@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 & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT)/ ; 2710086164//Commercialization Promotion Agency for R&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&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&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&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&E-based morphological analysis. Conclusions: NCMT enables HER2 score-aware virtual IHC generation from H&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.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
Male
Adult
Female
Language
Neural Networks, Computer
Young Adult
Support Vector Machine

@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.},
}

*Brain-Computer Interfaces
Electroencephalography/methods
Humans
Male
Adult
Female
Electromyography
Electrooculography
Young Adult
Wearable Electronic Devices

@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.},
}

Humans
*Emotions/physiology
*Neural Networks, Computer
Electroencephalography/methods
Uncertainty
Electrocardiography
Bayes Theorem
Algorithms
Entropy

@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.},
}

Humans
*Amyloid beta-Protein Precursor/metabolism/genetics
*CRISPR-Cas Systems
*Alzheimer Disease/genetics/metabolism
Amyloid beta-Peptides/metabolism/genetics
HEK293 Cells

@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 {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.

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.

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.

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.

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 & 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 {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 {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 & 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.

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 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.

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.

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.},
}

*Hand, Foot and Mouth Disease/epidemiology/virology
Humans
China/epidemiology
Incidence
Child, Preschool
Infant
Bayes Theorem
Female
Male
*Enterovirus A, Human/isolation & purification
Infant, Newborn

@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 & 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 & 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 & 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 {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.},
}

Humans
*Feedback, Sensory/physiology
*Upper Extremity/physiology
User-Computer Interface
Brain-Computer Interfaces
Touch
*Man-Machine Systems
Artificial Limbs
*Rehabilitation

@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.

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.

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.

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.

METHODS: A total of 151 individuals with treatment-naïve 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.

RESULTS: Abnormal CC thickness was observed in individuals with treatment-naïve 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.

CONCLUSIONS: Our findings demonstrate that CC thickness abnormalities in treatment-naïve 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.

METHODS: Twenty-five eligible patients from Tianjin Hospital's Rehabilitation Department were randomly divided into an experimental group (n = 13) and a control group (n = 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 min each session over 8 weeks. Sensory and motor functions were evaluated using the ASIA scales, and the BioNeuro Infiniti system measured RMS values and integrated electromyography.

RESULTS: Post-treatment, the experimental group demonstrated improved sensory and motor functions, functional independence, and biochemical blood markers compared to the control group (p < 0.05).

CONCLUSION: Both combined electrical stimulation and needle electrode therapy effectively treat incomplete spinal cord injuries over 8 weeks, with combined electrical stimulation showing greater efficacy.

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/showproj.html?proj=129549, identifier ChiCTR2100052017.},
}

@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},
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.

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.

MAIN RESULTS: Validated in non-human primate experiments, Dual-VCT achieved a classification accuracy of 0.930±0.023 in the 3-class spatial grasping task, and a Pearson correlation coefficient (CC) of 0.910±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.

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 & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; DC018805//U.S. Department of Health & 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 {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.

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.

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).

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).

METHODS: We enrolled 43 adolescents aged 12-18 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.

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 < 0.05), indicating mitigation of sympathetic pathological deterioration rather than autonomic normalization.

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 {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 {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.

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.

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.

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.

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 & 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 {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.},
}

Humans
*Electroencephalography/methods
Movement/physiology
Male
Adult
Female
*Signal Processing, Computer-Assisted
Young Adult
*Brain-Computer Interfaces
Motor Cortex/physiology
Hand/physiology

@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).

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 ≥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.

RESULTS: Among 94 treated patients, mean age was 69±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 ≤30-day mortality rate was 4% (2%-11%). QOL scores at 12 months (n=81) improved significantly from baseline (p<0.001).

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 {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.

METHODS: Publications related to non‑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.

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‑based signal types, neural mechanisms, algorithms, external devices, and their impact on functional rehabilitation after stroke.

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‑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.

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 {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&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.

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.

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 mg/day protected against fetal death (aOR .185, 95% CI .078-.428); doses exceeding .4 mg/day further reduced risk of composite adverse outcomes (aOR .343, 95% CI .162-.675), and doses above 1 mg additionally showed trends toward decreased preterm birth in ASM-exposed pregnancies (aOR .338, 95% CI .104-.943). Compared with .4 mg supplementation, doses above 1 mg/day were associated with a lower risk of LBW (aOR .208, 95% CI .05-.58).

SIGNIFICANCE: FA supplementation was associated with lower risks of composite adverse offspring outcomes in ASM-exposed pregnancies, specifically at doses exceeding .4 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},
doi = {10.1371/journal.pbio.3003447},
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.},
}

*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

@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 & 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 {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.

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.},
}

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

@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.},
}

*Machine Learning
*Astrocytes/metabolism/pathology
*Gliosis/pathology/etiology/metabolism
Animals
Glial Fibrillary Acidic Protein/metabolism
*Foreign-Body Reaction/pathology/etiology/metabolism
Male
Rats

@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 & 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.},
}

Animals
*Bortezomib/adverse effects
Mice
*Activating Transcription Factor 3/metabolism/genetics
*Catechin/analogs & 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

@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.},
}

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

@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.},
}

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

@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.

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.

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.

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 {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},
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 {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 & 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.},
}

*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

@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 {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 {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.

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.

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.

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.

CLINICAL TRIAL REGISTRATION: ChiCTR2400092275, Chinese Clinical Trial Registry (www.chictr.org.cn).},
}

@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 {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 {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 & histology ; *Frontal Lobe/physiology/anatomy & 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.},
}

Animals
Humans
*Callithrix/physiology/anatomy & histology
*Frontal Lobe/physiology/anatomy & histology
Male
Neural Pathways/physiology
Female
Language
Pan troglodytes
Brain Mapping
Speech/physiology
Macaca
Nerve Net/physiology

@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 = {},
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 {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},
doi = {10.1371/journal.pone.0346324},
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.},
}

*Commerce
*Neural Networks, Computer
Forecasting/methods
Machine Learning
Humans

@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 {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.},
}

Humans
*Electromyography/methods
Male
Adult
Female
Algorithms
Young Adult
Neural Networks, Computer
Regression Analysis
User-Computer Interface
*Machine Learning
Calibration
Brain-Computer Interfaces

@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},
doi = {10.1631/jzus.B2500307},
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).},
}

*Alzheimer Disease/pathology/diagnosis
Humans
Amyloid beta-Peptides/metabolism
Female
tau Proteins/metabolism
Aged
Male
Oxidative Stress
Brain/pathology

@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.},
}

*Hydrogels/chemistry
Electric Conductivity
*Brain-Computer Interfaces
Animals
*Electrocorticography/methods/instrumentation
Electrodes, Implanted

@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.

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.

RESULTS: We show that per-stage CKM increase elevates hazards of transitioning from healthy to mental disorders (HR = 1.24[1.22-1.26]) and subsequently to dementia (HR = 1.38[1.21-1.58]), or directly to dementia (HR = 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.

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.},
}