophrenia/drug therapy/blood ; Female ; Mice ; Mice, Inbred C57BL ; Middle Aged ; Young Adult ; }, abstract = {BACKGROUND: Dysregulation of coagulation function associated with antipsychotic treatment remains poorly understood. This study investigates the potential impact of paliperidone on serum D-dimer levels during the early stages of treatment.<br><br>METHODS: Nine patients diagnosed with first-episode schizophrenic spectrum disorder were assessed for serum D-dimer levels before and after a 2-week paliperidone regimen. Additionally, eight adult C57 mice in the experimental group (EG) received 3 mg/kg of paliperidone daily for 10 consecutive days, while eight mice in the control group (CG) were untreated. Venous blood was collected and analyzed for D-dimer at baseline, and on the 5th and 10th days in the EG, as well as on the 10th day for the CG.<br><br>RESULTS: No significant differences were observed in serum D-dimer levels before and after paliperidone treatment in the patient cohort. In animal experiments, compared to the CG on the 10th day, serum D-dimer levels in the EG on the 10th day showed no significant difference (p > 0.05), while the level in the EG on the 5th day was significantly lower (p < 0.05). Compared to its baseline, serum D-dimer levels within the EG on the 5th day was significantly decreased (p < 0.05).<br><br>CONCLUSION: Short-term paliperidone treatment had minimal effects on serum D-dimer levels in both human participants and mice, though transient changes were noted early in treatment. Nonetheless, the potential for drug-induced coagulation disruption should be considered in clinical practice.}, }
@article {pmid41115264, year = {2025}, author = {Ye, Y and Zhang, Y and Li, J and Wu, P and Zhang, T and Liao, T and Neculai, D and Lou, J and Li, Z and Chen, W and Hu, W}, title = {Nanoscale Mechanical Force Primes NOD1-LRR for Efficient Pathogen Recognition.}, journal = {The journal of physical chemistry letters}, volume = {16}, number = {43}, pages = {11196-11205}, doi = {10.1021/acs.jpclett.5c02875}, pmid = {41115264}, issn = {1948-7185}, mesh = {*Nod1 Signaling Adaptor Protein/chemistry/metabolism ; Molecular Dynamics Simulation ; Diaminopimelic Acid/analogs &amp; derivatives/chemistry/metabolism ; Humans ; Protein Domains ; }, abstract = {Detecting pathogens requires molecular sensors that can rapidly and precisely respond to local threats. While cytosolic innate immune receptors such as NOD1 are known as biochemical detectors, their ability to interpret physical cues remains a critical unknown. Here, we combine piconewton-resolution single-molecule manipulation, molecular dynamics simulations, and structural modeling to demonstrate that NOD1 is not a passive detector but an active nanomechanical sensor. We show that the receptor's LRR domain, with its curved, horseshoe-like nanoarchitecture, functions as a mechanical force concentrator. Physiologically relevant piconewton-scale forces, such as those at the membrane-cytosol interface, are concentrated into a high-stress hotspot that primes the domain for a conformational transition. This force-induced priming acts as an allosteric nanoswitch, transducing mechanical energy into a biochemical output: a dramatic increase in binding strength and sensitivity for its bacterial ligand iE-DAP. This mechanochemical coupling positions NOD1 as a force-responsive sensor, enabling rapid and spatially restricted immune activation. Our work establishes a new paradigm for cytosolic pathogen recognition and suggests that force-sensing LRR domains represent a generalizable design principle in nanobiology, bridging a conceptual gap between mechanobiology and innate immunity.}, }
@article {pmid41112520, year = {2025}, author = {Shi, J and Wang, J and Fei, W and Feleke, AG and Bi, L}, title = {Neuroanatomy-Informed Brain-Machine Hybrid Intelligence for Robust Acoustic Target Detection.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {6}, number = {}, pages = {0438}, pmid = {41112520}, issn = {2692-7632}, abstract = {Sound target detection (STD) plays a critical role in modern acoustic sensing systems. However, existing automated STD methods show poor robustness and limited generalization, especially under low signal-to-noise ratio (SNR) conditions or when processing previously unencountered sound categories. To overcome these limitations, we first propose a brain-computer interface (BCI)-based STD method that utilizes neural responses to auditory stimuli. Our approach features the Triple-Region Spatiotemporal Dynamics Attention Network (Tri-SDANet), an electroencephalogram (EEG) decoding model incorporating neuroanatomical priors derived from EEG source analysis to enhance decoding accuracy and provide interpretability in complex auditory scenes. Recognizing the inherent limitations of stand-alone BCI systems (notably their high false alarm rates), we further develop an adaptive confidence-based brain-machine fusion strategy that intelligently combines decisions from both the BCI and conventional acoustic detection models. This hybrid approach effectively merges the complementary strengths of neural perception and acoustic feature learning. We validate the proposed method through experiments with 16 participants. Experimental results demonstrate that the Tri-SDANet achieves state-of-the-art performance in neural decoding under complex acoustic conditions. Moreover, the hybrid system maintains reliable detection performance at low SNR levels while exhibiting remarkable generalization to unseen target classes. In addition, source-level EEG analysis reveals distinct brain activation patterns associated with target perception, offering neuroscientific validation for our model design. This work pioneers a neuro-acoustic fusion paradigm for robust STD, offering a generalizable solution for real-world applications through the integration of noninvasive neural signals with artificial intelligence.}, }
@article {pmid41110663, year = {2025}, author = {Gordleeva, S and Grigorev, N and Pitsik, E and Kurkin, S and Kazantsev, V and Hramov, A}, title = {Detection and rehabilitation of age-related motor skills impairment: Neurophysiological biomarkers and perspectives.}, journal = {Ageing research reviews}, volume = {113}, number = {}, pages = {102923}, doi = {10.1016/j.arr.2025.102923}, pmid = {41110663}, issn = {1872-9649}, abstract = {Age-related decline in motor control, manifesting as impaired posture, gait, and slowed movement execution, significantly diminishes the quality of life in older adults. These functional deficits are associated with alterations in neurophysiological data, which are analyzed using advanced techniques including spectral analysis, complexity measures, and functional connectivity network analysis. These methodologies provide valuable insights into the neurobiological mechanisms underpinning age-related motor function impairments, linking physiological changes to non-invasively recorded electrophysiological and hemodynamic responses. Recent investigations have demonstrated an age-dependent impairment in access to working memory during motor tasks, evidenced by significant correlations between electroencephalographic biomarkers and neural response latencies. Furthermore, these functional biomarkers are associated with the degradation of motor learning abilities in older individuals. There is a broad consensus that non-invasive assessment of brain activity accurately reflects the processes underlying age-related motor decline, thereby opening avenues for targeted intervention strategies. A key area of investigation is the utilization of motor system function for the early detection of neurodegenerative diseases. Seemingly, simple motor tasks engage cortical regions responsible for attention, vision, and memory through a process known as sensorimotor integration. Sensorimotor training implemented via brain-computer interfaces with neurofeedback demonstrates potential for ameliorating both cognitive and motor deficits in both healthy older adults and those with age-related conditions. This review synthesizes current research on age-related changes revealed through neuroimaging data analysis, highlighting how biomarkers derived from brain electrical and hemodynamic activity reflect both normative and pathological aging processes. Finally, we emphasize the considerable potential of neurophysiological data analysis for advancing the field of aging research. Digital medicine platforms, including brain-computer interfaces and a range of wearable monitoring devices, hold significant promise for transforming the diagnosis of age-related diseases. These technologies empower continuous, objective monitoring of older adults, paving the way for personalized, precision-based medical interventions.}, }
@article {pmid41110656, year = {2025}, author = {Huang, X and Xu, S}, title = {Mitigating choice overload: The interactive effects of set size and overall preference revealed by hierarchical drift diffusion modeling and electroencephalography.}, journal = {NeuroImage}, volume = {321}, number = {}, pages = {121542}, doi = {10.1016/j.neuroimage.2025.121542}, pmid = {41110656}, issn = {1095-9572}, mesh = {Humans ; Electroencephalography/methods ; Male ; Female ; Young Adult ; *Choice Behavior/physiology ; Adult ; *Evoked Potentials/physiology ; *Brain/physiology ; Attention/physiology ; }, abstract = {Excessive choice can overwhelm cognitive resources and trigger choice overload, yet its neurophysiological basis-particularly the moderating role of overall preference level-remains underexplored. This study employed a two-stage experimental paradigm manipulating choice set size (large vs. small) and overall preference level (high vs. low). We integrated event-related potentials (ERPs), multivariate pattern analysis (MVPA), and hierarchical drift diffusion modeling (HDDM) to investigate how these factors interactively shape decision processes. Behavioral and computational modeling results revealed that high-preference conditions enhanced participants' ability to identify satisfactory options, with this advantage persisting and significantly accelerating final selection speed, particularly for large choice sets. Conversely, low-preference conditions amplified choice set size effects, with large sets exacerbating choice overload. ERP analyses showed larger P2 amplitudes for small choice sets, indicating greater early attentional allocation. More negative N2 amplitudes consistently appeared for small sets across both overall preference levels, reflecting elevated conflict and cognitive control demands. Small-set/low-preference conditions elicited the largest P3 amplitudes, suggesting small sets triggered compensatory attentional allocation under low-preference conditions. MVPA identified stable and distinct neural representation patterns across all experimental conditions, confirming that overall preference level modulates neural encoding of choice overload. These findings demonstrate that subjective preference strength functions as a key regulatory factor in mitigating choice overload. Our multimodal approach advances theoretical accounts of value-based decision-making by revealing how internal preferences interact with external complexity to shape the temporal and computational architecture of cognitive control.}, }
@article {pmid41109958, year = {2025}, author = {Suffian, M and Ieracitano, C and Morabito, FC and Mammone, N}, title = {An Explainable 3D-Deep Learning Model for EEG Decoding in Brain-Computer Interface Applications.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2550073}, doi = {10.1142/S012906572550073X}, pmid = {41109958}, issn = {1793-6462}, abstract = {Decoding electroencephalographic (EEG) signals is of key importance in the development of brain-computer interface (BCI) systems. However, high inter-subject variability in EEG signals requires user-specific calibration, which can be time-consuming and limit the application of deep learning approaches, due to general need of large amount of data to properly train these models. In this context, this paper proposes a multidimensional and explainable deep learning framework for fast and interpretable EEG decoding. In particular, EEG signals are projected into the spatial-spectral-temporal domain and processed using a custom three-dimensional (3D) Convolutional Neural Network, here referred to as EEGCubeNet. In this work, the method has been validated on EEGs recorded during motor BCI experiments. Namely, hand open (HO) and hand close (HC) movement planning was investigated by discriminating them from the absence of movement preparation (resting state, RE). The proposed method is based on a global- to subject-specific fine-tuning. The model is globally trained on a population of subjects and then fine-tuned on the final user, significantly reducing adaptation time. Experimental results demonstrate that EEGCubeNet achieves state-of-the-art performance (accuracy of [Formula: see text] and [Formula: see text] for HC versus RE and HO versus RE, binary classification tasks, respectively) with reduced framework complexity and with a reduced training time. In addition, to enhance transparency, a 3D occlusion sensitivity analysis-based explainability method (here named 3D xAI-OSA) that generates relevance maps revealing the most significant features to each prediction, was introduced. The data and source code are available at the following link: https://github.com/AI-Lab-UniRC/EEGCubeNet.}, }
@article {pmid41109909, year = {2025}, author = {Yu, J and Chen, J and Zhang, Y and Lyu, H and Ma, T and Huang, H and Wang, Z and Xu, X and Hu, S and Xu, Y}, title = {Emoface: AI-assisted diagnostic model for differentiating major depressive disorder and bipolar disorder via facial biomarkers.}, journal = {Npj mental health research}, volume = {4}, number = {1}, pages = {52}, pmid = {41109909}, issn = {2731-4251}, support = {2025C01104, 2025C02108 and 2021C03107//Key R&amp;D Program of Zhejiang/ ; LZ23H180002 and LQ23F030001//Zhejiang Provincial Natural Science Foundation/ ; 62406280 and 72274170//National Natural Science Foundation of China/ ; 2022RC009//Cao Guangbiao High-tech Development Fund/ ; 20231203A13//Key Projects of Hangzhou Science and Technology Bureau/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation of Zhejiang Province/ ; }, abstract = {Affective disorders, including Major Depressive Disorder (MDD) and Bipolar Disorder (BD), exhibit significant mood abnormalities, making rapid diagnosis essential for social stability and healthcare efficiency. Traditional diagnostic solutions, including medical history collection and psychological assessments, often struggle to differentiate their similar clinical presentations, leading to time-consuming, laborious, and a high rate of misdiagnosis. Here, we propose Emoface, an AI-assisted diagnostic model that reads the emotional activities of faces in affective disorders. By analyzing videos from 353 participants exposed to various emotional stimuli, Emoface identified unique facial digital biomarkers distinguishing BD from MDD. Based on this, Emoface contributed to develop the first digital facial mapping for clinical and teaching use. In clinical practice with 347 patients, Emoface achieved precise diagnosis based on various facial visual signals, with accuracy rates of 95.29% for BD and 87.05% for MDD, offering a reliable face-based AI solution in a new era of affective disorders.}, }
@article {pmid41108907, year = {2025}, author = {Akazawa, A and Fujita, T and Uraguchi, K and Kitayama, M and Ito, T and Osaki, Y and Shirai, K and Yoshida, H and Yamamoto, N and Doi, K and Iwasaki, S and Oishi, N}, title = {Establishing a comprehensive national auditory implant registry in Japan: Trends and demographics from the first two years (2023-2024).}, journal = {Auris, nasus, larynx}, volume = {52}, number = {6}, pages = {679-686}, doi = {10.1016/j.anl.2025.09.009}, pmid = {41108907}, issn = {1879-1476}, abstract = {OBJECTIVE: To describe the establishment and initial findings of Japan's first comprehensive nationwide registry covering cochlear implants (CIs), active middle ear implants (AMEIs), and bone conduction implants (BCIs), launched in 2023. The registry aims to improve national data collection, support evidence-based policymaking, and track trends in surgical practice and patient demographics.<br><br>METHODS: A web-based electronic data capture (EDC) system was implemented to replace the previous paper-based reporting system. Between January 2023 and December 2024, data were voluntarily submitted by participating facilities across Japan. Collected data included patient demographics, implant types, hearing thresholds, etiologies, and manufacturer information. Registry completeness was assessed by comparison with Japan's National Database of Health Insurance Claims (NDB).<br><br>RESULTS: A total of 1880 patients were registered, and 1809 patients with surgical information entered from 104 facilities were selected for analysis, comprising 1723 CI cases and 86 AMEI or BCI cases (11 VSB, 22 BB, 53 Baha). Among 605 pediatric CI recipients, early-age implantation was increasingly observed, with 58 patients (10 %) aged under 1 year and 183 (30 %) aged 1 year. Among adult CI recipients, 271 patients were aged 75 years or older, including 40 patients aged 85 years or older. Additionally, simultaneous bilateral CI surgery was performed in 265 patients, of whom 175 were children, reflecting the expanding indications. Patients with better ear thresholds <90 dB HL accounted for 33 % of adults and 29 % of children. Congenital hearing loss predominated in children, while acquired causes were more common in adults. Among cases with a known etiology, hereditary deafness was the most common (24.5 %), although 39.6 % of etiologies were unknown. CI data completeness reached 73 % compared with NDB, indicating strong nationwide participation and a high level of data reliability.<br><br>CONCLUSION: This is the first comprehensive report from the national registry in Japan that includes not only CIs but also AMEIs and BCIs. The registry demonstrated reliable data capture and highlighted important trends in patient demographics and surgical practices. Continued data collection will enhance clinical decision-making and support policy development, ultimately improving care for auditory implant recipients.}, }
@article {pmid41107816, year = {2025}, author = {Chen, B and Gan, H and Yang, L and Yan, X and Lv, X and Zhang, X and Bu, J}, title = {A novel imagery-based retrieval-extinction training for intervention in nicotine addiction.}, journal = {BMC medicine}, volume = {23}, number = {1}, pages = {568}, pmid = {41107816}, issn = {1741-7015}, support = {32471140//National Natural Science Foundation of China/ ; 2021xkjT018//Scientific Research Improvement Project of Anhui Medical University/ ; 2022zhyx-C02//Research Fund of Anhui Institute of Translational Medicine/ ; YQZD2023018//Anhui Province Outstanding Young Teacher Cultivation Key Project/ ; JKS2023013//Research Funds of Center for Big Data and Population Health of IHM/ ; YESS20240007//Young Elite Scientists Sponsorship Program by CAST/ ; 2024AH030021//Excellent Youth of Natural Science Research Projects in Universities of Anhui Province/ ; }, mesh = {Humans ; Male ; *Tobacco Use Disorder/therapy/psychology ; Female ; Adult ; *Imagery, Psychotherapy/methods ; Craving ; *Extinction, Psychological ; Middle Aged ; Electroencephalography ; *Smoking Cessation/methods ; Young Adult ; *Mental Recall ; }, abstract = {BACKGROUND: Retrieval-extinction training based on the theory of memory reconsolidation has promising intervention effects for addiction. However, the conventional conditioned stimuli used in retrieval-extinction training have limitations in lack of contextual and selective activation of memories, which limits intervention efficacy and clinical translation. Therefore, we developed a novel imagery-based retrieval-extinction training (I-RE) and examined its effects on nicotine addiction.<br><br>METHODS: This study included 57 nicotine-dependent individuals randomly assigned to either the experimental (n&#x2009;=&#x2009;29) or control (n&#x2009;=&#x2009;28) group. Participants were exposed to a 5-min imagery script cue, followed by a 10-min rest period and 60-min extinction training session. Short- and long-term (1&#xa0;week, 1&#xa0;month, 3&#xa0;months, 6&#xa0;months, 12&#xa0;months) intervention effects were assessed via the smoking imagery vividness score, smoking craving, and daily cigarette consumption. Electroencephalogram (EEG) data were collected pre- and post-intervention.<br><br>RESULTS: Regarding short-term effects, smoking imagery vividness score [pre- vs. post-intervention: p&#x2009;<&#x2009;0.001; pre- vs. 1-day follow-up (FU): p&#x2009;=&#x2009;0.003] and craving significantly decreased (pre- vs. post-intervention: p&#x2009;<&#x2009;0.001; pre- vs. 1-day FU: p&#x2009;<&#x2009;0.001). Decreased imagery vividness score mediated decreased smoking craving induced by smoking-related I-RE. Moreover, the significant correlation observed between these variables at pre-intervention disappeared at post-intervention. For effects on EEG microstate, a significant decrease was observed in microstate C duration induced by the smoking-related imagery script cue reactivity task post-intervention (p&#x2009;<&#x2009;0.001). This mediated a decreased smoking craving induced by smoking-related I-RE. Degree of decrease in duration was positively correlated with addict imagery ability (p&#x2009;=&#x2009;0.035). Consistently, the microstate C occurrence rate significantly decreased during the memory reconsolidation phase (p&#x2009;<&#x2009;0.001). Regarding long-term effects, the smoking imagery vividness score (1-week FU: p&#x2009;=&#x2009;0.004; 1-month FU: p&#x2009;<&#x2009;0.001), smoking craving (1-week FU: p&#x2009;<&#x2009;0.001; 1-month FU: p&#x2009;<&#x2009;0.001), and daily cigarette consumption (1-week FU: p&#x2009;<&#x2009;0.001; 1-month FU: p&#x2009;<&#x2009;0.001) significantly decreased at 1-week and 1-month FU. Furthermore, decreased smoking craving mediated decreased Daily cigarette consumption in the experimental group. The significant correlation observed between the imagery vividness score and craving at pre-intervention disappeared at the 1-week and 1-month FU.<br><br>CONCLUSIONS: This novel I-RE demonstrated significant effects on nicotine addiction for 1&#xa0;month after a single intervention session, suggesting that it is a promising treatment tool.<br><br>TRIAL REGISTRATION: Chinese Clinical Trial Registry identifier: ChiCTR2200064469.}, }
@article {pmid41107249, year = {2025}, author = {Zhu, X and Jiang, L and Shi, L and Li, F and Yang, Q and Zhang, M and Li, Y and Yu, Q and Chen, J and Gao, X and Wang, Z and Wang, Y and Xu, P and Lu, L and Deng, J}, title = {Modulation of brain oscillations by continuous theta burst stimulation in patients with insomnia.}, journal = {Translational psychiatry}, volume = {15}, number = {1}, pages = {416}, pmid = {41107249}, issn = {2158-3188}, support = {82271528//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82201646//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; Male ; Female ; *Sleep Initiation and Maintenance Disorders/therapy/physiopathology ; Adult ; *Theta Rhythm/physiology ; Middle Aged ; Electroencephalography ; *Transcranial Magnetic Stimulation/methods ; Polysomnography ; Cross-Over Studies ; Wakefulness/physiology ; }, abstract = {Continuous theta burst stimulation (cTBS) induces long-lasting depression of cortical excitability in motor cortex. In the present study, we explored the modulation of cTBS on resting state electroencephalogram (rsEEG) during wakefulness and subsequent sleep in patients with insomnia disorder. Forty-one patients with insomnia received three sessions active and sham cTBS in a counterbalanced crossover design. Each session comprised 600 pulses over right dorsolateral prefrontal cortex. Closed-eyes rsEEG were recorded at before and after each session. Effects of cTBS in subsequent sleep were measured by overnight polysomnography screening. Power spectral density (PSD) and phase locking value (PLV) were used to calculate changes in spectral power and phase synchronization after cTBS during wakefulness and subsequent sleep. Compared with sham cTBS intervention, PSD of delta and theta bands were increased across global brain regions with a cumulative effect after three active cTBS sessions. PLV of delta and theta bands were enhanced between stimulated frontal area and occipital areas. Efficiency of information communication within frontal-occipital networks was consistently improved through three active sessions. Increased theta power during wakefulness was positively related with that during the first sleep cycle. Active cTBS significantly enhanced the spectral power of delta and theta bands during wakefulness, with a cumulative effect observed over time. This modulation also extended to influence theta power during subsequent sleep onset period. Collectively, these findings provide a robust theoretical foundation for further investigating the therapeutic potential of long-term cTBS in the treatment of insomnia disorders.}, }
@article {pmid41106071, year = {2025}, author = {Yasen, A and Sun, W and Gong, Y and Xu, G}, title = {Progress in the combined application of Brain-Computer Interface and non-invasive brain stimulation for post-stroke motor recovery.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {180}, number = {}, pages = {2111383}, doi = {10.1016/j.clinph.2025.2111383}, pmid = {41106071}, issn = {1872-8952}, abstract = {Stroke remains one of the leading causes of disability and death among adults globally. Both Brain-Computer Interface (BCI) and Non-invasive Brain Stimulation (NIBS) have shown significant potential in facilitating motor recovery in stroke patients. The combination of BCI and NIBS enhances brain functional reorganization and accelerates motor recovery post-stroke through a real-time feedback mechanism. By modulating neural plasticity, this combined approach can alter the trajectory of motor recovery, offering a novel therapeutic avenue for stroke rehabilitation. This review examines the application and recent advancements of BCI integrated with NIBS in motor function rehabilitation for stroke patients. Specifically, it outlines the advantages and challenges of this combined approach, including the use of TMS, tDCS, tACS, and other emerging neurostimulation technologies. While the integration of BCI and NIBS is still in the early stages of exploration, a unified, standardized protocol has yet to be established. Future research should focus on optimizing multimodal integration, investigating the underlying neuroplasticity mechanisms, and evaluating the long-term efficacy of BCI combined with NIBS.}, }
@article {pmid41105834, year = {2025}, author = {Clemesha, J and Chung, M}, title = {A different bimodal: case series of patients with a cochlear implant and a contralateral bone conduction implant.}, journal = {Cochlear implants international}, volume = {}, number = {}, pages = {1-8}, doi = {10.1080/14670100.2025.2571990}, pmid = {41105834}, issn = {1754-7628}, abstract = {INTRODUCTION: An increasing number of long-term users of bone conduction implants (BCI) have been observed to no longer obtain sufficient benefit from their device due to deteriorations in hearing thresholds. At the multidisciplinary auditory implant centre at the University College London Hospitals NHS Trust, these patients are assessed and considered for cochlear implantation (CI). This case series describes the history and outcomes of patients who became bimodal implant users, utilising electrical and vibratory auditory stimulation with a BCI and CI. This unique patient group has seldom been described in the literature.<br><br>METHODS: Case series from a retrospective chart review of patients who utilise the combination of electrical and vibratory auditory stimulation with the use of a bone conduction implant and cochlear implant, up to November 2023.<br><br>RESULTS: Six bimodal patients were identified from the patient cohort. Their case history and outcome are described.<br><br>CONCLUSION: The synergy of electrical and vibratory auditory stimulation observed in this case series provided subjective functional benefits and measurable speech perception benefits for some patients, while others experienced minimal or no measurable benefit and ceased usage.}, }
@article {pmid41105410, year = {2025}, author = {Saver, JL and Duncan, PW and Stein, J and Cramer, SC and Fox, EJ and Zorowitz, RD and Billinger, SA and Eickmeyer, SM and Kirshblum, SC and Androwis, GJ and Edwards, J and Savitz, SI and Koch, S and Shall, MB and Black-Schaffer, RM and Bonato, P and Cuccurullo, SJ and Barcikowski, J and Cao, N and Bornstein, NM and , }, title = {Electromagnetic Stimulation to Reduce Disability After Ischemic Stroke: The EMAGINE Randomized Clinical Trial.}, journal = {JAMA network open}, volume = {8}, number = {10}, pages = {e2537880}, pmid = {41105410}, issn = {2574-3805}, mesh = {Humans ; Male ; Female ; Middle Aged ; Double-Blind Method ; Aged ; *Stroke Rehabilitation/methods ; *Ischemic Stroke/therapy/rehabilitation ; *Magnetic Field Therapy/methods ; Treatment Outcome ; Upper Extremity/physiopathology ; Persons with Disabilities/rehabilitation ; }, abstract = {IMPORTANCE: Ischemic stroke remains a leading cause of disability worldwide. Preliminary studies have suggested that noninvasive, frequency-tuned, low-intensity electromagnetic network targeting field (ENTF) stimulation may have recovery benefit for patients with stroke.<br><br>OBJECTIVE: To evaluate the safety and effectiveness of ENTF therapy in reducing global disability among patients in the subacute ischemic stroke phase with moderate to severe disability and upper-extremity impairment.<br><br>This multicenter, double-blind, sham-controlled, randomized clinical trial was conducted at 15 US-based acute care and inpatient rehabilitation facilities from December 2021 to November 2023. Participants were enrolled 4 to 21 days after a stroke and had a baseline modified Rankin Scale (mRS) score of 3 or 4 (moderate or moderately severe global disability) and Fugl-Meyer Assessment for Upper Extremity score of 10 to 45 (higher scores indicating better arm function). Target sample size was 150 participants. Participants were randomly allocated to receive either active or sham ENTF stimulation. Modified intention-to-treat approach was used in primary efficacy and safety analyses.<br><br>INTERVENTION: Participants allocated to the active or sham ENTF stimulation were treated with a proprietary brain-computer interface-based stimulation device paired with an evidence-based, functional, repetitive, home-based physical and occupational exercise regimen for 45 one-hour sessions, 5 times per week within the first 90 days after a stroke.<br><br>MAIN OUTCOMES AND MEASURES: The primary end point was change in global disability, assessed with the mRS (score range: 0 [indicating normal or no symptoms] to 6 [indicating death]), from baseline to day 90. Secondary end points were change from baseline to day 90 in upper-limb impairment, arm motor function, gait speed, hand function, and physical and functional limitations as well as day-90 health-related quality of life, each of which was assessed with a specific metric.<br><br>RESULTS: The trial was stopped early after enrollment of 100 participants (50 in active group, 50 in sham group) when a promising zone threshold was not attained at planned interim analysis of the first 78 evaluable participants. Participants had a mean age of 59.0 (12.5) years and included 66 males (67.3%). The median (IQR) time from stroke to first ENTF treatment was 14 (12-19) days. Study groups were similar in age, sex, and baseline mRS scores, but imbalances were noted with participants in the active, compared with the sham, group having more right-hemisphere strokes (31 of 49 [63.3%] vs 22 of 49 [44.9%]), more severe upper-extremity impairment (Shoulder Abduction Finger Extension score <5; 31 of 49 [63.3%] vs 24 of 49 [49.0%]), and fewer small-vessel infarcts (14 of 49 [28.6%] vs 21 of 49 [42.9%]). For the primary outcome, the mean (SD) disability reduction on mRS at day 90 was not statistically significantly higher in the active group than in the sham group (-1.96 [0.12] vs -1.72 [0.12]), including mRS score of 0 to 1 attained in 12 participants (26.0%) vs 5 participants (10.0%) (odds ratio, 2.99; 95% CI, 0.96-9.30; P&#x2009;=&#x2009;.05). Point estimates for secondary outcomes favored the active group, although the differences were not statistically significant, in the prespecified analysis. No ENTF device-related serious adverse events were noted.<br><br>CONCLUSION AND RELEVANCE: This trial found that ENTF therapy is safe. Although the difference between groups was not statistically significant, ENTF therapy may reduce global disability in patients with severe baseline disability after ischemic stroke. These results warrant confirmation in a higher powered pivotal trial of ENTF therapy.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT05044507.}, }
@article {pmid41104953, year = {2025}, author = {Kamaleddin, MA}, title = {Simultaneous encoding of sensory features: the role of multiplexing and noise in tactile perception and neural representation.}, journal = {Biological reviews of the Cambridge Philosophical Society}, volume = {}, number = {}, pages = {}, doi = {10.1111/brv.70093}, pmid = {41104953}, issn = {1469-185X}, abstract = {The nervous system's capacity to process complex stimuli has long intrigued neuroscientists, with multiplexing now recognized as a fundamental neural coding strategy. Multiplexing refers to the simultaneous encoding of multiple stimulus features via vi distinct components of neuronal responses, such as firing rates and precise temporal spike patterns. This paper reviews the neural coding mechanisms underlying multiplexing, with a particular emphasis on the somatosensory system and its ability to represent tactile stimuli. The encoding of various sensory attributes, including vibration, texture, motion, and shape, is examined, highlighting the complementary roles of rate and temporal codes in capturing these features. The discussion further addresses how intrinsic and extrinsic noise, often viewed as detrimental, can facilitate multiplexed coding by supporting the concurrent encoding of both stimulus frequency and intensity. The relevance of multiplexing is also considered in translational contexts, such as the development of brain-machine interfaces. By synthesizing recent advances and integrating insights from empirical and theoretical studies, this review establishes multiplexing as a foundational principle in sensory neuroscience and identifies key directions for future research in both basic science and neuroengineering applications.}, }
@article {pmid41104690, year = {2025}, author = {Chehroudi, C and Chandrasekhar, V and Yu, H and De, S}, title = {Simple Prostatectomy is an Effective Option for BPH Patients With Hypocontractile Bladders.}, journal = {The Prostate}, volume = {}, number = {}, pages = {}, doi = {10.1002/pros.70079}, pmid = {41104690}, issn = {1097-0045}, support = {//The authors received no specific funding for this work./ ; }, abstract = {BACKGROUND: The impact of preoperative bladder function on outcomes of simple prostatectomy (SP) is unknown. The goal of this study was to determine if detrusor contractility affects postoperative catheter-free status in patients undergoing SP for benign prostatic hyperplasia (BPH).<br><br>METHODS: Patients who underwent SP (either open or minimally invasive) from 2017 to 2024 at our institution and had preoperative urodynamics were identified retrospectively. Bladder contractility index (BCI) was used to categorize patients as normocontractile (BCI&#x2009;&#x2265;&#x2009;100) or hypocontractile (BCI&#x2009;<&#x2009;100). Demographics, preoperative urodynamics, peri-operative characteristics, and postoperative variables were compared between the two groups with postoperative catheter status being the primary outcome.<br><br>RESULTS: Among 101 SP patients with preoperative urodynamics, 47 had hypocontractile bladders (median BCI 69 vs. 131). Both groups had similar median age, preoperative prostate specific antigen (PSA), and rates of diabetes. The majority of procedures in both the normocontracile and hypocontractile groups were robot-assisted (83% vs. 81%, respectively). Patients in the hypocontractile group were significantly more likely to be catheter dependent pre-operatively (77% vs. 57%, p&#x2009;=&#x2009;0.04). There was no difference in preoperative prostate size or use of BPH pharmacotherapy. Overall, 97% of hypocontractile and 100% of normocontractile patients were catheter-free following surgery. There were no differences in postoperative outcomes including pathology tissue weight and post-op PSA.<br><br>CONCLUSIONS: This is one of the first studies assessing outcomes of SP in patients with hypocontractile bladders. SP is an effective surgical option for patients with impaired detrusor function including those who are catheter dependent.}, }
@article {pmid41104354, year = {2025}, author = {Näher, T and Bastian, L and Vorreuther, A and Fries, P and Goebel, R and Sorger, B}, title = {Riemannian geometry boosts functional near-infrared spectroscopy-based brain-state classification accuracy.}, journal = {Neurophotonics}, volume = {12}, number = {4}, pages = {045002}, pmid = {41104354}, issn = {2329-423X}, abstract = {BACKGROUND: Functional near-infrared spectroscopy (fNIRS) has recently gained momentum as a reliable and accurate tool for assessing brain states based on the vascular response to neural activity. This increase in popularity is due to its robustness to movement, non-invasive nature, portability, and user-friendly application. However, compared with other hemodynamic functional brain-imaging methods such as functional magnetic resonance imaging (fMRI), fNIRS is constrained by its limited spatial resolution and coverage with a particularly limited penetration depth. In addition, due to comparatively fewer methodological advancements, the performance of fNIRS-based brain-state classification still lags behind more prevalent methods such as fMRI.<br><br>METHODS: We introduce a classification approach grounded in Riemannian geometry for the classification of kernel matrices, leveraging the temporal and spatial relationships between channels and the inherent duality of fNIRS signals, specifically oxygenated and deoxygenated hemoglobin. For the Riemannian-geometry-based models, we compared different kernel matrix estimators and two classifiers: Riemannian Support Vector Classifier and Tangent Space Logistic Regression. These were benchmarked against four models employing traditional feature extraction methods. Our approach was tested on seven participants in two brain-state classification scenarios based on the same fNIRS dataset: an eight-choice classification, which includes seven established plus an individually selected imagery task, and a two-choice classification of all possible 28 two-task combinations.<br><br>RESULTS: This approach achieved a mean eight-choice classification accuracy of 65%, significantly surpassing the mean accuracy of 42% obtained with traditional methods. In addition, the best-performing model achieved an average accuracy of 96% for two-choice classification across all task combinations, compared with 78% with traditional models.<br><br>CONCLUSION: To our knowledge, we are the first to demonstrate that the proposed Riemannian-geometry-based classification approach is both powerful and viable for fNIRS data, substantially increasing the accuracy in binary and multi-class classification of brain activation patterns.}, }
@article {pmid41104262, year = {2025}, author = {Bublitz, C and Chandler, JA and Molnár-Gábor, F and Navarro, MS and Kellmeyer, P and Soekadar, SR}, title = {A Moratorium on Implantable Non-Medical Neurotech Until Effects on the Mind are Properly Understood.}, journal = {Neuroethics}, volume = {18}, number = {3}, pages = {46}, pmid = {41104262}, issn = {1874-5490}, abstract = {The development of non-medical consumer neurotechnology is gaining momentum. As companies chart the course for future implanted and invasive brain-computer interfaces (BCIs) in non-medical populations, the time has come for concrete steps toward their regulation. We propose three measures: First, a mandatory Mental Impact Assessment that comprehensively screens for adverse mental effects of neurotechnologies under realistic use conditions needs to be developed and implemented. Second, until such an assessment is developed and further ethical concerns are effectively resolved, a moratorium on placing implantable non-medical devices on markets should be established. Third, implantable consumer neurotech for children should be banned. These measures are initial steps in a process seeking to define the necessary requirements for placing these devices on markets. They are grounded in a human rights-based approach to technology regulation that seeks to promote the interests protected by human rights while minimizing the risks posed to them. Neurotechnologies have the potential to profoundly alter cognitive, emotional, and other mental processes, with implications for the rights to mental health and integrity, and possibly for societal dynamics.}, }
@article {pmid41102402, year = {2025}, author = {Feng, Y and Zhao, W and Li, Y and Yin, Q and Wang, X and Huang, X and Li, L and Shan, X and Hu, W and Ming, Y and Wang, P and Xiao, J and Chen, H and Duan, X}, title = {Diffusion trajectory of atypical morphological development in autism spectrum disorder.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {1476}, pmid = {41102402}, issn = {2399-3642}, support = {82121003//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82322035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62333003//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273076//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62036003//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Autism Spectrum Disorder/diagnostic imaging/pathology/physiopathology ; Child ; Adolescent ; Male ; Female ; *Gray Matter/diagnostic imaging/pathology/growth &amp; development ; *Brain/growth &amp; development/diagnostic imaging/pathology/physiopathology ; Magnetic Resonance Imaging ; }, abstract = {Brain development from childhood through adolescence is crucial for understanding autism spectrum disorder (ASD). Yet how functional networks regulate developmental changes in brain morphology remains unclear. Here, we analyzed gray matter volume (GMV) and functional connectivity (FC) in 301 individuals with ASD and 375 typically developing controls (TDCs), aged 8-18 years, from the Autism Brain Imaging Data Exchange (ABIDE). Using a sliding-window approach, participants were stratified by age, and GMV distribution deviations (DEV) were quantified with Kullback-Leibler divergence and expected value analysis. Network diffusion modeling (NDM) was applied to predict developmental alterations and evaluate how functional networks constrain atypical neurodevelopment. Results revealed a developmental shift in GMV divergence: during early adolescence, ASD participants showed positive GMV deviations relative to TDCs, which shifted to negative in late adolescence. The largest DEV were observed in the superior temporal sulcus, cingulate gyrus, insula, and superior parietal lobule. Furthermore, NDM demonstrated cross-stage predictability, as DEV values of atypical brain regions at preceding age stages significantly predicting subsequent ones, constrained by network architecture. These findings highlight a dynamic developmental shift from GMV overgrowth to delayed maturation during adolescence in ASD and revealing the role of intrinsic functional networks in constraining atypical anatomical development.}, }
@article {pmid41101308, year = {2025}, author = {Zheng, D and Xin, Q and Jin, S and Zhou, A and Jia, X and Tan, Y and Hu, H}, title = {Neural mechanism of the sexually dimorphic winner effect in mice.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2025.09.029}, pmid = {41101308}, issn = {1097-4199}, abstract = {The "winner effect," where prior victories increase the likelihood of future wins, profoundly shapes social hierarchy dynamics and competitive motivation. Although human literature suggests a less pronounced winner effect in females, the neural mechanisms underlying these sex differences remain unclear. Here, we show that, compared with male mice, female mice take longer to form social hierarchies and exhibit a weaker winner effect. The dorsomedial prefrontal cortex (dmPFC), crucial for social dominance in males, plays a similar role in female mice. However, female mice exhibit reduced long-term potentiation (LTP) at the mediodorsal thalamus (MDT)-to-dmPFC synapses. In vitro recordings revealed that female mice have heightened excitability of dmPFC parvalbumin interneurons (PV-INs). Modulation of dmPFC PV-IN activity regulates LTP and the winner effect in a sexually dimorphic manner. This work identifies dmPFC PV-INs as a target for enhancing the winner effect, establishing a circuit-level framework for sex differences in competitive behaviors.}, }
@article {pmid41100980, year = {2026}, author = {Ban, S and Chong, D and Kwon, J and Lee, S and Huang, Y and Yoo, S and Yeo, WH}, title = {Advances in flexible high-density microelectrode arrays for brain-computer interfaces.}, journal = {Biosensors &amp; bioelectronics}, volume = {292}, number = {}, pages = {118102}, doi = {10.1016/j.bios.2025.118102}, pmid = {41100980}, issn = {1873-4235}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; Microelectrodes ; *Biosensing Techniques/instrumentation/methods ; Equipment Design ; Animals ; *Brain/physiology ; Electroencephalography/instrumentation ; }, abstract = {Recent advances in flexible high-density microelectrode arrays (FHD-MEA) have revolutionized brain-computer interfaces (BCIs) by providing high spatial resolution, mechanical compliance, and long-term biocompatibility. This technology enables stable neural recording and precise stimulation, addressing the shortcomings of conventional rigid BCI arrays. In this review, we outline the challenges of signal acquisition and stimulation of conventional low-density, rigid BCI systems. These include poor spatial resolution, micro-motor-induced instability, electrochemical degradation, wiring bottlenecks, off-target activation, and charge injection hazards. We then describe how these barriers are addressed through advanced materials, device designs, and system-level integration. We summarize representative applications of clinical therapy for sensory enhancement, human-machine interfaces, and neurological diseases, highlighting translational potential. Collectively, this review article presents recent progress and emerging trends in establishing FHD-MEAs as a crucial foundation for next-generation, clinically viable BCIs.}, }
@article {pmid41100231, year = {2025}, author = {Li, R and Liu, J and Liu, J and Yang, S and Liu, W and Deng, K and Wang, W}, title = {A Novel Grasping Robot Control Method Using Motion Execution BCI Combining Knowledge Reasoning.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3622255}, pmid = {41100231}, issn = {2168-2208}, abstract = {Recently, with the growing number of disabled people, brain-controlled technology offers a novel way to help patients restore their daily abilities. However, the conventional brain-controlled system based on the motion related task lacks intelligence in real-world environments. To address above problem, this study proposed a share controlled system combining a precise hand movement (PHM)-based brain computer interface (BCI) system and knowledge-driven reasoning method. Six types of precise hand movements were selected to design novel motion execution paradigm for BCI system. A feature intermediate fusion convolutional neural network was employed to accurately decode electroencephalogram. Furthermore, a shared control grasping technology based on knowledge based reasoning combined PHM-based BCI system was designed for grasping robot, which enhancing the system's intelligence and versatility in selecting objects. To verify the improvement of proposed method, experiments were conducted with 15 ࣥhealthy subjects and 2 patients. The proposed method achieved an average accuracy of 82.80±6.08%, with the highest accuracy reaching 94.27%. All the experimental results demonstrate the effectiveness of the proposed shared control method.}, }
@article {pmid41096996, year = {2025}, author = {Ga, YJ and Yeh, JY}, title = {siRNA Cocktail Targeting Multiple Enterovirus 71 Genes Prevents Escape Mutants and Inhibits Viral Replication.}, journal = {International journal of molecular sciences}, volume = {26}, number = {19}, pages = {}, pmid = {41096996}, issn = {1422-0067}, support = {2020//Incheon National University/ ; }, mesh = {*Enterovirus A, Human/genetics/physiology ; *Virus Replication/genetics ; *RNA, Small Interfering/genetics/pharmacology ; Humans ; *Mutation ; Enterovirus Infections/virology/genetics ; RNA Interference ; Animals ; Cell Line ; }, abstract = {RNA interference (RNAi) is a powerful mechanism of post-transcriptional gene regulation in which small interfering RNA (siRNA) is utilized to target and degrade specific RNA sequences. In this study, experiments were conducted to evaluate the efficacy of combination siRNA therapy against enterovirus 71 (EV71) and the potential of this therapy to delay or prevent the emergence of resistance in vitro. siRNAs targeting multiple genes of EV71 were designed, and the effects of a cocktail of siRNAs on viral replication were assessed compared to those of single-siRNA treatment. Cotransfection of multiple siRNAs targeting different protein-coding genes of the EV71 genome effectively suppressed escape mutants resistant to RNAi. Combination therapy with siRNAs targeting multiple viral genes successfully prevented viral escape mutations over five passages. By contrast, serial passaging with a single siRNA led to the rapid emergence of resistance, with mutations identified in the siRNA target sites. The combination of siRNAs specifically targeting different regions demonstrated an additive effect and was more effective than individual siRNAs at inhibiting EV71 replication. This study supports the effectiveness of combination therapy using siRNAs targeting multiple genes of EV71 to inhibit viral replication and prevent the emergence of resistant escape mutants. Overall, the findings identify RNAi targeting multiple viral genes as a potential strategy for therapeutic development against viral diseases and for preventing the emergence of escape mutants resistant to antiviral RNAi.}, }
@article {pmid41096009, year = {2025}, author = {von Altdorf, LAWR and Bracewell, M and Cooke, A}, title = {Effectiveness of Electroencephalographic Neurofeedback for Parkinson's Disease: A Systematic Review and Meta-Analysis.}, journal = {Journal of clinical medicine}, volume = {14}, number = {19}, pages = {}, pmid = {41096009}, issn = {2077-0383}, abstract = {Background: Electroencephalographic (EEG) neurofeedback training is gaining traction as a non-pharmacological treatment option for Parkinson's disease (PD). This paper reports the first pre-registered, integrated systematic review and meta-analysis of studies examining the effects of EEG neurofeedback on cortical activity and motor function in people with PD. Method: We searched Cochrane Databases, PubMed, Embase, Scopus, Web of Science, PsycInfo, grey literature repositories, and trial registers for EEG neurofeedback studies in people with PD. We included randomized controlled trials, single-group experiments, and case studies. We assessed risk of bias using the Cochrane Risk of Bias 2 and Risk of Bias in Non-Randomized Studies tools, and we used the Grading of Recommendations, Assessment, Development and Evaluations tool to assess certainty in the evidence and resultant interpretations. Random-effects meta-analyses were performed. Results: A total of 11 studies (143 participants; Hoehn and Yahr I-IV) met the criteria for inclusion. A first meta-analysis revealed that EEG activity is modified in the prescribed way by neurofeedback interventions. The effect size is large (SMD = 1.30, 95% CI = 0.50-2.10, p = 0.001). Certainty in the estimate is high. Despite successful cortical modulation, a subsequent meta-analysis revealed inconclusive effects of EEG neurofeedback on motor symptomology. The effect size is small (SMD = 0.10, 95% CI = -1.03-1.23, p = 0.86). Certainty in the estimates is low. Narrative evidence revealed that interventions are well-received and may yield specific benefits not detected by general symptomology reports. Conclusion: EEG neurofeedback successfully modulates cortical activity in people with PD, but downstream impacts on motor function remain unclear. The neuromodulatory potential of EEG neurofeedback in people with PD is encouraging. Additional well-powered and high-quality research into the effects of EEG neurofeedback in PD is warranted.}, }
@article {pmid41095845, year = {2025}, author = {Kollu, K and Yortanli, BC and Cicek, AN and Susam, E and Karakas, N and Kizilarslanoglu, MC}, title = {Investigation of the Prognostic Value of Novel Laboratory Indices in Patients with Sepsis in an Intensive Care Unit: A Retrospective Observational Study.}, journal = {Journal of clinical medicine}, volume = {14}, number = {19}, pages = {}, pmid = {41095845}, issn = {2077-0383}, abstract = {Background: This study aimed to evaluate the prognostic value of some novel laboratory indices in intensive care unit (ICU)-hospitalized sepsis patients. Methods: This retrospective, observational study included 400 patients with sepsis. The indices studied were the C-reactive protein/albumin ratio (CAR), hemoglobin, albumin lymphocyte, and platelet (HALP) score, lymphocyte/monocyte ratio (LMR), prognostic nutritional index (PNI), systemic immune inflammatory index (SII), vitamin B12xC-reactive protein index (BCI), systemic inflammatory response index (SIRI), and platelet/lymphocyte ratio (PLR). The predicting effects of these indices in ICU mortality, along with other clinical outcomes, were investigated. Results: The median age of the study population was 73 (18-95) years and 51.6% were males. The ICU mortality rate was 51.7%. Deceased patients with sepsis had an increased age and high APACHE II and SOFA scores compared to the survivors (p < 0.05 for all). In the multivariate logistic regression analysis, age (HR = 1.069, p = 0.038 in Model 1 vs. HR = 1.053, p = 0.001 in Model 2), SOFA score (HR = 2.145, p < 0.001 in Model 1 vs. HR = 1.740, p < 0.001 in Model 2), phosphorus levels (in Model 1, HR = 0.608, p = 0.037), and CAR (in Model 2, HR = 1.012, p = 0.023) were independent associated factors for ICU mortality. According to the ROC analyses, the SOFA (AUC = 0.879, p < 0.001) and APACHE II (AUC = 0.769, p < 0.001) scores showed high accuracy in predicting ICU mortality, while the PNI (AUC = 0.675, p < 0.001), CAR (AUC = 0.609, p < 0.001), and the BCI (AUC = 0.648, p < 0.001) showed limited accuracy. However, the HALP score did not reach a significant level in predicting ICU mortality (p = 0.067). Conclusions: Excluding the HALP score, the new laboratory indices mentioned above may be prognostic markers for predicting clinical outcomes in intensive care units for patients with sepsis. However, these indices need to be supported by larger patient populations.}, }
@article {pmid41095026, year = {2025}, author = {Reyes, D and Sieghartsleitner, S and Loaiza, H and Guger, C}, title = {Motor Imagery Acquisition Paradigms: In the Search to Improve Classification Accuracy.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {19}, pages = {}, pmid = {41095026}, issn = {1424-8220}, support = {Bicentenario 1st Call//Colfuturo/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Algorithms ; Electroencephalography/methods ; Stroke/physiopathology ; Male ; }, abstract = {In recent years, advances in medicine have been evident thanks to technological growth and interdisciplinary research, which has allowed the integration of knowledge, for example, of engineering into medical fields. This integration has generated developments and new methods that can be applied in alternative situations, highlighting, for example, aspects related to post-stroke therapies, Multiple Sclerosis (MS), or Spinal Cord Injury (SCI) treatments. One of the methods that has stood out and is gaining more acceptance every day is Brain-Computer Interfaces (BCIs), through the acquisition and processing of brain electrical activity, researchers, doctors, and scientists manage to transform this activity into control signals. In turn, there are several methods for operating a BCI, this work will focus on motor imagery (MI)-based BCI and three types of acquisition paradigms (traditional arrow, picture, and video), seeking to improve the accuracy in the classification of motor imagination tasks for naive subjects, which correspond to a MI task for both the left and the right hand. A pipeline and methodology were implemented using the CAR+CSP algorithm to extract the features and simple standard and widely used models such as LDA and SVM for classification. The methodology was tested with post-stroke (PS) subject data with BCI experience, obtaining 96.25% accuracy for the best performance, and with the novel paradigm proposed for the naive subjects, 97.5% was obtained. Several statistical tests were carried out in order to find differences between paradigms within the collected data. In conclusion, it was found that the classification accuracy could be improved by using different strategies in the acquisition stage.}, }
@article {pmid41094934, year = {2025}, author = {Zhang, Y and Yin, B and Yuan, X}, title = {TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {19}, pages = {}, pmid = {41094934}, issn = {1424-8220}, support = {62171152//National Natural Science Foundation of China/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; Spectroscopy, Near-Infrared/methods ; Algorithms ; Brain/physiology ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; }, abstract = {Unimodal brain-computer interfaces (BCIs) often suffer from inherent limitations due to the characteristic of using single modalities. While hybrid BCIs combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer complementary advantages, effectively integrating their spatiotemporal features remains a challenge due to inherent signal asynchrony. This study aims to develop a novel deep fusion network to achieve synergistic integration of EEG and fNIRS signals for improved classification performance across different tasks. We propose a novel Temporal-Spatial Fusion Network (TSFNet), which consists of two key sublayers: the EEG-fNIRS-guided Fusion (EFGF) layer and the Cross-Attention-based Feature Enhancement (CAFÉ) layer. The EFGF layer extracts temporal features from EEG and spatial features from fNIRS to generate a hybrid attention map, which is utilized to achieve more effective and complementary integration of spatiotemporal information. The CAFÉ layer enables bidirectional interaction between fNIRS and fusion features via a cross-attention mechanism, which enhances the fusion features and selectively filters informative fNIRS representations. Through the two sublayers, TSFNet achieves deep fusion of multimodal features. Finally, TSFNet is evaluated on motor imagery (MI), mental arithmetic (MA), and word generation (WG) classification tasks. Experimental results demonstrate that TSFNet achieves superior classification performance, with average accuracies of 70.18% for MI, 86.26% for MA, and 81.13% for WG, outperforming existing state-of-the-art multimodal algorithms. These findings suggest that TSFNet provides an effective solution for spatiotemporal feature fusion in hybrid BCIs, with potential applications in real-world BCI systems.}, }
@article {pmid41094901, year = {2025}, author = {Anzalone, A and Acampora, E and Liu, C and Hajra, SG}, title = {Passive Brain-Computer Interface Using Textile-Based Electroencephalography.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {19}, pages = {}, pmid = {41094901}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Textiles ; Support Vector Machine ; Male ; Adult ; Electrodes ; Female ; Machine Learning ; *Brain/physiology ; Cognition/physiology ; }, abstract = {Background: Passive brain-computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user's cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on sensor technologies that cannot easily be integrated into non-laboratory settings where pBCIs are most needed. Advances in textile-electrode-based EEG show promise in overcoming the operational limitations; however, no study has demonstrated their use in pBCIs. This study presents the first application of fully textile-based EEG for pBCIs in differentiating cognitive states. Methods: Cognitive state comparisons between eyes-open (EO) and eyes-closed (EC) conditions were conducted using publicly available data for both novel textile and traditional dry-electrode EEG. EO vs. EC differences across both EEG sensor technologies were assessed in delta, theta, alpha, and beta EEG power bands, followed by the application of a Support Vector Machine (SVM) classifier. The SVM was applied to each EEG system separately and in a combined setting, where the classifier was trained on dry EEG data and tested on textile EEG data. Results: The textile EEG system accurately captured the characteristic increase in alpha power from EO to EC (p < 0.01), but power values were lower than those of dry EEG across all frequency bands. Classification accuracies for the standalone dry and textile systems were 96% and 92%, respectively. The cross-sensor generalizability assessment resulted in a 91% classification accuracy. Conclusions: This study presents the first use of textile-based EEG for pBCI applications. Our results indicate that textile-based EEG can reliably capture changes in EEG power bands between EO and EC, and that a pBCI system utilizing non-traditional textile electrodes is both accurate and generalizable.}, }
@article {pmid41093880, year = {2025}, author = {Aguilera-Rodríguez, E and Cuevas-Romero, A and Mendoza-Franco, S and Wornovitzky-Green, J and Rivera-Cerros, E and Villanueva-Cazares, D and Muñoz-Ubando, LA and Ibarra-Zárate, D and Alonso-Valerdi, LM}, title = {An EEG-based Imagined Speech Database for comparing Paradigm Designs.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1644}, pmid = {41093880}, issn = {2052-4463}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Speech ; Female ; Male ; *Imagination ; Adult ; Video Games ; }, abstract = {Brain-computer interfaces (BCIs) attempt to establish a connection between the human mind and a computer system. While recent computational advances continue to improve these interfaces, human factors have been overlooked. Factors such as fatigue and attention play a key role in brain signal modulation. This arises the need for paradigms designed and implemented in terms of human factors. Therefore, it is proposed to improve the level of engagement to diminish fatigue and increase attention by a video game-based paradigm for an imagined speech BCI. For this purpose, a sample of 15 volunteers (females = 7) was recruited to study the quality of their imagined speech when it is evoked under an abstract scenario (traditional paradigm) and a video-game paradigm. This dataset helps to study the differences in imagined speech signals when using two different paradigms: (1) one that does not consider human factors, and (2) one that does. Additional applications may include designing imagined speech decoding models for BCI and studying the relationship between users' profile and their imagined speech signals.}, }
@article {pmid41092418, year = {2025}, author = {Oliveira, I and Russo, M and Almeida, AI and Vourvopoulos, A and Mendes Pereira, C}, title = {Recommendations for Combining Brain-Computer Interface, Motor Imagery, and Virtual Reality in Upper Limb Stroke Rehabilitation: Qualitative Participatory Design Study.}, journal = {JMIR rehabilitation and assistive technologies}, volume = {12}, number = {}, pages = {e71789}, pmid = {41092418}, issn = {2369-2529}, abstract = {BACKGROUND: The high incidence and prevalence of upper limb impairment post stroke highlights the need for advancements in rehabilitation. Brain-computer interfaces (BCIs) represent a promising technology by directly training the central nervous system. The integration of motor imagery (MI) and motor observation through virtual reality (VR) using BCIs provides valuable opportunities for rehabilitation. However, the diversity in intervention designs demonstrates the lack of guiding recommendations integrating neurorehabilitation principles for BCIs.<br><br>OBJECTIVE: This study aims to develop recommendations for BCI interventions using task specificity and ecological validity through simulated VR tasks for upper limb stroke survivors by gathering tacit knowledge from neurorehabilitation experts, patients' experiences, and engineers' expertise to ensure a comprehensive approach.<br><br>METHODS: A multiperspective qualitative study was conducted through collaborative design workshops involving stroke survivors (n=17), neurorehabilitation experts (n=13), and biomedical engineers (n=3), totaling 33 participants. This innovative approach aimed to actively engage stakeholders in developing multifaceted solutions for complex health interventions.<br><br>RESULTS: Six themes emerged from the thematic analysis: (1) importance of patient-centered approach, (2) clinical evaluation and patient selection, (3) recommendations for task design, (4) guidelines for structuring BCI intervention, (5) key factors influencing motivation, and (6) technology features. From these themes, the following recommendations (R) are established: (R1) MI-based VR-BCI interventions must be conducted through a patient-centered approach, based on individualized preferences, needs, and goals of the user, by an interdisciplinary team; (R2) selection criteria must include upper limb impairment, cognitive and communication assessment, and clinical traits, such as MI capacity, neglect, and depression must be assessed since they might influence intervention outcomes; (R3) tasks to perform should preferably be based on daily living activities, including unilateral and bilateral tasks, and a variety of tasks must be available for selection to ensure meaningfulness for the user and suitability to clinical traits; (R4) intervention must be structured by different progressing levels starting with simple, gross movements and adding complexity through additional movement features, cognitive demand, or MI difficulty; (R5) optimal levels of motivation must be sustained through task variability, gamification elements, and task demand adequacy; and (R6) multisensorial potential of MI-based VR-BCI must be effectively harnessed through the adequate adjustment of visual, haptic, and proprioceptive feedback modalities to the patient.<br><br>CONCLUSIONS: Current results contribute to establishing clear guidelines on patient selection, task design, intervention structuring, motivation factors, and tailoring of sensory feedback. This framework presents a foundation for optimal implementation of VR-BCI-based interventions that associate MI and motor observation, optimizing cortical activity during the intervention, patients' engagement, and clinical outcomes. Future research should explore the application of these guidelines for validation and investigate BCIs' efficacy according to different combinations of patients' profiles, task characteristics, and technology features.}, }
@article {pmid41091050, year = {2025}, author = {Levy, L and Feinsinger, A}, title = {Participant Engagement, Epistemic Injustice, and Early-Phase Implanted Neural Device Research.}, journal = {The Hastings Center report}, volume = {55}, number = {5}, pages = {18-28}, pmid = {41091050}, issn = {1552-146X}, support = {RF1 MH121373/MH/NIMH NIH HHS/United States ; //Dana Foundation/ ; RF1MH121373/NH/NIH HHS/United States ; }, mesh = {Humans ; *Social Justice ; Motivation ; *Biomedical Research/ethics ; Knowledge ; *Prostheses and Implants ; }, abstract = {In recent years, participant engagement initiatives in research on implanted neural devices have significantly increased. However, there remains little consensus on the motivations, goals, and best practices for engagement efforts. Drawing on the concept of participatory epistemic injustice, we argue that one core ethical motivation for engagement is epistemic in nature. Based on their subject positions, participants should be key knowledge contributors to implanted neurotech research. Therefore, we argue, participants experience participatory epistemic injustice when their insights do not result in changes to or otherwise influence research protocols, device development, and task design. We contend that engagement can resist this type of injustice only if it establishes robust methods not only to gather but also to actively incorporate participant knowledge into the research and development process.}, }
@article {pmid41090855, year = {2025}, author = {Liu, Y and Wu, H and Wang, S and Yang, Q and Zhang, B}, title = {The Implantable Electrode Co-Deposited with Iron Oxide Nanoparticles and PEDOT:PSS.}, journal = {Nanomaterials (Basel, Switzerland)}, volume = {15}, number = {19}, pages = {}, pmid = {41090855}, issn = {2079-4991}, support = {5216202252162022//National Natural Science Foundation of China/ ; 2021JJA160015//Guangxi Natural Science Foundation/ ; }, abstract = {Iron oxide nanoparticles (IONs) exhibit biocompatibility, ease of drug loading, and potential for generating forces and heat in a magnetic field, enhancing Magnetic Resonance Imaging (MRI). This study proposes coating IONs on electrode surfaces to improve performance and neuron bonding. Methods included synthesizing IONs, grafting chondroitin sulfate (CS), and co-depositing with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). Results showed reduced impedance, increased charge storage, and improved signal quality in vivo.}, }
@article {pmid41089660, year = {2025}, author = {Dai, Y and Chen, Z and Cao, TA and Zhou, H and Fang, M and Dai, Y and Jiang, L and Tong, J}, title = {A time-frequency feature fusion-based deep learning network for SSVEP frequency recognition.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1679451}, pmid = {41089660}, issn = {1662-4548}, abstract = {INTRODUCTION: Steady-state visual evoked potential (SSVEP) has emerged as a pivotal branch in brain-computer interfaces (BCIs) due to its high signal-to-noise ratio (SNR) and elevated information transfer rate (ITR). However, substantial inter-subject variability in electroencephalographic (EEG) signals poses a significant challenge to current SSVEP frequency recognition. In particular, it is difficult to achieve high cross-subject classification accuracy in calibration-free scenarios, and the classification performance heavily depends on extensive calibration data.<br><br>METHODS: To mitigate the reliance on large calibration datasets and enhance cross-subject generalization, we propose SSVEP time-frequency fusion network (SSVEP-TFFNet), an improved deep learning network fusing time-domain and frequency-domain features dynamically. The network comprises two parallel branches: a time-domain branch that ingests raw EEG signals and a frequency-domain branch that processes complex-spectrum features. The two branches extract the time-domain and frequency-domain features, respectively. Subsequently, these features are fused via a dynamic weighting mechanism and input to the classifier. This fusion strategy strengthens the feature expression ability and generalization across different subjects.<br><br>RESULTS: Cross-subject classification was conducted on publicly available 12-class and 40-class SSVEP datasets. We also compared SSVEP-TFFNet with traditional approaches and principal deep learning methods. Results demonstrate that SSVEP-TFFNet achieves an average classification accuracy of 89.72% on the 12-class dataset, surpassing the best baseline method by 1.83%. SSVEP-TFFNet achieves average classification accuracies of 72.11 and 82.50% (40-class datasets), outperforming the best controlled method by 7.40 and 6.89% separately.<br><br>DISCUSSION: The performance validates the efficacy of dynamic time-frequency feature fusion and our proposed method provides a new paradigm for calibration-free SSVEP-based BCI systems.}, }
@article {pmid41089381, year = {2025}, author = {Liu, B and Hu, C and Bao, P}, title = {Precision TMS through the integration of neuroimaging and machine learning: optimizing stimulation targets for personalized treatment.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1682852}, pmid = {41089381}, issn = {1662-5161}, abstract = {Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique based on electromagnetic induction, modulates cortical excitability by inducing currents with a magnetic field. TMS has demonstrated significant clinical potential in the treatment of various neuropsychiatric disorders, including depression, anxiety, and Parkinson's disease. However, conventional TMS targeting methods that rely on anatomical landmarks do not adequately account for individual differences in brain structure and functional networks, leading to considerable variability in treatment responses. In recent years, advances in neuroimaging techniques-such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI)-together with the application of machine learning (ML) and artificial intelligence (AI) algorithms in big data analysis, have provided novel approaches for precise TMS targeting and individualized treatment. This review summarizes the latest developments in the integration of multimodal neuroimaging and AI technologies for precision neuromodulation with TMS. It focuses on critical issues such as imaging resolution, AI model generalizability, real-time feedback modulation, as well as data privacy and ethical considerations. Future prospects including closed-loop TMS control systems, cross-modal data fusion, and AI-assisted brain-computer interfaces (BCIs) are also discussed. Overall, AI-driven personalized TMS strategies hold promise for markedly enhancing treatment precision and clinical efficacy, thereby offering new theoretical and practical guidance for individualized treatment in neuropsychiatric and neurodegenerative disorders.}, }
@article {pmid41088329, year = {2025}, author = {Han, F and Chen, H}, title = {Does brain-computer interface-based mind reading threaten mental privacy? ethical reflections from interviews with Chinese experts.}, journal = {BMC medical ethics}, volume = {26}, number = {1}, pages = {134}, pmid = {41088329}, issn = {1472-6939}, support = {21ZDA017//National Social Science Fund of China/ ; 21ZDA017//National Social Science Fund of China/ ; }, mesh = {Humans ; *Brain-Computer Interfaces/ethics ; China ; *Privacy ; Male ; Female ; Adult ; Interviews as Topic ; Reading ; Neurosciences/ethics ; Qualitative Research ; East Asian People ; }, abstract = {BACKGROUND: The rapid development of brain-computer interface (BCI) technology has sparked profound debates about the right to privacy, particularly concerning its potential to enable mind reading. While scholars have proposed the establishment of neurorights to safeguard mental privacy, questions remain about whether BCIs can genuinely decode inner thoughts and what makes their ethical implications distinctive.<br><br>METHODS: This study conducted semi-structured interviews with 20 Chinese experts in the BCI and neuroscience fields to explore their perspectives on the concept, feasibility, and limitations of BCI-based mind reading (BMR). The transcriptions of the interviews were analyzed through reflexive thematic analysis to identify key themes and insights.<br><br>RESULTS: The findings reveal a range of expert perspectives on the interpretations and feasibility of BMR. Most participants believe that current BCI technology cannot decode inner thoughts, although they acknowledge the potential for future advancements. Key technical challenges, such as signal quality and reliance on background information, are highlighted.<br><br>CONCLUSION: We summarize the interpretations, feasibility, and limitations of BMR and introduce a distinction between "strong BMR" and "weak BMR" to clarify their technical and ethical implications. Based on our analysis, we argue that current BMR does not pose unique ethical challenges compared with other forms of mind reading, and therefore does not yet justify the establishment of a distinct right to mental privacy.}, }
@article {pmid41088296, year = {2025}, author = {Tang, A and Chen, Y and Ding, J and Li, Z and Xu, C and Hu, S and Lai, J}, title = {Gut microbiota remodeling and sensory-emotional functional disruption in adolescents with bipolar depression.}, journal = {Journal of translational medicine}, volume = {23}, number = {1}, pages = {1083}, pmid = {41088296}, issn = {1479-5876}, support = {82201676//National Natural Science Foundation of China/ ; 82471542//National Natural Science Foundation of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Gastrointestinal Microbiome/drug effects/physiology ; Adolescent ; *Bipolar Disorder/microbiology/physiopathology/drug therapy/psychology ; Male ; Female ; *Emotions ; Quetiapine Fumarate/therapeutic use/pharmacology ; Magnetic Resonance Imaging ; Case-Control Studies ; Brain/physiopathology/diagnostic imaging ; Neuroimaging ; }, abstract = {BACKGROUND: Adolescence is the peak period of newly-onset bipolar disorder (BD). Accumulating studies have revealed disturbed gut microbiota can interfere with neurodevelopment in adolescents. In this study, we aimed to characterize the gut microbiota in adolescents with BD and its correlation with brain dysfunction.<br><br>METHODS: Thirty unmedicated BD adolescents within depressive episode were recruited and underwent four-week quetiapine treatment. Twenty-five age-, gender-, and BMI-matched healthy controls (HCs) were recruited. Fecal samples were collected from HCs and all BD adolescents before and after treatment and analyzed by metagenomic sequencing. Resting-state cranial functional magnetic images were collected from 21 BD adolescents before treatment. Random forest models were used to evaluate the discriminative power of gut microbiota and neuroimaging data for BD and the predictive power of treatment effect.<br><br>RESULTS: Although no significant difference was found in alpha-diversity, intra- and inter-group differences in beta-diversity were observed among HCs, pre- and post-treatment patients. Compared to HCs, unmedicated BD adolescents presented a differentiated gut microbial communities, which correlated to the short-chain fatty acids, choline, lipids, vitamins, polyamines, aromatic amino acids metabolic pathways. Four-week quetiapine treatment improved the abundance of specific genus, such as Odoribacter splanchnicus, Oribacterium sinus, Hafnia alvei, Fusobacterium periodonticum, Acidaminococcus interstini and Veillonella rogosae. Neuroimaging analysis revealed sensor-emotional brain regions were associated with BD severity. Finally, random forest models based on gut microbial biomarkers can well distinguish unmedicated BD from HCs (AUC&#x2009;=&#x2009;91.12%) and predict the treatment effect (AUC&#x2009;=&#x2009;91.84%). The random forest model integrating gut microbiota and neuroimaging data exhibited a better predictive efficacy than using microbiota data alone.<br><br>CONCLUSION: This study first characterized the gut microbiota architecture in adolescent BD. Combining gut microbiota and brain function biomarkers may benefit disease diagnosis and predict treatment outcome. Nonetheless, these findings should be carefully interpreted considering the limitations of a modest sample size and the absence of detailed mechanistic explorations. Trial registration NCT05480150. Registered 29 July 2022-Retrospectively registered, https://clinicaltrials.gov/study/NCT05480150 .}, }
@article {pmid41087533, year = {2025}, author = {Ge, Y and Dong, Y and Sun, H and Liu, Y and Wang, C}, title = {An incremental adversarial training method enables timeliness and rapid new knowledge acquisition.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {35826}, pmid = {41087533}, issn = {2045-2322}, support = {JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; 2022IT096//New Generation Information Technology Innovation Project of China University Industry, University and Research Innovation Fund/ ; }, mesh = {*Neural Networks, Computer ; Humans ; Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; }, abstract = {Adversarial training is an effective defense method for deep models against adversarial attacks. However, current adversarial training methods require retraining the entire neural network, which consumes a significant amount of computational resources, thereby affecting the timeliness of deep models and further hindering the rapid learning process of new knowledge. In response to the above problems, this article proposes an incremental adversarial training method (IncAT) and applies it to the field of brain computer interfaces (BCI). Within this method, we first propose a deep model called Neural Hybrid Assembly Network (NHANet) and then train it. Then, based on the original samples and the trained deep model, calculate the Fisher information matrix to evaluate the importance of deep neural network parameters on the original samples. Finally, when calculating the loss of adversarial samples and real labels, an Elastic Weight Consolidation (EWC) loss is added to limit the variation of important weights and bias parameters in the Neural Hybrid Assembly Network (NHANet). The above incremental adversarial training method was applied to the publicly available epilepsy brain computer interface dataset at the University of Bonn. The experimental results showed that when facing three different attack algorithms, including fast gradient sign method (FGSM), projected gradient descent (PGD) and basic iterative method (BIM), the method proposed in this paper achieved robust accuracies of 95.33%, 94.67%, and 93.60%, respectively, without affecting the accuracy of clean samples, which is 5.06%, 4.67%, and 2.67% higher than traditional training methods respectively, thus fully verifying the generalization and effectiveness of the method.}, }
@article {pmid41087504, year = {2025}, author = {Fang, T and Wang, R and Liu, W and Zhang, Y and Guo, Y and Hu, Y and Zhao, X and Chen, Y and Fan, Q and Ming, D}, title = {Edge participation coefficient unveiling the developmental dynamics of neonatal functional connectome.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {1463}, pmid = {41087504}, issn = {2399-3642}, mesh = {Humans ; *Connectome/methods ; Infant, Newborn ; *Brain/growth &amp; development/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; Infant, Premature/growth &amp; development ; Male ; Female ; *Nerve Net/growth &amp; development/physiology ; Infant ; }, abstract = {Understanding how the brain's functional connections develop during infancy is crucial for uncovering the complexities of early neural maturation. Traditional node-based analyses have advanced our knowledge, but may overlook the transient dynamics of interregional connectivity. Leveraging the large neonatal functional MRI dataset from the Developing Human Connectome Project (n = 781, including 494 full-term and 287 preterm infants), we introduce an edge-centric metric to quantify cross-module functional integration. Here we show that preterm infants exhibit higher edge participation coefficients than full-term peers, suggesting delayed network specialization. We mapped developmental changes in edge participation coefficients and found that between-network connections-particularly those involving visual and higher-order systems-undergo the most pronounced changes and are associated with cognitive outcomes at 18 months. By analyzing gene expression in a developing brain, we identified genes involved in neurodevelopmental processes and cellular signalling that may underlie these patterns. Our findings illustrate how interregional diversity evolves in early life and provide insight into the molecular basis of early brain development.}, }
@article {pmid41083759, year = {2025}, author = {Banaeian Far, S and Chalak Qazani, MR and Imani Rad, A}, title = {Cell-to-cell communication: from physical calling to remote emotional touching.}, journal = {Discover nano}, volume = {20}, number = {1}, pages = {178}, pmid = {41083759}, issn = {2731-9229}, abstract = {The emerging paradigm of cell-to-cell communication represents a transformative shift from device-mediated contact to bio-integrated, emotion-driven interactions. This article introduces a novel, multi-layered framework for enabling biologically integrated communication between cells, devices, and computational systems using the paradigm of Molecular Communication (MC). Moving beyond traditional digital interfaces, the proposed architecture, comprising in-body, on-chip, and external communication layers, models and processes intercellular signaling via molecular emissions, implantable biosensors, and nano-electronic processors. Theoretical foundations are extended to fractional-order diffusion systems and neuromorphic decoding, capturing complex behaviors in realistic biological environments. We further propose a cross-layer molecular digital twin model for context-aware interpretation and feedback. The framework's applications are grounded in the molecular underpinnings of emotion, where neurotransmitters like oxytocin and serotonin mediate prosocial behaviors and affective states through cell-to-cell signaling. For instance, remote emotional interfacing leverages MC to modulate oxytocin release, mimicking natural empathy circuits, while consensual telepathy draws from BCI-mediated neural pattern sharing, extending molecular-level decoding to cognitive-emotional relays. These are not mere metaphors but extensions of established neurochemical pathways, as evidenced by recent studies showing serotonin fluctuations amplify context-specific emotions. This work thus bridges cellular mechanisms to higher-order phenomena, ensuring scientific rigor in bio-digital systems .}, }
@article {pmid41082414, year = {2025}, author = {Chen, Q and Ye, C and Xiao, R and Pan, J and Li, J}, title = {SemSTNet: Medical EEG Semantic Metric Learning with Class Prototypes Generated by Pretrained Language Model.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3620754}, pmid = {41082414}, issn = {1558-2531}, abstract = {Electroencephalography (EEG) feature learning is crucial for brain-machine interfaces and medical diagnostics. Existing deep learning models for classification often overlook the intrinsic semantic relationships between different EEG classes and rely on overly complex models with a large number of parameters. To address these challenges, we propose SemSTNet, a novel and lightweight framework for EEG analysis. Firstly, we designed an e ficient, lightweight convolutional architecture that decouples spatial and temporal feature extraction. Then we propose a framework which introduces a novel semantic metric learning paradigm that uses class prototypes generated by a pretrained language model to better capture inter-class relationships and enhance intra-class compactness. These prototypes are extracted and stored offline, requiring no additional inference from the language model during training or deployment. This design significantly reduces model complexity, resulting in a model with only 23K parameters-over 100 times fewer than common Transformer-based models. Exten sive experiments demonstrate that SemSTNet outperforms state of-the-art approaches on tasks such as epilepsy classification and sleep staging, highlighting its effectiveness and efficiency. Our work demonstrates that integrating semantic knowledge with a purpose-built lightweight architecture provides a highly effective and efficient solution.}, }
@article {pmid41082173, year = {2025}, author = {Hodgkiss, DD and Balthazaar, SJT and Gee, CM and Boardley, ID and Janssen, TWJ and Krassioukov, AV and Nightingale, TE}, title = {Electroceuticals for Paralympic Athletes: A Fair Play and Classification Concern?.}, journal = {Sports medicine (Auckland, N.Z.)}, volume = {}, number = {}, pages = {}, pmid = {41082173}, issn = {1179-2035}, support = {NRB123//International Spinal Research Trust/ ; RG2698/21/23//Heart Research UK/ ; SBF009\1126/AMS_/Academy of Medical Sciences/United Kingdom ; }, abstract = {Electroceuticals such as brain computer interfaces and spinal cord stimulation (SCS) represent transformative strategies for neuromodulation. Research has demonstrated that SCS can ameliorate motor and autonomic cardiovascular dysfunctions, particularly in individuals with spinal cord injury (SCI). Notably, SCS has been shown to augment aerobic exercise performance. Owing to the nature of their injury, athletes with SCI are often predisposed to low resting blood pressure and impaired physiological responses to exercise. Therefore, some athletes intentionally induce autonomic dysreflexia ("boosting") to gain a competitive advantage - an act banned by the International Paralympic Committee (IPC). However, the emergence of electroceuticals facilitates an alternative performance enhancement strategy that could be considered unfair without equal access opportunities for all athletes. Currently, the World Anti-Doping Agency and the IPC have not acknowledged the potential impact of electroceuticals in parasport. Herein, we present an argument that the use of SCS meets the criteria for it to be placed on the World Anti-Doping Code Prohibited List (or at the very least be monitored) because collectively: SCS can enhance sport performance, represents a potential health risk to the athlete if misused, and may violate the spirit of sport. Acute and chronic use of SCS may also lead to classification changes, and increased opportunities for athletes to intentionally misrepresent, thereby raising concerns for the IPC. The growing access to electroceuticals (e.g. via clinical trial participation or private healthcare implantation) more than ever increases the likelihood of an athlete using SCS to gain an unfair advantage in parasport.}, }
@article {pmid41082005, year = {2025}, author = {Kolarijani, NR and Salehi, M and Mirzaii, M and Farahani, MK and Zamani, S and Fazli, M and Alizadeh, M}, title = {Synthesis and characterization of silver nanoparticle-loaded carboxymethylcellulose hydrogels: in vitro and in vivo evaluation of wound healing and antibacterial properties.}, journal = {Cell and tissue banking}, volume = {26}, number = {4}, pages = {46}, pmid = {41082005}, issn = {1573-6814}, mesh = {Animals ; *Silver/pharmacology/chemistry ; *Wound Healing/drug effects ; *Hydrogels/pharmacology/chemistry/chemical synthesis ; *Carboxymethylcellulose Sodium/chemistry/pharmacology ; *Anti-Bacterial Agents/pharmacology/chemistry/chemical synthesis ; *Metal Nanoparticles/chemistry/ultrastructure ; Rats ; Pseudomonas aeruginosa/drug effects ; Staphylococcus aureus/drug effects ; Microbial Sensitivity Tests ; Male ; Hemolysis/drug effects ; }, abstract = {The current research was conducted to assess wound healing activity and antibacterial properties of carboxymethyl cellulose (CMC) hydrogels loaded with silver nanoparticles (AgNPs) against excisional wounds (15 × 15 mm[2]) infected with Pseudomonas aeruginosa and Staphylococcus aureus in a rat model.CMC/AgNPs hydrogels were synthesized using varying concentrations of AgNPs and subsequently lyophilized. A comprehensive range of in vitro tests were conducted, including nanoparticle characterization, scanning electron microscopy (SEM) morphology study, water uptake (WUE) study, blood uptake capacity study (BUC), weight loss study (WLA), pH, hemolysis percentage (HP), blood coagulation index (BCI), antibacterial activity (minimum inhibitory concentration [MIC] and minimum bactericidal concentration [MBC]), and cell viability through the MTT assay. In vivo wound healing studies were conducted using infected excisional wound models in rats. SEM confirmed a porous structure with a mean pore size ranging from 68 to 152 μm. The hydrogels exhibited dosage-dependent swelling and sustained physiological pH (7.4-7.6) for a period of time. The 125 μg/mL AgNPs formulation showed a BUC of 97.68% in 22 h. Hemocompatibility assay showed minimal hemolysis and acceptable coagulation indices for all concentrations of AgNPs. MIC and MBC against both strains of bacteria were found to be 250 μg/mL and 500 μg/mL, respectively. CMC/AgNPs hydrogel with the concentration of 250 μg/mL showed the optimal cell viability and the optimal in vivo wound healing result. The findings indicate that AgNPs-loaded CMC hydrogels possess favorable physicochemical, biocompatible, and antimicrobial properties, suggesting their potential as a wound dressing for managing infected wounds and supporting the wound healing process.}, }
@article {pmid41081225, year = {2025}, author = {Cao, P and Guo, S and Zhang, G and Zan, X and Wang, J and Zhang, F and Muñoz, J and Lucke-Wold, B and Cheng, R}, title = {Brain-computer interface training for multimodal functional recovery in patients with brain injury: a case series.}, journal = {Quantitative imaging in medicine and surgery}, volume = {15}, number = {10}, pages = {9277-9293}, pmid = {41081225}, issn = {2223-4292}, abstract = {BACKGROUND: Patients with impaired brain function often face sequelae such as limb movement, cognitive, and language impairment, and there are limitations in the efficiency of traditional rehabilitation methods. This study examined whether motor imagery-based brain-computer interface (BCI) training could promote multimodal functional recovery-including limb movement, speech, and cognition-in patients with subacute brain injury. Unlike traditional BCI research focused on single functional domains, we combined multidimensional clinical assessments with multimodal neural analysis to examine cross-network plasticity.<br><br>METHODS: Five patients with subacute brain injury (four males and one female; mean age 54.4&#xb1;10.3 years) underwent 5 weeks of BCI training between 2021 and 2023. Pre- and post-intervention evaluations included the Fugl-Meyer Assessment Scale (FMA), Modified Ashworth Scale (MAS), Western Aphasia Battery (WAB), and Mini-Mental State Examination (MMSE). Neurophysiological metrics included classification accuracy (CA), power spectral density (PSD), and electroencephalography (EEG) topography. Functional connectivity analyses were conducted with functional magnetic resonance imaging (fMRI) and individualized connectomics based on the Human Connectome Project parcellation.<br><br>RESULTS: All five patients showed clinical improvement in motor, cognitive, or language functions. The average motor imagery CA increased by 14.2%. PSD flattening and event-related desynchronization (ERD) were observed in the central motor regions. EEG topographies showed enhanced activation converging toward the sensorimotor cortex. Patient-specific functional connectivity analyses revealed strengthened interactions among sensorimotor, language, and attention networks-most notably in one patient with marked clinical gains. Distinct patterns of connectivity reorganization were observed between patients with cortical and subcortical lesions. A critical 3-week time window for neural plasticity was identified.<br><br>CONCLUSIONS: Motor imagery-based BCI training may facilitate recovery across motor, language, and cognitive domains in patients with subacute brain injury. Functional gains were supported by neurophysiological and connectomics evidence of cross-network reorganization. These preliminary findings suggest that personalized BCI protocols could represent a promising avenue for multimodal neurorehabilitation.}, }
@article {pmid41079666, year = {2025}, author = {Jia, Q and Xu, Z and Wang, Y and Duan, Y and Liu, Y and Shan, J and Ma, J and Li, Q and Luo, J and Luo, Y and Wang, Y and Duan, S and Yu, Y and Wang, M and Cai, X}, title = {Targeted-Modified MultiTransm Microelectrode Arrays Simultaneously Track Dopamine and Cellular Electrophysiology in Nucleus Accumbens during Sleep-Wake Transitions.}, journal = {Research (Washington, D.C.)}, volume = {8}, number = {}, pages = {0944}, pmid = {41079666}, issn = {2639-5274}, abstract = {Cellular-level electrophysiological and neurotransmitter signals serve as key biomarkers of sleep depth, offering insights into the dynamic sleep transitions and the neural mechanisms underlying sleep regulation. Microelectrode arrays (MEAs) provide an innovative solution for in situ, simultaneous detection of these signals with high spatial and temporal resolution. However, despite substantial progress in electrode material development, current multimodal MEA systems remain fundamentally constrained by partial integration. This study aims to address the performance limitations of multimodal MEAs by developing a MultiTransm MEA (MT MEA), integrating a 3-electrode system with site-specific surface modifications: platinum nanoparticle (PtNP)/poly(3,4-ethylene dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)-modified sites for electrophysiology, PtNP/PEDOT:PSS/Nafion-modified sites for dopamine sensing, and iridium oxide (IrOx)-based on-probe reference electrodes. The directional surface modification strategy was employed to enable compact integration, minimize inter-channel crosstalk, preserve high spatiotemporal resolution for both electrophysiological and electrochemical detection, and ensure long-term operational stability. By incorporating electroencephalography (EEG) and electromyography (EMG), MT MEAs enable real-time in vivo monitoring of sleep dynamics within the nucleus accumbens. Three distinct spike types were identified, whose coordinated activity shaped the sleep architecture. In addition, EEG and local field potential (LFP) signals exhibited distinct patterns during wakefulness, indicating region-specific neural processing. Notably, dopamine release was lowest during non-rapid eye movement (NREM) sleep and peaked during wakefulness, suggesting a neuromodulatory role in sleep-wake transitions. These results demonstrate that MT MEAs are powerful tools for probing neural and neurochemical activity across sleep states, offering new insights into the physiological regulation of sleep.}, }
@article {pmid41079401, year = {2025}, author = {Esteves, D and Vagaja, K and Andrade, A and Vourvopoulos, A}, title = {When embodiment matters most: a confirmatory study on VR priming in motor imagery brain-computer interfaces training.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1681538}, pmid = {41079401}, issn = {1662-5161}, abstract = {BACKGROUND: Virtual Reality (VR) feedback is increasingly integrated into Brain-Computer Interface (BCI) applications, enhancing the Sense of Embodiment (SoE) toward virtual avatars and fostering more vivid motor imagery (MI). VR-based MI-BCIs hold promise for motor rehabilitation, but their effectiveness depends on neurofeedback quality. Although SoE may enhance MI training, its role as a priming strategy prior to VR-BCI has not been systematically examined, as prior work assessed embodiment only after interaction. This study investigates whether embodiment priming influences MI-BCI outcomes, focusing on event-related desynchronization (ERD) and BCI performance.<br><br>METHODS: Using a within-subject design, we combined data from a pilot study with an extended experiment, yielding 39 participants. Each completed an embodiment induction phase followed by MI training with EEG recordings. ERD and lateralization indices were analyzed across conditions to test the effect of prior embodiment.<br><br>RESULTS: Embodiment induction reliably increased SoE, yet no significant ERD differences were found between embodied and control conditions. However, lateralization indices showed greater variability in the embodied condition, suggesting individual differences in integrating embodied feedback.<br><br>CONCLUSION: Overall, findings indicate that real-time VR-based feedback during training, rather than prior embodiment, is the main driver of MI-BCI performance improvements. These results corroborate earlier findings that real-time rendering of embodied feedback during MI-BCI training constitutes the primary mechanism supporting performance gains, while highlighting the complex role of embodiment in VR-based MI-BCIs.}, }
@article {pmid41079152, year = {2025}, author = {Bassil, K and Jongsma, K}, title = {To Explant or not to Explant Neural Implants: an Empirical Study into Deliberations of Dutch Research Ethics Committees.}, journal = {Neuroethics}, volume = {18}, number = {3}, pages = {45}, pmid = {41079152}, issn = {1874-5490}, abstract = {UNLABELLED: Neural implants such as brain-computer interfaces and spinal cord stimulation offer therapeutic prospects for people with neurological and psychiatric disorders. As neural devices are increasingly tested in clinical research, the decision to explant requires carefully weighing both known and unknown medical and psychological risks, necessitating a thorough evaluation of the benefits and risks of each available option. Research Ethics Committees (RECs) play an important role in assessing research protocols and determining the conditions under which neural implants should be explanted, yet little is understood about how RECs make these decisions. To better understand the role of RECs in explantation decisions of neural implants, we approached REC secretaries within the Netherlands via email, with a list of open-ended questions of which the explantation of neural devices, on informed consent and post-trial care and responsibilities, and psychological harm associated with such trials. The findings highlight the differential technology-specific safety assessments conducted for different types of neural devices. Variability was observed in plans regarding clinical follow-up, post-trial access, and explantation options. While RECs emphasized clear participant information on device maintenance and longevity, the timing of this disclosure varied. Additionally, the psychological impact of explantation was rarely addressed in REC assessments, indicating a gap in ethical oversight. These results shed light on some remaining gaps and suggest the need for improvement in achieving more consistent and comprehensive evaluations of neural device clinical trials, particularly regarding explantation and post-trial access.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12152-025-09619-z.}, }
@article {pmid41076093, year = {2025}, author = {Althobaiti, M}, title = {Sensitivity analysis of the balloon model parameters in functional near-infrared spectroscopy simulation.}, journal = {Journal of neuroscience methods}, volume = {424}, number = {}, pages = {110599}, doi = {10.1016/j.jneumeth.2025.110599}, pmid = {41076093}, issn = {1872-678X}, mesh = {Spectroscopy, Near-Infrared/methods ; Humans ; *Computer Simulation ; Hemodynamics/physiology ; *Brain/physiology/blood supply ; *Models, Neurological ; Artifacts ; Signal Processing, Computer-Assisted ; Cerebrovascular Circulation/physiology ; }, abstract = {BACKGROUND: Accurate modeling of the hemodynamic response is critical for fNIRS data interpretation. While the Balloon model is a cornerstone for this, the quantitative impact of its key parameters on the fNIRS signal, particularly in the presence of realistic artifacts, remains under-characterized.<br><br>NEW METHOD: We developed an end-to-end fNIRS simulation pipeline. It incorporates a neural activity model, the Balloon model for hemodynamics, convolution for signal generation, and realistic motion, cardiac, and respiratory artifacts. We performed a sensitivity analysis by systematically varying Grubb's exponent (&#x3b1;) and transit time (&#x3c4;).<br><br>RESULTS: Both &#x3b1; and &#x3c4; significantly influence the simulated fNIRS response. &#x3b1; shows a non-linear relationship with peak amplitude, while &#x3c4; has a more linear effect on signal timing. Regression models quantifying these effects demonstrated a strong statistical fit (p&#x202f;<&#x202f;0.05, R&#xb2; > 0.9 for &#x3b1;).<br><br>Unlike prior fMRI-focused studies, this is the first quantitative sensitivity analysis specifically for fNIRS signals that incorporates a realistic noise model. Our framework characterizes the forward model's behavior, providing parameter-specific insights not previously available for fNIRS simulations.<br><br>CONCLUSIONS: The fNIRS hemodynamic response is highly sensitive to the Balloon model's &#x3b1; and &#x3c4; parameters. These findings highlight the importance of accounting for physiological variability in fNIRS analysis and provide a robust framework for generating synthetic data to test signal processing algorithms.}, }
@article {pmid41074421, year = {2025}, author = {Hui, Z and Zhang, Y and Su, Y and Kang, J and Qi, W and Li, S and Zhang, J and Shi, K and Wang, M and Yang, Y and Zhang, G and Yang, L and Chen, G and Li, S and Hu, Y and Zhu, D}, title = {Abnormal Brain Connectivity Patterns in Children with Global Developmental Delay Accompanied by Cognitive Impairment: A Resting-State EEG Study.}, journal = {Journal of integrative neuroscience}, volume = {24}, number = {9}, pages = {44410}, doi = {10.31083/JIN44410}, pmid = {41074421}, issn = {0219-6352}, support = {NHCKLBDP202508//Open Research Program of the NHC Key Laboratory of Birth Defects Prevention/ ; SBGJ202402069//Key Project of Medical Science and Technology Tackling Plan of Henan Province 2024/ ; }, mesh = {Humans ; Male ; Female ; Child ; Electroencephalography ; *Cognitive Dysfunction/physiopathology/etiology ; *Developmental Disabilities/physiopathology/complications ; *Nerve Net/physiopathology ; *Brain Waves/physiology ; *Connectome ; Support Vector Machine ; Child, Preschool ; }, abstract = {BACKGROUND: Global developmental delay (GDD) is a common childhood neurodevelopmental disorder characterized by the core symptoms of cognitive impairment. However, the underlying neural mechanisms of the cognitive impairment remain unclear. This study aimed to both analyze differences in electroencephalography (EEG) connectivity patterns between children with GDD and typical development (TD) using brain functional connectivity and to explore the neural mechanisms linking these differences to cognitive impairment.<br><br>METHODS: The study enrolled 60 children with GDD and 60 TD children. GDD participants underwent clinical assessment via the Gesell Developmental Schedule (GDS). Resting-state EEG data were subjected to brain functional connectivity analysis and graph theory metric-based network analysis, with intergroup functional differences compared. Subsequently, correlation analysis characterized the relationships between GDD subject's brain network metrics and GDS-derived cognitive developmental quotient (DQ). Finally, three support vector machine (SVM) models were constructed for GDD classification and feature weight factors were calculated to screen potential EEG biomarkers.<br><br>RESULTS: The two groups exhibited complex differences in functional connectivity. Compared with the TD group, the GDD group showed a large number of increased functional connections in the &#x3b8;, &#x3b1;, and &#x3b3;-bands, along with a small number of decreased functional connections in the &#x3b1; and &#x3b3;-bands (all p < 0.025). Brain network analysis revealed lower global efficiency, local efficiency, clustering coefficient and small-world coefficient, as well as higher characteristic path length in GDD children across multiple bands (all p < 0.05). Correlation analysis indicated that global efficiency and small-world coefficient in &#x3b8; and &#x3b3;-bands were positively correlated with the DQ, while the characteristic path length in &#x3b1; and &#x3b3;-bands was negatively correlated with DQ in the GDD group (all p < 0.05). Machine learning models showed that a quantum particle swarm optimization SVM (QPSO-SVM) achieved the highest classification performance, with characteristic path length in the &#x3b3;-band being the highest weighted metric.<br><br>CONCLUSIONS: Children with GDD exhibit abnormal patterns of brain functional connectivity, characterized by global hypo-connectivity and local hyper-connectivity. Specific network metrics under these abnormal patterns are significantly correlated with cognitive impairment in GDD. This study also highlights the potential of the &#x3b3;-band characteristic path length as an EEG biomarker for diagnosing GDD.}, }
@article {pmid41073181, year = {2025}, author = {Rudroff, T}, title = {Retraction notice to "Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution" [Brain Res. 1850 (2025) 149423].}, journal = {Brain research}, volume = {1868}, number = {}, pages = {149969}, doi = {10.1016/j.brainres.2025.149969}, pmid = {41073181}, issn = {1872-6240}, }
@article {pmid41073040, year = {2025}, author = {Wu, YJ and He, Q and Luo, FG and Li, T and Guo, WJ}, title = {Respiratory Dyskinesia With Refractory Tachypnea and Alkalosis Treated by Vesicular Monoamine Transporter 2 Inhibitor.}, journal = {Chest}, volume = {168}, number = {4}, pages = {e111-e113}, pmid = {41073040}, issn = {1931-3543}, mesh = {Humans ; Female ; Aged ; *Vesicular Monoamine Transport Proteins/antagonists &amp; inhibitors ; *Tachypnea/drug therapy/diagnosis/etiology ; *Alkalosis/drug therapy/diagnosis ; *Respiration Disorders/drug therapy/diagnosis ; Antipsychotic Agents/adverse effects ; Risperidone/adverse effects/therapeutic use ; Psychotic Disorders/drug therapy ; }, abstract = {We present the case of a 69-year-old woman with a 25-year history of psychosis, managed with risperidone, who developed refractory tachypnea and alkalosis over 2 weeks. Despite multidisciplinary evaluation, she was initially misdiagnosed with psychogenic hyperventilation. Ultimately, a diagnosis of respiratory dyskinesia (RD) was established, and substantial clinical improvement was achieved after initiation of a vesicular monoamine transporter 2 (VMAT2) inhibitor. The substantial effectiveness of this therapy was confirmed over a 7-month follow-up period, with monitoring of both clinical symptoms and arterial blood gas parameters. This case highlights the diagnostic challenges posed by RD and underscores the potential utility of VMAT2 inhibitor as a novel therapeutic option.}, }
@article {pmid41072470, year = {2025}, author = {Hecker, D and Pillong, L and Reuss, K and Friedrich, KH and Alexandersson, J and Rekrut, M and Linxweiler, M and Bozzato, A and Schick, B and Metzler, P}, title = {[Novel analysis method to determine the neural activation function of the inner hair cell].}, journal = {Laryngo- rhino- otologie}, volume = {}, number = {}, pages = {}, doi = {10.1055/a-2681-5401}, pmid = {41072470}, issn = {1438-8685}, abstract = {Sensorineural hearing loss (SNH) is one of the most common forms of hearing loss. A special form of SNH is hidden hearing loss (HHL) with subjective normal hearing. Current research results indicate that these patients demonstrate a reduced wave I in the averaged signal of brainstem audiometry (ABR). Since the averaging technique is not susceptible to latency jitter and amplitude height variation, a single sweep analysis is required for a deeper insight in HHL.A total of 14 mice with significantly different calcium currents in the IHC at normal hearing thresholds were analysed. For the analysis in order to calculate four new parameters from the single sweeps in the time window of wave I. These parameters also served to describe a neural activation function (NAV).Looking at the wild type all new parameters differ significantly or highly significantly. With the transgenic mouse, there are only non-significant to significant differences. There is also a significant difference in the neural activity demonstrated in the resting EEG between the wild-type mouse and the mutant. There is a negative correlation between the wave amplitudes for the wild mouse - after a strong amplitude follows a weak amplitude and after weak amplitude follows a strong amplitude.Using new parameters based on single sweeps, surprising results are obtained. Obviously the function of the IHC correlates more strongly with the new parameters than it does with the average amplitude of wave I. The new parameters appear to be excellently suited for the diagnosis of hearing disorders even when hearing thresholds are still according to norm values.}, }
@article {pmid41072287, year = {2025}, author = {Cao, X and Gong, P and Zhang, L and Zhang, D}, title = {EEG-CLIP: A transformer-based framework for EEG-guided image generation.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {194}, number = {}, pages = {108167}, doi = {10.1016/j.neunet.2025.108167}, pmid = {41072287}, issn = {1879-2782}, abstract = {Decoding visual perception from neural signals represents a fundamental step toward advanced brain-computer interfaces (BCIs), where functional magnetic resonance imaging (fMRI) has shown promising results despite practical constraints in deployment and costs. Electroencephalography (EEG), with its superior temporal resolution, portability, and cost-effectiveness, emerges as a promising alternative for real-time brain-computer interface (BCI) applications. While existing EEG-based approaches have advanced neural decoding capabilities, they remain constrained by inadequate architectural designs, limited reconstruction fidelity, and inconsistent evaluation protocols. To address these challenges, we present EEG-CLIP, a novel Transformer-based framework that systematically addresses each limitation: (1) We introduce a specialized EEG-ViT encoder that adeptly captures the spatial and temporal characteristics of EEG signals to augment model capacity, along with a Diffusion Prior Transformer architecture to approximate the image feature distribution. (2) We employ a dual-stage reconstruction pipeline that integrates class contrastive learning and pretrained diffusion models to enhance visual reconstruction quality. (3) We establish comprehensive evaluation protocols across multiple datasets. Our framework operates through two stages: first projecting EEG signals into CLIP image space via class contrastive learning and refining them into image priors, then reconstructing perceived images through a pretrained conditional diffusion model. Comprehensive empirical analysis, including temporal window sensitivity studies and regional brain activation visualization, demonstrates the framework's robustness. We demonstrate through ablations that EEG-CLIP's performance improvements over previous methods result from specialized architecture for EEG encoding and improved training techniques. Quantitative and qualitative evaluations on ThingsEEG and Brain2Image datasets establish EEG-CLIP's state-of-the-art performance in both classification and reconstruction tasks, advancing neural signal-based visual decoding capabilities.}, }
@article {pmid41072285, year = {2025}, author = {Wu, J and Tang, B and Wang, Y and Li, C and Yang, Q}, title = {A multi-level teacher assistant-based knowledge distillation framework with dynamic feedback for motor imagery EEG decoding.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {194}, number = {}, pages = {108180}, doi = {10.1016/j.neunet.2025.108180}, pmid = {41072285}, issn = {1879-2782}, abstract = {Deep learning has shown promise in motor imagery-based electroencephalogram (MI-EEG) decoding, a critical task in non-invasive brain-computer interfaces (BCIs). In response to the computational complexity of deep learning models to be deployed in practical BCI applications, knowledge distillation (KD) has emerged as a solution for model compression. However, vanilla KD methods struggle to effectively extract and transfer the abundant multi-level knowledge from MI-EEG signals under high compression ratios. This study proposes a novel knowledge distillation framework termed Motor Imagery Knowledge Distillation (MIKD), which compresses deep learning models for MI classification tasks while maintaining high performance. The MIKD framework consists of two key modules: (1) a multi-level teacher assistant knowledge distillation (ML-TAKD) module designed to extract and transfer local representations and global dependencies of MI-EEG signals from the complex teacher network to the much smaller student network, and (2) a dynamic feedback module that allows the teacher assistant to adjust its teaching strategy based on the student's learning progress. Extensive experiments on three public EEG datasets demonstrate that the MIKD framework achieves state-of-the-art performance. The proposed framework improves the baseline student model's accuracy by 6.61 %, 1.91 %, and 3.29 % on the three datasets, while reducing the model size by nearly 90 %.}, }
@article {pmid41072048, year = {2025}, author = {Li, C and Di, G and Xiong, Z and Sun, L and Li, Q and Li, H and Jiang, X and Wu, J}, title = {Three-dimensional microsurgical anatomy of the basal aspect of the cerebrum: a fiber dissection study.}, journal = {Journal of neurosurgery}, volume = {}, number = {}, pages = {1-13}, doi = {10.3171/2025.5.JNS242560}, pmid = {41072048}, issn = {1933-0693}, abstract = {OBJECTIVE: Due to the unique nature of the basal structures of the cerebrum, only a limited portion is exposed during surgery, leading to potential risk of damage to surrounding structures. The white matter fiber tracts in the basal cerebrum may be more critical than the cortex in determining the extent of resection. A thorough understanding of the 3D anatomy of these fiber tracts is essential for planning safe and precise surgical approaches and provides an anatomical foundation for studying brain function. This study aimed to examine the topographical anatomy of the fiber tracts and subcortical gray matter in the basal cerebrum, as well as their anatomical relationships with the cerebral cortex, ventricles, and associated nuclei.<br><br>METHODS: Using fiber dissection techniques and magnification ranging from &#xd7;6 to &#xd7;40, the authors studied 10 formalin-fixed human brains. The study focused on the fiber tracts and subcortical nuclei in the basal cerebrum, including the hippocampus, amygdala, and nucleus accumbens, and their relationships were documented through 3D photography.<br><br>RESULTS: The topographical relationships between the commissural, projection, and association fibers and the significant nuclei in the basal cerebrum were identified. Notable landmarks related to the fiber tracts include the cortical gyri and sulci, major basal nuclei, and lateral ventricles. The fiber tracts also exhibited consistent interrelationships.<br><br>CONCLUSIONS: The 3D microsurgical anatomy of the basal cerebrum provides valuable insights for planning precise and safe surgical approaches and offers anatomical evidence for further studies on brain function.}, }
@article {pmid41070190, year = {2025}, author = {Li, Y and Zhu, L and Huang, A and Zhang, J and Yuan, P}, title = {Multimodal MBC-ATT: cross-modality attentional fusion of EEG-fNIRS for cognitive state decoding.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1660532}, pmid = {41070190}, issn = {1662-5161}, abstract = {With the rapid development of brain-computer interface (BCI) technology, the effective integration of multimodal biological signals to improve classification accuracy has become a research hotspot. However, existing methods often fail to fully exploit cross-modality correlations in complex cognitive tasks. To address this, this paper proposes a Multi-Branch Convolutional Neural Network with Attention (MBC-ATT) for BCI based cognitive tasks classification. MBC-ATT employs independent branch structures to process electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals separately, thereby leveraging the advantages of each modality. To further enhance the fusion of multimodal features, we introduce a cross-modal attention mechanism to discriminate features, strengthening the model's ability to focus on relevant signals and thereby improving classification accuracy. We conducted experiments on the n-back and WG datasets. The results demonstrate that the proposed model outperforms conventional approaches in classification performance, further validating the effectiveness of MBC-ATT in brain-computer interfaces. This study not only provides novel insights for multimodal BCI systems but also holds great potential for various applications.}, }
@article {pmid41066375, year = {2025}, author = {, }, title = {Correction: Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.}, journal = {PloS one}, volume = {20}, number = {10}, pages = {e0334075}, pmid = {41066375}, issn = {1932-6203}, abstract = {[This corrects the article DOI: 10.1371/journal.pone.0311075.].}, }
@article {pmid41065125, year = {2025}, author = {Schnitzer, SA and DeFilippis, DM}, title = {Does increasing canopy liana density decrease the tropical forest carbon sink?.}, journal = {Ecology}, volume = {106}, number = {10}, pages = {e70196}, doi = {10.1002/ecy.70196}, pmid = {41065125}, issn = {1939-9170}, support = {DEB 06-13666//National Science Foundation/ ; DEB 20-01799//National Science Foundation/ ; IOS 15-58093//National Science Foundation/ ; }, mesh = {*Tropical Climate ; *Forests ; *Carbon Sequestration/physiology ; Panama ; *Plants/classification ; *Trees/physiology ; *Carbon/metabolism ; Time Factors ; }, abstract = {The ongoing decline in the American tropical forest carbon sink has serious ramifications for atmospheric carbon levels and global climate change. Increasing liana abundance may explain the decaying carbon sink because lianas reduce canopy tree growth and survival, which limits forest carbon storage. However, canopy lianas, not solely understory lianas, would have to be increasing for this hypothesis to be credible because canopy lianas compete especially intensely with canopy trees. We examined the change in canopy lianas over 10 years on Barro Colorado Island (BCI), Panama to test two main hypotheses. (1) Canopy lianas are increasing on BCI. (2) Increasing canopy lianas decrease aboveground canopy tree and forest carbon storage. We found that canopy liana density increased 8.3% over the 10-year period, and canopy lianas outnumbered canopy trees 3.59-1. There was a clear negative relationship between increasing canopy liana density and decreasing canopy tree carbon storage. Where liana density increased, tree carbon decreased, and where canopy lianas decreased, canopy tree carbon increased. Our findings indicate that lianas are the numerically dominant and diverse woody plant group in the BCI canopy, and this dominance is increasing, reducing forest-level carbon storage and possibly explaining the decaying American tropical forest carbon sink.}, }
@article {pmid41064793, year = {2025}, author = {Gong, J and Zhao, Z and Niu, X and Ji, Y and Sun, H and Shen, Y and Chen, B and Wu, B}, title = {AI reshaping life sciences: intelligent transformation, application challenges, and future convergence in neuroscience, biology, and medicine.}, journal = {Frontiers in digital health}, volume = {7}, number = {}, pages = {1666415}, pmid = {41064793}, issn = {2673-253X}, abstract = {The rapid advancement of artificial intelligence (AI) is profoundly transforming research paradigms and clinical practices across neuroscience, biology, and medicine with unprecedented depth and breadth. Leveraging its robust data-processing capabilities, precise pattern recognition techniques, and efficient real-time decision support, AI has catalyzed a paradigm shift toward intelligent, precision-oriented approaches in scientific research and healthcare. This review comprehensively reviews core AI applications within these domains. Within neuroscience, AI advances encompass brain-computer interface (BCI) development/optimization, intelligent analysis of neuroimaging data (e.g., fMRI, EEG), and early prediction/precise diagnosis of neurological disorders. In biological research, AI applications include enhanced gene-editing efficiency (e.g., CRISPR) with off-target effect prediction, genomic big-data interpretation, drug discovery/design (e.g., virtual screening), high-accuracy protein structure prediction (exemplified by AlphaFold), biodiversity monitoring, and ecological conservation strategy optimization. For medical research, AI empowers auxiliary medical image diagnosis (e.g., CT, MRI), pathological analysis, personalized treatment planning, health risk prediction with lifespan health management, and robot-assisted minimally invasive surgery (e.g., da Vinci Surgical System). This review not only synthesizes AI's pivotal role in enhancing research efficiency and overcoming limitations of conventional methodologies, but also critically examines persistent challenges, including data access barriers, algorithmic non-transparency, ethical governance gaps, and talent shortages. Building upon this analysis, we propose a tripartite framework ("Technology-Ethics-Talent") to advance intelligent transformation in scientific and medical domains. Through coordinated implementation, AI will catalyze a transition toward efficient, accessible, and sustainable healthcare, ultimately establishing a life-cycle preservation paradigm encompassing curative gene editing, proactive health management, and ecologically intelligent governance.}, }
@article {pmid41064747, year = {2025}, author = {Yue, J and Xiao, X and Wang, K and Yi, W and Jung, TP and Xu, M and Ming, D}, title = {Augmenting Electroencephalogram Transformer for Steady-State Visually Evoked Potential-Based Brain-Computer Interfaces.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {6}, number = {}, pages = {0379}, pmid = {41064747}, issn = {2692-7632}, abstract = {Objective: Advancing high-speed steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI) systems requires effective electroencephalogram (EEG) decoding through deep learning. However, challenges persist due to data sparsity and the unclear neural basis of most augmentation techniques. Furthermore, effective processing of dynamic EEG signals and accommodating augmented data require a more sophisticated model tailored to the unique characteristics of EEG signals. Approach: This study introduces background EEG mixing (BGMix), a novel data augmentation technique grounded in neural principles that enhances training samples by replacing background noise between different classes. Building on this, we propose the augment EEG Transformer (AETF), a Transformer-based model designed to capture the temporal, spatial, and frequential features of EEG signals, leveraging the advantages of Transformer architectures. Main results: Experimental evaluations of 2 publicly available SSVEP datasets show the efficacy of the BGMix strategy and the AETF model. The BGMix approach notably improved the average classification accuracy of 4 distinct deep learning models, with increases ranging from 11.06% to 21.39% and 4.81% to 25.17% in the respective datasets. Furthermore, the AETF model outperformed state-of-the-art baseline models, excelling with short training data lengths and achieving the highest information transfer rates (ITRs) of 205.82 ± 15.81 bits/min and 240.03 ± 14.91 bits/min on the 2 datasets. Significance: This study introduces a novel EEG augmentation method and a new approach to designing deep learning models informed by the neural processes of EEG. These innovations significantly improve the performance and practicality of high-speed SSVEP-based BCI systems.}, }
@article {pmid41062739, year = {2025}, author = {Abinaya, G and Dinakaran, K}, title = {ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {35178}, pmid = {41062739}, issn = {2045-2322}, mesh = {Humans ; *Electroencephalography/methods ; *Workload/psychology ; *Deep Learning ; Male ; *Cognition/physiology ; Task Performance and Analysis ; Adult ; Female ; Brain/physiology ; Neural Networks, Computer ; Young Adult ; Attention/physiology ; }, abstract = {Mental workload is an interdisciplinary construct that significantly influences human performance, particularly in tasks requiring sustained attention and cognitive processing. Effective mental workload assessment is critical for preventing cognitive overload, which can lead to errors and reduced efficiency in high-stakes environments. The approach leverages topographic neural manifolds (spatial electrode arrangements) and temporal neural manifolds (time-series patterns) to capture comprehensive brain activity representations.Traditional methods rely on subjective reports or task performance, but physiological signals like EEG provide a more objective and continuous means of monitoring cognitive states. Therefore, this paper proposes a hybrid novel approach ACXNet which integrates autoencoder, CNN and XGBoost to learn features of EEG from an individual cross task performance without prior subject-specific calibration or task specific pre-labeled .training data. Utilizing the STEW (Simultaneous Task EEG Workload) dataset, containing recordings from 48 participants experiencing different levels of cognitive demands. Unsupervised feature extraction was carried out using an autoencoder. Subsequently, a CNN was employed to capture the spatial-temporal dependencies in the data, and XGBoost was utilized for efficient mental workload classification. This research adopts a binary classification approach to differentiate between low and high mental workload during SIMKAP and No task. The ACXNet model proposed in this study outperforms the existing methods with an average accuracy of 92.10% for SIMKAP task and 89.94% for No task condition. These findings show that ACXNet significantly improves the robustness and precision of mental workload estimation, providing a scalable solution adaptable to real-world applications, opening new avenues for the development of intelligent systems in human-computer interaction, healthcare, and beyond.}, }
@article {pmid41061192, year = {2025}, author = {Bushnell, BD and Jarvis, BT and Jarvis, RC and Piller, CP and Baudier, RS}, title = {Minimal Stiffness After Rotator Cuff Repair With Bioinductive Collagen Implants.}, journal = {Journal of the American Academy of Orthopaedic Surgeons. Global research &amp; reviews}, volume = {9}, number = {10}, pages = {}, pmid = {41061192}, issn = {2474-7661}, support = {N/A//Smith and Nephew/ ; }, mesh = {Humans ; Retrospective Studies ; *Rotator Cuff Injuries/surgery ; *Collagen ; Male ; Female ; Middle Aged ; Aged ; Range of Motion, Articular ; *Postoperative Complications/epidemiology/etiology ; *Prostheses and Implants ; Adult ; Rotator Cuff/surgery ; Reoperation/statistics &amp; numerical data ; }, abstract = {BACKGROUND: Bioinductive collagen implants (BCIs) have been growing in popularity for use in rotator cuff repair (RCR) over the past several years, but recent literature has raised concerns about the implants contributing to postoperative stiffness. The purpose of this study was to investigate the incidence of stiffness over a decade of experience with the BCI.<br><br>METHODS: A retrospective review was conducted of all cases of RCR using a BCI performed between September 2014 and December 2023. The primary outcome measure was postoperative range of motion, with significant stiffness defined by parameters in the existing literature. The secondary outcome measure was any revision procedure for stiffness.<br><br>RESULTS: After application of inclusion and exclusion criteria to 522 cases of RCR, there were 432 cases (390 individual patients) available for outcome analysis with an average follow-up of 34.9 months (range, 6 months to 9.25 years). There were only 12 cases (2.8%) of significant postoperative stiffness. All of them required additional operative intervention for stiffness, and all but two patients had at least one risk factor for stiffness. Stiffness rates were 4 of 291 (1.4%) for full-thickness tears and 8 of 141 (5.7%) for partial-thickness tears (P = 0.0149).<br><br>CONCLUSION: This study, the largest single cohort to date analyzing BCIs in RCR, found a low incidence of significant postoperative stiffness in cases associated with the use of the implant. Stiffness rates were markedly higher for repairs of partial-thickness tears. To further improve understanding of postoperative stiffness after RCR with BCI, better definitions and prospective comparative studies across larger groups are needed.<br><br>LEVEL OF EVIDENCE: Level IV, retrospective cohort with no comparison group.}, }
@article {pmid41061070, year = {2025}, author = {Huang, K and Fu, P and Zhu, H and Feng, J and Zhang, L and Wang, B and Lu, Y and Zhang, D and Yao, M and Chen, L and Ying, Y and Chen, J and Li, X and Wu, Y and Xiong, W and Li, J and Wu, Y and Sun, J and Zhang, H and Lin, L}, title = {High-speed photoacoustic and ultrasonic computed tomography of the breast tumor for early diagnosis with enhanced accuracy.}, journal = {Science advances}, volume = {11}, number = {41}, pages = {eadz2046}, pmid = {41061070}, issn = {2375-2548}, mesh = {Humans ; *Breast Neoplasms/diagnostic imaging/diagnosis ; Female ; *Photoacoustic Techniques/methods ; *Tomography, X-Ray Computed/methods ; *Early Detection of Cancer/methods ; Middle Aged ; Adult ; Aged ; Ultrasonography, Mammary/methods ; }, abstract = {We have developed a high-speed dual-modal imaging system (HDMI), designed to concurrently reveal anatomical and hematogenous details of the human breast within seconds. Through innovative system design and technical advancements, HDMI integrates large-view photoacoustic and ultrasonic computed tomography with standardized scanning and batch data processing for computer-aided diagnosis. It achieves dual-modal imaging at a 10-hertz frame rate and completes a whole-breast scan in 12 seconds, providing penetration up to 5 centimeters in vivo. In a clinical study involving 170 patients with 186 breast tumors, we developed a diagnostic model leveraging combined photoacoustic and ultrasound features. In a triple-blinded comparison using pathological diagnosis as the ground truth, HDMI significantly improved diagnostic specificity from 22.5 to 75.0% compared to clinical ultrasonography. This technology shows strong potential for early breast tumor diagnosis, offering enhanced accuracy without the need for ionizing radiation, exogenous contrast agents, pain, invasiveness, operator dependence, or extended examination times.}, }
@article {pmid41060851, year = {2025}, author = {Nguyen, MD and Do, T and Tran, XT and Nguyen, QT and Lin, CT}, title = {Edge AI-Brain-Computer Interfaces System: A Survey.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {4051-4066}, doi = {10.1109/TNSRE.2025.3618688}, pmid = {41060851}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; *Artificial Intelligence ; Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Surveys and Questionnaires ; Software ; Equipment Design ; }, abstract = {Edge artificial intelligence (Edge AI) has emerged as a transformative paradigm for enhancing the performance, portability, and autonomy of brain-computer interface (BCI) systems. By integrating advanced AI capabilities directly into electroencephalography (EEG)-based devices, Edge AI enables real-time signal processing, reduces dependence on external computational resources, and improves data privacy. However, deploying AI on resource-constrained hardware introduces challenges related to computational capacity, power consumption, and system latency. This survey provides a comprehensive examination of Edge AI-enabled BCI systems, covering the full pipeline from EEG hardware specifications and on-device data acquisition to signal preprocessing techniques and lightweight deep learning models optimized for embedded platforms. We review existing frameworks, specialized hardware accelerators, and energy-efficient AI approaches that facilitate real-time BCI processing at the edge. Furthermore, the paper reviews state-of-the-art solutions, examines key technical challenges, and outlines future research directions in hardware-software co-design and application development. This work aims to serve as a reference for researchers and practitioners seeking to design efficient, portable, and practical Edge AI-powered BCI systems.}, }
@article {pmid41060788, year = {2025}, author = {Rosenthal, IA and Bashford, L and Bjanes, D and Pejsa, K and Lee, B and Liu, C and Andersen, RA}, title = {Visual context affects the perceived timing of tactile sensations elicited through intra-cortical microstimulation: a case study of two participants.}, journal = {Journal of neurophysiology}, volume = {}, number = {}, pages = {}, doi = {10.1152/jn.00518.2024}, pmid = {41060788}, issn = {1522-1598}, support = {N/A//T&amp;C Chen Brain-Interface Center/ ; N/A//James G. Boswell Foundation (Boswell Foundation)/ ; U01NS123127//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; T32 NS105595/NS/NINDS NIH HHS/United States ; }, abstract = {Intra-cortical microstimulation (ICMS) is a technique to provide tactile sensations for a somatosensory brain-machine interface (BMI). A viable BMI must function within the rich, multisensory environment of the real world, but how ICMS is integrated with other sensory modalities is poorly understood. To investigate how ICMS percepts are integrated with visual information, ICMS and visual stimuli were delivered at varying times relative to one another. Both visual context and ICMS current amplitude were found to bias the qualitative experience of ICMS. In two tetraplegic participants, ICMS and visual stimuli were more likely to be experienced as occurring simultaneously in a realistic visual condition compared to an abstract one, demonstrating an effect of visual context on the temporal binding window. The peak of the temporal binding window varied but was consistently offset from zero, suggesting that multisensory integration with ICMS can suffer from temporal misalignment. Recordings from primary somatosensory cortex (S1) during catch trials where visual stimuli were delivered without ICMS demonstrated that S1 represents visual information related to ICMS across visual contexts. This study was a part of a clinical trial (NCT01964261).}, }
@article {pmid41060749, year = {2025}, author = {Ji, J and Luo, H and Su, J and Wang, S and Chen, X and Song, J}, title = {Multisensory electronic skin with decoupled pressure-temperature-sensing capabilities for similar object recognition.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {41}, pages = {e2519693122}, pmid = {41060749}, issn = {1091-6490}, support = {2022YFC2401901//MOST | National Key Research and Development Program of China (NKPs)/ ; 12225209//MOST | National Natural Science Foundation of China (NSFC)/ ; 12321002//MOST | National Natural Science Foundation of China (NSFC)/ ; U21A20502//MOST | National Natural Science Foundation of China (NSFC)/ ; Smart Grippers for Soft Robotics (SGSR) Programme under the National Research Foundation Prime Min//Prime Minister's Office Singapore (PMO)/ ; }, mesh = {Humans ; Pressure ; Touch/physiology ; Temperature ; Robotics ; Skin ; *Touch Perception/physiology ; *Wearable Electronic Devices ; *Thermosensing/physiology ; }, abstract = {Multisensory electronic skin (e-skin), which mimics the tactile capabilities of human skin, is pivotal in equipping robots with intelligent perceptual functions. Despite numerous advances in multifunctional perceptions, e-skin with combined mechano- and thermosensation capabilities for accurately recognizing objects with similar characteristics is still challenging. Here, we report a multisensory e-skin with a skin-like multilayer construction for smart perceptions, which features the patterned protrusion texture mimicking the skin texture to enhance the pressure-sensing sensitivity, the temperature-sensing component mimicking the thermoreceptors, the pressure-sensing component mimicking the mechanoreceptors, and the heater mimicking the body heat source. This multisensory e-skin exhibits excellent decoupled sensing performances of pressure and temperature, enabling the development of a haptic perception system for evaluating some discernible characteristics (e.g., shape and size) and experience-driven features (e.g., modulus and thermal conductivity) of objects through a simple grasp. Demonstrations of accurate recognition and automatic classification of various objects even with extremely similar surface features highlight the significant potential of this multisensory e-skin in applications such as intelligent soft robotics, prosthetics, and other related fields.}, }
@article {pmid41059626, year = {2025}, author = {Meng, W and Hou, F and Chen, K and Ma, L and Liu, Q}, title = {Visually-Inspired Multimodal Iterative Attentional Network for High-Precision EEG-Eye-Movement Emotion Recognition.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2550072}, doi = {10.1142/S0129065725500728}, pmid = {41059626}, issn = {1793-6462}, abstract = {Advancements in artificial intelligence have propelled affective computing toward unprecedented accuracy and real-world impact. By leveraging the unique strengths of brain signals and ocular dynamics, we introduce a novel multimodal framework that integrates EEG and eye-movement (EM) features synergistically to achieve more reliable emotion recognition. First, our EEG Feature Encoder (EFE) uses a convolutional architecture inspired by the human visual cortex's eccentricity-receptive-field mapping, enabling the extraction of highly discriminative neural patterns. Second, our EM Feature Encoder (EMFE) employs a Kolmogorov-Arnold Network (KAN) to overcome the sparse sampling and dimensional mismatch inherent in EM data; through a tailored multilayer design and interpolation alignment, it generates rich, modality-compatible representations. Finally, the core Multimodal Iterative Attentional Feature Fusion (MIAFF) module unites these streams: alternating global and local attention via a Hierarchical Channel Attention Module (HCAM) to iteratively refine and integrate features. Comprehensive evaluations on SEED (3-class) and SEED-IV (4-class) benchmarks show that our method reaches leading-edge accuracy. However, our experiments are limited by small homogeneous datasets, untested cross-cultural robustness, and potential degradation in noisy or edge-deployment settings. Nevertheless, this work not only underscores the power of biomimetic encoding and iterative attention but also paves the way for next-generation brain-computer interface applications in affective health, adaptive gaming, and beyond.}, }
@article {pmid41059099, year = {2025}, author = {Zhang, C and Liu, Y and Wu, X}, title = {TFANet: a temporal fusion attention neural network for motor imagery decoding.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1635588}, pmid = {41059099}, issn = {1662-4548}, abstract = {INTRODUCTION: In the field of brain-computer interfaces (BCI), motor imagery (MI) classification is a critically important task, with the primary objective of decoding an individual's MI intentions from electroencephalogram (EEG) signals. However, MI decoding faces significant challenges, primarily due to the inherent complex temporal dependencies of EEG signals.<br><br>METHODS: This paper proposes a temporal fusion attention network (TFANet), which aims to improve the decoding performance of MI tasks by accurately modeling the temporal dependencies in EEG signals. TFANet introduces a multi-scale temporal self-attention (MSTSA) mechanism that captures temporal variation in EEG signals across different time scales, enabling the model to capture both local and global features. Moreover, the model adaptively adjusts the channel weights through a channel attention module, allowing it to focus on key signals related to motor imagery. This further enhances the utilization of temporal features. Moreover, by integrating the temporal depthwise separable convolution fusion network (TDSCFN) module, TFANet reduces computational burden while enhancing the ability to capture temporal patterns.<br><br>RESULTS: The proposed method achieves a within-subject classification accuracy of 84.92% and 88.41% on the BCIC-IV-2a and BCIC-IV-2b datasets, respectively. Furthermore, using a transfer learning approach on the BCIC-IV-2a dataset, a cross-subject classification accuracy of 77.2% is attained.<br><br>CONCLUSION: These results demonstrate that TFANet is an effective approach for decoding MI tasks with complex temporal dependencies.}, }
@article {pmid41058890, year = {2025}, author = {Benachour, A and Medvedev, V and Zinchenko, O}, title = {Mouse-tracking as a tool for investigating strategic behavior in Public Goods Game: an experimental pilot study.}, journal = {Frontiers in psychology}, volume = {16}, number = {}, pages = {1635677}, pmid = {41058890}, issn = {1664-1078}, abstract = {INTRODUCTION: Recent research has demonstrated the potential of utilizing mouse-tracking as a viable alternative method for examining attention-related attributes within the context of a multifaceted activity.<br><br>METHODS: In this study, a mouse-tracking technique was utilized to gather data from individuals who were involved in an online format of the Public Goods Game.<br><br>RESULTS: It was observed that participants exhibited distinct approaches to acquiring information while formulating decisions to propose high, moderate, or low offers. The mouse-tracking algorithm effectively distinguished between various types of offers made toward group funding, as evidenced by the measured distance of the cursor.<br><br>DISCUSSION: These findings suggest that mouse-tracking is a valuable tool for capturing decision-making processes and differentiating behavioral patterns in economic game contexts, offering insights into attention and choice mechanisms.}, }
@article {pmid41056741, year = {2025}, author = {Yin, Y and Zhang, Y and Xu, S}, title = {The influence of money priming on conformity consumption: The distinct roles of self-sufficiency and self-control.}, journal = {Acta psychologica}, volume = {260}, number = {}, pages = {105682}, doi = {10.1016/j.actpsy.2025.105682}, pmid = {41056741}, issn = {1873-6297}, mesh = {Humans ; *Self-Control/psychology ; Male ; Female ; Young Adult ; Adult ; *Social Conformity ; *Consumer Behavior ; *Choice Behavior ; China ; Adolescent ; }, abstract = {Despite the pervasive role of money in society and the known psychological effects of money priming, research into its influence on consumer choices, especially regarding conformity behavior in consumption, remains limited. This study examines the impact of money priming on individual conformity behaviors within the context of Chinese consumption through three behavioral studies. Study 1 revealed that priming with money concepts reduces the tendency to conform. Study 2 investigated how feelings of monetary abundance and deprivation, elicited by money priming, affect conformity in consumption. The findings showed that a perceived sense of monetary abundance decreases conformity in consumption, whereas a sense of deprivation increases it. While product types did affect conformity consumption, they did not significantly interact with monetary primes. Study 3 explored the mediating roles of self-sufficiency and self-control, confirming that monetary abundance decreases conformity by enhancing self-sufficiency, and monetary deprivation increases conformity by diminishing self-control. These results suggest that money priming can trigger distinct feelings of abundance and deprivation, each having differential effects on conformity consumption. Understanding these effects can enable marketers to tailor strategies for personalized marketing or group purchasing initiatives, effectively addressing different market segments.}, }
@article {pmid41055454, year = {2025}, author = {Xiang, Y and He, X and Cheng, T and Zhu, W and Pang, J and Cao, Y and Wu, M and Pei, R and Cao, Y}, title = {A Zwitterionic Conductive Hydrogel Interface for Enhanced Electrocorticography Signal Fidelity via High Conductivity, Antifouling, and Brain-Matched Mechanics.}, journal = {Biomacromolecules}, volume = {26}, number = {11}, pages = {7959-7973}, doi = {10.1021/acs.biomac.5c01412}, pmid = {41055454}, issn = {1526-4602}, mesh = {Animals ; Electric Conductivity ; *Hydrogels/chemistry ; *Electrocorticography/methods ; Rats ; *Brain/physiology ; Polymers/chemistry ; Biofouling/prevention &amp; control ; Rats, Sprague-Dawley ; Male ; Polystyrenes/chemistry ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; }, abstract = {Electrocorticography (ECoG) holds considerable promise for neural signal monitoring with high spatiotemporal resolution. However, conventional rigid ECoG electrodes are often hampered by poor mechanical compliance and insufficient resistance to biofouling, leading to high interfacial impedance and compromised signal quality. While integrating conductive hydrogels into ECoG interface offers a potential solution, concurrently achieving high conductivity, mechanical compatibility with brain tissue, biosafety, and robust antifouling remains a significant challenge. This study introduces SPP@NaCl, a novel zwitterionic conductive hydrogel synthesized by doping a poly(sulfobetaine methacrylate) (pSB) hydrogel matrix with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and employing NaCl as a Lewis acid to induce phase separation, thereby promoting an interconnected PEDOT network. The resultant SPP@NaCl hydrogel exhibits a compelling combination of properties: high electrical conductivity (∼9 S·m[-][1]), a low Young's modulus (1.74 kPa) that closely matches brain tissue, excellent conformability, and markedly reduced protein adsorption attributable to its zwitterionic structure. When integrated with commercial ECoG electrodes, the optimized SPP@NaCl-8 hydrogel dramatically lowers interfacial impedance. The resulting Au-SPP@NaCl electrodes enabled high-fidelity, real-time monitoring of cortical epileptiform discharges in a rat seizure model and demonstrated stable, long-term neural signal acquisition in anesthetized healthy rats. This work presents a new strategy for constructing ECoG interfaces that simultaneously deliver high conductivity, mechanical compliance, biosafety, and antifouling capabilities, highlighting the significant potential of these hydrogel-integrated ECoG electrodes for advanced brain-computer interface applications.}, }
@article {pmid41054887, year = {2025}, author = {Ye, Y and Chen, S and Zhang, Y and Zhang, T and Liao, T and Ren, Z and Chen, W and Hu, W}, title = {Mechano-Locking Strategy for Broad-Spectrum SARS-CoV-2 Neutralization.}, journal = {Small (Weinheim an der Bergstrasse, Germany)}, volume = {}, number = {}, pages = {e05582}, doi = {10.1002/smll.202505582}, pmid = {41054887}, issn = {1613-6829}, support = {T2394511//National Science Foundation of China/ ; T2394510//National Science Foundation of China/ ; 92359303//National Science Foundation of China/ ; 92269101//National Science Foundation of China/ ; LY23A020002//Natural Science Foundation of Zhejiang Province/ ; }, abstract = {Viral entry into host cells is typically initiated by interactions between viral surface proteins and host cell receptors. Conventional neutralization strategies aim to disrupt these interactions but often lose effectiveness against rapidly mutating viral strains. This challenge extends beyond SARS-CoV-2 to other viruses such as HIV and influenza. To overcome this limitation, a novel mechano-locking strategy is proposed, using SARS-CoV-2 as a model system, in which bispecific antibodies (bsAbs) lock the spike protein in its prefusion conformation by preventing force-induced conformational changes. These bsAbs demonstrate broad-spectrum neutralization efficacy against multiple SARS-CoV-2 variants in pseudoviral assays. Single-molecule magnetic tweezers experiments further reveal that these bsAbs significantly raise the mechanical force threshold required for S1-S2 dissociation, thereby enhancing spike protein mechano-stability. This stabilization mechanism offers a mutation-resistant approach to neutralization and introduces a new design paradigm for antiviral therapeutics. These findings establish a mechanistically driven framework for developing biomechanically enhanced strategies potentially applicable to a wide range of mechanically activated enveloped viruses.}, }
@article {pmid41052978, year = {2025}, author = {Liang, R and Fang, T and Wang, L and Ren, J and Meng, L and Zhao, M and Zheng, C and Fan, Q and Chen, Y and Yang, J and Ming, D}, title = {Multi-connectomics underpin emotional dysfunction in mouse exposed to simulated space composite environment.}, journal = {Translational psychiatry}, volume = {15}, number = {1}, pages = {359}, pmid = {41052978}, issn = {2158-3188}, mesh = {Animals ; Mice ; *Connectome ; Male ; Magnetic Resonance Imaging ; *Prefrontal Cortex/diagnostic imaging/physiopathology ; *Emotions/physiology ; Mice, Inbred C57BL ; Space Flight ; Behavior, Animal/physiology ; Gray Matter/diagnostic imaging/pathology/physiopathology ; *Space Simulation ; *Brain/diagnostic imaging/physiopathology ; Nerve Net/physiopathology/diagnostic imaging ; }, abstract = {Long-duration space exploration, including missions to the Moon and Mars, demands strategies to preserve astronauts' emotional well-being for optimal performance. This study combines behavioral phenotyping, multimodal MRI, in vivo calcium imaging, and brain-wide genomics to bridge macroscopic brain function with mesoscopic neural activity and microscopic genetic processes, providing a dynamic characterization of the mouse connectome under simulated spaceflight conditions. We observed a reduction in gray matter volume, particularly in the prefrontal cortex, with prolonged exposure. Simulated space composite environment (SSCE) disrupted multi-scale functional connectivity and altered the macro-organizational functional gradient, reversing the relationship between brain function and emotional behaviors. Neural activity in the medial prefrontal cortex demonstrated exposure-time-dependent changes across emotional tasks, while genetic analyses linked SSCE-induced alterations in functional profiles to synaptic function and ion channel activity. Our findings reveal how extreme environments impact emotional behaviors, brain networks, and neural activity, offering insights for interventions to maintain brain integrity during extended space missions.}, }
@article {pmid41052270, year = {2025}, author = {Lu, Y and Xiong, T and Liu, Y and Zhou, H and Xie, B and Guo, G and Pan, C and Ma, W and Yu, P}, title = {Gate Capacitance-Dependent Neuromorphic Functions of Organic Electrochemical Transistors.}, journal = {The journal of physical chemistry letters}, volume = {16}, number = {41}, pages = {10678-10684}, doi = {10.1021/acs.jpclett.5c02510}, pmid = {41052270}, issn = {1948-7185}, abstract = {Neuromorphic functions of organic electrochemical transistors (OECTs) have attracted enormous research attention due to their promising application in the field of brain-mimicking computing and brain-computer interfaces. However, the essential role of gate electrodes in the neuromorphic functions of these synaptic transistors remains unclear. Herein, we systematically investigated the influence of gate electrodes on the neuromorphic functions of synaptic OECTs by rationally choosing four kinds of typical gate electrodes: bare glass carbon electrode (Bare-GCE), carbon nanotube-modified GCE (CNT-GCE), PEDOT:PSS modified GCE (PEDOT:PSS-GCE), and Ag/AgCl electrode. Evaluations of the neuromorphic functions indicated that gate capacitance controlled the performance of synaptic OECTs by tuning the electrical field distribution and doping kinetics in the ionic circuits. This systematic exploration of the gate electrode influences on the OECTs offers rational guidance for the structural design of synaptic OECTs.}, }
@article {pmid41052170, year = {2025}, author = {Zhang, M and Zhao, S and Xie, L and Liu, T and Yao, D and Yin, E}, title = {Self-Supervised Contrastive Pre-Training for EEG-Based Recognition via Cross Device Representation Consistency.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3613730}, pmid = {41052170}, issn = {1558-2531}, abstract = {Electroencephalography (EEG) has emerged as a powerful tool for modeling human brain states. However, the widespread adoption of EEG-based recognition systems is hindered by low signal-to-noise ratios and the scarcity of labeled data. While existing studies often tackle these challenges in isolation, we propose a novel Cross-Device Representation Consistency (CDRC) pretraining paradigm that addresses both issues simultaneously. CDRC leverages self-supervised signals derived from representation distances and is trained through contrastive estimation. Specifically, our approach employs a transformer based dual-branch single-view embedding prediction task, combining with a contrastive feature alignment module to extract robust and discriminative representations. We first evaluate the CDRC model on a low signal-to-noise ratio emotion classification task involving wearable dry electrodes. Furthermore, we extend CDRC to a multimodal fusion setting to address a cross-device vigilance regression task involving heterogeneous physiological modalities. Extensive experiments on the PaDWEED and SEED-VIG datasets demonstrate that CDRC achieves performance comparable to fully supervised methods and reaches the stat-of-the-art results of existing self-supervised methods, setting a new benchmark in this field. Notably, its strong performance on subject-independent tasks highlights its effectiveness in mitigating subject variability. These results underscore the potential of CDRC to significantly enhance the practicality and scalability of EEG-based recognition systems, marking a meaningful step toward real-world brain-computer interfaces.}, }
@article {pmid41044400, year = {2025}, author = {Serafini, ERDS and Guerrero-Mendez, CD and Blanco-Diaz, CF and da Silva Fiorin, F and de Albuquerque, TS and A Dantas, AFO and Delisle-Rodriguez, D and do Espírito-Santo, CC}, title = {Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {34633}, pmid = {41044400}, issn = {2045-2322}, mesh = {Humans ; *Spinal Cord Injuries/physiopathology/rehabilitation/complications ; Male ; *Brain-Computer Interfaces ; Adult ; *Gait/physiology ; Middle Aged ; *Robotics/methods ; *Urinary Bladder, Neurogenic/physiopathology/etiology/rehabilitation/therapy ; *Urinary Bladder/physiopathology ; Electroencephalography ; *Imagery, Psychotherapy/methods ; Neurofeedback ; }, abstract = {Neurogenic bladder (NB) dysfunction in individuals with complete spinal cord injury (SCI) is a condition that significantly affects quality of life. Despite the prevalence of interventions, there is a substantial gap in effective treatments for this dysfunction. This study proposes robotic-assisted gait training combined with motor imagery (MI)-based brain-computer interface (BCI) to induce improved cortical modulation, and consequently improve bladder function in patients with SCI. The study involved seven men with complete and chronic SCI in a protocol comprising 24 sessions of robotic-assisted walking with BCI and MI. This regimen was designed to teach both mu (µ, 8-12 Hz) and beta (β, 15-20 Hz) modulation through MI practices using multi-channel EEG neurofeedback (NFB), focusing on sensorimotor rhythm (SMR) activation. Clinical outcomes were measured using the neurogenic bladder symptom score (NBSS), which revealed substantial improvements in bladder control among participants. EEG analysis confirmed a significant correlation between modulation of µ and β rhythms with decreased NBSS scores. Our findings support that robotic-assisted gait training combined with MI-based BCI effectively modulates with more precision the cortical µ and β rhythms and improves NB dysfunction in SCI individuals.}, }
@article {pmid41044308, year = {2025}, author = {Chen, Z and Cao, Y and Fu, Q and Hou, L}, title = {Hierarchical attention enhanced deep learning achieves high precision motor imagery classification in brain computer interfaces.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {34555}, pmid = {41044308}, issn = {2045-2322}, mesh = {*Brain-Computer Interfaces ; Humans ; *Deep Learning ; Electroencephalography/methods ; *Attention/physiology ; Male ; Adult ; *Brain/physiology ; Female ; *Imagination/physiology ; }, abstract = {Motor imagery-based Brain-Computer Interfaces (BCIs) hold transformative potential for individuals with severe motor impairments, yet their clinical deployment remains constrained by the inherent complexity of electroencephalographic (EEG) signal decoding. This study presents a systematic investigation of hierarchical deep learning architectures for motor imagery classification, introducing a novel attention-enhanced convolutional-recurrent framework that achieves state-of-the-art accuracy of 97.2477% on a custom four-class motor imagery dataset comprising 4,320 trials from 15 participants. By synergistically integrating spatial feature extraction through convolutional layers, temporal dynamics modeling via long short-term memory networks, and selective attention mechanisms for adaptive feature weighting, our approach significantly outperforms conventional methods while providing interpretable insights into the spatiotemporal signatures of motor imagery. Beyond demonstrating competitive performance, this work elucidates the critical role of attention mechanisms in capturing task-relevant neural patterns amidst the high-dimensional, non-stationary nature of EEG signals. Our findings demonstrate that biomimetic computational architectures that mirror the brain's own selective processing strategies can substantially enhance BCI reliability, offering immediate implications for neurorehabilitation technologies and broader applications in restorative neuroscience. Our code is available at https://github.com/Laboratory-EverythingAI/-EEG_Classification .}, }
@article {pmid41043460, year = {2025}, author = {Rasheed, S and Bennett, J and Yoo, PE and Burkitt, AN and Grayden, DB}, title = {Decoding saccadic eye movements from brain signals using an endovascular neural interface.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ae0f52}, pmid = {41043460}, issn = {1741-2552}, mesh = {Humans ; *Saccades/physiology ; *Brain-Computer Interfaces ; Male ; *Electroencephalography/methods ; Photic Stimulation/methods ; Middle Aged ; *Endovascular Procedures/methods ; Amyotrophic Lateral Sclerosis/physiopathology ; Female ; }, abstract = {Objective.An oculomotor brain-computer interface (BCI) records neural activity from brain regions involved in planning eye movements and translates this activity into control commands. While previous successful studies have relied on invasive implants in non-human primates or electrooculography artefacts in human electroencephalogram (EEG) data, this study aimed to demonstrate the feasibility of an oculomotor BCI using a minimally invasive endovascular Stentrode[TM]device implanted near the supplementary motor area of a patient with amyotrophic lateral sclerosis (ALS).Approach.One participant performed self-paced visually-guided and free-viewing saccade tasks in four directions (left, right, up, down) while endovascular EEG and eye gaze recordings were collected. Visually-guided saccades were cued with visual stimuli, whereas free-viewing saccades were self-directed without explicit cues. Brain signals were pre-processed to remove cardiac artefacts, downsampled, and classified using a Random Forest algorithm. For saccade onset classification (fixation vs saccade), features in time and frequency domains were extracted after xDAWN denoising, while for saccade direction classification, the downsampled time series were classified directly without explicit feature extraction.Main results.The neural responses of visually-guided saccades overlapped with cue-evoked potentials, while free-viewing saccades exhibited saccade-related potentials that began shortly before eye movement, peaked approximately 50 ms after saccade onset, and persisted for around 200 ms. In the frequency domain, these responses appeared as a low-frequency synchronisation below 15 Hz. Saccade onset classification was robust, achieving mean area under the receiver operating characteristic curve (AUC) scores of 0.88 within sessions and 0.86 across sessions. Saccade direction decoding yielded within-session AUC scores of 0.67 for four-class decoding and up to 0.75 for the best performing binary comparisons (left vs up and left vs down).Significance.This proof-of-concept study demonstrates the feasibility of an endovascular oculomotor BCI in a patient with ALS, establishing a foundation for future oculomotor BCI studies in human subjects.}, }
@article {pmid41042834, year = {2025}, author = {Guo, M and Zhang, J and Liu, H and Bai, Y and Ni, G}, title = {Signal-to-Noise Ratio Effects Frontoparietal Network Lateralization: Electroencephalogram Evidence in Underwater Auditory Target Recognition.}, journal = {Annals of the New York Academy of Sciences}, volume = {}, number = {}, pages = {}, doi = {10.1111/nyas.70081}, pmid = {41042834}, issn = {1749-6632}, support = {2023YFF1203500//National Key Research and Development Program of China/ ; }, abstract = {Accurately recognizing auditory targets within background interference remains challenging at a low signal-to-noise ratio (SNR). Using an oddball paradigm, this electroencephalogram study investigated the impact of SNR (0, -10, and -20 dB) on psychophysiological processes underlying underwater auditory target recognition in twenty normal-hearing participants. Reduced SNR impaired the N1-P2 component and led to P300 variations, with delayed latencies (N1: p = 0.0355; P300: p = 0.0075) and reduced amplitudes (P2: p = 0.0075; P300: p = 0.0277), indicating increased attentional demands. Microstate analysis highlighted 300-400 ms frontoparietal activation for attention orientation and sensory information integration. Reduced accuracy correlates with alpha-band activity and phase variations over frontoparietal areas (event-related spectral perturbation [ERSP]: p = 0.0388; inter-trial coherence [ITC]: p = 0.0059), implying suppression of task-relevant processing. Gamma-band activity and phase at lower SNR levels suggest changes in the parietal network's function (ERSP: p = 0.0183; ITC: p = 0.0113), influencing reaction times due to increased integration difficulty. Right-lateralized alpha- and gamma-band network shifts support the functional advantages of the right hemisphere in noise, with enhanced local efficiency (frontal alpha: p = 0.0100; parietal-occipital gamma: p = 0.0116). These findings provide insights into the psychophysiological mechanisms underlying auditory target recognition in noise.}, }
@article {pmid41042451, year = {2025}, author = {Huang, Y and Ke, Y and Li, J and Liu, S and Ming, D}, title = {Frontal Theta Modulation in Sequential Working Memory: the Impact of Spatial Regularity and Scenario.}, journal = {Brain topography}, volume = {38}, number = {6}, pages = {74}, pmid = {41042451}, issn = {1573-6792}, support = {No. 2021YFF1200603//the National Key Research and Development Program of China/ ; No. 62276184 and 61806141//the National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Memory, Short-Term/physiology ; *Theta Rhythm/physiology ; Male ; Female ; Young Adult ; Adult ; Electroencephalography ; *Frontal Lobe/physiology ; *Space Perception/physiology ; }, abstract = {Humans can quickly extract spatial regularities from sequences to reduce working memory (WM) load, yet the electrophysiological mechanisms remain unclear. Although previous studies have underscored the role of frontal-midline theta (FM-theta) in sequential WM processing, whether and how spatial regularity modulates FM-theta is unknown. To investigate this, we varied the spatial relation between successive items-more repetitions of the same displacement yielded fewer unique chunks and thus higher regularity-while sequence length stayed fixed. Participants were asked to encode, maintain and reproduce the temporal order of sequences utilizing their spatial structures. To enhance ecological validity, we further embedded the task in a complex scenario that included meaningful contexts, dispersed layouts, and variable stimulus sizes. Behavioral data revealed that sequences with higher regularity and the simple scenario yielded higher accuracy, confirming successful manipulations of regularity and scenario difficulty. The overall temporal dynamics of EEG data showed prominent theta enhancement and concurrent alpha/beta suppression during encoding and maintenance. Subsequent analyses across the 4-30 Hz and delay period demonstrated that theta power increased while alpha/beta power declined monotonically with sequence complexity. Notably, regularity-modulated alpha power differed in two scenarios. Moreover, the results found that only sequence regularity-not scenario difficulty-modulated fronto-posterior theta connectivity and slowed the FM-theta frequency. In sum, FM-theta, operating through long-range connectivity and frequency modulation, exclusively tracks spatial-regularity demands in sequential WM, while such neural mechanisms remain impervious to variations in scenario difficulty. These findings suggest that FM-theta may serve as a specific neural marker for spatial regularity processing, rather than a general index of task difficulty, thereby offering a concrete target for future neuromodulatory interventions.}, }
@article {pmid41042091, year = {2025}, author = {Sato, K and Tanaka, R and Ota, K}, title = {BCI-Mediated Warfare, Psychological Distance, and the Duty to Care.}, journal = {AJOB neuroscience}, volume = {16}, number = {4}, pages = {344-346}, doi = {10.1080/21507740.2025.2557822}, pmid = {41042091}, issn = {2150-7759}, }
@article {pmid41040967, year = {2025}, author = {Wood, C and Wang, H and Yang, WJ and Xi, Y}, title = {Facing the possibility of consciousness in human brain organoids.}, journal = {Patterns (New York, N.Y.)}, volume = {6}, number = {9}, pages = {101365}, pmid = {41040967}, issn = {2666-3899}, abstract = {Human brain organoids (HBOs) have emerged as transformative models for neurodevelopment and disease, yet ethical concerns persist regarding their potential to develop consciousness. Since 2020, a growing cohort of neuroscientists and philosophers has dismissed these concerns as unscientific, citing limited structural complexity, absence of bodily integration and environmental interaction, and a prevailing neuroscientific consensus against the feasibility of any, or any near-future, emergence of HBO consciousness, thus challenging any suggested revisions of ethical guidelines and safeguards. We argue that this dismissal is premature. Drawing on neuroscientific benchmarks, comparisons to the developing human brain, contemporary theories of consciousness, and principles of natural developmental progression, we question the basis for selectively excluding consciousness from among HBOs' expanding functional repertoire. We caution against enshrining such skepticism into dogma or using it to defer ethical engagement. Instead, we advocate for proactive, ongoing assessment of the moral implications of advancing HBO capabilities.}, }
@article {pmid41040697, year = {2025}, author = {Chetty, N and Kacker, K and Feldman, AK and Yoo, PE and Bennett, J and Fry, A and Tal, I and Hardy, NF and Ebrahimi, S and Echavarria, C and Sawyer, A and Schone, HR and Harel, NY and Nogueira, RG and Majidi, S and Levy, EI and Kandel, A and Hill, KK and Opie, NL and Lacomis, D and Collinger, JL and Oxley, TJ and Putrino, DF and Weber, DJ}, title = {Signal properties and stability of a chronically implanted endovascular brain computer interface.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {41040697}, support = {F32 MH139145/MH/NIMH NIH HHS/United States ; UH3 NS120191/NS/NINDS NIH HHS/United States ; }, abstract = {BACKGROUND: Implanted brain-computer interfaces (iBCIs) establish direct communication with the brain and hold the potential to enable people with severe disability to achieve control of digital devices, enabling communication and digital activities of daily living. The ability to access brain signals reliably and continuously over many years post-implantation is crucial for iBCIs to be effective and feasible. This study investigates the signal characteristics and long-term stability of neural activity recorded with a stent-electrode array over 1 year post-implant.<br><br>METHODS: We report on five participants with paralysis who were enrolled in an early feasibility clinical trial of an endovascular iBCI (Stentrode; ClinicalTrials.gov, NCT05035823). Each participant was implanted with a 16-channel stent-electrode array, deployed in the superior sagittal sinus to record bilaterally from the primary motor cortices. Neural activity was recorded during home-based sessions while the participants performed a set of standardized tasks. Metrics including motor signal strength during attempted movement, resting state signal features, and electrode impedances were quantified over time.<br><br>RESULTS: Motor-related modulation in neural activity was exhibited in the high-frequency bands (30-200 Hz) during attempted movements, with rest and attempted movement states showing sustained differentiation over time. Impedance and resting state band power for most channels did not change significantly over time.<br><br>CONCLUSIONS: These findings provide strong evidence that the endovascular BCIs may be suitable for long-term neural signal acquisition in the home environment, demonstrating the ability to record movement-related modulation over one year.}, }
@article {pmid41040692, year = {2025}, author = {Schone, HR and Yoo, P and Fry, A and Chetty, N and Sawyer, A and Herbers, C and Liu, F and Moon, CH and Hill, K and Majidi, S and Harel, NY and Nogueira, RG and Levy, E and Putrino, DF and Lacomis, D and Oxley, TJ and Weber, DJ and Collinger, JL}, title = {Motor Cortex Coverage Predicts Signal Strength of a Stentrode Endovascular Brain-Computer Interface.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {41040692}, abstract = {Brain-computer interfaces (BCIs) are an emerging assistive technology for individuals with motor impairments, enabling the command of digital devices using neural signals. The Stentrode BCI is an implant, positioned within the brain's neurovasculature, that can record movement-related electrocortical activity. Over 5 years, 10 participants (8 amyotrophic lateral sclerosis, 1 primary lateral sclerosis, 1 brainstem stroke) have been implanted with a Stentrode BCI and significant inter-participant variability has been observed in the recorded motor signal strength. This variability warrants a critical investigation to characterize potential predictors of signal strength to promote more successful BCI control in future participants. Therefore, we investigated the relationship between Stentrode BCI motor signal strength and a variety of user-specific factors: (1) clinical status, (2) pre-implant functional activity, (3) peri-implant neuroanatomy, (4) peri-implant neurovasculature, and (5) Stentrode device integrity. Data from 10 implanted participants, including clinical demographics, pre- and post-implant neuroimaging and longitudinal Stentrode BCI motor signal assessments were acquired over a year. Across all potential predictors, the strongest predictor of Stentrode motor signal strength was the degree to which the Stentrode BCI's deployment position overlapped with primary motor cortex (M1). These findings highlight the importance of targeting M1 during device deployment and, more generally, provides a scientific framework for investigating the role of user-specific factors on BCI device outcomes.}, }
@article {pmid41040179, year = {2025}, author = {Rigotti-Thompson, M and Nason-Tomaszewski, SR and Bechefsky, P and Acosta, A and Hahn, N and Avansino, D and Richards, B and Nicolas, C and Ali, YH and Henderson, JM and Hochberg, LR and AuYong, N and Pandarinath, C}, title = {Preparatory encoding of diverse features of intended movement in the human motor cortex.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.09.24.678356}, pmid = {41040179}, issn = {2692-8205}, abstract = {Over the course of a voluntary movement, motor cortical activity exhibits a transition from preparation to execution, with markedly different activity across these phases. Preparatory activity in particular might be used to improve brain-computer interfaces (BCIs) that harness brain activity to control external assistive devices, for example by anticipating a user's intended movement trajectory for quick and fluid performance. However, to leverage preparatory activity for clinical BCIs, we must first understand which features of upcoming movements are encoded by preparatory activity in humans. In this work, we collected intracortical recordings from 3 research participants in the BrainGate2 clinical trial to investigate whether diverse features of movement, such as direction, curvature, and distance, are encoded by preparatory activity in the human motor cortex. We first show that preparatory activity is tuned to the direction of upcoming movements, and this tuning is largely preserved across movements with different effectors. Further investigation demonstrated this preparatory activity is also informative of initial and endpoint directions of curved movement trajectories, and encodes movement distance and speed independently. Finally, we present an online control paradigm that leverages preparatory activity to predict movements towards intended directions in advance, yielding rapid, self-paced control of a computer cursor by human participants. Altogether, these results demonstrate that preparatory activity in the human motor cortex encodes rich features of upcoming movement, highlighting its potential use for high performance brain-computer interface applications.}, }
@article {pmid41039114, year = {2025}, author = {}, title = {High-resolution brain-computer interface with electrode scalability and minimally invasive surgery.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {41039114}, issn = {2157-846X}, }
@article {pmid41039113, year = {2025}, author = {Hettick, M and Ho, E and Poole, AJ and Monge, M and Papageorgiou, D and Takahashi, K and LaMarca, M and Trietsch, D and Reed, K and Murphy, M and Rider, S and Gelman, KR and Byun, YW and Miller, JS and Hanson, T and Tolosa, V and Lee, SH and Bhatia, S and Konrad, PE and Mager, M and Mermel, CH and Rapoport, BI}, title = {Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {41039113}, issn = {2157-846X}, abstract = {High-bandwidth brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes. Here we describe a cortical 1,024-channel thin-film microelectrode array and we demonstrate its minimally invasive surgical delivery that avoids craniotomy in porcine models and cadavers. We show recording and stimulation from the same electrodes to large portions of the cortical surface, and the reversibility of delivering the implants to multiple functional regions of the brain without damaging the cortical surface. We evaluate the performance of the interface for high-density neural recording and visualizing cortical surface activity at spatial and temporal resolutions and total spatial extents. We demonstrate accurate neural decoding of somatosensory, visual and volitional walking activity, and achieve focal neuromodulation through cortical stimulation at sub-millimetre scales. We report the feasibility of intraoperative use of the device in a five-patient pilot clinical study with anaesthetized and awake neurosurgical patients, characterizing the spatial scales at which sensorimotor activity and speech are represented at the cortical surface. The presented neural interface demonstrates the highly scalable nature of micro-electrocorticography and its utility for next-generation brain-computer interfaces.}, }
@article {pmid41039090, year = {2025}, author = {Zhou, H and Wang, M and Qi, S and Chen, Q and Lai, J and Wu, Z and Liu, R and Wang, L and Zhou, H and Zhang, S and Hu, S}, title = {Transcranial temporal interference stimulation for treating bipolar disorder with depressive episodes: a feasibility Study.}, journal = {Molecular psychiatry}, volume = {30}, number = {12}, pages = {6099-6106}, pmid = {41039090}, issn = {1476-5578}, support = {52407261//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Bipolar Disorder/therapy/physiopathology ; Male ; Female ; Adult ; Middle Aged ; Feasibility Studies ; *Transcranial Direct Current Stimulation/methods/adverse effects ; Treatment Outcome ; Nucleus Accumbens ; Psychiatric Status Rating Scales ; Depression/therapy ; *Transcranial Magnetic Stimulation/methods ; Brain ; Executive Function/physiology ; }, abstract = {Bipolar depression (BD-D) is a significant clinical challenge associated with high disease burden. Transcranial temporal interference stimulation (tTIS), a novel and noninvasive approach for targeting deep brain structures, was investigated for its efficacy and safety in BD-D patients in this trial. Thirty-six patients were recruited for a single-arm, open-label trial, and 25 completed the 5-day intervention consisting of 10 tTIS sessions targeting the left nucleus accumbens. Each session lasted 20 min, with a maximum current intensity of 2 mA and an envelope stimulation frequency of 40 Hz. Significant symptom reductions were observed following treatment, with mean HAMD-17 scores decreasing from 23.36 to 16.16 (p < 0.0001), MADRS scores from 39.12 to 31.28 (p < 0.01), HAMA scores from 19.68 to 15.44 (p < 0.05), and QIDS scores from 13.52to 9.68 (p < 0.001). Eleven participants (44.0%) met improvement criteria and seven (28.0%) achieved response. Cognitive assessments indicated improvements in memory and executive function, and changes in reward-related brain activity correlated positively with symptom reduction. Adverse events were mild, mainly transient scalp discomfort. These findings provide preliminary evidence supporting the efficacy and safety of tTIS for alleviating depressive symptoms and cognitive impairments in BD-D.}, }
@article {pmid41038246, year = {2025}, author = {Xie, H and Xu, H and Xu, K and Yu, C and Yang, W and Yang, C}, title = {Rat Robot Autonomous Border Detection Based on Wearable Sensors.}, journal = {Bioinspiration &amp; biomimetics}, volume = {}, number = {}, pages = {}, doi = {10.1088/1748-3190/ae0ee8}, pmid = {41038246}, issn = {1748-3190}, abstract = {Bio-robots, a novel type of robots created based on brain-machine interface, have shown great potential in search and rescue tasks. However, current research focuses on the bio-robot itself, such as locomotion, localization and navigation, but lacks interactions with the external environment. In this paper, we proposed a new system for rat robot to autonomously explore the border of unknown field out of sight, and then get the boundary map. We invented a wearable backpack, which is an embedded system with laser-ranging sensors, IMU and ultra-wide band (UWB) module, for the rat robot. Based on the wearable system, a classification method for motion states based on random forest (RF) and a navigation algorithm based on finite state machine (FSM) were developed for the autonomous exploration of border and tested in the locomotion experiment. Besides, with the localization and distance data from UWB and laser-ranging sensors, we mapped the distribution of the border, using Ramber-Douglas-Peucker (RDP) algorithm. The results show that the system could effectively navigate the rat robot to explore the field and accurately detect the border. The accuracy of classification reaches 97.86% and the error rate of border detection is 5.90%. This work provides a novel technology that has potential for practical applications such as prospect for minerals and search tasks. .}, }
@article {pmid41038061, year = {2025}, author = {Wang, X and Li, X and Li, J and Fu, Y and Zhang, D and Peng, Y}, title = {RimeSleepNet: A hybrid deep learning network for s-EEG sleep stage classification.}, journal = {Sleep medicine}, volume = {136}, number = {}, pages = {106835}, doi = {10.1016/j.sleep.2025.106835}, pmid = {41038061}, issn = {1878-5506}, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Sleep Stages/physiology ; Neural Networks, Computer ; Algorithms ; }, abstract = {Sleep stage classification is essential for sleep research and clinical diagnostics. However, frequency aliasing in sleep electroencephalogram (s-EEG) signals remains a significant challenge, existing methods have yet to effectively address this issue. This study proposes a hybrid deep-learning model, RimeSleepNet, comprising four key components. First, the rime optimization algorithm adaptively tunes variational mode decomposition (VMD) to reduce frequency aliasing by generating intrinsic mode functions (IMFs). Second, a convolutional neural network (CNN) automatically extracts stage-specific features from IMFs. A multi-head self-attention (MHSA) mechanism then dynamically weights these features to prioritize stage-specific patterns, followed by long short-term memory (LSTM) networks that model temporal dynamics for robust classification of NREM, REM, and WAKE stages. Evaluated on the Chengdu People's Hospital and Sleep-EDF datasets, RimeSleepNet achieves the highest F1 scores of 0.94, 0.89, and 0.92 for NREM, REM, and WAKE stages, respectively, with an AUC of 0.92, outperforming baseline models like CNN and LSTM. Cross-dataset validation confirms its robust generalization (Cohen's κ = 0.90), and it reduces validation loss by 53 % compared to LSTM, providing an advanced tool for automated sleep stage analysis in sleep disorder diagnosis and personalized monitoring.}, }
@article {pmid41036535, year = {2025}, author = {Arif, S and Rehman, MZU and Mushtaq, Z}, title = {Editorial: Advancements in smart diagnostics for understanding neurological behaviors and biosensing applications.}, journal = {Frontiers in computational neuroscience}, volume = {19}, number = {}, pages = {1693327}, doi = {10.3389/fncom.2025.1693327}, pmid = {41036535}, issn = {1662-5188}, }
@article {pmid41035957, year = {2025}, author = {Wang, W and Liu, Y and Shi, P and Zhang, J and Wang, G and Li, Y and Liu, W and Ming, D}, title = {Altered tactile abnormalities in children with ASD during tactile processing and recognition revealed by dynamic EEG features.}, journal = {Frontiers in psychiatry}, volume = {16}, number = {}, pages = {1611438}, pmid = {41035957}, issn = {1664-0640}, abstract = {INTRODUCTION: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by sensory processing abnormalities, particularly in tactile perception, highlighting the need for objective screening methods beyond current subjective behavioral assessments.<br><br>METHODS: This study developed a portable electro-tactile stimulation system with EEG to evaluate tactile processing differences in children with ASD (n=36) versus typically developing controls (n=36).<br><br>RESULTS: Revealing significantly reduced ERP amplitudes at key processing stages: P200 at FP2 (F(1,70)=10.82, p=0.0454), N200 at F3 (F(1,70)=58.33, p<0.0001), and P300 at C4 (F(1,70)=45.62, p<0.0001). Topographic analysis identified pronounced group differences (>10&#xec;V) across frontal, central, and parietal regions (F8, FC5/6, CP1/2/5/6, Pz, Oz), with ASD children exhibiting prolonged but less efficient tactile discrimination and compensatory prefrontal activation (FP2 CV: p=0.043). The paradigm demonstrated strong reliability (CV ICC: ASD=0.779, TD=0.729) and achieved 85.2% classification accuracy (AUC=0.91) using ANN, with optimal performance from F8 P300 features (sensitivity=87.5%, specificity=83.7%).<br><br>DISCUSSION: These findings provide an objective, efficient (15-minute) screening method that advances understanding of tactile processing abnormalities in ASD and supports the development of physiological biomarkers for early identification, overcoming limitations of questionnaire-based approaches.}, }
@article {pmid41035905, year = {2025}, author = {Xue, Y and Chen, Y and Wang, F and Zhao, L and Li, T and Gong, A and Nan, W and Fu, Y}, title = {Applications and interrelationships of brain function detection, brain-computer interfaces, and brain stimulation: a comprehensive review.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {161}, pmid = {41035905}, issn = {1871-4080}, abstract = {Brain-Computer Interface (BCI), Brain Function Detection (BFD), and Brain Stimulation (BS) are three pivotal technological domains in neuroscience and neuroengineering. Each plays a critical role in fundamental research, clinical applications, and human-computer interaction paradigms. Despite their distinct developmental pathways and application focuses, these technologies are frequently conflated or ambiguously referenced in both academic discourse and industrial practice, potentially leading to conceptual misinterpretations, suboptimal system designs, and clinical misapplications. Prior literature reviews have predominantly concentrated on BCI as a standalone subject, covering its historical evolution, specific neurophysiological signal modalities, or emergent technological trends. This manuscript's core contribution is critiquing the overuse of "passive BCI" (labeling feedback-absent monitoring as BCI). Through an application-oriented lens, it clarifies boundaries between BCI, BFD, and BS to resolve conceptual confusion. Further, the review interrogates the convergences and divergences among these modalities and critically evaluates the practical feasibility and challenges associated with their integrative deployment in clinical and experimental settings. Ultimately, this work aspires to provide a lucid, systematic, and conceptually coherent framework to support neuroscientific novices, interdisciplinary investigators, and clinical practitioners. By fostering precise comprehension and judicious utilization of BCI, BFD, and BS, it aims to propel their standardized advancement and enhance their translational impact across both research and clinical domains.}, }
@article {pmid41034549, year = {2025}, author = {Di, S and Luo, N and Shi, W and Yang, Z and Sui, J and Jiang, R and Cui, Y and Du, Z and Zhang, J and Ma, Y and Wang, H and Chu, C and Zhong, Y and Li, W and Lu, Y and Yan, H and Liao, J and Zhang, D and Calhoun, V and Song, M and Jiang, T}, title = {Physical Activity and Depressive Mood Share the Structural Connectivity Between Motor and Reward Networks.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {41034549}, issn = {1995-8218}, abstract = {In various studies, exercise has been revealed to have a positive effect on alleviating depressive symptoms. However, the neural basis behind this phenomenon remains unknown, as well as its underlying biological mechanism. In this study, we used a large neuroimaging cohort [n = 1,027, major depressive disorder (MDD)/healthy controls (HCs) = 492/535] from the UK Biobank to identify structural connectivity (SC) patterns simultaneously linked with physical activity and depression, as well as the biological interpretation. An SC pattern linked with exercise was identified to be both significantly correlated with depressive mood and group discrimination between MDDs and HCs, primarily located between the motor-related regions and reward-related regions. This pattern was associated with multiple neurotransmitter receptors, such as serotonin and GABA receptors, and enriched in pathways like synaptic signaling and the astrocyte cell type. The SC pattern and genetic results were also replicated in another independent MDD dataset (n = 3,496) and present commonalities with bipolar disorder (n = 81). Overall, these findings not only initially identified a reproducible shared SC pattern between physical activity and depressive mood, but also elucidated the underlying biological mechanisms, which enhance our understanding of how exercise helps alleviate depression and may inform the development of novel neuromodulation targets.}, }
@article {pmid41034219, year = {2025}, author = {Griggs, WS and Norman, SL and Tanter, M and Liu, C and Christopoulos, V and Shapiro, MG and Andersen, RA}, title = {Functional ultrasound neuroimaging reveals mesoscopic organization of saccades in the lateral intraparietal area.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {8752}, pmid = {41034219}, issn = {2041-1723}, support = {T32 GM008042/GM/NIGMS NIH HHS/United States ; R01NS123663//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; F30EY032799//U.S. Department of Health &amp; Human Services | NIH | National Eye Institute (NEI)/ ; T32GM008042//U.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; F30 EY032799/EY/NEI NIH HHS/United States ; R01 NS123663/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; *Saccades/physiology ; *Parietal Lobe/physiology/diagnostic imaging ; Male ; Macaca mulatta ; Ultrasonography/methods ; *Functional Neuroimaging/methods ; Brain Mapping/methods ; Magnetic Resonance Imaging ; }, abstract = {The lateral intraparietal cortex (LIP), contained within the posterior parietal cortex (PPC), is crucial for transforming spatial information into saccadic eye movements, yet its functional organization for movement direction remains unclear. Here, we used functional ultrasound imaging (fUSI), a technique with high sensitivity, large spatial coverage, and good spatial resolution, to map movement direction encoding across the PPC by recording local changes in cerebral blood volume within PPC as two male monkeys performed memory-guided saccades. Our analysis revealed a heterogeneous organization where small patches of neighboring LIP cortex encoded different directions. These subregions demonstrated consistent tuning across several months to years. A rough topography emerged where anterior LIP represented more contralateral downward movements and posterior LIP represented more contralateral upward movements. These results address two fundamental gaps in our understanding of LIP's functional organization: the neighborhood organization of patches and the stability of these populations across long periods of time. By tracking LIP populations over extended periods, we developed mesoscopic maps of direction specificity previously unattainable with fMRI or electrophysiology methods.}, }
@article {pmid41034198, year = {2025}, author = {Singh, A and Thomas, T and Li, J and Hickok, G and Pitkow, X and Tandon, N}, title = {Transfer learning via distributed brain recordings enables reliable speech decoding.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {8749}, pmid = {41034198}, issn = {2041-1723}, support = {U01 NS128921/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; *Brain/physiology ; Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Middle Aged ; Learning ; }, abstract = {Speech brain-computer interfaces (BCIs) combine neural recordings with large language models to achieve real-time intelligible speech. However, these decoders rely on dense, intact cortical coverage and are challenging to scale across individuals with heterogeneous brain organization. To derive scalable transfer learning strategies for neural speech decoding, we used minimally invasive stereo-electroencephalography recordings in a large cohort performing a demanding speech motor task. A sequence-to-sequence model enabled decoding of variable-length phonemic sequences prior to and during articulation. This enabled development of a cross-subject transfer learning framework to isolate shared latent manifolds while enabling individual model initialization. The group-derived decoder significantly outperformed models trained on individual data alone, enabling decoding robustness despite variable coverage and activation. These results highlight a pathway toward generalizable neural prostheses for speech and language disorders by leveraging large-scale intracranial datasets with distributed spatial sampling and shared task demands.}, }
@article {pmid41033466, year = {2025}, author = {Dong, Z and Xiang, Y and Wang, S}, title = {High - quality decoding of RGB images from the neuronal signals of the pigeon optic tectum.}, journal = {Journal of neuroscience methods}, volume = {424}, number = {}, pages = {110595}, doi = {10.1016/j.jneumeth.2025.110595}, pmid = {41033466}, issn = {1872-678X}, mesh = {Animals ; Columbidae ; *Superior Colliculi/physiology/cytology ; *Neurons/physiology ; *Image Processing, Computer-Assisted/methods ; Algorithms ; Photic Stimulation ; *Visual Perception/physiology ; Signal-To-Noise Ratio ; }, abstract = {BACKGROUND: Decoding neural activity to reverse-engineer sensory inputs advances understanding of neural encoding and boosts brain-computer interface and visual prosthesis tech. A major challenge is high-quality RGB image reconstruction from natural scenes, which this study tackles using pigeon optic tectum neurons.<br><br>NEW METHOD: We built a neural response dataset via microelectrode arrays capturing tectal neurons' ON-OFF responses to RGB images. A modular decoding algorithm, integrating a convolutional encoding network, linear decoder, and image enhancement network, enabled inverse RGB image reconstruction from neural signals.<br><br>RESULTS: Experimental results confirmed high-quality RGB image reconstruction by the proposed algorithm. For all test set reconstructions, average metrics were: correlation coefficient (R) of 0.853, structural similarity index (SSIM) of 0.618, peak signal-to-noise ratio (PSNR) of 19.94&#x202f;dB, and feature similarity index (FSIMc) of 0.801. These results confirm accurate recapitulation of both color and contour details of the original images.<br><br>In terms of key quantitative metrics, the proposed algorithm achieves a significant improvement over traditional linear reconstruction methods, with the correlation coefficient (R) increased by 12.65&#x202f;%, the structural similarity index (SSIM) increased by 38.92&#x202f;%, the peak signal-to-noise ratio (PSNR) increased by 12.65&#x202f;%, and the feature similarity index (FSIMc) increased by 9.28&#x202f;%.<br><br>CONCLUSIONS: This research provides a novel technical pathway for high-quality visual neural decoding, with robust experimental metrics validating its effectiveness. It also offers experimental evidence to support investigations into the information processing mechanisms of the avian visual pathway.}, }
@article {pmid41033328, year = {2025}, author = {Deng, X and Fan, Z and Dong, W}, title = {MEFD dataset and GCSFormer model: cross-subject emotion recognition based on multimodal physiological signals.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {6}, pages = {}, doi = {10.1088/2057-1976/ae0e28}, pmid = {41033328}, issn = {2057-1976}, mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; Male ; Female ; Heart Rate/physiology ; Adult ; Electrooculography ; *Signal Processing, Computer-Assisted ; Young Adult ; Algorithms ; Brain-Computer Interfaces ; Galvanic Skin Response ; Spectroscopy, Near-Infrared ; }, abstract = {Cross-subject emotion recognition is an important research direction in the fields of affective computing and brain-computer interfaces, aiming to identify the emotional states of different individuals through physiological signals such as functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG). Currently, most EEG-based emotion recognition datasets are unimodal or bimodal, which may overlook the emotional information reflected by other physiological signals of the subjects. In this paper, a multimodal dataset named Multimodal Emotion Four Category Dataset (MEFD) is constructed, which includes EEG, Heart Rate Variability (HRV), Electrooculogram (EOG), and Electrodermal Activity (EDA) data from 34 participants in four emotional states: sadness, happiness, fear, and calm. This will contribute to the development of multimodal emotion recognition research. To address the recognition difficulty caused by individual differences in cross-subject emotion recognition tasks, a classification model named Global Convolution Shifted Window Transformer (GCSFormer) composed of an EEG-Swin Convolution module and an improved Global Adaptive Transformer (GAT) module is proposed. By using a parallel network, the feature discrimination ability and generalization ability are enhanced. The model is applied to classify the EEG data in the self-built MEFD dataset, and the results are compared with those of mainstream methods. The experimental results show that the proposed EEG classification method achieves the best average accuracy of 85.36%, precision of 85.23%, recall of 86.35%, and F1 score of 84.52% in the cross-subject emotion recognition task. The excellent performance of GCSFormer in cross-subject emotion recognition task was verified.}, }
@article {pmid41032544, year = {2025}, author = {Ju, J and Zhuang, Y and Yi, C}, title = {An EEG-EMG-Based Hybrid Brain-Computer Interface for Decoding Tones in Silent and Audible Speech.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {4206-4216}, doi = {10.1109/TNSRE.2025.3616276}, pmid = {41032544}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; *Electromyography/methods ; Adult ; Female ; Young Adult ; Algorithms ; *Speech Perception/physiology ; Acoustic Stimulation ; *Speech/physiology ; Reproducibility of Results ; }, abstract = {Speech recognition can be widely applied to support people with language disabilities by enabling them to communicate through brain-computer interfaces (BCIs), thus improving their quality of life. Despite the essential role of tonal variations in conveying semantic meaning, there have been limited studies focusing on the neural signatures of tones and their decoding. This paper systematically investigates the neural signatures of the four tones of Mandarin. It explores the feasibility of tone decoding in both silent and audible speech using a multimodal BCI based on electroencephalography (EEG) and electromyography (EMG). The time-frequency analysis of EEG has revealed significant variations in neural activation patterns across various tones and speech modes. For example, in the silent speech condition, temporal-domain analysis shows significant tone-dependent activation in the frontal lobe (ANOVA p = 0.000, Tone1 vs Tone2: p = 0.000, Tone1 vs Tone4: p = 0.000, Tone2 vs Tone3: p = 0.000, Tone3 vs Tone4: p = 0.001) and in channel F8 (ANOVA p= 0.008, Tone1 vs Tone2: p= 0.014, Tone2 vs Tone3: p= 0.034). Spectral analysis shows significant differences between four tones in event-related spectral perturbation (ERSP) in the central region (p = 0.000) and channel C6 (p = 0.000). EMG analysis identifies a significant tone-related difference in activation of the left buccinator muscle (p = 0.023), and ERSP from the mentalis muscle also shows a marked difference across tones in both speech conditions (p = 0.00). Overall, tone-related neural differences were more pronounced in the audible speech condition than in the silent condition. For tone classification, RLDA and SVM classifiers achieved accuracies of 71.22% and 72.43%, respectively, using EEG temporal features in both speech modes. Additionally, the RLDA classifier with temporal features achieves binary tone classification accuracies of 90.92% (audible tones) and 91.00% (silent tones). The combination of EEG and EMG yields the highest speech modes decoding accuracy of 81.33%. These findings provide a potential strategy for speech restoration in tonal languages and further validate the feasibility of a speech brain-computer interface (BCI) as a clinically effective treatment for individuals with tonal language impairment.}, }
@article {pmid41031916, year = {2025}, author = {Liu, M and Guo, X and Cao, L and Cui, H and Li, Z and Lin, Y and Yin, Z and Quan, W and Feng, C and Ma, T and Zhao, Z and Yang, L and Yao, L and Zhang, X and Wang, G}, title = {Revolutionizing brain-computer interfaces: Compact and high-speed wireless neural signal acquisition.}, journal = {The Review of scientific instruments}, volume = {96}, number = {10}, pages = {}, doi = {10.1063/5.0287033}, pmid = {41031916}, issn = {1089-7623}, mesh = {*Brain-Computer Interfaces ; *Wireless Technology/instrumentation ; Animals ; *Signal Processing, Computer-Assisted/instrumentation ; Mice ; Electroencephalography/instrumentation ; Signal-To-Noise Ratio ; *Brain/physiology ; Humans ; }, abstract = {A brain-computer interface (BCI) facilitates the connection between the human brain and external devices by decoding neurophysiological signals, thereby enabling seamless interaction between humans and machines. However, existing neural signal acquisition systems often suffer from limited channel counts, low sampling rates, and challenges in miniaturization and wireless bandwidth, which restrict their ability to support large-scale and real-time neural recordings. Given the rapid advancements in BCI technologies and the increasing demand for high-resolution neural data, there is an imperative need for BCI systems that are high-throughput, high-speed, and miniaturized. This paper presents a wireless neural signal acquisition system based on FPGA technology, supporting 1024 channels at 32 kSPS and employing a stacked architecture for compact, low-power wireless transmission. Following the creation of the functional prototype, laboratory electrical performance tests were conducted. The system exhibited a noise voltage of 8.56 μVrms, which is in close proximity to the 6 μVrms specified by the chip. In addition, the system accurately captured weak sine wave inputs in both time and frequency domains, confirming its ability to record weak bioelectrical signals. Subsequent animal experiments involving mice implanted with EEG electrodes demonstrated that the system could reliably acquire brain neural signals in real time. The maximum and minimum values of signal-to-noise ratios among the channels were measured at 28.66 and 30.56 dB, thereby providing additional validation for the system's signal quality and consistency.}, }
@article {pmid41031500, year = {2025}, author = {Sisubalan, N and Vijay, N and Kesika, P and Newbegin, M and Shalini, R and Sivamaruthi, BS and Chaiysut, C}, title = {The Contribution of Wearable Devices and Artificial Intelligence to Promoting Healthy Aging.}, journal = {Current pharmaceutical biotechnology}, volume = {}, number = {}, pages = {}, doi = {10.2174/0113892010390500250911104231}, pmid = {41031500}, issn = {1873-4316}, abstract = {INTRODUCTION: Healthy aging involves consistently maximizing opportunities to maintain and enhance physical and mental well-being, fostering independence, and sustaining a high quality of life. This review examines recent technological innovations aimed at promoting the well-being of older adults. The scope encompasses wearable devices and telemedicine, showcasing their potential to enhance the health and overall well-being of older individuals. The review highlights the crucial role of assistive technologies, including mobility aids, hearing aids, and adaptive home devices, in addressing the specific challenges associated with aging.<br><br>METHODS: The relevant literature was collected and selected based on the objective of the study and reviewed.<br><br>RESULTS: Digital technologies, including brain-computer interfaces (BCIs), are explored as potential solutions to enhance communication between healthcare providers and aging patients, considering engagement levels and active interaction. Sophisticated BCIs, such as electroencephalograms, electrocorticography, and signal modeling for real-time identification, play a crucial role in event detection, with machine learning algorithms enhancing signal processing for accurate decoding. The exploration of smart wearable systems for health monitoring emerges as a dynamic and promising field in the context of aging.<br><br>DISCUSSION: Fitbit&#xae; showcases accurate step counting, making it suitable for monitoring physical activity in older adults engaged in slow walking. ActiGraph&#x2122; is evaluated for accuracy in monitoring physical activity in older adults, with results indicating reliable concurrence with Fitbit&#xae; devices. The study identifies several limitations, including sample size constraints, challenges in keeping pace with technological advancements, and the need for further investigation into the suitability of fitness trackers for individuals with significant mobility impairments.<br><br>CONCLUSION: The evolving landscape of wearable technologies, exemplified by Fitbit&#xae;, Acti- Graph&#x2122;, and other interventions, holds substantial promise for reshaping healthcare approaches for the aging population. Addressing the limitations will be crucial as research progresses to ensure the effective and ethical integration of wearables into geriatric care, maximizing their potential benefits.}, }
@article {pmid41028971, year = {2025}, author = {Korkmaz, I and Tepe, C}, title = {EEG-based motor execution classification of upper and lower extremities using machine learning.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-17}, doi = {10.1080/10255842.2025.2566260}, pmid = {41028971}, issn = {1476-8259}, abstract = {This study classifies upper- and lower-extremity motor execution from electroencephalography (EEG). We compared two feature extractors, statistical features and Common Spatial Patterns (CSP), and four classifiers: K-Nearest Neighbors, Linear Discriminant Analysis (LDA), Multilayer Perceptron, and Support Vector Machine. Metrics were accuracy, F1, precision, and recall. CSP with LDA achieved the best, most consistent performance (72.5% accuracy); statistical features underperformed. We report real-time feasibility benchmarks, post-cue time-window analysis, and significance tests for classifiers. Findings support BCI and neuroprosthesis development, while noting subject variability and dataset specificity. Future work is real-time use, cross-dataset generalization, and hybrid deep learning.}, }
@article {pmid41028569, year = {2025}, author = {Du, X and Liu, J and Wang, X}, title = {The transformational power of psychedelics: catalysts for creativity, consciousness, and mental health.}, journal = {Molecular psychiatry}, volume = {30}, number = {12}, pages = {6165-6171}, pmid = {41028569}, issn = {1476-5578}, support = {T2350008//National Natural Science Foundation of China (National Science Foundation of China)/ ; JCYJ20220804182935001//Shenzhen Science and Technology Innovation Commission/ ; }, mesh = {Humans ; *Hallucinogens/pharmacology/therapeutic use ; *Creativity ; *Consciousness/drug effects ; *Mental Health ; Lysergic Acid Diethylamide/pharmacology ; Psilocybin/pharmacology ; }, abstract = {Psychedelics, such as psilocybin, lysergic acid diethylamide (LSD), ketamine, and N,N-dimethyltryptamine (DMT), have captured the attention of scientists, artists, and seekers alike for their profound ability to alter consciousness and inspire creativity. The concept of "creation" encompasses multiple interpretations-ranging from generating novel ideas to fostering personal transformation. This perspective explores how psychedelics interact with the concept of creation, examining their role in enhancing artistic inspiration, facilitating spiritual experiences, and driving therapeutic breakthroughs in mental health treatment. By integrating findings from neurobiological research, clinical applications, and cultural analysis, we offer a holistic view of how psychedelics may catalyze innovative modes of thinking and awaken the mind's creative and transformative potential. As these substances gain prominence as tools for reshaping our understanding of consciousness and psychological healing, their broader integration into society requires careful consideration of legal complexities, ethical responsibilities, and cultural contexts to ensure their use is evidence-based, respectful, and responsibly guided.}, }
@article {pmid41025886, year = {2025}, author = {Chaudhary, J and Gupta, E and Singh, PK and Yadav, RK and Chaudhary, M and Singh, S}, title = {Designing behavioural change intervention module for tobacco cessation counselling among pregnant tobacco users in India: a methodology paper.}, journal = {Health education research}, volume = {40}, number = {6}, pages = {}, doi = {10.1093/her/cyaf041}, pmid = {41025886}, issn = {1465-3648}, support = {2020-5325//Indian Council of Medical Research, New Delhi/ ; }, mesh = {Humans ; Female ; Pregnancy ; India ; *Counseling/methods ; Prenatal Care/methods ; Adult ; *Tobacco Use Cessation/methods ; *Smoking Cessation/methods ; Tobacco Use ; }, abstract = {Tobacco use has detrimental effects on women's reproductive health and is associated with poor pregnancy outcomes. Antenatal care (ANC) check-ups provide health professionals with a unique opportunity to screen and counsel pregnant tobacco users to quit. Currently, in India, pregnant women are not being screened for tobacco use during antenatal care visits and healthcare providers lack formal training to provide tobacco cessation advice. This article describes the designing and development of a tailored behaviour change intervention (BCI) module for tobacco cessation and its delivery to pregnant women attending antenatal clinics. The BCI module was designed to incorporate the components of the Capability, Opportunity and Motivation Model and the Behaviour Change Wheel guide. The development was done in three steps-understanding the behaviour, developing intervention model, and identifying implementation options along with monitoring and evaluation strategies. The module has three tools-counselling flipbook for healthcare provider, take home pamphlets, and information posters for patient waiting areas. A gender- and culture-specific BCI module was developed and implemented to screen and counsel 105 pregnant tobacco users during antenatal visits, leading to high self-reported tobacco quit rate (69%) which corroborated with urine cotinine levels at baseline and end line.}, }
@article {pmid41025122, year = {2025}, author = {Mohan, A and Anand, RS}, title = {Innovative augmentation techniques and optimized ANN model for imagined speech decoding in EEG-based BCI.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {158}, pmid = {41025122}, issn = {1871-4080}, abstract = {Electroencephalogram (EEG) based Brain computer interface (BCI) emerges as a transformative technology with vast applications in neuroscience and rehabilitation. Imagined speech is the mental process of thinking and formulating words without vocalizing them through articulators. EEG signal is used to study imagined speech which can empower individuals with neurological impairments to communicate their thoughts effortlessly. The main challenge in decoding imagined speech is the nonstationary nature of EEG signals. Identifying robust features and scarcity of imagined speech datasets for properly training machine learning (ML) based algorithms is also a challenging task. The main objective of this study is to propose augmentation methods which mitigate data scarcity in EEG-based BCIs by introducing variations and strengthening model robustness through EEG data augmentation. The second objective is to propose a novel architecture capable of detecting variations in EEG signals for imagined speech datasets and show remarkable results. Seven diverse augmentation techniques are discussed, and the performance of the proposed model is analyzed in terms of accuracy, f1-score and kappa. The classification results are then compared with the case in which no data augmentation is used. The proposed model has shown remarkable accuracy of 91% for long words by using gaussian noise augmentation.}, }
@article {pmid41024222, year = {2025}, author = {Zhang, Q and Li, W and Zhang, T and Xiong, R and Zhang, J and Jin, Z and Li, L}, title = {Representation of top-down versus bottom-up attention in the right dorsolateral prefrontal cortex and superior parietal lobule.}, journal = {Behavioral and brain functions : BBF}, volume = {21}, number = {1}, pages = {31}, pmid = {41024222}, issn = {1744-9081}, support = {BX202402//Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows/ ; 2025ZNSFSC0453//Sichuan Science and Technology Program/ ; 62176045//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Attention/physiology ; *Parietal Lobe/physiology/diagnostic imaging ; Male ; Female ; Magnetic Resonance Imaging/methods ; Adult ; Young Adult ; *Dorsolateral Prefrontal Cortex/physiology/diagnostic imaging ; Brain Mapping/methods ; Visual Perception/physiology ; Photic Stimulation/methods ; *Prefrontal Cortex/physiology ; Neural Pathways/physiology ; }, abstract = {BACKGROUND: Visual selective attention can be categorized into top-down (goal-driven) and bottom-up (stimulus-driven) attention, with the fronto-parietal network serving as the primary neural substrate. However, fewer studies have focused on the specific roles of the right dorsolateral prefrontal cortex (DLPFC) and superior parietal lobule (SPL) in top-down and bottom-up attention. This study aimed to investigate the activity and connectivity of the right DLPFC and SPL in top-down and bottom-up attention.<br><br>METHODS: Visual pop-out task mainly induces bottom-up attention, while the visual search task mainly induces top-down attention. Fifty-four participants completed the pop-out and search tasks during functional magnetic resonance imaging (fMRI) scanning. We used univariate analyses, multivariate pattern analyses (MVPA), and generalized psychophysiological interaction (gPPI) to assess activity and functional connectivity.<br><br>RESULTS: Univariate analyses revealed stronger activation in the right DLPFC and SPL during the search&#x2009;>&#x2009;pop-out condition. The activation of the DLPFC was driven by its deactivation in the pop-out task, whereas the SPL showed significant activation in both tasks. MVPA demonstrated that activation patterns in the right DLPFC and SPL could distinguish between the pop-out and search tasks above chance level (0.5), with the right SPL exhibiting higher classification accuracy. The gPPI analyses showed that higher functional connectivity between the two seeds (right DLPFC and SPL) and bilateral precentral gyrus, left SPL, and right insula.<br><br>CONCLUSIONS: These results indicate that the right DLPFC and SPL showed stronger activity and connectivity under top-down versus bottom-up attention, allowing for neural representation of visual selective attention. This study provides evidence for understanding the role of the fronto-parietal network in visual selective attention.}, }
@article {pmid41022774, year = {2025}, author = {Li, L and Hartzler, A and Menendez-Lustri, DM and Zhang, J and Chen, A and Lam, DV and Traylor, B and Quill, E and Nethery, DE and Hoeferlin, GF and Pawlowski, CL and Bruckman, MA and Sen Gupta, A and Capadona, JR and Shoffstall, AJ}, title = {Dexamethasone-loaded platelet-inspired nanoparticles improve intracortical microelectrode recording performance.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {8579}, pmid = {41022774}, issn = {2041-1723}, support = {T32 EB004314/EB/NIBIB NIH HHS/United States ; GRANT12635707//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; HL121212//U.S. Department of Health &amp; Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)/ ; I01 RX003420/RX/RRD VA/United States ; T32EB004314//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; R01 HL121212/HL/NHLBI NIH HHS/United States ; }, mesh = {Animals ; *Dexamethasone/administration &amp; dosage/analogs &amp; derivatives/pharmacology ; Microelectrodes/adverse effects ; Blood-Brain Barrier/drug effects/metabolism ; Rats ; *Nanoparticles/chemistry ; Male ; *Blood Platelets/chemistry ; Neurons/drug effects ; Rats, Sprague-Dawley ; Electrodes, Implanted ; Brain-Computer Interfaces ; Drug Delivery Systems ; Anti-Inflammatory Agents/administration &amp; dosage ; }, abstract = {Long-term robust intracortical microelectrode (IME) neural recording quality is negatively affected by the neuroinflammatory response following microelectrode insertion. This adversely impacts brain-machine interface (BMI) performance for patients with neurological disorders or amputations. Recent studies suggest that the leakage of blood-brain barrier (BBB) and microhemorrhage caused by IME insertions contribute to increased neuroinflammation and reduced neural recording performance. Here, we evaluated dexamethasone sodium phosphate-loaded platelet-inspired nanoparticles (DEXSPPIN) to simultaneously augment local hemostasis and serve as an implant-site targeted drug-delivery vehicle. Weekly systemic treatment or control therapy was provided to rats for 8 weeks following IME implantation, while evaluating extracellular single-unit recording performance. End-point immunohistochemistry was performed to further assess the local tissue response to the IMEs. Treatment with DEXSPPIN significantly increased the recording capabilities of IMEs compared to controls over the 8-week observation period. Immunohistochemical analyses of neuron density, activated microglia/macrophage density, astrocyte density, and BBB permeability suggested that the improved neural recording performance may be attributed to reduced neuron degeneration and neuroinflammation. Overall, we found that DEXSPPIN treatment promoted an anti-inflammatory environment that improved neuronal density and enhanced IME recording performance.}, }
@article {pmid41022701, year = {2025}, author = {Li, J and Li, L and Gao, Z and Tian, Y}, title = {Molecular Dynamics and Neural Network Analysis Reveal Sequential Gating and Allosteric Communication in FMRFamide-Activated Sodium Channels.}, journal = {Journal of chemical information and modeling}, volume = {65}, number = {19}, pages = {10532-10548}, doi = {10.1021/acs.jcim.5c01255}, pmid = {41022701}, issn = {1549-960X}, mesh = {*Molecular Dynamics Simulation ; Allosteric Regulation/drug effects ; *Ion Channel Gating/drug effects ; *FMRFamide/pharmacology/metabolism ; *Neural Networks, Computer ; Ligands ; Protein Conformation ; }, abstract = {FMRFamide-activated sodium channels (FaNaCs) represent a unique class of neuropeptide-gated ion channels within the degenerin/epithelial sodium channel (DEG/ENaC) superfamily. While cryo-electron microscopy has revealed static binding architectures, the dynamic mechanisms underlying ligand recognition, allosteric signal transmission, and channel gating remain poorly understood. Here, we employed microsecond-scale molecular dynamics simulations coupled with neural relational inference analysis to elucidate the complete activation mechanism of FaNaC at atomic resolution. Our analysis revealed a sophisticated multistage activation process initiated by coordinated dynamics of FaNaC-specific insertions SI1 and SI2. Spontaneous FMRFamide-binding events suggested that SI1 functions as a dynamic gate that facilitates optimal ligand burial and stabilization, while SI2 appeared to serve as a conformational lid stabilizing the bound ligand through thermodynamically favorable induced-fit mechanisms. This ligand-induced conformational change, which involves the cooperative reorganization of the three peripheral loops (L1, L2, and L3) in the extracellular domain, propagates through the extracellular domain, particularly via a coordinated rigid-body motion of the β-ball/palm domain, leading to the reorganization of the central β-sheet in the extracellular vestibule and a subsequent conformational wave that compacts the intracellular vestibule. We further leveraged neural relational inference (NRI) to analyze residue-level allosteric networks, demonstrating that ligand binding enhances the network's connectivity and reorganizes allosteric communication pathways. These findings provide a high-resolution, dynamic view of FaNaC function, revealing a novel gating mechanism for the DEG/ENaC superfamily and laying the foundation for future studies into neuropeptide modulation.}, }
@article {pmid41022567, year = {2025}, author = {Chen, X and Cao, L and Wieske, RE and Prada, J and Gramann, K and Haendel, BF}, title = {Walking Modulates Active Auditory Sensing.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {45}, number = {45}, pages = {}, pmid = {41022567}, issn = {1529-2401}, mesh = {Humans ; Female ; Male ; *Walking/physiology ; Adult ; Young Adult ; *Auditory Perception/physiology ; Acoustic Stimulation/methods ; Electroencephalography ; *Evoked Potentials, Auditory/physiology ; }, abstract = {Walking provides the motor foundation for navigation, while navigation ensures that walking is purposeful and adaptive to environmental contexts. Sensory processing of environmental information acts as the informational bridge that connects walking and adaptive navigation. In the current study, we assessed if walking and the walking direction influences neuronal dynamics underlying environmental information processing. To this end, we conducted two experiments with 12 male and 18 female participants while they walked along an 8-shaped path. Auditory entrainment stimuli were continuously presented, and mobile electroencephalogram was recorded. We found increased auditory entrainment (auditory steady-state response) and early auditory evoked responses during walking compared with standing or stepping in place. We also replicated the well-established reduction of occipital alpha power during walking. The increase of auditory entrainment and the decrease of alpha power were correlated across participants. In the second experiment, randomly presented transient burst sounds led to a perturbation of the auditory entrainment response. The perturbation response was stronger during walking compared with standing; however, only when the burst sounds were presented to one ear but not to both ears. Most importantly, we found that the auditory entrainment was systematically modulated dependent on the walking path. The entrainment responses changed as a function of the turning direction. In general, the current work shows that walking changes auditory processing in a walking path-dependent way which might serve to optimize navigation. The walking path-related modulation might further reflect a shift of attention, marking a form of higher-order active sensing.}, }
@article {pmid41022118, year = {2025}, author = {Han, Y and Wang, S}, title = {E-Sort: empowering end-to-end neural network for multi-channel spike sorting with transfer learning and fast post-processing.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ae0d33}, pmid = {41022118}, issn = {1741-2552}, mesh = {*Neural Networks, Computer ; *Action Potentials/physiology ; Humans ; *Neurons/physiology ; *Machine Learning ; Animals ; Algorithms ; Brain-Computer Interfaces ; }, abstract = {Objective.Spike sorting, which involves detecting and attributing spikes to their putative neurons from extracellular recordings, is a common process in electrophysiology and brain-computer interface systems. Recent advances in large-scale neural recording technologies are challenging the conventional algorithms because of the intensive computational workloads required and the accuracy degradation suffered from time-variant spike patterns and significant levels of noise. Neural networks (NNs) have demonstrated promising performance in processing these large-scale neural recordings. However, their applications are constrained by the labor-intensive data labeling and the lack of fully vectorized frameworks with end-to-end NNs.Approach.We propose E-Sort, an end-to-end NN-based spike sorter with transfer learning and parallelizable post-processing to address both obstacles.Main results.We examined our framework in both synthetic and real datasets. The results of the processing of the synthetic datasets show that our approach can reduce the number of annotated spikes required for training by 44% compared to training from scratch, achieving up to 25.7% higher accuracy. We evaluated E-Sort on various probe geometries, noise levels, and drift patterns, which demonstrates that our design can achieve an accuracy that is comparable with Kilosort4 while sorting 50 s of data in only 1.32 s. To test with real datasets, we first sorted the spikes using Kilosort4 and used the sorted spikes at the initial period to pre-train the NN; then we compared and measured the agreement between the results from the trained model and those from Kilosort4. On average the pre-training process improved the result agreement by 30% approximately.Significance.E-Sort offers a scalable, efficient, and accurate NN-based framework for large-scale spike sorting, significantly reducing manual labeling effort and processing time.}, }
@article {pmid41021940, year = {2025}, author = {Bulfer, S and Gamez, J and Yan-Huang, A and Haghi, B and Pedroni, V and Andersen, RA and Emami, A}, title = {A 192-Channel 1D CNN-Based Neural Feature Extractor in 65nm CMOS for Brain-Machine Interfaces.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2025.3615121}, pmid = {41021940}, issn = {1940-9990}, abstract = {We present a 192-channel 1D convolutional neural network (1D CNN) based neural feature extractor for Brain-Machine Interfaces (BMI) that achieves state-of-the-art decoding stability at 1.8 $μ$W and 12801 $μ$m[2] per channel in 65nm CMOS technology. Our device is a fully configurable, scalable, area and power efficient solution that supports models with 2-8 feature layers and a total kernel length of up to 256. This architecture reduces caching requirements by 5× over conventional computation schemes. Channels and layers are individually power-switchable to further optimize power efficiency for a given neural application. We introduce an on-chip model, FENet-66, that achieves the highest cross-validated decoding performance compared to all previously reported feature sets. We show that this model maintains superior stability over time using recorded data from tetraplegic human participants with spinal cord injury. Our features have 18% higher overall average cross-validated R2 decoding performance compared to Spiking Band Power (SBP), with 28% better performance during the 4th year. Our proposed architecture can also extract mean wavelet power features at low power and latency. We show that custom 1D-CNN kernels achieve 10% better performance compared to wavelet features while compressing the neural data stream by 38×. The models and hardware were validated in real time with a human subject in online closed-loop center-out cursor control experiments with micro-electrode arrays that were implanted for 6 years. Decoders using features generated with this work substantially improve the viability of longterm neural implants compared to other feature extraction methods currently present in low power BMI hardware.}, }
@article {pmid41021638, year = {2025}, author = {Ferrea, E and Morel, P and Gail, A}, title = {Frontal and parietal planning signals encode adapted motor commands when learning to control a brain-computer interface.}, journal = {PLoS biology}, volume = {23}, number = {9}, pages = {e3003408}, pmid = {41021638}, issn = {1545-7885}, mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; *Parietal Lobe/physiology ; *Frontal Lobe/physiology ; Psychomotor Performance/physiology ; Male ; Feedback, Sensory/physiology ; *Learning/physiology ; Adaptation, Physiological ; Movement/physiology ; Neurons/physiology ; }, abstract = {Perturbing visual feedback is a powerful tool for studying visuomotor adaptation. However, unperturbed proprioceptive signals in common paradigms inherently co-varies with physical movements and causes incongruency with the visual input. This can create challenges when interpreting underlying neurophysiological mechanisms. We employed a brain-computer interface (BCI) in rhesus monkeys to investigate spatial encoding in frontal and parietal areas during a 3D visuomotor rotation task where only visual feedback was movement-contingent. We found that both brain regions better reflected the adapted motor commands than the perturbed visual feedback during movement preparation and execution. This adaptive response was observed in both local and remote neurons, even when they did not directly contribute to the BCI input signals. The transfer of adaptive changes in planning activity to corresponding movement corrections was stronger in the frontal than in the parietal cortex. Our results suggest an integrated large-scale visuomotor adaptation mechanism in a motor-reference frame spanning across frontoparietal cortices.}, }
@article {pmid41021378, year = {2025}, author = {de Camargo, PS and Santos E Souza, GO and Arévalo, A and Lepski, G}, title = {Intraoperative Techniques for Language Mapping in Brain Surgery: A Comparison Between Direct Electrical Stimulation (DES) and Electrocorticography (ECoG).}, journal = {Brain and behavior}, volume = {15}, number = {10}, pages = {e70900}, pmid = {41021378}, issn = {2162-3279}, support = {2018/18900-1//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; 2023/17520-9//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; }, mesh = {Humans ; *Electrocorticography/methods ; *Language ; *Brain Mapping/methods ; *Intraoperative Neurophysiological Monitoring/methods ; *Electric Stimulation/methods ; *Brain/surgery ; Neurosurgical Procedures/methods ; }, abstract = {PURPOSE: The purpose of this overview is to compare Direct Electrical Stimulation (DES) and Electrocorticography (ECoG) techniques, assessing their respective strengths, limitations, and roles in ensuring successful language mapping during awake brain surgeries.<br><br>METHOD: This overview aims to compare two techniques used in intraoperative language mapping during awake brain surgery: Direct Electrical Stimulation (DES) and Electrocorticography (ECoG). By summarizing recent advances in both methods, we highlight their respective mechanisms, applications, and roles in improving surgical outcomes. DES is widely considered the gold standard for cortical brain mapping and is applicable in both awake and anesthetized surgeries for treating epilepsy and brain tumors. In contrast, ECoG involves monitoring the brain's electrical activity with or without direct stimulation, as it provides valuable insight into high gamma activity (70-150&#xa0;Hz), which is strongly associated with speech production.<br><br>FINDING: ECoG offers a high-resolution approach to language mapping by detecting high-gamma activity, reducing the risk of intraoperative seizures, and serving as a complementary or alternative tool to DES in specific clinical scenarios. While DES continues to be the most reliable technique for identifying functional brain areas, it does carry a higher risk of inducing seizures. Furthermore, recent advancements in ECoG-based speech decoding and brain-computer interfaces (BCIs) underscore the growing potential of ECoG in restoring communication in patients with severe language impairments, extending its applications beyond surgical mapping.<br><br>CONCLUSION: In conclusion, while DES remains the gold standard for intraoperative language mapping, ECoG is emerging as a promising complementary or alternative technique in some clinical cases. This overview highlights the evolving role of ECoG, particularly in the context of speech decoding and BCIs, offering new possibilities for improving surgical outcomes and postoperative quality of life in patients.}, }
@article {pmid41017975, year = {2025}, author = {Adama, S and Bogdan, M}, title = {Assessing consciousness in patients with locked-in syndrome using their EEG.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1604173}, pmid = {41017975}, issn = {1662-4548}, abstract = {Research indicates that locked-in syndrome (LIS) patients retain both consciousness and cognitive functions, despite their inability to perform voluntary muscle movements or communicate. Brain-Computer Interfaces (BCIs) provide a means for these patients to communicate, which is crucial, as the ability to interact with their environment has been shown to significantly enhance their wellbeing and quality of life. This paper presents an innovative approach to analyzing electroencephalogram (EEG) data from four LIS patients to assess their consciousness levels, referred to as normalized consciousness levels (NCL) in this study. It consists of extracting different features based on frequency, complexity, and connectivity measures to maximize the probability of correctly determining the patients' actual states given the inexistence of ground truth. The consciousness levels derived from this approach aim to improve our understanding of the patients' condition, which is vital in order to build effective communication systems. Despite considerable inter-patient variability, the findings indicate that the approach is effective in detecting neural markers of consciousness and in differentiating between states across the majority of patients. By accurately assessing consciousness, this research aims to improve diagnosis in addition to determining the optimal time to initiate communication with these non-communicative patients. It is important to note that consciousness is a complex and difficult concept to define. In this study, the term "consciousness level" does not refer to a medical definition. Instead, it represents a scale of NCL values ranging from 0 to 1 representing the likelihood of the patient being fully conscious (1) or not (0).}, }
@article {pmid41017235, year = {2025}, author = {Chen, D and Lu, Y and Zhang, S and Zhang, W and Yu, Z and Wang, S and Qu, Z and Cheng, M and Yao, Y and Wang, D and Yang, Z and Dong, L}, title = {An Ultra-Flexible Neural Electrode with Bioelectromechanical Compatibility and Brain Micromotion Detection.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e03101}, doi = {10.1002/adhm.202503101}, pmid = {41017235}, issn = {2192-2659}, support = {62127810//National Natural Science Foundation of China/ ; CityU11213720//Research Grants Council of the Hong Kong Special Administrative Region/ ; CityU11217221//Research Grants Council of the Hong Kong Special Administrative Region/ ; 9680347//City University of Hong Kong/ ; 9610608//City University of Hong Kong/ ; 9680103//City University of Hong Kong/ ; }, abstract = {Neural electrodes, as core components of brain-computer interfaces(BCIs), face critical challenges in achieving stable mechanical coupling with brain tissue to ensure high-quality signal acquisition. Current flexible electrodes, including semi-invasive meningeal-attached types and implantable cantilever designs, exhibit significant mechanical mismatches (elastic modulus 5-6 orders higher than brain tissue) due to material/structural limitations, leading to interfacial slippage. While thread-like implants (e.g., Neuralink's electrodes) improve compliance via elongated structures, quantitative characterization of mechano-bioelectric interactions remains unexplored. This study proposes a bioelectromechanical coupling strategy, emphasizing synchronized motion between the electrode and the brain tissue through exposed-end deformation. A 4-channel ultra-flexible electrode (40 mm in length, 164 µm in width, and 3 µm in thickness) is optimized using finite-element simulations and zero relative-motion criteria, achieving an equivalent stiffness of 0.023 N m[-1]-matching brain tissue micromotion stiffness. A nanorobotic manipulator installed inside a scanning electron microscope(SEM) with an atomic force microscope(AFM) cantilever enabled precision characterization under the simulated displacement of 25 µm, revealing interfacial forces of 575 nN and piezoresistive sensitivities of 6.4 pA mm[-1] (length) and 10.2 pA µm[-1] (displacement). The dual-functionality (signal acquisition and micromotion sensing) electrodes demonstrate breakthrough potential, establishing quantitative design standards for next-generation bioelectronic implants.}, }
@article {pmid41016568, year = {2025}, author = {Li, J and Yang, W and Liu, X and Yang, K and Zhou, J and Yang, X}, title = {Research progress of lung organoids in infectious respiratory diseases.}, journal = {European journal of pharmacology}, volume = {1006}, number = {}, pages = {178201}, doi = {10.1016/j.ejphar.2025.178201}, pmid = {41016568}, issn = {1879-0712}, mesh = {*Organoids/virology/pathology/drug effects ; Humans ; *Lung/virology/pathology/cytology ; Animals ; COVID-19/virology/pathology ; SARS-CoV-2 ; }, abstract = {Infectious respiratory diseases are common epidemics that often exhibit phased outbreaks, increasing the healthcare burden. Past research models for these diseases were relatively simplistic, but the emergence of organoids has transformed this landscape. Organoids, three-dimensional in vitro tissue analogs that recapitulate specific spatial organ structures derived from stem cell culture, have advanced significantly over the decade since their inception. Compared to conventional animal models, organoids circumvent interspecies variations, enabling a more precise representation of human physiological and pathological traits. Relative to two-dimensional cell cultures, organoids exhibit enhanced complexity, incorporating diverse cell types and maintaining stable genomes, which facilitates a more faithful simulation of cellular interactions within the extracellular microenvironment. Consequently, as a three-dimensional in vitro model, lung organoids are pivotal for investigating lung organ development, infectious disease pathogenesis, and drug screening. Although SARS-CoV-2 is receding from the spotlight, advancing lung organoid development for addressing infectious respiratory diseases like influenza remains a priority. This review demonstrated the differentiation culture process of lung organoids and outlined advancements in utilizing organoids to elucidate pathogenic infection mechanisms, reveal virus-host interactions and screen therapeutic drugs over the past seven years. Additionally, we have summarized the advances in lung organoid model technologies and outlined their developmental directions.}, }
@article {pmid41016446, year = {2026}, author = {Wang, L and An, X and Jiang, Z and Wang, J and Ming, D}, title = {The individual differences analysis of audiovisual bounce-inducing effects.}, journal = {Behavioural brain research}, volume = {496}, number = {}, pages = {115851}, doi = {10.1016/j.bbr.2025.115851}, pmid = {41016446}, issn = {1872-7549}, mesh = {Humans ; Male ; *Individuality ; Female ; Young Adult ; Electroencephalography ; Adult ; Acoustic Stimulation ; *Auditory Perception/physiology ; Evoked Potentials/physiology ; Photic Stimulation ; *Visual Perception/physiology ; *Illusions/physiology ; *Brain/physiology ; }, abstract = {The audiovisual bounce-inducing effect (ABE) is a phenomenon that the brain integrates spatial and temporal information from different sensory modalities of vision and hearing. At present, some researchers have conducted research on the individual differences of the ABE, but have not considered the factor of audiovisual stimulus intervals. This study investigated the neural mechanisms underlying the intra- and inter-individual differences in subjects' ABE at different audiovisual stimulus onset asynchronies (SOAs). This study adopted the experimental paradigm of Stream/Bounce illusion, in which visual and auditory stimuli were presented in 7 different SOAs. We recorded behavioral and EEG data during the experiment, compared and analyzed the amplitude differences of event-related potentials (ERPs), calculated statistical indicators, and studied the intra- and inter-individual differences of the ABE under different SOAs. The results show that in terms of the inter-individual differences in the ABE, the amplitude of N1 is more significant in the High ABE Group than the Low ABE Group at SOAs of "V100A" and "0". Individual ABE tendencies are also significantly correlated with N1 amplitude at the two SOAs. These results reveal the effect of stimuli interval on the processing of audiovisual stimuli, there is a complex interplay between the individual's sensory processing mechanisms and the specific temporal dynamics of audiovisual integration.}, }
@article {pmid41015681, year = {2025}, author = {Parodi, F and Kording, KP and Platt, ML}, title = {Primate neuroethology: a new synthesis.}, journal = {Trends in cognitive sciences}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tics.2025.09.002}, pmid = {41015681}, issn = {1879-307X}, abstract = {Neuroscience has probed only a sliver of the rich cognitive, emotional, and social behaviors that enable primates to thrive in the real world. Technological breakthroughs allow us to quantify these behaviors alongside wireless neural recordings. New studies reveal that neural activity is intricately bound to movement and is profoundly modulated by behavioral context, emotional states, and social dynamics. We frame our review of primate neuroethology around Niko Tinbergen's four foundational questions - function, mechanism, development, and evolution - to unify classic ethological insights with modern neuroscience tools. We demonstrate that investigating natural behavior promises deep insights into primate cognition, which are relevant for advanced brain-machine interfaces, improved therapies for neurological disorders, and deeper understanding of natural and artificial intelligence.}, }
@article {pmid41011900, year = {2025}, author = {Tan, X and Tong, B and Zhang, K and Ni, C and Yang, D and Gao, Z and Huang, Y and Yao, N and Huang, L}, title = {Mechanical Behavior Analysis of Neural Electrode Arrays Implantation in Brain Tissue.}, journal = {Micromachines}, volume = {16}, number = {9}, pages = {}, pmid = {41011900}, issn = {2072-666X}, support = {2023BAA005//Major Program (JD) of Hubei Province(2023BAA005)./ ; }, abstract = {Understanding the mechanical behavior of implanted neural electrode arrays is crucial for BCI development, which is the foundation for ensuring surgical safety, implantation precision, and evaluating electrode efficacy and long-term stability. Therefore, a reliable FE models are effective in reducing animal experiments and are essential for a deeper understanding of the mechanics of the implantation process. This study established a novel finite element model to simulate neural electrode implantation into brain tissue, specifically characterizing the nonlinear mechanical responses of brain tissue. Synchronized electrode implantation experiments were conducted using ex vivo porcine brain tissue. The results demonstrate that the model accurately reproduces the dynamics of the electrode implantation process. Quantitative analysis reveals that the implantation force exhibits a positive correlation with insertion depth, the average implantation force per electrode within a multi-electrode array decreases with increasing electrode number, and elevation in electrode size, shank spacing, and insertion speed each contribute to a systematic increase in insertion force. This study provides a reliable simulation tool and in-depth mechanistic analysis for predicting the implantation forces of high-density neural electrode arrays and offer theoretical guidance for optimizing BCI implantation device design.}, }
@article {pmid41009567, year = {2025}, author = {Haghighi, P and Smith, TJ and Tahmasebi, G and Vargas, S and Jiang, MS and Massaquoi, AC and Huff, J and Capadona, JR and Pancrazio, JJ}, title = {Piezo1 and Piezo2 Ion Channels in Neuronal and Astrocytic Responses to MEA Implants in the Rat Somatosensory Cortex.}, journal = {International journal of molecular sciences}, volume = {26}, number = {18}, pages = {}, pmid = {41009567}, issn = {1422-0067}, support = {R01 NS110823/NS/NINDS NIH HHS/United States ; 1R01NS110823-06/NH/NIH HHS/United States ; }, mesh = {Animals ; *Ion Channels/metabolism/genetics ; *Somatosensory Cortex/metabolism/cytology ; *Astrocytes/metabolism ; Rats ; Microelectrodes/adverse effects ; *Neurons/metabolism ; Male ; Electrodes, Implanted/adverse effects ; Rats, Sprague-Dawley ; }, abstract = {Intracortical microelectrode arrays (MEAs) are tools for recording and stimulating neural activity, with potential applications in prosthetic control and treatment of neurological disorders. However, when chronically implanted, the long-term functionality of MEAs is hindered by the foreign body response (FBR), characterized by gliosis, neuronal loss, and the formation of a glial scar encapsulating layer. This response begins immediately after implantation and is exacerbated by factors such as brain micromotion and the mechanical mismatch between stiff electrodes and soft brain tissue, leading to signal degradation. Despite progress in mitigating these issues, the underlying mechanisms of the brain's response to MEA implantation remain unclear, particularly regarding how cells sense and respond to the associated mechanical forces. Mechanosensitive ion channels, such as the Piezo family, are key mediators of cellular responses to mechanical stimuli. In this study, silicon-based NeuroNexus MEAs consisting of four shanks were implanted in the rat somatosensory cortex for sixteen weeks. Weekly neural recordings were conducted to assess signal quality over time, revealing a decline in active electrode yield and signal amplitude. Immunohistochemical analysis showed an increase in GFAP intensity and decreased neuronal density near the implant site. Furthermore, Piezo1-but not Piezo2-was strongly expressed in GFAP-positive astrocytes within 25 µm of the implant. Piezo2 expression appeared relatively uniform within each brain slice, both in and around the MEA implantation site across cortical layers. Our study builds on previous work by demonstrating a potential role of Piezo1 in the chronic FBR induced by MEA implantation over a 16-week period. Our findings highlight Piezo1 as the primary mechanosensitive channel driving chronic FBR, suggesting it may be a target for improving MEA design and long-term functionality.}, }
@article {pmid41008372, year = {2025}, author = {Finnis, R and Mehmood, A and Holle, H and Iqbal, J}, title = {Exploring Imagined Movement for Brain-Computer Interface Control: An fNIRS and EEG Review.}, journal = {Brain sciences}, volume = {15}, number = {9}, pages = {}, pmid = {41008372}, issn = {2076-3425}, abstract = {Brain-Computer Interfaces (BCIs) offer a non-invasive pathway for restoring motor function, particularly for individuals with limb loss. This review explored the effectiveness of Electroencephalography (EEG) and function Near-Infrared Spectroscopy (fNIRS) in decoding Motor Imagery (MI) movements for both offline and online BCI systems. EEG has been the dominant non-invasive neuroimaging modality due to its high temporal resolution and accessibility; however, it is limited by high susceptibility to electrical noise and motion artifacts, particularly in real-world settings. fNIRS offers improved robustness to electrical and motion noise, making it increasingly viable in prosthetic control tasks; however, it has an inherent physiological delay. The review categorizes experimental approaches based on modality, paradigm, and study type, highlighting the methods used for signal acquisition, feature extraction, and classification. Results show that while offline studies achieve higher classification accuracy due to fewer time constraints and richer data processing, recent advancements in machine learning-particularly deep learning-have improved the feasibility of online MI decoding. Hybrid EEG-fNIRS systems further enhance performance by combining the temporal precision of EEG with the spatial specificity of fNIRS. Overall, the review finds that predicting online imagined movement is feasible, though still less reliable than motor execution, and continued improvements in neuroimaging integration and classification methods are essential for real-world BCI applications. Broader dissemination of recent advancements in MI-based BCI research is expected to stimulate further interdisciplinary collaboration among roboticists, neuroscientists, and clinicians, accelerating progress toward practical and transformative neuroprosthetic technologies.}, }
@article {pmid41008292, year = {2025}, author = {Hasegawa, RP and Watanabe, S}, title = {Neurodetector: EEG-Based Cognitive Assessment Using Event-Related Potentials as a Virtual Switch.}, journal = {Brain sciences}, volume = {15}, number = {9}, pages = {}, pmid = {41008292}, issn = {2076-3425}, support = {A19-46//AMED/ ; JP24K 12215//JSPS KAKENHI/ ; }, abstract = {Background/Objectives: Motor decline in older adults can hinder cognitive assessments. To address this, we developed a brain-computer interface (BCI) using electroencephalography (EEG) and event-related potentials (ERPs) as a motor-independent EEG Switch. ERPs reflect attention-related neural activity and may serve as biomarkers for cognitive function. This study evaluated the feasibility of using ERP-based task success rates as indicators of cognitive abilities. The main goal of this article is the development and baseline evaluation of the Neurodetector system (incorporating the EEG Switch) as a motor-independent tool for cognitive assessment in healthy adults. Methods: We created a system called Neurodetector, which measures cognitive function through the ability to perform tasks using a virtual one-button EEG Switch. EEG data were collected from 40 healthy adults, mainly under 60 years of age, during three cognitive tasks of increasing difficulty. Results: The participants controlled the EEG Switch above chance level across all tasks. Success rates correlated with task difficulty and showed individual differences, suggesting that cognitive ability influences performance. In addition, we compared the pattern-matching method for ERP decoding with the conventional peak-based approaches. The pattern-matching method yielded a consistently higher accuracy and was more sensitive to task complexity and individual variability. Conclusions: These results support the potential of the EEG Switch as a reliable, non-motor-dependent cognitive assessment tool. The system is especially useful for populations with limited motor control, such as the elderly or individuals with physical disabilities. While Mild Cognitive Impairment (MCI) is an important future target for application, the present study involved only healthy adult participants. Future research should examine the sources of individual differences and validate EEG switches in clinical contexts, including clinical trials involving MCI and dementia patients. Our findings lay the groundwork for a novel and accessible approach for cognitive evaluation using neurophysiological data.}, }
@article {pmid41006944, year = {2025}, author = {Huang, W and Li, H and Qin, F and Wu, D and Cheng, K and Chen, H}, title = {A Prompt-Guided Generative Language Model for Unifying Visual Neural Decoding Across Multiple Subjects and Tasks.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2550068}, doi = {10.1142/S0129065725500686}, pmid = {41006944}, issn = {1793-6462}, abstract = {Visual neural decoding not only aids in elucidating the neural mechanisms underlying the processing of visual information but also facilitates the advancement of brain-computer interface technologies. However, most current decoding studies focus on developing separate decoding models for individual subjects and specific tasks, an approach that escalates training costs and consumes a substantial amount of computational resources. This paper introduces a Prompt-Guided Generative Visual Language Decoding Model (PG-GVLDM), which uses prompt text that includes information about subjects and tasks to decode both primary categories and detailed textual descriptions from the visual response activities of multiple individuals. In addition to visual response activities, this study also incorporates a multi-head cross-attention module and feeds the model with whole-brain response activities to capture global semantic information in the brain. Experiments on the Natural Scenes Dataset (NSD) demonstrate that PG-GVLDM attains an average category decoding accuracy of 66.6% across four subjects, reflecting strong cross-subject generalization, and achieves text decoding scores of 0.342 (METEOR), 0.450 (Sentence-Transformer), 0.283 (ROUGE-1), and 0.262 (ROUGE-L), establishing state-of-the-art performance in text decoding. Furthermore, incorporating whole-brain response activities significantly enhances decoding performance by enabling the integration of distributed neural signals into coherent global semantic representations, underscoring its methodological importance for unified neural decoding. This research not only represents a breakthrough in visual neural decoding methodologies but also provides theoretical and technical support for the development of generalized brain-computer interfaces.}, }
@article {pmid41006379, year = {2025}, author = {Altaheri, H and Karray, F and Karimi, AH}, title = {Temporal convolutional transformer for EEG based motor imagery decoding.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {32959}, pmid = {41006379}, issn = {2045-2322}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; Algorithms ; }, abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) offer a transformative pathway for rehabilitation, communication, and control by translating imagined movements into actionable commands. However, accurately decoding motor imagery from electroencephalography (EEG) signals remains a significant challenge in BCI research. In this paper, we propose TCFormer, a temporal convolutional Transformer designed to improve the performance of EEG-based motor imagery decoding. TCFormer integrates a multi-kernel convolutional neural network (MK-CNN) for spatial-temporal feature extraction with a Transformer encoder enhanced by grouped query attention to capture global contextual dependencies. A temporal convolutional network (TCN) head follows, utilizing dilated causal convolutions to enable the model to learn long-range temporal patterns and generate final class predictions. The architecture is evaluated on three benchmark motor imagery and motor execution EEG datasets: BCIC IV-2a, BCIC IV-2b, and HGD, achieving average accuracies of 84.79, 87.71, and 96.27%, respectively, outperforming current methods. These results demonstrate the effectiveness of the integrated design in addressing the inherent complexity of EEG signals. The code is publicly available at https://github.com/altaheri/TCFormer .}, }
@article {pmid41005779, year = {2025}, author = {Kawakami, DMO and Karloh, M and Araujo, GHG and Colucci, MG and Pires Di Lorenzo, VA and Mendes, RG}, title = {Effects of an early behavioural change strategy following COPD exacerbation in hospital and outpatient settings in Brazil: protocol for a randomised clinical trial on cardiovascular risk, physical activity and functionality.}, journal = {BMJ open}, volume = {15}, number = {9}, pages = {e097954}, pmid = {41005779}, issn = {2044-6055}, mesh = {Humans ; *Pulmonary Disease, Chronic Obstructive/rehabilitation/psychology/physiopathology/complications ; Brazil ; Quality of Life ; *Exercise ; Randomized Controlled Trials as Topic ; Cost-Benefit Analysis ; *Cardiovascular Diseases/prevention &amp; control ; Disease Progression ; *Behavior Therapy/methods ; Outpatients ; }, abstract = {INTRODUCTION: Patients living with chronic obstructive pulmonary disease (COPD) experience periods of disease stability and exacerbations (ECOPD). COPD imposes a negative and impactful extrapulmonary impairment and commonly overlaps with multimorbidity, particularly cardiovascular disease. Pulmonary rehabilitation (PR) aims to improve physical activity (PA) and quality of life, while behavioural change interventions (BCIs) aim to promote lifestyle changes and autonomy. However, after ECOPD, a variety of barriers often delay patient referral to PR. This study aims to assess the effects of a BCI for patients after ECOPD, focusing on cardiovascular health, PA and functionality. Additionally, the study will assess 6-month sustainability of PA and conduct a cost-utility analysis comparing a non-intervention group in the Unified Health System.<br><br>METHODS AND ANALYSIS: This randomised clinical trial will assess patients with ECOPD over 12 weeks using a BCI based on self-determination theory to increase daily steps. First, the cardiovascular and functional profile will be evaluated. Afterwards, the patients will receive an accelerometer to monitor the PA level. After 7&#x2009;days, questionnaires will be applied on quality of life, symptoms and motivational levels for PA. Patients will be randomised into control group or intervention groups, both will receive educational booklets and IG will also receive an educational interview. PA will be tracked using activPAL accelerometer at weeks 1, 4 and 12, and follow-up at 6 months. Data analysis will include unpaired Student's t-test or Mann-Whitney test for group comparison, and a linear mixed model to assess intervention effects over time. Economic evaluation, using STATA (V.14), will involve correlation analysis, and p<0.05 significance will be considered.<br><br>ETHICS AND DISSEMINATION: This study has been approved by the Federal University of S&#xe3;o Carlos' Ethics Committee, Irmandade Santa Casa de Miseric&#xf3;rdia de S&#xe3;o Carlos and Base Hospital of S&#xe3;o Jos&#xe9; do Rio Preto. All procedures will be conducted in accordance with the Declaration of Helsinki, Good Clinical Practice guidelines and applicable regulatory requirements. All results will be presented in peer-reviewed medical journals and international conferences.<br><br>TRIAL REGISTRATION NUMBER: Brazilian Registry of Clinical Trials under the registration number RBR-6m9pwb7.}, }
@article {pmid41005749, year = {2025}, author = {Zhang, H and Xie, J and Yu, H and Du, F and Jin, Z and Chen, Y}, title = {Enhancing transient motion-onset visual evoked potentials via stochastic resonance: Unimodal and cross-modal noise effects.}, journal = {Journal of neuroscience methods}, volume = {424}, number = {}, pages = {110589}, doi = {10.1016/j.jneumeth.2025.110589}, pmid = {41005749}, issn = {1872-678X}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Stochastic Processes ; Male ; Adult ; Female ; Photic Stimulation/methods ; Young Adult ; *Motion Perception/physiology ; *Brain/physiology ; Acoustic Stimulation ; Signal Processing, Computer-Assisted ; Noise ; }, abstract = {BACKGROUND: Motion-onset visual evoked potential (mVEP) are transient brain responses triggered by sudden motion stimuli and are widely used in brain-computer interface (BCI) systems. However, the inherently weak nature of mVEP signals poses a significant challenge to achieving reliable and accurate BCI performance. Enhancing the signal quality of mVEP responses is therefore critical for improving system robustness and usability.<br><br>NEW METHOD: This study introduces a novel approach based on stochastic resonance (SR) theory, where appropriate levels of noise can enhance the performance of nonlinear systems such as the brain. By applying auditory and visual noise of varying intensities alongside mVEP stimuli, both unimodal SR and cross-modal SR effects were investigated. The method examines the effects of these noise conditions on brain activation and classification performance in mVEP-BCI.<br><br>RESULTS: The results show that moderate levels of auditory or visual noise significantly enhance the P2 component amplitude of mVEP and improve classification accuracy in BCI tasks. In contrast, excessive noise leads to suppression of neural responses, forming an inverted U-shaped relationship between noise intensity and mVEP amplitude.<br><br>Conventional mVEP enhancement techniques typically rely on signal processing methods such as spatial filtering or feature extraction. In comparison, the proposed noise modulation strategy directly enhances neural responses, offering a biologically inspired and computationally simple alternative that complements existing approaches.<br><br>CONCLUSIONS: Both unimodal and cross-modal SR effectively enhance mVEP responses and BCI performance. This strategy provides new insights into SR mechanisms and supports the development of more robust mVEP-BCI systems.}, }
@article {pmid41005327, year = {2025}, author = {Wang, N and Deng, X and Zhu, N and Wang, X and Wang, Y and Sun, B and Zheng, C}, title = {Bayesian decoding and its application in reading out spatial memory from neural ensembles.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ae0c3c}, pmid = {41005327}, issn = {1741-2552}, abstract = {Spatial memory serves as a foundation to establish cognitive map, supporting navigation and decision-making processes across species. Essential brain regions such as the hippocampus and entorhinal cortex enable these functions through spatially tuned neurons, particularly place cells, which encode an animal's precise location. The continuous spatial trajectories are then able to be represented by temporally sequential firing of these cells at neural ensemble level. Bayesian frameworks are powerful tools for reconstructing such "mind travel". In this article, we focus on the principles and advances of Bayesian decoding methods for extracting spatial memory information from neural ensembles. First, we review non-recursive approaches and recursive point process filters, paying special attention to clusterless decoding strategies. We also discuss emerging approaches such as neural manifolds within Bayesian estimation. Next, we discuss the advanced application of Bayesian decoding in understanding the neuronal coding mechanisms of memory consolidation and planning, and in supporting computational model establishment and closed-loop manipulation. Finally, we discuss the limitations and challenges of recent approaches, highlighting the promising strategies that could raise the decoding efficiency and adapt the growing scale of neural data. We believe that the developing of Bayesian decoding approach would significantly benefit for techniques and applications of memory-related brain machine interface.}, }
@article {pmid41005325, year = {2025}, author = {Botero, JP and Roberts, SM and Mackowiak, P and Witham, NS and Selzer, L and Srikanthan, B and Zoschke, K and Negi, S and Solzbacher, F}, title = {Neuralace: manufacture, parylene-C coating, and mechanical properties.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ae0c39}, pmid = {41005325}, issn = {1741-2552}, mesh = {*Xylenes/chemistry ; *Polymers/chemistry ; *Coated Materials, Biocompatible/chemistry/chemical synthesis ; *Electrodes, Implanted ; *Brain-Computer Interfaces ; Humans ; Equipment Design ; Materials Testing ; }, abstract = {Objective.This study investigates the mechanical properties of the Neuralace, a novel ultra-thin, high-channel-count mesh-type subdural electrode array, to characterize its mechanical compatibility with neural tissue (i.e., the forces exerted onto the brain upon conformation) for chronic brain-computer interface (BCI) applications.Approach.A full-factorial design of experiments was used to assess the effects of geometrical variations, orientation, and polymeric encapsulation on the stiffness of silicon-based Neuralace structures. A custom low-force four-point bending setup was developed to measure flexural stiffness in a physiologically relevant displacement range.Main results.The stiffness values of Neuralace structures ranged from 2.99 N m[-1]to 7.21 N m[-1], depending on the cell-wall thickness (CWT) of the lace, orientation, and parylene-C (PPXC) encapsulation. Orientation and CWT had the largest impact on the stiffness of the structures, while the effects of PPXC encapsulation were statistically significant but more subtle. The stiffest Neuralace configuration is expected to exert forces approximately 10-100 times lower than commercially available subdural implants would when conforming to the brain's topology (considering a 60 mm radius of the gyrus).Significance.Subdural electrode arrays have traditionally been used for epilepsy monitoring and surgical planning. These arrays are now transitioning from short-term implantation in epilepsy monitoring to long-term use in BCIs, which requires consideration of the foreign body response to ensure long-term durability and functionality. Biocompatibility challenges, such as fibrotic encapsulation and reactive astrogliosis, highlight the need for conformal subdural implant designs that minimize mechanical stress on neural tissue. This study establishes a rigorous and reproducible framework for mechanical characterization of conformable neural implants and demonstrates the feasibility of tuning design parameters to reduce implant-induced mechanical stress on cortical tissue. The results support future development of chronic BCI-compatible subdural electrodes with improved biocompatibility through mechanical design.}, }
@article {pmid41005322, year = {2025}, author = {Yue, J and Xiao, X and Zhang, H and Xu, M and Ming, D}, title = {BGTransform: a neurophysiologically informed EEG data augmentation framework.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ae0c3a}, pmid = {41005322}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Evoked Potentials, Visual/physiology ; *Deep Learning ; *Brain/physiology ; Adult ; Databases, Factual ; Event-Related Potentials, P300/physiology ; }, abstract = {Objective. Deep learning has emerged as a powerful approach for decoding electroencephalography (EEG)-based brain-computer interface (BCI) signals. However, its effectiveness is often limited by the scarcity and variability of available training data. Existing data augmentation methods often introduce signal distortions or lack physiological validity. This study proposes a novel augmentation strategy designed to improve generalization while preserving the underlying neurophysiological structure of EEG signals.Approach. We propose Background EEG Transform (BGTransform), a principled data augmentation framework that leverages the neurophysiological dissociation between task-related activity and ongoing background EEG. In contrast to existing methods, BGTransform generates new trials by selectively perturbing the background EEG component while preserving the task-related signal, thus enabling controlled variability without compromising class-discriminative features. We applied BGTransform to three publicly available EEG-BCI datasets spanning steady-state visual evoked potential and P300 paradigms. The effectiveness of BGTransform is evaluated using several widely adopted neural decoding models under three training regimes: (1) without augmentation (baseline model), (2) with conventional augmentation methods, and (3) with BGTransform.Main results. Across all datasets and model architectures, BGTransform consistently outperformed both baseline models and conventional augmentation techniques. Compared to models trained without BGTransform, it achieved average classification accuracy improvements of 2.45%-15.52%, 4.36%-17.15% and 7.55%-10.47% across the three datasets, respectively. In addition, BGTransform demonstrated greater robustness across subjects and tasks, maintaining stable performance under varying recording conditions.Significance. BGTransform provides a principled and effective approach to augmenting EEG data, informed by neurophysiological insight. By preserving task-related components and introducing controlled variability, the method addresses the challenge of data sparsity in EEG-BCI training. These findings support the utility of BGTransform for improving the accuracy, robustness, and generalizability of deep learning models in neural engineering applications.}, }
@article {pmid41005320, year = {2025}, author = {Berke Guney, O and Kucukahmetler, D and Ozkan, H}, title = {Source-free domain adaptation for SSVEP-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ae0c3d}, pmid = {41005320}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Male ; *Neural Networks, Computer ; Adult ; Female ; *Adaptation, Physiological/physiology ; Photic Stimulation/methods ; }, abstract = {Objective.Steady-state visually evoked potential-based Brain-computer interface (BCI) spellers assist individuals experiencing speech difficulties by enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in most prominent methods requires an extensive calibration period before using the system, leading to discomfort for new users. We address this issue by proposing a novel method that adapts a powerful deep neural network (DNN) pre-trained on data from source domains (data from former users or participants of previous experiments), to the new user (target domain) using only unlabeled target data.Approach.Our method adapts the pre-trained DNN to the new user by minimizing our proposed custom loss function composed of self-adaptation and local-regularity terms. The self-adaptation term uses the pseudo-label strategy, while the novel local-regularity term exploits the data structure and forces the DNN to assign similar labels to adjacent instances.Main results.Our method achieves excellent ITRs of 201.15 bits min[-1]and 145.02 bits min[-1]on the benchmark and BETA datasets, respectively, and outperforms the state-of-the-art alternatives. Our code is available athttps://github.com/osmanberke/SFDA-SSVEP-BCI.Significance.The proposed method prioritizes user comfort by removing the burden of calibration while maintaining an excellent character identification accuracy and ITR. Because of these attributes, our approach could significantly accelerate the adoption of BCI systems into everyday life.}, }
@article {pmid41004906, year = {2025}, author = {Cai, S and Lin, Z and Liu, X and Wei, W and Wang, S and Zhang, M and Schultz, T and Li, H}, title = {Spiking neural networks for EEG signal analysis: From theory to practice.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {194}, number = {}, pages = {108127}, doi = {10.1016/j.neunet.2025.108127}, pmid = {41004906}, issn = {1879-2782}, abstract = {The intricate and efficient information processing of the human brain, driven by spiking neural interactions, has led to the development of spiking neural networks (SNNs) as a cutting-edge neural network paradigm. Unlike traditional artificial neural networks (ANNs) that use continuous values, SNNs emulate the brain's spiking mechanisms, offering enhanced temporal information processing and computational efficiency. This review addresses the critical gap between theoretical advancements and practical applications of SNNs in EEG signal analysis. We provide a comprehensive examination of recent SNN methodologies and their application to EEG signals, highlighting their potential benefits over conventional deep learning approaches. The review encompasses foundational knowledge of SNNs, detailed implementation strategies for EEG analysis, and challenges inherent to SNN-based methods. Practical guidance is provided through step-by-step instructions and accessible code available on GitHub, aimed at facilitating researchers' adoption of these techniques. Additionally, we explore emerging trends and future research directions, emphasizing the potential of SNNs to advance brain-computer interfaces and neurofeedback systems. This paper serves as a valuable resource for bridging the gap between theoretical developments in SNNs and their practical implementation in EEG signal analysis.}, }
@article {pmid41004593, year = {2025}, author = {Zhang, L and Wang, S and Xia, J and Li, B and Zhang, S and Luo, J and Zhang, F and Zheng, T and Pan, G and Hasan, T and Yu, Y and Ding, G and Jin, H and Yang, Z and Dong, S}, title = {Monolithic multimodal neural probes for sustained stimulation and long-term neural recording.}, journal = {Science advances}, volume = {11}, number = {39}, pages = {eadu1753}, pmid = {41004593}, issn = {2375-2548}, mesh = {*Electrodes, Implanted ; Animals ; *Neurons/physiology ; Optical Fibers ; Biocompatible Materials/chemistry ; Electric Stimulation ; }, abstract = {Long-term implantable neural probes with dual-mode optical stimulation and simultaneous electrical recording are crucial for modulating neural loop activity in vivo. Traditional probes using "add-on" strategies often suffer from mechanical rigidity, compromised electrical performance, and insufficient biocompatibility, limiting their clinical applicability. In this study, we present a method for the direct laser writing of electrode arrays onto the curved surface of optical fibers, integrating them within a biocompatible polymer coating to create monolithic neural probes. The monolithic probes demonstrate high mechanical bending endurance, stable impedance, and improved biocompatibility, resulting in a lower inflammatory response compared to conventional systems. Furthermore, our method facilitates the multilayer integration of multilayer electrodes onto optical fibers, enabling high-density electrical readout channels. This advancement represents substantial progress in neuroengineering, with promising implications for future neural monitoring and modulation applications.}, }
@article {pmid41003965, year = {2025}, author = {Shao, X and Chang, C and Wang, H}, title = {Impact of fatigue levels on EEG-based personal recognition.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {41003965}, issn = {1741-0444}, support = {92270113//National Natural Science Foundation of China/ ; 62176054//National Natural Science Foundation of China/ ; }, abstract = {The uniqueness of the electroencephalogram (EEG), a distinct biometric marker inherent to each individual, yields significant advantages for user authentication and identification in brain-computer interface (BCI) systems. However, EEG features can easily change according to the user's state, which may affect the performance of biometric recognition systems based on EEG. Notably, in EEG data collection for such systems, fatigue levels can fluctuate over time-a factor that has yet to be thoroughly investigated concerning its impact on individual recognition performance. This study explores the implications of fatigue on EEG-based personal recognition systems. We derived six sub-datasets from two simulated driving datasets, each labeled with varying levels of fatigue. From each sub-dataset, we extracted six features for identity recognition within and across different fatigue levels. Single-session and cross-session studies revealed that the disparity of EEG fatigue levels between the training and testing sets increased, and system recognition accuracy experienced a decline. Specifically, recognition accuracy typically fell by over 30 % after 90 min of simulated driving. Furthermore, identity recognition results are better when the training set includes EEG in more fatigued states compared to the test set, rather than the other way around. Crucially, the method based on functional connectivity features shows the best recognition accuracy under different fatigue levels. This research emphasizes the potential benefits of considering fatigue variations in EEG-based personal recognition systems.}, }
@article {pmid41003117, year = {2025}, author = {Bizzarri, FP and Campetella, M and Recupero, SM and Bellavia, F and D'Amico, L and Rossi, F and Gavi, F and Filomena, GB and Russo, P and Palermo, G and Foschi, N and Totaro, A and Ragonese, M and Sighinolfi, MC and Racioppi, M and Sacco, E and Rocco, B}, title = {Female Sexual Function After Radical Treatment for MIBC: A Systematic Review.}, journal = {Journal of personalized medicine}, volume = {15}, number = {9}, pages = {}, pmid = {41003117}, issn = {2075-4426}, abstract = {Background: Sexuality in women with muscle-invasive bladder cancer (MIBC) undergoing radical treatment represents a crucial aspect of their overall quality of life, which is increasingly recognized as a key component of patient-centered care and long-term well-being. This review aimed to analyze the available literature to provide a comprehensive overview of the effects of treatments on female sexual function. Methods: We included all qualitative and quantitative studies addressing sexual function in patients treated for MIBC. Excluded were narrative reviews, case reports, conference abstracts, systematic reviews, and meta-analyses. The included studies involved women undergoing either robot-assisted radical cystectomy (RARC) or open RC (ORC), often with nerve-sparing, vaginal-sparing, or pelvic organ-preserving techniques. Data on oncological and functional outcomes were collected. Results: A systematic review of 29 studies including 1755 women was conducted. RC was performed via robotic/laparoscopic approaches in 39% of cases and open techniques in 61%. Urinary diversions included orthotopic neobladders (48%), ileal conduits (42%), ureterocutaneostomies (3%), and Indiana pouches (7%). Radiotherapy, used in 6% of patients, was mainly applied in a curative, trimodal setting. Sexual function was evaluated using various pre- and/or postoperative questionnaires, most commonly the EORTC QLQ-C22, FACT-BL, Bladder Cancer Index (BCI), LENT SOMA, and Female Sexual Function Index (FSFI). Radiotherapy was associated with reduced sexual function, though outcomes were somewhat better than with surgery. Among surgical approaches, no differences in sexual outcomes were observed. Conclusions: Further qualitative research is essential to better understand the experience of FSD after treatment. Incorporating both patient and clinician perspectives will be key to developing tailored interventions. In addition, efforts should be made to standardize the questionnaires used to assess female sexual dysfunction, in order to improve comparability across studies and ensure consistent evaluation.}, }
@article {pmid41002025, year = {2025}, author = {Tang, H and He, S and Tao, J and Wang, C and Wang, Z and Song, J}, title = {Mechanically Tunable Electromagnetic Metamaterials Based on Chains of Tension-rotation Coupling Units with Exceptional Reconfiguration Capability.}, journal = {Small methods}, volume = {}, number = {}, pages = {e01423}, doi = {10.1002/smtd.202501423}, pmid = {41002025}, issn = {2366-9608}, support = {12225209//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; 12302223//National Natural Science Foundation of China/ ; GZC20232293//Postdoctoral Fellowship Program of CPSF/ ; 2022M710126//China Postdoctoral Science Foundation/ ; 2023M743011//China Postdoctoral Science Foundation/ ; BX20220268//China National Postdoctoral Program for Innovative Talents/ ; }, abstract = {Controlling the out-of-plane rotation of split-ring resonators (SRRs) represents an effective strategy to realize mechanically tunable electromagnetic (EM) materials. However, designing structures that can achieve substantial angular rotations via straightforward stretching operations while keeping the resonators intact remains a challenge. Here, a mechanically tunable EM metamaterial constructed from parallel chains of tension-rotation units that enable substantial out-of-plane rigid rotations exceeding 120° of the SRRs through simple stretch is reported. Theoretical, numerical, and experimental studies are conducted to reveal the deformation mechanism and quantify the relationship between tensile strain and rotation angles of SRRs. Comprehensive experimental and numerical studies show that the proposed metamaterial can extensively modulate the transmissions of both linearly and circularly polarized waves. Specifically, the transmission of TE wave exhibits a distinctive two-stage increasing-decreasing behavior, and the CD presents a unique zero-positive-zero-negative profile during stretching, which are not easily accessible by existing mechanically tunable EM metamaterials due to their limited deformation capabilities. Moreover, structural reconfiguration of chain arrangements enables tunable resonance frequencies while maintaining the frequency position of maximum CD, demonstrating robust preservation of the dominant chiral eigenmode. This study provides a valuable design strategy for developing mechanically tunable EM metamaterials with high tunability and multifunctionality.}, }
@article {pmid41000855, year = {2025}, author = {Marin-Llobet, A and Lin, Z and Baek, J and Aljovic, A and Zhang, X and Lee, AJ and Wang, W and Lee, J and Shen, H and He, Y and Li, N and Liu, J}, title = {An AI Agent for cell-type specific brain computer interfaces.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {41000855}, issn = {2692-8205}, support = {DP1 DK130673/DK/NIDDK NIH HHS/United States ; R01 LM014465/LM/NLM NIH HHS/United States ; }, abstract = {Decoding how specific neuronal subtypes contribute to brain function requires linking extracellular electrophysiological features to underlying molecular identities, yet reliable in vivo electrophysiological signal classification remains a major challenge for neuroscience and clinical brain-computer interfaces (BCI). Here, we show that pretrained, general-purpose vision-language models (VLMs) can be repurposed as few-shot learners to classify neuronal cell types directly from electrophysiological features, without task-specific fine-tuning. Validated against optogenetically tagged datasets, this approach enables robust and generalizable subtype inference with minimal supervision. Building on this capability, we developed the BCI AI Agent (BCI-Agent), an autonomous AI framework that integrates vision-based cell-type inference, stable neuron tracking, and automated molecular atlas validation with real-time literature synthesis. BCI-Agent addresses three critical challenges for in vivo electrophysiology: (1) accurate, training-free cell-type classification; (2) automated cross-validation of predictions using molecular atlas references and peer-reviewed literature; and (3) embedding molecular identities within stable, low-dimensional neural manifolds for dynamic decoding. In rodent motor-learning tasks, BCI-Agent revealed stable, cell-type-specific neural trajectories across time that uncover previously inaccessible dimensions of neural computation. Additionally, when applied to human Neuropixels recordings-where direct ground-truth labeling is inherently unavailable-BCI-Agent inferred neuronal subtypes and validated them through integration with human single-cell atlases and literature. By enabling scalable, cell-type-specific inference of in vivo electrophysiology, BCI-Agent provides a new approach for dissecting the contributions of distinct neuronal populations to brain function and dysfunction.}, }
@article {pmid40999875, year = {2025}, author = {Balendra,  and Sharma, N and Sharma, S}, title = {Transformed wavelets for motor imagery EEG classification using hybrid CNN-modified vision transformer: an exploratory study of MI EEG.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-18}, doi = {10.1080/10255842.2025.2563351}, pmid = {40999875}, issn = {1476-8259}, abstract = {Wavelets capture signal characteristics across time and frequency, but traditional wavelets suffer from high time-bandwidth products (TBP), limiting feature discrimination in EEG classification. We propose transformed wavelets with improved TBP and frequency bandwidth, outperforming Morlet by 0.04 and 0.20, respectively. Using datasets BCI Competition IV 2a, 2b, and CLA, we evaluated both fundamental and transformed wavelets with a modified vision transformer (MViT). Enhanced scalograms generated through local mean and principal component analysis (PCA) consistently outperformed raw scalograms. A hybrid convolutional neural network (CNN)-MViT achieved 82.35% inter-subject and 89.02% intra-subject accuracy, with 3-4% average gains in motor imagery EEG decoding.}, }
@article {pmid40999234, year = {2025}, author = {Cai, C and Gao, L and Zhu, Z and Chen, W and Zhang, F and Yu, C and Xu, K and Zhu, J and Wu, H}, title = {Change in brain molecular landscapes following electrical stimulation of the nucleus accumbens.}, journal = {Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology}, volume = {}, number = {}, pages = {}, pmid = {40999234}, issn = {1740-634X}, support = {82401781//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82171519//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Deep brain stimulation (DBS) targeting the nucleus accumbens (NAc) is a promising therapeutic intervention for treatment-resistant neuropsychiatric disorders such as depression, anxiety, and addiction. However, the molecular mechanisms underlying the clinical efficacy of NAc DBS remain largely unknown. One approach to address this question is by performing spatial gene expression analysis on cells located in different regions of the same circuit following NAc DBS. In this study, we utilized high-resolution spatial transcriptomics (Stereo-seq) to investigate gene expression changes induced by NAc DBS in the mouse brain. Mice were randomly allocated to receive continuous electrical stimulation (0.1 mA, 130 Hz) or sham treatment (electrode implanted, no electrical stimulation given) for one week, and subsequent Stereo-seq analysis identified differentially expressed genes (DEGs) across various brain regions. Functional enrichment analysis highlighted changes in synaptic and neuroplasticity processes as well as stress and inflammatory responses in the NAc circuit. Single-cell resolution mapping further identified key molecular players, including Nlgn1, Snca, Pde10a, and Syt1, particularly in glutamate receptor-expressing neurons in the NAc. These genes are critical for synaptic plasticity and neurotransmitter release, and have been implicated in various psychiatric disorders. These findings shed light on the molecular underpinnings of NAc DBS and provide insights into its therapeutic potential in modulating neural circuits associated with neuropsychiatric disorders.}, }
@article {pmid40998792, year = {2025}, author = {Chen, G and Zhang, X and Hu, X and Liu, Y and Yang, Y and Wang, W}, title = {Chemical knowledge-informed framework for privacy-aware retrosynthesis learning.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {8389}, pmid = {40998792}, issn = {2041-1723}, abstract = {Chemical reaction data is a pivotal asset, driving advances in competitive fields such as pharmaceuticals, materials science, and industrial chemistry. Its proprietary nature renders it sensitive, as it often includes confidential insights and competitive advantages organizations strive to protect. However, in contrast to this need for confidentiality, the current standard training paradigm for machine learning-based retrosynthesis gathers reaction data from multiple sources into one single edge to train prediction models. This paradigm poses considerable privacy risks as it necessitates broad data availability across organizational boundaries and frequent data transmission between entities, potentially exposing proprietary information to unauthorized access or interception during storage and transfer. In the present study, we introduce the chemical knowledge-informed framework (CKIF), a privacy-preserving approach for learning retrosynthesis models. CKIF enables distributed training across multiple chemical organizations without compromising the confidentiality of proprietary reaction data. Instead of gathering raw reaction data, CKIF learns retrosynthesis models through iterative, chemical knowledge-informed aggregation of model parameters. In particular, the chemical properties of predicted reactants are leveraged to quantitatively assess the observable behaviors of individual models, which in turn determines the adaptive weights used for model aggregation. On a variety of reaction datasets, CKIF outperforms several strong baselines by a clear margin.}, }
@article {pmid40997885, year = {2025}, author = {Rouse, TC and Lupkin, SM and McGinty, VB}, title = {Using economic value signals from primate prefrontal cortex in neuro-engineering applications.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, pmid = {40997885}, issn = {1741-2552}, support = {K01 DA036659/DA/NIDA NIH HHS/United States ; }, mesh = {Animals ; *Brain-Computer Interfaces/economics ; *Prefrontal Cortex/physiology ; Macaca mulatta ; Male ; Choice Behavior/physiology ; Decision Making/physiology ; Reinforcement, Psychology ; Deep Learning ; }, abstract = {Objective.Brain-machine interface (BMI) research has shown the efficacy of using motor and sensory-related neural signals to assist physically impaired patients. Despite the comparable ability to extract more abstract cognitive signals from the brain, little effort has been devoted to leveraging these signals in neuro-engineering applications. In this study, we explore the use of neural signals related to economic value, a key cognitive construct, in a BMI context.Approach.Using multivariate time series data collected from the orbitofrontal cortex in non-human primates, we develop deep learning-based neural decoders to predict the monkeys' choices in a value-based decision-making task. We implement a reinforcement learning-based training approach to develop adaptive decoders that can be extended to handle multi-step decisions, which frequently arise in real-world settings.Main results.We develop neural decoders leveraging subjective value signals to predict the monkeys' choices with>70%accuracy on average, with above-chance accuracy even when choice options are objectively equal. We show that this same decoder architecture can be trained to execute choice-related actions and execute action sequences aligned with the user's goal. Finally, we explore a decoder architecture that uses a neural forecasting model equipped with task-related information, and show that it makes high accuracy predictions∼300 ms sooner than would otherwise be possible.Significance.These findings support the feasibility of user preference-informed neuro-engineering devices that leverage abstract cognitive signals to aid users in goal-directed behavior. They suggest that using abstract cognitive signals in real-world settings may be more accurate when combined with information from multiple sources, such as motor and sensory regions. This research also highlights the potential need for systems to measure their confidence in their actions when user input is minimal.}, }
@article {pmid40997041, year = {2025}, author = {Lee, Y and Chen, R and Bhattacharyya, S}, title = {An Online Learning Framework for Neural Decoding in Embedded Neuromodulation Systems.}, journal = {Brain connectivity}, volume = {}, number = {}, pages = {0}, doi = {10.1177/21580014251374627}, pmid = {40997041}, issn = {2158-0022}, abstract = {Introduction: Advancements in brain-computer interfaces (BCIs) have improved real-time neural signal decoding, enabling adaptive closed-loop neuromodulation. These systems dynamically adjust stimulation parameters based on neural biomarkers, enhancing treatment precision and adaptability. However, existing neuromodulation frameworks often depend on high-power computational platforms, limiting their feasibility for portable, real-time applications. Methods: We propose RONDO (Recursive Online Neural DecOding), a resource-efficient neural decoding framework that employs dynamic updating schemes in online learning with recurrent neural networks (RNNs). RONDO supports simple RNNs, long short-term memory networks, and gated recurrent units, allowing flexible adaptation to different signal type, accuracy, and real-time constraints. Results: Experimental results show that RONDO's adaptive model updating improves neural decoding accuracy by 35% to 45% compared to offline learning. Additionally, RONDO operates within real-time constraints of neuroimaging devices without requiring cloud-based or high-performance computing. Its dynamic updating scheme ensures high accuracy with minimal updates, improving energy efficiency and robustness in resource-limited settings. Conclusions: RONDO presents a scalable, adaptive, and energy-efficient solution for real-time closed-loop neuromodulation, eliminating reliance on cloud computing. Its flexibility makes it a promising tool for clinical and research applications, advancing personalized neurostimulation and adaptive BCIs.}, }
@article {pmid40996498, year = {2025}, author = {Zhang, M and Zhang, Y and Liu, W and Sun, S and Xu, G}, title = {Quantifying and evaluating motor imagery ability using EEG microstates in MI-BCI training.}, journal = {Experimental brain research}, volume = {243}, number = {10}, pages = {216}, pmid = {40996498}, issn = {1432-1106}, support = {2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; }, }
@article {pmid40995804, year = {2025}, author = {Kim, E and Chung, WG and Kim, E and Oh, M and Paek, J and Lee, T and Kim, D and An, SH and Kim, S and Park, JU}, title = {Multi-Channel Neural Interface for Neural Recording and Neuromodulation.}, journal = {Small methods}, volume = {}, number = {}, pages = {e01227}, doi = {10.1002/smtd.202501227}, pmid = {40995804}, issn = {2366-9608}, support = {//Ministry of Science &amp; ICT (MSIT)/ ; //Ministry of Trade, Industry and Energy/ ; 2023R1A2C2006257//National Research Foundation/ ; RS-2024-00464032//National Research Foundation/ ; RS-2025-16063568//National Research Foundation/ ; RS-2024-00460364//STEAM Research Programs/ ; RS-2024-00406240//ERC Program/ ; 2E33191//Korea Institute of Science and Technology/ ; 2E33190//Korea Institute of Science and Technology/ ; RS-2025-00514998//Sejong Science Fellowship/ ; IBS-R026-D1//Institute for Basic Science/ ; }, abstract = {Neural interfaces have emerged as pivotal platforms for advancing digital neurotherapies by enabling the real-time acquisition and monitoring of neural signals. Traditional single-channel systems are inherently limited in their capacity to capture the complex and large-scale interactions among diverse neuronal populations. In contrast, multi-channel systems provide the high spatiotemporal resolution necessary to decode the dynamic activity of neural circuits across multiple brain and spinal cord regions. This review provides a comprehensive overview of recent advances in multi-channel neural interface technologies, encompassing both penetrating and non-penetrating systems for high-resolution electrophysiological recording, as well as multifunctional platforms that integrate additional modalities such as drug delivery, optical stimulation, and chemical sensing. Recent progress in this field has been driven by advances in structural and material design, including the development of soft, flexible architectures and materials for both substrates and electrodes, which improve long-term stability and minimize tissue damage. In parallel, emerging data analysis techniques have enhanced the capacity to decode complex neural activity patterns from high-dimensional, multi-channel recordings. These technological advancements have broadened the potential applications of neural interfaces in brain-machine interfaces (BMIs), facilitating precise neuromodulation, real-time monitoring of neurological states, and integration with immersive systems such as virtual and augmented reality.}, }
@article {pmid40995145, year = {2025}, author = {Zabolotniy, A and Chan, RW and Moiseeva, V and Fedele, T}, title = {Convolutional neural networks decode finger movements in motor sequence learning from MEG data.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1623380}, pmid = {40995145}, issn = {1662-4548}, abstract = {OBJECTIVE: Non-invasive Brain-Computer Interfaces provide accurate classification of hand movement lateralization. However, distinguishing activation patterns of individual fingers within the same hand remains challenging due to their overlapping representations in the motor cortex. Here, we validated a compact convolutional neural network for fast and reliable decoding of finger movements from non-invasive magnetoencephalographic (MEG) recordings.<br><br>APPROACH: We recorded healthy participants in MEG performing a serial reaction time task (SRTT), with buttons pressed by left and right index and middle fingers. We devised classifiers to identify left vs. right hand movements and among four finger movements using a recently proposed decoding approach, Linear Finite Impulse Response Convolutional Neural Network (LF-CNN). We also compared LF-CNN to existing deep learning architectures such as EEGNet, FBCSP-ShallowNet, and VGG19.<br><br>RESULTS: Sequence learning was reflected by a decrease in reaction times during SRTT performance. Movement laterality was decoded with an accuracy superior to 95% by all approaches, while for individual finger movement, decoding was in the 80-85% range. LF-CNN stood out for (1) its low computational time and (2) its interpretability in both spatial and spectral domains, allowing to examine neurophysiological patterns reflecting task-related motor cortex activity.<br><br>SIGNIFICANCE: We demonstrated the feasibility of finger movement decoding with a tailored Convolutional Neural Network. The performance of our approach was comparable to complex deep learning architectures, while providing faster and interpretable outcome. This algorithmic strategy holds high potential for the investigation of the mechanisms underlying non-invasive neurophysiological recordings in cognitive neuroscience.}, }
@article {pmid40995144, year = {2025}, author = {Citarella, J and Siekierski, P and Ethridge, L and Westerkamp, G and Liu, Y and Blank, E and Voorhees, L and Batterink, L and Jones, SR and Smith, E and Reisinger, DL and Nelson, M and Binder, DK and Razak, KA and Miyakoshi, M and Wu, S and Gilbert, D and Horn, PS and De Stefano, LA and Erickson, CA and Pedapati, EV}, title = {FX ENTRAIN: scientific context, study design, and biomarker driven brain-computer interfaces in neurodevelopmental conditions.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1618804}, pmid = {40995144}, issn = {1662-4548}, abstract = {Fragile X Syndrome (FXS), caused by the loss of function of the Fmr1 gene, is characterized by varying degrees of intellectual disability, autistic features, and sensory hypersensitivity. Despite phenotypic rescue in animal deletion models, clinical trials in humans have been unsuccessful, likely due to the heterogeneous nature of FXS. To uncover the basis of individual- and subgroup-level variation driving treatment failures, we propose to test and modulate thalamocortical drive as a novel "bottom-up" neural probe to understand the mechanics of FXS-relevant circuits. Our study employs trial-level EEG analyses (neurodynamics) to detect fine-grained differences in brain activity using sensory and statistical learning paradigms in children with FXS, autism spectrum disorder (ASD), and typically developing controls. Parallel analysis in the FXS knockout mouse model will clarify its relevance to human FXS subgroups. In a randomized crossover study, we will evaluate the efficacy of closed-loop auditory entrainment, indexed on individual neurodynamic measures, aiming to normalize neural responses and enhance statistical learning performance. We anticipate this approach will yield opportunities to identify more effective early interventions that alter the trajectory of intellectual development in FXS.}, }
@article {pmid40993190, year = {2025}, author = {Merk, T and Köhler, RM and Brotons, TM and Vossberg, SR and Peterson, V and Lyra, LF and Vanhoecke, J and Chikermane, M and Binns, TS and Li, N and Walton, A and Neudorfer, C and Bush, A and Sisterson, N and Busch, J and Lofredi, R and Habets, J and Huebl, J and Zhu, G and Yin, Z and Zhao, B and Merkl, A and Bajbouj, M and Krause, P and Faust, K and Schneider, GH and Horn, A and Zhang, J and Kühn, AA and Mark Richardson, R and Neumann, WJ}, title = {Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {40993190}, issn = {2157-846X}, support = {R01NS110424//Bundesministerium fr Bildung und Forschung (Federal Ministry of Education and Research)/ ; R01NS110424//Bundesministerium fr Bildung und Forschung (Federal Ministry of Education and Research)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; R01NS110424//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; R01NS110424//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; R01 13478451//U.S. Department of Health Human Services | National Institutes of Health (NIH)/ ; 1R01NS127892-01//U.S. Department of Health Human Services | National Institutes of Health (NIH)/ ; UM1NS132358//U.S. Department of Health Human Services | National Institutes of Health (NIH)/ ; 101077060//European Commission (EC)/ ; }, abstract = {Brain-computer interface research can inspire closed-loop neuromodulation therapies, promising spatiotemporal precision for the treatment of brain disorders. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for invasive brain signal decoding from neural implants does not exist. Here we develop a platform that integrates brain signal decoding with magnetic resonance imaging connectomics and demonstrate its use across 123 h of invasively recorded brain data from 73 neurosurgical patients treated with brain implants for movement disorders, depression and epilepsy. We introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the United States, Europe and China. We reveal network targets for emotion decoding in left prefrontal and cingulate circuits in deep brain stimulation patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our study highlights the clinical use of brain signal decoding for deep brain stimulation and provides methods that allow for rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neurotherapies in response to the individual needs of patients.}, }
@article {pmid40990260, year = {2025}, author = {Lebani, BR and da Silva, AB and Silva, LT and Girotti, ME and Pinto, ER and Skaff, M and Almeida, FG}, title = {Is It Necessary to Remove the Maximum Prostate Tissue in All Patients? the Percentage of Resected Prostate Tissue and the Influence on Surgery Outcomes: A One-Year Follow Up Study.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.70152}, pmid = {40990260}, issn = {1520-6777}, support = {//The study developed includes only patients treated inside the Brazilian public health system. There were not any additional costs involved./ ; }, abstract = {INTRODUCTION: To investigate whether the volume of the prostate tissue resected on TURP influences on short and medium term follow up.<br><br>METHODS: It was developed a prospective study between May 2020 and August 2022, embracing patients with severe LUTS due to BPO, including clinical and urodynamic parameters meeting obstruction criteria (BOOI&#x2009;>&#x2009;40), and good detrusor function (BCI&#x2009;>&#x2009;100). Patients were assessed at 1, 6 and 12 months follow up. The primary endpoint was to compare whether the amount of resected tissue after TURP influences uroflowmetry at 12 months follow up (Qmax, ml/sec). The secondary endpoint was to compare different percentages of resected tissue (RPT) and its relation to the outcomes.<br><br>RESULTS: Ninety-six patients with mean age of 70,4&#x2009;&#xb1;&#x2009;7.96 years. At baseline, prostate volume was 78.5&#x2009;&#xb1;&#x2009;51.8 cc&#xb3;, Qmax was 6.03&#x2009;&#xb1;&#x2009;3.09&#x2009;ml/sec and post void residual (PVR) was 113&#x2009;&#xb1;&#x2009;132&#x2009;ml, IPSS of 24.9&#x2009;&#xb1;&#x2009;6.75. All of them were urodinamically obstructed (BOOI 86.7&#x2009;&#xb1;&#x2009;35.6) and good detrusor function (BCI 130&#x2009;&#xb1;&#x2009;28.6). The general RPT was 45.5&#x2009;&#xb1;&#x2009;27.7%. The higher the RTP, the lower the PSA at 1 month follow up (p&#x2009;<&#x2009;0.001, R&#x2009;=&#x2009;0.521). Nevertheless, it was not found correlation between the RTP and Qmax, IPSS or PVR.<br><br>CONCLUSION: TURP improves clinical and urodynamic parameters at 1 year follow up, independent of the amount of resected prostate tissue, in patients with bladder outlet obstruction and good detrusor function, since the surgery is effective.}, }
@article {pmid40990135, year = {2025}, author = {Jin, J and Wang, Z and Dai, L and Wang, A and Gao, L}, title = {An Exploratory Study of Loss Averse in Group Decision Contexts: Multiple Pieces of Evidence From ERPs and Machine Learning.}, journal = {Psychophysiology}, volume = {62}, number = {9}, pages = {e70155}, doi = {10.1111/psyp.70155}, pmid = {40990135}, issn = {1469-8986}, support = {72271166//National Natural Science Foundation of China/ ; 72501175//National Natural Science Foundation of China/ ; 22dz2261100//Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai)/ ; 41005067//Fundamental Research Funds for the Central Universities/ ; 2024DSYL051//Tutor Academic Leadership Program of shanghai international Studies University/ ; }, mesh = {Humans ; *Machine Learning ; Electroencephalography ; Male ; Female ; Young Adult ; *Decision Making/physiology ; Adult ; *Evoked Potentials/physiology ; *Group Processes ; Adolescent ; *Risk-Taking ; *Feedback, Psychological/physiology ; }, abstract = {Both laboratory and field evidence have shown differences in risk attitudes between individual and group decision contexts. Loss aversion, a crucial aspect of risk attitudes, whose behavioral performance and neural mechanism in group decision contexts remain unclear, differs from other risk attitudes such as risk aversion. Using behavioral and electroencephalography (EEG) experiments with non-student and student samples, we conducted an exploratory study to examine the behavioral performance and neural mechanisms of loss aversion in group decision contexts. Behaviorally, we found a reduction effect of loss aversion in group decision contexts compared to individual decision contexts. ERP results from the average and single-trial analyses jointly found that individuals are less sensitive to losses and gains in group (vs. individual) decision contexts, as evidenced by the vanishing Feedback-related Negativity (FRN) and P3b differences to losses and gains. We also found a significant negative correlation between the loss aversion coefficient and FRN amplitude induced by losses both in individual and group decision contexts, which indicated the relationship between loss aversion and neural signals that process loss outcomes. Furthermore, machine learning analyses revealed that EEG features exhibit a high accuracy rate of 81.25% in predicting the decision contexts. This finding underscores the intricate relationship between neural activity and loss aversion across varying decision contexts, highlighting the potential of neurophysiological activity to elucidate the underlying cognitive processes involved in loss aversion. This paper advances our understanding of loss aversion in group decision contexts by providing multiple pieces of evidence for behavioral performance, neural activities, and machine learning. Findings can help to optimize group decision-making processes and resource allocation, and to reduce inefficiencies caused by irrational behavior and resistance to beneficial changes.}, }
@article {pmid40989443, year = {2025}, author = {Su, K and Tian, L}, title = {Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research.}, journal = {PeerJ. Computer science}, volume = {11}, number = {}, pages = {e2938}, pmid = {40989443}, issn = {2376-5992}, abstract = {This article systematically reviews the latest developments in electroencephalogram (EEG)-based speech imagery brain-computer interface (SI-BCI). It explores the brain connectivity of SI-BCI and reveals its key role in neural encoding and decoding. It analyzes the research progress on vowel-vowel and vowel-consonant combinations, as well as Chinese characters, words, and long-words speech imagery paradigms. In the neural encoding section, the preprocessing and feature extraction techniques for EEG signals are discussed in detail. The neural decoding section offers an in-depth analysis of the applications and performance of machine learning and deep learning algorithms. Finally, the challenges faced by current research are summarized, and future directions are outlined. The review highlights that future research should focus on brain region mechanisms, paradigms innovation, and the optimization of decoding algorithms to promote the practical application of SI-BCI technology.}, }
@article {pmid40988031, year = {2025}, author = {Rab, P and Shirinskiy, IJ and Kimmeyer, M and Macken, AA and Calamita, AG and Colombini, AG and Buijze, GA and Lafosse, T}, title = {Augmentation of full-thickness rotator cuff tears with a bioinductive collagen implant does not reduce retear rates - a propensity matched cohort study.}, journal = {BMC musculoskeletal disorders}, volume = {26}, number = {1}, pages = {855}, pmid = {40988031}, issn = {1471-2474}, mesh = {Humans ; Female ; Male ; Middle Aged ; *Rotator Cuff Injuries/surgery/diagnostic imaging ; Retrospective Studies ; Aged ; *Collagen/administration &amp; dosage ; Range of Motion, Articular ; Treatment Outcome ; Propensity Score ; Follow-Up Studies ; *Prostheses and Implants ; }, abstract = {PURPOSE: To compare the clinical and radiographic outcomes after full-thickness RC repair with and without performing augmentation with a bioinductive collagen implant (BCI).<br><br>MATERIALS AND METHODS: Consecutive patients who underwent primary repair of a full-thickness supraspinatus tear between 05/2021 and 11/2023 were retrospectively identified. Patients at elevated risk for retear were defined by biological, radiographic, and intraoperative risk factors. Those who underwent repair with or without concomitant augmentation using a BCI and who had both clinical and radiographic follow-up at 1 year postoperatively were matched in a 1:1 ratio according to age, sex, body mass index, tear size, smoking status, diabetes, and American Society of Anesthesiologists physical status classification. Range of motion (ROM) as well as patient-reported outcome measures (Auto-Constant-Score (CS), American Shoulder and Elbow Surgeons (ASES) Score, Subjective Shoulder Value (SSV), and Visual Analog Scale (VAS) for pain) were recorded. Magnetic resonance imaging performed at 1 year postoperatively was analyzed and the presence of retear was recorded.<br><br>RESULTS: In total, 149 patients with a radiographic and clinical follow-up at 1 year postoperatively were identified. Of these, 23 patients with BCI augmentation were matched to 23 patients without placement of BCI (48% female, 59.2&#x2009;&#xb1;&#x2009;8.4 years at surgery). A retear occurred in 5 patients (21.7%) in the BCI augmentation group and in 3 patients (13.0%) in the control group (p&#x2009;=&#x2009;0.72). No significant difference was reported regarding the CS (77 [71-83] vs. 76 [63-81], p&#x2009;=&#x2009;0.5), ASES Score (92 [82-98] vs. 90 [84-95], p&#x2009;=&#x2009;0.8), SSV (90 [80-100] vs. 90 [88-95], p&#x2009;=&#x2009;0.9), VAS for pain (p&#x2009;=&#x2009;0.74), or ROM between the groups.<br><br>CONCLUSION: In this retrospective matched cohort of patients at elevated risk for retear, augmentation of full-thickness RC repair with a BCI was not associated with a reduced retear rate. Moreover, no significant differences regarding clinical and functional outcome were found between the two groups.<br><br>LEVEL OF EVIDENCE: III - Retrospective case series with a matched control group.}, }
@article {pmid40987603, year = {2025}, author = {Rana, D and Babushkina, N and Gini, M and Flores Cáceres, A and Li, H and Maybeck, V and Criscuolo, V and Mayer, D and Ienca, M and Musall, S and Rincon Montes, V and Offenhäusser, A and Santoro, F}, title = {Neural vs Neuromorphic Interfaces: Where Are We Standing?.}, journal = {Chemical reviews}, volume = {125}, number = {19}, pages = {9092-9139}, pmid = {40987603}, issn = {1520-6890}, mesh = {Humans ; *Brain-Computer Interfaces ; Animals ; *Neurons/physiology ; Brain/physiology ; }, abstract = {Neuromorphic interfaces represent a transformative frontier in neural engineering, enabling seamless communication between the nervous system and external devices through biologically inspired computing architectures. These systems offer promising avenues for diagnosing and treating neurological disorders by emulating the brain's computational strategies. Neural devices, including sensors and stimulators, monitor or modulate neural activity, playing a pivotal role in deciphering brain function and neuropathologies. Yet, clinical translation remains limited due to persistent challenges such as foreign body responses, low signal-to-noise ratios, and constraints in real-time data processing. Recent breakthroughs in neuromorphic hardware, neural recording, and stimulation technologies are addressing these challenges, paving the way for more adaptive and efficient brain-machine interfaces and neuroprosthetics. This review highlights the emerging class of neurohybrid interfaces, where neuromorphic systems might be integrated to enhance bidirectional neural communication. It emphasizes novel material strategies engineered for seamless neural interfacing and their incorporation into advanced neuromorphic chip architectures capable of real-time signal processing and closed-loop feedback. Furthermore, this review explores cutting-edge neuromorphic biointerfaces and evaluates the technological, biological, and ethical challenges involved in their clinical deployment. By bridging materials science, neuroscience, and neuromorphic engineering, these systems hold the potential to redefine the landscape of neurotechnology.}, }
@article {pmid40984876, year = {2025}, author = {Mishra, R and Agrawal, RK and Kirar, JS}, title = {Msst-eegnet: multi-scale spatio-temporal feature extraction using inception and temporal pyramid pooling for motor imagery classification.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {150}, pmid = {40984876}, issn = {1871-4080}, abstract = {Motor imagery classification is an essential component of Brain-computer interface systems to interpret and recognize brain signals generated during the visualization of motor imagery tasks by a subject. The objective of this work is to develop a novel DL model to extract discriminative features for better generalization performance to recognize motor imagery tasks. This paper presents a novel Multi-scale spatio-temporal network (MSST-EEGNet) to extract discriminative temporal, spectral, and spatial features for motor imagery task classification. The proposed MSST-EEGNet model includes three modules namely the inception module with dilated convolution, the temporal pyramid pooling module, and the classification module. Multi-scale temporal features along with spatial features are extracted using the inception block with the dilated convolution module. A set of multi-level fine-grained and coarse-grained features are extracted using a temporal pyramid pooling module. Further, categorical cross-entropy in combination with center loss is used as a loss function. Experiments are carried out on three benchmark datasets including the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset. The evaluation results shows that the proposed MSST-EEGNet model outperforms eight existing DL models in terms of classification accuracy for subject-specific and cross-session settings. It also outperforms eight existing DL models and six existing transfer-learning models for cross-subject setting. For the subject-specific classification the proposed MSST-EEGNet model achieved an accuracy of 0.8426 ± 0.1061, 0.7779 ± 0.0938, and 0.7365 ± 0.1477 on the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset respectively. For the cross-session setting, the proposed MSST-EEGNet model achieved an accuracy of 0.7709 ± 0.1098, 0.7524 ± 0.1017, and 0.6860 ± 0.0990 on the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset respectively. For the cross-subject setting, the proposed MSST-EEGNet model achieved an accuracy of 0.7288 ± 0.0730, 0.8161 ± 0.963, and 0.7075 ± 0.0746 on the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset respectively. Furthermore, a non-parametric Friedman statistical test demonstrates statistically significant superior performance of the proposed MSST-EEGNet model over the existing models.}, }
@article {pmid40983603, year = {2025}, author = {Kim, G and Jeong, H and Kim, K and Lee, S and Baeg, E and Yang, S and Kim, B and Yang, S}, title = {The Pre-clinical Safety of Graphene-based Electrodes Implanted on Rat Cerebral Cortex.}, journal = {Experimental neurobiology}, volume = {34}, number = {5}, pages = {214-223}, pmid = {40983603}, issn = {1226-2560}, abstract = {Graphene has emerged as a promising nanomaterial for brain-computer interface (BCI) applications due to its excellent electrical properties and biocompatibility. However, its long-term structural compatibility on the cerebral cortex requires further validation. This study assessed both functional compatibility and preservation of neural tissue architecture for graphene/parylene C composite electrodes implanted on the rat cortical surface, in accordance with ISO 10993-6 guideline weekly neurobehavioral assessments and comprehensive histopathological analyses were conducted for four weeks post-implantation. Our results revealed no significant differences in neurobehavioral outcomes between graphene-based and medical-grade silicone implants. Histopathological examination showed no noticeable inflammatory responses, changes in cellular morphology, myelination status, or neuronal degeneration. These findings indicate that graphene electrodes preserve tissue integrity comparable to medical‑grade silicone. Our study supports graphene's potential use in clinical neuroprosthetics and neuromodulation devices.}, }
@article {pmid40983076, year = {2025}, author = {He, Z and Wang, Y}, title = {TFDISNet: Temporal-frequency domain-invariant and domain-specific feature learning network for enhanced auditory attention decoding from EEG signals.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {5}, pages = {}, doi = {10.1088/2057-1976/ae09b2}, pmid = {40983076}, issn = {2057-1976}, mesh = {*Electroencephalography/methods ; Humans ; *Attention/physiology ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Algorithms ; *Machine Learning ; *Neural Networks, Computer ; *Auditory Perception/physiology ; Brain/physiology ; Adult ; }, abstract = {Auditory Attention Decoding (AAD) from Electroencephalogram (EEG) signals presents a significant challenge in brain-computer interface (BCI) research due to the intricate nature of neural patterns. Existing approaches often fail to effectively integrate temporal and frequency domain information, resulting in constrained classification accuracy and robustness. To address these shortcomings, a novel framework, termed the Temporal-Frequency Domain-Invariant and Domain-Specific Feature Learning Network (TFDISNet), is proposed to enhance AAD performance. A dual-branch architecture is utilized to independently extract features from the temporal and frequency domains, which are subsequently fused through an advanced integration strategy. Within the fusion module, shared features, common across both domains, are aligned by minimizing a similarity loss, while domain-specific features, essential for the task, are preserved through the application of a dissimilarity loss. Additionally, a reconstruction loss is employed to ensure that the fused features accurately represent the original signal. These fused features are then subjected to classification, effectively capturing both shared and unique characteristics to improve the robustness and accuracy of AAD. Experimental results show TFDISNet outperforms state-of-the-art models, achieving 97.1% accuracy on the KUL dataset and 88.2% on the DTU dataset with a 2 s window, validated across group, subject-specific, and cross-subject analyses. Component studies confirm that integrating temporal and frequency features boosts performance, with the full TFDISNet surpassing its variants. Its dual-branch design and advanced loss functions establish a robust EEG-based AAD framework, setting a new field standard.}, }
@article {pmid40982479, year = {2025}, author = {Zhong, Y and Song, M and Shi, W and Di, S and Yu, C and Jiang, T}, title = {Robust population orientation encoding by orientation-untuned neurons in macaque V1.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {35}, number = {9}, pages = {}, doi = {10.1093/cercor/bhaf264}, pmid = {40982479}, issn = {1460-2199}, support = {2024M753502//China Postdoctoral Science Foundation/ ; GZC20232999//China Postdoctoral Science Foundation/ ; 2024RC4028//Science and Technology Innovation Program of Hunan Province/ ; YJKYYQ20190040//Equipment Development Project of the Chinese Academy of Sciences/ ; 62403465//National Natural Science Foundation of China/ ; 82151307//National Natural Science Foundation of China/ ; 62327805//National Natural Science Foundation of China/ ; 2021ZD0200200//Science and Technology Innovation (STI) 2030-Major Projects/ ; }, mesh = {Animals ; *Neurons/physiology ; *Orientation/physiology ; Photic Stimulation ; Macaca mulatta ; *Primary Visual Cortex/physiology/cytology ; Visual Pathways/physiology ; Male ; *Visual Perception/physiology ; *Visual Cortex/physiology ; *Orientation, Spatial/physiology ; Neural Networks, Computer ; Models, Neurological ; }, abstract = {Orientation is one of the most fundamental stimulus features in visual perception. In the primary visual cortex (V1), while most neurons are orientation-selective, a small portion exhibits a lack of this selectivity. However, it remains unclear what roles the orientation-untuned V1 neurons play in population orientation discrimination. Here, we analyzed data from a 2-photon calcium imaging study that recorded the responses of thousands of V1 neurons to a grating stimulus at various orientations in awake macaques. Our population analysis reveals that orientation-untuned neurons can independently decode stimulus orientation with accuracy comparable to tuned neurons. Remarkably, we found that the more critical role of orientation-untuned neuronal populations in orientation encoding is to enhance coding robustness, specifically by reducing sensitivity to noise. Moreover, when using artificial neural networks to model the primate ventral visual pathway, we found that the V1-like layer also contains a proportion of orientation-untuned units. Removing these units leads to significant impairments in natural object recognition. Overall, these results indicate that orientation-untuned neurons encode orientation information and play a crucial role in primate visual perception. The study provides compelling evidence for a continuous distribution of visual features across neurons and challenges the notion of highly specialized units.}, }
@article {pmid40982226, year = {2025}, author = {Li, J and Yi, Y and Gao, X and Ren, Y and Gan, L and Zou, T and Qin, X and Tan, A and Yang, X and Jiang, F and Liu, X and Gao, H and Wang, Y and Aumont, E and Xiao, J and Zhou, B and Liao, W and Chen, H and Zhang, W and Montembeault, M and Rosa-Neto, P and Li, R}, title = {High brain network dynamics mediate audiovisual integration deficits and cognitive impairment in Alzheimer's disease.}, journal = {Journal of Alzheimer's disease : JAD}, volume = {108}, number = {1}, pages = {397-410}, doi = {10.1177/13872877251376717}, pmid = {40982226}, issn = {1875-8908}, mesh = {Humans ; *Alzheimer Disease/diagnostic imaging/psychology/physiopathology/complications ; Male ; Female ; Magnetic Resonance Imaging ; Aged ; *Cognitive Dysfunction/physiopathology/diagnostic imaging/psychology/etiology ; *Brain/diagnostic imaging/physiopathology ; *Visual Perception/physiology ; *Auditory Perception/physiology ; *Nerve Net/diagnostic imaging/physiopathology ; Neuropsychological Tests ; Middle Aged ; Photic Stimulation ; Aged, 80 and over ; Acoustic Stimulation ; }, abstract = {BackgroundAudiovisual integration deficits are frequent in patients with Alzheimer's disease (AD). In addition, patients with AD have altered functional brain networks, such as those supporting auditory and visual processing. However, the mechanisms driving this association remain unclear.ObjectiveTo investigate whether dynamic functional network disruptions underlie audiovisual integration and cognitive deficits in AD.MethodsSeventy-nine participants (41 AD, 38 controls) completed audiovisual stimuli tasks. A multilayer modularity algorithm was utilized to assess the resting-state fMRI-based brain dynamics of the primary sensory and higher-order functional networks. Mediation analysis was conducted to test our hypothesis.ResultsAD patients showed delayed response time and reduced peak benefit of audiovisual integration. Dynamic switching rates of primary sensory and higher-order networks were significantly increased in AD, particularly in the dynamic integration between the default mode network (DMN) and visual network (VN). The peak benefit of audiovisual integration negatively correlated with DMN-VN dynamic integration and positively with Mini-Mental State Examination, Montreal Cognitive Assessment, and Auditory Verbal Learning Test delayed scores. Notably, excessive integration between the DMN and VN mediated the relationship between audiovisual integration deficits and cognitive impairment in patients with AD.ConclusionsThese findings suggest that audiovisual integration impairment may disturb the dynamic integration between the DMN and VN, contributing to cognitive impairment in AD. The neural mechanisms underlying audiovisual integration deficit and cognitive decline might help with early diagnosis and intervention for AD.}, }
@article {pmid40978101, year = {2025}, author = {Liu, H and Liu, W and Du, Z and Wu, L and Chen, M and Gao, Z and Jiang, K and Li, L and Fan, Z and Shen, G}, title = {Encoding of blink information via wireless contact lens for eye-machine interaction.}, journal = {National science review}, volume = {12}, number = {10}, pages = {nwaf338}, pmid = {40978101}, issn = {2053-714X}, abstract = {Blinks controlled by ocular muscles and nerves can manifest as either involuntary physiological behaviors or volitional control actions, with the former serving spontaneous protective functions while the latter constitutes a biologically meaningful communicative signal. The encoding of blink information provides a novel eye-machine interaction (EMI) prototype within the realm of human-machine interaction, expanding human consciousness and capability boundaries. It facilitates motor and language rehabilitation, silent communication and even voluntary command execution. However, existing EMI devices face challenges related to wireless functionalities, ocular comfort and multi-route encoding/decoding orders. Here, we propose a wireless eye-wearable lens to encode conscious blink information via introduction of an RLC oscillating loop in the soft contact lens. The developed EMI contact lens incorporates a mechanosensitive capacitor, an inductive coil and the inherent loop resistance, generating characteristic resonance frequency for front-end capacitance signal transition or back-end control signal extraction. The EMI device delivers a sensitivity of 0.153 MHz/mmHg in the wide range of 0-70 mmHg for a normal intraocular pressure monitor and realizes conscious blink-based control command coding. A trial with participants having the EMI contact lens inserted demonstrates its wearability and biocompatibility. Finally, the five-route blink-based control command decoding mechanism is constructed via the EMI lens, linking blink counts to a drone's flight trajectory. The EMI contact lens offers an innovative prototype that transcends the capabilities of traditional brain-computer interfaces.}, }
@article {pmid40977080, year = {2025}, author = {Li, W and Zou, H and Yang, B and Xiao, L and Liu, S and Chen, Z and Xie, L and Zhu, W and Zhao, X and Wang, L and Li, T and Wang, T}, title = {From Electrophysiological to Biochemically-Modulated Interfaces: Evolution of Brain-Machine Communication.}, journal = {Small methods}, volume = {}, number = {}, pages = {e01471}, doi = {10.1002/smtd.202501471}, pmid = {40977080}, issn = {2366-9608}, support = {62235008//National Natural Science Foundation of China/ ; 62322108//Excellent Young Scholars of NSFC/ ; 62571260//General Program of NSFC/ ; 62201286//Young Scholars of NSFC/ ; 62301283//Young Scholars of NSFC/ ; 22405131//Young Scholars of NSFC/ ; 2023ZB587//Jiangsu Funding Program for Excellent Postdoctoral Talent/ ; NY222099//Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications/ ; BK20243057//Basic Research Program of Jiangsu Province/ ; }, abstract = {Brain-machine interfaces (BMIs) establish bidirectional communication between biological neural systems and external devices by decoding neural signals and delivering feedback stimulation. Achieving seamless integration with biological systems has driven the paradigmatic evolution of BMI technology through three interconnected dimensions. This review summarizes the shift from electrophysiological to biochemically-modulated BMIs, emphasizing key evolutionary trends that mirror biological neural characteristics. First, signal modalities have expanded from single electrophysiological detection to integrated biochemical sensing, enabling comprehensive neural circuit analysis through dual electrical-chemical communication pathways that capture both rapid electrical transmission and slower biochemical processes. Second, electrode morphology has transformed from rigid silicon structures to flexible, adaptive materials that mechanically match neural tissue properties, reducing mechanical mismatch and improving long-term biocompatibility. Third, system architectures have evolved from passive monitoring to active closed-loop platforms that incorporate neuromorphic intelligence and real-time therapeutic feedback, enabling dynamic neuromodulation based on multimodal signal analysis. Despite significant progress, challenges remain in achieving high electrode longevity, developing scalable multimodal interfaces, as well as understanding fundamental neural communication mechanisms. Future directions point toward biochemically-modulated brain interfaces incorporating living, adaptive, and evolutionarily responsive components that seamlessly integrate with biological neural networks for precision neurological therapeutics.}, }
@article {pmid40976830, year = {2025}, author = {Chen, W and Xie, C and Wang, Y and Jin, Y and Zhao, Y and Xu, Y and Zhang, C and Chen, A and Wang, X and Jia, Z}, title = {Efficacy analysis of 450 nm semiconductor blue laser enucleation of the prostate in treating benign prostatic hyperplasia with urinary retention.}, journal = {Lasers in medical science}, volume = {40}, number = {1}, pages = {377}, pmid = {40976830}, issn = {1435-604X}, mesh = {Humans ; Male ; *Prostatic Hyperplasia/surgery/complications ; *Urinary Retention/surgery/etiology ; Aged ; Retrospective Studies ; *Lasers, Semiconductor/therapeutic use ; Middle Aged ; Quality of Life ; Treatment Outcome ; Aged, 80 and over ; Urodynamics ; *Laser Therapy/methods ; *Prostatectomy/methods ; }, abstract = {To evaluate the clinical efficacy of 450 nm semiconductor blue laser enucleation of the prostate in patients with benign prostatic hyperplasia (BPH) complicated by acute urinary retention, and to assess its outcomes in patients with concomitant detrusor underactivity (DU).A retrospective analysis was conducted on clinical data from patients diagnosed with BPH and acute urinary retention who underwent 450 nm blue laser enucleation of the prostate in the Department of Urology at our hospital between February 2023 and May 2024. All patients had indwelling catheters due to acute urinary retention prior to surgery. Maximum urinary flow rate (Qmax), postvoid residual urine volume (PVR), International Prostate Symptom Score (IPSS), and quality of life (QoL) scores were compared before surgery and at 3 months postoperatively. Based on preoperative urodynamic testing, patients were divided into a DU group (bladder contractility index, BCI < 100) and a non-DU group (BCI ≥ 100). Surgical outcomes were compared between the two groups.A total of 62 patients were included in the study, with a mean age of 71.5 years. Of these, 32 (54.8%) were in the DU group and 28 (45.2%) in the non-DU group. At 3 months postoperatively, all patients showed significant improvements in Qmax, PVR, IPSS, and QoL scores compared with baseline (P < 0.001). In the DU group, 2 patients experienced recurrent urinary retention after catheter removal on postoperative day 3, but both recovered spontaneous urination after re-catheterization for 1 week. Intergroup comparisons showed that Qmax was lower and PVR was higher in the DU group than in the non-DU group at 3 months (P < 0.001), while no significant differences were observed in IPSS and QoL scores between the two groups (P > 0.05).The 450 nm semiconductor blue laser enucleation of the prostate is a safe and effective treatment for BPH complicated by acute urinary retention. Although patients with DU show less improvement in early postoperative voiding function compared to those without DU, the procedure effectively alleviates symptoms and may prevent further deterioration of detrusor function. These findings support its clinical application and wider adoption.}, }
@article {pmid40976794, year = {2025}, author = {Zhang, WL and Zeng, YH and Lai, YS}, title = {Spatial-temporal risk of Opisthorchis felineus infection in Western Siberia and the Ural Region of Russian Federation: a joint Bayesian modelling study based on survey and surveillance data.}, journal = {Infectious diseases of poverty}, volume = {14}, number = {1}, pages = {95}, pmid = {40976794}, issn = {2049-9957}, support = {82073665//The National Natural Science Foundation of China/ ; 2025A1515011200//Natural Science Foundation of Guangdong Province/ ; }, mesh = {Animals ; *Opisthorchiasis/epidemiology/parasitology ; Bayes Theorem ; *Opisthorchis/physiology ; Siberia/epidemiology ; Humans ; Russia/epidemiology ; Spatio-Temporal Analysis ; Prevalence ; Incidence ; Risk Factors ; }, abstract = {BACKGROUND: Opisthorchiasis infected by Opisthorchis felineus has represented a significant but understudied public health issue for the population residing in Western Siberia and the Ural Region of the Russian Federation. This study aimed to produce high-resolution spatial-temporal disease risk maps for guiding prevention strategy in the above region.<br><br>METHODS: Data on prevalence and surveillance data reflecting reported annual incidence rate of O. felineus infection in the study region were collected through systematic review and the annual reports of the Ministry of Health of the Russian Federation. Environmental, socioeconomic and demographic data were downloaded from different open-access data sources. An advanced multivariate Bayesian geostatistical modeling approach was developed to estimate the O. felineus infection risk at high-resolution spatial-temporal by joint analysis of survey and surveillance data, incorporating potential influencing factors and spatial-temporal random effects. The annual spatial-temporal risk maps of O. felineus infection at a resolution of 5&#x2009;&#xd7;&#x2009;5&#xa0;km[2] were produced.<br><br>RESULTS: The final dataset included 76 locations of survey data and 303 locations of surveillance data on O. felineus infection. The infection risk was high (>&#x2009;25%) in most part of central and eastern regions, and relatively low (<&#x2009;25%) in most part of western region, while temporal variations were observed across the sub-regions in recent decades. Particularly, in the densely populated eastern region, there was an increased trend of infection risk from 30.46% (95% Bayesian credible intervals, BCI 10.78-53.45%) in 1980 to 53.39% (95% BCI 13.77-91.93%) in 2019 and gradually transformed into high-risk. In the study region (excluding the western region due to data sparsity), the population-adjusted estimated prevalence was 46.61% (95% BCI 15.09-76.50%) in 2019, corresponding to approximately 7.91 million (95% BCI 2.56-12.98 million) people infected.<br><br>CONCLUSIONS: The high-resolution risk maps of O. felineus in Western Siberia and the Ural Region of the Russian Federation have effectively captured the risk profiles, suggesting the infection risk remains high in recent years and providing substantial evidence for spatial-target control and preventive strategies.}, }
@article {pmid40975869, year = {2025}, author = {Ottenhoff, MC and Verwoert, M and Goulis, S and Tousseyn, S and van Dijk, JP and Shanechi, MM and Sani, OG and Kubben, P and Herff, C}, title = {Decoding continuous goal-directed movement from human brain-wide intracranial recordings.}, journal = {Cell reports}, volume = {44}, number = {10}, pages = {116328}, doi = {10.1016/j.celrep.2025.116328}, pmid = {40975869}, issn = {2211-1247}, mesh = {Humans ; Male ; Movement/physiology ; Female ; Adult ; *Goals ; *Brain/physiology ; Motor Cortex/physiology ; Brain-Computer Interfaces ; Electroencephalography/methods ; Biomechanical Phenomena ; Middle Aged ; Young Adult ; Electrocorticography ; }, abstract = {Reaching out your hand is an effortless yet complex behavior that is indispensable in daily life. Neural correlates of reaching behavior have been observed and decoded beyond the motor cortex, but the degree and granularity of movement representation are not fully understood. Here, we decode 12 kinematics of goal-directed reaching behavior from 18 participants implanted with stereotactic-electroencephalography electrodes performing a 3D reaching task. The decoder is able to decode continuous movement kinematics using low-, mid-, and high-frequency information in all participants using preferential subspace identification. Neural correlates of movements are observed throughout the brain, including deeper structures. Switching to a goal-centric reference frame enables the decoder to decode hand position, indicating that low-frequency activity is involved in higher-order processing of movements. Our results strengthen the evidence that brain-wide motor-related dynamics can be decoded and may provide opportunities for brain-computer interfaces for individuals with a compromised motor cortex.}, }
@article {pmid40974874, year = {2025}, author = {Landau, O and Nissim, N}, title = {Mining multi-electrode and multi-wave electroencephalogram based time-interval temporal patterns for improved classification capabilities and explainability.}, journal = {Artificial intelligence in medicine}, volume = {170}, number = {}, pages = {103269}, doi = {10.1016/j.artmed.2025.103269}, pmid = {40974874}, issn = {1873-2860}, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Brain-Computer Interfaces ; *Data Mining/methods ; Electrodes ; Time Factors ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; }, abstract = {Brain-computer interface (BCI) systems, and particularly electroencephalogram (EEG) based BCI systems, have become more widely used in recent years and are utilized in various applications and domains ranging from medicine and marketing to games and entertainment. While different algorithms have been used to analyze EEG data and enable its classification, existing algorithms have two main drawbacks; both their classification and explainability capabilities are limited. Lacking in explainability, they cannot indicate which electrodes and waves led to a classification decision or explain how areas and frequencies of the brain's activity correlate to a specific task. In this study, we propose a novel extension for the time-interval temporal patterns mining algorithms aimed at enhancing the data mining process by enabling a richer set of patterns to be learned from the EEG data, thereby contributing to improved classification and explainability capabilities. The extended algorithm is designed to capture and leverage the unique nature of EEG data by decomposing it into different brain waves and modeling the relations among them and between different electrodes. Our evaluation of the proposed extended algorithm on multiple learning tasks and three EEG datasets demonstrated the extended algorithm's ability to mine richer patterns that improve the classification performance by 4-11 % based on the Area-Under the receiver operating characteristic Curve (AUC) metric, compared to the original version of the algorithm. Moreover, the algorithm was shown to shed light on the areas and frequencies of the brain's activity that are correlated with specific tasks.}, }
@article {pmid40974354, year = {2025}, author = {Tyagi, M and Shotwell, M and Power, AE and Singh, G and Kalra, DK}, title = {Cardiac Injury Causing Traumatic Ventricular Septal Rupture With Right Ventricular Pseudoaneurysm.}, journal = {JACC. Case reports}, volume = {30}, number = {35}, pages = {105475}, doi = {10.1016/j.jaccas.2025.105475}, pmid = {40974354}, issn = {2666-0849}, abstract = {BACKGROUND: Ventricular septal rupture (VSR) is a rare, potentially fatal consequence of blunt cardiac injury (BCI). Concomitant right ventricular (RV) pseudoaneurysm formation is even rarer, and the occurrence of both complications has not to our knowledge been previously reported.<br><br>CASE SUMMARY: A 63-year-old man presented with a VSR and a torn tricuspid chord, flail leaflet, and severe tricuspid regurgitation after BCI due to a motor vehicle accident. He declined surgery initially and presented a month later with severe heart failure symptoms. Imaging at that time demonstrated a persistent VSR and a new RV pseudoaneurysm. His condition was not deemed to be amenable to percutaneous closure, and he again declined open surgical repair.<br><br>DISCUSSION: VSR after BCI results from acute mechanical forces and/or delayed necrosis, with RV pseudoaneurysm developing as a delayed complication likely due to inflammatory necrosis. Multimodality imaging provides comprehensive anatomical assessment and tissue characterization and guides accurate diagnosis, prognostication, and therapeutic planning.<br><br>TAKE-HOME MESSAGE: This case emphasizes the importance of early recognition and the value of serial imaging in blunt cardiac trauma, with surgical repair recommended for significant defects and management tailored to the anatomy, timing of complications, and patient preferences.}, }
@article {pmid40973382, year = {2025}, author = {Ortner, J and Van Ewijk, R and Velthuis, L and Labenz, C and Arslanow, A and Nguyen-Tat, M and Wörns, MA and Reichert, MC and Farin-Glattacker, E and Binder, H and Fichtner, UA and Graf, E and Stelzer, D and Galle, PR and Lammert, F}, title = {Evaluating a population-based screening programme for early detection of liver fibrosis and cirrhosis in primary care in Germany: a cost assessment study.}, journal = {BMJ open}, volume = {15}, number = {9}, pages = {e090442}, pmid = {40973382}, issn = {2044-6055}, mesh = {Humans ; *Liver Cirrhosis/diagnosis/economics ; Germany ; Male ; Female ; Middle Aged ; *Primary Health Care/economics ; Adult ; Early Diagnosis ; *Mass Screening/economics/methods ; Aged ; Cost-Benefit Analysis ; Health Care Costs ; Elasticity Imaging Techniques ; Aspartate Aminotransferases/blood ; }, abstract = {OBJECTIVES: Structured Early detection of Asymptomatic Liver fibrosis and cirrhosis (SEAL) is a population-based screening programme using non-invasive tests for the early detection of liver fibrosis. This study evaluates the cost implications if the SEAL programme were to be implemented in routine care in Germany.<br><br>DESIGN: This study models cost differences with and without the SEAL screening programme. We regress costs of care on patient characteristics (age, comorbidities, sex, liver diseases, liver cancer and liver fibrosis and cirrhosis (LCI) stage) using statutory health insurance (SHI) data from routine care patients with LCI (n=4177). Based on these results, we predict per-patient costs for the patients newly diagnosed with LCI by SEAL (n=45). Costs with and without screening are estimated using patient age and LCI stage distributions from either SEAL or routine care.<br><br>SETTING: SEAL was conducted in two German states. Initial screening was performed by patients' primary care physicians.<br><br>PARTICIPANTS: Individuals insured by SHI without a prior diagnosis of LCI, eligible for Check-up 35, a general health check-up programme primarily targeting adults aged 35 and older, conducted by primary care physicians.<br><br>INTERVENTIONS: Screening via aspartate aminotransferase to platelet ratio index in primary care, for further evaluation serological diagnostics and ultrasound examinations in secondary care and specific assessment for definite diagnosis including transient elastography and liver biopsy for selected cases in tertiary care.<br><br>Primary outcome measures: expected 5-year cost changes for SEAL patients diagnosed with fibrosis or cirrhosis compared to costs without a screening programme.<br><br>SECONDARY OUTCOME MEASURES: case mix of leading chronic liver disease and LCI stages among patients diagnosed with advanced fibrosis or cirrhosis in SEAL versus routine care without screening.<br><br>RESULTS: Screening leads to fewer decompensated cases at initial diagnosis (4.6% in SEAL vs 22.8% in routine care) and thus savings in the costs of care within the first years of diagnosis: total expected costs per case were &#x20ac;2175 lower (bias-corrected bootstrap CIs (BCI): &#x20ac;527 to 3734), and LCI-associated costs were reduced by &#x20ac;1218 (BCI: &#x20ac;296 to 2164). Comparing the savings to the additional costs of diagnosis (range: &#x20ac;1575-1726 per detected LCI case) reveals that average changes in costs with screening range from moderate savings to moderate extra costs.<br><br>CONCLUSIONS: SEAL liver screening identifies patients in less advanced stages of LCI. If only costs were considered that are directly attributable to LCI, savings within 5 years are unlikely to fully outweigh the costs of screening. However, since this approach might miss additional LCI-related costs, SEAL appears to be cost-neutral compared with routine care when considering total healthcare costs.<br><br>REGISTRATION NUMBER: The SEAL registration number is DRKS00013460. This study relates to its results.}, }
@article {pmid40972658, year = {2025}, author = {Angrick, M and Luo, S and Rabbani, Q and Joshi, S and Candrea, DN and Milsap, GW and Gordon, CR and Rosenblatt, K and Clawson, L and Maragakis, N and Tenore, FV and Fifer, MS and Ramsey, NF and Crone, NE}, title = {Real-time detection of spoken speech from unlabeled ECoG signals: a pilot study with an ALS participant.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, pmid = {40972658}, issn = {1741-2552}, support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; }, mesh = {Female ; Humans ; Male ; Middle Aged ; *Amyotrophic Lateral Sclerosis/physiopathology/diagnosis/complications ; *Brain-Computer Interfaces ; Computer Systems ; *Electrocorticography/methods ; Pilot Projects ; *Speech/physiology ; }, abstract = {Objective. Brain-computer interfaces hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training-a major challenge when translating such approaches to people who have already lost their voice.Approach. In this pilot study, we made a first step toward scenarios in which no ground truth is available. We utilized a graph-based clustering approach to identify temporal segments of speech production from electrocorticographic (ECoG) signals alone. We then used the estimated speech segments to train a voice activity detection (VAD) model using only ECoG signals. We evaluated our approach using a leave-one-day-out cross-validation on open-loop recordings of a single dysarthric clinical trial participant living with ALS, and we compared the resulting performance to previous solutions trained with ground truth acoustic voice recordings.Main results. Our approach achieves a median timing error of around 530 ms with respect to the actual spoken speech. Embedded into a real-time BCI, our approach is capable of providing VAD results with a latency of only 10 ms.Significance. To the best of our knowledge, our results show for the first time that speech activity can be predicted purely from unlabeled ECoG signals, a crucial step toward individuals who cannot provide this information anymore due to their neurological condition, such as patients with locked-in syndrome.Clinical Trial Information. ClinicalTrials.gov, registration number NCT03567213.}, }
@article {pmid40972647, year = {2025}, author = {de Melo, GC and Forner-Cordero, A and Castellano, G}, title = {The role of the reference electrode in EEG recordings: looking from an inverted perspective.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {5}, pages = {}, doi = {10.1088/2057-1976/ae093f}, pmid = {40972647}, issn = {2057-1976}, mesh = {Humans ; *Electroencephalography/methods/instrumentation ; Electrodes ; Brain-Computer Interfaces ; Principal Component Analysis ; Male ; Adult ; Female ; Signal Processing, Computer-Assisted ; Algorithms ; Young Adult ; *Brain/physiology ; }, abstract = {The electroencephalographic signal variability caused by the active reference electrode is a major challenge for classification of motor tasks in Brain-Computer Interfaces. In this work a strategy to deal with the reference is proposed: use the information from all channels to extract more reliable information from the reference, the Inverted Perspective Reference Electrode (IPRE). In this novel approach the original set of signals is re-referenced to the electrode of interest, in contrast with all other available methods. At total, eight scenarios were analyzed independently: C3 and C4 as reference electrode, alpha and beta frequency bands, and motor imagery and motor execution tasks. Principal Component Analysis (PCA) was used to extract the information from the reference. This information was analyzed by means of the separability between motor tasks. Thirty-six subsets of electrodes were analyzed, including four typical choices of channels for comparison. A dataset with 109 subjects was used. Results showed that the quantity and location of electrodes are determinant to provide class-separable signals at the reference electrode. The IPRE showed greater separability compared to typical channel choices. Therefore, the strategy revealed better outcomes, encouraging further investigation with the inverted perspective to overcome the challenge of the active reference.}, }
@article {pmid40971842, year = {2025}, author = {Yang, X and Fang, X and Gao, M and Zhang, E and Zhu, B and Rao, H}, title = {Reducing Financial Misreporting Behavior with Noninvasive Brain Stimulation: The Moderating Effect of Moral Judgment.}, journal = {Social cognitive and affective neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1093/scan/nsaf094}, pmid = {40971842}, issn = {1749-5024}, abstract = {Building upon the distinct functions of the right dorsolateral prefrontal cortex (rDLPFC) and the right temporoparietal junction (rTPJ), this study investigates how moral judgment moderates the influence of these brain regions on financial misreporting-an effect that remains largely unknown. Employing transcranial direct current stimulation (tDCS), this study temporarily altered activity in these areas to investigate their influence on financial misreporting during a profit reporting task. Study 1 recruited university students, while Study 2 focused on finance professionals. The results showed that tDCS stimulation of rDLPFC and rTPJ reduced financial misreporting. However, the effects differed based on individuals' moral judgment levels. Those with lower moral judgment significantly reduced in misreporting with increased rDLPFC activity, whereas individuals with higher moral judgment remained consistent regardless of rDLPFC stimulation. In contrast, increased rTPJ activity reduced misreporting for subjects with higher moral judgment levels, whereas individuals with lower moral judgment remained consistent regardless of rTPJ stimulation. Importantly, these patterns hold whether participants are students or financial professionals. These findings emphasize distinct roles for rDLPFC and rTPJ in financial misreporting, highlighting the impact of individual moral judgment. This study has practical implications for enhancing ethical behavior by intervening in decision-making to effectively curb misreporting among individuals with different levels of moral judgment.}, }
@article {pmid40970086, year = {2025}, author = {Zhai, Y and Li, C and Cao, L and Zhang, S and Liu, X and Ren, J and Liu, Y}, title = {The m6A demethylase FTO suppresses glioma proliferation by regulating the EREG/PI3K/Akt signaling pathway.}, journal = {Frontiers in cell and developmental biology}, volume = {13}, number = {}, pages = {1667990}, pmid = {40970086}, issn = {2296-634X}, abstract = {BACKGROUND: Glioma, the most prevalent primary intracranial tumor, is characterized by aggressive proliferation and formidable treatment challenges. The N6-methyladenosine (m6A) demethylase, Fat mass and obesity-associated protein (FTO), is a critical regulator of gene expression, but its precise role in glioma remains controversial. This study aimed to elucidate the function and underlying molecular mechanisms of FTO in glioma progression.<br><br>METHODS: We integrated bioinformatic analysis of 1,027 glioma patients from public cohorts (TCGA and CGGA) with a comprehensive experimental approach. In vitro studies in U251 and U87MG glioma cells involved gain- and loss-of-function assays to assess proliferation, colony formation, and cell cycle progression. Mechanistic investigations included Western blotting, qRT-PCR, and mRNA stability assays. An in vivo subcutaneous xenograft model was used to validate the tumor-suppressive role of FTO.<br><br>RESULTS: Our analysis revealed that lower FTO expression is significantly associated with higher tumor grade and poorer overall survival in glioma patients. Functionally, FTO overexpression inhibited proliferation and induced G1 phase cell cycle arrest, whereas FTO knockdown enhanced these malignant phenotypes. Mechanistically, we identified Epiregulin (EREG) as a key downstream target of FTO. Loss of FTO increased global m6A levels and enhanced EREG mRNA stability, leading to its upregulation. This, in turn, activated the PI3K/Akt signaling pathway, evidenced by increased phosphorylation of PI3K and Akt and subsequent downregulation of p53 and p21. The in vivo model confirmed that FTO overexpression suppressed tumor growth, while its knockdown accelerated it.<br><br>CONCLUSION: Our findings establish FTO as a tumor suppressor in glioma. It inhibits proliferation by destabilizing EREG mRNA in an m6A-dependent manner, thereby inactivating the PI3K/Akt signaling cascade. These results highlight FTO as a potential prognostic biomarker and a promising therapeutic target for glioma.}, }
@article {pmid40969901, year = {2025}, author = {Zhang, J and Du, X and Li, X and Lv, X and Wang, X}, title = {Hypoxia, Psychedelics, and Terminal Lucidity: A Perspective on Neuroplasticity and Neuropsychiatric Disorders.}, journal = {ACS pharmacology &amp; translational science}, volume = {8}, number = {9}, pages = {2848-2854}, pmid = {40969901}, issn = {2575-9108}, abstract = {Hypoxia and psychedelics, despite their distinct origins, both induce altered states of consciousness and promote neuroplasticity, suggesting a shared underlying mechanism relevant to neuropsychiatric treatment and neurological recovery. Terminal lucidity, the transient resurgence of cognitive function in late-stage dementia, highlights the brain's latent capacity for rapid reorganization, a phenomenon that may be driven by transient hypoxia. Similarly, acute intermittent hypoxia and pharmacological agents like HypoxyStat, which modulate oxygen availability, have emerged as potential strategies for enhancing neural adaptability. This perspective explores the hypothesis that controlled reductions in oxygen availability(?)whether through psychedelics, near-death experiences, meditation, holotropic breathwork, or hypoxia therapies(?)trigger calcium signaling pathways that promote synaptogenesis and the formation of new neural circuits. Rather than restoring damaged connections, this process may enable functional rerouting, thereby supporting cognitive resilience and behavioral compensation in conditions such as stroke, Alzheimer's disease, and psychiatric disorders. By integrating insights from psychedelic research, hypoxia-based therapies, and neuroplasticity studies, we propose a unifying framework that leverages altered oxygen homeostasis as a novel therapeutic strategy for neuropsychiatric and neurodegenerative diseases.}, }
@article {pmid40969111, year = {2025}, author = {Kodama, T and Yoshikawa, M and Minamii, K and Nishimoto, K and Kadowaki, S and Inoue, Y and Ito, H and Shigeto, H and Okuyama, K and Maeda, K and Katayama, O and Murata, S and Morita, K}, title = {Investigating the Neural Mechanisms of Self-Controlled and Externally Controlled Movement with a Flexible Exoskeleton Using EEG Source Localization.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {11}, pages = {}, pmid = {40969111}, issn = {1424-8220}, support = {JP22H03445//Japan Society for the Promotion of Science/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Exoskeleton Device ; Adult ; Movement/physiology ; Young Adult ; Motor Cortex/physiology ; Fingers/physiology ; Hand Strength/physiology ; }, abstract = {BACKGROUND: Self-controlled motor imagery combined with assistive devices is promising for enhancing neurorehabilitation. This study developed a soft, Flexible Exoskeleton (flexEXO) for finger movements and investigated whether self-controlled motor tasks facilitate stronger cortical activation than externally controlled conditions.<br><br>METHODS: Twenty-one healthy participants performed grasping tasks under four conditions: Self-Controlled Motion (SCC), Other-Controlled Motion (OCC), Self-Controlled Imagery Only (SCIOC), and Other-Controlled Imagery Only (OCIOC). EEG data were recorded, focusing on event-related desynchronization (ERD) in the &#x3bc; and &#x3b2; bands during imagery and motion and event-related synchronization (ERS) in the &#x3b2; band during feedback. Source localization was performed using eLORETA.<br><br>RESULTS: Higher &#x3bc;ERD and &#x3b2;ERD were observed during self-controlled tasks, particularly in the primary motor cortex and supplementary motor area. Externally controlled tasks showed enhanced activation in the inferior parietal lobule and secondary somatosensory cortex. &#x3b2;ERS did not differ significantly across conditions. Source localization revealed that self-controlled tasks engaged motor planning and error-monitoring regions more robustly.<br><br>CONCLUSIONS: The flexEXO device and the comparison of brain activity under different conditions provide insights into the neural mechanisms of motor control and have implications for neurorehabilitation.}, }
@article {pmid40969011, year = {2025}, author = {He, R and Zhu, Y and Ye, J and Yao, D and Xu, P and Li, F and Jiang, L and Liang, Y}, title = {Brain Connectivity Variability Influences Anxiety Through the Behavioral Inhibition System.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2550055}, doi = {10.1142/S0129065725500558}, pmid = {40969011}, issn = {1793-6462}, abstract = {The behavioral inhibition system (BIS), mediating responses to punishment cues and avoidance behaviors, is implicated in anxiety. However, the neural dynamics underpinning BIS, particularly regarding the temporal variability of brain network interactions, remain less explored. Using resting-state functional magnetic resonance imaging (rs-fMRI) of 181 healthy adults, this study investigated the association between BIS sensitivity and the temporal variability of functional connectivity within and between functional brain networks. This finding revealed a significant positive correlation between BIS scores and temporal variability, specifically in the connectivity involving subnetworks' sensory somatomotor hand network (SSHN)-ventral attention network (VAN), and sensory somatomotor mouth network (SSMN)-VAN. Notably, the high-BIS sensitivity group exhibited significantly greater temporal variability between VAN and SSMN/SSHN compared to the low-BIS sensitivity group. Furthermore, predicted BIS scores based on network variability showed a strong correlation with actual BIS scores (Pearson's [Formula: see text]). Moreover, significant mediation effects highlighted the bridging role of BIS scores between brain network variability and anxiety scale scores. This enhances the comprehension of the relationship between BIS, anxiety, and brain function, while also offering new insights into the pathogenesis of anxiety.}, }
@article {pmid40968953, year = {2025}, author = {Li, K and El-Fiqi, H and Wang, M}, title = {Gate Control Mechanisms of Autoencoders for EEG Signal Reconstruction.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {11}, pages = {}, pmid = {40968953}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Brain/physiology ; Autoencoder ; }, abstract = {Electroencephalography (EEG) is a non-invasive and portable way to capture neurophysiological activity, which provides the basis for brain-computer interface systems and more innovative applications, from entertainment to security. However, the acquisition of EEG signals often suffers from noise contamination and even signal interruption problems due to poor contact of the electrodes, body movement, or heavy noise. Such heavily contaminated and lost signal segments are usually removed manually, which can hinder practical system deployment and application performance, especially in scenarios where continuous signals are required. In our previous work, we proposed the weighted gate layer autoencoder (WGLAE) and demonstrated its effectiveness in learning dependencies in EEG time series and encoding relationships among EEG channels. The WGLAE adopts a gate layer to encourage the AE to approximate multiple relationships simultaneously by controlling the data flow of each input variable. However, it only applies a sequential control scheme without taking into account the physical meaning of EEG channel locations. In this study, we investigate the gating mechanism for WGLAE and validate the importance of having a proper gating scheme for learning relationships between EEG channels. To this end, several gate control mechanisms are designed that embed EEG channel locations and their corresponding underlying physical meanings. The influences introduced by the proposed gate control mechanisms are examined on an open dataset with different scales and associated with various stimuli. The experimental results suggest that the gating mechanisms have varying influences on reconstructing EEG signals.}, }
@article {pmid40968884, year = {2025}, author = {Mihai Ungureanu, AS and Geman, O and Toderean, R and Miron, L and SharghiLavan, S}, title = {The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {11}, pages = {}, pmid = {40968884}, issn = {1424-8220}, mesh = {*Electroencephalography/methods/instrumentation ; Humans ; *Brain/physiology ; Electrodes ; Signal-To-Noise Ratio ; Monitoring, Physiologic/methods ; *Ear/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Electroencephalography (EEG) remains an essential method for monitoring brain activity, but the limitations of conventional systems due to the complexity of installation and lack of portability have led to the introduction and development of in-ear EEG technology. In-ear EEG is an emerging method of recording electrical activity in the brain and is an innovative concept that offers multiple advantages both from the point of view of the device itself, which is easily portable, and from the user's point of view, who is more comfortable with it, even in long-term use. One of the fundamental components of this type of device is the electrodes used to capture the EEG signal. This innovative method allows bioelectrical signals to be captured through electrodes integrated into an earpiece, offering significant advantages in terms of comfort, portability, and accessibility. Recent studies have demonstrated that in-ear EEG can record signals qualitatively comparable to scalp EEG, with an optimized signal-to-noise ratio and improved electrode stability. Furthermore, this review provides a comparative synthesis of performance parameters such as signal-to-noise ratio (SNR), common-mode rejection ratio (CMRR), signal amplitude, and comfort, highlighting the strengths and limitations of in-ear EEG systems relative to conventional scalp EEG. This study also introduces a visual model outlining the stages of technological development for in-ear EEG, from initial research to clinical and commercial deployment. Particular attention is given to current innovations in electrode materials and design strategies aimed at balancing biocompatibility, signal fidelity, and anatomical adaptability. This article analyzes the evolution of EEG in the ear, briefly presents the comparative aspects of EEG-EEG in the ear from the perspective of the electrodes used, highlighting the advantages and challenges of using this new technology. It also discusses aspects related to the electrodes used in EEG in the ear: types of electrodes used in EEG in the ear, improvement of contact impedance, and adaptability to the anatomical variability of the ear canal. A comparative analysis of electrode performance in terms of signal quality, long-term stability, and compatibility with use in daily life was also performed. The integration of intra-auricular EEG in wearable devices opens new perspectives for clinical applications, including sleep monitoring, epilepsy diagnosis, and brain-computer interfaces. This study highlights the challenges and prospects in the development of in-ear EEG electrodes, with a focus on integration into wearable devices and the use of biocompatible materials to improve durability and enhance user comfort. Despite its considerable potential, the widespread deployment of in-ear EEG faces challenges such as anatomical variability of the ear canal, optimization of ergonomics, and reduction in motion artifacts. Future research aims to improve device design for long-term monitoring, integrate advanced signal processing algorithms, and explore applications in neurorehabilitation and early diagnosis of neurodegenerative diseases.}, }
@article {pmid40968836, year = {2025}, author = {Zych, P and Filipek, K and Mrozek-Czajkowska, A and Kuwałek, P}, title = {Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {11}, pages = {}, pmid = {40968836}, issn = {1424-8220}, mesh = {*Neural Networks, Computer ; *Electroencephalography/classification ; Humans ; *Brain-Computer Interfaces ; Support Vector Machine ; Datasets as Topic ; Dimensionality Reduction ; Deep Learning ; *Movement ; *Brain/physiology ; *Gestures ; Male ; Female ; Young Adult ; Adult ; }, abstract = {Brain-computer interfaces (BCIs) have garnered significant interest due to their potential to enable communication and control for individuals with limited or no ability to interact with technologies in a conventional way. By applying electrical signals generated by brain cells, BCIs eliminate the need for physical interaction with external devices. This study investigates the performance of traditional classifiers-specifically, linear discriminant analysis (LDA) and support vector machines (SVMs)-in comparison with a hybrid neural network model for EEG-based gesture classification. The dataset comprised EEG recordings of seven distinct gestures performed by 33 participants. Binary classification tasks were conducted using both raw windowed EEG signals and features extracted via bandpower and the empirical wavelet transform (EWT). The hybrid neural network architecture demonstrated higher classification accuracy compared to the standard classifiers. These findings suggest that combining featuring extraction with deep learning models offers a promising approach for improving EEG gesture recognition in BCI systems.}, }
@article {pmid40967467, year = {2025}, author = {Cai, M and Xia, Z and Shao, C and Du, W and Cao, J and Yang, B and He, Q and Xu, X and Zhang, J and Shao, X and Ying, M}, title = {P2RY8::TSC22D3 is a novel fusion associated with chemoresistance in leukemia by activating PI3K-AKT pathway.}, journal = {Cancer letters}, volume = {633}, number = {}, pages = {218040}, doi = {10.1016/j.canlet.2025.218040}, pmid = {40967467}, issn = {1872-7980}, }
@article {pmid40967240, year = {2025}, author = {Del Sesto, MJ and Negoita, S and Bruzzone Giraldez, M and LaJoie, Z and Akhter Sathi, K and Wong, JK and Widge, AS and Okun, MS and Khalifa, A}, title = {Multitarget neurostimulation of the deep brain: clinical opportunities, challenges, and emerging technologies.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, pmid = {40967240}, issn = {1741-2552}, support = {DP2 EB037188/EB/NIBIB NIH HHS/United States ; }, mesh = {Humans ; *Deep Brain Stimulation/methods/trends/instrumentation ; Animals ; *Brain/physiology ; *Brain-Computer Interfaces/trends ; Electrodes, Implanted/trends ; }, abstract = {Recent computational, pre-clinical, and clinical studies have demonstrated the potential for using neuromodulation through simultaneous targeting of multiple deep brain regions. This approach has already been used for therapeutic and systems neuroscience applications. However, the broad clinical adoption of invasive distributed deep brain interfaces remains in its early stages. This review explores the barriers to implementation by addressing three key questions: do the benefits of implanting multiple electrodes justify the associated risks for specific applications? What is the risk-benefit ratio, and what technological advancements will be necessary to encourage clinical adoption? We also examine next-generation technologies that could enable multi-target brain interfaces, including system-on-chip micro-stimulators as well as nanoparticles. We highlight the role of novel machine learning algorithms in the optimization of stimulation parameters and for the guidance of device placement. Emerging hardware accelerators equipped with on-chip AI have demonstrated functionality that can be used to decode and to classify distributed neuronal data. This advance in hardware accelerators has also contributed to the potential for enhanced closed-loop stimulation control of devices. Despite these advances, significant technological and translational barriers persist, limiting the widespread clinical application of multi-target brain interfaces. This review provides a critical analysis of recent prototypes and novel hardware for use in multi-target systems. We will discuss both clinical and research applications. We will focus on the utilization of multi-site technologies to meet the needs of neurological diseases. We conclude that there exists a critical need for further innovation and integration of multi-site technologies into clinical practice.}, }
@article {pmid40967149, year = {2025}, author = {Park, S and Mun, S}, title = {AI-driven pupillary-computer interface via binary-coded flickering stimuli.}, journal = {Computers in biology and medicine}, volume = {197}, number = {Pt B}, pages = {111057}, doi = {10.1016/j.compbiomed.2025.111057}, pmid = {40967149}, issn = {1879-0534}, mesh = {Humans ; Male ; Female ; Adult ; *Pupil/physiology ; *Photic Stimulation ; *Reflex, Pupillary/physiology ; *Artificial Intelligence ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; }, abstract = {Pupillary-computer interface (PCI) refers to a novel interaction modality that leverages pupil size variations elicited by changes in visual stimulus brightness. The PCI based on the pupillary light reflex (PLR) induced by binary-coded visual stimuli was proposed. A novel PCI interface was devised to overcome the limitations of conventional electroencephalogram hardware, using artificial intelligence to model subtle pupil signal patterns induced by visual stimuli. The proposed PCI system exhibited high performance in terms of the number of commands, classification accuracy, and information transfer rate (ITR) using a simple binary coding scheme and convolutional neural network-based deep learning. Twelve healthy subjects (six men and six women, aged 28.6 ± 3.4 year) participated in three experimental conditions, each using 4-, 10-, and 20-class binary-coded visual stimuli. Each visual stimulus was constructed by dividing the 3-s period into ten phases of 0.3 s each, with a single brightness change (e.g., from dark to bright) occurring within this interval. The proposed system achieved a high classification accuracy (91.84 %, 93.84 %, and 98.61 %) and ITR (59.74, 62.04, and 69.36 bits/min) for 20-, 10-, and 4-class stimuli in the test dataset, considerably outperforming previous PLR-based interface studies. The findings indicate that the proposed PCI system provides a simple, cost-effective, and low-training-requirement interface solution that does not require user training and maintains long-term stability.}, }
@article {pmid40966615, year = {2025}, author = {Qian, X and Ng, KK and Yeo, SN and Loke, YM and Cheung, YB and Feng, L and Chong, MS and Ng, TP and Krishnan, KRR and Guan, C and Lee, TS and Zhou, JH}, title = {Brain-computer-interface-based intervention increases brain functional segregation in cognitively normal older adults.}, journal = {Age and ageing}, volume = {54}, number = {9}, pages = {}, pmid = {40966615}, issn = {1468-2834}, mesh = {Humans ; *Brain-Computer Interfaces ; Aged ; Male ; Female ; *Brain/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; *Cognition ; *Healthy Aging/psychology ; Brain Mapping/methods ; *Cognitive Aging/psychology ; Age Factors ; Middle Aged ; Aged, 80 and over ; }, abstract = {Brain-computer interface (BCI)-based cognitive training systems have shown promise in enhancing cognitive performance in cognitively normal older adults. However, the brain network changes underlying these behavioural improvements remain poorly understood. To address this gap, we investigated topological alterations in intrinsic brain functional networks following BCI-based training and their behavioural relevance in cognitively normal older adults using resting-state functional magnetic resonance imaging and graph theoretical analysis. Compared to a non-intervention waitlist (WL) group, the intervention (INT) group did not show significant behavioural improvements. However, they exhibited positive changes in brain network organisation. Specifically, the INT group demonstrated a reduced nodal participation coefficient, indicating enhanced strength of a node's connections within its community, primarily within control and subcortical networks, as well as increased system segregation after training. Additionally, the modular organisation of the brain functional network in the INT group became more segregated and more aligned with a young adult-based partition template (quantified using the adjusted Rand index) compared to the WL group. Importantly, decreased participation coefficients, particularly in subcortical regions, were associated with language improvement, while increases in the adjusted Rand index were linked to enhancements in everyday memory function. These findings suggest that BCI-based cognitive training may contribute to maintaining brain network organisation in cognitively normal ageing by enhancing functional network segregation, potentially supporting cognitive performance. This study provides insights into the neural mechanisms underlying the effectiveness of BCI-based cognitive training for cognitively normal ageing.}, }
@article {pmid40966144, year = {2025}, author = {Forenzo, D and Zhang, Y and Wittenberg, GF and He, B}, title = {Continuous Reaching and Grasping With a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {3888-3899}, pmid = {40966144}, issn = {1558-0210}, support = {R01 NS124564/NS/NINDS NIH HHS/United States ; RF1 NS124564/NS/NINDS NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics ; Male ; Electroencephalography ; Adult ; Female ; *Stroke Rehabilitation/methods/instrumentation ; *Hand Strength/physiology ; Stroke/physiopathology ; Middle Aged ; *Arm ; Movement ; Deep Learning ; Algorithms ; Imagination ; Signal Processing, Computer-Assisted ; Young Adult ; Healthy Volunteers ; Signal-To-Noise Ratio ; }, abstract = {Recent advancements in signal processing techniques have enabled non-invasive Brain-Computer Interfaces (BCIs) to control assistive devices, like robotic arms, directly with users' EEG signals. However, the applications of these systems are currently limited by the low signal-to-noise ratio and spatial resolution of EEG from which brain intention is decoded. In this study, we propose a motor-imagery (MI) paradigm, inspired by the mechanisms of a computer mouse, that adds an additional "click" signal to an established 2D movement BCI paradigm. The additional output signal increases the degrees of freedom of the BCI system and may enable more complex tasks. We evaluated this paradigm using deep learning (DL) based signal processing on both healthy subjects and stroke-survivors in online BCI tasks derived from two potential applications: clicking on virtual targets and moving physical objects with a robotic arm in a continuous reach-and-grasp task. The results show that subjects were able to control both movement and clicking simultaneously to grab, move, and place up to an average of 7 cups in a 5-minute run using the robotic arm. The proposed paradigm provides an additional degree of freedom to EEG BCIs, and improves upon existing systems by enabling continuous control of reach-and-grasp tasks instead of selecting from a discrete list of predetermined actions. The tasks studied in these experiments show BCIs may be used to control computer cursors or robotic arms for complex real-world or clinical applications in the near future, potentially improving the lives of both healthy individuals and motor-impaired patients.}, }
@article {pmid40966137, year = {2025}, author = {Gong, Y and Shi, K and Niu, X and Yang, L and Yang, X and Zheng, C}, title = {Multi-source Discriminant Dynamic Domain Adaptation for Cross-subject Motor Imagery EEG Recognition.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3610446}, pmid = {40966137}, issn = {2168-2208}, abstract = {Electroencephalography (EEG) has emerged as a widely utilized signal in motor imagery (MI) brain-computer interfaces(BCI) due to its convenience and safety. Recently, deep learning methods have rapidly developed in the field of brain computer interfaces. However, traditional EEG classification methods often face challenges related to limited generalization capability across subjects. To address this issue, this paper proposes a multi-source discriminant dynamic domain adaptation model(MSD-DDA) aimed at fully leveraging domain adaptation to enhance the accuracy of motor imagery classification. The model adeptly handles global and local disparities in motor imagery classification by dynamically minimizing differences between global domain and local subdomain. Furthermore, to ensure discriminability and diversity in the target domain, we introduce batch kernel norm maximization of the difference, thereby enhancing the model's discriminability in the target domain while preserving prediction diversity. To tackle variations in similarity between different source domains and the target domain, we devise a weighted joint prediction mechanism. This mechanism automatically adjusts the contribution weight of each source domain based on its similarity to the target domain, facilitating more precise discriminant prediction and improved adaptability to scenarios with multiple source domains. To evaluate our approach, we conducted a large number of experiments on datasets 1 and 2a of the Fourth BCI Competition and on the openBMI dataset, with average classification accuracy of 92.43%, 79.24% and 71.96%, respectively.Finally, we compare the proposed method with several classical and recent algorithms, and prove that its performance is better than the existing methods.}, }
@article {pmid40966133, year = {2025}, author = {Wang, H and Zhang, J and Yang, K and Xiong, J and Liu, X and Chen, T and Song, L}, title = {FourierMask: Explain EEG-based End-to-end Deep Learning Models in the Frequency Domain.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3610742}, pmid = {40966133}, issn = {2168-2208}, abstract = {The rise of EEG-based end-to-end deep learning models has underscored the need to elucidate how these models process time-series raw EEG signals to generate predictions. The frequency domain provides a more suitable perspective for this task due to two key advantages: the strong correlation with cognitive states and the inherent capacity to model long-range temporal dependencies. However, this perspective remains underexplored in existing research. To bridge this gap, we propose FourierMask, the first mask perturbation framework specifically designed for frequency-domain explanation of EEG-based end-to-end models. Our method introduces three key innovations. First, the Fourier-based domain transformation enables direct manipulation of spectral components. Second, A learnable mask mechanism jointly models the spectral-spatial couplings relationship for EEG explanation. Third, a perturbation generator constrained by a target alignment loss ensures natural perturbations by minimizing distribution shift via cluster-aware regularization. We validate our method through experiments on an EEG benchmark dataset across EEGNet, TSCeption, and DeepConvNet models. Our method reaches a 36.0% average accuracy drop gap (vs. 8.6% for LIME and 6.6% for easyPEASI) at the group-level. And, it reaches a 17.8% average accuracy drop gap (vs. 8.9% for LIME and 9.9% for easyPEASI) at the instance-level. Our model-agnostic framework provides a plug-and-play solution for enhancing transparency of EEG-based end-to-end deep learning models. It links model decisions to frequency biomarkers, with potential applications in neuromedicine and brain-computer interfaces.}, }
@article {pmid40963811, year = {2025}, author = {McDonald, C and Mayor, JJV and Lennon, O}, title = {Neurophysiological insights into sit-to-stand post stroke.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1646498}, pmid = {40963811}, issn = {1662-4548}, abstract = {INTRODUCTION: Stroke often results in the loss of ability to stand-up independently or to perform the transfer with compensatory movement patterns. While neurological disorders are associated with sit-to-stand disability, the neurophysiological mechanisms underlying the movement and the impact of injury at brain level remain poorly understood.<br><br>METHODS: Stroke participants (n =&#x202f;10, 4 males) performed five sets of three sit-to-stand transitions from an armless, backless seat adjusted to their knee joint height with three-dimensional kinematic data capture. Electromyography (EMG) was recorded from the bilateral vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius muscles. Surface electroencephalography (EEG) activity was recorded using eight focused bipolar channels over the sensorimotor cortex. Data were analyzed and compared with a reference dataset from healthy adults (n =&#x202f;10).<br><br>RESULTS: Kinematic data confirms post-stroke participants take significantly longer to complete a sit-to-stand transfer compared to healthy controls but maintain the same kinematic movement phases and temporal muscle activation patterns. EMG data indicates stroke survivors stand up using largely the same temporal muscle activation patterns, however they exhibit delayed peak activity of the vastus lateralis and biceps femoris compared to healthy controls. EEG data reveal stroke survivors demonstrate variable event-related spectral perturbation patterns and reduced event-related synchronization/de-synchronization in the alpha and beta frequency bands and increased asymmetry between brain hemispheres when compared to healthy controls.<br><br>CONCLUSION: EMG data supports the wider literature that confirms stroke survivors stand up using the same temporal muscle activation patterns compared to healthy controls, however peak activity of the vastus lateralis and biceps femoris are delayed. EEG data add new knowledge to our understanding of the central control of sit-to-stand transfers in a stroke population, highlighting differences in cortical activity from healthy controls, notably in ERSP patterns during sit to stand phases and in brain hemisphere asymmetry. Findings have relevance as a potential biomarker for stroke functional recovery and indicate that BCI-based applications of sit to stand may need to be trained individually in stroke survivors as they demonstrate variable cortical activation patterns compared to healthy controls.}, }
@article {pmid40963494, year = {2025}, author = {Kim, G and Hong, Y and Lee, H and Kim, M and Eun, J and Lee, J and Lee, S and Chou, N and Shin, H}, title = {Single-Step Patterning of Biocompatible Neural Electrodes Using Black-Pt Functionalized Laser-Induced Graphene for in Vivo Electrophysiology.}, journal = {Small methods}, volume = {}, number = {}, pages = {e01384}, doi = {10.1002/smtd.202501384}, pmid = {40963494}, issn = {2366-9608}, support = {//Neuro-Semi-AI Fusion Superhuman Project/ ; 2025-04812973//Technology Innovation Program/ ; //Ministry of Trade, Industry &amp; Energy (MOTIE, Korea)/ ; //National Research Foundation (NRF)/ ; //Bio&amp;Medical Technology Development Program/ ; //National Research Foundation of Korea (NRF)/ ; 2025-00557203//Korean government (MSIT)/ ; 2025-02243041//Korean government (MSIT)/ ; //Innovative Human Resource Development/ ; //Local Intellectualization program/ ; //Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP)/ ; 2025-RS-2022-00156389//Korea government (MSIT)/ ; 25-BR-04-01//Korea Brain Research Institute/ ; 25-BR-02-02//Korea Brain Research Institute/ ; }, abstract = {Neural electrodes are essential tools for monitoring electrophysiological activity in the brain, driving advances in neuroscience and neurotechnology. However, conventional semiconductor-based fabrication techniques suffer from high costs, complex procedures, and limited adaptability for customized designs. Here, a single-step patterning, scalable method is presented for fabricating biocompatible neural electrodes using laser-induced graphene (LIG) patterned directly onto polyimide substrates. This process requires only a standard CO2 laser system, a spray-coated biocompatible lubricant, and black-Platinum (Pt) functionalization to form conductive traces, electrode sites, and connector pads-eliminating the need for cleanroom infrastructure or photolithography. Selective laser ablation enables precise electrode exposure, allowing rapid prototyping across various formats, including electroencephalography (EEG), electrocorticography (ECoG), and penetrating neural probes. The entire fabrication process is completed within 5 h, reducing production time and cost by over two orders of magnitude compared to conventional approaches. Demonstrating mechanical robustness, reliable signal acquisition, and biocompatibility, the fabricated electrodes exhibit high fidelity in recording EEG, ECoG, and spike signals in anesthetized mice. These findings underscore the method's strong potential for rapid prototyping of personalized brain-computer interfaces, neurological monitoring systems, and scalable preclinical research tools.}, }
@article {pmid40962980, year = {2025}, author = {Xie, R and Han, F and Yu, Q and Li, D and Han, X and Xu, X and Yu, H and Huang, J and Zhou, X and Zhao, H and Deng, X and Tian, Q and Li, Q and Li, H and Zhao, Y and Ma, G and Li, G and Zheng, H and Zhu, M and Yan, W and Xu, T and Liu, Z}, title = {A movable long-term implantable soft microfibre for dynamic bioelectronics.}, journal = {Nature}, volume = {645}, number = {8081}, pages = {648-655}, pmid = {40962980}, issn = {1476-4687}, mesh = {Animals ; Humans ; Male ; Rats ; Biomechanical Phenomena ; *Electrodes, Implanted ; Electronics/instrumentation ; *Prostheses and Implants ; Rats, Sprague-Dawley ; Time Factors ; }, abstract = {Long-term implantable bioelectronics offer a powerful means to evaluate the function of the nervous system and serve as effective human-machine interfaces[1-3]. Here, inspired by earthworms, we introduce NeuroWorm-a soft, stretchable and movable fibre sensor designed for bioelectronic interface. Our approach involves rolling to transform 2D bioelectronic devices into 1D NeuroWorm, creating a multifunctional microfibre that houses longitudinally distributed electrode arrays for both bioelectrical and biomechanical monitoring. NeuroWorm effectively records high-quality spatio-temporal signals in situ while steerably advancing within the brain or on the muscle as needed. This allows for the dynamic targeting and shifting of desired monitoring sites. Implanted in muscle through a tiny incision, NeuroWorm provides stable bioelectrical monitoring in rats for more than 43 weeks. Even after 54 weeks of implantation in muscle, fibroblast encapsulation around the fibre remains negligible. Our NeuroWorm represents a platform that promotes a substantial advance in bioelectronics-from an immobile probe fixed in place to active, intelligent and living devices for long-term, minimally invasive and mobile evaluation of the nervous system.}, }
@article {pmid40962872, year = {2025}, author = {Qin, L and Guan, P and Shao, J and Xiao, Y and Yu, Y and Su, J and Zhang, C and Li, Y and Liu, S and Li, P and Ouyang, D and He, W and Liu, F and Zhu, K and Liu, K and Yao, Z and Wu, J and Zhao, Y and Li, H and Hui, F and Lin, P and Lanza, M and Li, Y and Zhai, T}, title = {Molecular crystal memristors.}, journal = {Nature nanotechnology}, volume = {}, number = {}, pages = {}, pmid = {40962872}, issn = {1748-3395}, abstract = {Memristors have emerged as a promising hardware platform for in-memory computing, but many current devices suffer from channel material degradation during repeated resistive switching. This leads to high energy consumption and limited endurance. Here we introduce a molecular crystal memristor, of which the representative channel material, Sb2O3, possesses a molecular crystal structure where molecular cages are interconnected via van der Waals forces. This unique configuration allows ions to migrate through intermolecular spaces with relatively low energy input, preserving the integrity of the crystal structure even after extensive switching cycles. Our molecular crystal memristor thus exhibits low energy consumption of 26 zJ per operation, with prominent endurance surpassing 10[9] switching cycles. The device delivers both reconfigurable non-volatile and volatile resistive switching behaviours over a broad range of device scales, from micrometres down to nanometres. Furthermore, we establish the scalability of this technology by fabricating large crossbar arrays on an 8 inch wafer. This enables the successful implementation of reservoir computing on a single CMOS-integrated chip using these memristors, achieving 100% accuracy in dynamic vision recognition.}, }
@article {pmid40962534, year = {2025}, author = {Wang, Y}, title = {[Promote the application and innovation of artificial intelligence in pediatric neurological diseases].}, journal = {Zhonghua er ke za zhi = Chinese journal of pediatrics}, volume = {63}, number = {10}, pages = {1045-1047}, doi = {10.3760/cma.j.cn112140-20250722-00671}, pmid = {40962534}, issn = {0578-1310}, mesh = {Humans ; *Artificial Intelligence ; Child ; *Nervous System Diseases/diagnosis/therapy ; Machine Learning ; *Pediatrics/methods ; Brain-Computer Interfaces ; Decision Support Systems, Clinical ; }, }
@article {pmid40961966, year = {2025}, author = {Ruszala, B and Mazurek, KA and Schieber, MH}, title = {Disentangling indirect versus direct effects of somatosensory cortex microstimulation on neurons in primary motor and ventral premotor cortex.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ae087e}, pmid = {40961966}, issn = {1741-2552}, mesh = {*Somatosensory Cortex/physiology ; *Motor Cortex/physiology/cytology ; Animals ; *Neurons/physiology ; Macaca mulatta ; Male ; Brain-Computer Interfaces ; Electric Stimulation/methods ; }, abstract = {Objective.Intracortical microstimulation in the primary somatosensory cortex (S1-ICMS) is being developed to provide on-line feedback for bidirectional brain-machine interfaces. Because S1-ICMS can alter the discharge of the motor cortex neurons used to decode motor intent, successful application of S1-ICMS feedback requires understanding the modulation it produces in motor cortex neuron activity.Approach.We investigated the effects of S1-ICMS on neurons in both the primary motor cortex (M1) and the ventral premotor cortex (PMv) during a task in which some trials were instructed with visual cues and other trials with S1-ICMS.Main results.We observed both indirect modulation during and/or after ICMS trains, as well as direct modulation time-locked to the individual S1-ICMS pulses within trains, with all possible combinations of the two types of modulation found among the majority of M1 and PMv neurons. Indirect effects were more prevalent and larger than direct effects. When S1-ICMS produced both indirect and direct modulation in the same neuron, the effects could both be excitatory, both inhibitory, or one excitatory and the other inhibitory. By simulating direct effects, we isolated the concurrent indirect effects, revealing that isolated direct effects failed to account for isolated indirect effects. Furthermore, indirect effects could be present 1 s or more after ICMS trains had terminated, when no direct effects could have occurred. Although the performance of movement decoders trained on visually-instructed trials was poor when applied to ICMS-instructed trials, decoders trained on ICMS-instructed trials performed well on ICMS-instructed trials, indicating that S1-ICMS altered the discharge of M1 and PMv neurons but did not degrade the decodable information available.Significance.When decoding movement intent from neural activity in M1 and/or PMv, accounting for indirect and direct modulation may improve the ability of bidirectional brain-machine interfaces to incorporate artificial somatosensory feedback delivered with S1-ICMS and restore functional movement.}, }
@article {pmid40961213, year = {2025}, author = {Kryt, G and Dougall, R and Borisoff, J}, title = {BCIT's BEAST wheelchair takes on Cybathlon with power, precision, and pilot-led design.}, journal = {Science robotics}, volume = {10}, number = {106}, pages = {eaeb1340}, doi = {10.1126/scirobotics.aeb1340}, pmid = {40961213}, issn = {2470-9476}, mesh = {*Wheelchairs ; Humans ; Equipment Design ; *Brain-Computer Interfaces ; *Robotics/instrumentation ; }, abstract = {An extending, articulating powered wheelchair competed and won the wheelchair race at Cybathlon 2024.}, }
@article {pmid40960388, year = {2025}, author = {Jhilal, S and Marchesotti, S and Thirion, B and Soudrie, B and Giraud, AL and Mandonnet, E}, title = {Implantable Neural Speech Decoders: Recent Advances, Future Challenges.}, journal = {Neurorehabilitation and neural repair}, volume = {}, number = {}, pages = {15459683251369468}, doi = {10.1177/15459683251369468}, pmid = {40960388}, issn = {1552-6844}, abstract = {The social life of locked-in syndrome (LIS) patients is significantly impacted by their difficulties to communicate. Consequently, researchers have started to explore how to decode intended speech from neural signals directly recorded from the cortex. The first studies in the late 2000s reported modest decoding accuracies. However, thanks to fast advances in machine learning, the most recent studies have reached decoding accuracies high enough to be optimistic about the clinical benefit of neural speech decoders in the near future. We first discuss the selection criteria for implanting a neural speech decoder in LIS patients, emphasizing the advantages and disadvantages associated with conditions such as brainstem stroke and amyotrophic lateral sclerosis. We examine the key design considerations for neural speech decoders, demonstrating how successful implantation requires careful optimization of multiple interrelated factors including language representation, cortical recording areas, neural features, training paradigms, and decoding algorithms. We then discuss current approaches and provide arguments for potential improvements in decoder design and implementation. Finally, we explore the crucial question of who should learn to use the neural speech decoder-the patient, the machine, or both. In conclusion, while neural speech decoders present promising avenues for improving communication for LIS patients, interdisciplinary efforts spanning neurorehabilitation, neuroscience, neuroengineering, and ethics are imperative to design future clinical trials.}, }
@article {pmid40959706, year = {2025}, author = {Kothe, C and Shirazi, SY and Stenner, T and Medine, D and Boulay, C and Grivich, MI and Artoni, F and Mullen, T and Delorme, A and Makeig, S}, title = {The lab streaming layer for synchronized multimodal recording.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {3}, number = {}, pages = {}, pmid = {40959706}, issn = {2837-6056}, support = {KL2 TR001999/TR/NCATS NIH HHS/United States ; R01 NS047293/NS/NINDS NIH HHS/United States ; }, abstract = {Accurately recording the interactions of humans or other organisms with their environment and other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) framework offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common local area network (LAN). Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features can ensure continuous, millisecond-precise data recording, even in the face of interruptions. In this paper, we present an overview of LSL architecture, core features, and performance in common experimental contexts. We also highlight practical considerations and known pitfalls when using LSL, including the need to take into account input device throughput delays that LSL cannot itself measure or correct. The LSL ecosystem has grown to support over 150 data acquisition device classes and to establish interoperability between client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording, now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis environments, and brain-computer interface applications. Beyond basic science, research, and development, LSL has been used as a resilient and transparent back-end in deployment scenarios, including interactive art installations, stage performances, and commercial products. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes occurring within and across multiple data streams on a common timeline.}, }
@article {pmid40959704, year = {2025}, author = {Wu, X and Hu, K and Fu, Z and Zhang, D}, title = {Improved evaluation of waveform reconstruction in speech decoding based on invasive brain-computer interfaces.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {3}, number = {}, pages = {}, pmid = {40959704}, issn = {2837-6056}, abstract = {Brain-computer interfaces (BCIs) that reconstruct speech waveforms from neural signals are a promising communication technology. However, the field lacks a standardized evaluation metric, making it difficult to compare results across studies. Existing objective metrics, such as correlation coefficient (CC) and mel cepstral distortion (MCD), are often used inconsistently and have intrinsic limitations. This study addresses the critical need for a robust and validated method for evaluating reconstructed waveform quality. Literature about waveform reconstruction from intracranial signals is reviewed, and issues with evaluation methods are presented. We collated reconstructed audio from 10 published speech BCI studies and collected Mean Opinion Scores (MOS) from human raters to serve as a perceptual ground truth. We then systematically evaluated how well combinations of existing objective metrics (STOI and MCD) could predict these MOS scores. To ensure robustness and generalizability, we employed a rigorous leave-one-dataset-out cross-validation scheme and compared multiple models, including linear and non-linear regressors. This work, for the first time, identifies a lack of a standard evaluation method, which prohibits cross-study comparison. Using 10 public datasets, our analysis reveals that a non-linear model, specifically a Random Forest regressor, provides the most accurate and reliable prediction of subjective MOS ratings (R[2] = 0.892). We propose this cross-validated Random Forest model, which maps STOI and MCD to a predicted MOS score, as a standardized objective evaluation metric for the speech BCI field. Its demonstrated accuracy and robust validation outperform the available methods. Moreover, it can provide the community with a reliable tool to benchmark performance, facilitate meaningful cross-study comparisons for the first time, and accelerate progress in speech neuroprosthetics.}, }
@article {pmid40956723, year = {2025}, author = {Darley, G and Bonnet, S}, title = {A Unified Framework for Matrix Backpropagation.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2025.3607405}, pmid = {40956723}, issn = {2162-2388}, abstract = {Computing matrix gradient has become a key aspect in modern signal processing/machine learning, with the recent use of matrix neural networks requiring matrix backpropagation. In this field, two main methods exist to calculate the gradient of matrix functions for symmetric positive definite (SPD) matrices, namely, the Daleckiǐ-Kreǐn/Bhatia formula and the Ionescu method. However, there appear to be a few errors. This brief aims to demonstrate each of these formulas in a self-contained and unified framework, to prove theoretically their equivalence, and to clarify inaccurate results of the literature. A numerical comparison of both methods is also provided in terms of computational speed and numerical stability to show the superiority of the Daleckiǐ-Kreǐn/Bhatia approach. We also extend the matrix gradient to the general case of diagonalizable matrices. Convincing results with the two backpropagation methods are shown on the EEG-based BCI competition dataset with the implementation of an SPDNet, yielding around 80% accuracy for one subject. Daleckiǐ-Kreǐn/Bhatia formula achieves an 8% time gain during training and handles degenerate cases.}, }
@article {pmid40956372, year = {2025}, author = {Wang, T and Yi, T and Chen, T and Khan, NU and Yuan, Y}, title = {Spinal Cord Injury 2.0: Bridging the Gap Between Neurobiology, Technology, and Hope in the Era of Precision Medicine.}, journal = {Stem cell reviews and reports}, volume = {21}, number = {8}, pages = {2597-2615}, pmid = {40956372}, issn = {2629-3277}, mesh = {Humans ; *Spinal Cord Injuries/therapy/pathology/physiopathology ; *Precision Medicine/methods ; Animals ; *Neurobiology ; Nerve Regeneration ; }, abstract = {Spinal cord injury (SCI) is a devastating neurological condition with profound motor, sensory, and autonomic consequences, affecting 10-83 individuals per million annually worldwide. This review explores the evolving SCI landscape, from acute ionic imbalance, excitotoxicity, and vascular disruption to chronic neuroinflammation and glial fibrosis, which collectively impede neural regeneration. Breakthroughs in regenerative bioengineering-such as stem cell-driven neurogenesis and CRISPR-Cas9-mediated axonal growth modulation-are converging with neurotechnological advances, including spinal neuromodulation, brain-computer interface integration, and AI-enhanced robotic locomotor systems, to redefine therapeutic frontiers. Precision medicine, guided by multi-omic biomarker stratification and patient-specific computational modeling, enables individualized intervention strategies. Despite unprecedented progress, translation to the clinic demands optimized preclinical models, harmonized trial methodologies, and ethical frameworks ensuring equitable access. Together, these innovations herald a shift from compensatory care toward structural repair and functional restoration in SCI.}, }
@article {pmid40956157, year = {2025}, author = {Yu, B and Li, P and Xu, H and Wang, Y and Xu, K and Hao, Y}, title = {Novel and optimized mouse behavior enabled by fully autonomous HABITS: Home-cage assisted behavioral innovation and testing system.}, journal = {eLife}, volume = {14}, number = {}, pages = {}, pmid = {40956157}, issn = {2050-084X}, support = {2021ZD0200405//STI 2030-Major Projects/ ; 62336007//National Natural Science Foundation of China/ ; SN-ZJU-SIAS-002//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024C03001//Pioneer R&amp;D Program of Zhejiang/ ; }, mesh = {Animals ; Mice ; *Behavior, Animal ; *Cognition ; Male ; *Habits ; Mice, Inbred C57BL ; Algorithms ; }, abstract = {Mice are among the most prevalent animal models used in neuroscience, benefiting from the extensive physiological, imaging, and genetic tools available to study their brain. However, the development of novel and optimized behavioral paradigms for mice has been laborious and inconsistent, impeding the investigation of complex cognitions. Here, we present a home-cage assisted mouse behavioral innovation and testing system (HABITS), enabling free-moving mice to learn challenging cognitive behaviors in their home-cage without any human involvement. Supported by the general programming framework, we have not only replicated established paradigms in current neuroscience research but also developed novel paradigms previously unexplored in mice, resulting in more than 300 mice demonstrated in various cognition functions. Most significantly, HABITS incorporates a machine-teaching algorithm, which comprehensively optimized the presentation of stimuli and modalities for trials, leading to more efficient training and higher-quality behavioral outcomes. To our knowledge, this is the first instance where mouse behavior has been systematically optimized by an algorithmic approach. Altogether, our results open a new avenue for mouse behavioral innovation and optimization, which directly facilitates investigation of neural circuits for novel cognitions with mice.}, }
@article {pmid40956015, year = {2025}, author = {Xu, JJ and Chen, YL and Sun, WB and Li, HF and Wu, ZY and Chen, DF}, title = {Functional Characterization and Pathogenicity Classification of PRRT2 Splice Variants in PRRT2-Related Disorders.}, journal = {Annals of clinical and translational neurology}, volume = {}, number = {}, pages = {}, doi = {10.1002/acn3.70189}, pmid = {40956015}, issn = {2328-9503}, support = {188020-193810101/089//distinguished scholar of Zhejiang University/ ; 81330025//National Natural Science Foundation of China/ ; }, abstract = {OBJECTIVE: Paroxysmal kinesigenic dyskinesia (PKD) is the most common hereditary paroxysmal movement disorder. The PRRT2 gene is the first identified causative gene and accounts for the majority of PKD. In this study, we investigated the pathogenicity of PRRT2 variants in the splice regions.<br><br>METHODS: Patients with clinically suspected PKD and no detectable pathogenic variants in the PRRT2 gene were included. Targeted next-generation sequencing technology was used to screen the full-length sequence of PRRT2. In silico analyses were performed on splice region variants identified in our cohort and compiled from the Human Gene Mutation Database (HGMD). Subsequently, a minigene system carrying these variants was constructed and introduced into HEK293T cells for functional assays to assess the pathogenicity.<br><br>RESULTS: Fourteen PRRT2 variants were analyzed, including four identified in patients with clinically suspected PKD from our center and 10 retrieved from HGMD. These variants comprised 10 intronic variants, two synonymous variants, one deletion, and one missense variant. In silico predictions suggested that all variants, except for one deep intronic variant, had the potential to affect normal splicing. Functional assays showed that 11 PRRT2 variants, including missense and intronic variants, caused aberrant splicing events, such as exon skipping and intron retention. The two synonymous variants and one deep intronic variant exhibited no splicing abnormalities. Based on these results, five patients with PRRT2 variants previously classified as variants of uncertain significance can now be genetically diagnosed with PKD or other PRRT2-related disorders.<br><br>INTERPRETATION: Combining in silico analyses with functional assays is essential for determining the pathogenicity of splice variants. It can help confirm the diagnosis of patients with clinically suspected PKD and other PRRT2-related disorders.}, }
@article {pmid40955442, year = {2025}, author = {Evenblij, D and Lührs, M and Rafeh, RW and Benitez Andonegui, A and Kurban, D and Valente, G and Sorger, B}, title = {Two Seconds to Speak: Increasing Communication Speed for fMRI-Based Brain-Computer Interfaces.}, journal = {Brain connectivity}, volume = {15}, number = {8}, pages = {283-299}, doi = {10.1177/21580014251376731}, pmid = {40955442}, issn = {2158-0022}, mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging/methods ; Male ; Female ; *Brain/physiology ; Adult ; Young Adult ; Brain Mapping/methods ; *Communication ; }, abstract = {Background: Brain-computer interfaces (BCIs) can provide alternative, motor-independent means of communication for people who have lost motor function. A promising variant is the functional magnetic resonance imaging (fMRI)-based BCI, which exploits information on hemodynamic brain activity evoked by performing different mental tasks. However, due to the sluggish nature of the hemodynamic response, a current challenge is to make these BCIs as efficient and fast as possible to allow useful clinical application. Furthermore, there is yet no consensus on optimal mental-task selection for multi-voxel pattern analysis-based decoding, nor whether certain tasks generalize well across users, or if individualized task selection would yield a higher decoding accuracy. Methods: To increase BCI efficiency, we tested whether distributed patterns of 3T-fMRI brain activation evoked by two-second mental tasks could be reliably discriminated in 2- to 7-class classification. In addition, we identified optimal mental-task combinations for high-accuracy classification across all classes. Finally, we examined whether individualized task selection-based on subjects' previous decoding performance (accuracy-based tasks) or their subjective preference (preference-based tasks)-was superior to the other in a yes/no communication paradigm. Results: The 2-class decoding resulted in a mean accuracy of 78% and 3- to 7-class accuracies were above chance level. Mental calculation and spatial navigation were most frequently associated with the highest decoding accuracy. Furthermore, subjects could encode yes/no answers using their accuracy-based and preference-based tasks with mean accuracies of 83% and 81%, respectively. This implies that this paradigm, using short encoding durations, is well-suited to the diversity of patients and could greatly increase BCI efficiency.}, }
@article {pmid40954927, year = {2025}, author = {Mehmood, A and Xu, S and Siddiqi, SM and Zhang, L and Huang, G and Liang, Z and Zhou, Y}, title = {Exploration of Nonsuicidal Self-Injury as an Addiction-Like Behaviour in Depressed Adolescents in the light of the I-PACE Model.}, journal = {Clinical psychology &amp; psychotherapy}, volume = {32}, number = {5}, pages = {e70147}, doi = {10.1002/cpp.70147}, pmid = {40954927}, issn = {1099-0879}, support = {62276169//National Natural Science Foundation of China/ ; 62201356//National Natural Science Foundation of China/ ; 2024YG008//Medical-Engineering Interdisciplinary Research Foundation of Shenzhen University/ ; 2023SHIBS0003//Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions/ ; 2021ZD0200500//STI 2030-Major Projects/ ; BMI2400008//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; JCYJ20241202124222027//Shenzhen Science and Technology Program/ ; JCYJ20241202124209011//Shenzhen Science and Technology Program/ ; }, mesh = {Humans ; Adolescent ; *Self-Injurious Behavior/psychology ; Male ; Female ; Child ; Resilience, Psychological ; *Behavior, Addictive/psychology ; Self Concept ; Models, Psychological ; *Depressive Disorder/psychology ; Rumination, Cognitive ; Executive Function ; }, abstract = {Nonsuicidal self-injury (NSSI) is increasingly conceptualized as an addiction-like behaviour characterized by dysregulated emotional and cognitive processes. Guided by the I-PACE model, this study investigated how person-level vulnerabilities interact with affective, mental and executive functioning to maintain NSSI in clinically depressed adolescents (N = 167, aged 12-18, M = 15.37 ± 1.75 years). Results revealed strong addiction-like patterns. Childhood trauma, depression and rumination demonstrated significant associations with NSSI frequency (r = 0.59-0.61), while resilience and self-esteem served as protective factors (r = -0.53 to -0.55). A hierarchical regression model explained 69% of variance, with trauma (OR = 1.12), depressive severity (OR = 1.11), rumination (OR = 1.11) and resilience (OR = 0.90) emerging as key predictors. Mediation analyses demonstrated how these factors operate in the addictive chain. Childhood trauma and borderline traits lead to affective dysregulation, which drives cognitive deficits that ultimately undermine resilience and increase NSSI risk (β = -0.28 and -0.24). These findings support the use of an addiction framework to conceptualize NSSI, while highlighting resilience-focused interventions as critical for breaking these maladaptive cycles.}, }
@article {pmid40954277, year = {2025}, author = {Zhou, D and Zhou, Y and Sun, Z and Ji, F and Zhang, D and Wang, Q and Ruan, Y and Wang, Y and Zhu, Y and Sun, X and Li, MJ and Yuan, C and Liu, K and Sun, L and Zhai, W and Fan, J and Zhu, K and Qiu, W and Yan, X and Ma, C and Shen, Y and Bao, A and Yue, W and Shi, Y and Chen, C and Yang, J and Duan, S and Zhang, J and , }, title = {The China Brain Multi-omics Atlas Project (CBMAP).}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {40954277}, issn = {1476-5578}, abstract = {The China Brain Multi-omics Atlas Project (CBMAP) aims to generate a comprehensive molecular reference map of over 1000 human brains (Phase I), spanning a broad age range and multiple regions in China, to address the underrepresentation of East Asian populations in brain research. By integrating genome, epigenome, transcriptome, proteome (including multiple post-translational modifications), and metabolome data, CBMAP is set to provide a rich and invaluable resource for investigating the molecular underpinnings of aging-related brain phenotypes and neuropsychiatric disorders. Leveraging high-throughput omics data and advanced technologies, such as spatial transcriptomics, proteomics, and single-nucleus 3D chromatin structure analysis, this atlas will serve as a crucial resource for the brain science community, illuminating disease mechanisms and enhancing the utility of data from genome-wide association studies (GWAS). CBMAP is also poised to accelerate drug discovery and precision medicine for brain disorders.}, }
@article {pmid40953646, year = {2025}, author = {Cao, Y and Pan, Z and Shen, X and Xu, Z and Yang, X and Yang, B and Luo, P and Yan, H and He, Q}, title = {CAMK2G in subcellular Ca[2+] homeostasis: Molecular mechanisms and therapeutic targeting.}, journal = {Biochemical pharmacology}, volume = {242}, number = {Pt 2}, pages = {117323}, doi = {10.1016/j.bcp.2025.117323}, pmid = {40953646}, issn = {1873-2968}, mesh = {Humans ; *Homeostasis/physiology/drug effects ; *Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism/antagonists &amp; inhibitors/genetics ; Animals ; *Calcium/metabolism ; *Calcium Signaling/physiology/drug effects ; Molecular Targeted Therapy/methods ; }, abstract = {The Ca[2+]/calmodulin-dependent protein kinase II (CAMK2) family, consisting of subtypes A, B, D, and G, plays a pivotal role in decoding Ca[2+] signals, an essential process in cellular communication and function. Among these, CAMK2G is notably widespread across various body tissues, with predominant expression in neurons and cardiomyocytes, where it significantly influences Ca[2+] signal transduction and the cellular response to stress. Ca[2+] serves as the most plentiful second messenger within the human body, orchestrating critical regulatory roles across numerous physiological and pathological contexts. It is instrumental in managing aspects of the tumor microenvironment, neurodegenerative conditions, cardiovascular diseases, and metabolic disorders. Maintaining Ca[2+] homeostasis is crucial for the proper functioning of different subcellular organelles, impacting overall cellular health and activity. Here, we describe the central connection between CAMK2G and subcellular Ca[2+] homeostasis, highlight the molecular functions of CAMK2G therein, and finally detail the cutting-edge therapeutic strategies targeting CAMK2G.}, }
@article {pmid40953427, year = {2025}, author = {Li, Z and Yan, C and Lan, Z and Xiang, X and Zhou, H and Lai, J and Tang, D}, title = {Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer-Based Dim Object Detection.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2025.3605710}, pmid = {40953427}, issn = {2162-2388}, abstract = {Advanced cognition can be measured from the human brain using brain-computer interfaces (BCIs). Integrating these interfaces with computer vision techniques, which possess efficient feature extraction capabilities, can achieve more robust and accurate detection of dim targets in aerial images. However, existing target detection methods primarily concentrate on homogeneous data, lacking efficient and versatile processing capabilities for heterogeneous multimodal data. In this article, we first build a brain-eye-computer-based object detection system for aerial images under few-shot conditions. This system detects suspicious targets using region proposal networks (RPNs), evokes the event-related potential (ERP) signal in electroencephalogram (EEG) through the eye-tracking-based slow serial visual presentation (ESSVP) paradigm, and constructs the EEG-image data pairs with eye movement data. Then, an adaptive modality balanced online knowledge distillation (AMBOKD) method is proposed to recognize dim objects with the EEG-image data. AMBOKD fuses EEG and image features using a multihead attention module, establishing a new modality with comprehensive features. To enhance the performance and robust capability of the fusion modality, simultaneous training and mutual learning between modalities are enabled by end-to-end online KD (OKD). During the learning process, an adaptive modality balancing module is proposed to ensure multimodal equilibrium by dynamically adjusting the weights of the importance and the training gradients across various modalities. The effectiveness and superiority of our method are demonstrated by comparing it with existing state-of-the-art methods. Additionally, experiments conducted on public datasets and real-world scenarios demonstrate the reliability and practicality of the proposed system and the designed method. The dataset and the source code can be found at: https://github.com/lizixing23/AMBOKD.}, }
@article {pmid40949716, year = {2025}, author = {Galang, EV and Velásquez, MA and Elcin, D and O'Connell, S and Wieck, J and McNair, S and Colombo, PJ}, title = {Systematic review and meta-analysis of the relationships between real-time neurofeedback training parameters and acquisition of neural modulation.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1652607}, pmid = {40949716}, issn = {1662-5161}, abstract = {INTRODUCTION: Real-time neurofeedback is an emerging method for regional modulation of neural activity with physiological and behavioral effects that persist beyond the duration of feedback sessions. However, many individuals fail to achieve successful modulation, a challenge known as the "neurofeedback inefficacy problem." This study examined how methodological parameters of neurofeedback training influence the acquisition and retention of neural modulation in healthy adults.<br><br>METHODS: A systematic search identified eligible studies, resulting in 55 participant groups included in the meta-analysis. Standardized mean differences (Hedges' g) were calculated for changes in neural activity from first to last session and from pre- to post-training. Subgroup analyses and meta-regression were conducted to assess the impact of discrete and continuous moderators.<br><br>RESULTS: This meta-analysis identified four parameters associated with significant neural modulation in the desired direction: the neurofeedback imaging device used, complexity of the feedback stimulus, presence of a pre-training rehearsal trial, and EEG target oscillations.<br><br>DISCUSSION: This meta-analysis highlights key methodological factors that shape neurofeedback efficacy in non-clinical populations. Findings may serve to better understand how methodological variables used in neurofeedback influence the acquisition and retention of neural modulation.<br><br>https://www.crd.york.ac.uk/PROSPERO/view/CRD42022357160, identifier: CRD42022357160.}, }
@article {pmid40949676, year = {2025}, author = {Cai, H and Hu, J and Zhao, C and Lin, J}, title = {Wearable devices in neurological disorders: a narrative review of status quo and perspectives.}, journal = {Annals of translational medicine}, volume = {13}, number = {4}, pages = {46}, pmid = {40949676}, issn = {2305-5839}, abstract = {BACKGROUND AND OBJECTIVE: Neurological disorders are a group of diseases involving motor, sensory, cognitive, and autonomic functions, among which stroke, Alzheimer's disease (AD), and Parkinson's disease (PD) are prevalent. Their management, especially in conditions with chronic courses or long-term sequelae, remains a substantial unmet need. With the growing comprehension of neuroscience, the development of digital technology, and the rising demand for quality of life, wearable devices offer a promising solution for disease management. The review aimed to evaluate the application and prospect of wearable devices in neurological disorders.<br><br>METHODS: We conducted the review by searching papers on the application of wearable devices and wearable technology in neurology and neurological disorders using multiple databases. We summarized the present development status of wearable devices, and outlined the potential value and future direction for further research.<br><br>KEY CONTENT AND FINDINGS: Existing wearable devices for neurological diseases can be applied to diagnosis and follow-up, as an electronic biomarker detector capturing subtle and objective changes in motor, sensory, and cognitive function. The devices can also be utilized for treatment and rehabilitation, mainly through exoskeletons and brain-computer interface. The application of wearable devices in neurology currently faces several critical limitations, including technical bottlenecks in the detection of fine motor and sensory functions, a lack of industry standards, and a limited sample size.<br><br>CONCLUSIONS: This review demonstrates the potential of wearable technology in people with neurological disorders, enabling disease management and clinical trials outside clinical settings in the future. Nevertheless, further research is required to develop lighter, more user-friendly devices with various functions. It is believed that with increasing demand and technical support, wearable devices would have a promising range of applications.}, }
@article {pmid40948973, year = {2025}, author = {De Pasquale, P and De Bartolo, D and Russo, M and Berger, DJ and Maselli, A and Borzelli, D and Colamarino, E and Mattia, D and Nissler, C and Nowak, M and Falomo, E and Soto Morras, J and Schiller, MR and Castellini, C and Morone, G and d'Avella, A}, title = {User-centered development of a personalized adaptive mirror therapy for upper-limb post-stroke rehabilitation using virtual reality and myoelectric control.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {13}, number = {}, pages = {1655416}, pmid = {40948973}, issn = {2296-4185}, abstract = {INTRODUCTION: Cerebral stroke often results in significant motor deficits, including contralateral hemiparesis of the upper limb. Rehabilitation protocols with high-intensity and task-specific exercises can improve these deficits. Recent technological advancements in virtual reality (VR), myoelectric control, and exergames may be exploited to enhance rehabilitation effectiveness. However, novel rehabilitation approaches combining these novel methodologies have rarely been developed with the active involvement of both therapists and patients.<br><br>METHODS: An interdisciplinary team developed a novel system, Validation of the Virtual Therapy Arm (VVITA), for post-stroke upper-limb rehabilitation combining VR, myoelectric control, and exergames using a user-centered design (UCD) approach. The VVITA hardware includes a head-mounted VR display, motion tracking devices integrated in the VR system, and wireless armbands to record electromyographic (EMG) signals, providing an interactive virtual environment for immersive rehabilitation exercises implementing a virtual mirror therapy. Assistance and task difficulty are adjusted dynamically based on patient performance, promoting active participation and motor learning.<br><br>RESULTS: The development process involved iterative phases, involving focus groups with stroke patients, therapists, and researchers. A pilot study with four stroke survivors assessed the system's feasibility, demonstrating its potential for personalized and adaptive rehabilitation.<br><br>CONCLUSION: The VVITA system enhances mirror therapy by integrating VR and myoelectric control, providing a tailored approach to upper-limb post-stroke rehabilitation. The UCD approach ensured the system met patient and therapist needs, showing promise for improving motor recovery and rehabilitation outcomes.}, }
@article {pmid40947448, year = {2025}, author = {Wang, Q and Dong, X and Jiang, D and Tian, S and Qiu, Y and Zhu, Y and Wu, J and Shang, S and Zhang, Y and Wang, P and Zhuang, L}, title = {Bioelectronic Interfaces and Sensors for Neural Organoids.}, journal = {Microsystems &amp; nanoengineering}, volume = {11}, number = {1}, pages = {172}, pmid = {40947448}, issn = {2055-7434}, support = {No. 82330064, 32250008, 62271443//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ24H090008//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {Neural organoids are emerging as promising in vitro models, offering a unique platform to partially recapitulate the structural and functional complexity of the human nervous system. These three-dimensional (3D) constructs, which mimic key aspects of organ architecture, can be reliably derived from pluripotent stem cells (iPSCs) or embryonic stem cells (ESCs). Their ability to faithfully model neural development and disease pathogenesis has positioned them as indispensable tools in neuroscience research. However, to further unleash their potential, there is a pressing need for long-term and stable monitoring of their dynamic functions in a 3D context. This review provides a brief overview on diverse types of neural organoids and their induction protocols. We further highlight recent advancements in bioelectronic interfaces and sensors tailored for 3D culture. Finally, we discuss future directions aimed at advanced methodologies for real-time, multidimensional functional analysis, ultimately paving the way for breakthroughs in understanding neural development and pathology.}, }
@article {pmid40946865, year = {2025}, author = {Zhao, R and Daly, I and Chen, Y and Wu, W and Liu, L and Wang, X and Cichocki, A and Jin, J}, title = {MSAttNet: Multi-scale attention convolutional neural network for motor imagery classification.}, journal = {Journal of neuroscience methods}, volume = {424}, number = {}, pages = {110578}, doi = {10.1016/j.jneumeth.2025.110578}, pmid = {40946865}, issn = {1872-678X}, mesh = {Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Imagination/physiology ; *Attention/physiology ; *Motor Activity/physiology ; Algorithms ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Convolutional Neural Networks ; }, abstract = {BACKGROUND: Convolutional neural networks (CNNs) are widely employed in motor imagery (MI) classification. However, due to cumbersome data collection experiments, and limited, noisy, and non-stationary EEG signals, small MI datasets present considerable challenges to the design of these decoding algorithms.<br><br>NEW METHOD: To capture more feature information from inadequately sized data, we propose a new method, a multi-scale attention convolutional neural network (MSAttNet). Our method includes three main components-a multi-band segmentation module, an attention spatial convolution module, and a multi-scale temporal convolution module. First, the multi-band segmentation module adopts a filter bank with overlapping frequency bands to enhance features in the frequency domain. Then, the attention spatial convolution module is used to adaptively adjust different convolutional kernel parameters according to the input through the attention mechanism to capture the features of different datasets. The outputs of the attention spatial convolution module are grouped to perform multi-scale temporal convolution. Finally, the output of the multi-scale temporal convolution module uses the bilinear pooling layer to extract temporal features and perform noise elimination. The extracted features are then classified.<br><br>RESULTS: We use four datasets, including BCI Competition IV Dataset IIa, BCI Competition IV Dataset IIb, the OpenBMI dataset and the ECUST-MI dataset, to test our proposed method. MSAttNet achieves accuracies of 78.20%, 84.52%, 75.94% and 78.60% in cross-session experiments, respectively.<br><br>Compared with state-of-the-art algorithms, MSAttNet enhances the decoding performance of MI tasks.<br><br>CONCLUSION: MSAttNet effectively addresses the challenges of MI-EEG datasets, improving decoding performance by robust feature extraction.}, }
@article {pmid40945816, year = {2025}, author = {Fan, YS and Yang, P and Zhu, Y and Jing, W and Xu, Y and Xu, Y and Guo, J and Lu, F and Yang, M and Huang, W and Chen, H}, title = {Neurodevelopmental deviations in schizophrenia: Evidences from multimodal connectome-based brain ages.}, journal = {Progress in neuro-psychopharmacology &amp; biological psychiatry}, volume = {142}, number = {}, pages = {111498}, doi = {10.1016/j.pnpbp.2025.111498}, pmid = {40945816}, issn = {1878-4216}, mesh = {Humans ; *Schizophrenia/diagnostic imaging/physiopathology/pathology ; *Connectome/methods ; Female ; Male ; *Brain/growth &amp; development/diagnostic imaging/physiopathology/pathology ; Adolescent ; Magnetic Resonance Imaging ; Young Adult ; Child ; Adult ; Machine Learning ; Multimodal Imaging ; }, abstract = {BACKGROUND: Pathologic schizophrenia processes originate early in brain development, leading to detectable brain alterations via structural and functional magnetic resonance imaging (MRI). Recent MRI studies have sought to characterize disease effects from a brain age perspective, but developmental deviations from the typical brain age trajectory in youths with schizophrenia remain unestablished. This study investigated brain development deviations in early-onset schizophrenia (EOS) patients by applying machine learning algorithms to structural and functional MRI data.<br><br>METHODS: Multimodal MRI data, including T1-weighted MRI (T1w-MRI), diffusion MRI, and resting-state functional MRI (rs-fMRI) data, were collected from 80 antipsychotic-naive first-episode EOS patients and 91 typically developing (TD) controls. The morphometric similarity connectome (MSC), structural connectome (SC), and functional connectome (FC) were separately constructed by using these three modalities. According to these connectivity features, eight brain age estimation models were first trained with the TD group, the best of which was then used to predict brain ages in patients. Individual brain age gaps were assessed as brain ages minus chronological ages.<br><br>RESULTS: Both the SC and MSC features performed well in brain age estimation, whereas the FC features did not. Compared with the TD controls, the EOS patients showed increased absolute brain age gaps when using the SC or MSC features, with opposite trends between childhood and adolescence. These increased brain age gaps for EOS patients were positively correlated with the severity of their clinical symptoms.<br><br>CONCLUSION: These findings from a multimodal brain age perspective suggest that advanced brain age gaps exist early in youths with schizophrenia.}, }
@article {pmid40945543, year = {2025}, author = {Bao, X and Feng, X and Chen, D and Huang, H and Cai, Y and Huang, Q and Li, Y}, title = {Thalamocortical dysrhythmia-related sleep spindle desynchronization in patients with tinnitus.}, journal = {Neurobiology of disease}, volume = {216}, number = {}, pages = {107081}, doi = {10.1016/j.nbd.2025.107081}, pmid = {40945543}, issn = {1095-953X}, mesh = {Humans ; *Tinnitus/physiopathology ; Female ; Male ; Middle Aged ; Adult ; Electroencephalography ; *Thalamus/physiopathology ; *Sleep/physiology ; *Cerebral Cortex/physiopathology ; Sleep Stages/physiology ; *Cortical Synchronization/physiology ; Aged ; }, abstract = {Patients with tinnitus commonly suffer from sleep problems, and the underlying neural mechanisms remain unclear. Previous studies have focused primarily on the correlation between patients' sleep structure and tinnitus, lacking exploration into the links between sleep problems and the underlying pathological mechanisms of tinnitus, such as thalamocortical dysrhythmia (TCD). Here, we present the first study on neural oscillatory patterns in patients with tinnitus during sleep spindles, a more precise subdivision of sleep that overlaps in neuropathological pathways with TCD. Sleep electroencephalogram (EEG) were recorded from 51 tinnitus participants and 51 healthy participants. During sleep spindles, patients with tinnitus exhibited a significant increase in 18-45 Hz and a stronger cross-frequency coupling, resembling the EEG abnormalities caused by TCD during wakefulness. With respect to spindle characteristics, tinnitus is linked to an increase in spindle quantity but a decrease in spindle root-mean-square and functional connectivity, suggesting that normal function of tinnitus spindles is impaired. Our findings indicated that neural oscillation dynamics related to TCD during sleep spindles serve as neural biomarkers for sleep disturbances in tinnitus participants. We demonstrate that the impact of the TCD pathological mechanism in tinnitus is not confined to the waking state but extends into the sleep stage as well, which advances our comprehension of the neural mechanisms underlying sleep-related problems in tinnitus.}, }
@article {pmid40944703, year = {2025}, author = {Elliss, H and Proctor, K and Robertson, M and Bagnall, J and Kasprzyk-Hordern, B}, title = {A new wide-scope, multi-biomarker wastewater-based epidemiology analytical method to monitor the health and well-being of inhabitants at a metropolitan scale.}, journal = {Analytical and bioanalytical chemistry}, volume = {417}, number = {26}, pages = {5983-6005}, pmid = {40944703}, issn = {1618-2650}, mesh = {Humans ; *Biomarkers/analysis ; *Wastewater/analysis/chemistry ; *Water Pollutants, Chemical/analysis ; *Wastewater-Based Epidemiological Monitoring ; Limit of Detection ; *Environmental Monitoring/methods ; Mass Spectrometry/methods ; }, abstract = {This manuscript establishes a new, comprehensive biomarker list and a multiresidue trace quantification method for community-wide health and well-being assessment at a metropolitan scale using wastewater-based epidemiology (WBE) and mass spectrometry pipelines. This method enables the quantification of 204 biochemical indicators (BCIs) across a range of biomarker classes within influent wastewater and includes illicit drug BCIs, pharmaceuticals as proxies for disease, health markers (hormones, oxidative stress, lipid peroxidation, etc.), Lifestyle chemicals, food BCIs, and hazardous chemicals in personal care products. This method facilitates the combined assessment of community exposure to chemicals and the effects of this exposure in the same framework. The method enables full quantification of 141 BCIs with method detection Limits varying from 0.01 ng/L for amlodipine to 23.8 ng/L for stachydrine. Total average method accuracies were 102.7% whereas precision was 10.4%. During an initial assessment of this method to test its suitability, 62% of all targets were detected and quantified during a week-long feasibility study of a large city with weekly average Daily BCI loads ranging from 40.0 ± 20.0 mg/day for salbutamol to 5836.5 ± 1697.1 g/day for creatinine. The inclusion of new endogenous markers such as advanced glycation end products, detected in wastewater for the first time, enables more accurate determination of community-level health and lifestyle habits. Alongside an unbiased and comprehensive health assessment through endogenous markers, health is further assessed via the use of pharmaceuticals, acting as a proxy for health and disease status whilst additionally providing insights into community lifestyle habits through the monitoring of licit/illicit drug use and food consumption. The analysis of all biomarker classes combined aims to provide insights to exposure and health effect outcomes at the community level.}, }
@article {pmid40942766, year = {2025}, author = {Han, Q and Ye, H and Sun, Y and Song, Z and Zhao, J and Shi, L and Kuang, Z}, title = {TopoTempNet: A High-Accuracy and Interpretable Decoding Method for fNIRS-Based Motor Imagery.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {17}, pages = {}, pmid = {40942766}, issn = {1424-8220}, support = {YDZJ202201ZYTS684//Development program project of the Science and Technology Department of Jilin Province, China/ ; }, mesh = {Humans ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Spectroscopy, Near-Infrared/methods ; }, abstract = {Functional near-infrared spectroscopy (fNIRS) offers a safe and portable signal source for brain-computer interface (BCI) applications, particularly in motor imagery (MI) decoding. However, its low sampling rate and hemodynamic delay pose challenges for temporal modeling and dynamic brain network analysis. To address these limitations in temporal dynamics, static graph modeling, and feature fusion interpretability, we propose TopoTempNet, an innovative topology-enhanced temporal network for biomedical signal decoding. TopoTempNet integrates multi-level graph features with temporal modeling through three key innovations: (1) multi-level topological feature construction using local and global functional connectivity metrics (e.g., connection strength, density, global efficiency); (2) a graph-modulated attention mechanism combining Transformer and Bi-LSTM to dynamically model key connections; and (3) a multimodal fusion strategy uniting raw signals, graph structures, and temporal representations into a high-dimensional discriminative space. Evaluated on three public fNIRS datasets (MA, WG, UFFT), TopoTempNet achieves superior accuracy (up to 90.04% ± 3.53%) and Kappa scores compared to state-of-the-art models. The ROC curves and t-SNE visualizations confirm its excellent feature discrimination and structural clarity. Furthermore, the statistical analysis of graph features reveals the model's ability to capture task-specific functional connectivity patterns, enhancing the interpretability of decoding outcomes. TopoTempNet provides a novel pathway for building interpretable and high-performance BCI systems based on fNIRS.}, }
@article {pmid40942721, year = {2025}, author = {Kauati-Saito, E and Pereira, ADS and Fontana, AP and de Sá, AMFLM and Soares, JGM and Tierra-Criollo, CJ}, title = {Classification of Different Motor Imagery Tasks with the Same Limb Using Electroencephalographic Signals.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {17}, pages = {}, pmid = {40942721}, issn = {1424-8220}, support = {CNPq grants 312592/ 2020-5 and 303066/2025-3//Brazilian institutions National Council for Scientific and Technological Development/ ; CAPES process No. 88887.853338/2023-00 and 23038.008788/2017-27//Coordination of Superior Level Staff Improvement/ ; FINEP process No. 01.24.0122.00//Financier for Studies and Projects/ ; FAPERJ process No. E-26/204.393/2024, 201.618/2025, E-211.635/2021, E-26/202.587/2019, and E-26/ 200.338/2023//the Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Movement/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Male ; *Extremities/physiology ; Adult ; Algorithms ; }, abstract = {Stroke is a neurological condition that often results in long-term motor deficits. Given the high prevalence of motor impairments worldwide, there is a critical need to explore innovative neurorehabilitation strategies that aim to enhance the quality of life of patients. One promising approach involves brain-computer interface (BCI) systems controlled by electroencephalographic (EEG) signals elicited when a subject performs motor imagery (MI), which is the mental simulation of movement without actual execution. Such systems have shown potential for facilitating motor recovery by promoting neuroplastic mechanisms. Controlling BCI systems based on MI-EEG signals involves the following sequential stages: recording the raw signal, preprocessing, feature extraction and selection, and classification. Each of these stages can be executed using several techniques and numerous parameter combinations. In this study, we searched for the combination of feature extraction technique, time window, frequency range, and classifier that could provide the best classification accuracy for the BCI Competition 2008 IV 2a benchmark dataset (BCI-C), characterized by EEG-MI data of different limbs (four classes, of which three were used in this work), and the NeuroSCP EEG-MI dataset, a custom experimental protocol developed in our laboratory, consisting of EEG recordings of different movements with the same limb (three classes-right dominant arm). The mean classification accuracy for BCI-C was 76%. When the subjects were evaluated individually, the best-case classification accuracy was 94% and the worst case was 54%. For the NeuroSCP dataset, the average classification result was 53%. The individual subject's evaluation best-case was 71% and the worst case was 35%, which is close to the chance level (33%). These results indicate that techniques commonly applied to classify different limb MI based on EEG features cannot perform well when classifying different MI tasks with the same limb. Therefore, we propose other techniques, such as EEG functional connectivity, as a feature that could be tested in future works to classify different MI tasks of the same limb.}, }
@article {pmid40938318, year = {2025}, author = {Dash, D and Iwane, F and Hayward, W and Salamanca-Giron, RF and Bönstrup, M and Buch, ER and Cohen, LG}, title = {Sequence action representations contextualize during early skill learning.}, journal = {eLife}, volume = {13}, number = {}, pages = {}, pmid = {40938318}, issn = {2050-084X}, support = {NINDS Intramural Research Program/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Learning/physiology ; *Motor Skills/physiology ; Male ; Magnetoencephalography ; Female ; Adult ; Fingers/physiology ; Young Adult ; Machine Learning ; *Brain/physiology ; }, abstract = {Activities of daily living rely on our ability to acquire new motor skills composed of precise action sequences. Here, we asked in humans if the millisecond-level neural representation of an action performed at different contextual sequence locations within a skill differentiates or remains stable during early motor learning. We first optimized machine learning decoders predictive of sequence-embedded finger movements from magnetoencephalographic (MEG) activity. Using this approach, we found that the neural representation of the same action performed in different contextual sequence locations progressively differentiated-primarily during rest intervals of early learning (offline)-correlating with skill gains. In contrast, representational differentiation during practice (online) did not reflect learning. The regions contributing to this representational differentiation evolved with learning, shifting from the contralateral pre- and post-central cortex during early learning (trials 1-11) to increased involvement of the superior and middle frontal cortex once skill performance plateaued (trials 12-36). Thus, the neural substrates supporting finger movements and their representational differentiation during early skill learning differ from those supporting stable performance during the subsequent skill plateau period. Representational contextualization extended to Day 2, exhibiting specificity for the practiced skill sequence. Altogether, our findings indicate that sequence action representations in the human brain contextually differentiate during early skill learning, an issue relevant to brain-computer interface applications in neurorehabilitation.}, }
@article {pmid40937924, year = {2025}, author = {Qu, Y and Hao, M and Hao, H and Ke, S and Li, Y and Wang, C and Xiao, Y and Jiang, B and Zhou, K and Ding, B and Chu, PK and Yu, XF and Wang, J}, title = {2D Vanadium Carbide/Oxide Heterostructure-Based Artificial Sensory Neuron for Multi-Color Near-Infrared Object Recognition.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e12238}, doi = {10.1002/adma.202512238}, pmid = {40937924}, issn = {1521-4095}, support = {2023YFA0915600//National Key R&amp;D Program of China/ ; 2024A1515030176//Natural Science Foundation of Guangdong Province/ ; 2025B1515020088//Natural Science Foundation of Guangdong Province/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; JCYJ20220818100806014//Shenzhen Science and Technology Program/ ; XDB0930000//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; 52273311//National Natural Science Foundation of China/ ; T2293693//National Natural Science Foundation of China/ ; KCXFZ2024090309420300//Shenzhen Innovation and Entrepreneurship Program-Science and Technology Major Project/ ; GZC20241837//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; DON-RMG 9229021//City University of Hong Kong Donation Research Grants/ ; 9220061//City University of Hong Kong Donation Research Grants/ ; 2025WK2013//Key Project of Research and Development Plan of Hunan Province/ ; }, abstract = {Near-infrared (NIR) photon detection and object recognition are crucial technologies for all-weather target identification in autonomous navigation, nighttime surveillance, and tactical reconnaissance. However, conventional NIR detection systems, which rely on photodetectors and von Neumann computing algorithms, are plagued by energy inefficiency and signal transmission bottlenecks. Herein, a vanadium carbide/oxide (V2C/V2O5-x) heterostructure is designed and synthesized by a topochemical conversion method. The V2C/V2O5-x heterostructure-based memristor exhibits stable threshold-type resistance switching (RS) behavior with low coefficient of variation in transition voltages (1.62% and 1.7%) over thousands of cycles, and maintains stable performance even after storage for 90 days. Benefiting from the NIR responsivity of V2C and the volatile RS enabled by vacancy-enriched V2O5-x, devices exhibit a linear variation in threshold voltage in response to NIR light power density and wavelength. Based on the multi-color NIR modulable RS characteristics and the YOLOv7 algorithm model, an artificial neural network (ANN) architecture achieves average recognition accuracies of 89.6% for cars and 85.9% for persons on the FLIR dataset. This work reveals a heterostructure with versatile functionalities for neuromorphic devices and establishes a memristor-based ANN platform for multi-color object detection and recognition in complex real-world scenarios.}, }
@article {pmid40936365, year = {2025}, author = {Liu, Y and Xu, G and Li, C and Ma, Y and Ji, N and Feng, X}, title = {Stretchable Multilevel Mesh Brain Electrodes for Neuroplasticity in Glioma Patients Undergoing Surgery.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e03358}, doi = {10.1002/adhm.202503358}, pmid = {40936365}, issn = {2192-2659}, support = {2023YFB3609002//National Basic Research Program of China/ ; 2023YFB3609002//National Basic Research Program of China/ ; 2022YFC2403905//National Basic Research Program of China/ ; U20A6001//National Natural Science Foundation of China/ ; 11921002//National Natural Science Foundation of China/ ; 12002190//National Natural Science Foundation of China/ ; 2023YFB3609002//National Key R&amp;D Program of China/ ; 2023YFB3609002//National Key R&amp;D Program of China/ ; 2022YFC2403905//National Key R&amp;D Program of China/ ; 2022-2-2047//Capital Health Research and Development of Special Fund/ ; }, abstract = {Brain disease surgical treatment usually leads to neurological dysfunction. Electroencephalogram (EEG)-based neuroplasticity study may facilitate patient nerve function recovery from injury, allowing a return to normal activities. Due to the limitations of wound infections and hair barrier effects, a traditional brain-computer interface system is not applicable to patients after tumor resection. Here, stretchable multilevel mesh brain electrodes with reconfigurable interfaces are developed. The electrode has a multilevel mesh and malleable structure to avoid hair blockage between the electrode and scalp, realizing the conformal attachment of the stretchable multilevel mesh brain electrodes to a nondevelopable curved brain surface. Moreover, the thermally reversible hydrogel forms a good reconfigurable interface contact between the electrode and scalp, reducing postoperative infection and secondary injury risks to ensure the high-quality acquisition EEGs. In this study, a newly invented stretchable multilevel mesh brain electrodes is applied to test the preoperative and postoperative EEGs of recurrent glioblastoma patients for the first time. The obvious inhibitory effects of tumors on brain activity (a-wave signals) are discovered. More importantly, the EEG signals gradually enhance with postoperative recovery, which is mutually confirmed with the Karnofsky score results, showing the possibility of neural function remodeling neurological rehabilitation in adults.}, }
@article {pmid40934551, year = {2025}, author = {Das, N and Chakraborty, M}, title = {EEGOpt: A performance efficient Bayesian optimization framework for automated EEG signal classification.}, journal = {Computers in biology and medicine}, volume = {197}, number = {Pt B}, pages = {111023}, doi = {10.1016/j.compbiomed.2025.111023}, pmid = {40934551}, issn = {1879-0534}, mesh = {*Electroencephalography/methods ; Humans ; Bayes Theorem ; *Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {BACKGROUND: Accurate classification of electroencephalography (EEG) signals depends on the optimal combination of signal processing, feature extraction, and classification methods. Since no single approach is suitable across different domains, identifying the best methods for each application remains a critical challenge.<br><br>OBJECTIVE: We propose EEGOpt, a Bayesian optimization framework designed to automate and optimize methodological choices in electroencephalography (EEG) signal processing and classification.<br><br>METHODS: EEGOpt employed Tree-Structured Parzen Estimator (TPE) to optimize signal denoising, feature extraction, and classifier selection. The search space included Empirical Mode Decomposition and Wavelet Packet Decomposition (WPD) for denoising; spatiotemporal, nonlinear, and spectral features; and classifiers with distinct decision boundaries. A modular caching mechanism was used to minimize redundant computations. EEGOpt was evaluated on three datasets and benchmarked against deep-learning models (EEGNet, ShallowConvNet, and DeepConvNet). TPE was compared with sampling methods, including Gaussian Process, Covariance Matrix Adaptation Evolution Strategy, Quasi-Monte Carlo, and random search.<br><br>RESULTS: EEGOpt achieved classification accuracies of up to 99.63&#xa0;%, outperforming EEGNet (96.20&#xa0;%), ShallowConvNet (90.83&#xa0;%), and DeepConvNet (90.29&#xa0;%). The caching mechanism reduced computation time by 74.69&#xa0;% compared to no caching, and by 95&#xa0;% compared to deep learning models. TPE was effective in navigating hierarchical search spaces to locate global optima. EEGOpt identified covariance and wavelet features, k-nearest neighbor classifier, and WPD denoising as optimal for music-based EEG classification.<br><br>CONCLUSION: EEGOpt is a scalable and interpretable framework that automatically identifies optimal signal processing and classification strategies adaptable to EEG datasets, making it a valuable tool for neuroscientific research, diagnostics, and brain-computer interface development.}, }
@article {pmid40933818, year = {2025}, author = {Tang, MY and Zhang, YY and Lin, L and Wu, LL and Hu, MT and Tan, LH and Yu, CX and Wang, H and Yu, YQ and Ding, Y and Han, JX and Hu, H and Li, XM and Lian, H}, title = {Medial preoptic CCKAR mediates anxiety and aggression induced by chronic emotional stress in male mice.}, journal = {National science review}, volume = {12}, number = {10}, pages = {nwaf152}, pmid = {40933818}, issn = {2053-714X}, abstract = {Anxiety disorders frequently accompany aggression, with their co-occurrence predicting greater functional impairment and poor prognosis. Nevertheless, the underlying neural mechanisms remain elusive, primarily due to a lack of appropriate animal models. Here, we designed a chronic conspecific outsider stress (CCS) model in which male mice underwent perceived social threats and exhibited increased anxiety-like behaviors accompanied by aggression. CCS led to Fos activation and hyperexcitability of GABAergic neurons in the medial preoptic area (mPOA). Inhibition of mPOA GABAergic (mPOA[Gad2]) neurons rescued CCS-induced anxiety-like and aggressive behaviors, whereas activating these cells induced susceptibility to CCS. Moreover, CCS upregulated the mRNA and protein expression of the sexual-dimorphic gene, cholecystokinin A receptor (CCKAR)-encoding Cckar gene in the mPOA. Importantly, the knock-down and overexpression of CCKAR in the mPOA[Gad2] neurons had alleviating and promoting effects on anxiety-like and aggressive behaviors, aligning with decreased and increased excitability by the anxiolytic CCKAR antagonist MK-329 and the anxiogenic CCKAR agonist A71623 in mPOA[Gad2] neurons, respectively. Overall, our study characterizes a novel mouse model of anxiety disorders accompanied by aggression and the neuronal subpopulation and molecular mediator of the aberrant behaviors provide potential targets of intervention for anxiety disorders with aggression.}, }
@article {pmid40933619, year = {2025}, author = {Hu, D and Li, H and Takahata, T and Tanigawa, H}, title = {Clustered architecture of ipsilateral and interhemispheric connections in macaque ventrolateral prefrontal cortex.}, journal = {Frontiers in neural circuits}, volume = {19}, number = {}, pages = {1635105}, pmid = {40933619}, issn = {1662-5110}, mesh = {Animals ; *Prefrontal Cortex/cytology/physiology ; *Neural Pathways/physiology/cytology ; Macaca mulatta ; Male ; *Neurons/cytology/physiology ; *Functional Laterality/physiology ; }, abstract = {The fine-scale organization of intrinsic and extrinsic connections in the primate ventrolateral prefrontal cortex (VLPFC), a region essential for higher cognitive functions, remains poorly understood. This contrasts with, for example, the well-documented stripe-like intrinsic circuits of the dorsolateral prefrontal cortex (DLPFC). To elucidate the circuit architecture supporting VLPFC function, we investigated the spatial organization of connections targeting the caudal VLPFC (primarily area 45A) in macaque monkeys using multiple retrograde tracers. Analyzing the distribution of labeled neurons in flattened tangential sections revealed that laterally projecting connections within the same hemisphere formed distinct clusters, not only in the VLPFC but also in the DLPFC. These clusters often spanned multiple cortical layers, suggesting a columnar-like organization. The width (minor axis) of these clusters was approximately 1.2 mm. Similarly, contralateral callosal projection neurons were also arranged in clusters. Additionally, inputs originating from the superior temporal sulcus were found to arise from discrete clusters of neurons. Our findings demonstrate that both long-range ipsilateral and interhemispheric connections of the caudal VLPFC share a common, fine-scale clustered architecture. This study provides an anatomical framework for understanding the structural basis of information processing and interhemispheric coordination within this critical association cortex, suggesting that this architecture is fundamental to VLPFC's role in complex cognitive functions.}, }
@article {pmid40932879, year = {2025}, author = {Attar, ET}, title = {EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1616456}, pmid = {40932879}, issn = {1662-5161}, abstract = {INTRODUCTION: This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.<br><br>METHODS: The study utilized data from 13 participants aged 24-58, which researchers obtained through an openly accessible OpenNeuro dataset.<br><br>RESULT: Examination of eventrelated potentials (ERPs) demonstrated that P300 amplitude showed significant growth when responding to oddball stimuli, which indicates increased attention allocation (p < 0.05). Spectral power analysis demonstrated an increase in frontal alpha and beta power during meditation while central theta power decreased, which suggests reduced cognitive load and enhanced internal focus. Meditation experience showed a statistical relationship with frontal alpha power, where r = 0.45 and p < 0.03. A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.<br><br>CONCLUSION: The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain-computer interfaces and mental health applications. The study of meditation's effects on brain activity reveals its benefits for emotional regulation and concentration improvement. The research findings deliver strong evidence that meditation induces distinct neural modifications detectable through ERP and spectral analysis. The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain-computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.}, }
@article {pmid40931087, year = {2025}, author = {Liu, W and Xiang, M and Ding, N}, title = {Active use of latent tree-structured sentence representation in humans and large language models.}, journal = {Nature human behaviour}, volume = {}, number = {}, pages = {}, pmid = {40931087}, issn = {2397-3374}, abstract = {Understanding how sentences are represented in the human brain, as well as in large language models (LLMs), poses a substantial challenge for cognitive science. Here we develop a one-shot learning task to investigate whether humans and LLMs encode tree-structured constituents within sentences. Participants (total N = 372, native Chinese or English speakers, and bilingual in Chinese and English) and LLMs (for example, ChatGPT) were asked to infer which words should be deleted from a sentence. Both groups tend to delete constituents, instead of non-constituent word strings, following rules specific to Chinese and English, respectively. The results cannot be explained by models that rely only on word properties and word positions. Crucially, based on word strings deleted by either humans or LLMs, the underlying constituency tree structure can be successfully reconstructed. Altogether, these results demonstrate that latent tree-structured sentence representations emerge in both humans and LLMs.}, }
@article {pmid40930287, year = {2025}, author = {Shirodkar, VR and Edla, DR and Kumari, A and Dharavath, R}, title = {Deep feature extraction and swarm-optimized enhanced extreme learning machine for motor imagery recognition in stroke patients.}, journal = {Journal of neuroscience methods}, volume = {424}, number = {}, pages = {110565}, doi = {10.1016/j.jneumeth.2025.110565}, pmid = {40930287}, issn = {1872-678X}, mesh = {Humans ; *Stroke/physiopathology ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; Male ; Female ; *Machine Learning ; Middle Aged ; *Motor Activity/physiology ; Neural Networks, Computer ; Aged ; Adult ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation ; Extreme Learning Machines ; }, abstract = {BACKGROUND: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.<br><br>NEW METHODS: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification. The approach begins with subject-specific frequency band selection based on event-related desynchronization (ERD), aimed at reducing non-stationarity and improving signal relevance. Spatial and temporal features are then extracted using a combination of the scale-invariant feature transform (SIFT) and a one-dimensional convolutional neural network (1D CNN), providing a comprehensive representation of EEG signal dynamics. These features are fused and classified using an enhanced extreme learning machine (EELM), with hidden layer weights optimized using differential evolution (DE), particle swarm optimization (PSO), and dynamic multi-swarm PSO (DMS-PSO).<br><br>RESULTS: Experimental validation on a dataset of 50 stroke patients demonstrated an average classification accuracy of 97% using DMS-PSO with 10-fold cross-validation. Additional evaluation on the BCI Competition IV 1a dataset yielded 95% and 91.56% on IV 2a, indicating strong generalization performance.<br><br>Unlike conventional BCI approaches, this method combines adaptive filtering, spatial-temporal hybrid feature learning, and metaheuristic optimization, resulting in a lightweight model with improved classification accuracy and robustness.<br><br>CONCLUSION: These findings demonstrate the effectiveness of evolutionary optimization in dealing with the constraints provided by high-dimensional, non-stationary EEG data, making it a promising strategy for real-time MI classification in BCI-based stroke applications.}, }
@article {pmid40929275, year = {2025}, author = {Kim, TY and Son, Y and Yook, KY and Lee, DG and Kim, Y and Kim, SJ and Park, K and Lee, Y and Lee, TK and Chung, JJ and Yang, C and Park, S and Seo, J}, title = {Bioadaptive liquid-infused multifunctional fibers for long-term neural recording via BDNF stabilization and enhanced neural interaction.}, journal = {Science advances}, volume = {11}, number = {37}, pages = {eadz1228}, pmid = {40929275}, issn = {2375-2548}, mesh = {*Brain-Derived Neurotrophic Factor/chemistry/metabolism ; *Neurons/physiology/metabolism/drug effects ; Animals ; *Brain-Computer Interfaces ; Astrocytes/metabolism ; Coated Materials, Biocompatible/chemistry ; Rats ; }, abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and computers. However, their long-term functionality remains limited due to signal degradation caused by acute insertion trauma, chronic foreign body reaction (FBR), and biofouling at the device-tissue interface. To address these challenges, we introduce a multifunctional surface modification strategy called targeting-specific interaction and blocking nonspecific adhesion (TAB) coating for flexible fiber, achieving a synergistic integration of mechanical compliance and biochemical stability. The coating combines brain-derived neurotrophic factor (BDNF) conjugation and a lubricant-infused surface. This dual-functional design enables selective interaction with neurons and astrocytes while preventing nonspecific adhesion. Notably, high-quality single-unit neural signals were stably recorded for more than 12 months after implantation, demonstrating exceptional long-term recording performance. Integrating mechanical compatibility, antifouling properties, and selective neural cell interaction, the TAB-coated multifunctional fiber represents a transformative approach for neural implants, bridging biological systems with computational systems.}, }
@article {pmid40928672, year = {2025}, author = {Mou, F and Lv, Z and Jin, X and Pan, J and Yun, L and Chen, Z}, title = {Decoding binocular color differences via EEG signals: linking ERP dynamics to chromatic disparity in CIELAB space.}, journal = {Experimental brain research}, volume = {243}, number = {10}, pages = {209}, pmid = {40928672}, issn = {1432-1106}, support = {62165019//National Science Foundation of China/ ; 202305 AC160084//Yunnan Youth and Middle-aged Academic and Technical Leaders Reserve Talent Program/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Color Perception/physiology ; Young Adult ; *Evoked Potentials, Visual/physiology ; Photic Stimulation ; *Vision, Binocular/physiology ; *Vision Disparity/physiology ; Neural Networks, Computer ; Event-Related Potentials, P300/physiology ; *Evoked Potentials/physiology ; Support Vector Machine ; }, abstract = {This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color differences, defined in the CIELAB color space with constant luminance and chroma, are investigated in this study. Analysis of Event-Related Potentials (ERPs) revealed a significant decrease in the amplitude of the P300 component as binocular color differences increased, suggesting a measurable brain response to these differences. Four classification models-Support Vector Machines (SVM), EEGNet, Temporal Convolutional Neural Network (T-CNN), and a hybrid CNN-LSTM model were employed to decode EEG data. The highest accuracy reached was 81.93% for binary classification tasks (the largest color differences) and 54.47% for a more nuanced four-class categorization, significantly exceeding random chance. This research offers the first evidence that binocular color differences can be objectively decoded through EEG signals, providing insights into the neural mechanisms of visual perception and forming a basis for developing color-based brain-computer interfaces (BCIs).}, }
@article {pmid40926818, year = {2025}, author = {Wu, S and Li, K and Long, J and Zhang, C and Li, R and Cheng, B and Cao, M and Deng, W}, title = {Risk Factors for Postictal Delirium in Geriatric Patients Undergoing Electroconvulsive Therapy: The Role of Lithium and Quetiapine.}, journal = {Alpha psychiatry}, volume = {26}, number = {4}, pages = {45431}, pmid = {40926818}, issn = {2757-8038}, abstract = {BACKGROUND: Postictal delirium (PID) is a significant and often underrecognized adverse effect associated with electroconvulsive therapy (ECT) in geriatric patients. Despite its clinical relevance, the specific risk factors contributing to the development of PID in this vulnerable population remain inadequately understood, which may affect treatment outcomes and patient safety.<br><br>METHODS: In this retrospective study, we analyzed data from 168 elderly patients who underwent ECT between 2009 and 2020 at a general hospital in China. Univariate analyses of sociodemographic and clinical characteristics were performed to identify variables for inclusion in a logistic regression model. Multiple binary logistic regression analysis was performed to determine the relationship between these variables and PID occurrence.<br><br>RESULTS: The incidence of PID was 20.8% (35/168) among the study cohort. Univariate analysis revealed statistically significant differences between PID and non-PID groups for lithium (&#x3c7; [2] = 6.67, p = 0.010), quetiapine (&#x3c7; [2] = 4.36, p = 0.037), number of ECT sessions (U = 3065.50, p = 0.003), and response rate (&#x3c7; [2] = 12.86, p < 0.001). Logistic regression analysis demonstrated that lithium (odds ratio (OR) = 5.128; p = 0.009) and quetiapine (OR = 2.562; p = 0.024) were significantly associated with PID.<br><br>CONCLUSION: Our findings indicate that lithium and quetiapine use significantly increase the risk of developing PID, underscoring the need for clinical vigilance. Careful consideration of these medications when planning ECT treatment is recommended to minimize the risk of postictal complications and optimize therapeutic outcomes.}, }
@article {pmid40925406, year = {2025}, author = {Chen, J and Yi, Z and Chen, T and Tong, H and Zhou, L and Hong, Z and Tan, C and Qin, J and Cai, F and Wu, Y and Li, J and Huang, Y}, title = {An adjustable three-layer skull phantom with realistic ultrasound transmission properties.}, journal = {Physics in medicine and biology}, volume = {70}, number = {18}, pages = {}, doi = {10.1088/1361-6560/ae0556}, pmid = {40925406}, issn = {1361-6560}, mesh = {*Phantoms, Imaging ; *Skull/diagnostic imaging ; Humans ; *Ultrasonic Waves ; Ultrasonography/instrumentation ; }, abstract = {Transcranial ultrasound research has garnered significant attention due to its non-invasive nature, absence of ionizing radiation, and portability, making it advantageous for both imaging and therapy. A critical aspect of advancing transcranial research lies in understanding the ultrasound transmission performance of the human skull. However, inherent variations in skull shape, physical parameters, and age-related changes pose challenges for comparative studies. To address these challenges, we designed a three-layer structured skull (TSS) phantom that closely mimics the structural and ultrasound transmission properties of real skulls. The TSS substrate is composed of epoxy resin/Al2O3powders, with purple perilla seeds incorporated into the middle layer to replicate the porous structure found in real skulls. Both simulation and experimental results demonstrate that TSS phantom achieves acoustic transmission properties closely approximating those of human skull bone within the 1.25-1.75 MHz frequency range. Experimentally, the TSS phantom containing 27 wt% purple perilla seeds shows a sound pressure transmission coefficient ranging from 5.0% to 6.6%, closely matching the skull's transmission characteristics (4.2%-9.8%). This performance represents a significant improvement over conventional phantom materials, outperforming epoxy resin plate phantoms (42.6%-48.4%) and polyetheretherketone phantoms (64.5%-75.2%). Notably, the transmission performance of TSS can be adjusted by varying the mass fraction of purple perilla seeds, making it adaptable to diverse research needs. The TSS phantom holds significant potential as a valuable tool in transcranial research, offering a reliable and accessible alternative for comprehensive investigations into ultrasound applications in brain therapy.}, }
@article {pmid40925395, year = {2025}, author = {Bisla, M and Anand, RS}, title = {Machine learning based classification of imagined speech electroencephalogram data from the amplitude and phase spectrum of frequency domain EEG signal.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {5}, pages = {}, doi = {10.1088/2057-1976/ae04ee}, pmid = {40925395}, issn = {2057-1976}, mesh = {Humans ; *Electroencephalography/methods ; *Machine Learning ; *Speech/physiology ; *Imagination/physiology ; Male ; Female ; *Signal Processing, Computer-Assisted ; Adult ; Algorithms ; *Brain/physiology ; Young Adult ; }, abstract = {Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to communicate through neural signals. The prime objective of this work is to propose an innovative machine learning (ML) based classification methodology that combines electroencephalogram (EEG) data augmentation using a sliding window technique with statistical feature extraction from the amplitude and phase spectrum of frequency domain EEG segments. This work uses an EEG dataset recorded from a 64-channel device during the imagination of long words, short words, and vowels with 15 human subjects. First, the raw EEG data is filtered between 1 Hz and 100 Hz, then segmented using a sliding window-based data augmentation technique with a window size of 100 and 50% overlap. The Fourier Transform is applied to each windowed segment to compute the amplitude and phase spectrum of the signal at each frequency point. The next step is to extract 50 statistical features from the amplitude and phase spectrum of frequency domain segments. Out of these, the 25 most statistically significant features are selected by applying the Kruskal-Walli's test. The extracted feature vectors are classified using six different machine learning based classifiers named support vector machine (SVM), K nearest neighbor (KNN), Random Forest (RF), XGBoost, LightGBM, and CatBoost. The CatBoost classifier outperforms other machine learning classifiers by achieving the highest accuracy of 91.72 ± 1.52% for long words classification, 91.68 ± 1.54% for long versus short word classification, 88.05 ± 3.07% for short word classification, and 88.89 ± 1.97% for vowel classification. The performance of the proposed model is assessed using five performance evaluation metrics: accuracy, F1-score, precision, recall, and Cohen's kappa. Compared to the existing literature, this study has achieved a 5%-7% improvement with the CatBoost classifier and extracted feature matrix.}, }
@article {pmid40922973, year = {2025}, author = {Denis-Robichaud, J and Nicola, I and Chupin, H and Roy, JP and Buczinski, S and Fauteux, V and Picard-Hagen, N and Dubuc, J}, title = {Nonesterified fatty acids during the dry period and their association with peripartum disorders, culling, and pregnancy in dairy cows.}, journal = {JDS communications}, volume = {6}, number = {5}, pages = {688-693}, pmid = {40922973}, issn = {2666-9102}, abstract = {The objective of this ambidirectional observational cohort study was to explore how nonesterified fatty acids (NEFA) 22 to 35 d before calving were related to NEFA 1 to 14 d before calving and to determine a threshold that could be used to identify cows at risk of poor postpartum health. We enrolled 855 dairy cows from 46 herds, 362 prospectively and 493 retrospectively. The NEFA concentrations were measured during the far-off period (foNEFA; 3 to 5 wk before calving) and in the close-up period (cuNEFA; up to 2 wk before calving), and postpartum infectious and metabolic disorders, reproduction success, and culling were recorded. Using a split dataset, we (1) determined a threshold maximizing the sum of sensitivity and specificity to identify peripartum conditions by classifying elevated NEFA and (2) assessed the associations between elevated NEFA and altered health and reproduction. The associations were expressed as the odds ratio (OR) and the 95% Bayesian credible interval (BCI). The concentration of foNEFA varied from 60 to 700 µmol/L (median = 149), and a threshold of ≥160 µmol/L was identified. Cows with elevated foNEFA had greater odds to have elevated cuNEFA (OR = 183, 95% BCI = 52.1-458), hyperketonemia (OR = 2.0, 95% BCI = 1.0-3.6), displaced abomasum (OR = 12.3, 95% BCI = 1.6-45.8), metritis (OR = 9.4, 95% BCI = 1.3-36.0), and clinical mastitis (OR = 5.8, 95% BCI = 1.9-12.1) than cows below the threshold. Our results suggest that foNEFA, using a threshold of ≥160 µmol/L, could be used by veterinarians as a monitoring or investigating tool to assess cows' negative energy balance before calving, even earlier than 2 wk prepartum. This monitoring could be used to implement early corrective actions to prevent the effect of negative energy balance on reproduction and peripartum health.}, }
@article {pmid40922815, year = {2025}, author = {Zhang, Y and Zhou, M and Liang, R and Chen, J and Shi, P and Zheng, Y and Luo, X and Wu, Y and Yu, X and Wu, Y and Liang, S and Deng, W and Bueber, MA and Phillips, MR and Li, T}, title = {Mental health literacy and the stigmatisation and discrimination of individuals affected by mental illnesses in China: a scoping review.}, journal = {The Lancet regional health. Western Pacific}, volume = {61}, number = {}, pages = {101642}, pmid = {40922815}, issn = {2666-6065}, abstract = {Low mental health literacy (MHL) could contribute to misconceptions about mental illnesses and reinforce various forms of stigma (public, personal, and associative), leading to discrimination, reduced help-seeking, and poorer mental health outcomes. To summarise the current state of the literature on MHL, stigma, and discrimination, this scoping review identified 387 studies published from 2000 to 2024 in five English and three Chinese databases: 60.7% focused on stigma, 31.8% on MHL, and only 7.5% on discrimination. Most studies (84.8%) were descriptive cross-sectional studies, 14.5% evaluated interventions, and 0.7% were non-intervention longitudinal studies. Methodological quality was generally low: reports about 88.4% of the cross-sectional studies, 75.6% of the randomised controlled trials, and 83.4% of the quasi-experimental studies lacked descriptions of key methodological or statistical details. After excluding researcher-developed tools only reported in a single study, 125 assessment tools remained, 26.4% of which were developed in China. Although 21 different mental health conditions were studied, 91.0% of the studies focused on a single condition. Study locations were geographically skewed (one-third of all studies were conducted in Guangdong, Beijing, and Shanghai), and study participants were not representative of the target cohort. The number of publications increased substantially after 2010. Most of the 56 intervention studies, which primarily used psychoeducational interventions, reported improved MHL and decreased stigma. Recommendations for future studies include: 1) Develop standardised instruments to improve comparability. 2) Ensure detailed statistical analyses and clearly defined sample characteristics. 3) Assess variations in MHL, stigmatisation, and discrimination across different mental health conditions. 4) Increase research in underserved regions and conduct nationwide longitudinal studies. 5) Include a broader range of participants in intervention studies and consider new intervention strategies (i.e., other than psychoeducation interventions). 6) Align research objectives with national mental health policies to enhance their relevance and impact.}, }
@article {pmid40921216, year = {2026}, author = {Saeed, S and Wang, H and Kong, L and Geng, Y and Zhang, J and Pan, Y and Le, X and Zhang, X and Liu, TT and Hu, S}, title = {Machine learning-enhanced mapping of suicide risk in Bipolar Disorder: A multi-modal analysis.}, journal = {Journal of affective disorders}, volume = {392}, number = {}, pages = {120183}, doi = {10.1016/j.jad.2025.120183}, pmid = {40921216}, issn = {1573-2517}, mesh = {Humans ; *Bipolar Disorder/psychology/diagnosis ; Male ; Female ; Cross-Sectional Studies ; Adult ; *Machine Learning ; *Suicide/psychology/statistics &amp; numerical data ; Middle Aged ; Risk Assessment/methods ; Psychiatric Status Rating Scales ; Suicidal Ideation ; Risk Factors ; }, abstract = {BACKGROUND: Bipolar disorder (BD) is associated with a high risk of suicide, but the complex interplay of factors contributing to this risk remains poorly understood. This study aimed to comprehensively analyze demographic, clinical, and biological factors associated with suicide risk in BD patients and develop a novel suicide risk assessment model integrating these factors.<br><br>METHODS: We conducted a cross-sectional study of 152 patients with BD, classified into four suicide-risk groups: no risk (n&#xa0;=&#xa0;19), low risk (n&#xa0;=&#xa0;45), moderate risk (n&#xa0;=&#xa0;38), and high risk (n&#xa0;=&#xa0;50). Participants underwent assessments using the Mini-International Neuropsychiatric Interview (M.I.N&#xb7;I.), Hamilton Depression Rating Scale-24 items (HAMD-24), Young Mania Rating Scale (YMRS), Montgomery-&#xc5;sberg Depression Rating Scale (MADRS), and Beck Scale for Suicide Ideation (BSSI). We evaluated thyroid function, inflammatory markers, and lymphocyte subsets. Univariate and multivariate analyses were performed to identify factors associated with suicide risk.<br><br>FINDINGS: Depressive symptoms were significantly associated with increased odds of medium (odds ratio (OR)&#xa0;=&#xa0;1.452, 95&#xa0;% confidence interval (CI): 1.122-1.878, P&#xa0;=&#xa0;0.005) and high (OR&#xa0;=&#xa0;1.405, 95&#xa0;% CI: 1.091-1.810, P&#xa0;=&#xa0;0.009) suicide risk. Lower free thyroxine 4 (FT4) levels were associated with higher odds of low (OR&#xa0;=&#xa0;0.581, 95&#xa0;% CI: 0.404-0.835, P&#xa0;=&#xa0;0.003) and medium (OR&#xa0;=&#xa0;0.694, 95&#xa0;% CI: 0.486-0.992, P&#xa0;=&#xa0;0.045) risk. The no-risk group exhibited higher levels of thyroid hormones and autoantibodies. CD3+ T-cell percentages varied significantly across risk groups, with the lowest mean percentage in the no-risk group (57.59&#xa0;&#xb1;&#xa0;14.64&#xa0;%). Our machine learning models achieved 87.1&#xa0;% accuracy in predicting suicide risk. Patient Health Questionnaire-9 items, Hamilton Depression Rating Scale-24 items, and Montgomery-&#xc5;sberg Depression Rating Scale scores were identified as the strongest predictors of suicide risk by a Random-Forest model with 100 decision trees. In addition, FT4 and interferon-&#x3b3; emerged as notable contributors to the model's predictions.<br><br>CONCLUSION: Depressive symptoms and thyroid function are crucial factors in assessing suicide risk in BD. Thyroid autoimmunity and T cell-mediated immunity emerge as potential biomarkers for risk stratification and therapeutic targets, offering new avenues for personalized intervention strategies.}, }
@article {pmid40920866, year = {2025}, author = {Nyawanda, BO and Sullivan, KM and Tinkitina, B and Beinamaryo, P and Nabatte, B and Kyarisiima, H and Mubangizi, A and Emerson, PM and Utzinger, J and Vounatsou, P}, title = {Geostatistical analysis to guide treatment decisions for soil-transmitted helminthiasis control in Uganda.}, journal = {PLoS neglected tropical diseases}, volume = {19}, number = {9}, pages = {e0013467}, pmid = {40920866}, issn = {1935-2735}, mesh = {Uganda/epidemiology ; Humans ; *Soil/parasitology ; *Helminthiasis/epidemiology/prevention &amp; control/drug therapy/transmission/parasitology ; Prevalence ; *Anthelmintics/therapeutic use/administration &amp; dosage ; Child ; Animals ; Bayes Theorem ; Male ; Female ; Child, Preschool ; Adolescent ; Ascaris lumbricoides ; Hookworm Infections/epidemiology ; Trichuris ; Trichuriasis/epidemiology ; }, abstract = {BACKGROUND: Soil-transmitted helminth (STH) infections remain a public health problem in Uganda despite biannual national deworming campaigns implemented since the early 2000s. Recent surveys have indicated a heterogeneous STH infection prevalence, suggesting that the current blanket deworming strategy may no longer be cost-effective. This study identified infection predictors, estimated the geographic distribution of STH infection prevalence by species, and calculated deworming needs for school-age children (SAC).<br><br>METHODOLOGY: Bayesian geostatistical models were applied to STH survey data (2021-2023) for each species (i.e., Ascaris lumbricoides, hookworm, and Trichuris trichiura). Climatic, environmental, and socioeconomic predictors were obtained from remote sensing sources, model-based databases, and demographic and health surveys. Prevalence was predicted on a 1&#x2009;&#xd7;&#x2009;1 km2 grid across Uganda, and district-level estimates were used to classify each district into treatment frequency categories and to determine its deworming tablet requirements.<br><br>PRINCIPAL FINDINGS: The national prevalence of A. lumbricoides, T. trichiura, and hookworm was estimated at 5.0% (95% Bayesian credible interval [BCI]: 0.8-11.8%), 3.5% (0.7-9.3%), and 7.2% (5.7-11.1%), respectively. The overall prevalence of any STH infection was 14.3% (9.6-21.8%). High intra-district variation in prevalence was observed. Of 146 implementation units (136 districts and 10 cities), 49 require twice-year treatment, 34 once-yearly treatment, 61 every other year treatment, and 2 had a prevalence <2%, indicating treatment suspension or event-based treatment. Approximately 17 million tablets will be needed for preventive chemotherapy aimed at SAC in 2025.<br><br>CONCLUSIONS/SIGNIFICANCE: The prevalence of STH infection has declined considerably across Uganda compared to the early 2000s. However, deworming needs remain heterogeneous across districts. Through geostatistical modeling, districts were classified according to the latest World Health Organization's (WHO) treatment guidelines. This approach optimizes treatment distribution and allows for prioritization of populations with the greatest needs. We estimated that tablet requirements are approximately 40% lower compared to the current twice-a-year deworming regimen, which contributes towards WHO's goal of halving the number of tablets required for preventive chemotherapy by 2030.}, }
@article {pmid40919632, year = {2025}, author = {Zhang, S and Ling, C and Wu, J and Li, J and Wang, J and Yu, Y and Liu, X and Lv, J and Vai, MI and Chen, R}, title = {EEG-ERnet: Emotion Recognition based on Rhythmic EEG Convolutional Neural Network Model.}, journal = {Journal of integrative neuroscience}, volume = {24}, number = {8}, pages = {41547}, doi = {10.31083/JIN41547}, pmid = {40919632}, issn = {0219-6352}, support = {2024B03J1361//Guangzhou Science and Technology Plan Project/ ; 2023B03J1327//Guangzhou Science and Technology Plan Project/ ; 2024SZFZ007//Research Fund of Key Laboratory of Numerical Simulation of Sichuan Provincial Universities/ ; 2025ZNSFSC0780//Sichuan Science and Technology Program/ ; 23XXK0402//Foundation of the 2023 Higher Education Science Research Plan of the China Association of Higher Education/ ; CSXL-25102//Foundation of the Sichuan Research Center of Applied Psychology (Chengdu Medical College)/ ; NJ2024ZD014//Neijiang Philosophy and Social Science Planning Project/ ; 2023KQNCX036//Guangdong Province Ordinary Colleges and Universities Young Innovative Talents Project/ ; 22GPNUZDJS17//Scientific Research Capacity Improvement Project of the Doctoral Program Construction Unit of Guangdong Polytechnic Normal University/ ; 2023YJSY04002//Graduate Education Demonstration Base Project of Guangdong Polytechnic Normal University/ ; 2025-M10//Open Research Fund of State Key Laboratory of Digital Medical Engineering/ ; 2022SDKYA015//Research Fund of Guangdong Polytechnic Normal University/ ; }, mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Neural Networks, Computer ; Adult ; Young Adult ; *Recognition, Psychology/physiology ; *Evoked Potentials/physiology ; Brain-Computer Interfaces ; Male ; *Brain Waves/physiology ; Female ; Convolutional Neural Networks ; }, abstract = {BACKGROUND: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition. Furthermore, emotions are inherently dynamic, and neglecting their temporal variability can lead to redundant or noisy data, thus reducing recognition performance. Complicating matters further, individuals may exhibit varied emotional responses to identical stimuli due to differences in experience, culture, and background, emphasizing the necessity for subject-independent classification models.<br><br>METHODS: To address these challenges, we propose a novel network model based on depthwise parallel CNNs. Power spectral densities (PSDs) from various rhythms are extracted and projected as 2D images to comprehensively encode channel, rhythm, and temporal properties. These rhythmic image representations are then processed by a newly designed network, EEG-ERnet (Emotion Recognition Network), developed to process the rhythmic images for emotion recognition.<br><br>RESULTS: Experiments conducted on the dataset for emotion analysis using physiological signals (DEAP) using 10-fold cross-validation demonstrate that emotion-specific rhythms within 5-second time intervals can effectively support emotion classification. The model achieves average classification accuracies of 93.27 &#xb1; 3.05%, 92.16 &#xb1; 2.73%, 90.56 &#xb1; 4.44%, and 86.68 &#xb1; 5.66% for valence, arousal, dominance, and liking, respectively.<br><br>CONCLUSIONS: These findings provide valuable insights into the rhythmic characteristics of emotional EEG signals. Furthermore, the EEG-ERnet model offers a promising pathway for the development of efficient, subject-independent, and portable emotion-aware systems for real-world applications.}, }
@article {pmid40919177, year = {2025}, author = {Sha, G and Liu, Y and Cao, Y and Zhang, Q and Zhang, Y and Chen, Y and Fan, Q and Cheng, Y}, title = {Structural and functional neural correlates of sensorimotor deficits in progression of hepatic encephalopathy.}, journal = {Magnetic resonance letters}, volume = {5}, number = {2}, pages = {200156}, pmid = {40919177}, issn = {2772-5162}, abstract = {Hepatic encephalopathy (HE) is a neurological condition that occurs as a complication of liver dysfunction that involves sensorimotor symptoms in addition to cognitive and behavioral changes, particularly in cases of severe liver disease or cirrhosis. Previous studies have reported spatially distributed structural and functional abnormalities related to HE, but the exact relationship between the structural and functional alterations with respect to disease progression remains unclear. In this study, we performed surface-based cortical thickness comparisons and functional connectivity (FC) analyses between three cross-sectional groups: healthy controls (HC, N = 51), patients with minimal hepatic encephalopathy (MHE, N = 50), patients with overt hepatic encephalopathy (OHE, N = 51). In addition to the distributed cortical thinning that is extensively thought to be associated with cognitive decline in HE, we found significant cortical thickening in the left parahippocampal gyrus cortex in the OHE group (p < 0.001, p = 0.009) as compared to the HC and MHE group respectively, which is further corroborated by the significant correlation between the cortical thickness and digit symbol test (DST) scores. Furthermore, the decreased FC between the right postcentral gyrus and several sensory regions (bilateral somatosensory and visual cortices) was found to be significant in MHE patients as compared to the HC group. Our results revealed cross-sectional structural and functional variations concerning disease progression across different subsystems (e.g., visual, motor and sensory), providing evidence that can potentially explain the mechanisms underlying the sensorimotor and cognitive deficits related to HE.}, }
@article {pmid40916208, year = {2025}, author = {Ahmadi Seyedkhani, S and Iraji Zad, A and Mohammadpour, R and Taghipoor, M and Vafaiee, M}, title = {Novel Brain-Inspired Hierarchical Micro-Nanostructured Poly(3,4-ethylenedioxythiophene)/Polydopamine Neural Interface on Titanium Nitride Electrodes for Electrophysiological Signal Recording.}, journal = {ACS applied bio materials}, volume = {8}, number = {10}, pages = {9332-9345}, doi = {10.1021/acsabm.5c01451}, pmid = {40916208}, issn = {2576-6422}, mesh = {*Titanium/chemistry ; *Polymers/chemistry ; *Bridged Bicyclo Compounds, Heterocyclic/chemistry ; *Indoles/chemistry ; Materials Testing ; *Biocompatible Materials/chemistry ; Surface Properties ; Particle Size ; *Nanostructures/chemistry ; *Brain ; Animals ; Electrodes ; Brain-Computer Interfaces ; }, abstract = {The development of high-performance neural interfaces is critical for advancing brain-machine communication and treating neurological disorders. A major challenge in neural electrode design is achieving a seamless biological-electronic interface with optimized electrochemical properties, mechanical stability, and biocompatibility. In this study, we introduce a hierarchical micronanostructured poly(3,4-ethylenedioxythiophene)-polydopamine (PEDOT-PDA) coating on titanium nitride (TiN) microelectrodes engineered to enhance electrophysiological signal recording and neural integration. The PEDOT-PDA films were synthesized via potentiodynamic electropolymerization, achieving a 90% reduction in impedance (∼353 Ω at 1 kHz) compared to conventional gold (Au) electrodes (∼3795 Ω) and a 60% decrease relative to TiN substrates (∼890 Ω). The brain-inspired hierarchical micronanostructure mimics the extracellular matrix (ECM), improving cell adhesion and biointegration. Wettability analysis revealed a 63% enhancement in hydrophilicity, reducing the water contact angle from ∼70° for pure PEDOT to ∼25° for PEDOT-PDA. Biocompatibility assessments demonstrated excellent cell viability of ∼97% for PEDOT-PDA electrodes and superior cell attachment with extended filopodia formation, promoting long-term neural interface stability. The PEDOT-PDA interface outperforms conventional PEDOT and metal-based electrodes in electrochemical stability, biocompatibility, and signal recording efficiency, making it a promising candidate for next-generation brain-computer interfaces (BCIs).}, }
@article {pmid40915552, year = {2025}, author = {Wang, X and Zou, T and Wang, H and Han, H and Chen, H and Calhoun, VD and Li, R}, title = {A dynamic spatiotemporal representation framework for deciphering personal brain function.}, journal = {NeuroImage}, volume = {319}, number = {}, pages = {121443}, doi = {10.1016/j.neuroimage.2025.121443}, pmid = {40915552}, issn = {1095-9572}, mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Male ; Female ; *Brain/physiology/diagnostic imaging ; Adult ; Young Adult ; Middle Aged ; *Nerve Net/physiology/diagnostic imaging ; *Brain Mapping/methods ; Adolescent ; }, abstract = {Functional magnetic resonance imaging (fMRI) opens a window on observing spontaneous activities of the human brain in vivo. However, the high complexity of fMRI signals makes brain functional representations intractable. Here, we introduce a state decomposition method to reduce this complexity and decipher individual brain functions at multiple levels. Briefly, brain dynamics are captured by temporal first-order derivatives and spatially divided into 'state sets' at each time point based on the velocity and direction of change. This approach transforms the original signals into discrete series consisting of four fundamental states, which efficiently encode individual-specific information. Subsequently, we designed a suite of state-based metrics to quantify regional activities and network interactions. Compared with conventional representations such as resting-state fluctuation amplitude and Pearson's functional connectivity, the state-based representations serve as more discriminative 'brain fingerprints' for individuals and produce reproducible spatial patterns across heterogeneous cohorts (n = 1015). Regarding functional organization, our proposed profiles extend previous representations into nonlinear domains, revealing not only the canonical default-mode dominant pattern but also patterns dominated by the attention network and basal ganglia. Moreover, we demonstrate that personal phenotypes (such as age and gender) can be decoded from regional representations with high accuracy. The equivalence between state series outperforms other existing network representations in predicting individual fluid intelligence. Overall, this framework establishes a foundation for enriching the repertoire of brain functional representations and enhancing the power of brain-phenotype modeling.}, }
@article {pmid40914696, year = {2025}, author = {Li, W and Shi, W and Wang, H and Li, J and Chu, C and Zhang, Y and Cui, Y and Cheng, L and Li, K and Lu, Y and Ma, L and Song, M and Yang, Z and Banaschewski, T and Bokde, ALW and Desrivières, S and Flor, H and Grigis, A and Garavan, H and Gowland, P and Walter, H and Brühl, R and Martinot, JL and Martinot, MP and Artiges, E and Nees, F and Orfanos, DP and Lemaitre, H and Poustka, L and Hohmann, S and Millenet, S and Fröhner, JH and Robinson, L and Smolka, MN and Winterer, J and Whelan, R and Fan, L and Jiang, T}, title = {Anatomical connectivity development constrains medial-lateral topography in the dorsal prefrontal cortex.}, journal = {Science bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.scib.2025.08.045}, pmid = {40914696}, issn = {2095-9281}, }
@article {pmid40914528, year = {2026}, author = {Chen, S and Guo, X and Liu, X and Liu, S and Ming, D}, title = {Transdiagnostic homogeneity, and diagnostic-specific biomarkers among major depressive disorder, bipolar disorder and schizophrenia during 40 Hz auditory steady-state response: a normative modeling analysis.}, journal = {Journal of affective disorders}, volume = {392}, number = {}, pages = {120189}, doi = {10.1016/j.jad.2025.120189}, pmid = {40914528}, issn = {1573-2517}, mesh = {Humans ; *Bipolar Disorder/physiopathology/diagnosis ; *Depressive Disorder, Major/physiopathology/diagnosis ; *Schizophrenia/physiopathology/diagnosis ; Male ; Female ; Adult ; Middle Aged ; *Evoked Potentials, Auditory/physiology ; Biomarkers ; Electroencephalography ; Case-Control Studies ; *Gamma Rhythm/physiology ; Young Adult ; }, abstract = {BACKGROUND: Abnormal gamma-band auditory steady-state response (gamma-ASSR) power has been reported in major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), but distinguishing between these disorders based solely on power remains challenging. Directed functional connectivity (DFC), which captures topological patterns of causal information flow, may provide more diagnostic-specific markers. However, conventional case-control framework often disregards the substantial individual heterogeneity, yielding unreliable biomarkers.<br><br>METHODS: An adapted framework integrating DFC heterogeneity with normative modeling was developed. 52 MDD, 33 BD, 39 SZ patients and 107 healthy controls (HC) participated in the 40&#xa0;Hz-ASSR task. The normative model was established using data from 71 HC to define the population baseline. Thereafter, deviation Z-scores and the proportion of extreme deviations in DFC were calculated.<br><br>RESULTS: The DFC deviations showed high individual heterogeneity at most DFCs, with fewer than 2.6&#xa0;% of individuals exhibiting extreme deviations at the same time point. However, a small proportion of DFC deviations with high overlap were embedded within common connectivity pathways in three disorders, particularly in the frontal and parietal regions. Furthermore, distinct diagnostic-specific patterns were identified: MDD mainly exhibited right temporal-frontal alterations, BD showed a parietal-driven temporo-occipital loop, and SZ presented a midline-centered pyramidal topology linking bilateral temporal-occipital regions. The Z-scores of DFC involved in these diagnostic-specific patterns achieved a maximum accuracy of 99.43&#xa0;% with the K-nearest neighbors (KNN) algorithm.<br><br>CONCLUSIONS: These findings offer novel insights into gamma-ASSR alterations and provide a robust framework for transdiagnostic and disorder-specific identification across MDD, BD, and SZ.}, }
@article {pmid40914440, year = {2025}, author = {Balam, VP}, title = {Automated EEG signal processing: A comprehensive investigation into preprocessing techniques and sub-band extraction for enhanced brain-computer interface applications.}, journal = {Journal of neuroscience methods}, volume = {424}, number = {}, pages = {110561}, doi = {10.1016/j.jneumeth.2025.110561}, pmid = {40914440}, issn = {1872-678X}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; Fourier Analysis ; Algorithms ; *Brain Waves/physiology ; Machine Learning ; Wavelet Analysis ; }, abstract = {The Electroencephalogram (EEG) is a vital physiological signal for monitoring brain activity and understanding neurological capacities, disabilities, and cognitive processes. Analyzing and classifying EEG signals are key to assessing an individual's reactions to various stimuli. Manual EEG analysis is time-consuming and labor-intensive, necessitating automated tools for efficiency. Machine learning techniques often rely on preprocessing and segmentation methods to integrate automated classification into EEG signal processing, with EEG sub-band components (δ,θ,α,β and γ) playing a crucial role. This paper presents a comprehensive exploration of EEG preprocessing methods, with a specific focus on sub-band extraction techniques used in Brain-Computer Interface (BCI) applications. Various methods-including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters, and wavelet transforms (DWT, WPT)-are evaluated through qualitative and quantitative parametric analysis, along with a review of their practical applicability. The study also includes an application-based evaluation using an open-access EEG dataset for drowsiness detection.}, }
@article {pmid40913810, year = {2026}, author = {Zheng, L and Su, Y and Li, S and Li, X and Zhang, Y and Tseomashko, NE and Sadikovna, AS and Wang, X}, title = {Injectable multifunctional sponges with rough sieve structure and efficient shape-recoverability for small-sized penetrating wound.}, journal = {Journal of colloid and interface science}, volume = {702}, number = {Pt 1}, pages = {138896}, doi = {10.1016/j.jcis.2025.138896}, pmid = {40913810}, issn = {1095-7103}, mesh = {Animals ; Rats ; Surface Properties ; Porosity ; *Wounds, Penetrating/therapy ; Rats, Sprague-Dawley ; *Hemostatics/chemistry/pharmacology/administration &amp; dosage ; Male ; Particle Size ; *Bandages ; Surgical Sponges ; }, abstract = {The emergence of special scenarios involving small-sized penetrating wounds has imposed stricter performance requirements on shape-recovery hemostatic materials, particularly regarding their shape fixity and water-triggered shape recovery efficiency. Herein, an efficient shape-recovery sponge dressing with high shape fixity and high-speed liquid absorption, designated as CQT, was developed by integrating a sieve structure with the rough surface coating. The sieve structure, characterized by microporous structures on macroporous walls, enhanced the multi-level and connectivity of the overall pore network, which could improve compressive fixity via enhancing the energy dissipation required for rebound and enabled efficient shape recovery through augmented capillary action during fluid absorption. Concurrently, the enhanced pore connectivity promoted rapid blood absorption (<0.5 s), expanded interfacial contact between blood and hydrophilic pore walls, and improved interception of blood active components, while the rough coating on the pore walls provided more binding sites along with its charge effect to enhance the adhesion and aggregation of blood cells (BCI of 7.8 %). The excellent in vivo hemostatic performance of the sponge (blood loss of 0.31 g and hemostasis time of 63 s) was further validated using a rat liver defect model, suggesting its potential for application in small-sized penetrating wounds. Additionally, this coating has antimicrobial and antioxidant properties that help to prevent infection and reduce inflammation. Thus, the unique sponge dressings possess excellent initial shape adaptability and efficient expansion hemostatic ability, making it very suitable for emergency hemostasis and subsequent repair of small-sized penetrating wounds.}, }
@article {pmid40913768, year = {2025}, author = {Verwoert, M and Ottenhoff, MC and Tousseyn, S and van Dijk, JP and Kubben, PL and Herff, C}, title = {Moving beyond the motor cortex: A brain-wide evaluation of target locations for intracranial speech neuroprostheses.}, journal = {Cell reports}, volume = {44}, number = {9}, pages = {116241}, doi = {10.1016/j.celrep.2025.116241}, pmid = {40913768}, issn = {2211-1247}, mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; *Speech/physiology ; Male ; Female ; Adult ; Electroencephalography ; Young Adult ; Electrocorticography ; Brain Mapping ; Middle Aged ; }, abstract = {Speech brain-computer interfaces (BCIs) offer a solution for those affected by speech impairments by decoding brain activity into speech. Current neuroprosthetics focus on the motor cortex, which might not be suitable for all patient populations. We investigate potential alternative targets for a speech BCI across a brain-wide distribution. Thirty participants are recorded with intracranial electroencephalography during speech production. We continuously predict speech from a brain-wide global to a single-channel local scale, across anatomical features. We find significant speech detection accuracy in both gray and white matter, no significant difference between gyri and sulci, and limited contribution from subcortical areas. Potential targets are located within the depths of and surrounding the lateral fissure bilaterally, such as the (sub)central sulcus, the transverse temporal gyrus, the supramarginal cortex, and parts of the insula. The results highlight the potential benefit of extending beyond the motor cortical surface and reaching the sulcal depth for speech neuroprostheses.}, }
@article {pmid40913530, year = {2025}, author = {Zhou, E and Wang, X and Liang, J and Liu, Y and Yang, Q and Ran, X and Xia, L and Zou, X and Liu, C and Sun, L and Peng, L and Chen, L and Mao, Y and Wu, Z and Tao, TH and Zhou, Z}, title = {Chronically Stable, High-Resolution Micro-Electrocorticographic Brain-Computer Interfaces for Real-Time Motor Decoding.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e06663}, doi = {10.1002/advs.202506663}, pmid = {40913530}, issn = {2198-3844}, support = {Y2023070//Youth Innovation Promotion Association for Excellent Members/ ; 22QA1410900//Shanghai Rising-Star Program/ ; ZDBS-LY-JSC024//Key Research Program of Frontier Sciences, CAS/ ; JCYJ-SHFY-2022-01//Shanghai Pilot Program for Basic Research-Chinese Academy of Science/ ; 82272116//National Natural Science Foundation of China/ ; 2021SHZDZX//Science and Technology Commission of Shanghai Municipality/ ; 2018AAA0103100//National Major Science and Technology Projects of China/ ; }, abstract = {Brain-computer interfaces (BCIs) enable communication between individuals and computers or other assistive devices by decoding brain activity, thereby reconstructing speech and motor functions for patients with neurological disorders. This study presents a high-resolution micro-electrocorticography (µECoG) BCI based on a flexible, high-density µECoG electrode array, capable of chronically stable and real-time motor decoding. Leveraging micro-nano manufacturing technology, the µECoG BCI achieves a 64-fold increase in electrode density compared to conventional clinical electrode arrays, enhancing spatial resolution while featuring scalability. Over a 203-day in vivo experiment, high-resolution µECoG carrying fine spatial specificity information demonstrated the potential to improve decoding performance while reduce implanted devices size. These advancements provide a pathway to overcome the limitations of conventional ECoG BCIs. During awake surgery, the µECoG BCI enabled game control after 7 min of model training. Furthermore, during practice of 19.87 h, the participant achieved cursor control with a bit rate of 1.13 bits per second (BPS) under full volitional control, and the bit rate reached up to 4.15 BPS with enhanced user interface. These results show that the µECoG BCI achieves comparable performance to intracortical electroencephalographic (iEEG) BCIs without intracortical invasiveness, marking a breakthrough in the clinical feasibility of flexible BCIs.}, }
@article {pmid40913389, year = {2025}, author = {Ji, Z and Li, L and Zheng, M and Ye, X and Yan, W and Wang, Z and Liu, Y and Wang, Y and Zhang, Y and Zhou, P and Yang, J and Wang, M and Lin, S and Haick, H and Wang, Y}, title = {Conductive Hydrogel-Enabled Electrode for Scalp Electroencephalography Monitoring.}, journal = {Small methods}, volume = {}, number = {}, pages = {e01242}, doi = {10.1002/smtd.202501242}, pmid = {40913389}, issn = {2366-9608}, support = {52303371//National Natural Science Foundation of China/ ; W2521021//National Natural Science Foundation of China/ ; STKJ2023075//Guangdong Science and Technology Department/ ; 2022A1515110209//Guangdong Science and Technology Department/ ; 2021B0301030005//Guangdong Science and Technology Department/ ; GCII-Seed-202406//GTIIT Changzhou Innovation Institute/ ; //Education Foundation of Guangdong Technion-Israel Institute of Technology/ ; //Key Discipline (KD) Fund/ ; //Start-Up fund from Guangdong Technion/ ; }, abstract = {Scalp electroencephalography (EEG) serves as a pivotal technology for the noninvasive monitoring of brain functional activity, diagnosing neurological disorders, and assessing cognitive states. However, inherent compatibility barriers between traditional rigid electrodes and the hairy scalp interface significantly compromise signal quality, long-term monitoring comfort, and user compliance. This review examines conductive hydrogel electrodes' pivotal role in advancing scalp EEG, particularly their unique capacity to overcome hair-interface barriers. The superiority of scalp EEG is first established over forehead/ear EEG for capturing diverse neural signals and defining core requirements for hair-compatible interfaces: scalp conformability, electrical conductivity, low contact impedance, and interfacial stability. Conductive hydrogel electrode applications are then detailed in alpha wave detection, sleep monitoring, event-related potential studies, and brain-computer interfaces. Finally, persisting challenges and future opportunities are discussed.}, }
@article {pmid40913111, year = {2025}, author = {Duan, C and Ma, S and Chen, M and Wang, J and Jiang, Y and Ye, M and Tan, Y and Cheng, S and Yang, X and Hu, H and Yang, Y and Huang, HF}, title = {Estrogen receptor beta in lateral habenula mediates antidepressant effects of estrogen in postpartum-hormone-withdrawal-induced depression.}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {40913111}, issn = {1476-5578}, abstract = {Dramatic drop in reproductive hormone, especially estrogen level, from pregnancy to postpartum period is known to contribute to postpartum depression (PPD), but the underlying mechanism and the role of the estrogen receptors (ERs) in this process were unclear. Here, we used an estrogen-withdrawal-induced PPD model following hormone simulated pregnancy (HSP) in female Sprague-Dawley rats to induce depressive-like behaviors. After estrogen withdrawal, we observe an up-regulation of astrocyte-specific potassium channel (Kir4.1) in the brain's anti-reward center lateral habenula (LHb), along with enhanced bursting and excitability of LHb neurons. Among all 3 subtypes of ERs in the LHb, only ERβ shows an HSP-correlated expression temporal dynamics. Systemic administration of selective ERβ agonist, but not agonists of other subtypes of ERs, inhibits neuronal bursting activities and blocks up-regulation of Kir4.1 in the LHb, as well as decreases estrogen-withdrawal-induced depressive-like behavior. Importantly, intra-LHb injection of ERβ agonist is sufficient to rescue depressive-like behaviors induced by estrogen withdrawal. Conversely, local knock-down of ERβ in the LHb suppresses the antidepressant-like effect of estrogen. Our results reveal a critical role of LHb in the pathogenesis of hormone-sensitive PPD and ERβ as a critical mediator of estrogen's antidepressant effects on PPD.}, }
@article {pmid40912944, year = {2025}, author = {Zhou, J and Li, W and Xu, S and Biswal, BB and Chen, H and Li, J and Liao, W}, title = {Multimodal, multifaceted, imaging-based human brain white matter atlas.}, journal = {Science bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.scib.2025.08.021}, pmid = {40912944}, issn = {2095-9281}, }
@article {pmid40911452, year = {2025}, author = {Cao, L and Li, H and Dong, Y and Liu, T and Li, J}, title = {Few-Shot Class-Incremental Learning with Dynamic Prototype Refinement for Brain Activity Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3605108}, pmid = {40911452}, issn = {2168-2208}, abstract = {The brain-computer interface (BCI) system facilitates efficient communication and control, with Electroencephalography (EEG) signals as a vital component. Traditional EEG signal classification, based on static deeplearning models, presents a challenge when new classes of the subject's brain activity emerge. The goal is to develop a model that can recognize new few-shot classes while preserving its ability to discriminate between existing ones. This scenario is referred to as Few-Shot Class-Incremental Learning (FSCIL). This work introduces IncrementEEG, a novel framework meticulously designed to tackle the distinct challenges of FSCIL in EEG-based brain activity classification, focusing specifically on emotion recognition and steady-state visual evoked potential (SSVEP). Our work analyzes the role of additive angular margin loss in improving the model's discrimination capabilities. The proposed method is designed to demonstrate robustness in open-world conditions and adaptability to new tasks. Furthermore, we introduce a prototype refinement module comprising a prototype augmentation block and an update block. The prototype augmentation block in the deep feature space preserves the decision boundary for prior tasks, and the prototype update block utilizes a shared embedding space to compute the relation matrix for bootstrapping prototype updates. Extensive experiments conducted across multiple datasets show the superior performance of the IncrementEEG framework compared to state-of-the-art methods. The proposed method advances FSCIL brain activity classification, offering promising potential for applications in Brain-Computer Interface systems.}, }
@article {pmid40911443, year = {2025}, author = {Zhang, J and Zhu, L and Kong, W and Zhang, J and Cao, J and Cichocki, A}, title = {Reinforcement Learning Decoding Method of Multi-User EEG Shared Information Based on Mutual Information Mechanism.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {9}, pages = {6588-6598}, doi = {10.1109/JBHI.2025.3565019}, pmid = {40911443}, issn = {2168-2208}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Reinforcement, Psychology ; Brain/physiology ; Adult ; Male ; Young Adult ; Algorithms ; Female ; Deep Learning ; }, abstract = {The multi-user motor imagery brain-computer interface (BCI) is a new approach that uses information from multiple users to improve decision-making and social interaction. Although researchers have shown interest in this field, the current decoding methods are limited to basic approaches like linear averaging or feature integration. They ignored accurately assessing the coupling relationship features, which results in incomplete extraction of multi-source information. To overcome these limitations, we propose a new reinforcement learning electroencephalography (EEG) decoding method based on mutual information mechanisms. Our method enhances the extraction of multi-source common information and uses a dynamic feedback model for inter-brain mutual information reward and punishment mechanisms in the reinforcement learning channel selection module. We feed the single-brain and inter-brain signals after channel selection into deep neural networks, which automatically extract coupled features. Finally, based on the attention indices calculated from EEG signals at prefrontal electrode positions, the output is obtained by voting. Our experimental results show that the average accuracy of dual-brain recognition is improved by 16% compared to single-brain mode. Furthermore, ablation experiments demonstrate that the reinforcement learning module and attention voting module enhance accuracy by 14.5% and 15.7%, respectively.}, }
@article {pmid40911279, year = {2025}, author = {Li, CP and Wang, YY and Zhou, CW and Ding, CY and Teng, P and Nie, R and Yang, SG}, title = {Cutting-edge technologies in neural regeneration.}, journal = {Cell regeneration (London, England)}, volume = {14}, number = {1}, pages = {38}, pmid = {40911279}, issn = {2045-9769}, support = {2024C03028//The Pioneer and Leading Goose R&amp;D Program of Zhejiang Province/ ; 2023R01005//The Leading Innovation and Entrepreneurship Team Program of Zhejiang Province/ ; }, abstract = {Neural regeneration stands at the forefront of neuroscience, aiming to repair and restore function to damaged neural tissues, particularly within the central nervous system (CNS), where regenerative capacity is inherently limited. However, recent breakthroughs in biotechnology, especially the revolutions in genetic engineering, materials science, multi-omics, and imaging, have promoted the development of neural regeneration. This review highlights the latest cutting-edge technologies driving progress in the field, including optogenetics, chemogenetics, three-dimensional (3D) culture models, gene editing, single-cell sequencing, and 3D imaging. Prospectively, the advancements in artificial intelligence (AI), high-throughput in vivo screening, and brain-computer interface (BCI) technologies promise to accelerate discoveries in neural regeneration further, paving the way for more precise, efficient, and personalized therapeutic strategies. The convergence of these multidisciplinary approaches holds immense potential for developing transformative treatments for neural injuries and neurological disorders, ultimately improving functional recovery.}, }
@article {pmid40909568, year = {2025}, author = {Gerrity, CG and Treuting, RL and Peters, RA and Womelsdorf, T}, title = {Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40909568}, issn = {2692-8205}, support = {R01 MH123687/MH/NIMH NIH HHS/United States ; }, abstract = {Recent advances in neuroscience enable recording neuronal signals across hundreds of channels while subjects perform complex tasks involving multiple stimulus dimensions. In this study, we developed a novel encode-decode-classify framework employing a gated recurrent variational autoencoder (VAE) to decode decision-making processes from over 300 simultaneously recorded neuronal channels in the prefrontal cortex and basal ganglia of monkeys performing a multidimensional feature-learning task. Using hierarchical stratified sampling and balanced accuracy, we trained and evaluated the model's ability to predict behavioral choices based on neuronal population dynamics. The results revealed distinct neural coding roles, with anterior cingulate cortex (ACC) channels encoding decision variables collectively and prefrontal cortex (PFC) channels contributing individually to decoding accuracy. This approach demonstrated decoding accuracy for decisions in multi-dimensional feature space that is comparable to single-label decoding accuracy for lower dimensional problems, highlighting the potential of machine learning frameworks to capture complex spatiotemporal neuronal interactions involved in multidimensional cognitive behaviors. The code has been released in https://github.com/cgerrity/Neural-Data-Reading.}, }
@article {pmid40907818, year = {2025}, author = {Pan, H and Gao, H and Zhang, Y and Yu, X and Li, Z and Lei, X and Mi, W}, title = {Design and implementation of a writing-stroke motor imagery paradigm for multi-character EEG classification.}, journal = {Neuroscience}, volume = {585}, number = {}, pages = {441-450}, doi = {10.1016/j.neuroscience.2025.08.058}, pmid = {40907818}, issn = {1873-7544}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; *Imagination/physiology ; Male ; Female ; Adult ; Neural Networks, Computer ; *Brain/physiopathology ; Young Adult ; Writing ; Movement/physiology ; }, abstract = {Motor imagery (MI) based brain-computer interfaces (BCI) decode neural activity to generate command outputs. However, the limited number of distinguishable commands in traditional MI-BCI systems restricts practical applications. To overcome this limitation, we propose a multi-character classification framework based on Electroencephalography (EEG) signals. A structurally simplified MI paradigm for stroke writing is designed, and maximize Euclidean distance trajectory optimization enhances neural separability among five stroke categories. The EEG data cover 11 motor imagery tasks, including five stroke-writing tasks and six related movement tasks such as hand, foot, tongue movements and eye blinks, collected from ten participants. Ensemble Empirical Mode Decomposition (EEMD) eliminates artifact-related Intrinsic Mode Functions (IMFs) and reconstructs the signals. Kernel Principal Component Analysis (KPCA) then conducts nonlinear dimensionality reduction to extract discriminative features. Finally, a recurrent neural network based on Gated Recurrent Units (GRU) performs classification, effectively modeling the temporal dynamics of EEG signals. Experimental results indicate that the optimized stroke paradigm achieves an average classification accuracy of 84.77%, outperforming the unoptimized version at 76.83%. Compared to existing MI-BCI methods, the proposed framework improves classification accuracy and expands the set of distinguishable commands, demonstrating enhanced practicality and effectiveness.}, }
@article {pmid40907530, year = {2025}, author = {J Bryan, M and Schwock, F and Yazdan-Shahmorad, A and P N Rao, R}, title = {Temporal basis function models for closed-loop neural stimulation.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ae036a}, pmid = {40907530}, issn = {1741-2552}, mesh = {Animals ; *Optogenetics/methods ; *Models, Neurological ; *Deep Brain Stimulation/methods ; Macaca mulatta ; Artificial Intelligence ; }, abstract = {Objective.Closed-loop neural stimulation provides novel therapies for neurological diseases such as Parkinson's disease (PD), but it is not yet clear whether artificial intelligence (AI) techniques can tailor closed-loop stimulation to individual patients or identify new therapies. Further advancements are required to address a number of difficulties with translating AI to this domain, including sample efficiency, training time, and minimizing loop latency such that stimulation may be shaped in response to changing brain activity.Approach.We propose temporal basis function models (TBFMs) to address these difficulties, and explore this approach in the context of excitatory optogenetic stimulation. We demonstrate the ability of TBF models to provide a single-trial, spatiotemporal forward prediction of the effect of optogenetic stimulation on local field potentials measured in two non-human primates. The simplicity of TBF models allow them to be sample efficient (<20 min of training data), rapid to train (<5 min), and low latency (<0.2 ms) on desktop CPUs.Main results.We demonstrate the model on 40 sessions of previously published excitatory optogenetic stimulation data. Surprisingly, on test sets it achieved a prediction accuracy 44% higher than a complex nonlinear dynamical systems model that requires hours to train, and 158% higher than a linear state-space model requiring 90 min to train. Additionally, in two simulations we show that it successfully allows a closed-loop stimulator to drive neural trajectories, and to achieve the user-preferred trade-offs between under- and over-stimulation, given the uncertainty in the model; it achieves an area under curve of ∼0.7 in both cases.Significance.By optimizing for sample efficiency, training time, and latency, our approach begins to bridge the gap between complex AI-based approaches to modeling dynamical systems and the vision of using such forward prediction models to develop novel, clinically useful closed-loop stimulation protocols.}, }
@article {pmid40906512, year = {2025}, author = {Zhou, L and Zhang, B and Kang, R and Wang, Y and Qin, J and Xiao, Q and Hui, V}, title = {Efficacy of the Conventional Rehabilitation Robot and bio-Signal Feedback-Based Rehabilitation Robot on Upper-Limb Function in Patients with Stroke: A Systematic Review and Network Meta-Analysis.}, journal = {NeuroRehabilitation}, volume = {57}, number = {2}, pages = {169-180}, doi = {10.1177/10538135251366668}, pmid = {40906512}, issn = {1878-6448}, mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; *Robotics ; *Upper Extremity/physiopathology ; Network Meta-Analysis as Topic ; *Stroke/physiopathology ; *Brain-Computer Interfaces ; Electromyography ; Randomized Controlled Trials as Topic ; }, abstract = {BackgroundWith the development of modern biomedical engineering, bio-signal feedback-based robots, such as electromyography (EMG)-based and brain-computer interface (BCI)-based rehabilitation robot, have emerged beyond conventional designs. However, their comparative effectiveness for improving upper limb function in stroke patients remains unassessed.ObjectiveTo evaluate the comparative effectiveness and ranking of the conventional rehabilitation robot and bio-signal feedback-based rehabilitation robot in improving upper limb function in stroke patients.MethodsPubMed, EMBASE, Cochrane Library, CINAHL, PEDro, EI, IEEEXplore, ClinicalTrials.gov, ICTRP, and ISRCTN Registry were searched for randomized controlled trials (RCTs) from their inception to December 25, 2024. The risk of bias was assessed using the Cochrane Risk of Bias tool (RoB 2.0) and evidence certainty with the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Network meta-analyses were performed using a random-effects model within a frequentist framework.Results59 RCTs with 3,387 participants were included. Based on the surface under the cumulative ranking curve (SUCRA), the BCI-based rehabilitation robot demonstrated the highest overall effects (SUCRA: 99.9%), short-term effects (SUCRA: 99.4%), and long-term effects (SUCRA: 85.1%), though its long-term effects were not significant (mean difference: 2.21; 95% confidence interval: -0.79, 5.21). The EMG-based rehabilitation robot outperformed the conventional rehabilitation robot in short-term interventions (SUCRA: 59.8% vs. 40.3%), but it did not have the same advantage in long-term interventions (SUCRA: 27.1% vs. 66.8%).ConclusionsThe BCI-based rehabilitation robot might be the best choice for improving upper limb function in stroke patients. Future studies should focus on the intervention time for the EMG-based rehabilitation robot.}, }
@article {pmid40904893, year = {2025}, author = {Patel, N and Verma, J and Jain, S}, title = {Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques.}, journal = {Frontiers in neuroinformatics}, volume = {19}, number = {}, pages = {1618050}, pmid = {40904893}, issn = {1662-5196}, abstract = {Electroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential. However, the inherent noise and complexity of EEG signals pose significant challenges. This study investigates the impact of three signal denoising techniques' Discrete Wavelet Transform(DWT), Discrete Fourier Transform(DFT), and Discrete Cosine Transform (DCT) Non EEG signal classification performance. The motivation behind this study is to identify the most effective preprocessing method for enhancing deep learning model performance in this domain. A novel DWT-CNN-BiGRU model is proposed, which leverages CNN layers for spatial feature extraction and BiGRU layers for capturing temporal dependencies. Experimental results show that the DWT-based approach, combined with standard scaling, achieves the highest accuracy of 94%, with a precision of 0.94, a recall of 0.95, and an F1-score of 0.94. Compared to the baseline DWT-CNN-BiLSTM model, the proposed method provides a modest yet meaningful improvement of approximately 17% in classification accuracy. These findings highlight the superiority of DWT as a preprocessing method and validate the proposed model's effectiveness for EEG-based classification, contributing to the development of more reliable medical diagnostic tools.}, }
@article {pmid40904422, year = {2025}, author = {Wang, X and Jin, X and Kong, W and Babiloni, F}, title = {CAGCNet: generalized contrastive learning for person identification based on channel aggregated EEG features.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {141}, pmid = {40904422}, issn = {1871-4080}, abstract = {Person identification method based on electroencephalograms (EEG) signals, or so called brainprint recognition is a novel way to distinguish identities with advantages of high security. However, existing methods neglect the distribution difference between training and test data, and the large distance between projected features in the latent space makes the performance of the model degrade in the unseen domain data. In this paper, we propose channel aggregated based generalized contrastive learning framework, which combines multiple modules to overcome this challenge. To capture features from different granularities, we involve multi-scale convolution with channel attention block. In face of distribution of unseen domain, we introduce feature enhancement-based generalized contrast learning to improve the model generalization ability. In the generalized contrast learning module, taking the difficulty of reconstructing EEG signals into consideration, we augment the source domain data at the feature level to improve the generalization ability of the model on the unseen domain data. Extensive experiments on two multi-session datasets shows that our model outperformed other baseline methods, demonstrating its capability of better generalization performance to unseen domain.}, }
@article {pmid40903968, year = {2025}, author = {Yu, H and Mu, Q and Liu, C and Wang, S and Sun, J}, title = {Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.}, journal = {Neural regeneration research}, volume = {}, number = {}, pages = {}, doi = {10.4103/NRR.NRR-D-25-00217}, pmid = {40903968}, issn = {1673-5374}, abstract = {Electroencephalography-based brain-computer interfaces have revolutionized the integration of neural signals with technological systems, offering transformative solutions across neuroscience, biomedical engineering, and clinical practice. This review systematically analyzes advancements in electroencephalography-based brain-computer interface architectures, emphasizing four pillars, namely signal acquisition, paradigm design, decoding algorithms, and diverse applications. The aim is to bridge the gap between technology and application and guide future research. In signal acquisition, noninvasive systems using wet, dry, and semi-dry electrodes are more comfortable and gentler on the skin compared to traditional methods. However, ensuring stable signal quality over long periods of time remains a challenge. Minimally invasive approaches, such as microneedle arrays and endovascular probes, achieve near-invasive signal fidelity without major surgery. Paradigm design explores task-specific neural encoders. Although motor imagery paradigms are widely used in rehabilitation, they require weeks of user training. Steady-state visually evoked potential and P300 speller paradigms enable rapid calibration, but cause visual and cognitive fatigue. Advanced systems currently combine electroencephalography with electromyography or eye-tracking to better handle real-world tasks. Decoding algorithms have advanced through Riemannian geometry for improved noise filtering, deep learning architectures for automated spatiotemporal feature extraction, and transfer learning frameworks to minimize cross-subject calibration. However, challenges remain in managing inconsistent electroencephalography, reducing processing demands, and ensuring compatibility across different electroencephalography devices. Clinical trials reveal a predominant focus on stroke rehabilitation, while emerging frontiers include astronaut neuromonitoring in space exploration. Challenges include improving signal accuracy, minimizing movement interference, addressing ethical data concerns, and ensuring real-world use. Future advancements focus on biocompatible nanomaterials, adaptive algorithms, and multimodal integration, positioning electroencephalography-based brain-computer interfaces as pivotal tools in next-generation neurotechnology.}, }
@article {pmid40902296, year = {2025}, author = {Bao, T and Wu, Y and Zhang, H and Cao, J and Wang, J and Liu, J and Fang, J}, title = {Determining microbial extracellular alkaline phosphatase activity in seawater based on surface-enhanced Raman spectroscopy.}, journal = {Marine environmental research}, volume = {212}, number = {}, pages = {107470}, doi = {10.1016/j.marenvres.2025.107470}, pmid = {40902296}, issn = {1879-0291}, abstract = {Microbial extracellular alkaline phosphatase (ALP) plays a significant role in marine phosphorus cycle. Therefore, it is of paramount importance to accurately and rapidly measure ALP activity (APA) in seawater. However, the applications of the existing APA measurement methods are constrained by cumbersome pre-processing, lengthy measurement times, and the influence of colored substances or suspended particles in seawater samples, which limit our accurate understanding of the marine phosphorus cycle. In this study, we developed a sensitive and rapid technique for the quantitative determination of microbial alkaline phosphatase activity in seawater based on surface-enhanced Raman spectroscopy (SERS). This method uses 5-bromo-4-chloro-3-indolyl phosphate (BCIP) as the substrate, and dimethyl sulfoxide (DMSO) as an internal standard to establish a model for quantifying APA in seawater samples. Our results show that the Raman intensity ratio (I600/I700) between the enzymatic reaction product 5-bromo-4-chloro-3-indole (BCI oxide dimers) (I600) and the internal standard (I700) is an ideal quantitation parameter, and there is a strong linear relationship between I600/I700 (y) and APA (x): y = 0.301x + 1.105, R[2] = 0.981. This method is capable of determining APA over a dynamic range of five orders of magnitude (from 0.1 to 10[4] mU L[-1]) with a detection limit of 0.1 mU L[-1]. The reliability of the method is confirmed by comparing the kinetic parameters of the fluorogenic method. Further, this method was tested and successfully applied to quantify APA in coastal and open ocean seawater samples from the Western Pacific Ocean, demonstrating the potential of this method for rapid and reliable detection of APA in the marine environment.}, }
@article {pmid40902051, year = {2025}, author = {Wang, G and Jiang, L and Song, X and Zhang, Y and Yao, D and Lu, J and Xu, P and Li, F and Liang, Y}, title = {Enhancing Neural Representations of Motor Imagery Through Action-Specific Brain Connectivity Patterns.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {3555-3564}, doi = {10.1109/TNSRE.2025.3605612}, pmid = {40902051}, issn = {1558-0210}, mesh = {Humans ; *Imagination/physiology ; Algorithms ; Male ; *Brain/physiology ; Female ; Adult ; Young Adult ; Functional Laterality/physiology ; Movement/physiology ; Magnetic Resonance Imaging ; Neural Pathways/physiology ; *Nerve Net/physiology ; Brain Mapping ; Psychomotor Performance/physiology ; Electroencephalography ; Brain-Computer Interfaces ; }, abstract = {Motor imagery (MI) is a cognitive process that allows individuals to mentally simulate movements without physical executio n. However, the exploration of functional connectivity (FC) and lateralization mechanisms under different MI actions remains insufficiently understood. In this work, the common orthogonal basis extraction (COBE) algorithm was employed to isolate action-specific components by removing shared background components from the raw FC of the MI process. We demonstrate that action-specific FC effectively captures the hemispheric statistical differences between left- and right-hand MI, outperforming traditional FC and temporal variability measures. And through a comprehensive analysis of network properties at three distinct levels, encompassing the whole-brain network properties, hemispherical properties, and individual nodal strength, complex lateralization patterns associated with diverse types of MI processes were successfully discerned. Furthermore, lateralization indices were further calculated to quantitatively reveal the degree of brain lateralization. Notably, the lateralization performance (LP) derived from action-specific FC exhibited a significant predictive capacity for MI performance, thereby suggesting its potential to evaluate individual MI capability. Collectively, these findings validate the action-specific FC patterns in characterizing neural mechanisms of MI processes and indicate that the LP could potentially be a useful tool to predict the MI performance of MI-based brain-computer inference (BCI), thereby contributing to the formulation of personalized therapeutic strategies for clinical rehabilitation from a new perspective.}, }
@article {pmid40899667, year = {2025}, author = {Han, NT and Yan, T and Zhuang, R and Kokkinakis, AV and Cao, L}, title = {Sensory Attenuation of Auditory P2 Responses is Modulated by the Sense of Action Timing Control.}, journal = {Psychophysiology}, volume = {62}, number = {9}, pages = {e70134}, pmid = {40899667}, issn = {1469-8986}, support = {32271078//National Natural Science Foundation of China/ ; 2023M733124//China Postdoctoral Science Foundation/ ; YJ20220315//China Postdoctoral Science Foundation/ ; 226-2024-00207//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; Male ; Female ; Electroencephalography ; *Evoked Potentials, Auditory/physiology ; Young Adult ; *Auditory Perception/physiology ; Adult ; Acoustic Stimulation ; *Psychomotor Performance/physiology ; Reaction Time/physiology ; }, abstract = {Sensory attenuation is a well-established phenomenon in which the neurophysiological response elicited by self-initiated stimuli is attenuated compared to identical externally generated stimuli. This phenomenon is mostly studied by comparing the N1 and P2 components of the auditory ERP. Sensory attenuation has also been linked to our sense of agency and control. In the present study, we investigated the role of action timing control in sensory attenuation. Previous studies that investigated the attenuation of the N1/P2 components instructed participants to generate self-initiated stimuli by having the participants perform a series of keypresses while EEG is recorded. ERP responses are then compared to a second condition where participants passively listen to identical sounds. Studies using this paradigm, known as the self-stimulation paradigm, have used a wide range of stimulus onset asynchronies (SOAs) for keypress timing. However, the choice of SOA is rarely explained, perhaps due to an assumption of trial independence. We found that as SOA increased, participants enacted more action timing control to maintain the specified SOA level. The degree of P2 suppression also increased as participants enacted more control. Contrary to most studies in the literature, we did not find N1 suppression but instead found N1 enhancement. The results suggest that P2 suppression may be related to action timing control while N1 enhancement may reflect factors other than motor predictions, in line with more recent interpretations of the N1 suppression effect.}, }
@article {pmid40899634, year = {2025}, author = {McGill, K and Bhullar, N and Carrandi, A and Batterham, PJ and Wayland, S and Maple, M}, title = {A Randomized Controlled Trial of an SMS-Based Brief Contact Intervention for People Bereaved by Suicide.}, journal = {Suicide &amp; life-threatening behavior}, volume = {55}, number = {5}, pages = {e70043}, pmid = {40899634}, issn = {1943-278X}, support = {//Suicide Prevention Australia/ ; }, mesh = {Humans ; Female ; Male ; *Bereavement ; *Text Messaging ; Adult ; *Suicide/psychology ; Middle Aged ; Suicidal Ideation ; Help-Seeking Behavior ; Psychological Distress ; Resilience, Psychological ; Young Adult ; }, abstract = {INTRODUCTION: Brief contact interventions (BCI) refer to short messages delivered proactively to a specific target population. The aim of this study was to test the effectiveness of a mobile phone short-message service (SMS) BCI for people bereaved by suicide.<br><br>METHODS: Participants were randomly allocated. The BCI group received text messages over a 6-week period. The active control group received the intervention website. Pre- and post-intervention surveys assessed demographic, suicide exposure and five key outcomes (psychological distress, suicidal ideation, complicated grief, resilience, and professional help-seeking intentions). BCI participants were also invited to participate in an interview post-intervention.<br><br>RESULTS: Of 99 participants randomized, 52 BCI and 47 control completed pre-intervention surveys. Post-intervention response rates were low (BCI: n&#x2009;=&#x2009;15; 28.85%; active control: n&#x2009;=&#x2009;16; 34.04%), with no statistically significant changes in key outcome measures. Eight BCI participants completed follow-up interviews. Relevance, timing of support, benefit to bereavement, and recommendations for scaling were identified.<br><br>CONCLUSIONS: Recruitment and retention challenges meant the effectiveness of the BCI could not be statistically determined. Qualitative evidence supported BCI acceptability for people bereaved by suicide. Recommendations to improve the intervention include embedding the BCI within existing postvention services offered soon after a death occurs and tailoring of messages to individuals' needs.<br><br>TRIAL REGISTRATION: This trial was registered with the Australian New Zealand Clinical Trial Register (ACTRN12621001430820).}, }
@article {pmid40898814, year = {2025}, author = {Zhao, Y and Lu, P and Wang, X and Yin, M}, title = {Bidirectional optimization of firing rate in a mouse neuronal brain-machine interface.}, journal = {Biology letters}, volume = {21}, number = {9}, pages = {20250176}, pmid = {40898814}, issn = {1744-957X}, support = {//High-level Talent Project of Natural Science Foundation of Hainan Province/ ; //Sanya Yazhou Bay Science and Technology City/ ; //'Rising Star of South China Sea' Project of Hainan Province/ ; //National Natural Science Foundation of China/ ; //STI 2030-Major Projects/ ; }, mesh = {Animals ; *Brain-Computer Interfaces ; Mice ; Reward ; *Neurons/physiology ; Male ; *Motor Cortex/physiology ; *Neuronal Plasticity ; Mice, Inbred C57BL ; Feedback, Sensory ; }, abstract = {Neuroplasticity enables the brain to adapt neural activity, but whether this can be harnessed for abstract optimization tasks like seeking curve extrema remains unclear. Here, we used a brain-machine interface in mice, pairing auditory feedback of neuronal firing rate with water rewards, to investigate whether motor cortex neurons can optimize activity along a unimodal curve ([Formula: see text]). The curve maps firing rate ([Formula: see text]) to sound frequency increase speed ([Formula: see text]), where the curve extremum accelerates reward acquisition. Over conditioning sessions, mice learned to modulate firing rates towards this peak, reducing reward time from 18.64 ± 7.30 s to 11.59 ± 4.38 s and increasing high-response events from 66 to 104 occurrences. Putative neurons increasingly prioritized high-response intervals, with positive proportion increments in upper intervals versus negative trends in lower ones. These findings demonstrate that cortical neurons can dynamically optimize activity along non-monotonic reward landscapes, revealing neuroplasticity as a substrate for adaptive self-optimization. This expands our understanding of how the brain learns abstract rules via feedback, with implications for neuroprosthetic design that leverage neural adaptability.}, }
@article {pmid40898635, year = {2025}, author = {Isakova, EV and Kotov, SV and Borisova, VA}, title = {[Effectiveness of "brain-computer" interfaces with biofeedback in the rehabilitation of cognitive impairment after a stroke].}, journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova}, volume = {125}, number = {8. Vyp. 2}, pages = {54-60}, doi = {10.17116/jnevro202512508254}, pmid = {40898635}, issn = {1997-7298}, mesh = {Humans ; Male ; *Brain-Computer Interfaces ; Female ; Middle Aged ; *Stroke/complications/psychology ; *Stroke Rehabilitation/methods ; *Biofeedback, Psychology ; Aged ; *Cognitive Dysfunction/rehabilitation/etiology ; Electroencephalography ; Adult ; Neuropsychological Tests ; }, abstract = {OBJECTIVE: Comparison of the effectiveness of two "brain-computer" interface (BCI) software complexes using biofeedback (BF) and standard therapy in restoring cognitive functions after a stroke.<br><br>MATERIAL AND METHODS: Eighty-nine stroke patients were examined. Neuropsychological testing was carried out using the Montreal Cognitive Assessment Scale (MoCA), the Tracking test, the Wechsler subtest 9 Kohs block design test, the Schulte tables, the Memorization of 10 Words test (according to A.R. Luria). Using the simple randomization method, three groups were formed: the main group (n=37), the comparison group (n=33) and the control group (n=19). In Group 1, sessions were conducted with BCI+BF based on the rhythm P300; in Group 2, with BCI+BF based on the mu-rhythm of electroencephalography (EEG), Group 3 received standard therapy.<br><br>RESULTS: An increase in the total MoCA score was reported in all three groups. The results in Groups 1 and 2 were comparable, exceeding those in Group 3 (p1-2=0.199, p1-3<0.001, p2-3=0.037). The effectiveness in Group 1 did not depend on the baseline MoCA score, exceeding the indicators in Group 3; in Group 2, the advantage over Group 3 was with a baseline MoCA of at least 22. According to the Schulte tables and the Tracking test, comparable statistically significant changes were obtained in Groups 1 and 2; no statistically significant change was reported in the control group. The Kohs block design test showed a more statistically significant change in the main group. The Memorization of 10 Words test by A.R. Luria also showed a more consistent improvement in mnestic disorders in the main group.<br><br>CONCLUSION: The effectiveness of BCI+BF exceeded standard therapy for post-stroke cognitive impairment. The advantage of IMC+BFB used in the main group over IMC+BFB in the comparison group was noted, which was due to a decrease in the effectiveness of the latter with a baseline MoCA score of less than 22 points, lower performance in the Memorizing 10 Words test and the Kohs block design test.}, }
@article {pmid40898590, year = {2025}, author = {Paveliev, M and Melnikova, A and Egorchev, AA and Parpura, V and Aganov, AV}, title = {Neuroimplants and the Glial Scar: What Makes the Brain-Computer Link Work?.}, journal = {Journal of neurochemistry}, volume = {169}, number = {9}, pages = {e70203}, doi = {10.1111/jnc.70203}, pmid = {40898590}, issn = {1471-4159}, support = {24-75-00123//Russian Science Foundation/ ; }, mesh = {Humans ; Animals ; *Brain-Computer Interfaces/trends ; *Cicatrix/pathology/prevention &amp; control ; *Neuroglia/pathology ; *Brain/pathology ; Tissue Engineering/methods ; *Brain Injuries/therapy/pathology ; }, abstract = {Neuroimplants are likely major technological breakthroughs of the next decade with the potential for unprecedented social impact. In addition to attractive and miracle-looking possibilities, the major obstacle for the industry is complicated, unpredictable, and unfavorable side effects due to tissue damage, biocompatibility limitations, and foreign body response at the brain-implant interface. Luckily, one major barrier preventing the connection of the neuroimplant to brain cells-the glial scar-has been studied previously for its role in brain trauma. This review highlights pharmacological and tissue engineering avenues that could be readily transferred from the brain trauma area to fast-growing neuroimplant engineering. The opportunities for chondroitinase ABC treatment, stem cells, and hydrogels for the prevention of glial scarring are emphasized. Alternatively, the glial scar may also be viewed not as an obstacle but as a possible regeneration-permissive component of the optimally working brain-neuroimplant interface. Feasible steps in that direction are discussed, including applications for chondroitin sulfate-binding peptides. Finally, the crucial role of new microscopy and data processing techniques for peri-implant glial scar monitoring is highlighted. To that end, we stress the importance of artificial intelligence, including artificial neuronal networks, for the analysis of cell morphology at the brain-neuroimplant interface.}, }
@article {pmid40897729, year = {2025}, author = {Sakakibara, Y and Kusutomi, T and Kondoh, S and Etani, T and Shimada, S and Imamura, Y and Naruse, Y and Fujii, S and Ibaraki, T}, title = {A Nostalgia Brain-Music Interface for enhancing nostalgia, well-being, and memory vividness in younger and older individuals.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {32337}, pmid = {40897729}, issn = {2045-2322}, mesh = {Humans ; *Music/psychology ; Male ; Female ; Adult ; Aged ; Electroencephalography ; Middle Aged ; *Brain/physiology ; Young Adult ; Mental Recall/physiology ; *Emotions/physiology ; *Neurofeedback/methods ; Auditory Perception/physiology ; *Memory/physiology ; Memory, Episodic ; }, abstract = {Music-evoked nostalgia has the potential to assist in recalling autobiographical memories and enhancing well-being. However, nostalgic music preferences vary from person to person, presenting challenges for applying nostalgia-based music interventions in clinical settings, such as a non-pharmacological approach. To address these individual differences, we developed the Nostalgia Brain-Music Interface (N-BMI), a neurofeedback system that recommends nostalgic songs tailored to each individual. This system is based on prediction models of nostalgic feelings, developed by integrating subjective nostalgia ratings, acoustic features and in-ear electroencephalographic (EEG) data during song listening. To test the effects of N-BMI on nostalgic feelings, state-level well-being, and memory recall, seventeen older and sixteen younger participants took part in the study. The N-BMI was personalized for each individual, and songs were recommended under two conditions: the "nostalgic condition", where songs were selected to enhance nostalgic feelings, and the "non-nostalgic condition", to reduce nostalgic feelings. We found nostalgic feelings, state-level well-being, and subjective memory vividness were significantly higher after listening to the recommended songs in the nostalgic condition compared to the non-nostalgic condition in both groups. This indicates that the N-BMI enhanced nostalgic feelings, state-level well-being, and memory recall across both groups. The N-BMI paves the way for innovative therapeutic interventions, including non-pharmacological approaches.}, }
@article {pmid40897258, year = {2025}, author = {Morozova, M and Yakovlev, L and Syrov, N and Lebedev, M and Kaplan, A}, title = {Cortical responses to tactile imagery: a high-density EEG study of the μ-rhythm event-related desynchronization and somatosensory evoked potentials.}, journal = {NeuroImage}, volume = {319}, number = {}, pages = {121440}, doi = {10.1016/j.neuroimage.2025.121440}, pmid = {40897258}, issn = {1095-9572}, mesh = {Humans ; *Evoked Potentials, Somatosensory/physiology ; Male ; Female ; Adult ; Electroencephalography/methods ; *Touch Perception/physiology ; *Somatosensory Cortex/physiology ; Young Adult ; *Imagination/physiology ; *Cortical Synchronization/physiology ; Attention/physiology ; }, abstract = {Tactile imagery (TI) engages somatosensory cortices in both hemispheres, along with widespread brain regions associated with the imagery process itself. Actively simulating touch can influence the processing of actual tactile stimuli, as reflected by modulations in somatosensory evoked potentials (SEPs) components. This study uses high-density electroencephalography (EEG) and sLORETA-based source localization to analyse cortical sources of SEPs components susceptible to active skin sensations imagery. Twenty healthy participants performed TI and tactile attention (TA) tasks. TI enhanced early SEP components (P100), indicating engagement of primary somatosensory cortical networks. This was accompanied with robust μ-rhythm event-related desynchronization (ERD) localized to the postcentral gyrus. While TA also elicited μ-ERD, its cortical distribution was broader, suggesting involvement of more distributed and possibly non-specific attentional mechanisms. Notably, sensor-space analysis revealed an enhanced late frontal P200 peak during TI, potentially indicating increased frontal activation. However, source-space analysis confirmed the absence of frontal pole involvement in SEPs during TI, underscoring the importance of accurate source localization. Thus, TI was found to significantly activate primary somatosensory cortices, influencing early stages of real tactile stimulus processing. Its effects were more spatially focused compared to those induced by TA. These findings suggest that TI could be a prospective approach for sensorimotor rehabilitation. On the other hand, TA could provide an effortless method for modulating sensorimotor rhythms in BCI control.}, }
@article {pmid40896338, year = {2025}, author = {Liu, M}, title = {Editorial: Neural dynamics for brain-inspired control and computing: advances and applications.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1666218}, doi = {10.3389/fnins.2025.1666218}, pmid = {40896338}, issn = {1662-4548}, }
@article {pmid40896268, year = {2025}, author = {Lee, D and Byun, K and Lee, S}, title = {Enhancing cognitive function through blood flow restriction: An effective resistance exercise modality for middle-aged women.}, journal = {Journal of exercise science and fitness}, volume = {23}, number = {4}, pages = {379-388}, pmid = {40896268}, issn = {1728-869X}, abstract = {PURPOSE: Cognitive decline progresses more rapidly in women than in men, with a higher prevalence of neurodegenerative diseases observed in females. Exercise has been shown to enhance cognitive function through the upregulation of neurotrophic factors such as brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF) and insulin-like growth factor-1 (IGF-1). However, high-load resistance exercise may not be suitable for all populations, particularly middle-aged women. Low-load resistance exercise with blood flow restriction (LLBFR) has emerged as an effective alternative. This study investigated the acute effects of LLBFR on neurotrophic factors and cognitive function in middle-aged women.<br><br>METHODS: Fifteen healthy middle-aged women completed a randomized crossover trial involving four conditions: control (CON), low-load resistance exercise (LLRE), LLBFR, and moderate-load resistance exercise (MLRE). Cognitive function was assessed before and after each session using the color-word matching Stroop task (CWST). Blood samples were analyzed for serum levels of BDNF, VEGF, and IGF-1, and lactate concentrations were measured to evaluate metabolic responses.<br><br>RESULTS: Only the LLBFR condition showed significant improvements in CWST reaction time (p&#xa0;=&#xa0;0.002) with no changes in error rates, indicating enhanced cognitive performance. Serum BDNF and VEGF levels increased significantly following both LLBFR (p&#xa0;<&#xa0;0.001, p&#xa0;=&#xa0;0.014, respectively) and MLRE (p&#xa0;<&#xa0;0.001, p&#xa0;=&#xa0;0.004, respectively), whereas IGF-1 levels remained unchanged across conditions. Increases in lactate concentrations were positively correlated with changes in BDNF and VEGF (p&#xa0;<&#xa0;0.001 for both), but not with IGF-1.<br><br>CONCLUSION: A single session of LLBFR improved cognitive function and upregulated neurotrophic factors, particularly BDNF and VEGF, in middle-aged women. These findings suggest that LLBFR may be an effective intervention for promoting cognitive health in this population.}, }
@article {pmid40894778, year = {2025}, author = {Tong, JQ and Binder, JR and Conant, LL and Mazurchuk, S and Anderson, AJ and Fernandino, L}, title = {A Common Representational Code for Event and Object Concepts in the Brain.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40894778}, issn = {2692-8205}, support = {R01 DC016622/DC/NIDCD NIH HHS/United States ; R01 DC020932/DC/NIDCD NIH HHS/United States ; }, abstract = {Events and objects are two fundamental ways in which humans conceptualize their experience of the world. Despite the significance of this distinction for human cognition, it remains unclear whether the neural representations of object and event concepts are categorically distinct or, instead, can be explained in terms of a shared representational code. We investigated this question by analyzing fMRI data acquired from human participants (males and females) while they rated their familiarity with the meanings of individual words (all nouns) denoting object and event concepts. Multivoxel pattern analyses indicated that both categories of lexical concepts are represented in overlapping fashion throughout the association cortex, even in the areas that showed the strongest selectivity for one or the other type in univariate contrasts. Crucially, in these areas, a feature-based model trained on neural responses to individual event concepts successfully decoded object concepts from their corresponding activation patterns (and vice versa), showing that these two categories share a common representational code. This code was effectively modeled by a set of experiential feature ratings, which also accounted for the mean activation differences between these two categories. These results indicate that neuroanatomical dissociations between events and objects emerge from quantitative differences in the cortical distribution of more fundamental features of experience. Characterizing this representational code is an important step in the development of theory-driven brain-computer interface technologies capable of decoding conceptual content directly from brain activity.}, }
@article {pmid40894619, year = {2025}, author = {Spalding, Z and Duraivel, S and Rahimpour, S and Wang, C and Barth, K and Schmitz, C and Lad, SP and Friedman, AH and Southwell, DG and Viventi, J and Cogan, GB}, title = {Shared latent representations of speech production for cross-patient speech decoding.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40894619}, issn = {2692-8205}, support = {R01 DC019498/DC/NIDCD NIH HHS/United States ; R01 NS129703/NS/NINDS NIH HHS/United States ; UG3 NS120172/NS/NINDS NIH HHS/United States ; UL1 TR002553/TR/NCATS NIH HHS/United States ; }, abstract = {Speech brain-computer interfaces (BCIs) can restore communication in individuals with neuromotor disorders who are unable to speak. However, current speech BCIs limit patient usability and successful deployment by requiring large volumes of patient-specific data collected over long periods of time. A promising solution to facilitate usability and accelerate their successful deployment is to combine data from multiple patients. This has proven difficult, however, due to differences in user neuroanatomy, varied placement of electrode arrays, and sparse sampling of targeted anatomy. Here, by aligning patient-specific neural data to a shared latent space, we show that speech BCIs can be trained on data combined across patients. Using canonical correlation analysis and high-density micro-electrocorticography (μECoG), we uncovered shared neural latent dynamics with preserved micro-scale speech information. This approach enabled cross-patient decoding models to achieve improved accuracies relative to patient-specific models facilitated by the high resolution and broad coverage of μECoG. Our findings support future speech BCIs that are more accurate and rapidly deployable, ultimately improving the quality of life for people with impaired communication from neuromotor disorders.}, }
@article {pmid40893910, year = {2025}, author = {Teng, J and Cho, S and Lee, SM}, title = {Tri-manual interaction in hybrid BCI-VR systems: integrating gaze, EEG control for enhanced 3D object manipulation.}, journal = {Frontiers in neurorobotics}, volume = {19}, number = {}, pages = {1628968}, pmid = {40893910}, issn = {1662-5218}, abstract = {Brain-computer interface (BCI) integration with virtual reality (VR) has progressed from single-limb control to multi-limb coordination, yet achieving intuitive tri-manual operation remains challenging. This study presents a consumer-grade hybrid BCI-VR framework enabling simultaneous control of two biological hands and a virtual third limb through integration of Tobii eye-tracking, NeuroSky single-channel EEG, and non-haptic controllers. The system employs e-Sense attention thresholds (>80% for 300 ms) to trigger virtual hand activation combined with gaze-driven targeting within 45° visual cones. A soft maximum weighted arbitration algorithm resolves spatiotemporal conflicts between manual and virtual inputs with 92.4% success rate. Experimental validation with eight participants across 160 trials demonstrated 87.5% virtual hand success rate and 41% spatial error reduction (σ = 0.23 mm vs. 0.39 mm) compared to traditional dual-hand control. The framework achieved 320 ms activation latency and 22% NASA-TLX workload reduction through adaptive cognitive load management. Time-frequency analysis revealed characteristic beta-band (15-20 Hz) energy modulations during successful virtual limb control, providing neurophysiological evidence for attention-mediated supernumerary limb embodiment. These findings demonstrate that sophisticated algorithmic approaches can compensate for consumer-grade hardware limitations, enabling laboratory-grade precision in accessible tri-manual VR applications for rehabilitation, training, and assistive technologies.}, }
@article {pmid40892657, year = {2025}, author = {Song, Z and Zhang, X and Li, M and Tan, J and Wang, Y}, title = {Online Neural-to-Movement Mapping Transfer for Task Switching and Retention in Brain-Machine Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {3674-3684}, doi = {10.1109/TNSRE.2025.3605246}, pmid = {40892657}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Animals ; Rats ; Algorithms ; Movement/physiology ; Male ; Machine Learning ; Reinforcement, Psychology ; Psychomotor Performance/physiology ; Brain/physiology ; Online Systems ; Memory ; Electroencephalography ; *Retention, Psychology/physiology ; *Brain Mapping/methods ; }, abstract = {Current brain-machine interfaces (BMIs) often rely on decoders trained for single tasks, limiting their flexibility in real-world applications. We propose an online learning framework that enables the transfer of neural-to-movement (knowledge) across tasks, supporting both task switching and memory retention. In our framework, neural activity is projected into a dynamical jPCA space to effectively dissociate into variant and invariant components. The variant components of the neural patterns are then aligned by deriving Gradient-based Kullback-Leibler Divergence Minimization (GKLD) for efficient online adaptation. A kernel reinforcement learning (KRL) model then decodes aligned neural signals while reusing prior neural-to-movement mapping. Evaluated on rats switching between a one-lever pressing and a two-lever discrimination task, the framework shows rapid convergence, over four times faster than the baseline method, and improves decoding accuracy by around 35% during task switching. Furthermore, when switching back to the original task, the framework successfully retains knowledge from the old task. Our method demonstrates general applicability to multiple task switching scenarios and maintains stable decoding across three representative days over a 21-day period, highlighting its potential for long-term, real-world use.}, }
@article {pmid40890094, year = {2025}, author = {Du, Z and Chu, C and Shi, W and Luo, N and Lu, Y and Wang, H and Zhao, B and Xiong, H and Yang, Z and Jiang, T}, title = {Connectome-constrained ligand-receptor interaction analysis for understanding brain network communication.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {8179}, pmid = {40890094}, issn = {2041-1723}, support = {62403465//National Natural Science Foundation of China (National Science Foundation of China)/ ; GZC20232999//China Postdoctoral Science Foundation/ ; 2024M753502//China Postdoctoral Science Foundation/ ; }, mesh = {*Connectome/methods ; *Brain/physiology/diagnostic imaging/metabolism ; Humans ; Algorithms ; Ligands ; *Nerve Net/physiology/diagnostic imaging ; Diffusion Magnetic Resonance Imaging ; }, abstract = {Both diffusion magnetic resonance imaging and transcriptomic technologies have provided unprecedented opportunities to dissect brain network communication, offering insights from structural connectivity and signaling molecules separately. However, incorporating these complementary modalities for characterizing the interregional communication remains challenging. By simplifying the communication processes into an optimal transport problem, which is defined as the ligand-receptor expression coupling constrained by structurally-derived communication cost, we develop a method called CLRIA (connectome-constrained ligand-receptor interaction analysis) to infer a low-rank representation of ligand-receptor interaction-mediated communication networks. To solve the proposed optimization problem, the block majorization minimization algorithm is adopted and proven to converge globally. We benchmark CLRIA on simulated and published data, validating its accuracy and computational efficiency. Subsequently, we demonstrate the CLRIA's utility in evaluating communication strategies and asymmetric communication using its solution. Furthermore, CLRIA-derived communication patterns successfully decode brain state transitions. Overall, our results highlight CLRIA as a valuable tool for understanding complex communication in the brain.}, }
@article {pmid40887906, year = {2025}, author = {Zhao, Y and Sun, R and Wang, Z and Ma, S and Wang, R and Li, F and Geng, H}, title = {Engineered Hydrogels as Functional Components in Controllable Neuromodulation for Translational Therapeutics.}, journal = {ACS applied bio materials}, volume = {8}, number = {9}, pages = {7587-7615}, doi = {10.1021/acsabm.5c01269}, pmid = {40887906}, issn = {2576-6422}, mesh = {*Hydrogels/chemistry/pharmacology ; Humans ; *Biocompatible Materials/chemistry/pharmacology/chemical synthesis ; Animals ; Tissue Engineering ; Materials Testing ; }, abstract = {Controllable neuromodulation leveraging multimodal triggers synergized with hydrogels represents a transformative therapeutic strategy for pro-regenerative neural repair. Strategic incorporation of programmable neuromodulatory interventions and engineered hydrogels within localized neural niches is critical for clinical translation, characterized by lower invasiveness and greater therapeutic efficacy. This review elucidates the physiochemical features of hydrogels, systematically classifying hydrogel-based neuromodulation into five distinct modes (electrical, ionic, biomechanical, optical, and biochemical) and highlighting the intrinsic multidimensional structural and chemical engineering employed to enhance neuromodulatory performance. Key principles of hydrogel design and fabrication are provided from the perspective of tissue-implant interactions, such as mechanical compatibility, electrointegration, adhesion, and wireless activation. Hydrogels embedded with low-impedance organic and inorganic components, such as conductive polymers and noble metals, are investigated to provide high-level evidence to enable precise cellular stimulation for intrinsic nerve repair, neural prosthesis, and brain-machine interface. Crucially, this review highlights the synergistic integration of these principles into multimodal, closed-loop systems, which combine functions like electrophysiological sensing with on-demand biochemical release for intelligent, autonomous therapies. Finally, this review confronts the critical challenges for clinical translation and discusses future directions, including the potential of artificial intelligence-driven materials design to accelerate the development of next-generation neural interfaces.}, }
@article {pmid40887182, year = {2025}, author = {Li, S and Fu, Y and Zhang, Y and Lu, G}, title = {[Research on fatigue recognition based on graph convolutional neural network and electroencephalogram signals].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {4}, pages = {686-692}, pmid = {40887182}, issn = {1001-5515}, mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Fatigue/diagnosis/physiopathology ; *Automobile Driving ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Convolutional Neural Networks ; }, abstract = {Electroencephalogram (EEG) serves as an effective indicator of detecting fatigue driving. Utilizing the open accessible Shanghai Jiao Tong University Emotion Electroencephalography Dataset (SEED-VIG), driving states are divided into three categories including awake, tired and drowsy for investigation. Given the characteristics of mutual influence and interdependence among EEG channels, as well as the consistency of the graph convolutional neural network (GCNN) structure, we designed an adjacency matrix based on the Pearson correlation coefficients of EEG signals among channels and their positional relationships. Subsequently, we developed a GCNN for recognition. The experimental results show that the average classification accuracy of driving state categories for 20 subjects, from the SEED-VIG dataset under the smooth feature of differential entropy (DE) linear dynamic system is 91.66%. Moreover, the highest classification accuracy can reach 98.87%, and the average Kappa coefficient is 0.83. This work demonstrates the reliability of this method and provides a guideline for the research field of safe driving brain computer interface.}, }
@article {pmid40887181, year = {2025}, author = {Xiao, N and Li, M}, title = {[Motor imagery classification based on dynamic multi-scale convolution and multi-head temporal attention].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {4}, pages = {678-685}, pmid = {40887181}, issn = {1001-5515}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Attention ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; }, abstract = {Convolutional neural networks (CNNs) are renowned for their excellent representation learning capabilities and have become a mainstream model for motor imagery based electroencephalogram (MI-EEG) signal classification. However, MI-EEG exhibits strong inter-individual variability, which may lead to a decline in classification performance. To address this issue, this paper proposes a classification model based on dynamic multi-scale CNN and multi-head temporal attention (DMSCMHTA). The model first applies multi-band filtering to the raw MI-EEG signals and inputs the results into the feature extraction module. Then, it uses a dynamic multi-scale CNN to capture temporal features while adjusting attention weights, followed by spatial convolution to extract spatiotemporal feature sequences. Next, the model further optimizes temporal correlations through time dimensionality reduction and a multi-head attention mechanism to generate more discriminative features. Finally, MI classification is completed under the supervision of cross-entropy loss and center loss. Experiments show that the proposed model achieves average accuracies of 80.32% and 90.81% on BCI Competition IV datasets 2a and 2b, respectively. The results indicate that DMSCMHTA can adaptively extract personalized spatiotemporal features and outperforms current mainstream methods.}, }
@article {pmid40887179, year = {2025}, author = {Pang, Z and Wang, Y and Dong, Q and Cheng, Z and Li, Z and Zhang, R and Cui, H and Chen, X}, title = {[Research on hybrid brain-computer interface based on imperceptible visual and auditory stimulation responses].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {4}, pages = {660-667}, pmid = {40887179}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Acoustic Stimulation ; *Photic Stimulation ; Electroencephalography ; Evoked Potentials, Auditory/physiology ; Adult ; }, abstract = {In recent years, hybrid brain-computer interfaces (BCIs) have gained significant attention due to their demonstrated advantages in increasing the number of targets and enhancing robustness of the systems. However, Existing studies usually construct BCI systems using intense auditory stimulation and strong central visual stimulation, which lead to poor user experience and indicate a need for improving system comfort. Studies have proved that the use of peripheral visual stimulation and lower intensity of auditory stimulation can effectively boost the user's comfort. Therefore, this study used high-frequency peripheral visual stimulation and 40-dB weak auditory stimulation to elicit steady-state visual evoked potential (SSVEP) and auditory steady-state response (ASSR) signals, building a high-comfort hybrid BCI based on weak audio-visual evoked responses. This system coded 40 targets via 20 high-frequency visual stimulation frequencies and two auditory stimulation frequencies, improving the coding efficiency of BCI systems. Results showed that the hybrid system's averaged classification accuracy was (78.00 ± 12.18) %, and the information transfer rate (ITR) could reached 27.47 bits/min. This study offers new ideas for the design of hybrid BCI paradigm based on imperceptible stimulation.}, }
@article {pmid40887178, year = {2025}, author = {Fu, Y and Lu, H}, title = {[Technical maturity and bubble risks of brain-computer interface (BCI): Considerations from research to industrial translation].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {4}, pages = {651-659}, pmid = {40887178}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual ; Electroencephalography ; Event-Related Potentials, P300 ; }, abstract = {Brain-computer interface (BCI) technology faces structural risks due to a misalignment between its technological maturity and industrialization expectations. This study used the Technology Readiness Level (TRL) framework to assess the status of major BCI paradigms-such as steady-state visual evoked potential (SSVEP), motor imagery, and P300-and found that they predominantly remained at TRL4 to TRL6, with few stable applications reaching TRL9. The analysis identified four interrelated sources of bubble risk: overly broad definitions of BCI, excessive focus on decoding performance, asynchronous translational progress, and imprecise terminology usage. These distortions have contributed to the misallocation of research resources and public misunderstanding. To foster the sustainable development of BCI, this paper advocated the establishment of a standardized TRL evaluation system, clearer terminological boundaries, stronger support for fundamental research, enhanced ethical oversight, and the implementation of inclusive and diversified governance mechanisms.}, }
@article {pmid40886590, year = {2025}, author = {Zhu, S and Cao, T and He, Q and Wang, N and Jia, Y and Chai, X and Yang, Y}, title = {Advanced neuroimaging techniques to decipher brain connectivity networks in patients with disorder of consciousness: a narrative review.}, journal = {NeuroImage. Clinical}, volume = {48}, number = {}, pages = {103864}, pmid = {40886590}, issn = {2213-1582}, abstract = {Advanced neuroimaging techniques have revolutionized our ability to decode brain networks in patients with disorders of consciousness (DoC), offering unprecedented insights into the structural and functional underpinnings of consciousness impairment. This review systematically examines and summarizes the clinical applications of modern neuroimaging methodologies-specifically functional MRI and diffusion MRI- for DoC patients from three key perspectives: (1) pathogenic mechanism and theory evolution, (2) accurate diagnosis and prognosis assessment, and (3) treatment strategy and efficacy evaluation. By integrating network neuroscience with clinical insights, we highlight the transformative role of neuroimaging in unraveling network-level damage, refining clinical assessments, and guiding therapeutic innovations. We further outline the potential applicational challenges associated with leveraging neuroimaging techniques to advance both scientific research on consciousness networks and clinical practice in DoC management, hoping to better address these complex conditions.}, }
@article {pmid40883792, year = {2025}, author = {Wan, C and Zhang, Q and Qiu, Y and Zhang, W and Nie, Y and Zeng, S and Wang, J and Shen, X and Yu, C and Wu, X and Zhang, Y and Li, Y}, title = {Effects of dual-task mode brain-computer interface based on motor imagery and virtual reality on balance and attention in patients with stroke: a randomized controlled pilot trial.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {187}, pmid = {40883792}, issn = {1743-0003}, support = {No. JBGS202414//Jiangsu Provincial People's Hospital, Clinical Diagnosis and Treatment Technology Innovation 'Open bidding for selecting the best candidates' Project/ ; No. ST242102//Major sports research projects of Jiangsu Sports Bureau/ ; 2024TGYY51//Ministry of Industry and Information Technology and National Health Commission High-end Equipment Promotion and Application Project/ ; }, mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; *Attention/physiology ; *Brain-Computer Interfaces ; Imagery, Psychotherapy/methods ; Imagination/physiology ; Pilot Projects ; *Postural Balance/physiology ; Single-Blind Method ; *Stroke/physiopathology/psychology ; *Stroke Rehabilitation/methods ; *Virtual Reality ; }, abstract = {BACKGROUND: Brain-computer interface (BCI) has been shown to be beneficial in improving lower limb motility in stroke, but their effectiveness on balance and attention is unclear. In addition, current BCIs are mostly in single-task mode. The BCI system used in this study was based on a dual-task model of motor imagery (MI) and virtual reality (VR). Previous studies have demonstrated that dual-task seems to be beneficial for balance and attention. The purpose of this study was to validate the effects of MI-VR-based dual-task BCI on balance and attention in participants with stroke.<br><br>METHODS: This pilot, single-blind, randomized controlled trial involved 38 stroke participants, randomized to the BCI (BCI pedaling training) or control group (conventional pedaling). Both groups trained 20&#xa0;min daily, 5&#xa0;days a week for 4&#xa0;weeks, alongside conventional rehabilitation. Thirty participants completed the program (mean age: 56.56&#xa0;years, mean disease duration: 4.48&#xa0;months). Assessments were made before and after 4&#xa0;weeks. The primary outcome was the Berg Balance Scale (BBS), and secondary outcomes included the Timed Up and Go Test (TUGT), Fugl-Meyer Lower Extremity Assessment (FMA-LE), Symbol Digit Modalities Test (SDMT), and average attention index.<br><br>RESULTS: 30 participants completed the study (14 in the BCI and 16 in the control group). The retention rates were 73.68% and 84.21% respectively. No adverse events were reported in this study and participants did not report any discomfort. The changes in BBS, TUGT and SDMT values in the BCI group were significantly better than those in the control group (P&#x2009;<&#x2009;0.05). Average attention index of the BCI group's participants grew with the number of training sessions, and there was a significant difference comparing pre- to post-treatment (p&#x2009;<&#x2009;0.05). The value of BBS change is linearly correlated with the value of SDMT change (F&#x2009;=&#x2009;8.778, y&#x2009;=&#x2009;0.59x&#x2009;+&#x2009;1.90, P&#x2009;<&#x2009;0.001).<br><br>CONCLUSIONS: This study initially showed positive effects of dual-task mode of BCI pedalling training on balance and attention in stroke participants. However, given the preliminary nature of this study and its limitations, the results need to be treated with caution. Trial registration Chinese Clinical Trial Registry Identifier: ChiCTR2300071522. Registered on 2023/05/17.}, }
@article {pmid40883351, year = {2025}, author = {Metwalli, D and Kiroles, AE and Radwan, YA and Mohamed, EA and Barakat, M and Ahmed, A and Omar, AM and Selim, S}, title = {ArEEG: an Open-Access Arabic Inner Speech EEG Dataset.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1513}, pmid = {40883351}, issn = {2052-4463}, mesh = {*Electroencephalography ; Humans ; *Brain-Computer Interfaces ; *Speech ; Language ; }, abstract = {Recent advancements in Brain-Computer Interface (BCI) technology are shifting towards inner speech over motor imagery due to its intuitive nature and broader command spectrum, enhancing interaction with electronic devices. However, the reliance on a large number of electrodes in available datasets complicates the development of cost-effective BCIs. Additionally, the lack of publicly available datasets hinder the development of this technology. To address this, we introduce a new Arabic Inner Speech dataset, featuring five distinct classes, exceeding the typical four-class datasets, and recorded using only eight electrodes, making it an economical solution. Our primary objective is to provide an open-access, multi-class Electroencephalographic (EEG) dataset in Arabic for inner speech, encompassing five commands. This dataset is designed to enhance our understanding of brain activity, facilitate the integration of BCI technologies in Arabic-speaking regions, and serve as a valuable resource for developing and testing real-world BCI applications. Through this contribution, we aim to bridge the gap between language-specific neural data and the field of neurotechnology, fostering innovation and inclusivity in BCI research.}, }
@article {pmid40885826, year = {2025}, author = {Vargas-Irwin, CE and Hosman, T and Gusman, JT and Pun, TK and Simeral, JD and Singer-Clark, T and Kapitonava, A and Nicolas, C and Shah, NP and Avansino, DT and Kamdar, F and Williams, ZM and Henderson, JM and Hochberg, LR}, title = {Gesture encoding in human left precentral gyrus neuronal ensembles.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {1315}, pmid = {40885826}, issn = {2399-3642}, support = {U01 NS123101/NS/NINDS NIH HHS/United States ; U01DC017844, R01DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; T32 MH115895/MH/NIMH NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; UH2NS095548, U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; 19CSLOI34780000//American Heart Association (American Heart Association, Inc.)/ ; T32MH115895//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; }, mesh = {Humans ; *Gestures ; *Motor Cortex/physiology ; Brain-Computer Interfaces ; Male ; Adult ; Female ; *Neurons/physiology ; Hand/physiology ; Middle Aged ; Spinal Cord Injuries/physiopathology ; }, abstract = {Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of ~70% for all 48 gestures (and ~90% for sets of 10). Our results show that single-unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.}, }
@article {pmid40881516, year = {2025}, author = {Swarnakar, R}, title = {Brain-Computer Interfaces in Rehabilitation: Implementation Models and Future Perspectives.}, journal = {Cureus}, volume = {17}, number = {7}, pages = {e88873}, pmid = {40881516}, issn = {2168-8184}, abstract = {Brain-computer interfaces (BCIs) represent an emerging advancement in rehabilitation, enabling direct communication between the brain and external devices to aid recovery in individuals with neurological impairments. BCIs can be classified into invasive, semi-invasive, non-invasive, or hybrid types. By interpreting neural signals and converting them into control commands, BCIs can bypass damaged pathways, offering therapeutic potential for conditions such as stroke, spinal cord injury, traumatic brain injury, and neurodegenerative diseases such as amyotrophic lateral sclerosis. BCIs' current applications, such as motor restoration via robotic exoskeletons and functional electrical stimulation, cognitive enhancement through neurofeedback and attention training, and communication tools for individuals with severe physical limitations, are largely being explored within research settings and are not yet part of routine clinical practice. Advances in EEG signal acquisition, machine learning, wearable and wireless systems, and integration with virtual reality are enhancing the clinical utility of BCIs by improving accuracy, adaptability, and usability. However, widespread clinical adoption faces challenges, including signal variability, training complexity, data privacy, and ethical and regulatory issues. Ethical challenges in BCI include issues related to the ownership and misuse of brain data, risks of neural interference, threats to autonomy and personal identity, as well as concerns around data privacy, user consent, emotional manipulation, and accountability in neural interventions. In this context, this editorial has also proposed one model (NEURO model checklist) for BCI implementation in rehabilitation. The future of BCIs in rehabilitation lies in developing personalized, closed-loop, and home-based systems, enabled by interdisciplinary collaboration among clinicians, engineers, neuroscientists, and policymakers. With continued research and ethical implementation, BCIs have the potential to transform neurorehabilitation and greatly enhance patient outcomes and quality of life.}, }
@article {pmid40858626, year = {2025}, author = {Hu, Y and Liu, Y and Hou, Y and Pan, Y and Gao, X}, title = {Dataset of natural conversations about appearance using fNIRS.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1486}, pmid = {40858626}, issn = {2052-4463}, mesh = {Humans ; Female ; Young Adult ; Spectroscopy, Near-Infrared ; *Brain/physiology ; *Body Image ; Adult ; }, abstract = {Self-objectification, marked by an overemphasis on how one's appearance is viewed by others, promotes increased body surveillance and dissatisfaction. Natural conversations centered around appearance, such as "fat talk"-where individuals, often women, engage in negative or self-deprecating remarks about their bodies or weight-are commonly used to induce a state of self-objectification. However, there is a notable lack of public datasets on brain signals during fat talk. In this dataset, we collected brain data from 31 female participants (aged 19.55 ± 0.89 years) using a 40-channel portable near-infrared device during fat talk and non-fat talk (topics such as travel and home decoration), primarily covering the frontal and parietal areas. Data analyses of subjective reports and fNIRS data revealed an increase in body surveillance and dissatisfaction, suggesting a significant activation of the self-objectification state. This dataset can be utilized to explore fNIRS data processing during natural interpersonal conversations and to gain insights into emotional and cognitive responses under conditions of self-dysregulation.}, }
@article {pmid40883960, year = {2026}, author = {Wang, Z and Tan, S and Lu, K and Li, Q and Jiao, B and Li, W and Wu, X and Zhang, L and Zeng, L and Bai, R}, title = {The contributions of aquaporin-4 to water exchange across the blood-brain barrier measured by filter-exchange imaging.}, journal = {Magnetic resonance in medicine}, volume = {95}, number = {1}, pages = {531-544}, doi = {10.1002/mrm.70049}, pmid = {40883960}, issn = {1522-2594}, support = {2024SSYS0019//Key R&amp;D Program of Zhejiang Province/ ; 82172050//National Natural Science Foundation of China/ ; 82222032//National Natural Science Foundation of China/ ; 92359303//National Natural Science Foundation of China/ ; 2022ZD0206000//STI2030-Major Projects of China/ ; }, mesh = {Animals ; *Aquaporin 4/metabolism ; *Blood-Brain Barrier/diagnostic imaging/metabolism ; Rats ; *Water/metabolism ; Male ; Ouabain/pharmacology ; Reproducibility of Results ; Rats, Sprague-Dawley ; Sodium-Potassium-Exchanging ATPase/metabolism/antagonists &amp; inhibitors ; *Magnetic Resonance Imaging/methods ; Endothelial Cells/metabolism ; Thiadiazoles/pharmacology ; Brain/diagnostic imaging/metabolism ; Niacinamide/analogs &amp; derivatives ; }, abstract = {PURPOSE: Water exchange across the blood-brain barrier (WEXBBB) is a promising biomarker for assessing the blood-brain barrier (BBB) integrity. However, the physiological mechanisms governing WEXBBB remain unclear. This study was conducted to investigate the contribution of Na[+]/K[+]-ATPase (NKA) on the luminal side of endothelial cells and aquaporin-4 (AQP4) to WEXBBB.<br><br>METHODS: WEXBBB was measured using filter-exchange imaging for BBB assessment (FEXI-BBB) on rats, and data were fitted using an adapted two-compartment crusher-compensated exchange rate (CCXR) model. Test-retest reliability of the vascular water efflux rate constant (kbo) was assessed. Ouabain and 2-(nicotinamide)-1,3,4-thiadiazole (TGN-020) were administered to inhibit NKA on the luminal side of endothelial cells and AQP4, respectively, to investigate their roles in WEXBBB measured by FEXI-BBB.<br><br>RESULTS: Fixing intravascular diffusivity in the two-compartment CCXR model significantly improved estimation accuracy and precision of kbo and other parameters. The test-retest experiment demonstrated that this method had good reproducibility in measuring kbo (intraclass correlation coefficient&#x2009;=&#x2009;0.79). Administering TGN-020, which inhibits AQP4, significantly decreased kbo by 32% (kbo&#x2009;=&#x2009;3.07&#x2009;&#xb1;&#x2009;0.81&#x2009;s[-1] vs. 2.09&#x2009;&#xb1;&#x2009;1.10&#x2009;s[-1], p&#x2009;<&#x2009;0.05). However, the ouabain-treated group showed no significant change in kbo compared with that of the control group (2.51&#x2009;&#xb1;&#x2009;0.58&#x2009;s[-1] vs. 2.37&#x2009;&#xb1;&#x2009;1.02&#x2009;s[-1], p&#x2009;=&#x2009;0.73) in the NKA inhibition experiment.<br><br>CONCLUSIONS: WEXBBB decreased by 32% after administering TGN-020, but no downward trend was noted after administering ouabain. Our findings indicate that AQP4 expression/function, but not NKA activity on the luminal side of endothelial cells, plays a significant role in regulating WEXBBB.}, }
@article {pmid40872076, year = {2025}, author = {Zhang, M and Qian, B and Gao, J and Zhao, S and Cui, Y and Luo, Z and Shi, K and Yin, E}, title = {Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {16}, pages = {}, pmid = {40872076}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/instrumentation/methods ; Humans ; Electrodes ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {As brain-computer interface (BCI) technology continues to advance, research on human brain function has gradually transitioned from theoretical investigation to practical engineering applications. To support EEG signal acquisition in a variety of real-world scenarios, BCI electrode systems must demonstrate a balanced combination of electrical performance, wearing comfort, and portability. Dry electrodes have emerged as a promising alternative for EEG acquisition due to their ability to operate without conductive gel or complex skin preparation. This paper reviews the latest progress in dry electrode EEG systems, summarizing key achievements in hardware design with a focus on structural innovation and material development. It also examines application advances in several representative BCI domains, including emotion recognition, fatigue and drowsiness detection, motor imagery, and steady-state visual evoked potentials, while analyzing system-level performance. Finally, the paper critically assesses existing challenges and identifies critical future research priorities. Key recommendations include developing a standardized evaluation framework to bolster research reliability, enhancing generalization performance, and fostering coordinated hardware-algorithm optimization. These steps are crucial for advancing the practical implementation of these technologies across diverse scenarios. With this survey, we aim to offer a comprehensive reference and roadmap for researchers engaged in the development and implementation of next-generation dry electrode EEG-based BCI systems.}, }
@article {pmid40872049, year = {2025}, author = {Khuntia, PK and Bhide, PS and Manivannan, PV}, title = {Preliminary Analysis and Proof-of-Concept Validation of a Neuronally Controlled Visual Assistive Device Integrating Computer Vision with EEG-Based Binary Control.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {16}, pages = {}, pmid = {40872049}, issn = {1424-8220}, support = {SB22230362MEPMRF000758//Prime Minister's Research Fellowship by The Government of India/ ; }, mesh = {*Electroencephalography/methods ; Humans ; Algorithms ; *Self-Help Devices ; Signal Processing, Computer-Assisted ; Robotics ; }, abstract = {Contemporary visual assistive devices often lack immersive user experience due to passive control systems. This study introduces a neuronally controlled visual assistive device (NCVAD) that aims to assist visually impaired users in performing reach tasks with active, intuitive control. The developed NCVAD integrates computer vision, electroencephalogram (EEG) signal processing, and robotic manipulation to facilitate object detection, selection, and assistive guidance. The monocular vision-based subsystem implements the YOLOv8n algorithm to detect objects of daily use. Then, audio prompting conveys the detected objects' information to the user, who selects their targeted object using a voluntary trigger decoded through real-time EEG classification. The target's physical coordinates are extracted using ArUco markers, and a gradient descent-based path optimization algorithm (POA) guides a 3-DoF robotic arm to reach the target. The classification algorithm achieves over 85% precision and recall in decoding EEG data, even with coexisting physiological artifacts. Similarly, the POA achieves approximately 650 ms of actuation time with a 0.001 learning rate and 0.1 cm[2] error threshold settings. In conclusion, the study also validates the preliminary analysis results on a working physical model and benchmarks the robotic arm's performance against human users, establishing the proof-of-concept for future assistive technologies integrating EEG and computer vision paradigms.}, }
@article {pmid40871906, year = {2025}, author = {Isaev, M and Bobrov, P and Mokienko, O and Fedotova, I and Lyukmanov, R and Ikonnikova, E and Cherkasova, A and Suponeva, N and Piradov, M and Ustinova, K}, title = {Hemodynamic Response Asymmetry During Motor Imagery in Stroke Patients: A Novel NIRS-BCI Assessment Approach.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {16}, pages = {}, pmid = {40871906}, issn = {1424-8220}, support = {No: 1021062411635-8-3.1.4 and Registration No: 122041800162-9//Ministry of Science and Higher Education of the Russian Federation/ ; }, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke/physiopathology ; Stroke Rehabilitation/methods ; *Hemodynamics/physiology ; Aged ; Hemoglobins/metabolism ; Adult ; }, abstract = {Understanding patterns of interhemispheric asymmetry is crucial for monitoring neuroplastic changes during post-stroke motor rehabilitation. However, conventional laterality indices often pose computational challenges when applied to functional near-infrared spectroscopy (fNIRS) data due to the bidirectional hemodynamic responses. In this study, we analyze fNIRS recordings from 15 post-stroke patients undergoing motor imagery brain-computer interface training across multiple sessions. We compare traditional laterality coefficients with a novel task response asymmetry coefficient (TRAC), which quantifies differential hemispheric involvement during motor imagery tasks. Both indices are calculated for oxygenated and deoxygenated hemoglobin responses using general linear model coefficients, and their day-to-day dynamics are assessed with linear regression. The proposed TRAC demonstrates greater sensitivity than conventional measures, revealing significantly higher oxygenated hemoglobin TRAC values (0.18 ± 0.19 vs. -0.05 ± 0.20, p < 0.05) and lower deoxygenated hemoglobin TRAC values (-0.15 ± 0.27 vs. 0.04 ± 0.23, p < 0.05) in lesioned compared to intact hemispheres. Among patients who exhibit substantial motor recovery, distinct daily TRAC dynamics were observed, with statistically significant temporal trends. Overall, the novel TRAC metric offers enhanced discrimination of interhemispheric asymmetry patterns and captures temporal neuroplastic changes not detected by conventional indices, providing a more sensitive biomarker for tracking rehabilitation progress in post-stroke brain-computer interface applications.}, }
@article {pmid40871892, year = {2025}, author = {Moreno-Castelblanco, SR and Vélez-Guerrero, MA and Callejas-Cuervo, M}, title = {Artificial Intelligence Approaches for EEG Signal Acquisition and Processing in Lower-Limb Motor Imagery: A Systematic Review.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {16}, pages = {}, pmid = {40871892}, issn = {1424-8220}, support = {SGI 3904//Universidad Pedag&#xf3;gica y Tecnol&#xf3;gica de Colombia/ ; }, mesh = {Humans ; Algorithms ; *Artificial Intelligence ; Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; *Lower Extremity/physiology ; Movement/physiology ; *Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: Motor imagery (MI) is defined as the cognitive ability to simulate motor movements while suppressing muscular activity. The electroencephalographic (EEG) signals associated with lower limb MI have become essential in brain-computer interface (BCI) research aimed at assisting individuals with motor disabilities.<br><br>OBJECTIVE: This systematic review aims to evaluate methodologies for acquiring and processing EEG signals within brain-computer interface (BCI) applications to accurately identify lower limb MI.<br><br>METHODS: A systematic search in Scopus and IEEE Xplore identified 287 records on EEG-based lower-limb MI using artificial intelligence. Following PRISMA guidelines (non-registered), 35 studies met the inclusion criteria after screening and full-text review.<br><br>RESULTS: Among the selected studies, 85% applied machine or deep learning classifiers such as SVM, CNN, and LSTM, while 65% incorporated multimodal fusion strategies, and 50% implemented decomposition algorithms. These methods improved classification accuracy, signal interpretability, and real-time application potential. Nonetheless, methodological variability and a lack of standardization persist across studies, posing barriers to clinical implementation.<br><br>CONCLUSIONS: AI-based EEG analysis effectively decodes lower-limb motor imagery. Future efforts should focus on harmonizing methods, standardizing datasets, and developing portable systems to improve neurorehabilitation outcomes. This review provides a foundation for advancing MI-based BCIs.}, }
@article {pmid40871810, year = {2025}, author = {Alahaideb, L and Al-Nafjan, A and Aljumah, H and Aldayel, M}, title = {Brain-Computer Interface for EEG-Based Authentication: Advancements and Practical Implications.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {16}, pages = {}, pmid = {40871810}, issn = {1424-8220}, support = {(13461-imamu-2023-IMIU-R-3-1-HW-).//The Research, Development, and Innovation Authority (RDIA) - Kingdom of Saudi Arabia/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Computer Security ; }, abstract = {Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We conducted a systematic review of the existing literature, followed by an experimental evaluation to assess the feasibility, limitations, and scalability of these systems in real-world scenarios. Data were collected from nine subjects using various approaches. Our results indicate that the CNN model achieved the highest accuracy of 99%, while Random Forest (RF) and Gradient Boosting (GB) classifiers also demonstrated strong performance with 94% and 93%, respectively. In contrast, classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) displayed significantly lower effectiveness, underscoring their limitations in capturing the complexities of EEG data. The findings suggest that EEG-based authentication systems have significant potential to enhance security measures, offering a promising alternative to traditional methods and paving the way for more robust and user-friendly authentication solutions.}, }
@article {pmid40868398, year = {2025}, author = {Kumar, R and Sporn, K and Kaur, H and Khanna, A and Paladugu, P and Zaman, N and Tavakkoli, A}, title = {Current Mechanobiological Pathways and Therapies Driving Spinal Health.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {8}, pages = {}, pmid = {40868398}, issn = {2306-5354}, abstract = {Spinal health depends on the dynamic interplay between mechanical forces, biochemical signaling, and cellular behavior. This review explores how key molecular pathways, including integrin, yeas-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ), Piezo, and Wingless/Integrated (Wnt) with β-catenin, actively shape the structural and functional integrity of spinal tissues. These signaling mechanisms respond to physical cues and interact with inflammatory mediators such as interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), driving changes that lead to disc degeneration, vertebral fractures, spinal cord injury, and ligament failure. New research is emerging that shows scaffold designs that can directly harness these pathways. Further, new stem cell-based therapies have been shown to promote disc regeneration through targeted differentiation and paracrine signaling. Interestingly, many novel bone and ligament scaffolds are modulating anti-inflammatory signals to enhance tissue repair and integration, as well as prevent scaffold degradation. Neural scaffolds are also arising. These mimic spinal biomechanics and activate Piezo signaling to guide axonal growth and restore motor function. Scientists have begun combining these biological platforms with brain-computer interface technology to restore movement and sensory feedback in patients with severe spinal damage. Although this technology is not fully clinically ready, this field is advancing rapidly. As implantable technology can now mimic physiological processes, molecular signaling, biomechanical design, and neurotechnology opens new possibilities for restoring spinal function and improving the quality of life for individuals with spinal disorders.}, }
@article {pmid40868333, year = {2025}, author = {Tonin, A and Semprini, M and Kiper, P and Mantini, D}, title = {Brain-Computer Interfaces for Stroke Motor Rehabilitation.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {8}, pages = {}, pmid = {40868333}, issn = {2306-5354}, abstract = {Brain-computer interface (BCI) technology holds promise for improving motor rehabilitation in stroke patients. This review explores the immediate and long-term effects of BCI training, shedding light on the potential benefits and challenges. Clinical studies have demonstrated that BCIs yield significant immediate improvements in motor functions following stroke. Patients can engage in BCI training safely, making it a viable option for rehabilitation. Evidence from single-group studies consistently supports the effectiveness of BCIs in enhancing patients' performance. Despite these promising findings, the evidence regarding long-term effects remains less robust. Further studies are needed to determine whether BCI-induced changes are permanent or only last for short durations. While evaluating the outcomes of BCI, one must consider that different BCI training protocols may influence functional recovery. The characteristics of some of the paradigms that we discuss are motor imagery-based BCIs, movement-attempt-based BCIs, and brain-rhythm-based BCIs. Finally, we examine studies suggesting that integrating BCIs with other devices, such as those used for functional electrical stimulation, has the potential to enhance recovery outcomes. We conclude that, while BCIs offer immediate benefits for stroke rehabilitation, addressing long-term effects and optimizing clinical implementation remain critical areas for further investigation.}, }
@article {pmid40867492, year = {2025}, author = {Guan, S and Meng, F and Wu, C}, title = {Authoritative Filial Piety Rather than Reciprocal Filial Piety Mediated the Relationship Between Parental Support, Career Decision Self-Efficacy, and Discrepancies Between Individual-Set and Parent-Set Career Goals.}, journal = {Behavioral sciences (Basel, Switzerland)}, volume = {15}, number = {8}, pages = {}, pmid = {40867492}, issn = {2076-328X}, support = {2023DSYL022//The Supervisor Guidance Program of Shanghai International Studies University/ ; 22YJC880018//General Project of Humanities and Social Sciences of the Ministry of Education 'Research on the Internationalization Path and Strategy of Vocational Education in China from the Perspective of Regional and Country Analysis'/ ; 23ZD010//Fundamental Research Funds for the Central Universities/ ; 2020EYY004//Shanghai Philosophy and Social Science Planning Youth Project/ ; 20CG40//Shanghai Chenguang Talent Program/ ; 2020114052//The Innovative Research Team of Shanghai International Studies University/ ; 2022KFKT009//Open project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; B202205//Open project of Key Laboratory of Multilingual Education with AI/ ; }, abstract = {Although a wealth of research has examined the predictors influencing the discrepancies between individual-set and parent-set career goals (DBIPCG), investigations grounded in collectivist cultural perspectives remain relatively scarce. Within collectivist societies, filial piety holds profound cultural significance. Drawing on a dual filial piety framework encompassing reciprocal filial piety (RFP) and authoritative filial piety (AFP), this study aims to explore the interconnections among parental support, self-efficacy in career decision-making, dual filial piety orientations, and DBIPCG. The results indicated that parental support was negatively associated with DBIPCG. By contrast, self-efficacy in career decision-making did not predict DBIPCG directly. Instead, self-efficacy indirectly influenced DBIPCG, an effect mediated specifically by AFP rather than RFP, Furthermore, AFP was found to mediate the link between parental support and DBIPCG. These findings underscore the role of parental support in minimizing differences in career goal formation between generations and highlight the potentially adverse implications of AFP in exacerbating such discrepancies.}, }
@article {pmid40867216, year = {2025}, author = {Zhao, Y and Cao, L and Ji, Y and Wang, B and Wu, W}, title = {Interpretable EEG Emotion Classification via CNN Model and Gradient-Weighted Class Activation Mapping.}, journal = {Brain sciences}, volume = {15}, number = {8}, pages = {}, pmid = {40867216}, issn = {2076-3425}, support = {4244100//Beijing Natural Science Foundation/ ; }, abstract = {Background/Objectives: Electroencephalography (EEG)-based emotion recognition plays an important role in affective computing and brain-computer interface applications. However, existing methods often face the challenge of achieving high classification accuracy while maintaining physiological interpretability. Methods: In this study, we propose a convolutional neural network (CNN) model with a simple architecture for EEG-based emotion classification. The model achieves classification accuracies of 95.21% for low/high arousal, 94.59% for low/high valence, and 93.01% for quaternary classification tasks on the DEAP dataset. To further improve model interpretability and support practical applications, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to identify the EEG electrode regions that contribute most to the classification results. Results: The visualization reveals that electrodes located in the right prefrontal cortex and left parietal lobe are the most influential, which is consistent with findings from emotional lateralization theory. Conclusions: This provides a physiological basis for optimizing electrode placement in wearable EEG-based emotion recognition systems. The proposed method combines high classification performance with interpretability and provides guidance for the design of efficient and portable affective computing systems.}, }
@article {pmid40867214, year = {2025}, author = {Han, Q and Sun, Y and Ye, H and Song, Z and Zhao, J and Shi, L and Kuang, Z}, title = {GAH-TNet: A Graph Attention-Based Hierarchical Temporal Network for EEG Motor Imagery Decoding.}, journal = {Brain sciences}, volume = {15}, number = {8}, pages = {}, pmid = {40867214}, issn = {2076-3425}, support = {YDZJ202201ZYTS684//Jilin Province Science and Technology Department/ ; }, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on motor imagery (MI) offer promising solutions for motor rehabilitation and communication. However, electroencephalography (EEG) signals are often characterized by low signal-to-noise ratios, strong non-stationarity, and significant inter-subject variability, which pose significant challenges for accurate decoding. Existing methods often struggle to simultaneously model the spatial interactions between EEG channels, the local fine-grained features within signals, and global semantic patterns.<br><br>METHODS: To address this, we propose the graph attention-based hierarchical temporal network (GAH-TNet), which integrates spatial graph attention modeling with hierarchical temporal feature encoding. Specifically, we design the graph attention temporal encoding block (GATE). The graph attention mechanism is used to model spatial dependencies between EEG channels and encode short-term temporal dynamic features. Subsequently, a hierarchical attention-guided deep temporal feature encoding block (HADTE) is introduced, which extracts local fine-grained and global long-term dependency features through two-stage attention and temporal convolution. Finally, a fully connected classifier is used to obtain the classification results. The proposed model is evaluated on two publicly available MI-EEG datasets.<br><br>RESULTS: Our method outperforms multiple existing state-of-the-art methods in classification accuracy. On the BCI IV 2a dataset, the average classification accuracy reaches 86.84%, and on BCI IV 2b, it reaches 89.15%. Ablation experiments validate the complementary roles of GATE and HADTE in modeling. Additionally, the model exhibits good generalization ability across subjects.<br><br>CONCLUSIONS: This framework effectively captures the spatio-temporal dynamic characteristics and topological structure of MI-EEG signals. This hierarchical and interpretable framework provides a new approach for improving decoding performance in EEG motor imagery tasks.}, }
@article {pmid40867208, year = {2025}, author = {Lian, X and Liu, C and Gao, C and Deng, Z and Guan, W and Gong, Y}, title = {A Multi-Branch Network for Integrating Spatial, Spectral, and Temporal Features in Motor Imagery EEG Classification.}, journal = {Brain sciences}, volume = {15}, number = {8}, pages = {}, pmid = {40867208}, issn = {2076-3425}, support = {62173007//National Natural Science Foundation of China/ ; }, abstract = {Background: Efficient decoding of motor imagery (MI) electroencephalogram (EEG) signals is essential for the precise control and practical deployment of brain-computer interface (BCI) systems. Owing to the complex nonlinear characteristics of EEG signals across spatial, spectral, and temporal dimensions, efficiently extracting multidimensional discriminative features remains a key challenge to improving MI-EEG decoding performance. Methods: To address the challenge of capturing complex spatial, spectral, and temporal features in MI-EEG signals, this study proposes a multi-branch deep neural network, which jointly models these dimensions to enhance classification performance. The network takes as inputs both a three-dimensional power spectral density tensor and two-dimensional time-domain EEG signals and incorporates four complementary feature extraction branches to capture spatial, spectral, spatial-spectral joint, and temporal dynamic features, thereby enabling unified multidimensional modeling. The model was comprehensively evaluated on two widely used public MI-EEG datasets: EEG Motor Movement/Imagery Database (EEGMMIDB) and BCI Competition IV Dataset 2a (BCIIV2A). To further assess interpretability, gradient-weighted class activation mapping (Grad-CAM) was employed to visualize the spatial and spectral features prioritized by the model. Results: On the EEGMMIDB dataset, it achieved an average classification accuracy of 86.34% and a kappa coefficient of 0.829 in the five-class task. On the BCIIV2A dataset, it reached an accuracy of 83.43% and a kappa coefficient of 0.779 in the four-class task. Conclusions: These results demonstrate that the network outperforms existing state-of-the-art methods in classification performance. Furthermore, Grad-CAM visualizations identified the key spatial channels and frequency bands attended to by the model, supporting its neurophysiological interpretability.}, }
@article {pmid40867186, year = {2025}, author = {Vanutelli, ME and Banzi, A and Cicirello, M and Folgieri, R and Lucchiari, C}, title = {Predicting State Anxiety Level Change Using EEG Parameters: A Pilot Study in Two Museum Settings.}, journal = {Brain sciences}, volume = {15}, number = {8}, pages = {}, pmid = {40867186}, issn = {2076-3425}, abstract = {Background: Museums are increasingly being recognized not only as cultural institutions but also as potential resources for enhancing psychological well-being. Prior research has shown that museum visits can reduce stress and anxiety, yet there is a pressing need for evidence-based interventions supported by neurophysiological data. While neuroscientific studies suggest a combined role of emotional and cognitive mechanisms in aesthetic experiences, less is known about the neural predictors of individual responsiveness to such interventions. Methods: This study was conducted in two Milan-based museums and included an initial profiling phase (sociodemographic information, trait anxiety, perceived stress, museum experience), followed by pre- and post-visit assessments of state anxiety and mood. Electrocortical activity was recorded via a portable brain-computer interface (BCI), focusing on the theta/beta ratio (TBR) as a marker of cortical-subcortical integration. Results: Museum visits were associated with significant improvements in mood (M = 1.17; p < 0.001) and reductions in state anxiety (M = -6.36; p < 0.001) in both arts and science museums. The baseline TBR predicted the magnitude of state anxiety change, alongside individual differences in trait anxiety and perceived stress. Conclusions: These findings support the idea that aesthetic experiences in museums engage both emotional and cognitive systems, and that resting state neurophysiological markers can help forecast individual responsiveness to well-being interventions. Such insights not only contribute to existing knowledge about the cognitive and emotional processes during aesthetic fruition, but could also guide future applications of personalized interventions in museum settings, further integrating cultural participation with mental health promotion.}, }
@article {pmid40867148, year = {2025}, author = {Serna, B and Salazar, R and Alonso-Silverio, GA and Baltazar, R and Ventura-Molina, E and Alarcón-Paredes, A}, title = {Fear Detection Using Electroencephalogram and Artificial Intelligence: A Systematic Review.}, journal = {Brain sciences}, volume = {15}, number = {8}, pages = {}, pmid = {40867148}, issn = {2076-3425}, abstract = {Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string ("fear detection" AND "artificial intelligence" OR "machine learning" AND NOT "fnirs OR mri OR ct OR pet OR image"). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing.}, }
@article {pmid40867138, year = {2025}, author = {Chen, X and Bao, X and Jitian, K and Li, R and Zhu, L and Kong, W}, title = {Hybrid EEG Feature Learning Method for Cross-Session Human Mental Attention State Classification.}, journal = {Brain sciences}, volume = {15}, number = {8}, pages = {}, pmid = {40867138}, issn = {2076-3425}, support = {62301196//National Science Foundation of China/ ; 2025C04001//"Pioneer" and "Leading &#xb7;Goose" R&amp;D &#xb7;Program of Zhejiang/ ; LQ24F020035//Zhejiang Provincial Natural Science Foundation of China/ ; }, abstract = {BACKGROUND: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human-computer interaction, and brain-computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking robustness in cross-session or inter-subject conditions.<br><br>METHODS: In this study, we propose a hybrid feature learning framework for robust classification of mental attention states, including focused, unfocused, and drowsy conditions, across both sessions and individuals. Our method integrates preprocessing, feature extraction, feature selection, and classification in a unified pipeline. We extract channel-wise spectral features using short-time Fourier transform (STFT) and further incorporate both functional and structural connectivity features to capture inter-regional interactions in the brain. A two-stage feature selection strategy, combining correlation-based filtering and random forest ranking, is adopted to enhance feature relevance and reduce dimensionality. Support vector machine (SVM) is employed for final classification due to its efficiency and generalization capability.<br><br>RESULTS: Experimental results on two cross-session and inter-subject EEG datasets demonstrate that our approach achieves classification accuracy of 86.27% and 94.01%, respectively, significantly outperforming traditional methods.<br><br>CONCLUSIONS: These findings suggest that integrating connectivity-aware features with spectral analysis can enhance the generalizability of attention decoding models. The proposed framework provides a promising foundation for the development of practical EEG-based systems for continuous mental state monitoring and adaptive BCIs in real-world environments.}, }
@article {pmid40859358, year = {2025}, author = {He, J and Yuan, Z and Quan, L and Xi, H and Guo, J and Zhu, D and Chen, M and Yang, B and Cui, Z and Zhu, S and Qiao, J}, title = {Multimodal assessment of a BCI system for stroke rehabilitation integrating motor imagery and motor attempts: a randomized controlled trial.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {185}, pmid = {40859358}, issn = {1743-0003}, support = {2022SF-379//Key R &amp; D Program of Shanxi Province/ ; 2022SF-379//Key R &amp; D Program of Shanxi Province/ ; QYJC05//Xi 'an Jiaotong University Medical Engineering Interdisciplinary Program/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Male ; Female ; Middle Aged ; Double-Blind Method ; Electroencephalography ; Aged ; Electromyography ; *Imagination/physiology ; Spectroscopy, Near-Infrared ; Recovery of Function/physiology ; Adult ; }, abstract = {BACKGROUND: Brain-computer interface (BCI) technology based on motor imagery (MI) or motor attempt (MA) has shown promise in enhancing motor function recovery in stroke patients. This study aimed to evaluate the effectiveness of BCI-based rehabilitation in improving motor function through multimodal assessment, and to explore the potential neuroplastic changes resulting from this intervention.<br><br>METHODS: We conducted a randomized double-blind controlled clinical trial with multimodal assessment to evaluate the efficacy of a BCI system for enhancing motor recovery. A total of 48 ischemic stroke patients completed the study (25 BCI, 23 control). The BCI group used an 8-electrode electroencephalogram (EEG) system, a virtual reality training module, and a rehabilitation training robot for real-time motor intention-based feedback. The control group used identical BCI devices but without displaying real-time data and feedback. Participants underwent 20-minute upper and lower limb training sessions for two weeks. Motor function (Fugl-Meyer Extremity scale), electromyography (EMG), and functional near-infrared spectroscopy (fNIRS) were assessed pre- and post-intervention.<br><br>RESULTS: The BCI group demonstrated significantly greater improvement in upper extremity motor function compared to the control group (&#x394;FMA-UE: 4.0 vs. 2.0, p&#x2009;=&#x2009;0.046). EEG results of the BCI group showed a significant decrease in both DAR (p&#x2009;=&#x2009;0.031) and DABR (p&#x2009;<&#x2009;0.001) compared to baseline. EMG analysis revealed that BCI treatment resulted in significant increases in deltoid and bicipital muscle activity during both shoulder and elbow flexion movements compared to baseline (p&#x2009;<&#x2009;0.01). fNIRS results indicated enhanced functional connectivity and activation in key motor-related brain regions, including the prefrontal cortex, supplementary motor area, and primary motor cortex in the BCI group.<br><br>CONCLUSION: BCI-based rehabilitation using an attention-motor dual-task paradigm significantly improved upper limb motor function and enhanced motor and cognitive network activity in stroke patients. Multimodal assessment supports the potential of BCI rehabilitation as an effective tool for leveraging neuroplasticity and promoting motor recovery.}, }
@article {pmid40881023, year = {2025}, author = {Zhang, S and Lu, Z and Zhang, B and Zhang, Y and Liang, Z and Zhang, L and Li, L and Huang, G and Zhang, Z and Li, Z}, title = {Graph-based feature learning methods for subject-dependent and subject-independent motor imagery EEG decoding.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {139}, pmid = {40881023}, issn = {1871-4080}, abstract = {UNLABELLED: The significant intra-individual variability and inter-individual differences in scalp electroencephalogram (EEG) make it difficult to learn task-distinguishable features, posing a challenge for motor imagery brain-computer interfaces. Current feature learning methods often produce an incomplete feature space, struggling to accommodate these variations and differences. Additionally, the weak discriminative nature of this feature space results in diminished EEG classification performance. This paper introduces novel graph-based feature learning methods to improve motor imagery decoding performance in both subject-dependent and subject-independent contexts. Firstly, construct a complete time-frequency-spatial-graph (TFSG) feature space. The original EEG signals are segmented into multiple time-frequency units using filter banks and sliding time windows. Spatial and brain network-based graph features are then extracted from each time-frequency unit and fused to create the TFSG features. This fused feature space is larger and more inclusive, effectively accommodating both intra- and inter-individual EEG variations. Secondly, learn a discriminative TFSG feature space. Two advanced methods are proposed. The first method employs a nonconvex sparse optimization model with log function regularization, which reduces bias in model estimation, thereby enabling more accurate learning of EEG patterns. The second method incorporates Fisher's criterion regularization into a sparse optimization framework to improve feature separability. A unified algorithmic framework is developed to solve the two new models. Our methods are validated on two motor imagery EEG datasets, achieving the highest average classification accuracies of 82.93, 68.52, and 71.69% for subject-dependent, subject-independent, and subject-adaptive evaluation methods, respectively. Experimental results demonstrate that the developed TFSG features significantly enhance both subject-dependent and subject-independent decoding performance, while the proposed regularization models improve the discriminability of the feature space, leading to further advancements in motor imagery decoding performance.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10291-5.}, }
@article {pmid40880337, year = {2025}, author = {Kim, J and Kim, SP}, title = {A Plug-and-Play P300-Based BCI With Zero-Training Application.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {3443-3454}, doi = {10.1109/TNSRE.2025.3603979}, pmid = {40880337}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; *Event-Related Potentials, P300/physiology ; Male ; Electroencephalography ; Adult ; Female ; Young Adult ; Neural Networks, Computer ; Algorithms ; Attention ; Calibration ; Electrodes ; }, abstract = {The practical deployment of P300-based brain-computer interfaces (BCIs) has long been hindered by the need for user-specific calibration and multiple stimulus repetitions. In this study, we build and validate a plug-and-play, zero-training P300 BCI system that operates in a single-trial setting using a pre-trained xDAWN spatial filter and a deep convolutional neural network. Without any subject-specific adaptation, participants could control an IoT device via the BCI system in real time, with decoding accuracy reaching 85.2% comparable to the offline benchmark of 87.8%, demonstrating the feasibility of realizing a plug-and-play BCI. Offline analyses revealed that a small set of parietal and occipital electrodes contributed most to decoding performance, supporting the viability of low-density, high-accuracy BCI configurations. A data sufficiency simulation provided quantitative guidelines for pre-training dataset size, and an error trial analysis showed that both stimulus timing and preparatory attentional state influenced real-time decoding performance. Together, these results demonstrate the real-time validation of a fully pre-trained, zero-training P300 BCI operating on a single-trial basis, without stimulus repetition or user-specific calibration, and offer practical insights for developing scalable, robust, and user-friendly BCI systems.}, }
@article {pmid40880336, year = {2025}, author = {Li, X and Wang, X and Chen, S and Zhu, W and Jin, R and Peng, W}, title = {Gamma-Band Binaural Beats Neuromodulation Enhances P300 Classification in an Auditory Brain-Computer Interface Paradigm.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {3455-3465}, doi = {10.1109/TNSRE.2025.3604016}, pmid = {40880336}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Event-Related Potentials, P300/physiology ; Adult ; Electroencephalography ; Young Adult ; Acoustic Stimulation/methods ; Cross-Over Studies ; *Gamma Rhythm/physiology ; Healthy Volunteers ; Algorithms ; Machine Learning ; Deep Learning ; }, abstract = {While established neuromodulation techniques like transcranial magnetic stimulation and transcranial direct current stimulation have shown potential for enhancing brain-computer interface (BCI) performance, their clinical adoption faces challenges including high implementation costs, technical complexity, and safety concerns. This study investigated binaural beats (BB), a non-invasive auditory neuromodulation method characterized by operational simplicity and minimal adverse effects, as a practical alternative for optimizing auditory P300-BCI. Employing a crossover experimental design, thirty healthy participants underwent gamma-band (40 Hz) and alpha-band (10 Hz) BB stimulation in separate sessions. Auditory oddball paradigm experiments were conducted before and after each BB intervention. Electroencephalogram (EEG) data were decoded using both a machine learning classifier and a deep learning model for P300 classification. Additionally, irregular-resampling auto-spectral analysis (IRASA) was applied to extract aperiodic components from EEG during BB stimulation to evaluate changes in brain state. The results demonstrated frequency-dependent modulation effects: gamma-BB significantly improved P300 classification accuracy while alpha-BB impaired performance. Neurophysiological analysis revealed that gamma-BB decreased the aperiodic exponent, indicating enhanced brain arousal level, whereas alpha-BB produced the opposite pattern. Importantly, the aperiodic parameter change showed a significant association with BCI performance improvement. These findings established gamma-BB as an effective, low-cost neuromodulation strategy for augmenting auditory P300-BCI through brain state modulation.}, }
@article {pmid40880333, year = {2025}, author = {Xu, M and Zhang, B and Zhang, L and Wang, D and Chen, Y}, title = {A Decade of Rapid Serial Visual Presentation Paradigm in Brain-Computer Interface for Target Detection: Current Status and Trends.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3603945}, pmid = {40880333}, issn = {1558-2531}, abstract = {OBJECTIVE: Electroencephalography (EEG)-based Rapid Serial Visual Presentation (RSVP) has steadily gained attention since 2015 as a paradigm to enhance image target detection in brain-computer interfaces (BCIs) used with healthy individuals.<br><br>METHODS: We reviewed the literature using Scopus and Web of Science as primary databases, covering publications from 2015 to 2024. After literature screening and filtering, a total of 86 papers on RSVP-BCI studies were analyzed over this decadelong period. The research categorizes RSVP into three dimensions: public datasets, paradigm encoding, and decoding methods, while exploring eight mode combinations involving target types, subject groups, and different modalities.<br><br>RESULTS: Our literature search revealed a scarcity of studies addressing diverse target types across different subject groups or modality combinations, indicating a promising direction for future RSVP-BCI development. Future efforts should prioritize inclusivity across all age groups, the design of user-friendly stimulus interfaces, and the development of advanced algorithms, with the goal of creating a more widely accessible RSVP-BCI system.<br><br>CONCLUSION: We have provided a comprehensive review of advances over the past decade in RSVP-based target detection, including datasets, encoding design, and decoding methods and potential applications.<br><br>SIGNIFICANCE: The present work aims to articulate prospective trajectories for the continued advancement of the RSVP community.}, }
@article {pmid40878633, year = {2025}, author = {Jiang, H and Ren, B and Zhang, Y and Zhou, Y and Wu, J and Yu, X and Yu, H and Ni, P and Xu, Y and Deng, W and Guo, W and Hu, X and Qi, X and Li, T}, title = {Alterations of plasma neural-derived extracellular vesicles microRNAs in patients with bipolar disorder.}, journal = {Psychological medicine}, volume = {55}, number = {}, pages = {e256}, doi = {10.1017/S0033291725000741}, pmid = {40878633}, issn = {1469-8978}, mesh = {Humans ; *Bipolar Disorder/genetics/blood/metabolism ; *Extracellular Vesicles/metabolism ; Female ; Male ; *MicroRNAs/metabolism/blood/genetics ; Adult ; Middle Aged ; Microglia/metabolism ; Case-Control Studies ; Prefrontal Cortex/metabolism ; }, abstract = {BACKGROUND: MicroRNAs (miRNAs) alterations in patients with bipolar disorder (BD) are pivotal to the disease's pathogenesis. Since obtaining brain tissue is challenging, most research has shifted to analyzing miRNAs in peripheral blood. One innovative solution is sequencing miRNAs in plasma extracellular vesicles (EVs), particularly those neural-derived EVs emanating from the brain.<br><br>METHODS: We isolated plasma neural-derived EVs from 85 patients with BD and 39 healthy controls (HC) using biotinylated antibodies targeting a neural tissue marker, followed by miRNA sequencing and expression analysis. Furthermore, we conducted bioinformatic analyses and functional experiments to delve deeper into the underlying pathological mechanisms of BD.<br><br>RESULTS: Out of the 2,656 neural-derived miRNAs in EVs identified, 14 were differentially expressed between BD patients and HC. Moreover, the target genes of miR-143-3p displayed distinct expression patterns in the prefrontal cortex of BD patients versus HC, as sourced from the PsychENCODE database. The functional experiments demonstrated that the abnormal expression of miR-143-3p promoted the proliferation and activation of microglia and upregulated the expression of proinflammatory factors, including IL-1&#x3b2;, IL-6, and NLRP3. Through weighted gene co-expression network analysis, a module linking to the clinical symptoms of BD patients was discerned. Enrichment analyses unveiled these miRNAs' role in modulating the axon guidance, the Ras signaling pathway, and ErbB signaling pathway.<br><br>CONCLUSIONS: Our findings provide the first evidence of dysregulated plasma miRNAs within neural-derived EVs in BD patients and suggest that neural-derived EVs might be involved in the pathophysiology of BD through related biological pathways, such as neurogenesis and neuroinflammation.}, }
@article {pmid40877476, year = {2025}, author = {Wu, Y and Qian, B and Li, T and Qin, Y and Guan, Z and Chen, T and Jia, Y and Zhang, P and Zeng, D and Moroi, S and Raman, R and Thinggaard, BS and Pedersen, F and Ñehe, JAO and Kamalden, TA and Zhou, Y and Jin, Y and Li, H and Ran, AR and Yang, D and Meng, Z and Peng, Q and Zheng, YF and Wang, D and Ji, H and Zang, P and Yin, C and Shen, J and Chen, Y and Yu, W and Dai, R and Zhang, C and Zhao, X and Wang, X and Chen, Y and Wu, Q and Xie, H and Szeto, SKH and Chan, JYY and Chan, VTT and Xie, HT and Wei, R and Li, J and Ma, W and Zhu, L and Wang, H and Fu, H and Wang, W and Lin, S and Xu, Z and Guan, N and Zhang, X and Grzybowski, A and Gołębiowska-Bogaj, M and Gawęcki, M and Smedowski, A and Szaraniec, W and Wu, Y and Wen, Y and Chen, X and Yao, Y and ,  and Lim, LL and Cheung, CY and Tan, GSW and Grauslund, J and Ruamviboonsuk, P and Sivaprasad, S and Keane, PA and Wang, YX and Tham, YC and Cheng, CY and Wong, TY and Sheng, B}, title = {An eyecare foundation model for clinical assistance: a randomized controlled trial.}, journal = {Nature medicine}, volume = {31}, number = {10}, pages = {3404-3413}, pmid = {40877476}, issn = {1546-170X}, support = {82388101//National Natural Science Foundation of China (National Science Foundation of China)/ ; IS23096//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; }, mesh = {Humans ; Male ; Female ; Middle Aged ; Double-Blind Method ; *Retinal Diseases/diagnosis ; Aged ; China ; Ophthalmologists ; Adult ; }, abstract = {In the context of an increasing need for clinical assessments of foundation models, we developed EyeFM, a multimodal vision-language eyecare copilot, and conducted a multifaceted evaluation, including retrospective validations, multicountry efficacy validation as a clinical copilot and a double-masked randomized controlled trial (RCT). EyeFM was pretrained on 14.5 million ocular images from five imaging modalities paired with clinical texts from global, multiethnic datasets. Efficacy validation invited 44 ophthalmologists across North America, Europe, Asia and Africa in primary and specialty care settings, highlighting its utility as a clinical copilot. The RCT-a parallel, single-center, double-masked study-assessed EyeFM as a clinical copilot in retinal disease screening among a high-risk population in China. A total of 668 participants (mean age 57.5 years, 79.5% male) were randomized to 16 ophthalmologists, equally allocated into intervention (with EyeFM copilot) and control (standard care) groups. The primary endpoint indicated that ophthalmologists with EyeFM copilot achieved higher correct diagnostic rate (92.2% versus 75.4%, P < 0.001) and referral rate (92.2% versus 80.5%, P < 0.001). Secondary outcome indicated improved standardization score of clinical reports (median 33 versus 37, P < 0.001). Participant satisfaction with the screening was similar between groups, whereas the intervention group demonstrated higher compliance with self-management (70.1% versus 49.1%, P < 0.001) and referral suggestions (33.7% versus 20.2%, P < 0.001) at follow-up. Post-deployment evaluations indicated strong user acceptance. Our study provided evidence that implementing EyeFM copilot can improve the performance of ophthalmologists and the outcome of patients. Chinese Clinical Trial Registry registration: ChiCTR2500095518 .}, }
@article {pmid40877466, year = {2025}, author = {Fan, YS and Xu, Y and Hettwer, MD and Yang, P and Sheng, W and Wang, C and Yang, M and Kirschner, M and Valk, SL and Chen, H}, title = {Neurodevelopmentally rooted epicenters in schizophrenia: sensorimotor-association spatial axis of cortical thickness alterations.}, journal = {Molecular psychiatry}, volume = {}, number = {}, pages = {}, pmid = {40877466}, issn = {1476-5578}, abstract = {Pathological disturbances in schizophrenia have been suggested to propagate via the functional and structural connectome across the lifespan. However, how the connectome guides early cortical reorganization of developing schizophrenia remains unknown. Here, we used early-onset schizophrenia (EOS) as a neurodevelopmental disease model to investigate putative early pathologic origins propagating through the functional and structural connectome. We compared 95 patients with antipsychotic-naïve first-episode EOS and 99 typically developing controls (total n = 194; 120 females; 7-17 years of age). While patients showed widespread cortical thickness reductions, thickness increases were observed in primary cortical areas. Using normative connectomics models, we found that epicenters of thickness reductions were located in association regions linked to language, affective, and cognitive functions, while epicenters of thickness increases in EOS were located in sensorimotor regions subserving visual, somatosensory, and motor functions. Using post-mortem transcriptomic data of six donors, we observed that the epicenter map differentiated oligodendrocyte-related transcriptional changes at its sensory apex, whereas the association end was related to the expression of excitatory/inhibitory neurons. More generally, the epicenter map was associated with dysregulation of neurodevelopmental disorder genes and human accelerated region genes, suggesting potential common genetic determinants across diverse neurodevelopmental conditions. Taken together, our results highlight the developmentally rooted pathological origins of schizophrenia and its transcriptomic overlap with other neurodevelopmental disorders.}, }
@article {pmid40876460, year = {2025}, author = {Marino, PJ and Bahureksa, L and Fisac, CF and Oby, ER and Smoulder, AL and Motiwala, A and Degenhart, AD and Grigsby, EM and Joiner, WM and Chase, SM and Yu, BM and Batista, AP}, title = {A posture subspace in the primary motor cortex.}, journal = {Neuron}, volume = {113}, number = {21}, pages = {3647-3660.e10}, doi = {10.1016/j.neuron.2025.07.030}, pmid = {40876460}, issn = {1097-4199}, mesh = {*Motor Cortex/physiology ; Animals ; *Posture/physiology ; Macaca mulatta ; Movement/physiology ; *Psychomotor Performance/physiology ; Male ; Neurons/physiology ; Action Potentials/physiology ; Brain Mapping ; }, abstract = {To generate movements, the brain must combine information about movement goal and body posture. The motor cortex (primary motor cortex [M1]) is a key node for the convergence of these information streams. How are posture and goal signals organized within M1's activity to permit the flexible generation of movement commands? To answer this question, we recorded M1 activity while monkeys performed a variety of tasks with the forearm in a range of postures. We found that posture- and goal-related components of neural population activity were separable and resided in nearly orthogonal subspaces. The posture subspace was stable across tasks. Within each task, neural trajectories for each goal had similar shapes across postures. Our results reveal a simpler organization of posture signals in M1 than previously recognized. The compartmentalization of posture and goal signals might allow the two to be flexibly combined in the service of our broad repertoire of actions.}, }
@article {pmid40876238, year = {2025}, author = {Azati, Y and Wang, X and Ye, X and Zhang, K}, title = {Refining the classification of combined alignment sections on mountainous freeways and analyzing the spatio-temporal effects on crash frequency.}, journal = {Accident; analysis and prevention}, volume = {221}, number = {}, pages = {108222}, doi = {10.1016/j.aap.2025.108222}, pmid = {40876238}, issn = {1879-2057}, mesh = {*Accidents, Traffic/statistics &amp; numerical data ; Humans ; Spatio-Temporal Analysis ; Weather ; *Environment Design ; *Automobile Driving/statistics &amp; numerical data ; Models, Statistical ; Seasons ; }, abstract = {Combined alignment sections of mountainous freeways often feature complex geometric configurations-such as downhill sag/convex curves, slope-changing curves, and uphill curves-that significantly affect crash risk. Existing studies typically apply homogeneous segmentation and broad classifications (e.g., downhill, uphill, sag/convex), which fail to capture the specific effects of geometric combinations on crash frequency. In addition, traffic operations and weather conditions in mountainous areas exhibit strong seasonal variation, and using annual data may obscure important patterns, making quarterly analysis necessary. The interaction of complex geometry, dynamic traffic flow, and adverse winter weather results in nonlinear spatio-temporal effects that conventional models cannot effectively capture. To address this, the study integrates road geometry, traffic operation, and environmental data into a Zero-Inflated Negative Binomial (ZINB) model enhanced with Gaussian processes, systematically analyzing the nonlinear spatio-temporal effects on crash frequency. Results show that the proposed model outperforms spatial- or temporal-only models in prediction accuracy (RMSE = 0.566) and model fit (LOOIC = 5961.2), with the variance of spatio-temporal interaction effects estimated at 1.35 (95 % BCI: 1.12-1.58), indicating substantial nonlinear influence. Key findings include a 56 % increase in crash frequency on straight downhill sag curves, a 2 % reduction on straight uphill convex curves, an 80.3 % increase for every additional 1,000 vehicles in daily traffic flow, and a 28.8 % decrease in crash frequency for each 1 °C rise in temperature. The study presents a refined classification and modeling framework that significantly improves crash risk identification and prediction for mountainous freeways, offering strong support for traffic safety management.}, }
@article {pmid40876195, year = {2025}, author = {Liu, Z and Hong, Q and Huang, L and Sha, L and Peng, A and Chen, L}, title = {Women with epilepsy during pregnancy: A systematic review of current guidelines.}, journal = {Epilepsy &amp; behavior : E&amp;B}, volume = {171}, number = {}, pages = {110658}, doi = {10.1016/j.yebeh.2025.110658}, pmid = {40876195}, issn = {1525-5069}, mesh = {Humans ; Pregnancy ; Female ; *Epilepsy/therapy/drug therapy ; *Pregnancy Complications/therapy/drug therapy ; Anticonvulsants/therapeutic use ; *Practice Guidelines as Topic ; }, abstract = {OBJECTIVE: To systematically evaluate the quality of existing guidelines for the management of pregnancy in women with epilepsy (WWE) and compare their key recommendations.<br><br>METHODS: A systematic review of available clinical practice guidelines and expert consensus statements was conducted. The quality of the literature was assessed using the Appraisal of Guidelines for Research &amp; Evaluation II (AGREE II) instrument. Core information was extracted using a predefined form and subjected to comparative analysis.<br><br>RESULTS: Only 14 guidelines on WWE pregnancy management have been published worldwide. Most guidelines performed well in scope definition, clarity of purpose, and presentation, but the evidence base was relatively weak. Recommendations were largely consistent across guidelines regarding preconception counseling, folic acid supplementation, vaginal delivery, breastfeeding, and avoidance of valproate. However, discrepancies were observed in the selection of certain antiseizure medications (ASMs), therapeutic drug monitoring, and the timing and dosage of folic acid supplementation. Current guidelines lack recommendations on newer ASMs and antinociceptive management during delivery.<br><br>CONCLUSION: The variability in recommendations among WWE pregnancy management guidelines reflects the insufficiency of the existing evidence base, highlighting the need for enhanced methodological rigor in guideline development and more comprehensive, evidence-based recommendations. Establishing large-scale prospective pregnancy registries is critical for improving WWE pregnancy management guidelines.}, }
@article {pmid40857498, year = {2025}, author = {Zhao, B and Huggins, JE and Kang, J}, title = {Bayesian Inference on Brain-Computer Interfaces via GLASS.}, journal = {Journal of the American Statistical Association}, volume = {}, number = {}, pages = {}, pmid = {40857498}, issn = {0162-1459}, support = {R01 DA048993/DA/NIDA NIH HHS/United States ; R01 GM124061/GM/NIGMS NIH HHS/United States ; R01 MH105561/MH/NIMH NIH HHS/United States ; R21 HD054697/HD/NICHD NIH HHS/United States ; }, abstract = {Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary users. To address these challenges, we introduce a novel Gaussian Latent channel model with Sparse time-varying effects (GLASS) under a Bayesian framework. GLASS is built upon a constrained multinomial logistic regression particularly designed for the imbalanced target and non-target stimuli. The novel latent channel decomposition efficiently alleviates strong spatial correlations between EEG channels, while the soft-thresholded Gaussian process (STGP) prior ensures sparse and smooth time-varying effects. We demonstrate GLASS substantially improves BCI's performance in participants with amyotrophic lateral sclerosis (ALS) and identifies important EEG channels (PO8, Oz, PO7, and Pz) in parietal and occipital regions that align with existing literature. For broader accessibility, we develop an efficient gradient-based variational inference (GBVI) algorithm for posterior computation and provide a user-friendly Python module available at https://github.com/BangyaoZhao/GLASS.}, }
@article {pmid40875414, year = {2025}, author = {Korik, A and Du Bois, N and Sanchez Bornot, J and McShane, N and Guger, C and Del Felice, A and Lennon, O and Coyle, D}, title = {Decoding the Variable Velocity of Lower-Limb Stepping Movements From EEG.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {3511-3523}, doi = {10.1109/TNSRE.2025.3603635}, pmid = {40875414}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Brain-Computer Interfaces ; Young Adult ; *Lower Extremity/physiology ; Deep Learning ; Neural Networks, Computer ; Movement/physiology ; Linear Models ; Algorithms ; Cues ; Biomechanical Phenomena ; Healthy Volunteers ; Walking/physiology ; Sensorimotor Cortex/physiology ; }, abstract = {Accurate decoding of lower-limb movement from electroencephalography (EEG) is essential for developing brain-computer interface (BCI) controlled exoskeletons in neurorehabilitation. This study investigates 3D velocity decoding at three fibular anatomical markers during overground stepping in healthy participants (${N}={9}$), using two approaches: (1) linear regression (LR) and (2) a deep learning (DL) framework combining convolutional neural networks (CNNs) and long short-term memory (LSTM) units. Participants were divided into two groups: G1 (${n}={5}$) performed cued forward and self-paced backward steps; G2 (${n}={4}$) performed cued forward and backward steps. The DL model significantly outperformed LR, achieving highest decoding accuracy (DA) in the forward-backward direction at the fibular head (R $= 0.63\pm 0.06$ , M±SD). Topographical analysis identified dominant contributions from the sensorimotor cortex (coupled with frontal regions in G2) within the 8-40 Hz band. Functional connectivity (FC) analysis revealed significant differences: only G2 showed statistically significant FC (${p}\lt {0.05}$), likely reflecting increased cognitive and sensorimotor demands under dual-cue conditions. In G2, FC occurred across delta (0-4 Hz), theta (4-8 Hz), alpha/mu (8-12 Hz), and low-beta (12-18 Hz) bands, linking motor areas associated with lower- and upper-limb control to other cortical regions, including the middle temporal gyrus (MTG), superior frontal gyrus (SFG), posterior cingulate cortex (PCC), superior parietal lobule (SPL), and supramarginal gyrus (SMG). These findings demonstrate that EEG-based 3D decoding of lower-limb kinematics is feasible during realistic locomotor tasks and suggest that cortical synchronization patterns vary with movement context. Our CNN-LSTM framework may support adaptive, intent-driven exoskeleton development for personalized neurorehabilitation.}, }
@article {pmid40875138, year = {2025}, author = {Zhou, Q and Song, J and Zhao, Y and Zhang, S and Du, Q and Ke, L}, title = {IF-MMCL: an individual focused network with multi-view and multi-modal contrastive learning for cross-subject emotion recognition.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {40875138}, issn = {1741-0444}, abstract = {Electroencephalography (EEG) usage in emotion recognition has garnered significant interest in brain-computer interface (BCI) research. Nevertheless, in order to develop an effective model for emotion identification, features need to be extracted from EEG data in terms of multi-view. In order to tackle the problems of multi-feature interaction and domain adaptation, we suggest an innovative network, IF-MMCL, which leverages multi-modal data in multi-view representation and integrates an individual focused network. In our approach, we build an individual focused network with multi-view that utilizes individual focused contrastive learning to improve model generalization. The network employs different structures for multi-view feature extraction and uses multi-feature relationship computation to identify the relationships between features from various views and modalities. Our model is validated using four public emotion datasets, each containing various emotion classification tasks. In leave-one-subject-out experiments, IF-MMCL performs better than the previous methods in model generalization with limited data.}, }
@article {pmid40874066, year = {2025}, author = {Tarara, P and Przybył, I and Schöning, J and Gunia, A}, title = {Motor imagery-based brain-computer interfaces: an exploration of multiclass motor imagery-based control for Emotiv EPOC X.}, journal = {Frontiers in neuroinformatics}, volume = {19}, number = {}, pages = {1625279}, pmid = {40874066}, issn = {1662-5196}, abstract = {INTRODUCTION: Enhancing the command capacity of motor imagery (MI)-based brain-computer interfaces (BCIs) remains a significant challenge in neuroinformatics, especially for real-world assistive applications. This study explores a multiclass BCI system designed to classify multiple MI tasks using a low-cost EEG device.<br><br>METHODS: A BCI system was developed to classify six mental states: resting state, left and right hand movement imagery, tongue movement, and left and right lateral bending, using EEG data collected with the Emotiv EPOC X headset. Seven participants underwent a body awareness training protocol integrating mindfulness and physical exercises to improve MI performance. Machine learning techniques were applied to extract discriminative features from the EEG signals.<br><br>RESULTS: Post-training assessments indicated modest improvements in participants' MI proficiency. However, classification performance was limited due to inter- and intra-subject signal variability and the technical constraints of the consumer-grade EEG hardware.<br><br>DISCUSSION: These findings highlight the value of combining user training with MI-based BCIs and the need to optimize signal quality for reliable performance. The results support the feasibility of scalable, multiclass MI paradigms in low-cost, user-centered neurotechnology applications, while pointing to critical areas for future system enhancement.}, }
@article {pmid40873632, year = {2025}, author = {Ye, Y and Tian, Y and Liu, H and Liu, J and Zhou, C and Xu, C and Zhou, T and Nie, Y and Wu, Y and Qin, L and Zhou, Z and Wei, X and Zhao, J and Wang, Z and Li, M and Tao, TH and Sun, L}, title = {High-Precision, Low-Threshold Neuromodulation With Ultraflexible Electrode Arrays for Brain-to-Brain Interfaces.}, journal = {Exploration (Beijing, China)}, volume = {5}, number = {4}, pages = {e70040}, pmid = {40873632}, issn = {2766-2098}, abstract = {Neuromodulation is crucial for advancing neuroscience and treating neurological disorders. However, traditional methods using rigid electrodes have been limited by large stimulating currents, low precision, and the risk of tissue damage. In this work, we developed a biocompatible ultraflexible electrode array that allows for both neural recording of spike firings and low-threshold, high-precision stimulation for neuromodulation. Specifically, mouse turning behavior can be effectively induced with approximately five microamperes of stimulating current, which is significantly lower than that required by conventional rigid electrodes. The array's densely packed microelectrodes enable highly selective stimulation, allowing precise targeting of specific brain areas critical for turning behavior. This low-current, targeted stimulation approach helps maintain the health of both neurons and electrodes, as evidenced by stable neural recordings after extended stimulations. Systematic validations have confirmed the durability and biocompatibility of the electrodes. Moreover, we extended the flexible electrode array to a brain-to-brain interface system that allows human brain signals to directly control mouse behavior. Using advanced decoding methods, a single individual can issue eight commands to simultaneously control the behaviors of two mice. This study underscores the effectiveness of the flexible electrode array in neuromodulation, opening new avenues for interspecies communication and potential neuromodulation applications.}, }
@article {pmid40863235, year = {2025}, author = {Shaw, J and Pyreddy, S and Rosendahl, C and Lai, C and Ton, E and Carter, R}, title = {Current Neuroethical Perspectives on Deep Brain Stimulation and Neuromodulation for Neuropsychiatric Disorders: A Scoping Review of the Past 10 Years.}, journal = {Diseases (Basel, Switzerland)}, volume = {13}, number = {8}, pages = {}, pmid = {40863235}, issn = {2079-9721}, abstract = {BACKGROUND: The use of neuromodulation for the treatment of psychiatric disorders has become increasingly common, but this emerging treatment modality comes with ethical concerns. This scoping review aims to synthesize the neuroethical discourse from the past 10 years on the use of neurotechnologies for psychiatric conditions.<br><br>METHODS: A total of 4496 references were imported from PubMed, Embase, and Scopus. The inclusion criteria required a discussion of the neuroethics of neuromodulation and studies published between 2014 and 2024.<br><br>RESULTS: Of the 77 references, a majority discussed ethical concerns of patient autonomy and informed consent for neuromodulation, with neurotechnologies being increasingly seen as autonomy enablers. Concepts of changes in patient identity and personality, especially after deep brain stimulation, were also discussed extensively. The risks and benefits of neurotechnologies were also compared, with deep brain stimulation being seen as the riskiest but also possessing the highest efficacy. Concerns about equitable access and justice were raised regarding the rise of private transcranial magnetic stimulation clinics and the current experimental status of deep brain stimulation.<br><br>CONCLUSIONS: Neuroethics discourse, particularly for deep brain stimulation, has continued to focus on how post-intervention changes in personality and behavior influence patient identity. Multiple conceptual frameworks have been proposed, though each faces critiques for addressing only parts of this complex phenomenon, prompting calls for pluralistic models. Emerging technologies, especially those involving artificial intelligence through brain computer interfaces, add new dimensions to this debate by raising concerns about neuroprivacy and legal responsibility for actions, further blurring the lines for defining personal identity.}, }
@article {pmid40863131, year = {2025}, author = {Gao, L and Han, L and Ma, X and Wang, H and Li, M and Cai, J}, title = {An Integrated Analysis of Transcriptomics and Metabolomics Elucidates the Role and Mechanism of TRPV4 in Blunt Cardiac Injury.}, journal = {Metabolites}, volume = {15}, number = {8}, pages = {}, pmid = {40863131}, issn = {2218-1989}, support = {YDZJ202101ZYTS086//jilin province science and technology development plan/ ; }, abstract = {BACKGROUND/OBJECTIVES: Blunt cardiac injury (BCI) is a severe medical condition that may arise as a result of various traumas, including motor vehicle accidents and falls. The main objective of this study was to explore the role and underlying mechanisms of the TRPV4 gene in BCI. Elucidating the function of TRPV4 in BCI may reveal potential novel therapeutic targets for the treatment of this condition.<br><br>METHODS: Rats in each group, including the SD control group (SDCON), the SD blunt-trauma group (SDBT), the TRPV4 gene-knockout control group (KOCON), and the TRPV4 gene-knockout blunt-trauma group (KOBT), were all freely dropped from a fixed height with a weight of 200 g and struck in the left chest with a certain energy, causing BCI. After the experiment, the levels of serum IL-6 and IL-1&#x3b2; were detected to evaluate the inflammatory response. The myocardial tissue structure was observed by HE staining. In addition, cardiac transcriptome analysis was conducted to identify differentially expressed genes, and metabolomics studies were carried out using UHPLC-Q-TOF/MS technology to analyze metabolites. The results of transcriptomics and metabolomics were verified by qRT-PCR and Western blot analysis.<br><br>RESULTS: Compared with the SDCON group, the levels of serum IL-6 and IL-1&#x3b2; in the SDBT group were significantly increased (p < 0.001), while the levels of serum IL-6 and IL-1&#x3b2; in the KOBT group were significantly decreased (p < 0.001), indicating that the deletion of the TRPV4 gene alleviated the inflammation induced by BCI. HE staining showed that myocardial tissue injury was severe in the SDBT group, while myocardial tissue structure abnormalities were mild in the KOBT group. Transcriptome analysis revealed that there were 1045 upregulated genes and 643 downregulated genes in the KOBT group. These genes were enriched in pathways related to inflammation, apoptosis, and tissue repair, such as p53, apoptosis, AMPK, PPAR, and other signaling pathways. Metabolomics studies have found that TRPV4 regulates nucleotide metabolism, amino-acid metabolism, biotin metabolism, arginine and proline metabolism, pentose phosphate pathway, fructose and mannose metabolism, etc., in myocardial tissue. The combined analysis of metabolic and transcriptional data reveals that tryptophan metabolism and the protein digestion and absorption pathway may be the key mechanisms. The qRT-PCR results corroborated the expression of key genes identified in the transcriptome sequencing, while Western blot analysis validated the protein expression levels of pivotal regulators within the p53 and AMPK signaling pathways.<br><br>CONCLUSIONS: Overall, the deletion of the TRPV4 gene effectively alleviates cardiac injury by reducing inflammation and tissue damage. These findings suggest that TRPV4 may become a new therapeutic target for BCI, providing new insights for future therapeutic strategies.}, }
@article {pmid40863003, year = {2025}, author = {Lee, J and Han, SY and Kwon, YW}, title = {Technological Advances and Medical Applications of Implantable Electronic Devices: From the Heart, Brain, and Skin to Gastrointestinal Organs.}, journal = {Biosensors}, volume = {15}, number = {8}, pages = {}, pmid = {40863003}, issn = {2079-6374}, support = {202302230001//Pusan National University Hospital/ ; }, mesh = {Humans ; Brain ; *Prostheses and Implants ; Skin ; Gastrointestinal Tract ; Heart ; }, abstract = {Implantable electronic devices are driving innovation in modern medical technology and have significantly improved patients' quality of life. This review comprehensively analyzes the latest technological trends in implantable electronic devices used in major organs, including the heart, brain, and skin. Additionally, it explores the potential for application in the gastrointestinal system, particularly in the field of biliary stents, in which development has been limited. In the cardiac field, wireless pacemakers, subcutaneous implantable cardioverter-defibrillators, and cardiac resynchronization therapy devices have been commercialized, significantly improving survival rates and quality of life of patients with cardiovascular diseases. In the field of brain-neural interfaces, biocompatible flexible electrodes and closed-loop deep brain stimulation have improved treatments of neurological disorders, such as Parkinson's disease and epilepsy. Skin-implantable devices have revolutionized glucose management in patients with diabetes by integrating continuous glucose monitoring and automated insulin delivery systems. Future development of implantable electronic devices incorporating pressure or pH sensors into biliary stents in the gastrointestinal system may significantly improve the prognosis of patients with bile duct cancer. This review systematically organizes the technological advances and clinical outcomes in each field and provides a comprehensive understanding of implantable electronic devices by suggesting future research directions.}, }
@article {pmid40862926, year = {2025}, author = {Dong, L and Xu, C and Xie, R and Wang, X and Yang, W and Li, Y}, title = {Enhanced SSVEP Bionic Spelling via xLSTM-Based Deep Learning with Spatial Attention and Filter Bank Techniques.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {8}, pages = {}, pmid = {40862926}, issn = {2313-7673}, support = {62306106//National Natural Science Foundation of China/ ; 2023AFB377//Natural Science Foundation of Hubei Province/ ; }, abstract = {Steady-State Visual Evoked Potentials (SSVEPs) have emerged as an efficient means of interaction in brain-computer interfaces (BCIs), achieving bioinspired efficient language output for individuals with aphasia. Addressing the underutilization of frequency information of SSVEPs and redundant computation by existing transformer-based deep learning methods, this paper analyzes signals from both the time and frequency domains, proposing a stacked encoder-decoder (SED) network architecture based on an xLSTM model and spatial attention mechanism, termed SED-xLSTM, which firstly applies xLSTM to the SSVEP speller field. This model takes the low-channel spectrogram as input and employs the filter bank technique to make full use of harmonic information. By leveraging a gating mechanism, SED-xLSTM effectively extracts and fuses high-dimensional spatial-channel semantic features from SSVEP signals. Experimental results on three public datasets demonstrate the superior performance of SED-xLSTM in terms of classification accuracy and information transfer rate, particularly outperforming existing methods under cross-validation across various temporal scales.}, }
@article {pmid40862891, year = {2025}, author = {Rusev, G and Yordanov, S and Nedelcheva, S and Banderov, A and Lafaye de Micheaux, H and Sauter-Starace, F and Aksenova, T and Koprinkova-Hristova, P and Kasabov, N}, title = {NEuroMOrphic Neural-Response Decoding System for Adaptive and Personalized Neuro-Prosthetics' Control.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {8}, pages = {}, pmid = {40862891}, issn = {2313-7673}, support = {101070891//European Commission/ ; }, abstract = {In our previous work, we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from the brain motor cortex, called Motor Control Decoder (MCD). Even though the training data are labeled based on the desired movement, there is no guarantee that the patient is satisfied by the action of the effectors. Hence, the need for the classification of brain signals as satisfactory/unsatisfactory is obvious. Based on previous work, we upgrade our neuromorphic MCD with a Neural Response Decoder (NRD) that is intended to predict whether ECoG data are satisfactory or not in order to improve MCD accuracy. The main aim is to design an actor-critic structure able to adapt via reinforcement learning the MCD (actor) based on NRD (critic) predictions. For this aim, NRD was trained using not only an ECoG signal but also the MCD prediction or prescribed intended movement of the patient. The achieved accuracy of the trained NRD is satisfactory and contributes to improved MCD performance. However, further work has to be carried out to fully utilize the NRD for MCD performance optimization in an on-line manner. Possibility to include feedback from the patient would allow for further improvement of MCD-NRD accuracy.}, }
@article {pmid40862879, year = {2025}, author = {Zare Lahijan, L and Meshgini, S and Afrouzian, R and Danishvar, S}, title = {Improved Automatic Deep Model for Automatic Detection of Movement Intention from EEG Signals.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {8}, pages = {}, pmid = {40862879}, issn = {2313-7673}, abstract = {Automated movement intention is crucial for brain-computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This work has compiled a database of EEG signals derived from left finger taps, right finger taps, and a resting condition. Following the requisite pre-processing, the captured signals are input into the proposed model, which is constructed based on graph theory and deep convolutional networks. In this study, we introduce a novel architecture based on six deep convolutional graph layers, specifically designed to effectively capture and extract essential features from EEG signals. The proposed model demonstrates a remarkable performance, achieving an accuracy of 98% in a binary classification task when distinguishing between left and right finger tapping. Furthermore, in a more complex three-class classification scenario, which includes left finger tapping, right finger tapping, and an additional class, the model attains an accuracy of 92%. These results highlight the effectiveness of the architecture in decoding motor-related brain activity from EEG data. Furthermore, relative to recent studies, the suggested model exhibits significant resilience in noisy situations, making it suitable for online BCI applications.}, }
@article {pmid40862861, year = {2025}, author = {Ortega-Robles, E and Carino-Escobar, RI and Cantillo-Negrete, J and Arias-Carrión, O}, title = {Brain-Computer Interfaces in Parkinson's Disease Rehabilitation.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {8}, pages = {}, pmid = {40862861}, issn = {2313-7673}, abstract = {Parkinson's disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain-computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers-such as beta-band synchrony, phase-amplitude coupling, and altered alpha-band activity-may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients' quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed.}, }
@article {pmid40857524, year = {2025}, author = {Golabchi, A and Wu, B and Du, ZJ and Cui, XT}, title = {Long-Term Neural Recording Performance of PEDOT/CNT/Dexamethasone Coated Electrode Array Implanted in Visual Cortex of Rats.}, journal = {Advanced nanobiomed research}, volume = {}, number = {}, pages = {}, pmid = {40857524}, issn = {2699-9307}, support = {R01 NS110564/NS/NINDS NIH HHS/United States ; R01 NS136622/NS/NINDS NIH HHS/United States ; }, abstract = {Implantable neural electrode arrays can be inserted in the brain to provide single-cell electrophysiology recording for neuroscience research and brain-machine interface applications. However, maintaining signal quality over time is complicated by inflammatory tissue responses and degradation of electrode materials. Organic electrode coatings offer a solution by enhancing recording and stimulation capabilities, including reduced impedance, increased charge injection capacity, and the ability to incorporate and release anti-inflammatory drugs. In this study, acid-functionalized multi-walled carbon nanotubes (CNTs) loaded with dexamethasone (Dex) were incorporated into poly (3,4-ethylendioxythiophene) (PEDOT) as electrode coatings. We investigated the electrochemical stability and recording performance of the PEDOT/CNT/Dex coating over an extended period of approximately 18 months. Cyclic voltammetric (CV) stimulation was used to trigger Dex release in half of the recording sites during the first 11 days of implantation to reduce the acute inflammation. The PEDOT/CNT/Dex coated floating microelectrode arrays demonstrated stable in vivo electrode impedance and successful detection of visually evoked neural activity from the rat visual cortex even at chronic time points. Additionally, the CV-stimulated sites exhibited higher single-unit recording yield, amplitudes, and signal-to-noise ratio compared to unstimulated sites. These results highlight the potential of anti-inflammatory treatments to improve the quality and longevity of chronic neural recordings.}, }
@article {pmid40864570, year = {2025}, author = {Chen, R and Xie, C and Zhang, J and You, Q and Pan, J}, title = {A Progressive Multi-Domain Adaptation Network With Reinforced Self-Constructed Graphs for Cross-Subject EEG-Based Emotion and Consciousness Recognition.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {3498-3510}, doi = {10.1109/TNSRE.2025.3603190}, pmid = {40864570}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Algorithms ; *Consciousness/physiology ; Brain-Computer Interfaces ; Male ; Female ; Adult ; *Neural Networks, Computer ; Young Adult ; Databases, Factual ; }, abstract = {Electroencephalogram (EEG)-based emotion recognition is a vital component in brain-computer interface applications. However, it faces two significant challenges: 1) extracting domain-invariant features while effectively preserving emotion-related information, and 2) aligning the joint probability distributions of data across different individuals. To address these challenges, we propose a progressive multi-domain adaptation network with reinforced self-constructed graphs. Specifically, we introduce EEG-CutMix to construct unlabeled mixed-domain data, facilitating the transition between source and target domains. Additionally, a reinforced self-constructed graphs module is employed to extract domain-invariant features. Finally, a progressive multi-domain adaptation framework is constructed to smoothly align the data distributions across individuals. Experiments on cross-subject datasets demonstrate that our model achieves state-of-the-art performance on the SEED and SEED-IV datasets, with accuracies of 97.03% $\pm ~1.65$ % and 88.18% $\pm ~4.55$ %, respectively. Furthermore, tests on a self-recorded dataset, comprising ten healthy subjects and twelve patients with disorders of consciousness (DOC), show that our model achieves a mean accuracy of 86.65% $\pm ~2.28$ % in healthy subjects. Notably, it successfully applies to DOC patients, with four subjects achieving emotion recognition accuracy exceeding 70%. These results validate the effectiveness of our model in EEG emotion recognition and highlight its potential for assessing consciousness levels in DOC patients. The source code for the proposed model is available at GitHub-seizeall/mycode.}, }
@article {pmid40862615, year = {2025}, author = {Gartner, MJ and Smith, ML and Dapat, C and Liaw, YW and Tran, T and Suryadinata, R and Chen, J and Sun, G and Shepherd, RA and Taiaroa, G and Roche, M and Lee, WS and Robinson, P and Polo, JM and Subbarao, K and Neil, JA}, title = {Contemporary seasonal human coronaviruses display differences in cellular tropism compared to laboratory-adapted reference strains.}, journal = {Journal of virology}, volume = {99}, number = {9}, pages = {e0068425}, pmid = {40862615}, issn = {1098-5514}, support = {CGCPT00021//Cumming Global Centre for Pandemic Therapeutics/ ; APP1177174//National Health and Medical Research Council/ ; }, mesh = {Humans ; *Viral Tropism ; *Coronavirus 229E, Human/genetics/physiology/isolation &amp; purification ; Epithelial Cells/virology ; *Coronavirus NL63, Human/genetics/physiology/isolation &amp; purification ; *Coronavirus OC43, Human/genetics/physiology/isolation &amp; purification ; Seasons ; Common Cold/virology ; Cell Line ; Spike Glycoprotein, Coronavirus/genetics ; *Coronavirus/physiology/genetics ; Coronavirus Infections/virology ; }, abstract = {Seasonal human coronaviruses (sHCoVs) cause 15%-30% of common colds. The reference strains used for research were isolated decades ago and have been passaged extensively, but contemporary sHCoVs have been challenging to study as they are notoriously difficult to grow in standard immortalized cell lines. Here, we addressed these issues by utilizing primary human nasal epithelial cells (HNECs) and immortalized human bronchial epithelial cells (BCi) differentiated at an air-liquid interface, as well as human embryonic stem cell-derived alveolar type II (AT2) cells to recover contemporary sHCoVs from human nasopharyngeal specimens. From 21 specimens, we recovered four HCoV-229e, three HCoV-NL63, and eight HCoV-OC43 viruses. All contemporary sHCoVs showed sequence differences from lab-adapted CoVs, particularly within the spike gene. Evidence of nucleotide changes in the receptor binding domains within HCoV-229e and detection of recombination for both HCoV-229e and HCoV-OC43 isolates was also observed. Importantly, we developed methods for the amplification of high-titer stocks of HCoV-NL63 and HCoV-229e that maintained sequence identity, and we established methods for the titration of contemporary sHCoV isolates. Comparison of lab-adapted and contemporary strains in immortalized cell lines and airway epithelial cells revealed differences in cell tropism, growth kinetics, and cytokine production between lab-adapted and contemporary sHCoV strains. These data confirm that contemporary sHCoVs differ from lab-adapted reference strains and, using the methods established here, should be used for the study of CoV biology and evaluation of medical countermeasures.IMPORTANCEZoonotic coronaviruses have caused significant public health emergencies. The occurrence of a similar spillover event in the future is likely, and efforts to further understand coronavirus biology should be a high priority. Several seasonal coronaviruses circulate within the human population. Efforts to study these viruses have been limited to reference strains isolated decades ago due to the difficulty in isolating clinical isolates. Here, we use human airway and alveolar epithelial cultures to recover contemporary isolates of human coronaviruses HCoV-NL63, HCoV-229e, and HCoV-OC43. We establish methods to make high-titer stocks and titrate HCoV-229e and HCoV-NL63 isolates. We show that contemporary isolates of HCoV-NL63 and HCoV-OC43 have a different tropism within the respiratory epithelium compared to lab-adapted strains. Although HCoV-229e clinical and lab-adapted strains similarly infect the respiratory epithelium, differences in host response and replication kinetics are observed. Using the methods developed here, future research should include contemporary isolates when studying coronavirus biology.}, }
@article {pmid40860492, year = {2025}, author = {Zhang, L and Guan, X and Wang, D and Wang, J and Liu, X and Liu, S and Ming, D}, title = {Understanding face processing in autism spectrum disorder: insights from cognitive neuroscience.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {137}, pmid = {40860492}, issn = {1871-4080}, abstract = {Faces convey critical information for social communication, such as identity, expression, and eye gaze. Unfortunately, individuals with autism spectrum disorder (ASD) often experience difficulties in processing this information, and these deficits lead to their suffering from social interactions. Importantly, since face processing is a social skill developed during early childhood, its deficits may be an early symptom of ASD. In recent years, researchers have made great progress in identifying face processing impairments in individuals with ASD and exploring their biological underpinnings. In this paper, we reviewed the research progress on face processing impairments in individuals with ASD. Moreover, we mainly summarized the mechanisms proposed to underlie these impairments, including the changes in brain structure and function, atypical social cognition, and genetic variation. Finally, we discussed the factors leading to the inconsistent results of existing studies. Focused efforts to research the alterations and mechanisms of face processing might improve our knowledge of this complex, heterogeneous neurodevelopmental disorder. The ultimate purpose is to help clinical diagnosis and treatment, thereby improving the function of individuals with ASD.}, }
@article {pmid40858781, year = {2025}, author = {Cui, Y and Sun, J and Zhang, B and Guo, T and Zhang, S and Li, Z and Chen, Y and Su, M and Wu, D and Wu, J and Wang, Q and Yuan, Y and Wang, J and Tian, Q and He, F and Wu, L and Li, X and Gong, Y and Qin, W}, title = {Efficacy and safety of transcutaneous auricular vagus nerve stimulation for patients with treatment-resistant schizophrenia with predominantly negative symptoms: a randomized clinical trial and efficacy sensitivity biomarkers.}, journal = {Molecular psychiatry}, volume = {30}, number = {11}, pages = {5437-5447}, pmid = {40858781}, issn = {1476-5578}, mesh = {Humans ; Male ; Female ; *Vagus Nerve Stimulation/methods/adverse effects ; Adult ; Double-Blind Method ; Treatment Outcome ; Biomarkers/metabolism ; Middle Aged ; *Schizophrenia/therapy ; *Schizophrenia, Treatment-Resistant/therapy/physiopathology ; *Transcutaneous Electric Nerve Stimulation/methods ; Psychiatric Status Rating Scales ; Antipsychotic Agents/therapeutic use ; Electroencephalography ; Tumor Necrosis Factor-alpha/metabolism ; }, abstract = {Negative symptoms in treatment-resistant schizophrenia (TRS) are notably persistent and minimally affected by antipsychotics, the transcutaneous auricular vagus nerve stimulation (taVNS) is a promising treatment approach. However, clinical trials are scarce, and further efficacy data are needed. We conducted a double-blind, sham-controlled, randomized clinical trial to determine the efficacy and safety of taVNS as an add-on treatment for patients with TRS with predominantly negative symptoms and to investigate potential biomarkers of efficacy. A total of 50 patients underwent a two-week intervention of active taVNS (n = 25) or sham taVNS (n = 25), followed by a two-week follow-up. Primary outcome was the change in the PANSS-factor score for negative symptoms (PANSS-FSNS) assessed after the intervention. In the intention-to-treat analysis, patients receiving active taVNS showed a significantly greater improvement in negative symptoms compared with those receiving the sham procedure (PANSS-FSNS difference, -1.36; effect size, -0.62; 95% CI, -1.20 to -0.04; p = 0.033), with effects sustained at follow-up and good tolerability. Inflammatory cytokines and EEG coherence showed that in the active group, the change in PANSS-FSNS scores after treatment was significantly correlated with changes in tumour necrosis factor (TNF)-α (r = 0.56, corrected p = 0.017) and beta-band coherence between the left frontal and parietal regions (r = -0.56, p = 0.004), but not in the sham group. This study suggests that taVNS may effectively and safely ameliorate negative symptoms in TRS, with TNF-α and beta-band coherence between the left frontal and parietal regions as potential sensitivity efficacy biomarkers. Chinese Clinical Trial Registry (http://www.chictr.org.cn .), ChiCTR2400085198.}, }
@article {pmid40858497, year = {2025}, author = {Tong, JQ and Binder, JR and Conant, LL and Mazurchuk, S and Anderson, AJ and Fernandino, L}, title = {A Common Representational Code for Event and Object Concepts in the Brain.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {45}, number = {41}, pages = {}, pmid = {40858497}, issn = {1529-2401}, mesh = {Humans ; Female ; Male ; Magnetic Resonance Imaging ; Adult ; Young Adult ; *Brain/physiology ; *Brain Mapping ; *Concept Formation/physiology ; *Recognition, Psychology/physiology ; Photic Stimulation/methods ; Image Processing, Computer-Assisted ; }, abstract = {Events and objects are two fundamental ways in which humans conceptualize their experience of the world. Despite the significance of this distinction for human cognition, it remains unclear whether the neural representations of object and event concepts are categorically distinct or, instead, can be explained in terms of a shared representational code. We investigated this question by analyzing fMRI data acquired from human participants (males and females) while they rated their familiarity with the meanings of individual words (all nouns) denoting object and event concepts. Multivoxel pattern analyses indicated that both categories of lexical concepts are represented in overlapping fashion throughout the association cortex, even in the areas that showed the strongest selectivity for one or the other type in univariate contrasts. Crucially, in these areas, a feature-based model trained on neural responses to individual event concepts successfully decoded object concepts from their corresponding activation patterns (and vice versa), showing that these two categories share a common representational code. This code was effectively modeled by a set of experiential feature ratings, which also accounted for the mean activation differences between these two categories. These results indicate that neuroanatomical dissociations between events and objects emerge from quantitative differences in the cortical distribution of more fundamental features of experience. Characterizing this representational code is an important step in the development of theory-driven brain-computer interface technologies capable of decoding conceptual content directly from brain activity.}, }
@article {pmid40857922, year = {2025}, author = {Hu, W and Zhang, D and Chen, W}, title = {ITSEF: Inception-based two-stage ensemble framework for P300 detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {193}, number = {}, pages = {108014}, doi = {10.1016/j.neunet.2025.108014}, pmid = {40857922}, issn = {1879-2782}, abstract = {To address the problems of low signal-to-noise ratio, significant individual differences between subjects, and class imbalance in P300-based brain-computer interface (BCI), this paper proposes a novel Inception-based two-stage ensemble framework (ITSEF) to improve detection accuracy. Firstly, an Inception-based convolutional neural network (ICNN) is designed to extract multi-scale features and conduct cross-channel learning. In addition, a two-stage ensemble framework (TSEF) combined with a pre-training and fine-tuning strategy is developed, aiming to enhance the classification performance of the minority class and improve the generalization ability of the model. The framework comprises a conventional learning branch and a re-balancing branch, each based on an ICNN pre-trained with a different loss function. The prediction results of both branches are dynamically weighted by a cumulative learning strategy, so that the model gradually shifts its learning focus from the majority class to the minority class, comprehensively improving the identification ability for both classes. Experimental results on two datasets, Dataset II of BCI Competition III and BCIAUT-P300, demonstrate that the proposed ITSEF achieves state-of-the-art performance in the P300 classification task, with average classification accuracies of 86.16 % and 92.13 %, respectively. Compared with the existing state-of-the-art methods, the ITSEF achieves improvements of 4.61 % and 1.01 % on the two datasets, respectively. Furthermore, it exhibits significant improvements compared to baseline models and widely used class re-balancing strategies. The proposed ITSEF method provides an innovative deep learning framework for P300 signal analysis and has application potential in the field of P300-BCI.}, }
@article {pmid40855086, year = {2025}, author = {Xiao, Q and Fan, LH and Ma, Q and Ning, YM and Gu, Z and Chen, L and Li, L and You, JW and Niu, YF and Cui, TJ}, title = {Secure wireless communication of brain-computer interface and mind control of smart devices enabled by space-time-coding metasurface.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {7914}, pmid = {40855086}, issn = {2041-1723}, support = {62288101//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92167202//National Natural Science Foundation of China (National Science Foundation of China)/ ; 72171044//National Natural Science Foundation of China (National Science Foundation of China)/ ; K201924//State Key Laboratory of Millimeter Waves (State Key Lab of Millimeter Waves)/ ; BK20230822//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; 2021M700761//China Postdoctoral Science Foundation/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Wireless Technology/instrumentation ; *Brain/physiology ; Electroencephalography ; *Computer Security ; Photic Stimulation ; Algorithms ; Brainwashing ; }, abstract = {Brain-computer interface (BCI) provides an interconnected pathway between the human brain and external devices and paves a potential route for mind manipulations. However, most existing BCI technologies are based on simple signal transmission and are independent of other interface devices, with limited consideration for the reliability and security of the human brain's information interaction in complicated wireless environments. Here, we propose a deep fusion coding scheme that combines the BCI visual stimulation coding with metasurface space-time coding at the physical layer, enabling reliable and secure information transfers between the human brain and external devices. A brain space-time-coding metasurface platform is designed to implement a secure wireless communication system by using harmonic-encrypted beams. We design and fabricate a proof-of-principle prototype and experimentally show that the proposed wireless BCI scheme can establish a remote but safeguarded paradigm for human-machine interactions and intelligent metasurfaces, providing a potential direction in future secure wireless communications.}, }
@article {pmid40856919, year = {2025}, author = {Arns, M and Sokhadze, E and Birbaumer, N}, title = {Neurofeedback and Brain-Machine Interfaces: Where are We Now?.}, journal = {Applied psychophysiology and biofeedback}, volume = {}, number = {}, pages = {}, pmid = {40856919}, issn = {1573-3270}, }
@article {pmid40856229, year = {2025}, author = {Yuan, Y and Gao, Z and Xiao, W}, title = {The Role of Oxytocin in Parental Care.}, journal = {Endocrinology}, volume = {166}, number = {9}, pages = {}, doi = {10.1210/endocr/bqaf129}, pmid = {40856229}, issn = {1945-7170}, support = {2021R52021//This work was supported by the Leading Talents in Science and Technology of Zhejiang Province/ ; 2021ZD0202700//National Science and Technology Innovation 2030-Major Projects/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; }, mesh = {*Oxytocin/physiology/metabolism ; Humans ; Animals ; *Maternal Behavior/physiology ; *Paternal Behavior/physiology ; Female ; }, abstract = {Parental behaviors are essential for offspring survival and shaped by hormonal changes and adaptations in the neural circuits. Oxytocin, a nonapeptide, has been shown to play an important role in promoting parental behaviors. Using cutting-edge tools, studies have recently uncovered how oxytocin mediates parental behaviors through modulation of different neural circuits. We highlight recent advances in identifying neural pathways contributing to the role of oxytocin in parental care, focusing on how infant-related cues activate the oxytocin system and how oxytocin enhances the salience of sensory cues to enable parental behaviors in this review. We also discuss future challenges to further elucidate mechanisms involved.}, }
@article {pmid40853583, year = {2025}, author = {Wang, S and Yang, Y and Hao, S and Sun, Y and Wang, H}, title = {Glutamatergic Periaqueductal Gray Projections to the Locus Coeruleus Orchestrate Adaptive Arousal States in Threatening Contexts.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {40853583}, issn = {1995-8218}, abstract = {The locus coeruleus (LC), a norepinephrine nucleus governing arousal states through tonic activity, requires precise regulatory mechanisms to maintain its dynamic activation levels. However, the neural circuitry underlying LC activity maintenance remains unclear. Here, we identify a glutamatergic projection from the ventrolateral periaqueductal gray (vlPAG) to the LC in mice as a critical regulator of arousal dynamics. Fiber photometry recordings revealed stress-induced Ca[2+] dynamics in vlPAG[CaMKIIα]-LC axon terminals across diverse threat paradigms. Slice electrophysiology demonstrated that this pathway mediates LC-norepinephrine (LC-NE) neuronal activity via glutamatergic transmission. Low-frequency pathway activation (1 Hz) mainly induced anxiety-like behaviors, whereas high-frequency stimulation (10 Hz) evoked more panic-like hyperlocomotion, establishing a frequency-dependent continuum of arousal states. Conversely, pathway inhibition reduced pupil size, a reliable biomarker for arousal, concurrently suppressing threat avoidance behaviors and alleviating anxiety-related behaviors without altering environmental preference. These findings reveal that the vlPAG[CaMKIIα]-LC pathway maintains baseline arousal while dynamically scaling threat-induced hyperarousal.}, }
@article {pmid40853288, year = {2025}, author = {Rahman, MM and Banik, N and Sunny, MSH and Zarif, MII and Bedolla-Martinez, D and Schultz, K and Ahamed, SI and Rahman, MH}, title = {Wheelchair-mounted robotic arms: a systematic review of technical design and activities of daily living outcomes.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {20}, number = {7}, pages = {2532-2556}, doi = {10.1080/17483107.2025.2547042}, pmid = {40853288}, issn = {1748-3115}, mesh = {Humans ; *Activities of Daily Living ; *Wheelchairs ; *Robotics/instrumentation ; Equipment Design ; *Persons with Disabilities/rehabilitation ; *Self-Help Devices ; Quality of Life ; }, abstract = {PURPOSE: This review examines wheelchair-mounted robotic arms (WMRAs) as an emerging assistive technology that enhances independence and quality of life for individuals with upper- and lower-limb disabilities. By enabling independent performance of activities of daily living (ADLs), WMRAs hold significant promise for disability and rehabilitation. The article aims to critically evaluate the state of the art in WMRA research and development, identifying persistent challenges and highlighting promising innovations.<br><br>MATERIALS AND METHODS: The review systematically analyzes literature on WMRAs published between 2001 and 2025. The analysis emphasizes design specifications, degrees of freedom, actuation methods, control strategies, and performance evaluations. A comparative synthesis is conducted to assess how existing systems support ADL execution, while also integrating technical considerations with user-centered outcomes.<br><br>RESULTS AND CONCLUSIONS: The findings indicate that current WMRA designs face significant limitations, including restricted workspace coverage, inadequate gripper dexterity, suboptimal kinematic configurations, limited payload capacity, high cost, and lack of modularity. Safety mechanisms remain underdeveloped, creating barriers to broader adoption. Nevertheless, advancements in AI-driven control systems, modular design strategies, and integration with complementary assistive technologies demonstrate promising progress. The review concludes that WMRAs have substantial potential to improve autonomy and daily functioning for individuals with disabilities. Addressing technical and practical shortcomings is essential to ensure successful real-world deployment. These insights contribute to disability and rehabilitation research, as they highlight pathways to enhance accessibility, safety, and cost-effectiveness in assistive technologies that support independent living.}, }
@article {pmid40852670, year = {2025}, author = {Zubayr, MO and Obimakinde, AM and Popoola, OA}, title = {PARENTAL RESPONSE AND COPING STRATEGIES FOR ADOLESCENTS' BEHAVIOURAL PROBLEMS: A COMMUNITY-BASED CROSS-SECTIONAL STUDY.}, journal = {Annals of Ibadan postgraduate medicine}, volume = {23}, number = {1}, pages = {15-23}, pmid = {40852670}, issn = {1597-1627}, abstract = {BACKGROUND: Adolescent behavioural problems can be burdensome for parental figures. The lack of good parental responses and coping strategies may worsen adolescent mental health issues. Research in this domain can be informative for effective management of adolescents' behavioural problems in resourcelimited settings like Nigeria.<br><br>AIM: We assessed parental responses and coping strategies for adolescents with behavioural problems.<br><br>METHODS: A cross-sectional community-based survey with cluster sampling was conducted. Coping strategies were assessed using the Brief Cope Inventory (BCI), dichotomized into Emotional-Based Strategies (EBS) and Problem- Based Strategies (PBS) coping. The Strength and Difficulty Questionnaire (SDQ) assessed adolescent behavioural problems. Data were analyzed using descriptive and inferential statistics.<br><br>RESULTS: Four hundred and ten (410) parental figures of adolescents aged 14.8&#xb1;2.3 years were recruited. Parental response to adolescent problem behaviours included corporal punishment in 44% and few (5.8%) sought medical or spiritual help for the adolescent. The most deployed parental coping strategy was 'active' coping (69%) while 'instrumental support' was the least adopted coping strategy. The age, gender, educational level and income of parental figures, were associated with the choice of utilizing PBS coping.<br><br>CONCLUSION: Parental figures employed more corporal punishment and utilized active coping, and planning as coping strategies when dealing with adolescents' problem behaviours. Interventions to discourage corporal punishment and promote more effective parental coping are needed.}, }
@article {pmid40852573, year = {2025}, author = {Chen, L and Yang, T and Liu, R and Xu, Q and Ge, Q and Wu, M and Yu, H}, title = {Sensory and neural responses to flavor compound 3-Methylbutanal in dry fermented sausages: Enhancing perceived overall aroma.}, journal = {Food chemistry: X}, volume = {29}, number = {}, pages = {102769}, pmid = {40852573}, issn = {2590-1575}, abstract = {This study investigated the impact of 3-methylbutanal (0, 60, 120, 180, 240, and 300 μg/kg) on aroma and neural responses in fermented sausages. Among 33 volatiles identified, 3-methylbutanal exhibited the highest odor activity value of 868, indicating its dominant contribution. Sensory analysis showed that samples with 180 μg/kg received the highest ratings for savory (7.0), caramelized (7.1), and nutty (4.4) notes, whereas the 300 μg/kg group showed the lowest overall aroma intensity. EEG analysis indicated global power and α-band activity peaked at 180 μg/kg, increasing by 65.8 % and 73.2 % over baseline, then declined at higher doses. Time-resolved topographies showed odor decoding began at 100 ms and peaked at 500 ms. Source localization identified increased activity in dorsolateral, orbitofrontal, and ventromedial prefrontal cortices at 180 μg/kg. These results demonstrate that moderate levels of 3-methylbutanal enhance aroma perception and evoke heightened neural activity in brain regions associated with olfactory processing and emotion.}, }
@article {pmid40852333, year = {2025}, author = {An, J and Goyal, P and Luft, AR and Schönhammer, JG}, title = {Functional near-infrared spectroscopy short-channel regression improves cortical activation estimates of working memory load.}, journal = {Neurophotonics}, volume = {12}, number = {3}, pages = {035009}, pmid = {40852333}, issn = {2329-423X}, abstract = {SIGNIFICANCE: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique commonly used to examine cognitive functions such as working memory (WM). However, fNIRS signals are often interfered with by extracerebral activity, such as scalp hemodynamics. Short separation channels (SSCs) allow direct measurement of these signals. Short-channel regression (SCR) is widely used to reduce scalp interference, but its added value in WM paradigms remains underexplored.<br><br>AIM: We aimed to examine the effect of SCR on improving the validity of fNIRS measurements for WM load (WML).<br><br>APPROACH: We used the N -Back task to induce WML-dependent brain activation by varying the " n " level. Data from 20 participants were collected using fNIRS with SSC. Hemodynamic responses were analyzed with generalized linear models and linear mixed models to assess SCR's effect on the sensitivity of cortical activation measures.<br><br>RESULTS: SCR enhanced the statistical effects of N -Back levels on measured hemodynamic responses at both group and subject levels, improving the validity and sensitivity of fNIRS.<br><br>CONCLUSIONS: SCR improves fNIRS measurement sensitivity and validity, even in tasks with minimal motor requirements.}, }
@article {pmid40850941, year = {2025}, author = {Zhang, X and Yang, Y}, title = {Gut: The gate and key to brain.}, journal = {Chinese medical journal}, volume = {138}, number = {18}, pages = {2207-2219}, pmid = {40850941}, issn = {2542-5641}, mesh = {Humans ; *Brain/physiology ; Animals ; Gastrointestinal Microbiome/physiology ; *Gastrointestinal Tract/physiology/microbiology ; }, abstract = {Brain science is the frontier of modern science, and new advances have been made in brain-like designs and brain-computer interfaces to simulate or develop brain functions. However, given that the brain is hermetically sealed within the skull, exploration and deciphering of the brain structure and functions are limited. Growing evidence suggests that the gut is not just a digestive organ. It not only provides essential nutrients and electrolytes for brain neurodevelopment and the maintenance of brain function, but it also transmits external environmental and intestinal wall signals from the intestinal lumen to the central nervous system through multiple pathways to regulate brain activity, function, and structure. A variety of gut-brain interaction pathways have been identified, including neural pathways, neuroimmune signaling, endocrine pathways, and biochemical messengers produced by gut microbes. Gut microbes interact with food and the gut to modulate gut-brain communication. The gut's important role and potential in neurodevelopment, maintenance of normal function, and disease development make it an increasingly important area of research in brain science and neuropsychiatric disorders. The gut's unique role in brain functions and its accessibility for research (compared to direct brain studies) establish it as a critical gate to understanding the mysteries of brain science. Crucially, intestinal nutrients and microbes provide two unique keys to unlock this gate-enabling neural regulation and novel treatments for neuropsychiatric diseases.}, }
@article {pmid40850344, year = {2025}, author = {Zhou, S and Liu, Y and Turnbull, A and Tapparello, C and Adeli, E and Lin, FV}, title = {Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework.}, journal = {Ageing research reviews}, volume = {112}, number = {}, pages = {102877}, doi = {10.1016/j.arr.2025.102877}, pmid = {40850344}, issn = {1872-9649}, mesh = {Humans ; *Cognition/physiology ; Aged ; *Aging/psychology/physiology ; *Precision Medicine/methods ; *Brain-Computer Interfaces ; }, abstract = {Emerging digitally delivered non-pharmacological interventions (dNPIs) offer scalable, low-risk solutions for enhancing cognitive function in older adults, yet their effectiveness remains inconsistent due to a lack of personalization and precise mechanisms of action. Generic, population-based designs often fail to predict individual gains, underscoring the need for more tailored approaches. To address this, we propose a closed-loop human-machine interface (HMI) framework for personalizing dNPIs by optimizing the engagement of neurocognitive resources for cognitive enhancement. Our framework tackles three major challenges: (1) comprehensive and effective neurobehavioral representations for cognitive decoding, (2) tailoring interventions for domain-specific cognitive processes, and (3) ensuring aging-friendly design on usability, validity, and reliability for long-term adherence. We provide reviews and perspectives to guide the development of closed-loop HMIs by outlining the operational details of three key components-sensor, controller, and external actuator-that monitor, analyze, and modulate neurobehavioral activities through real-time adaptive interventions. Centering on neurobehavioral characteristics of older adults, we propose to advance closed-loop HMIs toward (1) deploying multimodal sensor network that captures activities from both central and peripheral nervous systems, (2) artificial intelligence (AI)-powered cognitive decoding and modulation that integrates multi-modal easy-to-acquire neurobehavioral signals and predicts the cross-modal harder-to-acquire signals, and (3) targeting neurobehavioral processes via internal and/or external regulation. We envision that the proposed closed-loop HMI framework could provide personalized dNPI with enhanced effectiveness and scalability for cognitive enhancement in older adults, promoting brain resilience and healthy longevity in the aging population.}, }
@article {pmid40850267, year = {2025}, author = {Li, J and Zhang, W and Liao, Y and Qiu, Y and Zhu, Y and Zhang, X and Wang, C}, title = {Neural decoding reliability: Breakthroughs and potential of brain-computer interfaces technologies in the treatment of neurological diseases.}, journal = {Physics of life reviews}, volume = {55}, number = {}, pages = {1-40}, doi = {10.1016/j.plrev.2025.08.007}, pmid = {40850267}, issn = {1873-1457}, abstract = {Neurological disorders such as Parkinson's disease, stroke, and epilepsy frequently result in irreversible disability. Brain-computer interface (BCI) technologies offer the promise of recovering or replacing impaired sensory, motor, and cognitive functions by directly stimulating cortical activity or by converting self-generated cortical activity into commands for external assistive devices. In-depth studies of cerebral cortex connectivity, function and neural hierarchical coding mechanisms can provide novel solutions for BCI-based treatments. This review summarizes the fundamental principles and history of BCI technology and current research progress, including the utilization of known cortical functions and the potential impact of newly discovered cortical functions on the future development of BCI-based applications. The article then systematically reviews the application of BCI technology for the treatment of motor, cognitive, and psychiatric disorders, innovative uses of hydrogels and carbon nanomaterials in BCI systems, and the current limitations and future research directions of BCI systems with respect to the reliability of neural decoding. This article aims to provide clinicians and researchers with the latest progress and a comprehensive overview of BCI applications for diagnosing and treating neurological diseases from in-depth studies on cerebral cortex structure and function, and to propose potential future applications based on interdisciplinary approaches, especially in enhancing the reliability of neural decoding.}, }
@article {pmid40848671, year = {2025}, author = {Weng, Y and He, B and Zhou, J and Luo, P and Xu, Z and Yan, H and Yang, B and He, Q and Lu, J and Yang, X}, title = {Potential saviour of pulmonary fibrosis: multi-pathway treatment of natural products.}, journal = {Phytomedicine : international journal of phytotherapy and phytopharmacology}, volume = {147}, number = {}, pages = {157174}, doi = {10.1016/j.phymed.2025.157174}, pmid = {40848671}, issn = {1618-095X}, mesh = {Humans ; *Pulmonary Fibrosis/drug therapy ; *Biological Products/pharmacology/therapeutic use ; Oxidative Stress/drug effects ; Signal Transduction/drug effects ; Animals ; Phytotherapy ; Epithelial-Mesenchymal Transition/drug effects ; Inflammation/drug therapy ; }, abstract = {BACKGROUND: Pulmonary fibrosis (PF), a terminal manifestation of diverse interstitial lung diseases, remains incompletely understood in its pathogenesis. Natural products possess multifaceted biological activities and relatively favorable safety profiles, showing great advantage in treating complex disease including PF, though bioavailability limitations require formulation optimization.<br><br>PURPOSE: This review systematically consolidates insights into the underlying mechanisms of natural products and prospects several promising targets for the treatment of PF.<br><br>METHODS: A comprehensive literature search was conducted in PubMed, Web of Science, and specialized pharmacology texts using key terms related to pulmonary fibrosis, natural products (e.g., alkaloids, terpenoids, flavonoids, saponins), inflammation, and oxidative stress. The information was reviewed to emphasize the potential mechanisms of natural products in the treatment of PF.<br><br>RESULTS: Natural products ameliorate PF through multi-pathway interventions, including suppression of inflammation, antagonism of oxidative stress, inhibition of epithelial-mesenchymal transition and endothelial-to-mesenchymal transition, targeting of fibroblast activation, modulation of metabolic homeostasis, promotion of autophagy and repression of senescence and apoptosis. These effects are mediated by modulating intricate pathways such as the TGF-&#x3b2;1/SMAD, PI3K/Akt/mTOR, NOX4-Nrf2, AMPK, NF-&#x3ba;B and STAT3 signaling pathways. In addition, the toxicology and side effects of natural products for the treatment of pulmonary fibrosis, and various clinical questions and limitations are discussed.<br><br>CONCLUSION: These unveiling mechanisms provide robust support for the exploration of novel applications of existing medications. This review aims to contribute novel insights towards the further studies of natural products for the prevention and treatment of PF.}, }
@article {pmid40848318, year = {2025}, author = {Yu, SH and Park, HY and Lee, E and Kam, TE and Jeong, JH}, title = {DeepSMR: Decoding high-complex motor imagery via subject-dependent multi-feature refinement in deep convolutional networks.}, journal = {Computers in biology and medicine}, volume = {197}, number = {Pt A}, pages = {110920}, doi = {10.1016/j.compbiomed.2025.110920}, pmid = {40848318}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Fingers/physiology ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain/physiology ; }, abstract = {Electroencephalography (EEG) is a noninvasive neuroimaging technique that records electrical activity in the brain using electrodes placed on the scalp. It is widely used in neuroscience, clinical diagnosis, and brain-computer interface (BCI) applications to analyze brain signals in real time. This study proposes an advanced EEG-based BCI framework designed to decode and classify individual finger movements within a single hand during a finger-tapping task involving all five fingers. Our method employs a subject-dependent multi-feature refinement framework called DeepSMR, a novel deep convolutional network architecture optimized for feature extraction from EEG signals is introduced. This approach integrates spectral, temporal, and spatial analyses, leveraging event-related desynchronization/event-related synchronization (ERD/ERS), common spatial pattern (CSP), and power spectral density (PSD) techniques. Further, a subject-dependent multi-feature refinement framework. The DeepSMR achieved high classification accuracy for fine-motor tasks, achieving an average accuracy of 0.7471 (±0.0270) for the thumb and 0.7485 (±0.0314) for the index finger during motor execution tasks. DeepSMR outperformed EEGNet and DeepConvNet across all finger classes, showing an improvement of up to 15% in accuracy compared with the baseline models. Spectral feature analysis confirmed increased activity in the sensorimotor rhythm (SMR) frequency bands (8-13 Hz and 13-30 Hz), whereas temporal analysis revealed distinct patterns during the active and relaxed states. Spatial feature analysis highlighted class-specific features, further enhancing model performance. In the motor imagery session, DeepSMR maintained a superior performance, achieving the highest accuracy of 0.6984 (±0.0324) for the index finger. The results show that DeepSMR improves BCI performance by increasing the classification accuracy and computational efficiency, particularly for challenging finger-movement tasks. The framework could provide applications in neuroprosthetics, assistive robotics, and rehabilitation. In future work, the method could be expanded to include more motor tasks and integrate additional data types to further enhance the decoding accuracy for specific users and complex actions.}, }
@article {pmid40847250, year = {2025}, author = {Tan, H and Jin, S and Lv, W and Guo, L and Jiang, P and Li, Y and Shi, M and Wang, D and Wang, Y and Bao, A}, title = {Hypothalamic Oxytocin Neuronal Activation Induces Bipolar-Like Mood Changes in Mice in a Sex- and Dosage-Dependent Manner.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {40847250}, issn = {1995-8218}, abstract = {Clinical studies have suggested that increased plasma oxytocin (OT) levels are a promising biomarker for bipolar disorder (BD), and our earlier post-mortem study found increased OT activity in the hypothalamic paraventricular nucleus (OT[PVN]) in BD. However, the potential contribution of the supraoptic nucleus (SON, OT[SON]), a major part of the central OT system, to BD remains unknown. We therefore systematically performed independent acute or chronic chemogenetic activation of OT[PVN], OT[SON], or OT[PVN+SON] experiments in OT-cre mice. We found that acute activation of OT[PVN+SON] neurons led to slight mania-like (anti-depression-like) behaviors both in male and female mice, while chronic activation of OT[PVN] or OT[PVN+SON] led to sex-dependent behavioural changes from depression/anxiety-like to mania-like, accompanied by stress-related molecular changes in a sex- dependent manner in the medial prefrontal cortex. Our findings imply that OT may be involved in bipolar-like mood changes in a sex- and dosage-dependent manner.}, }
@article {pmid40841966, year = {2025}, author = {Yang, W and Lu, J and Luo, P and Xu, Z and Yan, H and Yang, B and He, Q and Zhou, J and Yang, X}, title = {An exhaustive examination of the research progress in identifying potential JAK inhibitors from natural products: a comprehensive overview.}, journal = {Chinese medicine}, volume = {20}, number = {1}, pages = {130}, pmid = {40841966}, issn = {1749-8546}, support = {No.2020YFE0204300//the National Key Research and Development Program/ ; No.82404960//Youth Fund of the National Natural Science Foundation of China/ ; No. 82274018//the National Natural Science Foundation of China/ ; }, abstract = {The JAK-STAT signaling pathway serves as a central regulator of diverse cellular processes encompassing proliferation, apoptosis, inflammation, and differentiation. Specifically, extracellular ligands such as interleukins, and colony-stimulating factors induce JAKs phosphorylation, subsequently triggering dimerization and nuclear translocation of STATs protein. In this way, the JAK-STAT pathway modulates target gene expression. Dysregulation of the JAK-STAT pathways has been implicated in the pathogenesis of multiple diseases, including inflammatory diseases, autoimmune diseases, malignant tumors. Therefore, JAK inhibitors have been considered promising therapeutic candidates with substantial clinical potential. While previous reviews have primarily focused on natural products targeting JAK-STAT signaling pathways for the specific disease application, this paper comprehensively collected 88 natural products demonstrating JAKs inhibitory activity across multiple pathological conditions. We mainly referenced nearly 20 years of literature from 2005 to 2025, comprising 294 different types of publications including review articles and research papers. Through systematic analysis of the compounds, we further classified these phytochemicals according to their structural characteristics (flavonoids, alkaloids, terpenoids) and molecular targets within the signaling cascades. This study provides novel insights into the pathophysiological relationships between diseases and JAK kinases, while offering valuable guidance for developing next-generation JAK inhibitors with improved therapeutic profiles.}, }
@article {pmid40844935, year = {2025}, author = {Zhu, Y and Chen, J and Cheng, L and Zhu, F and Zhang, X and Liu, Q}, title = {A Sparse-Integrated Filtering Residual Spiking Neural Network for High-Accuracy Spike Sorting and Co-optimization on Memristor Platforms.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2025.3601403}, pmid = {40844935}, issn = {1940-9990}, abstract = {Brain-computer interfaces rely on precise decoding of neural signals, where spike sorting is a critical step to extract individual neuronal activities from complex neural data. This works presents a spiking neural network (SNN) framework for efficient spike sorting, named SIFT-RSNN. In the SIFT-RSNN, raw neural signals are encoded into spike trains using a threshold-based temporal encoding strategy, then a sparse-integrated filtering module refines misfiring spikes, enhancing data sparsity for pattern learning. The RSNN module with a membrane shortcut structure ensures efficient feature transfer and improves generalization performance of the overall system. The SIFT-RSNN achieves an accuracy of 96.2% and 99.6% on the Difficult1 and Difficult2 subset of Leicester dataset, surpassing state-of-the-art methods. Also, we conducted it on a compute-in-memory platform with 8k memristor cells utilizing quantization-free mapping method and propose two algorithm-hardware co-optimization strategies to mitigate non-ideal hardware effects: weight outlier pre-constraint (WOP) and noise adaptation training (NAT). After optimization, our algorithm continues to outperform existing spike sorting methods, achieving accuracies of 94.2% and 99.7%, while also demonstrating improved robustness. The memristor platform only exhibits a 2% and 1.5% accuracy drop compared to software results on the two difficult subsets. Additionally, it achieves 3.52 μJ energy consumption and 0.5 ms latency per inference. This work offers promising solutions for brain-computer interfaces systems and neural prosthesis applications in the future.}, }
@article {pmid40843108, year = {2025}, author = {Hao, Y and Cheng, S}, title = {Motor imagery EEG classification method using 3D CNN and LSTM for rehabilitation application.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {131}, pmid = {40843108}, issn = {1871-4080}, abstract = {Due to the limitations in the accuracy and robustness of current EEG classification methods, applying motor imagery for practical Brain-Computer Interface applications remains challenging. Therefore, an EEG classification method with high accuracy and strong robustness is of significant importance. This paper proposed a method called 3D CNN and LSTM for Motor Imagery (3D-CLMI), which combines 3D CNN and LSTM network with attention to classify MI-EEG signals. This method combined MI-EEG signals from different channels into 3D features and extracted spatial features through convolution operations with multiple 3D convolutional kernels of different scales. At the same time, in order to ensure the integrity of the extracted temporal features of the MI-EEG signal, 3D-CLMI adopted a parallel structure to obtain spatial and temporal features respectively, and then combined the obtained features for classification. Experimental results showed that this method achieved a classification accuracy of 92.7% and an F1-score of 0.91 on BCI Competition IV 2a, which were both higher than the state-of-the-art methods in the field of MI tasks. Additionally, 12 participants were invited to complete a four-class MI task, and experiments on the collected dataset showed that our method also maintained the highest classification accuracy and F1-score. Our proposed method achieved the best results on both datasets, and we then demonstrated the effectiveness of each part of the proposed method through ablation experiments. Additionally, we designed a rehabilitation application system in a VR environment based on the proposed method, and the experimental results validated that it could assist patients with impaired hand motor function.}, }
@article {pmid40843107, year = {2025}, author = {Ding, P and Wang, F and Zhao, L and Gong, A and Fu, Y}, title = {HWI encoding/decoding of a non-invasive HWI-BCI paradigm based on temporal variation abundance scale.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {130}, pmid = {40843107}, issn = {1871-4080}, abstract = {The performance of non-invasive Handwriting Imagery (HWI) input in Brain-computer interface (BCI) systems is highly dependent on the paradigms employed, yet there is limited research on interpretable scales to measure how HWI-BCI paradigms and neural encoding designs affect performance. This study introduces the "Temporal Variation Abundance" metric and utilizes it to design two classes of handwriting imagery paradigms: Low Temporal Variation Abundance (LTVA) and High Temporal Variation Abundance (HTVA). A dynamic time warping algorithm based on random templates (rt-DTW) is proposed to align HWI velocity fluctuations using EEG. Comprehensive comparisons of these experimental paradigms are conducted in terms of feature space distance, offline and online classification accuracy, and cognitive load assessment using functional near-infrared spectroscopy. Results indicate that HTVA-HWI exhibits lower velocity stability but demonstrates higher spatial distance, offline classification accuracy, online testing classification accuracy, and lower cognitive load. This study provides deep insights into paradigm design for non-invasive HWI-BCI and scales of neural encoding, offering new theoretical support and methodological insights for future advancements in brain-computer interaction.}, }
@article {pmid40843078, year = {2025}, author = {Deb, N and Khan, Z and Sulaiman, M and Abu Bakar, M}, title = {Editorial: Interdisciplinary synergies in neuroinformatics, cognitive computing, and computational neuroscience.}, journal = {Frontiers in computational neuroscience}, volume = {19}, number = {}, pages = {1657167}, pmid = {40843078}, issn = {1662-5188}, }
@article {pmid40842871, year = {2025}, author = {Wang, Y and Wang, X}, title = {Entheogen: an evolutionary medicine for neuropsychiatric disorders.}, journal = {National science review}, volume = {12}, number = {8}, pages = {nwaf168}, pmid = {40842871}, issn = {2053-714X}, }
@article {pmid40839508, year = {2025}, author = {Liyanage, KA and Yoo, PE and Grayden, DB and Opie, NL and Oxley, TJ}, title = {Artifact Removal in Electrocorticography Devices With Cardiac Contamination.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {3400-3408}, doi = {10.1109/TNSRE.2025.3601445}, pmid = {40839508}, issn = {1558-0210}, mesh = {*Artifacts ; Humans ; *Electrocorticography/methods/instrumentation ; Electrocardiography/methods ; Algorithms ; Reproducibility of Results ; *Electroencephalography/methods ; Sensitivity and Specificity ; }, abstract = {Electrocorticography (ECoG) devices with electronics housed near the chest are susceptible to artifacts of a differing nature to electroencephalography (EEG) and standard ECoG. Using data obtained via an endovascular neural interface, we compared different artifact removal techniques in an offline setting with the aim of improving the quality and usefulness of clinically acquired data. Three different methods of filtration were applied and assessed: Common Average Referencing (CAR), Independent Component Analysis (ICA) with automated ECG channel selection, and Template-Based Removal (TBR). The automated ECG channel selection method was compared to manual selection. Methods were compared using signal-to-artifact root-mean-squared (RMS) values. The automated ECG source channel selection had high concordance with manual selection. All filtration methods decreased post-artifact RMS amplitudes and improved signal-to-artifact ratios. ICA took the most time to compute but had the most improved signal-to-artifact ratio. In regions with no ECG artifact, TBR preserved the underlying electrocorticography data better than the other methods. ICA with an automated method of ECG channel selection is the preferred method out of the three tested to remove ECG artifact while preserving the underlying signal. We establish methods that can be used to improve neural data of electrocorticography devices susceptible to cardiac contamination to facilitate translation as brain-computer interfaces.}, }
@article {pmid40837818, year = {2025}, author = {Si, JY and Lin, ZY and Gan, DG and Zhang, XY and Liu, YN and Hu, YX and Bao, YP and Wang, XQ and Sun, HQ and Yu, X and Lu, L}, title = {Informed consent competency assessment for brain-computer interface clinical research and application in psychiatric disorders: A systematic review.}, journal = {World journal of psychiatry}, volume = {15}, number = {8}, pages = {107593}, pmid = {40837818}, issn = {2220-3206}, abstract = {BACKGROUND: Brain-computer interface (BCI) technology is rapidly advancing in psychiatry. Informed consent competency (ICC) assessment among psychiatric patients is a pivotal concern in clinical research.<br><br>AIM: To analyze the assessment of ICC and form a framework with multi-dimensional elements involved in ICC of BCI clinical research among psychiatric disorders.<br><br>METHODS: A systematic review of studies regarding ICC assessments of BCI clinical research in patients with six kinds of psychiatric disorders was conducted. A systematic literature search was performed using PubMed, ScienceDirect, and Web of Science. Peer-reviewed articles and full-text studies were included in the analysis. There were no date restrictions, and all studies published up to February 27, 2025, were included.<br><br>RESULTS: A total of 103 studies were selected for this review. Fifty-eight studies included ICC factors, and forty-five were classified in ICC related ethical issues of BCI research in six kinds of psychiatric disorders. Executive function impairment is widely recognized as the most significant factor impacting ICC, and processing speed deficits are observed in schizophrenia, mood disorders, and Alzheimer's disease. Memory dysfunction, particularly episodic and working memory, contributes to compromised ICC. Five core ethical issues in BCI research should be addressed: BCI specificity, vulnerability, autonomy, dynamic ICC, comprehensiveness, and uncertainty.<br><br>CONCLUSION: A Five-Dimensional evaluative framework, including clinical, ethical, sociocultural, legal, and procedural dimensions, is constructed and proposed for future ICC research in BCI clinical research involving psychiatric disorders.}, }
@article {pmid40837785, year = {2025}, author = {Wang, P and Dai, AL and Guo, XR and Jiang, HT}, title = {Portable electroencephalography in early detection of depression: Progress and future directions.}, journal = {World journal of psychiatry}, volume = {15}, number = {8}, pages = {107725}, pmid = {40837785}, issn = {2220-3206}, abstract = {Traditional diagnostic tools for depression, such as the Patient Health Questionnaire-9, are susceptible to subjective bias, increasing the risk of misdiagnosis and emphasizing the critical need for objective biomarkers. This minireview evaluates the emerging role of portable electroencephalography (EEG) as a cost-effective, accessible solution for early depression detection. By synthesizing findings from 45 studies (selected from 764 screened articles), we highlight EEG's capacity to identify aberrant neural oscillations associated with core depressive symptoms, including anhedonia, excessive guilt, and persistent low mood. Advances in portable systems demonstrate promising classification accuracy when integrated with machine learning algorithms, with long short-term memory models achieving > 90% accuracy in recent trials. However, persistent challenges, such as signal quality variability, motion artifacts, and limited clinical validation, hinder widespread adoption. Further innovation in sensor optimization, multimodal data integration, and real-world clinical trials is essential to translate portable EEG into a reliable diagnostic tool. This minireview underscores the transformative potential of neurotechnology in psychiatry while advocating for rigorous standardization to bridge the gap between research and clinical practice.}, }
@article {pmid40836680, year = {2025}, author = {Riemann, D and Nissen, C and Geoffroy, PA and Feige, B and Ellis, J}, title = {Sleep and Dreams as Reflected by Science Fiction Literature and Films-Anything to Learn From?.}, journal = {Journal of sleep research}, volume = {34}, number = {5}, pages = {e70183}, pmid = {40836680}, issn = {1365-2869}, mesh = {Humans ; *Dreams/physiology/psychology ; *Sleep/physiology ; *Motion Pictures ; *Literature ; }, abstract = {Sleep and dreams are frequent themes in science fiction (Sci-Fi) literature and films, often used to explore questions about consciousness, reality, technology and the human experience. Sci-Fi authors and filmmakers utilise the enigmatic nature of sleep and dreams to blur the boundaries between reality and imagination, raising philosophical questions or extrapolating the effects of futuristic technologies on human life. In this article, we want to highlight some areas that have been recurring themes relating to sleep and dreams in Sci-Fi. These will include the concepts of so-called hypno-paedagogics, space hibernation, brain machine interfaces, electrostimulation, genetic engineering and the impact of substances (viruses, bacteria, drugs, toxins) on sleep and dreams. We will then confront Sci-Fi concepts with what is known from contemporary sleep science and judge what might be feasible, or not, in the future. A question we also want to address is how the relationship between sleep science and sleep Sci-Fi can be conceptualised: whether novel concepts have been instigated by Sci-Fi and taken up by sleep science or whether Sci-Fi merely reflects state of the art topics of sleep science, with just adding a touch of fiction.}, }
@article {pmid40836202, year = {2025}, author = {Agarwal, P and Kumar, S and Singh, R}, title = {Motor imagery-based neural networks for assisting tetraplegic patients.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {40836202}, issn = {1741-0444}, abstract = {Nowadays, deep network-based classification algorithms are used in a myriad of applications for brain-computer interfaces (BCIs). These interfaces can enhance the daily lives of quadriplegic patients. Electroencephalography (EEG) based motor imagery (MI) is an integral part of BCI, and the performance of the available deep classifiers is still limited. This paper presents a novel convolutional neural network (CNN) architecture designed to enhance the multiclass classification accuracy of motor imagery (MI) signals acquired through EEG-based sensing. We have selected the electrodes over the sensorimotor cortex region of the brain in the 8-30 Hz EEG frequency band. Further, we have computed the classification accuracy and kappa scores in an end-to-end deep classification network. Our framework surpasses the contemporary literature algorithms in classifying BCI competition IV-2a, a four-class MI dataset of nine subjects (left hand, right hand, both feet, tongue). The proposed network architecture has achieved an average and maximum accuracy of 95.19% and 99.28%, respectively. We have outperformed state-of-the-art accuracies of the individual subjects S1, S2, S3, S4, S5, S6, S8, and the average accuracy of the dataset by 8.28%, 40.97%, 5.54%, 14.83%, 19.09%, 25.5%, 10.43%, and 12.82% respectively.}, }
@article {pmid40835615, year = {2025}, author = {Zuo, C and Yin, Y and Wang, H and Zheng, Z and Ma, X and Yang, Y and Wang, J and Wang, S and Huang, ZG and Ye, C}, title = {Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1451}, pmid = {40835615}, issn = {2052-4463}, support = {NO.2024M764330//China Postdoctoral Science Foundation/ ; }, mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; Deep Learning ; *Electroencephalography ; Imagery, Psychotherapy ; *Lower Extremity/physiopathology ; *Osteoarthritis, Knee/physiopathology/rehabilitation ; }, abstract = {Chronic knee osteoarthritis pain significantly impacts patients' quality of life and motor function. While motor imagery (MI)-based brain-computer interface (BCI) systems have shown promise in rehabilitation, their application to lower-limb conditions, particularly in pain patients, is underexplored. This study evaluates the feasibility of applying an MI-BCI model to a large dataset of knee pain patients, utilizing a novel deep learning algorithm for signal decoding. This EEG data was collected and analysed from 30 knee pain patients, revealing significant event-related (de)synchronization (ERD/ERS) during MI tasks. Traditional decoding algorithms achieved accuracies of 51.43%, 55.71%, and 76.21%, while the proposed OTFWRGD algorithm reached an average accuracy of 86.41%. This dataset highlights the potential of lower-limb MI in enhancing neural plasticity and offers valuable insights for future MI-BCI applications in lower-limb rehabilitation, especially for patients with knee pain.}, }
@article {pmid40835596, year = {2025}, author = {Molokanova, E and Zhou, T and Vasupal, P and Cherkas, VP and Narute, P and Ferraz, MSA and Reiss, M and Almenar-Queralt, A and Chaldaiopoulou, G and de Souza, JS and Hemati, H and Downey, F and Olajide, OO and Thörn Perez, C and Puppo, F and Mesci, P and Pfaff, SL and Kireev, D and Muotri, AR and Savchenko, A}, title = {Non-genetic neuromodulation with graphene optoelectronic actuators for disease models, stem cell maturation, and biohybrid robotics.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {7499}, pmid = {40835596}, issn = {2041-1723}, support = {R01 MH127077/MH/NIMH NIH HHS/United States ; R43 MH124563/MH/NIMH NIH HHS/United States ; R01 MH123828/MH/NIMH NIH HHS/United States ; 1R01ES033636//U.S. Department of Health &amp; Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS)/ ; 1R43AG076088//U.S. Department of Health &amp; Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ ; 1R01MH128365//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; 1R43NS122666//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; MH123828//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; R01 NS123642/NS/NINDS NIH HHS/United States ; S10 OD026929/OD/NIH HHS/United States ; R01 ES033636/ES/NIEHS NIH HHS/United States ; R56 MH128365/MH/NIMH NIH HHS/United States ; R01NS123642//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R43 AG076088/AG/NIA NIH HHS/United States ; R43 NS122666/NS/NINDS NIH HHS/United States ; R01 NS105969/NS/NINDS NIH HHS/United States ; DISC2-13866//California Institute for Regenerative Medicine (CIRM)/ ; R44 DA050393/DA/NIDA NIH HHS/United States ; 5R44DA050393//U.S. Department of Health &amp; Human Services | NIH | National Institute on Drug Abuse (NIDA)/ ; 1R43MH124563//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; }, mesh = {*Graphite/chemistry ; *Robotics/methods/instrumentation ; Induced Pluripotent Stem Cells/cytology ; Humans ; Organoids/cytology ; Neurons/cytology ; Brain/cytology ; Alzheimer Disease/pathology/therapy ; Optogenetics/methods ; Cell Differentiation ; Animals ; }, abstract = {Light can serve as a tunable trigger for neurobioengineering technologies, enabling probing, control, and enhancement of brain function with unmatched spatiotemporal precision. Yet, these technologies often require genetic or structural alterations of neurons, disrupting their natural activity. Here, we introduce the Graphene-Mediated Optical Stimulation (GraMOS) platform, which leverages graphene's optoelectronic properties and its ability to efficiently convert light into electricity. Using GraMOS in longitudinal studies, we found that repeated optical stimulation enhances the maturation of hiPSC-derived neurons and brain organoids, underscoring GraMOS's potential for regenerative medicine and neurodevelopmental studies. To explore its potential for disease modeling, we applied short-term GraMOS to Alzheimer's stem cell models, uncovering disease-associated alterations in neuronal activity. Finally, we demonstrated a proof-of-concept for neuroengineering applications by directing robotic movements with GraMOS-triggered signals from graphene-interfaced brain organoids. By enabling precise, non-invasive neural control across timescales from milliseconds to months, GraMOS opens new avenues in neurodevelopment, disease treatment, and robotics.}, }
@article {pmid40835360, year = {2025}, author = {Xiao, X and Li, H}, title = {Improving brain-computer interface performance with optimized frequency interaction and enhancement techniques: CFC-PSO-XGBoost (CPX).}, journal = {Medical engineering &amp; physics}, volume = {143}, number = {}, pages = {104392}, doi = {10.1016/j.medengphy.2025.104392}, pmid = {40835360}, issn = {1873-4030}, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; Male ; Female ; Adult ; *Signal Processing, Computer-Assisted ; Young Adult ; Imagination ; Boosting Machine Learning Algorithms ; }, abstract = {PURPOSE: This work aims to increase the classification accuracy of motor imagery-based brain-computer interface (MI-BCI) by employing Cross-Frequency Coupling (CFC) and using spontaneous EEG as an input for the features to increase the system's robustness.<br><br>METHODS: Using a benchmark MI-BCI dataset, we examined 25 participants who completed two trials of a motor imagery task split into two classes. Our methodology involved preprocessing EEG data, using Phase-Amplitude Coupling (PAC) to extract CFC characteristics and Particle Swarm Optimization (PSO) to identify the optimal channels. The XGBoost method was utilized to classify the data, and 10-fold cross-validation was employed to verify the results. They are integrated into a single pipeline, named CFC-PSO-XGBoost (CPX).<br><br>RESULTS: With an average classification accuracy of 76.7 % &#xb1; 1.0 %, with only eight EEG channels, the suggested approach (CPX) outperformed cutting-edge techniques like CSP (60.2 % &#xb1; 12.4 %), FBCSP (63.5 % &#xb1; 13.5 %), FBCNet (68.8 % &#xb1; 14.6 %), and EEGNet. This significant improvement demonstrates the effectiveness of CFC features and PSO for channel selection in MI-BCI classification. Furthermore, the method was evaluated on the public BCI Competition IV-2a dataset, achieving an average multi-class classification accuracy of 78.3 % (95 % CI: 74.85-81.76 %), confirming the scalability and robustness of CPX on external benchmarks.<br><br>CONCLUSION: CPX leveraging spontaneous EEG signals and CFC features significantly improves classification accuracy. We anticipate this methodology will be a robust and practical solution in BCI applications, providing better brain-to-device communication with low-channel utilization and considerable performance metrics.}, }
@article {pmid40834866, year = {2025}, author = {Gupta, D and Brangaccio, J and Mojtabavi, H and Wolpaw, J and Hill, NJ}, title = {Extracting robust single-trial somatosensory evoked potentials for non-invasive brain computer interfaces.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, pmid = {40834866}, issn = {1741-2552}, support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Somatosensory/physiology ; Male ; Adult ; Female ; *Electroencephalography/methods ; Young Adult ; Electric Stimulation/methods ; Middle Aged ; Signal-To-Noise Ratio ; Tibial Nerve/physiology ; }, abstract = {Objective.Reliable extraction of single-trial somatosensory evoked potentials (SEPs) is essential for developing brain-computer interface (BCI) applications to support rehabilitation after brain injury. For real-time feedback, these responses must be extracted prospectively on every trial, with minimal post-processing and artifact correction. However, noninvasive SEPs elicited by electrical stimulation at recommended parameter settings (0.1-0.2 msec pulse width, stimulation at or below motor threshold, 2-5 Hz frequency) are typically small and variable, often requiring averaging across multiple trials or extensive processing. Here, we describe and evaluate ways to optimize the stimulation setup to enhance the signal-to-noise ratio (SNR) of noninvasive single-trial SEPs, enabling more reliable extraction.Approach.SEPs were recorded with scalp electroencephalography in tibial nerve stimulation in thirteen healthy people, and two people with CNS injuries. Three stimulation frequencies (lower than recommended: 0.2 Hz, 1 Hz, 2 Hz) with a pulse width longer than recommended (1 msec), at a stimulation intensity based on H-reflex and M-wave at Soleus muscle were evaluated. Detectability of single-trial SEPs relative to background noise was tested offline and in a pseudo-online analysis, followed by a real-time demonstration.Mainresults.SEP N70 was observed predominantly at the central scalp regions. Online decoding performance was significantly higher with Laplacian filter. Generalization performance showed an expected degradation, at all frequencies, with an average decrease of 5.9% (multivariate) and 6.5% (univariate), with an AUC score ranging from 0.78-0.90. The difference across stimulation frequencies was not significant. In individuals with injuries, AUC of 0.86 (incomplete spinal cord injury) and 0.81 (stroke) was feasible. Real-time demonstration showed SEP detection with AUC of 0.89.Significance.This study describes and evaluates a system for extracting single-trial SEPs in real-time, suitable for a BCI-based operant conditioning. It enhances SNR of individual SEPs by alternate electrical stimulation parameters, dry headset, and optimized signal processing.}, }
@article {pmid40834823, year = {2025}, author = {Zhang, Z and Meng, W and Sun, H and Pan, G}, title = {CausalCOMRL: Context-based offline meta-reinforcement learning with causal representation.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {193}, number = {}, pages = {107955}, doi = {10.1016/j.neunet.2025.107955}, pmid = {40834823}, issn = {1879-2782}, abstract = {Context-based offline meta-reinforcement learning (OMRL) methods have achieved appealing success by leveragingpre-collected offline datasets to develop task representations that guide policy learning. However, current context-based OMRL methods often introduce spurious correlations, where task components are incorrectly correlated due to confounders. These correlations can degrade policy performance when the confounders in the test taskdiffer from those in the training task. To address this problem, we propose CausalCOMRL, a context-based OMRL method that integrates causal representation learning. This approach uncovers causal relationships among the task components and incorporates the causal relationships into task representations, enhancing the generalizability of RL agents. We further improve the distinction of task representations from different tasks by using mutual information optimization and contrastive learning. Utilizing these causal task representations, we employSAC to optimize policies on meta-RL benchmarks. Experimental results show that CausalCOMRL achieves better performance than other methods on most benchmarks.}, }
@article {pmid40833932, year = {2025}, author = {Foster, MW and Sanhueza, C and Bahr, E and Kuo, JL and Wu, Y and Komolafe, DO and Blanchette, V and Brinza, T and Morgan-Daniel, J and Oshodi, Y and Sodimu, KA and Omuku, N and Akisanya, E and Trinder, L and Willmoth, S and Simpson, N and White, N and Shaw, TA and Moyse Fenning, H and Runefelt, A and Kolnik, M and Pokorn, M and Fietje, N and Sajnani, N}, title = {The effects of viewing visual artwork on patients, staff, and visitors in healthcare settings: A scoping review.}, journal = {PloS one}, volume = {20}, number = {8}, pages = {e0328215}, pmid = {40833932}, issn = {1932-6203}, mesh = {Humans ; *Health Personnel/psychology ; *Art ; *Patients/psychology ; Delivery of Health Care ; }, abstract = {BACKGROUND: The integration of visual art in healthcare settings has been demonstrated to contribute to well-being. However, the impact of visual arts in healthcare has been primarily evaluated among patients. Viewing visual art could be a health resource to a greater number of people in healthcare settings, including patients, staff, and visitors.<br><br>METHODS: We conducted a scoping review to synthesize literature on the impact of viewing visual artwork among patients, staff, and visitors in healthcare settings related to the reported outcomes of well-being, wellness, and belonging. The review was informed by Arksey and O'Malley and Joanna Briggs Institute frameworks with masked pairs of reviewers. Included studies were in English, with no restrictions on geographical settings or publication dates. Nine academic databases and twelve gray literature sources were searched, in addition to a hand search and global call for submissions.<br><br>RESULTS: From an initial 25,222 records, 68 publications met inclusion criteria across 20 locations. 35 were peer-reviewed studies and 33 constituted gray literature. Included publications that reported sample sizes reflected a total of 6,006 participants with the majority being patients (3,133) followed by staff (1,343), visitors (32), and other/unspecified participants (996). Reported outcomes for patients indicated that visual arts in hospitals reduced heart rates, improved reported mental health outcomes, increased well-being, and provided a positive distraction. Reported outcomes for healthcare staff included an increased well-being, belonging, and capacity to prioritize patient needs. Reported outcomes for visitors consisted of an improved experience in healthcare environments and increased well-being.<br><br>CONCLUSIONS: Our synthesis of evidence indicates that integration of visual arts within healthcare settings has positive outcomes for its viewers. Our findings are useful to promote the generation of evidence that can reliably inform the design and experience of healthcare environments.}, }
@article {pmid40833894, year = {2025}, author = {Liu, T and Wang, Z and Shakil, S and Tong, RK}, title = {Uncovering Low-Dimensional Manifolds of Neural Dynamics for Motor-Imagery Based Stroke Rehabilitation: An EEG-Based Brain-Computer Interface Study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {3281-3292}, doi = {10.1109/TNSRE.2025.3600824}, pmid = {40833894}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Stroke Rehabilitation/methods ; Male ; Female ; *Imagination/physiology ; Middle Aged ; Algorithms ; Adult ; Aged ; Stroke/physiopathology ; Brain/physiopathology ; }, abstract = {Stroke rehabilitation aims to repair neural circuits and dynamics through the remapping of neuronal functions. However, there is currently a gap in understanding the alteration of neural population dynamics-the fundamental computational unit driving functions-under clinical settings. In this study, we introduced a novel method to identify stable low-dimensional structures of neural population dynamics in stroke patients during motor tasks. Using whole-brain EEG recordings from chronic stroke patients performing motor imagery (MI) tasks before and after brain-computer interface (BCI) training, as well as a public EEG dataset of acute stroke patients performing MI tasks, we projected EEG signals from sensor space to voxel space via source localization (eLORETA), simulating neural population activity in regions of interest. By applying dimensionality reduction, we successfully obtained low-dimensional neural manifolds to represent neural population dynamics. Our analysis revealed three key findings: (1) For right-handed patients, task-related low-dimensional dynamics in the related brain regions remain stable across subjects, with their features holding potential as biomarkers for stroke rehabilitation; (2) BCI training promotes global and sustained restoration of neural population dynamics; (3) EEG theta-band oscillations show strong correlation with these dynamics, highlighting their macroscopic nature. This study proposes a new, simple, and powerful tool for comprehension and validation of stroke rehabilitation mechanisms confirming the effectiveness of BCI training in restoring neural dynamics.}, }
@article {pmid40832972, year = {2025}, author = {Wu, X and Tan, S and Zhang, Y and Yin, Y and Hsu, YC and Xue, R and Bai, R}, title = {Feasibility of relaxation-exchange magnetic resonance imaging (REXI) for measuring water exchange across the blood-CSF barrier in the human choroid plexus.}, journal = {Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism}, volume = {}, number = {}, pages = {271678X251369218}, doi = {10.1177/0271678X251369218}, pmid = {40832972}, issn = {1559-7016}, abstract = {The choroid plexus (CP) is important for cerebrospinal fluid (CSF) secretion and forms the blood-CSF barrier (BCSFB), which is essential for brain homeostasis. However, noninvasive methods for evaluating BCSFB function remain limited. Previously, we introduced a novel magnetic resonance imaging (MRI) technique, relaxation-exchange MRI (REXI), to quantify water exchange between CP and CSF in rats by leveraging the substantial difference in transverse relaxation times between CP tissue and CSF. Here, we adapted REXI to human applications by implementing segmented echo-planar imaging readout for enhanced acquisition speed, optimizing key parameters based on the Cramér-Rao lower bound, and refining the analysis methodology. We conducted simulations and phantom experiments for methodological validation. Subsequently, we performed a scan-rescan experiment in healthy volunteers (n = 6, mean-age ∼22 years), revealing relatively good repeatability in measurements of the apparent water exchange rate kBCSFB (intraclass correlation coefficient = 0.84). REXI detected a 34% decrease in kBCSFB among middle-aged healthy adults (n = 6, mean-age ∼55 years) compared with young healthy adults (n = 9, mean-age ∼23 years, p = 0.0048). These results demonstrate the feasibility of REXI in quantifying water exchange in human CP in vivo, providing a promising tool for future investigations of BCSFB function.}, }
@article {pmid40832808, year = {2025}, author = {Ma, C and Li, W and Gao, C and Li, X and She, J and Zou, Z and Zhang, D and Jin, Y and Xu, C and Liu, B and Luo, Z}, title = {Multifunctional Hydrogel Materials for Advanced Neural Interfaces.}, journal = {Small methods}, volume = {9}, number = {9}, pages = {e01134}, doi = {10.1002/smtd.202501134}, pmid = {40832808}, issn = {2366-9608}, support = {32471387//National Natural Science Foundation of China/ ; 2024BCB002//Key Research and Development Program of Hubei Province/ ; }, mesh = {*Hydrogels/chemistry ; Humans ; *Brain-Computer Interfaces ; Biocompatible Materials/chemistry ; Animals ; Nanotechnology ; }, abstract = {Conventional rigid neural electrodes mismatch the soft, wet nature of neural tissue, hindering long-term stable interfaces. Multifunctional hydrogels, with their tissue-like compliance, ionic conductivity, and biocompatibility, offer a promising solution to bridge bioelectronic systems and neural tissues. This review systematically examines critical hydrogel properties-mechanical compliance, adhesion, biocompatibility, conductivity, and injectability-for neural interfacing. It summarizes recent advances in hydrogel-based technologies, including hydrogel coatings, conductive hydrogel electrodes, and integrated hydrogel electronics. Future challenges involve balancing biodegradation with long-term stability, developing advanced fabrication strategies, and ensuring chronic performance stability. Key future directions include optimizing hydrogel properties for chronic applications, creating smart-responsive hydrogels, integrating artificial intelligence, and advancing wireless systems. Leveraging materials science, bioengineering, and nanotechnology, hydrogel-based neural interfaces are poised to unlock unprecedented capabilities in brain-computer interfaces, neural prosthetics, neuromodulation, and regenerative therapies, heralding a paradigm shift in neurotechnology.}, }
@article {pmid40832231, year = {2025}, author = {Jude, JJ and Haro, S and Levi-Aharoni, H and Hashimoto, H and Acosta, AJ and Card, NS and Wairagkar, M and Brandman, DM and Stavisky, SD and Williams, ZM and Cash, SS and Simeral, JD and Hochberg, LR and Rubin, DB}, title = {Decoding intended speech with an intracortical brain-computer interface in a person with longstanding anarthria and locked-in syndrome.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.08.12.668516}, pmid = {40832231}, issn = {2692-8205}, abstract = {Intracortical brain-computer interfaces (iBCIs) for decoding intended speech have provided individuals with ALS and severe dysarthria an intuitive method for high-throughput communication. These advances have been demonstrated in individuals who are still able to vocalize and move speech articulators. Here, we decoded intended speech from an individual with longstanding anarthria, locked-in syndrome, and ventilator dependence due to advanced symptoms of ALS. We found that phonemes, words, and higher-order language units could be decoded well above chance. While sentence decoding accuracy was below that of demonstrations in participants with dysarthria, we are able to attain an extensive characterization of the neural signals underlying speech in a person with locked-in syndrome and through our results identify several directions for future improvement. These include closed-loop speech imagery training and decoding linguistic (rather than phonemic) units from neural signals in middle precentral gyrus. Overall, these results demonstrate that speech decoding from motor cortex may be feasible in people with anarthria and ventilator dependence. For individuals with longstanding anarthria, a purely phoneme-based decoding approach may lack the accuracy necessary to support independent use as a primary means of communication; however, additional linguistic information embedded within neural signals may provide a route to augment the performance of speech decoders.}, }
@article {pmid40831738, year = {2025}, author = {}, title = {Corrigendum to "A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations".}, journal = {Computational intelligence and neuroscience}, volume = {2025}, number = {}, pages = {9842516}, doi = {10.1155/cone/9842516}, pmid = {40831738}, issn = {1687-5273}, abstract = {[This corrects the article DOI: 10.1155/2021/6685672.].}, }
@article {pmid40831229, year = {2025}, author = {Chen, D and Shi, J and Tao, B and Zhao, X and Zhao, Z and Li, S and Xu, Y and Ding, T and Zhang, P and Ye, Q and Chen, K and Wu, Z and Tang, Y and Jiang, W and Shu, K and Huang, L and You, Z and Zhang, P and Tang, Z}, title = {A Novel Transfer Learning-Based Hybrid EEG-fNIRS Brain-Computer Interface for Intracerebral Hemorrhage Rehabilitation.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e05426}, doi = {10.1002/advs.202505426}, pmid = {40831229}, issn = {2198-3844}, support = {2023BAA005//Major Program (JD) of Hubei Province/ ; YCJJ20251401//Fundamental Research Funds for the Central Universities/ ; 92148206//National Natural Science Foundation of China/ ; 2024020702030123//Key Research and Development Program of Wuhan/ ; 2024JCYJ044//Huazhong University of Science and Technology/ ; 2022ZHFY01//Research Fund of Tongji Hospital/ ; AI2024B03//Research Fund of Tongji Hospital/ ; }, abstract = {Motor imagery (MI)-based neurorehabilitation shows promise for intracerebral hemorrhage (ICH) recovery, yet conventional unimodal brain-computer interfaces (BCIs) face critical limitations in cross-subject generalization. This study presents a multimodal electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) fusion framework incorporating a Wasserstein metric-driven source domain selection method that quantifies inter-subject neural distribution divergence. Through comparative neuroactivation analysis of 17 normal controls and 13 ICH patients during MI tasks, the transfer learning model achieved 74.87% mean classification accuracy on patient data when trained with optimally selected normal templates. Cross-validation on two public hybrid EEG-fNIRS datasets demonstrated generalizability, increasing baseline accuracy to 82.30% and 87.24%, respectively. The proposed system synergistically combines the millisecond temporal resolution of EEG with the hemodynamic spatial specificity of fNIRS, establishing the first clinically viable multimodal analytical protocol for ICH rehabilitation. This paradigm advances neurotechnology translation by paving the way for personalized rehabilitation regimens through robust cross-subject neural pattern transfer while addressing the critical barrier of neurophysiological heterogeneity in post-ICH populations.}, }
@article {pmid40830580, year = {2025}, author = {Li, Y and Li, H and Wang, H and Wang, X}, title = {Exploring the therapeutic potential of psychedelics in treating substance use disorders.}, journal = {Molecular psychiatry}, volume = {30}, number = {12}, pages = {6134-6143}, pmid = {40830580}, issn = {1476-5578}, support = {T2350008//National Natural Science Foundation of China (National Science Foundation of China)/ ; 22207103//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2341003//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Substance-Related Disorders/drug therapy ; *Hallucinogens/therapeutic use/pharmacology ; Psilocybin/therapeutic use/pharmacology ; Animals ; }, abstract = {Psychedelics, particularly psilocybin, have garnered significant attention as potential therapeutic tools for treating substance use disorders (SUDs), such as those related to alcohol, nicotine, heroin (an opioid), or cocaine. Traditional treatments often fall short, leading to high relapse rates and an urgent need for innovative approaches. This article explores the emerging role of psychedelics in SUDs therapy, highlighting their ability to disrupt maladaptive neural circuits, promote neuroplasticity, and facilitate profound psychological insights that address the root causes of SUDs. Clinical trials demonstrate promising results across various forms of SUDs, with psilocybin-assisted therapy showing significant reductions in substance use and improved mental health outcomes. Despite the potential, challenges such as legal barriers, safety concerns, and the need for more rigorous research remain. The future of psychedelics in SUDs treatment is cautiously optimistic, with the possibility of transforming the field of SUDs therapy and offering hope to millions of individuals struggling with SUDs.}, }
@article {pmid40830488, year = {2025}, author = {Schippers, A and Vansteensel, MJ and Freudenburg, ZV and Luo, S and Crone, NE and Ramsey, NF}, title = {Don't put words in my mouth: speech perception can falsely activate a brain-computer interface.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {181}, pmid = {40830488}, issn = {1743-0003}, support = {101070939//HORIZON EUROPE European Innovation Council/ ; UH3 NS114439/NS/NINDS NIH HHS/United States ; UGT7685//Stichting voor de Technische Wetenschappen/ ; ERC-Advanced 'iConnect' project, grant ADV 320708/ERC_/European Research Council/International ; UH3NS114439/NS/NINDS NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; PPS-2021-02//Dutch Brain Foundation/ ; U01 DC016686/DC/NIDCD NIH HHS/United States ; SGW-406-18-GO-086//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Speech Perception/physiology ; Male ; Adult ; Electrocorticography ; Female ; Speech/physiology ; *Sensorimotor Cortex/physiology ; Support Vector Machine ; }, abstract = {BACKGROUND: Recent studies have demonstrated that speech can be decoded from brain activity which in turn can be used for brain-computer interface (BCI)-based communication. It is however also known that the area often used as a signal source for speech decoding BCIs, the sensorimotor cortex (SMC), is also engaged when people perceive speech, thus making speech perception a potential source of false positive activation of the BCI. The current study investigated if and how speech perception may interfere with reliable speech BCI control.<br><br>METHODS: We recorded high-density electrocorticography (HD-ECoG) data from five subjects while they performed a speech perception and a speech production task. We first evaluated whether speech perception and production activated the SMC. Second, we trained a support-vector machine (SVM) on the speech production data (including rest). To test the occurrence of false positives, this decoder was then tested on speech perception data where every perception segment that was classified as a produced syllable rather than rest was considered a false positive. Finally, we investigated whether perceived speech could be distinguished from produced speech and rest.<br><br>RESULTS: Our results show that both the perception and production of speech activate the SMC. In addition, we found that decoders that are highly reliable at detecting self-produced syllables from brain signals may generate false positive BCI activations during the perception of speech and that it is possible to distinguish perceived speech from produced speech and rest, with high accuracy.<br><br>CONCLUSIONS: We conclude that speech perception can interfere with reliable BCI control, and that efforts to limit the occurrence of false positives during daily-life BCI use should be implemented in BCI design to increase the likelihood of successful adoptation by end users.}, }
@article {pmid40830054, year = {2025}, author = {Brannigan, J and Kian, A and Eiber, C and Tarigoppula, VSA and Bogard, J and Siddiqui, AH and Rind, G and Berenstein, A and Majidi, S and Oxley, TJ}, title = {Characterizing superficial cerebral cortical venous anatomy for endovascular device implantation: a cross-sectional imaging study.}, journal = {Journal of neurointerventional surgery}, volume = {}, number = {}, pages = {}, doi = {10.1136/jnis-2025-023532}, pmid = {40830054}, issn = {1759-8486}, abstract = {BACKGROUND: Neurovascular electronic devices, including brain-computer interfaces (BCIs), offer a minimally invasive approach to diagnosing and treating neurological disorders. Implanting BCIs in superficial cortical veins, owing to their proximity to sensorimotor cortices, may improve motor function restoration. However, marked anatomical variability and the complex anteriorly directed connection with the superior sagittal sinus (SSS) complicate device navigation. This exploratory study aimed to characterize cortical venous anatomy to inform device design and procedural planning.<br><br>METHODS: Retrospective imaging data from 25 patients were analyzed using magnetic resonance venography (MRV) and computed tomography venography (CTV). Vessel segmentation and analysis quantified parameters such as vein presence, diameter, length, angulation, and tortuosity. In 12 patients, T1-weighted magnetic resonance imaging (MRI) was used to extract cortical gyri and sulci, assessing vessel-cortex relationships.<br><br>RESULTS: The superior anastomotic vein (vein of Trolard) was identified bilaterally in 84% of patients, with a mean entrance diameter of 4.4&#x2009;mm. Frequent transient constrictions (<2&#x2009;mm) were reported. The precentral vein was present bilaterally in 52% of cases. Most cortical veins exhibited take-off angles >90 degrees from the SSS, presenting challenges for endovascular navigation, with overall considerable anatomical variability observed.<br><br>CONCLUSION: The vein of Trolard shows promise as a target for endovascular BCIs given its consistent presence and favorable dimensions. Nonetheless, constrictions and steep angulation at the SSS confluence pose challenges for device deployment. A new framework is necessary for the classification of cortical venous anatomy, to guide patient selection and procedural planning, which will require further development and validation.}, }
@article {pmid40829351, year = {2025}, author = {Yadav, H and Maini, S}, title = {Decoding brain signals: A comprehensive review of EEG-Based BCI paradigms, signal processing and applications.}, journal = {Computers in biology and medicine}, volume = {196}, number = {Pt C}, pages = {110937}, doi = {10.1016/j.compbiomed.2025.110937}, pmid = {40829351}, issn = {1879-0534}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; Algorithms ; Evoked Potentials, Visual/physiology ; Event-Related Potentials, P300/physiology ; }, abstract = {Brain-computer interface (BCI) based on electroencephalography (EEG) is a fast-developing field with a wide range of applications such as assistive technology, neurorehabilitation, entertainment, cognitive enhancement, etc. Since EEG is a non-invasive technique that captures brain activity in real time, it is ideally suited for developing interfaces that enable direct brain-to-device communication. The different paradigms utilised in EEG-based BCIs, such as Motor Imagery (MI), Steady-State Visual Evoked Potentials (SSVEP), P300 Event-related Potentials (ERP), and Hybrid paradigms that integrate several strategies for enhanced performance, are the main emphasis of this systematic review. This paper also explores the signal processing techniques, feature extraction strategies, and classification algorithms necessary for handling low-amplitude and noisy EEG recordings. The applications of BCI in different fields, as well as the challenges and possible solutions of EEG-based BCIs, are also covered in this article. Overall, the state-of-the-art in EEG-based BCIs is thoroughly reviewed in this comprehensive review article, which also identifies important areas for further study and technological advancement.}, }
@article {pmid40829175, year = {2025}, author = {Liu, N and Wang, J and Wang, H and Gao, B and Lin, Z and Xu, TL and Duan, S and Xu, H}, title = {A noncanonical parasubthalamic nucleus-to-extended amygdala circuit converts chronic social stress into anxiety.}, journal = {The Journal of clinical investigation}, volume = {135}, number = {16}, pages = {}, pmid = {40829175}, issn = {1558-8238}, mesh = {Animals ; Mice ; *Stress, Psychological/physiopathology/metabolism/pathology ; *Anxiety/physiopathology/metabolism/pathology ; *Amygdala/physiopathology/metabolism/pathology ; Male ; *Septal Nuclei/physiopathology/metabolism ; Chronic Disease ; *Thalamus/physiopathology ; Shal Potassium Channels/metabolism/genetics ; Neurons/metabolism ; }, abstract = {Anxiety disorders pose a substantial threat to global mental health, with chronic stress identified as a major etiologic factor. Over the past few decades, extensive studies have revealed that chronic stress induces anxiety states through a distributed neuronal network of interconnected brain structures. However, the precise circuit mechanisms underlying the transition from chronic stress to anxiety remain incompletely understood. Employing the chronic social defeat stress (CSDS) paradigm in mice, we uncovered a critical role of the parasubthalamic nucleus (PSTh) in both the induction and expression of anxiety-like behavior. The anxiogenic effect was mediated by an excitatory trisynaptic circuitry involving the lateral parabrachial nucleus (LPB), PSTh, and bed nucleus of the stria terminalis (BNST). Furthermore, CSDS downregulated Kv4.3 channels in glutamatergic neurons of the PSTh. Reexpression of these channels dampened neuronal overexcitability and alleviated anxiety-like behavior in stressed animals. In parallel with the well-known anxiety network centered on the amygdala, here we identify a noncanonical LPB-PSTh-BNST pathway in the transformation of stress into anxiety. These findings suggest that the PSTh may serve as a potential therapeutic target for anxiety-related disorders.}, }
@article {pmid40828021, year = {2025}, author = {Martinez-Addiego, F and Liu, Y and Moon, K and Shytle, E and Amaral, L and O'Brien, C and Sen, S and Riesenhuber, M and Culham, JC and Striem-Amit, E}, title = {Action-type mapping principles extend beyond evolutionarily conserved actions, even in people born without hands.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {34}, pages = {e2503188122}, pmid = {40828021}, issn = {1091-6490}, support = {22YF1454200//Shanghai Youth Science and Technology Innovation Plan/ ; }, mesh = {Humans ; Magnetic Resonance Imaging ; Male ; Female ; *Hand/physiology ; Adult ; *Brain Mapping/methods ; *Sensorimotor Cortex/physiology ; Biological Evolution ; *Motor Cortex/physiology ; Young Adult ; Psychomotor Performance/physiology ; }, abstract = {How are actions represented in the motor system? Although the sensorimotor system is broadly organized somatotopically, higher-level sensorimotor areas encode action-type information for reaching and grasping actions-regardless of the acting body part. Does the brain similarly support generalization across acting body parts for more evolutionarily recent actions, such as tool-use? We tested whether there is a body-part-independent action-type organization in sensorimotor areas by examining fMRI responses for tool-use actions that participants performed with their hands or feet. We additionally included individuals born without hands to test whether hand sensorimotor experience is necessary for the development of this action-type organization. Across analyses, we found a consistent dissociation in the motor system. The primary sensorimotor cortices encoded concrete, body-part specific information in both groups. In contrast, higher-level motor areas within the tool-use network represent abstract, action-type information independent of the body part for both groups. Together, our results suggest that the hierarchical organization of the motor system is not dependent on a long evolutionary history of an action. Further, this organization is not dependent on an individual's manual sensorimotor experience. Our results also show that the functional reorganization in congenital handlessness follows the hierarchical organization of the intact cortex, revealing the limitations of brain plasticity. Finally, the results support using a readout of a more abstract code for hierarchical brain-computer interfaces.}, }
@article {pmid40827135, year = {2025}, author = {Qiao, MX and Yu, H and Fu, Z and Wei, W and Li, XJ and Deng, W and Guo, WJ and Li, T}, title = {Combination Therapy Against Mood and Anxiety Disorders: Association Between Efficacy and White Blood Cell Count.}, journal = {Neuropsychiatric disease and treatment}, volume = {21}, number = {}, pages = {1655-1668}, pmid = {40827135}, issn = {1176-6328}, abstract = {BACKGROUND: Numerous studies suggest that hyperactivation of the immuno-inflammatory system, as reflected in cytokine levels, is associated with more severe symptoms in mood and anxiety disorders and weaker response to treatment. Here we examined whether the efficacy of a combination of bright light therapy, repetitive transcranial magnetic stimulation and medication is associated with another immuno-inflammatory index, white blood cell count, before and/or after treatment, in a retrospective observational study.<br><br>METHODS: We retrospectively analyzed 467 inpatients with major depressive, bipolar, or generalized anxiety disorder who were treated with combination therapy for at least one week at Hangzhou Seventh People's Hospital between April 2022 and April 2024. Potential associations between remission incidences within four weeks after treatment and white blood cell count both before treatment and post-treatment were explored. We used mixed-effects linear modeling to examine the association between treatment characteristics and changes in white blood cell count and depressive symptoms.<br><br>RESULTS: Bipolar and major depressive disorders were associated with significantly higher white blood cell counts at baseline than generalized anxiety disorder as well as with significantly lower remission incidences. Bright light therapy's effects depended on baseline inflammation, more sessions led to greater reductions in the Hamilton Depression Rating Scale score with low baseline white blood cell count, and greater decreases in white blood cell count with high baseline count. In contrast, repetitive transcranial magnetic stimulation sessions showed no association with white blood cell count.<br><br>CONCLUSION: These results highlight the need to account for an individual's immuno-inflammatory state when personalizing treatment for mental health disorders.}, }
@article {pmid40825359, year = {2025}, author = {Voskoboynikov, A and Aliverdiev, M and Nekrasova, Y and Semenkov, I and Skalnaya, A and Sinkin, M and Ossadtchi, A}, title = {Towards stimulation-free automatic electrocorticographic speech mapping in neurosurgery patients.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, doi = {10.1088/1741-2552/adfc9c}, pmid = {40825359}, issn = {1741-2552}, mesh = {Humans ; *Electrocorticography/methods ; *Speech/physiology ; Male ; *Brain Mapping/methods ; Female ; Adult ; *Neurosurgical Procedures/methods ; Middle Aged ; Machine Learning ; Electrodes, Implanted ; Young Adult ; }, abstract = {Objective.The precise mapping of speech-related functions is crucial for successful neurosurgical interventions in epilepsy and brain tumor cases. Traditional methods like electrocortical stimulation mapping (ESM) are effective but carry a significant risk of inducing seizures.Methods.To address this, we have prepared a comprehensive ESM + electrocorticographic mapping (ECM) dataset from 14 patients with chronically implanted stereo-EEG electrodes. Then we explored several compact machine learning (ML) approaches to convert the ECM signals to the ground truth derived from the risky ESM procedure. Both procedures involved the standard picture naming task. As features, we used gamma-band power within successive temporal windows in the data averaged with respect to picture and voice onsets. We focused on a range of classifiers, including XGBoost, linear support vector classification (SVC), regularized logistic regression, random forest,k-nearest neighbors, decision tree, multi-Layer perceptron, AdaBoost and Gaussian Naive Bayes classifiers and equipped them with confidence interval estimates, crucial in a real-life application. We validated the ML approaches using a leave-one-patient-out procedure and computed ROC and Precision-Recall curves for various feature combinations.Results.For linear SVC we achieved ROC-AUC and PR-AUC scores of 0.91 and 0.88, respectively, which effectively distinguishes speech-related from non-related iEEG channels. We have also observed that the use of information on the voice onset moment notably improved the classification accuracy.Significance.We have for the first time rigorously compared the ECM and ESM results and mimicked a real-life use of the ECM technology. We have also provided public access to the comprehensive ECM+ESM dataset to pave the road towards safer and more reliable eloquent cortex mapping procedures.}, }
@article {pmid40824102, year = {2025}, author = {Fang, P and Li, GH and Rao, YB and Cheng, C and He, WL and Wang, J and Li, XY and Lu, YR}, title = {Serum Cytokines as Biomarkers for Comorbid Anxiety in Postpartum Depression: A Machine Learning Approach.}, journal = {Psychiatry and clinical psychopharmacology}, volume = {35}, number = {3}, pages = {245-252}, pmid = {40824102}, issn = {2475-0581}, abstract = {Background: This study aimed to investigate the serum levels of interleukin 2, interleukin 6 (IL-6), interleukin 10, and tumor necrosis factor-alpha in patients with postpartum depression (PPD) and to explore their potential as biomarkers for PPD and comorbid anxiety using machine learning techniques. Methods: Serum samples were collected from 53 patients diagnosed with PPD and 35 healthy controls. Cytokine levels were measured using a flow cytometer analyzer. Machine learning models, including Multinomial Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVMs), were developed to predict PPD and comorbid anxiety based on cytokine levels. Results: Patients with PPD exhibited significantly elevated serum levels of IL-6 compared to the control group. A positive correlation was found between psychological anxiety scores and IL-6 levels (r = 0.483, P < .001). Machine learning models, particularly the Random Forest and SVMs, demonstrated high accuracy in predicting PPD and comorbid anxiety, with IL-6 being identified as a key predictor. Conclusion: The activation of serum cytokines is evident in PPD patients, with IL-6 potentially serving as an auxiliary biomarker for the diagnosis of PPD and comorbid anxiety. The incorporation of machine learning techniques has enhanced the understanding of the complex relationships between cytokines and PPD, with IL-6 levels showing a correlation to the severity of clinical symptoms.}, }
@article {pmid40821552, year = {2025}, author = {Priya, S and Mohan, S and Kuppusamy, R and Suyambulingam, I and Baby, B and Ramesh, R and Han, SS}, title = {Advances in Bio-Microelectromechanical System-Based Sensors for Next-Generation Healthcare Applications.}, journal = {ACS omega}, volume = {10}, number = {31}, pages = {34088-34105}, pmid = {40821552}, issn = {2470-1343}, abstract = {Microelectromechanical system (MEMS)-based sensors have become essential in various fields, including healthcare, automotive, and industrial applications. These sensors integrate mechanical structures and electronics on a single chip, allowing precise, compact, and efficient measurements of parameters like pressure, force, acceleration, and chemical reactions. In this context, this review article presents the essential role of MEMS sensors in healthcare applications. In healthcare, MEMS sensors are widely used for monitoring vital signs, detecting glucose levels, managing cardiovascular and intracranial pressure, and enhancing drug delivery systems. They are also key in tactile sensing during surgeries and in improving neuromuscular monitoring through electromyography (EMG). Despite their advantages, such as small size, low energy consumption, and high performance, MEMS sensors face challenges like sensitivity drift, durability concerns, and long-term calibration stability. This article addresses these limitations and highlights ongoing advancements aimed at improving sensor accuracy, energy efficiency, and adaptability to diverse environments. By examining current trends and innovations, this review provides insights into how MEMS technology is driving breakthroughs in biomedical research, early cancer diagnosis, and bioimaging treatment. We have discussed inertial sensors, MEMS-based glucose sensors, intraocular pressure (IOP) sensors, intracranial pressure sensors, cardiovascular pressure sensors, tactile sensors, and smart inhalers. In addition, we have explored recent advancements in MEMS technologies applied to healthcare, particularly in microfluidic MEMS chips and brain-machine interfaces, with a focus on developments from the last five years. Future research directions focus on enhancing the flexibility, reliability, and energy efficiency of MEMS sensors, positioning them as key components in the next generation of healthcare and medical devices.}, }
@article {pmid40819304, year = {2026}, author = {Kontogianni, A and Yang, H and Chen, W}, title = {Brain insulin resistance and neuropsychiatric symptoms in Alzheimer's disease: A role for dopamine signaling.}, journal = {Neural regeneration research}, volume = {21}, number = {5}, pages = {1995-1996}, doi = {10.4103/NRR.NRR-D-25-00281}, pmid = {40819304}, issn = {1673-5374}, }
@article {pmid40819087, year = {2025}, author = {Kinreich, S}, title = {Neural transmission in the wired brain, new insights into an encoding-decoding-based neuronal communication model.}, journal = {Translational psychiatry}, volume = {15}, number = {1}, pages = {288}, pmid = {40819087}, issn = {2158-3188}, support = {R01 AA029448/AA/NIAAA NIH HHS/United States ; AA029448//U.S. Department of Health &amp; Human Services | NIH | National Institute on Alcohol Abuse and Alcoholism (NIAAA)/ ; }, mesh = {Humans ; Male ; Female ; Adult ; Aged ; Middle Aged ; Electroencephalography ; Young Adult ; Child ; Adolescent ; *Brain/physiopathology/physiology ; Aged, 80 and over ; Child, Preschool ; Schizophrenia/physiopathology ; Attention Deficit Disorder with Hyperactivity/physiopathology ; *Synaptic Transmission/physiology ; Parkinson Disease/physiopathology ; *Models, Neurological ; Obsessive-Compulsive Disorder/physiopathology ; }, abstract = {Brain activity is known to be rife with oscillatory activity in different frequencies, which are suggested to be associated with intra-brain communication. However, the specific role of frequencies in neuronal information transfer is still an open question. To this end, we utilized EEG resting state recordings from 5 public datasets. Overall, data from 1668 participants, including people with MDD, ADHD, OCD, Parkinson's, Schizophrenia, and healthy controls aged 5-89, were part of the study. We conducted a running window of Spearman correlation between the two frontal hemispheres' Alpha envelopes. The results of this analysis revealed a unique pattern of correlation states alternating between fully synchronized and desynchronized several times per second, likely due to the interference pattern between two signals of slightly different frequencies, also named "Beating". Subsequent analysis showed this unique pattern in every pair of ipsilateral/contralateral, across frequencies, either in eyes closed or open, and across all ages, underscoring its inherent significance. Biomarker analysis revealed significantly lower synchronization and higher desynchronization for people older than 50 compared to younger ones and lower ADHD desynchronization compared to age-matched controls. Importantly, we propose a new brain communication model in which frequency modulation creates a binary message encoded and decoded by brain regions for information transfer. We suggest that the binary-like pattern allows the neural information to be coded according to certain physiological and biological rules known to both the sender and recipient. This digital-like scheme has the potential to be exploited in brain-computer interaction and applied technologies such as robotics.}, }
@article {pmid40819020, year = {2025}, author = {Shao, X and Chung, RS and Cavaleri, JM and Del Campo-Vera, RM and Parra, M and Sundaram, S and Zhang, S and Surabhi, A and McGinn, RJ and Liu, CY and Kellis, SS and Lee, B}, title = {Directional hand movement can be classified from insular cortex SEEG signals using recurrent neural networks and high-gamma band features.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {29993}, pmid = {40819020}, issn = {2045-2322}, support = {K23 NS114190/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; Male ; Female ; Adult ; *Hand/physiology ; Movement/physiology ; *Gamma Rhythm/physiology ; *Electroencephalography/methods ; *Insular Cortex/physiology/physiopathology ; *Neural Networks, Computer ; Middle Aged ; Machine Learning ; Motor Cortex/physiology ; Young Adult ; Brain-Computer Interfaces ; Recurrent Neural Networks ; }, abstract = {Motor BCIs, with the help of Artificial Intelligence (AI) and machine learning, have shown promise in decoding neural signals for restoring motor function. Structures beyond motor cortex have provided additional sources for movement signals. New evidence points to the role of the insula in motor control, specifically directional hand-movements. In this study, we applied AI and machine learning techniques to decode directional hand-movements from high-gamma band (70-200 Hz) activity in the insular cortex. Seven participants with medication-resistant epilepsy underwent stereo electroencephalographic (SEEG) implantation of depth electrodes for seizure monitoring in the insula. SEEG data were sampled throughout a cued motor task involving three conditions: left-hand movement, right-hand movement, or no movement. Neural signal processing focused on high-gamma band activity. Demixed Principal Component Analysis (dPCA) was used for dimension reduction (d = 10) and feature extraction from the time-frequency analysis. For movement classification, we implemented a bidirectional Long Short-Term Memory (LSTM) architecture with a single layer, utilizing the capacity to process temporal sequences in forward and back directions for optimal decoding of movement direction. Our findings revealed robust directional-specific high-gamma modulation within the insular cortex during motor execution. Temporal decomposition through dPCA demonstrated distinct spatiotemporal patterns of high-gamma activity across movement conditions. Subsequently, LSTM networks successfully decoded these condition-specific neural signatures, achieving a classification accuracy of 72.6% ± 13.0% (mean ± SD), which significantly exceeded chance-level performance of 33.3% (p < 0.0001, n = 16 sessions). Furthermore, we identified a strong negative correlation between temporal distance of training-testing sessions and decoding performance (r = -0.868, p < 0.0001), indicating temporal difference of the neural representations. Our study highlights the potential role of deep brain structures, such as the insula, in conditional movement discrimination. We demonstrate that LSTM networks and high-gamma band analysis can advance the understanding of neural mechanisms underlying movement. These insights may pave the way for improvements in SEEG-based BCI.}, }
@article {pmid40818100, year = {2025}, author = {Wei, W and Li, C and Li, W and Jiang, M and Zhang, X and Xing, L and Qian, Z and Jin, X}, title = {Study of a non-water-cooled microwave ablation needle based on a vacuum needle rod to achieve carbonization-free operation: design, simulation, and experimental research.}, journal = {Minimally invasive therapy &amp; allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy}, volume = {}, number = {}, pages = {1-12}, doi = {10.1080/13645706.2025.2543894}, pmid = {40818100}, issn = {1365-2931}, abstract = {BACKGROUND: At present, the microwave ablation needle used in clinic needs to add water circulation in the needle rod to reduce the rod temperature. However, the water circulation will take away a lot of heat during the ablation process, which requires increasing the ablation dose to achieve the expected thermal coagulation target volume. This undoubtedly increases the risk of carbonization.<br><br>METHODS: A design scheme of non-water-cooled microwave ablation needle based on double-layer vacuum structure was proposed. Two types of non-water-cooled microwave ablation needles with long and short needles were designed, and the ablation simulation was carried out by establishing the finite element simulation model.<br><br>RESULTS: Simulation and experimental results indicate that, at 20&#x2009;W power, the long-needle vacuum tube ablation needle can create a carbonization-free solidification zone with a length of 34&#x2009;mm after 180&#x2009;s of ablation, whereas the short-needle vacuum tube ablation needle requires 300&#x2009;s to form a similar zone with a length of 30&#x2009;mm. Additionally, the axial ratio of the solidification zone created by the long-needle vacuum tube ablation needle exceeds that of the short-needle one. Consequently, the long-needle vacuum tube ablation needle is more apt for creating a larger solidification zone with minimal carbonization, while also achieving a more spherical shape.By comparing the ablation effects of long needle vacuum tube ablation needle and ky-2450b1 under low power,It is verified that the vacuum tube non-water-cooled ablation needle can ensure the effective ablation volume and non carbonization ablation under low-power and short-time ablation, which provides an important technical scheme for clinical optimization of the therapeutic effect of microwave ablation.<br><br>CONCLUSIONS: The LPH-W-F-MWA is more adept at creating a larger coagulation zone with minimal carbonization, achieving a more spherical shape to a greater extent. This ensures both an effective ablation volume and char-free ablation, offering a crucial technical solution for optimizing the therapeutic effect of MWA in clinical settings.}, }
@article {pmid40817330, year = {2025}, author = {Jiang, L and Genon, S and Ye, J and Zhu, Y and Wang, G and He, R and Valdes-Sosa, PA and Wan, F and Yao, D and Eickhoff, SB and Dong, D and Li, F and Xu, P}, title = {Gene transcription, neurotransmitter, and neurocognition signatures of brain structural-functional coupling variability.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {7623}, pmid = {40817330}, issn = {2041-1723}, support = {U54 MH091657/MH/NIMH NIH HHS/United States ; }, mesh = {Humans ; *Brain/physiology/anatomy &amp; histology/metabolism/diagnostic imaging ; Male ; Female ; Adult ; *Neurotransmitter Agents/metabolism ; *Cognition/physiology ; Young Adult ; *Transcription, Genetic ; Brain Mapping ; Transcriptome ; Magnetic Resonance Imaging ; Middle Aged ; Adolescent ; Emotions/physiology ; }, abstract = {The relationship between brain structure and function, known as structural-functional coupling (SFC), is highly dynamic. However, the temporal variability of this relationship, referring to the fluctuating extent to which functional profiles interact with anatomy over time, remains poorly elucidated. Here, we propose a framework to quantify SFC temporal variability and determine its neurocognitive map, genetic architecture, and neurochemical basis in 1206 healthy human participants. Results reveal regional heterogeneity in SFC variability and a composite emotion dimension co-varying with variability patterns involving the dorsal attention, somatomotor, and visual networks. The transcriptomic signatures of SFC variability are enriched in synapse- and cell cycle-related biological processes and implicated in emotion-related disorders. Moreover, regional densities of serotonin, glutamate, γ-aminobutyric acid, and opioid systems are predictive of SFC variability across the cortex. Collectively, SFC variability mapping provides a biologically plausible framework for understanding how SFC fluctuates over time to support macroscale neurocognitive specialization.}, }
@article {pmid40816597, year = {2025}, author = {Xu, T and Yu, L and Zheng, Y and Huang, S}, title = {BrainVision: Cross-domain EEG decoding for visual content retrieval and reconstruction.}, journal = {Neuroscience}, volume = {584}, number = {}, pages = {190-205}, doi = {10.1016/j.neuroscience.2025.07.047}, pmid = {40816597}, issn = {1873-7544}, mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Visual Perception/physiology ; Male ; Female ; Adult ; Emotions/physiology ; }, abstract = {Understanding human visual intent through brain signals remains a fundamental challenge in neuroscience and artificial intelligence. Despite recent advances in brain decoding, existing approaches typically operate within isolated datasets and modalities, limiting their generalization capabilities. This paper introduces BrainVision, a novel framework that bridges visual recognition and emotional EEG datasets to enable comprehensive visual content generation through cross-domain learning. BrainVision addresses the critical challenge of leveraging complementary information across heterogeneous EEG sources by implementing a unified cross-domain alignment strategy. Our framework maps neural patterns from the THINGS-EEG visual recognition dataset and the DEAP emotional response dataset into a shared representation space, enabling three distinct visual output capabilities: (1) accurate content retrieval and classification, (2) detailed linguistic descriptions through adapter-enhanced large language models, and (3) high-fidelity image reconstruction via stable diffusion models. Experimental results demonstrate that BrainVision significantly outperforms single-domain approaches, achieving a 15.3% increase in retrieval accuracy and a 12.7% improvement in structural similarity for reconstructed images compared to state-of-the-art methods. Furthermore, our framework demonstrates robust zero-shot generalization, maintaining 82% of its performance when applied to novel stimuli not seen during training. The multi-modal outputs provide complementary interpretations of neural activity, offering a more comprehensive understanding of visual intent. Our findings establish that integrating diverse neural datasets substantially enhances the capabilities of brain decoding systems, providing a promising direction for developing more intuitive and versatile brain-computer interfaces. BrainVision represents an important step toward bridging the gap between neural activity and rich visual experiences across different cognitive domains.}, }
@article {pmid40816538, year = {2025}, author = {Kong, K and Wang, J and Li, M and Zhang, T and Qi, E and Zhao, Q}, title = {Action sequence guidance with exposure trajectory technology improves performance of motor imagery-based brain-computer interface.}, journal = {Journal of neuroscience methods}, volume = {423}, number = {}, pages = {110553}, doi = {10.1016/j.jneumeth.2025.110553}, pmid = {40816538}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Electroencephalography/methods ; Female ; Adult ; Young Adult ; *Brain/physiology ; *Psychomotor Performance/physiology ; Movement/physiology ; }, abstract = {BACKGROUND: The paradigms greatly influence the performance of motor imagery (MI)-based brain-computer interfaces (BCI) by guiding subjects to imagine. How to make the guidance clear and intuitive is important for MI-BCI to improve performance.<br><br>NEW METHODS: This study proposes a novel MI-BCI paradigm based on action sequence (AS) guidance, which visualizes and choreographs sequential actions to support motor imagery. In a drawing task, the action exposure trajectory technique presents a gray nib at the starting point of the next stroke while the current stroke is being drawn, highlighting the order and details of the movement. Ten subjects participated in offline and online experiments under both AS and traditional MI conditions. EEG activation regarding multiple frequencies and periods, and MI-BCI performance are evaluated.<br><br>RESULTS: The AS paradigm evokes more significant ERD/ERS features, and improves offline and online BCI accuracies and information transfer rates to 85.69&#x202f;%, 78.77&#x202f;%, and 15.60 bits/min, which are 8.37&#x202f;%, 7.95&#x202f;%, and 7.13 bits/min higher than the traditional paradigm. In addition, the subjects are demonstrated more comfortable subjective feelings.<br><br>The AS paradigm offers clearer and more intuitive guidance, enhances EEG feature activation, and significantly improves MI-BCI performance in both offline and online experiments.<br><br>CONCLUSIONS: Dynamic action sequences action with exposure trajectory technique could enhance the subject's brian activation by offering richer content and more intuitive guidance, providing a new way for prompting BCI performance.}, }
@article {pmid40816265, year = {2025}, author = {Kunz, EM and Abramovich Krasa, B and Kamdar, F and Avansino, DT and Hahn, N and Yoon, S and Singh, A and Nason-Tomaszewski, SR and Card, NS and Jude, JJ and Jacques, BG and Bechefsky, PH and Iacobacci, C and Hochberg, LR and Rubin, DB and Williams, ZM and Brandman, DM and Stavisky, SD and AuYong, N and Pandarinath, C and Druckmann, S and Henderson, JM and Willett, FR}, title = {Inner speech in motor cortex and implications for speech neuroprostheses.}, journal = {Cell}, volume = {188}, number = {17}, pages = {4658-4673.e17}, pmid = {40816265}, issn = {1097-4172}, support = {U01 DC019430/DC/NIDCD NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; F32 HD112173/HD/NICHD NIH HHS/United States ; DP2 DC021055/DC/NIDCD NIH HHS/United States ; K23 DC021297/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; }, mesh = {Humans ; *Motor Cortex/physiology ; *Brain-Computer Interfaces ; *Speech/physiology ; Male ; Female ; Adult ; Neural Prostheses ; Young Adult ; }, abstract = {Speech brain-computer interfaces (BCIs) show promise in restoring communication to people with paralysis but have also prompted discussions regarding their potential to decode private inner speech. Separately, inner speech may be a way to bypass the current approach of requiring speech BCI users to physically attempt speech, which is fatiguing and can slow communication. Using multi-unit recordings from four participants, we found that inner speech is robustly represented in the motor cortex and that imagined sentences can be decoded in real time. The representation of inner speech was highly correlated with attempted speech, though we also identified a neural "motor-intent" dimension that differentiates the two. We investigated the possibility of decoding private inner speech and found that some aspects of free-form inner speech could be decoded during sequence recall and counting tasks. Finally, we demonstrate high-fidelity strategies that prevent speech BCIs from unintentionally decoding private inner speech.}, }
@article {pmid40816112, year = {2025}, author = {Chen, J and Chen, X and Tang, Z and Lei, L and Zhan, Y and Liu, S and Zhou, H and Wan, J and Chen, Z and Wu, Y and Luo, Z}, title = {Influence of eHealth literacy on acceptance of healthcare services with risks in China: chain-mediating effect of general risk propensity and self-efficacy.}, journal = {Public health}, volume = {247}, number = {}, pages = {105891}, doi = {10.1016/j.puhe.2025.105891}, pmid = {40816112}, issn = {1476-5616}, mesh = {Humans ; Male ; Female ; China ; *Self Efficacy ; *Health Literacy/statistics &amp; numerical data ; Cross-Sectional Studies ; *Telemedicine/statistics &amp; numerical data ; Middle Aged ; Adult ; *Patient Acceptance of Health Care/statistics &amp; numerical data/psychology ; COVID-19/prevention &amp; control ; Aged ; COVID-19 Vaccines/administration &amp; dosage ; Young Adult ; }, abstract = {OBJECTIVES: To investigate factors associated with the acceptance of healthcare services with risks among Chinese public.<br><br>STUDY DESIGN: This national cross-sectional study used data from the 2023 Psychology and Behavior Investigation of Chinese Residents.<br><br>METHODS: Structural equation modelling was used to analyse the chain-mediated pathways of e-health literacy acting through general risk propensity and self-efficacy on the acceptability of five risky healthcare services (COVID-19 vaccine booster shots, mixed vaccination with COVID-19 vaccine, telemedicine, internet-based home care, and brain-computer interface technology). Subgroup analyses were performed by gender, region, and age.<br><br>RESULTS: Mean acceptance ratings for the five services ranged from 53.01 to 65.62. eHealth literacy was positively associated with self-efficacy, general risk propensity, and acceptance of five services (r&#xa0;=&#xa0;0.012-0.048, P&#xa0;<&#xa0;0.05). General risk propensity was positively associated with mixed vaccination with COVID-19 vaccine, telemedicine, and brain-computer interface technology (r&#xa0;=&#xa0;0.009 to 0.041, P&#xa0;<&#xa0;0.05). After adjusting for covariates, the correlation between general risk propensity and acceptance of the COVID-19 vaccine booster shots and telemedicine was non-significant. eHealth literacy had a significant positive effect on five services, self-efficacy, and general risk propensity (P&#xa0;<&#xa0;0.05). Subgroup analyses showed that self-efficacy and general risk propensity acted as mediators in the relationship between e-health literacy and acceptance of four health services in addition to the mixed neocoronary vaccine in both male and urban populations.<br><br>CONCLUSIONS: This finding shows general risk propensity and self-efficacy mediate the link between eHealth literacy and risky healthcare acceptance, deepening understanding and providing practical guidance for promoting innovative healthcare services in China.}, }
@article {pmid40815626, year = {2025}, author = {Padrão, N and Gregoricchio, S and Eickhoff, N and Dong, J and Luzietti, L and Bossi, D and Severson, TM and Siefert, J and Calcinotto, A and Buluwela, L and Donaldson Collier, M and Ali, S and Young, L and Theurillat, JP and Varešlija, D and Zwart, W}, title = {TRIM24 as a therapeutic target in endocrine treatment-resistant breast cancer.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {33}, pages = {e2507571122}, pmid = {40815626}, issn = {1091-6490}, support = {813599//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; 9171640//KWF Kankerbestrijding (DCS)/ ; 016.156.401//ZonMw (Netherlands Organisation for Health Research and Development)/ ; 2014MayPR234//Breast Cancer Now (BCN)/ ; C37/A18784//Cancer Research UK (CRUK)/ ; 20/FFP-P/8597//Research Ireland/ ; 23/SPP/11783//Research Ireland/ ; 2019AugSF1310//Breast Cancer Now (BCN)/ ; 18239A01//Breast Cancer Ireland (BCI)/ ; 19/FFP/6443//Research Ireland/ ; 2021JulyPCC1460//Breast Cancer Now (BCN)/ ; }, mesh = {Humans ; *Breast Neoplasms/drug therapy/metabolism/genetics/pathology ; Female ; Estrogen Receptor alpha/metabolism/genetics ; *Drug Resistance, Neoplasm/drug effects/genetics ; Cell Line, Tumor ; *Carrier Proteins/metabolism/genetics/antagonists &amp; inhibitors ; Cell Proliferation/drug effects ; Gene Expression Regulation, Neoplastic/drug effects ; MCF-7 Cells ; Antineoplastic Agents, Hormonal/pharmacology ; Histones/metabolism ; }, abstract = {While Estrogen receptor alpha (ERα)+ breast cancer treatment is considered effective, resistance to endocrine therapy is common. Since ERα is still the main driver in most therapy-resistant tumors, alternative therapeutic strategies are needed to disrupt ERα transcriptional activity. In this work, we position TRIM24 as a therapeutic target in endocrine resistance, given its role as a key component of the ERα transcriptional complex. TRIM24 interacts with ERα and other well-known ERα cofactors to facilitate ERα chromatin interactions and allows for maintenance of active histone marks including H3K23ac and H3K27ac. Consequently, genetic perturbation of TRIM24 abrogates ERα-driven transcriptional programs and reduces tumor cell proliferation capacity. Using a recently developed degrader targeting TRIM24, ERα-driven transcriptional output and growth were blocked, effectively treating not only endocrine-responsive cell lines but also drug-resistant derivatives thereof as well as cell line models bearing activating ESR1 point mutations. Finally, using human tumor-derived organoid models, we could show the efficacy of TRIM24 degrader in the endocrine-responsive and -resistant setting. Overall, our study positions TRIM24 as a central component for the integrity and activity of the ERα transcriptional complex, with degradation-mediated perturbation of TRIM24 as a promising therapeutic avenue in the treatment of primary and endocrine resistance breast cancer.}, }
@article {pmid40815349, year = {2025}, author = {Li, Z and Li, M and Yang, Y}, title = {Motor imagery decoding network with multisubject dynamic transfer.}, journal = {Brain informatics}, volume = {12}, number = {1}, pages = {20}, pmid = {40815349}, issn = {2198-4018}, support = {Nos. 62173010//National Natural Science Foundation of China/ ; }, abstract = {Brain computer interface (BCI) provides a promising and intelligent rehabilitation method for motor function, and it is crucial to acquire the patient's movement intentions accurately through decoding motor imagery EEG (MI-EEG) . Because of the inter-individual heterogeneity, the decoding model should demonstrate dynamic adaptation abilities.Domain adaptation (DA) is effective to enhance model generalization by reducing the inherent distribution difference among subjects. However, the existing DA methods usually mix the multiple source domains into a new domain, the resulting multi-source domain conflict may cause negative transfer. In this paper, we propose a multi-source dynamic conditional domain adaptation network (MSDCDA). First, a multi-channel attention block is employed in the feature extractor to focus on the channels relevant to the corresponding MI task. Subsequently, the shallow spatial-temporal features are extracted using a spatial-temporal convolution block. And a dynamic residual block is applied in the feature extractor to dynamically adapt specific features of each subject to alleviate conflicts among multiple source domains since each domain is viewed as a distribution of electroencephalogram (EEG) signals. Furthermore, we employ the Margin Disparity Discrepancy (MDD) as the metric to achieve conditional distribution domain adaptation between the source and target domains through adversarial learning with an auxiliary classifier. MSDCDA achieved accuracies of 78.55 % and 85.08 % on Datasets IIa and IIb of BCI Competition IV, respectively. Our experimental results demonstrate that MSDCDA can effectively address multi-source domain conflicts and significantly enhance the decoding performance of target subjects. This study positively facilitates the application of BCI based on motor function rehabilitation.}, }
@article {pmid40813381, year = {2025}, author = {Hendry, MF and Cruz-Garza, JG and Delgado-Jiménez, EA and Lima-Carmona, YE and Aguilar-Herrera, AJ and Ramírez-Moreno, MA and Ravindran, AS and Paek, AY and Smith, M and Kan, J and Fors, M and Alam, A and Liu, R and Noble, A and Contreras-Vidal, JL}, title = {Mobile Brain-Body Imaging and Visual Data of Theatrical Actors During Rehearsal and Performance.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1421}, pmid = {40813381}, issn = {2052-4463}, support = {1757949//National Science Foundation (NSF)/ ; 2137255//National Science Foundation (NSF)/ ; 2412731//National Science Foundation (NSF)/ ; }, mesh = {Humans ; *Brain/physiology/diagnostic imaging ; Electroencephalography ; }, abstract = {This longitudinal Mobile Brain-Body Imaging dataset was acquired during six rehearsal sessions and three public performances of a scene from a play with highly emotional components. Three student actor dyads (N=6), one theatre director (N=1) and three audience members (N=3) participated in this study. The MoBI data recorded includes mobile electroencephalography, electrooculography, blood volume pulse, heart rate, body temperature, electrodermal activity, triaxial arm and head acceleration. The visual data includes five streams of video. This article describes the experimental setup, the multi-modal data streams acquired using a hyperscanning methodology, and an assessment of the data quality.}, }
@article {pmid40813218, year = {2025}, author = {Xie, J and Xu, G and Yang, Z and Su, H and Zhang, S}, title = {Modeling multiscale time-frequency complex networks on Riemannian manifolds for motor imagery BCI classification with graph convolutional networks.}, journal = {ISA transactions}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.isatra.2025.07.058}, pmid = {40813218}, issn = {1879-2022}, abstract = {Motor imagery brain-computer interface (MI-BCI) classification faces challenges such as low decoding accuracy and difficulty in capturing the spatiotemporal dynamics of EEG signals. The use of Riemannian geometry classifiers for this task has become one of the most popular classification methods. However, current Riemannian geometry classifiers typically compute the covariance matrix over a period of time to capture spatial features, neglecting the multiscale characteristics of EEG signals in both time and frequency, which limits their classification performance. To address these issues, this study proposes a novel framework. Specifically, we introduce graph convolutional network (GCN) on Riemannian geometry (GR) to process multiscale networks, using virtual nodes to capture global topological features and integrating spatial features across time and frequency domains. This method significantly enhances the feature extraction capability of Riemannian geometry classifiers. The proposed method was validated on three public datasets, with average classification accuracies of 91.87 % ± 7.33 %, 87.96 % ± 7.6 %, and 82.50 % ± 7.74 %, respectively. Ablation experiments show that, compared to traditional single-scale methods, the average classification accuracy improved by 9.85 %, highlighting the effectiveness and versatility of the proposed method. This research provides a new perspective for multiscale EEG signal analysis and advances the development of motor imagery BCI classification technology.}, }
@article {pmid40811166, year = {2025}, author = {Zhang, X and Zheng, W and Li, Z and Yang, Y and Liu, W and Cai, H and Zhu, J and Liu, J and Hu, B and Dong, Q}, title = {Constraint-Driven Causal Representation Learning for Vigilance Robust Estimation in Brain-Computer Interface.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2025.3594434}, pmid = {40811166}, issn = {2162-2388}, abstract = {Vigilance estimation is a critical task within the field of brain-computer interfaces, extensively applied in monitoring and optimizing user states during human-machine interaction using electroencephalography (EEG). However, most existing vigilance prediction frameworks are prone to spurious correlations stemming from inherent biases in collected data. These biases involve relevant but vigilance-independent information, which may lack robustness when applied to different data distributions, i.e., out-of-distribution (OOD) scenarios. The core idea of this study is to learn constraints that capture causal information from the input based on the assumed underlying data generating process. Leveraging the disentanglement and invariance principles behind the assumptions, we propose a constraint-driven causal representation learning (CCRL) to identify and separate spurious latent variables from biased training data for generalized vigilance estimation. The CCRL training process consists of two phases: self-supervised pretraining and constraint-driven causal information disentanglement. In the first phase, based on the masked autoencoder (MAE) architecture, unlabeled training data are used for reconstructing pretext tasks to capture the comprehensive and intrinsic contextual information from EEG data, which provides a powerful input for downstream disentanglement learning. In the second phase, we propose a novel disentanglement strategy to learn spurious-free latent representations causally related to the vigilance state driven by adversarial and invariance constraints. Comprehensive validation experiments conducted on two well-known public datasets demonstrate the effectiveness and superiority of the proposed framework. In general, this work has promising implications for addressing OOD challenges in vigilance estimation.}, }
@article {pmid40810162, year = {2025}, author = {Zhang, M and Zhai, H and Yang, L and Li, H and Wang, X}, title = {The Medial Prefrontal Cortex Modulates Psychedelic-like Effects of Psilocin.}, journal = {ACS pharmacology &amp; translational science}, volume = {8}, number = {8}, pages = {2767-2776}, pmid = {40810162}, issn = {2575-9108}, abstract = {Recent advancements in the study of psilocybin and its active metabolite psilocin have highlighted their unique psychedelic properties and potential therapeutic applications, particularly in the rapid and sustained treatment of depression. However, the potent acute psychedelic effects of psilocybin necessitate a deeper understanding of the neural mechanisms underlying its action. In this study, we investigated the psilocin-induced neural activity in male mice using c-Fos immunofluorescent labeling and identified brain regions associated with psychedelic-like activity. Among the medial prefrontal cortex (mPFC), orbitofrontal cortex (OFC), interstitial nucleus of the posterior limb of the anterior commissure (IPAC), and dorsomedial striatum (DMS), only the mPFC was specifically associated with the head twitch response (HTR), a hallmark of psychedelic-like behavior. A picomolar dose of psilocin in the mPFC was sufficient to induce significant HTR, suggesting that c-Fos-positive neurons in this region modulate psychedelic-like activity. To validate this hypothesis, optogenetic activation of these neurons significantly increased spontaneous HTR in TRAP2 mice, whereas acute inhibition suppressed drug-induced HTR. These findings establish the mPFC as a critical regulator of psilocin-induced psychedelic-like activity and provide valuable insights for enhancing the clinical safety and therapeutic application of psychedelics.}, }
@article {pmid40807942, year = {2025}, author = {Chen, J and Yang, C and Wei, R and Hua, C and Mu, D and Sun, F}, title = {Steady-State Visual-Evoked-Potential-Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {15}, pages = {}, pmid = {40807942}, issn = {1424-8220}, support = {BX2021157//Post Doctoral Innovative Talent Support Program under Grants/ ; 62103221//National Natural Science Foundation of China under Grant/ ; }, abstract = {In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19-50 Hz, 14-38 Hz, 9-26 Hz, and 3-14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP.}, }
@article {pmid40807891, year = {2025}, author = {Huang, Y and Cao, L and Chen, Y and Wang, T}, title = {Optimization of Dynamic SSVEP Paradigms for Practical Application: Low-Fatigue Design with Coordinated Trajectory and Speed Modulation and Gaming Validation.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {15}, pages = {}, pmid = {40807891}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; Female ; Young Adult ; *Fatigue/physiopathology ; Video Games ; }, abstract = {Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain-computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic motion trajectories with speed control. Using four frequencies (6, 8.57, 10, 12 Hz) and three waveform patterns (sinusoidal, square, sawtooth), speed was modulated at 1/5, 1/10, and 1/20 of each frequency's base rate. An offline experiment with 17 subjects showed that the low-speed sinusoidal and sawtooth trajectories matched the static accuracy (85.84% and 83.82%) while reducing cognitive workload by 22%. An online experiment with 12 subjects participating in a fruit-slicing game confirmed its practicality, achieving recognition accuracies above 82% and a System Usability Scale score of 75.96. These results indicate that coordinated trajectory and speed modulation preserves SSVEP signal quality and enhances user experience, offering a promising approach for fatigue-resistant, user-friendly BCI application.}, }
@article {pmid40807821, year = {2025}, author = {Aziz, MZ and Yu, X and Guo, X and He, X and Huang, B and Fan, Z}, title = {BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {15}, pages = {}, pmid = {40807821}, issn = {1424-8220}, support = {2025A1515011449//Natural Science Foundation of Guangdong Province/ ; 20240001053007//Aviation Science Foundation Project/ ; 62220106006//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Adult ; Male ; *Attention/physiology ; Female ; Algorithms ; }, abstract = {Motor imagery (MI) electroencephalograms (EEGs) are pivotal cortical potentials reflecting cortical activity during imagined motor actions, widely leveraged for brain-computer interface (BCI) system development. However, effectively decoding these MI EEG signals is often overshadowed by flawed methods in signal processing, deep learning methods that are clinically unexplained, and highly inconsistent performance across different datasets. We propose BCINetV1, a new framework for MI EEG decoding to address the aforementioned challenges. The BCINetV1 utilizes three innovative components: a temporal convolution-based attention block (T-CAB) and a spectral convolution-based attention block (S-CAB), both driven by a new convolutional self-attention (ConvSAT) mechanism to identify key non-stationary temporal and spectral patterns in the EEG signals. Lastly, a squeeze-and-excitation block (SEB) intelligently combines those identified tempo-spectral features for accurate, stable, and contextually aware MI EEG classification. Evaluated upon four diverse datasets containing 69 participants, BCINetV1 consistently achieved the highest average accuracies of 98.6% (Dataset 1), 96.6% (Dataset 2), 96.9% (Dataset 3), and 98.4% (Dataset 4). This research demonstrates that BCINetV1 is computationally efficient, extracts clinically vital markers, effectively handles the non-stationarity of EEG data, and shows a clear advantage over existing methods, marking a significant step forward for practical BCI applications.}, }
@article {pmid40807788, year = {2025}, author = {Siribunyaphat, N and Tohkhwan, N and Punsawad, Y}, title = {Investigation of Personalized Visual Stimuli via Checkerboard Patterns Using Flickering Circles for SSVEP-Based BCI System.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {15}, pages = {}, pmid = {40807788}, issn = {1424-8220}, support = {WU67260//Research and Innovation Institute of Excellence, Walailak University/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; *Photic Stimulation/methods ; Female ; Algorithms ; Young Adult ; }, abstract = {In this study, we conducted two steady-state visual evoked potential (SSVEP) studies to develop a practical brain-computer interface (BCI) system for communication and control applications. The first study introduces a novel visual stimulus paradigm that combines checkerboard patterns with flickering circles configured in single-, double-, and triple-layer forms. We tested three flickering frequency conditions: a single fundamental frequency, a combination of the fundamental frequency and its harmonics, and a combination of two fundamental frequencies. The second study utilizes personalized visual stimuli to enhance SSVEP responses. SSVEP detection was performed using power spectral density (PSD) analysis by employing Welch's method and relative PSD to extract SSVEP features. Commands classification was carried out using a proposed decision rule-based algorithm. The results were compared with those of a conventional checkerboard pattern with flickering squares. The experimental findings indicate that single-layer flickering circle patterns exhibit comparable or improved performance when compared with the conventional stimuli, particularly when customized for individual users. Conversely, the multilayer patterns tended to increase visual fatigue. Furthermore, individualized stimuli achieved a classification accuracy of 90.2% in real-time SSVEP-based BCI systems for six-command generation tasks. The personalized visual stimuli can enhance user experience and system performance, thereby supporting the development of a practical SSVEP-based BCI system.}, }
@article {pmid40807738, year = {2025}, author = {Leerskov, KS and Spaich, EG and Jochumsen, MR and Andreasen Struijk, LNS}, title = {Design and Demonstration of a Hybrid FES-BCI-Based Robotic Neurorehabilitation System for Lower Limbs.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {15}, pages = {}, pmid = {40807738}, issn = {1424-8220}, support = {A33234//Hartmann Fonden/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Neurological Rehabilitation/methods/instrumentation ; Male ; *Lower Extremity/physiology/physiopathology ; Adult ; *Robotics/methods/instrumentation ; Electric Stimulation/methods ; Female ; Movement/physiology ; Electroencephalography ; Young Adult ; }, abstract = {BACKGROUND: There are only a few available options for early rehabilitation of severely impaired individuals who must remain bedbound, as most exercise paradigms focus on out-of-bed exercises. To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain-computer interface (BCI), functional electrical stimulation (FES), and a robotic device.<br><br>METHODS: The BCI assessed the presence of a movement-related cortical potential (MRCP) and triggered the administration of FES to produce movement of the lower limb. The exercise trajectory was supported by the robotic device. To demonstrate the system, an experiment was conducted in an out-of-lab setting by ten able-bodied participants. During exercise, the performance of the BCI was assessed, and the participants evaluated the system using the NASA Task Load Index, Intrinsic Motivation Inventory, and by answering a few subjective questions.<br><br>RESULTS: The BCI reached a true positive rate of 62.6 &#xb1; 9.2% and, on average, predicted the movement initiation 595 &#xb1; 129 ms prior to the MRCP peak negativity. All questionnaires showed favorable outcomes for the use of the system.<br><br>CONCLUSIONS: The developed system was usable by all participants, but its clinical feasibility is uncertain due to the total time required for setting up the system.}, }
@article {pmid40803174, year = {2025}, author = {Kamali, S and Baroni, F and Varona, P}, title = {Mu and beta power effects of fast response trait double dissociate during precue and movement execution in the sensorimotor cortex.}, journal = {Computers in biology and medicine}, volume = {196}, number = {Pt C}, pages = {110874}, doi = {10.1016/j.compbiomed.2025.110874}, pmid = {40803174}, issn = {1879-0534}, mesh = {Humans ; Male ; Female ; Adult ; Electromyography ; *Sensorimotor Cortex/physiology ; Electroencephalography ; Movement/physiology ; Brain-Computer Interfaces ; Young Adult ; *Beta Rhythm/physiology ; Signal Processing, Computer-Assisted ; Motor Cortex/physiology ; Muscle, Skeletal/physiology ; }, abstract = {A better understanding of the neural and muscular mechanisms underlying motor responses is essential for advancing neurorehabilitation protocols, brain-computer interfaces (BCI), feature engineering for biosignal classification algorithms, and identifying biomarkers of disease and performance enhancement strategies. In this study, we examined the neuromuscular dynamics of healthy individuals during a sequential finger-pinching task, focusing on the relationships between cortical oscillations and muscle activity in simultaneous electroencephalography (EEG) and electromyography (EMG) recordings. We contrasted two pairs of subsets of the dataset based on the latency of EMG onset: an across-subjects trait-based comparison and a within-subjects state-based comparison. Trait-based analyses showed that fast responders had higher baseline beta power, indicating stronger motor inhibition and efficient resetting of motor networks, and greater mu desynchronization during movement, reflecting enhanced motor cortex activation. Visual association areas also displayed more pronounced changes in different phases of the task in subjects with lower latency. Fast responders exhibited lower baseline EMG activity and stronger EMG power during movement initiation, showing effective motor inhibition and rapid muscle activation. State-based analyses revealed no significant EEG differences between fast and slow trials, while EMG differences were only detected after movement onset. These results highlight that fast response trait is related to electrophysiological differences at specific frequency bands and task phases, offering insights for enhancing motor function in rehabilitation, biomarker identification and BCI applications.}, }
@article {pmid40801596, year = {2025}, author = {Henderson, FC and Tuchman, K}, title = {Angiogenic Cell Precursors and Neural Cell Precursors in Service to the Brain-Computer Interface.}, journal = {Cells}, volume = {14}, number = {15}, pages = {}, pmid = {40801596}, issn = {2073-4409}, mesh = {*Brain-Computer Interfaces ; Humans ; *Neural Stem Cells/cytology ; Animals ; *Neovascularization, Physiologic ; }, abstract = {The application of artificial intelligence through the brain-computer interface (BCI) is proving to be one of the great advances in neuroscience today. The development of surface electrodes over the cortex and very fine electrodes that can be stereotactically implanted in the brain have moved the science forward to the extent that paralyzed people can play chess and blind people can read letters. However, the introduction of foreign bodies into deeper parts of the central nervous system results in foreign body reaction, scarring, apoptosis, and decreased signaling. Implanted electrodes activate microglia, causing the release of inflammatory factors, the recruitment of systemic inflammatory cells to the site of injury, and ultimately glial scarring and the encapsulation of the electrode. Recordings historically fail between 6 months and 1 year; the longest BCI in use has been 7 years. This article proposes a biomolecular strategy provided by angiogenic cell precursors (ACPs) and nerve cell precursors (NCPs), administered intrathecally. This combination of cells is anticipated to sustain and promote learning across the BCI. Together, through the downstream activation of neurotrophic factors, they may exert a salutary immunomodulatory suppression of inflammation, anti-apoptosis, homeostasis, angiogenesis, differentiation, synaptogenesis, neuritogenesis, and learning-associated plasticity.}, }
@article {pmid40800758, year = {2025}, author = {Kostorz, K and Nguyen, T and Pan, Y and Melinscak, F and Steyrl, D and Hu, Y and Sorger, B and Hoehl, S and Scharnowski, F}, title = {Investigating short windows of interbrain synchrony: A step toward fNIRS-based hyperfeedback.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {3}, number = {}, pages = {}, pmid = {40800758}, issn = {2837-6056}, abstract = {Social interaction is of fundamental importance to humans. Prior research has highlighted the link between interbrain synchrony and positive outcomes in human social interaction. Neurofeedback is an established method to train one's brain activity and might offer a possibility to increase interbrain synchrony, too. Consequently, it would be advantageous to determine the feasibility of creating a neurofeedback system for enhancing interbrain synchrony to benefit human interaction. One vital step toward developing a neurofeedback setup is to determine whether the target metric can be determined in relatively short time windows. In this study, we investigated whether the most widely employed metric for interbrain synchrony, wavelet transform coherence, can be assessed accurately in short time windows using functional near-infrared spectroscopy (fNIRS), which is recognized for its mobility and ecological suitability for interactive research. To this end, we have undertaken a comprehensive approach where we created artificial data of different noise levels of a dyadic interaction and re-processed two human-interaction datasets. For both artificial and in vivo data, we computed short windows of interbrain synchrony of varying size and assessed significance at each window size. Our findings indicate that relatively short windows of wavelet transform coherence of integration durations of about 1 minute are feasible. This would align well with the methodology of an intermittent neurofeedback procedure. Our investigation lays a foundational step toward an fNIRS-based system to measure interbrain synchrony in real time and provide participants with information about their interbrain synchrony. This advancement is crucial for the future development of a neurofeedback training system tailored to enhance interbrain synchrony to potentially benefit human interaction.}, }
@article {pmid40800536, year = {2024}, author = {Papadopoulos, S and Darmet, L and Szul, MJ and Congedo, M and Bonaiuto, JJ and Mattout, J}, title = {Surfing beta burst waveforms to improve motor imagery-based BCI.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {2}, number = {}, pages = {}, pmid = {40800536}, issn = {2837-6056}, abstract = {Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during, and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non-invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient, and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article, we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors, thus being suitable for online applications. By adopting a time-resolved decoding approach, we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.}, }
@article {pmid40800510, year = {2024}, author = {Muraoka, Y and Iwama, S and Ushiba, J}, title = {Neurofeedback-induced desynchronization of sensorimotor rhythm elicits pre-movement downregulation of intracortical inhibition that shortens simple reaction time in humans: A double-blind, sham-controlled randomized study.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {2}, number = {}, pages = {}, pmid = {40800510}, issn = {2837-6056}, abstract = {Sensorimotor rhythm event-related desynchronization (SMR-ERD) is associated with the activities of cortical inhibitory circuits in the motor cortex. The self-regulation of SMR-ERD through neurofeedback training has demonstrated that successful SMR-ERD regulation improves motor performance. However, the training-induced changes in neural dynamics in the motor cortex underlying performance improvement remain unclear. Here, we hypothesized that SMR-neurofeedback based on motor imagery reduces cortical inhibitory activities during motor preparation, leading to shortened reaction time due to the repetitive recruitment of neural populations shared with motor imagery and movement preparation. To test this, we conducted a double-blind, sham-controlled study on 24 participants using neurofeedback training and pre- and post-training evaluation for simple reaction time tests and cortical inhibitory activity using short-interval intracortical inhibition (SICI). The results showed that veritable neurofeedback training effectively enhanced SMR-ERD in healthy male and female participants, accompanied by reduced simple reaction times and pre-movement SICI. Furthermore, SMR-ERD changes correlated with changes in pre-movement cortical disinhibition, and the disinhibition magnitude correlated with behavioral changes. These results suggest that SMR-neurofeedback modulates cortical inhibitory circuits during movement preparation, thereby enhancing motor performance.}, }
@article {pmid40798628, year = {2025}, author = {Zhao, SJ and Yin, ZY and Yu, SB and Wang, W and Yu, HZ and Li, WH and Tao, C}, title = {Block-based compressive imaging with a swin transformer.}, journal = {Optics express}, volume = {33}, number = {5}, pages = {9587-9603}, doi = {10.1364/OE.546585}, pmid = {40798628}, issn = {1094-4087}, abstract = {Block-based compressive imaging (BCI) is based on the compressive sensing principle, which uses a spatial light modulator and a low-resolution detector to perform parallel high-speed sampling, followed by super-resolution algorithm to reconstruct target image. When compared with traditional compressive imaging, BCI reduces the computational effort but introduces block artifacts. This paper proposes a data-driven deep neural network based on the swin transformer called SwinBCI, which introduces the local attention and shifted window mechanisms to improve the target image reconstruction quality. By using the dataset to train the model to obtain priori knowledge and performing graphics processing unit-accelerated computation, the computation time is greatly reduced to realize real-time BCI. We achieve better reconstruction performances with cake cutting-Hadamard matrix sampling than with Bernoulli matrix sampling. Comparison with three other classical compressed sensing reconstruction methods on four common image datasets and images acquired experimentally using the actual BCI system show that SwinBCI achieves faster high-quality reconstruction at each sampling rate.}, }
@article {pmid40797316, year = {2025}, author = {Tang, A and Jiang, H and Li, J and Chen, Y and Zhang, J and Wang, D and Hu, S and Lai, J}, title = {Gut microbiota links to cognitive impairment in bipolar disorder via modulating synaptic plasticity.}, journal = {BMC medicine}, volume = {23}, number = {1}, pages = {470}, pmid = {40797316}, issn = {1741-7015}, support = {82201676//National Natural Science Foundation of China/ ; 82471542//National Natural Science Foundation of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; No. 2021R52016//Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; 2022KTZ004//Chinese Medical Education Association/ ; }, mesh = {*Gastrointestinal Microbiome/physiology ; *Neuronal Plasticity/physiology ; Animals ; *Cognitive Dysfunction/microbiology/physiopathology/etiology ; *Bipolar Disorder/microbiology/complications/physiopathology/psychology ; Male ; Mice ; Humans ; Mice, Inbred C57BL ; Middle Aged ; Adult ; Fecal Microbiota Transplantation ; Female ; Disease Models, Animal ; Case-Control Studies ; }, abstract = {BACKGROUND: Cognitive impairment is an intractable clinical manifestation of bipolar disorder (BD), but its underlying mechanisms remain largely unexplored. Preliminary evidence suggests that gut microbiota can potentially influence cognitive function by modulating synaptic plasticity. Herein, we characterized the gut microbial structure in BD patients with and without cognitive impairment and explored its influence on neuroplasticity in mice.<br><br>METHODS: The gut structure of microbiota in BD without cognitive impairment (BD-nCI) patients, BD with cognitive impairment (BD-CI) patients, and healthy controls (HCs) were characterized, and the correlation between specific bacterial genera and clinical parameters was determined. ABX-treated C57 BL/J male mice were transplanted with fecal microbiota from BD-nCI, BD-CI patients or HCs and subjected to behavioral testing. The change of gut microbiota in recipient mice and its influence on the dendritic complexity and synaptic plasticity of prefrontal neurons were examined. Finally, microbiota supplementation from healthy individuals in the BD-CI mice was performed to further determine the role of gut microbiota.<br><br>RESULTS: 16S-ribosomal RNA gene sequencing reveals that gut microbial diversity and composition are significantly different among BD-nCI patients, BD-CI patients, and HCs. The Spearman correlation analysis suggested that glucose metabolism-related bacteria, such as Prevotella, Faecalibacterium, and Roseburia, were correlated with cognitive impairment test scores, and inflammation-related bacteria, such as Lachnoclostridium and Bacteroides, were correlated with depressive severity. Fecal microbiota transplantation resulted in depression-like behavior, impaired working memory and object recognition memory in BD-CI recipient mice. Compared with BD-nCI mice, BD-CI mice exhibited more severely impaired object recognition memory, along with greater reductions in dendritic complexity and synaptic plasticity. Supplementation of gut microbiota from healthy individuals partially reversed emotional and cognitive phenotypes and neuronal plasticity in BD-CI mice.<br><br>CONCLUSIONS: This study first characterized the gut microbiota in BD-CI patients and highlighted the potential role of gut microbiota in BD-related cognitive deficits by modulating neuronal plasticity in mice model.}, }
@article {pmid40797003, year = {2025}, author = {Lo, BWY and Fukuda, H}, title = {Advances in Ischemic Stroke Treatment: Current and Future Therapies.}, journal = {Neurology and therapy}, volume = {14}, number = {5}, pages = {1783-1796}, pmid = {40797003}, issn = {2193-8253}, abstract = {This review summarizes current concepts in our understanding of stroke anatomy, pathophysiology of cerebral hypoperfusion, and collateral circulation. It also provides an evidence-based update in stroke trials and treatments assessed using PRISMA guidelines. Intravenous thrombolysis, endovascular thrombectomy for anterior circulation strokes, blood pressure control after endovascular thrombectomy, and medical management principles are discussed. Endovascular thrombectomy and medical therapy improves functional independence at 90 days in anterior circulation strokes even in late windows up to 24 h post symptom onset regardless of infarct core size. Intensive systolic blood pressure control acutely post thrombectomy is associated with harm and worse outcomes. This review also provides an evidence-based update on neurorehabilitation strategies with emerging interventions such as brain-computer interface and robotics having the potential to maximize neuroplasticity for potential improvement and recovery post stroke.}, }
@article {pmid40796752, year = {2025}, author = {Li, D and Zalesky, A and Wang, Y and Wang, H and Ma, L and Cheng, L and Banaschewski, T and Barker, GJ and Bokde, ALW and Brühl, R and Desrivières, S and Flor, H and Garavan, H and Gowland, P and Grigis, A and Heinz, A and Lemaître, H and Martinot, JL and Martinot, MP and Artiges, E and Nees, F and Orfanos, DP and Poustka, L and Smolka, MN and Vaidya, N and Walter, H and Whelan, R and Schumann, G and Jia, T and Chu, C and Fan, L and , }, title = {Mapping the coupling between tract reachability and cortical geometry of the human brain.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {7489}, pmid = {40796752}, issn = {2041-1723}, support = {R01 DA049238/DA/NIDA NIH HHS/United States ; R56 AG058854/AG/NIA NIH HHS/United States ; U54 EB020403/EB/NIBIB NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; }, mesh = {Humans ; Male ; Female ; *White Matter/diagnostic imaging/anatomy &amp; histology/physiology ; Adult ; *Brain Mapping/methods ; Young Adult ; *Cerebral Cortex/diagnostic imaging/anatomy &amp; histology/physiology ; Adolescent ; Magnetic Resonance Imaging ; *Brain/physiology/diagnostic imaging/anatomy &amp; histology ; Diffusion Tensor Imaging/methods ; Neural Pathways/physiology/diagnostic imaging/anatomy &amp; histology ; Reproducibility of Results ; }, abstract = {The study of cortical geometry and connectivity is prevalent in human brain research. However, these two aspects of brain structure are usually examined separately, leaving the essential connections between the brain's folding patterns and white matter connectivity unexplored. In this study, we aim to elucidate the fundamental links between cortical geometry and white matter tract connectivity. We develop the concept of tract-geometry coupling (TGC) by optimizing the alignment between tract connectivity to the cortex and multiscale cortical geometry. We confirm in two independent datasets that cortical geometry reliably characterizes tract reachability, and that TGC demonstrates high test-retest reliability and individual-specificity. Interestingly, low-frequency TGC is more heritable and behaviorally informative. Finally, we find that TGC can reproduce task-evoked cortical activation patterns and exhibits non-uniform maturation during youth. Collectively, our study provides an approach to mapping cortical geometry-connectivity coupling, highlighting how these two aspects jointly shape the connected brain.}, }
@article {pmid40796392, year = {2025}, author = {Kim, J and Hong, SK and Lee, A and Kumar, SN and Suchi, M and Park, JI}, title = {Activity-Dependent Effects of ERK1/2 on Hepatic Ischemia-Reperfusion Injury.}, journal = {Transplantation proceedings}, volume = {57}, number = {8}, pages = {1659-1667}, doi = {10.1016/j.transproceed.2025.07.005}, pmid = {40796392}, issn = {1873-2623}, mesh = {Animals ; *Reperfusion Injury/pathology/enzymology/prevention &amp; control ; Male ; *Liver/pathology/enzymology/drug effects/blood supply ; Pyridones/pharmacology ; Rats, Sprague-Dawley ; Pyrimidinones/pharmacology ; Rats ; Disease Models, Animal ; *MAP Kinase Signaling System/drug effects ; *Mitogen-Activated Protein Kinase 3/metabolism/antagonists &amp; inhibitors ; *Mitogen-Activated Protein Kinase 1/metabolism/antagonists &amp; inhibitors ; Protein Kinase Inhibitors/pharmacology ; Liver Transplantation/adverse effects ; }, abstract = {BACKGROUND: Liver transplantation remains the only cure for end-stage liver disease, but ischemia-reperfusion injury (IRI) limits graft availability. Although extracellular signal-regulated kinase (ERK1/2) signaling is involved in cellular responses to IRI, its precise role in hepatic IRI remains unclear. We investigated the role of ERK1/2 in hepatic IRI by modulating its activity using small-molecule chemical inhibitors.<br><br>METHODS: ERK1/2 activation was monitored at different phases of hepatic IRI using a rat model in which liver ischemia was induced with varying reperfusion times. ERK1/2 activity was modulated in this model by administering different doses of trametinib (MEK1/2 inhibitor) and BCI (DUSP1/6 inhibitor). Liver injury was evaluated through histological assessment, serum markers, and molecular analysis of cell death pathways.<br><br>RESULTS: ERK1/2 activity increased early in the reperfusion phase and gradually decreased over 6 hours thereafter. Inhibiting the ERK1/2 activity increase using trametinib (0.3 mg/kg) as well as inhibiting its decreases using BCI (7.5 mg/kg) worsened the liver injury. However, the injury was reduced upon titrating ERK1/2 activity to a moderately increased level by BCI and trametinib coadministration. The reduced liver injury was accompanied by decreased expression of ferroptosis markers.<br><br>CONCLUSIONS: Our data demonstrate that ERK1/2 activity is required for hepatic cells to tolerate IRI. Our results suggest that modulation of ERK1/2 activity using existing drugs may be a potential therapeutic strategy for mitigating hepatic IRI.}, }
@article {pmid40795874, year = {2025}, author = {Li, J and Le, T and Fan, C and Chen, M and Shlizerman, E}, title = {Brain-to-text decoding with context-aware neural representations and large language models.}, journal = {Journal of neural engineering}, volume = {22}, number = {5}, pages = {}, doi = {10.1088/1741-2552/adfab1}, pmid = {40795874}, issn = {1741-2552}, mesh = {Humans ; *Brain/physiology ; *Language ; *Brain-Computer Interfaces ; Phonetics ; Large Language Models ; }, abstract = {Objective. Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mapping in the brain, leading to suboptimal decoding performance.Approach. In this work, we propose the use of diphone-an acoustic representation that captures the transitions between two phonemes-as the context-aware modeling target. We integrate diphones into existing phoneme decoding frameworks through a novel divide-and-conquer strategy in which we model the phoneme distribution by marginalizing over the diphone distribution. Our approach effectively leverages the enhanced context-aware representation of diphones while preserving the manageable class size of phonemes, a key factor in simplifying the subsequent phoneme-to-text conversion task.Main results. We demonstrate the effectiveness of our approach on the Brain-to-Text 2024 benchmark, where it achieves state-of-the-art phoneme error rate (PER) of 15.34% compared to 16.62% PER of monophone-based decoding. When coupled with finetuned large language models (LLMs), our method yields a Word error rate (WER) of 5.77%, significantly outperforming the 8.93% WER of the leading method in the benchmark.Significance. These results demonstrate the effectiveness of leveraging context-aware neural representations and LLMs for brain-to-text decoding, thereby expanding the capabilities of speech neuroprostheses and paving the way toward restoring communication in individuals with speech impairments.}, }
@article {pmid40795479, year = {2025}, author = {Albahri, AS and Hamid, RA and Alqaysi, ME and Al-Qaysi, ZT and Albahri, OS and Alamoodi, AH and Homod, RZ and Deveci, M and Sharaf, IM}, title = {Trust and explainability in robotic hand control via adversarial multiple machine learning models with EEG sensor data fusion: A fuzzy decision-making solution.}, journal = {Computers in biology and medicine}, volume = {196}, number = {Pt C}, pages = {110922}, doi = {10.1016/j.compbiomed.2025.110922}, pmid = {40795479}, issn = {1879-0534}, mesh = {*Electroencephalography ; Humans ; *Machine Learning ; *Fuzzy Logic ; *Brain-Computer Interfaces ; *Robotics ; *Hand/physiology ; *Signal Processing, Computer-Assisted ; }, abstract = {In the field of brain‒computer interfaces (BCIs), developing a reliable machine learning (ML) model for real-time robotic hand control systems based on motor imagery (MI) brain signals requires substantial research. For this purpose, a set of ML models has been developed and tested to identify robust models via MI sensor data fusion under both nonadversarial and adversarial attack conditions. This paper addresses numerous essential areas, including the development of ML models for electroencephalography (EEG) MI signal datasets, with a focus on proper preprocessing and evaluation under both nonadversarial and adversarial attack conditions. Three phases make up the process. In the first phase, raw MI-EEG datasets from the Graz University BCI competition are identified and preprocessed. The preprocessing encompasses six key stages: EEG-MI signal filtering, segmentation, time‒frequency domain feature extraction, merging and labeling, normalization (resulting in Dataset I), and feature fusion (resulting in Dataset II). In the second phase, both datasets are used to develop nine different ML methods and are evaluated via nine performance metrics. These models are trained and tested against adversarial and nonadversarial scenarios. In the third phase, the fuzzy decision by opinion score method (FDOSM) and the multiperspective decision matrix (MPDM) are combined to benchmark the ML models via the fuzzy multicriteria decision-making (MCDM) approach. The random forest (RF) model achieved the best overall performance, with the lowest FDOSM scores: 0.18241 for Dataset I and 0.21636 for Dataset II. A lower FDOSM score means better results across all evaluation criteria. To further assess the developed methodology, the RF model was tested on Dataset III, comprising EEG data from four participants collected via the EMOTIV EPOC. The mean classification accuracy achieved by the RF model was 83 % with standard preprocessing, and it improved to 86 % with the application of feature fusion techniques. Additionally, this study employed the local interpretability model-agnostic explanation (LIME) method to provide an understanding of the RF model's behavior and enhance the interpretability of the results in the context of individual predictions.}, }
@article {pmid40794110, year = {2025}, author = {Loss, J and von Sommoggy Und Erdödy, J and Rüter, J and Helten, J and Germelmann, CC and Tittlbach, S}, title = {[Using behavioral and cultural insights to promote physical activity among university students-the "Smart Moving" project].}, journal = {Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz}, volume = {68}, number = {9}, pages = {994-1005}, pmid = {40794110}, issn = {1437-1588}, mesh = {Humans ; *Students/statistics &amp; numerical data/psychology ; *Exercise/psychology ; Universities ; *Health Promotion/methods/organization &amp; administration ; Female ; Male ; Young Adult ; Germany ; Adult ; Adolescent ; Motivation ; Health Behavior ; Health Knowledge, Attitudes, Practice ; }, abstract = {BACKGROUND: Physical inactivity is widespread at universities. To promote physical activity among students, it is important to understand their needs. Behavioral and cultural insights (BCIs) help to identify barriers to physical activity and to develop appropriate interventions. The aim of "Smart Moving" was to use BCIs to implement measures to promote physical activity in two universities.<br><br>METHOD: "Smart Moving" was carried out at the universities of Bayreuth and Regensburg between 2018 and 2021. The project was implemented in four&#xa0;steps: (1)&#xa0;the target behavior was defined as students being physically active on campus; (2)&#xa0;knowledge about physical activity behavior was gained using a&#xa0;standardized survey of students, photo voice, and expert interviews; (3) a&#xa0;planning group at each university developed and implemented measures to promote physical activity; and (4)&#xa0;acceptance and short-term effects of selected measures were evaluated in short surveys.<br><br>RESULTS: University students spent an average of 34&#x202f;h per week sitting during their stay on campus. Factors influencing physical activity were assigned to the following categories: capability (cognitive/physical ability), opportunity (physical/social environment), and motivation. These included, for example, a&#xa0;lack of knowledge about access, poor accessibility of exercise opportunities, the prevailing norm that learning involves sitting, and shame when exercising in front of others. Various approaches to promote physical activity were developed: movement breaks in lectures, activating desk furniture with sitting/standing options, movement instructions in the outdoor area, and motivational interventions for exercise. The measures were well received by students.<br><br>DISCUSSION: The BCI data helped implement needs-based physical activity promotion at universities. Further studies are needed to investigate the long-term effects on physical activity behavior.}, }
@article {pmid40791388, year = {2025}, author = {Ponasso, GN and Drumm, DA and Oppermann, H and Wang, A and Noetscher, GM and Maess, B and Knösche, TR and Makaroff, SN and Haueisen, J}, title = {High-Resolution EEG Source Reconstruction from PCA-Corrected BEM-FMM Reciprocal Basis Funcions: A Study with Visual Evoked Potentials from Intermittent Photic Stimulation.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40791388}, issn = {2692-8205}, support = {R01 EB035484/EB/NIBIB NIH HHS/United States ; R01 MH130490/MH/NIMH NIH HHS/United States ; }, abstract = {Modern automated human head segmentations can generate high-resolution computational meshes involving many non-nested tissues. However, most source reconstruction software is limited to 3 -4 nested layers of low resolution and a small number of dipolar sources ~10,000. Recently, we introduced modeling techniques for source reconstruction of magnetoencephalographic (MEG) signals using the reciprocal approach and the boundary element fast multipole method (BEM-FMM). The technique of BEM-FMM can process both nested and non-nested models with as many as 4 million surface elements. In this paper, we present an analogue technique for source reconstruction of electroencephalographic (EEG) signals based on cortical global basis functions. The present work uses Helmholtz reciprocity to relate the reciprocally-generated lead-field matrices to their direct counterpart, while resolving the issue of possible biases toward the reference electrode. Our methodology is tested with experimental EEG data collected from a cohort of 12, young and healthy, volunteers subjected to intermittent photic stimulation (IPS). Our novel high-resolution source reconstruction models can have impact on mental health screening as well as brain-computer interfaces.}, }
@article {pmid40791170, year = {2025}, author = {Han, S and Pasquini, D and Sorieul, M and Boratto, MH and Gatecliff, L and Dickson, A and Jang, S and Davy, S and Malliaras, GG and Chen, Y}, title = {Implantable Ion-Selective Organic Electrochemical Transistors Enable Continuous, Long-Term, and In Vivo Plant Monitoring.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {12}, number = {41}, pages = {e04283}, pmid = {40791170}, issn = {2198-3844}, support = {NE/T012293/1//Natural Environment Research Council/ ; CSG-FRI12101//Royal Society Te Ap&#x101;rangi/ ; C04X2202//Ministry of Business, Innovation and Employment/ ; C04X1703//Ministry of Business, Innovation and Employment/ ; RS-2024-00452677//National Research Foundation of Korea/ ; RS-2024-00399300//National Research Foundation of Korea/ ; }, mesh = {*Biosensing Techniques/methods/instrumentation ; *Transistors, Electronic ; *Potassium/analysis ; *Xylem/chemistry/metabolism ; *Pinus/chemistry ; *Electrochemical Techniques/methods/instrumentation ; Ions ; }, abstract = {The development of plant-specific biosensors holds the potential to uncover new insights into plant physiology and advance precision agriculture. Current sensing platforms mainly focus on broad plant phenotypes (e.g., elongation and hydration) and local environmental monitoring (e.g., temperature and moisture). Here, an ion-selective organic electrochemical transistor (IS-OECT) is introduced that enables real-time monitoring of variations in potassium ion concentration within the xylem of pine trees. This work demonstrates that the high sensitivity of the IS-OECT enables the detection of subtle variations in potassium ion concentrations in the xylem sap of living trees, and the high stability of the sensor allows for in vivo measurements over five weeks. Furthermore, the implantable sensors are fabricated using processes that are compatible with low-cost manufacturing (i.e., lithography-free). This sensing technology, therefore, has great potential to be a game-changer in precision forestry and could extend to precision agriculture and horticulture practices.}, }
@article {pmid40789435, year = {2025}, author = {Tozzi, A and Jaušovec, K}, title = {Takens' theorem to assess EEG traces: Regional variations in brain dynamics.}, journal = {Neuroscience letters}, volume = {865}, number = {}, pages = {138352}, doi = {10.1016/j.neulet.2025.138352}, pmid = {40789435}, issn = {1872-7972}, mesh = {Humans ; *Electroencephalography/methods ; Male ; *Brain/physiology ; Adult ; Female ; Young Adult ; Occipital Lobe/physiology ; Brain Mapping/methods ; }, abstract = {Takens' theorem (TT) proves that the behaviour of a dynamical system can be effectively reconstructed within a multidimensional phase space. This offers a comprehensive framework for examining temporal dependencies, dimensional complexity and predictability of time series data. We applied TT to investigate the physiological regional differences in EEG brain dynamics of healthy subjects, focusing on three key channels: FP1 (frontal region), C3 (sensorimotor region), and O1 (occipital region). We provided a detailed reconstruction of phase spaces for each EEG channel using time-delay embedding. The reconstructed trajectories were quantified through measures of trajectory spread and average distance, offering insights into the temporal structure of brain activity that traditional linear methods struggle to capture. Variability and complexity were found to differ across the three regions, revealing notable regional variations. FP1 trajectories exhibited broader spreads, reflecting the dynamic complexity of frontal brain activity associated with higher cognitive functions. C3, involved in sensorimotor integration, displayed moderate variability, reflecting its functional role in coordinating sensory inputs and motor outputs. O1, responsible for visual processing, showed constrained and stable trajectories, consistent with repetitive and structured visual dynamics. These findings align with the functional specialization of different cortical areas, suggesting that the frontal, sensorimotor and occipital regions operate with autonomous temporal structures and nonlinear properties. This distinction may have significant implications for advancing our understanding of normal brain function and enhancing the development of brain-computer interfaces. In sum, we demonstrated the utility of TT in revealing regional variations in EEG traces, underscoring the value of nonlinear dynamics.}, }
@article {pmid40788303, year = {2025}, author = {Shotbolt, M and Bryant, J and Liang, P and Khizroev, S}, title = {Mechanism and applications of magnetoelectric nanoparticles in cancer therapy.}, journal = {Nanomedicine (London, England)}, volume = {20}, number = {19}, pages = {2469-2481}, pmid = {40788303}, issn = {1748-6963}, mesh = {Humans ; *Neoplasms/drug therapy/therapy ; *Nanoparticles/chemistry/therapeutic use ; Animals ; Drug Delivery Systems/methods ; Nanomedicine/methods ; Magnetic Fields ; *Antineoplastic Agents/therapeutic use/administration &amp; dosage ; }, abstract = {Cancer remains a major clinical challenge, with current therapies often hampered by off-target effects, drug resistance, and incomplete tumor eradication. There is a pressing need for more precise and effective treatment strategies. This review explores the mechanisms and applications of magnetoelectric nanoparticles (MENPs) in cancer therapy. MENPs, typically composed of magnetostrictive and piezoelectric materials in a core-shell structure, generate electric fields in response to magnetic fields, enabling targeted and noninvasive therapeutic actions. The literature search included recent advances in MENP synthesis, optimization of material composition and morphology, and preclinical studies demonstrating their ability to enhance drug delivery, disrupt tumor cell membranes, and induce tumor regression without systemic toxicity. Relevant studies were identified by searching electronic databases, including PubMed, Web of Science, Scopus, and Google Scholar. The search employed a combination of keywords and phrases such as "magnetoelectric nanoparticles," "MENPs," "cancer therapy," "nanomedicine," "core-shell nanoparticles," "magnetostrictive," "piezoelectric," "drug delivery," "magnetic field," "nano-electroporation," and "reactive oxygen species.." MENPs represent a promising option for precision oncology, offering remote control over therapeutic effects and the potential to overcome limitations of conventional treatments. Ongoing research should focus on optimizing MENP design for selectivity and efficacy, as well as advancing their clinical translation for cancer therapy.}, }
@article {pmid40786597, year = {2024}, author = {Chen, ZS}, title = {Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning.}, journal = {IEEE signal processing magazine}, volume = {41}, number = {6}, pages = {94-104}, pmid = {40786597}, issn = {1053-5888}, support = {R01 NS121776/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; P50 MH132642/MH/NIMH NIH HHS/United States ; RF1 DA056394/DA/NIDA NIH HHS/United States ; R01 MH139352/MH/NIMH NIH HHS/United States ; }, abstract = {Rapid advances in generative artificial intelligence (AI) and deep representation learning have revolutionized numerous engineering applications in signal processing, computer vision, speech recognition and translation, and natural language processing due to amazingly powerful representation power (e.g., [1,2]). Generative AI-empowered tools, such as ChatGPT and Sora, have fundamentally changed the landscape of human-computer communications research. One emerging application along this line is to link the brain to the computer (i.e., brain-computer interface or BCI) and to develop paradigm-shift brain-to-content technologies. This BCI system upgrade (i.e., BCI 2.0) is empowered by generative AI and deep learning ("new engine") and large amounts of data ("gas"). In this article, we will revisit the old song sung in a new tune, highlight some state-of-the-art progresses, and briefly discuss the future outlook.}, }
@article {pmid40786542, year = {2025}, author = {Zulfiqar, AA}, title = {Hypervitaminemia B12 in the Elderly: A Forgotten Marker of Serious Underlying Diseases.}, journal = {European journal of case reports in internal medicine}, volume = {12}, number = {8}, pages = {005553}, pmid = {40786542}, issn = {2284-2594}, abstract = {UNLABELLED: Hypervitaminemia B12, long neglected in clinical practice, is a biological anomaly whose pathological significance remains largely underestimated, particularly in the elderly. While medical attention has historically focused on vitamin B12 deficiency, several recent studies suggest that elevated levels of this vitamin may reveal serious underlying pathologies, such as solid neoplasia, haematological malignancies, chronic liver disease or renal failure. We report the case of a 91-year-old man hospitalized for asthenia, anorexia and altered general condition, in whom vitamin B12 assay revealed major hypervitaminemia (1318 pg/ml). The work-up revealed hepatic cirrhosis of alcoholic origin, complicated by hepatocellular carcinoma which was metastatic from the outset. This case illustrates the potential prognostic value of vitamin B12 dosage, particularly when coupled with C-reactive protein (BCI index), a high value (> 40,000) of which is associated with short-term mortality in patients with advanced cancer. Beyond hepatopathy, hypervitaminemia B12 is associated in the literature with increased haptocorrin release in myeloproliferative syndromes, excess transcobalamins in renal failure, or paradoxical elevation in certain inflammatory diseases. This biological marker, which is easy to obtain, could therefore become part of standardized geriatric assessment, particularly in oncogeriatrics, in order to guide diagnostic and prognostic strategy. The systematic inclusion of vitamin B12 assays in the general assessment of elderly patients, particularly in oncology settings, deserves to be reassessed.<br><br>LEARNING POINTS: Hypervitaminemia B12 is an often overlooked but potentially significant marker of serious underlying pathologies-including solid neoplasms, liver disease, renal failure, and hematologic malignancies-especially in elderly patients.The B12 &#xd7; C-reactive protein (CRP) index, easily obtainable from routine labs, may serve as a prognostic tool in oncology, with values over 40,000 being strongly associated with short-term mortality in advanced cancers.Routine screening for vitamin B12 levels in geriatric assessments should consider both deficiency and excess, with hypervitaminemia prompting systematic diagnostic evaluation to uncover latent or advanced disease.}, }
@article {pmid40783421, year = {2025}, author = {Hashemi, SI and Cheron, G and Demolin, D and Cebolla, AM}, title = {EEG oscillations and related brain generators of phonation phases in long utterances.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {29150}, pmid = {40783421}, issn = {2045-2322}, support = {ANR-20-CE23-0008//Agence Nationale de la Recherche/ ; }, mesh = {Humans ; Male ; Female ; *Electroencephalography ; Adult ; *Phonation/physiology ; *Brain/physiology ; Young Adult ; Electromyography ; *Speech/physiology ; }, abstract = {While the role of brain rhythms in respiratory and speech motor control has been mainly explored during brief utterances, the specific involvement of brain rhythms in the transition of regulating subglottic pressure phases which are concomitant to specific muscle activation during prolonged phonation remains unexplored. This study investigates whether power spectral variations of the electroencephalogram brain rhythms are related specifically to prolonged phonation phases. High-density EEG and surface EMG were recorded in nineteen healthy participants while they repeatedly produced the syllable [pa] without taking a new breath, until reaching respiratory exhaustion. Aerodynamic, acoustic, and electrophysiological signals were analyzed to detect the brain areas involved in different phases of prolonged phonation. Each phase was defined by successive thoracic and abdominal muscle activity maintaining estimated subglottic pressure. The results showed significant changes in power spectrum, with desynchronization and synchronization in delta, theta, low-alpha, and high-alpha bands during transitions among the phases. Brain source analysis estimated that the first phase (P1), associated with vocal initiation and elastic rib cage recoil, involved frontal regions, suggesting a key role in voluntary phonation preparation. Subsequent phases (P2, P3, P4) showed multiband dynamics, engaging motor and premotor cortices, anterior cingulate, sensorimotor regions, thalamus, and cerebellum, indicating progressive adaptation and fine-tuning of respiratory and articulatory muscle control. Additionally, the involvement of temporal and insular regions in delta rhythm suggests a role in maintaining phonetic representation and preventing spontaneous verbal transformations. These findings provide new insights into the mechanisms and brain regions involved in prolonged phonation. These findings pave the way for applications in vocal brain-machine interfaces, clinical biofeedback for respiratory and vocal disorders, and the development of more ecologically valid paradigms in speech neuroscience.}, }
@article {pmid40783082, year = {2025}, author = {Wu, K and Gao, L and Feng, Z and Kakkos, I and Li, C and Sun, Y}, title = {Multimodal brain network analysis reveals divergent dysconnectivity patterns during mental fatigue: A concurrent EEG-fMRI study.}, journal = {Brain research bulletin}, volume = {230}, number = {}, pages = {111505}, doi = {10.1016/j.brainresbull.2025.111505}, pmid = {40783082}, issn = {1873-2747}, mesh = {Humans ; Electroencephalography/methods ; *Mental Fatigue/physiopathology/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Male ; Female ; Adult ; *Brain/physiopathology/diagnostic imaging ; Young Adult ; *Nerve Net/physiopathology/diagnostic imaging ; Multimodal Imaging/methods ; Brain Mapping/methods ; Attention/physiology ; Psychomotor Performance/physiology ; }, abstract = {For the apparent importance of mental fatigue in neuroergonomics, continuous efforts have been made to reveal the underlying neural mechanisms. Using concurrent EEG-fMRI network analysis, this work aims to reveal fatigue-related brain network reorganization. Specifically, multimodal neuroimaging data were acquired from 35 healthy participants during a 15-min sustained attention task (i.e., psychomotor vigilance task). A monotonically decreasing pattern of behavioral performance was revealed where the first and last 3-min windows were determined as the most vigilant and fatigued states. Multimodal brain network architectures within these two states were then quantitatively compared. We found that EEG and fMRI networks exhibited divergent yet interrelated reorganizations. Specifically, MF-related deficiency in parallel information transmission was revealed in multiple EEG frequency bands, yet only local efficiency was altered in fMRI networks. Moreover, a convergent decrease of nodal efficiency mainly resided in the default mode network was found in both EEG and fMRI networks, indicating a decline in cognitive control capacity during mental fatigue. Overall, by integrating multimodal EEG-fMRI network analyses, this work provides novel insights into the dynamic neural adaptations to mental fatigue, enhancing our understanding of the underlying neural mechanisms.}, }
@article {pmid40780413, year = {2025}, author = {Qiu, SJ and Zhang, YL and Gong, WB and Ding, YH and Wu, JW and Wang, ZX and Yao, HW}, title = {BCI inhibits MKP3 by targeting the kinase-binding domain and disrupting ERK2 interaction.}, journal = {The Journal of biological chemistry}, volume = {301}, number = {9}, pages = {110570}, pmid = {40780413}, issn = {1083-351X}, mesh = {Humans ; *Mitogen-Activated Protein Kinase 1/metabolism/chemistry/antagonists &amp; inhibitors/genetics ; *Dual Specificity Phosphatase 3/antagonists &amp; inhibitors/metabolism/chemistry/genetics ; Protein Binding ; *Dual Specificity Phosphatase 6/metabolism/antagonists &amp; inhibitors/chemistry/genetics ; Protein Domains ; MAP Kinase Signaling System/drug effects ; }, abstract = {Mitogen-activated protein kinase phosphatase 3 (MKP3), also known as dual-specificity phosphatase 6, is a critical regulator of extracellular signal-regulated kinase (ERK) signaling, and its dysregulation is implicated in diseases, such as cancer. The small-molecule inhibitor BCI ((E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one) has been reported to inhibit MKP3, thereby enhancing ERK signaling and promoting selective cytotoxicity in cancer cells. However, the molecular mechanism underlying BCI-mediated MKP3 inhibition remains unclear. In this research, we characterized the interaction between BCI and MKP3 using NMR titration, microscale thermophoresis, enzymatic assays, and AlphaFold 3 modeling. Our results demonstrate that BCI selectively binds to the kinase-binding domain (KBD) of MKP3, rather than its catalytic domain, thereby disrupting the MKP3-ERK2 interaction and impairing MKP3 activation. Enzymatic assays further reveal that BCI significantly reduces ERK2-mediated MKP3 activity without directly interfering with substrate binding at the active site. AlphaFold 3 structural modeling suggests that BCI binding induces local conformational changes, notably an outward shift of the α4-helix, which exposes a hydrophobic pocket essential for BCI accommodation. Moreover, BCI exhibits differential binding affinities across the MKP family, showing significant interactions with the KBDs of MKPX and MKP5 but markedly weaker or negligible binding to those of MKP1, MKP2, and MKP4. Together, these findings uncover a novel KBD-targeting mechanism of MKP3 inhibition by BCI and highlight the potential of selectively modulating mitogen-activated protein kinase phosphatases through allosteric disruption of kinase-phosphatase interactions. This strategy may offer a new avenue for the design and optimization of targeted phosphatase inhibitors.}, }
@article {pmid40774731, year = {2025}, author = {Liang, R and Gao, J and Liu, X and Li, X and Chang, H and Yang, R and Yang, J and Ming, D}, title = {Regulatory measures for mitigating physical and mental health impacts in aerospace environment: A systematic review.}, journal = {Life sciences in space research}, volume = {46}, number = {}, pages = {106-114}, doi = {10.1016/j.lssr.2025.04.003}, pmid = {40774731}, issn = {2214-5532}, mesh = {Humans ; *Space Flight ; *Mental Health ; *Astronauts/psychology ; *Aerospace Medicine ; Weightlessness/adverse effects ; Exercise ; }, abstract = {Long-term spaceflight poses significant challenges to astronauts' physical and mental health, resulting in physiological issues such as osteoporosis, muscle atrophy, and cardiovascular dysfunction, as well as psychological problems like depression, anxiety, social withdrawal, and cognitive decline. As the duration of space missions continues to increase, the above challenges cannot be ignored. Therefore, identifying effective regulatory measures is essential. This article provides a concise review of the latest domestic and international research on strategies to mitigate physiological and psychological risks in aerospace environment. Including coping strategies for musculoskeletal, cardiovascular, and psychological problems, such as exercise, physical stimulation, psychotherapy, and medication, especially traditional Chinese medicine, which need to be further explored and applied. Its ultimate goal is to offer insights for ensuring the safe execution of space missions by astronauts and advancing the field of space medicine.}, }
@article {pmid40774087, year = {2025}, author = {Constant, M and Mandal, A and Asanowicz, D and Panek, B and Kotlewska, I and Yamaguchi, M and Gillmeister, H and Kerzel, D and Luque, D and Molinero, S and Vázquez-Millán, A and Pesciarelli, F and Borelli, E and Ramzaoui, H and Beck, M and Somon, B and Desantis, A and Castellanos, MC and Martín-Arévalo, E and Manini, G and Capizzi, M and Gokce, A and Özer, D and Soyman, E and Yılmaz, E and Eayrs, JO and London, RE and Steendam, T and Frings, C and Pastötter, B and Szaszkó, B and Baess, P and Ayatollahi, S and León Montoya, GA and Wetzel, N and Widmann, A and Cao, L and Low, X and Costa, TL and Chelazzi, L and Monachesi, B and Kamp, SM and Knopf, L and Itier, RJ and Meixner, J and Jost, K and Botes, A and Braddock, C and Li, D and Nowacka, A and Quenault, M and Scanzi, D and Torrance, T and Corballis, PM and Laera, G and Kliegel, M and Welke, D and Mushtaq, F and Pavlov, YG and Liesefeld, HR}, title = {A multilab investigation into the N2pc as an indicator of attentional selectivity: Direct replication of Eimer (1996).}, journal = {Cortex; a journal devoted to the study of the nervous system and behavior}, volume = {190}, number = {}, pages = {304-341}, doi = {10.1016/j.cortex.2025.05.014}, pmid = {40774087}, issn = {1973-8102}, mesh = {*Attention/physiology ; Humans ; Electroencephalography/methods ; *Evoked Potentials/physiology ; Male ; Female ; Adult ; Photic Stimulation ; Young Adult ; *Brain/physiology ; Reaction Time/physiology ; }, abstract = {The N2pc is widely employed as an electrophysiological marker of an attention allocation. This interpretation was largely driven by the observation of an N2pc elicited by an isolated relevant target object, which was reported as Experiment 2 in Eimer (1996). All subsequent refined interpretations of the N2pc had to take this crucial finding into account. Despite its central role for neurocognitive attention research, there have been no direct replications and only few conceptual replications of this seminal work. Within the context of #EEGManyLabs, an international community-driven effort to replicate the most influential EEG studies ever published, the present study was selected due to its strong impact on the study of selective attention. We revisit the idea of the N2pc being an indicator of attentional selectivity by delivering a high powered direct replication of Eimer's work through analysis of 779 datasets acquired from 22 labs across 14 countries. Our results robustly replicate the N2pc to form stimuli, but a direct replication of the N2pc to color stimuli technically failed. We believe that this pattern not only sheds further light on the functional significance of the N2pc as an electrophysiological marker of attentional selectivity, but also highlights a methodological problem with selecting analysis windows a priori. By contrast, the consistency of observed ERP patterns across labs and analysis pipelines is stunning, and this consistency is preserved even in datasets that were rejected for (ocular) artifacts, attesting to the robustness of the ERP technique and the feasibility of large-scale multilab EEG (replication) studies.}, }
@article {pmid40773224, year = {2025}, author = {Zhang, K and Chen, G and Choi, SH}, title = {Converging technologies: vagus nerve stimulation and brain-computer interfaces as catalysts for advancing post-stroke aphasia rehabilitation.}, journal = {International journal of surgery (London, England)}, volume = {}, number = {}, pages = {}, doi = {10.1097/JS9.0000000000003148}, pmid = {40773224}, issn = {1743-9159}, }
@article {pmid40772250, year = {2025}, author = {Wilkins, RB and Coffin, T and Pham, M and Klein, E and Marathe, M}, title = {Mind the gap: bridging ethical considerations and regulatory oversight in implantable BCI human subjects research.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1633627}, pmid = {40772250}, issn = {1662-5161}, abstract = {The advent of Brain-Computer Interface (BCI) technology brings groundbreaking advancements in medical science but also raises important ethical considerations. This manuscript explores the ethical dimensions of implantable BCIs (iBCIs), focusing on the central role of Institutional Review Boards (IRBs) in the United States, in safeguarding participant rights and welfare. As federally mandated bodies, IRBs ensure that informed consent is obtained ethically, emphasizing participant autonomy, preventing undue coercion, while supporting clear and practical communication of risks and benefits. As part of this discussion, this paper touches on the ethical challenges surrounding the enrollment of participants with impaired consent capacity and the long-term implications of implanted brain devices. Additionally, this work underscores the critical importance of robust cybersecurity measures to prevent data breaches and unauthorized manipulation of brain activity. By examining risk assessments, data management practices, and the need for external cybersecurity expertise, this work offers a comprehensive framework for IRB review of iBCI research. This perspective aims to guide ethical iBCI research and protect human subjects in this rapidly evolving field.}, }
@article {pmid40772248, year = {2025}, author = {Mohamed, MA and Giles, J and AlSaleh, M and Arvaneh, M}, title = {Associations between pre-cue parietal alpha oscillations and event related desynchronization in motor imagery-based brain-computer interface.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1625127}, pmid = {40772248}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor Imagery based brain-computer interfaces (MI-BCIs) offer a promising avenue for controlling external devices via neural signals generated through imagined movements. Despite their potential, the performance of MI-BCIs remains highly variable across users and sessions, presenting a barrier to broader adoption.<br><br>METHODS: This study explores the influence of pre-cue parietal alpha power on the quality of the event-related desynchronization (ERD) responses, a critical indicator of MI processes. Analyzing data from 102 sessions involving 77 participants.<br><br>RESULTS: We identified a robust significant correlation between pre-cue parietal alpha power and ERD magnitude, indicating that elevated pre-cue parietal alpha power is associated with enhanced ERD responses. Additionally, we observed a significant positive relationship between pre-cue parietal alpha power and MI-BCI classification accuracy, highlighting the potential relevance of this neurophysiological metric for BCI performance.<br><br>DISCUSSION: Our findings suggest that pre-cue parietal alpha power can serve as a potential marker for optimizing MI-BCI systems. Integrating this marker into individualized training protocols can potentially enhance MI-BCI systems' consistency, and overall accuracy.}, }
@article {pmid40770162, year = {2025}, author = {Oikonomidou, O and Beresford, MJ and Galve-Calvo, E and Woeckel, A and Parikh, RC and Hitchens, A and Chen, C and Doan, J and Li, B and Ansquer, VD and Frugier, G and Jimenez, MI and Davis, KL and Broughton, EI}, title = {Real-world clinical outcomes associated with first-line palbociclib and aromatase inhibitor therapy among patients with HR+/HER2- advanced breast cancer in Europe.}, journal = {Breast cancer research and treatment}, volume = {213}, number = {3}, pages = {299-312}, pmid = {40770162}, issn = {1573-7217}, mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy/pathology/mortality/metabolism ; *Pyridines/administration &amp; dosage/therapeutic use ; Receptor, ErbB-2/metabolism ; Retrospective Studies ; Middle Aged ; *Aromatase Inhibitors/therapeutic use/administration &amp; dosage ; Receptors, Estrogen/metabolism ; Aged ; *Piperazines/administration &amp; dosage/therapeutic use ; *Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Europe/epidemiology ; Adult ; Receptors, Progesterone/metabolism ; Aged, 80 and over ; Treatment Outcome ; }, abstract = {PURPOSE: Cyclin-dependent kinase 4/6 inhibitors (CDK4/6is) combined with endocrine therapy is the recommended first-line (1L) treatment for hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC). Real-world evidence (RWE) describing 1L CDK4/6i regimens and associated clinical outcomes in Europe is limited. The study objective was to describe clinical characteristics, tumor response, and survival outcomes in patients with HR+/HER2- ABC.<br><br>METHODS: This retrospective, observational cohort study used data from 52 treatment centers in the UK, Spain, and Germany and included patients who initiated 1L palbociclib&#x2009;+&#x2009;aromatase inhibitor (AI) therapy for ABC between 2016 and 2020. Primary endpoints were real-world progression-free survival (rwPFS) and overall survival (OS).<br><br>RESULTS: Data were abstracted from 856 patients. During treatment, complete response, partial response, or stable disease was achieved for 86.1% of patients in Spain, 80.7% in the UK, and 79.0% in Germany, while complete or partial response was achieved for 43.8% of patients in Spain, 34.9% in the UK, and 16.9% in Germany. Median rwPFS during treatment was 28.1&#xa0;months for patients in Spain, 33.9&#xa0;months in the UK, and 48.1&#xa0;months in Germany. Median OS was 51.3&#xa0;months (95% CI 46.6-NE) in the UK, 65.2&#xa0;months (95% CI 65.2-NE) in Germany, and not reached in Spain.<br><br>CONCLUSION: This RWE supports the clinical effectiveness of 1L palbociclib&#x2009;+&#x2009;AI in routine clinical practice in European countries-consistent with the efficacy observed in clinical trials-and further supports the implementation of palbociclib-based regimens as frontline treatment of HR+/HER2- ABC.}, }
@article {pmid40769403, year = {2025}, author = {Key, B and Brown, DJ}, title = {How pain fools everyone: An inference to the best explanation.}, journal = {Neuroscience and biobehavioral reviews}, volume = {177}, number = {}, pages = {106317}, doi = {10.1016/j.neubiorev.2025.106317}, pmid = {40769403}, issn = {1873-7528}, mesh = {Humans ; *Pain/physiopathology/psychology ; *Decision Making/physiology ; *Brain/physiopathology/physiology ; Animals ; }, abstract = {There is a commonly held assumption that feelings such as pain are causes of behaviour. We say we withdrew our hand from the hotplate because it hurt or that we flinched at the needle because it stung. The causal role of pain is widely implicated in theories of learning and decision-making. But what if this commonsense idea that feelings cause behaviour is just wrong? To date, there is no known mechanism for how subjectively experienced pain directly modulates neural activity and it is hard to see how there could be. There is no known mechanism by which pain could directly gate ion channels. On this basis, we contend that the real cause of behaviour is neural activity and that feelings of pain have no known causal role. This raises the question of whether pain has any function at all-i.e., whether it has causal powers or is merely epiphenomenal. Epiphenomenalism faces the intractable problem of explaining how such an attention-consuming feeling as pain could be epiphenomenal and yet still have survived evolutionary selection. In response, we infer from the available neuroscientific evidence that the best explanation is that pain has a novel, non-causal function and that decisions to act are instead caused by an internal decoding process involving threshold detection of accumulated evidence of pain rather than by pain per se. Because pain is necessarily implicated in the best explanation of subsequent decision-making, we do not conclude that pain is epiphenomenal or functionless even if it has no causal influence over decisions or subsequent actions. On this view, pain functions to mark neural pathways that are the causes of behaviour as salient, serving as a ground but not a cause of subsequent decision-making and action. This perspective has far-reaching implications for diverse fields including neuropsychiatry, biopsychosocial modelling, robotics, and brain-computer interfaces.}, }
@article {pmid40769034, year = {2025}, author = {Guggisberg, AG and Siebner, HR and Lundell, H and Madsen, MAJ and Madsen, KH and Wiggermann, V and Mégevand, P and Proix, T and Dalal, SS and Grouiller, F and Vulliémoz, S and Ušćumlić, M and Marchesotti, S}, title = {Emergent technologies in clinical neurophysiology to study the central nervous system: IFCN handbook chapter.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {178}, number = {}, pages = {2110942}, doi = {10.1016/j.clinph.2025.2110942}, pmid = {40769034}, issn = {1872-8952}, mesh = {Humans ; *Electroencephalography/methods/trends ; Magnetic Resonance Imaging/methods/trends ; Magnetoencephalography/methods/trends ; *Brain/physiology/physiopathology/diagnostic imaging ; *Brain Mapping/methods/trends ; *Central Nervous System/physiology/physiopathology ; *Neurophysiology/methods/trends ; }, abstract = {This chapter reviews recent breakthroughs in neurophysiological brain mapping, focusing on EEG, MEG, and MRI technologies and their integration with stimulation techniques. High-density and portable EEG systems now allow more precise, user-friendly, and mobile recordings. Machine learning enhances biomarker detection and diagnostic power, particularly in epilepsy, cognitive disorders, and sleep pathology. MEG has become more versatile with the development of wearable optically pumped magnetometers (OPMs), enabling recordings during natural movement and broadening clinical access. Intracranial EEG (iEEG) remains central in epilepsy surgery and neuroscience research, with innovations in seizure forecasting and high-resolution speech decoding via microelectrode arrays and Neuropixels probes. Structural and functional MRI have advanced through ultra-high field imaging, quantitative tissue characterization, and connectomics, while functional MRS (fMRS) enables real-time tracking of neurochemical changes. Crucially, these mapping tools increasingly converge with brain stimulation-TMS, TES, focused ultrasound, and deep brain stimulation-to enable real-time, individualized modulation of brain networks. Simultaneous EEG-fMRI and artifical intelligence-driven brain-computer interfaces further enhance precision interventions. Together, these technologies are transforming clinical neurophysiology, offering new insights into brain function and advancing personalized neuromodulation therapies for neurological and psychiatric disorders.}, }
@article {pmid40768522, year = {2025}, author = {Singh, G and Chharia, A and Upadhyay, R and Kumar, V and Longo, L}, title = {PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces.}, journal = {PloS one}, volume = {20}, number = {8}, pages = {e0327791}, pmid = {40768522}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Software ; Algorithms ; *Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. However, existing BCI frameworks face several limitations, including a lack of stage-wise flexibility essential for experimental research, steep learning curves for researchers without programming expertise, elevated costs due to reliance on proprietary software, and a lack of all-inclusive features leading to the use of multiple external tools affecting research outcomes. To address these challenges, we present PyNoetic, a modular BCI framework designed to cater to the diverse needs of BCI research. PyNoetic is one of the very few frameworks in Python that encompasses the entire BCI design pipeline, from stimulus presentation and data acquisition to channel selection, filtering, feature extraction, artifact removal, and finally simulation and visualization. Notably, PyNoetic introduces an intuitive and end-to-end GUI coupled with a unique pick-and-place configurable flowchart for no-code BCI design, making it accessible to researchers with minimal programming experience. For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. PyNoetic also includes a rich array of analytical tools such as machine learning models, brain-connectivity indices, systematic testing functionalities via simulation, and evaluation methods of novel paradigms. PyNoetic's strengths lie in its versatility for both offline and real-time BCI development, which streamlines the design process, allowing researchers to focus on more intricate aspects of BCI development and thus accelerate their research endeavors.}, }
@article {pmid40768143, year = {2025}, author = {Gallien, Y and Broussouloux, S and Demesmaeker, A and Fouillet, A and Mertens, C and Chin, F and Cassourret, G and Caserio-Schonemann, C and du Roscoät, E and Le Strat, Y and , }, title = {Outcomes and Cost-Benefit of a National Suicide Reattempt Prevention Program.}, journal = {JAMA network open}, volume = {8}, number = {8}, pages = {e2525671}, pmid = {40768143}, issn = {2574-3805}, mesh = {Humans ; Female ; Male ; Cost-Benefit Analysis ; Adult ; Retrospective Studies ; Middle Aged ; France/epidemiology ; *Suicide Prevention ; *Suicide, Attempted/statistics &amp; numerical data ; }, abstract = {IMPORTANCE: Suicide attempts (SA) are a major public health concern and a preventable cause of premature death with a significant societal cost. Suicide reattempt (SR) rates are high in the postdischarge period for an SA. Brief contact interventions (BCIs) aim to prevent SR by recontacting patients after discharge through crisis cards, calls, letters, or messages. A nationwide BCI was deployed in 6 French regions between 2015 and 2017.<br><br>OBJECTIVE: To assess the outcomes and the cost benefit of the program in reducing SR risk within 12 months after discharge.<br><br>Retrospective multicenter cohort study using nationwide data from the French health insurance database and emergency department surveillance system. Patients exposed to the program between 2015 and 2017 were matched 1:1 with unexposed patients based on age, sex, history of SA, and diagnosis codes using propensity scores and followed up for 12 months. Survival and cost-benefit analyses were conducted in [month to month] 2022.<br><br>EXPOSURE: Participation in the program, including structured follow-up using crisis cards, telephone calls, and/or postcards for up to 6 months after discharge.<br><br>MAIN OUTCOMES AND MEASURES: The primary outcome was time to first SR or suicide-related death within 12 months. The secondary outcome was the number of SRs and cost savings.<br><br>RESULTS: Among 23&#x202f;146 individuals, 14&#x202f;504 (62.6%) were female, 12&#x202f;244 (52.9%) had no history of SA, and the mean (SD) age was 39 (17) years. Exposure to the program was associated with a lower risk of SR (adjusted hazard ratio [aHR], 0.62; 95% CI, 0.59-0.67). This association was consistent regardless of patients' history of SAs (aHR, 0.63; 95% CI, 0.57-0.71 for those without prior attempts; aHR, 0.61; 95% CI, 0.56-0.66 for those with prior attempts) and appeared greater among female participants (aHR, 0.59; 95% CI, 0.54-0.68) than male participants (aHR, 0.68; 95% CI, 0.61-0.76). The program yielded a return on investment of &#x20ac;2.06 (95% CI, &#x20ac;1.58-&#x20ac;2.50) per euro spent.<br><br>CONCLUSION AND RELEVANCE: In this cohort study, exposure to the program was associated with a reduced risk of SR and favorable economic outcomes.}, }
@article {pmid40763175, year = {2025}, author = {Voola, M and Vignali, L and Mojallal, H and Bogdanov, C and Távora-Vieira, D}, title = {Using Cortical Auditory Evoked Potentials in Active Middle Ear and Bone Conduction Implant Users: An Objective Method to Optimize the Fitting.}, journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology}, volume = {46}, number = {9}, pages = {1037-1044}, doi = {10.1097/MAO.0000000000004581}, pmid = {40763175}, issn = {1537-4505}, mesh = {Humans ; *Bone Conduction/physiology ; *Evoked Potentials, Auditory/physiology ; Female ; Male ; Adult ; Middle Aged ; *Ossicular Prosthesis ; Aged ; *Hearing Aids ; Acoustic Stimulation ; Speech Perception/physiology ; *Prosthesis Fitting/methods ; Young Adult ; }, abstract = {OBJECTIVE: The study aimed to investigate whether cortical auditory evoked potential (CAEP) measures could be used to optimize active middle ear implant (aMEI) and bone conduction implant (BCI) fitting, with the goal of improving hearing outcomes in adults.<br><br>DESIGN: CAEPs were measured in response to LING sounds /OO/, /AH/, and /SH/ presented in sound field. If CAEP responses were recorded for all sounds, no map adjustments were performed. If a CAEP response was absent for one or more sounds, map parameters were optimized until a CAEP response could be induced. Functional outcomes were measured as pre- vs postoptimization adaptive speech-in-noise results. Subjective feedback was also collected.<br><br>RESULTS: Of the 15 participants, one was excluded from the study, three did not need optimization, nine were successfully optimized using CAEP measurements, and two could not be optimized. Comparison of CAEP morphology showed significant differences pre- vs postoptimization for middle- and high-frequency sounds (i.e., /AH/ and /SH/). Speech-in-noise testing revealed significant improvements pre- vs postoptimization, and participants were generally satisfied with the overall procedure.<br><br>CONCLUSION: These findings demonstrated that middle- and high-frequency tokens could be successfully optimized using CAEPs, resulting in significant improvements in hearing performance. Our results support the use of CAEPs for the optimization of aMEI and BCI adult users' fitting.}, }
@article {pmid40761593, year = {2025}, author = {Ding, S and Wang, K and Jiang, W and Xu, C and Bo, H and Ma, L and Li, H}, title = {DGAT: a dynamic graph attention neural network framework for EEG emotion recognition.}, journal = {Frontiers in psychiatry}, volume = {16}, number = {}, pages = {1633860}, pmid = {40761593}, issn = {1664-0640}, abstract = {INTRODUCTION: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, current graph neural network models rely on predefined fixed adjacency matrices during training, which imposes certain limitations on the model's adaptability and feature expressiveness.<br><br>METHODS: In this study, we propose a novel EEG emotion recognition framework known as the Dynamic Graph Attention Network (DGAT). This framework dynamically learns the relationships between different channels by utilizing dynamic adjacency matrices and a multi-head attention mechanism, allowing for the parallel computation of multiple attention heads. This approach reduces reliance on specific adjacency structures while enabling the model to learn information in different subspaces, significantly improving the accuracy of emotion recognition from EEG signals.<br><br>RESULTS: Experiments conducted on the EEG emotion datasets SEED and DEAP demonstrate that DGAT achieves higher emotion classification accuracy in both subject-dependent and subject-independent scenarios compared to other models. These results indicate that the proposed model effectively captures dynamic changes, thereby enhancing the accuracy and practicality of emotion recognition.<br><br>DISCUSSION: DGAT holds significant academic and practical value in the analysis of emotional EEG signals and applications related to other physiological signal data.}, }
@article {pmid40761318, year = {2025}, author = {Xiao, H and Huang, C and Wu, Y and Wang, JJ and Wang, H}, title = {Establishing a social behavior paradigm for female mice.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1630491}, pmid = {40761318}, issn = {1662-4548}, abstract = {INTRODUCTION: Social behavior assessment in female mice has been historically challenged by inconsistent results from the classic three-chamber test, which reliably detects social preferences in males but fails to capture female specific social dynamics.<br><br>METHODS: We developed a modified three-chamber paradigm by replacing standard social stimuli with familiar cagemates (co-housed for 2 weeks, 1 week or 24 hours) to better assess sociability and novelty preference in female mice.<br><br>RESULTS: In the sociability phase, female mice showed a significant preference for interacting with cagemates compared to empty chambers. Crucially, during the social preference phase, test females demonstrated robust novelty seeking behavior, spending significantly more time exploring novel conspecifics compared to 2-week cagemates or 1-week cagemates. This preference trended similarly, though non significantly, with 24-hour cagemates. Notably, our paradigm enhanced social preference indices without altering total interaction time, confirming its specificity for detecting novelty driven exploration.<br><br>DISCUSSION: These findings overcome the limitations of traditional paradigms and establish a validated framework for studying female social behavior, with critical implications for modeling neurodevelopmental disorders like autism spectrum disorder (ASD) in female preclinical research.}, }
@article {pmid40761312, year = {2025}, author = {Chen, Y and Xu, R and Lau, AT and He, X and Chen, W and Wang, X and Cichocki, A and Jin, J}, title = {Leveraging low-frequency components for enhanced high-frequency steady-state visual evoked potential based brain computer interface in fast calibration scenario.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {124}, pmid = {40761312}, issn = {1871-4080}, abstract = {High-frequency steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems offer improved user comfort but suffer from reduced performance compared to their low-frequency counterparts, limiting their practical application. To address this issue, we propose a transfer learning-based method that leverages low-frequency SSVEP data to enhance high-frequency SSVEP performance. A filtering mechanism is designed to extract informative components from low-frequency signals, and the least squares algorithm is employed to generate high-quality synthetic high-frequency data. Experiments conducted on two public datasets using TDCA, eTRCA, and advanced TRCA-based algorithms demonstrate significant performance improvements. Our approach requires only two calibration trials, achieving 9.03% and 14.49% accuracy increases for eTRCA and TDCA in Dataset 1, and 13.91% and 14.53% improvements in Dataset 2, all within 1.5 s. Moreover, our approach effectively addresses the issue of single calibration data for high-frequency SSVEP-BCI systems. These results support the feasibility of fast calibration and improved performance in real-world high-frequency BCI applications.}, }
@article {pmid40760397, year = {2025}, author = {Mondal, S and Nag, A}, title = {A computational eye state classification model using EEG signal based on data mining techniques: comparative analysis.}, journal = {Physical and engineering sciences in medicine}, volume = {}, number = {}, pages = {}, doi = {10.1007/s13246-025-01619-w}, pmid = {40760397}, issn = {2662-4737}, abstract = {Artificial Intelligence has shown great promise in healthcare, particularly in non-invasive diagnostics using bio signals. This study focuses on classifying eye states (open or closed) using Electroencephalogram (EEG) signals captured via a 14-electrode neuroheadset, recorded through a Brain-Computer Interface (BCI). A publicly available dataset comprising 14,980 instances was used, where each sample represents EEG signals corresponding to eye activity. Fourteen classical machine learning (ML) models were evaluated using a tenfold cross-validation approach. The preprocessing pipeline involved removing outliers using the Z-score method, addressing class imbalance with SMOTETomek, and applying a bandpass filter to reduce signal noise. Significant EEG features were selected using a two-sample independent t-test (p < 0.05), ensuring only statistically relevant electrodes were retained. Additionally, the Common Spatial Pattern (CSP) method was used for feature extraction to enhance class separability by maximizing variance differences between eye states. Experimental results demonstrate that several classifiers achieved strong performance, with accuracy above 90%. The k-Nearest Neighbours classifier yielded the highest accuracy of 97.92% with CSP, and 97.75% without CSP. The application of CSP also enhanced the performance of Multi-Layer Perceptron and Support Vector Machine, reaching accuracies of 95.30% and 93.93%, respectively. The results affirm that integrating statistical validation, signal processing, and ML techniques can enable accurate and efficient EEG-based eye state classification, with practical implications for real-time BCI systems and offering a lightweight solution for real-time healthcare wearable applications healthcare applications.}, }
@article {pmid40759633, year = {2025}, author = {Chen, C and Wu, J and Qin, C and Qiu, Y and Jiang, N and Wang, Q and Liu, M and Jiang, D and Yuan, Q and Wei, X and Zhuang, L and Wang, P}, title = {Planar-electroporated cell biosensor for investigating potential therapeutic effects of ectopic bitter receptors.}, journal = {Microsystems &amp; nanoengineering}, volume = {11}, number = {1}, pages = {147}, pmid = {40759633}, issn = {2055-7434}, support = {32201082//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62301481//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Bitter receptors were initially identified within the gustatory system. In recent years, bitter receptors have been found in various non-gustatory tissues, including the cardiovascular system, where they participate in diverse physiological processes. To investigate the electrophysiological and potential therapeutic implications of bitter receptors, we have developed a highly sensitive, multifunctional planar-electroporated cell biosensor (PECB) for high-throughput evaluation of the effects of bitter substances on cardiomyocytes. The PECB demonstrated the capability for high-throughput, stable, and reproducible detection of intracellular action potentials (IAPs). In comparison to conventional biosensors that utilize extracellular action potentials (EAPs) for data analysis, the IAPs recorded by the PECB provided high-resolution insights into action potentials, characterized by increased amplitudes and an enhanced signal-to-noise ratio (SNR). The PECB successfully monitored IAPs induced by the activation of bitter receptors by using three bitter substances: diphenidol, denatonium benzoate, and arbutin in cardiomyocytes. To further assess the drug development ability of our PECB, we established an in vitro long QT syndrome (LQTS) model to investigate the potential therapeutic effects of arbutin. The results indicated that arbutin altered the electrophysiological properties of cardiomyocytes and significantly shortened the repolarization time in the LQTS model. Moreover, it demonstrated its potential mechanistic pathway by activating bitter receptors to modulate cardiac ion channel activities. The developed PECB provides an effective platform for high-throughput screening of substrates of bitter receptors for the treatment of heart disease, presenting new opportunities for the development of antiarrhythmic therapies.}, }
@article {pmid40757371, year = {2025}, author = {Landsmeer, LPL and Hua, E and Abunahla, H and Siddiqi, MA and Ishihara, R and De Zeeuw, CI and Hamdioui, S and Strydis, C}, title = {Efficient implementation of the Hodgkin-Huxley potassium channel via a single volatile memristor.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1569397}, pmid = {40757371}, issn = {1662-4548}, abstract = {INTRODUCTION: In 2012, potassium and sodium ion channels in Hodgkin-Huxley-based brain models were shown to exhibit memristive behavior. This positioned memristors as strong candidates for implementing biologically accurate artificial neurons. Memristor-based brain simulations offer advantages in energy efficiency, scalability, and compactness, benefiting fields such as soft robotics, embedded systems, and neuroprosthetics.<br><br>METHODS: Previous approaches used current-controlled Mott memristors, which poorly matched the voltage-controlled nature of ion channels. This study employs volatile, oxide-based memristors that leverage electric-field-driven oxygen-vacancy migration to emulate voltage-dependent channel behavior. We selected candidate WOx and NbOx memristors and modeled their dynamics to verify performance as Hodgkin-Huxley potassium channels.<br><br>RESULTS: The device exhibits sigmoidal gating and voltage-dependent time constants consistent with the theoretical model. By scaling the passive circuitry around the memristors, we show that they capture the essential mechanisms of potassium ion-channels, although spike height is reduced due to strong non-linear voltage-dependence. Still, by cascading multiple compartments, typical spike propagation is retained.<br><br>DISCUSSION: This is the first demonstration of a voltage-controlled memristor replicating the Hodgkin-Huxley potassium channel, validating its potential for more efficient brain simulation hardware.}, }
@article {pmid40754610, year = {2025}, author = {Alghamdi, AM and Ashraf, MU and Bahaddad, AA and Almarhabi, KA and Al Shehri, WA and Daraz, A}, title = {Cross-subject EEG signals-based emotion recognition using contrastive learning.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {28295}, pmid = {40754610}, issn = {2045-2322}, support = {UJ-24-SUCH-1247//University of Jeddah/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain-Computer Interfaces ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; *Machine Learning ; Female ; }, abstract = {Electroencephalography (EEG) signals based emotion brain computer interface (BCI) is a significant field in the domain of affective computing where EEG signals are the cause of reliable and objective applications. Despite these advancements, significant challenges persist, including individual differences in EEG signals across subjects during emotion recognition. To cope this challenge, current study introduces a cutting-edge cross subject contrastive learning (CSCL) scheme for EEG signals representation of brain region. The proposed scheme addresses the generalisation across subjects directly, which is a primary challenge in EEG signals-based emotions recognition. The proposed CSCL scheme captures the complex patterns effectively by employing emotions and stimulus contrastive losses within hyperbolic space. CSCL is designed primarily to learn representations that can effectively distinguish signals originating from different brain regions. Further, we evaluate the significance of our proposed CSCL scheme on five different datasets, including SEED, CEED, FACED and MPED, and obtain 97.70%, 96.26%, 65.98%, and 51.30% respectively. The experimental results show that our proposed CSCL scheme demonstrates strong effectiveness while addressing the challenges related to cross subject variability and label noise in the EEG-based emotion recognition system.}, }
@article {pmid40754454, year = {2025}, author = {Ikegaya, Y}, title = {Semantics of Brain-Machine Hybrids.}, journal = {Biological &amp; pharmaceutical bulletin}, volume = {48}, number = {8}, pages = {1150-1164}, doi = {10.1248/bpb.b25-00285}, pmid = {40754454}, issn = {1347-5215}, mesh = {*Brain-Computer Interfaces ; Humans ; Semantics ; *Brain/physiology ; Animals ; Electroencephalography ; }, abstract = {Brain-machine interfaces, also known as brain-computer interfaces, represent a rapidly advancing field at the intersection of neuroscience and technology, enabling direct communication pathways between the brain and external devices. This review charts the historical evolution of brain-machine interfaces, from fundamental discoveries such as electroencephalography and volitional single-neuron control to sophisticated decoding of neural population activity for real-time control of robotics and sensory reconstruction. Clinical breakthroughs lead to unprecedented success in restoring motor function after paralysis through brain-spine interfaces, enabling high-speed communication through thought-to-text/speech systems, providing sensory feedback for prosthetics, and implementing closed-loop neuromodulation for the treatment of neurological disorders such as epilepsy and depression. Beyond therapeutic applications, brain-machine interfaces drive innovation in neurotech art (neuroart) and entertainment (neurogames), allowing neural activity to directly generate music, visual art, and interactive experiences. In addition, the potential for human augmentation is expanding, with technologies that enhance physical strength, sensory perception, and cognitive abilities. These converging advances challenge fundamental concepts of human identity and suggest that brain-machine interfaces may enable humanity to transcend inherent biological limitations, potentially ushering in an era of technologically guided evolution.}, }
@article {pmid40754053, year = {2025}, author = {Zhao, X and Xu, R and Zhang, Y and Lau, AT and Xu, R and Wang, X and Cichocki, A and Jin, J}, title = {A novel paradigm based on radar-like scanning for directional recognition in event-related potentials based brain-computer interfaces.}, journal = {Journal of neuroscience methods}, volume = {423}, number = {}, pages = {110546}, doi = {10.1016/j.jneumeth.2025.110546}, pmid = {40754053}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Electroencephalography/methods ; Young Adult ; Adult ; *Evoked Potentials/physiology ; *Brain/physiology ; Photic Stimulation/methods ; *Radar ; *Recognition, Psychology/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: Event-related potentials (ERPs) based brain-computer interface (BCI) systems have shown significant potential for directional control applications. Existing paradigms are constrained by the limited scalability of directional commands that demand interface reconfiguration for varying target numbers.<br><br>NEW METHOD: We propose a novel radar-like scanning (RS) paradigm for 32-directional recognition tasks to address these limitations. This paradigm continuously scans through directions using a sector-shaped visual stimulus, naturally evoking ERP responses without discrete directional indicators. During the online experiments, an early-stopping strategy is employed to enhance efficiency. Additionally, this study analyzes subjects' directional recognition performance using EEGNet under three sector rotation periods. Thirteen subjects participated in the experiments.<br><br>RESULTS: The grand-averaged ERP amplitudes exhibited a stronger negative deflection in the parietal, occipital, and temporoparietal regions. The results demonstrated that, with a 2&#x202f;s rotation period and early-stopping strategy, the best subject achieved an accuracy of 87.50&#x202f;% with a mean absolute angle error of 1.64&#xb0;. When the directional error tolerance was set to 11.25&#xb0;, the subject-averaged accuracy reached 91.83&#x202f;% under the same conditions. Longer rotation periods led to better subject-averaged recognition performance. When the rotation period was short (1&#x202f;s), targets close to the scanning center were challenging to recognize.<br><br>Compared with others, the RS paradigm enables more fine-grained directional target recognition and is unaffected by the target numbers.<br><br>CONCLUSIONS: The proposed paradigm demonstrates significant potential for applications in ERP-BCI systems.}, }
@article {pmid40749591, year = {2025}, author = {Elliss, H and Kevill, JL and Proctor, K and Farkas, K and Bailey, O and Shuttleworth, J and Jones, DL and Kasprzyk-Hordern, B}, title = {Flow-driven biomarker movement in gravitational sewers for wastewater-based epidemiology and public health monitoring.}, journal = {Water research}, volume = {287}, number = {Pt A}, pages = {124269}, doi = {10.1016/j.watres.2025.124269}, pmid = {40749591}, issn = {1879-2448}, mesh = {*Wastewater ; *Sewage ; Biomarkers/analysis ; *Environmental Monitoring ; Public Health ; *Wastewater-Based Epidemiological Monitoring ; Water Pollutants, Chemical ; Gravitation ; }, abstract = {The movement of biological (genetic viral, fungal or bacterial) and chemical indicators (BCIs) within sewer networks is critical to wastewater-based epidemiology (WBE) enabling accurate calculation of chemical and pathogen loads within a community. These quantified BCIs, which include genetic material from pathogens as well as pharmaceuticals, from a range of classes, serve as proxies for community-wide health and behaviour patterns. However, a critical knowledge gap exists in understanding how different BCIs move within complex sewer systems, which could lead to misinterpretation of community-level data. This study aims to address this gap by investigating the transport behaviour of 5 common BCIs (carbamazepine, metoprolol, naproxen, venlafaxine and PMMoV) in a real-world gravitational sewer network. In addition, we also spiked the wastewater with deuterated caffeine-d9, allowing discrimination from native caffeine present in the network and therefore, enabling an accurate assessment of recovery due to no public use. Our results revealed that all targets travelled with limited dispersion throughout the first stage of the gravitational sewer, 0.8 km after introduction into the network. It was observed that carbamazepine (logD = 2.8 at sewer pH), exhibited more dispersion throughout the remaining 2.3 km of the gravitational system, showing a broader, more asymmetric trace with increased tailing, which potentially indicates sorption to the solid phase, impacting its movement through the network. All other chemical targets had similar movement patterns, indicating a lower tendency to bind to the solid phase (logD < 1, at average sewer pH). Loads were calculated using dye-predicted flow rates and normalized to caffeine-d9. Carbamazepine loads were under-predicted by 74 %, attributed to losses to the solid phase throughout the sewer system. Conversely, metoprolol, naproxen, and venlafaxine loads were over-predicted (146 %, 32 %, and 129 %, respectively), likely due to additional public inputs. Our results demonstrate that more hydrophilic chemicals move throughout the sewer network with limited dispersion while hydrophobic compounds may experience significant losses. These findings have important implications for the accurate interpretation of WBE data, future BCI tracing studies and the selection of appropriate chemical markers for community health monitoring.}, }
@article {pmid40748806, year = {2025}, author = {Yuan, X and Zhang, Y and Rolfe, P}, title = {IIMCNet: Intra- and Inter-modality Correlation Network for Hybrid EEG-fNIRS Brain-Computer Interface.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3594203}, pmid = {40748806}, issn = {2168-2208}, abstract = {Hybrid Brain-Computer Interface (BCI) enhances accuracy and reliability by leveraging the complementary information provided by multi-modality signal fusion. EEG-fNIRS, a fusion of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS), have emerged as the suitable techniques for real-world BCI applications due to their portability and economic viability. Existing methods typically focus on the high-level feature representation with late-fusion or early-fusion strategies during the recognition tasks. However, they usually overlook the joint feature extraction of both intra-modality and inter-modality, which is crucial for optimizing BCI performance. In this study, we introduce an Intra- and Inter-modality Correlation Network (IIMCNet) to integrate both the inherent features derived from individual modalities: EEG, deoxygenated hemoglobin (HbR), and oxygenated hemoglobin (HbO), as well as the cross-modality features between EEG-HbR, EEG-HbO, and HbR-HbO data. The intra-modality correlation features are generated using a late fusion method (Intra-net), which combines the uni-modality features extracted by E-Net and f-Net. Concurrently, the inter-modality correlation features are extracted using an early fusion method (Inter-net). Inter-net is consist of three dilated convolution-based C-Nets that focus on neurovascular coupling across modalities. Finally, three intra-modality features, three inter-modality features, and the concatenate hybrid feature are fed into deep supervision module to enhance robustness and accuracy. Experiment results demonstrate the IIMCNet exhibits superior performance compared to methods that rely solely on either intra-modality or inter-modality correlation networks. Furthermore, IIMCNet outperforms other state-of-the-art methods in motor imagery and mental arithmetic tasks, respectively. (The code is available at: github.com/Y-xiaoyang/IIMCNet).}, }
@article {pmid40748802, year = {2025}, author = {Wang, J and Wang, Z and Xu, T and Li, A and Si, Y and Zhou, T and Zhao, X and Hu, H}, title = {Enhancing the Reliability of Affective Brain-Computer Interfaces by Using Specifically Designed Confidence Estimator.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3594219}, pmid = {40748802}, issn = {2168-2208}, abstract = {Recent years, the diverse applications of electroencephalography (EEG) - based affective brain-computer interfaces (aBCIs) are being extensively explored. However, due to adverse factors like noise and physiological variability, the recognition capability of aBCIs can unforeseeably suffer abrupt declines. Since the timing of these aBCI failures is unknown, placing trust in aBCIs without scrutiny can lead to undesirable consequences. To alleviate this issue, we propose an algorithm for estimating the reliability of aBCI (primarily Graph Convolutional Network), synchronously delivering a probabilistic confidence score upon aBCI decision completion, thereby reflecting the aBCI's real-time recognition capabilities. Methodologically, we use the Maximum Softmax Probability (MSP) from EEG recognition networks as confidence scores and leverage the Scaling Operator to calibrate them. Then, the Projection Operator is employed to address confidence estimation biases caused by noise and subject variability. For the numerical concentration of MSP, we provide fresh insights into its causes and propose corresponding solutions. The derivation of the estimator from the Maximum Entropy Principle is also substantiated for robust theoretical underpinnings. Finally, we confirm theoretically that the estimator does not compromise BCI performance. In experiments conducted on public datasets SEED and SEED-IV, the proposed algorithm demonstrates superior performance in estimating aBCIs reliability compared to other benchmarks, and commendable adaptability to new subjects. This research has the potential to lead to more trustworthy aBCIs and advance their broader application in complex real-world scenarios.}, }
@article {pmid40746978, year = {2025}, author = {Enemark, C}, title = {Loyal Wingmen, Artificial Intelligence, and Cognitive Enhancement: A Warning against Cyborg-Drone Warfare.}, journal = {Journal of military ethics}, volume = {24}, number = {1}, pages = {4-20}, pmid = {40746978}, issn = {1502-7589}, abstract = {Some states are planning to acquire armed drones that incorporate artificial intelligence (AI) and fly alongside inhabited aircraft. The use of drones according to this "Loyal Wingman" concept is an example of tactical human-machine teaming, and it could be militarily advantageous in future aerial warfare. Incorporating AI into the operation of a weapon system's critical functions (selecting and engaging targets) nevertheless carries an ethical risk: that a human will be unable to exercise adequate control over the use of force and unable to take responsibility for any injustice caused. To reduce this risk, one potential approach is to pursue "meaningful human control" over armed and AI-enabled drones by increasing their human supervisors' cognitive capacity. The use of brain-computer interfaces (BCIs) to achieve such an increase might be beneficial from the perspective of military ethics if it enabled faster human interventions to prevent unjust, AI-associated harms. However, as this article shows, that benefit would be outweighed by the ethical downsides of waging cyborg-drone warfare: the undermining of pilots' hors de combat noncombatant status and of human moral agency in the use of force.}, }
@article {pmid40746851, year = {2025}, author = {Aziz, NA and Ng, K and Alifrangis, C and Tran, B and Conduit, C and Liow, E and Ackerman, C and Georgescu, R and Jamal, T and Relton, C and Mayer, E and Nicol, D and Cazzaniga, W and Huddart, R and Reid, A and Shamash, J and Rajan, P}, title = {Therapy de-escalation for testicular cancer (THERATEST): A multi-centre observational cohort feasibility study of de-escalation therapies for good prognosis stage II germ cell tumours.}, journal = {BJUI compass}, volume = {6}, number = {8}, pages = {e70057}, pmid = {40746851}, issn = {2688-4526}, abstract = {BACKGROUND: Standard of care (SOC) treatments for International Germ Cell Cancer Collaborative Group (IGCCCG) good prognosis stage II germ cell tumours (GCT) involve primary orchidectomy followed by combination chemotherapy for both seminoma and non-seminomatous germ cell tumours (NSGCT). Alternatively, external beam radiotherapy may be used for seminoma and retroperitoneal lymph node dissection (RPLND) for NSGCT. While these treatments achieve high cure rates, they are associated with significant toxicities. De-escalation strategies including three cycles of Carboplatin AUC10 or robotic RPLND with or without adjuvant chemotherapy have demonstrated potential to reduce treatment-related toxicity in stage II seminoma while preserving oncological efficacy. However, these approaches are not widely adopted due to limited prospective comparative trials.<br><br>STUDY DESIGN: The THERATEST trial is a prospective multicentre observational feasibility study evaluating participants receiving SOC treatments for good prognosis stage II seminoma and NSGCT or de-escalated treatments for stage II seminoma.<br><br>ENDPOINTS: The primary endpoints are to assess feasibility of recruitment and retention. Secondary endpoints include assessing health-related quality of life (HRQOL), sexual function and satisfaction, progression-free survival (PFS), overall survival (OS) and safety and treatment-related complications.<br><br>PATIENTS AND METHODS: Thirty participants with good prognosis stage II seminoma or NSGCTs will be recruited over 18&#x2009;months into two cohorts: de-escalation arm and SOC arm. The de-escalation cohort will receive either Carboplatin AUC10 or robotic RPLND with or without adjuvant therapy depending on institutional SOC. Participants who decline or are ineligible for de-escalation will receive SOC treatment: combination chemotherapy or radiotherapy for seminoma and combination chemotherapy for NSGCT. All participants will be followed for two years post-treatment or until withdrawal. Data collection includes recruitment and retention rates, disease status, surgical outcomes, adverse events and patient-reported outcomes using validated questionnaire: EORTC QLQ-TC26, EORTC QLQ-C30, Brief Male Sexual Function Inventory (BMSFI) and additional enquiries on anejaculation.<br><br>COORDINATING CENTRE: THERATEST Trial Coordinator, Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University of London, Old Anatomy Building, Charterhouse Square, London, EC1M 6BQ|T: 0207882 8497|E: bci-theratest@qmul.ac.uk.<br><br>TRIAL REGISTRATION NUMBER: ISRCTN61007118.}, }
@article {pmid40746199, year = {2025}, author = {Madhavan, S}, title = {Harnessing Neuroplasticity: The Role of Priming in Enhancing Post Stroke Motor Function.}, journal = {Restorative neurology and neuroscience}, volume = {}, number = {}, pages = {9226028251358162}, doi = {10.1177/09226028251358162}, pmid = {40746199}, issn = {1878-3627}, abstract = {Stroke remains a leading cause of disability worldwide, highlighting the need for innovative neurorehabilitation strategies to enhance recovery. Recent advancements emphasize neuroplasticity-the brain's ability to reorganize and form new connections-through targeted interventions. Among these, cortical priming has emerged as a promising approach to enhance neuroplasticity and improve motor recovery post-stroke by modulating brain excitability for optimal motor learning. This review explores the role of cortical priming in stroke rehabilitation, highlighting its ability to enhance neural excitability and plasticity in motor-related brain regions. Various priming techniques, including non-invasive brain stimulation (rTMS, tDCS), deep brain stimulation (DBS), vagus nerve stimulation (VNS), brain-computer interfaces (BCIs), movement-based priming, aerobic exercise, and sensory stimulation, are examined. Despite promising findings, challenges remain in optimizing protocols and addressing individual variability. Future directions focus on biomarker-driven rehabilitation, personalized strategies, and large-scale trials to integrate cortical priming into clinical practice.}, }
@article {pmid40745321, year = {2025}, author = {Chen, J and Liu, Q and Chen, G and Cai, G and Jiang, J and Yang, X and Tan, C and Zhang, C and Xu, G and Lan, Y}, title = {iTBS on RDLPFC improves performance of motor imagery: a brain-computer interface study combining EEG and fNIRS.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {172}, pmid = {40745321}, issn = {1743-0003}, support = {82072548//National Science Foundation of China/ ; 82472619//National Science Foundation of China/ ; 2022YFC2009700//Natural Key Research and Development Program of China/ ; 202206010197 and 202201020378//Guangzhou Municipal Science and Technology Program/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Spectroscopy, Near-Infrared ; Electroencephalography/methods ; *Imagination/physiology ; Adult ; Young Adult ; *Transcranial Magnetic Stimulation/methods ; *Dorsolateral Prefrontal Cortex/physiology ; Neuronal Plasticity/physiology ; Psychomotor Performance/physiology ; }, abstract = {BACKGROUND: Some individuals using brain-computer interfaces (BCIs) exhibit ineffective control during motor imagery-based BCI (MI-BCI) training. MI-BCI performance correlates with the activation in the frontoparietal attention network, premotor-parietal network, and supplementary motor area (SMA). This study aimed to enhance motor imagery ability and MI-BCI performance by modulating the excitability of the right dorsolateral prefrontal cortex (RDLPFC) through intermittent theta-burst stimulation (iTBS), inducing neuroplastic changes.<br><br>METHODS: Fifty-two healthy right-handed participants were randomly assigned to either the iTBS or sham group. They undertook two MI-BCI training sessions, with electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) used to assess acute neuroplasticity changes. The intervention was administered between sessions. Corticospinal excitability and motor imagery vividness were assessed using single-pulse transcranial magnetic stimulation (spTMS) and the Kinesthetic and Visual Imagery Questionnaire-20 (KVIQ-20) before and following the trial.<br><br>RESULTS: The iTBS group significantly improved motor state percentage (MSP). Significant &#xb5; event-related desynchronization (&#xb5;-ERD) was observed at the F4 electrode in the iTBS group. Functional connectivity (FC) analyses revealed decreased connectivity among several electrodes during the post-intervention period. The hemodynamic response function (HRF) indicated significant activation in the right PMC and SMA, with reduced FC among motor areas. No significant differences in MEP, CSP, and KVIQ-20 scores were found between groups.<br><br>CONCLUSION: iTBS targeting the RDLPFC may improve MI-BCI training performance and address the "BCI inefficiency" problem. RDLPFC stimulation induced changes in FC of brain regions associated with motor imagery and increased the activation of motor areas, suggesting that the RDLPFC could be a promising target for enhancing motor imagery and optimizing BCI systems.}, }
@article {pmid40745252, year = {2025}, author = {Ke, Y and Han, Y and Liu, P and Ming, D}, title = {Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1338}, pmid = {40745252}, issn = {2052-4463}, support = {62276184 and 81925020//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography ; *Augmented Reality ; Vision, Binocular ; }, abstract = {Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown significant promise for practical applications. The integration of SSVEP-BCIs with head-mounted augmented-reality (AR) displays is expected to foster wearable, portable systems; nevertheless, empirical resources for such configurations are scarce, especially for paradigms employing innovative stimulation paradigms. Here we present a curated SSVEP dataset recorded with a binocular AR headset that independently modulates the visual input to each eye and a lightweight electroencephalography recorder. Beyond the conventional binocular-congruent single-frequency stimulation adopted in AR-SSVEP studies, the dataset systematically explores binocular-incongruent dual-frequency encoding whereby the two lenses render flickers with distinct frequencies and/or phases. We report comparative analyses of SSVEP characteristics and BCI performance under congruent versus incongruent protocols, and delineate the influence of inter-ocular frequency and phase disparities. The results substantiate the feasibility of wearable AR-SSVEP-BCIs and highlight binocular-incongruent dual-frequency stimulation as a compelling strategy for improving target separability. The dataset should accelerate research on portable SSVEP-BCIs, novel encoding schemes, and the neural mechanisms of binocular vision.}, }
@article {pmid40744250, year = {2025}, author = {Li, Q and Ping, A and Feng, Y and Xu, B and Zhang, B and Roe, AW and Gao, L and Li, X}, title = {Mesoscale functional connectivity of amygdala to the auditory and prefrontal cortex of macaque monkeys revealed by INS-fMRI.}, journal = {NeuroImage}, volume = {318}, number = {}, pages = {121406}, doi = {10.1016/j.neuroimage.2025.121406}, pmid = {40744250}, issn = {1095-9572}, mesh = {Animals ; *Prefrontal Cortex/physiology/diagnostic imaging ; *Amygdala/physiology ; *Auditory Cortex/physiology ; *Magnetic Resonance Imaging/methods ; Male ; Macaca mulatta ; Neural Pathways/physiology ; Brain Mapping/methods ; Acoustic Stimulation ; }, abstract = {Mammals rely heavily on their auditory system to perceive environmental threats, socially communicate, and care for the young. As an extension of the multiple sensory system including the auditory system, the amygdala evaluates the emotional salience of acoustic stimuli, and mediates its impact on sensory, cognitive, and physiological aspects of emotional processing via the lateral amygdala (LA), basal amygdala (BA), and central amygdala (CeA) nuclei of the amygdala in acoustic domain. However, the functional connections of LA, BA, and CeA with the auditory cortex (AC) and the prefrontal cortex (PFC) remain unclear, particularly at the mesoscale level. Here we employed a novel method called INS-fMRI (Infrared Neural Stimulation combined with high-resolution functional magnetic resonance imaging) in Macaque monkeys, this method permits stimulation of multiple sites within single animals in vivo, so that the relative organization of auditory networks can be studied. We found that: (1) Focal INS stimulation of the amygdala elicited robust and reliable responses in both the AC and the PFC; (2) Amygdala stimulation mainly activated ipsilateral AC and PFC; (3) The stimulation of the amygdala mainly activated the secondary AC, and the dorsolateral PFC; (4) The connection between the amygdala and the cortex is mainly mediated by neurons in LA and BA connection area. Our study further revealed the functional connectivity among the amygdala subnucleus, the auditory cortex and the prefrontal cortex, and will shed light on the research for processing biologically meaningful complex sounds.}, }
@article {pmid40744238, year = {2025}, author = {Wen, Z and Yang, D and Yang, Y and Hu, J and Parviainen, A and Chen, X and Li, Q and VanDeusen, E and Ma, J and Tay, F}, title = {The path to biotechnological singularity: Current breakthroughs and outlook.}, journal = {Biotechnology advances}, volume = {84}, number = {}, pages = {108667}, doi = {10.1016/j.biotechadv.2025.108667}, pmid = {40744238}, issn = {1873-1899}, mesh = {Humans ; *Biotechnology/trends ; Gene Editing ; Artificial Intelligence ; Synthetic Biology ; Regenerative Medicine ; CRISPR-Cas Systems ; Brain-Computer Interfaces ; }, abstract = {Fueled by rapid advances in gene editing, synthetic biology, artificial intelligence, regenerative medicine, and brain-computer interfaces, biotechnology is approaching a transformative era often referred to as biotechnological singularity. CRISPR-based gene editing has revolutionized genetic engineering, enabling precise modifications for treating hereditary diseases and cancer. Synthetic biology facilitates sustainable biomaterial production and innovative therapeutic applications. Artificial intelligence accelerates drug discovery, enhances diagnostic accuracy, and personalizes treatment through deep learning models. Driven by stem cell research, regenerative medicine offers promising avenues for reversing aging and treating degenerative diseases. Brain-computer interfaces merge human cognition with technology, enabling direct neural control of prosthetics and expanding human-machine interactions. These breakthroughs, however, raise ethical, regulatory, and societal concerns, including equitable access, biosecurity risks, and the implications of human enhancement. The convergence of biological and computational technologies challenges traditional boundaries, necessitating comprehensive governance frameworks. By embracing responsible innovation, society can harness these advancements for transformative health interventions, environmental sustainability, and extended longevity. The realization of biotechnological singularity depends on interdisciplinary collaboration among scientists, policymakers, and the public to ensure that progress aligns with the well-being of humanity and ethical considerations.}, }
@article {pmid40743699, year = {2025}, author = {Amiri, G and Shalchyan, V}, title = {Decoding muscle activity via CNN-LSTM from 3D spatiotemporal EEG.}, journal = {Computer methods and programs in biomedicine}, volume = {271}, number = {}, pages = {108983}, doi = {10.1016/j.cmpb.2025.108983}, pmid = {40743699}, issn = {1872-7565}, mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Male ; Adult ; Electromyography ; Female ; Deep Learning ; Signal Processing, Computer-Assisted ; Linear Models ; *Muscle, Skeletal/physiology ; Young Adult ; Algorithms ; }, abstract = {OBJECTIVE: Reconstructing muscle activity from electromyogram (EMG) data using non-invasive electroencephalogram (EEG) signals could lead to significant advancements in brain-computer interfaces (BCIs). However, extracting muscle-related signals from EEG poses considerable challenges due to the mixed nature of signals captured by EEG sensors from various cortical regions.<br><br>APPROACH: This study introduces a new method for estimating muscle activity from non-invasive EEG signals while participants performed the grasp and lift (GAL) task. Envelopes of the delta, theta, alpha, beta, and gamma frequency bands were chosen as EEG features for the decoding models, computed similarly to muscle activity (EMG envelopes). These were converted into three-dimensional spatiotemporal matrices based on EEG electrode locations. A deep learning model, combining convolutional neural networks (CNN) for spatial and long short-term memory (LSTM) network for temporal EEG information extraction, was applied. This model was compared with two linear and nonlinear decoding methods: multivariate linear regression (mLR) and multilayer perceptron (MLP).<br><br>MAIN RESULTS: The average &#xb1; standard deviation of the normalized root mean square error (nRMSE), coefficient of determination (R&#xb2;), and correlation coefficient (CC) between the estimated and actual muscle activity of two muscles in five participants were 0.21 &#xb1; 0.05, 0.54 &#xb1; 0.17, and 0.76 &#xb1; 0.10, respectively. The CNN-LSTM model outperformed both mLR and MLP approaches (p-value < 0.016), with higher frequencies proving more effective for decoding.<br><br>SIGNIFICANCE: The proposed model effectively captures nonlinear relationships between brain and muscle activities, indicating its potential to enhance the accuracy and reliability of non-invasive BCIs.}, }
@article {pmid40742862, year = {2025}, author = {Heo, D and Kim, SP}, title = {Freeing P300-Based Brain-Computer Interfaces From Daily Recalibration by Extracting Daily Common ERPs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2977-2987}, doi = {10.1109/TNSRE.2025.3594341}, pmid = {40742862}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; *Event-Related Potentials, P300/physiology ; Male ; Algorithms ; Electroencephalography/methods ; Adult ; Female ; Young Adult ; Calibration ; Reproducibility of Results ; }, abstract = {When people use brain-computer interfaces (BCIs) based on event-related potentials (ERPs) over different days, they often need to repeatedly calibrate BCIs every day using ERPs acquired on the same day. This cumbersome recalibration procedure would make it difficult to use BCIs daily. We aim to address the daily recalibration issue by examining across-day variations of the BCI performance and proposing a method to avoid daily recalibration. To this end, we implemented a P300-based BCI system designed to control a home appliance over five days. We first examined how the BCI performance varied across days with or without daily recalibration. On each day, the BCIs were tested using recalibration-based and recalibration-free decoders (RB and RF), with an RB or an RF decoder being built on the training data on each day or those on the first day, respectively. Using the RF decoder resulted in lower BCI performance on subsequent days compared to the RB decoder. Then, we developed a method to extract daily common ERP patterns from observed ERP signals using the sparse dictionary learning algorithm. We applied this method to the RF decoder and retested the BCI performance over days. Using the proposed method improved the RF decoder performance on subsequent days; the performance was closer to the level of the RB decoder compared to the original RF decoder. The method may provide a novel approach to addressing the daily-recalibration issue for P300-based BCIs, which is essential to implementing BCIs into daily life.}, }
@article {pmid40741299, year = {2025}, author = {Si, Y and Sun, Y and Wu, K and Gao, L and Wang, S and Xu, M and Qi, X}, title = {Effects of ASMR on mental fatigue recovery revealed by EEG power and brain network analysis.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1619424}, pmid = {40741299}, issn = {1662-5161}, abstract = {INTRODUCTION: Mental fatigue, resulting from prolonged cognitive tasks or sleep deprivation, significantly impacts safety and performance, particularly in high-risk environments. However, effective intervention methods are limited, highlighting the urgent need for new approaches to alleviate mental fatigue. This study explores the effectiveness of Autonomous Sensory Meridian Response (ASMR) as a novel intervention for alleviating mental fatigue.<br><br>METHODS: A within-subject design was employed in this work, where 28 healthy young subjects (M/F = 17/11, age = 21.82 &#xb1; 0.37 years) were requested to perform a continuous 30 min sustained attention task (named No-Break session) and a 30 min task with a 4-min mid-task ASMR break (named ASMR-Break session) at a counterbalanced order. The immediate effect and general effect of ASMR were quantitatively assessed on behavioral performance and EEG characteristics.<br><br>RESULTS: Behaviorally, only significant immediate effect was revealed as showing in reduced reaction time. Further interrogation of brain dynamics showed complex patterns of spatio-spectrum alterations and an interaction in small-world metric in theta band. Specifically, the ASMR intervention prevented an increase in small-worldness, and the correlation between changes in small-worldness and reaction times diminished after the intervention.<br><br>DISCUSSION: In sum, this preliminary investigation provides insight into ASMR's neural mechanisms and suggests it may help attenuate fatigue. Further research in larger, more diverse samples will be necessary to confirm its utility for mental fatigue management in real-world settings.}, }
@article {pmid40741298, year = {2025}, author = {Zhu, T and Tang, H and Jiang, L and Li, Y and Li, S and Wu, Z}, title = {A study of motor imagery EEG classification based on feature fusion and attentional mechanisms.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1611229}, pmid = {40741298}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor imagery EEG-based action recognition is an emerging field arising from the intersection of brain science and information science, which has promising applications in the fields of neurorehabilitation and human-computer collaboration. However, existing methods face challenges including the low signal-to-noise ratio of EEG signals, inter-subject variability, and model overfitting.<br><br>METHODS: We propose HA-FuseNet, an end-to-end motor imagery action classification network. This model integrates feature fusion and attention mechanisms to classify left hand, right hand, foot, and tongue movements. Its innovations include: (1) multi-scale dense connectivity, (2) hybrid attention mechanism, (3) global self-attention module, and (4) lightweight design for reduced computational overhead.<br><br>RESULTS: On BCI Competition IV Dataset 2A, HA-FuseNet achieved 77.89% average within-subject accuracy (8.42% higher than EEGNet) and 68.53% cross-subject accuracy.<br><br>CONCLUSION: The model demonstrates robustness to spatial resolution variations and individual differences, effectively mitigating key challenges in motor imagery EEG classification.}, }
@article {pmid40741296, year = {2025}, author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y}, title = {Classification of finger movements through optimal EEG channel and feature selection.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1633910}, pmid = {40741296}, issn = {1662-5161}, abstract = {INTRODUCTION: Electrencephalography (EEG)-based brain-computer interfaces (BCIs) have become popular as EEG is accepted as the simplest and non-invasive neuroimaging modality to record the brain's electrical activity. In the current BCI research context, apart from predicting extremity movements, recent BCI studies have been interested in accurately predicting finger movements of the same hand using different pattern recognition methods over EEG data collected based on motor imagery (MI), through which a mental image of the desired action is generated when a person ideally simulates or imagines carrying out a certain motor task. Although several pattern recognition methods have already been recommended in literature, majority of the studies focusing on classifying five finger movements, were based on study designs that neglected or excluded the idle state of brain (i.e., no mental task state) during which brain does not carry out any MI task. This study design may result in an increasing number of false positives and a significant decrease in the prediction rates and classification performance. Moreover, recent studies have focused on improving prediction performance using complex feature extraction and machine learning algorithms while ignoring comprehensive EEG channels and feature investigation in the prediction of finger movements from EEGs. Therefore, the objectives of this study are threefold: (i) to develop a more viable and practical system to predict the movements of five fingers and the no mental task (NoMT) state from EEG signals (ii) to analyze the effects of the statistical-significance based feature selection method over four different feature domains (nonlinear domain, time-domain, frequency-domain and time-frequency domain) and their combinations, and (iii) to test these feature sets with different and prominent classifiers.<br><br>METHODS: In this study, our major goal is not to explore the best machine algorithm performance, but to investigate the best EEG channels and features that can be used in the classification of finger movements. Hence, the comprehensive analysis of the effectiveness of EEG channels and features is performed utilizing a statistically significant feature distribution over 19 EEG channels for each feature set independently. A bulky dataset of electroencephalographic MI for EEG-based BCIs is used in this study. A total of 1102 EEG features supplied from different feature domains have been investigated. Subsequently, these features were tested with eight well-known classifiers, comprising Decision tree, Discriminant analysis, Naive Bayes, Support vector machine, k-nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.<br><br>RESULTS: For subject-dependent analysis, the maximum accuracy of 59.17% was obtained using the EEG features that were selected the most (including (i) energy and variance of five frequency bands in frequency-domain feature set, (ii) all feature types in time-domain, time-frequency domain, and nonlinear domain feature sets) and all EEG channels by the Support vector machine algorithm. For subject-independent analysis, the maximum accuracy of 39.30% was obtained using the mostly selected EEG features (which are (i) all feature types excluding the waveform length, average amplitude change value, absolute difference in standard deviation, and slope-change value feature types in time-domain feature set, (ii) the energy and variance values of all frequency bands except gamma frequency band in frequency-domain feature set, (iii) the entropy value of five frequency bands in time-frequency-domain feature set, and (iv) SD 2 and SD 1/SD 2 values where lag = 1 in nonlinear feature set) and EEG channels (which are (i) some definite EEG channels including 2nd, 3rd, 7th, 11th, 13th, 14th, and 15th channels in time-frequency-domain feature set and (ii) all EEG channels in time-domain, frequency-domain, and nonlinear feature sets) by the Support vector machine classifier.<br><br>DISCUSSION: Experimental results demonstrate that despite the high-class number, the proposed approach obtained a modest yet considerable advancement in finger movement prediction when the results are compared to the results of similar studies. Additionally, for almost all feature sets, the statistical significance-based feature reduction method improves the prediction performance in the most of classifiers, contributing elaborate EEG channel and feature analysis. Nonetheless, in this study, we used an EEG dataset recorded from only 13 healthy subjects; therefore, a dataset covering more subjects is necessary to reach a more general conclusion.}, }
@article {pmid40740060, year = {2025}, author = {Kumar, A and Kumar, A}, title = {BiLSTM-Based Human Emotion Classification Using EEG Signal.}, journal = {Clinical EEG and neuroscience}, volume = {}, number = {}, pages = {15500594251364017}, doi = {10.1177/15500594251364017}, pmid = {40740060}, issn = {2169-5202}, abstract = {Emotion recognition using electroencephalography (EEG) signals has garnered significant attention due to its applications in affective computing, human-computer interaction, and healthcare. This study employs a Bidirectional Long Short-Term Memory (BiLSTM) network to classify emotions using EEG data from four well-established datasets: SEED, SEED-IV, SEED-V, and DEAP. By leveraging the temporal dependencies inherent in EEG signals, the BiLSTM model demonstrates robust learning of emotional states. The model achieved notable classification accuracies, with 92.30% for SEED, 99.98% for SEED-IV, 99.97% for SEED-V, and 88.33% for DEAP, showcasing its effectiveness across datasets with varying class distributions. The superior performance on SEED-IV and SEED-V underscores the BiLSTM's capability to capture bidirectional temporal information, which is crucial for emotion recognition tasks. Moreover, this work highlights the importance of utilizing diverse datasets to validate the generalizability of EEG-based emotion recognition models. The integration of both dimensional and discrete emotion models in the study demonstrates the framework's flexibility in addressing various emotion representation paradigms. Future directions include optimizing the framework for real-world applications, such as wearable EEG devices, and exploring transfer learning techniques to enhance cross-subject and cross-cultural adaptability. Overall, this study advances EEG-based emotion recognition methodologies, establishing a robust foundation for integrating affective computing into various domains and paving the way for real-time, reliable emotion recognition systems.}, }
@article {pmid40739107, year = {2025}, author = {Jia, O and Tan, Q and Zhang, S and Jia, K and Gong, M}, title = {The precision of attention selection during reward learning influences the mechanisms of value-driven attention.}, journal = {NPJ science of learning}, volume = {10}, number = {1}, pages = {49}, pmid = {40739107}, issn = {2056-7936}, support = {2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 32371087//National Natural Science Foundation of China/ ; 32300855//National Natural Science Foundation of China/ ; 226-2024-00118//Fundamental Research Funds for the Central University/ ; 2021ZD0200409//National Science and Technology Innovation 2030-Major Project/ ; }, abstract = {Reward-predictive items capture attention even when task-irrelevant. While value-driven attention typically generalizes to stimuli sharing critical reward-associated features (e.g., red), recent evidence suggests an alternative generalization mechanism based on feature relationships (e.g., redder). Here, we investigated whether relational coding of reward-associated features operates across different learning contexts by manipulating search mode and target-distractor similarity. Results showed that singleton search training induced value-driven relational attention regardless of target-distractor similarity (Experiments 1a-1b). In contrast, feature search training produced value-driven relational attention only when targets and distractors were dissimilar, but not when they were similar (Experiments 2a-2c). These findings indicate that coarse selection training (singleton search or feature search among dissimilar items) promotes relational coding of reward-associated features, while fine selection (feature search among similar items) engages precise feature coding. The precision of target selection during reward learning thus critically determines value-driven attentional mechanisms.}, }
@article {pmid40737169, year = {2025}, author = {Xiong, D and Hu, L and Jin, J and Ding, Y and Tan, C and Zhang, J and Tian, Y}, title = {Interpretable Cross-Modal Alignment Network for EEG Visual Decoding With Algorithm Unrolling.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {36}, number = {11}, pages = {19894-19908}, doi = {10.1109/TNNLS.2025.3592646}, pmid = {40737169}, issn = {2162-2388}, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Neural Networks, Computer ; Signal-To-Noise Ratio ; *Visual Perception/physiology ; Photic Stimulation/methods ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Machine Learning ; }, abstract = {Accurate decoding in electroencephalography (EEG) technology, particularly for rapid visual stimuli, remains challenging due to the low signal-to-noise ratio (SNR). Additionally, existing neural networks struggle with issues related to generalization and interpretability. This article proposes a cross-modal aligned network, E2IVAE, which leverages shared information from multiple modalities for self-supervised alignment of EEG to images for extracting visual perceptual information and features a novel EEG encoder, ISTANet, based on algorithm unrolling. This network framework significantly enhances the accuracy and stability of EEG decoding for object recognition in novel classes while reducing the extensive neural data typically required for training neural decoders. The proposed ISTANet employs algorithm unrolling to transform the multilayer sparse coding algorithm into an end-to-end format, extracting features from noisy EEG signals while incorporating the interpretability of traditional machine learning. The experimental results demonstrate that our method achieves SOTA top-1 accuracy of 62.39% and top-5 accuracy of 88.98% on a comprehensive rapid serial visual presentation (RSVP) dataset for public comparison in a 200-class zero-shot neural decoding task. Additionally, ISTANet enables visualization and analysis of multiscale atom features and overall reconstruction features, exploring biological plausibility across temporal, spatial, and spectral dimensions. On another more challenging RSVP large-scale dataset, the proposed framework also achieves significantly above chance-level performance, proving its robustness and generalization. This research provides critical insights into neural decoding and brain-computer interfaces (BCIs) within the fields of cognitive science and artificial intelligence.}, }
@article {pmid40735361, year = {2025}, author = {Zhang, Q and Zhang, C and Ji, H and Chen, J and Wang, X and Zhang, T and Liu, P and Wang, Z and Xu, Y}, title = {Ethical governance of clinical research on the brain-computer interface for mental disorders: a modified Delphi study.}, journal = {General psychiatry}, volume = {38}, number = {4}, pages = {e101755}, pmid = {40735361}, issn = {2517-729X}, abstract = {BACKGROUND: Clinical brain-computer interface (BCI) for mental disorders is an emerging interdisciplinary research field, posing new ethical concerns and challenges, yet lacking practical ethical governance guidelines for stakeholders and the entire community.<br><br>AIMS: This study aims to establish a multidisciplinary consensus of principles for ethical governance of clinical BCI research for mental disorders and offer practical ethical guidance to stakeholders involved.<br><br>METHODS: A systematic literature review, symposium and roundtable discussions, and a pre-Delphi (round 0) survey were conducted to form the questionnaire for the three-round modified Delphi study. Two rounds of surveys, followed by a third round of independent interviews of 25 experts from BCI-related research domains, were involved. We conducted quantitative analysis of responses and agreements among experts to reveal the consensus and differences regarding the ethical governance of mental BCI research from a multidisciplinary perspective.<br><br>RESULTS: The Delphi panel emphasised important concerns of ethical review practices and ethical principles within the BCI context, identified qualified and highly influential institutions and personnel in conducting and advancing clinical BCI research, and recognised prioritised aspects in the risk-benefit evaluation. Experts expressed diverse opinions on specific ethical concerns, including concerns about invasive technology, its impact on humanity and potential social consequences. Agreement was reached that the practices of ethical governance of clinical BCI for mental disorders should focus on patient voluntariness, autonomy, long-term effects and related assessments of BCI interventions, as well as privacy protection, transparent reporting and ensuring that the research is conducted in qualified institutions with strong data security.<br><br>CONCLUSIONS: Ethical governance of clinical research on BCI for mental disorders should include interdisciplinary experts to balance various needs and incorporate the expertise of different stakeholders to avoid serious ethical issues. It requires scientifically grounded approaches, continuous monitoring and interdisciplinary collaboration to ensure evidence-based policies, comprehensive risk assessments and transparency, thereby promoting responsible innovations and protecting patient rights and well-being.}, }
@article {pmid40735214, year = {2025}, author = {Ran, J and Xu, J and Luo, D and Li, T and Xu, J}, title = {Problematic internet use and aggression in Chinese middle school students: mediation effect of reality social connectedness.}, journal = {Frontiers in public health}, volume = {13}, number = {}, pages = {1587400}, pmid = {40735214}, issn = {2296-2565}, mesh = {Humans ; *Aggression/psychology ; China/epidemiology ; Male ; Female ; Cross-Sectional Studies ; *Students/psychology/statistics &amp; numerical data ; Adolescent ; Surveys and Questionnaires ; *Internet Addiction Disorder/psychology/epidemiology ; *Internet Use/statistics &amp; numerical data ; East Asian People ; }, abstract = {INTRODUCTION: Problematic internet use (PIU) has become a prevalent concern worldwide and is associated with increased aggression. However, the underlying effect of PIU on aggression remains unclear. In this study, we aimed to investigate the potential influence of reality social connectedness (RSC) on the relationship between PIU and aggression.<br><br>METHODS: We used cross-sectional data from a large survey conducted among middle school students in four provinces of China between September 2022 and March 2023. PIU, RSC, and aggression were assessed using Young's 20-item Internet Addiction Test (IAT-20), the modified Social Connectedness Scale-Revised (SCS-R), and the Buss-Perry Aggression Questionnaire (BPAQ), respectively.<br><br>RESULTS: We found that students who experienced PIU had significantly higher scores on the BPAQ, which reflects the aggression levels, compared to students without PIU. Specifically, all four dimensions of aggression-verbal aggression, physical aggression, hostility, and anger-were elevated in the PIU group. Additionally, RSC was significantly reduced among individuals with PIU. Notably, RSC significantly mediated the relationship between PIU and aggression, accounting for 18.89% of the total effect. Among the four dimensions of aggression, the mediating effect of RSC was strongest for hostility, followed by anger and physical aggression, with the weakest observed for verbal aggression.<br><br>DISCUSSION: RSC significantly mediated the relationship between PIU and aggression, suggesting that reduced RSC partially explains how PIU exacerbates aggression. This result highlights the importance of fostering RSC as a strategy to reduce aggression related to PIU.}, }
@article {pmid40734822, year = {2025}, author = {Liu, ZY and Zhang, L and Wang, ZD and Huang, ZQ and Li, MC and Lu, Y and Hu, JP and Chen, QL and Chen, XY}, title = {Magnetic resonance imaging for spinocerebellar ataxia: a bibliometric analysis based on web of science.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1512800}, pmid = {40734822}, issn = {1664-2295}, abstract = {The objective of this study was to review the history of magnetic resonance imaging (MRI) research on spinocerebellar ataxia (SCA) over the last 16 years. We conducted a comprehensive bibliometric analysis of relevant scientific literature that explores the use of MRI in studying SCA using CiteSpace. A total of 761 scientific manuscripts, published between January 2009 and March 2025 and available in the Web of Science (WoS) database, were included in this analysis. A total of 197 out of 761 articles were analyzed using CiteSpace to determine the number and centrality of publications, countries, institutions, journals, authors, cited references, and keywords related to MRI and SCA. Overall, the number of publications that use MRI to study SCA has gradually increased over the years. The United States, China, Italy, Germany, and Brazil are at the forefront in this research field; a total of 420 authors from 317 research institutions in these nations have published articles in neuroscience-related journals. Among the most cited publications are an article by Rezende et al. on brain structural damage in SCA3 patients and an review by Klockgether et al. on spinocerebellar ataxia. The keyword "spinocerebellar ataxia" has the highest frequency of occurrence. However, "feature" may become a research hotspot in the coming years based on the analysis of the keyword's citation burst. The findings of this bibliometric study provide a summary of the last 16 years of SCA research using MRI technology. More importantly, the present study identifies current trends and future research hotspots in the field, helping researchers to identify new and unexplored research areas.}, }
@article {pmid40731219, year = {2025}, author = {Motiwala, A and Soldado-Magraner, J and Batista, AP and Smith, MA and Yu, BM}, title = {Brain-computer interfaces as a causal probe for scientific inquiry.}, journal = {Trends in cognitive sciences}, volume = {}, number = {}, pages = {}, pmid = {40731219}, issn = {1879-307X}, support = {R01 MH118929/MH/NIMH NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; }, abstract = {Establishing causal relationships between neural activity and brain function requires experimental perturbations of neural activity. Many existing perturbation methods modify activity by directly applying external signals to the brain. We review an alternative approach where brain-computer interfaces (BCIs) leverage volitional control of neural activity to manipulate and causally perturb it. We highlight the potential of BCIs to manipulate neural activity in ways that are flexible, accurate, and adhere to intrinsic biophysical and network-level constraints to investigate the consequences of configuring neural population activity in specified ways. We discuss the advantages and disadvantages of using BCIs as a perturbation tool compared with other perturbation methods and how BCIs can expand the scope of questions that can be addressed about brain function.}, }
@article {pmid40731189, year = {2025}, author = {Zhao, X and Yu, J and Xu, B and Xu, Z and Lei, X and Han, S and Luo, S and Zhang, C and Peng, G and Li, J and Yu, J and Ling, Y and Fan, Z and Mo, W and Yang, Y and Zhang, J}, title = {Gut-derived bacterial vesicles carrying lipopolysaccharide promote microglia-mediated synaptic pruning.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {21}, number = {8}, pages = {e70331}, pmid = {40731189}, issn = {1552-5279}, support = {82020108012//National Natural Science Foundation of China/ ; 82371250//National Natural Science Foundation of China/ ; 2024C03098//Key Research and Development Program of Zhejiang Province/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; LZ23H090002//Natural Science Foundation of Zhejiang Province/ ; LY24H090006//Natural Science Foundation of Zhejiang Province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; }, mesh = {*Lipopolysaccharides/metabolism ; *Microglia/metabolism ; Humans ; *Gastrointestinal Microbiome/physiology ; Animals ; Mice ; *Extracellular Vesicles/metabolism ; *Alzheimer Disease/metabolism ; *Neuronal Plasticity/physiology ; Blood-Brain Barrier/metabolism ; Male ; Female ; Brain/metabolism ; }, abstract = {INTRODUCTION: Growing evidence links gut microbiota (GM) to Alzheimer's disease (AD). Elevated lipopolysaccharide (LPS) levels, a Gram-negative bacteria component, are found in AD brains, but how LPS breaches the blood-brain barrier (BBB) remains unclear. Hypotheses suggest that bacteria-derived extracellular vesicles (bEVs) may transport LPS across the BBB.<br><br>METHODS: bEVs were extracted from human and mouse feces and blood, and LPS levels were measured. In vivo imaging and immunofluorescence confirmed the transport of blood LPS-carrying bEVs across the BBB. The role of these bEVs in microglia was investigated both in vivo and in vitro.<br><br>RESULTS: Elevated LPS-containing bEVs were detected in the plasma of AD patients compared to healthy individuals. These bEVs activated microglial Piezo1, consequently precipitating an excessive synaptic pruning process mediated by the C1q-C3 complement pathway.<br><br>DISCUSSION: These findings illuminate the complex interplay between the gut microbiota, bEVs, neuroinflammation, and synaptic plasticity - a key early event in AD - offering insights for potential therapeutic interventions.<br><br>HIGHLIGHTS: GM-derived bEVs can traverse the BBB. LPS was necessary for bEVs' penetration into the brain, and bEVs might be closely related to AD progression. bEVs mediated microglial activation and synaptic pruning via C1q-C3 complement pathway. Microglia Piezo1 was involved in bEV-induced excessive synaptic pruning.}, }
@article {pmid40730254, year = {2025}, author = {Alouani, Z and Gannour, OE and Saleh, S and El-Ibrahimi, A and Daanouni, O and Cherradi, B and Bouattane, O}, title = {A novel contrastive Dual-Branch Network (CDB-Net) for robust EEG-Based Alzheimer's disease diagnosis.}, journal = {Brain research}, volume = {1865}, number = {}, pages = {149863}, doi = {10.1016/j.brainres.2025.149863}, pmid = {40730254}, issn = {1872-6240}, mesh = {*Alzheimer Disease/diagnosis/physiopathology ; Humans ; *Electroencephalography/methods ; Deep Learning ; *Neural Networks, Computer ; Male ; Brain/physiopathology ; Aged ; Female ; Signal Processing, Computer-Assisted ; }, abstract = {Alzheimer's Disease (AD) is neurodegenerative disorder that causes cognitive decline, memory loss, confusion, and changes in behavior. Early and accurate detection is important for timely intervention, current diagnostic methods can be slow, expensive, and have limited sensitivity. Electroencephalography (EEG) offers a simple and non-invasive way to measure brain activity, and it has shown promise in supporting AD diagnosis. However, EEG signals are often affected by noise-such as muscle movement, blinking, or electrical interference-which can make it harder for models to give reliable results. To address these challenges, we introduce CDB-Net (Contrastive Dual-Branch Network), a deep learning model built to improve the accuracy and robustness of EEG-based AD classification. The model uses two parallel branches: one processes clean EEG data, while the other processes a noisy version of the same data. By training these branches together using contrastive learning, the model learns to focus on features that stay consistent even when the signal is distorted by noise. A classification head is trained jointly using cross-entropy loss for downstream diagnosis. We tested our method on a public EEG dataset and found that CDB-Net achieved 97.92% accuracy on clean data and 83.41% accuracy even under adversarial attacks (FGSM), outperforming traditional machine learning classifiers and deep learning baselines models. These results highlight the effectiveness of contrastive dual-branch learning in enhancing model generalization and robustness, positioning CDB-Net as a promising tool for reliable EEG-based clinical decision support in the context of Alzheimer's Disease diagnosis.}, }
@article {pmid40728869, year = {2025}, author = {Kawaguchi, N and Koyano, K and Morita, H and Pengiran Mohamad Fadly, DNRAC and Shinabe, Y and Noguchi, Y and Arioka, M and Nakao, Y and Ozaki, M and Nakamura, S and Kondo, S and Konishi, Y and Kuboi, T and Okada, H and Yasuda, S and Itoh, S and Murao, K and Kusaka, T}, title = {Quantitative effects of bilirubin photoisomers on the measurement of direct bilirubin by the enzymatic bilirubin oxidase method.}, journal = {Annals of clinical biochemistry}, volume = {}, number = {}, pages = {45632251367245}, doi = {10.1177/00045632251367245}, pmid = {40728869}, issn = {1758-1001}, abstract = {BackgroundBilirubin photoisomers, generated during phototherapy or incidental light exposure, may interfere with direct bilirubin (DB) measurement using the bilirubin oxidase method. This interference is particularly relevant in neonates, who physiologically exhibit elevated levels of unconjugated bilirubin.MethodsResidual serum samples from 30 neonates were irradiated under controlled conditions to selectively produce bilirubin configurational isomers (BCIs) and structural isomers (BSIs). DB and total bilirubin (TB) were measured pre- and post- irradiation using the bilirubin oxidase method. BCI and BSI concentrations were quantified using high-performance liquid chromatography (HPLC), and their contributions to DB values were evaluated using linear and multiple regression analyses.ResultsPost-irradiation, DB levels increased significantly in correlation with BCI and BSI concentrations. Approximately 11% of BCI and 32% of BSI were quantified as DB using the bilirubin oxidase method. These findings were consistent across both individual and multiple regression models.ConclusionsBilirubin photoisomers significantly influence DB values measured by the bilirubin oxidase method, potentially leading to overestimation of conjugated bilirubin. In neonatal care, accurate interpretation of DB values requires attention to sample handling and awareness of photoisomer interference, particularly under light-expose conditions.}, }
@article {pmid40727297, year = {2025}, author = {Ebrahimibasabi, S and Golshahi, M and Shahraki, N and Tamjid Shabestari, D and Sajjadi, M and Hashemi, S and Borchert, A and Baker, I and Khalifehzadeh, L and Arami, H}, title = {Designing parylene coating for implantable brain-machine interfaces.}, journal = {RSC advances}, volume = {15}, number = {33}, pages = {26660-26672}, pmid = {40727297}, issn = {2046-2069}, abstract = {Parylene is widely recognized as an effective candidate for encapsulating implantable bioelectronics due to its outstanding chemical stability, conformity and biocompatibility. However, its weak adhesion to inorganic substrates remains a significant challenge. Here, we explored various pre- and post-deposition treatments to enhance adhesion and stability of parylene coating for implantable brain-machine interfaces (BMIs). We utilized 0%, 0.5%, 1%, and 1.5% (v/v) 3-(trimethoxysilyl)propyl-methacrylate as an adhesion promoter for substrate treatment prior to deposition. Deposited samples were subsequently subjected to post-heat treatments at various temperatures. Samples were exposed to an in vitro accelerated aging bath at 87 °C for 7 days to assess their post-implantation durability. Cytotoxicity and in vivo biocompatibility were also investigated to further evaluate biocompatibility and encapsulation efficiency of parylene coatings on commonly used rigid and flexible bioelectronic substrates. The emergence of carboxyl groups in FTIR and chlorine abstraction in EDS analyses, indicated that the as-deposited samples were degraded during aging. The chemical stability of these coatings was improved in heat-treated samples due to their higher crystallinity. Additionally, delamination and microcrack initiation/growth reduced due to post-heat treatments. We found the optimal heat treatment temperature to be 150 °C; any increase beyond this compromised coating quality by increasing delamination and defect formation. Increasing the concentration of adhesion promoter enhanced coating adhesion to the substrates in both as-deposited samples and the ones heat-treated at 150 °C. In contrast, the adhesion strength decreased when heat-treatment was performed at higher temperatures, even when the concentration of adhesion promoter was increased. Numerical analysis was used to assess the effect of parylene coating on the electrical performance of a typical implantable, wirelessly powered model device. The results demonstrated that the presence of the parylene layer not only preserved the wireless coupling between this device and the pickup probe, but also enhanced it. In addition to these favourable physiochemical improvements, parylene also promoted general in vivo brain compatibility and cell viability of the devices. This study revealed the synergistic effects of pre- and post-deposition treatments and systematically optimized adhesion and stability of parylene coatings for implantable BMIs for the first time.}, }
@article {pmid40722830, year = {2025}, author = {Ga, YJ and Go, YY and Yeh, JY}, title = {Small Interfering RNAs Targeting VP4, VP3, 2B, or 3A Coding Regions of Enterovirus A71 Inhibit Viral Replication In Vitro.}, journal = {Biomedicines}, volume = {13}, number = {7}, pages = {}, pmid = {40722830}, issn = {2227-9059}, support = {2019//Incheon National University/ ; }, abstract = {Background: Enterovirus A71 (EV-A71) is considered as the primary causative agent of hand, foot, and mouth disease (HFMD) in young children, leading to severe neurological complications and contributing to substantial mortalities in recent HFMD outbreaks across Asia. Despite this, there is currently no effective antiviral treatment available for EV-A71. RNA interference (RNAi) is a powerful mechanism of post-transcriptional gene regulation that utilizes small interfering RNA (siRNA) to target and degrade specific RNA sequences. Objectives: The aim of this study was to design various siRNAs targeting EV-A71 genomic regions and evaluate the RNAi efficacy against a novel, previously genetically uncharacterized EV-A71 strain. Methods: A novel EV-A71 strain was first sequenced to design target-specific siRNAs. The viral titers, viral protein expression, cytopathic effects, and cell viability of EV-A71-infected HeLa cells were examined to evaluate the specific viral inhibition by the siRNAs. Results: A substantial reduction in viral titers and viral protein synthesis was observed in EV-A71-infected HeLa cells treated with specific siRNAs targeting the VP4, VP3, 2B, and 3A genes. siRNAs delayed cytopathic effects and increased cell viability of EV-A71-infected HeLa cells. Nonspecific interferon induction caused by siRNAs was not observed in this study. In contrast, replication of coxsackievirus B3, another important member of the Enterovirus genus, remained unaffected. Conclusions: Overall, the findings demonstrate that RNAi targeting genomic regions of EV-A71 VP4, VP3, 2B, or 3A could become a potential strategy for controlling EV-A71 infection, and this promising result can be integrated into future anti-EV-A71 therapy developments.}, }
@article {pmid40722467, year = {2025}, author = {Zhan, H and Li, X and Song, X and Lv, Z and Li, P}, title = {MCTGNet: A Multi-Scale Convolution and Hybrid Attention Network for Robust Motor Imagery EEG Decoding.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {7}, pages = {}, pmid = {40722467}, issn = {2306-5354}, support = {No. 2108085MF207//Anhui Natural Science Foundation/ ; No. 2024AH050054//Natural Science Research Project of Anhui Educational Committee under Grant/ ; No. 2208085J05//Distinguished Youth Foundation of Anhui Scientific Committee/ ; No. 62476004//National Natural Science Foundation of China (NSFC)/ ; }, abstract = {Motor imagery (MI) EEG decoding is a key application in brain-computer interface (BCI) research. In cross-session scenarios, the generalization and robustness of decoding models are particularly challenging due to the complex nonlinear dynamics of MI-EEG signals in both temporal and frequency domains, as well as distributional shifts across different recording sessions. While multi-scale feature extraction is a promising approach for generalized and robust MI decoding, conventional classifiers (e.g., multilayer perceptrons) struggle to perform accurate classification when confronted with high-order, nonstationary feature distributions, which have become a major bottleneck for improving decoding performance. To address this issue, we propose an end-to-end decoding framework, MCTGNet, whose core idea is to formulate the classification process as a high-order function approximation task that jointly models both task labels and feature structures. By introducing a group rational Kolmogorov-Arnold Network (GR-KAN), the system enhances generalization and robustness under cross-session conditions. Experiments on the BCI Competition IV 2a and 2b datasets demonstrate that MCTGNet achieves average classification accuracies of 88.93% and 91.42%, respectively, outperforming state-of-the-art methods by 3.32% and 1.83%.}, }
@article {pmid40722306, year = {2025}, author = {Deniz, SM and Ademoglu, A and Duru, AD and Demiralp, T}, title = {Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG Data.}, journal = {Brain sciences}, volume = {15}, number = {7}, pages = {}, pmid = {40722306}, issn = {2076-3425}, abstract = {Background/Objectives: Emotion and cognition, two essential components of human mental processes, have traditionally been studied independently. The exploration of emotion and cognition is fundamental for gaining an understanding of human mental functioning. Despite the availability of various methods to measure and evaluate emotional states and cognitive processes, physiological measurements are considered to be one of the most reliable methods due to their objective approach. In particular, electroencephalography (EEG) provides unique insight into emotional and cognitive activity through the analysis of event-related potentials (ERPs). In this study, we discriminated pleasant/unpleasant emotional moods and low/high cognitive states using graph-theoretic features extracted from spatio-temporal components. Methods: Emotional data were collected at the Physiology Department of Istanbul Medical Faculty at Istanbul University, whereas cognitive data were obtained from the DepositOnce repository of Technische Universität Berlin. Wavelet coherence values for the N100, N200, and P300 single-trial ERP components in the delta, theta, alpha, and beta frequency bands were investigated individually. Then, graph-theoretic analyses were performed using wavelet coherence-based connectivity maps. Global and local graph metrics such as energy efficiency, strength, transitivity, characteristic path length, and clustering coefficient were used as features for classification using support vector machines (SVMs), k-nearest neighbor(K-NN), and linear discriminant analysis (LDA). Results: The results show that both pleasant/unpleasant emotional moods and low/high cognitive states can be discriminated, with average accuracies of up to 92% and 89%, respectively. Conclusions: Graph-theoretic metrics based on wavelet coherence of ERP components in the delta band with the SVM algorithm allow for the discrimination of emotional and cognitive states with high accuracy.}, }
@article {pmid40722299, year = {2025}, author = {Zhang, Z and Lu, G}, title = {Multimodal Knowledge Distillation for Emotion Recognition.}, journal = {Brain sciences}, volume = {15}, number = {7}, pages = {}, pmid = {40722299}, issn = {2076-3425}, abstract = {Multimodal emotion recognition has emerged as a prominent field in affective computing, offering superior performance compared to single-modality methods. Among various physiological signals, EEG signals and EOG data are highly valued for their complementary strengths in emotion recognition. However, the practical application of EEG-based approaches is often hindered by high costs and operational complexity, making EOG a more feasible alternative in real-world scenarios. To address this limitation, this study introduces a novel framework for multimodal knowledge distillation, designed to improve the practicality of emotion decoding while maintaining high accuracy, with the framework including a multimodal fusion module to extract and integrate interactive and heterogeneous features, and a unimodal student model structurally aligned with the multimodal teacher model for better knowledge alignment. The framework combines EEG and EOG signals into a unified model and distills the fused multimodal features into a simplified EOG-only model. To facilitate efficient knowledge transfer, the approach incorporates a dynamic feedback mechanism that adjusts the guidance provided by the multimodal model to the unimodal model during the distillation process based on performance metrics. The proposed method was comprehensively evaluated on two datasets based on EEG and EOG signals. The accuracy of the valence and arousal of the proposed model in the DEAP dataset are 70.38% and 60.41%, respectively. The accuracy of valence and arousal in the BJTU-Emotion dataset are 61.31% and 60.31%, respectively. The proposed method achieves state-of-the-art classification performance compared to the baseline method, with statistically significant improvements confirmed by paired t-tests (p < 0.05), and the framework effectively transfers knowledge from multimodal models to unimodal EOG models, enhancing the practicality of emotion recognition while maintaining high accuracy, thus expanding the applicability of emotion recognition in real-world scenarios.}, }
@article {pmid40722278, year = {2025}, author = {Yazıcı, M and Ulutaş, M and Okuyan, M}, title = {Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery.}, journal = {Brain sciences}, volume = {15}, number = {7}, pages = {}, pmid = {40722278}, issn = {2076-3425}, abstract = {The electroencephalogram (EEG) is one of the most popular neurophysiological methods in neuroscience. Scalp EEG measurements are obtained using various numbers of channels for both clinical and research applications. This pilot study explores the effect of EEG channel count on motor imagery classification using source analysis in brain-computer interface (BCI) applications. Different channel configurations are employed to evaluate classification performance. This study focuses on mu band signals, which are sensitive to motor imagery-related EEG changes. Common spatial patterns are utilized as a spatiotemporal filter to extract signal components relevant to the right hand and right foot extremities. Classification accuracies are obtained using configurations with 19, 30, 61, and 118 electrodes to determine the optimal number of electrodes in motor imagery studies. Experiments are conducted on the BCI Competition III Dataset Iva. The 19-channel configuration yields lower classification accuracy when compared to the others. The results from 118 channels are better than those from 19 channels but not as good as those from 30 and 61 channels. The best results are achieved when 61 channels are utilized. The average accuracy values are 83.63% with 19 channels, increasing to 84.70% with 30 channels, 84.73% with 61 channels, and decreasing to 83.95% when 118 channels are used.}, }
@article {pmid40721281, year = {2025}, author = {Ognard, J and Douri, D and El Hajj, G and Ghozy, S and Rohleder, M and Gentric, JC and Kadirvel, R and Kallmes, DF and Brinjikji, W}, title = {Future is Ven(o)us: A 5-year narrative update on the venous route for therapeutics in Neurointervention.}, journal = {AJNR. American journal of neuroradiology}, volume = {}, number = {}, pages = {}, doi = {10.3174/ajnr.A8942}, pmid = {40721281}, issn = {1936-959X}, abstract = {Over the past five years, transvenous (TV) techniques have rapidly expanded the neurointerventional landscape, offering new diagnostic and therapeutic strategies for a range of cerebrovascular conditions. This narrative review synthesizes contemporary evidence and technical advances across multiple venous applications, including TV embolization for arteriovenous malformations and dural fistulas, treatment of cerebrospinal fluid-venous fistulas, and venous sinus stenting for pulsatile tinnitus, intracranial hypertension, and skull-base leaks. Recent data underscore high efficacy rates and favorable safety profiles in carefully selected patients, often matching or surpassing traditional arterial approaches. Innovations such as fetal vein of Galen embolization, vein-targeted brain-computer interface implantation, and endovascular cerebrospinal fluid shunting exemplify the therapeutic versatility of venous access. However, procedural challenges, such as venous anatomy, access, and embolic control, require meticulous planning and advanced skillsets. Trials like TATAM and DIVE-IIN are and will shape evidence-based indications for TV therapy. With expanding indications and growing operator expertise, the venous route is evolving from a niche adjunct into a cornerstone of neurovascular care.ABBREVIATIONS: bAVM(s)＝ brain arteriovenous malformation(s); CVF(s)＝ cerebrospinal fluid-venous fistula(s); CVT＝ cerebral venous thrombosis; DAVF(s)＝ dural arteriovenous fistula(s); EVT＝ endovascular therapy; EVOH＝ ethylene-vinyl alcohol copolymer; IIH＝ idiopathic intracranial hypertension; JR-NET3＝ Japanese Registry of NeuroEndovascular Therapy; PT＝ pulsatile tinnitus; RPCT＝ retrograde pressure-cooker technique; SIH＝ spontaneous intracranial hypotension; sCSFL＝ spontaneous cerebrospinal fluid leak; SSWA＝ sigmoid sinus wallabnormality/abnormalities; TV＝ transvenous; TVE＝ transvenous embolization; VSS＝ venous sinus stenting.}, }
@article {pmid40720979, year = {2025}, author = {Silva, AB and Liu, JR and Anderson, VR and Kurtz-Miott, CM and Hallinan, IP and Littlejohn, KT and Brosler, SC and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF}, title = {Implications of shared motor and perceptual activations on the sensorimotor cortex for neuroprosthetic decoding.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, pmid = {40720979}, issn = {1741-2552}, support = {F30 DC021872/DC/NIDCD NIH HHS/United States ; U01 DC018671/DC/NIDCD NIH HHS/United States ; }, mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; *Electrocorticography/methods ; Motor Cortex/physiology ; *Neural Prostheses ; *Sensorimotor Cortex/physiology ; *Speech Perception/physiology ; Clinical Trials as Topic ; }, abstract = {Objective.Neuroprostheses can restore communicative ability to people with paralysis by decoding intended speech motor movements from the sensorimotor cortex (SMC). However, overlapping neural populations in the SMC are also engaged in visual and auditory perceptual processing. The nature of these shared motor and perceptual activations and their potential to interfere with decoding are particularly relevant questions for speech neuroprostheses, as reading and listening are essential daily functions.Approach.In two participants with vocal-tract paralysis and anarthria (ClinicalTrials.gov; NCT03698149), we developed an online electrocorticography (ECoG) based speech-decoding system that maintained accuracy and specificity to intended speech, even during common daily tasks like reading and listening. Offline, we studied the spectrotemporal characteristics and spatial distribution of reading, listening, and attempted-speech responses across our participants' ECoG arrays.Main results.Across participants, the speech-decoding system had zero false-positive activations during 63.2 min of attempted speech and perceptual tasks, maintaining accuracy and specificity to volitional speech attempts. Offline, though we observed shared neural populations that responded to attempted speech, listening, and reading, we found they leveraged different neural representations with differentiable spectrotemporal responses. Shared populations localized to the middle precentral gyrus and may have a distinct role in speech-motor planning.Significance.Potential neuroprosthesis users strongly desire reliable systems that will retain specificity to volitional speech attempts during daily use. These results demonstrate a decoding framework for speech neuroprostheses that maintains this specificity and further our understanding of shared perceptual and motor activity on the SMC.}, }
@article {pmid40720264, year = {2025}, author = {Cobilean, V and Mavikumbure, HS and Drake, D and Stuart, M and Manic, M}, title = {Investigating Membership Inference Attacks Against CNN Models for BCI Systems.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {11}, pages = {8164-8174}, doi = {10.1109/JBHI.2025.3593443}, pmid = {40720264}, issn = {2168-2208}, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; *Neural Networks, Computer ; *Deep Learning ; *Computer Security ; Algorithms ; *Signal Processing, Computer-Assisted ; }, abstract = {As Deep Learning (DL) algorithms become more widely adopted in healthcare applications, there is a greater emphasis on understanding and addressing the potential privacy risks associated with these models. The purpose of this study is to investigate the privacy vulnerabilities of the Convolutional Neural Network (CNN) classifiers for Electroencephalogram (EEG) data in the Brain-Computer Interfaces (BCIs). Specifically, it focuses on the Membership Inference Attack (MIA), which seeks to determine if data from an individual were used in model training. The novelty of this work lies in its empirical analysis of MIA, by addressing two key challenges that are less common in other domains: 1) heterogeneous datasets and 2) spatio-temporal design choices. Motivated by these challenges, we investigate the susceptibility to MIA based on: 1) the specifics of the training data set (number of participants, demographics), and 2) specifics of the CNN (such as architecture, regularization). Our experiments revealed that an adversary with limited knowledge of the model and its training process can compromise the privacy of training participants, noting that the same attack is not effective against deep learning models trained on image and tabular datasets. Some of our findings are: 1) training on diverse participant datasets improves the privacy of most participants but increases risks of memorization and vulnerabilities for underrepresented groups; 2) regularization is less effective in defending against the MIA on EEG data CNN classifiers when compared to other types of input data; 3) the depth and width of the model architecture have no impact on the effectiveness of membership attack. We hope that the insights presented will help future researchers develop more privacy-aware deep learning-based BCI systems.}, }
@article {pmid40720262, year = {2025}, author = {Wang, K and Liu, Y and Tian, F and Yi, W and Zhang, Y and Jung, TP and Xu, M and Ming, D}, title = {Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2956-2966}, doi = {10.1109/TNSRE.2025.3592988}, pmid = {40720262}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Neurofeedback/methods ; Male ; Female ; Electroencephalography ; Adult ; *Imagination/physiology ; Young Adult ; *Virtual Reality ; *Video Games ; Psychomotor Performance/physiology ; Healthy Volunteers ; Hand/physiology ; Sensorimotor Cortex/physiology ; Algorithms ; }, abstract = {Neurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that simulates real-life motor tasks to improve training efficiency. We conducted a detailed comparative analysis of the efficiency of the VR-based NFT and traditional Graz-based NFT. Forty-eight healthy subjects were randomly assigned to five groups and underwent various NFT protocols. Among them, the subjects in the four experimental groups were required to perform the NFT three times over five days, including virtual or real scenarios, as well as unilateral or bilateral hands training. We evaluated training effects by analyzing EEG features and classification performance, while online recognition duration served as the primary measure for assessing the adaptive NFT strategy. EEG analysis showed that VR-based NFT significantly enhanced the Event-related desynchronization (ERD) activations in the sensorimotor cortices over five days. The VR-based NFT group achieved a classification accuracy of 81.85%, representing a 10.14% improvement from baseline, which exceeded the 6.43% increase observed in the Graz-based NFT group. Furthermore, implementing the adaptive NFT strategy reduced the mean task duration by over 30% compared to the fixed-time training protocol. The results demonstrated that the adaptive MI-BCI-based NFT in a VR game achieves superior training outcomes while reducing training duration. These findings suggest the promising potential for applying MI-BCI NFT with VR games in motor rehabilitation following a stroke.}, }
@article {pmid40719991, year = {2025}, author = {Luo, X and Dong, J and Li, T}, title = {The Role of CCL11-CCR3 Induced Mitochondrial Dysfunction and Oxidative Stress in Cognitive Impairment in Early-onset Schizophrenia: Insights from Preclinical Studies.}, journal = {Inflammation}, volume = {}, number = {}, pages = {}, pmid = {40719991}, issn = {1573-2576}, support = {81920108018//National Nature Science Foundation of China Key Project/ ; }, abstract = {Abnormal cytokine expression has been implicated as a potential contributor to neurodegeneration. This study aimed to investigate the plasma cytokine profiles in patients with early-onset schizophrenia (SCZ) and to explore the molecular mechanisms underlying the role of the key cytokine CCL11 in contributing to cognitive impairment. Plasma concentrations of 44 cytokines were quantified in individuals with SCZ. The effects of CCL11 on mitochondrial function were examined in vitro using primary hippocampal neurons. An in vivo model was subsequently developed by administering CCL11 into the lateral ventricle. The impact of the CCL11-CCR3 signaling pathway on mitochondrial function, oxidative stress, and cognitive function within the hippocampus was assessed using a combination of behavioral testing, molecular biology experiments, transcriptomic analysis, and non-targeted metabolomics. In individuals with SCZ, CCL11 and IL-13 levels were notably higher than in controls. In vitro, CCL11 exposure caused mitochondrial dysfunction and increased reactive oxygen species in hippocampal neurons. In vivo, CCL11-treated mice showed cognitive deficits, mitochondrial fission, and neuroinflammation in the hippocampus. Comprehensive integration of transcriptomic and metabolomic data revealed that CCL11 significantly disrupted the Glucokinase/Glucose-6-phosphate metabolism pathway, coinciding with elevated metabolites indicative of oxidative damage. Finally, downregulation of the CCR3 receptor in the hippocampus mitigated CCL11-induced oxidative stress, mitochondrial dysfunction, and cognitive impairment. CCL11 causes cytotoxicity in neurons by increasing oxidative stress and mitochondrial dysfunction. In a mouse model, knockout of the CCR3 receptor alleviates CCL11-induced cognitive impairment, mitochondrial dysfunction, and oxidative stress.}, }
@article {pmid40719383, year = {2025}, author = {Greenbaum, D}, title = {Enhancing the Warfighter: Ethical, Legal, and Strategic Implications of Brain-Machine Interface-Enabled Military Exoskeletons.}, journal = {AJOB neuroscience}, volume = {16}, number = {4}, pages = {222-247}, doi = {10.1080/21507740.2025.2530952}, pmid = {40719383}, issn = {2150-7759}, mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Military Personnel/legislation &amp; jurisprudence ; *Exoskeleton Device/ethics ; }, abstract = {The integration of brain-machine interfaces (BMIs) with military exoskeletons represents a significant development in human-machine interaction, raising complex ethical, legal, and strategic challenges. Unlike conventional human enhancement technologies, BMI-exoskeleton systems translate neural intent directly into mechanical movement, generating new concerns regarding agency, accountability, long-term health outcomes, and the governance of neuroadaptive changes. This paper offers a structured interdisciplinary analysis, developing taxonomies of current technologies, tracing the historical trajectory of military exoskeleton development, and critically assessing the emerging convergence between exoskeletal augmentation and neural interface systems. We argue that BMI-exoskeletons constitute a distinct category of augmentation that blurs traditional boundaries between operator and tool, requiring governance frameworks attentive to both operational effectiveness and the ethical implications for individual service members, military institutions, and broader society. Drawing on research in engineering, neuroscience, military studies, and bioethics, we outline a comprehensive ethical-legal framework designed to guide the entire lifecycle of human enhancement-from recruitment and informed consent processes through active service, operational deployment, and post-discharge reintegration. Particular attention is given to autonomy, cybersecurity vulnerabilities, distributive justice, gender equity, and the risks associated with de-enhancement and neuroplastic adaptation. Recognizing the preliminary and rapidly evolving nature of empirical evidence in this domain, we emphasize the need for anticipatory, adaptive policy approaches that safeguard the dignity, rights, and long-term welfare of enhanced warfighters while ensuring that technological innovation proceeds with responsible, ethically-informed oversight.}, }
@article {pmid40719065, year = {2025}, author = {Xu, G and Wang, Z and Xu, K and Zhu, J and Zhang, J and Wang, Y and Hao, Y}, title = {Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain-to-Text Communication.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {12}, number = {40}, pages = {e05492}, pmid = {40719065}, issn = {2198-3844}, mesh = {Humans ; *Brain-Computer Interfaces ; *Handwriting ; *Brain/physiology ; Male ; Adult ; Electroencephalography/methods ; Female ; }, abstract = {The potential to decode handwriting trajectories from brain signals has yet to be fully explored in clinical brain-computer interfaces (BCIs). Here, intracortical neural signals are recorded from a paralyzed individual during attempted handwriting of complex characters. An innovative decoding framework is introduced to address both shape and temporal distortions between neural activity and movement, effectively resolving the misalignment issue commonly encountered in clinical BCIs due to the lack of accurate movement labels. The results demonstrated the reconstruction of highly accurate and human-recognizable handwriting trajectories, significantly outperforming conventional methods. Furthermore, the new framework enabled effective multi-day data fusion, leading to additional improvements in trajectory quality. By employing a dynamic time warping approach to translate trajectories into text, a recognition rate up to 91.1% is achieved within a 1000-character database. Additionally, the framework is applied to reconstruct single-trial trajectories of English letters using a previously published dataset, achieving similarly high recognition rates. Collectively, these findings present a novel BCI decoding scheme capable of accurately reconstructing handwriting trajectories, demonstrating its applicability to both alphabetic and logographic brain-to-text translation. This approach has the potential to revolutionize communication for individuals with motor impairments by enabling accurate brain-to-text translation across diverse languages.}, }
@article {pmid40718756, year = {2025}, author = {Kim, R and Liu, Y and Zhang, J and Xie, C and Luan, L}, title = {Towards Precise Synthetic Neural Codes: High-dimensional Stimulation with Flexible Electrodes.}, journal = {Npj flexible electronics}, volume = {9}, number = {1}, pages = {}, pmid = {40718756}, issn = {2397-4621}, support = {R01 EY036094/EY/NEI NIH HHS/United States ; R01 NS102917/NS/NINDS NIH HHS/United States ; U01 NS115588/NS/NINDS NIH HHS/United States ; U01 NS131086/NS/NINDS NIH HHS/United States ; }, abstract = {Neural representations arise from the spatiotemporally structured activity of neuron populations, inherently residing in high-dimensional spaces. Writing specific information into the central nervous system requires precisely manipulating neural states within this framework. However, current neuromodulation methods lack the precision to fully address this complexity, presenting a significant challenge for advancing effective bidirectional interfaces. In this perspective, we advocate for high-dimensional stimulation as a systematic approach capable of approximating the high dimensionality of natural neural code for brain-machine interface applications. We outline key technological requirements on resolution, coverage, and safety, review recent advances in critical application areas, and highlight the promise of flexible electrode technology in enabling a transformative leap towards precise synthetic neural codes.}, }
@article {pmid40718596, year = {2025}, author = {Chang, L and Yang, B and Zhang, J and Li, T and Feng, J and Xu, W}, title = {DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {118}, pmid = {40718596}, issn = {1871-4080}, abstract = {Accurate decoding and strong feature interpretability of Motor Imagery (MI) are expected to drive MI applications in stroke rehabilitation. However, the inherent nonstationarity and high intra-class variability of MI-EEG pose significant challenges in extracting reliable spatio-temporal features. We proposed the Dynamic Spatio-Temporal Feature Augmentation Network (DSTA-Net), which combines DSTA and the Spatio-Temporal Convolution (STC) modules. In DSTA module, multi-scale temporal convolutional kernels tailored to the α and β frequency bands of MI neurophysiological characteristics, while raw EEG serve as a baseline feature layer to retain original information. Next, Grouped Spatial Convolutions extract multi-level spatial features, combined with weight constraints to prevent overfitting. Spatial convolution kernels map EEG channel information into a new spatial domain, enabling further feature extraction through dimensional transformation. And STC module further extracts features and conducts classification. We evaluated DSTA-Net on three public datasets and applied it to a self-collected stroke dataset. In tenfold cross-validation, DSTA-Net achieved average accuracy improvements of 6.29% (p < 0.01), 3.05% (p < 0.01), 5.26% (p < 0.01), and 2.25% over the ShallowConvNet on the BCI-IV-2a, OpenBMI, CASIA, and stroke dataset, respectively. In hold-out validation, DSTA-Net achieved average accuracy improvements of 3.99% (p < 0.01) and 4.2% (p < 0.01) over the ShallowConvNet on the OpenBMI and CASIA datasets, respectively. Finally, we applied DeepLIFT, Common Spatial Pattern, and t-SNE to analyze the contributions of individual EEG channels, extract spatial patterns, and visualize features. The superiority of DSTA-Net offers new insights for further research and application in MI. The code is available in https://github.com/CL-Cloud-BCI/DSTANet-code.}, }
@article {pmid40718569, year = {2025}, author = {Lopez Blanco, C and Tyler, WJ}, title = {The vagus nerve: a cornerstone for mental health and performance optimization in recreation and elite sports.}, journal = {Frontiers in psychology}, volume = {16}, number = {}, pages = {1639866}, pmid = {40718569}, issn = {1664-1078}, abstract = {Decades of physiological and psychological research into human performance and wellness have established a critical role for vagus nerve signaling in peak physical and cognitive performance. We outline models and perspectives that have emerged through neuroscience and psychophysiology studies to elucidate how the vagus nerve governs human performance through its influence on central nervous system functions and autonomic nervous system activity. These functions include the monitoring and regulation of cardio-respiratory activity, emotional responses, inflammation and physical recovery, cognitive control, stress resilience, and team cohesion. We briefly review some useful interventions such as transcutaneous auricular vagus nerve stimulation, heart-rate variability biofeedback, and controlled breathing as accessible tools for enhancing vagal tone, improving executive functioning under pressure, and mitigating fatigue and burnout. We describe how these approaches and their biological underpinnings are rooted by psychological models like the Yerkes-Dodson law and Polyvagal theory to contextualize their effects on athletic performance. These perspectives suppor recent shifts in sports science toward integrating vagal-centered approaches as scalable, evidence-based strategies that can enhance human performance and wellness.}, }
@article {pmid40717726, year = {2025}, author = {Otarbay, Z and Kyzyrkanov, A}, title = {SVM-enhanced attention mechanisms for motor imagery EEG classification in brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1622847}, pmid = {40717726}, issn = {1662-4548}, abstract = {Brain-Computer Interfaces (BCIs) leverage brain signals to facilitate communication and control, particularly benefiting individuals with motor impairments. Motor imagery (MI)-based BCIs, utilizing non-invasive electroencephalography (EEG), face challenges due to high signal variability, noise, and class overlap. Deep learning architectures, such as CNNs and LSTMs, have improved EEG classification but still struggle to fully capture discriminative features for overlapping motor imagery classes. This study introduces a hybrid deep neural architecture that integrates Convolutional Neural Networks, Long Short-Term Memory networks, and a novel SVM-enhanced attention mechanism. The proposed method embeds the margin maximization objective of Support Vector Machines directly into the self-attention computation to improve interclass separability during feature learning. We evaluate our model on four benchmark datasets: Physionet, Weibo, BCI Competition IV 2a, and 2b, using a Leave-One-Subject-Out (LOSO) protocol to ensure robustness and generalizability. Results demonstrate consistent improvements in classification accuracy, F1-score, and sensitivity compared to conventional attention mechanisms and baseline CNN-LSTM models. Additionally, the model significantly reduces computational cost, supporting real-time BCI applications. Our findings highlight the potential of SVM-enhanced attention to improve EEG decoding performance by enforcing feature relevance and geometric class separability simultaneously.}, }
@article {pmid40715543, year = {2025}, author = {Xu, H and Huang, Q and Song, P and Chen, Y and Li, Q and Zhai, Y and Du, X and Ye, H and Bao, X and Mehmood, I and Tanigawa, H and Niu, W and Tu, Z and Chen, P and Zhang, T and Zhang, L and Zhao, X and Zhang, L and Wen, W and Cao, L and Yu, X}, title = {EEG neural indicator of temporal integration in the human auditory brain with clinical implications.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {1109}, pmid = {40715543}, issn = {2399-3642}, support = {32171044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100827//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32271078//National Natural Science Foundation of China (National Science Foundation of China)/ ; LGF22H170006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; *Auditory Perception/physiology ; Acoustic Stimulation ; Young Adult ; Middle Aged ; *Auditory Cortex/physiology ; *Brain/physiology ; Evoked Potentials, Auditory ; }, abstract = {Temporal integration, the process by which the auditory system combines sound information over a certain period to form a coherent auditory experience, is essential for auditory perception, yet its neural mechanisms remain underexplored. We use a "transitional click train" paradigm, which concatenates two click trains with slightly differing inter-click intervals (ICIs), to investigate temporal integration in the human brain. Using a 64-channel electroencephalogram (EEG), we recorded responses from healthy participants exposed to regular and irregular transitional click trains and conducted change detection tasks. Regular transitional click trains elicited significant change responses in the human brain, indicative of temporal integration, whereas irregular trains did not. These neural responses were modulated by length, contrast, and regularity of ICIs. Behavioral data mirrored EEG findings, showing enhanced detection for regular conditions compared to irregular conditions and pure tones. Furthermore, variations in change responses were associated with decision-making processes. Temporal continuity was critical, as introducing gaps between click trains diminished both behavioral and neural responses. In clinical assessments, 22 coma patients exhibited diminished or absent change responses, effectively distinguishing them from healthy individuals. Our findings identify distinct neural markers of temporal integration and highlight the potential of transitional click trains for clinical diagnostics.}, }
@article {pmid40715225, year = {2025}, author = {Das, A and Singh, S and Kim, J and Ahanger, TA and Pise, AA}, title = {Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {27161}, pmid = {40715225}, issn = {2045-2322}, support = {No.RS-2022-00155857//Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; No.RS-2022-00155857//Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Deep Learning ; *Brain/physiology ; Neural Networks, Computer ; Support Vector Machine ; Bayes Theorem ; Signal Processing, Computer-Assisted ; }, abstract = {Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning techniques. The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. The research evaluates the performance of five traditional machine learning classifiers- K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)-using the "PhysioNet EEG Motor Movement/Imagery Dataset". This dataset encompasses EEG data from various motor tasks, including both actual and imagined movements. Among the traditional classifiers, Random Forest achieved the highest accuracy of 91%, underscoring its efficacy in motor imagery classification within BCI systems. In addition to conventional approaches, the study also explores deep learning techniques, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks yielding accuracies of 88.18% and 16.13%, respectively. However, the proposed hybrid model, which synergistically combines CNN and LSTM, significantly surpasses both traditional machine learning and individual deep learning methods, achieving an exceptional accuracy of 96.06%. This substantial improvement highlights the potential of hybrid deep learning models to advance the state of the art in BCI systems, offering a more robust and precise approach to motor imagery classification.}, }
@article {pmid40714477, year = {2025}, author = {Wang, J and Zhao, S and Luo, Z and Zhou, Y and Li, S and Pan, G}, title = {EEGMamba: An EEG foundation model with Mamba.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {192}, number = {}, pages = {107816}, doi = {10.1016/j.neunet.2025.107816}, pmid = {40714477}, issn = {1879-2782}, abstract = {Electroencephalography (EEG) captures brain activity and has been widely used in clinic and brain-computer interfaces (BCIs). Classic EEG decoding methods rely on supervised learning, limiting their performance and generalizability. Inspired by the revolutionary impact of large models in other fields, researchers are now investigating EEG foundation models. Recently, state space models (SSMs), such as Mamba, have demonstrated strong sequence modeling capabilities, which may be suitable to model the spatiotemporal dependencies of EEG signals. However, the application of Mamba for EEG representation learning remains largely unexplored. In this paper, we investigate the potential of Mamba for learning generic EEG representations and propose a novel EEG foundation model, EEGMamba. Specifically, we employ Mamba encoder as the backbone of EEGMamba to model the spatiotemporal dependencies among EEG patches. Meanwhile, we use patch-based masked EEG reconstruction to learn generic EEG representations. EEGMamba is pre-trained on a large and diverse EEG corpus (16,724 h) from five datasets. We evaluate EEGMamba on up to six downstream BCI tasks using six public datasets. EEGMamba achieves the state-of-the-art performance across all the tasks, demonstrating its strong capability and generalizability.}, }
@article {pmid40714230, year = {2025}, author = {Gao, C and Wu, X and Ma, L and Li, D and Wang, Y and Guo, C and Li, W and Wang, H and Chu, C and Madsen, KH and Fan, L}, title = {Iterative prior-guided parcellation (iPGP) for capturing inter-subject and inter-nuclei variability in thalamic mapping.}, journal = {NeuroImage}, volume = {318}, number = {}, pages = {121399}, doi = {10.1016/j.neuroimage.2025.121399}, pmid = {40714230}, issn = {1095-9572}, mesh = {Humans ; Male ; Female ; Adult ; Adolescent ; Young Adult ; *Thalamus/diagnostic imaging/anatomy &amp; histology ; *Brain Mapping/methods ; Reproducibility of Results ; *Image Processing, Computer-Assisted/methods ; Middle Aged ; Magnetic Resonance Imaging/methods ; Child ; }, abstract = {The thalamus, a critical relay station in the brain, consists of multiple nuclei that play essential roles in various brain circuits. Identifying these nuclei is crucial for understanding how thalamic structures influence cognitive functions. However, genetic and environmental factors introduce substantial variability in thalamic parcellation patterns, posing both challenges and opportunities for individualized mapping of thalamic function. This study proposes an iterative prior-guided parcellation (iPGP) framework to construct individualized thalamic parcellations. The iPGP method utilizes the Morel histological atlas as prior guidance, incorporates spatially constrained local diffusion characteristics as features, and employs an iterative framework to optimize an individual-specific parcellation model. As a result, iPGP automatically adapts to individual thalamic contrast variations, producing personalized and anatomically consistent parcellations. Through test-retest assessments, iPGP demonstrated a high degree of intra-subject reproducibility. By evaluating inter-subject and inter-nuclei variability, iPGP exhibited strong adaptability across different age groups while capturing subject-specific and region-specific variability. Furthermore, thalamic parcellations generated by iPGP showed significant associations with adolescent age and adult behavioral-cognitive scores. Our findings suggest that iPGP effectively captures inter-subject and inter-nuclei variability in thalamic parcellation, highlighting its potential for advancing thalamic mapping in exploring brain function.}, }
@article {pmid40712594, year = {2025}, author = {Huang, J and Yang, P and Xiong, B and Lv, Y and Wang, Q and Wan, B and Zhang, ZQ}, title = {Mixup-based data augmentation for enhancing few-shot SSVEP detection performance.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/adf467}, pmid = {40712594}, issn = {1741-2552}, mesh = {*Evoked Potentials, Visual/physiology ; Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Photic Stimulation/methods ; Adult ; Female ; Young Adult ; Algorithms ; }, abstract = {Objective.Few-shot steady-state visual evoked potential (SSVEP) detection remains a major challenge in brain-computer interface (BCI) systems, as limited calibration data often leads to degraded performance. This study aims to enhance few-shot SSVEP detection through an effective data augmentation (DA) strategy.Approach.We propose a mixup-based DA method that generates synthetic trials by linearly interpolating between real SSVEP signals extracted using a sliding window strategy. The interpolation weight is optimized by maximizing the similarity between the mixed signal and both the template and reference signals. The augmented data is then used to train spatial filters for improved SSVEP detection.Main results.The proposed method was evaluated on two benchmark SSVEP datasets using task-related component analysis and incorporating neighboring stimuli data as spatial filters. Results demonstrate that the mixup-based augmentation significantly improves detection accuracy under few-shot conditions, outperforming existing augmentation and baseline methods.Significance.The mixup-based method offers an effective and practical solution for enhancing SSVEP decoding with limited data, reducing calibration time, and improving BCI systems' usability in real-world scenarios.}, }
@article {pmid40712572, year = {2025}, author = {Wang, J and Liu, Y and Ma, Y and Feng, Y and Lin, L and Ping, A and Tian, F and Zhang, X and Berman, AJL and Bollmann, S and Polimeni, JR and Roe, AW}, title = {In vivo 7 Tesla MRI of non-human primate intracortical microvascular architecture.}, journal = {Neuron}, volume = {113}, number = {16}, pages = {2621-2635.e5}, doi = {10.1016/j.neuron.2025.05.028}, pmid = {40712572}, issn = {1097-4199}, mesh = {Animals ; *Magnetic Resonance Imaging/methods ; *Microvessels/diagnostic imaging/anatomy &amp; histology ; *Cerebral Cortex/blood supply/diagnostic imaging ; *Cerebrovascular Circulation/physiology ; Male ; Macaca mulatta ; Arterioles/diagnostic imaging ; }, abstract = {Intracortical arterioles are key locations for blood flow regulation and oxygen supply in the brain and are critical to brain health and disease. However, imaging such small (<100-μm-sized) vessels in humans is challenging. Here, using non-human primates as a model, we developed a capability for imaging microvasculature in vivo with a clinical 7 T MRI scanner. Using simulations, we identified parameters for imaging intracortical vessels with slow flow and combined this with high-resolution imaging (64 × 64 μm[2] in-plane). Across large swaths of occipital, parietal, and temporal cortex, arrays of intracortical arterioles and venules were observed in gyral crowns and deep within sulcal folds. Systematic arteriole-venule patterns revealed potential architecture of input-output flow relationships. Even single vessels could be followed across cortical laminae. As a first step toward imaging microvasculature in humans, this method introduces a new technology and animal model for understanding relationships between functional and vascular architectures.}, }
@article {pmid40712216, year = {2025}, author = {Li, S and Xu, R and Wang, X and Cichocki, A and Jin, J}, title = {Dual branch neural network with dynamic learning mechanism for P300-based brain-computer interfaces.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {192}, number = {}, pages = {107876}, doi = {10.1016/j.neunet.2025.107876}, pmid = {40712216}, issn = {1879-2782}, abstract = {Brain-computer interface (BCI) system offers an alternative or supplementary means of interaction for individuals with disabilities. P300 speller is a commonly utilized BCI system due to its high stability, and reliability and without intensive user training. Nevertheless, the inherent class imbalance within P300 datasets predisposes the system to overfit, potentially impacting the classification performances. Existing class rebalancing methods mainly rely on resampling or adjusting the class weight with a fixed value, thus it is still tricky to ensure that the output is evenly balanced. To mitigate the above class imbalance issue, this study proposes a dual branch learning (DBL) method that concurrently considers feature representation and class imbalance. This approach involves the ingestion of two distinct sample types-uniformly sampled and reverse-sampled data-into the feature extraction and classification modules during the training phase. Furthermore, a dynamic learning mechanism is implemented to incrementally emphasize minority class samples (specifically the P300 component) as training progresses. The effectiveness of the proposed DBL method is proved using both publicly accessible and self-collected datasets in a subject-dependent scheme. The proposed DBL method can achieve an accuracy of 97.37 % and 88.72 % in the above datasets. Besides, it provides superior and more reliable results compared with several deep learning and rebalancing methods. These findings highlight the promising potential of the proposed DBL framework in P300-based BCI.}, }
@article {pmid40710265, year = {2025}, author = {Ma, S and Situ, Z and Peng, X and Li, Z and Huang, Y}, title = {Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {7}, pages = {}, pmid = {40710265}, issn = {2313-7673}, support = {2024ZD0715801//The National Science and Technology Major Project of China/ ; }, abstract = {Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical application. This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. This study developed an EEG acquisition protocol specifically for use with lower-limb rehabilitation motor imagery (MI). It systematically explored preprocessing techniques, feature extraction strategies, and multi-classification algorithms for multi-task MI-EEG signals. A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. Evaluations on a self-collected dataset demonstrated that the proposed model achieved a peak classification accuracy of 66.32%, substantially outperforming conventional approaches and demonstrating notable progress in the multi-class classification of lower-limb motor imagery tasks.}, }
@article {pmid40709513, year = {2025}, author = {Guo, W and Wang, H and Deng, W and Dong, Z and Liu, Y and Luo, S and Yu, J and Huang, X and Chen, Y and Ye, J and Song, J and Jiang, Y and Li, D and Wang, W and Sun, X and Kuang, W and Qiu, C and Cheng, N and Li, W and Zhang, W and Liu, Y and Tang, Z and Du, X and Greenshaw, AJ and Zhang, L and Li, T}, title = {Impact of early detection and management of emotional distress on length of stay in non-psychiatric inpatients: A retrospective hospital-based cohort study.}, journal = {Chinese medical journal}, volume = {}, number = {}, pages = {}, pmid = {40709513}, issn = {2542-5641}, abstract = {BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients.<br><br>METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS (n = 178,883) and non-BS-GPS (n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score &#x2265;11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively.<br><br>RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge.<br><br>CONCLUSION: Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.}, }
@article {pmid40708811, year = {2025}, author = {Tsytsarev, V and Volnova, A and Rojas, L and Sanabria, P and Ignashchenkova, A and Ortiz-Rivera, J and Alves, J and Inyushin, M}, title = {Vectorial principles of sensorimotor decoding.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1612626}, pmid = {40708811}, issn = {1662-5161}, support = {SC3 GM143983/GM/NIGMS NIH HHS/United States ; }, abstract = {This review explores the vectorial principles underlying sensorimotor decoding across diverse biological systems. From the encoding of light wavelength in retinal cones to direction-specific motor cortex activity in primates, neural representations frequently rely on population vector coding-a scheme, in which neurons with directional or modality-specific preferences integrate their activity to encode stimuli or motor commands. Early studies on color vision and motor control introduced concepts of vector summation and neuronal tuning, evolving toward more precise models such as the von Mises distribution. Research in invertebrates, including leeches and snails, reveals that even simple nervous systems utilize population vector principles for reflexes and coordinated movements. Furthermore, analysis of joint limb motion suggests biomechanical optimization aligned with Fibonacci proportions, facilitating efficient neural and mechanical control. The review highlights that motor units and neurons often display multimodal or overlapping tuning fields, reinforcing the need for population-based decoding strategies. These findings suggest a unifying vectorial framework for sensory and motor coding, with implications for periprosthetic and brain-machine interface.}, }
@article {pmid40708808, year = {2025}, author = {Chowdhury, AT and Hassanein, A and Al Shibli, AN and Khanafer, Y and AbuHaweeleh, MN and Pedersen, S and Chowdhury, MEH}, title = {Neural signals, machine learning, and the future of inner speech recognition.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1637174}, pmid = {40708808}, issn = {1662-5161}, abstract = {Inner speech recognition (ISR) is an emerging field with significant potential for applications in brain-computer interfaces (BCIs) and assistive technologies. This review focuses on the critical role of machine learning (ML) in decoding inner speech, exploring how various ML techniques improve the analysis and classification of neural signals. We analyze both traditional methods such as support vector machines (SVMs) and random forests, as well as advanced deep learning approaches like convolutional neural networks (CNNs), which are particularly effective at capturing the dynamic and non-linear patterns of inner speech-related brain activity. Also, the review covers the challenges of acquiring high-quality neural signals and discusses essential preprocessing methods for enhancing signal quality. Additionally, we outline and synthesize existing approaches for improving ISR through ML, that can lead to many potential implications in several domains, including assistive communication, brain-computer interfaces, and cognitive monitoring. The limitations of current technologies were also discussed, along with insights into future advancements and potential applications of machine learning in inner speech recognition (ISR). Building on prior literature, this work synthesizes and organizes existing ISR methodologies within a structured mathematical framework, reviews cognitive models of inner speech, and presents a detailed comparative analysis of existing ML approaches, thereby offering new insights into advancing the field.}, }
@article {pmid40707971, year = {2025}, author = {Ji, X and Lu, X and Xu, Y and Zhang, W and Yang, H and Yin, C and Wang, H and Ren, C and Ji, Y and Li, Y and Huang, G and Shen, Y}, title = {Effects and neural mechanisms of a brain-computer interface-controlled soft robotic glove on upper limb function in patients with subacute stroke: a randomized controlled fNIRS study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {171}, pmid = {40707971}, issn = {1743-0003}, support = {No.Q202414//Youth Project of the Wuxi Municipal Health Commission/ ; No.2022YFC2009700//National Key Research &amp; Development Program of China/ ; No.BE2023023-2//the Key Project of Jiangsu Province's Key Research and Development Program/ ; No.BE2023034//the Competitive Project of Jiangsu Province's Key Research and Development Program/ ; No.JBGS202414//Jiangsu Province Hospital clinical diagnosis and treatment of technological innovation "Open bidding for selecting the best candidates" project/ ; 2025-K10//Open Research Fund of State Key Laboratory of Digital Medical Engineering/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Spectroscopy, Near-Infrared ; *Robotics/instrumentation ; Aged ; *Stroke/physiopathology/complications ; *Paresis/rehabilitation/physiopathology/etiology ; Adult ; }, abstract = {BACKGROUND AND PURPOSE: The brain-computer interface-based soft robotic glove (BCI-SRG) holds promise for upper limb rehabilitation in subacute stroke patients, yet its efficacy and neural mechanisms are unclear. This study aimed to investigate the therapeutic effects and neural mechanisms of BCI-SRGs by functional near-infrared spectroscopy (fNIRS).<br><br>METHODS: Forty subacute stroke patients with left-sided hemiparesis were randomized into the BCI-SRG (n&#x2009;=&#x2009;20) and soft robotic glove (SRG) (n&#x2009;=&#x2009;20) groups. Both groups received 20 sessions of intervention over 4&#xa0;weeks in addition to conventional rehabilitation. The BCI-SRG group was trained using a soft robotic glove controlled by a brain&#x2012;computer interface (BCI), whereas the SRG group used the same soft robotic glove without BCI control. The clinical outcomes included the Action Research Arm Test (ARAT), the Fugl-Meyer Assessment Upper Limb (FMA-UL), and Modified Barthel Index (MBI) scores. In addition, fNIRS was used to explore potential clinical brain mechanisms. All assessments were performed before treatment and after 4&#xa0;weeks of treatment.<br><br>RESULTS: A total of 39 participants completed the intervention and clinical assessments (BCI-SRG: n&#x2009;=&#x2009;20; SRG: n&#x2009;=&#x2009;19). Compared with the SRG group, the BCI-SRG group showed greater improvements in the ARAT (Z&#x2009;=&#x2009;-&#x2009;2.139, P&#x2009;=&#x2009;0.032) and FMA-UL (Z&#x2009;=&#x2009;-&#x2009;2.588, P&#x2009;=&#x2009;0.010), with no notable difference in the MBI (Z&#x2009;=&#x2009;-&#x2009;1.843, P&#x2009;=&#x2009;0.065). fNIRS data were available for 35 participants (BCI-SRG: n&#x2009;=&#x2009;17; SRG: n&#x2009;=&#x2009;18). Within-group comparisons revealed significant postintervention increases in cortical activation in the bilateral sensorimotor cortex (SMC) and medial prefrontal cortex (MPFC) in the BCI-SRG group, whereas no significant changes were observed in the SRG group. Between-group comparisons further revealed significantly greater changes in HbO concentrations in the BCI-SRG group than in the SRG group across the same cortical regions. Moreover, changes in prefrontal activation (post-pre) were positively correlated with improvements in ARAT scores, with significant correlations observed in the left dorsal lateral prefrontal cortex (LDLPFC) (Ch9, r&#x2009;=&#x2009;0.592, P&#x2009;=&#x2009;0.012; Ch25, r&#x2009;=&#x2009;0.488, P&#x2009;=&#x2009;0.047) and right dorsal lateral prefrontal cortex (RDLPFC) (Ch19, r&#x2009;=&#x2009;0.671, P&#x2009;=&#x2009;0.003).<br><br>CONCLUSIONS: BCI-SRG training significantly enhances upper limb function and facilitates bilateral motor and sensory cortical reorganization. PFC activation is correlated with functional improvements, suggesting a potential mechanism underlying the benefits of rehabilitation in stroke patients.<br><br>TRIAL REGISTRATION: This trial was registered under the Chinese Clinical Trial Registry (ChiCTR2400082786) and was retrospectively registered on April 8, 2024.}, }
@article {pmid40707673, year = {2025}, author = {Zhang, X and Li, M and Chen, Y and Liu, J and Zhang, J and Shao, C and Deng, B and Zhang, J and Wang, T and Cao, J and Xu, X and He, Q and Yang, B and Shao, X and Ying, M}, title = {Deubiquitinase USP6 stabilizes oncogenic RUNX1 fusion proteins to promote the leukemic potential and malignant progression.}, journal = {Leukemia}, volume = {39}, number = {10}, pages = {2355-2363}, pmid = {40707673}, issn = {1476-5551}, support = {No. 82273942//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Core Binding Factor Alpha 2 Subunit/genetics/metabolism ; Animals ; *Oncogene Proteins, Fusion/genetics/metabolism ; Mice ; *Ubiquitin Thiolesterase/metabolism/genetics ; *Leukemia/pathology/genetics/metabolism ; Disease Progression ; Cell Proliferation ; Cell Line, Tumor ; }, abstract = {RUNX1-rearranged leukemia is one of the most common subtypes of leukemia associated with genetic abnormalities. Although the majority of patients respond to chemotherapy, relapse and long-term adverse effects remain significant challenges. RUNX1 fusions, resulting from chromosomal rearrangements, are pivotal oncogenic drivers, with over 70 distinct variants identified. Therefore, elucidating their regulatory mechanisms may help to develop novel therapeutic strategies. Herein, we identify a universal deubiquitinase, USP6, that stabilizes RUNX1 fusion proteins with different partners. Importantly, USP6 is specifically upregulated in RUNX1-rearranged leukemia and strongly correlates with poor patient outcomes. Mechanistically, USP6 stabilizes RUNX1 fusions to facilitate the formation of phase separation, leading to robust transcriptional activation of the fusions. Depletion of USP6 dramatically inhibits proliferation and induces differentiation of RUNX1-rearranged leukemic cells. The marketed drug auranofin is identified as a potential USP6 inhibitor, which induces degradation of different RUNX1 fusions, further triggering myeloid differentiation and arresting xenograft tumor growth. Notably, auranofin exhibits selective therapeutic efficacy in patient-derived leukemia blasts from RUNX1-rearranged cases. Together, we not only uncover a new biological function of USP6 in regulating the transcriptional activity of RUNX1 fusions but also validate USP6 as a promising drug target and auranofin as a candidate therapy for RUNX1-rearranged leukemia.}, }
@article {pmid40706724, year = {2025}, author = {Kim, YS and Kim, CU and Han, H and Kim, MY and Choi, SI and Im, CH}, title = {Performance enhancement of steady-state visual evoked field-based brain-computer interfaces using spatial distribution of synchronization index in MEG channel space.}, journal = {NeuroImage}, volume = {318}, number = {}, pages = {121391}, doi = {10.1016/j.neuroimage.2025.121391}, pmid = {40706724}, issn = {1095-9572}, mesh = {Humans ; *Magnetoencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Adult ; Male ; *Evoked Potentials, Visual/physiology ; Female ; Young Adult ; Signal Processing, Computer-Assisted ; }, abstract = {The development of helmet-type magnetoencephalography (MEG) systems that do not require liquid helium (e.g., OPM-MEG) has sparked growing interest in steady-state visual evoked field (SSVEF)-based brain-computer interfaces (BCIs). Unlike electroencephalography (EEG), MEG records less distorted signals with a high spatial resolution, covering the entire head without requiring cumbersome electrode attachment. However, conventional algorithms, such as the filter bank-driven multivariate synchronization index (FBMSI), are prone to misclassification in ambiguous cases where the differences between synchronization indices (S indices) are minimal. Additionally, these algorithms fail to fully exploit high spatial resolution and whole-head coverage of MEG. To address these limitations, this study proposes a novel, calibration-free SSVEF classification algorithm termed Spatial Distribution Analysis (SDA). The SDA algorithm utilizes the center of gravity of the S index distribution in the MEG channel space to enhance classification accuracy. Experimental evaluations with 20 participants using a helmet-type SQUID MEG system demonstrated that the proposed SDA algorithm achieved significantly higher classification accuracy and information transfer rate (ITR) across all window sizes. Notably, the largest improvements of 5.76 % in accuracy and 4.87 bits/min in ITR were reported for a window size of 2.5 s. Furthermore, the generalizability of the SDA algorithm was validated on an OPM-MEG dataset, showing performance improvements across all window sizes. The SDA algorithm also mitigated misclassification due to adjacent stimuli and showed short time delay of 0.0907 s, enough to be used for real-time BCIs. These findings highlight the potential of SDA algorithm to enhance the overall performance of SSVEF-based BCI.}, }
@article {pmid40705590, year = {2025}, author = {Ko, BK and Lee, SH and Lee, SW}, title = {Imagined Speech Detection Using Multi-Receptive CNN for Asynchronous BCI Communication and Neurorehabilitation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2904-2914}, doi = {10.1109/TNSRE.2025.3592312}, pmid = {40705590}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Imagination/physiology ; *Speech/physiology ; *Neurological Rehabilitation/methods ; *Neural Networks, Computer ; Male ; Adult ; Female ; Algorithms ; Young Adult ; Communication Devices for People with Disabilities ; Communication ; Signal Processing, Computer-Assisted ; }, abstract = {Imagined speech-based brain-computer interface (BCI) facilitates brain signal-driven intuitive communication which holds great promise as an effective speech rehabilitation tool, enabling real-time, hands-free interaction for individuals with speech and motor impairments. While speech-based assistant systems rely on wake-word detection (e.g., "Hey Siri"), BCI-based communication system must capture imagined onset from EEG signals to turn on the 'brain switch' to further convey user's imagined command. Nevertheless, the absence of reliable ground truth for the endogenous paradigm adds to the complexity to train the model to capture exact onset from continuous EEG. To address these issues, we introduce a multi-receptive field convolutional neural network, designed to capture speech and idle states based on behaviorally-aligned EEG features. We propose a voice-based ground truth alignment method with voting strategy that aims to synchronize imagined speech with overt speech onset and offset, providing a structured approach for capturing speech events in asynchronous BCI systems. Furthermore, spectral and phonological analyses revealed that beta and alpha bands, as well as syllable count, appear to influence speech state discriminability. Evaluations on imagined and overt speech tasks, including pseudo-online experiments, demonstrate the potential to enhance asynchronous BCI systems, supporting real-time communication for both healthy and impaired individuals.}, }
@article {pmid40703721, year = {2025}, author = {Alkhoury, L and O'Sullivan, J and Scanavini, G and Dou, J and Arora, J and Hamill, L and Patchell, A and Radanovic, A and Watson, WD and Lalor, EC and Schiff, ND and Hill, NJ and Shah, SA}, title = {Leveraging meaning-induced neural dynamics to detect covert cognition via EEG during natural language listening-a case series.}, journal = {Frontiers in psychology}, volume = {16}, number = {}, pages = {1616963}, pmid = {40703721}, issn = {1664-1078}, abstract = {At least a quarter of adult patients with severe brain injury in a disorder of consciousness may have cognitive abilities that are hidden due to motor impairment. In this case series, we developed a tool that extracted acoustic and semantic processing biomarkers from electroencephalography recorded while participants listened to a story. We tested our method on two male adolescent survivors of severe brain injury and showed evidence of acoustic and semantic processing. Our method identifies cognitive processing while obviating demands on attention, memory, and executive function. This lays a foundation for graded assessments of cognition recovery across the spectrum of covert cognition.}, }
@article {pmid40703668, year = {2025}, author = {Wang, F and Luo, Z and Lv, W and Zhu, X}, title = {DTCNet: finger flexion decoding with three-dimensional ECoG data.}, journal = {Frontiers in computational neuroscience}, volume = {19}, number = {}, pages = {1627819}, pmid = {40703668}, issn = {1662-5188}, abstract = {ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more accurate decoding of brain activity compared to traditional EEG. By obtaining cortical ECoG signals directly from the cerebral cortex, complex motor commands, such as finger movement trajectories, can be decoded more efficiently. However, existing studies still face significant challenges in accurately decoding finger movement trajectories. Specifically, current models tend to confuse the movement information of different fingers and fail to fully exploit the dependencies within time series when predicting long sequences, resulting in limited decoding performance. To address these challenges, this paper proposes a novel decoding method that transforms 2D ECoG data samples into 3D spatio-temporal spectrograms with time-stamped features via wavelet transform. The method further enables accurate decoding of finger bending by using a 1D convolutional network composed of Dilated-Transposed convolution, which together extract channel band features and temporal variations in tandem. The proposed method achieved the best performance among three subjects in BCI Competition IV. Compared with existing studies, our method made the correlation coefficient between the predicted multi-finger motion trajectory and the actual multi-finger motion trajectory exceed 80% for the first time, with the highest correlation coefficient reaching 82%. This approach provides new insights and solutions for high-precision decoding of brain-machine signals, particularly in precise command control tasks, and advances the application of BCI systems in real-world neuroprosthetic control.}, }
@article {pmid40703402, year = {2025}, author = {Borra, D and Ma, M and Martinez-Martin, E and Xia, L}, title = {Editorial: Methods in brain-computer interfaces: 2023.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1647584}, pmid = {40703402}, issn = {1662-5161}, }
@article {pmid40703200, year = {2025}, author = {Li, K and Zhang, J and Yu, B and Ward, MP and Liu, M and Liu, Y and Wang, Z and Chen, Z and Li, W and Wang, N and Zhao, Y and Yang, X and Yang, F and Wang, P and Zhang, Z}, title = {Meteorological, Socioeconomic, and Environmental Factors Influencing Human Brucellosis Occurrence in Yunnan, China, 2006-2021: A Bayesian Spatiotemporal Modeling Study.}, journal = {Transboundary and emerging diseases}, volume = {2025}, number = {}, pages = {8872434}, pmid = {40703200}, issn = {1865-1682}, mesh = {Humans ; China/epidemiology ; *Brucellosis/epidemiology ; Bayes Theorem ; Socioeconomic Factors ; Spatio-Temporal Analysis ; Risk Factors ; Meteorological Concepts ; Environment ; }, abstract = {Background: Brucellosis epidemics in Yunnan Province in southern China have increased and caused more impact in recent years. However, the epidemiological characteristics and driving factors for brucellosis have not been clearly described. The aim of this study was to analyze the spatiotemporal distribution and potential factors for human brucellosis (HB) in Yunnan Province, 2006-2021. Methods: HB data were obtained from the China National Notifiable Infectious Diseases Reporting Information System. Global spatial autocorrelation and spatial scanning statistics were used to analyze the spatial patterns of brucellosis. Zero-inflated negative binomial (ZINB) Bayesian spatiotemporal models were applied to the analysis of potential risk factors, including environmental, meteorological, and socioeconomic factors. Findings: Between 2006 and 2021, a total of 2794 brucellosis cases were reported. The central and western regions were the most severely affected. GDP showed a positive correlation with brucellosis risk when in the range 0-30.9 billion RMB, peaking with a relative risk (RR) of 13.64 (95% Bayesian credible interval [BCI]: 4.10, 49.10) at around 2.3 billion RMB. Conversely, a negative correlation was observed for GDP between 101 and 135 billion RMB, with the RR dropping to 0.14 (95% BCI: 0.01, 0.89) at 135 billion RMB. Brucellosis cases increased by 4.90% (95% BCI: 1.82%, 7.95%) per 1°C increase in temperature, while a 1° increase in slope reduced cases by 17.06% (95% BCI: 4.01%, 28.81%). Interpretation: Our findings suggest that socioeconomic factors play the greatest role in the occurrence of brucellosis in both northern and southern China; however, the effects of the environmental factors may be different between these areas. Differences in factors affecting each region need to be fully considered, and brucellosis prevention and control need to be adapted to these differences.}, }
@article {pmid40702984, year = {2025}, author = {Yu, Y and Wang, RM and Dong, Y and Jia, XZ and Wu, ZY}, title = {Neuroimaging correlates of genetics in patients with Wilson's disease.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {35}, number = {7}, pages = {}, doi = {10.1093/cercor/bhaf186}, pmid = {40702984}, issn = {1460-2199}, support = {81125009//National Natural Science Foundation of China/ ; 81701126//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholars of Zhejiang University/ ; }, mesh = {Humans ; *Hepatolenticular Degeneration/genetics/diagnostic imaging/pathology/physiopathology ; Male ; Female ; Adult ; *Brain/pathology/diagnostic imaging/physiopathology ; Young Adult ; *Mutation/genetics ; Magnetic Resonance Imaging ; Neuroimaging ; Copper-Transporting ATPases/genetics ; Adolescent ; Middle Aged ; Atrophy ; }, abstract = {Wilson's disease is an inherited disorder of copper metabolism. Despite significant advancements in neuroimaging studies, prior research into the pathological mechanism of Wilson's disease has ignored the crucial impact of mutation on the disease. This study examined brain imaging in relation to mutation in patients with Wilson's disease. A total of 57 Wilson's disease patients and 25 healthy controls were recruited in the current research. Patients were classified as having either the p.R778L or the p.P992L mutation (N = 43) or other mutations (N = 14). Utilizing the amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and voxel-based morphology, the brain function and structure of Wilson's disease were explored. Compared to healthy controls, Wilson's disease patients with the p.R778L or p.P992L mutation showed greater atrophy in the bilateral putamen, caudate, globus pallidus, thalamus, amygdala, insula, and hippocampus. And these patients showed altered spontaneous neural activity in many more brain regions than healthy controls in three frequency bands. Significant correlation was found between altered brain volume and Unified Wilson's Disease Rating Scale neurological subscale scores. These findings reveal the functional and structural characteristics of Wilson's disease and emphasize the importance of exploring the neuroimaging correlation of genetic mutations in Wilson's disease.}, }
@article {pmid40702747, year = {2025}, author = {Yang, A and Lv, X and Wang, H and Wang, X}, title = {Psychedelics, Spirituality, and Fundamentalism: A Brain Network Approach to Cognitive Flexibility and Rigidity.}, journal = {ACS chemical neuroscience}, volume = {16}, number = {15}, pages = {2750-2752}, doi = {10.1021/acschemneuro.5c00509}, pmid = {40702747}, issn = {1948-7193}, mesh = {Humans ; *Hallucinogens/pharmacology ; *Cognition/drug effects/physiology ; *Brain/drug effects/physiology ; *Spirituality ; Psilocybin/pharmacology ; Mysticism ; Cognitive Flexibility ; }, abstract = {This viewpoint reconceptualizes mysticism and fundamentalism as brain network disorders, with psychedelics like psilocybin, lysergic acid diethylamide, and N,N-dimethyltryptamine offering potential to modulate these states. By disrupting rigid neural patterns, psychedelics may foster cognitive flexibility, challenge inflexible belief systems, and offer therapeutic value for extremism and mental health disorders.}, }
@article {pmid40702190, year = {2025}, author = {Kaifosh, P and Reardon, TR and , }, title = {A generic non-invasive neuromotor interface for human-computer interaction.}, journal = {Nature}, volume = {645}, number = {8081}, pages = {702-711}, pmid = {40702190}, issn = {1476-4687}, mesh = {Humans ; Gestures ; *Brain-Computer Interfaces ; *Electromyography/instrumentation/methods ; Male ; Female ; *User-Computer Interface ; Adult ; Young Adult ; Wrist/physiology ; }, abstract = {Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive and universal. While diverse modalities have been developed, including keyboards, mice and touchscreens, they require interaction with a device that can be limiting, especially in on-the-go scenarios. Gesture-based systems use cameras or inertial sensors to avoid an intermediary device, but tend to perform well only for unobscured movements. By contrast, brain-computer or neuromotor interfaces that directly interface with the body's electrical signalling have been imagined to solve the interface problem[1], but high-bandwidth communication has been demonstrated only using invasive interfaces with bespoke decoders designed for single individuals[2-4]. Here, we describe the development of a generic non-invasive neuromotor interface that enables computer input decoded from surface electromyography (sEMG). We developed a highly sensitive, easily donned sEMG wristband and a scalable infrastructure for collecting training data from thousands of consenting participants. Together, these data enabled us to develop generic sEMG decoding models that generalize across people. Test users demonstrate a closed-loop median performance of gesture decoding of 0.66 target acquisitions per second in a continuous navigation task, 0.88 gesture detections per second in a discrete-gesture task and handwriting at 20.9 words per minute. We demonstrate that the decoding performance of handwriting models can be further improved by 16% by personalizing sEMG decoding models. To our knowledge, this is the first high-bandwidth neuromotor interface with performant out-of-the-box generalization across people.}, }
@article {pmid40701672, year = {2025}, author = {Kundi, H and Popma, JJ and Granada, JF and Leon, MB and Kodesh, A and Ascione, G and George, I and Latib, A and Thompson, JB and Popma, A and Alu, MC and Cohen, DJ}, title = {Outcomes in Older Patients Undergoing Surgical Aortic Valve Replacement With Concomitant Procedures.}, journal = {Journal of the American College of Cardiology}, volume = {86}, number = {4}, pages = {280-283}, doi = {10.1016/j.jacc.2025.05.021}, pmid = {40701672}, issn = {1558-3597}, }
@article {pmid40700800, year = {2025}, author = {Peng, J and Jia, S and Zhang, J and Wang, Y and Yu, Z and Liu, JK}, title = {Decoding natural visual scenes via learnable representations of neural spiking sequences.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {192}, number = {}, pages = {107863}, doi = {10.1016/j.neunet.2025.107863}, pmid = {40700800}, issn = {1879-2782}, abstract = {Visual input underpins cognitive function by providing the brain with essential environmental information. Neural decoding of visual scenes seeks to reconstruct pixel-level images from neural activity, a vital capability for vision restoration via brain-computer interfaces. However, extracting visual content from time-resolved spiking activity remains a significant challenge. Here, we introduce the Wavelet-Informed Spike Augmentation (WISA) model, which applies multilevel wavelet transforms to spike trains to learn compact representations that can be directly fed into deep reconstruction networks. When tested on recorded retinal spike data responding to natural video stimuli, WISA substantially improves reconstruction accuracy, especially in recovering fine-grained details. These results emphasize the value of temporal spike patterns for high-fidelity visual decoding and demonstrate WISA as a promising model for visual decoding.}, }
@article {pmid40700312, year = {2025}, author = {Correia, P and Quintão, C and Quaresma, C and Vigário, R}, title = {A Framework for Corticomuscle Control Studies Using a Serious Gaming Approach.}, journal = {Methods and protocols}, volume = {8}, number = {4}, pages = {}, pmid = {40700312}, issn = {2409-9279}, support = {UI/BD/151321/2021//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia (FCT, Portugal)/ ; }, abstract = {Sophisticated voluntary movements are essential for everyday functioning, making the study of how the brain controls muscle activity a central challenge in neuroscience. Investigating corticomuscular control through non-invasive electrophysiological recordings is particularly complex due to the intricate nature of neuronal signals. To address this challenge, we present a novel experimental methodology designed to study corticomuscular control using electroencephalography (EEG) and electromyography (EMG). Our approach integrates a serious gaming biofeedback system with a specialized experimental protocol for simultaneous EEG-EMG data acquisition, optimized for corticomuscular studies. This work introduces, for the first time, a method for assessing brain-muscle functional connectivity during the execution of a demanding motor task. By identifying neuronal sources linked to muscular activity, this methodology has the potential to advance our understanding of motor control mechanisms. These insights could contribute to improving clinical practices and fostering the development of novel brain-computer interface technologies.}, }
@article {pmid40699544, year = {2025}, author = {Pan, H and Chen, Z and Xu, N and Wang, B and Hu, Y and Zhou, H and Perry, A and Kong, XZ and Shen, M and Gao, Z}, title = {Dissecting Social Working Memory: Neural and Behavioral Evidence for Externally and Internally Oriented Components.}, journal = {Neuroscience bulletin}, volume = {41}, number = {11}, pages = {2049-2062}, pmid = {40699544}, issn = {1995-8218}, mesh = {Humans ; *Memory, Short-Term/physiology ; Male ; Female ; *Empathy/physiology ; Young Adult ; Magnetic Resonance Imaging ; Adult ; *Brain/physiology/diagnostic imaging ; Brain Mapping ; Facial Expression ; *Social Behavior ; Facial Recognition/physiology ; *Social Perception ; Personality/physiology ; }, abstract = {Social working memory (SWM)-the ability to maintain and manipulate social information in the brain-plays a crucial role in social interactions. However, research on SWM is still in its infancy and is often treated as a unitary construct. In the present study, we propose that SWM can be conceptualized as having two relatively independent components: "externally oriented SWM" (e-SWM) and "internally oriented SWM" (i-SWM). To test this external-internal hypothesis, participants were tasked with memorizing and ranking either facial expressions (e-SWM) or personality traits (i-SWM) associated with images of faces. We then examined the neural correlates of these two SWM components and their functional roles in empathy. The results showed distinct activations as the e-SWM task activated the postcentral and precentral gyri while the i-SWM task activated the precuneus/posterior cingulate cortex and superior frontal gyrus. Distinct multivariate activation patterns were also found within the dorsal medial prefrontal cortex in the two tasks. Moreover, partial least squares analyses combining brain activation and individual differences in empathy showed that e-SWM and i-SWM brain activities were mainly correlated with affective empathy and cognitive empathy, respectively. These findings implicate distinct brain processes as well as functional roles of the two types of SWM, providing support for the internal-external hypothesis of SWM.}, }
@article {pmid40697162, year = {2025}, author = {Dohle, E and Swanson, E and Jovanovic, L and Yusuf, S and Thompson, L and Horsfall, HL and Muirhead, W and Bashford, L and Brannigan, J}, title = {Toward the Clinical Translation of Implantable Brain-Computer Interfaces for Motor Impairment: Research Trends and Outcome Measures.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {12}, number = {32}, pages = {e01912}, pmid = {40697162}, issn = {2198-3844}, support = {FC001153/WT_/Wellcome Trust/United Kingdom ; //Rosetrees Trust and Stoneygate Trust/ ; }, mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Translational Research, Biomedical/trends ; Outcome Assessment, Health Care ; Electrocorticography ; }, abstract = {Implantable brain-computer interfaces (iBCIs) decode neural signals to control external effectors, offering potential to restore function in individuals with severe motor impairments, such as loss of limb function or speech. This systematic review examines the evolution of iBCI research and key bottlenecks to clinical translation, particularly the absence of standardized, clinically meaningful outcome measures. A comprehensive search of MEDLINE, Embase, and CINAHL identifies 112 studies, nearly half (49.1%) published since 2020. Eighty unique iBCI participants were identified, providing the most up-to-date estimate of global users. Research remains concentrated in the United States (83%), with growing contributions from Europe, China, and Australia. Electrocorticography (ECoG)-based devices increasingly emerge alongside micro-electrode arrays. iBCI devices are now being used to control a broader range of effectors, including robotic prosthetics and digital technologies. Although most (69.6%) studies reported outcome measures prospectively, these primarily related to decoding (69.6%) and task performance (62.5%), with only 17.9% assessing clinical outcomes. When cassessed, clinical outcomes were highly heterogeneous due to varied approaches across target populations. iBCIs show potential to restore functional independence at scale. However, challenges remain around cross-subject generalization, scalable implantation, and outcome standardization. Novel measures should be developed collaboratively with engineers, clinicians, and individuals with lived experience of motor impairment.}, }
@article {pmid40696184, year = {2025}, author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y}, title = {Author Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.}, journal = {Nature}, volume = {644}, number = {8075}, pages = {E12}, doi = {10.1038/s41586-025-09404-1}, pmid = {40696184}, issn = {1476-4687}, }
@article {pmid40695313, year = {2025}, author = {de Borman, A and Wittevrongel, B and Van Dyck, B and Van Rooy, K and Carrette, E and Meurs, A and Van Roost, D and Van Hulle, MM}, title = {Speech mode classification from electrocorticography: transfer between electrodes and participants.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/adf2de}, pmid = {40695313}, issn = {1741-2552}, mesh = {Humans ; *Electrocorticography/methods/instrumentation/classification ; *Brain-Computer Interfaces ; *Speech/physiology ; Male ; Female ; Adult ; *Electrodes, Implanted ; Middle Aged ; Speech Perception/physiology ; Young Adult ; }, abstract = {Objective.Speech brain-computer interfaces (BCIs) aim to restore communication for individuals who have lost the ability to speak by interpreting their brain activity and decoding the intended speech. As an initial component of these decoders, speech detectors have been developed to distinguish between the intent to speak and silence. However, it is important that these detectors account for real-life scenarios in which users may engage language-related brain areas-such as during reading or listening-without any intention to speak.Approach.In this study, we analyze the interplay between different speech modes: speaking, listening, imagining speaking, reading and mouthing. We gathered a large dataset of 29 participants implanted with electrocorticography electrodes and developed a speech mode classifier. We also assessed how well classifiers trained on data from a specific participant transfer to other participants, both in the case of a single- and multi-electrode classifier.Main results.High accuracy was achieved using linear classifiers, for both single-electrode and multi-electrode configurations. Single-electrode classification reached 88.89% accuracy and multi-electrode classification 96.49% accuracy in distinguishing among three classes (speaking, listening, and silence). The best performing electrodes were located on the superior temporal gyrus and sensorimotor cortex. We found that single-electrode classifiers could be transferred across recording sites. For multi-electrode classifiers, we observed that transfer performance was higher for binary classifiers compared to multiclass classifiers, with the optimal source subject of the binary classifiers depending on the speech modes being classified.SignificanceAccurately detecting speech from brain signals is essential to prevent spurious outputs from a speech BCI and to advance its use beyond lab settings. To achieve this objective, the transfer between participants is particularly valuable as it can reduce training time, especially in cases where subject training is challenging.}, }
@article {pmid40694675, year = {2025}, author = {Izac, M and N'Kaoua, B and Pillette, L and Jeunet-Kelway, C}, title = {[Improve athletes' performance with neurofeedback].}, journal = {Biologie aujourd'hui}, volume = {219}, number = {1-2}, pages = {51-58}, doi = {10.1051/jbio/2025001}, pmid = {40694675}, issn = {2105-0686}, mesh = {Humans ; *Neurofeedback/methods/physiology ; *Athletic Performance/physiology/psychology ; *Athletes/psychology ; Electroencephalography ; Cognition/physiology ; }, abstract = {In order to optimise their performance, athletes are looking for innovative, efficient and reliable training approaches. The development of electroencephalography and neurofeedback (NF) offers the opportunity to create innovative cognitive training procedures. Indeed, these technologies allow athletes to benefit from a feedback during mental training sessions and to objectively assess performance and progress. In addition, NF makes it possible to guide the athletes towards optimal cognitive strategies according to their objectives, and has a motivational dimension that pushes them to engage in the sessions. We first introduce the usefulness of NF to improve sports performance. Then, we review the current results concerning its efficiency. Finally, we provide an overview of the literature showing the heterogeneity of the studies published on the subject, focusing mainly on the aspects that could explain the variability of reported data.}, }
@article {pmid40694476, year = {2025}, author = {Rangwani, R and Abbasi, A and Gulati, T}, title = {Effective cerebellar neuroprosthetic control after stroke.}, journal = {Cell reports}, volume = {44}, number = {8}, pages = {116030}, pmid = {40694476}, issn = {2211-1247}, support = {R00 NS097620/NS/NINDS NIH HHS/United States ; R01 NS128469/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; *Cerebellum/physiopathology ; *Stroke/physiopathology ; *Brain-Computer Interfaces ; Rats ; Motor Cortex/physiopathology ; Male ; Neurons/physiology ; Disease Models, Animal ; }, abstract = {Brain-machine interfaces (BMIs) offer a viable option for restoring function in patients with motor disabilities post-stroke. Most BMI systems rely on signals from the motor cortex (M1), which is often compromised after stroke. The cerebellum, a subcortical structure involved in motor control, remains an underexplored source for neuroprosthetic control. Using chronic electrophysiological recordings in a rat stroke model, we show that cerebellar neural activity can effectively drive BMI control, performing comparably to M1-driven control. We observed this even in animals with motor impairments post-stroke. Simultaneous M1-cerebellum recordings during cerebellar BMI control revealed that cerebellar "direct" neurons driving the interface were influenced by both local cerebellar and distant M1 neurons. While cerebellar influence remained stable, M1's interaction with cerebellar direct neurons shifted from longer to shorter timescales after stroke. These findings highlight that cerebellar direct neural control is possible in the stroke brain and reveal changes in M1-cerebellar network dynamics post-stroke.}, }
@article {pmid40694466, year = {2025}, author = {Zhang, C and Li, G and Wu, X and Gao, X}, title = {A Novel Hybrid Brain-Computer Interface Integrating Motor Imagery and Multiple Visual Stimuli.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2847-2857}, doi = {10.1109/TNSRE.2025.3591616}, pmid = {40694466}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; Male ; *Imagination/physiology ; Evoked Potentials, Visual/physiology ; Electroencephalography ; Adult ; Female ; *Photic Stimulation/methods ; Young Adult ; Algorithms ; Movement/physiology ; Attention/physiology ; Reproducibility of Results ; Psychomotor Performance/physiology ; Arm/physiology ; }, abstract = {Brain-Computer Interface (BCI) that integrate Motor Imagery (MI) with Steady-State Visual Evoked Potentials (SSVEP) or Overt Spatial Attention (OSA) have demonstrated superior performance compared to MI only BCI. Nonetheless, the exploration of BCI that combine MI with visual tasks remains limited, and the synchronization between MI and visual tasks is often weak. To address this gap, our study introduces a novel BCI paradigm that combines MI with two visual tasks: SSVEP and OSA. In this paradigm, dynamic images depicting left and right arm movements flash at distinct frequencies, serving as visual stimuli positioned on both sides of the screen. Four classification methods are used for testing. The MI+SSVEP+OSA paradigm achieves higher average accuracy than the MI, MI+SSVEP, and MI+OSA paradigms. This validates the effectiveness of our novel paradigm and confirms the feasibility of simultaneously integrating MI with two visual stimuli. Moreover, we observe that the integration of SSVEP offers significant improvements, especially for participants who exhibit limited performance in the MI only paradigm. Additionally, our results indicate comparable performance between the MI+SSVEP and MI+OSA paradigms. Overall, this study offers valuable insights that can guide future research in hybrid BCI development, paving the way for more efficient and user-friendly BCI.}, }
@article {pmid40694230, year = {2025}, author = {Afkhaminia, F and Shamsollahi, MB and Bahraini, T}, title = {A distributed adaptive network framework for ERP-Based classification of multichannel EEG signals.}, journal = {Physical and engineering sciences in medicine}, volume = {48}, number = {3}, pages = {1207-1224}, pmid = {40694230}, issn = {2662-4737}, mesh = {*Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; *Evoked Potentials ; Algorithms ; Brain-Computer Interfaces ; Machine Learning ; }, abstract = {Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffusion strategy. Our approach models the brain as a multitask network, where EEG electrodes are considered as nodes of this network. The network parameters are dynamically optimized based on the data from the nodes and inter-node cooperation. The proposed framework, which comprises network modeling and diffusion-based adaptation using the adapt then combine (ATC) algorithm, has been validated on different types of data. Experimental results indicate that the proposed framework outperforms common methods in classifying EEG data based on event-related potential (ERP) pattern identification, particularly in scenarios where machine learning-based models struggle with limited data. Furthermore, its ability to adapt to the non-stationary and dynamic nature of EEG signals and its efficient real-time implementation make this approach ideal for brain-computer interface (BCI), cognitive neuroscience, and clinical applications.}, }
@article {pmid40694026, year = {2025}, author = {Guérin, V}, title = {Veteran and Brain-Computer Interfaces: The Duty to Care.}, journal = {AJOB neuroscience}, volume = {16}, number = {4}, pages = {300-308}, doi = {10.1080/21507740.2025.2530948}, pmid = {40694026}, issn = {2150-7759}, mesh = {*Brain-Computer Interfaces/psychology ; Humans ; *Veterans/psychology ; United States ; *Military Personnel/psychology ; }, abstract = {Anticipated by science fiction, the enhanced soldier crystallized in the United States at the dawn of the 21st century within the Pentagon's scientific agency, the Defense Advanced Research Projects Agency (DARPA). Fueled by the fear of being overtaken by the enemy, and then by its own technology, this agency's new vision produced a "bifurcation" within anthropotechnics: the modification of humans for war. The soldier is now at the heart of a process of radical innovation, with as yet unknown implications. Emblematic of this enhancement, the use of the brain-computer interfaces (BCIs) will not only expose the soldier to previously unknown psychocognitive and emotional effects, but also offer the enemy potential access to his/her inner self. By giving birth to a new kind of veteran, this hybridization will generate new responsibilities for military commanders and politicians, as well as a new type of care.}, }
@article {pmid40694018, year = {2025}, author = {Fan, C and Ding, Y and Zhang, H}, title = {A commentary on "Brain-computer interfaces: the innovative to unlocking neurological conditions".}, journal = {International journal of surgery (London, England)}, volume = {}, number = {}, pages = {}, doi = {10.1097/JS9.0000000000003094}, pmid = {40694018}, issn = {1743-9159}, }
@article {pmid40691442, year = {2025}, author = {Wang, X and Chen, S and Li, J and Gao, Y and Li, S and Li, M and Liu, X and Liu, S and Ming, D}, title = {Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.}, journal = {Schizophrenia (Heidelberg, Germany)}, volume = {11}, number = {1}, pages = {104}, pmid = {40691442}, issn = {2754-6993}, abstract = {Positive symptoms are a prominent feature of schizophrenia. Despite antipsychotic treatment, ~30% of patients develop refractory positive symptoms (RPSs). Current research fails to elucidate the potential neurophysiological mechanisms underlying RPSs, thereby hindering the development of additional treatments. This study, which included 37 patients with RPSs and 40 with non-refractory positive symptoms (NRPSs), aimed to explore their underlying neural mechanisms. Outcome measures were relative power spectrum density and interregional synchronization across frequency bands and theta-gamma phase-amplitude coupling (θ-γ PAC). The single-frequency analysis indicated that RPSs exhibited elevated theta power and reduced lateralization in the left temporal lobe and temporo-parietal junction, along with enhanced functional connectivity in the left frontocentral region. The cross-frequency analysis revealed that RPSs exhibited slightly higher θ-γ coupling at the left temporo-parietal junction compared to NRPSs. Correlation analysis revealed significant associations among theta power, the lateralization index, functional connectivity, and the severity of positive symptoms. The aberrant activation of the theta rhythm in the left temporo-parietal region may lead to increased functional asymmetry in the brain, impeding interregional and inter-frequency information transmission and thus significantly impairing the normal processing of auditory information. These findings offer potential insights into the neurophysiological basis of positive symptoms in schizophrenia and may inform future clinical interventions.}, }
@article {pmid40690341, year = {2025}, author = {Wang, J and Yao, L and Wang, Y}, title = {Enhanced Online Continuous Brain-Control by Deep Learning-Based EEG Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2834-2846}, doi = {10.1109/TNSRE.2025.3591254}, pmid = {40690341}, issn = {1558-0210}, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Neural Networks, Computer ; Young Adult ; *Brain/physiology ; Algorithms ; Imagination/physiology ; Online Systems ; }, abstract = {OBJECTIVE: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces (BCIs) still predominantly adopt machine learning decoders while falling short of high BCI performance. Yet, the generalization and advantages of deep learning-based EEG decoding in realistic BCI systems remain far unclear.<br><br>METHODS: We conduct a randomized and cross-session online MI-BCI study on 2D center-out tasks in 15 BCI-naive subjects. A newly proposed deep learning model named interactive frequency convolutional neural network (IFNet) is leveraged and rigorously compared with the prevailing benchmark namely filter-bank common spatial pattern (FBCSP) for online MI decoding.<br><br>RESULTS: Through extensive online analysis, the deep learning decoder consistently outperforms the classical counterpart across various performance metrics. In particular, IFNet significantly improves the average online task accuracy by 20% and 27% in two sessions compared with FBCSP, respectively. Moreover, a significant cross-session training effect is observed by the IFNet model (${P}={0}.{017}$) while not for the controlled method (${P}={0}.{337}$). Further offline evaluations also demonstrate the superior performance of IFNet over state-of-the-art deep learning models. Moreover, we present unique behavioral and neurophysiological insights underlying online brain-machine interaction.<br><br>CONCLUSION: We present one of the first studies about online MI-BCIs using deep learning, achieving substantially enhanced online performance for continuous BCI control.<br><br>SIGNIFICANCE: This study suggests the good utility of deep learning in MI-BCIs and has implications for clinical applications such as stroke rehabilitation.}, }
@article {pmid40688356, year = {2025}, author = {Sonntag, J and Yu, L and Wang, X and Schack, T}, title = {Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1617748}, pmid = {40688356}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor imagery-based brain-computer interfaces (BCIs) are a technique for decoding and classifying the intention of motor execution, solely based on imagined (rather than executed) movements. Although deep learning techniques have increased the potential of BCIs, the complexity of decoding unilateral upper limb motor imagery remains challenging. To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.<br><br>METHODS: In this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-based machine learning classifiers (SVM, LDA) were used to classify imagined right elbow flexion and extension from participants using electroencephalography data. From two recorded resting states (eyes-open, eyes-closed), absolute and relative alpha and beta power of the frontal, fronto-central and central electrodes were used to predict the accuracy of the different classifiers.<br><br>RESULTS: The prediction of classifier accuracies by neurophysiological features revealed negative correlations between the relative alpha band and classifier accuracies and positive correlations between the absolute and relative beta band and classifiers accuracies. Most ipsilateral EEG channels yielded significant correlations with classifier accuracies, especially for the machine learning classifier.<br><br>DISCUSSION: This pattern contrasts with previous findings from bilateral MI paradigms, where contralateral alpha and beta activity were more influential. These inverted correlations suggest task-specific neurophysiological mechanisms in unilateral MI, emphasizing the role of ipsilateral inhibition and attentional processes.}, }
@article {pmid40685778, year = {2025}, author = {Niu, X and Jiang, L and Hu, J and Jia, Y and Zhao, S and Ma, Y and Qiu, Z and Lian, Y and Zhu, E and Ni, J}, title = {Femtosecond Laser-Engineered Multifunctional Bio-Metasurface for the Inhibition of Thrombosis and Bacterial Infections.}, journal = {ACS applied materials &amp; interfaces}, volume = {17}, number = {30}, pages = {43761-43776}, doi = {10.1021/acsami.5c05001}, pmid = {40685778}, issn = {1944-8252}, mesh = {*Thrombosis/prevention &amp; control/drug therapy ; Humans ; *Lasers ; Staphylococcus aureus/drug effects ; Surface Properties ; *Anti-Bacterial Agents/pharmacology/chemistry ; Escherichia coli/drug effects ; Platelet Adhesiveness/drug effects ; *Bacterial Infections ; Carbon/chemistry ; *Coated Materials, Biocompatible/chemistry/pharmacology ; }, abstract = {Surface engineering is an effective strategy for addressing thrombosis and bacterial infection associated with blood-contacting implants (BCIs). However, most functional surfaces rely on a single mechanism and surface engineering poses substantial processing challenges for chemically inert and difficult-to-process materials such as pyrolytic carbon. Herein, a multifunctional bio-metasurface (LDT surface) synergizing liquid-repellent (L), drag-reduction (D), and turbulence-attenuation (T) strategies is proposed. The LDT surface is achieved through the synergistic interplay of surface texture-mediated flow control and interfacial lubrication effects. The textured LDT surface with microgrooves exhibits a hemodynamic modulation capability, exhibiting an effective turbulence-attenuation effect. The slippery coating on the LDT surface exhibits liquid-repellent and drag-reduction effects, regulating bio (blood and bacteria)-material interfacial interactions. The complex, hierarchical micro-groove, micro-hole, and nano-ripples/gaps/protrusions structures on the surface are fabricated on pyrolytic carbon via temporally shaped femtosecond laser texturing, followed by functional coating. The LDT surface exhibits excellent stability under continuous turbulent flow, with no toxic byproducts generated during processing. The computational fluid dynamics simulation results confirm that the streamwise microgrooves on the wall significantly attenuate turbulence. Compared to the pristine sample surface, the experimental results reveal a 98.2% reduction in platelet adhesion on the LDT surface, with a platelet adhesion rate of only 0.22% and no detected activated platelets, while denatured fibrinogen adhesion decreases by 55.3%. Moreover, the antiadhesion capacities of the LDT surface against Staphylococcus aureus and Escherichia coli improve by 99.4% and 98.4%, respectively, relative to the pristine sample surface, without viable residual bacteria or biofilm formation. The study offers a promising strategy to mitigate BCI-associated thrombosis and bacterial infection on BCIs, particularly those made from difficult-to-machine materials.}, }
@article {pmid40683976, year = {2025}, author = {Joshi, A and Matharu, PS and Malviya, L and Kumar, M and Jadhav, A}, title = {Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {26267}, pmid = {40683976}, issn = {2045-2322}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Machine Learning ; Wavelet Analysis ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; }, abstract = {Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inherent in EEG data. This paper explores the use of Convolutional Spiking Neural Networks (CSNNs) to enhance EEG signal classification. We apply Discrete Wavelet Transform (DWT) for feature extraction and evaluate CSNN performance on the Physionet EEG dataset, benchmarking it against traditional deep learning and machine learning methods. The findings indicate that CSNNs achieve high accuracy, reaching 98.75% in 10-fold cross-validation, and an impressive F1 score of 98.60%. Notably, this F1-score represents an improvement over previous benchmarks, highlighting the effectiveness of our approach. Along with offering advantages in temporal precision and energy efficiency, CSNNs emerge as a promising solution for next-generation EEG analysis systems.}, }
@article {pmid40683565, year = {2025}, author = {Wu, Y and Lv, K and Zhao, Y and Yang, G and Hao, X and Zheng, B and Lv, C and An, Z and Zhou, H and Yuan, Q and Song, T}, title = {Prediction Model for Detrusor Underactivity via Noninvasive Clinical Parameters in Men With Benign Prostatic Hyperplasia.}, journal = {Urology}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.urology.2025.07.021}, pmid = {40683565}, issn = {1527-9995}, abstract = {OBJECTIVE: To develop a clinical prediction model for detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).<br><br>METHODS: A retrospective review was conducted on 546 individuals with BPH who had undergone urodynamic testing between January 2012 and May 2022. The bladder contractility index (BCI) was assessed using a pressure-flow study (PFS). Patients were categorized into DU (BCI <100, n = 196) and non-DU (BCI &#x2265;100, n = 350) groups. Univariate logistic regression was initially performed to identify potential DU-related factors, followed by multivariate analysis to determine independent risk factors.<br><br>RESULTS: A predictive model for DU in patients with BPH was developed using the coefficient of these independent risk factors. Among the 546 cases, 196 (35.9%) were diagnosed with DU. Older age, smaller prostate volume, lower urgency symptom score, lower incomplete emptying symptom score, higher straining symptom score, and lower maximum flow rate (Qmax) were identified as independent predictors of DU in patients with BPH. The model demonstrated an area under the curve of 0.78 (95% CI, 0.74-0.82), with internal validation yielding 0.75 (95% CI, 0.74-0.75).<br><br>CONCLUSION: We developed a predictive model that effectively estimates the DU probability in patients with BPH without requiring invasive pressure-flow study.}, }
@article {pmid40683191, year = {2025}, author = {Li, Y and Sun, Y and Wan, F and Yuan, Z and Jung, TP and Wang, H}, title = {MetaNIRS: A general decoding framework for fNIRS based motor execution/imagery.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {192}, number = {}, pages = {107873}, doi = {10.1016/j.neunet.2025.107873}, pmid = {40683191}, issn = {1879-2782}, abstract = {Functional near-infrared spectroscopy (fNIRS) is a crucial brain activity monitoring tool with remarkable potential applications in brain-computer interfaces (BCI), particularly in rehabilitation therapy for disabilities. However, the performance of fNIRS-based BCI systems remains suboptimal, such as motor execution (ME) and motor imagery (MI) decoding. Inspired by the successful application of the PoolFormer framework in visual tasks, we first proposed a novel long-range dilation multilayer perceptron (LongDilMLP) to utilize the hemodynamic characteristics of fNIRS. Furthermore, the LongDilMLP was integrated with the PoolFormer framework, called as MetaNIRS in this study. The proposed framework MetaNIRS was employed for both ME and MI classification tasks, achieving rigorous validation of its effectiveness and practical applicability. To evaluate the performance of MetaNIRS, two publicly available ME datasets (A and C) and one self-collected MI dataset (B) were employed. The experimental results demonstrated that the average accuracy were 76.00 %, 57.45 %, and 84.14 %, with cross-subject accuracy of 77.24 %, 58.55 %, and 85.52 %, respectively. Moreover, sensitivity experiments of model parameters showed the robustness. Ablation experiments highlighted the significance of each MetaNIRS component and the efficacy of LongDilMLP over traditional MLP. Additionally, visualization techniques enhanced the interpretability of MetaNIRS, indicating the main contribution of the first half signals for classification. Using the first half of signals, the average accuracy only reduced 4.30 %, 1.69 %, and 1.11 %, respectively. These findings suggest that the superior performance of MetaNIRS, which provide an efficient general decoding framework for ME and MI.}, }
@article {pmid40683189, year = {2025}, author = {Chen, H and Zeng, W and Chen, C and Cai, L and Wang, F and Shi, Y and Wang, L and Zhang, W and Li, Y and Yan, H and Siok, WT and Wang, N}, title = {EEG Emotion Copilot: Optimizing lightweight LLMs for emotional EEG interpretation with assisted medical record generation.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {192}, number = {}, pages = {107848}, doi = {10.1016/j.neunet.2025.107848}, pmid = {40683189}, issn = {1879-2782}, abstract = {In the fields of affective computing (AC) and brain-computer interface (BCI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognition, significant challenges persist in achieving end-to-end emotion computation, including rapid processing, individual adaptation, and seamless user interaction. This paper presents the EEG Emotion Copilot, a system optimizing a lightweight large language model (LLM) with 0.5B parameters operating in a local setting, which first recognizes emotional states directly from EEG signals, subsequently generates personalized diagnostic and treatment suggestions, and finally supports the automation of assisted electronic medical records. Specifically, we demonstrate the critical techniques in the novel data structure of prompt, model pruning and fine-tuning training, and deployment strategies aiming at improving performance and computational efficiency. Extensive experiments show that our optimized lightweight LLM-based copilot achieves an enhanced intuitive interface for participant interaction, superior accuracy of emotion recognition and assisted electronic medical records generation, in comparison to such models with similar scale parameters or large-scale parameters such as 1.5B, 1.8B, 3B and 7B. In summary, through these efforts, the proposed copilot is expected to advance the application of AC in the medical domain, offering innovative solution to mental health monitoring. The codes will be released at https://github.com/NZWANG/EEG_Emotion_Copilot.}, }
@article {pmid40681665, year = {2025}, author = {Schrag, E and Comaduran Marquez, D and Kirton, A and Kinney-Lang, E}, title = {An investigation into the comfort and neural response of textured visual stimuli in pediatric SSVEP-based BCI.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {26168}, pmid = {40681665}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Female ; Male ; Adolescent ; Child ; *Photic Stimulation/methods ; Electroencephalography/methods ; Child, Preschool ; Signal-To-Noise Ratio ; }, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) are widely used due to their reliability and possible training-free setup. Common SSVEP stimuli are high contrast and solidly colored, potentially causing discomfort and visual fatigue, particularly when high stimulation frequencies are employed. To address this, textured stimuli, which may evoke visual responses in higher processing systems, have been proposed as an alternative to conventional flashing stimuli. We evaluate the effectiveness of textured stimuli for SSVEP-based BCIs by examining both user comfort and neural responses across different EEG channel subsets. Neurotypical participants aged 5-18 (n = 35, 57% female) were exposed to traditional and textured stimuli at three frequencies (9, 14, and 33 Hz) and asked to report perceived comfort. While textured stimuli were consistently rated as more comfortable, especially at lower frequencies, signal-to-noise ratio analysis indicated that they did not enhance neural responses compared to conventional stimuli. Classification accuracy was driven primarily by stimulation frequency rather than stimulus type and there was a sharp decline in accuracy at 33 Hz. These findings suggest that while textured stimuli improve user comfort, their utility in enhancing BCI performance remains unclear, warranting further investigation into stimulus design for SSVEP-based BCIs.}, }
@article {pmid40681115, year = {2025}, author = {Deepika, D and Rekha, G}, title = {Multi-class mental Task Classification based Brain-Computer Interface using Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network model.}, journal = {Journal of neuroscience methods}, volume = {423}, number = {}, pages = {110536}, doi = {10.1016/j.jneumeth.2025.110536}, pmid = {40681115}, issn = {1872-678X}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Fuzzy Logic ; *Deep Learning ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; }, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) offer a promising avenue for individuals with severe motor disabilities to interact with the world. By decoding brain signals, BCIs can enable users to control devices and communicate thoughts. However, challenges such as noise in EEG signals and limited data availability hinder the development of accurate and reliable BCI systems. Nonetheless, problems persist, including limited data availability, noisy EEG signals, real-time performance limitations, and reduced classification accuracy.<br><br>NEW METHOD: To overcome this, the present work proposes an efficient Multi-Class Mental Task Classification based BCI using deep learning techniques. Initially, the obtained EEG data is pre-processed with a Finite Linear Haar wavelet-based Filtering (FLHF) technique to remove disturbances in EEG data. Afterwards, optimal feature extraction utilizes a Hybrid dynamic centre binary pattern and multi-threshold-based ternary pattern (H-DCBP-MTTP) technique to extract characteristics from pre-processed EEG data. Finally, the Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network (IRDCANFIN) model is used to classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using an Improved Remora optimization approach (IROA).<br><br>RESULTS: The proposed approach's performance is examined using the BCI laboratory dataset and the EEG Psychiatric Disorders Dataset, which yield accuracy results of 99.3&#x202f;% and 99.56&#x202f;%, respectively. Furthermore, evaluation results show that the proposed approach outperforms existing models.<br><br>Compared to existing models, such as DQN with a 1D-CNN-LSTM, GSP-ML, Shallow 1D-CNN, KNN, and SVM, and the proposed approach yields effective results in terms of accuracy, robustness, and computational efficiency.<br><br>CONCLUSION: The proposed IRDCANFIN classifier is used to classify multiple classes of mental tasks like baseline, counting, multiplication, letter composing, and rotation.}, }
@article {pmid40681114, year = {2025}, author = {Zhang, R and Li, Z and Pan, X and Cui, H and Chen, X}, title = {Hybrid BCI for upper limb rehabilitation: integrating MI with peripheral field SSVEP stimulation.}, journal = {Journal of neuroscience methods}, volume = {423}, number = {}, pages = {110537}, doi = {10.1016/j.jneumeth.2025.110537}, pmid = {40681114}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Female ; Adult ; *Upper Extremity/physiopathology/physiology ; *Imagination/physiology ; Electroencephalography ; *Stroke Rehabilitation/methods ; Robotics ; Photic Stimulation ; Young Adult ; Middle Aged ; }, abstract = {BACKGROUND: Rehabilitation systems based on brain-computer interfaces (BCIs) hold significant potential for stroke patients. Existing systems, predominantly relying on motor imagery (MI), have room for improvement in both performance and user comfort. This study aims to enhance these aspects by developing a hybrid BCI system integrating MI with steady-state visual evoked potentials (SSVEPs) elicited by peripheral visual field stimulation.<br><br>NEW METHODS: The system is coupled with a soft robotic hand for feedback, forming a closed-loop framework. The design incorporates concentric rings with 7&#xb0; and 10&#xb0; eccentricities as peripheral stimuli, flashing at frequencies of 34&#x202f;Hz and 35&#x202f;Hz for left and right sides, respectively, to evoke SSVEPs. A central video (304&#x202f;&#xd7;304 pixels) of left-hand/right-hand grasping motions guides subjects in performing synchronized MI tasks simply by focusing on it, which could also complete the SSVEP task.<br><br>RESULTS: The offline results of 11 subjects showed that the classification result of MI was 70.65&#x202f;&#xb1;&#x202f;3.38&#x202f;%, and the SSVEP result was 96.04&#x202f;&#xb1;&#x202f;3.33&#x202f;%, and the fusion result reached 96.23&#x202f;&#xb1;&#x202f;3.21&#x202f;%, which confirmed the validity of the fusion method. The online experiment of 11 subjects achieved a result of 97.12&#x202f;&#xb1;&#x202f;2.09&#x202f;%, validating the feasibility of the system.<br><br>The proposed system improves the comfort level while ensuring the performance of the system as compared to the existing systems.<br><br>CONCLUSION: The feasibility of the proposed system was verified by offline and online experiments to advance the clinical applications.}, }
@article {pmid40680338, year = {2025}, author = {Sun, R and Ma, D and Pan, G}, title = {Post-training quantization for efficient ANN-SNN conversion.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {191}, number = {}, pages = {107832}, doi = {10.1016/j.neunet.2025.107832}, pmid = {40680338}, issn = {1879-2782}, mesh = {*Neural Networks, Computer ; *Neurons/physiology ; Algorithms ; Humans ; *Action Potentials/physiology ; }, abstract = {Spiking Neural Networks (SNNs), inspired by the behavior of biological neurons, offer a promising direction for next-generation neural computing. Two primary methodologies have emerged for training deep SNNs: Direct Training, which optimizes SNNs using surrogate gradients, and ANN-to-SNN Conversion, which derives SNNs from Artificial Neural Networks (ANNs). In this work, we focus on the latter and investigate the conversion error that arises during the transformation. We provide a theoretical analysis showing that channel-wise thresholds are more effective than traditional layer-wise thresholds in mitigating this error. To achieve this efficiently, we leverage post-training quantization (PTQ), which enables calibration using only a small dataset without requiring retraining. Compared to conventional direct training and ANN-to-SNN conversion methods, our approach significantly reduces training time while improving accuracy on both static image and neuromorphic datasets.}, }
@article {pmid40679899, year = {2025}, author = {Kim, YS and Han, H and Kim, CU and Choi, SI and Kim, MY and Im, CH}, title = {Performance Enhancement of Steady-State Visual Evoked Field-Based Brain-Computer Interfaces Incorporating MEG Source Imaging.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2806-2813}, doi = {10.1109/TNSRE.2025.3590576}, pmid = {40679899}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; *Magnetoencephalography/methods ; Algorithms ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; Young Adult ; Electroencephalography ; Reproducibility of Results ; Brain/physiology ; }, abstract = {Recent advancements in helmet-type magneto-encephalography (MEG) systems that operate without liquid helium, such as optically pumped magnetometer (OPM)-based MEG, have increased interest in MEG-based brain-computer interfaces (BCIs). Among various BCI paradigms, steady-state visual evoked field (SSVEF)-based BCIs have been actively studied owing to their high information transfer rate (ITR) and low demand for calibration sessions. Although MEG provides excellent spatial resolution and whole-head coverage, conventional algorithms such as the filter bank-driven multivariate synchronization index (FBMSI) do not fully exploit these advantages. To overcome this limitation, this study employed MEG source imaging to utilize information from whole-head MEG recordings fully and developed a novel weighting method called the averaged source location-based weighting (ASLW). ASLW leverages the averaged source locations of SSVEF signals to enhance BCI performance. Experimental results with 20 participants demonstrated that integrating ASLW with the FBMSI algorithm (ASLW-FBMSI) significantly improved both the classification accuracy and ITR across all tested window sizes. Notably, the largest performance gains included a 13.9% accuracy improvement at a 3-s window size and a 13.1 bits/min increase in ITR at a 2.5-s window size. Additionally, the ASLW-FBMSI algorithm exhibited a short processing delay of 0.107 s at a 4-s data length and was successfully validated in online BCI experiments with 20 participants. Although tested in SQUID-MEG in this study, our findings demonstrate the effectiveness of ASLW in significantly enhancing the overall performance of SSVEF-based BCIs.}, }
@article {pmid40678831, year = {2025}, author = {Wang, J and Chen, H and Wang, X}, title = {Tirzepatide Induces Ferroptosis in Glioblastoma Cell Lines via the SOX2/SLC7A11 Axis: A Potential Therapeutic Strategy for Glioma Treatment.}, journal = {Journal of biochemical and molecular toxicology}, volume = {39}, number = {8}, pages = {e70392}, doi = {10.1002/jbt.70392}, pmid = {40678831}, issn = {1099-0461}, support = {//This study was supported by the Fifth Affiliated Hospital of Zhengzhou University./ ; }, mesh = {*Ferroptosis/drug effects ; Humans ; Cell Line, Tumor ; *Amino Acid Transport System y+/metabolism ; *SOXB1 Transcription Factors/metabolism ; *Glioblastoma/metabolism/drug therapy/pathology ; Cell Proliferation/drug effects ; Lipid Peroxidation/drug effects ; *Neoplasm Proteins/metabolism ; *Brain Neoplasms/metabolism/drug therapy/pathology ; Cell Movement/drug effects ; Tirzepatide ; }, abstract = {Tirzepatide, a dual agonist for glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptors used in type 2 diabetes and obesity management, was investigated for its effects on glioma cells, focusing on its potential to induce ferroptosis. Tirzepatide treatment significantly inhibited glioma cell proliferation and migration, as demonstrated by the CCK-8 and Transwell migration assays. Tirzepatide also induced lipid peroxidation, evidenced by increased ROS levels, elevated MDA production, and reduced SOD activity, while the GSH/GSSG ratio was decreased, reflecting oxidative stress. Ferroptosis was further confirmed by increased Fe[2+] concentrations and alterations in iron metabolism-related genes (Ferritin and TFR1) and lipid metabolism-related genes (ACSL4 and GPX4). Tirzepatide also inhibited the SOX2/SLC7A11 axis, which plays a critical role in resisting ferroptosis. Fer-1, a ferroptosis inhibitor, or SOX2 overexpression, markedly reduced Tirzepatide's effects on proliferation, migration, lipid peroxidation, and ferroptosis, highlighting the critical role of the SOX2/SLC7A11 axis in mediating these effects. These findings indicate that Tirzepatide inhibits glioma cell growth by inducing ferroptosis, presenting a potential therapeutic approach for glioma.}, }
@article {pmid40678346, year = {2025}, author = {Tsay, JJ and Velez, A and Collazo, D and Laniado, I and Bessich, J and Murthy, V and DeMaio, A and Rafeq, S and Kwok, B and Darawshy, F and Pillai, R and Wong, K and Li, Y and Schluger, R and Lukovnikova, A and Roldan, S and Blaisdell, M and Paz, F and Krolikowski, K and Gershner, K and Liu, Y and Gong, J and Borghi, S and Zhou, F and Tsirigos, A and Pass, H and Segal, LN and Sterman, DH}, title = {A Phase I Dose-Escalation Clinical Trial of Bronchoscopic Cryoimmunotherapy in Advanced-Stage NSCLC.}, journal = {JTO clinical and research reports}, volume = {6}, number = {8}, pages = {100849}, pmid = {40678346}, issn = {2666-3643}, abstract = {INTRODUCTION: Outcomes for NSCLC remain suboptimal. Recent data suggest that cryoablation can generate antitumor immune effects. In this first-in-human phase I clinical trial, we investigated the safety and feasibility of bronchoscopic cryoimmunotherapy (BCI) delivered during standard-of-care bronchoscopy and explored associated systemic immune responses.<br><br>METHODS: Subjects with known or suspected advanced-stage NSCLC were recruited. BCI was delivered in dose-escalated freeze-thaw cycles to determine maximum dose tolerance. Feasibility assessment was determined with a pre-set goal of achieving successful BCI in more than or equal to 80% of subjects. Safety was assessed by review of BCI-related complications, including grades 2 to 3 bleeding, pneumothorax requiring intervention, and National Cancer Institute Common Terminology Criteria for Adverse Events grade 3 to 5 adverse events. Pre- and post-BCI blood samples were collected to explore changes in the systemic immune profile.<br><br>RESULTS: Subjects with predominantly clinical TNM stage 3 or 4 adenocarcinoma or squamous cell carcinoma were enrolled. We reached the maximum dose of 30 seconds with 100% feasibility and no BCI-related adverse events. In peripheral blood analysis, we observed a significant decrease in derived neutrophil-to-lymphocyte ratio in the high-dose BCI group in comparison to the low-dose BCI cohort. We also observed increases in inflammatory cytokines-GM-CSF, IFN-&#x3b3;, IL-1&#x3b2;, IL-17A, and IL-2-and effector memory T cells post-BCI.<br><br>CONCLUSION: BCI is safe and feasible. In addition, we provide preliminary evidence that at higher dose levels there is a systemic immune response consistent with a cytotoxic profile. Further immune analyses will determine the potential of BCI as an adjunctive therapy in combination with immune checkpoint inhibition in NSCLC treatment.}, }
@article {pmid40677333, year = {2025}, author = {Zheng, ZW and Xu, MH and Fan, LN and Wang, RM and Xu, WQ and Yang, GM and Guo, LY and Liu, C and Dong, Y and Wu, ZY}, title = {Renal Impairment in Wilson's Disease.}, journal = {Kidney international reports}, volume = {10}, number = {7}, pages = {2453-2456}, pmid = {40677333}, issn = {2468-0249}, }
@article {pmid40674496, year = {2025}, author = {Nair, A}, title = {Unraveling the emergent chorus of the mind: Machine learning reveals how a hidden neural code orchestrates diverse emotion states.}, journal = {Science (New York, N.Y.)}, volume = {389}, number = {6757}, pages = {245}, doi = {10.1126/science.adx7811}, pmid = {40674496}, issn = {1095-9203}, mesh = {*Emotions/physiology ; *Machine Learning ; Humans ; *Brain/physiology ; *Neurons/physiology ; }, abstract = {Machine learning reveals how a hidden neural code orchestrates diverse emotion states.}, }
@article {pmid40672704, year = {2025}, author = {Rekrut, M and Ihl, J and Jungbluth, T and Krüger, A}, title = {How low can you go: evaluating electrode reduction methods for EEG-based speech imagery BCIs.}, journal = {Frontiers in neuroergonomics}, volume = {6}, number = {}, pages = {1578586}, pmid = {40672704}, issn = {2673-6195}, abstract = {Speech imagery brain-computer interfaces (SI-BCIs) aim to decode imagined speech from brain activity and have been successfully established using non-invasive brain measures such as electroencephalography (EEG). However, current EEG-based SI-BCIs predominantly rely on high-resolution systems with 64 or more electrodes, making them cumbersome to set up and impractical for real-world use. In this study, we evaluated several electrode reduction algorithms in combination with various feature extraction and classification methods across three distinct EEG-based speech imagery datasets to identify the optimal number and position of electrodes for SI-BCIs. Our results showed that, across all datasets, the original 64 channels could be reduced by 50% without a significant performance loss in classification accuracy. Furthermore, the relevant areas were not limited to the left hemisphere, widely known to be responsible for speech production and comprehension, but were distributed across the cortex. However, we could not identify a consistent set of optimal electrode positions across datasets, indicating that electrode configurations are highly subject-specific and should be individually tailored. Nonetheless, our findings support the move away from extensive and costly high-resolution systems toward more compact, user-specific setups, facilitating the transition of SI-BCIs from laboratory settings to real-world applications.}, }
@article {pmid40672675, year = {2025}, author = {Zhang, C and Wang, Y and Wang, X}, title = {Reimagining Neuropsychiatric and Neurological Disorders through the Lens of Brain Network Dynamics: Psychedelics as Catalysts for System-Level Plasticity.}, journal = {ACS pharmacology &amp; translational science}, volume = {8}, number = {7}, pages = {2308-2311}, pmid = {40672675}, issn = {2575-9108}, abstract = {Neuropsychiatric disorders reflect disruptions in brain network dynamics along an "order-complexity-chaos" continuum. Psychedelics may therapeutically increase neural entropy, disrupt maladaptive patterns, and promote network reorganization. This system-level framework emphasizes dynamic connectome remodeling over static molecular correction, offering a novel strategy for treating psychiatric and neurological conditions through controlled neural destabilization and reconnection.}, }
@article {pmid40672502, year = {2025}, author = {Chaichanasittikarn, O and Diaz, L and Thomas, N and Candrea, D and Luo, S and Nathan, K and Tenore, FV and Fifer, MS and Crone, NE and Christie, B and Osborn, LE}, title = {High-gamma electrocorticography activity represents perceived vibration intensity in human somatosensory cortex.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {40672502}, support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; }, abstract = {Haptic feedback can play a useful role in rehabilitation and brain-computer interface applications by providing users with information about their system or performance. One challenge delivering tactile stimulation is not knowing how the haptic sensation is actually perceived, irrespective of the stimulation amplitude, during real-world use and beyond controlled psychophysical experiments. In a participant with chronically implanted electrocorticography arrays, we observed that perceived intensity of haptic vibration on the fingertips was represented in the high-gamma (HG) frequency band (70-170 Hz) in the somatosensory cortex. The five fingers of the participant's right hand were represented by distinct channels in the implanted array and modulated by the vibration amplitude at the fingertips. Although it reliably varied with the vibration amplitude, we found that HG activity had a stronger relationship with the actual perceived intensity of haptic stimulation (r s = 0.45, p < 10 [-6]). These results demonstrate that neural signals, specifically HG activity, in the somatosensory cortex can represent qualities of perceived haptic intensity regardless of the stimulation amplitude, which could enable a new way to passively quantify or ensure effective haptic feedback to a user.}, }
@article {pmid40672280, year = {2025}, author = {Lei, T and Scheid, MR and Glaser, JI and Slutzky, MW}, title = {Active Dissociation of Intracortical Spiking and High Gamma Activity.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40672280}, issn = {2692-8205}, support = {R00 NS119787/NS/NINDS NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; R01 NS112942/NS/NINDS NIH HHS/United States ; RF1 NS125026/NS/NINDS NIH HHS/United States ; K08 NS060223/NS/NINDS NIH HHS/United States ; T32 NS047987/NS/NINDS NIH HHS/United States ; R01 NS099210/NS/NINDS NIH HHS/United States ; T32 EB009406/EB/NIBIB NIH HHS/United States ; }, abstract = {Cortical high gamma activity (HGA) is used in many scientific investigations, yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly- predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. We trained subjects to decouple spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them indicated that HGA is not primarily generated by summed local spiking. Instead, HGA correlated with neuronal population co-firing of neurons that were widely distributed across millimeters. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises predominantly from summed postsynaptic potentials triggered by synchronous co-firing of widely distributed neurons.}, }
@article {pmid40668700, year = {2025}, author = {Robinson, JT}, title = {Making Heads and Tails of the Coming Era of Neural Devices, Could Moore's Law Address the Declining Mental Health Trend.}, journal = {IEEE pulse}, volume = {16}, number = {3}, pages = {50-55}, doi = {10.1109/MPULS.2025.3572593}, pmid = {40668700}, issn = {2154-2317}, mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Mental Health ; *Transcranial Magnetic Stimulation ; }, abstract = {Despite major advances in medicine and technology, mental health outcomes have declined globally over the past several decades. Fortunately we are in the early phases of exponential growth neurotech similar to Moore's Law. These emerging neural devices may provide a solution to the growing mental health crisis. Clinical data shows promising outcomes from technologies such as transcranial magnetic stimulation (TMS) leading to exponential improvement in performance improvements and cost reductions. As a result, neurotechnology could follow a similar path to personal computing going from a handful of niche markets to ubiquity over the next decade. Indeed, next generation therapeutic brain-computer interfaces (BCIs)-particularly minimally invasive implants-could become mass-market solutions for regulating mental states. The future may be one where neural devices help individuals thrive in an increasingly complex world, not by augmenting human intelligence but by enhancing emotional well-being and preserving the most precious aspects of our humanity.}, }
@article {pmid40668693, year = {2025}, author = {Banks, J}, title = {Silicon Synapses: The Bold Frontier of Brain-Computer Integration.}, journal = {IEEE pulse}, volume = {16}, number = {3}, pages = {5-9}, doi = {10.1109/MPULS.2025.3572569}, pmid = {40668693}, issn = {2154-2317}, mesh = {*Brain-Computer Interfaces ; Humans ; *Silicon ; *Synapses/physiology ; Spinal Cord Injuries ; *Brain/physiology ; }, abstract = {The allure of Neuralink is attracting investors to funnel money into the development of brain-computer interface (BCI) technology, primarily aimed at treating spinal cord injury (SCI) patients. But what is the payoff? Jim Banks examines the inspired innovation in BCI that is reestablishing connections for patients with the world.}, }
@article {pmid40668691, year = {2025}, author = {Goktas, P and Tun, NN}, title = {EEG-Based Brain-Computer Interfaces: Pioneering Frontier Research in the 21st Century.}, journal = {IEEE pulse}, volume = {16}, number = {3}, pages = {36-39}, doi = {10.1109/MPULS.2025.3572556}, pmid = {40668691}, issn = {2154-2317}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Electroencephalography/methods/trends ; Artificial Intelligence ; *Signal Processing, Computer-Assisted ; Brain/physiology ; }, abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) systems are inevitably needed to set up non-invasive therapies in neurorehabilitation. Along with the artificial intelligence (AI) techniques trending, constructing EEG-based brain computer interfaces is still in demand with high classification accuracy for advancing the state-of-the-art BCIs. From the perspective of pioneering frontier research, this article highlights the 21st-century's EEG-based BCI systems, their challenges, and its future direction for neuroscientists and clinical applications.}, }
@article {pmid40668688, year = {2025}, author = {Zaman, MH}, title = {The Potential of Brain-Computer Interface Technologies in Low- and Middle-Income Countries Global Health Perspective.}, journal = {IEEE pulse}, volume = {16}, number = {3}, pages = {40-42}, doi = {10.1109/MPULS.2025.3572574}, pmid = {40668688}, issn = {2154-2317}, mesh = {*Brain-Computer Interfaces/economics ; Humans ; *Developing Countries ; *Global Health ; }, abstract = {Historically, brain-computer interface (BCI) technologies have almost exclusively been available in high-income countries. What would it take for them to become more available and accessible in low- and middle-income countries, and in complex settings?}, }
@article {pmid40668686, year = {2025}, author = {Grifantini, K}, title = {From Headsets to Healing: The Rise of Wearable Brain Tech and Its Impact on Mental Illness and Cognitive Health.}, journal = {IEEE pulse}, volume = {16}, number = {3}, pages = {25-29}, doi = {10.1109/MPULS.2025.3572580}, pmid = {40668686}, issn = {2154-2317}, mesh = {Humans ; *Brain-Computer Interfaces ; *Cognition/physiology ; *Mental Disorders/therapy ; Mental Health ; *Wearable Electronic Devices ; }, abstract = {The rapidly evolving field of noninvasive brain-machine interfaces (BMIs) is transforming wearable technology from science fiction into a powerful tool for health care, offering a surgery-free and drug-free alternative to traditional treatments. Such devices are currently being used to target conditions such as depression, anxiety, PTSD, insomnia and more through targeted neurostimulation techniques.}, }
@article {pmid40668685, year = {2025}, author = {Bates, M}, title = {Why Consumer Neurofeedback Devices Are More Than Hype for Brain Health.}, journal = {IEEE pulse}, volume = {16}, number = {3}, pages = {21-24}, doi = {10.1109/MPULS.2025.3572577}, pmid = {40668685}, issn = {2154-2317}, mesh = {Humans ; *Neurofeedback/instrumentation ; *Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; }, abstract = {Neurofeedback uses a brain-computer interface to measure a person's brain activity and show it to them in real time. A number of companies offer neurofeedback devices directly to consumers, with promises of improving meditation and enhancing concentration. However, whether neurofeedback is actually effective remains controversial among researchers.}, }
@article {pmid40668684, year = {2025}, author = {Anderson, C}, title = {Industry Corner Live With Synchron CEO Tom Oxley.}, journal = {IEEE pulse}, volume = {16}, number = {3}, pages = {43-49}, doi = {10.1109/MPULS.2025.3572578}, pmid = {40668684}, issn = {2154-2317}, mesh = {Humans ; Artificial Intelligence ; *Biomedical Engineering ; *Brain-Computer Interfaces ; }, abstract = {Pulse's Industry Corner Live featured a dynamic live Q&amp;A session between IEEE Pulse Editor-in-Chief Chad Andresen and Dr. Tom Oxley, CEO and co-founder of Synchron, a leader in minimally invasive brain-computer interface (BCI) technology. The discussion explored the intersection of neurotechnology, artificial intelligence, and the evolving landscape of entrepreneurship in the MedTech sector. Dr. Oxley shared insights into Synchron's pioneering work with endovascular BCIs, offering a less invasive alternative to traditional neurosurgical approaches, and how this technology is reshaping the possibilities for restoring communication in patients with paralysis. The conversation delved into the growing role of AI in decoding neural signals and driving clinical translation, while also addressing the regulatory, financial, and ethical challenges faced by entrepreneurs in the neurotechnology space. With candid reflections on his journey from clinician to startup founder, Oxley provided an inside look at what it takes to bring disruptive technologies from concept to clinic. This session offered a rare glimpse into the mindset of a neurotech innovator navigating the high-stakes interface of science, medicine, and industry.}, }
@article {pmid40668677, year = {2025}, author = {Sorrell, E and Wilson, DE and Rule, ME and Yang, H and Forni, F and Harvey, CD and O'Leary, T}, title = {An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation.}, journal = {Cell reports}, volume = {44}, number = {7}, pages = {115862}, doi = {10.1016/j.celrep.2025.115862}, pmid = {40668677}, issn = {2211-1247}, mesh = {Animals ; *Brain-Computer Interfaces ; *Parietal Lobe/physiology ; Mice ; *Goals ; *Spatial Navigation/physiology ; Male ; Mice, Inbred C57BL ; Virtual Reality ; }, abstract = {Cortical circuits contain diverse sensory, motor, and cognitive signals, and they form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We develop a calcium-imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we find that mice can immediately navigate toward goal locations when control is switched to the BMI. No learning or adaptation is observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decouple from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.}, }
@article {pmid40667422, year = {2025}, author = {Schroeder, F and Fairclough, S and Dehais, F and Richins, M}, title = {The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting.}, journal = {Frontiers in neuroergonomics}, volume = {6}, number = {}, pages = {1582724}, pmid = {40667422}, issn = {2673-6195}, abstract = {Neuroadaptive technologies are a type of passive Brain-computer interface (pBCI) that aim to incorporate implicit user-state information into human-machine interactions by monitoring neurophysiological signals. Evaluating machine learning and signal processing approaches represents a core aspect of research into neuroadaptive technologies. These evaluations are often conducted under controlled laboratory settings and offline, where exhaustive analyses are possible. However, the manner in which classifiers are evaluated offline has been shown to impact reported accuracy levels, possibly biasing conclusions. In the current study, we investigated one of these sources of bias, the choice of cross-validation scheme, which is often not reported in sufficient detail. Across three independent electroencephalography (EEG) n-back datasets and 74 participants, we show how metrics and conclusions based on the same data can diverge with different cross-validation choices. A comparison of cross-validation schemes in which train and test subset boundaries either respect the block-structure of the data collection or not, illustrated how the relative performance of classifiers varies significantly with the evaluation method used. By computing bootstrapped 95% confidence intervals of differences across datasets, we showed that classification accuracies of Riemannian minimum distance (RMDM) classifiers may differ by up to 12.7% while those of a Filter Bank Common Spatial Pattern (FBCSP) based linear discriminant analysis (LDA) may differ by up to 30.4%. These differences across cross-validation implementations may impact the conclusions presented in research papers, which can complicate efforts to foster reproducibility. Our results exemplify why detailed reporting on data splitting procedures should become common practice.}, }
@article {pmid40667167, year = {2025}, author = {Sun, L and Qin, W and Liang, X and Wang, C and Men, W and Duan, Y and Fan, XR and Cai, Q and Qiu, S and Wang, M and Gong, Q and Tian, Y and Liang, P and Liu, Z and Zhang, X and Song, H and Ye, Z and Zhang, P and Dong, Q and Tao, S and Zhu, W and Zhang, J and Xie, F and Feng, J and Zhang, J and Liu, C and Qian, Q and Zhang, B and Meng, M and Hu, L and Gao, JH and Jiang, T and Zhu, X and Zhang, Y and Liu, L and Liu, H and Liao, W and Wang, D and Wang, H and Guo, T and Dai, Z and Lui, S and Xu, K and Li, L and Xie, P and Feng, C and Cui, G and Wu, J and Yin, X and Ding, G and Xian, J and Zhao, L and Lu, J and Liu, Z and Han, Y and Yuan, Z and Zhang, X and Si, T and Zhou, F and Bi, Y and Wu, D and Gao, F and Wang, F and Qin, S and Wang, G and Chen, F and Zhang, Z and Sui, J and Chen, H and Cai, J and Liu, S and Geng, Z and Zhang, C and Mao, N and Yin, H and Liu, B and Ma, H and Gao, B and Miao, Y and Kong, XZ and Zhou, Y and Liu, L and Hu, J and Wang, L and Zhang, Q and Shu, H and Wang, P and Lee, TMC and Cao, Q and Yang, L and Zhang, X and Luo, W and Liang, M and Yao, H and Li, M and Huang, H and Peng, Y and Han, Z and Zhou, C and Xu, H and Feng, M and Shen, W and Hu, Y and Chen, H and Wang, Y and Gong, G and Yan, Z and Xu, X and Liu, J and Chen, G and Wang, P and Yang, Y and Yao, D and Han, T and He, H and Chen, C and Zou, Q and Liu, H and Zhang, H and Chai, C and Lu, C and Tu, Y and Liu, Y and Lin, D and Zhao, W and Xu, X and Liu, X and Cui, Z and Wang, Z and Huang, R and Li, Z and Liu, Y and Li, X and Yang, X and Zhang, N and Chen, A and Zhang, B and Qin, P and Liu, C and Yao, Z and Wei, Y and Yuan, H and Wang, F and Zhang, Y and Zhang, Q and Hu, F and Xie, H and Wu, X and Wang, J and Fan, G and Wang, Z and Zhang, D and Zhong, H and Wang, Y and Bai, L and Li, Y and Wei, X and Wang, J and Zhang, Y and He, H and Li, S and Zhang, T and Jiang, F and Yang, J and Chen, F and Liu, F and Liu, H and Chen, N and Yang, J and Hou, B and Huang, CC and Zhu, J and Cai, H and Wei, D and Chen, Q and Wei, Y and Miao, P and Li, Y and Liu, Y and Yang, N and Gao, X and Liu, Y and Shen, Y and Huang, X and Ji, GJ and ,  and Zhang, L and Qiu, J and Yu, Y and Lin, CP and Feng, F and Li, K and Yu, C and He, Y}, title = {Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40667167}, issn = {2692-8205}, support = {U24 DA041147/DA/NIDA NIH HHS/United States ; U01 DA051039/DA/NIDA NIH HHS/United States ; U01 DA041120/DA/NIDA NIH HHS/United States ; U01 DA051018/DA/NIDA NIH HHS/United States ; U01 AG024904/AG/NIA NIH HHS/United States ; U24 DA041123/DA/NIDA NIH HHS/United States ; U01 DA051037/DA/NIDA NIH HHS/United States ; U01 DA051016/DA/NIDA NIH HHS/United States ; U01 DA041106/DA/NIDA NIH HHS/United States ; U01 DA041148/DA/NIDA NIH HHS/United States ; U01 MH110274/MH/NIMH NIH HHS/United States ; P50 MH086385/MH/NIMH NIH HHS/United States ; U01 DA041174/DA/NIDA NIH HHS/United States ; U01 DA041093/DA/NIDA NIH HHS/United States ; U01 MH109589/MH/NIMH NIH HHS/United States ; U01 DA051038/DA/NIDA NIH HHS/United States ; R21 MH107045/MH/NIMH NIH HHS/United States ; U01 DA041134/DA/NIDA NIH HHS/United States ; U01 DA041022/DA/NIDA NIH HHS/United States ; U01 DA041156/DA/NIDA NIH HHS/United States ; U01 DA050987/DA/NIDA NIH HHS/United States ; U01 DA041025/DA/NIDA NIH HHS/United States ; U01 DA050989/DA/NIDA NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; U01 DA041089/DA/NIDA NIH HHS/United States ; U01 DA050988/DA/NIDA NIH HHS/United States ; R03 MH096321/MH/NIMH NIH HHS/United States ; U01 DA041117/DA/NIDA NIH HHS/United States ; U01 DA041028/DA/NIDA NIH HHS/United States ; U01 DA041048/DA/NIDA NIH HHS/United States ; K23 MH087770/MH/NIMH NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; }, abstract = {Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine [1-7]. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.}, }
@article {pmid40666891, year = {2025}, author = {Wang, Y and Cheng, L and Li, D and Lu, Y and Hopkins, WD and Sherwood, CC and Xu, T and Liu, C and Paxinos, G and Jiang, T and Chu, C and Fan, L}, title = {Evolutionary Convergence of the Arcuate Fasciculus in Marmosets and Humans.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40666891}, issn = {2692-8205}, support = {R24 NS092988/NS/NINDS NIH HHS/United States ; P41 EB015897/EB/NIBIB NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; R01 AG067419/AG/NIA NIH HHS/United States ; R01 AG087945/AG/NIA NIH HHS/United States ; R01 HG011641/HG/NHGRI NIH HHS/United States ; }, abstract = {The marmoset is a highly vocal platyrrhine monkey that shares key anatomical and functional features with humans, offering insights into the evolution of brain connectivity. Although similarities in vocalization features with humans have been reported, it remains unclear whether marmosets possess an arcuate fasciculus (af) homolog. This study delineated white matter tracts in marmosets, establishing homologies with those observed in other primates, including macaques, chimpanzees, and humans. The presence of an af homolog in marmosets was confirmed by tracer and ultra-high-resolution diffusion magnetic resonance imaging datasets. We compared cortical connectivity patterns across these species and found the af in marmosets terminates in the ventral frontal cortex, with greater similarity to humans than macaques. Furthermore, we linked af connectivity with vocalization-related brain activation in both marmosets and humans. Collectively, our findings suggest that a dorsal pathway, which emerged early in marmoset evolution, has evolved convergently with humans, despite their distant phylogenetic kinship.}, }
@article {pmid40666829, year = {2025}, author = {Bo, W and Che, R and Jia, F and Sun, K and Liu, Q and Guo, L and Zhang, X and Gong, Y}, title = {Study on the Effect of the Envelope of Terahertz Unipolar Stimulation on Cell Membrane Communication-Related Variables.}, journal = {Research (Washington, D.C.)}, volume = {8}, number = {}, pages = {0755}, pmid = {40666829}, issn = {2639-5274}, abstract = {The development of terahertz science and technology has shown new application prospects in artificial intelligence. Terahertz stimulation can lead to information communication of cells. Terahertz unipolar picosecond pulse train stimulation can activate cell membrane hydrophilic pores and protein ion channels. However, the effect of the envelope of the terahertz unipolar stimulation remains unknown. This paper studies the effect of the envelope on membrane communication-related variables and the accompanying energy consumption by a cell model with considerations of hydrophilic pores and Na[+], K[+]-ATPase. According to the results, terahertz unipolar picosecond pulse train stimulation can deliver the signal contained in its envelope into the variation rates of membrane potentials no matter whether the hydrophilic pores are activated or not and also into the variation rates of the ion flow via the pores after activation of the pores. In contrast, the ion flow via Na[+], K[+]-ATPase seems irrelevant to the signal in the envelope. Moreover, the ion flows show a modulation effect on the variation rates of membrane potentials. The accompanying power dissipations in the cases of different envelopes are similar, as low as around the level of 10[-11] W. The results lay the foundations for application in artificial intelligence, like brain-machine communications.}, }
@article {pmid40664224, year = {2025}, author = {Wang, X and Meng, J and Zheng, Y and Wei, Y and Wang, F and Ding, H and Zhuo, Y}, title = {Characterizing the neural representations and decoding performance of foot rhythmic motor execution or imagery guided by action observation.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/adf011}, pmid = {40664224}, issn = {1741-2552}, mesh = {Humans ; Male ; Female ; Magnetoencephalography/methods ; *Imagination/physiology ; Adult ; Young Adult ; *Foot/physiology ; Electroencephalography/methods ; *Psychomotor Performance/physiology ; Brain-Computer Interfaces ; Movement/physiology ; *Periodicity ; }, abstract = {Objective. The limited spatial resolution inherent in electroencephalography (EEG), a widely-adopted non-invasive neuroimaging technique, combined with the intrinsic complexity of performing unilateral lower-limb motor imagery (MI), restricts decoding accuracy. To address these challenges, we propose a paradigm based on action observation-guided rhythmic motor execution (AO-ME) and motor imagery (AO-MI), designed to simplify task demands and enhance decoding performance. Magnetoencephalography (MEG) serves as the data acquisition method, leveraging its superior spatiotemporal resolution.Approach. Spatiotemporal and spectral features were characterized at the sensor level, and source imaging techniques were employed to examine cortical activation patterns. Ensemble task-related component analysis (eTRCA) facilitated decoding of unilateral tasks. And multiple decoding algorithms were employed to validate the effectiveness of the proposed paradigm.Main results. Robust lateralized neural responses were observed, exhibiting low-frequency phase-locked components that distinctly reflected the task frequency and its second harmonic within sensorimotor, parietal, and occipital cortices. Moreover, significant contralateral suppression of the sensorimotor rhythm was observed. Decoding accuracies reached 95.22 ± 4.75% for AO-ME and 88.66 ± 8.52% for AO-MI across twenty participants based on the phase-locked features using eTRCA.Significance. Collectively, our findings demonstrate that the proposed paradigm provides an effective approach for eliciting robust, distinguishable neural responses, enabling high decoding performance of unilateral lower-limb movements. This work offers new insights into the underexplored domain of lower-limb MI and highlights the paradigm's potential for brain-computer interface applications.}, }
@article {pmid40664221, year = {2025}, author = {Jochumsen, M and Petersen, BS and Vestergaard, LM and Falborg, NF and Wisler, L and Olesen, MV and Andersen, MS and Sørensen, NB and Jørgensen, ST}, title = {Detection of movement-related cortical potentials associated with upper and low limb movements in patients with multiple sclerosis for brain-computer interfacing.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/adf010}, pmid = {40664221}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Multiple Sclerosis/physiopathology/diagnosis ; Male ; Female ; Middle Aged ; Movement/physiology ; Adult ; Electroencephalography/methods ; *Upper Extremity/physiology/physiopathology ; *Lower Extremity/physiology/physiopathology ; *Evoked Potentials, Motor/physiology ; }, abstract = {Objectives.Brain-computer interface (BCI) training has been shown to be effective for inducing neural plasticity and for improving motor function in stroke patients. BCI training could potentially have a positive effect on people with multiple sclerosis (MS) as well by pairing movement-related brain activity with congruent afferent feedback from e.g. functional electrical stimulation. In the current study, the aim was to detect movement-related cortical potentials (MRCPs) from single-trial EEG in people with MS across two separate days using different classifier calibration schemes to estimate the performance of a BCI that can be used for neurorehabilitation.Approach.Fifteen individuals with MS performed 100 wrist movements and 100 ankle movements while continuous EEG was recorded. Also, idle brain activity was recorded. This was repeated on a separate day. The data were filtered and divided into epochs containing data prior to the movement onset. Temporal, spectral and template matching features were extracted and classified with a random forest classifier using different calibration schemes to estimate the performance when training the classifier on data from the same day and same participant, different day but same participant, and across different participants.Main Results.Clear MRCPs were elicited across both recording days, and it was possible to discriminate between idle activity and movement-related brain activity with accuracies between ∼80%-90% when training and testing the classifier on data from the same day and participant. The performance decreased when using data from a separate day but same participant (∼70%-80%) or data from separate participants (∼70%) for training the classifier.Significance.The results showed that it is feasible for people with MS to use a BCI for inducing neural plasticity.}, }
@article {pmid40663645, year = {2025}, author = {Wang, A and Lin, C and Mao, W and Jin, J}, title = {More generosity, less inequity aversion? Neural correlates of fairness perception under social distance and of its relation to generosity.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {35}, number = {7}, pages = {}, doi = {10.1093/cercor/bhaf152}, pmid = {40663645}, issn = {1460-2199}, support = {//Shanghai Philosophy and Social Sciences Planning Project/ ; //National Nature Science Foundation of China/ ; }, mesh = {Humans ; Male ; Female ; Young Adult ; Electroencephalography ; Adult ; *Psychological Distance ; *Social Behavior ; *Social Perception ; *Brain/physiology ; Interpersonal Relations ; Games, Experimental ; *Altruism ; Adolescent ; }, abstract = {Humans instinctively react negatively to inequity, while generosity counters this tendency. Previous studies show that both fairness perception and generosity involve balancing behaviors and motivations in social interactions. However, their relationship remains underexplored, limiting our understanding of the complex psychological processes underlying social behavior. Using a social discounting task, we assessed individual generosity, while an Ultimatum Game task with concurrent electroencephalogram recording allowed us to quantify inequity aversion and fairness perception by manipulating social distance and inequity levels. We found that both generosity and fairness perception decrease with increasing social distance, whereas inequity aversion increases. Modeling the decay of generosity across social distances, we found that decayed generosity was positively associated with inequity aversion in the friend condition and negatively correlated with the attenuation of fairness perception. These results suggest that the decay of generosity with social distance is linked to reduced sensitivity to inequity toward friends and heightened neural differences in fairness perception across social relationships. Our study provides electrophysiological evidence of individual variability in generosity and inequity aversion influenced by social distance, expanding inequity aversion theory.}, }
@article {pmid40662561, year = {2025}, author = {Vikal, A and Maurya, R and Patel, BB and Patel, P and Kumar, M and Kurmi, BD}, title = {A Mini-Review on Unlocking Cognitive Enhancement: An Innovative Strategy for Optimal Brain Functions.}, journal = {Central nervous system agents in medicinal chemistry}, volume = {}, number = {}, pages = {}, doi = {10.2174/0118715249357704250702140026}, pmid = {40662561}, issn = {1875-6166}, abstract = {Cognitive enhancement, aimed at improving or preserving memory, attention, and executive functions, has gained significant interest from both the scientific community and the public. This review explores various strategies for enhancing cognitive function, including natural compounds, synthetic enhancers, and behavioural approaches. Natural compounds like curcumin, Ginkgo biloba, Panax ginseng, and Rhodiola rosea are examined for their cognitive benefits, with ongoing research on their mechanisms and potential nanoformulation-based drug delivery. Synthetic enhancers such as Modafinil, Piracetam, Methylphenidate, and Noopept show promise in improving cognitive functions. Additionally, substances influencing brain metabolism, like Creatine and Coenzyme Q10, are discussed. Behavioural interventions, including sleep optimization, meditation, and physical exercise, are evaluated for their cognitive-enhancing effects. Noninvasive brain stimulation techniques, such as TMS and tDCS, along with innovative methods like whole-body vibration and brain-machine interfaces, are also explored. The review emphasizes the complex interplay of these strategies and the need for continued research to fully exploit their potential. By highlighting natural compounds, synthetic drugs, and behavioural approaches, the review advocates for a multifaceted approach to cognitive enhancement and calls for more detailed and longitudinal studies to understand their long-term benefits and mechanisms.}, }
@article {pmid40661574, year = {2025}, author = {Hou, X and Iacobacci, C and Card, NS and Wairagkar, M and Singer-Clark, T and Kunz, EM and Fan, C and Kamdar, F and Hahn, N and Hochberg, LR and Henderson, JM and Willett, FR and Brandman, DM and Stavisky, SD}, title = {Error encoding in human speech motor cortex.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40661574}, issn = {2692-8205}, support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; }, abstract = {Humans monitor their actions, including detecting errors during speech production. This self-monitoring capability also enables speech neuroprosthesis users to recognize mistakes in decoded output upon receiving visual or auditory feedback. However, it remains unknown whether neural activity related to error detection is present in the speech motor cortex. In this study, we demonstrate the existence of neural error signals in speech motor cortex firing rates during intracortical brain-to-text speech neuroprosthesis use. This activity could be decoded to enable the neuroprosthesis to identify its own errors with up to 86% accuracy. Additionally, we observed distinct neural patterns associated with specific types of mistakes, such as phonemic or semantic differences between the person's intended and displayed words. These findings reveal how feedback errors are represented within the speech motor cortex, and suggest strategies for leveraging these additional cognitive signals to improve neuroprostheses.}, }
@article {pmid40660069, year = {2025}, author = {Jeppsen, C and McMurray, B}, title = {Reduced Cochlear Implant Performance in Listeners with Single-Sided Deafness: Comparison with Bilateral Listeners.}, journal = {Journal of the Association for Research in Otolaryngology : JARO}, volume = {26}, number = {4}, pages = {477-489}, pmid = {40660069}, issn = {1438-7573}, support = {P50 DC000242/DC/NIDCD NIH HHS/United States ; R01 DC008089/DC/NIDCD NIH HHS/United States ; P50 DC00242//Foundation for the National Institutes of Health/ ; }, mesh = {Humans ; Female ; Male ; *Cochlear Implants ; Middle Aged ; *Speech Perception ; Adult ; Aged ; *Deafness ; *Hearing Loss, Unilateral ; }, abstract = {PURPOSE: The efficacy of the Cochlear Implant (CI) in listeners with single-sided deafness (SSD) was evaluated by comparing single-ear speech perception in SSD listeners and bilateral cochlear implant listeners (BCI).<br><br>METHODS: Consonant-nucleus-consonant (CNC) speech perception scores for the CI-only ear in SSD listeners (N&#x2009;=&#x2009;55; 36 female, 19 male) were compared to single-ear performance in age and device experience-matched BCI listeners (N&#x2009;=&#x2009;55; 29 female, 26 male). Separate analyses examined: (1) a matched ear from the BCI listeners (for sequentially implanted BCI listeners, the first-implanted ear in sequential BCI listeners, or, for simultaneously implanted BCI listeners, the ear on the same side as the CI in the matching SSD listener), and (2) the lower-performing ear across BCI listeners. Additional models included moderators such as age, time since activation, CI usage, and etiology. A final analysis compared first and second implants for sequential BCI listeners.<br><br>RESULTS: SSD listeners showed significantly lower CNC performance after controlling for age, time since activation, CI usage, and etiology. Sequential BCI listeners exhibited significantly lower CNC performance on their second ear, compared to their first ear.<br><br>CONCLUSION: Speech perception with CIs is reduced in SSD listeners compared to BCI users, likely due to blocking, where the normal-hearing ear diminishes reliance on the CI. Lower performance in the second implanted ear of sequential BCI listeners also suggests greater reliance on the more experienced ear. These findings highlight the need for additional training, resources, and support to optimize CI performance in SSD listeners, despite prior evidence of positive CNC outcomes.}, }
@article {pmid40659530, year = {2025}, author = {Hu, Y and Ma, B and Jin, J}, title = {Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {45}, number = {32}, pages = {}, pmid = {40659530}, issn = {1529-2401}, mesh = {Humans ; Female ; Male ; *Friends/psychology ; Adult ; Young Adult ; *Social Networking ; *Brain/physiology/diagnostic imaging ; *Consumer Behavior ; Magnetic Resonance Imaging ; Longitudinal Studies ; *Social Behavior ; Adolescent ; Brain Mapping ; }, abstract = {The endogenous aspect of social influence, reflected in the spontaneous alignment of behaviors within close social relationships, plays a crucial role in understanding human social behavior. In two studies involving 222 human subjects (Study 1: n = 175, 106 females; Study 2: n = 47, 33 females), we used a longitudinal behavioral study and a naturalistic stimuli neuroimaging study to investigate the endogenous consumer behavior similarities and their neural basis in real-world social networks. The findings reveal that friends, compared with nonfriends, exhibit higher similarity in product evaluation, which undergoes dynamic changes as the structure of social networks changes. Both neuroimaging and meta-analytic decoding results indicate that friends exhibit heightened neural synchrony, which is linked to cognitive functions such as object perception, attention, memory, social judgment, and reward processing. Stacking machine learning-based predictive models demonstrate that the functional connectivity maps of brain activity can predict the purchase intention of their friends or their own rather than strangers. Based on the significant neural similarity which exists among individuals in close relationships within authentic social networks, the current study reveals the predictive capacity of neural activity in predicting the behavior of friends.}, }
@article {pmid40658672, year = {2025}, author = {Eser, A and Erdoğan, SB}, title = {Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.}, journal = {PloS one}, volume = {20}, number = {7}, pages = {e0325850}, pmid = {40658672}, issn = {1932-6203}, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; *Emotions/physiology ; Male ; Female ; Adult ; Young Adult ; *Supervised Machine Learning ; Algorithms ; Prefrontal Cortex/physiology ; Support Vector Machine ; }, abstract = {Automated detection of emotional states through brain-computer interfaces (BCIs) offers significant potential for enhancing user experiences and personalizing services across domains such as mental health, adaptive learning and interactive entertainment. Within this advancing field, the aim of this study was to test the feasibility of a functional near-infrared spectroscopy (fNIRS)-based BCI system for accurate prediction and objective identification of three fundamental emotional states that involved positive, negative and neutral conditions. Consequently, the efficacy of fNIRS signals in predicting the valence of standardized stimuli from the International Affective Picture System (IAPS) was assessed. fNIRS data were collected from twenty healthy participants while images from the IAPS database were presented. The images varied in both valence (i.e., positive, neutral, negative) and arousal (i.e., high, low) level. Hemodynamic responses of prefrontal cortical (PFC) regions were recorded with a twenty-two channel system. Twenty fNIRS derived time domain features were extracted from HbO time traces of each channel corresponding to each stimulus period. Classification performances of three machine learning algorithms, namely the k-Nearest Neighbors (kNN), Ensemble (Subspace kNN) and Support Vector Machines (SVM), in two class and three class classification of positive, neutral and negative states were evaluated with ten runs of a tenfold cross-validation procedure through splitting the data into test, train and validation groups at each run. Three class classification performances of all algorithms were above 90% in terms of accuracy, sensitivity, specificity, F-1 score and precision metrics while two class accuracy performances of all algorithms were above 93% in terms of each performance metric. The high-performance classification results highlight the potential of fNIRS-based BCI systems for real-time, objective detection of basic emotional states for daily life and clinical applications. fNIRSbased BCIs may show promise for future developments in personalized user experiences and clinical applications due to their practicality and low computational complexity.}, }
@article {pmid40658035, year = {2025}, author = {Niu, Y and Li, Z and Zeng, G and Zhang, Y and Yao, L and Wu, X}, title = {Electroencephalogram-Based Satisfaction Assessment Brain-Computer Interface in Emerging Video Service by Using Graph Representation Learning.}, journal = {Brain connectivity}, volume = {}, number = {}, pages = {}, doi = {10.1177/21580014251359107}, pmid = {40658035}, issn = {2158-0022}, abstract = {Background: Emerging video services (EVS) offer users various multimedia presentations, and satisfaction assessment is crucial for enhancing their user experience and competitiveness. However, existing research methods are unable to provide a quantitative satisfaction assessment. Electroencephalogram (EEG), as a popular signal source in brain-computer interface (BCI), with the advantage of being difficult to disguise and containing rich brain activity information, has gained increasing attention from researchers. This article aims to investigate the advantages of employing EEG for modeling satisfaction in EVS. Unlike the subjective metrics assessment in traditional video services, generating satisfaction in EVS involves a range of cognitive functions, including cognitive load, emotion, and audiovisual perception, which are difficult to characterize using a single feature. The representation of brain states for complex cognitive functions has been a major challenge for EEG modeling approaches. Methods: To address this challenge, we propose an EEG-based EVS satisfaction assessment BCI by raising a Point-to-Global graph representation learning strategy (P2G) that efficiently identifies satisfaction level through a parallel coding module and a graph-based brain region perception module. P2G captures satisfaction-sensitive graph representations in EEG samples based on coding and integrating point features and the global topography. Results: We validate the effectiveness of introducing a P2G learning strategy in EVS satisfaction modeling using a self-constructed dataset and a relevant public dataset, and our method outperforms existing methods. Additionally, we provide a detailed visual analysis to unveil neural markers associated with EVS satisfaction, thereby laying a scientific foundation for the optimization and development of video services.}, }
@article {pmid40656548, year = {2025}, author = {Mueller, NN and Ocoko, MYM and Kim, Y and Li, K and Gisser, K and Glusauskas, G and Lugo, I and Dernelle, P and Hermoso, AC and Wang, J and Duncan, J and Druschel, LN and Graham, F and Capadona, JR and Hess-Dunning, A}, title = {Mechanically-adaptive, resveratrol-eluting neural probes for improved intracortical recording performance and stability.}, journal = {Npj flexible electronics}, volume = {9}, number = {1}, pages = {64}, pmid = {40656548}, issn = {2397-4621}, abstract = {Intracortical microelectrodes are used for recording activity from individual neurons, providing both a valuable neuroscience tool and an enabling medical technology for individuals with motor disabilities. Standard neural probes carrying the microelectrodes are rigid silicon-based structures that can penetrate the brain parenchyma to interface with the targeted neurons. Unfortunately, within weeks after implantation, neural recording quality from microelectrodes degrades, owing largely to a neuroinflammatory response. Key contributors to the neuroinflammatory response include mechanical mismatch at the device-tissue interface and oxidative stress. We developed a mechanically-adaptive, resveratrol-eluting (MARE) neural probe to mitigate both mechanical mismatch and oxidative stress and thereby promote improved neural recording quality and longevity. In this work, we demonstrate that compared to rigid silicon controls, highly-flexible MARE probes exhibit improved recording performance, more stable impedance, and a healing tissue response. With further optimization, MARE probes can serve as long-term, robust neural probes for brain-machine interface applications.}, }
@article {pmid40656455, year = {2025}, author = {Komosar, M and Tamburro, G and Graichen, U and Comani, S and Haueisen, J}, title = {Combination of spatial and temporal de-noising and artifact reduction techniques in multi-channel dry EEG.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1576954}, pmid = {40656455}, issn = {1662-4548}, abstract = {INTRODUCTION: Dry electroencephalography (EEG) allows for recording cortical activity in ecological scenarios with a high channel count, but it is often more prone to artifacts as compared to gel-based EEG. Spatial harmonic analysis (SPHARA) and ICA-based methods (Fingerprint and ARCI) have been separately used in previous studies for dry EEG de-noising and physiological artifact reduction. Here, we investigate if the combination of these techniques further improves EEG signal quality. For this purpose, we also introduced an improved version of SPHARA.<br><br>METHODS: Dry 64-channel EEG was recorded from 11 healthy volunteers during a motor performance paradigm (left and right hand, feet, and tongue movements). EEG signals were denoised separately using Fingerprint + ARCI, SPHARA, a combination of these two methods, and a combination of these two methods including an improved SPHARA version. The improved version of SPHARA includes an additional zeroing of artifactual jumps in single channels before application of SPHARA. The EEG signal quality after application of each denoising method was calculated by means of standard deviation (SD), signal to noise ratio (SNR), and root mean square deviation (RMSD), and a generalized linear mixed effects (GLME) model was used to identify significant changes of these parameters and quantify the changes in the EEG signal quality.<br><br>RESULTS: The grand average values of SD improved from 9.76 (reference preprocessed EEG) to 8.28, 7.91, 6.72, and 6.15 &#x3bc;V for Fingerprint + ARCI, SPHARA, Fingerprint + ARCI + SPHARA, and Fingerprint + ARCI + improved SPHARA, respectively. Similarly, the RMSD values improved from 4.65 to 4.82, 6.32, and 6.90 &#x3bc;V, and the SNR values changed from 2.31 to 1.55, 4.08, and 5.56 dB.<br><br>DISCUSSION: Our results demonstrate the different performance aspects of Fingerprint + ARCI and SPHARA, artifact reduction and de-noising techniques that complement each other. We also demonstrated that a combination of these techniques yields superior performance in the reduction of artifacts and noise in dry EEG recordings, which can be extended to infant EEG and adult MEG applications.}, }
@article {pmid40655558, year = {2025}, author = {Alonso-Vázquez, D and Mendoza-Montoya, O and Caraza, R and Martinez, HR and Antelis, JM}, title = {From pronounced to imagined: improving speech decoding with multi-condition EEG data.}, journal = {Frontiers in neuroinformatics}, volume = {19}, number = {}, pages = {1583428}, pmid = {40655558}, issn = {1662-5196}, abstract = {INTRODUCTION: Imagined speech decoding using EEG holds promising applications for individuals with motor neuron diseases, although its performance remains limited due to small dataset sizes and the absence of sensory feedback. Here, we investigated whether incorporating EEG data from overt (pronounced) speech could enhance imagined speech classification.<br><br>METHODS: Our approach systematically compares four classification scenarios by modifying the training dataset: intra-subject (using only imagined speech, combining overt and imagined speech, and using only overt speech) and multi-subject (combining overt speech data from different participants with the imagined speech of the target participant). We implemented all scenarios using the convolutional neural network EEGNet. To this end, twenty-four healthy participants pronounced and imagined five Spanish words.<br><br>RESULTS: In binary word-pair classifications, combining overt and imagined speech data in the intra-subject scenario led to accuracy improvements of 3%-5.17% in four out of 10 word pairs, compared to training with imagined speech only. Although the highest individual accuracy (95%) was achieved with imagined speech alone, the inclusion of overt speech data allowed more participants to surpass 70% accuracy, increasing from 10 (imagined only) to 15 participants. In the intra-subject multi-class scenario, combining overt and imagined speech did not yield statistically significant improvements over using imagined speech exclusively.<br><br>DISCUSSION: Finally, we observed that features such as word length, phonological complexity, and frequency of use contributed to higher discriminability between certain imagined word pairs. These findings suggest that incorporating overt speech data can improve imagined speech decoding in individualized models, offering a feasible strategy to support the early adoption of brain-computer interfaces before speech deterioration occurs in individuals with motor neuron diseases.}, }
@article {pmid40654838, year = {2025}, author = {Lin, X and Zhang, X and Wang, Z and Chen, J and Lee, J and Lee, AJ and Yang, H and Remy, A and Shen, H and He, Y and Zhao, H and Zhang, X and Wang, W and Aljović, A and Vlassak, JJ and Lu, N and Liu, J}, title = {Plastic-elastomer heterostructure for robust flexible brain-computer interfaces.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.04.29.651325}, pmid = {40654838}, issn = {2692-8205}, abstract = {Electronics for neural signal recording must be robust across multiple and deep brain regions while preserving tissue-level flexibility to ensure stable tracking over months or years. However, existing electronics cannot simultaneously achieve robustness and tissue-level flexibility, limiting their potential for customizable and scalable neuroscience research and clinical applications. Here, we introduce FlexiSoft, an electronic platform based on a plastic-elastomer heterostructure that uniquely integrates mechanical robustness and tissue-level flexibility. Compared to conventional flexible electronics of similar thickness, the FlexiSoft platform demonstrates an order-of- magnitude improvement in both mechanical robustness (critical energy release rate) and flexibility (flexural rigidity). Leveraging these mechanical advantages, we developed FlexiSoft probe for robust implantation, demonstrated by its ability to withstand repeated insertion and removal, as well as to reach centimeter-scale depths comparable to those in the human brain. The platform enables long-term recording from the same neurons across the hippocampus (HPC) and primary motor cortex (M1) during a months-long motor learning task, thereby revealing long-term dynamic changes in neuronal firing patterns. Additionally, FlexiSoft's unique robustness and flexibility enable curved implantation routes, opening new directions of customizable implantation pathways. In summary, we present FlexiSoft as a novel, robust, and tissue-level flexible heterostructure electronics platform that advances flexible brain-computer interfaces (BCIs) with strong translational potential for neuroscience and clinical applications.}, }
@article {pmid40653584, year = {2025}, author = {Monteiro, RV and Amarante, JEV and Bona, VS and Lins, RBE and Lopes, GC and Blackburn, M and Swanson, C and Latorre, JA and De Souza, GM}, title = {Microshear bond strength of conventional and bioactive restorative materials to irradiated and non-irradiated dentin: an in vitro study.}, journal = {Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer}, volume = {33}, number = {8}, pages = {688}, pmid = {40653584}, issn = {1433-7339}, mesh = {Humans ; *Dentin/radiation effects/ultrastructure ; *Composite Resins/chemistry ; *Dental Bonding/methods ; Materials Testing ; In Vitro Techniques ; Shear Strength ; Microscopy, Electron, Scanning ; Resin Cements/chemistry ; Dentin-Bonding Agents/chemistry ; Surface Properties ; Time Factors ; }, abstract = {PURPOSE: To evaluate the effect of conventional and bioactive restorative materials on bond strength to control (non-irradiated) and irradiated dentin.<br><br>METHODS: Human dentin fragments (240) were polished and divided into non-irradiated dentin (NI; n&#x2009;=&#x2009;120) and irradiated dentin (ID; n&#x2009;=&#x2009;120). ID specimens received 70&#xa0;Gy irradiation (2&#xa0;Gy/fraction, 5&#xa0;days/week for 7&#xa0;weeks). All dentin surfaces were bonded to restorative materials with Scotchbond universal adhesive in self-etching mode. Microshear bond strength cylinders were built on the bonded surface according to the restorative material (4 subgroups, n&#x2009;=&#x2009;30): conventional resin composite (CC-Filtek Z250) and three bioactive restorative composites (BCI-Activa BioActive-Restorative; BCII-Beautiful II; BCIII-Predicta Bulk). Specimens were stored in water at 37&#xa0;&#xb0;C for 24&#xa0;h or 30&#xa0;days and subjected to microshear bond strength test. The data was analyzed using two-way ANOVA and Tukey's post-hoc test (&#x237a;&#x2009;<&#x2009;0.05). The morphological surface of both NI and ID dentin (n&#x2009;=&#x2009;3) was analyzed using Scanning Electron Microscopy (SEM).<br><br>RESULTS: Two-way ANOVA revealed a significant effect of the Time/Radiation (p&#x2009;<&#x2009;0.001). Restorative material (p&#x2009;=&#x2009;0.191) and the interaction Time/Radiation*Restorative material (p&#x2009;=&#x2009;0.169) were not significant. Irradiation decreased the bond strength of CC specimens at both 24&#xa0;h (p&#x2009;<&#x2009;0.001) and 30&#xa0;days (p&#x2009;<&#x2009;0.001). None of the bioactive materials were significantly affected by irradiation and storage time. The SEM analysis revealed morphological changes in the ID specimens.<br><br>CONCLUSION: Ionizing radiation-induced morphological changes in the dentin surface. These changes negatively affected the conventional resin composite bond strengths to dentin. However, these morphological alterations did not affect the bond strength of the bioactive restorative materials.}, }
@article {pmid40649800, year = {2025}, author = {Jaszczuk, P and Bratelj, D and Capone, C and Rudnick, M and Pötzel, T and Verma, RK and Fiechter, M}, title = {Advances in Neuromodulation and Digital Brain-Spinal Cord Interfaces for Spinal Cord Injury.}, journal = {International journal of molecular sciences}, volume = {26}, number = {13}, pages = {}, pmid = {40649800}, issn = {1422-0067}, mesh = {Humans ; *Spinal Cord Injuries/therapy/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; *Spinal Cord Stimulation/methods ; Spinal Cord/physiopathology ; Animals ; }, abstract = {Spinal cord injury (SCI) results in a significant loss of motor, sensory, and autonomic function, imposing substantial biosocial and economic burdens. Traditional approaches, such as stem cell therapy and immune modulation, have faced translational challenges, whereas neuromodulation and digital brain-spinal cord interfaces combining brain-computer interface (BCI) technology and epidural spinal cord stimulation (ESCS) to create brain-spine interfaces (BSIs) offer promising alternatives by leveraging residual neural pathways to restore physiological function. This review examines recent advancements in neuromodulation, focusing on the future translation of clinical trial data to clinical practice. We address key considerations, including scalability, patient selection, surgical techniques, postoperative rehabilitation, and ethical implications. By integrating interdisciplinary collaboration, standardized protocols, and patient-centered design, neuromodulation has the potential to revolutionize SCI rehabilitation, reducing long-term disability and enhancing quality of life globally.}, }
@article {pmid40648603, year = {2025}, author = {Bonanno, M and Saracino, B and Ciancarelli, I and Panza, G and Manuli, A and Morone, G and Calabrò, RS}, title = {Assistive Technologies for Individuals with a Disability from a Neurological Condition: A Narrative Review on the Multimodal Integration.}, journal = {Healthcare (Basel, Switzerland)}, volume = {13}, number = {13}, pages = {}, pmid = {40648603}, issn = {2227-9032}, abstract = {BACKGROUND/OBJECTIVES: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains.<br><br>METHODS: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders.<br><br>RESULTS: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain-computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach.<br><br>CONCLUSIONS: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain-computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users' physiological and behavioral data to optimize support in daily tasks.}, }
@article {pmid40648390, year = {2025}, author = {Jezierska, K and Turoń-Skrzypińska, A and Rotter, I and Syroka, A and Łukowiak, M and Rawojć, K and Rawojć, P and Rył, A}, title = {Latency and Amplitude of Cortical Activation in Interactive vs. Passive Tasks: An fNIRS Study Using the NefroBall System.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {13}, pages = {}, pmid = {40648390}, issn = {1424-8220}, mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Brain Mapping/methods ; Brain-Computer Interfaces ; Movement/physiology ; *Prefrontal Cortex/physiology ; *Spectroscopy, Near-Infrared/methods ; *Visual Cortex/physiology ; }, abstract = {Functional near-infrared spectroscopy (fNIRS) allows non-invasive assessment of cortical activity during naturalistic tasks. This study aimed to compare cortical activation dynamics-specifically the latency (tmax) and amplitude (ΔoxyHb) of oxygenated haemoglobin changes-in passive observation and an interactive task using the Nefroball system. A total of 117 healthy adults performed two tasks involving rhythmic hand movements: a passive protocol and an interactive game-controlled condition. fNIRS recorded signals from the visual, parietal, motor, and prefrontal cortices of the left hemisphere. The Mann-Whitney test revealed significantly shorter tmax in all areas during the interactive task, suggesting faster recruitment of cortical networks. ΔoxyHb amplitude was significantly higher only in the visual cortex during the interactive task, indicating increased visual processing demand. No significant ΔoxyHb differences were observed in the motor, prefrontal, or parietal cortices. Weak but significant positive correlations were found between tmax and ΔoxyHb in the motor and prefrontal regions, but only in the passive condition. These findings support the notion that interactive tasks elicit faster, though not necessarily stronger, cortical responses. The results have potential implications for designing rehabilitation protocols and brain-computer interfaces involving visual-motor integration.}, }
@article {pmid40648293, year = {2025}, author = {Zhang, J and Zhao, D and Li, Y and Ming, G and Pei, W}, title = {Four-Dimensional Adjustable Electroencephalography Cap for Solid-Gel Electrode.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {13}, pages = {}, pmid = {40648293}, issn = {1424-8220}, support = {62401325//National Natural Science Foundation of China/ ; }, mesh = {*Electroencephalography/instrumentation/methods ; Humans ; Electrodes ; Adult ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Male ; Head ; Equipment Design ; Female ; }, abstract = {Currently, the electroencephalogram (EEG) cap is limited to a finite number of sizes based on head circumference, lacking the mechanical flexibility to accommodate the full range of skull dimensions. This reliance on head circumference data alone often results in a poor fit between the EEG cap and the user's head shape. To address these limitations, we have developed a four-dimensional (4D) adjustable EEG cap. This cap features an adjustable mechanism that covers the entire cranial area in four dimensions, allowing it to fit the head shapes of nearly all adults. The system is compatible with 64 channels or lower electrode counts. We conducted a study with numerous volunteers to compare the performance characteristics of the 4D caps with the commercial (COML) caps in terms of contact pressure, preparation time, wearing impedance, and performance in brain-computer interface (BCI) applications. The 4D cap demonstrated the ability to adapt to various head shapes more quickly, reduce impedance during testing, and enhance measurement accuracy, signal-to-noise ratio (SNR), and comfort. These improvements suggest its potential for broader application in both laboratory settings and daily life.}, }
@article {pmid40648241, year = {2025}, author = {Carìa, A}, title = {Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {13}, pages = {}, pmid = {40648241}, issn = {1424-8220}, support = {no number available//5xMille Unitn/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Language ; Artificial Intelligence ; *Communication ; Brain/physiology ; Deep Learning ; Electroencephalography ; Large Language Models ; }, abstract = {Integration of advanced artificial intelligence with neurotechnology offers transformative potential for assistive communication. This perspective article examines the emerging convergence between non-invasive brain-computer interface (BCI) spellers and large language models (LLMs), with a focus on predictive communication for individuals with motor or language impairments. First, I will review the evolution of language models-from early rule-based systems to contemporary deep learning architectures-and their role in enhancing predictive writing. Second, I will survey existing implementations of BCI spellers that incorporate language modeling and highlight recent pilot studies exploring the integration of LLMs into BCI. Third, I will examine how, despite advancements in typing speed, accuracy, and user adaptability, the fusion of LLMs and BCI spellers still faces key challenges such as real-time processing, robustness to noise, and the integration of neural decoding outputs with probabilistic language generation frameworks. Finally, I will discuss how fully integrating LLMs with BCI technology could substantially improve the speed and usability of BCI-mediated communication, offering a path toward more intuitive, adaptive, and effective neurotechnological solutions for both clinical and non-clinical users.}, }
@article {pmid40648159, year = {2025}, author = {Ionita, S and Coman, DA}, title = {Narrowband Theta Investigations for Detecting Cognitive Mental Load.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {13}, pages = {}, pmid = {40648159}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Theta Rhythm/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Adult ; Female ; }, abstract = {The way in which EEG signals reflect mental tasks that vary in duration and intensity is a key topic in the investigation of neural processes concerning neuroscience in general and BCI technologies in particular. More recent research has reinforced historical studies that highlighted theta band activity in relation to cognitive performance. In our study, we propose a comparative analysis of experiments with cognitive load imposed by arithmetic calculations performed mentally. The analysis of EEG signals captured with 64 electrodes is performed on low theta components extracted by narrowband filtering. As main signal discriminators, we introduced an original measure inspired by the integral of the curve of a function-specifically the signal function over the period corresponding to the filter band. Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. The results present EEG activity during mental tasks and show the behavioral pattern across 64 channels. The most precise and specific EEG channels for discriminating cognitive tasks induced by arithmetic tests are also identified.}, }
@article {pmid40646750, year = {2025}, author = {Li, S and Tang, Z and Li, M and Yang, L and Shang, Z}, title = {Neural Correlates of Flight Acceleration in Pigeons: Gamma-Band Activity and Local Functional Network Dynamics in the AId Region.}, journal = {Animals : an open access journal from MDPI}, volume = {15}, number = {13}, pages = {}, pmid = {40646750}, issn = {2076-2615}, support = {62301496//the National Natural Science Foundation of China/ ; GZC20232447//the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; 252102311095//the Key Scientific and Technological Projects of Henan Province/ ; 252102210008//the Key Scientific and Technological Projects of Henan Province/ ; }, abstract = {Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration-exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems.}, }
@article {pmid40645213, year = {2025}, author = {Meng, L and Zhao, H and Dong, M and Wang, Q and Shi, Y and Wang, D and Zhu, X and Xu, R and Ming, D}, title = {Cortical changes induced by increased cognitive task difficulty during dual task balancing correlate with postural instability in elders and patients with Parkinson's disease.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/adeeca}, pmid = {40645213}, issn = {1741-2552}, mesh = {Humans ; *Parkinson Disease/physiopathology/psychology/diagnosis ; Male ; Female ; *Postural Balance/physiology ; Aged ; *Psychomotor Performance/physiology ; *Cerebral Cortex/physiopathology ; Electroencephalography/methods ; Middle Aged ; *Cognition/physiology ; Memory, Short-Term/physiology ; Adult ; Young Adult ; }, abstract = {Objective. The flexibility of cognitive resource allocation is deteriorated due to aging and neurological degenerative diseases, such as Parkinson's disease (PD). Dual task performance reflects a subject's ability to allocate cognitive resources, and the investigation of cortical activation changes during dual tasking can provide a deep insight into the reallocation of neural resources. However, the cortical changes induced by increased cognitive task difficulty during dual tasking with changes in behavioral outcomes have not been explored in PD and older adults (OAs).Approach.We designed a novel dual task paradigm comprising of balance maintenance and visual working memory (VWM) task to assess cognitive-motor interaction. Nineteen early-stage PD, 13 age-matched OA and 15 young adults completed 4 blocks of 25 trials each for two VWM difficulty levels (2 squares and 4 squares). Behavioral performance, postural stability, and 32-channel EEG were recorded. One-way ANOVA was used to examine group and task effects while Spearman's correlation analysis assessed associations between EEG changes and behavioral performance.Main results.Both PD and OA groups exhibited significantly longer reaction time, reduced postural stability, prolonged P300 latency and less alpha event related desynchronization (ERD) enhancement in response to the increased VWM task difficulty. Moreover, PD patients demonstrated significantly alpha ERD reduction at FC3, C3 and P4 in the 500-700 ms compared to the OAs. The ERD changes at the central and parietal regions were found to be significantly related to postural stability and clinical scores, respectively.Significance.The results provide novel evidence that cortical EEG responses during dual tasking may reflect deficits in attention resource reallocation and reduced cognitive flexibility in PD and OA groups. These observed cortical changes with increasing cognitive task difficulty are correlated with postural instability, highlighting their potential as neurophysiological biomarkers for dual-task dysfunction.}, }
@article {pmid40645212, year = {2025}, author = {Li, Y and Zhao, Z and Liu, J and Peng, Y and Camilleri, K and Kong, W and Cichocki, A}, title = {EEG-based speech imagery decoding by dynamic hypergraph learning within projected and selected feature subspaces.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/adeec8}, pmid = {40645212}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Machine Learning ; *Speech/physiology ; Male ; Adult ; Female ; Young Adult ; }, abstract = {Objective.Speech imagery is a nascent paradigm that is receiving widespread attention in current brain-computer interface (BCI) research. By collecting the electroencephalogram (EEG) data generated when imagining the pronunciation of a sentence or word in human mind, machine learning methods are used to decode the intention that the subject wants to express. Among existing decoding methods, graph is often used as an effective tool to model the data structure; however, in the field of BCI research, the correlations between EEG samples may not be fully characterized by simple pairwise relationships. Therefore, this paper attempts to employ a more effective data structure to model EEG data.Approach.In this paper, we introduce hypergraph to describe the high-order correlations between samples by viewing feature vectors extracted from each sample as vertices and then connecting them through hyperedges. We also dynamically update the weights of hyperedges, the weights of vertices and the structure of the hypergraph in two transformed subspaces, i.e. projected and feature-weighted subspaces. Accordingly, two dynamic hypergraph learning models, i.e. dynamic hypergraph semi-supervised learning within projected subspace (DHSLP) and dynamic hypergraph semi-supervised learning within selected feature subspace (DHSLF), are proposed for speech imagery decoding.Main results.To validate the proposed models, we performed a series of experiments on two EEG datasets. The obtained results demonstrated that both DHSLP and DHSLF have statistically significant improvements in decoding imagined speech intentions to existing studies. Specifically, DHSLP achieved accuracies of 78.40% and 66.64% on the two datasets, while DHSLF achieved accuracies of 71.07% and 63.94%.Significance.Our study indicates the effectiveness of the learned hypergraphs in characterizing the underlying semantic information of imagined contents; besides, interpretable results on quantitatively exploring the discriminative EEG channels in speech imagery decoding are obtained, which lay the foundation for further exploration of the physiological mechanisms during speech imagery.}, }
@article {pmid40644990, year = {2025}, author = {Cai, G and Chen, Y and Yang, B and Yang, Y and Ma, T and Wang, Y}, title = {CGNet: A Complex-valued Graph Network for jointly learning amplitude-phase information in EEG-based brain-computer interfaces.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {191}, number = {}, pages = {107795}, doi = {10.1016/j.neunet.2025.107795}, pmid = {40644990}, issn = {1879-2782}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Algorithms ; *Brain/physiology ; *Deep Learning ; }, abstract = {The synergy between amplitude and phase in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provides comprehensive and essential insights into neural oscillatory processes. However, constrained by real-valued computation paradigms, most deep learning methods have to process amplitude and phase independently, neglecting their crucial interaction mechanisms. To address this issue, we construct a Complex-valued Graph Network (CGNet) to capture comprehensive information from EEG signals, where both amplitude and phase information are encoded into the complex-valued representation. Specifically, we design a two-scale complex-valued convolutional network to learn local spatio-temporal information, develop a spatial attention module to enhance spatial information learning, and formulate a dynamic graph convolution to capture global temporal dependencies. Furthermore, we extend CGNet to Filter-Band CGNet (FBCGNet), enhancing the model's adaptability to broadband EEG data. Applied to motor imagery and execution BCI tasks, CGNet achieves state-of-the-art classification performance while maintaining computational efficiency comparable to other advanced algorithms. Notably, FBCGNet further improves CGNet's performance. Visualization results show that CGNet can identify the key spatio-temporal information consistent with paradigm principles. In addition, compared with using amplitude or phase alone, CGNet can capture more comprehensive task-related neural activities, thereby showing higher classification performance. CGNet is a promising tool for mining amplitude-phase information and offering more comprehensive neural decoding in EEG-based BCIs.}, }
@article {pmid40644885, year = {2025}, author = {Al-Hadithy, SS and Abdalkafor, AS and Al-Khateeb, B}, title = {Emotion recognition in EEG Signals: Deep and machine learning approaches, challenges, and future directions.}, journal = {Computers in biology and medicine}, volume = {196}, number = {Pt A}, pages = {110713}, doi = {10.1016/j.compbiomed.2025.110713}, pmid = {40644885}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Deep Learning ; *Signal Processing, Computer-Assisted ; *Machine Learning ; Neural Networks, Computer ; *Brain-Computer Interfaces ; }, abstract = {A crucial part of brain-computer interfaces is the use of electroencephalogram (EEG) signals for human emotion identification, which analyzes patterns of brain activity to determine the emotional state. This field of study is becoming increasingly important for developing advanced applications that enhance brain machine interaction and improve brain health assessment systems. However, EEG signal analysis faces significant challenges due to their subject-specific nature, high noise levels, and the scarcity of high-quality labeled data, which collectively limit model generalizability and complicate signal analysis. Traditional approaches have employed handcrafted features with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests (RF) for EEG feature extraction and classification. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable automatic feature learning from raw data to extract temporal, spatial, and spectral properties. The study employs a literature review approach and the analysis of the popular datasets (e.g., DEAP, SEED, AMIGOS). Despite technological advances, the fundamental challenges of noisy subject variability, and limited labeled data persist, requiring future research to focus on improving model robustness, scalability, and interpretability while addressing current limitations.}, }
@article {pmid40644284, year = {2025}, author = {Petrich, LC and Neumann, S and Pilarski, PM and Fyshe, A}, title = {Neural Network Sparsity in Brain-Body-Machine Interfaces.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {1-8}, doi = {10.1109/ICORR66766.2025.11062950}, pmid = {40644284}, issn = {1945-7901}, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; Algorithms ; *Signal Processing, Computer-Assisted ; }, abstract = {Brain-body-machine interfaces acquire, process, and translate brain signals for individuals with severe motor impairments to communicate and control the assistive technology that supports their daily life activities. Electroencephalography (EEG) is a standard approach for acquiring such brain signals due to its low cost and high temporal resolution. EEG signals can be thought of as a proxy for the user's intent. One established method for translating this intent into inferences and actions are neural networks. However, densely connected neural networks can be computationally expensive-a problem for real-time, deployed brain-body-machine interface systems. In this paper we investigate the use of sparsity in neural networks for EEG-based motor classification, with the goal of reducing the number of neuronal connections without sacrificing a system's performance. We compare two sparsity-inducing algorithms, weight pruning and sparse evolutionary training, with a dense neural network under three experimental conditions. Overall, our results show that sparse neural networks can achieve higher performance accuracy and generalization than their densely-connected counterparts for an EEG-based classification task. We found that sparse evolutionary training achieves the highest and most stable performance across all experiments. Introducing sparsity into the network is a viable option for efficient EEG-based control, with promising applications in a range of related rehabilitation and assistive technologies. This brings us closer to helping individuals with severe motor impairments reclaim independence through more computationally realizable methods of interacting with their technology and the world around them.}, }
@article {pmid40644274, year = {2025}, author = {Patarini, F and Maronati, C and Manuello, J and Cuturi, LF and Monti, M and Savina, G and Ferrari, E and Iarrobino, I and Iani, C and Rubichi, S and Ciaramidaro, A and Astolfi, L and Cavallo, A and Toppi, J}, title = {Handling Kinematic Features in an Action Observation Task to Optimize a Brain Computer Interface-Based Rehabilitation Training.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {1078-1082}, doi = {10.1109/ICORR66766.2025.11062958}, pmid = {40644274}, issn = {1945-7901}, mesh = {Humans ; *Brain-Computer Interfaces ; Biomechanical Phenomena/physiology ; Electroencephalography ; *Stroke Rehabilitation ; Male ; Female ; Adult ; Middle Aged ; }, abstract = {Brain-Computer Interface (BCI) technology promotes neuroplasticity mechanisms which favor the functional motor recovery in stroke survivors. Patients' residual motor abilities determine the intention, which, once detected by the BCI is fed back via an effector. The majority of studies aimed at optimizing the feedback branch, but not enough attention has been posed to supporting patient in the movement intention that should be detected by the BCI system. The inclusion of a visual motor priming (observed action before a task) in a BCI could promote the retrieval of movements from the patient's own impaired motor repertoire. None of the motor priming proposed until so far have been tailored to the patients' residual motor ability, although it is well known that the human brain recognizes movements closer from a kinematic perspective to its own repertoire more easily. The aim of this study was to investigate how individual motor style in an action observation task would affect the observer's cortical excitability. EEG signals were recorded from 10 individuals during an action observation task where different levels of motor distance between the observer and the agent were modulated. EEG-based group spectral activations shown an involvement of bilateral parietal areas in beta band in case of more unpredictable kinematics. The results would open the way for the design of a kinematic-based visual motor priming to be embedded in a BCI system for post-stroke rehabilitation.}, }
@article {pmid40644240, year = {2025}, author = {Gonzalez-Cely, AX and Soekadar, SR and Delisle-Rodriguez, D and Bastos-Filho, T}, title = {Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {76-81}, doi = {10.1109/ICORR66766.2025.11063181}, pmid = {40644240}, issn = {1945-7901}, mesh = {Humans ; *Brain-Computer Interfaces ; *Lower Extremity/physiology ; Male ; Adult ; Electroencephalography ; *Exercise Test ; *Imagination/physiology ; Female ; Signal Processing, Computer-Assisted ; }, abstract = {Lower-limb rehabilitation traditionally relies on physical therapy, but motor imagery(MI)-based brain- computer interfaces (BCIs) promise to facilitate neuroplasticity and adaptation by closing the perception-action cycle. Here, we present a BCI system based on kinesthetic MI that enables treadmill velocity control, establishing a closed-loop feedback mechanism. The system was tested in a healthy participant translating mu (8-12 Hz) and high-beta (18-24 Hz) rhythm modulation into treadmill velocity control commands. Feature extraction techniques, including power spectral density (PSD) and Riemannian geometry (RG), were used for MI- and resting state classification. Additionally, Logistic Regression (LR), k-nearest neighbors, support vector machine, and Linear Discriminant Analysis (LDA) were employed and optimized for accuracy. The results showed increased mu and highbeta activation modulation at the vertex. The online RG+LDA classifier achieving an average accuracy of 72%, while the pseudo-online RG+LR reached 95%. The study's novelty lies in combining kinesthetic MI with treadmill control and employing RG for feature extraction, demonstrating its potential to enhance cortical modulation during rehabilitation. Future work will have to validate the system in poststroke patients for clinical applicability.}, }
@article {pmid40644193, year = {2025}, author = {Mannan, MMN and Lloyd, DG and Pizzolato, C}, title = {Optimising Continuous Control of Real-Time Brain-Computer Interfaces Through Trial Length and Feedback Update Interval Selection.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {284-288}, doi = {10.1109/ICORR66766.2025.11063010}, pmid = {40644193}, issn = {1945-7901}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; Adult ; Female ; Signal Processing, Computer-Assisted ; Young Adult ; }, abstract = {Brain-computer interfaces (BCIs) offer promising potential to aid neurorehabilitation by transforming motor imagery (MI) signals into control commands, bypassing damaged neural pathways to support motor recovery. However, a key challenge in BCI research is achieving an effective balance between classification accuracy and real-time responsiveness, as both are critical for enhancing user embodiment and control for neurorehabilitation outcomes. This study investigates the impact of trial length and feedback update interval (FUI) on classification accuracy in an MI-based BCI system. Using EEG data from five subjects across 50 sessions, we evaluated classification performance across various trial length (1-5 seconds) and FUI (0.2-1 second) configurations. Results revealed that both trial length and FUI significantly influenced classification accuracy, with longer trial length (4-5 seconds) and FUI (0.4-1 seconds) yielding the highest accuracy. However, post-hoc analyses indicated a saturation effect, with no significant differences in the accuracy for these parameters. These findings underscore the importance of balancing signal stability with responsiveness for optimal BCI performance, providing insights into parameter settings that can enhance BCI usability in neurorehabilitation. Future work may explore adaptive approaches to dynamically adjust these parameters based on real-time requirements, potentially offering a more responsive and efficient BCI for clinical rehabilitation.}, }
@article {pmid40644184, year = {2025}, author = {Koellner, J and Wimpff, M and Gizzi, L and Vujaklija, I and Yang, B}, title = {Exploring Cortical Responses to Blood Flow Restriction through Deep Learning.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {546-552}, doi = {10.1109/ICORR66766.2025.11063023}, pmid = {40644184}, issn = {1945-7901}, mesh = {Humans ; *Deep Learning ; Magnetoencephalography ; Male ; Adult ; Female ; Brain-Computer Interfaces ; *Cerebral Cortex/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Resistance Training ; }, abstract = {Blood flow restriction (BFR) training, which combines low-intensity resistance exercises with restricted blood flow, is effective in promoting muscle hypertrophy and strength. However, its impact on cortical activity remains largely unexplored, presenting an opportunity to investigate neural mechanisms using brain-computer interfaces (BCIs). Deep learning (DL)-based BCIs, with their large capacity for decoding complex brain signals, offer a promising avenue for such exploration. This study utilized magnetoencephalography (MEG) to analyze cortical responses in six subjects across three conditions-before, during, and after BFR. After preprocessing steps, such as data standardization and Euclidean-space alignment to optimize performance, the BaseNet architecture was utilized to classify the data. The models were tested using within-subject, cross-subject, and cross-time data splits. The results revealed classification accuracy well above 90% for individual subjects, indicating that cortical responses to BFR are detectable on a personal level. However, cross-subject models achieved only chance-level accuracy (33%), highlighting significant variability between individuals. Cross-time models showed better performance, with accuracy exceeding 50%. These findings suggest that while BFR elicits distinct cortical activity patterns, these responses are highly individualized, presenting challenges for generalization.}, }
@article {pmid40644160, year = {2025}, author = {Toppi, J and Savina, G and Colamarino, E and De Seta, V and Patarini, F and Cincotti, F and Pichiorri, F and Mattia, D}, title = {Hybrid Brain Computer Interface-Based Rehabilitation Addressing Post-Stroke Maladaptive Movement Patterns.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {431-436}, doi = {10.1109/ICORR66766.2025.11062988}, pmid = {40644160}, issn = {1945-7901}, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Middle Aged ; Female ; Aged ; Movement/physiology ; Adult ; Stroke/physiopathology ; Electromyography ; }, abstract = {Hybrid Brain-Computer Interfaces (hBCI) integrate brain and muscle signals to enhance motor rehabilitation of stroke survivors, by closing the loop between the lesioned brain and the paretic limb. To date, little attention has been devoted to their potential efficacy in managing the maladaptive movement patterns that afflict post-stroke motor outcome (unwanted abnormal co-contrations, spasticity). This study proposes a comparison of Cortico-Muscular Coherence (CMC) patterns assessed in stroke patients before and after a 1-month rehabilitation intervention based on a hBCI-controlled Functional Electrical Stimulation (FES) treatment, which included a module to monitor non-physiological movement patterns. Results demonstrated the efficacy of this type of assistive technology for post-stroke rehabilitation, addressing patient-tailored interventions able to reduce the maladaptive mechanisms.}, }
@article {pmid40644144, year = {2025}, author = {Bastos-Filho, T and Gonzalez-Cely, AX and Mehrpour, S and Souza, F and Villa-Parra, AC and Cabral, F}, title = {Rehabilitation of Chronic Stroke Using Neurofeedback, Functional Electrical Stimulation and Cerebrospinal Direct Current Stimulation.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {1203-1208}, doi = {10.1109/ICORR66766.2025.11063073}, pmid = {40644144}, issn = {1945-7901}, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Neurofeedback/methods ; Male ; Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; Chronic Disease ; Electromyography ; Middle Aged ; Stroke/physiopathology ; }, abstract = {This work presents the application of a rehabilitation protocol using a novel Non-Invasive Brain Stimulation (NIBS) technique, called cerebrospinal Direct Current Stimulation (csDCS), together with the use of a Brain-Computer Interface (BCI) based on Motor Imagery (MI) with Neurofeedback (NFB), and applying Functional Electrical Stimulation (FES) plus the use of a pedal exerciser. This protocol uses the concept of Alternating Treatment Design (ATD), in which a chronic post-stroke subject is submitted to these techniques to recover his left hand and leg movements. The rehabilitation progress was verified through metrics, such as Fugl Meyer Assessment (FMA), Functional Independence Measure (FIM), Ashworth Scale, Muscle Strength Grading (MSG), and surface Electromyography (sEMG). Results from these metrics include a 41% gain in hand function recovery, a 5% gain in performance in motor and cognitive/social domains, and a 50% improvement in both wrist extensor muscle strength and finger extensor muscle strength. In addition, there was a 17% gain of Maximum Voluntary Contraction (MVC) for the tibialis anterior muscle of the patient's left leg. On the other hand, there was a worsening in some values of EMG, probably due to the participant having received application of botulinum toxin in his hand.}, }
@article {pmid40644135, year = {2025}, author = {Sun, Q and Merino, EC and Yang, L and Faes, A and Van Hulle, MM}, title = {On the Impact of Proprioception in EEG Representations and Decoding During Human-Hand Exoskeleton Interaction.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {186-192}, doi = {10.1109/ICORR66766.2025.11063039}, pmid = {40644135}, issn = {1945-7901}, mesh = {Humans ; *Electroencephalography/methods ; *Proprioception/physiology ; Male ; *Exoskeleton Device ; Female ; Adult ; Brain-Computer Interfaces ; Young Adult ; *Hand/physiology ; Movement/physiology ; Fingers/physiology ; }, abstract = {Controlling a hand exoskeleton based on electroencephalogram (EEG)-based brain-computer interfacing (BCI) holds promise for human motor augmentation and neurore-habilitation. To achieve natural control, a critical step is to understand the impact of proprioception provided by the exoskeleton during interaction. In this study, we aim to approach the goal by quantifying EEG representations and BCI performance. We monitored 25 healthy subjects' full-scalp EEG while performing different finger movement tasks with a cable-driven hand exoskeleton. Each task involves three movement modalities, i.e., imagined (IM), passive (PM), and congruent imagined and passive (IPM) finger flexion. We found that alpha (8 - 13 Hz) and beta (13 - 30 Hz) band desynchronization in the sensorimotor area was significantly stronger for PM and IPM tasks compared to IM, with no significant difference between PM and IPM. Using machine learning models, we achieved a high accuracy in classifying exoskeleton-assisted movements from the rest condition (IPM vs. REST: 0.80 ± 0.07, PM vs. REST: 0.72 ± 0.10), with the IPM modality returning the highest accuracy. However, distinguishing between IPM and PM yielded only 0.61 ± 0.09, significantly lower than the condition of intention detection without the exoskeleton (IM vs. REST: 0.73 ± 0.08). Our findings suggest that sensorimotor EEG activity can track proprioceptive feedback induced by the hand exoskeleton. While this feedback is pronounced and distinguishable, detecting motor intention during exoskeleton movement remains highly challenging. This highlights the need for advanced decoders and control strategies for the future development of continuous BCI-actuated hand exoskeletons.}, }
@article {pmid40644105, year = {2025}, author = {Shevchenko, O and Yeremeieva, S and Laschowski, B}, title = {Comparative Analysis of Neural Decoding Algorithms for Brain-Machine Interfaces.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {222-227}, doi = {10.1109/ICORR66766.2025.11063033}, pmid = {40644105}, issn = {1945-7901}, mesh = {*Brain-Computer Interfaces ; Humans ; *Algorithms ; Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Machine Learning ; Neural Networks, Computer ; }, abstract = {Accurate neural decoding of brain dynamics remains an open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of the largest comparative studies to evaluate different combinations of state-of-the-art algorithms for motor neural decoding in order to find the optimal combination. We studied three signal processing methods (i.e., artifact subspace reconstruction, surface Laplacian filtering, and data normalization), four feature extractors (i.e., common spatial patterns, independent component analysis, short-time Fourier transform, and no feature extraction), and four machine learning classifiers (i.e., support vector machine, linear discriminant analysis, convolutional neural networks, and long short-term memory networks). Using a large-scale EEG dataset, we optimized each combination for individual subjects (i.e., resulting in 672 total experiments) and evaluated performance based on classification accuracy. We also compared the computational and memory storage requirements, which are important for real-time embedded computing. Our comparative analysis provides novel insights that can help inform the design of next-generation neural decoding algorithms for brain-machine interfaces.}, }
@article {pmid40644100, year = {2025}, author = {Feng, Z and Kakkos, I and Matsopoulos, GK and Guan, C and Sun, Y}, title = {Explaining E/MEG Source Imaging and Beyond: An Updated Review.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3588350}, pmid = {40644100}, issn = {2168-2208}, abstract = {E/MEG source imaging (ESI) provides noninvasive measurements of brain activity with high spatial and temporal resolution. In particular, the wearability and portability of EEG make it an attractive area of research not only in the biomedical communities especially when considering the wide applications prospects including brain-computer interface (BCI), neuromarketing, neuroergonomics, etc. Although there are already some valuable and impressive reviews on ESI, these reviews introduce the ESI models in a relatively isolated way and lack the recent advances in ESI. In this work, we aim to: 1) provide a timely in-depth review of the widely-explored/state-of-the art ESI models including their underlying neurophysiological assumptions and mathematical derivations; 2) list the primary applications of ESI and highlight the crucial steps regarding its implementations; 3) discuss the challenges in ESI and suggest several future research prospects; 4) demonstrate practical usage and implementation details of various representative ESI models along with open-source dataset/codes (link). As a rapidly expanding field, the development of ESI is continuously growing and evolving to embrace new technologies. We believe the widespread applications of ESI is happening, and it will dramatically expand our understanding of brain dynamics.}, }
@article {pmid40644042, year = {2025}, author = {Kim, M and Jo, S and Cho, H and Ye, S and Kim, Y and Park, HS}, title = {Development of Multimodal EEG-EMG Human Machine Interface for Hand-Wrist Rehabilitation: A Preliminary Study.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2025}, number = {}, pages = {1564-1569}, doi = {10.1109/ICORR66766.2025.11063079}, pmid = {40644042}, issn = {1945-7901}, mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; *Electromyography/methods/instrumentation ; *Wrist/physiology ; Male ; Adult ; Wearable Electronic Devices ; Stroke Rehabilitation ; *Robotics/instrumentation ; Female ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; }, abstract = {Patients with neurological disorders, such as stroke, often undergo upper limb motor impairments, severely limiting their ability to perform activities of daily living (ADL). Wearable robots have been developed to provide intensive and precise repetitive training for upper limb rehabilitation. Effective rehabilitation requires aligning robotic assistance with patient movement intention to promote brain plasticity. Additionally, robotic assistance must accommodate the complex, coordinated upper limb motions required for ADL tasks, including not only isolated hand movements but also integrated hand and wrist actions. This paper presents a multimodal human-machine interface (HMI) for integrated hand-wrist rehabilitation using both EEG and EMG signals. A three-degrees-of-freedom (3-DOF) soft wearable robot, combining a robotic hand glove and forearm skin brace, was designed to assist coordinated hand and wrist movements during reaching and grasping. EEG signals classified rest and grasp states using a Riemannian geometry approach, while EMG signals from three forearm muscles detected reaching onset to trigger the wrist adjustment. Preliminary tests with four healthy participants demonstrated 85% accuracy in EEG-based classification and sufficient EMG amplitude for motion onset detection. Future studies will expand participant testing to improve system robustness and evaluate its effectiveness for stroke rehabilitation.}, }
@article {pmid40640801, year = {2025}, author = {Gao, W and Yan, Z and Zhou, H and Xie, Y and Wang, H and Yang, J and Yu, J and Ni, C and Liu, P and Xie, M and Huang, L and Ye, Z}, title = {Revolutionizing brain‒computer interfaces: overcoming biocompatibility challenges in implantable neural interfaces.}, journal = {Journal of nanobiotechnology}, volume = {23}, number = {1}, pages = {498}, pmid = {40640801}, issn = {1477-3155}, support = {National Innovation Platform Development Program (No. 2020021105012440), the National Natural Science Foundation of China (No. 82172524, 81974355)//Zhewei Ye/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electrodes, Implanted ; Animals ; *Biocompatible Materials/chemistry ; Brain/physiology ; }, abstract = {Brain‒computer interfaces (BCIs) exhibit significant potential for various applications, including neurofeedback training, neurological injury management, and language, sensory and motor rehabilitation. Neural interfacing electrodes are positioned between external electronic devices and the nervous system to capture complex neuronal activity data and promote the repair of damaged neural tissues. Implantable neural electrodes can record and modulate neural activities with both high spatial and high temporal resolution, offering a wide window for neuroscience research. Despite significant advancements over the years, conventional neural electrode interfaces remain insufficient for fully achieving these objectives, particularly in the context of long-term implantation. The primary limitation stems from the poor biocompatibility and mechanical mismatch between the interfacing electrodes and neural tissues, which induce a local immune response and scar tissue formation, thus decreasing the performance and useful lifespan. Therefore, neural interfaces should ideally exhibit appropriate stiffness and minimal foreign body reactions to mitigate neuroinflammation and enhance recording quality. This review provides an exhaustive analysis of the current understanding of the critical failure modes that may impact the performance of implantable neural electrodes. Additionally, this study provides a comprehensive overview of the current research on coating materials and design strategies for implanted neural interfaces and discusses the primary challenges currently facing long-term implantation of neural electrodes. Finally, we present our perspective and propose possible future research directions to improve implantable neural interfaces for BCIs.}, }
@article {pmid40640486, year = {2025}, author = {Pierrieau, E and Dussard, C and Plantey-Veux, A and Guerrini, C and Lau, B and Pillette, L and George, N and Jeunet-Kelway, C}, title = {Changes in cortical beta power predict motor control flexibility, not vigor.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {1041}, pmid = {40640486}, issn = {2399-3642}, support = {ANR-20-CE37-0012//Agence Nationale de la Recherche (French National Research Agency)/ ; }, mesh = {Humans ; Male ; Female ; *Beta Rhythm/physiology ; Adult ; Electroencephalography ; Young Adult ; *Motor Cortex/physiology ; Neurofeedback ; Brain-Computer Interfaces ; Movement/physiology ; Psychomotor Performance/physiology ; }, abstract = {The amplitude of beta-band activity (β power; 13-30 Hz) over motor cortical regions is used to assess and decode movement in clinical settings and brain-computer interfaces, as β power is often assumed to predict the strength of the brain's motor output, or "vigor". However, recent conflicting evidence challenges this assumption and underscores the need to clarify the relationship between β power and movement. In this study, sixty participants were trained to self-regulate β power using electroencephalography-based neurofeedback before performing different motor tasks. Results show that β power modulations can impact different motor variables, or the same variables in opposite directions, depending on task constraints. Importantly, downregulation of β power is associated with better task performance regardless of whether performance implied increasing or decreasing motor vigor. These findings demonstrate that β power should be interpreted as a measure of motor flexibility, which underlies adaptation to environmental constraints, rather than vigor.}, }
@article {pmid40638250, year = {2025}, author = {Zhang, X and Ma, D and Wang, J and Su, N and Guo, J}, title = {Structures and Molecular Mechanisms of Insect Odorant and Gustatory Receptors.}, journal = {Physiology (Bethesda, Md.)}, volume = {40}, number = {6}, pages = {0}, doi = {10.1152/physiol.00011.2025}, pmid = {40638250}, issn = {1548-9221}, support = {2020YFA0908501//Ministry of Science and Technology of China/ ; 32371204//National Science Foundation of China/ ; LD25C050004//Zhejiang Provincial Natural Science Foundation/ ; //Foundamental Research Funds for the Central Universities/ ; //Ministry of Education Frontier Science Center for Brain Science &amp; Brain-Machine Integration/ ; //K.C. Wong Education Foundation/ ; }, mesh = {*Receptors, Odorant/chemistry/metabolism ; Animals ; *Insecta/metabolism/physiology ; *Receptors, Cell Surface/chemistry/metabolism ; Ligands ; *Insect Proteins/chemistry/metabolism ; }, abstract = {Insects rely on chemoreceptors in sensory neurons to detect and discriminate various chemicals in constantly changing environments. Among the chemoreceptors, odorant receptors (ORs) and gustatory receptors (GRs) play essential roles in sensing different odorant and tastant molecules, thereby regulating insects' physiology and behaviors such as feeding, mating, and alarming. ORs and GRs are evolutionarily related seven-transmembrane helical proteins that constitute a large family of tetrameric ion channels. In recent years, great progress has been made in the structures and molecular mechanisms of insect ORs and GRs. In this review, we summarize the available structures of insect ORs and GRs, analyze their diverse ligand recognition modes, and examine their conserved ligand activation mechanisms. These structural analyses will not only enhance our understanding of the molecular basis of insect ORs and GRs but also provide critical insights for the future discovery of repellents and attractants.}, }
@article {pmid40636103, year = {2025}, author = {Qi, R and Lin, Y and Liu, S and Cao, X and Xie, M and Yu, C and Sun, H and Gao, L and Li, X}, title = {Vocal taking turns is premature at birth and improved by the postnatal phonetic environment in marmosets.}, journal = {National science review}, volume = {12}, number = {7}, pages = {nwaf162}, pmid = {40636103}, issn = {2053-714X}, abstract = {Precisely time-controlled vocal antiphony is crucial for the social communication of arboreal marmosets. However, it remains unclear when this antiphony is formed and how postnatal acoustic environments affect its development. In the present study, we systematically recorded the emitted calls of infant marmosets in an antiphonal calling scenario from postnatal day one (P1) to postnatal 10 weeks (W10). We found that infant marmosets emit most types of adult calls and engage in turn-taking as early as in P1. In addition, parent-reared infants emitted more antiphonal phee calls than hand-reared marmosets in W10. Call transitions in parent-reared W10 animals mainly occurred between phee calls or from phee calls to other call types. In contrast, P1 and hand-reared W10 marmosets displayed call transitions among various types of calls. These findings suggest that the antiphony in marmosets emerges on P1 but remains immature, and the antiphony skills can be improved by development environments, especially by the vocal feedback from parents.}, }
@article {pmid40633885, year = {2025}, author = {Li, J and Chen, H and Liao, W}, title = {Biologically Annotated Heterogeneity of Depression Through Neuroimaging Normative Modeling.}, journal = {Biological psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.biopsych.2025.07.002}, pmid = {40633885}, issn = {1873-2402}, abstract = {Depression is not a unitary disorder; it is heterogeneous in nature. Likewise, no 2 individuals with depression are entirely alike, and therefore their associated symptoms are highly personalized. Over the past decade, numerous approaches have been developed to identify neuroimaging-derived biomarkers to advance our understanding of the neurobiology of patients with depression at the group level. However, substantial clinical heterogeneity among individuals with depression hinders the development of biomarkers for personalized interventions. Recently, publicly available resources have enabled researchers to investigate precision neuromarkers for depression using integrative multineuroimaging approaches. In this review, we systematically revisit previous findings and discuss the advances in data-driven neuroimaging analyses for depression heterogeneity, including the disentangling of dimensional and overlapping strategies, individual-specific abnormal patterns based on normative modeling frameworks, and associations between multiscale organizations. We also discuss the limitations, challenges, and future directions for depression heterogeneity. A summary of these advances is crucial for enhancing the understanding of the neurobiology of depression and will facilitate more accurate diagnoses and personalized interventions.}, }
@article {pmid40632037, year = {2025}, author = {Kumar, R and Soni, A and Ahmed, T and Beniwal, K}, title = {Experiences and Well-Being of Early-Career Trauma Nurses in India: A Mixed Methods Study.}, journal = {Journal of trauma nursing : the official journal of the Society of Trauma Nurses}, volume = {32}, number = {4}, pages = {189-200}, pmid = {40632037}, issn = {1078-7496}, mesh = {*Trauma Nursing ; *Nurses/psychology ; *Psychological Well-Being ; *Burnout, Professional/psychology ; Sleep Quality ; Anxiety ; *Occupational Stress/psychology ; Quality of Life ; Resilience, Psychological ; India ; Patient Acuity ; Sleep Initiation and Maintenance Disorders/etiology ; Humans ; Male ; Female ; Young Adult ; Adult ; Compassion Fatigue/etiology ; Qualitative Research ; Job Satisfaction ; *Nursing Staff, Hospital ; *Trauma Centers ; }, abstract = {BACKGROUND: Trauma nursing is fast-paced and high-pressure work that can affect nurses' physical and mental health. However, these effects remain unexplored among novice trauma nurses in a newly established trauma center in India.<br><br>OBJECTIVE: To examine relationships between professional quality of life, sleep disturbances, anxiety, and resilience and to explore the experiences of novice trauma nurses in a newly established trauma center in India.<br><br>METHODS: This sequential mixed-methods study was conducted between April and June 2024 in a newly established trauma center in India. A purposive sample of 80 nurses was surveyed using a demographic questionnaire, the Brief Resilience Scale, the Generalized Anxiety Disorder Scale, the Insomnia Severity Index, and the Professional Quality of Life Scale. Nine nurses were interviewed to explore their trauma experiences. The analysis included descriptive and inferential statistics, bootstrap-based mediation testing, and thematic content analysis.<br><br>RESULTS: A total of 80 nurses completed the survey (response rate: 67.8%) with a mean age of 27.7&#xa0;years (standard deviation [SD]&#xa0;=&#xa0;2.89) and average years of trauma experience of 2.08&#xa0;years (SD&#xa0;=&#xa0;1.93). Higher compassion satisfaction correlated with fewer sleep disturbances (r =&#xa0;-.23, p&#xa0;=&#xa0;.037). Burnout positively correlated with anxiety (r =&#xa0;.24, p =&#xa0;.033) and sleep disturbances (r =&#xa0;.34, p =&#xa0;.023), and negatively with nurses' resilience (r =&#xa0;-.12, p&#xa0;=&#xa0;.049). Professional quality of life significantly correlated with resilience (r =&#xa0;.18, p&#xa0;=&#xa0;.048). Resilience mediated the relationship between anxiety and both burnout (&#x3b2;&#xa0;=&#xa0;.24, bootstrap confidence interval [BCI]&#xa0;=&#xa0;0.04, 0.46, p =&#xa0;.041) and secondary traumatic stress (&#x3b2;&#xa0;=&#xa0;.24, BCI&#xa0;=&#xa0;0.03, 0.52, p&#xa0;=&#xa0;.029). Qualitative analysis revealed three major themes: personal and professional adaptation to trauma life, adverse physical and psychological issues, and challenges faced in trauma care.<br><br>CONCLUSION: Our findings highlight the adverse impact of trauma nursing on sleep, resilience, anxiety, and professional quality of life among novice nurses in a newly established Level I trauma center in India. Targeted interventions are required to enhance resilience and mitigate the impact of caring for trauma patients.}, }
@article {pmid40631920, year = {2025}, author = {Wang, X and Jun, F and Lin, C and Wang, X}, title = {Psychedelics and the Gut Microbiome: Unraveling the Interplay and Therapeutic Implications.}, journal = {ACS chemical neuroscience}, volume = {16}, number = {15}, pages = {2747-2749}, doi = {10.1021/acschemneuro.5c00418}, pmid = {40631920}, issn = {1948-7193}, mesh = {*Gastrointestinal Microbiome/drug effects/physiology ; Humans ; *Hallucinogens/pharmacology/therapeutic use/metabolism ; Probiotics ; Animals ; Receptor, Serotonin, 5-HT2A/metabolism ; Neuronal Plasticity/drug effects/physiology ; }, abstract = {Classic psychedelics and the gut microbiome interact bidirectionally through mechanisms involving 5-HT2A receptor signaling, neuroplasticity, and microbial metabolism. This viewpoint highlights how psychedelics may reshape microbiota and how microbes influence psychedelic efficacy, proposing microbiome-informed strategies─such as probiotics or dietary interventions─to personalize and enhance psychedelic-based mental health therapies.}, }
@article {pmid40631106, year = {2025}, author = {Ding, Y and Dunn, SLS and Sakon, JJ and Aghajan, ZM and Duan, C and Zhang, Y and Berger, JI and Rhone, AE and Nourski, KV and Kawasaki, H and Howard, MA and Roychowdhury, VP and Fried, I}, title = {Reading specific memories from human neurons before and after sleep.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40631106}, issn = {2692-8205}, support = {R01 NS084017/NS/NINDS NIH HHS/United States ; U01 NS123128/NS/NINDS NIH HHS/United States ; }, abstract = {The ability to retrieve a single episode encountered just once is a hallmark of human intelligence and episodic memory[1]. Yet, decoding a specific memory from neuronal activity in the human brain remains a formidable challenge. Here, we develop a transformer neural network model[2, 3] trained on neuronal spikes from intracranial microelectrodes recorded during a single viewing of an audiovisual episode. Combining spikes throughout the brain via cross-channel attention[4], capable of discovering neural patterns spread across brain regions and timescales, individual participant models predict memory retrieval of specific concepts such as persons or places. Brain regions differentially contribute to memory decoding before and after sleep. Models trained using only medial temporal lobe (MTL) spikes significantly decode concepts before but not after sleep, while models trained using only frontal cortex (FC) spikes decode concepts after but not before sleep. These findings suggest a system-wide distribution of information across neural populations that transforms over wake/sleep cycles[5]. Such decoding of internally generated memories suggests a path towards brain-computer interfaces to treat episodic memory disorders through enhancement or muting of specific memories.}, }
@article {pmid40631097, year = {2025}, author = {Lee, W and Scherschligt, X and Nishimoto, M and Rouse, AG}, title = {Neural trajectories improve motor precision.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40631097}, issn = {2692-8205}, support = {R00 NS101127/NS/NINDS NIH HHS/United States ; }, abstract = {Populations of neurons in motor cortex signal voluntary movement. Most classic neural encoding models and current brain-computer interface decoders assume individual neurons sum together along a neural dimension to represent movement features such as velocity or force. However, large population neural analyses continue to identify trajectories of neural activity evolving with time that traverse multiple dimensions. Explanations for these neural trajectories typically focus on how cortical circuits processes learn, organize, and implement movements. However, descriptions of how these neural trajectories might improve performance, and specifically motor precision, are lacking. In this study, we proposed and tested a computational model that highlights the role of neural trajectories, through the selective co-activation and selective timing of firing rates across the neural populations, for improving motor precision. Our model uses experimental results from a center-out reaching task as inspiration to create several physiologically realistic models for the neural encoding of movement. Using a recurrent neural network to simulate how a downstream population of neurons might receive such information, like the spinal cord and motor units, we show that movements are more accurate when neural information specific to the phase and/or amplitude of movement are incorporated across time instead of an instantaneous, velocity-only tuning model. Our finding suggests that precise motor control arises from spatiotemporal recruitment of neural populations that create distinct neural trajectories. We anticipate our results will significantly impact not only how neural encoding of movement in motor cortex is described but also future understating for how brain networks communicate information for planning and executing movements. Our model also provides potential inspiration for how to incorporate selective activation across a neural population to improve future brain-computer interfaces.}, }
@article {pmid40630938, year = {2025}, author = {Liu, L and Wang, F and Chen, X and Liu, L and Wang, Y and Bei, J and Lei, L and Zhao, Z and Tang, C}, title = {Designing Multifunctional Microneedles in Biomedical Engineering: Materials, Methods, and Applications.}, journal = {International journal of nanomedicine}, volume = {20}, number = {}, pages = {8693-8728}, pmid = {40630938}, issn = {1178-2013}, mesh = {*Needles ; Humans ; *Drug Delivery Systems/instrumentation/methods ; Tissue Engineering/methods ; *Biomedical Engineering/methods/instrumentation ; Animals ; Biocompatible Materials/chemistry ; Equipment Design ; *Microinjections/instrumentation ; Brain-Computer Interfaces ; }, abstract = {This review focuses on the emerging technology of multifunctional microneedles (MNs) within the biomedical engineering (BME) field, highlighting their potential in drug delivery, diagnostics, and therapeutics. Previous studies have explored MNs in various applications; however, their diverse functionalities across different material types and advanced application domains have been rarely comprehensively explored. This review bridges this gap by providing insights into the application of MNs in materials science, drug delivery, diagnostic monitoring, and tissue engineering. The unique properties and skin effects of various inorganic (eg, silicon, metals) and organic materials (eg, polysaccharides, polymers, proteins) used in MNs are examined. The analysis emphasizes the advantages of different MN materials, ie, their biocompatibility, degradation rates, and application specificity. In addition, the preparation processes and application scenarios of each MN type, such as minimally invasive drug delivery in transdermal applications and their benefits in tissue engineering for promoting repair, regeneration, and precise delivery of cells and growth factors in tissues like skin, cartilage, muscle, bone, and nerves, are discussed. Furthermore, this review explores the innovative use of MNs in brain-computer interfaces-an area not yet thoroughly examined. This novel application offers significant opportunities in neuroscience and clinical practice. Overall, this review provides valuable insights into the current research landscape and unexplored areas of MNs, contributing to future advancements in BME.}, }
@article {pmid40630584, year = {2025}, author = {Hahn, NV and Stein, E and ,  and Donoghue, JP and Simeral, JD and Hochberg, LR and Willett, FR}, title = {Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {40630584}, support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; U01 NS123101/NS/NINDS NIH HHS/United States ; R01 NS062092/NS/NINDS NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; RC1 HD063931/HD/NICHD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; U01 DC019430/DC/NIDCD NIH HHS/United States ; }, abstract = {Brain-computer interfaces have enabled people with paralysis to control computer cursors, operate prosthetic limbs, and communicate through handwriting, speech, and typing. Most high-performance demonstrations have used silicon microelectrode "Utah" arrays to record brain activity at single neuron resolution. However, reports so far have typically been limited to one or two individuals, with no systematic assessment of the longevity, decoding accuracy, and day-to-day stability properties of chronically implanted Utah arrays. Here, we present a comprehensive evaluation of 20 years of neural data from the BrainGate and BrainGate2 pilot clinical trials. This dataset spans 2,319 recording sessions and 20 arrays from the first 14 participants in these trials. On average, arrays successfully recorded neural spiking waveforms on 35.6% of electrodes, with only a 7% decline over the study enrollment period (up to 7.6 years, with a mean of 2.8 years). We assessed movement intention decoding performance using a "decoding signal-to-noise ratio" (dSNR) metric, and found that 11 of 14 arrays provided meaningful movement decoding throughout study enrollment (dSNR > 1). Three arrays reached a peak dSNR greater than 4.5, approaching that achieved during able-bodied computer mouse control (6.29). We also found that dSNR increases logarithmically with the number of electrodes, providing a pathway for scaling performance. Longevity and reliability of Utah array recordings in this study were better than in prior nonhuman primate studies. However, achieving peak performance consistently will require addressing unknown sources of variability.}, }
@article {pmid40629288, year = {2025}, author = {Paret, C and Jindrová, M and Kleindienst, N and Eck, J and Breman, H and Lührs, M and Barth, B and Ethofer, T and Fallgatter, AJ and Goebel, R and Hoell, A and Lockhofen, D and Reinhold, AS and Maier, S and Matthies, S and Mulert, C and Schönholz, C and van Elst, LT and Schmahl, C}, title = {A randomised controlled trial of amygdala fMRI-neurofeedback versus sham-feedback in borderline-personality disorder - systematic literature review and introduction to the BrainSTEADy trial.}, journal = {BMC psychiatry}, volume = {25}, number = {1}, pages = {687}, pmid = {40629288}, issn = {1471-244X}, support = {PA 3107/4-1//Deutsche Forschungsgemeinschaft/ ; SCHM 1526/26-1//Deutsche Forschungsgemeinschaft/ ; }, mesh = {Adult ; Female ; Humans ; Male ; *Amygdala/physiopathology/diagnostic imaging ; *Borderline Personality Disorder/therapy/physiopathology/diagnostic imaging ; Emotional Regulation ; *Magnetic Resonance Imaging/methods ; *Neurofeedback/methods ; Randomized Controlled Trials as Topic ; Multicenter Studies as Topic ; }, abstract = {BACKGROUND: Individuals with Borderline-Personality Disorder (BPD) experience intensive, unstable negative emotions. Hyperactivity of the amygdala is assumed to drive exaggerated emotional responses in BPD. Functional Magnetic Resonance Imaging (fMRI)-based neurofeedback is an endogenous neuromodulation method intended to address the imbalance of neural circuits and thus holds the potential as a treatment for BPD. Many original articles and meta-analyses show that fMRI-neurofeedback can improve psychiatric symptoms. In contrast, there is a lack of publications that aggregate and evaluate data of the safety of the treatment. Furthermore, evidence on the efficacy of fMRI-neurofeedback for the treatment of BPD is limited. Preliminary evidence suggests that downregulation of amygdala hyperactivation through fMRI-neurofeedback can ameliorate emotion dysregulation. To test this assumption, BrainSTEADy (Brain Signal Training to Enhance Affect Down-regulation), a multi-center clinical trial, is conducted. First, we present a systematic literature review evaluating the safety of fMRI-neurofeedback and assessing clinical performance in BPD. Second, we describe the study protocol of BrainSTEADy.<br><br>METHODS: Literature research: From 2,609 screened paper abstracts, 758 were identified as potentially relevant. Twenty studies reported adverse events or undesirable side effects. Two papers provided relevant data for the assessment of clinical performance in BPD. BrainSTEADy study protocol: During four sessions, patients will receive graphical fMRI-neurofeedback from their right amygdala or sham-feedback while viewing images with aversive content. The primary endpoint, 'negative affect intensity', will be assessed after the last neurofeedback session using Ecological Momentary Assessment (EMA). Secondary endpoints will be assessed after the last neurofeedback session, at 3-month and at 6-month follow-up. This trial is a multi-center, patient- and investigator-blind, randomized, parallel-group superiority study with a planned interim-analysis once half of the recruitment target is met (N&#x2009;=&#x2009;82).<br><br>DISCUSSION: As suggested by literature review, fMRI-neurofeedback is a safe treatment for patients, although future studies should systematically assess and report adverse events. Although fMRI-neurofeedback showed promising effects in BPD, current evidence is limited and calls for a randomized controlled trial such as BrainSTEADy, which aims to test whether amygdala-fMRI-neurofeedback specifically reduces emotion instability in BPD beyond nonspecific benefit. Endpoint measures encompassing EMA, clinical interviews, psychological questionnaires, quality of life, and neuroimaging will enable a comprehensive analysis of effects and mechanisms of neurofeedback treatment.<br><br>TRIAL REGISTRATION: The study protocol was first posted 2024/10/04 on ClinicalTrials.gov and received the ID NCT06626789.}, }
@article {pmid40629037, year = {2025}, author = {Wood, H}, title = {Brain-computer interface restores naturalistic speech to a man with ALS.}, journal = {Nature reviews. Neurology}, volume = {21}, number = {8}, pages = {409}, pmid = {40629037}, issn = {1759-4766}, }
@article {pmid40628758, year = {2025}, author = {Mathiyazhagan, S and Devasena, MSG}, title = {Motor imagery EEG signal classification using novel deep learning algorithm.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {24539}, pmid = {40628758}, issn = {2045-2322}, mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; Algorithms ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Wavelet Analysis ; *Imagination/physiology ; }, abstract = {Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges and exhibit reduced performances due to signal noise, inter-subject variability, and real-time processing demands. Thus, to overcome these limitations a novel model is presented in this research work for motor imagery (MI) EEG signal classification. To begin, the preprocessing stage of the proposed approach includes an innovative hybrid approach that combines empirical mode decomposition (EMD) for extracting intrinsic signal modes. In addition to that, continuous wavelet transform (CWT) is used for multi-resolution analysis. For spatial feature enhancement the proposed approach utilizes source power coherence (SPoC) integrated with common spatial patterns (CSP) for robust feature extraction. For final feature classification, an adaptive deep belief network (ADBN) is proposed. To attain enhanced performance the parameters of the classifier network are optimized using the Far and near optimization (FNO) algorithm. This combined approach provides superior classification accuracy and adaptability to diverse conditions in EEG signal analysis. The evaluations of the proposed approach were conducted using benchmark BCI competition IV Dataset 2a and Physionet dataset. On the BCI dataset, the proposed approach achieves 95.7% accuracy, 96.2% recall, 95.9% precision, and 97.5% specificity. In addition, it delivers 94.1% accuracy, 94.0% recall, 93.6% precision, and 95.0% specificity on the PhysioNet dataset. With better results, the proposed model attained superior performance compared to existing methods such as CNN, LSTM, and BiLSTM algorithms.}, }
@article {pmid40628277, year = {2025}, author = {Afdideh, F and Shamsollahi, MB}, title = {Subject-specific feature extraction approach for a three-class motor imagery-based brain-computer interface enabling navigation in a virtual environment: open-access framework.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {5}, pages = {}, doi = {10.1088/2057-1976/aded19}, pmid = {40628277}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Male ; *Virtual Reality ; Adult ; *Imagination/physiology ; Female ; Young Adult ; *Brain/physiology ; Movement ; User-Computer Interface ; }, abstract = {Brain-Computer Interface (BCI) is a system that aids individuals with disabilities to establish a novel communication channel between the brain and computer. Among various electrophysiological sources that can drive a BCI system, Motor Imagery (MI) facilitates more natural communication for users with motor disabilities, whereas electroencephalogram (EEG) is considered the most practical brain imaging modality. However, subject training is a critical aspect of such a type of BCI. One possible solution to address this challenge is to leverage the Virtual Reality (VR) technology. This study proposes a VR in MI- and EEG-based BCI (MI-EEG-BCI-VR) framework wherein users navigate a Virtual Environment (VE) following cue-based training, and employing a subject-specific feature extraction approach. The assigned task involves performing the left hand, right hand, and feet movement imagination to navigate from the start station to the end station as quickly as possible. The generated brain signals are collected using three bipolar EEG channels only. The proposed open-access MATLAB-based MI-EEG-BCI-VR framework was validated with eight healthy participants. One participant demonstrated satisfactory performance in navigating the VE. Notably, it achieved the highest performance of 82.28 ± 5.11% for MI and 97.72 ± 4.55% for Motor Execution (ME) after just a single training session.}, }
@article {pmid40628276, year = {2025}, author = {Fedosov, N and Medvedeva, D and Shevtsov, O and Ossadtchi, A}, title = {A reliable and reproducible real-time access to sensorimotor rhythm with a small number of optically pumped magnetometers.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/aded35}, pmid = {40628276}, issn = {1741-2552}, mesh = {Humans ; Male ; Adult ; *Brain-Computer Interfaces ; Female ; Reproducibility of Results ; *Magnetometry/instrumentation/methods ; Young Adult ; Equipment Design ; Movement/physiology ; Imagination/physiology ; *Sensorimotor Cortex/physiology ; Computer Systems ; *Magnetoencephalography/instrumentation/methods ; }, abstract = {Objective.Recent advances in biomagnetic sensing have led to the development of compact, wearable devices capable of detecting weak magnetic fields generated by biological activity. Optically pumped magnetometers (OPMs) have shown significant promise in functional neuroimaging. Brain rhythms play a crucial role in diagnostics, cognitive research, and neurointerfaces. Here we demonstrate that a small number of OPMs can reliably capture sensorimotor rhythms (SMRs).Approach.We conducted movement execution and motor imagery (MI) experiments with nine participants in two distinct magnetically shielded rooms (MSRs), each equipped with different ambient field suppression systems. We used only 4 OPMs located above the sensorimotor region and standard common-spatial-patterns (CSPs) based processing to decode the real and imaginary movement intentions of our participants. We evaluated reproducibility of the CSP components' spectral profiles and assessed the decoding accuracy deterioration with reduction of OPM's count. We also assessed the influence of the magnetic field orientation on the decoding accuracy and implemented a real-time MI brain-computer interface (BCI) solution.Main results.Under optimal conditions, OPM sensors deliver informative signals suitable for practical MI BCI applications. Those subjects who participated in the experiments in both MSRs exhibit highly reproducible SMR spectral patterns across two different magnetically shielded environments. The magnetic field components with radial orientation yield higher decoding accuracy than their tangential counterparts. In some subjects we observed more than 80% of binary decoding accuracy using a single OPM sensor. Finally we demonstrate real-time performance of our system along with clearly pronounced and behaviorally relevant fluctuations of the SMR power.Significance.For the first time, we demonstrated reliable and reproducible tracking of SMR components using a small number of contactless OPM sensors during movement execution and MI. Our findings pave the way for more efficient post-stroke neurorehabilitation by enabling MI-based BCI solutions to accelerate functional recovery.}, }
@article {pmid40627787, year = {2025}, author = {Li, Y and Zhang, J}, title = {Utilizing statistical analysis for motion imagination classification in brain-computer interface systems.}, journal = {PloS one}, volume = {20}, number = {7}, pages = {e0327121}, pmid = {40627787}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Imagination/physiology ; Algorithms ; *Motion ; *Brain/physiology ; Male ; }, abstract = {In this study, we introduce a novel Field-Agnostic Riemannian-Kernel Alignment (FARKA) method to advance the classification of motion imagination in Brain-Computer Interface (BCI) systems. BCI systems enable direct control of external devices through brain activity, bypassing peripheral nerves and muscles. Among various BCI technologies, electroencephalography (EEG) based on non-intrusive cortical potential signals stands out due to its high temporal resolution and non-invasive nature. EEG-based BCI technology encodes human brain intentions into cortical potentials, which are recorded and decoded into control commands. This technology is crucial for applications in motion rehabilitation, training optimization, and motion control. The proposed FARKA method combines Riemannian Alignment for sample alignment, Riemannian Tangent Space for spatial representation extraction, and Knowledge Kernel Adaptation to learn field-agnostic kernel matrices. Our approach addresses the limitations of current methods by enhancing classification performance and efficiency in inter-individual MI tasks. Experimental results on three public EEG datasets demonstrate the superior performance of FARKA compared to existing methods.}, }
@article {pmid40627473, year = {2025}, author = {Zhao, Z and Cao, Y and Yu, H and Yu, H and Huang, J}, title = {CNNViT-MILF-a: A Novel Architecture Leveraging the Synergy of CNN and ViT for Motor Imagery Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3587026}, pmid = {40627473}, issn = {2168-2208}, abstract = {Accurate motor imagery (MI) classification in EEG-based brain-computer interfaces (BCIs) is essential for applications in engineering, medicine, and artificial intelligence. Due to the limitations of single-model approaches, hybrid model architectures have emerged as a promising direction. In particular, convolutional neural networks (CNNs) and vision transformers (ViTs) demonstrate strong complementary capabilities, leading to enhanced performance. This study proposes a series of novel models, termed as CNNViT-MI, to explore the synergy of CNNs and ViTs for MI classification. Specifically, five fusion strategies were defined: parallel integration, sequential integration, hierarchical integration, early fusion, and late fusion. Based on these strategies, eight candidate models were developed. Experiments were conducted on four datasets: BCI competition IV dataset 2a, BCI competition IV dataset 2b, high gamma dataset, and a self-collected MI-GS dataset. The results demonstrate that CNNViT-MILF-a achieves the best performance among all candidates by leveraging ViT as the backbone for global feature extraction and incorporating CNN-based local representations through a late fusion strategy. Compared to the best-performing state-ofthe-art (SOTA) methods, mean accuracy was improved by 2.27%, 2.31%, 0.74%, and 2.50% on the respective datasets, confirming the model's effectiveness and broad applicability, other metrics showed similar improvements. In addition, significance analysis, ablation studies, and visualization analysis were conducted, and corresponding clinical integration and rehabilitation protocols were developed to support practical use in healthcare.}, }
@article {pmid40627471, year = {2025}, author = {Chen, X and Fu, Z and Zhang, P and Chen, X and Huang, J}, title = {Intracortical Brain-Machine Interfaces with High-Performance Neural Decoding through Efficient Transfer Meta-learning.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3586870}, pmid = {40627471}, issn = {1558-2531}, abstract = {Implantable brain-machine interfaces (iBMIs) have emerged as a groundbreaking neural technology for restoring motor function and enabling direct neural communication pathways. Despite their therapeutic potential in neurological rehabilitation, the critical challenge of neural decoder calibration persists, particularly in the context of transfer learning. Traditional calibration approaches assume the availability of extensive neural recordings, which is often impractical in clinical settings due to patient fatigue and neural signal variability. Furthermore, the inherent constraints of implanted neural processors-including limited computational capacity and power consumption requirements-demand streamlined processing algorithms. To address these clinical and technical challenges, we developed DMM-WcycleGAN (Dimensionality Reduction Model-Agnostic Meta-Learning based Wasserstein Cycle Generative Adversarial Networks), a novel neural decoding framework that integrates meta-learning principles with optimal transfer learning strategies. This innovative approach enables efficient decoder calibration using minimal neural data while implementing dimensionality reduction techniques to optimize computational efficiency in implanted devices. In vivo experiments with non-human primates demonstrated DMM-WcycleGAN's superior performance in mitigating neural signal distribution shifts between historical and current recordings, achieving a 3% enhancement in neural decoding accuracy using only ten calibration trials while reducing the calibration duration by over 70%, thus significantly improving the clinical viability of iBMI systems.}, }
@article {pmid40626564, year = {2025}, author = {Hu, Z and Luo, K and Liu, Y}, title = {Classification of motor imagery based on multi-scale feature extraction and fusion-residual temporal convolutional network.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-12}, doi = {10.1080/10255842.2025.2528892}, pmid = {40626564}, issn = {1476-8259}, abstract = {Brain-computer interface (BIC) decodes electroencephalogram (EEG) signals to realize the interaction between brain and external devices. However, traditional methods show limited performance in motor imagery electroencephalogram (MI-EEG) classification. In this paper, we introduce a multi-scale temporal convolutional network (MS-TCNet) that employs parallel multi-scale convolutions for spatiotemporal feature extraction, efficient channel attention (ECA) for channel weights optimization, and fusion-residual temporal convolution (FR-TCN) for high-level temporal feature capture. Experimental results show that MS-TCNet achieved remarkable decoding accuracies of 87.85% and 92.85% on the BCI IV-2a and BCI IV-2b datasets, respectively. The proposed MS-TCNet surpasses existing baseline models across various performance metrics, demonstrating its effectiveness in advancing MI-EEG decoding.}, }
@article {pmid40624803, year = {2025}, author = {Li, S and Gao, S and Hu, Y and Xu, J and Sheng, W}, title = {Brain-Computer Interfaces in Spinal Cord Injury: A Promising Therapeutic Strategy.}, journal = {The European journal of neuroscience}, volume = {62}, number = {1}, pages = {e70183}, doi = {10.1111/ejn.70183}, pmid = {40624803}, issn = {1460-9568}, support = {2023TSYCLJ0031//Program of Technological Leading Talent of Tianshan Talent/ ; 2023YFY-QKMS-06//Youth Foundation of Research and Development/ ; 2021D01D18//Key Program of Natural Science Foundation of Xinjiang Uygur Autonomous Region/ ; 82360257//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Spinal Cord Injuries/rehabilitation/physiopathology/therapy ; *Neurological Rehabilitation/methods ; Animals ; }, abstract = {The current treatment regimen for spinal cord injury (SCI), a neurological disorder with a high incidence of disability, is based on early surgical decompression and administration of pharmacological agents. However, the efficacy of such an approach remains limited, and most patients have sensory and functional deficits below the level of injury, which seriously affects their quality of life. This necessitates further exploration into effective treatment modalities. In recent years, considerable advancements have been made in developing and utilizing brain-computer interfaces (BCI), which facilitate neurorehabilitation and enhance motor function by transforming brain signals into diverse forms of output commands. BCI-assisted systems provide alternative means of rehabilitative exercise or limb movement in patients with SCI, including electrical stimulation and exoskeleton robots. BCI shows great potential in the rehabilitation of patients with SCI. This review summarizes the current research status and limitations of BCI for SCI to provide novel insights into the concept of multimodal rehabilitation and treatment of SCI and facilitate BCI's future development.}, }
@article {pmid40624755, year = {2025}, author = {Barios, JA and Vales, Y and Catalán, JM and Blanco-Ivorra, A and Martínez-Pascual, D and García-Aracil, N}, title = {Post-Movement Beta Rebound for Longitudinal Monitoring of Motor Rehabilitation in Stroke Patients Using an Exoskeleton-Assisted Paradigm.}, journal = {International journal of neural systems}, volume = {35}, number = {9}, pages = {2550044}, doi = {10.1142/S0129065725500443}, pmid = {40624755}, issn = {1793-6462}, mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Middle Aged ; Female ; Aged ; *Stroke/physiopathology/diagnosis ; *Exoskeleton Device ; *Sensorimotor Cortex/physiopathology ; *Beta Rhythm/physiology ; Electroencephalography ; Adult ; Longitudinal Studies ; *Motor Activity/physiology ; Brain-Computer Interfaces ; Movement/physiology ; }, abstract = {Task-oriented rehabilitation is essential for hand function recovery in stroke patients, and recent advancements in BCI-controlled exoskeletons and neural biomarkers - such as post-movement beta rebound (PMBR) - offer new pathways to optimize these therapies. Movement-related EEG signals from the sensorimotor cortex, particularly PMBR (post-movement) and event-related desynchronization (ERD, during movement), exhibit high task specificity and correlate with stroke severity. This study evaluated PMBR in 34 chronic stroke patients across two cohorts, along with a control group of 16 healthy participants, during voluntary and exoskeleton-assisted movement tasks. Longitudinal tracking in the second cohort enabled the analysis of PMBR changes, with EEG recordings acquired at three timepoints over a 30-session rehabilitation program. Findings revealed significant PMBR alterations in both passive and active movement tasks: patients with severe impairment lacked a PMBR dipole in the ipsilesional hemisphere, while moderately impaired patients showed a diminished response. The marked differences in PMBR patterns between stroke patients and controls highlight the extent of sensorimotor cortex disruption due to stroke. ERD showed minimal task-specific variation, underscoring PMBR as a more reliable biomarker of motor function impairment. These findings support the use of PMBR, particularly the PMBR/ERD ratio, as a biomarker for EEG-guided monitoring of motor recovery over time during exoskeleton-assisted rehabilitation.}, }
@article {pmid40622874, year = {2025}, author = {Annett, EG and Shook, JR and Giordano, J}, title = {Super Soldiers or Social Burden? Ethical Exploration of the Benefits and Costs of Military Bioenhancement.}, journal = {AJOB neuroscience}, volume = {16}, number = {4}, pages = {212-221}, doi = {10.1080/21507740.2025.2519457}, pmid = {40622874}, issn = {2150-7759}, mesh = {Humans ; *Military Personnel/psychology ; *Biomedical Enhancement/ethics/economics ; }, abstract = {Biotechnological enhancements for military personnel arouse scrutiny, beyond the ethics of experimental research and due care during operational service, to the eventual return to a civilian life. Reversal of enhancements-by withdrawal, extraction, deactivation, modification, destruction, etc.-will be just as experimental and consequential. Super soldiering may not smoothly transition to ordinary habilitation and lifestyle. Complete reversions of dramatic augmentations, such as prosthetics or brain-computer interfacing, could be more damaging to the person than the initial installation. Partial reversions would be just as perplexing, as discharged personnel retain workable technology to prevent disability while other careers next beckon for a suitably empowered individual. Either way, all such biotechnological enhancements must be treated as ethical and social experiments having both positive and negative potential outcomes. Life stages of technologically modified military personnel require special ethical consideration beyond the lifecycle of the technology itself. The post-enhancement veteran is a largely unexplored area, and we propose that these civilian "supra-soldiers" will become a cohort of increasing interest, requiring continued care and ethical support. To that end, we suggest a system of guidelines to ensure ethically sound support for those who serve, and have served, in national defense.}, }
@article {pmid40622660, year = {2025}, author = {Kong, L and Zhu, B and Zhuang, Y and Lai, J and Hu, S}, title = {Viewing Psychiatric Disorders Through Viruses: Simple Architecture, Burgeoning Implications.}, journal = {Neuroscience bulletin}, volume = {41}, number = {9}, pages = {1669-1688}, pmid = {40622660}, issn = {1995-8218}, mesh = {Humans ; *Mental Disorders/virology ; Animals ; *Brain/virology ; *Gastrointestinal Microbiome/physiology ; *Viruses ; *Virus Diseases/complications ; }, abstract = {A growing interest in the comprehensive pathogenic mechanisms of psychiatric disorders from the perspective of the microbiome has been witnessed in recent decades; the intrinsic link between microbiota and brain function through the microbiota-gut-brain axis or other pathways has gradually been realized. However, little research has focused on viruses-entities characterized by smaller dimensions, simpler structures, greater diversity, and more intricate interactions with their surrounding milieu compared to bacteria. To date, alterations in several populations of bacteriophages and viruses have been documented in both mouse models and patients with psychiatric disorders, including schizophrenia, major depressive disorder, autism spectrum disorder, and Alzheimer's disease, accompanied by metabolic disruptions that may directly or indirectly impact brain function. In addition, eukaryotic virus infection-mediated brain dysfunction provides insights into the psychiatric pathology involving viruses. Efforts towards virus-based diagnostic and therapeutic approaches have primarily been documented. However, limitations due to the lack of large-scale cohort studies, reliability, clinical applicability, and the unclear role of viruses in microbiota interactions pose a challenge for future studies. Nevertheless, it is conceivable that investigations into viruses herald a new era in the field of precise psychiatry.}, }
@article {pmid40621214, year = {2025}, author = {Kwon, J and Min, BK}, title = {Deep learning-based electroencephalic decoding of the phase-lagged transcranial alternating current stimulation.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1545726}, pmid = {40621214}, issn = {1662-5161}, abstract = {We investigated whether the phase-lag types of cross-frequency coupled alternating current stimulation (CFC-tACS), a non-invasive technique aimed at enhancing cognitive functions, could be decoded using task-based electroencephalographic (EEG) signals. EEG recordings were obtained from 21 healthy individuals engaged in a modified Sternberg task. CFC-tACS was administered online for 6 s during the middle of the retention period with either a 45° or 180° phase lag between the central executive network and the default mode network. To decode different phase-lag tACS conditions, we trained a modified EEGNet using task-based EEG signals before and after the online tACS application. When utilizing parietal EEG signals, the model achieved a decoding accuracy of 81.73%. Feature maps predominantly displayed EEG beta activity in the parietal region, suggesting that the model heavily weighted the beta band, indicative of top-down cognitive control influenced by tACS phase-lag type. Thus, EEG signals can decode online stimulation types, and task-related EEG spectral characteristics may indicate neuromodulatory activity during brain stimulation. This study could advance communicative strategies in brain-machine interfacing (BMI)-neuromodulation within a closed-loop system.}, }
@article {pmid40620352, year = {2025}, author = {Ying, A and Lv, J and Huang, J and Wang, T and Si, P and Zhang, J and Zuo, G and Xu, J}, title = {A feature fusion network with spatial-temporal-enhanced strategy for the motor imagery of force intensity variation.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1591398}, pmid = {40620352}, issn = {1662-4548}, abstract = {INTRODUCTION: Motor imagery (MI)-based brain-computer interfaces (BCI) offers promising applications in rehabilitation. Traditional force-based MI-BCI paradigms generally require subjects to imagine constant force during static or dynamic state. It is challenging to meet the demands of dynamic interaction with force intensity variation in MI-BCI systems.<br><br>METHODS: To address this gap, we designed a novel MI paradigm inspired by daily life, where subjects imagined variations in force intensity during dynamic unilateral upper-limb movements. In a single trial, the subjects were required to complete one of three combinations of force intensity variations: large-to-small, large-to-medium, or medium-to-small. During the execution of this paradigm, electroencephalography (EEG) features exhibit dynamic coupling, with subtle variations in intensity, timing, frequency coverage, and spatial distribution, as the force intensity imagined by the subjects changed. To recognize these fine-grained features, we propose a feature fusion network with a spatial-temporal-enhanced strategy and an information reconstruction (FN-SSIR) algorithm. This model combines a multi-scale spatial-temporal convolution module with a spatial-temporal-enhanced strategy, a convolutional auto-encoder for information reconstruction, and a long short-term memory with self-attention, enabling the comprehensive extraction and fusion of EEG features across fine-grained time-frequency variations and dynamic spatial-temporal patterns.<br><br>RESULTS: The proposed FN-SSIR achieved a classification accuracy of 86.7% &#xb1; 6.6% on our force variation MI dataset, and 78.4% &#xb1; 13.0% on the BCI Competition IV 2a dataset.<br><br>DISCUSSION: These findings highlight the potential of this paradigm and algorithm for advancing MI-BCI systems in rehabilitation training based on dynamic force interactions.}, }
@article {pmid40619564, year = {2025}, author = {Beressa, G and Feyissa, GT and Murimi, M and Muhammed, AH and Abdulkadir, A and Jema, AT and Alenko, A and Kebede, A and Lencha, B and Sahiledengle, B and Solomon, D and Atlaw, D and Gomora, D and Zenbaba, D and Dibaba, D and Nigussie, E and Nugusu, F and Desta, F and Ejigu, N and Wake, SK and Girma, S and Jidha, TD and Yazew, T and Tadesse, TM and Elala, T and Tekalegn, Y and Belachew, T}, title = {Nutritional status and associated factors among school age children in Southeast Ethiopia using a bayesian analysis approach.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {24141}, pmid = {40619564}, issn = {2045-2322}, mesh = {Humans ; Child ; Ethiopia/epidemiology ; *Nutritional Status ; Bayes Theorem ; Adolescent ; Male ; Female ; Cross-Sectional Studies ; *Growth Disorders/epidemiology ; Prevalence ; Body Mass Index ; *Thinness/epidemiology ; Malnutrition/epidemiology ; Schools ; }, abstract = {Undernutrition among school-age children is a major public health concern in sub-Saharan Africa. This study aimed to assess the nutritional status and associated factors among school-age children in the hard-to-reach pastoral communities in Southeast Ethiopia. We conducted a school-based cross-sectional study among 395 randomly selected schoolchildren aged 7-14 years in pastoral communities in Bale Zone. We employed a hybrid of multistage sampling and systematic random sampling to select the respondents. We used the Z scores of height for age (HAZ) and body mass index for age (BAZ) based on the World Health Organization (WHO) guidance to classify nutritional status of the school-age children. We conducted a Bayesian linear regression analysis estimation using Markov chain Monte Carlo (MCMC). We calculated the mean, along with a 95% Bayesian credible interval (BCI), to identify factors associated with nutritional status. The overall prevalence of stunting and thinness among school-age children 7-14 years was 26.6% (95% CI: 21.8, 31.4%) and 28.9% (95% CI: 24.3, 33.2%), respectively. The mean and SD of HAZ and BAZ scores were -0.82 (2.13) and -0.87 (1.73), respectively. A unit increment in the age of the child and a unit increment in dietary diversity score were associated with an increment in HAZ scores by 0.122 and 0.120 units, respectively. Travelling to school for more than 30 min and more (compared to travelling less than 30 min) and being a child of a literate father (compared to being a child of an illiterate father) were associated with a decrement in the mean HAZ scores by 0.81 and 0.675 units, respectively. Children who come from rich families had BAZ scores, which are about 0.50 units higher when compared to those children coming from poor families. The high burden of stunting and thinning among the hard-to-reach pastoral communities underscores the importance of strengthening nutrition intervention programs such as school feeding and multisectoral collaboration and economic empowerment to improve accessibility of diversified food among school-age children in the hard-to-reach pastoral communities. Younger school children, children from poor families and children who have less access to school and diverse diets should be prioritised during school based nutritional interventions.}, }
@article {pmid40616172, year = {2025}, author = {Ji, X and Zhang, J and Chen, D and Qin, Q and Huang, F}, title = {Research on transcranial magnetic stimulation for stroke rehabilitation: a visual analysis based on CiteSpace.}, journal = {European journal of medical research}, volume = {30}, number = {1}, pages = {575}, pmid = {40616172}, issn = {2047-783X}, support = {No.CRSI2022CZ-17//China Rehabilitation Research Center under the Central Public Welfare Scientific Research Institute Basic Research Business Fund Project/ ; }, mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; *Stroke Rehabilitation/methods ; *Stroke/therapy ; Bibliometrics ; }, abstract = {OBJECTIVE: This study aimed to analyze recent research and emerging trends in transcranial magnetic stimulation (TMS) for stroke rehabilitation.<br><br>METHODS: We employed bibliometric methods to retrieve relevant Chinese and English literature on TMS for stroke rehabilitation from China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection (WOSCC) respectively, including publications up to April 10, 2025. CiteSpace 6.4.R1 was utilized to generate knowledge maps, focusing on authors, institutions, countries, and keywords.<br><br>RESULTS: We identified 1301 publications since the inception of the database through April 10, 2025, including 797 articles in Chinese and 504 articles in English. The number of articles available in both languages increased over time. Fudan University and University of Manchester were the institutions with the most outputs. Co-occurrence and clustering keyword analyses revealed similarities between Chinese and English terms, with key research areas include the role of TMS in motor cortex areas, post-stroke cognitive impairment (PSCI), and dysphagia, and TMS has been integrated with other therapeutic approaches for stroke patients.<br><br>CONCLUSION: TMS, a noninvasive brain stimulation technique, has been applied to improve stroke patients' functional outcomes and daily living skills. Future investigations should integrate TMS with cutting-edge technologies including artificial intelligence and brain&#x2012;computer interfaces to uncover its full potential in restoring neural function in stroke survivors.}, }
@article {pmid40615688, year = {2025}, author = {Li, Y and Wang, YJ and Su, C and Deng, F and Pan, Y}, title = {Bidirectional information flow in cooperative learning reflects emergent leadership.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {1000}, pmid = {40615688}, issn = {2399-3642}, support = {Nos. 62207025, 62337001//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. LMS25C090002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Humans ; *Leadership ; Male ; Female ; *Learning/physiology ; *Cooperative Behavior ; Adult ; Spectroscopy, Near-Infrared ; Young Adult ; *Brain/physiology ; }, abstract = {Advances in social neuroscience have shown that one of the fundamental characteristics of cooperative learning is synchronization between learners' brains. However, the directionality of this synchronization, and the role of emergent leadership (i.e., a group leader emerges naturally), in cooperative learning remain unclear. Here, we investigated the directionality and dynamics of information flow by leveraging functional near-infrared spectroscopy (fNIRS) hyperscanning and Granger causality analysis (GCA). Through a 6 min dyadic cooperative learning task, we observed that dyads' utterance score increased over time and remained stable at the end of interaction, suggesting successful cooperative learning. At the neural level, we found a stronger leader-to-follower Granger causality in the left middle temporal gyrus, alongside a more pronounced follower-to-leader causality in the left sensorimotor cortex. Moreover, we found that information transfer in both directions increased and peaked around the first half of time into the task, followed by a decline. These temporally similar yet spatially dissociable patterns of directional information flow suggest a hierarchical organization of bidirectional communication during cooperative learning with emergent leadership.}, }
@article {pmid40615618, year = {2025}, author = {Hobbs, FDR and Dorward, J and Hayward, G and Yu, LM and Saville, BR and Butler, CC and , }, title = {The PRINCIPLE randomised controlled open label platform trial of hydroxychloroquine for treating COVID19 in community based patients at high risk.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {23850}, pmid = {40615618}, issn = {2045-2322}, support = {CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; }, mesh = {Humans ; *Hydroxychloroquine/therapeutic use/adverse effects/administration &amp; dosage ; *COVID-19 Drug Treatment ; Female ; Male ; Aged ; Middle Aged ; United Kingdom/epidemiology ; SARS-CoV-2 ; COVID-19/virology ; *Antiviral Agents/therapeutic use ; Hospitalization/statistics &amp; numerical data ; Prospective Studies ; Aged, 80 and over ; Treatment Outcome ; }, abstract = {Early on in the COVID-19 pandemic, we aimed to assess the effectiveness of hydroxychloroquine on reducing the need for hospital admission in patients in the community at higher risk of complications from COVID-19 syndromic illness (testing was largely unavailable at the time, hence not microbiologically confirmed SARS-CoV-2 infection), as part of the national open-label, multi-arm, prospective, adaptive platform, randomised clinical trial in community care in the United Kingdom (UK). People aged 65 and over, or aged 50 and over with comorbidities, and who had been unwell for up to 14 days with suspected COVID-19 were randomised to usual care with the addition of hydroxychloroquine, 200 mg twice a day for seven days, or usual care without hydroxychloroquine (control). Participants were recruited based on symptoms and approximately 5% had confirmed SARS-COV2 infection. The primary outcome while hydroxychloroquine was in the trial was hospital admission or death related to suspected COVID-19 infection within 28 days from randomisation. First recruitment was on April 2, 2020, and the hydroxychloroquine arm was suspended by the UK Medicines Regulator on May 22, 2020. 207 were randomised to hydroxychloroquine and 206 to usual care, and 190 and 194 contributed to the primary analysis results presented, respectively. There was no swab result available within 28 days of randomisation for 39% in both groups: 107 (54%) in the hydroxychloroquine group and 111 (55%) in the usual care group tested negative for SARS-Cov-2, and 13 (7%) and 11 (5%) tested positive. 13 participants, (seven (3·7%) in the usual care plus hydroxychloroquine and six (3.1%) in the usual care group were hospitalized (odds ratio 1·04 [95% BCI 0·36 to 3.00], probability of superiority 0·47). There was one serious adverse event, in the usual care group. More people receiving hydroxychloroquine reported nausea. We found no evidence from this treatment arm of the PRINCIPLE trial, stopped early and therefore under-powered for reasons external to the trial, that hydroxychloroquine reduced hospital admission or death in people with suspected, but mostly unconfirmed COVID-19.}, }
@article {pmid40615558, year = {2025}, author = {Xi, C and Lu, B and Guo, X and Qin, Z and Yan, C and Hu, S}, title = {Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression.}, journal = {Molecular psychiatry}, volume = {30}, number = {11}, pages = {5150-5160}, pmid = {40615558}, issn = {1476-5578}, mesh = {Humans ; *Bipolar Disorder/physiopathology/drug therapy ; Connectome/methods ; Male ; Female ; Magnetic Resonance Imaging/methods ; Brain/physiopathology ; Adult ; Nerve Net/physiopathology ; Quetiapine Fumarate/therapeutic use/pharmacology ; Middle Aged ; Neural Pathways/physiopathology ; Treatment Outcome ; Machine Learning ; }, abstract = {Aberrant functional connectivity (FC) between brain networks has been indicated closely associated with bipolar disorder (BD). However, the previous findings of specific brain network connectivity patterns have been inconsistent, and the clinical utility of FCs for predicting treatment outcomes in bipolar depression was underexplored. To identify robust neuro-biomarkers of bipolar depression, a connectome-based analysis was conducted on resting-state functional MRI (rs-fMRI) data of 580 bipolar depression patients and 116 healthy controls (HCs). A subsample of 148 patients underwent a 4-week quetiapine treatment with post-treatment clinical assessment. Adopting machine learning, a predictive model based on pre-treatment brain connectome was then constructed to predict treatment response and identify the efficacy-specific networks. Distinct brain network connectivity patterns were observed in bipolar depression compared to HCs. Elevated intra-network connectivity was identified within the default mode network (DMN), sensorimotor network (SMN), and subcortical network (SC); and as to the inter-network connectivity, increased FCs were between the DMN, SMN and frontoparietal (FPN), ventral attention network (VAN), and decreased FCs were between the SC and cortical networks, especially the DMN and FPN. And the global network topology analyses revealed decreased global efficiency and increased characteristic path length in BD compared to HC. Further, the support vector regression model successfully predicted the efficacy of quetiapine treatment, as indicated by a high correspondence between predicted and actual HAMD reduction ratio values (r(df=147)=0.4493, p = 2*10[-4]). The identified efficacy-specific networks primarily encompassed FCs between the SMN and SC, and between the FPN, DMN, and VAN. These identified networks further predicted treatment response with r = 0.3940 in the subsequent validation with an independent cohort (n = 43). These findings presented the characteristic aberrant patterns of brain network connectome in bipolar depression and demonstrated the predictive potential of pre-treatment network connectome for quetiapine response. Promisingly, the identified connectivity networks may serve as functional targets for future precise treatments for bipolar depression.}, }
@article {pmid40614757, year = {2025}, author = {Mishler, J and Yun, R and Perlmutter, S and Rao, RPN and Fetz, E}, title = {Manipulation of neuronal activity by an artificial spiking neural network implemented on a closed-loop brain-computer interface in non-human primates.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, pmid = {40614757}, issn = {1741-2552}, support = {P51 OD010425/OD/NIH HHS/United States ; P51 RR000166/RR/NCRR NIH HHS/United States ; U42 OD011123/OD/NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; *Brain-Computer Interfaces ; *Neurons/physiology ; *Action Potentials/physiology ; *Neural Networks, Computer ; Macaca mulatta ; Motor Cortex/physiology ; Male ; }, abstract = {Objective.Closed-loop brain-computer interfaces can be used to bridge, modulate, or repair damaged connections within the brain to restore functional deficits. Towards this goal, we demonstrate that small artificial spiking neural networks can be bidirectionally interfaced with single neurons (SNs) in the neocortex of non-human primates (NHPs) to create artificial connections between the SNs to manipulate their activity in predictable ways.Approach.Spikes from a small group of SNs were recorded from primary motor cortex of two awake NHPs during rest. The SNs were then interfaced with a small network of integrate-and-fire units (IFUs) that were programmed on a custom clBCI. Spikes from the SNs evoked excitatory and/or inhibitory postsynaptic potentials in the IFUs, which themselves spiked when their membrane potentials exceeded a predetermined threshold. Spikes from the IFUs triggered single pulses of intracortical microstimulation (ICMS) to modulate the activity of the cortical SNs.Main results.We show that the altered closed-loop dynamics within the cortex depends on several factors including the connectivity between the SNs and IFUs, as well as the precise timing of the ICMS. We additionally show that the closed-loop dynamics can reliably be modeled from open-loop measurements.Significance.Our results demonstrate a new type of hybrid biological-artificial neural system based on a clBCI that interfaces SNs in the brain with artificial IFUs to modulate biological activity in the brain. Our model of the closed-loop dynamics may be leveraged in the future to develop training algorithms that shape the closed-loop dynamics of networks in the brain to correct aberrant neural activity and rehabilitate damaged neural circuits.}, }
@article {pmid40614457, year = {2025}, author = {Yan, H and Wang, Z and Li, J}, title = {MSC-transformer-based 3D-attention with knowledge distillation for multi-action classification of separate lower limbs.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {191}, number = {}, pages = {107806}, doi = {10.1016/j.neunet.2025.107806}, pmid = {40614457}, issn = {1879-2782}, mesh = {Humans ; *Lower Extremity/physiology ; Electroencephalography/methods ; *Attention/physiology ; *Deep Learning ; Neural Networks, Computer ; *Imagination/physiology ; Movement/physiology ; }, abstract = {Deep learning has been extensively applied to motor imagery (MI) classification using electroencephalogram (EEG). However, most existing deep learning models do not extract features from EEG using dimension-specific attention mechanisms based on the characteristics of each dimension (e.g., spatial dimension), while effectively integrate local and global features. Furthermore, implicit information generated by the models has been ignored, leading to underutilization of essential information of EEG. Although MI classification has been relatively thoroughly investigated, the exploration of classification including real movement (RM) and motor observation (MO) is very limited, especially for separate lower limbs. To address the above problems and limitations, we proposed a multi-scale separable convolutional Transformer-based filter-spatial-temporal attention model (MSC-T3AM) to classify multiple lower limb actions. In MSC-T3AM, spatial attention, filter and temporal attention modules are embedded to allocate appropriate attention to each dimension. Multi-scale separable convolutions (MSC) are separately applied after the projections of query, key, and value in self-attention module to improve computational efficiency and classification performance. Furthermore, knowledge distillation (KD) was utilized to help model learn suitable probability distribution. The comparison results demonstrated that MSC-T3AM with online KD achieved best performance in classification accuracy, exhibiting an elevation of 2 %-19 % compared to a few counterpart models. The visualization of features extracted by MSC-T3AM with online KD reiterated the superiority of the proposed model. The ablation results showed that filter and temporal attention modules contributed most for performance improvement (improved by 2.8 %), followed by spatial attention module (1.2 %) and MSC module (1 %). Our study also suggested that online KD was better than offline KD and the case without KD. The code of MSC-T3AM is available at: https://github.com/BICN001/MSC-T3AM.}, }
@article {pmid40611671, year = {2025}, author = {Alemu, RZ and Blakeman, A and Fung, AL and Hazen, M and Negandhi, J and Papsin, BC and Cushing, SL and Gordon, KA}, title = {Children With Bilateral Cochlear Implants Show Emerging Spatial Hearing of Stationary and Moving Sound.}, journal = {Trends in hearing}, volume = {29}, number = {}, pages = {23312165251356333}, pmid = {40611671}, issn = {2331-2165}, mesh = {Humans ; *Sound Localization ; *Cochlear Implants ; Child ; Male ; Female ; *Cochlear Implantation/instrumentation ; Auditory Threshold ; Speech Perception ; Adolescent ; Cues ; Acoustic Stimulation ; *Persons with Hearing Disabilities/rehabilitation/psychology ; Case-Control Studies ; Eye Movements ; Noise/adverse effects ; Head Movements ; *Hearing Loss, Bilateral/physiopathology/rehabilitation/psychology ; }, abstract = {Spatial hearing in children with bilateral cochlear implants (BCIs) was assessed by: (a) comparing localization of stationary and moving sound, (b) investigating the relationship between sound localization and sensitivity to interaural level and timing differences (ILDs/ITDs), (c) evaluating effects of aural preference on sound localization, and (d) exploring head and eye (gaze) movements during sound localization. Children with BCIs (n = 42, MAge = 12.3 years) with limited duration of auditory deprivation and peers with typical hearing (controls; n = 37, MAge = 12.9 years) localized stationary and moving sound with unrestricted head and eye movements. Sensitivity to binaural cues was measured by a lateralization task to ILDs and ITDs. Spatial separation effects were measured by spondee-word recognition thresholds (SNR thresholds) when noise was presented in front (colocated/0°) or with 90° of left/right separation. BCI users had good speech reception thresholds (SRTs) in quiet but higher SRTs in noise than controls. Spatial separation of noise from speech revealed a greater advantage for the right ear across groups. BCI users showed increased errors localizing stationary sound and detecting moving sound direction compared to controls. Decreased ITD sensitivity occurred with poorer localization of stationary sound in BCI users. Gaze movements in BCI users were more random than controls for stationary and moving sounds. BCIs support symmetric hearing in children with limited duration of auditory deprivation and promote spatial hearing which is albeit impaired. Spatial hearing was thus considered to be "emerging." Remaining challenges may reflect disruptions in ITD sensitivity and ineffective gaze movements.}, }
@article {pmid40611622, year = {2025}, author = {Dahò, M and Monzani, D}, title = {The multifaceted nature of inner speech: Phenomenology, neural correlates, and implications for aphasia and psychopathology.}, journal = {Cognitive neuropsychology}, volume = {42}, number = {1-2}, pages = {1-21}, doi = {10.1080/02643294.2025.2527983}, pmid = {40611622}, issn = {1464-0627}, mesh = {Humans ; *Aphasia/physiopathology/diagnostic imaging ; *Speech/physiology ; *Theory of Mind/physiology ; *Brain/physiopathology ; }, abstract = {This narrative review explores the phenomenon of inner speech - mental speech without visible articulation - and its implications for cognitive science and clinical practice. Despite its importance, the many neural mechanisms underlying inner speech remain unclear. We propose classifying inner speech into monologic, dialogal, elicited, and spontaneous forms, and discuss related phenomenological and neural correlates theories. A literature review on PubMed (1990-2024) identified 83 studies. Dialogal forms recruit Theory of Mind networks, compared to monologic forms. Task-elicited inner speech activates the left inferior frontal gyrus more strongly, while spontaneous inner speech engages Heschl's gyrus, suggesting auditory involvement. Evidence regarding aphasia suggests inner speech may be partially preserved even when overt speech is impaired, offering a potential route for rehabilitation. Future research should also address the emotional aspects of inner speech, its role in psychopathology, and its developmental trajectory. Such studies may improve interventions for disorders related to dysfunctional inner speech.Abbreviation: ACC: anterior cingulate cortex; ALE: activation likelihood estimation; AVH: auditory verbal hallucination; BMI: brain-machine interface; CD: corollary discharge; ConDialInt: consciousness-dialogue-intentionality; DES: descriptive experience sampling; DTI: diffusion tensor imaging; dPMC: dorsal premotor cortex; dmPFC: dorsomedial prefrontal cortex; IFG: inferior frontal gyrus; M1: primary motor cortex; MedFG: medial frontal gyrus; MFG: middle frontal gyrus; MTG: middle temporal gyrus; MRI: magnetic resonance imaging; preSMA: presupplementary motor area; PrG: precentral gyrus; SMA: supplementary motor area; SMG: supramarginal gyrus; SPC: superior parietal cortex; SPL: superior parietal lobule; STG: superior temporal gyrus; STS: superior temporal sulcus; TVA: temporal vocal areas; ToM: theory of mind; vmPFC: ventromedial prefrontal cortex.}, }
@article {pmid40611619, year = {2025}, author = {Ponomarev, T and Vasilyev, A and Novikova, E and Pokidko, A and Zaitseva, N and Zaitsev, D and Kaplan, A}, title = {Brain mechanisms of (dis)agreement: ERP evidence from binary choice responses.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {35}, number = {7}, pages = {}, doi = {10.1093/cercor/bhaf167}, pmid = {40611619}, issn = {1460-2199}, support = {121032300070-1//Lomonosov Moscow State University/ ; }, mesh = {Humans ; Male ; Female ; *Evoked Potentials/physiology ; Electroencephalography ; *Brain/physiology ; Young Adult ; Adult ; *Choice Behavior/physiology ; Brain-Computer Interfaces ; *Decision Making/physiology ; }, abstract = {Agreement and disagreement are essential brain processes that enable effective communication and decision-making. However, a clear neurophysiological framework explaining their organization is still lacking. The present study aimed to identify EEG correlates of implicit agreement and disagreement, developing a novel experimental paradigm to model these internal responses. Participants were tasked with mentally responding to binary ("yes" or "no") questions and evaluating the accuracy of a computer system's attempts to "guess" their responses. Event-related potentials (ERP) revealed distinct patterns associated with agreement and disagreement in two key contexts: when participants read the final word of a question and when they observed the computer's "guess." Disagreement, compared to agreement, elicited larger ERP amplitudes, specifically an enhanced N400 component in the first context and increased feedback-related negativity in the second. Considering the associations of these ERP components with cognitive processes, this research offers robust evidence linking agreement and disagreement to the brain's effort in reconciling personal beliefs and expectations with new information. Furthermore, the experimental framework and findings provide a foundation for the development of brain-computer interfaces (BCIs) capable of detecting "yes" and "no" commands based on their intrinsic EEG predictors, offering promising applications in assistive technologies and neural communication systems.}, }
@article {pmid40611612, year = {2025}, author = {Saeed, S and Wang, H and Jia, M and Liu, TT and Xu, L and Zhang, X and Hu, SH}, title = {The spectrum of overlapping anti-NMDAR encephalitis and demyelinating syndromes: a systematic review of presentation, diagnosis, management, and outcomes.}, journal = {Annals of medicine}, volume = {57}, number = {1}, pages = {2517813}, pmid = {40611612}, issn = {1365-2060}, mesh = {Humans ; *Anti-N-Methyl-D-Aspartate Receptor Encephalitis/diagnosis/therapy/complications/immunology ; *Demyelinating Diseases/diagnosis/therapy/immunology/complications ; Autoantibodies ; Treatment Outcome ; }, abstract = {BACKGROUND: Anti-NMDAR encephalitis frequently overlaps with demyelinating diseases (MOGAD, NMOSD, MS), creating complex syndromes with diverse presentations and challenging management.<br><br>METHODS: Systematic search of databases including MEDLINE, Google Scholar, Embase, Scopus, Cochrane Library, and Web of Science up to March 2024 for studies on co-existing anti-NMDAR encephalitis and demyelinating syndromes. Data extracted on clinical characteristics, diagnostics, treatments, and outcomes.<br><br>RESULTS: Twenty-five studies identified 256 patients (16.2%) with co-existing Anti-NMDAR encephalitis and demyelinating syndromes, primarily MOGAD (94.5%), with fewer cases involving NMOSD or MS. The Anti-NMDAR&#xa0;+&#xa0;MOGAD subgroup exhibited seizures (51-72.7%), psychiatric symptoms (45.5-71.4%), cognitive dysfunction (30.6%), and movement disorders (30.6%). All patients had CSF anti-NMDAR antibodies, with MOG (60%) or AQP4 (25%) antibodies. Use of standardized, cell-based assays and adherence to established criteria are essential to avoid false positives, particularly for MOG. MRI abnormalities were seen in 75% of patients. First-line immunotherapies were effective in 70% of cases; 80% of refractory cases responded to second-line therapies.<br><br>CONCLUSIONS: Anti-NMDAR encephalitis overlapping with demyelinating diseases is challenging. Tailored treatments based on detailed immune profiles are key to better outcomes.}, }
@article {pmid40611081, year = {2025}, author = {Wei, Y and Xu, Y and Chen, W and Zheng, J and Chen, H and Chen, S}, title = {Can heart rate variability demonstrate the effects and the levels of mindfulness? A repeated-measures study on experienced and novice mindfulness practitioners.}, journal = {BMC complementary medicine and therapies}, volume = {25}, number = {1}, pages = {231}, pmid = {40611081}, issn = {2662-7671}, mesh = {Humans ; *Heart Rate/physiology ; *Mindfulness ; Male ; Female ; Adult ; Young Adult ; Middle Aged ; Meditation ; }, abstract = {BACKGROUND: Heart rate variability (HRV) is a potential biomarker that might demonstrate the effects of mindfulness, but it might be influenced by practice experiences. This study wanted to elucidate the possibility of using HRV metrics to reveal the effects of mindfulness and examine its variation between novice and experienced mindfulness practitioners.<br><br>METHODS: Forty-six participants (20 experienced practitioners, 26 novices) were enrolled to practice 14-day mindfulness training. HRV data were collected during three phases (20&#xa0;min baseline, T1; 20&#xa0;min mindfulness, T2; 20&#xa0;min post-mindfulness, T3) using Holter monitoring. The linear mixed model was conducted to explore the effects of group and time based on standardized data.<br><br>RESULTS: The experienced group had higher full-scale scores of FFMQ both in the pre-test (t&#x2009;=&#x2009;-3.34, df&#x2009;=&#x2009;44, p&#x2009;=&#x2009;0.002) and the post-test (t&#x2009;=&#x2009;-2.35, df&#x2009;=&#x2009;44, p&#x2009;=&#x2009;0.025). Both groups showed significant changes in HRV indices (e.g., RMSSD, SDNN, LnHF) from T1 to T2 or T3 (p&#x2009;<&#x2009;0.05). In the experienced group, significant fluctuations (p&#x2009;<&#x2009;0.05) were observed at T2, followed by recovery at T3, in SD1/SD2, Sample Entropy, normalized High Frequency (HFn), DFA_&#x3b1;1, and DFA_&#x3b1;2. In contrast, the novice participants only showed monotonic changes in SD1/SD2 and DFA_&#x3b1;1.<br><br>CONCLUSIONS: This study revealed significant HRV changes during mindfulness practice, with distinct patterns observed between novice and experienced practitioners.}, }
@article {pmid40609489, year = {2026}, author = {Cui, H and Hu, D and Yang, T and Huang, C and Yang, Z and Dong, S}, title = {Humidity sensors based on surface-functionalized tunable photonic crystal grating.}, journal = {Talanta}, volume = {296}, number = {}, pages = {128521}, doi = {10.1016/j.talanta.2025.128521}, pmid = {40609489}, issn = {1873-3573}, abstract = {Photonic crystal (PC)-based humidity sensors detect changes in humidity using periodic structural color variations and have significant potential in the humidity detection field. However, current technologies typically rely on observing these structural color changes with the human eye. The human eye has limited color discrimination, thus resulting in insufficient detection accuracy. Meanwhile, viewing angles and ambient lighting can also disrupt observations. Here, we propose a humidity sensor based on surface-functionalized tunable PC grating. The tunable PC grating consists of a 600 nm polystyrene (PS) microsphere PC and a humidity-sensitive hydrogel. As ambient humidity increases, the hydrophilic amide groups (-CONH2) inside the hydrogel interact with the hydrogen bonds between water molecules and triggers hydrogel swelling, exerts interfacial stress on the PS microsphere lattice, thus expanding the lattice spacing of the PS microspheres and causing a red shift in the reflected wavelength. Integrating the surface-functionalized tunable PC grating into a Czerny-Turner (C-T) optical system enables us to directly translate humidity into precise spectral shifts, overcoming the limitations of human eye-based observations. Experimental results demonstrate a strong linear response over the range of 24-94 % relative humidity (RH), as well as excellent repeatability and long-term stability. We provide an innovative solution for high-precision optical humidity sensing.}, }
@article {pmid40609413, year = {2025}, author = {Wang, Y and Gao, Y and He, R and Gao, Y and Xu, Z and Wang, C and Liu, F}, title = {Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance.}, journal = {The Science of the total environment}, volume = {993}, number = {}, pages = {179856}, doi = {10.1016/j.scitotenv.2025.179856}, pmid = {40609413}, issn = {1879-1026}, abstract = {CO2 exchange at air-sea interface is crucial for global carbon cycle. Uncertainties in CO2 flux quantification are constrained by ocean surface partial pressure of CO2 (pCO2) variations. While regional pCO2 retrieval algorithms exist, the impact of mesoscale eddies on accuracy remains understudies. We improve the global ocean surface pCO2 retrieval algorithm using XGBoost, incorporating sea surface temperature (SST), chlorophyll-a (Chl-a), sea surface salinity (SSS), mixed layer depth (MLD), and sea surface height (SSH), achieving high performance (R[2]= 0.95, RMSE = 10.52 μatm) at daily resolution. The SHAP method and the sequential feature removal method were used to assesses the individual impacts. The results reveal that SSH significantly enhances model accuracy, increasing R[2] by ∼10% and decreasing RMSE by ∼38%. Regional evaluations show better performance in the Atlantic, with overestimation (underestimation) at ocean gyre fronts (interiors). The models perform better in summer, while in winter, more overestimation is observed in the North Pacific. The future prediction in global field shows excellent spatiotemporal extrapolation performance. The results verify mesoscale dynamics significantly impact the retrieval accuracy in energetic regions. Relative error normalized quantities were calculated for cyclonic and anticyclonic eddies in eddy-active regions to analyze the influence of energetic mesoscale dynamic, suggesting that regional and seasonal variations in errors are linked to differences in eddy-induced nutrient flux and baroclinic instabilities.}, }
@article {pmid40609325, year = {2025}, author = {Ren, X and Zhou, C and Jiang, Y and Zhao, J and Tina, X and Xu, N and Fu, M and Ni, P and Li, T and Zhang, X}, title = {Generation of an induced pluripotent stem cell line (HZSMHCi002-A) from a patient with neuronal intranuclear inclusion disease carrying GGC repeat expansion in the NOTCH2NLC gene.}, journal = {Stem cell research}, volume = {87}, number = {}, pages = {103761}, doi = {10.1016/j.scr.2025.103761}, pmid = {40609325}, issn = {1876-7753}, mesh = {Humans ; *Induced Pluripotent Stem Cells/metabolism/cytology/pathology ; Female ; *Intranuclear Inclusion Bodies/pathology/genetics/metabolism ; *Neurodegenerative Diseases/genetics/pathology/metabolism ; *Trinucleotide Repeat Expansion/genetics ; Cell Line ; Cell Differentiation ; Nerve Tissue Proteins ; Intercellular Signaling Peptides and Proteins ; }, abstract = {The NOTCH2NLC gene contains a GGC repeat expansion in its 5' untranslated region. This expansion is associated with neuronal intranuclear inclusion disease (NIID). NIID is a rare neurodegenerative disorder. Its clinical features include cognitive decline, paroxysmal symptoms, and autonomic dysfunction. We generated an induced pluripotent stem cell (iPSC) line from a female patient's PBMCs carrying a high GGC repeat expansion in NOTCH2NLC. The iPSC line displayed typical pluripotent morphology. It expressed key pluripotency markers and demonstrated differentiation potential in teratoma assays. This cell line serves as a useful model for studying disease mechanisms and developing therapeutic strategies.}, }
@article {pmid40609285, year = {2025}, author = {Xu, JJ and Chen, YL and Yu, H and Chen, DF and Li, HF and Wu, ZY}, title = {Genetic and Clinical Features of SLC2A1-Related Paroxysmal Exercise-Induced Dyskinesia.}, journal = {Pediatric neurology}, volume = {170}, number = {}, pages = {31-37}, doi = {10.1016/j.pediatrneurol.2025.06.006}, pmid = {40609285}, issn = {1873-5150}, mesh = {Adolescent ; Adult ; Child ; Female ; Humans ; Male ; *Chorea/genetics/physiopathology ; *Exercise/physiology ; Exome Sequencing ; *Glucose Transporter Type 1/genetics ; Mutation, Missense ; Pedigree ; }, abstract = {BACKGROUND: Paroxysmal exercise-induced dyskinesia (PED) is a rare movement disorder characterized by choreoathetosis and dystonia triggered by sustained exercise, commonly affecting the lower extremities. PED is an autosomal dominant disorder genetically linked to mutations in the SLC2A1 gene. The transmembrane protein Glut1, encoded by the SLC2A1 gene, can transport glucose from blood to the brain. This study aimed to characterize the genetic and clinical features of SLC2A1-related PED.<br><br>METHODS: We reported two Chinese PED families presenting with involuntary movements after prolonged exercise. Whole-exome sequencing was performed on two probands, and cosegregation analysis was subsequently carried out in available family members. Additionally, we summarized and analyzed the genetic and clinical features of SLC2A1-related PED by retrieving information from the literature.<br><br>RESULTS: Genetic testing identified two missense mutations in SLC2A1 in these families, including a known disease-causing mutation, c.997C>T (p.R333W), and a novel mutation, c.823G>C (p.A275P). Upon review of the literature, mutations in certain regions of the Glut1 protein, particularly in transmembrane segments 3, 4, 5, 7, and 8, together with the intracellular domain, were more frequently seen in PED. Among the various types of epilepsy, absence seizures were the most common in patients with PED. Furthermore, familial PED had a later onset and a higher cerebrospinal fluid/blood glucose ratio. Patients with missense mutations exhibited a later onset than those with truncated mutations.<br><br>CONCLUSIONS: Our study identified a new disease-causing mutation and, through an extensive literature review, provided a detailed genetic and clinical description of PED associated with SLC2A1 mutations.}, }
@article {pmid40608885, year = {2025}, author = {Yang, Z and Si, X and Jin, W and Huang, D and Zang, Y and Yin, S and Ming, D}, title = {SEEG Emotion Recognition Based on Transformer Network With Channel Selection and Explainability.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {11}, pages = {8153-8163}, doi = {10.1109/JBHI.2025.3585528}, pmid = {40608885}, issn = {2168-2208}, mesh = {Humans ; *Emotions/physiology/classification ; *Electroencephalography/methods ; Male ; Adult ; *Signal Processing, Computer-Assisted ; Female ; *Brain-Computer Interfaces ; Young Adult ; Brain/physiology ; Neural Networks, Computer ; Algorithms ; }, abstract = {Brain-computer interface (BCI) technology for emotion recognition holds significant potential for future applications in the treatment of refractory emotional disorders. Stereo-electroencephalography (SEEG), being less invasive, can precisely record neural activities originating from the cortex and the deep structures of the brain. Thus, it has broad application prospects in constructing emotion recognition BCI. In this study, SEEG data from nine subjects were collected to construct an emotion dataset, and a Spatial Transformer-based Hybrid Network (STHN) was proposed for SEEG emotion recognition. The triple-classification accuracy of STHN reached 83.56%, outperforming the baseline methods such as EEGNet, TSception, and the deep convolution neural network. Moreover, STHN can assign weights to each SEEG channel and select those channels that contribute more significantly to emotion recognition. It was found that when using the top 30% weighted SEEG channels as model inputs, the accuracy did not decrease significantly. Most of the channels with higher weights were located in brain regions strongly associated with emotions, such as the frontal lobe, the temporal lobe, and the hippocampus. This indicates that STHN is not merely a "black-box" model but possesses a degree of explainability. To the best of our knowledge, this is the first study to develop an SEEG emotion recognition algorithm, which is expected to play a crucial role in the monitoring and treatment of patients with refractory emotional disorders in the future.}, }
@article {pmid40608881, year = {2025}, author = {Yu, X and Yu, X}, title = {Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model Predictive Control.}, journal = {IEEE transactions on cybernetics}, volume = {55}, number = {9}, pages = {4311-4321}, doi = {10.1109/TCYB.2025.3580726}, pmid = {40608881}, issn = {2168-2275}, mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Algorithms ; *Signal Processing, Computer-Assisted ; *Robotics/methods/instrumentation ; *Brain/physiology ; Computer Simulation ; }, abstract = {Brain-controlled systems have experienced significant advancements in overall performance, largely driven by continuous optimization and innovation in electroencephalography (EEG) acquisition experimental paradigms and decoding algorithms. However, their applications still face challenges, including limited control precision and low efficiency. In this article, we focus on a wheeled mobile robot (WMR) as the control object and propose a novel brain-controlled framework that combines a probabilistic brain-computer interface (BCI) and a model predictive controller (MPC). First, the probabilistic BCI is developed, featuring the sigmoid fitting-filter bank canonical correlation analysis (SF-FBCCA) algorithm, which serves as the core of the BCI system by decoding EEG signals and generating brain commands along with their associated probabilities. Second, an auxiliary MPC is integrated into the probabilistic BCI system to provide decision-making assistance while preserving the users' primary brain control authority. The weights of the cost function are adaptively determined based on the command probabilities. Finally, simulation-based evaluations were conducted using the WMR in a path-keeping scenario. The results demonstrate that the proposed framework significantly improves control accuracy and efficiency compared to direct brain control approaches, reducing the average lateral error by 58.02% and the average yaw angle error by 60.06%. Additionally, the MPC employing adaptive weights further improves overall performance. These findings offer theoretical insights and technical references for future research on BCI-based control frameworks.}, }
@article {pmid40606836, year = {2025}, author = {Cantillo-Negrete, J and Rodríguez-García, ME and Carrillo-Mora, P and Arias-Carrión, O and Ortega-Robles, E and Galicia-Alvarado, MA and Valdés-Cristerna, R and Ramirez-Nava, AG and Hernandez-Arenas, C and Quinzaños-Fresnedo, J and Pacheco-Gallegos, MDR and Marín-Arriaga, N and Carino-Escobar, RI}, title = {The ReHand-BCI trial: a randomized controlled trial of a brain-computer interface for upper extremity stroke neurorehabilitation.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1579988}, pmid = {40606836}, issn = {1662-4548}, abstract = {BACKGROUND: Brain-computer interfaces (BCI) are a promising complementary therapy for stroke rehabilitation due to the close-loop feedback that can be provided with these systems, but more evidence is needed regarding their clinical and neuroplasticity effects.<br><br>METHODS: A randomized controlled trial was performed using the ReHand-BCI system that provides feedback with a robotic hand orthosis. The experimental group (EG) used the ReHand-BCI, while sham-BCI was given to the control group (CG). Both groups performed 30 therapy sessions, with primary outcomes being the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Secondary outcomes were hemispheric dominance, measured with electroencephalography and functional magnetic resonance imaging, white matter integrity via diffusion tensor imaging, and corticospinal tract integrity and excitability, measured with transcranial magnetic stimulation.<br><br>RESULTS: At post-treatment, patients in both groups had significantly different FMA-UE scores (EG: baseline&#x202f;=&#x202f;24.5[20, 36], post-treatment 28[23, 43], CG: baseline&#x202f;=&#x202f;26[16, 37.5], post-treatment&#x202f;=&#x202f;34[17.3, 46.5]), while only the EG had significantly different ARAT scores at post-treatment (EG: baseline&#x202f;=&#x202f;8.5[5, 26], post-treatment&#x202f;=&#x202f;20[7, 36], CG: baseline&#x202f;=&#x202f;3[1.8, 30.5], post-treatment&#x202f;=&#x202f;15[2.5, 40.8]). In addition, across the intervention, the EG showed trends of more pronounced ipsilesional cortical activity and higher ipsilesional corticospinal tract integrity, although these differences were not statistically different compared to the control group, likely due to the study's sample size.<br><br>CONCLUSION: To the authors' knowledge, this is the first clinical trial that has assessed such a wide range of physiological effects across a long BCI intervention, implying that a more pronounced ipsilesional hemispheric dominance is associated with upper extremity motor recovery. Therefore, the study brings light into the neuroplasticity effects of a closed-loop BCI-based neurorehabilitation intervention in stroke.<br><br>CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/, identifier NCT04724824.}, }
@article {pmid40606655, year = {2025}, author = {Jacob, JE and Chandrasekharan, S}, title = {Editorial: Advanced EEG analysis techniques for neurological disorders.}, journal = {Frontiers in neuroinformatics}, volume = {19}, number = {}, pages = {1637890}, doi = {10.3389/fninf.2025.1637890}, pmid = {40606655}, issn = {1662-5196}, }
@article {pmid40605914, year = {2025}, author = {Yang, L and Zhu, W}, title = {Mifnet: a MamBa-based interactive frequency convolutional neural network for motor imagery decoding.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {106}, pmid = {40605914}, issn = {1871-4080}, abstract = {Motor imagery (MI) decoding remains a critical challenge in brain-computer interface (BCI) systems due to the low signal-to-noise ratio, non-stationarity, and complex spatiotemporal dynamics of electroencephalography (EEG) signals. Although deep learning architectures have advanced MI-EEG decoding, existing approaches-including convolutional neural networks (CNNs), Transformers, and recurrent neural networks (RNNs)-still face limitations in capturing global temporal dependencies, maintaining positional coherence, and ensuring computational efficiency. To address these challenges, we propose MIFNet, a MamBa-based Interactive Frequency Convolutional Neural Network that systematically integrates spectral, spatial, and temporal feature extraction. Specifically, MIFNet incorporates: non-overlapping frequency decomposition, which selectively extracts motor imagery-related mu (8-12 Hz) and beta (12-32 Hz) rhythms; a ConvEncoder module, which autonomously learns to fuse spectral-spatial features from both frequency bands; and a MamBa-based temporal module, leveraging selective state-space models (SSMs) to efficiently capture long-range dependencies with linear complexity. Extensive experiments on three public MI-EEG datasets (BCIC-IV-2A, OpenBMI, and High Gamma) demonstrate that MIFNet outperforms existing models, achieving an average classification accuracy improvement of 12.3%, 8.3%, 4.7%, and 5.5% over EEGNet, FBCNet, IFNet, and Conformer, respectively. Ablation studies further validate the necessity of each component, with the MamBa module contributing a 5.5% improvement in accuracy on the BCIC-IV-2A dataset. Moreover, MIFNet exhibits strong generalization performance in cross-validation settings, establishing a robust foundation for real-time BCI applications. Our findings highlight the potential of hybridizing CNNs with state-space models (SSMs) for improving EEG decoding performance, effectively bridging the gap between localized feature extraction and global temporal modeling.}, }
@article {pmid40603471, year = {2025}, author = {Liao, W and Liu, H and Wang, W}, title = {Advancing BCI with a transformer-based model for motor imagery classification.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {23380}, pmid = {40603471}, issn = {2045-2322}, support = {2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Neural Networks, Computer ; Algorithms ; Machine Learning ; Deep Learning ; }, abstract = {Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise. This paper introduces EEGEncoder, a deep learning framework that employs modified transformers and Temporal Convolutional Networks (TCNs) to surmount these limitations. We propose a novel fusion architecture, named Dual-Stream Temporal-Spatial Block (DSTS), to capture temporal and spatial features, improving the accuracy of Motor Imagery classification task. Additionally, we use multiple parallel structures to enhance the model's performance. When tested on the BCI Competition IV-2a dataset, our proposed model achieved an average accuracy of 86.46% for subject dependent and average 74.48% for subject independent.}, }
@article {pmid40603333, year = {2025}, author = {Isaev, MR and Mokienko, OA and Lyukmanov, RK and Ikonnikova, ES and Cherkasova, AN and Suponeva, NA and Piradov, MA and Bobrov, PD}, title = {Correction: A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1132}, doi = {10.1038/s41597-025-05466-y}, pmid = {40603333}, issn = {2052-4463}, }
@article {pmid40602422, year = {2025}, author = {Jin, J and Liang, W and Xu, R and Chen, W and Xu, R and Wang, X and Cichocki, A}, title = {A transformer-based network with second-order pooling for motor imagery EEG classification.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/adeae8}, pmid = {40602422}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods/classification ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; Deep Learning ; Brain/physiology ; }, abstract = {Objective. Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning models have been developed to decode EEG signals. Although deep learning models, particularly those based on convolutional neural networks, have shown promise in decoding EEG signals, most existing methods focus on attention mechanisms while neglecting high-order statistical dependencies that are critical for accurately capturing the complex structure of EEG data.Approach. To address this limitation, we propose a neural network integrating a transpose-attention mechanism and second-order pooling (SecTNet). The proposed model tackles two fundamental challenges in EEG decoding. It metrics the covariance structure of EEG signals using Riemannian geometry on symmetric positive definite (SPD) matrices, and it enhances the discriminability of these SPD features by introducing attention mechanisms that adaptively model inter-channel dependencies. Specifically, SecTNet is composed of three key components. First, a multi-scale spatial-temporal convolution module extracts detailed local features. Second, a transpose-attention mechanism captures dependency information from the internal interactions between channels. Lastly, a second-order pooling layer captures high-order statistical correlations in the EEG feature space.Main results. SecTNet is evaluated on two publicly available EEG datasets, namely BCI competition IV 2a dataset and OpenBMI dataset. It achieves an average accuracy of 86.88% on the BCI competition IV dataset 2a and 74.99% on the OpenBMI dataset. Moreover, results show that SecTNet maintains competitive performance even when trained on only 50% of the data, demonstrating strong generalization under limited data conditions.Significance. These results demonstrate the broad applicability and effectiveness of SecTNet in enhancing MI-BCI performance. SecTNet provides a robust and generalizable framework for EEG decoding, supporting the development of BCI applications across diverse real-world scenarios.}, }
@article {pmid40602419, year = {2025}, author = {Li, L and Wei, B}, title = {A two-stage EEG zero-shot classification algorithm guided by class reconstruction.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/adeaea}, pmid = {40602419}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods/classification ; *Algorithms ; Brain-Computer Interfaces ; *Brain/physiology ; Photic Stimulation/methods ; Adult ; Classification Algorithms ; }, abstract = {Objective. Researchers have long been dedicated to decoding human visual representations from neural signals. These studies are crucial in uncovering the mechanisms of visual processing in the human brain. Electroencephalogram (EEG) signals have garnered widespread attention recently due to their non-invasive nature and low cost. EEG classification is one of the most popular topics in brain-computer interface research. However, most traditional EEG classification algorithms are difficult to generalize to unseen classes that were not involved in the training phase. The main objective of this work is to improve the performance of these EEG classification algorithms for unseen classes.Approach. In this work, we propose a two-stage zero-shot EEG classification algorithm guided by class reconstruction. The method is specifically designed with a two-stage training strategy based on class reconstruction. This structure and training strategy enable the model to thoroughly learn the relations and distinctions among EEG embeddings of different classes. The contrastive language-image pre-training (CLIP) model has a well-aligned latent space and powerful cross-modality generalization ability.Main results. We conducted experiments on the ImageStimulus-EEG dataset to evaluate the performance of the proposed method. Meanwhile, it was compared with the state-of-the-art model and the baseline model. The experimental results demonstrate that our model achieves superior performance in among Top-1, Top-3, and Top-5 classification accuracy for a 50-way zero-shot classification task, reaching 17.77%, 38.76% and 54.75%, respectively.Significance. The proposed method bridges the modality gap between EEG, images, and text using CLIP features. It significantly improves the model's performance in unseen classes. The experimental results validate the effectiveness of it in EEG zero-shot classification.}, }
@article {pmid40602315, year = {2025}, author = {Del Pup, F and Zanola, A and Tshimanga, LF and Bertoldo, A and Finos, L and Atzori, M}, title = {The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study.}, journal = {Computers in biology and medicine}, volume = {196}, number = {Pt A}, pages = {110608}, doi = {10.1016/j.compbiomed.2025.110608}, pmid = {40602315}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Deep Learning ; Alzheimer Disease/physiopathology ; Parkinson Disease/physiopathology ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Reproducibility of Results ; }, abstract = {Deep learning is significantly advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns within the signals. Data partitioning and cross-validation are crucial for assessing model performance and ensuring study comparability, as they can produce varied results and data leakage due to specific signal properties (e.g., biometric). Such variability in model evaluation leads to incomparable studies and, increasingly, overestimated performance claims, which are detrimental to the field. Nevertheless, no comprehensive guidelines for proper data partitioning and cross-validation exist in the domain, nor is there a quantitative evaluation of the impact of different approaches on model accuracy, reliability, and generalizability. To assist researchers in identifying optimal experimental strategies, this paper thoroughly investigates the role of data partitioning and cross-validation in evaluating EEG deep learning models. Five cross-validation settings are compared across three supervised cross-subject classification tasks (brain-computer interfaces, Parkinson's, and Alzheimer's disease classification) and four established architectures of increasing complexity (ShallowConvNet, EEGNet, DeepConvNet, and Temporal-based ResNet). The comparison of over 100,000 trained models underscores, first, the importance of using subject-based cross-validation strategies for evaluating EEG deep learning architectures, except when within-subject analyses are acceptable (e.g., BCI). Second, it highlights the greater reliability of nested approaches (e.g., N-LNSO) compared to non-nested counterparts, which are prone to data leakage and favor larger models overfitting to validation data. In conclusion, this work provides EEG deep learning researchers with an analysis of data partitioning and cross-validation and offers guidelines to avoid data leakage, currently undermining the domain with potentially overestimated performance claims.}, }
@article {pmid40602314, year = {2025}, author = {Huang, S and Wei, Q}, title = {A deep learning model combining convolutional neural networks and a selective kernel mechanism for SSVEP-Based BCIs.}, journal = {Computers in biology and medicine}, volume = {196}, number = {Pt A}, pages = {110691}, doi = {10.1016/j.compbiomed.2025.110691}, pmid = {40602314}, issn = {1879-0534}, mesh = {Humans ; *Brain-Computer Interfaces ; Convolutional Neural Networks ; *Deep Learning ; Electroencephalography ; *Evoked Potentials, Visual/physiology ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; }, abstract = {Existing deep learning methods for brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) face several challenges, such as overfitting when training data are insufficient, and the difficulty of effectively capturing global temporal features due to limited receptive fields. To address these challenges, we propose a novel deep learning model, FBCNN-TKS, which extracts harmonic components from SSVEP signals using a filter bank technique, followed by feature extraction through convolutional neural networks (CNNs) and a temporal kernel selection (TKS) module, and finally the weighted sum of cross-entropy loss and center loss is used as the objective function for model optimization. The key innovation of our approach lies in the introduction of the TKS module, which significantly enhances feature extraction capability by providing a broader receptive field. Additionally, dilated and grouped convolutions are used in TKS module to reduce the number of model parameters, minimizing the risk of overfitting and improving classification accuracy. Experimental results manifest that FBCNN-TKS outperforms state-of-the-art methods in terms of classification accuracy and information transfer rate (ITR). Specifically, FBCNN-TKS achieved the highest ITRs of 251.54 bpm and 203.47 bpm with the highest accuracies of 83.10 % and 72.98 % on public datasets Benchmark and BETA respectively at the data length of 0.4s, exhibiting superior performance. The FBCNN-TKS model bears big potential for the development of high-performance SSVEP-BCI character spelling systems.}, }
@article {pmid40601454, year = {2025}, author = {Zhang, Y and Yu, Y and Li, H and Wu, A and Chen, X and Liu, J and Zeng, LL and Hu, D}, title = {DMAE-EEG: A Pretraining Framework for EEG Spatiotemporal Representation Learning.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {36}, number = {10}, pages = {17664-17678}, doi = {10.1109/TNNLS.2025.3581991}, pmid = {40601454}, issn = {2162-2388}, mesh = {*Electroencephalography/methods ; Humans ; Algorithms ; Brain/physiology ; *Machine Learning ; Signal Processing, Computer-Assisted ; Artifacts ; Neural Networks, Computer ; Signal-To-Noise Ratio ; Databases, Factual ; Spatio-Temporal Analysis ; }, abstract = {Electroencephalography (EEG) plays a crucial role in neuroscience research and clinical practice, but it remains limited by nonuniform data, noise, and difficulty in labeling. To address these challenges, we develop a pretraining framework named DMAE-EEG, a denoising masked autoencoder for mining generalizable spatiotemporal representation from massive unlabeled EEG. First, we propose a novel brain region topological heterogeneity (BRTH) division method to partition the nonuniform data into fixed patches based on neuroscientific priors. Second, we design a denoised pseudo-label generator (DPLG), which utilizes a denoising reconstruction pretext task to enable the learning of generalizable representations from massive unlabeled EEG, suppressing the influence of noise and artifacts. Furthermore, we utilize an asymmetric autoencoder with self-attention as the backbone in the proposed DMAE-EEG, which captures long-range spatiotemporal dependencies and interactions from unlabeled EEG data across 14 public datasets. The proposed DMAE-EEG is validated on both generative (signal quality enhancement) and discriminative tasks (motion intention recognition). In the quality enhancement, DMAE-EEG outperforms existing statistical methods with normalized mean squared error (nMSE) reduction of 27.78%-50.00% under corruption levels of 25%, 50%, and 75%, respectively. In motion intention recognition, DMAE-EEG achieves a relative improvement of 2.71%-6.14% in intrasession classification balanced accuracy across 2-6 class motor execution and imagery tasks, outperforming state-of-the-art methods. Overall, the results suggest that the pretraining framework DMAE-EEG can capture generalizable spatiotemporal representations from massive unlabeled EEG and enhance the knowledge transferability across sessions, subjects, and tasks in various downstream scenarios, advancing EEG-aided diagnosis and brain-computer communication and control, and other clinical practice.}, }
@article {pmid40601441, year = {2025}, author = {Si, X and Han, Y and Li, S and Zhang, S and Ming, D}, title = {The Cortical Spatial Responses and Decoding of Emotion Imagery Toward a Novel fNIRS-Based Affective BCI.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2577-2586}, doi = {10.1109/TNSRE.2025.3584765}, pmid = {40601441}, issn = {1558-0210}, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; Male ; *Emotions/physiology ; Female ; Adult ; *Imagination/physiology ; Young Adult ; *Cerebral Cortex/physiology ; Brain Mapping/methods ; Algorithms ; Motor Cortex/physiology ; Hemodynamics ; }, abstract = {Functional near-infrared spectroscopy (fNIRS), with its non-invasive and high spatial resolution, holds promise in developing novel affective brain-computer interface (BCI). Similar to motor imagery BCI, emotion imagery BCI could recognize internal emotions and convey them to the external world. This holds clinical value for expressing emotions in patients with neurological impairments and serves as a proactive emotion regulation method. However, the fNIRS features of emotion imagery for affective BCI and the discriminability of different emotion categories remain unclear. Here, this study designed a novel emotion verbal imagery paradigm (imagining descriptions of happy or sad scenes). First, task-related hemodynamic responses were analyzed from 17 subjects. Then, statistical analyses were then conducted to reveal the significant cortical spatial response patterns. Additionally, decoding experiments and model interpretability are employed to assist in validating the feasibility of the emotion imagery BCI. Results showed: 1) Happy imagery recruited frontoparietal regions, such as the left dorsal secondary motor cortex, ventral secondary motor cortex, and inferior parietal lobe. 2) Sad imagery mainly recruited the right dorsolateral prefrontal cortex. 3) The left dorsal sensorimotor cortex exhibited selective responsiveness to happy imagery and sad imagery. 4) The classification results of the emotion imagery task exceeded the random level. 5) Emotional categories activation responses showed significant similarity with the hemodynamic responses of the imagination tasks. Taken together, by proposing the emotion imagery fNIRS paradigm, this work could shed light on the development of feature non-invasive BCI.}, }
@article {pmid40600191, year = {2025}, author = {Ma Thi, C and Nguyen The, HA and Nguyen Minh, K and Vu Thanh, L and Nguyen Dinh, H and Huynh Thi, NY and Ha Thi, TH and Hoang Tien, TN and Au Dao, DT and Nguyen Hoang, KL and Huynh Kha, V and Le Hoang, TL}, title = {UET175: EEG dataset of motor imagery tasks in Vietnamese stroke patients.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1580931}, pmid = {40600191}, issn = {1662-4548}, }
@article {pmid40598468, year = {2025}, author = {Chen, Y and Zhao, N and Zhang, J and Wu, X and Huang, J and Xu, X and Cai, F and Chen, S and Xu, L and Yan, W and Hong, Y and Wang, Y and Ling, H and Ji, J and Chen, G and Gu, H and Zhang, J and Wu, Q}, title = {Molecular signatures of invasive and non-invasive pituitary adenomas: a comprehensive analysis of DNA methylation and gene expression.}, journal = {BMC medicine}, volume = {23}, number = {1}, pages = {373}, pmid = {40598468}, issn = {1741-7015}, mesh = {Humans ; *DNA Methylation ; *Pituitary Neoplasms/genetics/pathology ; *Adenoma/genetics/pathology ; Male ; Female ; Middle Aged ; Adult ; *Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics ; Neoplasm Invasiveness ; Gene Expression Profiling ; }, abstract = {BACKGROUND: Pituitary adenomas (PAs) are benign tumors in the pituitary gland. However, 30-40% of these tumors are invasive, complicating diagnosis and treatment. Invasive pituitary adenomas (IPAs) often respond poorly to conventional therapies, emphasizing the need for better diagnostic and therapeutic strategies. Understanding DNA methylation patterns in IPAs may reveal new biomarkers and therapeutic targets, leading to more effective management of this challenging disease.<br><br>METHODS: Reduced representation bisulfite sequencing (RRBS) and RNA sequencing (RNA-seq) were performed on 129 samples from the Second Affiliated Hospital of Zhejiang University, including 69 tissue samples from invasive and non-invasive pituitary adenomas (NPA) and 60 blood samples from IPA, NPA and healthy individuals. Differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were identified in tissues. Pearson correlation analysis was used to identify associations between DNA methylation status and gene expression, as well as the effect of methylation on gene expression at different sites. Blood samples were analyzed to detect DMRs and DEGs, correlating with tissue-derived findings. Finally, ROC analysis and a random forest model were used to identify biomarkers for discriminating invasive from non-invasive phenotypes.<br><br>RESULTS: We identified 347 DMRs between IPA and NPA, of which 63% (219/347) were hypomethylated. Additionally, 543 mRNAs showed differential expression, with 350 upregulated and 193 downregulated. 17 genes demonstrated concurrent aberrant methylation and expression, primarily within introns, promoters, and CpG islands (CGIs). Notably, only protein tyrosine phosphatase receptor type T (PTPRT) exhibited a remarkably high correlation (r&#x2009;=&#x2009;0.81) between its DNA methylation levels and mRNA expression levels. This correlation was observed within the intronic region/opensea of the gene's CGIs. Plasma sample analysis revealed 852 DMRs between IPA and NPA, with 52% (447/852) being hypomethylated. Three tumor tissue-derived blood biomarkers (MIR4535, SLC8A1-AS1, and TTC34) accurately discriminated between IPA and NPA patients with a combined AUC of 0.980. These markers also differentiated NPA from healthy controls, though with different methylation patterns.<br><br>CONCLUSIONS: The relationship between DNA methylation and gene expression is complex. Plasma-based DNA methylation markers can effectively discriminate between IPA and NPA, as well as between NPA and healthy individuals (N group).}, }
@article {pmid40598460, year = {2025}, author = {Lu, R and Pang, Z and Gao, T and He, Z and Hu, Y and Zhuang, J and Zhang, Q and Gao, Z}, title = {Multisensory BCI promotes motor recovery via high-order network-mediated interhemispheric integration in chronic stroke.}, journal = {BMC medicine}, volume = {23}, number = {1}, pages = {380}, pmid = {40598460}, issn = {1741-7015}, support = {82372570//the National Science Foundation of China/ ; 82372570//the National Science Foundation of China/ ; 82271422//the National Science Foundation of China/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 22ZR1479000//Shanghai Natural Science Foundation/ ; 20234Y0043//Shanghai Municipal Health Commission/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Recovery of Function/physiology ; *Stroke/physiopathology ; Aged ; *Feedback, Sensory/physiology ; Chronic Disease ; Magnetic Resonance Imaging ; Adult ; Neuronal Plasticity ; }, abstract = {BACKGROUND: Chronic stroke patients often experience persistent motor impairments, and current rehabilitation therapies rarely achieve substantial functional recovery. Sensory feedback during movement plays a pivotal role in driving neuroplasticity. This study introduces a novel multi-modal sensory feedback brain-computer interface (Multi-FDBK-BCI) system that integrates proprioceptive, tactile, and visual stimuli into motor imagery-based training. We aimed to explore the potential therapeutic efficacy and elucidate its neural mechanisms underlying motor recovery.<br><br>METHODS: Thirty-nine chronic stroke patients were randomized to either the Multi-FDBK-BCI group (n = 20) or the conventional motor imagery therapy group (n = 19). Motor recovery was assessed using the Fugl-Meyer Assessment (primary outcome), Motor Status Scale, Action Research Arm Test, and surface electromyography. Functional MRI was used to examine brain activation patterns during upper limb tasks, while Granger causality analysis and machine learning evaluated inter-regional connectivity changes and their predictive value for recovery.<br><br>RESULTS: Multi-FDBK-BCI training led to significantly greater motor recovery compared to conventional therapy. Functional MRI revealed enhanced activation of high-order transmodal networks-including the default mode, dorsal/ventral attention, and frontoparietal networks-during paralyzed limb movement, with activation strength positively correlated with motor improvement. Granger causality analysis identified a distinct information flow pattern: signals from the lesioned motor cortex were relayed through transmodal networks to the intact motor cortex, promoting interhemispheric communication. These functional connectivity changes not only supported motor recovery but also served as robust predictors of therapeutic outcomes.<br><br>CONCLUSIONS: Our findings highlight the Multi-FDBK-BCI system as a promising strategy for chronic stroke rehabilitation, leveraging activity-dependent neuroplasticity within high-order transmodal networks. This multi-modal approach holds significant potential for patients with limited recovery options and sheds new light on the neural drivers of motor restoration, warranting further investigation in clinical neurorehabilitation.<br><br>TRIAL REGISTRATION: All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (ChiCTR-ONC-17010739, registered 26 February 2017, starting from 10 January 2017).}, }
@article {pmid40596215, year = {2025}, author = {Rabbani, M and Sabith, NUS and Parida, A and Iqbal, I and Mamun, SM and Khan, RA and Ahmed, F and Ahamed, SI}, title = {EEG based real time classification of consecutive two eye blinks for brain computer interface applications.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {21007}, pmid = {40596215}, issn = {2045-2322}, mesh = {Humans ; *Blinking/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Support Vector Machine ; Neural Networks, Computer ; Young Adult ; Machine Learning ; Signal Processing, Computer-Assisted ; Brain/physiology ; }, abstract = {Human eye blinks are considered a significant contaminant or artifact in electroencephalogram (EEG), which impacts EEG-based medical or scientific applications. However, eye blink detection can instead be transformed into a potential application of brain-computer interfaces (BCI). This study introduces a novel real-time EEG-based framework for classifying three blink states: no blink, single blink, and two consecutive blinks in one model. EEG data were collected from ten healthy participants using an 8-channel wearable headset under controlled blinking conditions. The data were preprocessed and analyzed using four feature extraction techniques: basic statistical, time-domain, amplitude-driven, and frequency-domain methods. The most significant features were selected to develop three machine learning models: XGBoost, support vector machine (SVM), and neural network (NN). We achieved the highest accuracy of 89.0% for classifying multiple-eye blink detection. To further enhance the model's capacity and suitability for real-life BCI applications, we trained and employed the You Only Look Once (YOLO) model, achieving a recall of 98.67%, a precision of 95.39%, and mAP50 of 99.5%, demonstrating its superior accuracy and robustness in classifying two consecutive eye blinks. In conclusion, this study will be the first groundwork and open a new dimension in EEG-based BCI research by classifying multiple-eye blink detection.}, }
@article {pmid40595635, year = {2025}, author = {Liu, L and Gao, Z and Niu, X and Yu, H and Xin, X and Gu, Y and Ma, G and Gu, Y and Liu, Y and Fang, S and Marquardt, T and Wang, L}, title = {SEMA3B switches axon-axon to axon-glia interactions required for unmyelinated axon envelopment and integrity.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {5433}, pmid = {40595635}, issn = {2041-1723}, support = {32100758//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; *Axons/metabolism ; *Semaphorins/metabolism/genetics ; Mice ; Schwann Cells/metabolism ; Hyperalgesia/metabolism ; Male ; Mice, Inbred C57BL ; *Nerve Fibers, Unmyelinated/metabolism ; Peripheral Nerve Injuries/metabolism ; Endocytosis ; *Neuroglia/metabolism ; Cell Communication ; }, abstract = {During peripheral nerve (PN) development, unmyelinated axons (nmAs) tightly fasciculate before being separated and enveloped by non-myelinating Schwann cells (nmSCs), glial cells essential for maintaining nmA integrity. How such a switch from axon-axon to axon-glia interactions is achieved remains poorly understood. Here, we find that inactivating SC-derived SEMA3B or its axonal receptor components in mice leads to incomplete nmA separation and envelopment by nmSCs, eliciting hyperalgesia and allodynia. Conversely, increasing SEMA3B levels in SCs accelerates nmA separation and envelopment. SEMA3B transiently promotes nmA defasciculation accompanied by cell adhesion molecule (CAM) endocytosis, subsequently facilitating nmA-nmSC association. Restoring SEMA3B expression following PN injury promotes nmA-nmSC re-association and alleviates hyperalgesia and allodynia. We propose that SEMA3B-induced CAM turnover facilitates a switch from axon-axon to axon-glia interactions promoting nmA envelopment by nmSCs, which may be exploitable for alleviating PN injury-induced pain by accelerating the restoration of nmA integrity.}, }
@article {pmid40594904, year = {2025}, author = {Sayem, M and Rafi, MA and Mishu, ID and Mahmud, Z}, title = {Comprehensive genomic analysis reveals virulence and antibiotic resistance genes in a multidrug-resistant Bacillus cereus isolated from hospital wastewater in Bangladesh.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {22915}, pmid = {40594904}, issn = {2045-2322}, mesh = {*Bacillus cereus/genetics/pathogenicity/isolation &amp; purification/drug effects ; *Wastewater/microbiology ; Bangladesh ; *Drug Resistance, Multiple, Bacterial/genetics ; Phylogeny ; Hospitals ; Virulence/genetics ; Genome, Bacterial ; Whole Genome Sequencing ; Genomics/methods ; Anti-Bacterial Agents/pharmacology ; Virulence Factors/genetics ; Humans ; }, abstract = {Hospital wastewater represents a significant reservoir for antimicrobial-resistant bacteria, including multidrug-resistant (MDR) Bacillus cereus, a pathogen of growing concern due to its potential impact on public health and environmental safety. This study characterizes the genomic features, antimicrobial resistance (AMR) mechanisms, and virulence potential of Bacillus cereus MBC, isolated from hospital wastewater in Dhaka, Bangladesh. Using whole-genome sequencing (WGS) and advanced bioinformatics, we analyzed the isolate's taxonomy, phylogenetics, functional annotation, and biosynthetic potential. The genome, spanning 5.6 Mb with a GC content of 34.84%, contained 5,881 protein-coding sequences, including 1,424 hypothetical proteins, and 28 genes associated with AMR. Phylogenetic analysis revealed a close genetic relationship with Bacillus cereus ATCC 14579, sharing virulence factors such as hemolysin BL (HBL), non-hemolytic enterotoxin (NHE), and cytotoxin K (CytK), all contributing to its pathogenicity. The ability to form biofilms further enhances the strain's persistence and resistance in hospital environments. AMR profiling identified genes conferring resistance to beta-lactams (e.g., BcI, BcII, BcIII), tetracyclines (tetB(P)), glycopeptides (vanY), and fosfomycin, highlighting the bacterium's capacity to resist a wide array of antibiotics. Functional annotation revealed metabolic pathways involved in iron acquisition and the biosynthesis of siderophores such as petrobactin and bacillibactin, reinforcing the bacterium's adaptability in nutrient-limited environments. Mobile genetic elements, including prophages, CRISPR-Cas systems, and transposable elements, suggest significant horizontal gene transfer (HGT), enhancing genetic plasticity and resistance spread. Pangenomic analysis, involving 125 B. cereus strains, revealed a high degree of genetic diversity and close relationships with strains from clinical, food, and agricultural environments, emphasizing the overlap between clinical and environmental reservoirs of resistance. The strain's isolation from hospital wastewater underscores the complex interplay between environmental contaminants and bacterial evolution, which fosters MDR traits. Our findings underscore the urgent need for enhanced genomic surveillance and wastewater management strategies to mitigate the spread of MDR B. cereus and AMR genes in hospital environments.}, }
@article {pmid40594760, year = {2025}, author = {Kanna, RK and Shoran, P and Yadav, M and Ahmed, MN and Burje, S and Shukla, G and Sinha, A and Hussain, MR and Khalid, S}, title = {Improving EEG based brain computer interface emotion detection with EKO ALSTM model.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {20727}, pmid = {40594760}, issn = {2045-2322}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Algorithms ; *Brain/physiology ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; }, abstract = {Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscular or muscle channels. Various techniques for automatic emotion identification based on body language, speech, or facial expressions are nowadays in use. However, the monitoring of exterior emotions, which are easily manipulated, limits the applicability of these procedures. EEG-based emotion detection research might yield significant benefits for enhancing BCI application performance and user experience. To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based brain-computer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. Then, feature extraction is performed using DWT from pre-processed data. Specifically, the proposed approach is implemented using Python software. The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. The results demonstrated that the proposed method achieved better performance in EEG-based BCI emotion detection with an accuracy of 97.93%, a positive predictive value of 96.24%, a sensitivity of 97.81%, and a specificity of 97.75%. This study emphasizes that innovative approaches have significantly increased the accuracy of emotion identification when applied to EEG-based emotion recognition systems. Additionally, the findings suggest that integrating advanced machine learning techniques can further enhance the effectiveness and reliability of these systems in real-world applications, paving the way for more responsive and intuitive BCI technologies.}, }
@article {pmid40594416, year = {2025}, author = {Wechakarn, P and Klomchitcharoen, S and Jatupornpoonsub, T and Jirakittayakorn, N and Puttanawarut, C and Likitsuntonwong, W and Chaimongkolnukul, K and Wongsawat, Y}, title = {Modified stereotactic neurosurgery techniques for rodent surgery enhance survival and reduce surgery time in a severe traumatic brain injury model.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {22166}, pmid = {40594416}, issn = {2045-2322}, mesh = {Animals ; *Brain Injuries, Traumatic/surgery/mortality ; *Stereotaxic Techniques ; Disease Models, Animal ; Rats ; *Neurosurgical Procedures/methods/instrumentation ; Male ; Operative Time ; Rats, Sprague-Dawley ; Mice ; }, abstract = {Controlled cortical impact (CCI) is the most widely used mechanical model of traumatic brain injury (TBI) in rodent brains. This neurosurgical procedure generally involves the use of a stereotaxic system, which requires reaching a specific brain region with the most accurate position possible. In this study, a modified stereotaxic system for TBI induction was developed to evaluate preclinical research in rodents for conducting neural stimulation experiments by using an implanted electrode to assist in rehabilitation after severe TBI. The proposed model aims to reduce animal mortality during surgery and alleviate the negative side effects potentially caused by prolonged anesthesia drug usage. Isoflurane is applied as an anesthetic drug before stereotaxic surgery in rodents, which promotes hypothermia in the animal body. The result showed notable improvement in rodent survival after applying an active warming pad system to prevent hypothermia. Compared with the conventional stereotaxic system, the modified CCI device with a mounted 3D-printed header significantly improved performance in the surgical procedure, decreasing the total operation time by 21.7%, especially in the Bregma‒Lambda measurement. These findings indicate the tangible capability of our modified stereotaxic system, which allows surgeons to perform stereotaxic surgery faster and lowers the risk of intraoperative mortality.}, }
@article {pmid40594365, year = {2025}, author = {Hadi-Saleh, Z and Mosleh, M and Al-Shahe, MA and Mosleh, M}, title = {Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {21202}, pmid = {40594365}, issn = {2045-2322}, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Movement/physiology ; Brain/physiology ; }, abstract = {The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impressive because of the possibility of analyzing and translating brain signals related to movement intentions. This technology has many applications in the fields of medicine, rehabilitation, mind-controlled computers and assistive technologies. Despite significant progress in EEG-based BCI systems, there are challenges such as signal noise, low decoding accuracy, instability and changeability of signals, etc. To address these limitations, this article presents a new approach to classify MI from EEG signals with the help of synergistic Hilbert-Huang Transform(HHT) as pre-processing, Permutation Conditional Mutual Information Common Space Pattern (PCMICSP) as features and optimized back propagation neural network(BPNN) based on Honey Badger Algorithm(HBA) as classifier. Using the ergodicity of the HBA, along with chaotic mechanisms and global convergence, this approach encodes and optimizes the weights and thresholds of a BPNN. Initially, a comprehensive optimal solution is obtained through the honey badger algorithm. Subsequently, this solution is further refined to reach a more precise optimal state by introducing chaotic disturbances. The proposed method efficiency was confirmed through experimental analysis on a set of data of benchmark that is generally accessible of EEGMMIDB (imagery database or motor movement of EEG). Our experimental analysis outcome showed that mechanism development is important. Now, two EEG signal levels were taken into consideration: the first being an epileptic and the other being non-epileptic. The presented technique generated a max accuracy of 89.82% in comparison with other methods.}, }
@article {pmid40590757, year = {2025}, author = {Chang, T and Cho, SI and Chai, JY and Min, KD}, title = {Implications of predator species richness in terms of zoonotic spillover transmission of filovirus diseases in Africa.}, journal = {Transactions of the Royal Society of Tropical Medicine and Hygiene}, volume = {119}, number = {11}, pages = {1277-1287}, doi = {10.1093/trstmh/traf065}, pmid = {40590757}, issn = {1878-3503}, support = {NRF-2021R1C1C2012611//National Research Foundation of Korea/ ; }, mesh = {Animals ; Humans ; *Zoonoses/epidemiology/transmission/virology ; *Biodiversity ; Africa/epidemiology ; *Disease Outbreaks ; *Strigiformes/virology ; *Marburg Virus Disease/transmission/epidemiology ; *Hemorrhagic Fever, Ebola/transmission/epidemiology ; *Predatory Behavior ; Ebolavirus ; }, abstract = {BACKGROUND: A rich biodiversity of predators has been suggested to suppress the risk of zoonotic spillover by regulating prey abundance and behavior. We evaluated the association between predator species richness and spillover events of Ebolavirus and Marburgvirus in Africa.<br><br>METHODS: Historical records of filovirus outbreaks, along with ecological, geographical and socioeconomic factors, were considered in this environmental study. We used the maximum entropy approach (Maxent modeling) and stacked species distribution models to estimate predator species richness. Logistic regression analyses accounting for spatiotemporal autocorrelations were conducted to assess the association between predator species richness and spillover risk, adjusting for potential confounders.<br><br>RESULTS: Higher species richness of certain predators-the order Strigiformes and the family Colubridae-was associated with lower risks of Ebolavirus spillover, but not with Marburgvirus spillover. The third quartile (OR=0.02, 95% Bayesian credible interval [BCI]=0.00-0.84) and fourth quartile (OR=0.07, 95% BCI=0.00-0.42) of Strigiformes species richness, as well as the third quartile (OR=0.15, 95% BCI=0.01-0.73) and fourth quartile (OR=0.53, 95% BCI=0.03-0.85) of Colubridae species richness, were significantly associated with reduced odds of Ebolavirus index cases.<br><br>CONCLUSION: These findings support a possible role for predator species richness in suppressing zoonotic spillover.}, }
@article {pmid40590380, year = {2025}, author = {Kushwaha, N and Mishra, N and Lalawat, RS and Padhy, PK and Gupta, VK}, title = {Automated posture adjustment system for immobilized patients using EEG signals.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/10255842.2025.2523322}, pmid = {40590380}, issn = {1476-8259}, abstract = {This paper presents a Brain Computing Interface (BCI) system utilizing Electroencephalography (EEG) for human posture Identification. The proposed approach follows a structured five-step process, ensuring accurate and efficient classification. The dataset collected using the MindRove EEG device captures brain activity during four motor imagery tasks: Leftward, Rightward, Upward, and Zeroth. Pre-processing involved filtering, followed by feature extraction using a Convolutional Recurrent Denoising Autoencoder (CRDAE) model. After that Classification is performed using artificial intelligence (AI) models, including Gated Recurrent Unit (GRU) with Attention, Temporal Transformer (TT), Bidirectional Long Short-Term Memory with attention mechanisms (Bi-LSTM with AM), and proposed Graph Transformer All Attention (GTAA). The GTAA model demonstrates superior performance, achieving the highest classification accuracy among the evaluated models. Additionally, the proposed system validated against the BCI Competition IV 2a datasets and ten-fold subject cross-validation, demonstrating its reliability and efficiency for real-time BCI applications. This study underscores the potential of integrating advanced AI techniques with EEG signal measurement and instrumentation for practical implementations.}, }
@article {pmid40590025, year = {2025}, author = {Deuel, TA and Wenlock, J and McGovern, A and Rosenthal, J and Pampin, J}, title = {Musical auditory feedback BCI: clinical pilot study of the Encephalophone.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1592640}, pmid = {40590025}, issn = {1662-5161}, abstract = {INTRODUCTION: Therapeutic strategies for patients with severe acquired motor disability are relatively limited and show variable efficacy. Innovative technologies such as brain-computer interfaces (BCIs) have been developed recently that might benefit certain types of patients.<br><br>METHODS: Here, we tested a previously described auditory BCI, the Encephalophone, which may offer new options to improve quality of life and function. Eleven subjects with acquired moderate to severe motor disability, who had lost their ability to express themselves musically, were enrolled and 10 completed a clinical pilot study of the hands-free Encephalophone brain-computer interface (BCI). Subjects were briefly instructed on the use of the Encephalophone BCI, which uses EEG measured motor imagery to allow users to generate musical notes in real time without requiring movement. Subjects then underwent a pitch-matching task, a measure of accuracy, to attempt to match a given target pitch 3 times within 10&#x202f;s. They were allowed free play, where they could improvise music over a backing track. After 2-3 songs - approximately 10&#x202f;min - of freely improvised playing, subjects repeated the pitch-matching task. There were 3 sessions of testing and free play per subject, within 2&#x202f;weeks, with at least 1&#x202f;day separating sessions.<br><br>RESULTS: All subjects, on average, improved their pitch-matching accuracy by 15.6 percentage points and increased their number of hits by 58.7% over the 3 sessions, with all subjects scoring accuracy percentages significantly above random probability (19.05%). A subjective self-reporting survey of ratings of such factors as a feeling of expressing oneself, enjoyment, discomfort, and feeling of control showed a generally favorable response.<br><br>DISCUSSION: We suggest that this training approach using an auditory BCI may provide an innovative solution to challenges in recovery from motor disability.<br><br>CLINICAL TRIAL REGISTRATION: https://research.providence.org/clinical-research, Swedish Health Services #: STUDY2017000301.}, }
@article {pmid40589299, year = {2025}, author = {George, I and Rao, DP and Jain, A and Ascione, G and Sharma, M and Meharwal, ZS and Sarkar, B and Kochar, N and Gan, MD and Shastri, N and Runt, J and Whisenant, B and Wilson, B and Kiser, A and Leon, MB and Pandey, K}, title = {1-Year Results From a Multicenter Trial of a Polymer Surgical Mitral Valve: Insights Into New Technology.}, journal = {Journal of the American College of Cardiology}, volume = {86}, number = {7}, pages = {515-526}, doi = {10.1016/j.jacc.2025.06.017}, pmid = {40589299}, issn = {1558-3597}, mesh = {Humans ; Female ; Male ; Middle Aged ; Adult ; Aged ; *Heart Valve Prosthesis ; *Mitral Valve/surgery/diagnostic imaging ; Prospective Studies ; *Polymers ; *Heart Valve Prosthesis Implantation/methods/instrumentation ; Prosthesis Design ; Young Adult ; India/epidemiology ; Treatment Outcome ; *Mitral Valve Insufficiency/surgery ; Follow-Up Studies ; }, abstract = {BACKGROUND: Polymer leaflet material may extend the durability of surgical mitral valve replacement (SMVR) to provide stable long-term hemodynamics. The India Mitral Surgical Trial sought to evaluate the safety and performance of a novel polymer leaflet material as part of a surgical mitral valve (MV) prosthesis.<br><br>OBJECTIVES: In this study, the authors sought to report 1-year outcomes in patients undergoing SMVR for MV disease using the Tria Mitral Valve (Foldax).<br><br>METHODS: Adult patients requiring MV replacement were enrolled in a prospective single-arm multicenter trial at 8 clinical sites in India from April to November 2023. An independent physician screening committee reviewed each patient for study eligibility before enrollment. Safety events were adjudicated per standard Valve Academic Research Consortium 3 criteria guidelines, and valve performance was assessed by means of echocardiographic and computed tomographic imaging at 30 days and 1 year. Patients were maintained on a vitamin K antagonist (target international normalized ratio: 2.5).<br><br>RESULTS: Sixty-seven patients, of whom 64% were female (48% of childbearing age), with a mean age of 42 years (range: 19-67 years), mean body mass index of 22.7 kg/m[2], and body surface area of 1.6 cm[2] were treated with SMVR with 100% technical success. Most patients (54%) were NYHA functional class III or IV at baseline. The mean Society of Thoracic Surgeons score was 1.4%. The etiology of MV disease was stenosis in 27%, regurgitation in 30%, and mixed in 43% of patients, primarily secondary to rheumatic heart disease. The 1-year rates for all-cause mortality, thromboembolic events, stroke, structural valve deterioration, and valve reintervention were 9.1%, 7.5%, 4.9%, 0%, and 0%, respectively. No death was valve related. One-year effective orifice area and mean inflow gradient were 1.4 &#xb1; 0.4 cm[2] and 4.6 &#xb1; 1.7 mm Hg, respectively. There were 2 thrombotic events and 3 ischemic strokes, all in patients with subtherapeutic international normalized ratio.<br><br>CONCLUSIONS: The polymer surgical MV demonstrated an acceptable safety profile and maintained stable hemodynamic performance through 1 year in patients undergoing MV replacement. Further study of this promising polymer leaflet technology is ongoing. (Clinical Investigation for the Foldax Tria Mitral Valve-India; NCT06191718).}, }
@article {pmid40588550, year = {2025}, author = {Tian, Y and Li, H and Ye, W and Yuan, X and Guo, X and Guo, F}, title = {Temperature-dependent modulation of light-induced circadian responses in Drosophila melanogaster.}, journal = {The EMBO journal}, volume = {44}, number = {16}, pages = {4552-4576}, pmid = {40588550}, issn = {1460-2075}, support = {32171008//the National Natural Science Foundation of China/ ; 32471210//the National Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2025ZFJH01-01//the Fundamental Research Funds for the Central Universities/ ; 226-2024-00133//the Fundamental Research Funds for the Central Universities/ ; }, mesh = {Animals ; *Drosophila melanogaster/physiology/radiation effects ; *Circadian Rhythm/physiology ; *Temperature ; *Light ; Neurons/physiology/metabolism ; Drosophila Proteins/metabolism/genetics ; *Circadian Clocks/physiology ; Neuropeptides/metabolism ; }, abstract = {Animals entrain their circadian rhythms to multiple external signals, such as light and temperature, which are integrated in master clock neurons to adjust circadian phases. However, the precise mechanisms underlying this process remain unclear. Here, we use in vivo two-photon calcium imaging while precisely controlling temperature to investigate how the Drosophila melanogaster circadian clock integrates light and temperature inputs in circadian neurons. We show that light responses modulate the circadian clock in central pacemaker neurons, with temperature acting as a fine-tuning mechanism to achieve optimal adaptation. Our results suggest that temperature-sensitive dorsal clock neurons DN1as regulate the light-induced firing of s-LNv circadian pacemaker neurons and release of the neuropeptide PDF through inhibitory glutamatergic signaling. Specifically, higher temperatures suppress s-LNv firing upon light exposure, while lower temperatures enhance this response. Behavioral analyses further indicate that lower temperatures accelerate phase adjustment, whereas higher temperatures decelerate them in response to new light-dark cycles. This novel mechanism of temperature-dependent modulation of circadian phase adjustment provides new insights into the adaptive strategies of animals for survival in fluctuating environments.}, }
@article {pmid40588517, year = {2025}, author = {Ding, Y and Udompanyawit, C and Zhang, Y and He, B}, title = {EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {5401}, pmid = {40588517}, issn = {2041-1723}, support = {R01 NS124564/NS/NINDS NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; NS124564, NS131069, NS127849, NS096761//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS127849/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Robotics/methods/instrumentation ; *Fingers/physiology ; Male ; Adult ; Female ; *Hand/physiology ; Young Adult ; Movement/physiology ; Brain/physiology ; Neural Networks, Computer ; Imagination/physiology ; }, abstract = {Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive mappings and imprecise control. In this study, we present a real-time noninvasive robotic control system using movement execution (ME) and motor imagery (MI) of individual finger movements to drive robotic finger motions. The proposed system advances state-of-the-art electroencephalography (EEG)-BCI technology by decoding brain signals for intended finger movements into corresponding robotic motions. In a study involving 21 able-bodied experienced BCI users, we achieved real-time decoding accuracies of 80.56% for two-finger MI tasks and 60.61% for three-finger tasks. Brain signal decoding was facilitated using a deep neural network, with fine-tuning enhancing BCI performance. Our findings demonstrate the feasibility of naturalistic noninvasive robotic hand control at the individuated finger level.}, }
@article {pmid40588007, year = {2025}, author = {Mahoney, TB and Grayden, DB and John, SE}, title = {Sub-scalp EEG for sensorimotor brain-computer interface.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ade9f1}, pmid = {40588007}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Animals ; *Electroencephalography/methods ; Sheep ; *Evoked Potentials, Somatosensory/physiology ; *Sensorimotor Cortex/physiology ; }, abstract = {Objective. To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity.Approach. Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment.Main results. We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models.Significance. These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.}, }
@article {pmid40587936, year = {2025}, author = {Vadivelan D, S and Sethuramalingam, P}, title = {A hybrid approach for EEG motor imagery classification using adaptive margin disparity and knowledge transfer in convolutional neural networks.}, journal = {Computers in biology and medicine}, volume = {195}, number = {}, pages = {110675}, doi = {10.1016/j.compbiomed.2025.110675}, pmid = {40587936}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Convolutional Neural Networks ; }, abstract = {- Motor Imagery (MI) using Electroencephalography (EEG) is essential in Brain-Computer Interface (BCI) technology, enabling interaction with external devices by interpreting brain signals. Recent advancements in Convolutional Neural Networks (CNNs) have significantly improved EEG classification tasks; however, traditional CNN-based methods rely on fixed convolution modes and kernel sizes, limiting their ability to capture diverse temporal and spatial features from one-dimensional EEG-MI signals. This paper introduces the Adaptive Margin Disparity with Knowledge Transfer 2D Model (AMD-KT2D), a novel framework designed to enhance EEG-MI classification. The process begins by transforming EEG-MI signals into 2D time-frequency representations using the Optimized Short-Time Fourier Transform (OptSTFT), which optimizes windowing functions and time-frequency resolution to preserve dynamic temporal and spatial features. The AMD-KT2D framework integrates a guide-learner architecture where Improved ResNet50 (IResNet50), pre-trained on a large-scale dataset, extracts high-level spatial-temporal features, while a Customized 2D Convolutional Neural Network (C2DCNN) captures multi-scale features. To ensure feature alignment and knowledge transfer, the Adaptive Margin Disparity Discrepancy (AMDD) loss function minimizes domain disparity, facilitating multi-scale feature learning in C2DCNN. The optimized learner model then classifies EEG-MI images into left and right-hand movement motor imagery classes. Experimental results on the real-world EEG-MI dataset collected using the Emotiv Epoc Flex system demonstrated that AMD-KT2D achieved a classification accuracy of 96.75 % for subject-dependent and 92.17 % for subject-independent, showcasing its effectiveness in leveraging domain adaptation, knowledge transfer, and multi-scale feature learning for advanced EEG-based BCI applications.}, }
@article {pmid40587626, year = {2025}, author = {Li, Z and Huang, Z and Li, J and Tang, Y and Li, J and Ding, X}, title = {Shear-Aligned Flexible Polarized Fluorescent Antennas for Wearable Visible Light Communications.}, journal = {ACS applied materials &amp; interfaces}, volume = {17}, number = {28}, pages = {40915-40927}, doi = {10.1021/acsami.5c06121}, pmid = {40587626}, issn = {1944-8252}, abstract = {Wearable visible light communication systems face fundamental limitations in dense multi-input multioutput configurations due to signal crosstalk between channels. Here, we demonstrate shear-aligned flexible polarized fluorescent antennas (FPFAs) fabricated through a scalable thermally assisted brush-coating induction (BCI) process. By systematically investigating the synergistic effects of ″coffee-ring″ phenomena and shear forces on halloysite nanotube alignment, we reveal the underlying physical mechanism enabling the formation of highly ordered structures with an orientation degree of 0.89. We encapsulate these structures in a sandwich configuration that maintains polarization performance while exhibiting mechanical stability, with parallel fracture strength 4.25 times higher than conventional designs. When integrated with quantum dot fluorescent conversion layers, these FPFAs achieve a 4.95-fold improvement in signal-to-noise ratio (SNR) compared to traditional receivers across wide viewing angles, even under extreme bending conditions. The resulting wearable communication system maintains 85.1% transmission accuracy at distances up to 9 m under ambient lighting, a 935% improvement over conventional approaches, with superior resilience to environmental disturbances including rain and fog. This work establishes an effective strategy for polarization multiplexing in wearable optical communications, with applications spanning healthcare monitoring, secure communications, and augmented reality interfaces in dynamic environments.}, }
@article {pmid40586414, year = {2025}, author = {Tian, Y and Wallace, DM and Cederna, PS and Chestek, CA and Kemp, SWP}, title = {Toward Natural Limb Function: A New Era in Prosthetic Innovation.}, journal = {Annals of neurology}, volume = {98}, number = {5}, pages = {913-928}, pmid = {40586414}, issn = {1531-8249}, mesh = {Humans ; *Artificial Limbs/trends ; *Brain-Computer Interfaces/trends ; *Extremities/physiology ; Electroencephalography ; }, abstract = {The past decade has witnessed groundbreaking clinical implementation of neuroprosthetic limbs driven by signals from peripheral targets (eg, nerves and muscle) and the brain to restore limb function for individuals with limb loss or impairment. In this review, we highlight recent key clinical trials in peripheral neuroprosthetic interfaces directly with nerve, residual muscle, and reinnervated muscle. We then highlight the key advances in brain interfaces, including clinical trials using electroencephalography, electrocorticography, and intracortical electrodes to control neuroprosthetics. Finally, we explore the future of neuroprosthetic control where both peripheral and brain interfaces can be combined to improve neuroprosthetic performance. ANN NEUROL 2025;98:913-928.}, }
@article {pmid40586134, year = {2025}, author = {Zhang, Q and Liu, B and Wang, Z and Zhou, J and Yang, X and Zhou, Q and Zhao, Y and Li, S and Zhou, J and Wang, C}, title = {Training-Free Regulation of Grasping by Intracortical Tactile Feedback Designed via S1-M1 Communication.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {12}, number = {36}, pages = {e03011}, pmid = {40586134}, issn = {2198-3844}, support = {2021ZD0201600//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 2021ZD0201604//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 82327810//National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China/ ; }, mesh = {Animals ; *Motor Cortex/physiology ; *Somatosensory Cortex/physiology ; *Hand Strength/physiology ; *Feedback, Sensory/physiology ; *Touch/physiology ; Male ; Brain-Computer Interfaces ; Macaca mulatta ; }, abstract = {Tactile feedback is essential for grip force control when operating a neuroprosthesis. Due to limited knowledge of cortical sensorimotor coordination, artificial feedback is mostly counterintuitive, requiring training to be associated with grasping behaviors. The current study investigates sensorimotor communication by recording neural activities from the primary sensory cortex (S1) and the primary motor cortex (M1) while macaques grasp targets of various textures and loads. Intracortical micro-stimulation is also delivered to S1 to validate the intervention of sensorimotor communication in grasping. The findings identify an S1→M1 functional pathway through which tactile information is transferred. The pathway is shared by both natural and artificial neural propagations. Moreover, it is demonstrated that sensory and motor decoding of neural activities in M1, as well as the actual grip force, are modulated by stimulation designed via S1→M1 communication, without prior training. The work provides a biomimetic strategy to design intuitive haptic feedback for brain-machine interfaces utilizing the S1→M1 pathway.}, }
@article {pmid40585760, year = {2025}, author = {Zheng, Q and Wu, Y and Zhu, J and Feng, K and Bai, Y and Li, G and Ni, G}, title = {Applications and Challenges of Auditory Brain-Computer Interfaces in Objective Auditory Assessments for Pediatric Cochlear Implants.}, journal = {Exploration (Beijing, China)}, volume = {5}, number = {3}, pages = {20240078}, pmid = {40585760}, issn = {2766-2098}, abstract = {Cochlear implants (CI) are the premier intervention for individuals with severe to profound hearing impairment. Worldwide, an estimated 600,000 individuals have enhanced their hearing through cochlear implantation, with nearly half being children. The evaluations after implantation are crucial for appropriate clinical interventions and care. Current clinical practice lacks methods to assess the recovery of advanced auditory functions in cochlear-implanted children. Yet, recent advancements in electroencephalographic (EEG) techniques show promise in accurately evaluating auditory rehabilitation in this demographic. This review elucidates the evolution of brain-computer interface (BCI) technology for auditory assessment, focusing primarily on its application in pediatric cochlear implant recipients. Emphasis is placed on promising clinical biomarkers for auditory rehabilitation and the neural adaptability accompanying cortical adjustments after implantation. Additionally, we discuss emerging challenges and prospects in applying BCI technology to these children.}, }
@article {pmid40584823, year = {2025}, author = {Jiang, M and Pan, X and Wang, X and Luo, Q}, title = {Will the embedded semantic radicals be activated when recognizing Chinese phonograms?.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1550536}, pmid = {40584823}, issn = {1662-5161}, abstract = {INTRODUCTION: A majority of Chinese characters are phonograms composed of phonetic and semantic radicals that serve different functions. While radical processing in character recognition has drawn significant interest, there is inconsistency regarding the semantic activation of embedded semantic radicals, and little is known about the duration of such sub-lexical semantic activation.<br><br>METHODS: Using a priming character decision task and a between-subjects design, this study examined whether semantic radicals embedded in SP phonograms (semantic radicals on the left and phonetic radicals on the right) can be automatically activated and how long such activation persists. We manipulated semantic relatedness between embedded radicals and target characters, prime frequency, and stimulus onset asynchronies (SOAs).<br><br>RESULTS: Facilitatory effects were observed on targets preceded by low-frequency primes at an SOA of 500 ms. No significant priming effects were found at SOAs of 100 ms or 1000 ms, regardless of prime frequency.<br><br>DISCUSSION: These findings suggest that sub-lexical semantic activation can occur and remain robust at 500 ms but may dissipate before 1000 ms. The study contributes valuable evidence for the automaticity and time course of embedded semantic radical processing in Chinese phonogram recognition, thereby enhancing our understanding of sub-lexical semantic processing in logographic writing systemse.}, }
@article {pmid40584523, year = {2025}, author = {Shen, Y and Jiang, L and Lai, J and Hu, J and Liang, F and Zhang, X and Ma, F}, title = {A comprehensive review of rehabilitation approaches for traumatic brain injury: efficacy and outcomes.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1608645}, pmid = {40584523}, issn = {1664-2295}, abstract = {Traumatic Brain Injury (TBI), particularly in moderate-to-severe cases, remains a leading cause of long-term disability worldwide, affecting over 64 million individuals annually. Its complex and multifactorial nature demands an integrated, multidisciplinary rehabilitation approach to address the diverse physical, cognitive, behavioral, and psychosocial impairments that follow injury. We conducted a structured literature search using PubMed, Scopus, and Web of Science databases for suitable studies. This comprehensive review critically examines key rehabilitation strategies for TBI, including neuropsychological assessments, cognitive and neuroplasticity-based interventions, psychosocial support, and community reintegration through occupational therapy. The review emphasizes emerging technological innovations such as virtual reality, robotics, brain-computer interfaces, and tele-rehabilitation, which are expanding access to care and enhancing recovery outcomes. Furthermore, it also explores regenerative approaches, such as stem cell therapies and nanotechnology, highlighting their future potential in neurorehabilitation. Special attention is given to the importance of rigorous outcome evaluation, including standardized functional measures, neuropsychological testing, and advanced statistical methodologies to assess treatment efficacy and clinical significance. Patient-centered care is emphasized as a core element-rehabilitation plans are tailored to each individual's cognitive profile, functional needs, and life goals. Studies show this approach leads to better outcomes in executive functioning, emotional wellbeing, and community reintegration. It identifies gaps in current research, such as the lack of longitudinal studies, predictors of individualized treatment success, cost-benefit evaluations, and strategies to manage comorbidities like PTSD. Thus, combining conventional and technology-assisted rehabilitation-guided by patient-centered strategies-can enhance recovery in moderate-to-severe TBI. Future research should focus on long-term effectiveness, cost-efficiency, and scalable personalized care models.}, }
@article {pmid40584436, year = {2025}, author = {Zamani, S and Sadeghi, J and Kamalabadi-Farahani, M and Aghayan, SN and Arabpour, Z and Djalilian, AR and Salehi, M}, title = {Comparison of cellular, mechanical, and optical properties of different polymers for corneal tissue engineering.}, journal = {Iranian journal of basic medical sciences}, volume = {28}, number = {8}, pages = {1082-1099}, pmid = {40584436}, issn = {2008-3866}, abstract = {OBJECTIVES: The invention of corneal tissue engineering is essential for vision due to the lack of effective treatments and donated corneas. Finding the right polymer is crucial for reducing inflammation, ensuring biocompatibility, and mimicking natural cornea properties.<br><br>MATERIALS AND METHODS: In this study, solvent casting and physical crosslinking (freeze-thaw cycles) were used to fabricate polymeric scaffolds of Polyvinyl alcohol, alginate, gelatin, carboxymethyl chitosan, carboxymethyl cellulose, polyacrylic acid, polyvinyl pyrrolidone, and their combinations. The mechanical evaluation of scaffolds for tension and suture ability was conducted. Biodegradability, swelling, water vapor, bacterial permeability, anti-inflammatory properties, blood compatibility, Blood Clotting Index (BCI), pH alterations, and cell compatibility with human Mesenchymal Stem cells (MSCs) were investigated with MTT. The hydrophilicity of the samples and the ability to adhere to surfaces were also compared with the contact angle and adhesive test, respectively. Finally, quantitative and qualitative analysis was used to check the transparency of the samples.<br><br>RESULTS: The mechanical strength of polyvinyl alcohol and polyvinyl pyrrolidone samples was highest, showing good suture ability. All samples had blood compatibility below 5% and cell compatibility above 75%. Polyvinyl alcohol was the most transparent at around 93%. Carboxymethyl chitosan effectively inhibited bacterial permeability, while its anti-inflammatory potential showed no significant difference.<br><br>CONCLUSION: This study aims to choose the best polymer composition for corneal tissue engineering. The selection depends on the study's goals, like mechanical strength or transparency. Comparing polymers across different dimensions provides better insight for polymer selection.}, }
@article {pmid40584269, year = {2025}, author = {Ji, D and Yu, H and Xiao, X and Huang, Y and Zhou, X and Xu, M and Jung, TP and Ming, D}, title = {A user-friendly BCI encoding by high frequency single-frequency-SDMA SSaVEF using MEG.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {101}, pmid = {40584269}, issn = {1871-4080}, abstract = {Magnetoencephalography (MEG) delivers high spatial resolution and superior detection performance for high-frequency signals compared to Electroencephalography (EEG). Therefore, researchers can leverage MEG for high-frequency steady-state asymmetric visual evoked potential (SSaVEP). Current SSaVEP encoding typically uses low-frequency stimulation with relatively large stimulus areas, hindering the applicability of this encoding method in user-friendly brain-computer interface (BCI) systems. This study introduces an ultra critical flicker frequency (ultra-CFF) single-frequency-SDMA steady-state asymmetric visual evoked field (SSaVEF) encoding powered by MEG and presents an eight-command SSaVEF-BCI system. The BCI system features a 60 Hz SSVEF visual stimulus landmark and eight visual targets spaced 45° apart. Ten participants took part in the offline experiments, during which data from 41 channels in the occipital region were collected. This study analyzed the spatiotemporal characteristics, frequency-space characteristics, signal-to-noise ratio, and other features of the SSaVEF signals. We also evaluated the system's performance using the multi-DCPM algorithm. Using the multi-DCPM algorithm, the system achieved an impressive average classification accuracy of 81.65% with 4-s length data. With a data length of 1 s, the system achieved an average Information Transfer Rate (ITR) of 32.05 bits/min, with the highest individual ITR reached an astonishing 64.45 bits/min. This study represents the exploration of a high-frequency spatial encoding SSVEF-BCI system based on MEG. The results demonstrate MEG's feasibility and potential of applying MEG in such BCI systems, providing both theoretical and practical value for the further development and implementation of future BCI systems.}, }
@article {pmid40584164, year = {2025}, author = {Sharma, MK and Chaudhary, S and Shenoy, S}, title = {Development and testing of range of motion driven motor unit recruitment device for knee rehabilitation: A randomized controlled trial.}, journal = {MethodsX}, volume = {14}, number = {}, pages = {103382}, pmid = {40584164}, issn = {2215-0161}, abstract = {Existing research on neuromuscular electrical stimulation (NMES) identifies two primary control approaches: therapist-operated systems and participant-controlled systems. Therapist-operated NMES devices typically employ switches and potentiometers for control, whereas participant-controlled systems offer diverse input methods, including switches, buttons, joysticks, electromyography electrodes, voice-activated commands, and sip-and-puff devices. A critical limitation of current NMES technology lies in its failure to mimic the body's natural muscle recruitment process during electrical stimulation, resulting in premature fatigue and diminished user engagement. A particularly significant drawback is the absence of joint range-of-motion dependency observed during voluntary movements and active involvement of participant. This limitation prevents precise control over spatial and temporal parameters, such as modulating motor unit recruitment relative to joint position, during neuromuscular rehabilitation. Furthermore, existing devices cannot accurately reproduce the co-contraction dynamics and reciprocal activation patterns seen in synergistic, agonist, and antagonist muscle groups during natural movement. Addressing these challenges requires developing innovative NMES technology capable of activating the neuromuscular system while replicating natural voluntary recruitment patterns. Such advancements would not only improve muscle strengthening outcomes but also enhance participant adherence through more effective cortical and peripheral neuromuscular engagement.•Development of neuromuscular electrical stimulation (NMES) device to replicate natural neuromuscular activation patterns through bio-inspired stimulation protocols.•Engineered to mitigate existing limitations of conventional NMES systems, optimizing therapeutic applications for neuromuscular re-education and functional recovery.•Integrates muscle synergy principles, enabling synchronized synergistic, agonist and antagonist activation for enhanced cortical and peripheral neuromuscular engagement and optimize functional rehabilitation outcomes.•Advances rehabilitation strategies by combining dual focus on muscular reconditioning and neural adaptation for holistic recovery.•Demonstrates potential to amplify strength gains while fostering neuroplasticity, supporting long-term functional recovery in neuromuscular rehabilitation.}, }
@article {pmid40581689, year = {2025}, author = {Olza, A and Soto, D and Santana, R}, title = {Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.}, journal = {Brain informatics}, volume = {12}, number = {1}, pages = {17}, pmid = {40581689}, issn = {2198-4018}, support = {IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; KK-2023/00090//Elkartek/ ; KK-2023/00090//Elkartek/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; CEX2020-001010-S//Severo Ochoa programme/ ; CEX2020-001010-S//Severo Ochoa programme/ ; }, abstract = {In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.}, }
@article {pmid40581220, year = {2025}, author = {Alsamri, J and Alamgeer, M and Alamri, MZ and Ghaleb, M and Asklany, SA and Almansour, H and Alsafari, S and Alghamdi, EA}, title = {Longitudinal EEG-based assessment of neuroplasticity and adaptive responses to transcranial focused ultrasound stimulation.}, journal = {Journal of neuroscience methods}, volume = {422}, number = {}, pages = {110521}, doi = {10.1016/j.jneumeth.2025.110521}, pmid = {40581220}, issn = {1872-678X}, mesh = {Humans ; *Electroencephalography/methods ; *Neuronal Plasticity/physiology ; Male ; Adult ; Female ; Longitudinal Studies ; *Brain/physiology ; Neural Networks, Computer ; Young Adult ; *Adaptation, Physiological/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: An emerging non-invasive neuromodulation technique named Transcranial-focused ultrasound stimulation (tFUS) offered several advantages than the conventional methods in terms of high spatial precision and penetration depth. In neurological disorders, this emerging method have gained a lot of attention, because of has the potential for therapeutic modulation of brain activity. Then, lack of standardized, Real-Time (RT) assessment protocols will result in unclear comprehension regarding the way the repeated tFUS applications may impacts the neuroplasticity and adaptive brain responses in a long-term. Here, the short-term and long-term neuroplastic modifications were effectively identified by the the longitudinal integration of EEG biomarkers with tFUS stimulation sessions. An adaptive modulation strategies customized for individual neural responses are also facilitated by this hypothesis.<br><br>NEW METHODS: To integrate the tFUS with high-resolution electroencephalogram (EEG) monitoring in many sessions, Integrated Longitudinal Evaluation Protocol (ILEP) model was suggested in this study. To extract amplitude, latency, spectral dynamics, and connectivity features from evoked potentials, pre-, during-, and post-stimulation EEG signals were identified by the protocol. Then, for monitoring neuroadaptive trajectories over time, the intrgration of the statistical modeling and neural network (NN)-based pattern recognition was employed, and it will assist in analysing those features. For the purpose of differentiating the short-term oscillatory effects from long-term neuroplastic shifts, the following ways will helps in processing the EEG signals: time-frequency decomposition, event-related potential (ERP) analysis, and machine learning (ML) classifiers. Here, the subject-specific response patterns and temporal evolution of brain dynamics were effectively detected by the application of the Deep learning (DL) models.<br><br>RESULTS ANALYSIS: After the tFUS, both the short-term and long-term modifications in brain activity were effectively detected by the application of ILEP, and it was demonstrated by the outcomes of the simulation and empirical data. Here, the location-specific, session-dependent EEG modifications are consistent with the adaptive neuroplastic processes, and it was revealed by the outcomes of the simulation. Then, accurate neuroadaptive signals were separated from noise and temporary conditions, and it was facilitated by the potential of the model.<br><br>A dynamic, session-over-session monitoring of brain responses was facilitated by the ILEP model. But static images was offered by those conventional methods. With an integration of closed-loop feedback and advanced neural modelling, the suggested model executes better than the conventional methods. This suggested model also facilitates in offering a customized neuromodulation therapies.<br><br>CONCLUSION: For monitoring the neuroplastic modifications induced by tFUS,this suggested ILEP model becomes an effective, sacalable. So, this suggested model facilitates an adaptive assessment model for that tracking, and it was demonstrated in this study. The future, RT, closed-loop neuromodulation systems in therapeutic and cognitive enhancement contexts may get benefits from the integration of EEG feedback mechanisms in the suggested model.}, }
@article {pmid40579488, year = {2025}, author = {Ibáñez, J and Zicher, B and Burdet, E and Baker, SN and Mehring, C and Farina, D}, title = {Peripheral neural interfaces for reading high-frequency brain signals.}, journal = {Nature biomedical engineering}, volume = {9}, number = {9}, pages = {1391-1402}, pmid = {40579488}, issn = {2157-846X}, support = {EP/T020970/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/ ; V00896X//RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)/ ; BB/V00896X/1//RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)/ ; 899626//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Euratom (H2020 Euratom Research and Training Programme 2014-2018)/ ; 810346//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)/ ; 101077693//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)/ ; }, mesh = {Humans ; *Motor Neurons/physiology ; *Brain/physiology ; *Brain-Computer Interfaces ; Animals ; Deep Learning ; Muscle, Skeletal/physiology ; }, abstract = {Accurate and robust recording and decoding from the central nervous system (CNS) is essential for advances in human-machine interfacing. Technologies for direct measurements of CNS activity are limited by their resolution, sensitivity to interference and invasiveness. Motor neurons (MNs) represent the motor output layer of the CNS, receiving and sampling signals from different regions in the nervous system and generating the neural commands that control muscles. Muscle recordings and deep learning decode the spiking activity of spinal MNs in real time and with high accuracy. The input signals to MNs can be estimated from MN outputs. Here we argue that peripheral neural interfaces using muscle sensors represent a promising, non-invasive approach to estimate some of the neural activity from the CNS that reaches the MNs but does not directly modulate force production. We discuss the evidence supporting this concept and the advances needed to consolidate and test MN-based CNS interfaces in controlled and real-world settings.}, }
@article {pmid40579374, year = {2025}, author = {Yang, C and Zhang, L and Liu, J and Li, K and Li, S and Yang, Z and Bishop, JR and Deng, W and Yao, L and Lui, S and Gong, Q}, title = {More Severe Brain Network Hierarchy Disorganization in Treatment-Naive Deficit Compared to Non-deficit Schizophrenia and Underlying Neurotransmitter Associations.}, journal = {Schizophrenia bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1093/schbul/sbae231}, pmid = {40579374}, issn = {1745-1701}, support = {82102007//National Natural Science Foundation of China/ ; 82120108014//National Natural Science Foundation of China/ ; 82071908//National Natural Science Foundation of China/ ; 82202110//National Natural Science Foundation of China/ ; 2022YFC2009901//National Key Research and Development Program of China/ ; 2022YFC2009900//National Key Research and Development Program of China/ ; 2021JDTD0002//Sichuan Science and Technology Program/ ; 2022-YF09-00062-SN//Chengdu Science and Technology Office, major technology application demonstration project/ ; 2022-GH03-00017-HZ//Chengdu Science and Technology Office, major technology application demonstration project/ ; ZYGD23003//West China Hospital, Sichuan University/ ; ZYAI24010//West China Hospital, Sichuan University/ ; ZYGX2022YGRH008//Fundamental Research Funds for the Central Universities/ ; GZB20240493//Postdoctoral Fellowship Program of CPSF/ ; T2019069//Humboldt Foundation Friedrich Wilhelm Bessel Research Award and Chang Jiang Scholars/ ; }, abstract = {BACKGROUND AND HYPOTHESIS: Deficit schizophrenia (DS) represents a distinct entity characterized by primary and enduring negative symptoms, yet the neurobiological differences between DS and non-DS (NDS) remain undetermined. Using a gradient-based approach, we hypothesize that DS and NDS will exhibit convergent and divergent brain functional hierarchy patterns, each with a specific underlying neurotransmitter architecture.<br><br>STUDY DESIGN: Resting-state functional magnetic resonance imaging images were acquired from 44 treatment-naive DS, 55 treatment-naive NDS, and 60 matched healthy controls (HCs). Gradient metrics were calculated using the BrainSpace toolbox. The spatial correlation between gradient abnormalities in DS or NDS and density maps of 10 neurotransmitters derived by the JuSpace toolbox was analyzed to link the neuroimaging to underlying neurotransmitter information.<br><br>STUDY RESULTS: Both DS and NDS exhibited compressed gradient patterns compared to HC, suggesting reduced network differentiation, with more severe disorganization in DS. The ventral attention network was associated with depression symptoms in DS, whereas the visual network was related to total, general, and paranoid symptom scores in NDS. Moreover, spatial correlation of neurotransmitter analysis revealed that the gradient alterations of DS were primarily related to the serotonergic system while those of NDS were predominantly associated with the dopamine system.<br><br>CONCLUSIONS: The study suggests that independent from the potential effects of antipsychotic medication, DS and NDS are characterized by different neuropathology in brain hierarchy patterns, potentially linked to neurochemical metabolic distinction. Our findings support the hypothesis that DS is a distinct subtype versus NDS from neurodevelopmental perspective.}, }
@article {pmid40578761, year = {2025}, author = {Brands, R and Fuchs, L and Seyffer, JM and Bajcinca, N and Bartsch, J and Peuker, UA and Schmidt, V and Thommes, M}, title = {Penetration depth and effective sample size characterization of UV/Vis radiation into pharmaceutical tablets.}, journal = {Journal of pharmaceutical sciences}, volume = {}, number = {}, pages = {103889}, doi = {10.1016/j.xphs.2025.103889}, pmid = {40578761}, issn = {1520-6017}, abstract = {The pharmaceutical industry is moving from off-line to real-time release testing (RTRT) to enhance quality while reducing costs. UV/Vis spectroscopy has emerged as a promising tool for RTRT given its simplicity, sensitivity and cost-effectiveness. Nevertheless, the effective sample size must be characterized in relation to the penetration depth to justify its representativeness and suitability for RTRT. In this study, bilayer tablets were produced using a hydraulic tablet press. The lower layer contained titanium dioxide and microcrystalline cellulose (MCC), while the upper layer consisted of MCC, lactose or a combination with theophylline. The thickness of the upper layer was stepwise increased. Spectra from 224 to 820 nm were recorded with an orthogonally aligned UV/Vis probe. Thereby, the experimental penetration depth reached up to 0.4 mm, while the Kubelka-Munk model yielded a theoretical maximum penetration depth of 1.38 mm. Based on these values, the effective sample sizes were determined. Considering a parabolic penetration profile, the maximum volume was 2.01 mm[3]. The results indicated a wavelength and particle size dependency. Micro-CT analysis confirmed the even distribution of the API in the tablets proving the sufficiency of the UV/Vis sample size. Consequently, UV/Vis spectroscopy is a reliable alternative for RTRT in tableting.}, }
@article {pmid40578508, year = {2025}, author = {Metin, S and Altan, H and Tercan, E and Dedeoglu, BG and Gurdal, H}, title = {DUSP1 protein's impact on breast cancer: Anticancer response and sensitivity to cisplatin.}, journal = {Biochimica et biophysica acta. Gene regulatory mechanisms}, volume = {1868}, number = {3}, pages = {195103}, doi = {10.1016/j.bbagrm.2025.195103}, pmid = {40578508}, issn = {1876-4320}, mesh = {*Cisplatin/pharmacology ; Humans ; *Dual Specificity Phosphatase 1/genetics/metabolism/antagonists &amp; inhibitors ; Female ; Cell Line, Tumor ; Cell Proliferation/drug effects ; *Triple Negative Breast Neoplasms/drug therapy/genetics/pathology/metabolism ; *Antineoplastic Agents/pharmacology ; Cell Movement/drug effects ; Animals ; Gene Expression Regulation, Neoplastic/drug effects ; MAP Kinase Signaling System/drug effects ; Drug Resistance, Neoplasm/genetics ; Phosphorylation/drug effects ; p38 Mitogen-Activated Protein Kinases/metabolism ; Mice ; }, abstract = {Dual-Specificity Phosphatase 1 (DUSP1) modulates the activity of members of the Mitogen-Activated Protein Kinase (MAPK) family, including p38, JNK, and ERK1/2, which affects various cellular functions in cancer. Moreover, DUSP1 is known to influence the outcomes of cancer chemotherapy. This study aimed to reduce DUSP1 protein expression using CRISPR/Cas9 and siRNA and assess its effects on cell proliferation, migration, and tumor growth potential in triple-negative breast cancer (TNBC) cells. We examined the expression levels of p38, JNK, and ERK1/2, along with their phosphorylated forms, and investigated DUSP1's influence to cisplatin sensitivity. Our findings revealed that the downregulation of DUSP1 expression inhibited the proliferation, migration, and tumor growth potential of TNBC cells. Additionally, BCI, an inhibitor of DUSP1/6, demonstrated anti-proliferative effects on these cells. Decreasing the expression of DUSP1 increased the phosphorylation ratio of p38 and JNK, but not ERK1/2. Moreover, the anticancer response induced by cisplatin was enhanced by reducing DUSP1 expression or by treating the cells with BCI. Notably, cisplatin treatment increased p38 phosphorylation, which was significantly augmented by reduced DUSP1 expression. We also demonstrated that the DUSP1 inhibition-induced anticancer response in these cells predominantly relied on p38 activity. These findings contribute to a better understanding of the role of DUSP1 in breast cancer and offer insights into potential therapeutic strategies targeting DUSP1 to enhance the efficacy of cisplatin treatment. Our study highlights that decreased DUSP1 protein expression and activity mediates an anticancer response and increases the sensitivity of MDA-MB231 cells to cisplatin by regulating p38.}, }
@article {pmid40578406, year = {2025}, author = {Cao, Y and Chen, Z and Yin, Y and Kang, X and Zhang, Y and Xu, Z and Yang, X and Yang, B and He, Q and Yan, H and Luo, P}, title = {Autophagy-dependent hepatocyte apoptosis mediates gilteritinib-induced hepatotoxicity.}, journal = {Toxicology letters}, volume = {410}, number = {}, pages = {189-196}, doi = {10.1016/j.toxlet.2025.06.018}, pmid = {40578406}, issn = {1879-3169}, mesh = {Animals ; *Autophagy/drug effects ; *Apoptosis/drug effects ; *Hepatocytes/drug effects/pathology/metabolism ; Humans ; *Chemical and Drug Induced Liver Injury/pathology/etiology/metabolism/genetics ; Mice, Inbred C57BL ; *Pyrazines/toxicity ; Autophagy-Related Protein 7/genetics/metabolism ; *Aniline Compounds/toxicity ; Mice ; Male ; Mice, Knockout ; *Protein Kinase Inhibitors/toxicity ; }, abstract = {Gilteritinib, a dual FLT3/AXL inhibitor, is clinically effective for relapsed/refractory FLT3-mutated acute myeloid leukemia (AML) but is limited by severe hepatotoxicity. This study investigates the molecular mechanisms underlying gilteritinib-induced liver injury, focusing on the interplay between autophagy and apoptosis. In vitro and in vivo models, including human hepatocyte HL-7702 cells and C57BL/6 J mice, were employed. Gilteritinib treatment significantly upregulated autophagy markers (LC3-II) and induced autophagosome formation, as confirmed by western blot, TEM, and mCherry-GFP-LC3 reporter assays. Concurrently, apoptosis markers (cleaved-PARP, cleaved-Caspase3, Annexin V/PI staining) increased dose- and time-dependently. Pharmacological inhibition of autophagy with autophagy inhibitor 3-methyladenine (3-MA, 5 mM) or gene silence of Atg7 attenuated apoptosis, mitochondrial membrane potential loss, and ROS overproduction, while autophagy induction by Torin1 (100 nM) exacerbated hepatocyte death. In vivo, gilteritinib-treated mice exhibited elevated serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) levels, alongside histopathological damage, all of which were mitigated in Atg7-deficient mice. These findings demonstrate that gilteritinib triggers excessive autophagy, which drives hepatocyte apoptosis and liver injury. Targeting autophagy pathways, represents a potential therapeutic strategy to alleviate gilteritinib-induced hepatotoxicity, enabling safer clinical use of this vital AML therapy. This study elucidates a critical autophagy-apoptosis axis in drug-induced liver injury and provides actionable insights for managing adverse effects of targeted cancer therapies.}, }
@article {pmid40578388, year = {2025}, author = {Lin, Z and Jiang, X and Dai, C and Jia, F}, title = {Towards real time efficient and robust ECoG decoding for mobile brain-computer interface.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ade917}, pmid = {40578388}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electrocorticography/methods ; Algorithms ; Male ; Adult ; Female ; Computer Systems ; *Brain/physiology ; }, abstract = {Objective. Decoding locomotion-related brain activities from electrocorticographic (ECoG) signals is essential in brain-computer interfaces (BCIs). Most previous ECoG decoders are computationally demanding and sensitive to noises/outliers. Mobile and robust BCIs are particularly important for physically disabled patients to restore motor ability in outdoor scenarios, where the processing pipeline should be implemented efficiently using constrained computation resources. In this work, we aim to explore the optimal pipeline for mobile BCI decoding.Approach. We comprehensively evaluated the trade-off between the decoding precision, computational efficiency and robustness of diverse decoding algorithms on a combined ECoG dataset of 12 subjects conducting individual finger movement, including partial-least-square and their N-way variants, Bayesian ridge regression, least absolute shrinkage and selection operator, support vector regression, neural networks (NNs) with diverse architectures, and random forest (RF). We further explored the feature optimization technique for selected models by using their inherent model explainability. We also compared the decoding performance of updatable algorithms when the data is split into multiple batches and used sequentially.Main results. The RF model, not valued by previous studies, can achieve the best trade-off between precision and efficiency, achieving an average Pearson's correlation coefficient (r) of 0.466 with only 0.5 K floating-point operations per second (FLOPs) per inference and a model size of 900KiB. We also demonstrate the inherent superior robustness of RF model on corrupted ECoG electrodes, with a>2×decoding precision on noisy signals compared with all state-of-the-art deep NNs. The optimized RF processing pipeline was deployed on a STM32-based embedded platform with only a 15.2 ms computation delay.Significance. In this study, we systematically explored the performance and efficiency of ECoG decoding algorithms in finger movement analysis. The proposed decoding pipeline is implemented on a compact embedded platform to achieve low-latency, power-efficient real-time decoding. This research accelerates the translation of mobile BCI into real-life practices.}, }
@article {pmid40578216, year = {2025}, author = {Zhang, H and Wang, H and An, J and Zheng, S and Wu, D}, title = {A lightweight spiking neural network for EEG-based motor imagery classification.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {191}, number = {}, pages = {107741}, doi = {10.1016/j.neunet.2025.107741}, pmid = {40578216}, issn = {1879-2782}, mesh = {Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Brain/physiology ; *Action Potentials/physiology ; }, abstract = {Spiking neural networks (SNNs) aim to simulate the human brain neural network, using sparse spike event streams for effective and energy-efficient spatio-temporal signal processing. This paper proposes a lightweight SNN model for electroencephalogram (EEG) based motor imagery (MI) classification, a classical brain-computer interface paradigm. The model has three desirable characteristics: (1) it has a brain-inspired architecture; (2) it is energy efficient; and, (3) it is dataset agnostic. Within-subject and cross-subject experiments on three public datasets demonstrated the superiority of our SNN model over four classical convolutional neural network based models in EEG based MI classification.}, }
@article {pmid40576544, year = {2025}, author = {Wu, Y and Bao, K and Liang, J and Li, Z and Shi, Y and Tang, R and Xu, K and Wei, M and Chen, Z and Jian, J and Luo, Y and Tang, Y and Deng, Q and Dai, H and Sun, C and Zhang, W and Lin, H and Zhang, K and Li, L}, title = {Photonic Interfaces: an Innovative Wearable Sensing Solution for Continuous Monitoring of Human Motion and Physiological Signals.}, journal = {Small methods}, volume = {}, number = {}, pages = {e2500727}, doi = {10.1002/smtd.202500727}, pmid = {40576544}, issn = {2366-9608}, support = {10300000H062401/001//Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China/ ; 2024SDXHDX0005//"Pioneer" and "Leading Goose" Key Research and Development Program of Zhejiang Province/ ; 12104375//National Natural Science Foundation of China/ ; 62175202//National Natural Science Foundation of China/ ; 2024C03150//Key Research and Development Program of Zhejiang Province/ ; 2023GD003/110500Y0022303//Key Project of Westlake Institute for Optoelectronics/ ; }, abstract = {Flexible integrated photonic sensors are gaining prominence in intelligent wearable sensing due to their compact size, exceptional sensitivity, rapid response, robust immunity to electromagnetic interference, and the capability to enable parallel sensing through optical multiplexing. However, integrating these sensors for practical applications, such as monitoring human motions and physiological activities together, remains a significant challenge. Herein, it is presented an innovative fully packaged integrated photonic wearable sensor, which features a delicately designed flexible necklace-shaped microring resonator (MRR), along with a pair of grating couplers (GCs) coupled to a fiber array (FA). The necklace-shaped MRR is engineered to minimize waveguide sidewall-induced scattering loss, with a measured intrinsic quality factor (Qint) of 1.68 × 10[5], ensuring highly sensitive and precise signal monitoring. GCs and FA enhance the seamless wearability of devices while maintaining superior sensitivity to monitor various human motions and physiological signs. These are further classified signals using machine learning algorithms, achieving an accuracy rate of 97%. This integrated photonic wearable sensor shows promise for human-machine interfaces, touch-responsive wearable monitors, and artificial skin, especially in environments susceptible to electromagnetic interference, such as intensive care units (ICUs) and spacecraft. This work significantly advances the field of smart wearable technology.}, }
@article {pmid40575493, year = {2025}, author = {Mehta, D}, title = {Brain-Computer Interface tool use and the Contemplation Conundrum: a blueprint of mental action, agency, and control.}, journal = {Oxford open neuroscience}, volume = {4}, number = {}, pages = {kvaf002}, pmid = {40575493}, issn = {2753-149X}, abstract = {This paper approaches the role of intentional action in brain-computer interface (BCI) tool use to allow for an ethical discourse regarding the development and usage of neurotechnology. The exploration of mental actions and user control in BCI tool use brings us closer to understanding the philosophical underpinnings of intentions and agency for BCI-mediated actions. The author presents that under some theories of intentional action, certain BCI-mediated overt movements qualify as both voluntary and unintentional. This plausibly magnifies the ethical considerations surrounding BCI tool use. This problem is referred by the author as the contemplation conundrum. Thus, the paper proposes research scope for the neural correlates of intention formation and the neural correlates of imagination aimed at clarifying implementational control and safeguarding privacy of thought in BCI tool use.}, }
@article {pmid40574626, year = {2025}, author = {Kaszás, A and Meszéna, D and Fiáth, R and Slézia, A and Ulbert, I and Katona, G}, title = {Capturing the Electrical Activity of all Cortical Neurons: Are Solutions Within Reach?.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {12}, number = {32}, pages = {e06225}, pmid = {40574626}, issn = {2198-3844}, support = {TKP2021-EGA-42//Thematic Programme of Excellence/ ; NAP2022-I-2/2022//Hungarian Brain Research Program/ ; RRF-2.3.1-21-2022-00015//Pharmaceutical Research and Development Laboratory Project/ ; HUN-REN-HAZAHIVO-2023//Hungarian Research Network/ ; KSZF-174/2023//Hungarian Research Network/ ; 2019-2.1.7-ERA-NET-2021-00023//ERA-NET/ ; //Bolyai J&#xe1;nos Scholarship of the Hungarian Academy of Sciences/ ; 150574//National Research, Development and Innovation Office/ ; PD143582//National Research, Development and Innovation Office/ ; }, mesh = {*Neurons/physiology ; Humans ; *Cerebral Cortex/physiology ; Animals ; *Electrodes, Implanted ; }, abstract = {Recent advancements in high-density implantable intracortical electrode technology have significantly improved neural interfaces for both research and clinical applications. However, a significant challenge persists: scaling up these devices to achieve recording of nearly all single-unit activity across large brain volumes. This critical review explores recent progress in neural electrode design, focusing on the challenges of achieving scalable solutions for this ambitious goal. The physical and technical constraints of both rigid and flexible probes are addressed, highlighting the limitations imposed by shank stiffness, mechanical tissue damage, and foreign body response. It is identified that the physics of inserting the electrodes into the brain tissue poses a fundamental constraint, which inherently restricts achievable electrode density. Biohybrid strategies, integrating biological and synthetic components, have shown promise, but they have yet to overcome the major challenges necessary to achieve a scalable functional interface. It is concluded that, given the current limitations of available techniques, there is a pressing need to explore fundamentally novel approaches to realize the vision of recording the electrical activity of every cortical neuron within the brain.}, }
@article {pmid40573719, year = {2025}, author = {Safarov, F and Kutlimuratov, A and Khojamuratova, U and Abdusalomov, A and Cho, YI}, title = {Enhanced AlexNet with Gabor and Local Binary Pattern Features for Improved Facial Emotion Recognition.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {12}, pages = {}, pmid = {40573719}, issn = {1424-8220}, support = {20022362//Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy in 2024/ ; 2410003714//Establishment of standardization basis for BCI and AI Interoperability/ ; }, mesh = {Humans ; *Emotions/physiology ; *Facial Recognition/physiology ; *Facial Expression ; Algorithms ; Deep Learning ; *Pattern Recognition, Automated/methods ; Face/physiology ; *Automated Facial Recognition/methods ; Neural Networks, Computer ; Convolutional Neural Networks ; }, abstract = {Facial emotion recognition (FER) is vital for improving human-machine interactions, serving as the foundation for AI systems that integrate cognitive and emotional intelligence. This helps bridge the gap between mechanical processes and human emotions, enhancing machine engagement with humans. Considering the constraints of low hardware specifications often encountered in real-world applications, this study leverages recent advances in deep learning to propose an enhanced model for FER. The model effectively utilizes texture information from faces through Gabor and Local Binary Pattern (LBP) feature extraction techniques. By integrating these features into a specially modified AlexNet architecture, our approach not only classifies facial emotions more accurately but also demonstrates significant improvements in performance and adaptability under various operational conditions. To validate the effectiveness of our proposed model, we conducted evaluations using the FER2013 and RAF-DB benchmark datasets, where it achieved impressive accuracies of 98.10% and 93.34% for the two datasets, with standard deviations of 1.63% and 3.62%, respectively. On the FER-2013 dataset, the model attained a precision of 98.2%, a recall of 97.9%, and an F1-score of 98.0%. Meanwhile, for the other dataset, it achieved a precision of 93.54%, a recall of 93.12%, and an F1-score of 93.34%. These results underscore the model's robustness and its capability to deliver high-precision emotion recognition, making it an ideal solution for deployment in environments where hardware limitations are a critical concern.}, }
@article {pmid40573479, year = {2025}, author = {Sasatake, Y and Matsushita, K}, title = {P300 ERP System Utilizing Wireless Visual Stimulus Presentation Devices.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {12}, pages = {}, pmid = {40573479}, issn = {1424-8220}, support = {JPMJSP2125//JST SPRING/ ; none//THERS/ ; }, mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Wireless Technology/instrumentation ; Brain-Computer Interfaces ; Male ; Adult ; *Photic Stimulation/methods ; Electroencephalography/methods ; Female ; Young Adult ; }, abstract = {The P300 event-related potential, evoked by attending to specific sensory stimuli, is utilized in non-invasive brain-computer interface (BCI) systems and is considered the only interface through which individuals with complete paralysis can operate devices based on their intention. Conventionally, visual stimuli used to elicit P300 have been presented using displays; however, placing a display directly in front of the user obstructs the field of view and prevents the user from perceiving their surrounding environment. Moreover, every time the user changes posture, the display must be repositioned accordingly, increasing the burden on caregivers. To address these issues, we propose a novel system that employs wirelessly controllable LED visual stimulus presentation devices distributed throughout the surrounding environment, rather than relying on traditional displays. The primary challenge in the proposed system is the communication delay associated with wireless control, which introduces errors in the timing of stimulus presentation-an essential factor for accurate P300 analysis. Therefore, it is necessary to evaluate how such delays affect P300 detection accuracy. The second challenge lies in the variability of visual stimulus strength due to differences in viewing distance caused by the spatial distribution of stimulus devices. This also requires the validation of its impact on P300 detection. In Experiment 1, we evaluated system performance in terms of wireless communication delay and confirmed an average delay of 352.1 ± 30.9 ms. In Experiment 2, we conducted P300 elicitation experiments using the wireless visual stimulus presentation device under conditions that allowed the precise measurement of stimulus presentation timing. We compared P300 waveforms across three conditions: (1) using the exact measured stimulus timing, (2) using the stimulus timing with a fixed compensation of 350 ms for the wireless delay, and (3) using the stimulus timing with both the 350 ms fixed delay compensation and an additional pseudo-random error value generated based on a normal distribution. The results demonstrated the effectiveness of the proposed delay compensation method in preserving P300 waveform integrity. In Experiment 3, a system performance verification test was conducted on 21 participants using a wireless visual presentation device. As a result, statistically significant differences (p < 0.01) in amplitude between target and non-target stimuli, along with medium or greater effect sizes (Cohen's d: 0.49-0.61), were observed under all conditions with an averaging count of 10 or more. Notably, the P300 detection accuracy reached 85% with 40 averaging trials and 100% with 100 trials. These findings demonstrate that the system can function as a P300 speller and be utilized as an interface equivalent to conventional display-based methods.}, }
@article {pmid40571414, year = {2025}, author = {Yang, L and Li, M and Yang, L and Wang, Z and Shang, Z}, title = {Hippocampal LFP Responses during Pigeon Homing Flight in Outdoors.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {45}, number = {30}, pages = {}, pmid = {40571414}, issn = {1529-2401}, mesh = {Animals ; *Columbidae/physiology ; *Hippocampus/physiology ; Male ; *Homing Behavior/physiology ; *Flight, Animal/physiology ; Female ; *Spatial Navigation/physiology ; Theta Rhythm/physiology ; }, abstract = {The hippocampal formation (HF) plays a key role in avian spatial navigation. Previous studies suggest that the HF may serve different functions at various stages in pigeons' long-distance outdoor homing flight. However, it remains unclear whether the HF exhibits specific neural responses during these stages. In this study, we employed a wearable bimodal data recording system to simultaneously capture flight trajectories and hippocampal local field potential (LFP) signals of pigeons (either sex) during outdoor homing navigation. Our results revealed significant differences in hippocampal neural responses across the initial decision-making (DM) and en route navigation (ER) stages. Specifically, elevated LFP power in theta (4-12 Hz) and beta (12-30 Hz) bands was detected during the DM stage compared with the ER stage, while the high-gamma (60-120 Hz) band exhibited the opposite pattern. In addition, we examined typical theta-beta phase-amplitude coupling during the ER stage. Additionally, stage-specific hippocampal responses remained consistent across release sites. Notably, the difference in hippocampal responses across stages diminished along with the accumulation of homing experience. These results offer new insights into the role of the avian HF in homing flight navigation and suggest parallels between avian and mammalian hippocampal mechanisms in spatial learning.}, }
@article {pmid40567082, year = {2025}, author = {Lachkar, S and Ibrahimi, A and Boualaoui, I and Sayegh, HE and Nouini, Y}, title = {Botulinum toxin A in idiopathic overactive bladder: a narrative review of 5410 cases.}, journal = {The Canadian journal of urology}, volume = {32}, number = {3}, pages = {145-165}, pmid = {40567082}, issn = {1488-5581}, mesh = {Humans ; *Urinary Bladder, Overactive/drug therapy ; *Botulinum Toxins, Type A/therapeutic use/adverse effects ; *Neuromuscular Agents/therapeutic use/adverse effects ; Treatment Outcome ; }, abstract = {INTRODUCTION: When conservative treatments fail, botulinum toxin A (BoNT-A) is an option for refractory idiopathic overactive bladder (OAB). This review evaluates the efficacy, safety, and predictive factors for BoNT-A in this situation.<br><br>MATERIALS AND METHODS: A literature search up to January 2025 was performed using PubMed, Google Scholar, and Embase to assess efficacy, safety, and predictors of adverse events (AE) related to BoNT-A. The risk of bias was assessed using the Risk of Bias 2 (RoB 2) tool for randomized studies and the Critical Appraisal Skills Programme (CASP) checklist for cohort studies. The quality of the review was evaluated based on the Oxford criteria, following the Strengthening the Assessment of Narrative Review Articles (SANRA) guidelines, and by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews.<br><br>RESULTS: 31 studies were included, involving 5410 patients. BoNT-A improves OAB symptoms even after reinjections. Higher doses do not enhance efficacy but increase AE. AE includes high post-void residual (PVR), clean intermittent self-catheterization (CISC), and Urinary Tract Infection (UTI). Predictors of CISC include age, male gender, hysterectomy, &#x2265;3 vaginal deliveries, mixed incontinence, prior mid-urethral sling (MUS), high PVR, low Pressure at Pdet at First Micturition (PIP1) in women, low Bladder Compliance Index (BCI) in men, and high Bladder Outlet Obstruction Index (BOOI). Diabetes and heart failure increase PVR. UTIs are more frequent in women and men with benign prostatic hyperplasia, with CISC increasing the risk fivefold. Severe complications are rare. Predictors of poor response include male gender, high BOOI, low urinary flow, and diabetes.<br><br>DISCUSSION: BoNT-A is effective for OAB, especially for incontinence. AE is dose-dependent and limits treatment adherence. Their link with poor response remains unclear.<br><br>CONCLUSION: BoNT-A effectively treats refractory idiopathic OAB, improving symptoms and quality of life with repeated injections.}, }
@article {pmid40566931, year = {2025}, author = {Lin, K and Chen, J and Pan, J and Wang, R and Wu, S and Wen, W and Li, Y and Wang, L and Yuan, F}, title = {Electro-Acupuncture to Treat Disorder of Consciousness (AcuDoc): Study Protocol for a Randomized Sham-Controlled Trial.}, journal = {Brain and behavior}, volume = {15}, number = {6}, pages = {e70637}, pmid = {40566931}, issn = {2162-3279}, support = {//Health Commission of Guangzhou City/ ; //NATCM's Project of High-level Construction of Key TCM Disciplines/ ; //Guangzhou Municipal Science and Technology Bureau/ ; //2023A04J0473/ ; }, mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; *Brain Injuries, Traumatic/complications/therapy/physiopathology ; *Consciousness Disorders/therapy/etiology/physiopathology ; *Electroacupuncture/methods ; Electroencephalography ; Randomized Controlled Trials as Topic ; }, abstract = {BACKGROUND: Treatment of disorders of consciousness (DOC) remains a clinical challenge. Electroacupuncture (EA) was shown to have the potential to promote the recovery of consciousness. This trial aims to explore the therapeutic effects and mechanisms of EA in patients with DOC due to traumatic brain injury (TBI) through a multimodal approach.<br><br>METHODS: A total of 50 adult patients with DOC due to TBI and 25 healthy subjects will be enrolled in the study. Patients enrolled in the study will be assigned to the EA group or the sham-EA group through stratified randomization. All patients receive behavioral assessments (CRS-R and brain-computer interface), neurophysiological evaluations (EEG, somatosensory evoked potentials, brainstem auditory evoked potentials), and neuroimaging evaluations (rs-fMRI, amide proton transfer, intravoxel incoherent motion, neurite orientation dispersion and density imaging) before and after the 14-day EA or sham-EA treatment. Each healthy subject will receive a set of neurophysiological and neuroimaging examinations but no treatments. The practitioner administering EA and sham-EA is the only one aware of the grouping results. In the sham-EA group, sham-acupoints, sham-acupuncture, and sham-wire are utilized. The primary outcome measurement is the change in CRS-R score after 14 days of treatment compared with the baseline CRS-R score.<br><br>DISCUSSION: The AcuDoc trial will be the first randomized sham-controlled study to investigate the clinical benefits of EA in patients with DOC. This trial will elucidate the role of EA in the treatment of DOC due to TBI and provide evidence of its therapeutic mechanisms.}, }
@article {pmid40566772, year = {2025}, author = {Zhang, T and Chen, J and Lu, Y and Xu, D and Yan, S and Ouyang, Z}, title = {[The analysis of invention patents in the field of artificial intelligent medical devices].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {3}, pages = {504-511}, pmid = {40566772}, issn = {1001-5515}, mesh = {*Artificial Intelligence ; *Patents as Topic ; Humans ; *Inventions ; China ; Brain-Computer Interfaces ; Telemedicine ; *Equipment and Supplies ; Robotics ; Algorithms ; }, abstract = {The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine and intelligent care. However, the artificial intelligent medical device sector still faces significant challenges, such as data privacy protection and algorithm reliability. This study, based on invention patent analysis, revealed the technological innovation trends in the field of artificial intelligent medical devices from aspects such as patent application time trends, hot topics, regional distribution, and innovation players. The results showed that global invention patent applications had remained active, with technological innovations primarily focused on medical image processing, physiological signal processing, surgical robots, brain-computer interfaces, and intelligent physiological parameter monitoring technologies. The United States and China led the world in the number of invention patent applications. Major international medical device giants, such as Philips, Siemens, General Electric, and Medtronic, were at the forefront of global technological innovation, with significant advantages in patent application volumes and international market presence. Chinese universities and research institutes, such as Zhejiang University, Tianjin University, and the Shenzhen Institute of Advanced Technology, had demonstrated notable technological innovation, with a relatively high number of patent applications. However, their overseas market expansion remained limited. This study provides a comprehensive overview of the technological innovation trends in the artificial intelligent medical device field and offers valuable information support for industry development from an informatics perspective.}, }
@article {pmid40566769, year = {2025}, author = {Wu, H and Chen, S and Jia, J}, title = {[Research progress on brain mechanism of brain-computer interface technology in the upper limb motor function rehabilitation in stroke patients].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {3}, pages = {480-487}, pmid = {40566769}, issn = {1001-5515}, mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; *Upper Extremity/physiopathology ; *Brain/physiopathology ; Electroencephalography ; Stroke/physiopathology ; }, abstract = {Stroke causes abnormality of brain physiological function and limb motor function. Brain-computer interface (BCI) connects the patient's active consciousness to an external device, so as to enhance limb motor function. Previous studies have preliminarily confirmed the efficacy of BCI rehabilitation training in improving upper limb motor function after stroke, but the brain mechanism behind it is still unclear. This paper aims to review on the brain mechanism of upper limb motor dysfunction in stroke patients and the improvement of brain function in those receiving BCI training, aiming to further explore the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function after stroke. The results of this study show that in the fields of imaging and electrophysiology, abnormal activity and connectivity have been found in stroke patients. And BCI training for stroke patients can improve their upper limb motor function by increasing the activity and connectivity of one hemisphere of the brain and restoring the balance between the bilateral hemispheres of the brain. This article summarizes the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function in stroke in both imaging and electrophysiology, and provides a reference for the clinical application and scientific research of BCI in stroke rehabilitation in the future.}, }
@article {pmid40566768, year = {2025}, author = {Liu, X and Yang, B and Gan, A and Zhang, J}, title = {[Study on speech imagery electroencephalography decoding of Chinese words based on the CAM-Net model].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {3}, pages = {473-479}, pmid = {40566768}, issn = {1001-5515}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Speech/physiology ; Algorithms ; Male ; Adult ; Imagination ; }, abstract = {Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant words-"Help me", "Sit up" and "Turn over"-and collected electroencephalography (EEG) data from 15 healthy subjects. Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies. Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between "Sit up" and "Turn over". This work provides a promising approach for future Chinese speech imagery BCI research and applications.}, }
@article {pmid40566767, year = {2025}, author = {Li, X and Cao, X and Wang, J and Zhu, W and Huang, Y and Wan, F and Hu, Y}, title = {[Performance evaluation of a wearable steady-state visual evoked potential based brain-computer interface in real-life scenario].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {3}, pages = {464-472}, pmid = {40566767}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; *Wearable Electronic Devices ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; }, abstract = {Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.}, }
@article {pmid40566766, year = {2025}, author = {Zhu, Y and Ji, Z and Li, S and Wang, H and Fu, Y and Wang, H}, title = {[A portable steady-state visual evoked potential brain-computer interface system for smart healthcare].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {3}, pages = {455-463}, pmid = {40566766}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; Signal Processing, Computer-Assisted ; Software ; Adult ; Male ; }, abstract = {This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.}, }
@article {pmid40566765, year = {2025}, author = {Chai, X and Wang, N and Song, J and Yang, Y}, title = {[Detection of motor intention in patients with consciousness disorder based on electroencephalogram and functional near infrared spectroscopy combined with motor brain-computer interface paradigm].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {3}, pages = {447-454}, pmid = {40566765}, issn = {1001-5515}, mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; *Electroencephalography/methods ; *Consciousness Disorders/physiopathology/diagnosis ; Male ; Movement ; Adult ; Female ; Intention ; Persistent Vegetative State/physiopathology/diagnosis ; }, abstract = {Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks (P < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.}, }
@article {pmid40566764, year = {2025}, author = {Pan, J and Zhang, Z and Zhang, Y and Wang, F and Xiao, J}, title = {[Brain-computer interface technology and its applications for patients with disorders of consciousness].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {3}, pages = {438-446}, pmid = {40566764}, issn = {1001-5515}, mesh = {Humans ; *Brain-Computer Interfaces ; *Consciousness Disorders/diagnosis/rehabilitation/physiopathology ; Electroencephalography ; Brain/physiopathology ; Consciousness ; }, abstract = {With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.}, }
@article {pmid40566763, year = {2025}, author = {Pan, H and Ding, P and Wang, F and Li, T and Zhao, L and Nan, W and Gong, A and Fu, Y}, title = {[Evaluation methods for the rehabilitation efficacy of bidirectional closed-loop motor imagery brain-computer interface active rehabilitation training systems].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {3}, pages = {431-437}, pmid = {40566763}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Imagery, Psychotherapy/methods ; }, abstract = {The bidirectional closed-loop motor imagery brain-computer interface (MI-BCI) is an emerging method for active rehabilitation training of motor dysfunction, extensively tested in both laboratory and clinical settings. However, no standardized method for evaluating its rehabilitation efficacy has been established, and relevant literature remains limited. To facilitate the clinical translation of bidirectional closed-loop MI-BCI, this article first introduced its fundamental principles, reviewed the rehabilitation training cycle and methods for evaluating rehabilitation efficacy, and summarized approaches for evaluating system usability, user satisfaction and usage. Finally, the challenges associated with evaluating the rehabilitation efficacy of bidirectional closed-loop MI-BCI were discussed, aiming to promote its broader adoption and standardization in clinical practice.}, }
@article {pmid40566538, year = {2025}, author = {Pilipović, K and Janković, T and Rajič Bumber, J and Belančić, A and Mršić-Pelčić, J}, title = {Traumatic Brain Injury: Novel Experimental Approaches and Treatment Possibilities.}, journal = {Life (Basel, Switzerland)}, volume = {15}, number = {6}, pages = {}, pmid = {40566538}, issn = {2075-1729}, support = {UIP-2017-05-9517//Croatian Science Foundation/ ; uniri-iskusni-biomed-23-56//University of Rijeka/ ; uniri-mladi-biomed-23-38//University of Rijeka/ ; uniri-iskusni-biomed-23-82//University of Rijeka/ ; }, abstract = {Traumatic brain injury (TBI) remains a critical global health issue with limited effective treatments. Traditional care of TBI patients focuses on stabilization and symptom management without regenerating damaged brain tissue. In this review, we analyze the current state of treatment of TBI, with focus on novel therapeutic approaches aimed at reducing secondary brain injury and promoting recovery. There are few innovative strategies that break away from the traditional, biological target-focused treatment approaches. Precision medicine includes personalized treatments based on biomarkers, genetics, advanced imaging, and artificial intelligence tools for prognosis and monitoring. Stem cell therapies are used to repair tissue, regulate immune responses, and support neural regeneration, with ongoing development in gene-enhanced approaches. Nanomedicine uses nanomaterials for targeted drug delivery, neuroprotection, and diagnostics by crossing the blood-brain barrier. Brain-machine interfaces enable brain-device communication to restore lost motor or neurological functions, while virtual rehabilitation and neuromodulation use virtual and augmented reality as well as brain stimulation techniques to improve rehabilitation outcomes. While these approaches show great potential, most are still in development and require more clinical testing to confirm safety and effectiveness. The future of TBI therapy looks promising, with innovative strategies likely to transform care.}, }
@article {pmid40564460, year = {2025}, author = {Liu, Z and Fan, K and Gu, Q and Ruan, Y}, title = {Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {6}, pages = {}, pmid = {40564460}, issn = {2306-5354}, abstract = {The study of electroencephalogram (EEG) signals is crucial for understanding brain function and has extensive applications in clinical diagnosis, neuroscience, and brain-computer interface technology. This paper addresses the challenge of recognizing motor imagery EEG signals with few channels, which is essential for portable and real-time applications. A novel framework is proposed that applies a continuous wavelet transform to convert time-domain EEG signals into two-dimensional time-frequency representations. These images are then concatenated into channel-dependent multilayer EEG time-frequency representations (CDML-EEG-TFR), incorporating multidimensional information of time, frequency, and channels, allowing for a more comprehensive and enriched brain representation under the constraint of few channels. By adopting a deep convolutional neural network with EfficientNet as the backbone and utilizing pre-trained weights from natural image datasets for transfer learning, the framework can simultaneously learn temporal, spatial, and channel features embedded in the CDML-EEG-TFR. Moreover, the transfer learning strategy effectively addresses the issue of data sparsity in the context of a few channels. Our approach enhances the classification accuracy of motor imagery EEG signals in few-channel scenarios. Experimental results on the BCI Competition IV 2b dataset show a significant improvement in classification accuracy, reaching 80.21%. This study highlights the potential of CDML-EEG-TFR and the EfficientNet-based transfer learning strategy in few-channel EEG signal classification, laying a foundation for practical applications and further research in medical and sports fields.}, }
@article {pmid40564444, year = {2025}, author = {Garcia-Palencia, O and Fernandez, J and Shim, V and Kasabov, NK and Wang, A and The Alzheimer's Disease Neuroimaging Initiative, }, title = {Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {6}, pages = {}, pmid = {40564444}, issn = {2306-5354}, support = {Project 22-UOA-120, 23-UOA-055-CSG//Health Research Council of New Zealand and Royal Society Catalyst/ ; 23-UOA-055-CSG//University of Auckland/ ; }, abstract = {Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain's biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain-computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives.}, }
@article {pmid40564430, year = {2025}, author = {Darvishi, H and Mohammadi, A and Maghami, MH and Sadeghi, M and Sawan, M}, title = {EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain-Computer Interface Performance.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {6}, pages = {}, pmid = {40564430}, issn = {2306-5354}, abstract = {Brain-computer interfaces (BCIs) translate electroencephalography (EEG) signals into control commands, offering potential solutions for motor-impaired individuals. While traditional BCI studies often focus solely on amplitude variations or inter-channel connectivity, movement-related brain activity is inherently dynamic, involving interactions across regions and frequency bands. We propose that combining amplitude-based (filter bank common spatial patterns, FBCSP) and phase-based connectivity features (phase-locking value, PLV) improves decoding accuracy. EEG signals from ten healthy subjects were recorded during arm movements, with electromyography (EMG) as ground truth. After preprocessing (resampling, normalization, bandpass filtering), FBCSP and multi-lag PLV features were fused, and the ReliefF algorithm selected the most informative subset. A feedforward neural network achieved average metrics of: Pearson correlation 0.829 ± 0.077, R-squared value 0.675 ± 0.126, and root mean square error (RMSE) 0.579 ± 0.098 in predicting EMG amplitudes indicative of arm movement angles. Analysis highlighted contributions from both FBCSP and PLV, particularly in the 4-8 Hz and 24-28 Hz bands. This fusion approach, augmented by data-driven feature selection, significantly enhances movement decoding accuracy, advancing robust neuroprosthetic control systems.}, }
@article {pmid40563754, year = {2025}, author = {Gkintoni, E and Vassilopoulos, SP and Nikolaou, G and Vantarakis, A}, title = {Neurotechnological Approaches to Cognitive Rehabilitation in Mild Cognitive Impairment: A Systematic Review of Neuromodulation, EEG, Virtual Reality, and Emerging AI Applications.}, journal = {Brain sciences}, volume = {15}, number = {6}, pages = {}, pmid = {40563754}, issn = {2076-3425}, abstract = {Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual's age and education level but not severe enough to significantly interfere with daily activities, with variable trajectories that may remain stable, progress to dementia, or occasionally revert to normal cognition. This systematic review examines neurotechnological approaches to cognitive rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), cognitive training, physical exercise, and artificial intelligence (AI) applications. Methods: A systematic review following PRISMA guidelines was conducted on 34 empirical studies published between 2014 and 2024. Studies were identified through comprehensive database searches and included if they employed neurotechnological interventions targeting cognitive outcomes in individuals with MCI. Results: Evidence indicates promising outcomes across multiple intervention types. Neuromodulation techniques showed beneficial effects on memory and executive function. EEG analyses identified characteristic neurophysiological markers of MCI with potential for early detection and monitoring. Virtual reality enhanced assessment sensitivity and rehabilitation engagement through ecologically valid environments. Cognitive training demonstrated the most excellent efficacy with multi-domain, adaptive approaches. Physical exercise interventions yielded improvements through multiple neurobiological pathways. Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. Conclusions: Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. Neurophysiological monitoring provides valuable biomarkers for diagnosis and treatment response. Future research should focus on more extensive clinical trials, standardized protocols, and accessible implementation models to translate these technological advances into clinical practice.}, }
@article {pmid40563743, year = {2025}, author = {Mróz, K and Jonak, K}, title = {Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study.}, journal = {Brain sciences}, volume = {15}, number = {6}, pages = {}, pmid = {40563743}, issn = {2076-3425}, support = {FD-20/II-3/999//Lublin University of Technology/ ; }, abstract = {Background: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities for diagnosing and treating mental disorders. However, challenges such as data variability, noise, and model interpretability remain significant. This study reviews the current limitations of EEG-based anxiety detection and explores the potential of advanced AI models, including transformers and VAE-D2GAN, to improve diagnostic accuracy and real-time monitoring. Methods: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. Additionally, it discusses the role of brain-computer interfaces (BCIs) in assisting individuals with disabilities by enabling device control through brain activity. Results: Experimental EEG research on BCI applications was conducted, focusing on motor imagery-based brain activity. Findings indicate that successive training sessions improve signal classification accuracy, emphasizing the need for personalized and adaptive EEG analysis methods. Challenges in BCI usability and technological constraints in EEG processing are also addressed. Conclusions: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.}, }
@article {pmid40563723, year = {2025}, author = {Fodor, MA and Cantürk, A and Heisenberg, G and Volosyak, I}, title = {Streamlining cVEP Paradigms: Effects of a Minimized Electrode Montage on Brain-Computer Interface Performance.}, journal = {Brain sciences}, volume = {15}, number = {6}, pages = {}, pmid = {40563723}, issn = {2076-3425}, support = {101118964//This project has received funding from the European Union's research and innovation programme under the Marie Sk&#x142;odowska-Curie grant agreement No 101118964./ ; }, abstract = {(1) Background: Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals, offering potential applications in assistive technology and neurorehabilitation. Code-modulated visual evoked potential (cVEP)-based BCIs employ code-pattern-based stimulation to evoke neural responses, which can then be classified to infer user intent. While increasing the number of EEG electrodes across the visual cortex enhances classification accuracy, it simultaneously reduces user comfort and increases setup complexity, duration, and hardware costs. (2) Methods: This online BCI study, involving thirty-eight able-bodied participants, investigated how reducing the electrode count from 16 to 6 affected performance. Three experimental conditions were tested: a baseline 16-electrode configuration, a reduced 6-electrode setup without retraining, and a reduced 6-electrode setup with retraining. (3) Results: Our results indicate that, on average, performance declines with fewer electrodes; nonetheless, retraining restored near-baseline mean Information Transfer Rate (ITR) and accuracy for those participants for whom the system remained functional. The results reveal that for a substantial number of participants, the classification pipeline fails after electrode removal, highlighting individual differences in the cVEP response characteristics or inherent limitations of the classification approach. (4) Conclusions: Ultimately, this suggests that minimal cVEP-BCI electrode setups capable of reliably functioning across all users might only be feasible through other, more flexible classification methods that can account for individual differences. These findings aim to serve as a guideline for what is currently achievable with this common cVEP paradigm and to highlight where future research should focus in order to move closer to a practical and user-friendly system.}, }
@article {pmid40562060, year = {2025}, author = {Xu, F and Liu, Y and Li, Y and Zhang, C and Han, Z and He, T and Xiao, X and Feng, C and Leng, J and Xu, M}, title = {Research on coding and decoding algorithm of binocular brain-controlled unmanned vehicle.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ade829}, pmid = {40562060}, issn = {1741-2552}, mesh = {Humans ; *Algorithms ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; Photic Stimulation/methods ; Female ; *Vision, Binocular/physiology ; Young Adult ; *Automobile Driving ; }, abstract = {Objective. With the rapid development of brain-computer interface (BCI) technology, steady-state visual evoked potential (SSVEP) has emerged as an effective method for high-efficiency information transmission. However, traditional single-frequency stimulation methods face limitations in command set scalability and visual comfort.Approach. To address these issues, we propose a novel binocular SSVEP stimulation paradigm for brain-controlled unmanned vehicles. (UV) This system uses a checkerboard and phase encoding for stimulus presentation, encoding a single target with two frequencies to expand the command set. The frequencies are set between 30-35 Hz to enhance visual comfort. By leveraging polarized light technology, each eye receives distinct frequencies, suppressing intermodulation components and reducing the stimulated area for each eye. We also introduce an improved filter bank dual-frequency task-discriminant component analysis (FBD-TDCA) algorithm.Main results. Experimental results show that, in a 15-command simulation, only six frequencies successfully encoded all commands, achieving comparable performance to traditional single-frequency paradigms. Furthermore, the FBD-TDCA algorithm outperformed existing methods such as filter bank task-related component analysis and filter bank canonical correlation analysis, achieving a classification accuracy of 89.27% ± 3.67 and an information translate rate of 163.87 ± 14.32 bits min[-1], with statistical significance confirmed through pairedt-tests. The system's practical application was further demonstrated in an online 12-command UV control task. Participants achieved an average classification accuracy of 90.34% ± 8.75%, with most maintaining low path deviation rates during navigation tasks.Significance. The proposed binocular SSVEP stimulation paradigm and FBD-TDCA algorithm address the limitations of traditional methods, offering enhanced command set scalability, improved visual comfort, and superior performance, paving the way for more efficient and user-friendly BCI applications in real-world scenarios.}, }
@article {pmid40561510, year = {2025}, author = {Almanna, MA and Elkaim, LM and Alvi, MA and Levett, JJ and Li, B and Mamdani, M and Al-Omran, M and Alotaibi, NM}, title = {Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study.}, journal = {JMIR formative research}, volume = {9}, number = {}, pages = {e60859}, pmid = {40561510}, issn = {2561-326X}, mesh = {Humans ; *Brain-Computer Interfaces/psychology ; *Social Media/statistics &amp; numerical data ; Natural Language Processing ; *Public Opinion ; Male ; Emotions ; Female ; Adult ; }, abstract = {BACKGROUND: Given the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education.<br><br>OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods.<br><br>METHODS: A mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users' demographics by matching predefined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.<br><br>RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; &#x3c4;=0.266; P<.001). Most posts were objective (50,847/65,340, 77.81%), with a smaller proportion being subjective (14,393/65,340, 22.02%). Biographic analysis showed that the "broadcasting" group contributed the most to BCI discussions (17,803/58,030, 30.67%), while the "scientific" group, contributing 27.58% (n=16,005), had the highest overall engagement metrics. The emotional analysis identified anticipation (score = 10,802/52,618, 20.52%), trust (score=9244/52,618, 17.56%), and fear (score=7344/52,618, 13.95%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.<br><br>CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies.}, }
@article {pmid40561478, year = {2025}, author = {Chen, CS and Chang, SH and Liu, CW and Pan, TM}, title = {Exploring the Potential of Electroencephalography Signal-Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis.}, journal = {JMIR medical informatics}, volume = {13}, number = {}, pages = {e72027}, pmid = {40561478}, issn = {2291-9694}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Brain/physiology/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; Adult ; }, abstract = {BACKGROUND: Electroencephalography (EEG) has been widely used to measure brain activity, but its potential to generate accurate images from neural signals remains a challenge. Most EEG-decoding research has focused on tasks such as motor imagery, emotion recognition, and brain wave classification, which involve EEG signal analysis and classification. Some studies have explored the correlation between EEG and images, primarily focusing on EEG-image pair classification or transformation. However, EEG-based image generation remains underexplored.<br><br>OBJECTIVE: The primary goal of this study was to extend EEG-based classification to image generation, addressing the limitations of previous methods and unlocking the full potential of EEG for image synthesis. To achieve more meaningful EEG-to-image generation, we developed a novel framework, Neural-Cognitive Multimodal EEG-Informed Image (NECOMIMI), which was specifically designed to generate images directly from EEG signals.<br><br>METHODS: We developed a 2-stage NECOMIMI method, which integrated the novel Neural Encoding Representation Vectorizer (NERV) EEG encoder that we designed with a diffusion-based generative model. The Category-Based Assessment Table (CAT) score was introduced to evaluate the semantic quality of EEG-generated images. In addition, the ThingsEEG dataset was used to validate and benchmark the CAT score, providing a standardized measure for assessing EEG-to-image generation performance.<br><br>RESULTS: The NERV EEG encoder achieved state-of-the-art performance in several zero-shot classification tasks, with an average accuracy of 94.8% (SD 1.7%) in the 2-way task and 86.8% (SD 3.4%) in the 4-way task, outperforming models such as Natural Image Contrast EEG, Multimodal Similarity-Keeping Contrastive Learning, and Adaptive Thinking Mapper ShallowNet. This highlighted its superiority as a feature extraction tool for EEG signals. In a 1-stage image generation framework, EEG embeddings often resulted in abstract or generalized images such as landscapes instead of specific objects. Our proposed 2-stage NECOMIMI architecture effectively extracted semantic information from noisy EEG signals, showing its ability to capture and represent underlying concepts derived from brain wave activity. We further conducted a perturbation study to test whether the model overly depended on visual cortex EEG signals for scene-based image generation. The perturbation of visual cortex EEG channels led to a notable increase in Fr&#xe9;chet inception distance scores, suggesting that our model relied heavily on posterior brain signals to generate semantically coherent images.<br><br>CONCLUSIONS: NECOMIMI demonstrated the potential of EEG-to-image generation, revealing the challenges of translating noisy EEG data into accurate visual representations. The novel NERV EEG encoder for multimodal contrastive learning reached state-of-the-art performance both on n-way zero-shot and EEG-informed image generation. The introduction of the CAT score provided a new evaluation metric, paving the way for future research to refine generative models. In addition, this study highlighted the significant clinical potential of EEG-to-image generation, particularly in enhancing brain-machine interface systems and improving quality of life for individuals with motor impairments.}, }
@article {pmid40554057, year = {2025}, author = {Park, S and Ha, J and Kim, L}, title = {Improving single-trial detection of error-related potentials by considering the effect of heartbeat-evoked potentials in a motor imagery-based brain-computer interface.}, journal = {Computers in biology and medicine}, volume = {195}, number = {}, pages = {110563}, doi = {10.1016/j.compbiomed.2025.110563}, pmid = {40554057}, issn = {1879-0534}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Heart Rate/physiology ; Adult ; Electroencephalography ; Young Adult ; *Evoked Potentials/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {OBJECTIVE: This study aimed to determine the effect of heartbeat-evoked potentials (HEPs) on changes in the error-related potential (ErrP) epoch and classification performance in single trials, specifically distinguishing between correct and error conditions in a three-class motor imagery-based brain-computer interface.<br><br>METHODS: Eleven individuals participated in this study, with 10 participants assigned to the offline group and 10 to the real-time group. The experiment consisted of 360 motor imagery trials, involving both correct and erroneous feedback. The ErrP trial was categorized into three conditions based on whether the heartbeat was within the ErrP epoch time window or not: (1) including heartbeat trials (ErrPIHB), (2) excluding heartbeat trials (ErrPEHB), and (3) total trials (ErrPT).<br><br>RESULTS: A small negativity was observed at approximately 200&#xa0;ms, followed by a subsequent positivity at approximately 300&#xa0;ms. The prominent amplitudes at approximately 200 and 300&#xa0;ms in the ErrPEHB condition notably differed from those in the ErrPT and ErrPIHB conditions, showing the highest classification accuracy. In the offline experiment dataset of 10 participants, the ErrPEHB condition demonstrated the highest classification accuracy (0.89). This was higher by 0.07 and 0.11 compared to the ErrPT (0.82) and ErrPIHB (0.78) conditions, respectively. For real-time analysis, the novel ErrP paradigm proposed in this study achieved a classification accuracy of 0.89 for 10 participants, a 0.05 increase compared with that of the conventional ErrP paradigm.<br><br>CONCLUSION AND SIGNIFICANCE: These findings can contribute to obtaining pure or clear ErrP epochs associated with the target response and significantly improve classification performance.}, }
@article {pmid40553977, year = {2025}, author = {Jiang, H and Qi, H and Tang, A and Hu, S and Lai, J}, title = {Start the engine of neuroregeneration: A mechanistic and strategic overview of direct astrocyte-to-neuron reprogramming.}, journal = {Ageing research reviews}, volume = {110}, number = {}, pages = {102808}, doi = {10.1016/j.arr.2025.102808}, pmid = {40553977}, issn = {1872-9649}, mesh = {Humans ; Animals ; *Astrocytes/physiology/metabolism ; *Neurons/physiology/metabolism ; *Cellular Reprogramming/physiology ; *Nerve Regeneration/physiology ; Cell Transdifferentiation/physiology ; *Neurogenesis/physiology ; *Aging/physiology ; Neurodegenerative Diseases/therapy/pathology ; }, abstract = {The decline of adult neurogenesis and neuronal function during aging underlies the onset and progression of neurodegenerative diseases such as Alzheimer's disease. Conventional therapies, including neurotransmitter modulators and antibodies targeting pathogenic proteins, offer only symptomatic improvement. As the most abundant glial cells in the brain, astrocytes outnumber neurons nearly fivefold. However, their proliferative and transdifferentiation potential renders them ideal candidates for in situ neuronal replacement. Direct astrocyte-to-neuron reprogramming offers a promising regenerative approach to restore damaged neural circuits. Herein, we propose a "car start-up" model to conceptualize this process, emphasizing the need to inhibit non-neuronal fate pathways (release the handbrake), suppress transcriptional repressors (release the footbrake), and activate neuron-specific gene expression (step on the gas). Additionally, overcoming metabolic barriers in the cytoplasm is essential for successful lineage conversion. Viral or non-viral vectors deliver reprogramming factors, while small molecules serve as metabolic and epigenetic fuel to boost efficiency. In summary, we review the current evidence supporting direct astrocyte-to-neuron reprogramming as a viable regenerative strategy in the aging brain. We also highlight the conceptual "car start-up" model as a useful framework to dissect the molecular logic of lineage conversion and emphasize its promising therapeutic potential for combating neurodegenerative diseases.}, }
@article {pmid40553738, year = {2025}, author = {Zhang, HG and Wang, JF and Jialin, A and Zhao, XY and Wang, C and Deng, W}, title = {Relationship between multimorbidity burden and depressive symptoms in older Chinese adults: A prospective 10-year cohort study.}, journal = {Journal of affective disorders}, volume = {389}, number = {}, pages = {119714}, doi = {10.1016/j.jad.2025.119714}, pmid = {40553738}, issn = {1573-2517}, mesh = {Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology ; Chronic Disease/epidemiology/psychology ; *Depression/epidemiology ; Longitudinal Studies ; *Multimorbidity ; Proportional Hazards Models ; Prospective Studies ; Risk Factors ; }, abstract = {BACKGROUND: Recent research indicates that multimorbidity clusters due to common mechanisms and risk factors, leading to different effects on the development of depressive symptoms (DS) in older populations. This study innovatively examined the association of both the number and specific patterns of multimorbidity with DS.<br><br>METHODS: A total of 1988 participants aged 60&#xa0;years and older were selected from the China Health and Retirement Longitudinal Study (CHARLS) and monitored for DS between June 2011 and September 2020. Twelve chronic conditions were assessed through self-reports. DS was evaluated using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Latent class analysis (LCA) was used to identify multimorbidity patterns, and Cox proportional hazards regression models examined the associations of specific diseases, multimorbidity count and multimorbidity patterns with DS.<br><br>RESULTS: During the 9.17-year follow-up period, 986 cases of DS were identified. Hypertension (adjusted hazard ratio [HR]&#xa0;=&#xa0;1.21, 95&#xa0;% confidence interval [CI]&#xa0;=&#xa0;1.05-1.39), stroke (HR&#xa0;=&#xa0;1.77, 95%CI&#xa0;=&#xa0;1.20-2.63), stomach or other digestive disease (HR&#xa0;=&#xa0;1.28, 95%CI&#xa0;=&#xa0;1.11-1.48), arthritis or rheumatism (HR&#xa0;=&#xa0;1.41, 95%CI&#xa0;=&#xa0;1.24-1.60), chronic lung diseases (HR&#xa0;=&#xa0;1.25, 95%CI&#xa0;=&#xa0;1.03-1.52) and kidney disease (HR&#xa0;=&#xa0;1.38, 95%CI&#xa0;=&#xa0;1.07-1.78) were significantly associated with increased DS risk. Each additional chronic condition increased the DS hazard by 13&#xa0;% (adjusted HR&#xa0;=&#xa0;1.13, 95&#xa0;% CI&#xa0;=&#xa0;1.08-1.18). Four multimorbidity patterns were identified by LCA, with the digestion/arthritis pattern (HR&#xa0;=&#xa0;1.47, 95&#xa0;% CI&#xa0;=&#xa0;1.22-1.77) and respiratory pattern (HR&#xa0;=&#xa0;1.47, 95&#xa0;% CI&#xa0;=&#xa0;1.07-2.04) showing higher DS risk compared to the relatively healthy group.<br><br>CONCLUSION: The number and patterns of multimorbidity are significantly associated with heightened DS risk in older populations. Older adults in complex health conditions, particularly those with digestion/arthritis and respiratory multimorbidity patterns, should receive closer mental health monitoring.}, }
@article {pmid40551292, year = {2025}, author = {Chu, J and Yao, J and Li, Z and Li, J and Zhang, Y and Liu, C and He, H and Li, B and Wei, H}, title = {Brain tissue electrical conductivity as a promising biomarker for dementia assessment using MRI.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {21}, number = {6}, pages = {e70270}, pmid = {40551292}, issn = {1552-5279}, support = {2024YFC2421100//National Key Research and Development Program of China/ ; //National Natural Science Foundation of China/ ; //62471296, 82271441, 62071299, 82372036, 82001342/ ; 23TS1400200//Shanghai Science and Technology Development Funds/ ; STAR 20220103 YG2023LC02//SJTU Trans-med Awards Research/ ; }, mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Biomarkers ; *Brain/diagnostic imaging/metabolism/physiopathology ; *Dementia/diagnostic imaging/diagnosis/metabolism ; Male ; Female ; Amyloid beta-Peptides/metabolism ; *Electric Conductivity ; tau Proteins/metabolism ; Aged ; Cognitive Dysfunction ; Positron-Emission Tomography ; }, abstract = {INTRODUCTION: Dementia, particularly Alzheimer's disease, involves cognitive decline linked to amyloid beta (Aβ) and tau protein aggregation. Magnetic resonance imaging (MRI)-based brain tissue conductivity, which increases in dementia, may serve as a non-invasive biomarker for protein aggregation. We investigate the relationship between MRI-based brain electrical conductivity, protein aggregation, cognition, and gene expression.<br><br>METHODS: Brain conductivity maps were reconstructed and correlated with PET protein signals, cognitive performance, and plasma protein levels. The diagnostic potential of conductivity for dementia was assessed, and transcriptomic analysis using the Allen Human Brain Atlas elucidated the underlying biological processes.<br><br>RESULTS: Increased brain conductivity was associated with A&#x3b2; and tau aggregation in specific brain regions, cognitive decline, and plasma protein levels. Conductivity also improved dementia discrimination performance, and higher gene expression related to ion transport, cellular development, and signaling pathways was observed.<br><br>DISCUSSION: Brain electrical conductivity shows promise as a biomarker for dementia, correlating with protein aggregation and relevant cellular processes.<br><br>HIGHLIGHTS: Brain tissue conductivity correlates with A&#x3b2; and tau aggregation in dementia. Brain tissue conductivity correlates with cognitive scores and GMV. CSF conductivity correlates with plasma protein levels. Combining conductivity with GMV improves dementia diagnosis accuracy. Gene expression in ion processes, cell development, and signaling links to conductivity.}, }
@article {pmid40550006, year = {2025}, author = {Liu, Y and Fan, P and Pan, Y and Ping, J}, title = {Flexible Microinterventional Sensors for Advanced Biosignal Monitoring.}, journal = {Chemical reviews}, volume = {125}, number = {17}, pages = {8246-8318}, doi = {10.1021/acs.chemrev.5c00115}, pmid = {40550006}, issn = {1520-6890}, mesh = {*Biosensing Techniques/instrumentation/methods ; Humans ; Animals ; Electrodes ; }, abstract = {Flexible microinterventional sensors represent a transformative technology that enables the minimal intervention required to access and monitor complex biosignals (e.g., bioelectrical, biophysical, and biochemical signals) originating from deep tissues, thereby providing accurate data for diagnostics, robotics, prosthetics, brain-computer interfaces, and therapeutic systems. However, fully unlocking their potential hinges on establishing a nondisruptive, intimate, and nonrestrictive interface with the tissue surface, facilitating efficient integration between the microinterventional sensor and the target tissue. In this comprehensive review, we highlight the critical tissue characteristics in both physiologically and pathologically relevant contexts that are pivotal for the design of microinterventional sensors. We also summarize recent advancements in flexible substrate materials and conductive materials, which are tailored to facilitate effective information interaction between bioelectronic components and biological tissues. Furthermore, we classify various electrode architectures─spanning 1D, 2D, and 3D─designed to accommodate the mechanics of soft tissues and enable nonrestrictive interfaces in diverse sensing scenarios. Additionally, we outline critical challenges for next-generation microinterventional sensors and propose integrating advanced materials, innovative fabrication, and embedded intelligence to drive breakthroughs in biosignal sensing. Ultimately, we aim to both provide foundational understanding and highlight emerging strategies in biosignal capture, leveraging recent advancements in these critical components.}, }
@article {pmid40549688, year = {2025}, author = {Meijs, S and Andreis, FR and Kjærgaard, B and Janjua, TAM and Jensen, W}, title = {Chronic Cranial Window Technique for Repeated Cortical Recordings During Anesthesia in Pigs.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {220}, pages = {}, doi = {10.3791/67931}, pmid = {40549688}, issn = {1940-087X}, mesh = {Animals ; Swine ; *Electrocorticography/methods/instrumentation ; *Anesthesia/methods ; *Somatosensory Cortex/physiology ; Dura Mater/surgery ; Electrodes, Implanted ; }, abstract = {Cortical recordings are essential for extracting neuronal signals to inform various applications, including brain-computer interfaces and disease diagnostics. Each application places specific requirements on the recording technique, and invasive solutions are often selected for long-term recordings. However, invasive recording methods are challenged by device failure and adverse tissue responses, which compromise long-term signal quality. To improve the reliability and quality of chronic cortical recordings while minimizing risks related to device failure and tissue reactions, we developed a cranial window technique. In this protocol, we report methods to implant and access a cranial window in juvenile landrace pigs, which facilitates temporary electrocorticography (ECoG) array placement on the dura mater. We further describe how cortical signals can be recorded using the cranial window technique. Cranial window access can be repeated several times, but a minimum of 2 weeks between implant and access surgeries is advised to facilitate recovery and tissue healing. The cranial window approach successfully minimized common electrode failure modes and tissue responses, resulting in stable and reliable cortical recordings over time. We recorded event-related potentials (ERPs) from the primary somatosensory cortex as an example. The method provided highly reliable recordings, which also allowed the assessment of the effect of an intervention (high-frequency stimulation) on the ERPs. The absence of significant device failures and the reduced number of electrodes used (two electrodes, 43 recording sessions, 16 animals) suggest an improved research economy. While minor surgical access is required for electrode placement, the method offers advantages such as reduced infection risk and improved animal welfare. This study presents a scalable, reliable, and reproducible method for chronic cortical recordings, with potential applications in various fields of neuroscience, including pain research and neurological disease diagnosis. Future adaptations may extend its use to other species and recording modalities, such as intracortical recordings and imaging techniques.}, }
@article {pmid40549518, year = {2025}, author = {Yu, F and Rao, Z and Chen, N and Liu, L and Jiang, M}, title = {ArmBCIsys: Robot Arm BCI System With Time-Frequency Network for Multiobject Grasping.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {36}, number = {10}, pages = {18327-18341}, doi = {10.1109/TNNLS.2025.3579332}, pmid = {40549518}, issn = {2162-2388}, mesh = {Humans ; *Brain-Computer Interfaces ; *Hand Strength/physiology ; *Robotics/methods/instrumentation ; Electroencephalography/methods ; Algorithms ; *Neural Networks, Computer ; Signal-To-Noise Ratio ; *Arm/physiology ; Adult ; }, abstract = {Brain-computer interface (BCI) offers a direct communication and control channel between the human brain and external devices, presenting new pathways for individuals with physical disabilities to operate robotic arms for complex tasks. However, achieving multiobject grasping tasks under low signal-to-noise ratio (SNR) consumer-grade EEG signals is a significant challenge due to the lack of robust decoding algorithms and precise visual tracking methods. This article proposes, ArmBCIsys, an integrated robotic arm system that combines a novel dual-branch frequency-enhanced network (DBFENet) to robustly decode EEG signals under noisy conditions with the high-precision vision-guided grasping module. The proposed DBFENet designs the scaling temporal convolution block (STCB) to extract multiscale spatiotemporal features from the time domain, while the designed DropScale projected Transformer (DSPT) utilizes discrete cosine transform (DCT) to capture key frequency-domain features, significantly improving decoding robustness. We fine-tune the masked-attention mask Transformer (Mask2Former) model on the Jacquard dataset and incorporate the multiframe centroid-intersection over union (IoU) tracking algorithm to build visual grasp segmenter (VisGraspSeg), enabling reliable segmentation and dynamic tracking for diverse daily objects. Experimental validations on both self-built code-modulated visual evoked potential (c-VEP) dataset (1344 samples) and two public c-VEP datasets demonstrate that DBFENet achieves the state-of-the-art recognition performance, and the system integrates the DBFENet and proposed vision-guided module and ensures stable multiobject selecting and automatic object grasping in dynamic environments, extending promising applications in healthcare robotics, assistive technology, and industrial automation. The self-built dataset has been made publicly accessible at https://github.com/wtu1020/ ArmBCIsys-Self-built-cVEP-Dataset.}, }
@article {pmid40548156, year = {2025}, author = {Tzimourta, KD}, title = {Human-Centered Design and Development in Digital Health: Approaches, Challenges, and Emerging Trends.}, journal = {Cureus}, volume = {17}, number = {6}, pages = {e85897}, pmid = {40548156}, issn = {2168-8184}, abstract = {Human-centered design (HCD) has emerged as a critical approach for developing digital health technologies that are usable, acceptable, and effective within complex healthcare environments. Rooted in systems theory, ergonomics, and information systems research, HCD prioritizes the needs, capabilities, and limitations of diverse user groups - including patients, clinicians, and caregivers - throughout the design and implementation process. This review synthesizes current applications of HCD in four key domains: brain-computer interfaces (BCIs), augmented and virtual reality (AR/VR), artificial intelligence (AI)-based clinical decision support systems AI-CDSS, and mobile health (mHealth) technologies. It explores design frameworks, usability strategies, and models of human-technology collaboration that contribute to successful adoption and sustained use. Ethical and legal considerations - such as data privacy, informed consent, and algorithmic fairness - are also addressed, particularly in contexts involving biometric and neurophysiological data. While HCD practices have shown substantial potential to improve digital health outcomes, their implementation remains uneven across technologies and settings. Emerging trends, including adaptive personalization, explainable AI, and participatory co-design, are identified as promising directions for the development of more inclusive, trustworthy, and sustainable digital health innovations.}, }
@article {pmid40546334, year = {2025}, author = {Cruz, MV and Jamal, S and Sethuraman, SC}, title = {A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.}, journal = {Journal of medical signals and sensors}, volume = {15}, number = {}, pages = {16}, pmid = {40546334}, issn = {2228-7477}, abstract = {The brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in health care. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in health care and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.}, }
@article {pmid40545006, year = {2025}, author = {Feng, J and Jia, W and Li, Z}, title = {Electroencephalography: A valuable tool for assessing motor impairment and recovery post-stroke.}, journal = {Journal of neuroscience methods}, volume = {422}, number = {}, pages = {110518}, doi = {10.1016/j.jneumeth.2025.110518}, pmid = {40545006}, issn = {1872-678X}, mesh = {Humans ; *Electroencephalography/methods ; *Stroke/physiopathology/diagnosis/complications ; *Recovery of Function/physiology ; *Stroke Rehabilitation/methods ; Brain-Computer Interfaces ; *Brain/physiopathology ; }, abstract = {Stroke is a leading cause of adult disability, and restoring motor function post-stroke is critical to improving the well-being and quality of life of affected individuals. Accurate and timely assessment of motor function is essential for developing effective rehabilitation strategies and predicting recovery outcomes. Electroencephalography (EEG) offers a non-invasive, real-time monitoring of brain activity, offering personalized insights into motor impairment and recovery. Its simplicity and bedside applicability make EEG a valuable tool and a potential biomarker for brain function. In recent years, the integration of EEG with the brain-computer interface technology and neuromodulation techniques has revolutionized personalized rehabilitation therapy, offering new hope for patients with motor dysfunction following stroke. This review synthesizes evidence on EEG-derived biomarkers and their integration with brain-computer interface and neuromodulation techniques, proposing a framework for personalized rehabilitation strategies in stroke recovery.}, }
@article {pmid40544658, year = {2025}, author = {Mathon, B and Navarro, V and Pons, T and Lecas, S and Roussel, D and Charpier, S and Carpentier, A}, title = {Ultrasound-induced blood-brain barrier opening and selenium-nanoparticle injection lower seizure activity: A mouse model of temporal lobe epilepsy.}, journal = {Ultrasonics}, volume = {155}, number = {}, pages = {107734}, doi = {10.1016/j.ultras.2025.107734}, pmid = {40544658}, issn = {1874-9968}, mesh = {Animals ; *Blood-Brain Barrier ; *Selenium/administration &amp; dosage ; Mice ; *Epilepsy, Temporal Lobe/therapy/chemically induced/drug therapy ; Disease Models, Animal ; *Nanoparticles/administration &amp; dosage ; Male ; Kainic Acid ; Microbubbles ; *Ultrasonic Waves ; Mice, Inbred C57BL ; }, abstract = {BACKGROUND: Given the limitations of current treatment options for drug-resistant mesial temporal lobe epilepsy (MTLE), the development of novel, nonablative and minimally invasive surgical techniques is essential.<br><br>OBJECTIVE AND METHODS: In this study, low-intensity pulsed ultrasound (LIPU)- and microbubble-induced (henceforth LIPU) blood-brain barrier (BBB) opening combined with selenium-nanoparticle (SeNP) intravenous injection in a mouse model of mesial temporal lobe optimized the latter's bioavailability in the brain epileptic tissue of the kainic acid (KA) mouse model of MTLE. We aimed to assess the safety and antiepileptic potential of LIPU-enhanced SeNP delivery against KA-induced seizures using long-term intracranial electroencephalogram video recordings and evaluating neuroinflammation, astrogliosis, neuronal apoptosis and neurogenesis in the hippocampal tissues of mice.<br><br>RESULTS: First, we established that SeNP intravenous injection combined with LIPU-induced BBB disruption was the most effective method to achieve high and sustained selenium levels in the brain. The safety of this treatment was demonstrated after three treatment sessions, 1-week apart, with no adverse effects observed. Our results further showed a significantly lower frequency of epileptic seizures (-90&#xa0;%, P = 0.001) in KA mice treated with LIPU&#xa0;+&#xa0;SeNPs compared to sham-treated controls. Short- and long-term histological changes were seen after that combined regimen, including less aberrant neurogenesis in the hippocampus hilum, less neuronal death throughout the hippocampus and less hippocampal microglial activation, which might collectively contribute to the observed antiseizure effect.<br><br>CONCLUSION: SeNP injection combined with LIPU-induced BBB disruption demonstrated potential as a promising approach to reduce seizure activity in MTLE; however, statistical comparison did not conclusively establish superiority over SeNPs alone. Further investigations are necessary to consider translational studies in humans.}, }
@article {pmid40542951, year = {2025}, author = {Chen, J and Sun, G and Zhang, Y and Chen, W and Zheng, X and Zhang, S and Hao, Y}, title = {Interactively Integrating Reach and Grasp Information in Macaque Premotor Cortex.}, journal = {Neuroscience bulletin}, volume = {41}, number = {11}, pages = {1991-2009}, pmid = {40542951}, issn = {1995-8218}, mesh = {Animals ; *Motor Cortex/physiology ; *Hand Strength/physiology ; Macaca mulatta ; *Psychomotor Performance/physiology ; Neurons/physiology ; Male ; Cues ; Movement/physiology ; Gestures ; }, abstract = {Reach-to-grasp movements require integrating information on both object location and grip type, but how these elements are planned and to what extent they interact remains unclear. We designed a new experimental paradigm in which monkeys sequentially received reach and grasp cues with delays, requiring them to retain and integrate both cues to grasp the goal object with appropriate hand gestures. Neural activity in the dorsal premotor cortex (PMd) revealed that reach and grasp were similarly represented yet not independent. Upon receiving the second cue, the PMd continued encoding the first, but over half of the neurons displayed incongruent modulations: enhanced, attenuated, or even reversed. Population-level analysis showed significant changes in encoding structure, forming distinct neural patterns. Leveraging canonical correlation analysis, we identified a shared subspace preserving the initial cue's encoding, contributed by both congruent and incongruent neurons. Together, these findings reveal a novel perspective on the interactive planning of reach and grasp within the PMd, providing insights into potential applications for brain-machine interfaces.}, }
@article {pmid40541755, year = {2025}, author = {Yang, A and Tian, J and Wang, W and Zhou, L and Zhou, K}, title = {Shared and distinct neural signatures of feature and spatial attention.}, journal = {NeuroImage}, volume = {317}, number = {}, pages = {121296}, doi = {10.1016/j.neuroimage.2025.121296}, pmid = {40541755}, issn = {1095-9572}, mesh = {Humans ; *Attention/physiology ; Male ; Female ; Adult ; Young Adult ; Machine Learning ; Magnetic Resonance Imaging/methods ; *Space Perception/physiology ; Brain Mapping/methods ; *Brain/physiology ; *Nerve Net/physiology/diagnostic imaging ; *Visual Perception/physiology ; }, abstract = {The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent activation during various visual attention tasks. However, these studies have been limited by small sample sizes and methodological constraints inherent in univariate analysis. Here, we utilized a between-subject whole-brain machine learning approach with a large sample size (N=235) to investigate the neural signatures of FA (FAS) and SA (SAS). Both FAS and SAS showed cross-task predictive capabilities, though inter-task prediction was weaker than intra-task prediction, suggesting both shared and distinct mechanisms. Specifically, the frontoparietal network exhibited the highest predictive performance for FA, while the visual network excelled in predicting SA, highlighting their respective prominence in the two attention processes. Moreover, both signatures demonstrated distributed representations across large-scale brain networks, as each cluster within the signatures was sufficient for predicting FA and SA, but none of them were deemed necessary for either FA or SA. Our study challenges traditional network-centric models of attention, emphasizing distributed brain functioning in attention, and provides comprehensive evidence for shared and distinct neural mechanisms underlying FA and SA.}, }
@article {pmid40541523, year = {2025}, author = {Cao, S and Yin, Y and Li, W and Liu, Z and Chen, Z}, title = {Time-varying formation control for heterogeneous multi-agent systems in the presence of actuator faults and deception attacks.}, journal = {ISA transactions}, volume = {165}, number = {}, pages = {54-63}, doi = {10.1016/j.isatra.2025.06.004}, pmid = {40541523}, issn = {1879-2022}, abstract = {This paper explores the control of time-varying formations in a class of heterogeneous multi-agent systems. The key innovation lies in the simultaneous consideration of hybrid actuator faults and deception attacks. To achieve the control objective, a novel distributed double-layer control scheme, comprising a network layer and a physical layer, is proposed. In the network layer, a distributed observer with secure output feedback control is developed to mitigate severe deception attacks, ensuring that the mean square observer error remains within an acceptable range. In the physical layer, fault compensators are designed to address both additive and multiplicative faults. As a result, the followers achieve time-varying formation control, and closed-loop stability analysis is conducted using the Lyapunov method. Finally, to verify the validity of the theoretical findings, numerical simulations are subsequently conducted.}, }
@article {pmid40538971, year = {2025}, author = {Miao, Y and Li, K and Zhao, W and Zhang, Y}, title = {EA-EEG: a novel model for efficient motor imagery EEG classification with whitening and multi-scale feature integration.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {94}, pmid = {40538971}, issn = {1871-4080}, abstract = {Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience and brain-computer interfaces (BCI) due to its high temporal resolution. In motor imagery EEG (MI-EEG) tasks, EEG signals reflect movement-related brain activity, making them ideal for BCI control. However, the non-stationary nature of MI-EEG signals poses significant challenges for classification, as frequency characteristics vary across tasks and individuals. Traditional preprocessing methods, such as bandpass filtering and standardization, may struggle to adapt to these variations, potentially limiting classification performance. To address this issue, this study introduces EA-EEG, an improved MI-EEG classification model that incorporates whitening as a preprocessing step to reduce channel correlation and enhance the model feature extraction ability. EA-EEG further leverages a multi-scale pooling strategy, combining convolutional networks and root mean square pooling to extract key spatial and temporal features, and applies prototype-based classification to improve MI-EEG classification performance. Experiments on the BCI4-2A and BCI4-2B datasets demonstrate that EA-EEG achieves state-of-the-art performance, with 85.33% accuracy (Kappa = 0.804) on BCI4-2A and 88.05% accuracy (Kappa = 0.761) on BCI4-2B, surpassing existing approaches. These results confirm EA-EEG's effectiveness in handling non-stationary MI-EEG signals, demonstrating its potential for robust BCI applications, including rehabilitation, prosthetic control, and cognitive monitoring.}, }
@article {pmid40538970, year = {2025}, author = {Lin, C and Lu, H and Pan, C and Ma, S and Zhang, Z and Tian, R}, title = {MBRSTCformer: a knowledge embedded local-global spatiotemporal transformer for emotion recognition.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {95}, pmid = {40538970}, issn = {1871-4080}, abstract = {Emotion recognition is an essential prerequisite for realizing generalized BCI, which possesses an extensive range of applications in real life. EEG-based emotion recognition has become mainstream due to its real-time mapping of brain emotional activities, so a robust EEG-based emotion recognition model is of great interest. However, most existing deep learning emotion recognition methods treat the EEG signal as a whole feature extraction, which will destroy its local stimulation differences and fail to extract local features of the brain region well. Inspired by the cognitive mechanisms of the brain, we propose the multi-brain regions spatiotemporal collaboration transformer (MBRSTCfromer) framework for EEG-based emotion recognition. First, inspired by the prior knowledge, we propose the Multi-Brain Regions Collaboration Network. The EEG data are processed separately after being divided by brain regions, and stimulation scores are presented to quantify the stimulation produced by different brain regions and feedback on the stimulation degree to the MBRSTCfromer. Second, we propose a Cascade Pyramid Spatial Fusion Temporal Convolution Network for multi-brain regions EEG features fusion. Finally, we conduct comprehensive experiments on two mainstream emotion recognition datasets to validate the effectiveness of our proposed MBRSTCfromer framework. We achieved 98.63 % , 98.15 % , and 98.58 % accuracy on the three dimensions (arousal, valence, and dominance) on the DEAP dataset; and 97.66 % , 97.07 % , and 97.97 % on the DREAMER dataset.}, }
@article {pmid40536865, year = {2025}, author = {Li, Y and Su, D and Yang, X and Wang, X and Zhao, H and Zhang, J}, title = {From Frequency to Temporal: Three Simple Steps Achieve Lightweight High-Performance Motor Imagery Decoding.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3579528}, pmid = {40536865}, issn = {1558-2531}, abstract = {OBJECTIVE: To address the challenges of high data noise and substantial model computational complexity in Electroencephalography (EEG)-based motor imagery decoding, this study aims to develop a decoding method with both high accuracy and low computational cost.<br><br>METHODS: First, frequency domain analysis was performed to reveal the frequency modeling patterns of deep learning models. Utilizing prior knowledge from brain science regarding the key frequency bands for motor imagery, we adjusted the convolution kernels and pooling sizes of EEGNet to focus on effective frequency bands. Subsequently, a residual network was introduced to preserve high-frequency detailed features. Finally, temporal convolution modules were used to deeply capture temporal dependencies, significantly enhancing feature discriminability.<br><br>RESULTS: Experiments were conducted on the BCI Competition IV 2a and 2b datasets. Our method achieved average classification accuracies of 86.23% and 86.75% respectively, surpassing advanced models like EEG-Conformer and EEG-TransNet. Meanwhile, the Multiply-accumulate operations (MACs) were 27.16M, a reduction of over 50% compared to the comparison models, and the Forward/Backward Pass Size was 14.33MB.<br><br>CONCLUSION: By integrating prior knowledge from brain science with deep learning techniques-specifically frequency domain analysis, residual networks, and temporal convolutions-it is possible to effectively improve the accuracy of EEG motor imagery decoding while substantially reducing model computational complexity.<br><br>SIGNIFICANCE: This paper employs the simplest and most fundamental techniques in its design, highlighting the critical role of brain science knowledge in model development. The proposed method demonstrates broad application potential.}, }
@article {pmid40536747, year = {2025}, author = {Fei, SW and Chen, JL and Hu, YB}, title = {A novel time-frequency feature extraction method of EEG signals utilizing fractional synchrosqueezing wavelet transform.}, journal = {Physical and engineering sciences in medicine}, volume = {48}, number = {3}, pages = {1237-1247}, pmid = {40536747}, issn = {2662-4737}, mesh = {*Electroencephalography/methods ; *Wavelet Analysis ; Humans ; Algorithms ; Time Factors ; *Signal Processing, Computer-Assisted ; }, abstract = {In order to improve the accuracy of Electroencephalogram (EEG) classification, Fractional Synchrosqueezing Wavelet Transform (FSSWT) is proposed to effectively overcome the contradiction between energy concentration and frequency separation in traditional time-frequency analysis methods. Firstly, the principle of FSSWT is introduced, and the time-frequency transformation equation for FSSWT applied to multi-frequency signals is established. The examples of synthetic signal and EEG signal show that the proposed method can suppress the mode aliasing of MI-EEG significantly while maintaining high resolution characteristics, and the energy concentration and related intermediate indexes perform well. The experimental results show that the proposed FSSWT-EEGDNN-ResNet model achieves an average classification accuracy of 95.17% under the condition of the MI-EEG signals processed by FSSWT of eight subjects, demonstrating the effectiveness of FSSWT in EEG signal feature extraction and classification.}, }
@article {pmid40536356, year = {2025}, author = {Rizzo, M and Dawson, JD}, title = {AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.}, journal = {Annals of neurology}, volume = {98}, number = {2}, pages = {211-230}, pmid = {40536356}, issn = {1531-8249}, support = {R01AG017177/AG/NIA NIH HHS/United States ; U54 GM115458/GM/NIGMS NIH HHS/United States ; U54GM115458/GM/NIGMS NIH HHS/United States ; R01 AG017177/AG/NIA NIH HHS/United States ; //University of Nebraska Foundation/ ; }, mesh = {Humans ; *Neurology/methods/trends ; *Artificial Intelligence/trends ; Machine Learning ; Brain-Computer Interfaces ; *Nervous System Diseases/therapy/diagnosis ; }, abstract = {Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3-part series, provides neurologists and neuroscientists with a foundational understanding of AI's key concepts, terminology, and applications. We begin by tracing AI's origins in mathematics, human logic, and brain-inspired neural networks to establish a context for its development. The review highlights AI's growing role in neurological diagnostics and treatment, emphasizing machine learning applications, such as computer vision, brain-machine interfaces, and precision care. By mapping the evolution of AI tools and linking them to neuroscience and human reasoning, we illustrate how AI is reshaping neurological practice and research. We end the review with an overview of model selection in AI and a case scenario illustrating how AI may drive precision neurological care. Part 1 sets the stage for part 2, which will focus on practical applications of AI in real-world scenarios where humans and AI collaborate as joint cognitive systems. Part 3 will examine AI's integration with extensive healthcare and neurology networks, innovative clinical trials, and massive datasets, expanding our vision of AI's global impact on neurology, healthcare systems, and society. ANN NEUROL 2025;98:211-230.}, }
@article {pmid40535306, year = {2025}, author = {Xavier Fidêncio, A and Grün, F and Klaes, C and Iossifidis, I}, title = {Hybrid brain-computer interface using error-related potential and reinforcement learning.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1569411}, pmid = {40535306}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using electroencephalography (EEG) often suffer from performance limitations due to non-stationarities arising from changes in mental state or device characteristics. Addressing these challenges motivates the development of adaptive systems capable of real-time adjustment. This study investigates a novel approach for creating an adaptive, error-related potential (ErrP)-based BCI using reinforcement learning (RL) to dynamically adapt to EEG signal variations. The framework was validated through experiments on a publicly available motor imagery dataset and a novel fast-paced protocol designed to enhance user engagement. Results showed that RL agents effectively learned control policies from user interactions, maintaining robust performance across datasets. However, findings from the game-based protocol revealed that fast-paced motor imagery tasks were ineffective for most participants, highlighting critical challenges in real-time BCI task design. Overall, the results demonstrate the potential of RL for enhancing BCI adaptability while identifying practical constraints in task complexity and user responsiveness.}, }
@article {pmid40535187, year = {2025}, author = {Bao, Y and Zhou, H and Geng, F and Hu, Y}, title = {The relation between game disorder and interruption during game is mediated by game craving.}, journal = {Frontiers in psychology}, volume = {16}, number = {}, pages = {1579016}, pmid = {40535187}, issn = {1664-1078}, abstract = {The burgeoning user base and potential negative effects of excessive involvement in gaming, particularly Internet Gaming Disorder (IGD), demand significant attention. While existing research has explored the susceptibility of individuals with IGD to game-related stimuli, the question of why it is challenging for these individuals to disengage from gaming remains under-explored. Drawing parallels with the concept of interruption, we hypothesize that negative emotions triggered during gaming interruptions would drive individuals' craving for the game and compelling them to continue playing, reinforcing the IGD cycle. In this study, 42 male 'League of Legends' players, aged 19 to 29, experienced controlled interruptions every 3 min during gaming and non-gaming control tasks. Our findings demonstrate that interruptions during gaming elicited significantly higher levels of anger and anxiety compared to the control tasks. Further, we found a positive correlation between the severity of IGD symptoms and the intensity of anger and anxiety induced by gaming interruptions. Additionally, our analysis suggests that craving partially mediates the relationship between anger arousal during gaming interruptions and IGD severity. These findings provide new insights into how emotional responses to gaming interruptions contribute to IGD, offering a novel perspective for future research and potential treatment approaches.}, }
@article {pmid40534746, year = {2025}, author = {Zheng, K and Guo, L and Liang, W and Liu, P}, title = {Comparison of the effects of transcranial direct current stimulation combined with different rehabilitation interventions on motor function in people suffering from stroke-related symptoms: a systematic review and network meta-analysis.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1586685}, pmid = {40534746}, issn = {1664-2295}, abstract = {BACKGROUND: This study employs network meta-analysis to assess the efficacy of transcranial direct current stimulation (tDCS) combined with different rehabilitation approaches in enhancing motor function in people suffering from stroke-related symptoms (PSSS). The objective is to determine the most effective tDCS-based rehabilitation approach and offer valuable evidence to guide clinical decision-making.<br><br>METHODS: This study included randomized controlled trials (RCTs) published before September 23, 2024. We conducted a systematic search across eight databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), China Biology Medicine (SinoMed), Wanfang, and VIP. Network meta-analysis (NMA) was conducted utilizing R Studio and Stata 15.0 for data analysis.<br><br>RESULTS: A total of 74 RCTs were included in this study, encompassing 4,335 PSSS and 11 intervention strategies. The NMA revealed that brain-computer interface therapy (BCIT) in combination with tDCS [surface under the cumulative ranking curve (SUCRA)&#x202f;=&#x202f;88.34%] was the most effective tDCS-based intervention for improving the Fugl-Meyer Assessment for Upper Extremity score in PSSS. Mirror therapy (MT) in combination with tDCS (SUCRA&#x202f;=&#x202f;85.96%) was identified as the optimal intervention for enhancing the Action Research Arm Test score in PSSS. MT&#x202f;+&#x202f;tDCS (SUCRA&#x202f;=&#x202f;84.29%) was the best approach for improving the Fugl-Meyer Assessment for Lower Extremity score. Additionally, acupuncture and moxibustion (AM) in combination with tDCS (SUCRA&#x202f;=&#x202f;77.16%) was the most effective intervention for increasing the Berg Balance Scale score in PSSS. The two-dimensional clustering analysis showed that MT&#x202f;+&#x202f;tDCS (SUCRA&#x202f;=&#x202f;75.83%/85.96%) was the optimal tDCS-based rehabilitation strategy for treating upper limb motor dysfunction in PSSS, while AM+tDCS (SUCRA&#x202f;=&#x202f;76.94%/77.16%) was the best tDCS-based rehabilitation strategy for improving lower limb motor dysfunction in PSSS.<br><br>CONCLUSION: BCIT+tDCS was identified as the optimal tDCS-based rehabilitation strategy for improving upper limb motor ability in PSSS, MT&#x202f;+&#x202f;tDCS was the most effective intervention for enhancing arm mobility, MT&#x202f;+&#x202f;tDCS was the best protocol for improving lower limb motor ability, while AM+tDCS was the best strategy for improving balance ability. Furthermore, MT&#x202f;+&#x202f;tDCS was the optimal tDCS-based rehabilitation approach for treating upper limb motor dysfunction, whereas AM+tDCS was the most effective strategy for addressing lower limb motor dysfunction in PSSS. Future studies may focus on investigating the therapeutic effects of MT combined with tDCS on Berg Balance Scale score in PSSS, as well as the effects of AM combined with tDCS on Action Research Arm Test score, in order to further explore the therapeutic potential of these two intervention strategies.<br><br>https://www.crd.york.ac.uk/PROSPERO/view/CRD42024621998, Identifier PROSPERO CRD42024621998.}, }
@article {pmid40534671, year = {2025}, author = {Zhai, H and Wang, H and Li, H and Wang, X}, title = {The Intersection of Psychedelics and Sleep: Exploring the Impacts on Sleep Architecture, Dream States, and Therapeutic Implications.}, journal = {ACS pharmacology &amp; translational science}, volume = {8}, number = {6}, pages = {1832-1836}, pmid = {40534671}, issn = {2575-9108}, abstract = {The interplay between psychedelics, such as psilocybin, lysergic acid diethylamide (LSD) and dimethyltryptamine (DMT), and sleep is an emerging area, but their impact on sleep remains relatively underexplored. This viewpoint provides a perspective on how psychedelics may alter sleep phases, dreaming, and their potential therapeutic applications for sleep disorders.}, }
@article {pmid40533772, year = {2025}, author = {Lv, S and Ran, X and Xia, M and Zhang, Y and Pang, T and Zhou, X and Zhao, Z and Yu, Y and Gao, Z}, title = {Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {137}, pmid = {40533772}, issn = {1743-0003}, support = {221100310500//the Major Science and Technology Projects of Henan Province/ ; 82302298//the National Natural Science Foundation of China/ ; 82201709//the National Natural Science Foundation of China/ ; 24IRTSTHN042//Innovative Research Team (in Science and Technology) in University of Henan Province/ ; XTkf01//the Open Project Program of Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder/ ; 242102521012//International Science and Technology Cooperation Project of Henan Province/ ; 242102310055//the Science and Technology Research Project of Henan Province/ ; }, mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Stroke/physiopathology ; Aged ; *Hand/physiopathology ; *Stroke Rehabilitation/methods ; *Functional Laterality/physiology ; Support Vector Machine ; Adult ; }, abstract = {BACKGROUND: Stroke is one of the leading causes of adult disability, often resulting in motor dysfunction and brain network reorganization. Brain-computer interface (BCI) systems offer a novel approach to post-stroke motor rehabilitation, with motor imagery (MI) serving as a key paradigm that requires decoding left and right-hand MI differences to optimize system performance. However, the neural dynamics underlying these differences, especially from the perspective of Electroencephalography(EEG) microstate, remain poorly understood in acute stroke patients.<br><br>METHODS: This study enrolled 14 acute stroke patients and recorded their EEG data during left and right-hand MI tasks. Four EEG microstate (A, B, C, and D) were analyzed to extract temporal feature parameters, including Duration, Occurrence Coverage, and transition probabilities(TP). Significant features were used to construct classification models using Linear Discriminant Analysis(LDA), Support Vector Machines(SVM), and K-Nearest Neighbors(KNN) algorithms.<br><br>RESULTS: Microstate analysis revealed significant differences in temporal features of microstate A and C during left and right-hand MI tasks. During left-hand MI, microstate A exhibited longer Duration(Pfdr=0.032), higher Occurrence(Pfdr=0.018), and greater Coverage(Pfdr=0.004) compared to the right-hand, whereas microstate C showed the opposite pattern(Pfdr=0.044, Pfdr=0.004, Pfdr=0.004). Additionally, the TP from microstate B&#x2192;A, D&#x2192;A and D&#x2192;C also demonstrated significant differences(Pfdr=0.04, Pfdr<0.001, Pfdr=0.006). Among classification models, the KNN algorithm achieved the highest accuracy of 75.00%, outperforming LDA and SVM. Fisher analysis indicated that the Occurrence of microstate C was the most discriminative feature for distinguishing between left and right-hand MI tasks in acute stroke patients.<br><br>CONCLUSION: Differences in EEG microstate features during left and right-hand MI tasks in acute stroke patients may reflect lateralized mechanisms of brain network reorganization. Microstate features hold significant potential for both post-stroke brain function assessment and the optimization of BCI systems. These features could enhance adaptive BCI strategies in acute stroke rehabilitation.}, }
@article {pmid40532880, year = {2025}, author = {Luo, X and Dong, J and Li, T}, title = {Comparative cytokine signatures and cognitive deficits in early-onset schizophrenia and adolescent major depression: Toward refined diagnostic classification frameworks.}, journal = {Journal of affective disorders}, volume = {389}, number = {}, pages = {119667}, doi = {10.1016/j.jad.2025.119667}, pmid = {40532880}, issn = {1573-2517}, mesh = {Humans ; *Depressive Disorder, Major/blood/diagnosis/classification/psychology/complications ; Male ; Adolescent ; Female ; *Schizophrenia/blood/diagnosis/classification/complications ; *Cytokines/blood ; *Cognitive Dysfunction/blood/diagnosis ; Neuropsychological Tests ; Machine Learning ; Biomarkers/blood ; }, abstract = {BACKGROUND: This study analyzed plasma cytokine patterns in individuals with schizophrenia (SCZ), major depressive disorder (MDD), and healthy controls, explored the link between cytokine levels and cognitive function, and created machine learning models to evaluate the diagnostic potential of cytokine and cognitive assessments.<br><br>METHODS: This study involved 64 early-onset SCZ patients, 53 adolescents with MDD, and 33 healthy controls. The plasma concentrations of 44 cytokines were measured using the LUMINEX multiplex assay. Cognitive function was tested with the Cambridge Neuropsychological Test Automated Battery. Random Forest and Extreme Gradient Boosting models were used for classification, with their effectiveness evaluated via ROC curve analysis.<br><br>RESULTS: SCZ patients exhibited significantly elevated levels of CCL11, IL-2 and IL-13, while MDD patients displayed increased CXCL2 and G-CSF levels but decreased CCL20 and CCL11 levels. SCZ patients showed significant cognitive impairments compared to healthy controls. Elevated CCL11 were associated with poorer memory accuracy, and higher G-CSF were linked to worse executive function. The XGBoost model was more sensitive in classifying MDD than the Random Forest model, but both struggled to differentiate SCZ patients from healthy controls due to low specificity.<br><br>CONCLUSION: Early-onset SCZ and adolescent MDD patients showed unique peripheral cytokine profiles, with SCZ patients experiencing significant cognitive deficits. The cytokine CCL11 was found to have a significant association with cognitive dysfunction. Cytokine levels and cognitive assessments may serve as potential biomarkers for the diagnosis of MDD.}, }
@article {pmid40530005, year = {2025}, author = {Shao, W and Meng, W and Zuo, J and Li, X and Ming, D}, title = {Opportunities and Challenges of Brain-on-a-Chip Interfaces.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {6}, number = {}, pages = {0287}, pmid = {40530005}, issn = {2692-7632}, abstract = {The convergence of life sciences and information technology is driving a new wave of scientific and technological innovation, with brain-on-a-chip interfaces (BoCIs) emerging as a prominent area of focus in the brain-computer interface field. BoCIs aim to create an interactive bridge between lab-grown brains and the external environment, utilizing advanced encoding and decoding technologies alongside electrodes. Unlike classical brain-computer interfaces that rely on human or animal brains, BoCIs employ lab-grown brains, offering greater experimental controllability and scalability. Central to this innovation is the advancement of stem cell and microelectrode array technologies, which facilitate the development of neuro-electrode hybrid structures to ensure effective signal transmission in lab-grown brains. Furthermore, the evolution of BoCI systems depends on a range of stimulation strategies and novel decoding algorithms, including artificial-intelligence-driven methods, which has expanded BoCI applications to pattern recognition and robotic control. Biological neural networks inherently grant BoCI systems neuro-inspired computational properties-such as ultralow energy consumption and dynamic plasticity-that surpass those of conventional artificial intelligence. Functionally, BoCIs offer a novel framework for hybrid intelligence, merging the cognitive capabilities of biological systems (e.g., learning and memory) with the computational efficiency of machines. However, critical challenges span 4 domains: optimizing neural maturation and functional regionalization, engineering high-fidelity bioelectronic interfaces for robust signal transduction, enhancing adaptive neuroplasticity mechanisms in lab-grown brains, and achieving biophysically coherent integration with artificial intelligence architectures. Addressing these limitations could offer insights into emergent intelligence while enabling next-generation biocomputing solutions.}, }
@article {pmid40529543, year = {2025}, author = {Faber, J and Tsytsarev, V and Pais-Vieira, M and Aksenova, T}, title = {Editorial: Sensorimotor decoding: characterization and modeling for rehabilitation and assistive technologies, volume II.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1619232}, pmid = {40529543}, issn = {1662-5161}, }
@article {pmid40527877, year = {2025}, author = {Moreira, JPC and Carvalho, VR and Mendes, EMAM and Fallah, A and Sejnowski, TJ and Lainscsek, C and Comstock, L}, title = {An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {1017}, pmid = {40527877}, issn = {2052-4463}, mesh = {Humans ; *Electroencephalography ; *Speech ; *Brain-Computer Interfaces ; Phonetics ; Transcranial Magnetic Stimulation ; Adult ; Male ; Female ; }, abstract = {Electroencephalography (EEG) holds promise for brain-computer interface (BCI) devices as a non-invasive measure of neural activity. With increased attention to EEG-based BCI systems, publicly available datasets incorporating the complex stimuli found in naturalistic speech are necessary to establish a common standard of performance within the BCI community. Effective solutions must overcome noise in the EEG signal and remain reliable across sessions and stimuli that reflect types of real-world linguistic complexity without overfitting to a dataset or task. We present two validated datasets (N=8 and N=16) for classification at the phoneme and word level and by the articulatory properties of phonemes. EEG signals were recorded from 64 channels while subjects listened to and repeated six consonants and five vowels. Individual phonemes were combined in different phonetic environments to produce coarticulated variation in 40 consonant-vowel pairs, 20 real words, and 20 pseudowords. Phoneme pairs and words were presented during a control condition and during transcranial magnetic stimulation (TMS) to assess whether stimulation would augment the EEG signal associated with specific articulatory processes.}, }
@article {pmid40527666, year = {2025}, author = {Toner, AA and Eberlin, L and Pichaimuthu, R and Tompkins, T and Szekeres, M}, title = {The use of robotics and artificial intelligence in upper extremity rehabilitation following traumatic injury: A scoping review.}, journal = {Journal of hand therapy : official journal of the American Society of Hand Therapists}, volume = {38}, number = {2}, pages = {254-265}, doi = {10.1016/j.jht.2025.04.009}, pmid = {40527666}, issn = {1545-004X}, mesh = {Humans ; *Artificial Intelligence ; *Robotics ; *Upper Extremity/injuries ; *Arm Injuries/rehabilitation ; }, abstract = {BACKGROUND: With the recent advances in technology and its increased use in society, healthcare practices work to identify areas where technology can be implemented to enhance patient care. Rehabilitation has begun to incorporate the use of robotics and artificial intelligence to facilitate positive outcomes and assist in achieving patient goals following injury. While traumatic upper extremity injuries can result in increased levels of pain and disability for an individual, it is not clear how robotics and artificial intelligence have been used in hand rehabilitation to address these issues.<br><br>PURPOSE: The objective of this study is to understand the extent of the use of robotics and artificial intelligence for traumatic upper extremity injuries.<br><br>STUDY DESIGN: Scoping review.<br><br>METHODS: The search strategy was conducted in Embase, CINAHL, MEDLINE, and PsycINFO and identified 7105 studies published between 2014 and 2024. Following title and abstract screening and removal of duplicates, 122 full-text articles were screened. A total of 13 papers were included that used artificial intelligence, robotics, or other technology in rehabilitation programs for individuals with traumatic upper extremity injuries.<br><br>RESULTS: Of the 13 included studies: 11 used robotics such as the KINARM Exoskeleton, the Hybrid Assistive Limb, and the WRISTBOT, and two used artificial intelligence including chatbots and brain-computer interface. Multiple outcomes were reported with the most common including range of motion, strength, pain, function, and joint sense.<br><br>CONCLUSIONS: Currently, there is a wide variety of different forms of robotics with very little reported use of artificial intelligence for traumatic upper extremity injuries. There exists opportunities for future research to further investigate how these technologies can influence clinical outcomes for patients with traumatic upper extremity injuries.}, }
@article {pmid40527337, year = {2025}, author = {Temmar, H and Willsey, MS and Costello, JT and Mender, MJ and Cubillos, LH and DeMatteo, JC and Lam, JL and Wallace, DM and Kelberman, MM and Patil, PG and Chestek, CA}, title = {Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, pmid = {40527337}, issn = {1741-2552}, support = {T32 NS007222/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; Male ; Macaca mulatta ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Linear Models ; Fingers/physiology ; Movement/physiology ; }, abstract = {Objective.Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how nonlinear and linear approaches predict individuated finger movements in open and closed-loop settings.Approach.Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex and performed a 2D dexterous finger movement task for a juice reward. Multiple linear and nonlinear 'decoders' were used to map from recorded spiking band power into movement kinematics. Performance of these decoders was compared and analyzed to determine how nonlinear decoders perform in both open and closed-loop scenarios.Main Results.We show that nonlinear decoders enable control which more closely resembles true hand movements, producing distributions of velocities 80.7% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of temporally-convolved feedforward neural network convergence by up to 188.9%, along with improving average performance and training speed. Finally, we show that TCNs and long short-term memory can effectively leverage training data from multiple task variations to improve generalization.Significance.The results of this study support artificial neural networks of all kinds as the future of BMI decoding and show potential for generalizing over less constrained tasks.}, }
@article {pmid40527331, year = {2025}, author = {Xin, H and Li, H and Qi, H}, title = {A novel paradigm for two-degree-of-freedom BCI control based on ERP in-duced by overt and covert visual attention.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ade56a}, pmid = {40527331}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Attention/physiology ; Male ; *Electroencephalography/methods ; Female ; Young Adult ; Adult ; *Evoked Potentials/physiology ; *Photic Stimulation/methods ; *Visual Perception/physiology ; *Evoked Potentials, Visual/physiology ; Psychomotor Performance/physiology ; }, abstract = {Objective.This study developed a novel brain-computer interface (BCI) paradigm based on event-related potentials (ERPs) to achieve simultaneous two-degree-of-freedom control through overt and covert visual selective attention.Approach.In this paradigm, three stimuli were arranged equidistantly around the cursor. Participants selected two stimuli as attention targets based on the relative position of the cursor and the intended movement destination, focusing overtly on one while covertly attending to the other. EEG data collected during offline experiments were used to train classifiers for overt and covert targets (CT), and the outputs of these classifiers were employed in online experiments to construct movement vectors for controlling the cursor in a 2D space.Main results.EEG analysis demonstrated that overt and CT elicited distinct ERP signals, with classification accuracies of 96.2% and 92.4%, respectively. The accuracy of simultaneously identifying both targets reached 91.0%. In online experiments, the success rate of moving the cursor to the target region was 92.6%, and 88.2% of cursor movements were in the desired direction. These results confirm the feasibility of achieving 2D control through ERP based selective attention and validate the effectiveness of the proposed paradigm.Significance.This study introduces a novel EEG-based approach for multi-degree-of-freedom control, expanding the capabilities of traditional ERP based BCIs, which have primarily been limited to single-degree-of-freedom applications.}, }
@article {pmid40526548, year = {2025}, author = {Li, C and Cao, Z and Pan, Y and Zhu, P and Li, P and Li, F and Chen, H and Lu, BL and Wan, F and Yao, D and Xu, P}, title = {EEG-Based Emotion Monitoring and Regulation System by Learning the Discriminative Brain Network Manifold.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {36}, number = {10}, pages = {17751-17765}, doi = {10.1109/TNNLS.2025.3576182}, pmid = {40526548}, issn = {2162-2388}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain/physiology ; *Neural Networks, Computer ; Male ; *Machine Learning ; Adult ; Young Adult ; Algorithms ; Female ; *Emotional Regulation/physiology ; Brain-Computer Interfaces ; }, abstract = {Emotion recognition based on electroencephalogram (EEG) is fundamentally associated with human-like intelligence system. However, due to the noise-sensitive characteristics of EEGs and the individual variability of emotions, it is very challenging to extract inherent emotion dependent patterns from emotional EEG signals. In this work, we propose a L1-norm space defined discriminative brain network manifold learning model (L1-SGL), in which the EEG noise outliers can be effectively separated and the pseudolabeled samples caused by subjective feelings can be automatically corrected. Off-line experimental results consistently indicate that the L1-SGL can effectively suppress the influence of noise and achieve an incomparable superiority performance over other existing methods in EEG emotion recognition. Besides, benefiting from the time efficiency of the L1-SGL, an online emotion monitoring and regulation system is further implemented in this work. On-line emotion decoding experimental results (86.30%) of 25 participants prove that the L1-SGL can effectively satisfy the real-time requirements of on-line emotional monitoring applications, and the significant negative emotion regulation experimental results ($p \lt 0.001$) further confirm the feasibility and effectiveness of L1-SGL model in real-time emotion regulation and interactive applications. Overall, the L1-SGL provides a promising solution for the real-time online affective brain-computer interfaces (aBCIs) and the intelligent clinical closed-loop treatments.}, }
@article {pmid40526539, year = {2025}, author = {Jia, T and Long, H and Ji, L and Guan, X}, title = {EEG-based Spatial-Channel Interaction Attention Neural Networks for Detecting Empathy in Motor Collaboration.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3580617}, pmid = {40526539}, issn = {2168-2208}, abstract = {Embodied intelligence and humanoid robots aim to mimic interpersonal interactions to achieve affective human-robot interaction (HRI). A major challenge in advancing HRI lies in effectively emulating interpersonal affective interactions and evaluating the resulting artificial empathy. To address these challenges, we propose SpatialChannel Interaction Attention Neural Networks (SCIANN)-a novel EEG-based architecture that combines topological brain activation and connectivity patterns to decode empathy in motor collaboration. A private EEG dataset from a collaborative brain-computer interface motor control experiment and a public EEG dataset from a dyadic perceptual crossing experiment were used for evaluating SCIANN's performance with comparisons with five baseline models. Results showed that SCIANN outperformed the state-of-the-art baseline models. In the private dataset, SCIANN reached an accuracy of 100% both in inter-subject and cross-subject tests for detecting whether empathy is induced or not. For classifying 4-class empathy, it achieved an accuracy of 98.3% in the inter-subject test, and 48.1% in the cross-subject test. In the public dataset, SCIANN reached a classification accuracy of 92.2% in inter-subject and 91.7% in cross-subject tests for detecting whether empathy is induced or not. Feature visualization results revealed that contributing EEG channel importance features and channel interaction features aligned with established neurophysiological findings. These results collectively demonstrate SCIANN's potential as a robust, generalizable framework for artificial empathy assessment in HRI applications.}, }
@article {pmid40526534, year = {2025}, author = {Luo, T and Zhang, J and Qiu, Y and Zhang, L and Hu, Y and Yu, Z and Liang, Z}, title = {M3D: Manifold-Based Domain Adaptation With Dynamic Distribution for Non-Deep Transfer Learning in Cross-Subject and Cross-Session EEG-Based Emotion Recognition.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {11}, pages = {8126-8139}, doi = {10.1109/JBHI.2025.3580612}, pmid = {40526534}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology/classification ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Adult ; Male ; Female ; Deep Learning ; Young Adult ; Algorithms ; Machine Learning ; }, abstract = {Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces (aBCIs) is crucial for affective computing but is hindered by EEG's non-stationarity, individual variability, and the high cost of large-scale labeled data. Deep learning-based approaches, while effective, require substantial computational resources and large datasets, limiting their practicality. To address these challenges, we propose Manifold-based Domain Adaptation with Dynamic Distribution (M3D), a lightweight non-deep transfer learning framework. M3D includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning. The data undergoes a transformation onto an optimal Grassmann manifold space, enabling dynamic alignment of the source and target domains. This process prioritizes both marginal and conditional distributions according to their significance, ensuring enhanced adaptation efficiency across various types of data. In the classifier learning, the principle of structural risk minimization is integrated to develop robust classification models. This is complemented by dynamic distribution alignment, which refines the classifier iteratively. Additionally, the ensemble learning module aggregates the classifiers obtained at different stages of the optimization process, which leverages the diversity of the classifiers to enhance the overall prediction accuracy. The proposed M3D framework is evaluated on three benchmark EEG emotion recognition datasets using two validation protocols (cross-subject single-session and cross-subject cross-session), as well as on a clinical EEG dataset of Major Depressive Disorder (MDD). Experimental results demonstrate that M3D outperforms traditional non-deep learning methods, achieving an average improvement of 6.67%, while achieving deep learning-comparable performance with significantly lower data and computational requirements. These findings highlight the potential of M3D to enhance the practicality and applicability of aBCIs in real-world scenarios.}, }
@article {pmid40524963, year = {2025}, author = {Zhao, W and Lu, H and Zhang, B and Zheng, X and Wang, W and Zhou, H}, title = {TCANet: a temporal convolutional attention network for motor imagery EEG decoding.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {91}, pmid = {40524963}, issn = {1871-4080}, abstract = {Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain-computer interface (BCI) systems. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal dependencies by progressively integrating local, fused, and global features. Specifically, TCANet employs a multi-scale convolutional module to extract local spatiotemporal representations across multiple temporal resolutions. A temporal convolutional module then fuses and compresses these multi-scale features while modeling both short- and long-term dependencies. Subsequently, a stacked multi-head self-attention mechanism refines the global representations, followed by a fully connected layer that performs MI-EEG classification. The proposed model was systematically evaluated on the BCI IV-2a and IV-2b datasets under both subject-dependent and subject-independent settings. In subject-dependent classification, TCANet achieved accuracies of 83.06% and 88.52% on BCI IV-2a and IV-2b respectively, with corresponding Kappa values of 0.7742 and 0.7703, outperforming multiple representative baselines. In the more challenging subject-independent setting, TCANet achieved competitive performance on IV-2a and demonstrated potential for improvement on IV-2b. The code is available at https://github.com/snailpt/TCANet.}, }
@article {pmid40523119, year = {2025}, author = {Kamitani, Y and Tanaka, M and Shirakawa, K}, title = {Visual Image Reconstruction from Brain Activity via Latent Representation.}, journal = {Annual review of vision science}, volume = {11}, number = {1}, pages = {611-634}, doi = {10.1146/annurev-vision-110423-023616}, pmid = {40523119}, issn = {2374-4650}, mesh = {Humans ; *Brain/physiology ; *Visual Perception/physiology ; Neural Networks, Computer ; *Image Processing, Computer-Assisted/methods ; Brain-Computer Interfaces ; }, abstract = {Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and generative models. This review traces the field's evolution from early classification approaches to sophisticated reconstructions that capture detailed, subjective visual experiences, emphasizing the roles of hierarchical latent representations, compositional strategies, and modular architectures. Despite notable progress, challenges remain, such as achieving true zero-shot generalization for unseen images and accurately modeling the complex, subjective aspects of perception. We discuss the need for diverse datasets, refined evaluation metrics aligned with human perceptual judgments, and compositional representations that strengthen model robustness and generalizability. Ethical issues, including privacy, consent, and potential misuse, are underscored as critical considerations for responsible development. Visual image reconstruction offers promising insights into neural coding and enables new psychological measurements of visual experiences, with applications spanning clinical diagnostics and brain-machine interfaces.}, }
@article {pmid40522886, year = {2025}, author = {Nam, J and Shin, H and You, C and Baeg, E and Kim, JG and Yang, S and Han, MR}, title = {Cortical Stimulation-Based Transcriptome Shifts on Parkinson's Disease Animal Model.}, journal = {ASN neuro}, volume = {17}, number = {1}, pages = {2513881}, pmid = {40522886}, issn = {1759-0914}, mesh = {Animals ; *Transcriptome/physiology ; Male ; Disease Models, Animal ; *Motor Cortex/metabolism ; *Parkinson Disease/genetics/therapy/metabolism ; Mice ; Mice, Inbred C57BL ; }, abstract = {Parkinson's disease is the second most prevalent neurodegenerative disorder and is characterized by the degeneration of dopaminergic neurons. Significant improvements in gait balance, particularly in step length and velocity, were observed with less invasive wireless cortical stimulation. Transcriptome sequencing was performed to demonstrate the cellular mechanism, specifically targeting the primary motor cortex, where stimulation was applied. Our findings indicated that 38 differentially expressed genes (DEGs), initially downregulated following Parkinson's disease induction, were subsequently restored to normal levels after cortical stimulation. These 38 DEGs are potential targets for the treatment of motor disorders in Parkinson's disease. These genes are implicated in crucial processes, such as astrocyte-mediated blood vessel development and microglia-mediated phagocytosis of damaged motor neurons, suggesting their significant roles in improving behavioral disorders. Moreover, these biomarkers not only facilitate the rapid and accurate diagnosis of Parkinson's disease but also assist in precision medicine approaches.}, }
@article {pmid40522806, year = {2025}, author = {Wang, P and Qi, Y and Pan, G}, title = {Partial Domain Adaptation for Stable Neural Decoding in Disentangled Latent Subspaces.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3577222}, pmid = {40522806}, issn = {1558-2531}, abstract = {OBJECTIVE: Brain-Computer Interfaces (BCI) have demonstrated significant potential in neural rehabilitation. However, the variability of non-stationary neural signals often leads to instabilities of behavioral decoding, posing critical obstacles to chronic applications. Domain adaptation technique offers a promising solution by obtaining the invariant neural representation against non-stationary signals through distribution alignment. Here, we demonstrate domain adaptation that directly applied to neural data may lead to unstable performance, mostly due to the common presence of task-irrelevant components within neural signals. To address this, we aim to identify task-relevant components to achieve more stable neural alignment.<br><br>METHODS: In this work, we propose a novel partial domain adaptation (PDA) framework that performs neural alignment within the task-relevant latent subspace. With pre-aligned short-time windows as input, the proposed latent space is constructed based on a causal dynamical system, enabling more flexible neural decoding. Within this latent space, task-relevant dynamical features are disentangled from task-irrelevant components through VAE-based representation learning and adversarial alignment. The aligned task-relevant features are then employed for neural decoding across domains.<br><br>RESULTS: Using Lyapunov theory, we analytically validated the improved stability of late our neural representations through alignment. Experiments with various neural datasets verified that PDA significantly enhanced the cross-session decoding performance.<br><br>CONCLUSION: PDA successfully achieved stable neural representations across different experimental days, enabling reliable long-term decoding.<br><br>SIGNIFICANCE: Our approach provides a novel aspect for addressing the challenge of chronic reliability in real-world BCI deployments.}, }
@article {pmid40522801, year = {2025}, author = {Yao, Y and De Swaef, W and Geirnaert, S and Bertrand, A}, title = {EEG-Based Decoding of Selective Visual Attention in Superimposed Videos.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {10}, pages = {7248-7261}, doi = {10.1109/JBHI.2025.3580261}, pmid = {40522801}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; Adult ; Male ; Female ; Young Adult ; *Signal Processing, Computer-Assisted ; Eye Movements/physiology ; Photic Stimulation ; Video Recording ; *Visual Perception/physiology ; }, abstract = {Selective attention enables humans to efficiently process visual stimuli by enhancing important elements and filtering out irrelevant information. Locating visual attention is fundamental in neuroscience with potential applications in brain-computer interfaces. Conventional paradigms often use synthetic stimuli or static images, but visual stimuli in real life contain smooth and highly irregular dynamics. We show that these irregular dynamics can be decoded from electroencephalography (EEG) signals for selective visual attention decoding. To this end, we propose a free-viewing paradigm in which participants attend to one of two superimposed videos, each showing a center-aligned person performing a stage act. Superimposing ensures that the relative differences in the neural responses are not driven by differences in object locations. A stimulus-informed decoder is trained to extract EEG components correlated with the motion patterns of the attended object, and can detect the attended object in unseen data with significantly above-chance accuracy. This shows that the EEG responses to naturalistic motion are modulated by selective attention. Eye movements are also found to be correlated to the motion patterns in the attended video, despite the spatial overlap with the distractor. We further show that these eye movements do not dominantly drive the EEG-based decoding and that complementary information exists in EEG and gaze data. Moreover, our results indicate that EEG may also capture neural responses to unattended objects. To our knowledge, this study is the first to explore EEG-based selective visual attention decoding on natural videos, opening new possibilities for experiment design.}, }
@article {pmid40522765, year = {2026}, author = {Kinfe, T and Brenner, S and Etminan, N}, title = {Brain-computer interfaces re-shape functional neurosurgery.}, journal = {Neural regeneration research}, volume = {21}, number = {3}, pages = {1122-1123}, pmid = {40522765}, issn = {1673-5374}, }
@article {pmid40522539, year = {2025}, author = {Ullah, A and Bookwalter, J and Sant, H and Azapagic, A and Shea, J and Berlet, R and Jha, N and Bailes, J and Gale, BK}, title = {An Osmosis-driven 3D-printed brain implant for drug delivery.}, journal = {Biomedical microdevices}, volume = {27}, number = {3}, pages = {29}, pmid = {40522539}, issn = {1572-8781}, mesh = {*Printing, Three-Dimensional ; *Osmosis ; *Drug Delivery Systems/instrumentation ; *Brain/surgery/metabolism ; *Brain Neoplasms/drug therapy ; Humans ; *Prostheses and Implants ; Glioblastoma/drug therapy ; }, abstract = {Glioblastoma is a highly malignant brain tumor with limited survival rates due to challenges in complete surgical excision, high recurrence (> 90%), and the inefficacy of systemic drug delivery. Significant efforts have been made to develop drug-loaded brain implants, catheters, and wafers aimed at enhancing survival rates by suppressing tumor recurrence. However, these devices often fail due to clogging, reflux, and the inability to be fully implanted intracranially. Furthermore, a lack of tissue penetration, diffusion distance, and duration of therapy have limited effectiveness of these implants. To address existing challenges, this study reports an osmosis-driven, 3D-printed brain implant with the potential for precise device customization to meet therapeutic needs, while negating systemic toxicity. It is capable of being loaded with two distinct therapeutic agents and implanted directly into the tumor resection cavity during surgery. The device features dual reservoirs, osmotic membranes, and precision-engineered needles for anchoring the device in the resection cavity and perfusing. Further, the device was characterized in vitro using 0.2% agarose gel as a brain tissue analog, with food dye as a drug analog and sodium chloride serving as an osmogen. A design of experiment approach was implemented to investigate various parameters, including membrane pore size, osmogen concentration, needle length, and their effects on release rates. The results demonstrated that the optimized implant achieves flow rates of 2.5 ± 0.1 µl/Hr and diffusion distance of up to 15.5 ± 0.4 mm, using 25 nm pore osmotic membranes with 25.3% osmogen concentration, aligning with model predictions.}, }
@article {pmid40520823, year = {2025}, author = {Ivan Brown, A and MacDuffie, KE and Goering, S and Klein, E}, title = {The "wheels that keep me goin'": invisible forms of support for brain pioneers.}, journal = {Neuroethics}, volume = {18}, number = {1}, pages = {}, pmid = {40520823}, issn = {1874-5490}, support = {R01 MH130457/MH/NIMH NIH HHS/United States ; }, abstract = {Research participants in long-term, first-in-human trials of implantable neural devices (i.e., brain pioneers) are critical to the success of the emerging field of neurotechnology. How these participants fare in studies can make or break a research program. Yet, their ability to enroll, participate, and seamlessly exit studies relies on both the support of family/caregivers and care from researchers that is often hidden from view. The present study offers an initial exploration of the different kinds of support that play a role in neural device trials from the perspectives of brain pioneers and their support partners (spouses, paid caregivers, parents, etc.). Using a mixed methods approach (semi-structured, open-ended interviews and a survey) with interpretive grounded theory, we present narratives from a study of six pioneers -- four in brain-computer interface (BCI) trials, and two in deep brain stimulation (DBS) trials -- and five support partners, about their experiences of being supported and supporting participants in implantable neural device studies. Our findings indicate the substantial amount of work involved on the part of pioneers - and some support partners - to make these studies successful. A central finding of the study is that non-logistical forms of support - social, emotional, and epistemic support - play a role, alongside more widely acknowledged forms of support, such as transportation and physical and clinical care. We argue that developing a better understanding of the kinds of support that enable neurotechnology studies to go well can help bridge the gap between abstract ethical principles of caring for subjects and on-the-ground practice.}, }
@article {pmid40520820, year = {2025}, author = {Aubinet, C and Gillet, A and Regnier, A}, title = {Disorders of Consciousness, Language and Communication Following Severe Brain Injury.}, journal = {Psychologica Belgica}, volume = {65}, number = {1}, pages = {169-188}, pmid = {40520820}, issn = {2054-670X}, abstract = {Patients with severe brain injuries and disorders of consciousness (DoC) represent a complex clinical population in terms of diagnosis, prognosis, and management, including critical ethical considerations. Behavioral assessment scales remain the primary tools for evaluating the level of consciousness of these patients following a coma; however, they heavily depend on language and communication abilities. This reliance can lead to underestimating residual consciousness in cases where language impairments go undetected. Accordingly, the latest international guidelines on DoC diagnosis have highlighted aphasia as a significant confounding factor that must be addressed. On the other hand, accurately assessing residual language abilities is essential for better characterizing the patient's cognitive profile. This, in turn, enables neuropsychologists and speech-language therapists to tailor and plan effective rehabilitation programs. This review examines the current literature on language function and communication skills in patients with DoC, detailing the latest tools for assessing and managing language and consciousness in individuals with severe brain injuries. We explore the critical role of language function in evaluating residual consciousness, particularly in DoC behavioral diagnoses and in identifying covert consciousness through neuroimaging passive or active paradigms. Furthermore, we discuss how therapies aimed at recovering consciousness-such as pharmacological treatments, electromagnetic therapies, sensory or cognitive stimulation, and communication aids like brain-computer interfaces-may also impact or rely on language function and communication abilities. Further research is needed to refine methodologies and better understand the interplay between language processing, communication and levels of consciousness.}, }
@article {pmid40519866, year = {2025}, author = {Wang, K and Ren, S and Jia, Y and Yan, X and Wang, L and Fan, Y}, title = {Neuromorphic chips for biomedical engineering.}, journal = {Mechanobiology in medicine}, volume = {3}, number = {3}, pages = {100133}, pmid = {40519866}, issn = {2949-9070}, abstract = {The modern medical field faces two critical challenges: the dramatic increase in data complexity and the explosive growth in data size. Especially in current research, medical diagnostic, and data processing devices relying on traditional computer architecture are increasingly showing limitations when faced with dynamic temporal and spatial processing requirements, as well as high-dimensional data processing tasks. Neuromorphic devices provide a new way for biomedical data processing due to their low energy consumption and high dynamic information processing capabilities. This paper aims to reveal the advantages of neuromorphic devices in biomedical applications. First, this review emphasizes the urgent need of biomedical engineering for diversify clinical diagnostic techniques. Secondly, the feasibility of the application in biomedical engineering is demonstrated by reviewing the historical development of neuromorphic devices from basic modeling to multimodal signal processing. In addition, this paper demonstrates the great potential of neuromorphic chips for application in the fields of biosensing technology, medical image processing and generation, rehabilitation medical engineering, and brain-computer interfaces. Finally, this review provides the pathways for constructing standardized experimental protocols using biocompatible technologies, personalized treatment strategies, and systematic clinical validation. In summary, neuromorphic devices will drive technological innovation in the biomedical field and make significant contributions to life health.}, }
@article {pmid40519178, year = {2025}, author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG}, title = {Response to letter to the editor on 'the potential power of Neuralink - how brain-machine interfaces can revolutionize medicine'.}, journal = {Expert review of medical devices}, volume = {22}, number = {8}, pages = {781-782}, doi = {10.1080/17434440.2025.2521399}, pmid = {40519178}, issn = {1745-2422}, }
@article {pmid40519177, year = {2025}, author = {Cordero, DA}, title = {Letter to the editor on 'the potential power of neuralink - how brain-machine interfaces can revolutionize medicine'.}, journal = {Expert review of medical devices}, volume = {22}, number = {8}, pages = {779-780}, doi = {10.1080/17434440.2025.2521393}, pmid = {40519177}, issn = {1745-2422}, }
@article {pmid40514107, year = {2025}, author = {Zuo, M and Chen, X and Sui, L}, title = {A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.}, journal = {Medical engineering &amp; physics}, volume = {141}, number = {}, pages = {104363}, doi = {10.1016/j.medengphy.2025.104363}, pmid = {40514107}, issn = {1873-4030}, mesh = {*Virtual Reality ; *Electroencephalography ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Brain-Computer Interfaces ; Female ; Young Adult ; Attention ; *Neural Networks, Computer ; }, abstract = {Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, education, and healthcare, offering more immersive and interactive experiences than traditional 2D formats. This study investigates EEG classification in response to 2D and 3D VR stimuli to deepen our understanding of the neural mechanisms driving VR interactions, with implications for brain-computer interfaces (BCIs). We introduce STA-EEGNet, an innovative model that enhances EEGNet by incorporating spatial-temporal attention (STA), improving EEG signal classification from VR environments. A one-way analysis of variance (ANOVA) was utilized to optimize channel selection, enhancing model accuracy. Comparative experiments showed that STA-EEGNet surpassed traditional EEGNet, achieving a peak accuracy of 99.78 % with channel selection. These findings highlight the benefits of spatial-temporal attention and optimal channel selection in classifying VR-evoked EEG data. This study underscores the importance of integrating spatial-temporal attention with compact convolutional neural networks like EEGNet, not only improving EEG signal classification but also advancing neural decoding and optimizing BCI applications.}, }
@article {pmid40513959, year = {2025}, author = {Yang, Q and Guo, W and Wang, L and Zhang, Y and Tian, Y and Ming, D and Xiao, X and Yang, J}, title = {Effects of Fstl1 on neuroinflammation and microglia activation in lipopolysaccharide-induced acute depression-like mice.}, journal = {Behavioural brain research}, volume = {493}, number = {}, pages = {115696}, doi = {10.1016/j.bbr.2025.115696}, pmid = {40513959}, issn = {1872-7549}, mesh = {Animals ; *Follistatin-Related Proteins/metabolism/genetics ; *Microglia/metabolism/drug effects ; Lipopolysaccharides/pharmacology ; Mice ; Male ; *Depression/metabolism/chemically induced ; Female ; Disease Models, Animal ; Hippocampus/metabolism ; *Neuroinflammatory Diseases/metabolism ; Cytokines/metabolism ; Mice, Inbred C57BL ; Behavior, Animal/physiology ; Neurons/metabolism ; Inflammation/metabolism ; }, abstract = {Depression is the most prevalent psychiatric illness, and its pathogenesis is associated with neuroinflammation. Follistatinlike protein 1 (FSTL1), a novel inflammatory protein, participates in the pathogenesis of diseases related to neuroinflammation. Therefore, we aimed to investigate the effect of FSTL1 in the pathogenesis of depression mediated using neuroinflammation-mediated models. Our results showed that lipopolysaccharide (LPS) administration could induce despair-like behavior and increase proinflammatory cytokine levels in both male and female mice. Then, a significant positive correlation between hippocampal Fstl1 mRNA expression, microglial activation and despair-like behaviors was observed in male mice. Moreover, knockdown FSTL1 significantly reduced microglial activation and the expression of proinflammatory cytokines, while overexpression of Fstl1 in hippocampus could exacerbate the activation of microglial under the LPS-induced condition in male mice. Mechanically, knockdown Fstl1 inhibited LPS-induced activation of BV2 microglia and reduced the production of proinflammatory cytokines, thereby protecting the survival of HT22 neurons. In conclusion, our results implied that Fstl1 may modulate despair-like behaviors through regulation of microglial activation and neuronal viability, which would lay the experimental and theoretical foundation for the neuroinflammatory mechanisms underlying depression.}, }
@article {pmid40513226, year = {2025}, author = {Vasilyev, AN and Svirin, EP and Dubynin, IA and Butorina, AV and Nuzhdin, YO and Ossadtchi, AE and Stroganova, TA and Shishkin, SL}, title = {Intentionally versus spontaneously prolonged Gaze: A MEG study of active gaze-based interaction.}, journal = {Cortex; a journal devoted to the study of the nervous system and behavior}, volume = {189}, number = {}, pages = {76-96}, doi = {10.1016/j.cortex.2025.05.010}, pmid = {40513226}, issn = {1973-8102}, mesh = {Humans ; Magnetoencephalography/methods ; Male ; *Fixation, Ocular/physiology ; Female ; Adult ; Young Adult ; *Attention/physiology ; *Eye Movements/physiology ; Saccades/physiology ; *Brain/physiology ; *Intention ; Frontal Lobe/physiology ; }, abstract = {Eye fixations are increasingly employed to control computers through gaze-sensitive interfaces, yet the brain mechanisms supporting this non-visual use of gaze remain poorly understood. In this study, we employed 306-channel magnetoencephalography (MEG) to find out what is specific to brain activity when gaze is used voluntarily for control. MEG was recorded while participants played a video game controlled by their eye movements. Each move required object selection by fixating it for at least 500 msec. Gaze dwells were classified as intentional if followed by a confirmation gaze on a designated location and as spontaneous otherwise. We identified both induced oscillatory and sustained phase-locked MEG activity differentiating intentional and spontaneous gaze dwells. Induced power analysis revealed prominent alpha-beta band synchronization (8-30 Hz) localized in the frontal cortex, with location broadly consistent with the frontal eye fields. This synchronization began 500-750 msec before intentional fixation onset and peaked shortly after it, suggesting proactive inhibition of saccadic activity. Sustained evoked responses further distinguished the two conditions, showing gradually rising cortical activation with a maximum at 200 msec post-onset in the inferior temporal cortex during intentional fixations, likely indicative of focused attentional engagement on spatial targets. These findings illuminate the neural dynamics underlying intentional gaze control, shedding light on the roles of proactive inhibitory mechanisms and attentional processes in voluntary behavior. By leveraging a naturalistic gaze-based interaction paradigm, this study offers a novel framework for investigating voluntary control under free behavior conditions and holds potential applications for enhancing hybrid eye-brain-computer interfaces.}, }
@article {pmid40512634, year = {2025}, author = {Yan, T and Ming, Z and Huang, Y and Liu, Z and Chen, Q and Zhang, D and Liu, M and Suo, D and Zhang, J and Liu, S}, title = {Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2498-2507}, doi = {10.1109/TNSRE.2025.3579373}, pmid = {40512634}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Robotics/methods/instrumentation ; Electroencephalography/methods ; Male ; Adult ; Support Vector Machine ; Algorithms ; Female ; Photic Stimulation/methods ; Young Adult ; Reproducibility of Results ; Brain/physiology ; }, abstract = {Brain-computer interface (BCI) systems based on steady-state visually evoked potentials (SSVEPs) have been widely adopted because of their efficiency and accuracy. However, the traditional SSVEP method has limitations, including visual fatigue and interference between different stimuli. To address these issues, a new BCI paradigm, namely, a spatial encoding-visually evoked potential (SE-VEP) model, is proposed in this work. This paradigm involves deploying four target points to implement gaze restrictions around a stimulus block and optimizing the locations of these target points through offline data acquisition. This design facilitates electroencephalogram (EEG) encoding for four instructions while using only one stimulus block. Data with varying eccentricities are classified using the Riemann kernel-based support vector machine (R-SVM) approach, which achieves a classification accuracy of up to 86.11%. As the eccentricity increases, the classification accuracy initially increases but subsequently decreases. By evaluating the information transfer rate (ITR), the optimal time window length for online BCIs is determined to be 1.2 s. Additionally, an online brain-controlled robotic virtual system is developed to validate the feasibility of the proposed paradigm for online brain-computer interface applications. The results confirm the effectiveness of the proposed paradigm in implementing an online BCI control system. An evaluation conducted with scales and the information transfer rate for a single stimulus (ITRSS) indicates that compared with the traditional BCI system, the proposed paradigm yields greater reductions in user fatigue (2.8 ± 0.5 vs. 4.1 ± 0.6) and stimulus block utilization (24.6 ± 2.3 vs. 8.2 ± 1.1 bits/min).}, }
@article {pmid40511861, year = {2025}, author = {Ramirez, P}, title = {Alternative ways to access AAC technologies.}, journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)}, volume = {41}, number = {3}, pages = {297-299}, doi = {10.1080/07434618.2025.2513902}, pmid = {40511861}, issn = {1477-3848}, mesh = {Humans ; *Communication Devices for People with Disabilities ; *Brain-Computer Interfaces ; Robotics ; *Communication Disorders/rehabilitation ; }, abstract = {More than 21 years ago, I had a car accident that led to a brain stem stroke, leaving me paralyzed and unable to speak. I was desperate to communicate. One day, my sister wrote down the alphabet and pointed to each letter accordingly. I nodded, yes or no, and she wrote my message down. Later, I used a laser light with a letter board and then a laptop with a head pointer. More recently, I started using a gyroscopic air mouse. During outings, I use the laser and the letter board. They are easy to carry and use. Plus, I can communicate in English and Spanish which is very important because my family does not speak English. I am currently enrolled in a clinical trial at the University of California, San Francisco to investigate brain computer interface to control a robotic arm and communicate. They placed an implant in the surface of my brain; the implant connects to a computer system that collects brain signals and translates neural activity from my sensorimotor cortex into intended speech and motor actions. This type of research is needed to enhance communication and improve lives.}, }
@article {pmid40510263, year = {2025}, author = {Amande, TJ and Kaszyk, V and Brown, F}, title = {Identification of OqxB Efflux Pump and Tigecycline Resistance Gene Cluster tmexC3D2-toprJ3 in Multidrug-Resistant Pseudomonas Stutzeri Isolate G3.}, journal = {Infection and drug resistance}, volume = {18}, number = {}, pages = {2889-2899}, pmid = {40510263}, issn = {1178-6973}, abstract = {PURPOSE: To identify antibiotic resistance genes (ARGs) and understand the molecular basis of multidrug resistance in P. stutzeri isolate G3.<br><br>METHODS: Whole-genome sequencing of isolate G3 was conducted at 30X coverage using Illumina NovaSeq 6000. Reads were trimmed using Trimmomatic and assessed using a combination of scripts that incorporated Samtools, BedTools, and bwa-mem. De novo assembly was performed using SPAdes, and assembly metrics were evaluated using QUAST. The assembled genome was uploaded to a Type Strain Genome Server (TYGS) for taxonomic identification. Genome annotation was performed using the KBase and Proksee software using PROKKA. ARGs were identified using the Comprehensive Antibiotic Resistance Database (CARD).<br><br>RESULTS: P. stutzeri isolate G3 demonstrated resistance to most antibiotics tested, including meropenem (10&#xa0;&#xb5;g), ciprofloxacin (5&#xa0;&#xb5;g), gentamicin (10&#xa0;&#xb5;g), and tetracycline (30&#xa0;&#xb5;g). The ARGs identified were PmpM, AdeF, rsmA, vgb(A), BcI, cipA, OCH-2, and tet(45). A tigecycline-resistant gene cluster, tmexC3D2-toprJ3, was found in NODE_84, while the oqxB gene, encoding a resistance-nodulation-division (RND) efflux pump, was in NODE_309. Phylogenetic analysis showed OqxB clustered with Pseudomonas species, distinct from Klebsiella and Enterobacter. Comparative analysis of oqxB revealed P. stutzeri isolate G3 shared 78-100% identity with Pseudomonas aeruginosa strain 1334/14 in key components of the multidrug efflux system, including the transcriptional regulator MexT, periplasmic adaptor subunit MexE, and permease subunit MexF.<br><br>CONCLUSION: Our findings offer new insights into the reservoir of ARGs in the draft genome of Pseudomonas stutzeri isolate G3, including the tmexC3D2-toprJ3 and oqxB genes, highlighting its genomic plasticity and public health significance. This adaptability enables P. stutzeri to thrive in clinical environments, despite its natural habitat association. This study advances our understanding of the molecular mechanisms driving resistance in P. stutzeri and offers valuable insights to inform strategies for combating the spread of antimicrobial resistance in clinical and environmental settings.}, }
@article {pmid40510210, year = {2025}, author = {Gazerani, P}, title = {A Hybrid Digital-4E Strategy for comorbid migraine and depression: a medical hypothesis on an AI-driven, neuroadaptive, and exposome-aware approach.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1587296}, pmid = {40510210}, issn = {1664-2295}, abstract = {OBJECTIVE: The co-occurrence of migraines and depression presents a critical clinical challenge, affecting up to 50% of individuals with either condition. This comorbidity leads to greater disability, higher healthcare costs, and poorer treatment outcomes than either disorder alone. Despite a bidirectional pathophysiological relationship, current models remain static and fragmented, treating each condition separately. This paper proposes a Hybrid Digital-4E Strategy, deployed on an AI-driven neuroadaptive digital health platform, integrating closed-loop therapy, digital phenotyping, and exposome tracking to enable real-time, personalized care.<br><br>METHODS: Grounded in the 4E cognition framework (Embodied, Embedded, Enactive, and Extended cognition), this strategy reconceptualizes migraine-depression as an interactive system rather than two separate conditions. The platform integrates real-time biomarker tracking, neuromorphic AI, and precision environmental analytics to dynamically optimize treatment. Adaptive chronotherapy, brain-computer interfaces (BCIs), and virtual reality (VR)-based neuroplasticity training further enhance intervention precision.<br><br>CONCLUSION: A closed-loop, AI-driven neuroadaptive system could improve outcomes by enabling early detection, real-time intervention, and precision care tailored to individual neurophysiological and environmental profiles. Addressing AI bias, data privacy, and clinical validation is crucial for implementation. If validated, this Hybrid Digital-4E Strategy could redefine migraine-depression management, paving the way for precision neuropsychiatry.}, }
@article {pmid40506548, year = {2025}, author = {Wairagkar, M and Card, NS and Singer-Clark, T and Hou, X and Iacobacci, C and Miller, LM and Hochberg, LR and Brandman, DM and Stavisky, SD}, title = {An instantaneous voice-synthesis neuroprosthesis.}, journal = {Nature}, volume = {644}, number = {8075}, pages = {145-152}, pmid = {40506548}, issn = {1476-4687}, support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces ; Humans ; Male ; *Voice/physiology ; *Speech/physiology ; Amyotrophic Lateral Sclerosis/physiopathology/rehabilitation/complications ; Dysarthria/rehabilitation/physiopathology ; *Neural Prostheses ; Electrodes, Implanted ; Microelectrodes ; Communication Devices for People with Disabilities ; }, abstract = {Brain-computer interfaces (BCIs) have the potential to restore communication for people who have lost the ability to speak owing to a neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text[1-3]. However, text communication fails to capture the nuances of human speech, such as prosody and immediately hearing one's own voice. Here we demonstrate a brain-to-voice neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding neural activity from 256 microelectrodes implanted into the ventral precentral gyrus of a man with amyotrophic lateral sclerosis and severe dysarthria. We overcame the challenge of lacking ground-truth speech for training the neural decoder and were able to accurately synthesize his voice. Along with phonemic content, we were also able to decode paralinguistic features from intracortical activity, enabling the participant to modulate his BCI-synthesized voice in real time to change intonation and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.}, }
@article {pmid40506484, year = {2025}, author = {Liu, J and Liu, H and Zhu, J and Han, X and Bai, Y and Ni, G and Ming, D}, title = {A Dataset of Pinna-Related Transfer Functions Using High-Resolution Pinna Models.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {992}, pmid = {40506484}, issn = {2052-4463}, mesh = {Humans ; *Sound Localization ; *Ear Auricle/physiology ; Acoustics ; }, abstract = {The pinna-related transfer function (PRTF) is critical for localizing and perceiving sound in three-dimensional space. PRTF largely depends on individual spectral cues and the unique physiology of the pinna, necessitating high-resolution data for accurate acoustic modeling. The accuracy of personalized acoustic models could be significantly improved using high-precision physiological data and incorporating advanced simulation methods such as the boundary element method (BEM). We describe a comprehensive dataset of 150 bilateral PRTFs from 75 participants to support developing, improving, and validating personalized PRTF modeling methods. The dataset includes simulated results from binaural laser-scanned models that are accurately validated through empirical measurements. This comprehensive dataset will contribute to acoustic and spatial audio research and support the ongoing advancements in personalized PRTF modeling techniques.}, }
@article {pmid40505916, year = {2025}, author = {Bikiaris, RE and Matschek, NI and Koumentakou, I and Niti, A and Kyzas, GZ}, title = {Synergistic effects of arginine and tannic acid on chitosan matrices: An approach for hemostatic sponge development.}, journal = {International journal of biological macromolecules}, volume = {318}, number = {Pt 3}, pages = {145105}, doi = {10.1016/j.ijbiomac.2025.145105}, pmid = {40505916}, issn = {1879-0003}, mesh = {*Chitosan/chemistry/pharmacology ; *Arginine/chemistry/pharmacology ; *Tannins/chemistry/pharmacology ; *Hemostatics/pharmacology/chemistry ; Animals ; Humans ; Hydrogels/chemistry ; Bandages ; Alginates/chemistry ; Biocompatible Materials/chemistry/pharmacology ; Porosity ; Blood Coagulation/drug effects ; Antioxidants/pharmacology/chemistry ; Wound Healing/drug effects ; Polyphenols ; }, abstract = {This study presents the development of a novel multifunctional hydrogel biocomposite sponge designed to address the complexities of wound healing, including rapid hemostasis, infection prevention, and tissue regeneration. Recognizing the limitations of conventional wound dressings that lack multifunctionality, this study introduces a 3D chitosan/tannic acid (CS/TA) hydrogel. After testing three chitosan/tannic acid (CS/TA) ratios, CS/TA-1 (1:0.16), CS/TA-2 (1:0.25), and CS/TA-3 (1:0.34), the most effective formulation, CS/TA-2, was enhanced with sodium alginate (SA) and arginine (Arg) for optimal performance. Arginine, with its guanidinium functional group, served as a green crosslinker through physical interactions, enhancing the sponge's mechanical strength while also improving its hemostatic performance and biocompatibility, promoting cellular interactions. Its inclusion significantly amplified antioxidant activity (>90 %), mitigating oxidative stress and contributing to enhanced therapeutic outcomes. Ionic crosslinking and freeze-drying created a porous, absorbent sponge with high water retention and compression resilience. SEM confirmed the sponge's interconnected porosity, enabling cell infiltration and nutrient exchange. Blood Clotting Index (BCI) assessments demonstrated the hemostatic effectiveness of CS/TA/SA/Arg-3, with 25 % BCI at 5 min and 20 % at 15 min, along with excellent hemocompatibility, achieving a 2.08 % hemolysis rate. These results suggest the hydrogel sponge's potential for effective wound management in emergencies and clinical applications.}, }
@article {pmid40505654, year = {2025}, author = {Becker, B}, title = {Will our social brain inherently shape and be shaped by interactions with AI?.}, journal = {Neuron}, volume = {113}, number = {13}, pages = {2037-2041}, doi = {10.1016/j.neuron.2025.04.034}, pmid = {40505654}, issn = {1097-4199}, mesh = {Animals ; Humans ; *Artificial Intelligence ; *Brain/physiology ; *Brain-Computer Interfaces ; *Interpersonal Relations ; *Social Behavior ; *Social Interaction ; }, abstract = {Social-specific brain circuits enable rapid understanding and affiliation in interpersonal interactions. These evolutionarily and experience-shaped mechanisms will influence-and be influenced by-interactions with conversational AI agents (chatbots, avatars). This NeuroView explores fundamental circuits, computations, and societal implications.}, }
@article {pmid40503091, year = {2025}, author = {Li, Q and Pan, Y}, title = {Mobile eye-tracking and neuroimaging technologies reveal teaching and learning on the move: bibliometric mapping and content analysis.}, journal = {Psychoradiology}, volume = {5}, number = {}, pages = {kkaf013}, pmid = {40503091}, issn = {2634-4416}, abstract = {Mobile psychophysiological technologies, such as portable eye tracking, electroencephalography, and functional near-infrared spectroscopy, are advancing ecologically valid findings in cognitive and educational neuroscience research. Staying informed on the field's current status and main themes requires continuous updates. Here, we conducted a bibliometric and text-based content analysis on 135 articles from Web of Science, specifically parsing publication trends, identifying prolific journals, authors, institutions, and countries, along with influential articles, and visualizing the characteristics of cooperation among authors, institutions, and countries. Using a keyword co-occurrence analysis, five clusters of research trends were identified: (i) cognitive and emotional processes, intelligent education, and motor learning; (ii) professional vision and collaborative learning; (iii) face-to-face social learning and real classroom learning; (iv) cognitive load and spatial learning; and (v) virtual reality-based learning, child learning, and technology-assisted special education. These trends illustrate a consistent growth in the use of portable technologies in education over the past 20 years and an emerging shift towards "naturalistic" approaches, with keywords such as "face-to-face" and "real-world" gaining prominence. These observations underscore the need to further generalize the current research to real-world classroom settings and call for interdisciplinary collaboration between researchers and educators. Also, combining multimodal technologies and conducting longitudinal studies will be essential for a comprehensive understanding of teaching and learning processes.}, }
@article {pmid40502712, year = {2025}, author = {Chen, H and Zhang, M and Ye, T and Wolpert, MA and Ding, N}, title = {Low-frequency cortical activity reflects context-dependent parsing of word sequences.}, journal = {iScience}, volume = {28}, number = {6}, pages = {112650}, pmid = {40502712}, issn = {2589-0042}, abstract = {During speech listening, it has been hypothesized that the brain builds representations of linguistic structures like sentences, which are tracked by neural activity entrained to the rhythm of these structures. Alternatively, others proposed that these sentence-tracking neural activities may reflect the predictability or syntactic properties of individual words. Here, to disentangle the neural responses to sentences and words, we design word sequences that are parsed into different sentences in different contexts. By analyzing neural activity recorded by magnetoencephalography, we find that low-frequency neural activity strongly depends on context-the difference between MEG responses to the same word sequence in two contexts yields a low-frequency signal, which precisely tracks sentences. The predictability and syntactic properties of words can partly explain the neural response in each context but not the difference between contexts. In summary, low-frequency neural activity encodes sentences and can reliably reflect how same-word sequences are parsed in different contexts.}, }
@article {pmid40502202, year = {2025}, author = {Srinivasan, A and Wairagkar, M and Iacobacci, C and Hou, X and Card, NS and Jacques, BG and Pritchard, AL and Bechefsky, PH and Hochberg, LR and AuYong, N and Pandarinath, C and Brandman, DM and Stavisky, SD}, title = {Encoding of speech modes and loudness in ventral precentral gyrus.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40502202}, issn = {2692-8205}, support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; }, abstract = {The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with an accuracy of 94% and 89% for two participants respectively, and corresponding neural preparatory activity could be detected hundreds of milliseconds before speech onset. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.}, }
@article {pmid40501187, year = {2025}, author = {Wang, YJ and Jie, Z and Liu, Y and Pan, Y}, title = {Dyad averaged BMI-dependent interbrain synchrony during continuous mutual prediction in social coordination.}, journal = {Social neuroscience}, volume = {20}, number = {3}, pages = {195-204}, doi = {10.1080/17470919.2025.2517068}, pmid = {40501187}, issn = {1747-0927}, mesh = {Humans ; *Body Mass Index ; Male ; Female ; Spectroscopy, Near-Infrared ; *Brain/physiology/diagnostic imaging ; Young Adult ; Adult ; *Social Interaction ; *Interpersonal Relations ; Obesity/psychology/physiopathology ; }, abstract = {Obesity is linked to notable psychological risks, particularly in social interactions where individuals with high body mass index (BMI) often encounter stigmatization and difficulties in forming and maintaining social connections. Although awareness of these issues is growing, there is a lack of research on real-time, dynamic interactions involving dyads with various BMI levels. To address this gap, our study employed a joint finger-tapping task, where participant dyads engaged in coordinated activity while their brain activity was monitored using functional near-infrared spectroscopy (fNIRS). Our findings showed that both Bidirectional and Unidirectional Interaction conditions exhibited higher levels of behavioral and interbrain synchrony compared to the No Interaction condition. Notably, only in the Bidirectional Interaction condition, higher dyadic BMI was significantly correlated with poorer behavioral coordination and reduced interbrain synchrony. This finding suggests that the ability to maintain social coordination, particularly in scenarios requiring continuous mutual prediction and adjustment, is modulated by dyads' BMI.}, }
@article {pmid40499369, year = {2025}, author = {Banovoth, RS and K V, K}, title = {Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.}, journal = {Computers in biology and medicine}, volume = {194}, number = {}, pages = {110397}, doi = {10.1016/j.compbiomed.2025.110397}, pmid = {40499369}, issn = {1879-0534}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; *Brain/physiology ; *Imagination/physiology ; *Models, Neurological ; }, abstract = {The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit rich neurodynamic features in the spatiotemporal domain, making them well-suited for processing brain signals, mainly those captured using the widely used non-invasive Electroencephalography (EEG) technique. However, the structural limitations of SNN hinder their feature extraction capabilities for motor imagery signal classification, which leads to under performance of the task. To address the aforementioned challenge, the proposed study introduces a novel model that incorporates Roman Domination within a Spiking Neural Network (RDSNN), where Roman domination identifies the most highly correlated channels or nodes. These channels generate an appropriate threshold for spike generation in the signals, which are then classified using the SNN. The model's performance was evaluated on three typically representative motor imagery datasets: PhysioNet, BCI Competition IV-2a, and BCI Competition IV-2b. RDSNN achieved 73.65% accuracy on PhysioNet, 81.75% on BCI IV-2a, and 84.56% on BCI IV-2b. The results demonstrate not only superior accuracy compared to State-Of-the-Art (SOTA) methods but also a 35% reduction in computation time, attributed to the application of Roman domination.}, }
@article {pmid40499342, year = {2025}, author = {Silveira, I and Varandas, R and Gamboa, H}, title = {Cognitive Lab: A dataset of biosignals and HCI features for cognitive process investigation.}, journal = {Computer methods and programs in biomedicine}, volume = {269}, number = {}, pages = {108863}, doi = {10.1016/j.cmpb.2025.108863}, pmid = {40499342}, issn = {1872-7565}, mesh = {Humans ; *Cognition ; Attention ; Male ; Emotions ; Adult ; Female ; Electroencephalography ; Learning ; User-Computer Interface ; Workload ; Young Adult ; Fatigue ; Machine Learning ; }, abstract = {BACKGROUND AND OBJECTIVE: Attention, cognitive workload/fatigue, and emotional states significantly influence learning outcomes, cognitive performance, and human-machine interactions. However, existing assessment methodologies fail to fully capture the multimodal nature of these cognitive processes, limiting their application in adaptive learning environments. This study presents the Cognitive Lab, a comprehensive multimodal dataset designed to investigate these cognitive processes across real-time learning scenarios. Specifically, it aims to capture and enable the classification of (1) attention and cognitive workload states using standard cognitive tasks, (2) cognitive fatigue arising from prolonged digital activities, and (3) emotional and learning states during interactive lessons.<br><br>METHODS: The Cognitive Lab dataset consists of three distinct subsets, each developed through specific experimental scenarios targeting different aspects of learning. Dataset 1 comprises recordings from eight participants performing N-Back and mental subtraction tasks, aimed at assessing attention and cognitive workload. Dataset 2 includes data from 10 participants engaged in a digital lesson, complemented by Corsi block-tapping and concentration tasks, to evaluate cognitive fatigue. Lastly, Dataset 3 captures data from 18 participants during an interactive Jupyter Notebook lesson, focusing on emotional states and learning processes. Each scenario combined biosignals (accelerometry, ECG, EDA, EEG, fNIRS, respiration) with Human-Computer Interaction (HCI) features (mouse-tracking, keyboard activity, screenshots). Machine learning models were applied to classify cognitive states, with cross-validation ensuring robust results.<br><br>RESULTS: The dataset enabled accurate classification of learning states, achieving up to 87% accuracy in differentiating learning states using mouse-tracking data. Furthermore, it successfully differentiated attention, cognitive workload, and cognitive fatigue states using biosignal and HCI data, with fNIRS, EEG, and ECG emerging as key contributors to classification performance. Variability across participants highlighted the potential for subject-specific calibration to enhance model accuracy.<br><br>CONCLUSIONS: The Cognitive Lab dataset represents a resource for investigating cognitive phenomena in real-world learning scenarios. Its integration of biosignals and HCI features enables the classification of cognitive states and supports advancements in adaptive learning systems, cognitive neuroscience, and brain-computer interface technologies.}, }
@article {pmid40498623, year = {2025}, author = {Lian, Q and Wang, Y and Qi, Y}, title = {Dynamic Instance-Level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {11}, pages = {8348-8360}, doi = {10.1109/JBHI.2025.3578627}, pmid = {40498623}, issn = {2168-2208}, mesh = {Humans ; *Signal Processing, Computer-Assisted ; *Electrocorticography/methods ; *Seizures/diagnosis/physiopathology ; *Neural Networks, Computer ; Deep Learning ; *Epilepsy/diagnosis/physiopathology ; Brain-Computer Interfaces ; *Electroencephalography/methods ; Adult ; }, abstract = {Brain-computer interface (BCI) technology is emerging as a valuable tool for diagnosing and treating epilepsy, with deep learning-based feature extraction methods demonstrating remarkable progress in BCI-aided systems. However, accurately identifying causal relationships in temporal dynamics of epileptic intracranial electroencephalography (iEEG) signals remains a challenge. This paper proposes a Dynamic Instance-level Graph Learning Network (DIGLN) for seizure prediction using iEEG signals. The DIGLN comprises two core components: a grouped temporal neural network that extracts node features and a graph structure learning method to capture the causality from intra-channel to inter-channel. Furthermore, we propose a graphical interactive writeback technique to enable DIGLN to capture the causality from inter-channel to intra-channel. Consequently, our DIGLN enables patient-specific dynamic instance-level graph learning, facilitating the modelling of evolving signals and functional connectivities through end-to-end data-driven learning. Experimental results on the Freiburg iEEG dataset demonstrate the superior performance of DIGLN, surpassing other deep learning-based seizure prediction methods. Visualization results further confirm DIGLN's capability to learn interpretable and diverse connections.}, }
@article {pmid40496741, year = {2025}, author = {Tiwari, N and Anwar, S and Bhattacharjee, V}, title = {EEG dataset for natural image recognition through visual stimuli.}, journal = {Data in brief}, volume = {60}, number = {}, pages = {111639}, pmid = {40496741}, issn = {2352-3409}, abstract = {Electroencephalography (EEG) is a technique for measuring the brain's electrical activity in the form of action potentials with electrodes placed on the scalp. Because of its non-invasive nature and ease of use, the approach is becoming increasingly popular for investigations. EEG reveals a wide spectrum of human brain potentials, such as event-related, sensory, and visually evoked potentials (VEPs), which aids in the development of intricate applications. Developing Apps or Brain-Computer Interface (BCI) devices demands data on these potentials. The present dataset comprises EEG recordings generated by thirty-two individuals in reaction to visual stimuli (VEPs). The rationale behind gathering this data is its ability to support EEG-based image classification and reconstruction while also advancing visual decoding. The primary purpose is to examine the cognitive processes behind both familiar and unfamiliar observations. A standardized experimental setup comprising many experimental phases was employed to capture the essence of the investigation and gather the dataset.}, }
@article {pmid40496017, year = {2025}, author = {Reid, LV and Spalluto, CM and Wilkinson, TMA and Staples, KJ}, title = {Influenza-induced microRNA-155 expression is altered in extracellular vesicles derived from the COPD epithelium.}, journal = {Frontiers in cellular and infection microbiology}, volume = {15}, number = {}, pages = {1560700}, pmid = {40496017}, issn = {2235-2988}, mesh = {*MicroRNAs/genetics/metabolism ; Humans ; *Pulmonary Disease, Chronic Obstructive ; *Extracellular Vesicles/metabolism ; *Epithelial Cells/virology/metabolism ; *Influenza, Human/virology ; Cells, Cultured ; Gene Expression Profiling ; Cell Line ; }, abstract = {BACKGROUND: Influenza virus particularly affects those with chronic lung conditions such as Chronic Obstructive Pulmonary Disease (COPD). Airway epithelial cells are the first line of defense and primary target of influenza infection and release extracellular vesicles (EVs). EVs can transfer of biological molecules such as microRNAs (miRNAs) that can modulate the immune response to viruses through control of the innate and adaptive immune systems. The aim of this work was to profile the EV miRNAs released from bronchial epithelial cells in response to influenza infection and discover if EV miRNA expression was altered in COPD.<br><br>METHODS: Influenza infection of air-liquid interface (ALI) differentiated BCi-NS1.1 epithelial cells were characterized by analyzing the expression of antiviral genes, cell barrier permeability and cell death. EVs were isolated by filtration and size exclusion chromatography from the apical surface wash of ALI cultured bronchial epithelial cells. The EV miRNA cargo was sequenced and reads mapped to miRBase. The BCi sequencing results were further investigated by RT-qPCR and by using healthy and COPD primary epithelial cells.<br><br>RESULTS: Infection of ALI cultured BCi cells with IAV at 3.6 x 10[6] IU/ml for 24 h led to significant upregulation of anti-viral genes without high levels of cell death. EV release from ALI-cultured BCi cells was confirmed using electron microscopy and detection of known tetraspanin EV markers using western blot and the ExoView R100 platform. Differential expression analyses identified 5 miRNA that had a fold change of >0.6: miR-155-5p, miR-122-5p, miR-378a-3p, miR-7-5p and miR-146a-5p (FDR<0.05). Differences between EV, non-EV and cellular levels of these miRNA were detected. Primary epithelial cell release of EV and their miRNA cargo was similar to that observed for BCi. Intriguingly, miR-155 expression was decreased in EVs derived from COPD patients compared to EVs from control samples.<br><br>CONCLUSION: Epithelial EV miRNA release may be a key mechanism in modulating the response to IAV in the lungs. Furthermore, changes in EV miRNA expression may play a dysfunctional role in influenza-induced exacerbations of COPD. However, further work to fully characterize the function of EV miRNA in response to IAV in both health and COPD is required.}, }
@article {pmid40495523, year = {2025}, author = {Gou, H and Bu, J and Cheng, Y and Liu, C and Gan, H and Liu, M and Zhao, Q and Chen, X and Ren, J and Hong, W and Wang, R and Cao, Y and Yu, C and Chen, X and Zhang, X}, title = {Improved Response Inhibition Through Cognition-Guided EEG Neurofeedback in Men With Methamphetamine Use Disorder.}, journal = {The American journal of psychiatry}, volume = {182}, number = {9}, pages = {861-877}, doi = {10.1176/appi.ajp.20240475}, pmid = {40495523}, issn = {1535-7228}, mesh = {Humans ; Male ; *Neurofeedback/methods ; *Amphetamine-Related Disorders/psychology/physiopathology/therapy ; Adult ; *Methamphetamine/adverse effects ; Electroencephalography ; *Inhibition, Psychological ; *Cognition/physiology ; Cues ; Young Adult ; Middle Aged ; }, abstract = {OBJECTIVE: Impaired response inhibition is the core cognitive deficit in methamphetamine use disorder (MUD), and methamphetamine cue reactivity is a major factor that reduces inhibition efficiency. The authors sought to use cognition-guided neurofeedback to deactivate methamphetamine cue-related brain reactivity patterns in men with MUD to improve their response inhibition.<br><br>METHODS: A cognition-guided, closed-loop EEG-based neurofeedback protocol was employed. Methamphetamine cue-related brain activity patterns were identified offline using multivariate pattern analysis of EEG data from all channels during a methamphetamine cue reactivity task. In the real-time feedback phase, participants were trained to deactivate their methamphetamine cue-related patterns, which were presented as feedback. The study included two samples, totaling 99 men with MUD. In sample 1, 66 men received 10 neurofeedback sessions based either on their own brain activity patterns (real neurofeedback group 1, N=33) or on randomly matched participants' patterns (yoke neurofeedback group, N=33). Sample 2, which was used to validate findings in sample 1, included a real feedback group (real neurofeedback group 2; N=17) and a standard rehabilitation group (N=16) that received only standard rehabilitation without additional intervention. Response inhibition was assessed using a go/no-go task based on methamphetamine-related cues before and after the intervention.<br><br>RESULTS: Compared to the yoke feedback group, real neurofeedback group 1 successfully deactivated methamphetamine cue-related brain reactivity patterns, resulting in significantly enhanced response inhibition (d-prime, Cohen's f=0.31). Neurofeedback performance in real neurofeedback group 1 was significantly correlated with improved response inhibition. Additionally, response inhibition improvements could be predicted by initial neurofeedback performance and baseline characteristics. Sample 2 replicated these findings, showing that response inhibition in real neurofeedback group 2 was improved and predictable. Notably, these intervention effects in real neurofeedback group 2 were better than those in the standard rehabilitation group.<br><br>CONCLUSIONS: These findings underscore the efficacy of cognition-guided neurofeedback for treating MUD, thereby suggesting its potential applicability in other addiction interventions.}, }
@article {pmid40495436, year = {2025}, author = {Rozovsky, R and Wolfe, M and Abdul-Waalee, H and Chobany, M and Malgireddy, G and Hart, JA and Lepore, B and Vahedifard, F and Phillips, ML and Birmaher, B and Skeba, A and Diler, RS and Bertocci, MA}, title = {Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.}, journal = {Brain and behavior}, volume = {15}, number = {6}, pages = {e70589}, pmid = {40495436}, issn = {2162-3279}, support = {R01-MH-121451/MH/NIMH NIH HHS/United States ; }, mesh = {Humans ; Adolescent ; *Bipolar Disorder/diagnostic imaging/pathology ; Male ; Female ; *Gray Matter/diagnostic imaging/pathology ; Inpatients ; Magnetic Resonance Imaging/methods ; Machine Learning ; Support Vector Machine ; *Brain/pathology/diagnostic imaging ; *Mental Disorders/diagnostic imaging ; }, abstract = {BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis.<br><br>METHODS: Five support vector machine (SVM) models were used to detect differences in GMVs between inpatient adolescents aged 13-17 with BD-I/II (n = 34), other specified BD (OSB) (n = 106), other non-bipolar psychopathology (OP) (n = 52), and healthy controls (HC) (n = 27). We examined the most discriminative GMVs and tested their associations with clinical symptoms.<br><br>RESULTS: Whole-brain classifiers in the model BD-I/II versus OSB achieved total accuracy of 79%, (AUC = 0.70, p = 0.002); BD versus OP 66%, (AUC = 0.61, p = 0.014); BD versus HC 66%, (AUC = 0.67, p = 0.011); OSB versus HC 77%, (AUC = 0.61, p = 0.01); OP versus HC 68%, (AUC = 0.70, p = 0.001). The most discriminative GMVs that contributed to the classification were in areas associated with movement, sensory processing, and cognitive control. Correlations between these GMVs and self-reported mania, negative affect, or anxiety were observed in all inpatient groups.<br><br>CONCLUSIONS: These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well-characterized BD-I/II from other forms of psychopathology, including other specified BD, in a pediatric population. These results may contribute to enhancing diagnostic accuracy and guiding earlier, more targeted interventions.}, }
@article {pmid40494420, year = {2025}, author = {Spinelli, R and Sanchís, I and Siano, A}, title = {Fighting Alzheimer's naturally: Peptides as multitarget drug leads.}, journal = {Bioorganic &amp; medicinal chemistry letters}, volume = {127}, number = {}, pages = {130305}, doi = {10.1016/j.bmcl.2025.130305}, pmid = {40494420}, issn = {1464-3405}, mesh = {*Alzheimer Disease/drug therapy/metabolism ; Humans ; *Peptides/chemistry/pharmacology/therapeutic use ; Animals ; *Cholinesterase Inhibitors/chemistry/pharmacology/therapeutic use ; Monoamine Oxidase/metabolism ; Acetylcholinesterase/metabolism ; *Biological Products/chemistry/pharmacology ; }, abstract = {In this review, we provide a comprehensive analysis of the role of natural peptides-particularly those derived from amphibian skin secretions-as multitarget-directed ligands (MTDLs) in the context of Alzheimer's disease (AD). Given the multifactorial nature of AD, where cholinergic dysfunction intersects with amyloid-β aggregation, tau hyperphosphorylation, oxidative stress, metal ion imbalance, and monoamine oxidase dysregulation, therapeutic strategies capable of modulating several pathological pathways simultaneously are urgently needed. We begin by revisiting the cholinergic hypothesis and its molecular and structural underpinnings, emphasizing the relevance of key binding sites such as the catalytic active site (CAS) and the peripheral anionic site (PAS) of cholinesterases. The central axis of this review lies in the exploration of naturally occurring peptides that have demonstrated dual or multiple activities against AD-related targets. We highlight our group's pioneering work on amphibian-derived peptides such as Hp-1971, Hp-1935, and BcI-1003, which exhibit non-competitive inhibition of AChE and BChE, MAO-B modulation, and antioxidant properties. Furthermore, we describe additional peptide-rich extracts and bioactive sequences from various amphibians and other animal or plant sources, expanding the landscape of natural molecules with neuroprotective potential. We also delve into peptide modification strategies-such as amino acid substitution, cyclization, D-amino acid incorporation, and terminal/side-chain functionalization-that have been employed to enhance peptide stability, blood-brain barrier permeability, and target affinity. These strategies not only improve the pharmacokinetic profiles of native peptides but also open the door for the rational design of next-generation peptide therapeutics. Overall, this review underscores the vast potential of natural peptides as scaffolds for the development of multifunctional agents capable of intervening in the complex cascade of Alzheimer's pathology.}, }
@article {pmid40494387, year = {2025}, author = {Wu, EG and Rudzite, AM and Bohlen, MO and Li, PH and Kling, A and Cooler, S and Rhoades, C and Brackbill, N and Gogliettino, AR and Shah, NP and Madugula, SS and Sher, A and Litke, AM and Field, GD and Chichilnisky, EJ}, title = {Decomposition of retinal ganglion cell electrical images for cell type and functional inference.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ade344}, pmid = {40494387}, issn = {1741-2552}, mesh = {*Retinal Ganglion Cells/physiology/classification/cytology ; Animals ; Macaca mulatta ; *Action Potentials/physiology ; Algorithms ; }, abstract = {Objective.Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision.Approach.The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose EI into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments.Main results.The decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches.Significance.These findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.}, }
@article {pmid40494367, year = {2025}, author = {Thielen, J}, title = {Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {4}, pages = {}, doi = {10.1088/2057-1976/ade316}, pmid = {40494367}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Evoked Potentials, Visual/physiology ; Female ; Electroencephalography/methods ; Adult ; Young Adult ; Photic Stimulation ; Heart Rate ; Attention ; *Brain/physiology ; }, abstract = {Objective.This study investigated the presence of brain-computer interface (BCI) inefficiency in BCIs using the code-modulated visual evoked potential (c-VEP). It further explored neurophysiological predictors of performance variability and evaluated a wide range of binary stimulus sequences in terms of classification accuracy and user comfort, aiming to identify strategies to mitigate c-VEP BCI inefficiency.Approach.In a comprehensive empirical analysis, ten different binary stimulus sequences were offline evaluated. These sequences included five code families (m-sequence, de Bruijn sequence, Golay sequence, Gold code, and a Gold code set), each in original and modulated form. To identify predictors of performance variability, resting-state alpha activity, heart rate and heart rate variability, sustained attention, and flash-VEP characteristics were studied.Main Results.Results confirmed substantial inter-individual variability in c-VEP BCI efficiency. While all participants reached a near-perfect classification accuracy, their obtained speed varied substantially. Four flash-VEP features were found to significantly correlate with the observed performance varibility: the N2 latency, the P2 latency and amplitude, and the N3 amplitude. Among the tested stimulus conditions, the m-sequence emerged as the best-performing universal stimulus. However, tailoring stimulus selection to individuals led to significant improvements in performance. Cross-decoding was successful between modulated stimulus conditions, but showed challenges when generalizing across other stimulus conditions. Lastly, while overall comfort ratings were comparable across conditions, stimulus modulation was associated with a significant decrease in user comfort.Significance.This study challenges the assumption of universal efficiency in c-VEP BCIs. The findings highlight the importance of accounting for individual neurophysiological differences and underscore the need for personalized stimulus protocols and decoding strategies to enhance both performance and user comfort.}, }
@article {pmid40493465, year = {2025}, author = {Rabiee, A and Ghafoori, S and Cetera, A and Norouzi, M and Besio, W and Abiri, R}, title = {A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3578235}, pmid = {40493465}, issn = {1558-2531}, abstract = {This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninvasive tripolar concentric ring electrode electroencephalography (tEEG) with conventional electroencephalography (EEG) technology. The goal is to determine which EEG technology is more effective in measuring and decoding different grasp-related neural signals. The approach involves experimenting on ten healthy participants who performed two distinct reach-and-grasp movements: power grasp and precision grasp, with a no-movement condition serving as the baseline. Our research compares EEG and tEEG in decoding grasping movements, focusing on signal-to-noise ratio (SNR), spatial resolution, and wavelet time-frequency analysis. Additionally, our study involved extracting and analyzing statistical features from the wavelet coefficients, and both binary and multiclass classification methods were employed. Four machine learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)-were used to evaluate the decoding accuracies. Our results indicated that tEEG demonstrated higher quality performance compared to conventional EEG in various aspects. This included a higher signal-to-noise ratio and improved spatial resolution. Furthermore, wavelet timefrequency analyses validated these findings, with tEEG exhibiting increased power spectra, thus providing a more detailed and informative representation of neural dynamics. The use of tEEG led to significant improvements in decoding accuracy for differentiating grasp movement types. Specifically, tEEG achieved around 90.00% accuracy in binary and 75.97% for multiclass classification. These results exceed those from conventional EEG, which recorded a maximum of 77.85% and 61.27% in similar tasks, respectively.}, }
@article {pmid40493396, year = {2025}, author = {Xiang, S and Chen, P and Shi, X and Cai, H and Shen, Z and Liu, L and Xu, A and Zhang, J and Zhang, X and Bing, S and Wang, J and Shao, X and Cao, J and Yang, B and He, Q and Ying, M}, title = {Disruption of the KLHL37-N-Myc complex restores N-Myc degradation and arrests neuroblastoma growth in mouse models.}, journal = {The Journal of clinical investigation}, volume = {135}, number = {14}, pages = {}, pmid = {40493396}, issn = {1558-8238}, mesh = {*Neuroblastoma/metabolism/pathology/genetics/drug therapy ; Animals ; *N-Myc Proto-Oncogene Protein/metabolism/genetics ; Humans ; Mice ; *Proteolysis ; Cell Line, Tumor ; Xenograft Model Antitumor Assays ; Proto-Oncogene Proteins c-myc ; }, abstract = {The N-Myc gene MYCN amplification accounts for the most common genetic aberration in neuroblastoma and strongly predicts the aggressive progression and poor clinical prognosis. However, clinically effective therapies that directly target N-Myc activity are limited. N-Myc is a transcription factor, and its stability is tightly controlled by ubiquitination-dependent proteasomal degradation. Here, we discovered that Kelch-like protein 37 (KLHL37) played a crucial role in enhancing the protein stability of N-Myc in neuroblastoma. KLHL37 directly interacted with N-Myc to disrupt N-Myc-FBXW7 interaction, thereby stabilizing N-Myc and enabling tumor progression. Suppressing KLHL37 effectively induced the degradation of N-Myc and had a profound inhibitory effect on the growth of MYCN-amplified neuroblastoma. Notably, we identified RTA-408 as an inhibitor of KLHL37 to disrupt the KLHL37-N-Myc complex, promoting the degradation of N-Myc and suppressing neuroblastoma in vivo and in vitro. Moreover, we elucidated the therapeutic potential of RTA-408 for neuroblastoma using patient-derived neuroblastoma cell and patient-derived xenograft tumor models. RTA408's antitumor effects may not occur exclusively via KLHL37, and specific KLHL37 inhibitors are expected to be developed in the future. These findings not only uncover the biological function of KLHL37 in regulating N-Myc stability, but also indicate that KLHL37 inhibition is a promising therapeutic regimen for neuroblastoma, especially in patients with MYCN-amplified tumors.}, }
@article {pmid40493186, year = {2025}, author = {Alawieh, H and Liu, D and Madera, J and Kumar, S and Racz, FS and Fey, AM and Del R Millán, J}, title = {Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {24}, pages = {e2418920122}, pmid = {40493186}, issn = {1091-6490}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Adult ; *Learning/physiology ; *Spinal Cord Injuries/physiopathology/rehabilitation ; Female ; *Spinal Cord Stimulation/methods ; *Motor Cortex/physiology ; Young Adult ; *Motor Skills/physiology ; }, abstract = {Injuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, noninvasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.}, }
@article {pmid40490658, year = {2025}, author = {Norizadeh Cherloo, M and Kashefi Amiri, H and Mijani, AM and Zhan, L and Daliri, MR}, title = {A comprehensive study of template-based frequency detection methods in SSVEP-based brain-computer interfaces.}, journal = {Behavior research methods}, volume = {57}, number = {7}, pages = {196}, pmid = {40490658}, issn = {1554-3528}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Recently, SSVEP-based brain-computer interfaces (BCIs) have received increasing attention from researchers due to their high signal-to-noise ratios (SNR), high information transfer rates (ITR), and low user training. Therefore, various methods have been proposed to recognize the frequency of SSVEPs. This paper reviewed the state-of-the-art frequency detection methods in SSVEP-based BCIs. Nineteen multi-channel SSVEP detection methods, organized into four categories based on different analytical approaches, were studied. All methods are template-based approaches and classified into four groups according to the basic models they employ: canonical correlation analysis (CCA), multivariate synchronization index (MSI), task-related component analysis (TRCA), and correlated component analysis (CORRCA). Each group consists of methods that use one of these basic models as the core model for their approach. This paper provides a description, a clear flowchart, and MATLAB code for each method and helps researchers use or develop the existing SSVEP detection methods. Although all methods were evaluated in separate studies, a comprehensive comparison of methods is still missing. In this study, several experiments were conducted to assess the performance of SSVEP detection methods. The benchmark 40-class SSVEP dataset from 35 subjects was used to evaluate methods. All methods were applied to the dataset and were evaluated in terms of classification accuracy, information transfer rate (ITR), and computational time. The experiment results revealed that four factors efficiently design an accurate, robust SSVEP detection method. (1) employing filter bank analysis to incorporate fundamental and harmonics frequency components, (2) utilizing calibration data to construct optimized reference signals, (3) integrating spatial filters of all stimuli to construct classification features, and (4) calculating spatial filters using training trials. Furthermore, results showed that filter bank ensemble task-related components (FBETRCA) achieved the highest performance.}, }
@article {pmid40490007, year = {2025}, author = {Chen, Y and Peng, Y and Tang, J and Camilleri, T and Camilleri, K and Kong, W and Cichocki, A}, title = {EEG-based affective brain-computer interfaces: recent advancements and future challenges.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ade290}, pmid = {40490007}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces/trends/psychology ; *Electroencephalography/trends/methods ; *Emotions/physiology ; *Brain/physiology ; Forecasting ; *Affect/physiology ; }, abstract = {Objective. As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e. depression, anxiety) are detected, which are considered as the two basic functions of aBCI systems. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems.Approach. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders.Main results. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information', 'outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis and regulation of affective disorders'. In addition, future opportunities were concluded by focusing on the main challenges in hindering the aBCI system to move from laboratory to real applications. Moreover, the neural mechanisms and theoretical basis behind EEG emotion recognition and regulation are also introduced to provide support for the advancements and challenges in aBCI.Significance. This review summarizes the current practices and performance outcomes in emotion recognition and regulation. Future directions in response to the existing challenges are provided with the expectation of guiding the aBCI research to focus on the necessary key technologies of aBCI systems in practical deployment.}, }
@article {pmid40490003, year = {2025}, author = {Tates, A and Matran-Fernandez, A and Halder, S and Daly, I}, title = {Speech imagery brain-computer interfaces: a systematic literature review.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ade28e}, pmid = {40490003}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Imagination/physiology ; *Speech/physiology ; *Brain/physiology ; Electroencephalography/methods ; }, abstract = {Objective:Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whose sensations are similar to their non-imagery counterparts. SI underpins language processes and may have similar building blocks to overt speech without the final articulatory outcome. The kinesthetic experience of SI has been proposed to be a projection of the expected articulatory outcome in a top-down processing manner. As SI seems to be a core human cognitive task it has been proposed as a paradigm for Brain-Computer Interfaces (BCI). One important aspect of BCI designs is usability, and SI may present an intuitive paradigm, which has brought the attention of researchers to attempt to decode SI from brain signals. In this paper we review the important aspects of SI-BCI decoding pipelines.Approach. We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. Specifically, we filtered peer-reviewed reports via a search of Google Scholar and PubMed. We selected a total of 104 reports that attempted to decode SI from neural activity.Main results. Our review reveals a growing interest in SI decoding in the last 20 years, and shows how different neuroimaging modalities have been employed to record SI in distinct ways to instruct participants to perform this task. We discuss the signal processing methods used along with feature extraction techniques and found a high preference for Deep Learning models. We have summarized and compared the decoding attempts by quantifying the efficacy of decoding by measuring Information Transfer Rates. Notably, fewer than 6% of studies reported real-time decoding, with the vast majority focused on offline analyses. This suggests existing challenges of this paradigm, as the variety of approaches and outcomes prevents a clear identification of the field's current state-of-the-art. We offer a discussion of future research directions.SignificanceSI is an attractive BCI paradigm. This review outlines the increasing interest in SI, the methodological trends, the efficacy of different approaches, and the current progress toward real-time decoding systems.}, }
@article {pmid40489280, year = {2025}, author = {Wang, Z and Zhang, Y and Zhang, Z and Xie, SQ and Lanzon, A and Heath, WP and Li, Z}, title = {Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3577813}, pmid = {40489280}, issn = {2168-2208}, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-related component analysis (iTRCA), which leverages knowledge from source subjects while considering their individual contributions. iTRCA extracts two types of features: (1) the subject-general feature, capturing shared information between source and target subjects in a common latent space, and (2) the subject-specific feature, preserving the unique characteristics of the target subject. To mitigate negative transfer, we further design an enhanced framework, subject selection-based iTRCA (SS-iTRCA), which integrates a similarity-based subject selection strategy to identify appropriate source subjects for transfer based on their task-related components (TRCs). Comparative evaluations on the Benchmark, BETA, and a self-collected dataset demonstrate the effectiveness of the proposed iTRCA and SS-iTRCA frameworks. This study provides a potential solution for developing high-performance SSVEP-based BCIs with reduced target subject data.}, }
@article {pmid40485770, year = {2025}, author = {Tyler, WJ and Adavikottu, A and Blanco, CL and Mysore, A and Blais, C and Santello, M and Unnikrishnan, A}, title = {Neurotechnology for enhancing human operation of robotic and semi-autonomous systems.}, journal = {Frontiers in robotics and AI}, volume = {12}, number = {}, pages = {1491494}, pmid = {40485770}, issn = {2296-9144}, abstract = {Human operators of remote and semi-autonomous systems must have a high level of executive function to safely and efficiently conduct operations. These operators face unique cognitive challenges when monitoring and controlling robotic machines, such as vehicles, drones, and construction equipment. The development of safe and experienced human operators of remote machines requires structured training and credentialing programs. This review critically evaluates the potential for incorporating neurotechnology into remote systems operator training and work to enhance human-machine interactions, performance, and safety. Recent evidence demonstrating that different noninvasive neuromodulation and neurofeedback methods can improve critical executive functions such as attention, learning, memory, and cognitive control is reviewed. We further describe how these approaches can be used to improve training outcomes, as well as teleoperator vigilance and decision-making. We also describe how neuromodulation can help remote operators during complex or high-risk tasks by mitigating impulsive decision-making and cognitive errors. While our review advocates for incorporating neurotechnology into remote operator training programs, continued research is required to evaluate the how these approaches will impact industrial safety and workforce readiness.}, }
@article {pmid40484831, year = {2025}, author = {Zhao, JZ}, title = {[A historical review and future outlook of neurosurgery in China].}, journal = {Zhonghua yi xue za zhi}, volume = {105}, number = {21}, pages = {1679-1685}, doi = {10.3760/cma.j.cn112137-20250325-00727}, pmid = {40484831}, issn = {0376-2491}, mesh = {*Neurosurgery/trends/history ; China ; Humans ; History, 20th Century ; History, 21st Century ; Societies, Medical ; Artificial Intelligence ; }, abstract = {Since its inception in the early 20th century at Peking Union Medical College Hospital, neurosurgery in China has gone through a century-long process from its initial establishment, development to modernization, forming a complete system, covering vascular diseases, tumors, epilepsy, and other diseases. This article reviews the key pioneers and historical milestones in Chinese neurosurgery, highlights the founding and advancement of the Society of Neurosurgery of Chinese Medical Association, and shows major achievements in standardization, training, and international cooperation, etc. At present, with the application of technologies such as artificial intelligence and brain-computer interfaces, network-based neurosurgery has emerged and developed rapidly, marking the transition to Neurosurgery 4.0. In the future, Chinese neurosurgery is poised to further promote interdisciplinary integration and clinical translation in support of the high-quality development of brain science.}, }
@article {pmid40483841, year = {2025}, author = {Zakrzewski, S and Stasiak, B and Wojciechowski, A}, title = {Supervised factor selection in tensor decomposition of EEG signal.}, journal = {Computer methods and programs in biomedicine}, volume = {269}, number = {}, pages = {108866}, doi = {10.1016/j.cmpb.2025.108866}, pmid = {40483841}, issn = {1872-7565}, mesh = {*Electroencephalography/methods/statistics &amp; numerical data ; Humans ; Algorithms ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Factor Analysis, Statistical ; }, abstract = {BACKGROUND AND OBJECTIVE: Tensor decomposition methods are important tools for multidimensional data analysis, which have also proved useful for EEG signal processing. However, their direct application for tasks involving supervised training, such as EEG data classification in systems based on brain-computer interfaces, is limited by the inherently unsupervised nature of the optimization algorithms used for tensor factorization.<br><br>METHODS: In this work, we propose a solution for a motor imagery classification task based on parallel factor analysis (PARAFAC) of EEG data. The individual factors obtained through PARAFAC decomposition are subjected to statistical analysis, enabling us to select signal components relevant to the classification problem. To choose the rank of the decomposition, we propose a special score function based on cosine similarity of all possible pairs of decompositions.<br><br>RESULTS: The proposed method was shown to significantly increase the classification accuracy in the case of the best-performing subjects, when provided with an EEG signal satisfying certain conditions, such as sufficient trial length. Besides, representation of the statistically significant components in the form of a heatmap, defined over the space-frequency plane, proved suitable for direct interpretation in the context of event-related synchronization/desynchronization of cortical activity.<br><br>CONCLUSION: The proposed approach, joining universal tensor decomposition methods with statistical evaluation of the obtained components, has the potential to yield high accuracy and explainability of the results while significantly reducing the input space dimensionality.}, }
@article {pmid40483616, year = {2025}, author = {Han, J and Zhan, G and Wang, L and Liang, D and Zhang, H and Zhang, L and Kang, X}, title = {Decoding EEG-based cognitive load using fusion of temporal and functional connectivity features.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-16}, doi = {10.1080/10255842.2025.2514132}, pmid = {40483616}, issn = {1476-8259}, abstract = {Evaluating cognitive load using electroencephalogram (EEG) signals is a crucial research area in the field of Brain-Computer Interfaces (BCI). However, achieving high accuracy and generalization in feature extraction and classification for cognitive load assessment remains a challenge, primarily due to the low signal-to-noise ratio of EEG signals and the inter-individual variability in EEG data. In this study, we propose a novel deep learning architecture that integrates temporal information features and functional connectivity features to enhance EEG signal analysis. Functional connectivity features capture inter-channel information, while temporal features are extracted from continuous signal segments using a Long Short-Term Memory (LSTM) network enhanced with an attention mechanism. The fusion strategy combines these two information streams to leverage their complementary strengths, resulting in improved classification performance. We evaluated our architecture on two publicly available datasets, and the results demonstrate its robustness in cognitive load recognition. Achieving performance comparable to the best existing methods on two public datasets. Ablation studies further substantiate the contributions of each module, demonstrating the importance of combining temporal and functional connectivity features for optimal results. These findings underscore the robustness and versatility of the proposed approach, paving the way for more effective EEG-based BCI applications.}, }
@article {pmid40482972, year = {2025}, author = {M V, H and K, K and B, SB}, title = {An EEG-based imagined speech recognition using CSP-TP feature fusion for enhanced BCI communication.}, journal = {Behavioural brain research}, volume = {493}, number = {}, pages = {115652}, doi = {10.1016/j.bbr.2025.115652}, pmid = {40482972}, issn = {1872-7549}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Female ; Adult ; *Speech/physiology ; Young Adult ; *Brain/physiology ; Support Vector Machine ; Signal Processing, Computer-Assisted ; Machine Learning ; }, abstract = {BACKGROUND: Imagined speech has emerged as a promising paradigm for intuitive control of brain-computer interface (BCI)-based communication systems, providing a means of communication for individuals with severe brain disabilities. In this work, a non-invasive electroencephalogram (EEG)-based automated imagined speech recognition model was proposed to assist communication to convey the individual's intentions or commands. The proposed approach uses Common Spatial Patterns (CSP) and Temporal Patterns (TP) for feature extraction, followed by feature fusion to capture both spatial and temporal dynamics in EEG signals. This fusion of the CSP and TP domains enhances the discriminative power of the extracted features, leading to improved classification accuracy.<br><br>NEW METHOD: An EEG data set was collected from 15 subjects while performing an imagined speech task with a set of ten words that are more suitable for paralyzed patients. The EEG signals were preprocessed and a set of statistical characteristics was extracted from the fused CSP and TP domains. Spectral analysis of the signals was performed with respect to ten imagined words to identify the underlying patterns in EEG. Machine learning models, including Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), were employed for pairwise and multiclass classification.<br><br>RESULTS: The proposed model achieved average classification accuracies of 83.83% &#xb1; 5.94 and 64.58% &#xb1; 10.43 and maximum accuracies of 97.78% and 79.22% for pairwise and multiclass classification, respectively. These results demonstrate the effectiveness of the CSP-TP feature fusion approach, outperforming existing state-of-the-art methods in imagined speech recognition.<br><br>CONCLUSION: The findings suggest that EEG-based automatic imagined speech recognition (AISR) using CSP and TP techniques has significant potential for use in BCI-based assistive technologies, offering a more natural and intuitive means of communication for individuals with severe communication limitations.}, }
@article {pmid40481499, year = {2025}, author = {Zhang, N and Huang, Z and Xia, Y and Tao, S and Wu, T and Sun, S and Zhu, Y and Jiang, G and Lu, X and Gao, Y and Guo, F and Cao, R and Chen, S and Zhang, L and Zou, X and Chen, M and Zhang, G}, title = {Remote ischemia precondition protects against renal IRI through apoptosis associated vesicles carrying MIF protein via modulating DUSP6/JNK signaling axis.}, journal = {Journal of nanobiotechnology}, volume = {23}, number = {1}, pages = {422}, pmid = {40481499}, issn = {1477-3155}, support = {tsgn202103116//Tai-Shan Scholar Program from Shandong Province/ ; 81900618//the National Natural Science Foundation of China/ ; 2023GX026//the Program of Scientific and Technological Development of Weifang/ ; GSP-LCYJFH11//Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds/ ; 2023YXZDXK02//Jiangsu Provincial Key Discipline and Laboratory Construction Funds of Urology/ ; CZXM-ZK-47//National clinical key discipline construction funds/ ; 202305033//Nanjing Key Science and Technology Special Project (Life and Health) - Medical-Engineering Collaborative Project/ ; 82100732//Natural Science Foundation of China/ ; }, abstract = {BACKGROUND: Remote ischemic preconditioning (rIPC) has been reported to protect against kidney ischemia-reperfusion injury (IRI) through the delivery of extracellular vesicles (EVs). Among these, apoptosis-induced compensatory proliferation signaling-related vesicles (ACPSVs) can transmit proliferation signals to surrounding cells. However, the underlying mechanisms remain unclear. This study aimed to investigate the role of ACPSVs in renal IRI following rIPC and to elucidate the associated mechanisms.<br><br>RESULTS: We demonstrated that rIPC plasma or ACPSVs alleviated renal damage and inflammation, with the protective effects abolished upon the removal of ACPSVs from the plasma. EVs isolated via differential centrifugation exhibited defining characteristics of ACPSVs. Co-culture experiments revealed that ACPSVs reduced apoptosis and enhanced the viability of HK-2 cells under hypoxia/reoxygenation (H/R) conditions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses highlighted the critical role of macrophage migration inhibitory factor (MIF) protein in ACPSVs. Using CRISPR/Cas9 technology, we generated MIF-knockout HeLa cells to induce the production of MIF-deficient ACPSVs. The protective effects of ACPSVs were significantly attenuated when MIF was knocked out. Transcriptome sequencing and chromatin immunoprecipitation (ChIP) assays revealed that MIF suppresses dual-specificity phosphatase 6 (DUSP6) expression by promoting H3K9 trimethylation (H3K9me3) in the DUSP6 promoter region, thereby activating the JNK signaling pathway. In rescue experiments, treatment with the DUSP6 inhibitor BCI effectively restored the protective function of MIF-deficient ACPSVs.<br><br>CONCLUSION: This study underscores the protective role of ACPSVs derived from rIPC-treated rats and serum-starved cells against renal IRI through the MIF/DUSP6/JNK signaling axis, offering a potential clinical therapeutic strategy for acute kidney injury induced by IRI.<br><br>GRAPHICAL ABSTRACT: [Image: see text]<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12951-025-03505-9.}, }
@article {pmid40481295, year = {2025}, author = {Zheng, J and Yu, J and Xu, M and Guan, C and Fu, Y and Shen, M and Chen, H}, title = {Expectation violation enhances short-term source memory.}, journal = {Psychonomic bulletin &amp; review}, volume = {}, number = {}, pages = {}, pmid = {40481295}, issn = {1531-5320}, abstract = {Recent studies of short-term source amnesia demonstrated that source information is rapidly forgotten in memory, reflecting a highly selective mode of memory encoding. In this study, we explored the flexibility of memory selection by investigating whether short-term source amnesia is affected by expectation violations. In seven experiments, we first replicated the short-term source amnesia phenomenon and then induced various forms of expectation violations. The results consistently showed that the short-term source amnesia was significantly reduced or attenuated when expectation violation occurred, indicating a strengthening effect of expectation violation on short-term source memory. This effect occurred quite quickly, nearly at the same time as the occurrence of unexpected events. Moreover, the source memory was improved even when the unexpected events were completely irrelevant to the task set or target stimuli. These findings suggest that short-term memory tends to encode and maintain more detailed source information when encountering expectation violations, which might be an adaptive mechanism for handling unexpected environmental changes.}, }
@article {pmid40481078, year = {2025}, author = {Peng, L and Wang, L and Wu, S and Gu, M and Deng, S and Liu, J and Cheng, CK and Sui, X}, title = {Biomechanics characterization of an implantable ultrathin intracortical electrode through finite element method.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {19938}, pmid = {40481078}, issn = {2045-2322}, support = {No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; }, mesh = {Finite Element Analysis ; *Electrodes, Implanted ; Biomechanical Phenomena ; Microelectrodes ; Brain/physiology ; Humans ; Stress, Mechanical ; Brain-Computer Interfaces ; }, abstract = {Neural electrodes are widely used in brain-computer interfaces and neuroprosthesis for the treatment of various neurological disorders. However, as components that come into direct contact with neural tissue, implanted neural electrodes could cause mechanical damage during surgical insertions or while inside the brain. Thus, accurately and timely assessing this damage was vital for chronic implantation, which posed a significant challenge. This study aimed to evaluate the biomechanical effects and clinical application risks of a polyimide-based ultrathin flexible intracortical microelectrode through the finite element method (FEM). It analyzed the electrode-brain biomechanical effects during the electrode's insertion process and under steady-state acceleration with the electrode inside the brain. Furthermore, the study examined the impact of factors including implantation depth (ranging from 5 to 5000 μm), cortical thickness (0.5 mm, 2.5 mm, and 4.5 mm), step displacement (from 1 to 5 μm) during insertion, and acceleration direction (vertical and horizontal) on the electrode's biomechanical effects. The primary findings showed that the 98th percentile Von Mises Strain (ε98) and Von Mises Stress (σ98) in the region of interest (ROI) decreased dual-exponentially with increasing implantation depth and increased linearly with larger step displacements. Compared to the Von Mises strain threshold of 14.65%, as proposed by Sahoo et al., indicating a 50% risk of diffuse axonal injury (DAI), it was recommended to limit the initial step displacement during insertion to 1 μm, increasing to 5 μm at deeper locations (over 500 μm) to balance safety and efficiency. Additionally, it was found that cortical thickness had a negligible impact during insertion and while experiencing steady-state acceleration in vivo, with the three fitted curves almost coinciding when cortical thicknesses were 0.5 mm, 2.5 mm, and 4.5 mm. The flexible electrode exhibited excellent mechanical performance under steady-state acceleration in vivo, with ε98 being less than 0.3% and σ98 being less than 50 Pa, although it was more sensitive to horizontal acceleration. Thus, it could be concluded that during long-duration accelerations from transportation modes such as elevators and high-speed trains, the electrode's mechanical effects on brain tissue could be neglected, demonstrating long-term mechanical stability. This research was significant for guiding surgical insertion and clinical applications of flexible electrodes.}, }
@article {pmid40481044, year = {2025}, author = {Yi, W and Chen, J and Wang, D and Hu, X and Xu, M and Li, F and Wu, S and Qian, J}, title = {A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {953}, pmid = {40481044}, issn = {2052-4463}, support = {12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Electroencephalography ; Spectroscopy, Near-Infrared ; Brain-Computer Interfaces ; *Upper Extremity/physiology ; *Imagination ; *Joints/physiology ; Movement ; }, abstract = {As one of the important brain-computer interface (BCI) paradigms, motor imagery (MI) enables the control of external devices via identification of motor intention by decoding the features of Electroencephalography (EEG). Movement imagination of multi-types of joints from the same limb allows the development of more accurate and intuitive BCI systems. In this work, we reported an open dataset including EEG and functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects performing eight MI tasks from four types of joints including hand open/close, wrist flexion/extension, wrist abduction/adduction, elbow pronation/supination, elbow flexion/extension, shoulder pronation/supination, shoulder abduction/adduction, and shoulder flexion/extension, resulting in a total of 5760 trials. The validity of multi-modal data was verified both from the EEG/fNIRS activation patterns and the classification performance. It is expected that this dataset will facilitate the development and innovation of decoding algorithms for MI of multi-types of joints based on multi-modal EEG-fNIRS data.}, }
@article {pmid40480870, year = {2025}, author = {Gunda, NK and Khalaf, MI and Bhatnagar, S and Quraishi, A and Gudala, L and Venkata, AKP and Alghayadh, FY and Alsubai, S and Bhatnagar, V}, title = {Retraction notice to "Lightweight attention mechanisms for EEG emotion recognition for brain computer interface".}, journal = {Journal of neuroscience methods}, volume = {422}, number = {}, pages = {110502}, doi = {10.1016/j.jneumeth.2025.110502}, pmid = {40480870}, issn = {1872-678X}, }
@article {pmid40480308, year = {2025}, author = {Zhang, T and Jia, Y and Wang, N and Chai, X and He, Q and Cao, T and Mu, Q and Lan, Q and Zhao, J and Yang, Y}, title = {Recent advances in potential mechanisms of epidural spinal cord stimulation for movement disorders.}, journal = {Experimental neurology}, volume = {392}, number = {}, pages = {115330}, doi = {10.1016/j.expneurol.2025.115330}, pmid = {40480308}, issn = {1090-2430}, mesh = {Humans ; *Spinal Cord Stimulation/methods/trends ; *Movement Disorders/therapy/physiopathology ; Animals ; *Spinal Cord/physiology ; Neuronal Plasticity/physiology ; Epidural Space/physiology ; }, abstract = {BACKGROUND: Epidural spinal cord stimulation (eSCS) has emerged as a promising neuromodulation technique for treating movement disorders. The underlying mechanisms of eSCS are still being explored, making it a compelling area for further research.<br><br>OBJECTIVE: This review aims to provide a comprehensive analysis of the mechanisms of eSCS, its stimulation parameters, and its clinical applications in movement disorders. It seeks to synthesize the current understanding of how eSCS interacts with the central nervous system to enhance motor function and promotes neural plasticity for sustained recovery.<br><br>METHODS: A literature search was performed in databases such as Web of Science, Scopus, and PubMed to identify studies on eSCS for movement disorders.<br><br>RESULTS: The therapeutic effects of eSCS are achieved through both immediate facilitative actions and long-term neural reorganization. By activating sensory neurons in the dorsal root, facilitating proprioceptive input and modulating spinal interneurons, eSCS enhances motor neuron excitability. Additionally, eSCS influences corticospinal interactions, increasing cortical excitability and promoting corticospinal circuit remodeling. Neuroplasticity plays a critical role in the long-term efficacy of eSCS, with evidence suggesting that stimulation can enhance axonal sprouting, synaptic formation, and neurotrophic factor expression while reducing neuroinflammation. Its regulation of the sympathetic nervous system further enhances recovery by improving blood flow, muscle tone, and other physiological parameters.<br><br>CONCLUSIONS: Epidural spinal cord stimulation shows promise in enhancing motor function and promoting neuroplasticity, but further research is needed to optimize treatment protocols and establish long-term efficacy.}, }
@article {pmid40480249, year = {2025}, author = {Pritchard, M and Campelo, F and Goldingay, H}, title = {An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ade1f9}, pmid = {40480249}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods/classification ; *Electromyography/methods/classification ; *Gestures ; *Upper Extremity/physiology ; Male ; Adult ; Female ; Algorithms ; Young Adult ; Machine Learning ; Brain-Computer Interfaces ; }, abstract = {Objective. Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often limited in methodological rigour, the extent to which generalisation is demonstrated, and the granularity of gestures classified. This work evaluates three architectures for multimodal fusion of EMG &amp; EEG data in gesture classification, including a novel Hierarchical strategy, in both subject-specific and subject-independent settings.Approach. We propose an unbiased methodology for designing classifiers centred on Automated Machine Learning through Combined Algorithm Selection &amp; Hyperparameter Optimisation (CASH); the first application of this technique to the biosignal domain. Using CASH, we introduce an end-to-end pipeline for data handling, algorithm development, modelling, and fair comparison, addressing established weaknesses among biosignal literature.Main results. EMG-EEG fusion is shown to provide significantly higher subject-independent accuracy in same-hand multi-gesture classification than an equivalent EMG classifier. Our CASH-based design methodology produces a more accurate subject-specific classifier design than recommended by literature. Our novel Hierarchical ensemble of classical models outperforms a domain-standard CNN architecture. We achieve a subject-independent EEG multiclass accuracy competitive with many subject-specific approaches used for similar, or more easily separable, problems.Significance. To our knowledge, this is the first work to establish a systematic framework for automatic, unbiased designing and testing of fusion architectures in the context of multimodal biosignal classification. We demonstrate a robust end-to-end modelling pipeline for biosignal classification problems which if adopted in future research can help address the risk of bias common in multimodal BCI studies , enabling more reliable and rigorous comparison of proposed classifiers than is usual in the domain. We apply the approach to a more complex task than typical of EMG-EEG fusion research, surpassing literature-recommended designs and verifying the efficacy of a novel Hierarchical fusion architecture.}, }
@article {pmid40479831, year = {2025}, author = {Li, C and Di, G and Li, Q and Sun, L and Wang, W and Wang, Y and Jiang, X and Wu, J}, title = {Microsurgical anatomy of the fiber tracts and vascular structures lateral to the internal capsule.}, journal = {Journal of neurosurgery}, volume = {143}, number = {4}, pages = {1068-1076}, doi = {10.3171/2025.2.JNS243025}, pmid = {40479831}, issn = {1933-0693}, mesh = {Humans ; *Microsurgery/methods ; *Internal Capsule/anatomy &amp; histology/surgery/blood supply ; *White Matter/anatomy &amp; histology/surgery/blood supply ; *Cerebral Cortex/anatomy &amp; histology/blood supply/surgery ; Cadaver ; }, abstract = {OBJECTIVE: The cerebral structures lateral to the internal capsule are frequently involved in studies of nervous system functions and diseases. This study aimed to investigate the fiber tracts and vascular structures of the brain lateral to the internal capsule using cranial specimens and specimen perfusion techniques.<br><br>METHODS: Ten cranial specimens were perfused via arteries and veins using specimen perfusion techniques and then processed using the fiber dissection method. The authors studied the fiber tracts and vascular structures from the cerebral cortex to the internal capsule, moving from lateral to medial.<br><br>RESULTS: The topographical relationships between the fiber tracts, nuclei, and vascular structures were identified. This was achieved by examining structures from the gray matter cortex of the brain's lateral surface, including U fibers, long association fiber tracts, and the insular lobe, extending to the level of the internal capsule.<br><br>CONCLUSIONS: Understanding the anatomical structures of white matter fiber tracts and vascular structures from the brain's lateral surface to the level of the internal capsule aids in planning safe, effective, and minimally invasive surgical procedures. It also contributes to advancements in neuroscience research.}, }
@article {pmid40478867, year = {2025}, author = {Jiang, M and Luo, Q and Wang, X and Tan, Y}, title = {The "Dogs' Catching Mice" conjecture in Chinese phonogram processing.}, journal = {PloS one}, volume = {20}, number = {6}, pages = {e0324848}, pmid = {40478867}, issn = {1932-6203}, mesh = {Adult ; Animals ; Female ; Humans ; Male ; Young Adult ; China ; *Language ; *Phonetics ; Semantics ; }, abstract = {In Chinese phonogram processing studies, it is not strange that phonetic radicals contribute phonologically to phonograms' phonological recognition. The present study, however, based on previous findings of phonetic radicals' proneness to semantic activation, as well as free-standing phonetic radicals' possession of triadic interconnections of orthography, phonology, and semantics at the lexical level, proposed that phonetic radicals may contribute semantically to the host phonograms' phonological recognition. We label this speculation as the "Dogs' Catching Mice" Conjecture. To examine this conjecture, three experiments were conducted. Experiment 1 was designed to confirm whether phonetic radicals, when embedded in phonograms, can contribute semantically to their host phonograms' phonological recognition. Experiment 2 was intended to show that the embedded phonetic radicals employed in Experiment 1 were truly semantically activated. Experiment 3, on top of the first two experiments, was devoted to demonstrating that the semantically activated phonetic radicals, when used as independent characters, can truly contribute semantically to their phonological recognition. Results from the three experiments combine to confirm the conjecture. The implication drawn is that phonetic radicals may have forged two paths in contributing to the host phonograms' phonological recognition: one is the regular "Cats' Catching Mice" Path, the other is the novel "Dogs' Catching Mice" Path.}, }
@article {pmid40478707, year = {2025}, author = {Li, H and Zhang, H and Chen, Y}, title = {Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {9}, pages = {6524-6537}, doi = {10.1109/JBHI.2025.3577611}, pmid = {40478707}, issn = {2168-2208}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Algorithms ; }, abstract = {The decoding of electroencephalography (EEG) signals allows access to user intentions conveniently, which plays an important role in the fields of human-machine interaction. To effectively extract sufficient characteristics of the multichannel EEG, a novel decoding architecture network with a dual-branch temporal-spectral-spatial transformer (Dual-TSST) is proposed in this study. Specifically, by utilizing convolutional neural networks (CNNs) on different branches, the proposed processing network first extracts the temporal-spatial features of the original EEG and the temporal-spectral-spatial features of time-frequency domain data converted by wavelet transformation, respectively. These perceived features are then integrated by a feature fusion block, serving as the input of the transformer to capture the global long-range dependencies entailed in the non-stationary EEG, and being classified via the global average pooling and multi-layer perceptron blocks. To evaluate the efficacy of the proposed approach, the competitive experiments are conducted on three publicly available datasets of BCI IV 2a, BCI IV 2b, and SEED, with the head-to-head comparison of more than ten other state-of-the-art methods. As a result, our proposed Dual-TSST performs superiorly in various tasks, which achieves the promising EEG classification performance of average accuracy of 82.79% in BCI IV 2a, 89.38% in BCI IV 2b, and 96.65% in SEED, respectively. Extensive ablation experiments conducted between the Dual-TSST and comparative baseline model also reveal the enhanced decoding performance with each module of our proposed method. This study provides a new approach to high-performance EEG decoding and has great potential for future CNN-Transformer based applications.}, }
@article {pmid40476694, year = {2025}, author = {Nazareth, G}, title = {Speaking from the heart: a story about innovation, resilience, and infinite possibilities with AAC.}, journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)}, volume = {41}, number = {3}, pages = {248-249}, doi = {10.1080/07434618.2025.2508491}, pmid = {40476694}, issn = {1477-3848}, mesh = {Humans ; Artificial Intelligence ; Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; *Communication Disorders/rehabilitation ; *Resilience, Psychological ; }, abstract = {Communication is the cornerstone of human connection, impacting everything from our personal relationships to our professional success. This concept became heartbreakingly real for me when I was diagnosed with motor neuron disease at the age of 25. The rapid decline of my speech left me feeling all alone and isolated. After experimenting with AAC options, I yearned for a system that was lightweight, portable and stylish. This sparked my entrepreneurial spirit, leading me to assemble components catering to my diverse interests and professional pursuits. Over the years, I have built multiple AAC systems using different hardware platforms. Currently, I am focused on integrating emotional expression and faster communication speeds into AAC technology. Artificial intelligence, multi-modal inputs and non-invasive brain-computer interfaces hold immense potential for people who use AAC. Building my communication tools has revealed profound truths about living life to the fullest, accepting complete responsibility for our lives and embracing the good, the bad and the ugly. Through innovation and resilience, I have discovered infinite possibilities and I continue to use AAC to work miracles in my own life.}, }
@article {pmid40475558, year = {2025}, author = {Sicorello, M and Gianaros, PJ and Wright, AGC and Bogdan, P and Kraynak, TE and Manuck, SB and Schmahl, C and Wager, TD}, title = {The functional neurobiology of negative affective traits across regions, networks, signatures, and a machine learning multiverse.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40475558}, issn = {2692-8205}, support = {P01 HL040962/HL/NHLBI NIH HHS/United States ; }, abstract = {Understanding the neural basis of negative affective traits like neuroticism remains a critical challenge across psychology, neuroscience, and psychiatry. Here, we investigate which level of brain organization-regions, networks, or validated whole-brain machine-learning signatures-best explains negative affective traits in a community sample of 458 adults performing the two most widely used affective fMRI tasks, viewing emotional faces and scenes. Neuroticism could not be predicted from brain activity, with Bayesian evidence against all theory-guided neural measures. However, preregistered whole-brain models successfully decoded vulnerability to stress, a lower-level facet of neuroticism, with results replicating in a hold-out sample. The neural stress vulnerability pattern demonstrated good psychometric properties and indicated that negative affective traits are best represented by distributed whole-brain patterns related to domain-general stimulation rather than localized activity. Together with results from a comprehensive multiverse analysis across 14 traits and 1,176 models-available for exploration in an online app-the findings speak against simplistic neurobiological theories of negative affective traits, highlight a striking gap between predicting individual differences (r<.35) and within-person emotional states (r=.88), and underscore the importance of aligning psychological constructs with neural measures at the appropriate level of granularity.}, }
@article {pmid40472937, year = {2025}, author = {Qian, MB and Huang, JL and Wang, L and Zhou, CH and Zhu, TJ and Zhu, HH and He, YT and Zhou, XN and Lai, YS and Li, SZ}, title = {Clonorchiasis in China: Geospatial modeling of the population infected and at risk, based on national surveillance.}, journal = {The Journal of infection}, volume = {91}, number = {1}, pages = {106528}, doi = {10.1016/j.jinf.2025.106528}, pmid = {40472937}, issn = {1532-2742}, mesh = {Humans ; China/epidemiology ; *Clonorchiasis/epidemiology ; Male ; Female ; Middle Aged ; Prevalence ; Adult ; Adolescent ; Child ; Aged ; Young Adult ; Bayes Theorem ; Child, Preschool ; Clonorchis sinensis ; Infant ; Animals ; Risk Factors ; Aged, 80 and over ; Infant, Newborn ; Epidemiological Monitoring ; Spatial Analysis ; }, abstract = {OBJECTIVES: Clonorchiasis is highly endemic in China. The unavailability of fine-scale distribution of population with infection and at risk hinders the control.<br><br>METHODS: This study established Bayesian geostatistical models to estimate age- and gender-specific prevalence of Clonorchis sinensis infection at high spatial resolution (5 &#xd7; 5&#xa0;km[2]), based on the surveillance data in China between 2016 and 2021, together with socioeconomic, environmental and behavioral determinants. The population at risk and under infection, as well as chemotherapy need were then estimated.<br><br>RESULTS: In 2020, population-weighted prevalence of 0.67% (95% Bayesian credible interval (BCI): 0.58%-0.77%) was estimated for C. sinensis infection in China, corresponding to 9.46 million (95% BCI: 8.22 million-10.88 million) persons under infection. High prevalence was demonstrated in southern areas, including Guangxi (8.92%, 95% BCI: 7.10%-10.81%) and Guangdong (2.99%, 95% BCI: 2.43%-3.74%). A conservative estimation of 99.13 million (95% BCI: 88.61 million-114.40 million) people were at risk of infection, of which 51.69 million (95% BCI: 45.48 million-57.84 million) need chemotherapy.<br><br>CONCLUSIONS: Clonorchiasis is an important public health problem in China, especially in southern areas, due to the huge population at risk and large number of people under infection. Implementation of chemotherapy is urged to control the morbidity.}, }
@article {pmid40472336, year = {2025}, author = {Ranieri, A and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Toppi, J}, title = {SPectral graph theory And Random walK (SPARK) toolbox for static and dynamic characterization of (di)graphs: A tutorial.}, journal = {PloS one}, volume = {20}, number = {6}, pages = {e0319031}, pmid = {40472336}, issn = {1932-6203}, mesh = {Humans ; Algorithms ; Electroencephalography ; Stroke/physiopathology ; *Software ; Brain/physiopathology/physiology ; }, abstract = {Spectral graph theory and its applications constitute an important forward step in modern network theory. Its increasing consensus over the last decades fostered the development of innovative tools, allowing network theory to model a variety of different scenarios while answering questions of increasing complexity. Nevertheless, a comprehensive understanding of spectral graph theory's principles requires a solid technical background which, in many cases, prevents its diffusion through the scientific community. To overcome such an issue, we developed and released an open-source MATLAB toolbox - SPectral graph theory And Random walK (SPARK) toolbox - that combines spectral graph theory and random walk concepts to provide a both static and dynamic characterization of digraphs. Once described the theoretical principles grounding the toolbox, we presented SPARK structure and the list of available indices and measures. SPARK was then tested in a variety of scenarios including: two-toy examples on synthetic networks, an example using public datasets in which SPARK was used as an unsupervised binary classifier and a real data scenario relying on functional brain networks extracted from the EEG data recorded from two stroke patients in resting state condition. Results from both synthetic and real data showed that indices extracted using SPARK toolbox allow to correctly characterize the topology of a bi-compartmental network. Furthermore, they could also be used to find the "optimal" vertex set partition (i.e., the one that minimizes the number of between-cluster links) for the underlying network and compare it to a given a priori partition. Finally, the application to real EEG-based networks provides a practical case study where the SPARK toolbox was used to describe networks' alterations in stroke patients and put them in relation to their motor impairment.}, }
@article {pmid40471721, year = {2025}, author = {Li, J and Fu, B and Li, F and Gu, W and Ji, Y and Li, Y and Liu, T and Shi, G}, title = {Applying SSVEP BCI on Dynamic Background.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2225-2237}, doi = {10.1109/TNSRE.2025.3576984}, pmid = {40471721}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; Male ; Adult ; Algorithms ; Female ; Neural Networks, Computer ; Young Adult ; Photic Stimulation/methods ; Color ; }, abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high efficiency and accuracy. The SSVEP paradigm and decoding methods have been extensively studied and achieved remarkable results. This study proposed two modulation methods for the SSVEP paradigm, namely color inversion modulation and brightness compression modulation. Color inversion modulation adjusts the stimulus to adapt to the changing background, while brightness compression modulation ensures high contrast by reducing the background brightness. Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. We conduct experiments to evaluate the performance of the proposed modulation and decoding methods. Compared with color inversion modulation and no modulation, the brightness compression modulation method achieved the best performance. In addition, MTSGNN outperforms the best competitive decoding method by 11.98%, 3.9% and 5.15% under color inversion modulation, brightness compression modulation and no modulation, respectively. The experimental results demonstrate the effectiveness of the proposed modulation methods and the robustness of the proposed decoding method. This study significantly improves the performance of SSVEP in dynamic backgrounds and effectively expands the practical application scenarios of BCI.}, }
@article {pmid40471491, year = {2025}, author = {Liu, X and Jia, Z and Xun, M and Wan, X and Lu, H and Zhou, Y}, title = {MSFHNet: a hybrid deep learning network for multi-scale spatiotemporal feature extraction of spatial cognitive EEG signals in BCI-VR systems.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {40471491}, issn = {1741-0444}, support = {62276022//National Natural Science Foundation of China/ ; 62206014//National Natural Science Foundation of China/ ; }, abstract = {The integration of brain-computer interface (BCI) and virtual reality (VR) systems offers transformative potential for spatial cognition training and assessment. By leveraging artificial intelligence (AI) to analyze electroencephalogram (EEG) data, brain activity patterns during spatial tasks can be decoded with high precision. In this context, a hybrid neural network named MSFHNet is proposed, optimized for extracting spatiotemporal features from spatial cognitive EEG signals. The model employs a hierarchical architecture where its temporal module uses multi-scale dilated convolutions to capture dynamic EEG variations, while its spatial module integrates channel-spatial attention mechanisms to model inter-channel dependencies and spatial distributions. Cross-stacked modules further refine discriminative features through deep-level fusion. Evaluations demonstrate the superiority of MSFHNet in the beta2 frequency band, achieving 98.58% classification accuracy and outperforming existing models. This innovation enhances EEG signal representation, advancing AI-powered BCI-VR systems for robust spatial cognitive training.}, }
@article {pmid40470749, year = {2025}, author = {Li, W and Gao, C and Li, Z and Diao, Y and Li, J and Zhou, J and Zhou, J and Peng, Y and Chen, G and Wu, X and Wu, K}, title = {BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {12}, number = {32}, pages = {e17408}, pmid = {40470749}, issn = {2198-3844}, support = {2023YFC2414500//National Key Research and Development Program of China/ ; 2023YFC2414504//National Key Research and Development Program of China/ ; 81971585//Natural Science Foundation of China/ ; 72174082//Natural Science Foundation of China/ ; 82271953//Natural Science Foundation of China/ ; 82301688//Natural Science Foundation of China/ ; 2021B1515020064//Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project/ ; 2023B0303020001//Key Research and Development Program of Guangdong/ ; 2023B0303010003//Key Research and Development Program of Guangdong/ ; 2022A1515140142//Basic and Applied Basic Research Foundation of Guangdong Province/ ; 2024A1515013058//Natural Science Foundation of Guangdong Province/ ; 202206060005//Science and Technology Program of Guangzhou/ ; 202206080005//Science and Technology Program of Guangzhou/ ; 202206010077//Science and Technology Program of Guangzhou/ ; 202206010034//Science and Technology Program of Guangzhou/ ; 202201010093//Science and Technology Program of Guangzhou/ ; 2023A03J0856//Science and Technology Program of Guangzhou/ ; 2023A03J0839//Science and Technology Program of Guangzhou/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Software ; Spectroscopy, Near-Infrared/methods ; Electrocardiography/methods ; *Brain/physiology ; Reproducibility of Results ; Electromyography/methods ; }, abstract = {This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain-computer interface (BCI) and brain-body interaction research. While electroencephalography (EEG) -based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real-world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross-modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within-subject EEG-functional near-infrared spectroscopy (fNIRS) motor imagery classification using ensemble modeling and 80.2% accuracy in EEG-electrocardiography (ECG) sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG) , and ECG, BrainFusion provides a low-code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world.}, }
@article {pmid40469097, year = {2025}, author = {Wang, Z and Wang, Y}, title = {Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1599960}, pmid = {40469097}, issn = {1662-5161}, abstract = {Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering significant potential for brain-computer interface (BCI) applications. However, due to the complex, noisy, and non-stationary nature of EEG signals, traditional classification methods-such as Common Spatial Pattern (CSP) and Power Spectral Density (PSD)-struggle to extract meaningful, multidimensional features. While deep learning models like CNNs and RNNs have shown promise, they often focus on single-dimensional aspects and lack interpretability, limiting their neuroscientific relevance. This study proposes a novel multi-branch deep learning framework, termed Multi-Branch GAT-GRU-Transformer, to enhance EEG-based MI classification. The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. Feature fusion is employed to integrate these heterogeneous representations. To improve interpretability, the model incorporates SHAP (SHapley Additive exPlanations) and Phase Locking Value (PLV) analyses. Notably, PLV is also used to construct the GAT adjacency matrix, embedding biologically-informed spatial priors into the learning process. The proposed model was evaluated on the Kaya dataset, achieving a five-class MI classification accuracy of 55.76%. Ablation studies confirmed the effectiveness of each architectural component. Furthermore, SHAP and PLV analyses identified C3 and C4 as critical EEG channels and highlighted the Beta frequency band as highly relevant, aligning with known motor-related neural activity. The Multi-Branch GAT-GRU-Transformer effectively addresses key challenges in EEG-based MI classification by integrating domain-relevant spatial, temporal, and frequency features, while enhancing model interpretability through biologically grounded mechanisms. This work not only improves classification performance but also provides a transparent framework for neuroscientific investigation, with broad implications for BCI development and cognitive neuroscience research.}, }
@article {pmid40469096, year = {2025}, author = {Li, P and Yu, D and Cheng, L and Wang, K}, title = {Influence of attentional state on EEG-based motor imagery of lower limb.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1545492}, pmid = {40469096}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor imagery (MI) has emerged as a promising technique for enhancing motor skill acquisition and facilitating neural adaptation training. Attention plays a key role in regulating the neural mechanisms underlying MI. This study aims to investigate how attentional states modulate EEG-based lower-limb motor imagery performance by influencing event-related desynchronization (ERD) and the alpha modulation index (AMI) and to develop a real-time attention monitoring method based on the theta/beta ratio (TBR).<br><br>METHODS: Fourteen healthy right-handed subjects (aged 21-23) performed right-leg MI tasks, while their attentional states were modulated via a key-press paradigm. EEG signals were recorded using a 32-channel system and preprocessed with independent component analysis (ICA) to remove artifacts. Attentional states were quantified using both the traditional offline AMI and the real-time TBR index, with time-frequency analysis applied to assess ERD and its relationship with attention.<br><br>RESULTS: The results indicated a significant increase in ERD during high attentional states compared to low attentional states, with AMI values showing a strong positive correlation with ERD (r&#x202f;=&#x202f;0.9641, p&#x202f;<&#x202f;0.01). Cluster-based permutation testing confirmed that this &#x3b1;-band ERD difference was significant (corrected p&#x202f;<&#x202f;0.05). Moreover, the TBR index proved to be an effective real-time metric, decreasing significantly under focused attention. Offline paired t-tests showed a significant TBR reduction [t (13)&#x202f;=&#x202f;5.12, p&#x202f;=&#x202f;2.4&#x202f;&#xd7;&#x202f;10[-5]], and online analyses further validated second-by-second discrimination (Bonferroni-corrected p&#x202f;<&#x202f;0.01). These findings confirm the feasibility and efficacy of TBR for real-time attention monitoring and suggest that enhanced attentional focus during lower-limb MI can improve signal quality and overall performance.<br><br>CONCLUSION: This study reveals that attentional states significantly influence the neural efficiency of lower-limb motor imagery by modulating ERD/AMI and demonstrates that the TBR can serve as a real-time indicator of attention, providing a novel tool for optimizing attentional processes in motor skill training.}, }
@article {pmid40468342, year = {2025}, author = {Wang, M and Zhou, H and Zhang, X and Chen, Q and Tong, Q and Han, Q and Zhao, X and Wang, D and Lai, J and He, H and Zhang, S and Hu, S}, title = {Alleviating cognitive impairments in bipolar disorder with a novel DTI-guided multimodal neurostimulation protocol: a double-blind randomized controlled trial.}, journal = {BMC medicine}, volume = {23}, number = {1}, pages = {334}, pmid = {40468342}, issn = {1741-7015}, support = {52407261, 82201675//National Natural Science Foundation of China/ ; 52407261, 82201675//National Natural Science Foundation of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; 2022KTZ004//Chinese Medical Education Association/ ; 226-2022-00193, 226-2022-00002, 2023ZFJH01-01, 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; Double-Blind Method ; Female ; Male ; *Bipolar Disorder/therapy/complications/psychology ; *Diffusion Tensor Imaging/methods ; Adult ; *Transcranial Magnetic Stimulation/methods ; *Cognitive Dysfunction/therapy/etiology/diagnostic imaging ; Middle Aged ; *Transcranial Direct Current Stimulation/methods ; Treatment Outcome ; }, abstract = {BACKGROUND: Traditional neuromodulation strategies show promise in enhancing cognitive abilities in bipolar disorder (BD) but remain suboptimal. This study introduces a novel multimodal neurostimulation (MNS) protocol to improve therapeutic outcomes.<br><br>METHODS: The novel MNS protocol used individualized diffusion tensor imaging (DTI) data to identify fiber tracts between the dorsolateral prefrontal cortex and dorsal&#xa0;anterior cingulate cortex. The highest structural connectivity point is selected as the individualized stimulation site, which is then targeted using a combination of optimized transcranial alternating current stimulation (tACS) and robot-assisted navigated repetitive transcranial magnetic stimulation (rTMS). A double-blind randomized controlled trial was conducted to investigate the clinical efficacy of this innovative neuromodulation approach on cognitive abilities in stable-phase BD patients. One hundred BD patients were randomly assigned to four groups: group A (active tACS-active rTMS (MNS protocol)), group B (sham tACS-active rTMS), group C (active tACS-sham rTMS), and group D (sham tACS-sham rTMS). Participants underwent 15 sessions over 3&#xa0;weeks. Cognitive assessments (THINC integrated tool) were conducted at baseline (week 0) and post-treatment (week 3).<br><br>RESULTS: Sixty-six participants completed all 15 sessions. Group A (MNS protocol) showed superior improvements in Spotter CRT, TMT, and DSST scores compared to other groups at week 3. Only group A exhibited significant activation in the left frontal region post-MNS intervention. The novel MNS protocol was well tolerated, with no significant side effects observed.<br><br>CONCLUSIONS: The study indicates that DTI-guided multimodal neurostimulation mode significantly improves cognitive impairments and is safe for stable-phase BD patients.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05964777.}, }
@article {pmid40467567, year = {2025}, author = {Pang, J and Xu, J and Chen, L and Teng, H and Su, C and Zhang, Z and Gao, L and Zhang, R and Liu, G and Chen, Y and He, J and Pang, Y and Li, H}, title = {Family history, inflammation, and cerebellum in major depression: a combined VBM and dynamic functional connectivity study.}, journal = {Translational psychiatry}, volume = {15}, number = {1}, pages = {188}, pmid = {40467567}, issn = {2158-3188}, support = {222102310205//Science and Technology Department of Henan Province (Henan Provincial Department of Science and Technology)/ ; 62103377//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Depressive Disorder, Major/physiopathology/diagnostic imaging/pathology/genetics/blood ; Female ; Male ; Adult ; *Cerebellum/diagnostic imaging/physiopathology/pathology ; Magnetic Resonance Imaging ; *Inflammation/blood ; Interleukin-6/blood ; Gray Matter/diagnostic imaging/pathology ; C-Reactive Protein/metabolism/analysis ; Middle Aged ; Prefrontal Cortex/diagnostic imaging/physiopathology ; Case-Control Studies ; Young Adult ; }, abstract = {A family history (FH) of depression significantly influences the progress of major depressive disorder (MDD). However, the underlying neural mechanism of FH remains unclear. This study examined the association between brain structural and connectivity alterations, inflammation, and FH in MDD. A total of 134 MDD patients with (FH group, n = 43) and without (NFH group, n = 91) first-degree FH and 96 demographic-matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) and sliding-window dynamic functional connectivity (dFC) analyses were performed, and inflammatory biomarkers (C-reactive protein (CRP) and interleukin-6 (IL-6)) were detected. Compared with HCs, FH and NFH groups showed decreased gray matter volume (GMV) in the left cerebellum posterior lobe and increased dFC between this region and the left inferior parietal lobule. The FH group showed increased dFC between the cerebellum region and medial prefrontal cortex (mPFC) compared to NFH and HCs. The combination of these brain measurements further differentiated between FH and NFH. Moreover, the GMV of the cerebellum was positively correlated with CRP in the NFH group, while the dFC between the cerebellum and mPFC was positively correlated with IL-6 in the FH group. The present findings indicate that cerebellar structure and dynamic function vary according to FH of MDD and are related to inflammatory factors, potentially offering novel insights into the underlying pathogenic mechanisms of MDD.}, }
@article {pmid40465456, year = {2025}, author = {Li, C and Hasegawa, I and Tanigawa, H}, title = {Protocol for assisting frequency band definition and decoding neural dynamics using hierarchical clustering and multivariate pattern analysis.}, journal = {STAR protocols}, volume = {6}, number = {2}, pages = {103870}, pmid = {40465456}, issn = {2666-1667}, mesh = {Animals ; *Electrocorticography/methods ; Multivariate Analysis ; Cluster Analysis ; *Signal Processing, Computer-Assisted ; Macaca ; Brain/physiology ; }, abstract = {Traditional fixed frequency band divisions often limit neural data analysis accuracy. Here, we present a protocol for assisting frequency band definition for multichannel neural data using macaque electrocorticography (ECoG) data. We describe steps for performing time-frequency analysis on preprocessed signals and applying hierarchical clustering to frequency power profiles to identify data-informed groupings. We then detail procedures for defining frequency bands guided by these clusters and using multivariate pattern analysis (MVPA) on the derived bands for functional validation via time-series decoding. For complete details on the use and execution of this protocol, please refer to Tanigawa et al.[1].}, }
@article {pmid40463690, year = {2025}, author = {Rabiee, A and Ghafoori, S and Cetera, A and Shahriari, Y and Abiri, R}, title = {Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.}, journal = {ArXiv}, volume = {}, number = {}, pages = {}, pmid = {40463690}, issn = {2331-8422}, support = {P20 GM103430/GM/NIGMS NIH HHS/United States ; }, abstract = {This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.}, }
@article {pmid40462746, year = {2025}, author = {Song, J and Chai, X and Zhang, X and Lv, Z and Wan, F and Yang, Y and Shan, X and Liu, J}, title = {HEGNet: EEG and EMG fusion decoding method in motor imagery and actual movement.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-14}, doi = {10.1080/10255842.2025.2512877}, pmid = {40462746}, issn = {1476-8259}, abstract = {The widespread adoption od brain-computer interface (BCI) has been hindered by the limited classification accuracy of electroencephalography (EEG) signals alone. This study proposes a novel BCI model, HEGNet, that addresses this challenge by fusing EEG and electromyography (EMG) signals. HEGNet incorporates an EMG feature extraction component to mitigate the inherent instability and low signal-to-noise ratio limitations of relying solely on EEG data. Additionally, HEGNet employs a feature fusion module to dynamically adjust the focus on EEG and EMG features, thereby enhancing its overall robustness. These findings suggest that EMG information can serve as a valuable supplement to EEG data.}, }
@article {pmid40461535, year = {2025}, author = {He, X and Chen, J and Zhong, Y and Cen, P and Shen, L and Huang, F and Wang, J and Jin, C and Zhou, R and Zhang, X and Wang, A and Fan, J and Wu, S and Tu, M and Qin, X and Luo, X and Zhou, Y and Peng, J and Zhou, Y and Civelek, AC and Tian, M and Zhang, H}, title = {Forebrain neural progenitors effectively integrate into host brain circuits and improve neural function after ischemic stroke.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {5132}, pmid = {40461535}, issn = {2041-1723}, support = {82030049, 32027802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82102095//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302262//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302267//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82394433//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY23H180005//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Animals ; *Neural Stem Cells/transplantation/metabolism/cytology ; Rats ; Humans ; Forkhead Transcription Factors/metabolism/genetics ; *Prosencephalon/cytology ; Nerve Tissue Proteins/metabolism/genetics ; Neurons/metabolism/cytology ; *Ischemic Stroke/therapy/physiopathology/diagnostic imaging ; Induced Pluripotent Stem Cells/cytology/transplantation/metabolism ; Cell Differentiation ; Male ; Stem Cell Transplantation/methods ; Recovery of Function ; Rats, Sprague-Dawley ; Neurogenesis ; Disease Models, Animal ; *Stroke/therapy ; Positron-Emission Tomography ; Synapses ; }, abstract = {Human cortical neural progenitor cell transplantation holds significant potential in cortical stroke treatment by replacing lost cortical neurons and repairing damaged brain circuits. However, commonly utilized human cortical neural progenitors are limited in yield a substantial proportion of diverse cortical neurons and require an extended period to achieve functional maturation and synaptic integration, thereby potentially diminishing the optimal therapeutic benefits of cell transplantation for cortical stroke. Here, we generated forkhead box G1 (FOXG1)-positive forebrain progenitors from human inducible pluripotent stem cells, which can differentiate into diverse and balanced cortical neurons including upper- and deep-layer excitatory and inhibitory neurons, achieving early functional maturation simultaneously in vitro. Furthermore, these FOXG1 forebrain progenitor cells demonstrate robust cortical neuronal differentiation, rapid functional maturation and efficient synaptic integration after transplantation into the sensory cortex of stroke-injured adult rats. Notably, we have successfully utilized the non-invasive [18]F-SynVesT-1 PET imaging technique to assess alterations in synapse count before and after transplantation therapy of FOXG1 progenitors in vivo. Moreover, the transplanted FOXG1 progenitors improve sensory and motor function recovery following stroke. These findings provide systematic and compelling evidence for the suitability of these FOXG1 progenitors for neuronal replacement in ischemic cortical stroke.}, }
@article {pmid40460359, year = {2025}, author = {Chen, Z and Zhang, Y and Ding, J and Li, Z and Tian, Y and Zeng, M and Wu, X and Su, B and Jiang, J and Wu, C and Wei, D and Sun, J and Lim, CT and Fan, H}, title = {Hydrogel-Based Multifunctional Deep Brain Probe for Neural Sensing, Manipulation, and Therapy.}, journal = {ACS nano}, volume = {19}, number = {23}, pages = {21600-21613}, doi = {10.1021/acsnano.5c03865}, pmid = {40460359}, issn = {1936-086X}, mesh = {Animals ; *Hydrogels/chemistry/pharmacology ; Rats ; Rats, Sprague-Dawley ; *Neurons/drug effects ; Optogenetics ; *Brain ; Photochemotherapy ; Hippocampus ; Male ; }, abstract = {Implantable deep brain probes (DBPs) constitute a vital component of brain-machine interfaces, facilitating direct interaction between neural tissues and the external environment. Most multifunctional DBPs used for neural system sensing and modulation are currently fabricated through thermal tapering of polymeric materials. However, this approach faces a fundamental challenge in selecting materials that simultaneously accommodate the thermal stretching process and yet match the modulus of brain tissues. Here, we introduce a kind of multifunctional hydrogel-based fiber (HybF) designed for neural sensing, on-demand deep brain manipulation, and photodynamic therapy, and was achieved by integrating ion chelation/dechelation effects with templating methods throughout the entire wet-spinning process. With a low bending stiffness of approximately 0.3 N/m and a high conductivity of about 97 S/m at 1 kHz, HybF facilitates a high-quality signal recording (SNR ∼10) while minimizing immune rejection. It also effectively mediates deep brain optogenetic stimulation, successfully manipulating the behavior of hippocampal neurons in hSyn-ChrimsonR-tdTomato SD rats. Importantly, by leveraging HybF, this study explores the use of a spatiotemporally controllable photodynamic strategy in antiepilepsy, in which the high-amplitude abnormal electrical discharges were instantaneously eliminated without affecting normal cognitive/memory abilities. The above innovative approach established a distinct paradigm for deep brain manipulation and degenerative disease treatment, providing interesting insights into brain circuits and bioelectronic devices.}, }
@article {pmid40459463, year = {2025}, author = {Savitz, BL and Dean, YE and Popa, NK and Cornely, RM and Byers, V and Gutama, BW and Abbott, EN and Torres-Guzman, R and Alter, N and Stehr, JD and Hill, JB and Elmaraghi, S}, title = {Targeted Muscle Reinnervation and Regenerative Peripheral Nerve Interface for Myoelectric Prosthesis Control: The State of Evidence.}, journal = {Annals of plastic surgery}, volume = {94}, number = {6S Suppl 4}, pages = {S572-S576}, doi = {10.1097/SAP.0000000000004273}, pmid = {40459463}, issn = {1536-3708}, mesh = {Humans ; *Artificial Limbs ; Electromyography ; *Nerve Regeneration/physiology ; *Muscle, Skeletal/innervation ; *Peripheral Nerves/physiology/surgery ; *Amputation Stumps/innervation ; Phantom Limb/prevention &amp; control ; *Amputation, Surgical/rehabilitation ; }, abstract = {Prosthetic rehabilitation after amputation poses significant challenges, often due to functional limitations, residual limb pain (RLP), and phantom limb pain (PLP). These issues not only affect physical health but also mental well-being and quality of life. In this review, we describe targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) and explore their clinical role in the evolution of myoelectric prosthetic control as well as postamputation pain and neuroma management. Early myoelectric prostheses, which detected electrical potentials from muscles to control prosthetic limbs, faced limitations such as inconsistent signal acquisition and complex control modes. Novel microsurgical techniques at the turn of the century such as TMR and RPNI significantly advanced myoelectric prosthetic control. TMR involves reinnervating denervated muscles with residual nerves to create electromyography (EMG) potentials and prevent painful neuromas. Similarly, RPNI relies on small muscle grafts to amplify EMG signals and distinguish from stochastic noise for refined prosthetic control. Techniques like TMR and RPNI not only improved prosthetic function, but also significantly reduced postamputation pain, making them critical in improving amputees' quality of life. Modern myoelectric prostheses evolved with advancements in microprocessor and sensor technologies, enhancing their functionality and user experience. Today, researchers have developed more intuitive and reliable prosthetic control by utilizing pattern recognition software and machine learning algorithms that may supersede reliance on surgically amplifying EMG signals. Future developments in brain-computer interfaces and machine learning hold promise for even greater advancements in prosthetic technology, emphasizing the importance of continued innovation in this field.}, }
@article {pmid40459258, year = {2025}, author = {Seibert, B and Caceres, CJ and Gay, LC and Shetty, N and Faccin, FC and Carnaccini, S and Walters, MS and Marr, LC and Lowen, AC and Rajao, DS and Perez, DR}, title = {Air-liquid interface model for influenza aerosol exposure in vitro.}, journal = {Journal of virology}, volume = {99}, number = {7}, pages = {e0061925}, pmid = {40459258}, issn = {1098-5514}, support = {75N93021C00014/AI/NIAID NIH HHS/United States ; 75N93021C00017/AI/NIAID NIH HHS/United States ; }, mesh = {Animals ; Humans ; Dogs ; Aerosols ; Madin Darby Canine Kidney Cells ; *Influenza, Human/virology/transmission ; Influenza A Virus, H3N2 Subtype/pathogenicity ; Influenza A Virus, H1N1 Subtype/pathogenicity/physiology ; Swine ; Epithelial Cells/virology ; *Influenza A virus/pathogenicity ; Influenza A Virus, H9N2 Subtype/pathogenicity/physiology ; Cell Line ; *Air Microbiology ; }, abstract = {UNLABELLED: Airborne transmission is an essential mode of infection and spread of influenza viruses among humans. However, most studies use liquid inoculum for virus infection. To better replicate natural airborne infections in vitro, we generated a calm-aerosol settling chamber system designed to examine the aerosol infectivity of influenza viruses in different cell types. Aerosol inoculation was characterized for multiple influenza A virus (FLUAV) subtypes, including pandemic 2009 H1N1, seasonal swine H3N2, and avian H9N2, using this exposure system. While each FLUAV strain displayed high infectivity within MDCK cells via liquid inoculation, differences in infectivity were observed during airborne inoculation. This was further observed in recently developed immortalized differentiated human airway epithelial cells (BCi-NS1.1) cultured in an air-liquid interface. The airborne infectious dose 50 for each virus was based on the exposure dose per well. Our findings indicate that this system has the potential to enhance our understanding of the factors influencing influenza transmission via the airborne route. This could be invaluable for conducting risk assessments, potentially reducing the reliance on extensive and costly in vivo animal studies.<br><br>IMPORTANCE: This study presents a significant advancement in influenza research by developing a novel in vitro system to assess aerosol infectivity, a crucial aspect of influenza transmission. The system's ability to differentiate between mammalian-adapted and avian-adapted influenza viruses based on their aerosol infectivity offers a valuable tool for pre-screening the pandemic potential of different strains. This could potentially streamline the risk assessment process and inform public health preparedness strategies. Moreover, the system's capacity to examine aerosol infectivity in human airway epithelial cells provides a more relevant model for studying virus-host interactions in natural airborne infections. Overall, this study provides an accessible platform for investigating aerosol infectivity, which could significantly contribute to our understanding of influenza transmission and pandemic preparedness.}, }
@article {pmid40459142, year = {2025}, author = {Gao, J and Jiang, D and Wang, H and Wang, X}, title = {Opioid Enantiomers: Exploring the Complex Interplay of Stereochemistry, Pharmacodynamics, and Therapeutic Potential.}, journal = {Journal of medicinal chemistry}, volume = {68}, number = {11}, pages = {10540-10555}, doi = {10.1021/acs.jmedchem.5c00136}, pmid = {40459142}, issn = {1520-4804}, mesh = {Stereoisomerism ; Humans ; *Analgesics, Opioid/chemistry/pharmacology/therapeutic use ; Animals ; Structure-Activity Relationship ; Neuralgia/drug therapy ; Morphine/chemistry/pharmacology/therapeutic use ; Receptors, Opioid/metabolism ; }, abstract = {Opioids have been essential in pain management, particularly when other analgesics prove insufficient, but their use is complicated by risks of addiction, tolerance, and a range of adverse effects. These challenges are further exacerbated by the presence of opioid enantiomers that interact in a variety of ways with biological systems. This Perspective provides a comprehensive exploration of opioid enantiomers, focusing on their synthesis, pharmacodynamics, and potential therapeutic applications beyond traditional pain management. It highlights the complexity of synthesizing morphine enantiomers and additional challenges in producing the less-studied (+)-morphine. The Perspective also examines structure-activity relationship studies on (+)-opioid compounds, revealing their potential in modulating neuroinflammatory responses through non-opioid pathways and suggesting new therapeutic avenues for conditions like neuropathic pain and drug addiction. Furthermore, it discusses the differential safety profiles of opioid enantiomers, emphasizing the need for future research to advance precision medicine in opioid therapy, ultimately aiming to develop safer and more effective pain management strategies.}, }
@article {pmid40458259, year = {2025}, author = {Zhang, W and Wang, T and Qin, C and Xu, B and Hu, H and Wang, T and Shen, Y}, title = {Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {13}, number = {}, pages = {1591316}, pmid = {40458259}, issn = {2296-4185}, abstract = {INTRODUCTION: The application of non-invasive brain-computer interfaces (BCIs) in robotic control is limited by insufficient signal quality and decoding capabilities. Enhancing the robustness of BCIs without increasing the cognitive load remains a major challenge in brain-control technology.<br><br>METHODS: This study presents a teleoperation robotic system based on hybrid control of electroencephalography (EEG) and eye movement signals, and utilizes vibration stimulation to assist motor imagery (MI) training and enhance control signals. A control experiment involving eight subjects was conducted to validate the enhancement effect of this tactile stimulation technique.<br><br>RESULTS: Experimental results showed that during the MI training phase, the addition of vibration stimulation improved the brain region activation response speed in the tactile group, enhanced the activation of the contralateral motor areas during imagery of non-dominant hand movements, and demonstrated better separability (p = 0.017). In the robotic motion control phase, eye movement-guided vibration stimulation effectively improved the accuracy of online decoding of MI and enhanced the robustness of the control system and success rate of the grasping task.<br><br>DISCUSSION: The vibration stimulation technique proposed in this study can effectively improve the training efficiency and online decoding rate of MI, helping users enhance their control efficiency while focusing on control tasks. This tactile enhancement technology has potential applications in robot-assisted elderly care, rehabilitation training, and other robotic control scenarios.}, }
@article {pmid40457516, year = {2025}, author = {Zhang, Y and Deng, X and Wang, S and Zhou, W and Wu, Z and Tang, X and Lee, HJ and Zhang, D}, title = {High-Specificity Spatiotemporal Cholesterol Detection by Quadrature Phase-Shifted Polarization Stimulated Raman Imaging.}, journal = {Angewandte Chemie (International ed. in English)}, volume = {64}, number = {32}, pages = {e202505038}, pmid = {40457516}, issn = {1521-3773}, support = {2024YFA1408900//National Key Research and Development Program of China/ ; 82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities of China/ ; }, mesh = {*Cholesterol/analysis ; *Spectrum Analysis, Raman/methods ; Caenorhabditis elegans/chemistry/metabolism ; Animals ; }, abstract = {Visualizing cholesterol dynamics in living systems in situ remains a fundamental challenge in biomedical imaging. Although fluorescence microscopy requires bulky tags that perturb small molecule behavior, stimulated Raman scattering (SRS) microscopy enables label-free detection of CH-rich molecules. However, conventional SRS probes only polarized Raman components, limiting molecular specificity by seemingly overlapped peaks. Here, we extend SRS microscopy to achieve rapid, comprehensive detection of Raman tensor through quadrature phase-shifted polarization SRS (QP[2]-SRS) microscopy. This technique exploits the underlying molecular signatures by detecting both polarized and depolarized components of third-order nonlinear susceptibility χ[(3)] that originates from molecular structural features. We adopt a specialized optical delay line that rapidly alternates between parallel- and perpendicular-polarization states. QP[2]-SRS enables unprecedented distinction of similar molecular species in complex mixtures, demonstrating approximately 10× enhancement in chemical specificity and 5× improvement in analytical accuracy. This enhanced sensitivity enables real-time monitoring of lipid dynamics in living C. elegans and reveals component heterogeneity and morphological changes of LD in NAFLD livers. QP[2]-SRS creates new opportunities for investigating cholesterol-dependent biological processes in their native environment, with broad potential for chemical imaging with enhanced molecular specificity.}, }
@article {pmid40457127, year = {2025}, author = {Xiao, Z and She, Q and Fang, F and Meng, M and Zhang, Y}, title = {Auxiliary classifier adversarial networks with maximum subdomain discrepancy for EEG-based emotion recognition.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {40457127}, issn = {1741-0444}, support = {62371172//National Natural Science Foundation of China/ ; 62271181//National Natural Science Foundation of China/ ; ZY2024025//Wenzhou Institute of Biomaterials and Engineering/ ; }, abstract = {Domain adaptation (DA) is considered to be effective solutions for unsupervised emotion recognition cross-session and cross-subject tasks based on electroencephalogram (EEG). However, the cross-domain shifts caused by individual differences and sessions differences seriously limit the generalization ability of existing models. Moreover, existing models often overlook the discrepancies among task-specific subdomains. In this study, we propose the auxiliary classifier adversarial networks (ACAN) to tackle these two key issues by aligning global domains and subdomains and maximizing subdomain discrepancies to enhance model effectiveness. Specifically, to address cross-domain discrepancies, we deploy a domain alignment module in the feature space to reduce inter-domain and inter-subdomain discrepancies. Meanwhile, to maximum subdomain discrepancies, the auxiliary adversarial classifier is introduced to generate distinguishable subdomain features by promoting adversarial learning between feature extractor and auxiliary classifier. System experiment results on three benchmark databases (SEED, SEED-IV, and DEAP) validate the model's effectiveness and superiority in cross-session and cross-subject experiments. The method proposed in this study outperforms other state-of-the-art DA, that effectively address domain shifts in multiple emotion recognition tasks, and promote the development of brain-computer interfaces.}, }
@article {pmid40456926, year = {2025}, author = {Slutzky, MW and Vansteensel, MJ and Herff, C and Gaunt, RA}, title = {A brain-computer interface working definition.}, journal = {Nature biomedical engineering}, volume = {9}, number = {6}, pages = {792}, pmid = {40456926}, issn = {2157-846X}, }
@article {pmid40456256, year = {2025}, author = {Tangermann, M and Chevallier, S and Dold, M and Guetschel, P and Kobler, R and Papadopoulo, T and Thielen, J}, title = {Learning from small datasets-review of workshop 6 of the 10th International BCI Meeting 2023.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/addf80}, pmid = {40456256}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Congresses as Topic/trends ; *Datasets as Topic ; Deep Learning ; *Machine Learning/trends ; }, abstract = {In a brain-computer interface (BCI), a primary objective is to reduce calibration time by recording as few as possible novel data points to (re-)train decoder models.Objective.Minimizing the calibration can be crucial for enhancing the usability of a BCI application with patients, increasing the acceptance by healthy users, facilitating a fast adaptation during non-stationary recordings, or transferring between sessions.Approach.At the 10th International BCI Meeting in 2023, our workshop addressed the latest proposed techniques to train classification or regression machine learning models with small datasets.Main results.We explored methodologies from both traditional machine learning and deep learning. In addition to talks and discussions, we discussed Python toolboxes for various presented methods and for the benchmarking of classification models.Significance.This review provides a comprehensive overview of the workshop's content and discusses the insights that were obtained.}, }
@article {pmid40456243, year = {2025}, author = {Galiotta, V and Caracci, V and Toppi, J and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Riccio, A}, title = {P300-based brain-computer interface for communication in assistive technology centres: influence of users' profile on BCI access.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/addf7f}, pmid = {40456243}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces/psychology ; Male ; Female ; *Event-Related Potentials, P300/physiology ; Adult ; Middle Aged ; Electroencephalography/methods ; *Self-Help Devices ; Young Adult ; *Communication Devices for People with Disabilities ; }, abstract = {Objective. Assistive technology (AT) refers to any product that enables people to live independently and with dignity and to participate in activities of daily life. A brain-computer interface (BCI) is an AT that provides an alternative output, based on neurophysiological signals, to control an external device. The aim of the study is to screen patients accessing an AT-centre to investigate their eligibility for BCI access and the factors influencing the BCI control.Approach. Thirty-five users and 11 healthy subjects were included in the study. Participants were required to operate a P300-speller BCI. We evaluated the influence of clinical diagnosis, socio-demographic factors, level of dependence and disability of users, neuropsychological profile on BCI performance.Main results. The 7.1% of the users controlled the system with a mean accuracy of 93.6 ± 8.0%, while 8 users had an online accuracy below 70%. We found that the neuropsychological profile significantly affected online accuracy and ITR.Significance. The percentage of users who had an accuracy considered functional for communication is an encouraging data in terms of BCI effectiveness. The results regarding accuracy and factors influencing (and not influencing) it, are a contribution to the introduction of BCIs in the AT-centres, considering the BCI for communication both as an AT and as an additional input to provide multimodal access to AT.}, }
@article {pmid40456242, year = {2025}, author = {Schmid, P and Sweeney-Reed, CM and Dürschmid, S and Reichert, C}, title = {Stimulus predictability has little impact on decoding of covert visual spatial attention.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/addf81}, pmid = {40456242}, issn = {1741-2552}, mesh = {Humans ; *Attention/physiology ; Male ; Female ; *Brain-Computer Interfaces ; Adult ; Young Adult ; *Photic Stimulation/methods ; Electroencephalography/methods ; *Space Perception/physiology ; Eye Movements/physiology ; *Visual Perception/physiology ; }, abstract = {Objective. Brain-computer interfaces (BCI) that are aimed at supporting completely locked-in patients require independence from eye movements. Since visual spatial attention (VSA) shifts precede eye movements, they can be used for non-invasive, gaze-independent BCI control. In VSA tasks, stimuli locations and presentation onsets are commonly unpredictable. In this study we investigated the impact of predictability of potential target stimuli on the decoding accuracy of a BCI.Approach. We presented visual stimuli simultaneously to the left and right visual fields while participants shifted attention to a target stimulus. Using canonical correlation analysis, we decoded the direction of attention under different combinations of temporal and spatial predictability and compared the performance.Main results. We found no variation in decoding accuracies with spatial predictability. In addition, jittered timing did not alter the decoding accuracy compared to a constant stimulus onset asynchrony (SOA). Finally, reducing the SOA enabled faster BCI communication without accuracy loss. Using time-resolved decoding and interpretable models, we show that a later positive difference wave (between 300 ms and 350 ms post-stimulus onset) at occipital sites, rather than the N2pc, primarily contributes to decoding the target receiving attention.Significance. Our results demonstrate that stimulus predictability has no beneficial impact on decoding accuracy, but the paradigm proved robust to alterations in various stimulus parameters, making VSA a promising cognitive process for use in non-invasive, gaze-independent BCI-based communication.}, }
@article {pmid40456241, year = {2025}, author = {Pang, Z and Zhang, R and Li, M and Li, Z and Cui, H and Chen, X}, title = {SSVEP-based BCI using ultra-low-frequency and high-frequency peripheral flickers.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/addf82}, pmid = {40456241}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; *Photic Stimulation/methods ; Young Adult ; *Electroencephalography/methods ; *Flicker Fusion/physiology ; }, abstract = {Objective. existing steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems predominantly employ a flicker frequency range of 8-20 Hz, which often induces visual fatigue in users, thereby compromising system performance. Considering that, this study introduces an innovative paradigm to enhance the user experience of SSVEP-based BCIs while maintaining the performance.Approach. the system encodes 12 targets by integrating ultra-low-frequency (2.00-3.32 Hz) and high-frequency (34.00-35.32 Hz) flickers with peripheral stimulation, and task-related component analysis is employed for SSVEP signal identification.Main results. the feasibility of the ultra-low-frequency peripheral stimulation paradigm was validated through online experiments, achieving an average accuracy of 89.03 ± 9.95% and an information transfer rate (ITR) of 66.74 ± 15.44 bits min[-1]. For the high-frequency peripheral stimulation paradigm, only the stimulation frequency changed, the paradigm, the signal processing algorithm and the step of frequency and phase were unchanged. The online experiments demonstrated an average accuracy of 93.55 ± 3.02% and an ITR of 51.88 ± 3.74 bits min[-1].Significance. the performance of the proposed system has reached a relatively high level among the current user-friendly SSVEP-based BCI systems. This study successfully innovates the paradigm for SSVEP-based BCIs, offering new insights into the development of user-friendly systems that balance high performance and user comfort.}, }
@article {pmid40456131, year = {2025}, author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C}, title = {Correction: Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.}, journal = {Journal of medical Internet research}, volume = {27}, number = {}, pages = {e78147}, doi = {10.2196/78147}, pmid = {40456131}, issn = {1438-8871}, abstract = {[This corrects the article DOI: 10.2196/71741.].}, }
@article {pmid40456094, year = {2025}, author = {Arpaia, P and Esposito, A and Galdieri, F and Natalizio, A}, title = {Acquisition Delay of Wireless EEG Instruments in Time-Sensitive Applications.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2151-2159}, doi = {10.1109/TNSRE.2025.3575695}, pmid = {40456094}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/instrumentation/methods ; *Brain-Computer Interfaces ; *Wireless Technology/instrumentation ; Reproducibility of Results ; Event-Related Potentials, P300/physiology ; Time Factors ; Male ; Adult ; Equipment Design ; Algorithms ; Female ; Sensitivity and Specificity ; Young Adult ; }, abstract = {The aim of this study is to characterize the acquisition delay in wireless EEG instruments and evaluate its impact on the detection of time-locked neural phenomena, such as P300 and movement-related cortical potentials (MRCP). Accurate timing is critical for both research and clinical applications, especially for real-time brain-computer interfaces (BCI). A measurement setup was thus developed to assess acquisition delays and their uncertainty. Delays were measured at both the start and stop of a reference signal generation to investigate the consistency and reliability of the devices. BCI experiments were also performed to evaluate the impact of the measured delay on the detection of the time-locked phenomena. Statistical tests confirmed significant differences in delays across devices and configurations (e.g., from few tens to a hundred ms). These delays directly impacted P300 and MRCP detection, raising concerns about potential misclassification. Nonetheless, the correction of the measured acquisition delay proved beneficial, especially with regard to the P300 latency measured through low-cost instrumentation.}, }
@article {pmid40456080, year = {2025}, author = {Jin, L and Song, Y and Zhao, H and Cao, J and Cheung, VCK and Liao, WH}, title = {Frequency-Aware Spatial-Temporal Attention Explainable Network for EEG Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {10}, pages = {7175-7185}, doi = {10.1109/JBHI.2025.3576088}, pmid = {40456080}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Brain/physiology ; *Neural Networks, Computer ; Emotions/physiology ; Adult ; }, abstract = {Representation learning in spatial and temporal domains has shown significant potential in EEG decoding, advancing the field of brain-computer interfaces (BCIs). However, the critical role of frequency information, closely tied to the brain's neurological mechanism, has been largely neglected. In this paper, we propose FSTNet, which integrates frequency-spatial-temporal domains synergistically. The network allows broadband EEG signals as input and adaptively learns informative frequency signatures. A frequency-aware module emphasizes the importance of frequency information by selectively assigning weights to latent representations in the frequency space. Subsequently, self-attention captures spatial and temporal dependencies, extracting discriminative neural signatures for EEG decoding. We conducted extensive experiments on EEG datasets for motor imagery and emotion recognition, achieving superior results on SEED, PhysioNet, and OpenBMI datasets in both individual and cross-subject scenarios. Additionally, visualization reveals that the network captures informative frequency ranges and spatial patterns associated with specific tasks, aligning with known physiological mechanisms. This enhances the transparency of the network's learning process. In conclusion, our method exhibits the potential for decoding EEG and advancing the understanding of neurological processes in the brain.}, }
@article {pmid40455568, year = {2025}, author = {Chen, Y and Fan, Z and Shi, N and Cheng, B and Huang, C and Liu, X and Gao, X and Liu, R}, title = {MXene-Based Microneedle Electrode for Brain-Computer Interface in Diverse Scenarios.}, journal = {ACS applied materials &amp; interfaces}, volume = {17}, number = {23}, pages = {33451-33464}, doi = {10.1021/acsami.5c03798}, pmid = {40455568}, issn = {1944-8252}, mesh = {*Brain-Computer Interfaces ; Electroencephalography/instrumentation ; *Needles ; Humans ; Electrodes ; Evoked Potentials, Visual/physiology ; *Brain/physiology ; Male ; Nitrites ; Transition Elements ; }, abstract = {In this study, we introduce a brain-computer interface (BCI) framework incorporating MXene microneedle EEG electrodes, tailored for versatile deployment. The dry electrodes, configured as 1 mm[2] microneedles, underwent meticulous processing to establish a cohesive integration with the MXene conductive material. The microneedle architecture facilitates epidermal penetration, yielding low contact impedance, enabling the recording of spontaneous EEG and induced brain activity, and ensuring high precision in steady-state visual evoked potential (SSVEP) speller. Simultaneously, the microneedle electrode demonstrates commendable biological compatibility and superior nuclear magnetic resonance compatibility. It exhibits minimal artifact generation and manifests no heating-related adaptations in nuclear magnetic environments. The inherent microneedle electrode structure endows it with robust anti-interference capabilities. In vibrational environments, the SSVEP text input accuracy of the microneedle electrode remains comparable to that of gel electrodes, maintaining consistent impedance and delivering high-fidelity EEG acquisition during real-motion scenarios. The microneedle electrode devised in this study serves as a reliable signal acquisition tool, thereby advancing the development of BCI systems tailored for practical usage scenarios.}, }
@article {pmid40454682, year = {2025}, author = {Pitt, KM and Mikuls, A and Ousley, CL and Boster, JB and Mahmoudi, M and McCarthy, J and Burnison, J}, title = {Considering whether brain-computer interfaces have prospective potential to support children who have the physical abilities for touch-based AAC access: a forum manuscript.}, journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)}, volume = {}, number = {}, pages = {1-9}, pmid = {40454682}, issn = {1477-3848}, support = {R21 DC021496/DC/NIDCD NIH HHS/United States ; }, abstract = {Augmentative and alternative communication (AAC) may help address communication challenges for both those with developmental disabilities (DD) and intellectual and developmental disabilities (IDD). This forum manuscript explores the possibility of various future applications of brain-computer interface technology for AAC control (BCI-AAC) by children who have the physical abilities to utilize touch-based AAC access. Due to the early status of BCI-AAC research, the forum focuses on those with DD, though considerations for those with IDD are also discussed. Departing from the prevalent focus on severe speech and physical impairments (SSPI), this work shifts the spotlight toward children who may employ touch selection for AAC access, exploring the challenges and prospective possibilities within this population. Applying the International Classification of Functioning, Disability, and Health (ICF) framework, we explore potential BCI-AAC considerations across Activities and Participation, Functions and Structures, Environmental Factors, and Personal Factors. Proposing prospective BCI-AAC strategies, such as leveraging brain activity for functional intent recognition and emotion detection, this paper is designed to fuel discussion on tailoring AAC interventions to the diverse profiles of children with DD and IDD. Acknowledging the significant hurdles faced by BCI-AAC technology, we support the inclusive consideration of individuals in BCI-AAC development. While not seeking to lay a definitive roadmap, this forum aims to serve as a catalyst for future interdisciplinary dialogues, including those who use AAC and their support network, laying the groundwork for considering diverse BCI-AAC applications in children.}, }
@article {pmid40450930, year = {2025}, author = {Fan, C and Song, Y and Mao, X}, title = {A classification method of motor imagery based on brain functional networks by fusing PLV and ECSP.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {190}, number = {}, pages = {107684}, doi = {10.1016/j.neunet.2025.107684}, pmid = {40450930}, issn = {1879-2782}, mesh = {Humans ; *Brain/physiology ; *Imagination/physiology ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Deep Learning ; Hand Strength/physiology ; Hand/physiology ; }, abstract = {In order to enhance the decoding ability of brain states and evaluate the functional connection changes of relevant nodes in brain regions during motor imagery (MI), this paper proposes a brain functional network construction method which fuses edge features and node features. And we use deep learning methods to realize MI classification of left and right hand grasping tasks. Firstly, we use phase locking value (PLV) to extract edge features and input a weighted PLV to enhanced common space pattern (ECSP) to extract node features. Then, we fuse edge features and node features to construct a novel brain functional network. Finally, we construct an attention and multi-scale feature convolutional neural network (AMSF-CNN) to validate our method. The performance indicators of the brain functional network on the SHU_Dataset in the corresponding brain region will increase and be higher than those in the contralateral brain region when imagining one hand grasping. The average accuracy of our method reaches 79.65 %, which has a 25.85 % improvement compared to the accuracy provided by SHU_Dataset. By comparing with other methods on SHU_Dataset and BCI IV 2a Dataset, the average accuracies achieved by our method outperform other references. Therefore, our method provides theoretical support for exploring the working mechanism of the human brain during MI.}, }
@article {pmid40450863, year = {2025}, author = {Niu, X and Zhang, J and Peng, Y and Kong, Y and Li, Y and Han, Y and Shi, L and Zheng, G}, title = {Extraction and analysis of abnormal EEG features in children with amblyopia.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {175}, number = {}, pages = {2110765}, doi = {10.1016/j.clinph.2025.2110765}, pmid = {40450863}, issn = {1872-8952}, mesh = {Humans ; *Amblyopia/physiopathology/diagnosis ; *Electroencephalography/methods ; Male ; Female ; Child ; *Evoked Potentials, Visual/physiology ; Child, Preschool ; *Brain/physiopathology ; }, abstract = {OBJECTIVE: Early and accurate diagnosis of amblyopia is crucial for the healthy development of children. Existing clinical diagnostic methods rely on patient cooperation, which can easily lead to misdiagnosis. The commonly used features derived from visual evoked potentials (VEP) only provided limited information for characterizing the whole brain, highlighting the need for integrating additional data sources, such as brain network metrics, to achieve a more comprehensive understanding.<br><br>METHODS: We extracted 488 features from 64-channel EEG data recorded from thirty amblyopic children. The features mainly derived from a weighted functional brain network based on coherence across different frequency bands. Feature selection and linear classification techniques were employed to assess their effectiveness in distinguishing amblyopia from normal children.<br><br>RESULTS: Abnormal EEG features were distributed not only in the occipital lobe but also in non-visual regions, with a higher prevalence in the alpha and beta bands. Their decoding performance surpassed traditional VEP features, and their combination achieved the highest accuracy (89.00%). Moreover, features beyond the occipital lobe exhibited limited decoding performance when considered individually, yet they still have an obvious contribution.<br><br>CONCLUSIONS: The study identified novel abnormal EEG features associated with amblyopia and demonstrated the potential of multi-channel EEG recordings to assist in the diagnosis of amblyopia.<br><br>SIGNIFICANCE: The study suggests amblyopia may impair more abilities beyond visual cognition and further provides objective biomarkers for diagnosing amblyopia, which is essential for effective treatment.}, }
@article {pmid40450806, year = {2025}, author = {Wosnick, N and Dörfer, T and Turner, M and Nicholls, C and Richardson, M and Génier, I and Hauser-Davis, RA}, title = {Assessing the potential physiological impacts of urban development around lemon shark (Negaprion brevirostris) nurseries: effects on neonate and juvenile health.}, journal = {Marine pollution bulletin}, volume = {218}, number = {}, pages = {118233}, doi = {10.1016/j.marpolbul.2025.118233}, pmid = {40450806}, issn = {1879-3363}, mesh = {Animals ; *Sharks/physiology ; *Urbanization ; *Environmental Monitoring ; }, abstract = {Urbanization driven by population growth, development and tourism increasingly threatens even remote areas, potentially impacting biodiversity. This is particularly concerning given the ecological and economic importance of biodiversity, especially for island nations, where ecotourism plays a crucial role in the economy. This study examines urban-driven degradation effects on the nurseries of lemon sharks, a predator with strong site fidelity to its birthing and nursery areas. Six sites in South Eleuthera, The Bahamas, were assessed, analyzing proxies indicative of body condition (triglycerides/cholesterol ratio, body condition index) and energetic stress markers (glucose, β-hydroxybutyrate, triglycerides, total cholesterol) in neonates and juveniles compared across nurseries relative to degradation scores. While TAG/CHOL and BCI were not significantly different between nurseries, energetic markers were overall higher in more degraded nurseries. Moreover, total urban score was a significant predictor for glucose, β-hydroxybutyrate, and triglyceride ciruclating concentrations. These findings, coupled with prior studies carried out in Bimini, suggest that urban development around lemon shark nurseries in The Bahamas may negatively impact shark health. Cooperative monitoring, community initiatives for mangrove preservation, and stronger urbanization laws are required to mitigate these impacts. As urbanization and environmental degradation are universal threats to mangroves worldwide, this approach can be adapted to study urbanization impacts on other species in regions such as Southeast Asia, the Caribbean, the Pacific Islands, and the coasts of Africa and South America, which face similar urban encroachment, habitat degradation, and biodiversity loss challenges.}, }
@article {pmid40450046, year = {2025}, author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S}, title = {Human voices communicating trustworthy intent: A demographically diverse speech audio dataset.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {921}, pmid = {40450046}, issn = {2052-4463}, mesh = {Humans ; Adult ; Middle Aged ; Female ; Male ; *Voice ; Young Adult ; Adolescent ; *Speech ; *Trust ; }, abstract = {The multi-disciplinary field of voice perception and trustworthiness lacks accessible and diverse speech audio datasets representing diverse speaker demographics, including age, ethnicity, and sex. Existing datasets primarily feature white, younger adult speakers, limiting generalisability. This paper introduces a novel open-access speech audio dataset with 1,152 utterances from 96 untrained speakers, across white, black and south Asian backgrounds, divided into younger (N = 60, ages 18-45) and older (N = 36, ages 60+) adults. Each speaker recorded both, their natural speech patterns (i.e. "neutral" or no intent), and their attempt to convey their trustworthy intent as they perceive it during speech production. Our dataset is described and evaluated through classification methods between neutral and trustworthy speech. Specifically, extracted acoustic and voice quality features were analysed using linear and non-linear classification models, achieving accuracies of around 70%. This dataset aims to close a crucial gap in the existing literature and provide additional research opportunities that can contribute to the generalisability and applicability of future research results in this field.}, }
@article {pmid40448829, year = {2025}, author = {Marques, LM and Strauss, A and Castellani, A and Barbosa, S and Simis, M and Fregni, F and Battistella, L}, title = {Dynamics of sensorimotor-related brain oscillations: EEG insights from healthy individuals in varied upper limb movement conditions.}, journal = {Experimental brain research}, volume = {243}, number = {7}, pages = {160}, pmid = {40448829}, issn = {1432-1106}, support = {#21/05897-5//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #21/12790-2//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #20/08512-4//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #17/12943-8//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #17/12943-8//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; }, mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; Cross-Sectional Studies ; Electroencephalography ; *Sensorimotor Cortex/physiology ; *Upper Extremity/physiology ; *Brain Waves/physiology ; Movement/physiology ; *Motor Activity/physiology ; Brain-Computer Interfaces ; *Psychomotor Performance/physiology ; *Cortical Synchronization/physiology ; Imagination/physiology ; Middle Aged ; }, abstract = {Event-related desynchronization (ERD) and event-related synchronization (ERS) are critical neurophysiological phenomena associated with motor execution and inhibitory processes. Their utility spans neurophysiological biomarker research and Brain-Computer Interface (BCI) development. However, standardized frameworks for analyzing ERD and ERS oscillations across motor tasks and frequency ranges remain scarce. This study conducted a cross-sectional analysis of 76 healthy participants from the DEFINE cohort to explore ERD and ERS variations across four motor-related tasks (Motor Execution, Motor Imagery, Active Observation, and Passive Observation) and six frequency bands (Delta, Theta, Low Alpha, High Alpha, Low Beta, and High Beta) using C3 electrode activity. Repeated measures ANOVA revealed task-sensitive ERD and ERS power modulations, with oscillatory responses spanning the 1-30 Hz spectrum. Beta activity exhibited pronounced differences between tasks, highlighting its relevance in motor control, while other bands showed distinct task-dependent variations. These findings underscore the variability in ERD/ERS patterns across different tasks and frequency bands, reinforcing the importance of further research into standardized analytical frameworks. By refining ERD/ERS analyses, our study contributes to developing reference frameworks that can enhance clinical and Brain-Computer Interface (BCI) applications.}, }
@article {pmid40448287, year = {2025}, author = {Cao, L and Zheng, Q and Wu, Y and Liu, H and Guo, M and Bai, Y and Ni, G}, title = {A dual-modality study on the neural features of cochlear implant simulated tone and consonant perception.}, journal = {Annals of the New York Academy of Sciences}, volume = {1549}, number = {1}, pages = {260-273}, doi = {10.1111/nyas.15380}, pmid = {40448287}, issn = {1749-6632}, support = {2023YFF1203500//National Key Research and Development Program of China/ ; 824B2056//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Cochlear Implants ; Female ; Male ; *Speech Perception/physiology ; Adult ; Spectroscopy, Near-Infrared/methods ; Electroencephalography ; Evoked Potentials, Auditory/physiology ; Phonetics ; Young Adult ; Acoustic Stimulation ; }, abstract = {Accurately perceiving lexical tones and consonants is critical for understanding speech in tonal languages. Cochlear implant (CI) users exhibit reduced phonetic perception due to spectral loss in CI encoding, yet the underlying neural mechanisms remain unclear. This study combined electroencephalography and functional near-infrared spectroscopy (fNIRS) to investigate the neural processing mechanisms of CI-simulated channelized speech in 26 normal-hearing adults during the processing of tones (T1-T4) and consonants ("ba," "da," "ga," "za"). Results showed that the N1 amplitude in auditory evoked potentials was significantly lower for channelized speech than a natural human voice (NH), particularly for T2 and T4 tones, indicating a weaker perception of channelized speech. Functional connectivity analysis revealed that an NH exhibited significantly higher synchrony in the δ and θ frequency bands than channelized speech, which was more pronounced in the right temporal lobe. This finding was also observed with "za" consonants. fNIRS results showed stronger right temporal lobe activation for channelized speech, suggesting that the brain requires greater auditory effort to process channelized speech. Combining both modalities revealed neural compensatory mechanisms underlying channelized speech-manifesting as "low-efficiency perception with high cognitive load." This study provides potential biomarkers for CI rehabilitation assessment and a foundation for future research.}, }
@article {pmid40446349, year = {2025}, author = {Wang, S and Chen, G and Xie, J and Yang, R and Wang, X and Shan, Q and Liu, W and Zhao, D and Wang, F and Li, K and Zhang, Q and Guo, Y}, title = {Development and validation of a predictive model for poor initial outcomes after Gamma Knife radiosurgery for trigeminal neuralgia: a prognostic correlative analysis.}, journal = {Journal of neurosurgery}, volume = {143}, number = {4}, pages = {987-998}, doi = {10.3171/2025.2.JNS242655}, pmid = {40446349}, issn = {1933-0693}, mesh = {Humans ; *Trigeminal Neuralgia/surgery/diagnosis ; *Radiosurgery/methods ; Male ; Female ; Middle Aged ; Aged ; Treatment Outcome ; Prognosis ; Adult ; Pain Measurement ; Aged, 80 and over ; Retrospective Studies ; Recurrence ; Carbamazepine/therapeutic use ; }, abstract = {OBJECTIVE: The present study aimed to develop a reliable predictive model for identifying preoperative predictors of poor initial outcomes in patients with primary trigeminal neuralgia (PTN) treated with Gamma Knife radiosurgery (GKRS) and further elucidate the clinical significance of these predictors in initial outcomes and long-term pain recurrence.<br><br>METHODS: A total of 217 PTN patients were divided into a training set (n = 167) and a validation set (n = 50). The initial outcomes of GKRS treatment were assessed based on the Barrow Neurological Institute pain intensity scale. A predictive model was developed through multivariate regression and validated with repeated sampling. The differences in predictors of long-term pain recurrence were assessed using Kaplan-Meier analysis. The association between predictors was tested using chi-square tests, and subgroup analyses were performed to compare initial outcomes and long-term pain recurrence between two clinically significant correlates.<br><br>RESULTS: The training and validation sets showed areas under the curve of 0.85 and 0.88, respectively. Calibration curves and decision curve analysis indicated significant clinical benefits in both sets. Independent risk factors for poor initial outcomes included hyperglycemia, absence of neurovascular contact, carbamazepine insensitivity, and atypical pain (trigeminal neuralgia type 2 [TN2]). Carbamazepine insensitivity was moderately associated with TN2 and predicted long-term pain recurrence. Patients with both phenotypes had significantly worse initial outcomes compared with other subgroups (adjusted p = 0.0125).<br><br>CONCLUSIONS: Patients with both TN2 and carbamazepine insensitivity have the poorest initial treatment outcomes and face an increased risk of recurrence. Furthermore, this predictive model is highly accurate and useful, offering a comprehensive method of identifying PTN patients likely to experience poor initial outcomes based on clinical characteristics and imaging perspectives. The authors believe that the nomogram presented in this model enables clinicians to calculate multiple variables and predict the probability of adverse events.}, }
@article {pmid40446280, year = {2025}, author = {Liang, X and Ding, Y and Yuan, Z and Han, Y and Zhou, Y and Jiang, J and Xie, Z and Fei, P and Sun, Y and Jia, P and Gu, G and Zhong, Z and Chen, F and Si, G and Gong, Z}, title = {Mechanics of Soft-Body Rolling Motion without External Torque.}, journal = {Physical review letters}, volume = {134}, number = {19}, pages = {198401}, doi = {10.1103/PhysRevLett.134.198401}, pmid = {40446280}, issn = {1079-7114}, mesh = {Animals ; Robotics ; Larva/physiology ; *Models, Biological ; Biomechanical Phenomena ; *Drosophila/physiology ; Muscle Contraction/physiology ; Torque ; }, abstract = {The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.}, }
@article {pmid40443843, year = {2025}, author = {Yang, H and Li, T and Zhao, L and Wei, Y and Chen, X and Pan, J and Fu, Y}, title = {Guiding principles and considerations for designing a well-structured curriculum for the brain-computer interface major based on the multidisciplinary nature of brain-computer interface.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1554266}, pmid = {40443843}, issn = {1662-5161}, abstract = {Brain-computer interface (BCI) is a novel human-computer interaction technology, and its rapid development has led to a growing demand for skilled BCI professionals, culminating in the emergence of the BCI major. Despite its significance, there is limited literature addressing the curriculum design for this emerging major. This paper seeks to bridge this gap by proposing and discussing a curricular framework for the BCI major, based on the inherently multidisciplinary nature of BCI research and development. The paper begins by elucidating the primary factors behind the emergence of the BCI major, the increasing demand for both medical and non-medical applications of BCI, and the corresponding need for specialized talent. It then delves into the multidisciplinary nature of BCI research and offers principles for curriculum design to address this nature. Based on these principles, the paper provides detailed suggestions for structuring a BCI curriculum. Finally, it discusses the challenges confronting the development of the BCI major, including the lack of consensus and international collaboration in the construction of the BCI major, as well as the inadequacy or lack of teaching materials. Future work needs to improve the curriculum design of the BCI major from a competency-oriented perspective. It is expected that this paper will provide a reference for the curriculum design and construction of the BCI major.}, }
@article {pmid40442937, year = {2025}, author = {Wang, F and Wang, L and Zhu, X and Lu, Y and Du, X}, title = {Neuron-Inspired Ferroelectric Bioelectronics for Adaptive Biointerfacing.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {37}, number = {35}, pages = {e2416698}, doi = {10.1002/adma.202416698}, pmid = {40442937}, issn = {1521-4095}, support = {B2302045//Shenzhen Medical Research Fund/ ; 52022102//National Natural Science Foundation of China/ ; 52261160380//National Natural Science Foundation of China/ ; 32471042//National Natural Science Foundation of China/ ; 32300845//National Natural Science Foundation of China/ ; 2017YFA0701303//National Key R&amp;D Program of China/ ; Y2023100//Youth Innovation Promotion Association of CAS/ ; RCJC20221008092729033//Fundamental Research Program of Shenzhen/ ; JCYJ20220818101800001//Fundamental Research Program of Shenzhen/ ; 2024A1515010645//Basic and Applied Basic Research Foundation of Guangdong Province/ ; }, mesh = {*Neurons ; *Biocompatible Materials ; Titanium ; Barium Compounds ; Indoles ; Polymers ; *Nanoparticles ; Vinyl Compounds ; Animals ; Mice ; Peripheral Nervous System/physiology ; Central Nervous System/physiology ; *Brain-Computer Interfaces ; *Vagus Nerve Stimulation ; Humans ; Mice, Inbred BALB C ; Male ; }, abstract = {Implantable bioelectronics, which are essential to neuroscience studies, neurological disorder treatment, and brain-machine interfaces, have become indispensable communication bridges between biological systems and the external world through sensing, monitoring, or manipulating bioelectrical signals. However, conventional implantable bioelectronic devices face key challenges in adaptive interfacing with neural tissues due to their lack of neuron-preferred properties and neuron-similar behaviors. Here, innovative neuron-inspired ferroelectric bioelectronics (FerroE) are reported that consists of biocompatible polydopamine-modified barium titanate nanoparticles, ferroelectric poly(vinylidene fluoride-co-trifluoroethylene) copolymer, and cellular-scale micropyramid array structures, imparting adaptive interfacing with neural systems. These FerroE not only achieve neuron-preferred flexible and topographical properties, but also offer neuron-similar behaviors including highly efficient and stable light-induced polarization change, superior capability of producing electric signals, and seamless integration and adaptive communication with neurons. Moreover, the FerroE allows for adaptive interfacing with both peripheral and central neural networks of mice, enabling regulation of their heart rate and motion behavior in a wireless, non-genetic, and non-contact manner. Notably, the FerroE demonstrates unprecedented structural and functional stability and negligible immune response even after 3 months of implantation in vivo. Such bioinspired FerroE are opening new opportunities for next-generation brain-machine interfaces, tissue engineering materials, and biomedical devices.}, }
@article {pmid40442546, year = {2025}, author = {Wang, L and Li, T and Li, X and Liu, F and Feng, C}, title = {Mapping trait justice sensitivity in the Brain: Whole-brain resting-state functional connectivity as a predictor of other-oriented not self-oriented justice sensitivity.}, journal = {Cognitive, affective &amp; behavioral neuroscience}, volume = {25}, number = {6}, pages = {1834-1849}, pmid = {40442546}, issn = {1531-135X}, support = {2024B0303390003//Research Center for Brain Cognition and Human Development, Guangdong, China/ ; 32020103008//National Natural Science Foundation of China/ ; 32271126//National Natural Science Foundation of China/ ; 81922036//National Natural Science Foundation of China/ ; }, mesh = {Humans ; Male ; Magnetic Resonance Imaging ; Female ; Machine Learning ; Young Adult ; Adult ; *Brain/physiology/diagnostic imaging ; *Social Justice ; Brain Mapping/methods ; Neural Pathways/physiology ; *Connectome/methods ; }, abstract = {Justice sensitivity (JS) reflects personal concern and commitment to the principle of justice, showing considerable heterogeneity among the general population. Despite a growing interest in the behavioral characteristics of JS over the past decades, the neurobiological substrates underlying trait JS are not well comprehended. We addressed this issue by employing a machine learning approach to decode the trait JS, encompassing its various orientations, from whole-brain resting-state functional connectivity. We demonstrated that the machine-learning model could decode the individual trait of other-oriented JS but not self-oriented JS from resting-state functional connectivity across multiple neural systems, including functional connectivity between and within parietal lobe and motor cortex as well as their connectivity with other brain systems. Key nodes that contributed to the prediction model included the parietal, motor, temporal, and subcortical regions that have been linked to other-oriented JS. Additionally, the machine learning model can distinctly distinguish between the distinct roles associated with other-oriented JS, including observer, perpetrator, and beneficiary, with key brain regions in the predictive networks exhibiting both similarities and disparities. These findings remained robust using different validation procedures. Collectively, these results support the separation between other-oriented JS and self-oriented JS, while also highlighting the distinct intrinsic neural correlates among the three roles of other-oriented JS: observer, perpetrator, and beneficiary.}, }
@article {pmid40442206, year = {2025}, author = {Akama, T and Zhang, Z and Li, P and Hongo, K and Minamikawa, S and Polouliakh, N}, title = {Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {18869}, pmid = {40442206}, issn = {2045-2322}, mesh = {*Music ; Humans ; *Neural Networks, Computer ; Electroencephalography ; *Brain/physiology ; Male ; *Auditory Perception/physiology ; Female ; Adult ; Acoustic Stimulation ; Young Adult ; Brain-Computer Interfaces ; }, abstract = {Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representations to cortical representations. Building upon this concept, our approach reverses the direction of prediction: we utilize ANN representations as a supervisory signal to train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, we focus on constructing a recognition model for music identification, where electroencephalography (EEG) brain recordings collected during music listening serve as input. By training an EEG recognition model to predict ANN representations-representations associated with music identification-we observed a significant improvement in classification accuracy. This study introduces a novel approach to developing recognition models for brain recordings in response to external auditory stimuli. It holds promise for advancing brain-computer interfaces (BCI), neural decoding techniques, and our understanding of music cognition. Furthermore, it provides new insights into the relationship between auditory brain activity and ANN representations.}, }
@article {pmid40442062, year = {2025}, author = {Shah, NP and Avansino, D and Kamdar, F and Nicolas, C and Kapitonava, A and Vargas-Irwin, C and Hochberg, LR and Pandarinath, C and Shenoy, KV and Willett, FR and Henderson, JM}, title = {Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {5008}, pmid = {40442062}, issn = {2041-1723}, support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; Milton Safenowtiz Postdoctoral Scholarship//Amyotrophic Lateral Sclerosis Association (ALS Association)/ ; R01 DC014034/DC/NIDCD NIH HHS/United States ; }, mesh = {Humans ; *Fingers/physiology ; *Motor Cortex/physiology/physiopathology ; Male ; Movement/physiology ; Adult ; Quadriplegia/physiopathology ; }, abstract = {How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such as hand gestures, or playing the piano)? To address this question, motor cortical activity was recorded using intracortical multi-electrode arrays in two male people with tetraplegia as they attempted single, pairwise and higher-order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity exhibited normalization, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers changed significantly as a result of the movement of more strongly represented fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.}, }
@article {pmid40441574, year = {2025}, author = {Rahman, MH and Mondal, MIH}, title = {Investigation of neem-oil-loaded PVA/chitosan biocomposite film for hydrophobic dressing, rapid hemostasis and wound healing applications.}, journal = {International journal of biological macromolecules}, volume = {316}, number = {Pt 1}, pages = {144712}, doi = {10.1016/j.ijbiomac.2025.144712}, pmid = {40441574}, issn = {1879-0003}, mesh = {*Chitosan/chemistry ; *Wound Healing/drug effects ; *Bandages ; Hydrophobic and Hydrophilic Interactions ; Animals ; *Hemostasis/drug effects ; *Polyvinyl Alcohol/chemistry ; Anti-Bacterial Agents/pharmacology/chemistry ; *Biocompatible Materials/chemistry/pharmacology ; Hemostatics/pharmacology/chemistry ; Male ; Humans ; Blood Coagulation/drug effects ; }, abstract = {The present work aims to develop a hydrophobic dressing with a blood-repellent surface that achieves fast clotting without blood loss, having antibacterial properties, clot self-detachment, and superior wound healing activity. For these reasons, a novel approach was applied by producing a hydrophobic film made of PVA, chitosan, and neem seed oil (NSO). The film had the necessary hydrophobicity, mechanical strength, stability and was able to transmit water vapor to be suitable for the wound skin surface and demonstrated faster blood clotting (BCI = 91.44 % in 5 min and 85.22 % in 10 min). The proportion of red blood cells (2.78 %) and platelets (17.33 %) attached to the film proved its excellent hemostatic activity. It was anti-adhesive, created spontaneous clot detachment, and exhibited antibacterial properties at the wound site, as evidenced by in vivo testing. Moreover, in vivo testing and histopathological findings showed enhanced wound healing activity, greater re-epithelialization, and decreased granulation tissue. Additionally, the film's eco-friendliness was evaluated using a soil burial degradation test, and the results show that it deteriorated into the soil but did so slowly because of its hydrophobic property. Thus, PVA/CS/NSO composite film may be a green biomedical material for hemostasis and wound healing.}, }
@article {pmid40440260, year = {2025}, author = {Tong, B and Li, G and Bu, X and Wang, Y and Yu, X}, title = {A deep learning-based algorithm for the detection of personal protective equipment.}, journal = {PloS one}, volume = {20}, number = {5}, pages = {e0322115}, pmid = {40440260}, issn = {1932-6203}, mesh = {*Personal Protective Equipment ; *Deep Learning ; Humans ; *Algorithms ; Neural Networks, Computer ; Construction Industry ; }, abstract = {Personal protective equipment (PPE) is critical for ensuring the safety of construction workers. However, site surveillance images from construction sites often feature multi-size and multi-scale targets, leading to low detection accuracy for PPE in existing models. To address this issue, this paper proposes an improved model based on YOLOv8n.By enriching feature diversity and enhancing the model's adaptability to geometric transformations, the detection accuracy is improved.A Multi-Scale Group Convolution Module (MSGP) was designed to extract multi-level features using different convolution kernels. A Multi-Scale Feature Diffusion Pyramid Network (MFDPN) was developed, which aggregates multi-scale features through the Multiscale Feature Focus (MFF) module and diffuses them across scales, providing each scale with detailed contextual information. A customized Task Alignment Module was introduced to integrate interactive features, optimizing both classification and localization tasks. The DCNV2(Deformable Convolutional Networks v2) module was incorporated to handle geometric scale transformations by generating spatial offsets and feature masks from interactive features, thereby improving localization accuracy and dynamically selecting weights to enhance classification precision.The improved model incorporates rich multi-level and multi-scale features, allowing it to better adapt to tasks involving geometric transformations and aligning with the image data distribution in construction scenarios. Additionally, structured pruning techniques were applied to the model at varying levels, further reducing computational and parameter loads. Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. The proposed MFD-YOLO(1.5) model achieves significant progress in detecting personal protective equipment on construction sites, with a substantial reduction in parameter count, making it suitable for deployment on resource-constrained edge devices.}, }
@article {pmid40438784, year = {2025}, author = {Du, Y and Yang, X and Wang, M and Lv, Q and Zhou, H and Sang, G}, title = {Longitudinal changes in children with autism spectrum disorder receiving applied behavior analysis or early start denver model interventions over six months.}, journal = {Frontiers in pediatrics}, volume = {13}, number = {}, pages = {1546001}, pmid = {40438784}, issn = {2296-2360}, abstract = {BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication difficulties, restricted interests, repetitive behaviors, and sensory abnormalities. The rising prevalence of ASD presents a significant public health concern, with no pharmacological treatments available for its core symptoms. Therefore, early and effective behavioral interventions are crucial to improving developmental outcomes for children with ASD. Current interventions primarily focus on educational rehabilitation methods, including Applied behavior Analysis (ABA) and the Early Start Denver Model (ESDM).<br><br>OBJECTIVE: This study aims to examine the developmental changes in children with ASD following six months of ABA therapy or ESDM intervention.<br><br>METHODS: From December 2021 to December 2023, 30 children receiving ABA therapy at the Zhejiang Rehabilitation Medical Center (40&#x2005;min/session, 4 sessions/day, 5 days/week), while another 30 children undergoing ESDM training at Hangzhou Children's Hospital (2&#x2005;h of one-on-one sessions and 0.5&#x2005;h of group sessions/day, 5 days/week). Both groups participated in their respective interventions for six months. Pre- and post-treatment assessments were conducted using the Psycho-educational Profile-Third Edition (PEP-3).<br><br>RESULTS: Both groups showed significant improvements in PEP-3 scores post-treatment, including cognitive verbal/pre-verbal, expressive language, receptive language, social reciprocity, small muscles, imitation, emotional expression, and verbal and nonverbal behavioral characteristics.<br><br>CONCLUSION: Both ABA and ESDM interventions were associated with comprehensive improvements in children with ASD over a six-month period.}, }
@article {pmid40438090, year = {2025}, author = {Ding, W and Liu, A and Chen, X and Xie, C and Wang, K and Chen, X}, title = {Reducing calibration efforts of SSVEP-BCIs by shallow fine-tuning-based transfer learning.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {81}, pmid = {40438090}, issn = {1871-4080}, abstract = {The utilization of transfer learning (TL), particularly through pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has substantially reduced the calibration efforts. However, commonly employed fine-tuning approaches, including end-to-end fine-tuning and last-layer fine-tuning, require data from target subjects that encompass all categories (stimuli), resulting in a time-consuming data collection process, especially in systems with numerous categories. To address this challenge, this study introduces a straightforward yet effective ShallOw Fine-Tuning (SOFT) method to substantially reduce the number of calibration categories needed for model fine-tuning, thereby further mitigating the calibration efforts for target subjects. Specifically, SOFT involves freezing the parameters of the deeper layers while updating those of the shallow layers during fine-tuning. Freezing the parameters of deeper layers preserves the model's ability to recognize semantic and high-level features across all categories, as established during pre-training. Moreover, data from different categories exhibit similar individual-specific low-level features in SSVEP-BCIs. Consequently, updating the parameters of shallow layers-responsible for processing low-level features-with data solely from partial categories enables the fine-tuned model to efficiently capture the individual-related features shared by all categories. The effectiveness of SOFT is validated using two public datasets. Comparative analysis with commonly used end-to-end and last-layer fine-tuning methods reveals that SOFT achieves higher classification accuracy while requiring fewer calibration categories. The proposed SOFT method further decreases the calibration efforts for target subjects by reducing the calibration category requirements, thereby improving the feasibility of SSVEP-BCIs for real-world applications.}, }
@article {pmid40437332, year = {2025}, author = {Cherukuri, SB and Ramakrishnan, S}, title = {Eye-blink artifact removal in single-channel electroencephalogram using K-means and Savitzky Golay-singular Spectrum Analysis hybrid technique.}, journal = {Physical and engineering sciences in medicine}, volume = {48}, number = {3}, pages = {1127-1136}, pmid = {40437332}, issn = {2662-4737}, mesh = {*Electroencephalography/methods ; *Blinking/physiology ; *Artifacts ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; *Spectrum Analysis ; Signal-To-Noise Ratio ; }, abstract = {Electroencephalogram (EEG) acquisition systems are used to record the neural condition of humans for diagnosing various neural problems. The eye-blink or Electrooculogram (EOG) artifact caused by eye-lid movements, influences the EEG signal measurements and interferes with the diagnosis. The complete removal of eye-blink artifact while preserving the EEG content is a challenging task that needs highly efficient denoising methods, particularly from Single-Channel EEG which is widely used for Out-Of-Hospital (OOH) neurological patients and for Brain-Computer-Interface (BCI) applications. When compared to multi-channel EEG systems, Single-channel EEG system suffers certain difficulties such as lack of spatial information, redundancy, etc. This paper proposes an innovative hybrid method combining K-Means clustering and Savitzky Golay-Singular Spectrum Analysis (SG-SSA) methods for effective eye-blink artifact removal from single channel EEG. The eye-blink artifact is extracted and then subtracted from the noisy EEG signal, so that the EEG content available in the eye-blink periods are preserved. Through extensive experiments with synthetic as well as real time EEG, we show that our proposed method outperforms the other contemporary methods from literature. Our proposed hybrid approach achieves a significant reduction in Mean Absolute Error (MAE) and Relative Root Mean Square Error (RRMSE) than the Fourier-Bessel Series Expansion based Empirical Wavelet Transform (FBSE-EWT), SSA combined with independent component analysis (SSA-ICA) and Ensemble Empirical Mode Decomposition combined with ICA (EEMD-ICA), proposed in recent literature.}, }
@article {pmid40436265, year = {2025}, author = {Chopra, M and Kumar, H}, title = {Navigating the complexities of spinal cord injury: an overview of pathology, treatment strategies and clinical trials.}, journal = {Drug discovery today}, volume = {30}, number = {6}, pages = {104387}, doi = {10.1016/j.drudis.2025.104387}, pmid = {40436265}, issn = {1878-5832}, mesh = {*Spinal Cord Injuries/therapy/pathology/physiopathology/drug therapy ; Humans ; Animals ; Clinical Trials as Topic ; Quality of Life ; }, abstract = {Spinal cord injury (SCI) is a debilitating neurological condition characterized by sensory and motor deficits. It significantly affects patient quality of life and poses a substantial socioeconomic burden. The complex and multifaceted pathophysiology of SCI complicates its effective treatment. Following the primary mechanical insult, a secondary injury cascade disrupts the microenvironment at the injury site, exacerbating the tissue damage. Despite extensive research, no fully effective treatment is currently available. This review explores current pharmacological and non-pharmacological treatment strategies at the preclinical and clinical stages, providing insights into promising interventions. The findings highlight potential avenues for future research aimed at improving SCI management.}, }
@article {pmid40434889, year = {2025}, author = {Ruszala, BM and Schieber, MH}, title = {Injecting information in the cortical reach-to-grasp network is effective in ventral but not dorsal nodes.}, journal = {Cell reports}, volume = {44}, number = {5}, pages = {115664}, pmid = {40434889}, issn = {2211-1247}, support = {F31 NS129099/NS/NINDS NIH HHS/United States ; R01 NS107271/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; *Motor Cortex/physiology ; *Somatosensory Cortex/physiology ; Brain-Computer Interfaces ; *Hand Strength/physiology ; Macaca mulatta ; *Parietal Lobe/physiology ; Male ; Movement/physiology ; Electric Stimulation ; }, abstract = {Although control of movement involves many cortical association areas, bidirectional brain-machine interfaces (BMIs) typically decode movement intent from the motor cortex and deliver feedback information to the primary somatosensory cortex (S1). Compared to the S1, the parietal and premotor areas encode more complex information about object properties, hand pre-shaping, and reach trajectories. BMIs therefore might deliver richer information to those cortical association areas than to primary areas. Here, we investigated whether instructions for a center-out task could be delivered via intracortical microstimulation (ICMS) in the anterior intraparietal area (AIP), dorsal posterior parietal cortex (dPPC), or dorsal premotor cortex (PMd) as well as the ventral premotor cortex (PMv) and S1. Two monkeys successfully learned to use AIP, PMv, or S1-ICMS instructions, but neither learned to use dPPC- or PMd-ICMS instructions. The AIP, PMv, and S1 may thus be effective cortical territory for delivering information to the brain, whereas the dPPC or PMd may be comparatively ineffective.}, }
@article {pmid40434816, year = {2025}, author = {Liu, L}, title = {Did you see it?.}, journal = {eLife}, volume = {14}, number = {}, pages = {}, pmid = {40434816}, issn = {2050-084X}, mesh = {Humans ; *Brain/physiology ; *Consciousness/physiology ; }, abstract = {Cautious reporting choices can artificially enhance how well analyses of brain activity reflect conscious and unconscious experiences, making distinguishing between the two more challenging.}, }
@article {pmid40434551, year = {2025}, author = {Sokhadze, E}, title = {Neurofeedback and Brain-Computer Interface-Based Methods for Post-stroke Rehabilitation.}, journal = {Applied psychophysiology and biofeedback}, volume = {}, number = {}, pages = {}, pmid = {40434551}, issn = {1573-3270}, abstract = {Stroke has been identified as a major public health concern and one of the leading causes contributing to long-term neurological disability. People suffering from stroke often present with upper limb paralysis impacting their quality of life and ability to work. Motor impairments in the upper limb represent the most prevalent symptoms in stroke sufferers. There is a need to develop novel intervention strategies that can be used as stand-alone techniques or combined with current gold standard post-stroke rehabilitation procedures. There was reported evidence about the utility of rehabilitation protocols with motor imagery (MI) used either alone or in combination with physical therapy resulting in enhancement of post-stroke functional recovery of paralyzed limbs. Brain-Computer Interface (BCI) and EEG neurofeedback (NFB) training can be considered as novel technologies to be used in conjunction with MI and motor attempt (MA) to enable direct translation of EEG induced by imagery or attempted movement to arrange training that has potential to enhance functional motor recovery of upper limbs after stroke. There are reported several controlled trials and multiple cases series that have shown that stroke patients are able to learn modulation of their EEG sensorimotor rhythm in BCI mode to control external devices, including exoskeletons, prosthetics, and such interventions were shown promise in facilitation of recovery in stroke sufferers. A review of the literature suggests there has been significant progress in the development of new methods for post-stroke rehabilitation procedures. There are reviewed findings supportive of NFB and BCI methods as evidence-based treatment for post-stroke motor function recovery.}, }
@article {pmid40433677, year = {2025}, author = {He, J and Zhou, G and Sun, B and Yan, L and Lang, X and Yang, Y and Hao, H}, title = {Graphene quantum dots induced performance enhancement in memristors.}, journal = {Nanoscale}, volume = {17}, number = {23}, pages = {14082-14102}, doi = {10.1039/d5nr00597c}, pmid = {40433677}, issn = {2040-3372}, abstract = {With the rapid development of information technology, the demand for miniaturization, integration, and intelligence of electronic devices is growing rapidly. As a key device in the non-von Neumann architecture, memristors can perform computations while storing data, enhancing computational efficiency and reducing power consumption. Memristors have become pivotal in driving the advancement of artificial intelligence (AI) and Internet of Things technologies. Combining the electronic properties of graphene with the size effects of quantum dots, graphene quantum dot (GQD)-based memristors exhibit potential applications in constructing brain-inspired neuromorphic computing systems and achieving AI hardware acceleration, making them a focal point of research interest. This review provides an overview of the preparation, mechanism, and application of GQD-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQD-based memristors are presented from three perspectives: the metal conductive filament mechanism, the electron trapping and detrapping mechanism, and the oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQD-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interfaces. Finally, the challenges and future development prospects of GQD-based memristors are discussed.}, }
@article {pmid40431969, year = {2025}, author = {You, Z and Guo, Y and Zhang, X and Zhao, Y}, title = {Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {10}, pages = {}, pmid = {40431969}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; }, abstract = {Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward.}, }
@article {pmid40431893, year = {2025}, author = {Sasatake, Y and Matsushita, K}, title = {EEG Baseline Analysis for Effective Extraction of P300 Event-Related Potentials for Welfare Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {10}, pages = {}, pmid = {40431893}, issn = {1424-8220}, support = {JPMJSP2125//JST SPRING/ ; Not Applicable//THERS Make New Standards Program for the Next Generation Researchers/ ; }, mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Photic Stimulation ; Signal Processing, Computer-Assisted ; }, abstract = {Enabling individuals with complete paralysis to operate devices voluntarily requires an effective interface; EEG-based P300 event-related potential (ERP) interfaces are considered a promising approach. P300 is an EEG peak generated in response to specific sensory stimuli recognized by an individual. Accurate detection of this peak necessitates a stable pre-stimulus baseline EEG signal, which serves as the reference for baseline correction. Previous studies have commonly employed either a single-time-point amplitude (e.g., at 100 ms before stimulus onset) or a time-range-averaged amplitude over a specified pre-stimulus period (e.g., 0-200 ms) as a baseline correction method, assuming these provide the most stable EEG reference. However, in assistive P300 interfaces, continuous visual stimuli at 400 ms intervals are typically used to efficiently evoke P300 peaks. Since stimuli are presented before the EEG stabilizes, it remains unclear whether conventional neuroscience baseline correction methods are suitable for such applications. To address this, the present study conducted a P300 induction experiment based on continuous 400 ms interval visual stimuli. Using EEG data recorded from 0 to 1000 ms before each visual stimulus (sampled at 1 ms intervals), we applied three baseline correction methods-single-time-point amplitude, time-range-averaged amplitude, and multi-time-point amplitude-to determine the most effective EEG reference and evaluate the impact on P300 detection performance. The results showed that baseline correction using an amplitude at a single point in time is unstable when the basic EEG rhythm and low-frequency noise remain, while time-range-averaged baseline correction using the 0-200 ms pre-stimulus period led to relatively effective P300 detection. However, it was also found that using only one value averaged over the amplitude from 0 to 200 ms did not result in an accurate EEG reference potential, resulting in an error. Finally, this study confirmed that the multi-time-point baseline correction method, through which the amplitude state from 0 to 200 ms before the visual stimulus is comprehensively evaluated, may be the most effective method for P300 determination.}, }
@article {pmid40431780, year = {2025}, author = {Polo-Hortigüela, C and Ortiz, M and Soriano-Segura, P and Iáñez, E and Azorín, JM}, title = {Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {10}, pages = {}, pmid = {40431780}, issn = {1424-8220}, support = {PID2021-124111OB-C31//MICIU /AEI/10.13039/501100011033 and by ERDF, EU/ ; PRE2022-103336//MICIU/AEI/10.13039 501100011033/ ; //Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI), Generalitat Valenciana and European Union/ ; //Project "Neurokit" funded by Centro Internacional para la Investigaci&#xf3;n del Envejecimiento de la Fundaci&#xf3;n de la Comunitat Valenciana (ICAR)/ ; 101118964//European Union's research and innovation programme under the Marie Sk&#x142;odowska-Curie/ ; }, mesh = {Humans ; Electroencephalography/methods ; Brain-Computer Interfaces ; Movement/physiology ; Male ; *Ankle/physiology ; *Exoskeleton Device ; Adult ; Biomechanical Phenomena ; Foot/physiology ; Female ; Wearable Electronic Devices ; Fourier Analysis ; }, abstract = {Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain-machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert-Huang (HHT), and Chirplet (CT) methods. The 8-20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies.}, }
@article {pmid40428890, year = {2025}, author = {Endzelytė, E and Petruševičienė, D and Kubilius, R and Mingaila, S and Rapolienė, J and Rimdeikienė, I}, title = {Integrating Brain-Computer Interface Systems into Occupational Therapy for Enhanced Independence of Stroke Patients: An Observational Study.}, journal = {Medicina (Kaunas, Lithuania)}, volume = {61}, number = {5}, pages = {}, pmid = {40428890}, issn = {1648-9144}, mesh = {Humans ; Male ; Female ; *Brain-Computer Interfaces/standards/trends ; *Occupational Therapy/methods/standards ; *Stroke Rehabilitation/methods/standards ; Middle Aged ; Aged ; Activities of Daily Living/psychology ; Upper Extremity/physiopathology ; Adult ; Stroke/complications ; }, abstract = {Background and Objectives: Brain-computer interface (BCI) technology is revolutionizing stroke rehabilitation by offering innovative neuroengineering solutions to address neurological deficits. By bypassing peripheral nerves and muscles, BCIs enable individuals with severe motor impairments to communicate their intentions directly through control signals derived from brain activity, opening new pathways for recovery and improving the quality of life. The aim of this study was to explore the beneficial effects of BCI system-based interventions on upper limb motor function and performance of activities of daily living (ADL) in stroke patients. We hypothesized that integrating BCI into occupational therapy would result in measurable improvements in hand strength, dexterity, independence in daily activities, and cognitive function compared to baseline. Materials and Methods: An observational study was conducted on 56 patients with subacute stroke. All patients received standard medical care and rehabilitation for 54 days, as part of the comprehensive treatment protocol. Patients underwent BCI training 2-3 times a week instead of some occupational therapy sessions, with each patient completing 15 sessions of BCI-based recoveriX treatment during rehabilitation. The occupational therapy program included bilateral exercises, grip-strengthening activities, fine motor/coordination tasks, tactile discrimination exercises, proprioceptive training, and mirror therapy to enhance motor recovery through visual feedback. Participants received ADL-related training aimed at improving their functional independence in everyday activities. Routine occupational therapy was provided five times a week for 50 min per session. Upper extremity function was evaluated using the Box and Block Test (BBT), Nine-Hole Peg Test (9HPT), and dynamometry to assess gross manual dexterity, fine motor skills, and grip strength. Independence in daily living was assessed using the Functional Independence Measure (FIM). Results: Statistically significant improvements were observed across all the outcome measures (p < 0.001). The strength of the stroke-affected hand improved from 5.0 kg to 6.7 kg, and that of the unaffected hand improved from 29.7 kg to 40.0 kg. Functional independence increased notably, with the FIM scores rising from 43.0 to 83.5. Cognitive function also improved, with MMSE scores increasing from 22.0 to 26.0. The effect sizes ranged from moderate to large, indicating clinically meaningful benefits. Conclusions: This study suggests that BCI-based occupational therapy interventions effectively improve upper extremity motor function and daily functions and have a positive impact on the cognition of patients with subacute stroke.}, }
@article {pmid40988938, year = {2024}, author = {Oganesian, LL and Shanechi, MM}, title = {Brain-computer interfaces for neuropsychiatric disorders.}, journal = {Nature reviews bioengineering}, volume = {2}, number = {8}, pages = {653-670}, pmid = {40988938}, issn = {2731-6092}, support = {R01 MH123770/MH/NIMH NIH HHS/United States ; R61 MH135407/MH/NIMH NIH HHS/United States ; }, abstract = {Neuropsychiatric disorders such as major depression are a leading cause of disability worldwide with standard treatments, including psychotherapy or medication, failing many patients. Deep brain stimulation holds great potential as an alternative therapy for treatment-resistant cases; however, improving the efficacy of stimulation therapy for neuropsychiatric disorders is hindered by the complexity as well as inter- and/or intra-individual variability in symptom manifestations, neural representations and response to therapy. These challenges motivate the development of brain-computer interfaces (BCIs) that can decode a patient's symptom-state from brain activity as feedback to personalize the stimulation therapy in closed loop. Here, we review progress on developing BCIs for neuropsychiatric care, focusing on neural biomarkers for decoding symptom-states, stimulation site selection and closed-loop stimulation strategies. Moreover, we highlight promising data-driven machine learning and system design approaches and provide a roadmap for realizing these BCIs. Finally, we review current limitations, discuss extensions to other treatment modalities, and outline the required scientific and technological advances. These advances can enable next-generation BCIs that provide an alternative therapy for treatment-resistant neuropsychiatric disorders.}, }
@article {pmid40800282, year = {2024}, author = {Ladouce, S and Dehais, F}, title = {Frequency tagging of spatial attention using periliminal flickers.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {2}, number = {}, pages = {}, pmid = {40800282}, issn = {2837-6056}, abstract = {Steady-State Visually Evoked Potentials (SSVEPs) manifest as a sustained rhythmic activity that can be observed in surface electroencephalography (EEG) in response to periodic visual stimuli, commonly referred to as flickers. SSVEPs are widely used in fundamental cognitive neuroscience paradigms and Brain-Computer Interfaces (BCI) due to their robust and rapid onset. However, they have drawbacks related to the intrusive saliency of flickering visual stimuli, which may induce eye strain, cognitive fatigue, and biases in visual exploration. Previous findings highlighted the potential of altering features of flicker stimuli to improve user experience. In this study, we propose to reduce the amplitude modulation depth of flickering stimuli down to the individuals' perceptual visibility threshold (periliminal) and below (subliminal). The stimulus amplitude modulation depth represents the contrast difference between the two alternating states of a flicker. A simple visual attention task where participants responded to the presentation of spatially cued target stimuli (left and right) was used to assess the validity of such periliminal and subliminal frequency-tagging probes to capture spatial attention. The left and right sides of the screen, where target stimuli were presented, were covered by large flickers (13 and 15 Hz, respectively). The amplitude modulation depth of these flickers was manipulated across three conditions: control, periliminal, and subliminal. The latter two levels of flickers amplitude modulation depth were defined through a perceptual visibility threshold protocol on a single-subject basis. Subjective feedback indicated that the use of periliminal and subliminal flickers substantially improved user experience. The present study demonstrates that periliminal and subliminal flickers evoked SSVEP responses that can be used to derive spatial attention in frequency-tagging paradigms. The single-trial classification of attended space (left versus right) based on SSVEP response reached an average accuracy of 81.1% for the periliminal and 58% for the subliminal conditions. These findings reveal the promises held by the application of inconspicuous flickers to both cognitive neuroscience research and BCI development.}, }
@article {pmid40800520, year = {2024}, author = {Banville, H and Jaoude, MA and Wood, SUN and Aimone, C and Holst, SC and Gramfort, A and Engemann, DA}, title = {Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {2}, number = {}, pages = {}, pmid = {40800520}, issn = {2837-6056}, abstract = {Electroencephalography (EEG) is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications, including brain-computer interfaces, epilepsy monitoring, and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using the brain age framework and four-channel consumer EEG data. We analyzed recordings from more than 5200 human subjects (18-81 years) during meditation and sleep, to predict age at the time of recording. With cross-validated R 2 scores between 0.3 - 0.5 , prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine-learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.}, }
@article {pmid40800349, year = {2024}, author = {Zubarev, I and Nurminen, M and Parkkonen, L}, title = {Robust discrimination of multiple naturalistic same-hand movements from MEG signals with convolutional neural networks.}, journal = {Imaging neuroscience (Cambridge, Mass.)}, volume = {2}, number = {}, pages = {}, pmid = {40800349}, issn = {2837-6056}, abstract = {Discriminating patterns of brain activity corresponding to multiple hand movements are a challenging problem at the limit of the spatial resolution of magnetoencephalography (MEG). Here, we use the combination of MEG, a novel experimental paradigm, and a recently developed convolutional-neural-network-based classifier to demonstrate that four goal-directed real and imaginary movements-all performed by the same hand-can be detected from the MEG signal with high accuracy: > 70 % for real movements and > 60 % for imaginary movements. Additional experiments were used to control for possible confounds and to establish the empirical chance level. Investigation of the patterns informing the classification indicated the primary contribution of signals in the alpha (8-12 Hz) and beta (13-30 Hz) frequency range in the contralateral motor areas for the real movements, and more posterior parieto-occipital sources for the imagined movements. The obtained high accuracy can be exploited in practical applications, for example, in brain-computer interface-based motor rehabilitation.}, }
@article {pmid40918004, year = {2023}, author = {Zong, F and Liu, H and Bai, R and Galvosas, P}, title = {Data inversion of multi-dimensional magnetic resonance in porous media.}, journal = {Magnetic resonance letters}, volume = {3}, number = {2}, pages = {127-139}, pmid = {40918004}, issn = {2772-5162}, abstract = {Since its inception in the 1970s, multi-dimensional magnetic resonance (MR) has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions. MR spectroscopy beyond one dimension allows the study of the correlation, exchange processes, and separation of overlapping spectral information. The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media. Apart from Fourier transform, methods have been developed for processing the multi-dimensional time-domain data, identifying the fluid components, and estimating pore surface permeability via joint relaxation and diffusion spectra. Through the resolution of spectroscopic signals with spatial encoding gradients, multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases. Signals in each voxel are usually expressed as multi-exponential decay, representing microstructures or environments along multiple pore scales. The separation of contributions from different environments is a common ill-posed problem, which can be resolved numerically. Moreover, the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra. This paper reviews the algorithms that have been proposed to process multi-dimensional MR datasets in different scenarios. Detailed information at the microscopic level, such as tissue components, fluid types and food structures in multi-disciplinary sciences, could be revealed through multi-dimensional MR.}, }
@article {pmid40620639, year = {2023}, author = {Zhang, Y and He, T and Boussard, J and Windolf, C and Winter, O and Trautmann, E and Roth, N and Barrell, H and Churchland, M and Steinmetz, NA and ,  and Varol, E and Hurwitz, C and Paninski, L}, title = {Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.}, journal = {Advances in neural information processing systems}, volume = {36}, number = {}, pages = {77604-77631}, pmid = {40620639}, issn = {1049-5258}, support = {/WT_/Wellcome Trust/United Kingdom ; K99 MH128772/MH/NIMH NIH HHS/United States ; U19 NS123716/NS/NINDS NIH HHS/United States ; }, abstract = {Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.}, }
@article {pmid40477046, year = {2020}, author = {Amoo-Adare, EA}, title = {The Art of (Un)Thinking: When Hyper Productivity Says 'Enough!', Is a Feast.}, journal = {Postdigital science and education}, volume = {2}, number = {3}, pages = {606-613}, pmid = {40477046}, issn = {2524-4868}, }
@article {pmid40428702, year = {2025}, author = {Ma, X and Miao, T and Xie, F and Zhang, J and Zheng, L and Liu, X and Hai, H}, title = {Development of Wearable Wireless Multichannel f-NIRS System to Evaluate Activities.}, journal = {Micromachines}, volume = {16}, number = {5}, pages = {}, pmid = {40428702}, issn = {2072-666X}, abstract = {Functional near-infrared spectroscopy is a noninvasive neuroimaging technique that uses optical signals to monitor subtle changes in hemoglobin concentrations within the superficial tissue of the human body. This technology has widespread applications in long-term brain-computer interface monitoring within both traditional medical domains and, increasingly, domestic settings. The popularity of this approach lies in the fact that new single-channel brain oxygen sensors can be used in a variety of scenarios. Given the diverse sensor structure requirements across applications and numerous approaches to data acquisition, the accurate extraction of comprehensive brain activity information requires a multichannel near-infrared system. This study proposes a novel distributed multichannel near-infrared system that integrates two near-infrared light emissions at differing wavelengths (660 nm, 850 nm) with a photoelectric receiver. This substantially improves the accuracy of regional signal sampling. Through a basic long-time mental arithmetic paradigm, we demonstrate that the accompanying algorithm supports offline analysis and is sufficiently versatile for diverse scenarios relevant to the system's functionality.}, }
@article {pmid40428683, year = {2025}, author = {Hong, S}, title = {Wireless Optogenetic Microsystems Accelerate Artificial Intelligence-Neuroscience Coevolution Through Embedded Closed-Loop System.}, journal = {Micromachines}, volume = {16}, number = {5}, pages = {}, pmid = {40428683}, issn = {2072-666X}, support = {N/A//Hongik University/ ; }, abstract = {Brain-inspired models in artificial intelligence (AI) originated from foundational insights in neuroscience. In recent years, this relationship has been moving toward a mutually reinforcing feedback loop. Currently, AI is significantly contributing to advancing our understanding of neuroscience. In particular, when combined with wireless optogenetics, AI enables experiments without physical constraints. Furthermore, AI-driven real-time analysis facilitates closed-loop control, allowing experimental setups across a diverse range of scenarios. And a deeper understanding of these neural networks may, in turn, contribute to future advances in AI. This work demonstrates the synergy between AI and miniaturized neural technology, particularly through wireless optogenetic systems designed for closed-loop neural control. We highlight how AI is now revolutionizing neuroscience experiments from decoding complex neural signals and quantifying behavior, to enabling closed-loop interventions and high-throughput phenotyping in freely moving subjects. Notably, AI-integrated wireless implants can monitor and modulate biological processes with unprecedented precision. We then recount how neuroscience insights derived from AI-integrated neuroscience experiments can potentially inspire the next generation of machine intelligence. Insights gained from these technologies loop back to inspire more efficient and robust AI systems. We discuss future directions in this positive feedback loop between AI and neuroscience, arguing that the coevolution of the two fields, grounded in technologies like wireless optogenetics and guided by reciprocal insight, will accelerate progress in both, while raising new challenges and opportunities for interdisciplinary collaboration.}, }
@article {pmid40428114, year = {2025}, author = {Zheng, Y and Wu, S and Chen, J and Yao, Q and Zheng, S}, title = {Cross-Subject Motor Imagery Electroencephalogram Decoding with Domain Generalization.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {5}, pages = {}, pmid = {40428114}, issn = {2306-5354}, abstract = {Decoding motor imagery (MI) electroencephalogram (EEG) signals in the brain-computer interface (BCI) can assist patients in accelerating motor function recovery. To realize the implementation of plug-and-play functionality for MI-BCI applications, cross-subject models are employed to alleviate time-consuming calibration and avoid additional model training for target subjects by utilizing EEG data from source subjects. However, the diversity in data distribution among subjects limits the model's robustness. In this study, we investigate a cross-subject MI-EEG decoding model with domain generalization based on a deep learning neural network that extracts domain-invariant features from source subjects. Firstly, a knowledge distillation framework is adopted to obtain the internally invariant representations based on spectral features fusion. Then, the correlation alignment approach aligns mutually invariant representations between each pair of sub-source domains. In addition, we use distance regularization on two kinds of invariant features to enhance generalizable information. To assess the effectiveness of our approach, experiments are conducted on the BCI Competition IV 2a and the Korean University dataset. The results demonstrate that the proposed model achieves 8.93% and 4.4% accuracy improvements on two datasets, respectively, compared with current state-of-the-art models, confirming that the proposed approach can effectively extract invariant features from source subjects and generalize to the unseen target distribution, hence paving the way for effective implementation of the plug-and-play functionality in MI-BCI applications.}, }
@article {pmid40426690, year = {2025}, author = {Taha, BN and Baykara, M and Alakuş, TB}, title = {Neurophysiological Approaches to Lie Detection: A Systematic Review.}, journal = {Brain sciences}, volume = {15}, number = {5}, pages = {}, pmid = {40426690}, issn = {2076-3425}, abstract = {Background and Objectives: Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues and susceptibility to countermeasures. In recent years, electroencephalography (EEG) and particularly the Event-Related Potential (ERP) P300 component have gained prominence for identifying concealed information. This systematic review aims to evaluate recent studies (2017-2024) on EEG-based lie detection using ERP P300 responses, especially in relation to recognized and unrecognized face stimuli. The goal is to summarize commonly used EEG signal processing techniques, feature extraction methods, and classification algorithms, identifying those that yield the highest accuracy in lie detection tasks. Methods: This review followed PRISMA guidelines for systematic reviews. A comprehensive literature search was conducted using IEEE Xplore, Web of Science, Scopus, and Google Scholar, restricted to English-language articles from 2017 to 2024. Studies were included if they focused on EEG-based lie detection, utilized experimental protocols like Concealed Information Test (CIT), Guilty Knowledge Test (GKT), or Deceit Identification Test (DIT), and evaluated classification accuracy using ERP P300 components. Results: CIT with ERP P300 was the most frequently employed protocol. The most used preprocessing method was Bandpass Filtering (BPF), and the Discrete Wavelet Transform (DWT) emerged as the preferred feature extraction technique due to its suitability for non-stationary EEG signals. Among classification algorithms, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN) were frequently utilized. These findings demonstrate the effectiveness of hybrid and deep learning-based models in enhancing classification performance. Conclusions: EEG-based lie detection, particularly using the ERP P300 response to face recognition tasks, shows promising accuracy and robustness compared to traditional polygraph methods. Combining advanced signal processing methods with machine learning and deep learning classifiers significantly improves performance. This review identifies the most effective methodologies and suggests that future research should focus on real-time applications, cross-individual generalization, and reducing system complexity to facilitate broader adoption.}, }
@article {pmid40426631, year = {2025}, author = {Mao, Q and Zhu, H and Yan, W and Zhao, Y and Hei, X and Luo, J}, title = {MCL-SWT: Mirror Contrastive Learning with Sliding Window Transformer for Subject-Independent EEG Recognition.}, journal = {Brain sciences}, volume = {15}, number = {5}, pages = {}, pmid = {40426631}, issn = {2076-3425}, support = {23JK0556//the Scientific Research Program Founded by Shaanxi Provincial Education Department of China/ ; 61906152, 62376213 and U21A20524//the National Natural Science Foundation of China/ ; }, abstract = {Background: In brain-computer interfaces (BCIs), transformer-based models have found extensive application in motor imagery (MI)-based EEG signal recognition. However, for subject-independent EEG recognition, these models face challenges: low sensitivity to spatial dynamics of neural activity and difficulty balancing high temporal resolution features with manageable computational complexity. The overarching objective is to address these critical issues. Methods: We introduce Mirror Contrastive Learning with Sliding Window Transformer (MCL-SWT). Inspired by left/right hand motor imagery inducing event-related desynchronization (ERD) in the contralateral sensorimotor cortex, we develop a mirror contrastive loss function. It segregates feature spaces of EEG signals from contralateral ERD locations while curtailing variability in signals sharing similar ERD locations. The Sliding Window Transformer computes self-attention scores over high temporal resolution features, enabling efficient capture of global temporal dependencies. Results: Evaluated on benchmark datasets for subject-independent MI EEG recognition, MCL-SWT achieves classification accuracies of 66.48% and 75.62%, outperforming State-of-the-Art models by 2.82% and 2.17%, respectively. Ablation studies validate the efficacy of both the mirror contrastive loss and sliding window mechanism. Conclusions: These findings underscore MCL-SWT's potential as a robust, interpretable framework for subject-independent EEG recognition. By addressing existing challenges, MCL-SWT could significantly advance BCI technology development.}, }
@article {pmid40426214, year = {2025}, author = {Li, K and Liang, H and Qiu, J and Zhang, X and Cai, B and Wang, D and Zhang, D and Lin, B and Han, H and Yang, G and Zhu, Z}, title = {Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {118}, pmid = {40426214}, issn = {1743-0003}, support = {2024XHSZ-Y08//Zhejiang Health Information Association Research Program/ ; 82401786//National Natural Science Foundation of China/ ; 82201637//National Natural Science Foundation of China/ ; 2024KY246//Zhejiang Provincial Medical and Health Technology Project/ ; BMI2400025//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; 2024C03150//Key R&amp;D Program of Zhejiang Province/ ; J-202402//Qiushi Youth Program from Scientific Research Cultivation Foundation/ ; }, mesh = {*Brain/physiology/cytology ; Humans ; Microscopy, Fluorescence/methods ; Animals ; *Single-Cell Analysis/methods ; *Neurons/physiology ; Optogenetics ; *Brain Mapping/methods ; }, abstract = {As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms-quantity encoding, spatial encoding, temporal encoding, and frequency encoding-enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.}, }
@article {pmid40425805, year = {2025}, author = {Liu, CW and Wang, YM and Chen, SY and Lu, LY and Liang, TY and Fang, KC and Chen, P and Lee, IC and Liu, WC and Kumar, A and Kuo, SH and Lee, JC and Lo, CC and Wu, SC and Pan, MK}, title = {The cerebellum shapes motions by encoding motor frequencies with precision and cross-individual uniformity.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {40425805}, issn = {2157-846X}, support = {NTUMC 110C101-011//NTU | College of Medicine, National Taiwan University (College of Medicine, National Taiwan University)/ ; NSC-145-11//National Taiwan University Hospital (NTUH)/ ; 113-UN0013//National Taiwan University Hospital (NTUH)/ ; 108-039//National Taiwan University Hospital (NTUH)/ ; 112-UN0024//National Taiwan University Hospital (NTUH)/ ; 113-E0001//National Taiwan University Hospital (NTUH)/ ; AS-TM-112-01-02//Academia Sinica/ ; NHRI-EX113-11303NI//National Health Research Institutes (NHRI)/ ; 109-2326-B-002-013-MY4//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 107-2321-B-002-020//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-002-059-MY2//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 110-2321-B-002-012//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 111-2628-B-002-036//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 112-2628-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 113-2628-B-002-002//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; R01NS118179//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS104423//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS124854//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; }, abstract = {Understanding brain behaviour encoding or designing neuroprosthetics requires identifying precise, consistent neural algorithms across individuals. However, cerebral microstructures and activities are individually variable, posing challenges for identifying precise codes. Here, despite cerebral variability, we report that the cerebellum shapes motor kinematics by encoding dynamic motor frequencies with remarkable numerical precision and cross-individual uniformity. Using in vivo electrophysiology and optogenetics in mice, we confirm that deep cerebellar neurons encode frequencies using populational tuning of neuronal firing probabilities, creating cerebellar oscillations and motions with matched frequencies. The mechanism is consistently presented in self-generated rhythmic and non-rhythmic motions triggered by a vibrational platform or skilled tongue movements of licking in all tested mice with cross-individual uniformity. The precision and uniformity allowed us to engineer complex motor kinematics with designed frequencies. We further validate the frequency-coding function of the human cerebellum using cerebellar electroencephalography recordings and alternating current stimulation during voluntary tapping tasks. Our findings reveal a cerebellar algorithm for motor kinematics with precision and uniformity, the mathematical foundation for a brain-computer interface for motor control.}, }
@article {pmid40425792, year = {2025}, author = {Chen, ZP and Zhao, X and Wang, S and Cai, R and Liu, Q and Ye, H and Wang, MJ and Peng, SY and Xue, WX and Zhang, YX and Li, W and Tang, H and Huang, T and Zhang, Q and Li, L and Gao, L and Zhou, H and Hang, C and Zhu, JN and Li, X and Liu, X and Cong, Q and Yan, C}, title = {GABA-dependent microglial elimination of inhibitory synapses underlies neuronal hyperexcitability in epilepsy.}, journal = {Nature neuroscience}, volume = {28}, number = {7}, pages = {1404-1417}, pmid = {40425792}, issn = {1546-1726}, support = {82373856//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31900824//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371074//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32071097//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82471481//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200778//National Natural Science Foundation of China (National Science Foundation of China)/ ; 020813005031//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; 2019M651779//Postdoctoral Research Foundation of China (China Postdoctoral Research Foundation)/ ; }, mesh = {Animals ; *Microglia/physiology/metabolism ; Female ; Male ; *Synapses/physiology ; Mice ; *gamma-Aminobutyric Acid/metabolism ; *Neural Inhibition/physiology ; Mice, Inbred C57BL ; *Neurons/physiology ; *Epilepsy/physiopathology/pathology ; Synaptic Transmission/physiology ; Phagocytosis/physiology ; Mice, Transgenic ; }, abstract = {Neuronal hyperexcitability is a common pathophysiological feature of many neurological diseases. Neuron-glia interactions underlie this process but the detailed mechanisms remain unclear. Here, we reveal a critical role of microglia-mediated selective elimination of inhibitory synapses in driving neuronal hyperexcitability. In epileptic mice of both sexes, hyperactive inhibitory neurons directly activate surveilling microglia via GABAergic signaling. In response, these activated microglia preferentially phagocytose inhibitory synapses, disrupting the balance between excitatory and inhibitory synaptic transmission and amplifying network excitability. This feedback mechanism depends on both GABA-GABAB receptor-mediated microglial activation and complement C3-C3aR-mediated microglial engulfment of inhibitory synapses, as pharmacological or genetic blockage of both pathways effectively prevents inhibitory synapse loss and ameliorates seizure symptoms in mice. Additionally, putative cell-cell interaction analyses of brain tissues from males and females with temporal lobe epilepsy reveal that inhibitory neurons induce microglial phagocytic states and inhibitory synapse loss. Our findings demonstrate that inhibitory neurons can directly instruct microglial states to control inhibitory synaptic transmission through a feedback mechanism, leading to the development of neuronal hyperexcitability in temporal lobe epilepsy.}, }
@article {pmid40425030, year = {2025}, author = {Dehgan, A and Abdelhedi, H and Hadid, V and Rish, I and Jerbi, K}, title = {Artificial neural networks for magnetoencephalography: a review of an emerging field.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/addd4a}, pmid = {40425030}, issn = {1741-2552}, mesh = {*Magnetoencephalography/methods/trends ; Humans ; *Neural Networks, Computer ; *Brain/physiology ; Machine Learning/trends ; }, abstract = {Objective. Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. While MEG data analytics have traditionally relied on advanced signal processing and mathematical and statistical tools, the recent surge in artificial intelligence has led to the growing use of machine learning (ML) methods for MEG data classification. An emerging trend in this field is the use of artificial neural networks (ANNs) to address various MEG-related tasks. This review aims to provide a comprehensive overview of the state of the art in this area.Approach. This topical review included studies that applied ANNs to MEG data. Studies were sourced from PubMed, Google Scholar, arXiv, and bioRxiv using targeted search queries. The included studies were categorized into three groups: 'Classification', 'Modeling', and 'Other'. Key findings and trends were summarized to provide a comprehensive assessment of the field.Main results. We identified 119 relevant studies, with 70 focused on 'Classification', 16 on 'Modeling', and 33 in the 'Other' category. 'Classification' studies addressed tasks such as brain decoding, clinical diagnostics, and brain-computer interfaces implementations, often achieving high predictive accuracy. 'Modeling' studies explored the alignment between ANN activations and brain processes, offering insights into the neural representations captured by these networks. The 'Other' category demonstrated innovative uses of ANNs for artifact correction, preprocessing, and neural source localization.Significance. By establishing a detailed portrait of the current state of the field, this review highlights the strengths and current limitations of ANNs in MEG research. It also provides practical recommendations for future work, offering a helpful reference for seasoned researchers and newcomers interested in using ANNs to explore the complex dynamics of the human brain with MEG.}, }
@article {pmid40425024, year = {2025}, author = {Wolpaw, JR}, title = {Making brain-computer interfaces as reliable as muscles.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/addd47}, pmid = {40425024}, issn = {1741-2552}, support = {I01 CX001812/CX/CSRD VA/United States ; I01 BX002550/BX/BLRD VA/United States ; R01 NS069551/NS/NINDS NIH HHS/United States ; R01 HD036020/HD/NICHD NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; P01 HD032571/HD/NICHD NIH HHS/United States ; R01 NS061823/NS/NINDS NIH HHS/United States ; R01 NS022189/NS/NINDS NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Muscle, Skeletal/physiology ; *Brain/physiology ; Reproducibility of Results ; }, abstract = {Objective.While brain-computer interfaces (BCIs) can restore basic communication to people lacking muscle control, they cannot yet restore actions that require the extremely high reliability of natural (i.e. muscle-based) actions. Most BCI research focuses on neural engineering; it seeks to improve the measurement and analysis of brain signals. But neural engineering alone cannot make BCIs reliable.Approach.A BCI does not simply decode brain activity; it enables its user to acquire a skill that is produced not by nerves and muscles but rather by the BCI. Thus, BCI research should focus also on neuroscience; it should seek to develop BCI skills that emulate natural skills.Main results.A natural skill is produced by a network of neurons and synapses that may extend from cortex to spinal cord. This network has been given the nameheksor, from the ancient Greek wordhexis. A heksor changes through life; it modifies itself as needed to maintain the key features of its skill, the attributes that make the skill satisfactory. Heksors overlap; they share neurons and synapses. Through their concurrent changes, heksors keep neuronal and synaptic properties in anegotiated equilibriumthat enables each to produce its skill satisfactorily. A BCI-based skill is produced by asynthetic heksor, a network of neurons, synapses, and software that produces a BCI-based skill and should change as needed to maintain the skill's key features.Significance.A synthetic heksor shares neurons and synapses with natural heksors. Like natural heksors, it can benefit from multimodal sensory feedback, using signals from multiple brain areas, and maintaining the skill's key features rather than all its details. A synthetic heksor also needs successful co-adaptation between its central nervous system and software components and successful integration into the negotiated equilibrium that heksors establish and maintain. With due attention to both neural engineering and neuroscience, BCIs could become as reliable as muscles.}, }
@article {pmid40425023, year = {2025}, author = {Wu, D}, title = {Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/addd49}, pmid = {40425023}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Machine Learning ; Algorithms ; *Transfer, Psychology/physiology ; }, abstract = {Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.}, }
@article {pmid40424668, year = {2025}, author = {Sawyer, A and Brannigan, J and Spielman, L and ,  and Putrino, D and Fry, A}, title = {Development of a novel clinical outcome assessment: digital instrumental activities of daily living.}, journal = {EBioMedicine}, volume = {116}, number = {}, pages = {105732}, pmid = {40424668}, issn = {2352-3964}, mesh = {Humans ; *Activities of Daily Living ; *Outcome Assessment, Health Care/methods ; Delphi Technique ; Surveys and Questionnaires ; Focus Groups ; Male ; Female ; }, abstract = {BACKGROUND: Digital technology is integral to activities of daily living, particularly instrumental activities of daily living (IADLs). However, tools that accommodate digital performance of IADLs are lacking. The aim of this study was to develop a novel Digital IADL Scale.<br><br>METHODS: The multi-stage methodology included: (i) deductive item generation via a systematic review and assignment to domains using a Delphi process, (ii) inductive item generation via a survey of individuals with lived experience (IWLE) of severe paralysis, (iii) item refinement via item rating surveys of content experts and IWLE, and (iv) focus group discussions with key opinion leaders.<br><br>FINDINGS: The systematic review identified 1250 IADL items from validated IADL measures, of which 353 met criteria. Deduplication reduced the deductive item set to 77, of which 42 remained following the Delphi process. IWLE generated 152 items, of which 132 met criteria. Deduplication reduced the inductive item set to 41. The combined item pool was reduced to 69 following the item rating surveys. Following focus group feedback, a list of nine domains, containing 37 items, and suggested response scale options are presented.<br><br>INTERPRETATION: We describe the initial development of a scale to assess functional independence within IADLs that may be completed digitally, which will be submitted to further validation.<br><br>FUNDING: Support for this project was provided in kind by the Abilities Research Center. No formal funding was received.}, }
@article {pmid40423756, year = {2025}, author = {Moeller, A and Andres Porras, JM}, title = {Human enhancement, past and present.}, journal = {Monash bioethics review}, volume = {}, number = {}, pages = {}, pmid = {40423756}, issn = {1836-6716}, abstract = {One important role the medical humanities might and should play relates to public education. In this instance, we mean helping persons to think about their own aims or purposes as potential receivers of enhancement interventions, and similarly helping to inform the developers of said interventions. This article argues that, in the light of real and speculative applications of emerging biotechnologies and artificial intelligence aimed at human enhancement-including germline genetic engineering, the linking of the human brain with an artificial general intelligence by way of a brain-computer interface, and various interventions directed toward life extension-historians would do well to consider the following three practices as they participate in the medical humanities and the shared task of public education: (1) Taking under scrutiny a broad swath of topics and timeframes as it relates to past efforts aimed at human enhancement; (2) Focusing on past engagement with enhancement efforts and their perceived relation to the pursuit of living well; and (3) Entering into debates on enhancement as equal participants. In support of these assertions, this article takes efforts directed towards the prolongation of life in medieval Europe as an illustrative example. It also highlights continuities and discontinuities between past and present justifications for human enhancement, and addresses how similarities and differences can shape and challenge contemporary bioethical arguments.}, }
@article {pmid40423554, year = {2025}, author = {Brackman, KN and Taychert, MT and Serrell, EC and Gralnek, D and Manakas, C and Knoedler, M and Antar, A and Allen, GO and Grimes, MD}, title = {Clinical Outcomes of Holmium Laser Enucleation of the Prostate in Patients With Diminished Bladder Contractility.}, journal = {Urology practice}, volume = {12}, number = {5}, pages = {524-532}, pmid = {40423554}, issn = {2352-0787}, support = {K12 DK100022/DK/NIDDK NIH HHS/United States ; }, mesh = {Humans ; Male ; *Lasers, Solid-State/therapeutic use ; Retrospective Studies ; *Prostatic Hyperplasia/surgery/complications ; Aged ; *Urinary Bladder Neck Obstruction/surgery/etiology/physiopathology ; Treatment Outcome ; *Prostatectomy/methods ; *Urinary Bladder/physiopathology ; Middle Aged ; Urodynamics ; Aged, 80 and over ; }, abstract = {INTRODUCTION: Bladder outlet obstruction (BOO) due to benign prostatic hyperplasia (BPH) is common in aging men and can be treated with holmium laser enucleation of the prostate (HoLEP). However, diminished bladder contractility (DC) is also highly prevalent (9%-48%) and can be clinically indistinguishable from BOO without urodynamics (UDS). While HoLEP effectively treats BPH/BOO, clinical outcomes data for patients with DC are limited and mixed. We aim to compare the prevalence and risk factors of catheter dependence among patients with and without DC after HoLEP.<br><br>METHODS: A retrospective cohort study was conducted on 179 patients with preoperative UDS who underwent HoLEP between June 2018 and December 2023. Diminished contractility was defined as Bladder Contractility Index (BCI) < 100. Statistical analyses included univariate and multivariate logistic regression.<br><br>RESULTS: Among 179 patients, 103 (57.5%) had DC (BCI < 100). After HoLEP, all patients with normal contractility (NC) were voiding while 7.8% of patients with DC were catheter dependent (P = .01) at a mean follow-up of 28 months. Preoperative BCI was associated with post-HoLEP catheter dependence (OR = 0.97, 95% CI 0.95-1.00, P = .046). Postoperative International Prostate Symptom Scores were significantly higher in DC compared with NC groups despite similar preoperative scores.<br><br>CONCLUSIONS: HoLEP rendered 95.5% (171/179) of patients catheter free. However, patients with DC were more likely to require catheterization postoperatively and reported worse urinary symptoms compared with patients with NC. Our results support obtaining UDS when there is clinical concern for DC because this may guide shared decision-making before pursuing HoLEP.}, }
@article {pmid40422053, year = {2025}, author = {Avital, N and Shulkin, N and Malka, D}, title = {Automatic Calculation of Average Power in Electroencephalography Signals for Enhanced Detection of Brain Activity and Behavioral Patterns.}, journal = {Biosensors}, volume = {15}, number = {5}, pages = {}, pmid = {40422053}, issn = {2079-6374}, mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Female ; Adult ; Young Adult ; }, abstract = {Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain-computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration.}, }
@article {pmid40421845, year = {2025}, author = {Pizzolante, S and Covelli, E and Filippi, C and Barbara, M}, title = {Percutaneous Bone Implant Surgery: A MIPS Modified Technique.}, journal = {The Laryngoscope}, volume = {135}, number = {9}, pages = {3378-3381}, pmid = {40421845}, issn = {1531-4995}, mesh = {Humans ; *Hearing Aids ; *Minimally Invasive Surgical Procedures/methods ; *Prosthesis Implantation/methods ; }, abstract = {Since their introduction, passive percutaneous hearing aids have undergone substantial evolution, including changes in implant production, improvements in the sound processor, and simplification of surgical implantation techniques. The latest innovation comes from the minimally invasive technique proposed for the PONTO system (MIPS), which does not involve the creation of a mucoperiosteal flap in order to leave the surrounding soft tissue and vascular microcirculation intact. This study proposes a modified surgical technique compared to the one proposed for the PONTO system in order to overcome some steps of the traditional surgical technique for the placement of the Baha Connect prosthesis. Our technique does not involve any incision but the exposure of the periosteum using a skin punch and subsequent drilling without the use of any protective cannula. The described procedure allows one to overcome some steps of the traditional surgical technique and, consequently, also some post-operative complications. Moreover, a minimally invasive procedure can help reduce surgical time and the invasiveness of the application.}, }
@article {pmid40420994, year = {2025}, author = {Esteves, D and Valente, M and Bendor, SE and Andrade, A and Vourvopoulos, A}, title = {Identifying EEG biomarkers of sense of embodiment in virtual reality: insights from spatio-spectral features.}, journal = {Frontiers in neuroergonomics}, volume = {6}, number = {}, pages = {1572851}, pmid = {40420994}, issn = {2673-6195}, abstract = {The Sense of Embodiment (SoE) refers to the subjective experience of perceiving a non-biological body part as one's own. Virtual Reality (VR) provides a powerful platform to manipulate SoE, making it a crucial factor in immersive human-computer interaction. This becomes particularly relevant in Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), especially motor imagery (MI)-BCIs, which harness brain activity to enable users to control virtual avatars in a self-paced manner. In such systems, a strong SoE can significantly enhance user engagement, control accuracy, and the overall effectiveness of the interface. However, SoE assessment remains largely subjective, relying on questionnaires, as no definitive EEG biomarkers have been established. Additionally, methodological inconsistencies across studies introduce biases that hinder biomarker identification. This study aimed to identify EEG-based SoE biomarkers by analyzing frequency band changes in a combined dataset of 41 participants under standardized experimental conditions. Participants underwent virtual SoE induction and disruption using multisensory triggers, with a validated questionnaire confirming the illusion. Results revealed a significant increase in Beta and Gamma power over the occipital lobe, suggesting these as potential EEG biomarkers for SoE. The findings underscore the occipital lobe's role in multisensory integration and sensorimotor synchronization, supporting the theoretical framework of SoE. However, no single frequency band or brain region fully explains SoE. Instead, it emerges as a complex, dynamic process evolving across time, frequency, and spatial domains, necessitating a comprehensive approach that considers interactions across multiple neural networks.}, }
@article {pmid40420178, year = {2025}, author = {Jiang, M and Luo, Q and Wang, X and Qu, D}, title = {Semantic radicals' semantic attachment to their composed phonograms.}, journal = {BMC psychology}, volume = {13}, number = {1}, pages = {559}, pmid = {40420178}, issn = {2050-7283}, support = {20BYY095//National Social Science Fund of China/ ; 2019YBYY131//Chongqing Social Science Planning Fund/ ; 22SKGH236//Humanities and Social Sciences Research Project Fund of Chongqing Municipal Education Commission/ ; }, mesh = {Humans ; *Semantics ; Female ; Male ; Young Adult ; Reaction Time ; Adult ; Decision Making ; *Reading ; }, abstract = {In Chinese character processing studies, it is widely accepted that semantic radicals, whether character or non-character ones, can undergo semantic activation. However, there is a notable absence of studies dedicated to understanding the nature and operation of the semantic radicals' semantic information. To address this gap, the present study employed a masked semantic priming paradigm combined with a part-of-speech decision task and a lexical decision task across three experiments. Experiment 1 was designed to examine the semantic autonomy and the semantic attachment of semantic radicals in transparent phonograms. Experiment 2 sought to further investigate the degree of semantic autonomy of semantic radicals in opaque phonograms. Experiment 3 was crafted to further probe into the presence of semantic attachment of semantic radicals in pseudo-characters. Results showed significant priming effects in both transparent and opaque phonogram conditions, with faster reaction times and higher accuracy for semantically related prime-target pairs. However, no such priming effect was observed in the pseudo-character condition, indicating that semantic radicals are not activated in non-lexical contexts. These findings suggest that semantic radicals were semantically activated when embedded in both transparent and opaque phonograms, but not when planted in pseudo-characters. The plausible account put forward is that semantic radicals stand on pars with their composed phonograms in possessing their own semantic information, but the former is semantically strongly attached to the latter, such that it cannot live without the latter's semantic company.}, }
@article {pmid40419791, year = {2025}, author = {Chen, Y and Ding, K and Zheng, S and Gao, S and Xu, X and Wu, H and Zhou, F and Wang, Y and Xu, J and Wang, C and Ling, C and Xu, J and Wang, L and Wu, Q and Giamas, G and Chen, G and Zhang, J and Yi, C and Ji, J}, title = {Post-translational modifications in DNA damage repair: mechanisms underlying temozolomide resistance in glioblastoma.}, journal = {Oncogene}, volume = {44}, number = {23}, pages = {1781-1792}, pmid = {40419791}, issn = {1476-5594}, support = {82203035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82403931//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Glioblastoma/drug therapy/genetics/pathology ; *Temozolomide/therapeutic use/pharmacology ; *Drug Resistance, Neoplasm/genetics ; *DNA Repair/drug effects ; *Protein Processing, Post-Translational/drug effects ; *DNA Damage ; *Antineoplastic Agents, Alkylating/therapeutic use/pharmacology ; *Brain Neoplasms/drug therapy/genetics/pathology ; Animals ; }, abstract = {Temozolomide (TMZ) resistance is one of the critical factors contributing to the poor prognosis of glioblastoma (GBM). As a first-line chemotherapeutic agent for GBM, TMZ exerts its cytotoxic effects through DNA alkylation. However, its therapeutic efficacy is significantly compromised by enhanced DNA damage repair (DDR) mechanisms in GBM cells. Although several DDR-targeting drugs have been developed, their clinical outcomes remain suboptimal. Post-translational modifications (PTMs) in GBM cells play a pivotal role in maintaining the genomic stability of DDR mechanisms, including methylguanine-DNA methyltransferase-mediated repair, DNA mismatch repair dysfunction, base excision repair, and double-strand break repair. This review focuses on elucidating the regulatory roles of PTMs in the intrinsic mechanisms underlying TMZ resistance in GBM. Furthermore, we explore the feasibility of enhancing TMZ-induced cytotoxicity by targeting PTM-related enzymatic to disrupt key steps in PTM-mediated DDR pathways. By integrating current preclinical insights and clinical challenges, this work highlights the potential of modulating PTM-driven networks as a novel therapeutic strategy to overcome TMZ resistance and improve treatment outcomes for GBM patients.}, }
@article {pmid40419502, year = {2025}, author = {Rajabi, N and Zanettin, I and Ribeiro, AH and Vasco, M and Björkman, M and Lundström, JN and Kragic, D}, title = {Exploring the feasibility of olfactory brain-computer interfaces.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {18404}, pmid = {40419502}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Odorants/analysis ; Male ; Adult ; Female ; *Smell/physiology ; Feasibility Studies ; Neural Networks, Computer ; Young Adult ; *Olfactory Perception/physiology ; *Brain/physiology ; }, abstract = {In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel electrobulbogram (EBG) recordings, both in sensor and source space, and compare these with commonly used electroencephalogram (EEG) signals. Despite having fewer data channels, EBG shows comparable performance to EEG. We also examine whether breathing patterns contain relevant information for this task. By comparing a logistic regression classifier, which requires hand-crafted features, with an end-to-end convolutional deep neural network, we find that end-to-end approaches can be as effective as classic methods. However, due to the high dimensionality of the data, the current dataset is insufficient for either classifier to robustly differentiate odor and non-odor trials. Finally, we identify key challenges in olfactory BCIs and suggest future directions for improving odor detection systems.}, }
@article {pmid40419488, year = {2025}, author = {Wang, D and Xue, H and Xia, L and Li, Z and Zhao, Y and Fan, X and Sun, K and Wang, H and Hamalainen, T and Zhang, C and Cong, F and Li, Y and Song, F and Lin, J}, title = {A tough semi-dry hydrogel electrode with anti-bacterial properties for long-term repeatable non-invasive EEG acquisition.}, journal = {Microsystems &amp; nanoengineering}, volume = {11}, number = {1}, pages = {105}, pmid = {40419488}, issn = {2055-7434}, support = {2022 ZD0210700//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; }, abstract = {Non-invasive brain-computer interfaces (NI-BCIs) have garnered significant attention due to their safety and wide range of applications. However, developing non-invasive electroencephalogram (EEG) electrodes that are highly sensitive, comfortable to wear, and reusable has been challenging due to the limitations of conventional electrodes. Here, we introduce a simple method for fabricating semi-dry hydrogel EEG electrodes with antibacterial properties, enabling long-term, repeatable acquisition of EEG. By utilizing N-acryloyl glycinamide and hydroxypropyltrimethyl ammonium chloride chitosan, we have prepared electrodes that not only possess good mechanical properties (compression modulus 65 kPa) and anti-fatigue properties but also exhibit superior antibacterial properties. These electrodes effectively inhibit the growth of both Gram-negative (E. coli) and Gram-positive (S. epidermidis) bacteria. Furthermore, the hydrogel maintains stable water retention properties, resulting in an average contact impedance of <400 Ω measured over 12 h, and an ionic conductivity of 0.39 mS cm[-1]. Cytotoxicity and skin irritation tests have confirmed the high biocompatibility of the hydrogel electrodes. In an N170 event-related potential (ERP) test on human volunteers, we successfully captured the expected ERP signal waveform and a high signal-to-noise ratio (20.02 dB), comparable to that of conventional wet electrodes. Moreover, contact impedance on the scalps remained below 100 kΩ for 12 h, while wet electrodes became unable to detect signals after 7-8 h due to dehydration. In summary, our hydrogel electrodes are capable of detecting ERPs over extended periods in an easy-to-use manner with antibacterial properties. This reduces the risk of bacterial infection associated with prolonged reuse and expands the potential of NI-BCIs in daily life.}, }
@article {pmid40419083, year = {2025}, author = {Chen, L and Zhang, L and Wang, Z and Li, Q and Gu, B and Ming, D}, title = {Task-related reconfiguration patterns of frontoparietal network during motor imagery.}, journal = {Neuroscience}, volume = {579}, number = {}, pages = {302-311}, doi = {10.1016/j.neuroscience.2025.05.035}, pmid = {40419083}, issn = {1873-7544}, mesh = {Humans ; Male ; Female ; *Imagination/physiology ; *Parietal Lobe/physiology ; Adult ; Young Adult ; *Frontal Lobe/physiology ; Electroencephalography ; *Brain Waves/physiology ; *Nerve Net/physiology ; *Motor Activity/physiology ; }, abstract = {Motor imagery (MI) is closely associated with the frontoparietal network that includes prefrontal and posterior parietal regions. Studying task-related network reconfiguration after brain shifts from the resting state to the MI task is an important way to understand the brain's response process. However, how the brain modulates functional connectivity of the frontoparietal network when it shifts to MI has not been thoroughly studied. In this study, we attempted to characterize the frontoparietal network reconfiguration patterns as the brain transitioned to motor imagery tasks. We performed the analysis using EEG signals from 52 healthy subjects during left- and right-hand MI tasks. The results indicated distinct reconfiguration patterns in the frontoparietal network across four typical brain wave rhythms (theta (4 ∼ 7 Hz), alpha (8 ∼ 13 Hz), beta (14 ∼ 30 Hz), and gamma (31 ∼ 45 Hz)). Meanwhile, there was a significant positive correlation between the frontoparietal network reconfiguration and the event-related desynchronization of alpha and beta rhythms in the sensorimotor cortex. We further found that subjects with better MI-BCI performance exhibited greater reconfiguration of the frontoparietal network in alpha and beta rhythms. These findings implied that MI was accompanied by a shift in information interaction between brain regions, which might contribute to understanding the neural mechanisms of MI.}, }
@article {pmid40418615, year = {2025}, author = {Song, Y and Wang, Y and He, H and Gao, X}, title = {Recognizing Natural Images From EEG With Language-Guided Contrastive Learning.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {36}, number = {9}, pages = {15896-15910}, doi = {10.1109/TNNLS.2025.3562743}, pmid = {40418615}, issn = {2162-2388}, mesh = {Humans ; *Electroencephalography/methods ; *Language ; Semantics ; *Machine Learning ; Neural Networks, Computer ; Algorithms ; *Image Processing, Computer-Assisted/methods ; *Pattern Recognition, Automated/methods ; Signal-To-Noise Ratio ; }, abstract = {Electroencephalography (EEG), known for its convenient noninvasive acquisition but moderate signal-to-noise ratio, has recently gained much attention due to the potential to decode image information. However, previous works have not delivered sufficient evidence of this task, primarily limited by performance and biological plausibility. In this work, we first introduce a self-supervised framework to demonstrate the feasibility of recognizing images from EEG signals. Contrastive learning is leveraged to align the representations of EEG responses with image stimuli. Then, language descriptions of the stimuli generated by large language models (LLMs) help guide learning core semantic information. With the framework, we attain significantly above-chance results on the THINGS-EEG2 dataset, achieving a top-1 accuracy of 19.7% and a top-5 accuracy of 51.5% in challenging 200-way zero-shot tasks. Furthermore, we conduct thorough experiments to resolve the human visual responses with EEG from temporal, spatial, spectral, and semantic perspectives. These results provide evidence of feasibility and plausibility regarding EEG-based image recognition, substantiated by comparative studies with the THINGS-Magnetoencephalography (MEG) dataset. The findings offer valuable insights for neural decoding and real-world applications of brain-computer interfaces (BCIs), such as health care and robot control. The code is available at https://github.com/eeyhsong/NICE-LLM.}, }
@article {pmid40416647, year = {2025}, author = {Teng, Y and Song, L and Shi, J and Lv, Q and Hou, S and Ramakrishna, S}, title = {Advancing electrospinning towards the future of biomaterials in biomedical engineering.}, journal = {Regenerative biomaterials}, volume = {12}, number = {}, pages = {rbaf034}, pmid = {40416647}, issn = {2056-3418}, abstract = {Biomaterial is a material designed to take a form that can direct, through interactions with living systems, the course of any therapeutic or diagnostic procedure. Growing demand for improved and affordable healthcare treatments and unmet clinical needs seek further advancement of biomaterials. Over the past 25 years, the electrospinning method has been innovated to enhance biomaterials at nanometer and micrometer length scales for diverse healthcare applications. Recent developments include intelligent (smart) biomaterials and sustainable biomaterials. Intelligent materials can sense, adapt to and respond to external stimuli, autonomously adjusting to enhance functionality and performance. Sustainable biomaterials possess several key characteristics, including renewability, a low carbon footprint, circularity, durability, biocompatibility, biodegradability and others. Herein, advances in electrospun biomaterials, encompassing process innovations, working principles and the effects of process variables, are presented succinctly. The potential of electrospun intelligent biomaterials and sustainable biomaterials in specific biomedical applications, including tissue engineering, regenerative medicine, drug delivery systems, brain-computer interfaces, biosensors, personal protective equipment and wearable devices, is explored. More effective healthcare demands further advancements in electrospun biomaterials. In the future, the distinctive characteristics of intelligent biomaterials and sustainable biomaterials, integrated with various emerging technologies (such as AI and data transmission), will enable physicians to conduct remote diagnosis and treatment. This advancement significantly enhances telemedicine capabilities for more accurate disease prediction and management.}, }
@article {pmid40416500, year = {2025}, author = {Mokienko, OA}, title = {The Potential of Near-Infrared Spectroscopy as a Therapeutic Tool Following a Stroke (Review).}, journal = {Sovremennye tekhnologii v meditsine}, volume = {17}, number = {2}, pages = {73-83}, pmid = {40416500}, issn = {2309-995X}, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology/therapy/diagnosis/diagnostic imaging ; }, abstract = {The advancement of novel technologies for the rehabilitation of post-stroke patients represents a significant challenge for a range of interdisciplinary fields. Near-infrared spectroscopy (NIRS) is an optical neuroimaging technique based on recording local hemodynamic changes at the cerebral cortex level. The technology is typically employed in post-stroke patients for diagnostic purposes, including the assessment of neuroplastic processes accompanying therapy, the study of hemispheric asymmetry, and the examination of functional brain networks. However, functional NIRS can also be used for therapeutic purposes, including the provision of biofeedback during rehabilitation tasks, as well as the navigation method during transcranial stimulation. The effectiveness of therapeutic NIRS application in stroke patients remains insufficiently studied, despite existing scientific evidence confirming its promising potential as a treatment method. The review examines the published literature on the therapeutic applications of NIRS after stroke, evaluating its potential role in the rehabilitation process. The paper describes NIRS features, advantages, and disadvantages, determining its position among other neuroimaging technologies; analyzes the findings of neurophysiological studies, which justified the clinical trials of NIRS technology; and evaluates the results of the studies on the therapeutic use of NIRS in post-stroke patients. Two potential applications of NIRS for therapeutic purposes following a stroke were suggested: the first was to provide real-time feedback during movement training (motor or ideomotor ones, including that in brain-computer interface circuits), and the second was to facilitate navigation during transcranial stimulation. Based on a comprehensive literature review, there were proposed and justified further research lines and development in this field.}, }
@article {pmid40414967, year = {2025}, author = {Qian, L and Jia, C and Wang, J and Shi, L and Wang, Z and Wang, S}, title = {The dynamics of stimulus selection in the nucleus isthmi pars magnocellularis of avian midbrain network.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {18260}, pmid = {40414967}, issn = {2045-2322}, support = {2024M752934//China Postdoctoral Science Foundation/ ; }, mesh = {Animals ; *Mesencephalon/physiology ; Neurons/physiology ; Photic Stimulation ; *Nerve Net/physiology ; Birds/physiology ; }, abstract = {The nucleus isthmi pars magnocellularis (Imc) serves as a critical node in the avian midbrain network for encoding stimulus salience and selection. While reciprocal inhibitory projections among Imc neurons (inhibitory loop) are known to govern stimulus selection, existing studies have predominantly focused on stimulus selection under stimuli of constant relative intensity. However, animals typically encounter complex and changeable visual scenes. Thus, how Imc neurons represent stimulus selection under varying relative stimulus intensities remains unclear. Here, we examined the dynamics of stimulus selection by in vivo recording of Imc neurons' responses to spatiotemporally successive visual stimuli divided into two segments: the previous stimulus and the post stimulus. Our data demonstrate that Imc neurons can encode sensory memory of the previous stimulus, which modulates competition and salience representation in the post stimulus. This history-dependent modulation is also manifested in persistent neural activity after stimulus cessation. We identified, through neural tracing, focal inactivation, and computational modeling experiments, projections from the nucleus isthmi pars parvocellularis (Ipc) to "shepherd's crook" (Shc) neurons, which could be either direct or indirect. These projections enhance Imc neurons' responses and persistent neural activity after stimulus cessation. This connectivity supports a Shc-Ipc-Shc excitatory loop in the midbrain network. The coexistence of excitatory and inhibitory loops provides a neural substrate for continuous attractor network models, a proposed framework for neural information representation. This study also offers a potential explanation for how animals maintain short-term attention to targets in complex and changeable environments.}, }
@article {pmid40414233, year = {2025}, author = {Paton, NI and Cousins, C and Sari, IP and Burhan, E and Ng, NK and Dalay, VB and Suresh, C and Kusmiati, T and Chew, KL and Balanag, VM and Lu, Q and Ruslami, R and Djaharuddin, I and Sugiri, JJR and Veto, RS and Sekaggya-Wiltshire, C and Avihingsanon, A and Saini, JK and Papineni, P and Nunn, AJ and Crook, AM and , }, title = {Efficacy and safety of 8-week regimens for the treatment of rifampicin-susceptible pulmonary tuberculosis (TRUNCATE-TB): a prespecified exploratory analysis of a multi-arm, multi-stage, open-label, randomised controlled trial.}, journal = {The Lancet. Infectious diseases}, volume = {25}, number = {10}, pages = {1084-1096}, pmid = {40414233}, issn = {1474-4457}, support = {/WT_/Wellcome Trust/United Kingdom ; }, mesh = {Humans ; *Rifampin/therapeutic use/administration &amp; dosage/adverse effects ; Adult ; Male ; Female ; Middle Aged ; *Tuberculosis, Pulmonary/drug therapy/microbiology ; *Antitubercular Agents/administration &amp; dosage/therapeutic use/adverse effects ; Treatment Outcome ; Young Adult ; Aged ; Adolescent ; Pyrazinamide/therapeutic use/administration &amp; dosage ; Drug Therapy, Combination ; Isoniazid/therapeutic use/administration &amp; dosage ; Ethambutol/therapeutic use/administration &amp; dosage ; Drug Administration Schedule ; Linezolid/administration &amp; dosage/therapeutic use ; Diarylquinolines/administration &amp; dosage/therapeutic use ; Mycobacterium tuberculosis/drug effects ; Clofazimine/administration &amp; dosage/therapeutic use ; }, abstract = {BACKGROUND: WHO recommends a 2-month optimal duration for new drug regimens for rifampicin-susceptible tuberculosis. We aimed to investigate the efficacy and safety of the 8-week regimens that were assessed as part of the TRUNCATE management strategy of the TRUNCATE-TB trial.<br><br>METHODS: TRUNCATE-TB was a multi-arm, multi-stage, open-label, randomised controlled trial in which participants aged 18-65 years with rifampicin-susceptible pulmonary tuberculosis were randomly assigned via a web-based system, using permuted blocks, to 24-week standard treatment (rifampicin, isoniazid, pyrazinamide, and ethambutol) or the TRUNCATE management strategy comprising initial 8-week treatment, then post-treatment monitoring and re-treatment where needed. The four 8-week regimens comprised five drugs, modified from standard treatment: high-dose rifampicin and linezolid, or high-dose rifampicin and clofazimine, or bedaquiline and linezolid, all given with isoniazid, pyrazinamide, and ethambutol; and rifapentine, linezolid, and levofloxacin, given with isoniazid and pyrazinamide. Here, we report the efficacy (proportion with unfavourable outcome; and difference from standard treatment, assessed via Bayesian methods) and safety of the 8-week regimens, assessed in the intention-to-treat population. This prespecified exploratory analysis is distinct from the previously reported 96-week outcome of the strategy in which the regimens were deployed. This trial is registered with ClinicalTrials.gov (NCT03474198).<br><br>FINDINGS: Between March 21, 2018, and March 26, 2020, 675 participants (674 in the intention-to-treat population) were enrolled and randomly assigned to the standard treatment group or one of the four 8-week regimen groups. Two 8-week regimens progressed to full enrolment. An unfavourable outcome (mainly relapse) occurred in seven (4%) of 181 participants on standard treatment; 46 (25%) of 184 on the high-dose rifampicin and linezolid-containing regimen (adjusted difference 21&#xb7;0%, 95% Bayesian credible interval [BCI] 14&#xb7;3-28&#xb7;1); and 26 (14%) of 189 on the bedaquiline and linezolid-containing regimen (adjusted difference 9&#xb7;3% [4&#xb7;3-14&#xb7;9]). Grade 3-4 adverse events occurred in 24 (14%) of 181 participants on standard treatment, 20 (11%) of 184 on the rifampicin-linezolid regimen, and 22 (12%) of 189 on the bedaquiline-linezolid regimen.<br><br>INTERPRETATION: Efficacy was worse with 8-week regimens, although the difference from standard treatment varied between regimens. Even the best 8-week regimen (bedaquiline-linezolid) should only be used as part of a management strategy involving post-treatment monitoring and re-treatment if necessary.<br><br>FUNDING: Singapore National Medical Research Council; UK Department of Health and Social Care; UK Foreign, Commonwealth, and Development Office; UK Medical Research Council; Wellcome Trust; and UK Research and Innovation Medical Research Council.}, }
@article {pmid40411529, year = {2025}, author = {Sun, Y and Guan, M and Chen, X and Feng, F and He, R and Huang, L and Tong, X and Zhou, H and Liu, X and Ming, D}, title = {Deep learning-based classification and segmentation of interictal epileptiform discharges using multichannel electroencephalography.}, journal = {Epilepsia}, volume = {66}, number = {9}, pages = {3398-3410}, doi = {10.1111/epi.18463}, pmid = {40411529}, issn = {1528-1167}, support = {020/0903065111//Tianjin University Innovation Fund/ ; 2021YFF1200602//National Key Technologies Research and Development Program/ ; c02022088//National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; }, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods/classification ; *Epilepsy/physiopathology/diagnosis/classification ; Male ; Adult ; }, abstract = {OBJECTIVE: This study was undertaken to develop a deep learning framework that can classify and segment interictal epileptiform discharges (IEDs) in multichannel electroencephalographic (EEG) recordings with high accuracy, preserving both spatial information and interchannel interactions.<br><br>METHODS: We proposed a novel deep learning framework, U-IEDNet, for detecting IEDs in multichannel EEG. The U-IEDNet framework employs convolutional layers and bidirectional gated recurrent units as a temporal encoder to extract temporal features from single-channel EEG, followed by the use of transformer networks as a spatial encoder to fuse multichannel features and extract interchannel interaction information. Transposed convolutional layers form a temporal decoder, creating a U-shaped architecture with the encoder. This upsamples features to estimate the probability of each EEG sampling point falling within the IED range, enabling segmentation of IEDs from background activity. Two datasets, a public database with 370 patient recordings and our own annotated database with 43 patient recordings, were used for model establishment and validation.<br><br>RESULTS: The results showed prominent advantage compared with other methods. U-IEDNet achieved a recall of .916, precision of .911, F1-score of .912, and false positive rate (FPR) of .030 on the public database. The classification performance in our own annotated database achieved a recall of .905, a precision of .902, an F1-score of .903, and an FPR of .072. The segmentation performance had a recall of .903, a precision of .916, and an F1-score of .909. Additionally, this study analyzes attention weights in the transformer network based on brain network theory to elucidate the spatial feature fusion process, enhancing the interpretability of the IED detection model.<br><br>SIGNIFICANCE: In this paper, we aim to present an artificial intelligence-based toolbox for IED detection, which may facilitate epilepsy diagnosis at the bedside in the future. U-IEDNet demonstrates great potential to improve the accuracy and efficiency of IED detection in multichannel EEG recordings.}, }
@article {pmid40409524, year = {2025}, author = {Li, Y and Pan, Y and Zhao, D}, title = {Understanding the Neurobiology and Computational Mechanisms of Social Conformity: Implications for Psychiatric Disorders.}, journal = {Biological psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.biopsych.2025.05.011}, pmid = {40409524}, issn = {1873-2402}, abstract = {Social conformity and psychiatric disorders share overlapping brain regions and neural pathways, arousing our interest in uncovering their potentially shared underlying neural and computational mechanisms. Critically, the dynamics of group behavior may either mitigate or exacerbate mental health conditions, highlighting the need to bridge social neuroscience and psychiatry. Our work examines how aberrant neurobiological circuits and computations influence social conformity. We propose a hierarchical computational framework, based on dynamic systems and active inference, to facilitate the interpretation of the multilayered interplay among processes that drive social conformity. We underscore the significant implications of this hierarchical computational framework for guiding future research on psychiatry, particularly with respect to the clinical translation of interventions such as targeted pharmacotherapy and neurostimulation techniques. Interdisciplinary efforts hold the potential to propel the fields of social and clinical neuroscience forward, fostering the emergence of more efficacious and individualized therapeutic approaches tailored to psychiatric disorders characterized by aberrant social behaviors.}, }
@article {pmid40408764, year = {2025}, author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C}, title = {Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.}, journal = {Journal of medical Internet research}, volume = {27}, number = {}, pages = {e71741}, pmid = {40408764}, issn = {1438-8871}, mesh = {Humans ; *Students, Nursing/psychology ; *Mindfulness/methods ; Female ; Male ; *Mental Health ; China ; *Neurofeedback/methods ; Adult ; Young Adult ; Anxiety/therapy ; *Internet ; Depression/therapy ; *Internet-Based Intervention ; East Asian People ; }, abstract = {BACKGROUND: Nursing students experience disproportionately high rates of mental health challenges, underscoring the urgent need for innovative, scalable interventions. Web-based mindfulness programs, and more recently, neurofeedback-enhanced approaches, present potentially promising avenues for addressing this critical issue.<br><br>OBJECTIVE: This study aimed to explore the effectiveness of the neurofeedback-assisted online mindfulness intervention (NAOM) and the conventional online mindfulness intervention (COM) in reducing mental health symptoms among Chinese nursing students.<br><br>METHODS: A 3-armed randomized controlled trial was conducted among 147 nursing students in Beijing, China, using a 6-week web-based mindfulness program. Participants received NAOM, COM, or general mental health education across 6 weeks. Electroencephalogram and validated tools such as the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to primarily assess symptoms of depression and anxiety at baseline, immediately after the intervention, and at 1 and 3 months after the intervention. Generalized estimating equations were used to evaluate the effects of intervention and time.<br><br>RESULTS: A total of 155 participants enrolled in the study, and 147 finished all assessments. Significant reductions in the symptoms of depression, anxiety, and fatigue were observed in the NAOM (mean difference [MD]=-3.330, Cohen d=0.926, P<.001; MD=-3.468, Cohen d=1.091, P<.001; MD=-2.620, Cohen d=0.743, P<.001, respectively) and the COM (MD=-1.875, Cohen d=0.490, P=.03; MD=-1.750, Cohen d=0.486, P=.02; MD=-2.229, Cohen d=0.629, P=.01, respectively) groups compared with the control group at postintervention assessment. Moreover, the NAOM group showed significantly better effects than the COM group in alleviating depressive symptoms (MD=-1.455; Cohen d=0.492; P=.04) and anxiety symptoms (MD=-1.718; Cohen d=0.670; P=.04) and improving the level of mindfulness (MD=-3.765; Cohen d=1.245; P<.001) at the postintervention assessment. However, no significant difference except for the anxiety symptoms was observed across the 3 groups at the 1- and 3-month follow-ups.<br><br>CONCLUSIONS: This 6-week web-based mindfulness intervention, both conventional and neurofeedback-assisted, effectively alleviated mental health problems in the short term among nursing students. The addition of neurofeedback demonstrated greater short-term benefits; however, but these effects were not sustained over the long term. Future research should focus on long-term interventions using a more robust methodological approach.<br><br>TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) ChiCTR2400080314; https://www.chictr.org.cn/bin/project/edit?pid=211845.}, }
@article {pmid40408491, year = {2025}, author = {Zhou, H and Wu, J and Li, J and Pan, Z and Lu, J and Shen, M and Wang, T and Hu, Y and Gao, Z}, title = {Event cache: An independent component in working memory.}, journal = {Science advances}, volume = {11}, number = {21}, pages = {eadt3063}, pmid = {40408491}, issn = {2375-2548}, mesh = {*Memory, Short-Term/physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Female ; Adult ; Young Adult ; Brain Mapping ; *Cerebellum/physiology ; Brain/physiology ; }, abstract = {Working memory (WM) has been a major focus of cognitive science and neuroscience for the past 50 years. While most WM research has centered on the mechanisms of objects, there has been a lack of investigation into the cognitive and neural mechanisms of events, which are the building blocks of our experience. Using confirmatory factor analysis, psychophysical experiments, and resting-state and task functional magnetic resonance imaging methods, our study demonstrated that events have an independent storage space within WM, named as event cache, with distinct neural correlates compared to object storage in WM. We found the cerebellar network to be the most essential network for event cache, with the left cerebellum Crus I being particularly involved in encoding and maintaining events. Our findings shed critical light on the neuropsychological mechanism of WM by revealing event cache as an independent component of WM and encourage the reconsideration of theoretical models for WM.}, }
@article {pmid40408214, year = {2025}, author = {Chen, W and Li, Y and Zheng, N and Shi, W}, title = {DenoiseMamba: An Innovative Approach for EEG Artifact Removal Leveraging Mamba and CNN.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {9}, pages = {6551-6564}, doi = {10.1109/JBHI.2025.3573042}, pmid = {40408214}, issn = {2168-2208}, mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; *Deep Learning ; Brain/physiology ; Algorithms ; }, abstract = {Electroencephalography (EEG) is a widely used tool for monitoring brain activity, but it is often disturbed by various artifacts, such as electrooculography (EOG), electromyography (EMG), and electrocardiography (ECG), which degrade signal quality and affect subsequent analysis. Effective EEG denoising is critical for enhancing the performance of EEG-based applications, including disease diagnosis and brain-computer interfaces (BCIs). While recent deep learning (DL) approaches have shown promise in this area, they often struggle to efficiently model the temporal dependencies inherent in EEG signals, as well as to capture local contextual information simultaneously. In this work, we introduce DenoiseMamba, a novel deep learning-based EEG denoising model. The model incorporates the ConvSSD module, which integrates convolutional neural networks (CNNs) with structured state-space duality (SSD) mechanisms. This allows DenoiseMamba to capture both local and global spatiotemporal features, resulting in more effective artifact suppression. Extensive experiments on three semi-simulated datasets demonstrate that DenoiseMamba outperforms existing methods in EEG reconstruction accuracy, effectively eliminating myoelectric, electrooculographic, and electrocardiographic artifacts while preserving critical EEG signal details.}, }
@article {pmid40408213, year = {2025}, author = {Lan, Z and Li, Z and Yan, C and Xiang, X and Tang, D and Wu, M and Chen, Z}, title = {MTSNet: Convolution-Based Transformer Network With Multi-Scale Temporal-Spectral Feature Fusion for SSVEP Signal Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {11}, pages = {8034-8047}, doi = {10.1109/JBHI.2025.3573410}, pmid = {40408213}, issn = {2168-2208}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Adult ; *Neural Networks, Computer ; Algorithms ; Male ; Female ; Young Adult ; }, abstract = {Improving the decoding performance of steady-state visual evoked (SSVEP) signals is crucial for the practical application of SSVEP-based brain-computer interface (BCI) systems. Although numerous methods have achieved impressive results in decoding SSVEP signals, most of them focus only on the temporal or spectral domain information or concatenate them directly, which may ignore the complementary relationship between different features. To address this issue, we propose a dual-branch convolution-based Transformer network with multi-scale temporal-spectral feature fusion, termed MTSNet, to improve the decoding performance of SSVEP signals. Specifically, the temporal branch extracts temporal features from the SSVEP signals using the multi-level convolution- based Transformer (Convformer) that can adapt to the dynamic fluctuations of SSVEP signals. In parallel, the spectral branch takes the complex spectrum converted from temporal signals by the zero-padding fast Fourier transform as input and uses the Convformer to extract spectral features. These extracted temporal and spectral features are then integrated by the multi-scale feature fusion module to obtain comprehensive features with different scale information, thereby enhancing the interactions between the features and improving the effectiveness and robustness. Extensive experimental results on two widely used public SSVEP datasets, Benchmark and BETA, show that the proposed MTSNet significantly outperforms the state-of-the-art calibration-free methods in terms of accuracy and ITR. The superior performance demonstrates the effectiveness of our method in decoding SSVEP signals, which may facilitate the practical application of SSVEP-based BCI systems.}, }
@article {pmid40408200, year = {2025}, author = {Ingolfsson, TM and Kartsch, V and Benini, L and Cossettini, A}, title = {A Wearable Ultra-Low-Power System for EEG-Based Speech-Imagery Interfaces.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {19}, number = {4}, pages = {743-755}, doi = {10.1109/TBCAS.2025.3573027}, pmid = {40408200}, issn = {1940-9990}, mesh = {Humans ; *Electroencephalography/instrumentation ; *Brain-Computer Interfaces ; *Wearable Electronic Devices ; *Speech/physiology ; Signal Processing, Computer-Assisted ; Machine Learning ; Neural Networks, Computer ; Adult ; Male ; }, abstract = {Speech imagery-the process of mentally simulating speech without vocalization-is a promising approach for brain-computer interfaces (BCIs), enabling assistive communication for individuals with speech impairments or to enhance privacy. However, existing EEG-based speech imagery systems remain impractical for use outside specialized laboratories due to their reliance on high-channel-count and resource-intensive machine learning models running on external computing platforms. In this work, we present the first end-to-end demonstration of EEG-based speech imagery decoding on a low-channel, ultra-low-power wearable device. Building on our previous work on vowel imagery, we introduce an extended framework leveraging the BioGAP platform and VowelNet, a lightweight neural network optimized for embedded speech imagery classification. In particular, we demonstrate state-of-the-art accuracy in the classification of an expanded vocabulary comprising vowels, commands, and rest states (13 classes) with a subject-specific training approach, achieving up to 50.0% for one subject (42.8% average) in multi-class classification. We deploy our model on an embedded biosignal acquisition and processing platform (BioGAP), based on the GAP9 processor, for real-time inference with minimal power consumption (25.93 mW). Our system achieves continuous execution for more than 21 hours on a small LiPo battery while maintaining classification latencies of 40.9 ms. Finally, we also explore the benefits of applying Continual Learning techniques to progressively improve the system's performance throughout its operational lifetime, and we demonstrate that electrodes located on the temporal area contribute the most to the overall accuracy. This work marks a significant step toward practical, real-time, and unobtrusive speech imagery BCIs, unlocking new opportunities for covert communication and assistive technologies.}, }
@article {pmid40407663, year = {2025}, author = {Astefanei, O and Martu, C and Cozma, S and Radulescu, L}, title = {Cochlear and Bone Conduction Implants in Asymmetric Hearing Loss and Single-Sided Deafness: Effects on Localization, Speech in Noise, and Quality of Life.}, journal = {Audiology research}, volume = {15}, number = {3}, pages = {}, pmid = {40407663}, issn = {2039-4330}, abstract = {BACKGROUND: Single-sided deafness (SSD) and asymmetric hearing loss (AHL) impair spatial hearing and speech perception, often reducing quality of life. Cochlear implants (CIs) and bone conduction implants (BCIs) are rehabilitation options used in SSD and AHL to improve auditory perception and support functional integration in daily life.<br><br>OBJECTIVE: We aimed to evaluate hearing outcomes after auditory implantation in SSD and AHL patients, focusing on localization accuracy, speech-in-noise understanding, tinnitus relief, and perceived benefit.<br><br>METHODS: In this longitudinal observational study, 37 patients (adults and children) received a CI or a BCI according to clinical indications. Outcomes included localization and spatial speech-in-noise assessment, tinnitus ratings, and SSQ12 scores. Statistical analyses used parametric and non-parametric tests (p < 0.05).<br><br>RESULTS: In adult CI users, localization error significantly decreased from 81.9&#xb0; &#xb1; 15.8&#xb0; to 43.7&#xb0; &#xb1; 13.5&#xb0; (p < 0.001). In children, regardless of the implant type (CI or BCI), localization error improved from 74.3&#xb0; to 44.8&#xb0;, indicating a consistent spatial benefit. In adult BCI users, localization error decreased from 74.6&#xb0; to 69.2&#xb0;, but the improvement did not reach statistical significance. Tinnitus severity, measured on a 10-point VAS scale, decreased significantly in CI users (mean reduction: 2.8 &#xb1; 2.0, p < 0.001), while changes in BCI users were small and of limited clinical relevance. SSQ12B/C scores improved in all adult groups, with the largest gains observed in spatial hearing for CI users (2.1 &#xb1; 1.2) and in speech understanding for BCI users (1.6 &#xb1; 0.9); children reported high benefits across all domains. Head shadow yielded the most consistent benefit across all groups (up to 4.9 dB in adult CI users, 3.8 dB in adult BCI users, and 4.6 dB in children). Although binaural effects were smaller in BCI users, positive gains were observed, especially in pediatric cases. Correlation analysis showed that daily device use positively predicted SSQ12 improvement (r = 0.57) and tinnitus relief (r = 0.42), while longer deafness duration was associated with poorer localization outcomes (r = -0.48).<br><br>CONCLUSIONS: CIs and BCIs provide measurable benefits in SSD and AHL rehabilitation. Outcomes vary with age, device, and deafness duration, underscoring the need for early intervention and consistent auditory input.}, }
@article {pmid40406801, year = {2025}, author = {Moumdjian, RA}, title = {Bioethics of neurotechnologies: a field in effervescence.}, journal = {Neurological research}, volume = {47}, number = {8}, pages = {756-759}, doi = {10.1080/01616412.2025.2499896}, pmid = {40406801}, issn = {1743-1328}, mesh = {Humans ; *Bioethics ; *Brain-Computer Interfaces/ethics ; *Neurosciences/ethics ; }, abstract = {Brain-Computer Interface (BCI) comprises a device that detects brain signals conveying specific intentions and translates them into executable outputs by a machine. It enables neurologically impaired patients to regain some control over their environment, thereby aiding in their rehabilitation. Some authors argue that 'the use of BCI is the greatest ethical challenge that neuroscience faces today. Ethical issues highlighted in the literature include safety, justice, privacy, security, and the balance of risks and benefits.}, }
@article {pmid40403087, year = {2025}, author = {Essam, AA and Ibrahim, A and Seif Al-Nasr, A and El-Saqa, M and Mohamed, S and Anwar, A and Eldeib, A and Akcakaya, M and Khalaf, A}, title = {Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.}, journal = {PloS one}, volume = {20}, number = {5}, pages = {e0311075}, pmid = {40403087}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Ultrasonography, Doppler, Transcranial/methods ; Male ; Adult ; Female ; Young Adult ; Bayes Theorem ; *Brain/physiology ; Algorithms ; }, abstract = {Brain-computer interfaces (BCIs) provide alternative means of communication and control for individuals with severe motor or speech impairments. Multimodal BCIs have been introduced recently to enhance the performance of BCIs utilizing single modality. In this paper, we aim to advance the state of the art in multimodal BCIs combining Electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) by introducing advanced analysis approaches that enhance system performance. Our EEG-fTCD BCIs employ two distinct paradigms to infer user intent: motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. In the MI paradigm, we introduce the use of Filter Bank Common Spatial Pattern (FBCSP) for the first time in an EEG-fTCD BCI, while in the flickering MR/WG paradigm, we extend FBCSP application to non-motor imagery tasks. Additionally, we extract previously unexplored time-series features from the envelope of fTCD signals, leveraging richer information from cerebral blood flow dynamics. Furthermore, we employ a Bayesian fusion framework that allows EEG and fTCD to contribute unequally to decision-making. The multimodal EEG-fTCD system achieved high classification accuracies across tasks in both paradigms. In the MI paradigm, accuracies of 94.53%, 94.9%, and 96.29% were achieved for left arm MI vs. baseline, right arm MI vs. baseline, and right arm MI vs. left arm MI, respectively - outperforming EEG-only accuracy by 3.87%, 3.80%, and 5.81%, respectively. In the MR/WG paradigm, the system achieved 95.27%, 85.93%, and 96.97% for MR vs. baseline, WG vs. baseline, and MR vs. WG, respectively, showing accuracy improvements of 2.28%, 4.95%, and 1.56%, respectively compared to EEG-only results. Overall, the proposed analysis approach improved classification accuracy for 5 out of 6 binary classification problems within the MI and MR/WG paradigms, with gains ranging from 0.64% to 9% compared to our previous EEG-fTCD studies. Additionally, our results demonstrate that EEG-fTCD BCIs with the proposed analysis techniques outperform multimodal EEG-fNIRS BCIs in both accuracy and speed, improving classification performance by 2.7% to 24.7% and reducing trial durations by 2-38 seconds. These findings highlight the potential of the proposed approach to advance assistive technologies and improve patient quality of life.}, }
@article {pmid40402697, year = {2025}, author = {Chaisaen, R and Autthasan, P and Ditthapron, A and Wilaiprasitporn, T}, title = {AlphaGrad: Normalized Gradient Descent for Adaptive Multi-Loss Functions in EEG-Based Motor Imagery Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {10}, pages = {7116-7128}, doi = {10.1109/JBHI.2025.3572197}, pmid = {40402697}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; *Imagination/physiology ; Algorithms ; Neural Networks, Computer ; Adult ; Male ; }, abstract = {In this study, we propose AlphaGrad, a novel adaptive loss blending strategy for optimizing multi-task learning (MTL) models in motor imagery (MI)-based electroencephalography (EEG) classification. AlphaGrad is the first method to automatically adjust multi-loss functions with differing metric scales, including mean square error, cross-entropy, and deep metric learning, within the context of MI-EEG. We evaluate AlphaGrad using two state-of-the-art MTL-based neural networks, MIN2Net and FBMSNet, across four benchmark datasets. Experimental results show that AlphaGrad consistently outperforms existing strategies such as AdaMT, GradApprox, and fixed-weight baselines in classification accuracy and training stability. Compared to baseline static weighting, AlphaGrad achieves over 10% accuracy improvement on subject-independent MI tasks when evaluated on the largest benchmark dataset. Furthermore, AlphaGrad demonstrates robust adaptability across various EEG paradigms-including steady-state visually evoked potential (SSVEP) and event-related potential (ERP), making it broadly applicable to brain-computer interface (BCI) systems. We also provide gradient trajectory visualizations highlighting AlphaGrad's ability to maintain training stability and avoid local minima. These findings underscore AlphaGrad's promise as a general-purpose solution for adaptive multi-loss optimization in biomedical time-series learning.}, }
@article {pmid40401160, year = {2025}, author = {Zhao, L}, title = {Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective.}, journal = {Psychoradiology}, volume = {5}, number = {}, pages = {kkaf007}, pmid = {40401160}, issn = {2634-4416}, abstract = {Functional magnetic resonance imaging (fMRI) provides a powerful tool for studying brain function by capturing neural activity in a non-invasive manner. Mapping brain function from fMRI data enables researchers to investigate the spatial and temporal dynamics of neural processes, providing insights into how the brain responds to various tasks and stimuli. In this review, we explore the evolution of deep learning-based methods for brain function mapping using fMRI. We begin by discussing various network architectures such as convolutional neural networks, recurrent neural networks, and transformers. We further examine supervised, unsupervised, and self-supervised learning paradigms for fMRI-based brain function mapping, highlighting the strengths and limitations of each approach. Additionally, we discuss emerging trends such as fMRI embedding, brain foundation models, and brain-inspired artificial intelligence, emphasizing their potential to revolutionize brain function mapping. Finally, we delve into the real-world applications and prospective impact of these advancements, particularly in the diagnosis of neural disorders, neuroscientific research, and brain-computer interfaces for decoding brain activity. This review aims to provide a comprehensive overview of current techniques and future directions in the field of deep learning and fMRI-based brain function mapping.}, }
@article {pmid40401149, year = {2025}, author = {Leong, F and Micera, S and Shokur, S}, title = {Optimization frameworks for bespoke sensory encoding in neuroprosthetics.}, journal = {APL bioengineering}, volume = {9}, number = {2}, pages = {020901}, pmid = {40401149}, issn = {2473-2877}, abstract = {Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain-machine interface with sensory feedback would provide the user with sensation about movement, pressure, curvature, texture, etc. Despite advances in neural interfaces that allow for complex stimulation patterns (e.g., multisite stimulation or the possibility of targeting a precise neural ensemble), a key question remains: How can we best exploit the potential of these technologies? The increasing number of electrodes coupled with more parameters being explored leads to an exponential increase in the number of possible combinations, making a brute-force approach, such as systematic search, impractical. This Perspective outlines three different optimization frameworks-namely, the explicit, physiological, and self-optimized methods-allowing one to potentially converge faster toward effective parameters. Although our focus will be on the somatosensory system, these frameworks are flexible and applicable to various sensory systems (e.g., vision) and stimulator types.}, }
@article {pmid40399603, year = {2025}, author = {Wang, Y and Fukuma, R and Seymour, B and Yang, H and Kishima, H and Yanagisawa, T}, title = {Neurofeedback modulation of insula activity via MEG-based brain-machine interface: a double-blind randomized controlled crossover trial.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {770}, pmid = {40399603}, issn = {2399-3642}, support = {JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JP19dm0307008//Japan Agency for Medical Research and Development (AMED)/ ; 214251/Z/18/Z//Wellcome Trust (Wellcome)/ ; JP20H05705//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; JP24wm0625517//Japan Agency for Medical Research and Development (AMED)/ ; 22H04998//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; EP/W03509X/1//DH | National Institute for Health Research (NIHR)/ ; 19dm0207070h//Japan Agency for Medical Research and Development (AMED)/ ; 203316//DH | National Institute for Health Research (NIHR)/ ; JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; /WT_/Wellcome Trust/United Kingdom ; }, mesh = {Humans ; *Neurofeedback/methods ; *Magnetoencephalography/methods ; Double-Blind Method ; Male ; Cross-Over Studies ; *Brain-Computer Interfaces ; Female ; Adult ; Young Adult ; *Insular Cortex/physiology ; Pain Threshold/physiology ; *Cerebral Cortex/physiology ; }, abstract = {Insula activity has often been linked to pain perception, making it a potential target for therapeutic neuromodulation strategies such as neurofeedback. However, it is not known whether insula activity is under cognitive control and, if so, whether this activity is consequently causally related to pain. Here, we conducted a double-blind randomized controlled crossover trial to test the modulation of insula activity and pain thresholds using neurofeedback training. Nineteen healthy subjects underwent neurofeedback training for upmodulation and downmodulation of right insula activity using our magnetoencephalography (MEG)-based brain-machine interface. We observed significant differences in insula activity between the upmodulation and downmodulation training sessions. Furthermore, resting-state insula activity significantly decreased following downmodulation training compared to following upmodulation training. Compared with upmodulation training, downmodulation training was also associated with increased pain thresholds, albeit with no significant interaction effect. These findings show that humans can cognitively modulate insula activity as a potential route to develop therapeutic MEG neurofeedback systems for clinical testing. However, the present findings do not provide direct evidence of a causal link between modulation of insula activity and changes in pain thresholds.}, }
@article {pmid40398443, year = {2025}, author = {Jehn, C and Kossmann, A and Katerina Vavatzanidis, N and Hahne, A and Reichenbach, T}, title = {CNNs improve decoding of selective attention to speech in cochlear implant users.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/addb7b}, pmid = {40398443}, issn = {1741-2552}, mesh = {Humans ; *Cochlear Implants ; *Attention/physiology ; *Speech Perception/physiology ; Female ; Male ; Electroencephalography/methods ; Middle Aged ; Adult ; *Neural Networks, Computer ; Aged ; Acoustic Stimulation/methods ; Support Vector Machine ; }, abstract = {Objective. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory attention decoding (AAD) aims to decode the target speaker of a listener from electroencephalography (EEG), and then use this information to steer an auditory prosthesis towards this speech signal. In normal-hearing individuals, deep neural networks (DNNs) have been shown to improve AAD compared to simpler linear models. We aim to demonstrate that DNNs can improve attention decoding in CI users too, which would make them the state-of-the-art candidate for a neuro-steered CI.Approach. To this end, we first collected an EEG dataset on selective auditory attention from 25 bilateral CI users, and then implemented both a linear model as well as a convolutional neural network (CNN) for attention decoding. Moreover, we introduced a novel, objective CI-artifact removal strategy and evaluated its impact on decoding accuracy, alongside learnable speaker classification using a support vector machine (SVM).Main results. The CNN outperformed the linear model across all decision window sizes from 1 to 60 s. Removing CI artifacts modestly improved the CNN's decoding accuracy. With SVM classification, the CNN decoder reached a peak mean decoding accuracy of 74% at the population level for a 60 s decision window.Significance. These results demonstrate the superior potential of CNN-based decoding for neuro-steered CIs, which could improve speech perception of its users in cocktail party situations significantly.}, }
@article {pmid40398442, year = {2025}, author = {Peterson, V and Spagnolo, V and Galván, CM and Nieto, N and Spies, RD and Milone, DH}, title = {Towards subject-centered co-adaptive brain-computer interfaces based on backward optimal transport.}, journal = {Journal of neural engineering}, volume = {22}, number = {4}, pages = {}, doi = {10.1088/1741-2552/addb7a}, pmid = {40398442}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Imagination/physiology ; Young Adult ; *Adaptation, Physiological/physiology ; Algorithms ; }, abstract = {Objective. Controlling a motor imagery brain-computer interface (MI-BCI) can be challenging, requiring several sessions of practice. Electroencephalography (EEG)-based BCIs are particularly affected by cross-session variability. In this scenario, it is crucial to implement co-adaptive systems, where the machine adapts the decoding algorithm while the user learns how to control the BCI. To support the user learning process, it is essential to measure and provide real-time feedback on self-modulation skills. This study aims to develop a method for online assessment of MI modulation capability to build co-adaptive BCIs that improve both user performance and system accuracy.Approach. Backward optimal transport for domain adaptation allows across-session MI-BCI usage without classifier retraining. Using the cued label to guide the adaptation, a supportive backward adaptation (SBA) method is defined. The required model effort to perform a trial adaptation is proposed as an online metric of MI modulation skills. We conducted experiments on both real and simulated data to demonstrate that this metric effectively informs about the the discriminability and stability of the EEG patterns related to the MI task. The proposed metric is validated by means of Riemannian distinctiveness metrics.Main Results. Our findings show that the associated effort when applying SBA provides a meaningful way of evaluating EEG patterns discriminability, being significantly correlated with Riemannian distinctiveness metrics.Significance. This study introduces a novel framework for co-adaptive BCI learning that performs data adaptation while assessing the MI-BCI skills of the user. The proposed SBA approach can enhance BCI performance by facilitating session-to-session adaptation and empowering users with valuable feedback based on their current MI modulation strategy. This framework represents a significant advancement in developing user-centered, co-adaptive MI-BCIs that effectively support and enhance user capabilities.}, }
@article {pmid40398440, year = {2025}, author = {van der Eerden, JHM and Liu, PC and Villalobos, J and Yanagisawa, T and Grayden, DB and John, SE}, title = {Decoding cortical responses from visual input using an endovascular brain-computer interface.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/addb7c}, pmid = {40398440}, issn = {1741-2552}, mesh = {Animals ; *Brain-Computer Interfaces ; *Visual Cortex/physiology ; *Evoked Potentials, Visual/physiology ; Sheep ; *Electrocorticography/methods/instrumentation ; *Photic Stimulation/methods ; Electrodes, Implanted ; *Endovascular Procedures/methods/instrumentation ; }, abstract = {Objective.Implantable neural interfaces enable recording of high-quality brain signals that can improve our understanding of brain function. This work examined the feasibility of using a minimally invasive endovascular neural interface (ENI) to record interpretable cortical activity from the visual cortex.Approach. A sheep model (n= 5) was used to record and decode visually evoked potentials from the cortex both with an ENI and a subdural electrode grid. Sets of distinct experimental visual stimuli were presented to attempt decoding from the recorded cortical potentials, using perceptual categories of colour, contrast, movement direction orientation, spatial frequency and temporal frequency. Decoding performances are presented as accuracy scores from K-fold cross-validation of a stratified random forest classification model. The study compared the signal quality and decoding performance between the ENI and electrocorticography (ECoG) electrodes.Main results. Recordings from the ENI array resulted in lower decoding performances than the ECoG array, but the classification scores were significantly above chance in the stimuli categories of colour, contrast, direction and temporal frequency. This study is the first report of visually evoked neural activity using a minimally-invasive ENI.Significance. Overall, the results show that implantable macro-electrodes yield sufficient neural signal definition to discern primary visual percepts, using both endo-vascular and intracranial surgical placements.}, }
@article {pmid40398391, year = {2025}, author = {Huang, W and Shu, N}, title = {AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.}, journal = {Cell reports. Medicine}, volume = {6}, number = {5}, pages = {102132}, pmid = {40398391}, issn = {2666-3791}, mesh = {Humans ; *Precision Medicine/methods ; *Multimodal Imaging/methods ; *Mental Disorders/diagnostic imaging/therapy ; *Artificial Intelligence ; *Neuroimaging/methods ; }, abstract = {Neuropsychiatric disorders have complex pathological mechanism, pronounced clinical heterogeneity, and a prolonged preclinical phase, which presents a challenge for early diagnosis and development of precise intervention strategies. With the development of large-scale multimodal neuroimaging datasets and advancement of artificial intelligence (AI) algorithms, the integration of multimodal imaging with AI techniques has emerged as a pivotal avenue for early detection and tailoring individualized treatment for neuropsychiatric disorders. To support these advances, in this review, we outline multimodal neuroimaging techniques, AI methods, and strategies for multimodal data fusion. We highlight applications of multimodal AI based on neuroimaging data in precision medicine for neuropsychiatric disorders, discussing challenges in clinical adoption, their emerging solutions, and future directions.}, }
@article {pmid40398228, year = {2025}, author = {Xiao, S and Huang, X and He, X and Chen, Z and Li, X and Wei, X and Liu, Q and Dong, H and Zeng, X and Bai, W}, title = {Interactions between curcumin and fish-/bovine-derived (type I and II) collagens: Preparation of nanoparticle and their application in Pickering emulsions.}, journal = {Food chemistry}, volume = {487}, number = {}, pages = {144781}, doi = {10.1016/j.foodchem.2025.144781}, pmid = {40398228}, issn = {1873-7072}, mesh = {*Curcumin/chemistry ; Animals ; *Nanoparticles/chemistry ; Cattle ; Emulsions/chemistry ; Hydrophobic and Hydrophilic Interactions ; *Collagen Type I/chemistry ; Fishes ; *Collagen Type II/chemistry ; Molecular Dynamics Simulation ; *Fish Proteins/chemistry ; Hydrogen Bonding ; Protein Binding ; }, abstract = {This study aims to elucidate the interaction mechanisms between curcumin (Cur) and four collagen subtypes (fish type I [FCI], bovine type I [BCI], fish type II [FCII], bovine type II [BCII]), with parallel characterization of the structural and functional attributes of their derived nanoparticles. Type I Collagen/Cur nanoparticles exhibited superior solution stability compared to type II. Cur binding significantly enhanced the surface hydrophobicity, absolute ζ potential, and surface tension of collagen, while reduced dynamic interfacial tension. The binding type of Cur to collagen was static, and binding process was enthalpy-driven exothermic reaction. Molecular dynamics simulations revealed that hydrophobic interactions, hydrogen bonds, and electrostatic forces dominated the binding process. The binding affinity followed the order: FCI/Cur > BCI/Cur > FCII/Cur > BCII/Cur. The binding sites of Cur to type I collagen and type II collagen were around Ser129-Glu135 and Asn179-Ser183 residues. Collagen/Cur nanoparticle stabilized emulsions and improved oxidative stability and storage modulus.}, }
@article {pmid40395924, year = {2025}, author = {Russell, M and Hincks, S and Wang, L and Babar, A and Chen, Z and White, Z and Jacob, RJK}, title = {Visualization and workload with implicit fNIRS-based BCI: toward a real-time memory prosthesis with fNIRS.}, journal = {Frontiers in neuroergonomics}, volume = {6}, number = {}, pages = {1550629}, pmid = {40395924}, issn = {2673-6195}, abstract = {Functional Near-Infrared Spectroscopy (fNIRS) has proven in recent time to be a reliable workload-detection tool, usable in real-time implicit Brain-Computer Interfaces. But what can be done in terms of application of neural measurements of the prefrontal cortex beyond mental workload? We trained and tested a first prototype example of a memory prosthesis leveraging a real-time implicit fNIRS-based BCI interface intended to present information appropriate to a user's current brain state from moment to moment. Our prototype implementation used data from two tasks designed to interface with different brain networks: a creative visualization task intended to engage the Default Mode Network (DMN), and a complex knowledge-worker task to engage the Dorsolateral Prefrontal Cortex (DLPFC). Performance of 71% from leave-one-out cross-validation across participants indicates that such tasks are differentiable, which is promising for the development of future applied fNIRS-based BCI systems. Further, analyses within lateral and medial left prefrontal areas indicates promising approaches for future classification.}, }
@article {pmid40395688, year = {2025}, author = {Tibermacine, IE and Russo, S and Citeroni, F and Mancini, G and Rabehi, A and Alharbi, AH and El-Kenawy, EM and Napoli, C}, title = {Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1583342}, pmid = {40395688}, issn = {1662-5161}, abstract = {INTRODUCTION: Electroencephalography (EEG) signals frequently contain substantial noise and interference, which can obscure clinically and scientifically relevant features. Traditional denoising approaches, such as linear filtering or wavelet thresholding, often struggle with nonlinear or time-varying artifacts. In response, the present study explores a Generative Adversarial Network (GAN) framework to enhance EEG signal quality, focusing on two variants: a conventional GAN model and a Wasserstein GAN with Gradient Penalty (WGAN-GP).<br><br>METHODS: Data were obtained from two distinct EEG datasets: a "healthy" set of 64-channel recordings collected during various motor/imagery tasks, and an "unhealthy" set of 18-channel recordings from individuals with orthopedic impairments. Both datasets underwent comprehensive preprocessing, including band-pass filtering (8-30 Hz), channel standardization, and artifact trimming. The training stage involved adversarial learning, in which a generator sought to reconstruct clean EEG signals while a discriminator (or critic in the case of WGAN-GP) attempted to distinguish between real and generated signals. The model evaluation was conducted using quantitative metrics such as signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), correlation coefficient, mutual information, and dynamic time warping (DTW) distance.<br><br>RESULTS: Experimental findings indicate that adversarial learning substantially improves EEG signal fidelity across multiple quantitative metrics. Specifically, WGAN-GP achieved an SNR of up to 14.47 dB (compared to 12.37 dB for the standard GAN) and exhibited greater training stability, as evidenced by consistently lower relative root mean squared error (RRMSE) values. In contrast, the conventional GAN model excelled in preserving finer signal details, reflected in a PSNR of 19.28 dB and a correlation coefficient exceeding 0.90 in several recordings. Both adversarial frameworks outperformed classical wavelet-based thresholding and linear filtering methods, demonstrating superior adaptability to nonlinear distortions and dynamic interference patterns in EEG time-series data.<br><br>DISCUSSION: By systematically comparing standard GAN and WGAN-GP architectures, this study highlights a practical trade-off between aggressive noise suppression and high-fidelity signal reconstruction. The demonstrated improvements in signal quality underscore the promise of adversarially trained models for applications ranging from basic neuroscience research to real-time brain-computer interfaces (BCIs) in clinical or consumer-grade settings. The results further suggest that GAN-based frameworks can be easily scaled to next-generation wireless networks and complex electrophysiological datasets, offering robust and dynamic solutions to long-standing challenges in EEG denoising.}, }
@article {pmid40395354, year = {2025}, author = {Zargarian, SS and Rinoldi, C and Ziai, Y and Zakrzewska, A and Fiorelli, R and Gazińska, M and Marinelli, M and Majkowska, M and Hottowy, P and Mindur, B and Czajkowski, R and Kublik, E and Nakielski, P and Lanzi, M and Kaczmarek, L and Pierini, F}, title = {Chronic Probing of Deep Brain Neuronal Activity Using Nanofibrous Smart Conducting Hydrogel-Based Brain-Machine Interface Probes.}, journal = {Small science}, volume = {5}, number = {5}, pages = {2400463}, pmid = {40395354}, issn = {2688-4046}, abstract = {The mechanical mismatch between microelectrode of brain-machine interfaces (BMIs) and soft brain tissue during electrophysiological investigations leads to inflammation, glial scarring, and compromising performance. Herein, a nanostructured, stimuli-responsive, conductive, and semi-interpenetrating polymer network hydrogel-based coated BMIs probe is introduced. The system interface is composed of a cross-linkable poly(N-isopropylacrylamide)-based copolymer and regioregular poly[3-(6-methoxyhexyl)thiophene] fabricated via electrospinning and integrated into a neural probe. The coating's nanofibrous architecture offers a rapid swelling response and faster shape recovery compared to bulk hydrogels. Moreover, the smart coating becomes more conductive at physiological temperatures, which improves signal transmission efficiency and enhances its stability during chronic use. Indeed, detecting acute neuronal deep brain signals in a mouse model demonstrates that the developed probe can record high-quality signals and action potentials, favorably modulating impedance and capacitance. Evaluation of in vivo neuronal activity and biocompatibility in chronic configuration shows the successful recording of deep brain signals and a lack of substantial inflammatory response in the long-term. The development of conducting fibrous hydrogel bio-interface demonstrates its potential to overcome the limitations of current neural probes, highlighting its promising properties as a candidate for long-term, high-quality detection of neuronal activities for deep brain applications such as BMIs.}, }
@article {pmid40395337, year = {2025}, author = {Yao, J and Zhou, Z and Tong, Q and Li, L and Wei, J and Lu, J and Hu, S and Bao, A and He, H}, title = {Magnetic resonance imaging of postmortem human brain specimens: methodological considerations and prospects in psychoradiology.}, journal = {Psychoradiology}, volume = {5}, number = {}, pages = {kkaf012}, pmid = {40395337}, issn = {2634-4416}, abstract = {Ex vivo magnetic resonance imaging (MRI) has revolutionized psychoradiological research by enabling detailed structural and pathological assessments of the brain in conditions ranging from psychiatric disorders to neurodegenerative diseases. By providing high-resolution images of postmortem brain tissue, ex vivo MRI overcomes several limitations inherent in in vivo imaging, offering unparalleled insights into the underlying pathophysiology of mental disorders. This review critically summarizes the state-of-the-art ex vivo MRI methodologies for neuroanatomical mapping and pathological characterization in psychoradiology, while also establishing standardized specimen processing protocols. Furthermore, we explore the prospects of application in ex vivo MRI in schizophrenia, major depressive disorder and bipolar disorder, highlighting its role in understanding neuroanatomical alterations, disease progression, and the validation of in vivo neuroimaging biomarkers.}, }
@article {pmid40395088, year = {2025}, author = {Zhang, S and Gu, J and Yang, Y and Li, J and Ni, L}, title = {Evolution Trend of Brain Science Research: An Integrated Bibliometric and Mapping Approach.}, journal = {Brain and behavior}, volume = {15}, number = {5}, pages = {e70451}, pmid = {40395088}, issn = {2162-3279}, support = {2020Z388//Jiangsu Postdoctoral Research Foundation/ ; //Top Talent Support Program for young and middle-aged people of the Wuxi Health Committee/ ; M202033//Wuxi Health Commission Scientific Research Project/ ; 24CC00903//Beijing Academy of Science and Technology Think Tank Research Project/ ; ZYYB05//Wuxi Administration of Traditional Chinese Medicine/ ; }, mesh = {*Bibliometrics ; Humans ; *Biomedical Research/trends ; *Neurosciences/trends ; *Brain/physiology ; United States ; China ; }, abstract = {BACKGROUND: Brain science research is considered the crown jewel of 21st-century scientific research; the United States, the United Kingdom, and Japan have elevated brain science research to a national strategic level. This study employs bibliometric analysis and knowledge graph visualization to map global trends, research hotspots, and collaborative networks in brain science, providing insights into the field's evolving landscape and future directions.<br><br>METHODS: We analyzed 13,590 articles (1990-2023) from the Web of Science Core Collection using CiteSpace and VOSviewer. Metrics included publication volume, co-authorship networks, citation patterns, keyword co-occurrence, and burst detection. Analytical tools such as VOSviewer, CiteSpace, and online bibliometric platforms were employed to facilitate this investigation.<br><br>RESULTS: The United States, China, and Germany dominated research output, with China's publications rising from sixth to second globally post-2016, driven by national initiatives like the China Brain Project. However, China exhibited limited international collaboration compared to the United States and European Union. Key journals included Human Brain Mapping and Journal of Neural Engineering, while emergent themes centered on "task analysis," "deep learning," and "brain-computer interfaces" (BCIs). Research clusters revealed three focal areas: (1) Brain Exploration (e.g., fMRI, diffusion tensor imaging), (2) Brain Protection (e.g., stroke rehabilitation, amyotrophic lateral sclerosis therapies), and (3) Brain Creation (e.g., neuromorphic computing, BCIs integrated with AR/VR). Despite China's high output, its influence lagged in highly cited scholars, reflecting a "quantity-over-quality" challenge.<br><br>CONCLUSION: Brain science research is in a golden period of development. This bibliometric analysis offers the first comprehensive review, encapsulating research trends and progress in brain science. It reveals current research frontiers and crucial directions, offering a strategic roadmap for researchers and policymakers to navigate countries when planning research layouts.}, }
@article {pmid40395013, year = {2025}, author = {Pyo, YW and Kim, H and Park, HG}, title = {Graphene-Integrated Ultrathin Neural Probe for Multiregional Cortical Recordings.}, journal = {ACS nano}, volume = {19}, number = {21}, pages = {19951-19961}, doi = {10.1021/acsnano.5c03145}, pmid = {40395013}, issn = {1936-086X}, mesh = {*Graphite/chemistry ; Animals ; Mice ; *Somatosensory Cortex/physiology ; *Neurons/physiology ; Electric Stimulation ; Mice, Inbred C57BL ; }, abstract = {Electrophysiological measurement techniques are essential for understanding the functions of the central and peripheral nervous systems. Specifically, noninvasive neural probes, such as surface electrode arrays, provide stable electrophysiological recordings without eliciting an immunological response. However, the ability to capture complex interactions across multiple brain regions is limited by their localized recording site. Here, we present the "large-area NeuroWeb (LNW)", an ultrathin, minimally invasive neural probe designed for extensive cortical recording and stimulation. LNW consists of four recording areas, each containing 16-channel platinum electrodes interconnected by graphene networks. In vivo experiments of the mouse brain exhibit stable, high-quality single-unit spike recordings for up to 7 days post-surgery. Simultaneous high-resolution neural activity recordings are performed across left/right somatosensory cortex and cerebellum, simplifying the experimental procedure by eliminating the necessity for multiple synchronized probes, thus reducing tissue displacement and inflammation. Furthermore, whisker and electrical stimulations demonstrate that the LNW has precise and bidirectional connections with neurons for reliable, region-specific signal acquisition and activation. These findings highlight the capability of LNW to facilitate comprehensive and accurate mapping of neuronal dynamics, thereby advancing brain-machine interfaces and neural prostheses.}, }
@article {pmid40393988, year = {2025}, author = {Garro, F and Fenoglio, E and Ceroni, I and Forsiuk, I and Canepa, M and Mozzon, M and Bruschi, A and Zippo, F and Laffranchi, M and De Michieli, L and Buccelli, S and Chiappalone, M and Semprini, M}, title = {An EEG-EMG dataset from a standardized reaching task for biomarker research in upper limb assessment.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {831}, pmid = {40393988}, issn = {2052-4463}, mesh = {Humans ; *Electroencephalography ; *Upper Extremity/physiology ; *Electromyography ; Biomarkers ; Adult ; Movement ; }, abstract = {This work describes a dataset containing high-density EEG (hd-EEG) and surface electromiography (sEMG) to capture neuromechanical responses during a reaching task with and without the assistance of an upper-limb exoskeleton. It was designed to explore electrophysiological biomarkers for assessing assistive technologies. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks. A custom-designed touch panel was built to standardize and simulate natural upper-limb movements relevant to daily activities. The dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. To provide an overview of data quality, we present subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency- domain decomposition for EMG. Beyond providing a methodology for evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.}, }
@article {pmid40393212, year = {2025}, author = {Lu, Q and Yi, M and Jiang, J}, title = {Bioelectronic nose for ultratrace odor detection via brain-computer interface with olfactory bulb electrode arrays.}, journal = {Biosensors &amp; bioelectronics}, volume = {285}, number = {}, pages = {117585}, doi = {10.1016/j.bios.2025.117585}, pmid = {40393212}, issn = {1873-4235}, mesh = {Animals ; *Olfactory Bulb/physiology ; *Electronic Nose ; Rats ; *Odorants/analysis ; *Biosensing Techniques/instrumentation ; *Brain-Computer Interfaces ; Male ; Smell ; Support Vector Machine ; Trinitrotoluene/isolation &amp; purification ; Equipment Design ; Rats, Sprague-Dawley ; Electrodes ; }, abstract = {Rapid and accurate detection of hazardous volatile compounds is crucial for public health and environmental safety. While conventional methods, including electronic noses, typically exhibit detection thresholds in the parts-per-million (ppm) range, many harmful substances pose risks at parts-per-billion (ppb) concentrations or lower. To address this challenge, we leverage the exceptional sensitivity of the mammalian olfactory system, specifically that of Rattus norvegicus (lab rat), which has evolved to detect and discriminate a vast array of odors at extremely low concentrations. In this study, we developed a novel bio-hybrid system that integrates behavioral training with in vivo electrophysiological recordings from the olfactory bulb (OB). Rats were operantly conditioned to recognize target odors, namely TNT (2,4,6-trinitrotoluene), TNP (2,4,6-trinitrophenol), and chlorine gas (Cl2), at ppb levels. Concurrent with behavioral testing, we recorded neural activity from both the dorsal and ventral OB using a customdesigned, multi-channel electrode array optimized for the rat OB's cytoarchitecture. Electrophysiological data were decoded using a Support Vector Machine algorithm, achieving a mean accuracy of over 90 % in classifying odor identity at ppb concentrations based on OB activity patterns. These results demonstrate the feasibility of utilizing a brain-computer interface with the olfactory system to achieve ultratrace detection of hazardous substances. This bio-hybrid approach offers significantly enhanced sensitivity compared to existing electronic nose technologies, paving the way for highly effective environmental and biomedical sensing applications.}, }
@article {pmid40390719, year = {2025}, author = {Leung, ES and Mofatteh, M}, title = {Investigating the Feasibility and Safety of Osseointegration With Neural Interfaces for Advanced Prosthetic Control.}, journal = {Cureus}, volume = {17}, number = {4}, pages = {e82567}, pmid = {40390719}, issn = {2168-8184}, abstract = {Osseointegrated neural interfaces (ONI), particularly in conjunction with peripheral nerve interfaces (PNIs), have emerged as a promising advancement for intuitive neuroprosthetics. PNIs can decode neural signals and allow precise prosthetic movement control and bidirectional communication for haptic feedback, while osseointegration can address limitations of traditional socket-based prosthetics, such as poor stability, limited dexterity, and lack of sensory feedback. This review explores advancements in ONIs, including screw-fit and press-fit systems and their integration with PNIs for bidirectional communication. ONIs integrated with PNIs (OIPNIs) have shown improvements in signal fidelity, motor control, and sensory feedback compared to popular surface electromyography (sEMG) systems. Additionally, emerging technologies such as hybrid electrode designs (e.g., cuff and sieve electrode (CASE)) and regenerative peripheral nerve interfaces (RPNIs) show potential for enhancing selectivity and reducing complications such as micromotion and scarring. Despite these advances, challenges remain, including infection risk, electrode degradation, and variability in long-term signal stability. Osseointegration combined with advanced neural interfaces represents a transformative approach to prosthetic control, offering more natural and intuitive movement with sensory feedback. Further research is needed to address long-term biocompatibility, reduce surgical invasiveness, and explore emerging technologies such as machine learning for personalized ONI designs. The findings of this review underscore the potential of ONIs to enhance embodiment and quality of life for amputees and highlight current pitfalls and possible areas of improvement and future research.}, }
@article {pmid40389429, year = {2025}, author = {Karpowicz, BM and Ali, YH and Wimalasena, LN and Sedler, AR and Keshtkaran, MR and Bodkin, K and Ma, X and Rubin, DB and Williams, ZM and Cash, SS and Hochberg, LR and Miller, LE and Pandarinath, C}, title = {Stabilizing brain-computer interfaces through alignment of latent dynamics.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {4662}, pmid = {40389429}, issn = {2041-1723}, support = {K12 HD073945/HD/NICHD NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; T32 EB025816/EB/NIBIB NIH HHS/United States ; RF1 DA055667/DA/NIDA NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces ; Animals ; *Motor Cortex/physiology ; Macaca mulatta ; Neural Networks, Computer ; Male ; Movement/physiology ; Humans ; }, abstract = {Intracortical brain-computer interfaces (iBCIs) restore motor function to people with paralysis by translating brain activity into control signals for external devices. In current iBCIs, instabilities at the neural interface result in a degradation of decoding performance, which necessitates frequent supervised recalibration using new labeled data. One potential solution is to use the latent manifold structure that underlies neural population activity to facilitate a stable mapping between brain activity and behavior. Recent efforts using unsupervised approaches have improved iBCI stability using this principle; however, existing methods treat each time step as an independent sample and do not account for latent dynamics. Dynamics have been used to enable high-performance prediction of movement intention, and may also help improve stabilization. Here, we present a platform for Nonlinear Manifold Alignment with Dynamics (NoMAD), which stabilizes decoding using recurrent neural network models of dynamics. NoMAD uses unsupervised distribution alignment to update the mapping of nonstationary neural data to a consistent set of neural dynamics, thereby providing stable input to the decoder. In applications to data from monkey motor cortex collected during motor tasks, NoMAD enables accurate behavioral decoding with unparalleled stability over weeks- to months-long timescales without any supervised recalibration.}, }
@article {pmid40387950, year = {2025}, author = {Wu, P and Zhu, J and He, Q and Wang, Z and Shi, L}, title = {Visual numerical cognition in pigeons: conformity to the Weber-Fechner law.}, journal = {Animal cognition}, volume = {28}, number = {1}, pages = {39}, pmid = {40387950}, issn = {1435-9456}, mesh = {Animals ; *Columbidae/physiology ; *Cognition ; *Visual Perception ; Male ; }, abstract = {As representatives of a basal bird lineage, pigeons have exhibited remarkable visual numerical cognition, comparable even to that of monkeys. Nevertheless, whether visual numerical cognition in pigeons conforms to the Weber-Fechner law remains unknown. To address this, we designed a fully automated apparatus tailored for pigeons and used it to train them to perform a delayed match-to-numerosity task. The results showed that on a linear scale, pigeons represented smaller numerosities with higher precision and larger numerosities with lower precision, exhibiting a numerical magnitude effect. When the linear scale was compressed into a logarithmic scale, this magnitude effect was offset, resulting in similar representational characteristics across different numerosities. This finding suggests that the mental number line of pigeons is logarithmic rather than linear, consistent with the Weber-Fechner law. While biological brains seek precision in representing numerical information, they must also take computational load into account. This representational strategy may be the optimal outcome of the trade-off between computational precision and computational load that biological brains have achieved through long-term evolution.}, }
@article {pmid40382989, year = {2025}, author = {Wang, T and Dai, Q and Xiong, W}, title = {Escarcitys: A framework for enhancing medical image classification performance in scarcity of trainable samples scenarios.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {189}, number = {}, pages = {107573}, doi = {10.1016/j.neunet.2025.107573}, pmid = {40382989}, issn = {1879-2782}, mesh = {Humans ; *Deep Learning ; Neural Networks, Computer ; *Diagnostic Imaging/classification/methods ; *Image Processing, Computer-Assisted/methods ; }, abstract = {In the field of healthcare, the acquisition and annotation of medical images present significant challenges, resulting in a scarcity of trainable samples. This data limitation hinders the performance of deep learning models, creating bottlenecks in clinical applications. To address this issue, we construct a framework (EScarcityS) aimed at enhancing the success rate of disease diagnosis in scarcity of trainable medical image scenarios. Firstly, considering that Transformer-based deep learning networks rely on a large amount of trainable data, this study takes into account the unique characteristics of pathological regions. By extracting the feature representations of all particles in medical images at different granularities, a multi-granularity Transformer network (MGVit) is designed. This network leverages additional prior knowledge to assist the Transformer network during training, thereby reducing the data requirement to some extent. Next, the importance maps of particles at different granularities, generated by MGVit, are fused to construct disease probability maps corresponding to the images. Based on these maps, a disease probability map-guided diffusion generation model is designed to generate more realistic and interpretable synthetic data. Subsequently, authentic and synthetical data are mixed and used to retrain MGVit, aiming to enhance the accuracy of medical image classification in scarcity of trainable medical image scenarios. Finally, we conducted detailed experiments on four real medical image datasets to validate the effectiveness of EScarcityS and its specific modules.}, }
@article {pmid40382679, year = {2025}, author = {Covelli, E and Filippi, C and Lazzerini, F and Tromboni, E and Tarentini, S and Pizzolante, S and Forli, F and Berrettini, S and Bruschini, L}, title = {Traditional and adaptive speech audiometry in single-sided deaf (SSD) subjects rehabilitated by bone conductive implants (BCI), quality of life and long-term utilization.}, journal = {Acta oto-laryngologica}, volume = {145}, number = {7}, pages = {633-639}, doi = {10.1080/00016489.2025.2504032}, pmid = {40382679}, issn = {1651-2251}, mesh = {Humans ; *Quality of Life ; Female ; Male ; Retrospective Studies ; Middle Aged ; Adult ; *Bone Conduction ; *Hearing Loss, Unilateral/rehabilitation ; *Audiometry, Speech/methods ; Aged ; Young Adult ; }, abstract = {BACKGROUND: Single-sided deafness (SSD) encompasses the presence of a profoundly deaf ear with a normal, contralateral one. Patients with SSD may have difficulty with speech intelligibility in noise and localizing sounds.<br><br>AIMS/OBJECTIVES: This retrospective study aims to evaluate the long-term effectiveness of bone conduction implant (BCI) in a group of patients with SSD.<br><br>MATERIAL AND METHODS: Audiologic benefit was assessed through conventional speech audiometry and adaptive Matrix test. Impact on quality of life was evaluated with the Glasgow Benefit Inventory (GBI) questionnaire. BCI usage data were also obtained from each subject.<br><br>RESULTS: Thirty-two patients were included. No statistically significant improvements were found at standard audiometric tests using BCI, but at Matrix test the mean SRT is reached at S/N -1.16&#x2009;dB without BCI and -2.07 with BCI with a statistically significant difference (p&#x2009;=&#x2009;0.026). The mean GBI score was 25.12, ranging from -8.3 to 47.2. Ten subjects (31%) discontinued the BCI use overtime.<br><br>CONCLUSIONS AND SIGNIFICANCE: Benefit assessment of BCI in SSD recipients can be difficult. Adaptive audiometric test could be useful. Quality of life measures seem to suggest potential 'beyond-auditory' benefits. SSD recipients can be inconsistent users of BCI.}, }
@article {pmid40382338, year = {2025}, author = {Zhou, S and Zhu, Y and Du, A and Niu, S and Du, Y and Yang, Y and Chen, W and Du, S and Sun, L and Liu, Y and Wu, H and Lou, H and Li, XM and Duan, S and Yang, H}, title = {A midbrain circuit mechanism for noise-induced negative valence coding.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {4610}, pmid = {40382338}, issn = {2041-1723}, support = {LR24C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Animals ; *Ventral Tegmental Area/physiology/cytology ; *Noise ; Mice ; *Inferior Colliculi/physiology/cytology ; GABAergic Neurons/physiology/metabolism ; Male ; Mice, Inbred C57BL ; Acoustic Stimulation ; *Emotions/physiology ; Geniculate Bodies/physiology ; *Mesencephalon/physiology ; Auditory Pathways/physiology ; Optogenetics ; Auditory Perception/physiology ; Female ; Dopamine/metabolism ; Neurons/physiology ; }, abstract = {Unpleasant sounds elicit a range of negative emotional reactions, yet the underlying neural mechanisms remain largely unknown. Here we show that glutamatergic neurons in the central inferior colliculus (CIC[glu]) relay noise information to GABAergic neurons in the ventral tegmental area (VTA[GABA]) via the cuneiform nucleus (CnF), encoding negative emotions in mice. In contrast, the CIC[glu]→medial geniculate (MG) canonical auditory pathway processes salient stimuli. By combining viral tracing, calcium imaging, and optrode recording, we demonstrate that the CnF acts downstream of CIC[glu] to convey negative valence to the mesolimbic dopamine system by activating VTA[GABA] neurons. Optogenetic or chemogenetic inhibition of any connection within the CIC[glu]→CnF[glu] → VTA[GABA] circuit, or direct excitation of the mesolimbic dopamine (DA) system is sufficient to alleviate noise-induced negative emotion perception. Our findings highlight the significance of the CIC[glu]→CnF[glu] → VTA[GABA] circuit in coping with acoustic stressors.}, }
@article {pmid40381460, year = {2025}, author = {Qi, G and Zhao, S and Yu, J and Li, P and Guan, W}, title = {Recognizing autonomous driving disengagement scenarios using the transferable knowledge from human driver's EEG cognitive data.}, journal = {Accident; analysis and prevention}, volume = {219}, number = {}, pages = {108102}, doi = {10.1016/j.aap.2025.108102}, pmid = {40381460}, issn = {1879-2057}, mesh = {Humans ; *Electroencephalography ; *Automobile Driving/psychology ; Male ; *Cognition/physiology ; Adult ; Female ; Computer Simulation ; }, abstract = {Without human participation in driving operations, the adoption of autonomous driving (AD) technology greatly enhances driving safety by reducing human errors. Even though AD can handle common scenarios properly, some exceptions still call for the human takeover with AD failing to engage due to the incomprehensible or intensely conflict situations that rarely occur. To help AD understand and recognize the disengagement scenarios effectively, this paper incorporates the human electroencephalogram (EEG) cognitive data into modeling and proposes a transfer learning framework to let AD absorb the integrative knowledge from the manual driving (MD). Several disengagement scenarios are designed using a driving simulator and EEG data are collected from both "drivers" in MD and "supervisors" in AD. A conditional maximum mean discrepancy (CMMD) function is introduced to identify the common brain activity characteristics, allowing the recognition model to be transferred from the cognitively demanding domain of MD to the less demanding domain of AD. The results indicate that the proposed model can achieve an 80 % recognition rate for typical disengagement scenarios, such as static obstacles, intersection conflict and vehicle cut-in, using only 30 % of AD training labels. The transferable common feature space from EEG data improves the recognition accuracy by 21.2 % compared with the model only using AD domain data. By accurately recognizing the type of disengagement scenarios, the AD system can activate appropriate safety mechanisms or provide more explicit takeover prompts, which could effectively reduce the risk of accidents due to delayed or incorrect takeovers.}, }
@article {pmid40380329, year = {2025}, author = {Chen, W and Chen, H and Jiang, W and Chen, C and Xu, M and Ruan, H and Chen, H and Yu, Z and Chen, S}, title = {Heart rate variability and heart rate asymmetry in adolescents with major depressive disorder during nocturnal sleep period.}, journal = {BMC psychiatry}, volume = {25}, number = {1}, pages = {497}, pmid = {40380329}, issn = {1471-244X}, support = {A20240472//Hangzhou Municipal General Medical and Health Plan/ ; }, mesh = {Humans ; *Depressive Disorder, Major/physiopathology ; *Heart Rate/physiology ; Adolescent ; Male ; Female ; Electrocardiography ; *Sleep/physiology ; Case-Control Studies ; }, abstract = {BACKGROUND: Although reduced heart rate variability (HRV) has been observed in adolescents with major depressive disorder (MDD), substantial between-study heterogeneity and conflicting outcomes exist. Moreover, few studies have investigated heart rate asymmetry (HRA) features despite the high sensitivity of nonlinear indices to heart rate fluctuations. This study aimed to investigate the variations in HRV measures, especially the nonlinear features of HRA, among adolescents with MDD during the nocturnal sleep period.<br><br>METHODS: Adolescents with MDD and healthy controls completed the clinical assessment of depressive symptom severity and sleep quality followed by a three-night sleep electrocardiogram (ECG) monitoring. Traditional time-domain and frequency-domain HRV measures, nonlinear HRA measures, and the prevalence of different HRA forms and HRA compensation were calculated.<br><br>RESULTS: A total of 61 participants with 154 nocturnal ECG time series were available for analysis. Vagally-mediated HRV measures, such as RMSSD, PNN50, and HF, as well as C1d were statistically lower in clinically depressed adolescents compared with healthy controls, whereas C2d was significantly higher. A substantial decrease in the prevalence of short-term HRA, long-term HRA, and the corresponding compensation effect were also observed. In contrast to the medium to large effect sizes observed in traditional HRV indices, nonlinear HRA features showed extremely large effect sizes in discriminating MDD (C1d: Cohen's d= -&#x2009;1.38; C2d: Cohen's d&#x2009;=&#x2009;1.11), and exhibited a statistical correlation with the severity of depression (C1d: rho = -&#x2009;0.269; C2d: rho&#x2009;=&#x2009;0.243). Moreover, there were no significant differences in the distributions of nocturnal HRA measures collected over various nights.<br><br>CONCLUSION: Adolescents with MDD suffered a significant decrease in vagal tone compared to healthy controls, and the features focusing on the directionality of heart rate variations may provide further information on cardiac autonomic activity associated with depression.}, }
@article {pmid40379686, year = {2025}, author = {Bom, MS and Brak, AMA and Raemaekers, M and Ramsey, NF and Vansteensel, MJ and Branco, MP}, title = {Large-scale fMRI dataset for the design of motor-based Brain-Computer Interfaces.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {804}, pmid = {40379686}, issn = {2052-4463}, mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging ; Child ; Adolescent ; Aged ; Adult ; Aged, 80 and over ; Middle Aged ; Young Adult ; Male ; Female ; }, abstract = {Functional Magnetic Resonance Imaging (fMRI) data is commonly used to map sensorimotor cortical organization and to localise electrode target sites for implanted Brain-Computer Interfaces (BCIs). Functional data recorded during motor and somatosensory tasks from both adults and children specifically designed to map and localise BCI target areas throughout the lifespan is rare. Here, we describe a large-scale dataset collected from 155 human participants while they performed motor and somatosensory tasks involving the fingers, hands, arms, feet, legs, and mouth region. The dataset includes data from both adults and children (age range: 6-89 years) performing a set of standardized tasks. This dataset is particularly relevant to study developmental patterns in motor representation on the cortical surface and for the design of paediatric motor-based implanted BCIs.}, }
@article {pmid40378852, year = {2025}, author = {Ding, W and Liu, A and Cheng, L and Chen, X}, title = {Data augmentation using masked principal component representation for deep learning-based SSVEP-BCIs.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/add9d1}, pmid = {40378852}, issn = {1741-2552}, mesh = {*Deep Learning ; Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Principal Component Analysis/methods ; Photic Stimulation/methods ; Male ; Adult ; }, abstract = {Objective.Data augmentation has been demonstrated to improve the classification accuracy of deep learning models in steady-state visual evoked potential-based brain-computer interfaces (BCIs), particularly when dealing with limited electroencephalography (EEG) data. However, current data augmentation methods often rely on signal-level manipulations, which may lead to significant distortion of EEG signals. To overcome this limitation, this study proposes a component-level data augmentation method called masked principal component representation (MPCR).Approach.MPCR utilizes a principal component-based reconstruction approach, integrating a random masking strategy applied to principal component representations. Specifically, certain principal components are randomly selected and set to zero, thereby introducing random perturbations in the reconstructed samples. Furthermore, reconstructing samples via linear combinations of the remaining components effectively preserves the primary inherent structure of EEG signals. By expanding the input space covered by training samples, MPCR helps the trained model learn more robust features. To validate the efficacy of MPCR, experiments are performed on two widely utilized public datasets.Main results.Experimental results indicate that MPCR substantially enhances classification accuracy across diverse deep learning models. Additionally, in comparison to various state-of-the-art data augmentation approaches, MPCR demonstrates both greater performance and high compatibility.Significance.This study proposes a simple yet effective component-level data augmentation method, contributing valuable insights for advancing data augmentation methods in EEG-based BCIs.}, }
@article {pmid40377015, year = {2025}, author = {Yang, T and Zhang, D and Huang, H and Liu, F and Wu, J and Ma, X and Liu, S and Huang, M and Zhou, YD and Shen, Y}, title = {Astrocytic mGluR5-dependent calcium hyperactivity promotes amyloid-β pathology and cognitive impairment.}, journal = {Brain : a journal of neurology}, volume = {}, number = {}, pages = {}, doi = {10.1093/brain/awaf186}, pmid = {40377015}, issn = {1460-2156}, abstract = {Astrocytic dysfunction is a crucial factor for the pathogenesis of Alzheimer's disease. Metabotropic glutamate receptor 5 (mGluR5) is ubiquitously expressed in the brain and is a key molecule that regulates synaptic transmission and plasticity. It has been shown that mGluR5 is elevated in astrocytes in Alzheimer's disease. However, it remains elusive how astrocytic mGluR5 contributes to the pathogenesis of Alzheimer's disease. Here, we first quantified a high expression level of astrocytic mGluR5 in the hippocampus of Alzheimer's disease brains and demonstrated that the expression of astrocytic mGluR5 was positively correlated with Alzheimer's disease progression in both humans and mice. Upregulating astrocytic mGluR5 in the CA1 area at an early stage accelerated, whereas downregulating these receptors rescued, Aβ pathology and cognitive impairment in Alzheimer's disease mice. Moreover, the activation of mGluR5 led to calcium hyperactivity in astrocytes, causing Aβ pathology progression due to dysregulated Aβ uptake and degradation in astrocytes. Importantly, attenuating astrocytic calcium hyperactivity in the hippocampal CA1 area in the prodromal phase ameliorated Aβ pathology and cognitive defects in Alzheimer's disease mice. Our findings thus reveal a fundamental contribution of astrocytic mGluR5 in presymptomatic Alzheimer's disease that may serve as a potential diagnostic and therapeutic target for early Alzheimer's disease pathogenesis.}, }
@article {pmid40374051, year = {2025}, author = {Wang, M and Wang, Y and Yang, Y}, title = {Dynamic and low-dimensional modeling of brain functional connectivity on Riemannian manifolds.}, journal = {NeuroImage}, volume = {314}, number = {}, pages = {121243}, doi = {10.1016/j.neuroimage.2025.121243}, pmid = {40374051}, issn = {1095-9572}, mesh = {Humans ; *Brain/physiology ; *Models, Neurological ; Algorithms ; *Connectome/methods ; Magnetic Resonance Imaging/methods ; }, abstract = {Modeling brain functional connectivity (FC) is key in investigating brain functions and dysfunctions. FC is typically quantified by symmetric positive definite (SPD) matrices that are located on a Riemannian manifold rather than the regular Euclidean space, whose modeling faces three challenges. First, FC can be time-varying and the temporal dynamics of FC matrix time-series need to be modeled within the constraint of the SPD Riemannian manifold geometry, which remains elusive. Second, the FC matrix time-series exhibits considerable stochasticity, whose probability distribution is difficult to model on the Riemannian manifold. Third, FC matrices are high-dimensional and dimensionality reduction methods for SPD matrix time-series are still lacking. Here, we develop a Riemannian state-space modeling framework to simultaneously address the challenges. First, we construct a new Riemannian state-space model (RSSM) to define a hidden SPD matrix state to achieve dynamic, stochastic, and low-dimensional modeling of FC matrix time-series on the SPD Riemannian manifold. Second, we develop a new Riemannian Particle Filter (RPF) algorithm to estimate the hidden low-dimensional SPD matrix state and predict the FC matrix time-series. Third, we develop a new Riemannian Expectation Maximization (REM) algorithm to fit the RSSM parameters. We evaluate the proposed RSSM, RPF, and REM using simulation and real-world EEG datasets, demonstrating that the RSSM enables accurate prediction of the EEG FC time-series and classification of emotional states, outperforming traditional Euclidean methods. Our results have implications for modeling brain FC on the SPD Riemannian manifold to study various brain functions and dysfunctions.}, }
@article {pmid40373768, year = {2025}, author = {Luo, T and Liu, C and Cheng, T and Zhao, GQ and Huang, Y and Luan, JY and Guo, J and Liu, X and Wang, YF and Dong, Y and Xiao, Y and He, E and Sun, RZ and Chen, X and Chen, J and Ma, J and Megason, S and Ji, J and Xu, PF}, title = {Establishing dorsal-ventral patterning in human neural tube organoids with synthetic organizers.}, journal = {Cell stem cell}, volume = {32}, number = {7}, pages = {1071-1086.e8}, doi = {10.1016/j.stem.2025.04.011}, pmid = {40373768}, issn = {1875-9777}, mesh = {*Organoids/cytology/metabolism ; Humans ; *Neural Tube/cytology/embryology/metabolism ; Animals ; *Body Patterning ; Zebrafish ; Pluripotent Stem Cells/cytology/metabolism ; Wnt Signaling Pathway ; Mice ; }, abstract = {Precise dorsal-ventral (D-V) patterning of the neural tube (NT) is essential for the development and function of the central nervous system. However, existing models for studying NT D-V patterning and related human diseases remain inadequate. Here, we present organizers derived from pluripotent stem cell aggregate fusion ("ORDER"), a method that establishes opposing BMP and SHH gradients within neural ectodermal cell aggregates. Using this approach, we generated NT organoids with ordered D-V patterning from both zebrafish and human pluripotent stem cells (hPSCs). Single-cell transcriptomic analysis revealed that the synthetic human NT organoids (hNTOs) closely resemble the human embryonic spinal cord at Carnegie stage 12 (CS12) and exhibit greater similarity to human NT than to mouse models. Furthermore, using the hNTO model, we demonstrated the critical role of WNT signaling in regulating intermediate progenitors, modeled TCTN2-related D-V patterning defects, and identified a rescue strategy.}, }
@article {pmid40372852, year = {2025}, author = {Lo, YT and Maggi, A and Wu, K and Zhong, H and Choi, W and Nguyen, TD and Abedi, A and Agyeman, K and Sakellaridi, S and Reggie Edgerton, V and Kreydin, E and Lee, D and Sideris, C and Liu, CY and Christopoulos, VN}, title = {Exploring the Feasibility of Bidirectional Spinal Cord Machine Interface Through Sensing and Stimulation of Axonal Bundles.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {2004-2012}, doi = {10.1109/TNSRE.2025.3570324}, pmid = {40372852}, issn = {1558-0210}, mesh = {*Axons/physiology ; *Spinal Cord/physiology ; Feasibility Studies ; Action Potentials/physiology ; Animals ; Spinal Cord Injuries/rehabilitation/physiopathology ; Microelectrodes ; Electrodes, Implanted ; Electric Stimulation ; Rats ; Proprioception/physiology ; Rats, Sprague-Dawley ; Male ; Touch/physiology ; Female ; }, abstract = {Spinal cord injury (SCI) patients experience long-term deficits in motor and sensory functions. While brain-machine interface (BMI) has shown great promise for restoring neurological functions after SCI, spinal cord-machine interface (SCMI) offers unique advantages, such as more defined somatotopy and the compact organization of neural elements in the spinal cord. In the current study, we aim to demonstrate the feasibility of sensing and evoking compound action potentials (CAPs) via electrode implantation in spinal cord axonal bundles, an essential prerequisite for advancing SCMI development. To do so, we designed microelectrode arrays (MEA) optimized for recording and stimulation in the spinal cord. For sensory mapping, the MEAs were inserted into the lumbar dorsal column (i.e., the fasciculus gracilis) to determine somatotopic representations corresponding to tactile stimulation across lower body regions and assess proprioceptive signals with varying hip positions. For stimulations, at the L3 level, we delivered electrical pulses both rostrally, along ascending afferent tracts (dorsal column), and caudally, down descending corticospinal tract. We successfully captured axonal CAPs from the dorsal columns with high spatial precision that corresponded to known dermatomal somatotopy. Proprioceptive changes produced by abduction at the hip resulted in modulation of discharge frequency in the dorsal column axons. We demonstrated that stimulation pulses emitted by a caudally placed electrode could be propagated up the ascending fibers and be intercepted by a rostrally placed electrode array along the same axonal tracts. We also confirmed that electrical pulses can be directed down descending corticospinal tracts resulting in specific activations of lower limb muscles. These findings set a critical groundwork for developing closed-loop, bidirectional SCMI systems capable of sensing and modulating spinal cord activity.}, }
@article {pmid40371570, year = {2025}, author = {Ding, P and Tan, L and Pan, H and Gong, A and Nan, W and Fu, Y}, title = {The Lack of Neurofeedback Training Regulation Guidance and Process Evaluation May be a Source of Controversy in Post-Traumatic Stress Disorder-Neurofeedback Research: A Systematic Review and Statistical Analysis.}, journal = {Brain connectivity}, volume = {}, number = {}, pages = {}, doi = {10.1089/brain.2024.0084}, pmid = {40371570}, issn = {2158-0022}, abstract = {Objectives: Neurofeedback (NF) based on brain-computer interface (BCI) is an important direction in adjunctive interventions for post-traumatic stress disorder (PTSD). However, existing research lacks comprehensive methodologies and experimental designs. There are concerns in the field regarding the effectiveness and mechanistic interpretability of NF, prompting this study to conduct a systematic analysis of primary NF techniques and research outcomes in PTSD modulation. The study aims to explore reasons behind these concerns and propose directions for addressing them. Methods: A search conducted in the Web of Science database up to December 1, 2023, yielded 111 English articles, of which 80 were excluded based on predetermined criteria irrelevant to this study. The remaining 31 original studies were included in the literature review. A checklist was developed to assess the robustness and credibility of these 31 studies. Subsequently, these original studies were classified into electroencephalogram-based NF (EEG-NF) and functional magnetic resonance imaging-based NF (fMRI-NF) based on BCI type. Data regarding target brain regions, target signals, modulation protocols, control group types, assessment methods, data processing strategies, and reported outcomes were extracted and synthesized. Consensus theories from existing research and directions for future improvements in related studies were distilled. Results: Analysis of all included studies revealed that the average sample size of PTSD patients in EEG and fMRI NF studies was 17.4 (SD 7.13) and 14.6 (SD 6.37), respectively. Due to sample and neurofeedback training protocol constraints, 93% of EEG-NF studies and 87.5% of fMRI-NF studies used traditional statistical methods, with minimal utilization of basic machine learning (ML) methods and no studies utilizing deep learning (DL) methods. Apart from approximately 25% of fMRI NF studies supporting exploratory psychoregulatory strategies, the remaining EEG and fMRI studies lacked explicit NF modulation guidance. Only 13% of studies evaluated NF effectiveness methods involving signal classification, decoding during the NF process, and lacking in process monitoring and assessment means. Conclusion: In summary, NF holds promise as an adjunctive intervention technique for PTSD, potentially aiding in symptom alleviation for PTSD patients. However, improvements are necessary in the process evaluation mechanisms for PTSD-NF, clarity in NF modulation guidance, and development of ML/DL methods suitable for PTSD-NF with small sample sizes. To address these challenges, it is crucial to adopt more rigorous methodologies for monitoring NF, and future research should focus on the integration of advanced data analysis techniques to enhance the effectiveness and precision of PTSD-NF interventions. Impact Statement The implications of this study are to address the limited application of Neurofeedback training (NFT) in post-traumatic stress disorder (PTSD) research, where a significant portion of the approaches, foundational research, and conclusions lack consensus. There is a notable absence of retrospective statistical analyses on NFT interventions for PTSD. This study provides a comprehensive statistical analysis and discussion of existing research, offering valuable insights for future studies. The findings hold significance for researchers, clinicians, and practitioners in the field, providing a foundation for informed, evidence-based interventions for PTSD treatment.}, }
@article {pmid40370566, year = {2025}, author = {Hong, W and Ma, H and Yang, Z and Wang, J and Peng, B and Wang, L and Du, Y and Yang, L and Zhang, L and Li, Z and Huang, H and Zhu, D and Yang, B and He, Q and Wang, J and Weng, Q}, title = {Optineurin restrains CCR7 degradation to guide type II collagen-stimulated dendritic cell migration in rheumatoid arthritis.}, journal = {Acta pharmaceutica Sinica. B}, volume = {15}, number = {3}, pages = {1626-1642}, pmid = {40370566}, issn = {2211-3835}, abstract = {Dendritic cells (DCs) serve as the primary antigen-presenting cells in autoimmune diseases, like rheumatoid arthritis (RA), and exhibit distinct signaling profiles due to antigenic diversity. Type II collagen (CII) has been recognized as an RA-specific antigen; however, little is known about CII-stimulated DCs, limiting the development of RA-specific therapeutic interventions. In this study, we show that CII-stimulated DCs display a preferential gene expression profile associated with migration, offering a new perspective for targeting DC migration in RA treatment. Then, saikosaponin D (SSD) was identified as a compound capable of blocking CII-induced DC migration and effectively ameliorating arthritis. Optineurin (OPTN) is further revealed as a potential SSD target, with Optn deletion impairing CII-pulsed DC migration without affecting maturation. Function analyses uncover that OPTN prevents the proteasomal transport and ubiquitin-dependent degradation of C-C chemokine receptor 7 (CCR7), a pivotal chemokine receptor in DC migration. Optn-deficient DCs exhibit reduced CCR7 expression, leading to slower migration in CII-surrounded environment, thus alleviating arthritis progression. Our findings underscore the significance of antigen-specific DC activation in RA and suggest OPTN is a crucial regulator of CII-specific DC migration. OPTN emerges as a promising drug target for RA, potentially offering significant value for the therapeutic management of RA.}, }
@article {pmid40369268, year = {2025}, author = {Pan, S and Cai, Y and Liu, R and Jiang, S and Zhao, H and Jiang, J and Lin, Z and Liu, Q and Lu, H and Liang, S and Fan, W and Chen, X and Wu, Y and Wang, F and Chen, Z and Hu, R and Yang, L}, title = {Targeting 5-HT to Alleviate Dose-Limiting Neurotoxicity in Nab-Paclitaxel-Based Chemotherapy.}, journal = {Neuroscience bulletin}, volume = {41}, number = {7}, pages = {1229-1245}, pmid = {40369268}, issn = {1995-8218}, mesh = {*Paclitaxel/adverse effects/toxicity ; Animals ; *Albumins/toxicity/adverse effects ; *Serotonin/blood/metabolism ; Mice ; Humans ; Male ; Female ; *Venlafaxine Hydrochloride/pharmacology/therapeutic use ; *Neurotoxicity Syndromes/drug therapy/etiology/metabolism ; Middle Aged ; Schwann Cells/drug effects/metabolism ; *Peripheral Nervous System Diseases/chemically induced/drug therapy ; *Antineoplastic Agents ; }, abstract = {Chemotherapy-induced peripheral neurotoxicity (CIPN) is a severe dose-limiting adverse event of chemotherapy. Presently, the mechanism underlying the induction of CIPN remains unclear, and no effective treatment is available. In this study, through metabolomics analyses, we found that nab-paclitaxel therapy markedly increased serum serotonin [5-hydroxtryptamine (5-HT)] levels in both cancer patients and mice compared to the respective controls. Furthermore, nab-paclitaxel-treated enterochromaffin (EC) cells showed increased 5-HT synthesis, and serotonin-treated Schwann cells showed damage, as indicated by the activation of CREB3L3/MMP3/FAS signaling. Venlafaxine, an inhibitor of serotonin and norepinephrine reuptake, was found to protect against nerve injury by suppressing the activation of CREB3L3/MMP3/FAS signaling in Schwann cells. Remarkably, venlafaxine was found to significantly alleviate nab-paclitaxel-induced CIPN in patients without affecting the clinical efficacy of chemotherapy. In summary, our study reveals that EC cell-derived 5-HT plays a critical role in nab-paclitaxel-related neurotoxic lesions, and venlafaxine co-administration represents a novel approach to treating chronic cumulative neurotoxicity commonly reported in nab-paclitaxel-based chemotherapy.}, }
@article {pmid40368962, year = {2025}, author = {Dias, C and Sousa, T and Cruz, A and Costa, D and Mouga, S and Castelhano, J and Pires, G and Castelo-Branco, M}, title = {A role for preparatory midfrontal theta in autism as revealed by a high executive load brain-computer interface reverse spelling task.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {16671}, pmid = {40368962}, issn = {2045-2322}, support = {10.54499/UI/BD/150832/2021, https://doi.org/10.54499/UI/BD/150832/2021//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; CEEC: 2021.01469.CEECIND//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; PTDC/EEI-AUT/30935/2017;//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; UIDB/4950/2020, https://doi.org/10.54499/UIDB/04950/2020//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; PT/FB/BL-2018-306//Funda&#xe7;&#xe3;o Bial/ ; CAIXA Impulse 2024//'la Caixa' Foundation/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Autistic Disorder/physiopathology ; *Theta Rhythm/physiology ; Female ; Adult ; *Executive Function/physiology ; Electroencephalography ; Young Adult ; Memory, Short-Term/physiology ; *Frontal Lobe/physiopathology ; }, abstract = {Midfrontal theta oscillations have been linked to executive function, yet their role in autism-where this function is often compromised-remains unclear. We hypothesized that preparatory increases in theta power may help normalize performance in autism. To test this, we used a challenging interactive executive function task designed to impose a high working memory load and require constant error monitoring. An electroencephalogram (EEG)-based brain-computer interface (BCI) was used to maximize cognitive load and engagement. Neural activity from autistic and non-autistic adults was compared while participants were asked to mentally reverse pseudowords (engaging working memory) and write them using the BCI, which provided real-time performance feedback (maximizing error monitoring). The study focused on theta power modulation during the preparatory (pre-response) and feedback (post-response) periods but also explored the role of posterior alpha oscillations. Results showed similar task performance between groups, but distinct recruitment of brain resources, particularly during the preparatory period. The finding of an increased preparatory theta in autism favors the hypothesis of compensatory recruitment of cognitive control and attentional mechanisms to achieve accurate results.}, }
@article {pmid40367961, year = {2025}, author = {Partovi, A and Grayden, DB and Burkitt, AN}, title = {POC-CSP: a novel parameterised and orthogonally-constrained neural network layer for learning common spatial patterns (CSP) in EEG signals.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/add8bc}, pmid = {40367961}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Machine Learning ; Brain-Computer Interfaces ; Adult ; Male ; Imagination/physiology ; }, abstract = {Objective. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it has shortcomings including sensitivity to noise and rigidity in the value of the weights. Our goal was to transform CSP into a trainable machine learning model that can learn from data, be regularized, and be integrated into end-to-end classification networks.Approach. We developed a novel parameterised and orthogonally-constrained neural network layer for learning CSPs (POC-CSP) that maintains CSP's mathematical properties while allowing trainable weights. The layer uses parameterisation based on Lie Group theory to convert constrained optimisation into unconstrained optimisation, enabling integration with standard neural network (NN) training methods. We evaluated the approach on two public motor imagery datasets, focusing on both subject-specific and multi-subject paradigms.Main results. POC-CSP outperformed both conventional CSP and existing NN implementations in subject-specific classification tasks. In a novel multi-subject paradigm, POC-CSP achieved superior generalisation. When fine-tuned with just 50% of a new subject's data, POC-CSP achieved 0.95 average accuracy across subjects, substantially outperforming subject-specific models trained with more data.Significance. These findings demonstrate that combining CSP's proven effectiveness with NNs' flexibility can significantly improve EEG signal processing performance. The ability to generalize across subjects and achieve high accuracy with minimal subject-specific training data makes POC-CSP particularly valuable for practical brain-computer interface applications, where collecting large amounts of training data from each new user is often impractical or unfeasible.}, }
@article {pmid40367953, year = {2025}, author = {Xu, X and Drougard, N and Roy, RN}, title = {Does topological data analysis work for EEG-based brain-computer interfaces?.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/add8bd}, pmid = {40367953}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Adult ; Male ; *Brain/physiology ; Female ; Databases, Factual ; Imagination/physiology ; *Data Analysis ; }, abstract = {Objective.Brain-computer interfaces (BCIs) are systems that establish a direct communication pathway with machines through brain activity only, recorded for example via electroencephalography (EEG). Topological data analysis (TDA) extracts topological features of the shape of the data and showed promising results in various applications. However, the work evaluating TDA systematically on EEG-based BCI is rare. Our study aims to fill this gap.Approach.The hypothesis is that the topology of the EEG dynamics is different under different mental states so that the topological features are discriminant. By adopting a dynamical system point of view, the non-stationary nature of EEG is respected. In practice, topological information is encoded by the persistence diagram. To turn it into a feature vector, some classical vector- and function-based representations are used. Each feature vector is then classified by several basic linear and non-linear classifiers.Main results.A benchmark comparing TDA with the gold standard methods was established on 3 publicly available datasets (2 active BCI datasets based on motor-imagery, 1 passive BCI dataset for mental workload estimation). TDA had significantly lower performance in intra-subject classification, yet comparable and sometimes higher performance in inter-subject classification. The persistence consistently outperformed all other topological features. We explained theoretically the link between persistence and spectral power and demonstrated it experimentally.Significance.To our knowledge, this is the first study that evaluates TDA in both intra- and inter-subject classification on various types of datasets. Insights on the connection between persistence and classical EEG features are also given for the first time.}, }
@article {pmid40367199, year = {2025}, author = {Yashinski, M}, title = {Neuroprosthesis converts brain activity to speech.}, journal = {Science robotics}, volume = {10}, number = {102}, pages = {eady7192}, doi = {10.1126/scirobotics.ady7192}, pmid = {40367199}, issn = {2470-9476}, mesh = {Humans ; *Speech/physiology ; *Brain-Computer Interfaces ; *Brain/physiology ; *Neural Prostheses ; }, abstract = {A neuroprosthesis decodes short bits of neural activity and synthesizes speech synchronously with a user's vocal intent.}, }
@article {pmid40366622, year = {2025}, author = {Yang, L and Li, H and Wang, X}, title = {Psilocybin and Obsessive-Compulsive Disorder: Exploring New Therapeutic Horizons.}, journal = {Neuroscience bulletin}, volume = {41}, number = {7}, pages = {1302-1306}, pmid = {40366622}, issn = {1995-8218}, }
@article {pmid40366280, year = {2025}, author = {Liu, M and Chang, S and Chen, M and Li, P and Roe, AW and Hu, JM}, title = {How shape information is coded by V4 cortical response of macaque monkey.}, journal = {Journal of neurophysiology}, volume = {133}, number = {6}, pages = {2016-2028}, doi = {10.1152/jn.00520.2024}, pmid = {40366280}, issn = {1522-1598}, support = {32471052//MOST | National Natural Science Foundation of China (NSFC)/ ; 32100802//MOST | National Natural Science Foundation of China (NSFC)/ ; }, mesh = {Animals ; Macaca mulatta ; *Visual Cortex/physiology/cytology ; *Neurons/physiology ; Male ; *Form Perception/physiology ; *Pattern Recognition, Visual/physiology ; }, abstract = {Previous neural recording studies have shown that monkey V4 can process shape information across populations of neurons. The responses recorded from each single neuron make it possible to retrieve shape information. However, these studies did not fully characterize the spatial distribution of activity in the cortex. There are multiple types of functional columns (orientation, curvature) in V4; how do these structures respond to different shapes? Here, with intrinsic optical imaging, we explored the cortical responses of V4 to contours (straight and curved) and shapes (circle and square). We found that in V4 the response of neurons to different shapes is highly dependent on the compositional features contained in the shape. A specific local network would have a higher response magnitude to its corresponding shape than other shapes. Meanwhile, the cortical response of V4 exhibits a tolerance to the shift of stimulus location. Our results suggest that two essential cortical response features in V4 are the specificity of the activated response pattern in the cortex and tolerance to the stimulus location variance. These features can help decode shape information from imaging results.NEW &amp; NOTEWORTHY At the cortical response level, the V4 area of the macaque monkey employs two critical principles of shape coding: specificity of the activated response pattern to shape components within the stimuli and tolerance to variations in stimulus location.}, }
@article {pmid40364497, year = {2025}, author = {Kim, JH and Nam, H and Won, D and Im, CH}, title = {Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.}, journal = {Experimental neurobiology}, volume = {34}, number = {3}, pages = {119-130}, pmid = {40364497}, issn = {1226-2560}, abstract = {Electroencephalography (EEG) provides high temporal resolution and noninvasiveness for a range of practical applications, including emotion recognition. However, inherent variability across subjects poses significant challenges to model generalizability. In this study, we systematically evaluated twelve approaches by combining four domain generalization (DG) techniques, Deep CORAL, GroupDRO, VREx, and DANN, with three representative deep learning architectures (ShallowFBCSPNet, EEGNet, and TSception) to enable improved subject-independent EEG-based emotion recognition. The performances of the DG-integrated deep learning models were quantitatively evaluated using two emotional EEG datasets collected by the authors. Data from each subject were treated as distinct domains in each model. Binary classification tasks were conducted to identify the valence or arousal state of each participant based on a ten-fold cross-validation strategy. The results indicated that the application of DG methods consistently enhanced classification accuracy across datasets. In one dataset, TSception combined with VREx achieved the highest performance for both valence and arousal classifications. In the other dataset, TSception with VREx still yielded the highest valence classification accuracy, while TSception combined with GroupDRO showed the best arousal classification performance among the twelve models, slightly outperforming TSception with VREx. These findings underscore the potential of DG approaches to mitigate distributional shifts caused by intersubject and intersession variabilities to implement robust subject-independent EEG-based emotion recognition systems.}, }
@article {pmid40363359, year = {2025}, author = {Deng, X and Huo, H and Ai, L and Xu, D and Li, C}, title = {A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {9}, pages = {}, pmid = {40363359}, issn = {1424-8220}, support = {No. XTZW2024-KF02//Chongqing Key Laboratory of Germplasm Innovation 755 and Utilization of Native Plants under Grant/ ; }, mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Signal-To-Noise Ratio ; Movement/physiology ; Deep Learning ; Algorithms ; }, abstract = {Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite the significant clinical and application value of MI-BCI technology, accurately decoding high-dimensional and low signal-to-noise ratio (SNR) electroencephalography (EEG) signals remains challenging. Moreover, traditional deep learning approaches exhibit limitations in processing EEG signals, particularly in capturing the intrinsic correlations between electrode channels and long-distance temporal dependencies. To address these challenges, this research introduces a novel end-to-end decoding network that integrates convolutional neural networks (CNNs) and a Swin Transformer, aiming at enhancing the classification accuracy of the MI paradigm in EEG signals. This approach transforms EEG signals into a three-dimensional data structure, utilizing one-dimensional convolutions along the temporal dimension and two-dimensional convolutions across the EEG electrode distribution for initial spatio-temporal feature extraction, followed by deep feature exploration using a 3D Swin Transformer module. Experimental results show that on the BCI Competition IV-2a dataset, the proposed method achieves 83.99% classification accuracy, which is significantly better than the existing deep learning methods. This finding underscores the efficacy of combining a CNN and Swin Transformer in a 3D data space for processing high-dimensional, low-SNR EEG signals, offering a new perspective for the future development of MI-BCI. Future research could further explore the applicability of this method across various BCI tasks and its potential clinical implementations.}, }
@article {pmid40363182, year = {2025}, author = {Hougaard, BI and Knoche, H and Kristensen, MS and Jochumsen, M}, title = {Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {9}, pages = {}, pmid = {40363182}, issn = {1424-8220}, support = {22357//VELUX FONDEN/ ; }, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Video Games ; Aged ; Adult ; *Stroke/physiopathology ; User-Computer Interface ; }, abstract = {Designers of digital rehabilitation experiences can accommodate error-prone input devices like brain-computer interfaces (BCIs) by incorporating virtual help mechanisms to adjust the difficulty, but it is unclear on what grounds users are willing to accept such help. To study users' experience of virtual help mechanisms, we used three help mechanisms in a blink-controlled game simulating a BCI-based stroke rehabilitation exercise. A mixed-method, simulated BCI study was used to evaluate game help by 19 stroke patients who rated their frustration and perceived control when experiencing moderately high input recognition. None of the help mechanisms affected ratings of frustration, which were low throughout the study, but two mechanisms affected patients' perceived control ratings positively and negatively. Patient ratings were best explained by the amount of positive feedback, including game help, which increased perceived control ratings by 8% and decreased frustration ratings by 3%. The qualitative analysis revealed appeal, interference, self-blame, and prominence as deciding experiential factors of help, but it was unclear how they affected frustration and perceived control ratings. Building upon the results, we redesigned and tested self-reported measures of help quantity, help appeal, irritation, and pacing with game-savvy adults in a follow-up study using the same game. Help quantity appeared larger when game help shielded players from negative feedback, but this did not necessarily appeal to them. Future studies should validate or control for the constructs of perceived help quantity and appeal.}, }
@article {pmid40361409, year = {2025}, author = {Marín-Liébana, S and Llor, P and Serrano-García, L and Fernández-Murga, ML and Comes-Raga, A and Torregrosa, D and Pérez-García, JM and Cortés, J and Llombart-Cussac, A}, title = {Gene Expression Signatures for Guiding Initial Therapy in ER+/HER2- Early Breast Cancer.}, journal = {Cancers}, volume = {17}, number = {9}, pages = {}, pmid = {40361409}, issn = {2072-6694}, abstract = {In triple-negative (TNBC) and human epidermal growth factor receptor 2-positive (HER2+) breast cancer patients, neoadjuvant systemic therapy is the standard recommendation for tumors larger than 2 cm. Monitoring the response to primary systemic therapy allows for the assessment of treatment effects, the need for breast-conserving surgery (BCS), and the achievement of pathological complete responses (pCRs). In estrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer, the benefit of neoadjuvant strategies is controversial, as they have shown lower tumor downstaging and pCR rates compared to other breast cancers. In recent decades, several gene expression assays have been developed to tailor adjuvant treatments in ER+/HER2- early breast cancer (EBC) to identify the patients that will benefit the most from adjuvant chemotherapy (CT) and those at low risk who could be spared from undergoing CT. It is still a challenge to identify patients who will benefit from neoadjuvant systemic treatment (CT or endocrine therapy (ET)). Here, we review the published data on the most common gene expression signatures (MammaPrint (MP), BluePrint (BP), Oncotype Dx, PAM50, the Breast Cancer Index (BCI), and EndoPredict (EP)) and their ability to predict the response to neoadjuvant treatment, as well as the possibility of using them on core needle biopsies. Additionally, we review the changes in the gene expression signatures after neoadjuvant treatment, and the ongoing clinical trials related to the utility of gene expression signatures in the neoadjuvant setting.}, }
@article {pmid40360495, year = {2025}, author = {Li, J and Mo, D and Hu, J and Wang, S and Gong, J and Huang, Y and Li, Z and Yuan, Z and Xu, M}, title = {PEDOT:PSS-based bioelectronics for brain monitoring and modulation.}, journal = {Microsystems &amp; nanoengineering}, volume = {11}, number = {1}, pages = {87}, pmid = {40360495}, issn = {2055-7434}, support = {30802-110690303//Guangdong Science and Technology Department (Science and Technology Department, Guangdong Province)/ ; 2021ZD0204300//National Science Foundation of China | Major Research Plan/ ; MYRGGRG2023-00038-FHS//Universidade de Macau (University of Macau)/ ; 28709-312200502501//Beijing Normal University (BNU)/ ; }, abstract = {The growing demand for advanced neural interfaces that enable precise brain monitoring and modulation has catalyzed significant research into flexible, biocompatible, and highly conductive materials. PEDOT:PSS-based bioelectronic materials exhibit high conductivity, mechanical flexibility, and biocompatibility, making them particularly suitable for integration into neural devices for brain science research. These materials facilitate high-resolution neural activity monitoring and provide precise electrical stimulation across diverse modalities. This review comprehensively examines recent advances in the development of PEDOT:PSS-based bioelectrodes for brain monitoring and modulation, with a focus on strategies to enhance their conductivity, biocompatibility, and long-term stability. Furthermore, it highlights the integration of multifunctional neural interfaces that enable synchronous stimulation-recording architectures, hybrid electro-optical stimulation modalities, and multimodal brain activity monitoring. These integrations enable fundamentally advancing the precision and clinical translatability of brain-computer interfaces. By addressing critical challenges related to efficacy, integration, safety, and clinical translation, this review identifies key opportunities for advancing next-generation neural devices. The insights presented are vital for guiding future research directions in the field and fostering the development of cutting-edge bioelectronic technologies for neuroscience and clinical applications.}, }
@article {pmid40360243, year = {2025}, author = {Schurzig, D and Iseke, R and Maier, H and Prenzler, NK an