@article {pmid42174338,
year = {2026},
author = {Pan, Y and Porteous, F and Rosenbaum, D and Fallgatter, A and Ehlis, AC and Koole, SL and Dikker, S},
title = {Inter-brain coupling tracks emotional co-regulation.},
journal = {Cognitive, affective & behavioral neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42174338},
issn = {1531-135X},
abstract = {When we have a negative emotional experience, we often recount this experience to others. Such emotional sharing plays a key role in building and maintaining interpersonal relationships, and is a vital component of most psychotherapies. Yet, while research shows the importance of social relationships for brain health, the neural underpinnings of emotional sharing remain largely unknown. Here, we asked whether successful processing and regulation of negative emotions can be linked to shared brain responses between dyads during and after emotional sharing. Participants watched videos eliciting either negative or neutral emotions, after which they shared their feelings about these videos with a friend. We simultaneously recorded the brain activity of both friends using functional near-infrared spectroscopy (fNIRS) and compared inter-brain coupling within sharer-listener dyads before, during, and after sharing sessions. We found that shifts in inter-brain coupling were associated with changes in mood. Specifically, an increase in inter-brain coupling after recounting a video that elicited negative emotions was associated with reduced anger and dejection in listeners and increased vigor in sharers. These findings suggest that inter-brain coupling facilitates the co-regulation of negative emotions, and thereby maintaining a healthy homeostatic balance. This knowledge holds potential relevance for informing psychotherapeutic interventions.},
}

@article {pmid42179096,
year = {2026},
author = {Hari, K and Anand, A and Naveed, A and P, V},
title = {Genetic algorithm-optimized machine learning approaches for EEG-based silent speech decoding.},
journal = {Journal of medical engineering & technology},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/03091902.2026.2676658},
pmid = {42179096},
issn = {1464-522X},
abstract = {The phases of human communication consist of speech perception, production, and imagination. The objective of this work is to understand and analyse the changes that occur in the neural signals during the hearing phase by examining electroencephalogram (EEG) patterns of the subject for different sentences. We propose optimising the decoding process using Genetic Algorithms (GA). Six different experiments are performed on Dataset 3 of coSpeech EEG Database. Both handcrafted features and CNN-based features are used for classification. GA is used for two purposes - channel selection as well as feature selection. Two classifiers - decision trees and SVMs are used for sentence classification. A benchmark accuracy of 41.92% is obtained using the proposed methods. Accuracy improves in the alpha, beta and gamma frequency sub-bands (41.79%, 40.92%, 40.27% respectively). Channel selection using GA reduces the computational load significantly (∼ 90%) while producing comparable results (34.37%, 33.20%, 32.93% in the alpha, beta and gamma sub-bands). This work highlights that EEG is a viable, non-invasive way to decode speech from subjects, which would help people with speech disorders communicate in a better way without exertion. Silent speech decoding has applications in assisting speech-impaired individuals, ensuring private communication, and enhancing human-computer interaction.},
}

@article {pmid42179723,
year = {2026},
author = {Choudhary, PK and Choudhary, S and Saha, S and Rajpoot, YS and Ciocan, VC and Nicolae-Lucian, V and Gorgan, CM and Șufaru, C},
title = {Brain-computer interfaces and neural synchronization in esports: a systematic review of effects on reaction time, decision-making, and cognitive performance.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1774230},
pmid = {42179723},
issn = {1662-5161},
abstract = {BACKGROUND: The rapid expansion of esports has intensified interest in the cognitive and neurophysiological mechanisms underlying elite performance, particularly reaction time (RT), decision-making (DM), and neural efficiency. Advances in brain-computer interfaces (BCIs) offer targeted neural modulation that may enhance these abilities through improved neural synchronization. To systematically review evidence on the effects of BCI-based neural synchronization, including motor imagery (MI) BCIs, visual evoked potential (VEP/c-VEP) systems, neural entrainment, and dual-brain coupling, on RT, DM, and related cognitive outcomes in esports athletes and competitive gamers.

METHODS: Following PRISMA 2020 guidelines, comprehensive searches were conducted across PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, ScienceDirect, and Google Scholar. Studies examining BCI-induced neural modulation and its cognitive or performance effects in esports players or experienced gamers were included. Eighteen studies met the criteria, comprising controlled trials, pre-post interventions, cross-sectional neurophysiology studies, comparative behavioural analyses, and supporting systematic reviews. Due to methodological heterogeneity, results were synthesised narratively. Although the review follows PRISMA 2020 guidelines for systematic study identification and selection, the synthesis adopts a structured integrative narrative approach due to substantial heterogeneity in study designs, BCI modalities, and outcome measures.

RESULTS: Across studies, BCI-mediated neural synchronization produced consistent improvements in RT, DM accuracy, cortical oscillatory stability, and neural connectivity. MI-BCI and gamified systems enhanced MI accuracy, user engagement, and cognitive load regulation. VEP-based BCIs accelerated perceptual processing by improving signal reliability and reducing latency. Dual-brain coupling improved coordinated decision behaviour. Additional evidence indicates that experienced gamers display superior working memory, attentional control, and visuomotor coordination compared with non-gamers. However, variability in study design, small samples, and moderate risk of bias limit the strength of causal inference.

DISCUSSION: BCI-based neural synchronization shows promise as a tool for enhancing neurocognitive performance in esports athletes. Future studies should prioritize standardized training protocols, multimodal neural-measurement methods, and longitudinal designs to determine long-term effectiveness and real-world applicability.},
}

@article {pmid42181257,
year = {2026},
author = {Xia, Y and Jin, S and Zhou, W and Huang, R and Huang, S},
title = {A comparative study of five telerehabilitation therapies for improving core symptoms in stroke patients: A network meta-analysis (2,833 patients).},
journal = {iScience},
volume = {29},
number = {6},
pages = {115774},
pmid = {42181257},
issn = {2589-0042},
abstract = {This network meta-analysis demonstrates that virtual reality therapy exhibits significant advantages in specific functional domains of remote rehabilitation: Remote virtual reality technology demonstrated the most pronounced effects in improving gait (SUCRA = 92.4%, standardized mean difference [SMD] = -1.27) and upper limb functional recovery (SUCRA = 71.3%, SMD = -0.64), while remote brain-computer interfaces showed the most significant effects in fine motor control. SMD = -1.27) and upper limb functional recovery (SUCRA = 71.3%, SMD = -0.64), while remote brain-computer interfaces showed the greatest effect in fine motor control (SUCRA = 87.6%, SMD = -1.20). Regarding quality of life improvement, exoskeleton training yielded the best results (SUCRA = 62.4%, SMD = 0.05). The findings of this study provide evidence-based support for developing personalized telerehabilitation protocols tailored to specific rehabilitation goals in clinical practice. This approach facilitates a shift in the telerehabilitation field from empirical selection to precision-targeted intervention strategies.},
}

@article {pmid42182055,
year = {2026},
author = {Feng, C and Zhang, E and Jia, Y and Zhu, Z and Zhu, J and Wu, D and Xu, K},
title = {Distributed cortico-subcortical networks enable robust speech state detection from sparse intracranial recordings.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1816455},
pmid = {42182055},
issn = {1662-4548},
abstract = {INTRODUCTION: Accurate and reliable detection of speech state transitions is a prerequisite for practical speech brain-computer interfaces (BCIs). While cortical language areas have been extensively studied, it remains unclear whether speech onset information is exclusively localized to these regions or distributed across a broader cortico-subcortical network. Here, we investigated the feasibility of decoding speech state transitions using sparse stereo-electroencephalography (SEEG) recordings that sample both cortical and subcortical structures.

METHODS: Four Mandarin-speaking epilepsy patients undergoing clinical SEEG monitoring performed a sentence-reading task. Neural signals were segmented and labeled as rest or speech based on acoustic onset. A convolutional neural network was trained to classify speech states using broadband or high-gamma features derived from different anatomical channel subsets. We further evaluated continuous decoding performance, model robustness to channel dropout, and the specific contributions of different brain regions.

RESULTS: Speech state decoding accuracy exceeded chance level (50%) in all participants, with peak single-participant accuracies surpassing 90%. Models integrating both cortical and subcortical signals generally outperformed those restricted to a single anatomical domain. Notably, broadband signals yielded higher classification accuracy than high-gamma features. In continuous decoding simulations, performance remained above chance, although reduced relative to discretized evaluation. Crucially, decoding accuracy was robust to random channel reduction (up to 50%) and remained above 70% even after excluding classical speech-related cortical regions. Contribution analyses indicated participant-specific patterns of model sensitivity, with relatively higher contributions observed in frontal regions and the thalamus in multiple participants.

DISCUSSION: These findings support the hypothesis that speech state information is represented in a distributed cortico-subcortical network rather than being confined to canonical language areas. The robustness of decoding performance despite channel reduction and regional exclusion suggests that sparsely sampled SEEG data can effectively drive speech detection modules. This study demonstrates the feasibility of utilizing deep brain recordings for speech BCIs, offering a pathway toward more stable and generalized implantable systems. Moreover, such autonomous speech state detection may also serve as an ethical safeguard, ensuring that neural language decoding is activated only during intended communicative acts.},
}

@article {pmid42182066,
year = {2026},
author = {Feng, C and Ni, H and Zhu, Z and Jiang, H and Zheng, Z and Ming, W and Wang, S and Xu, K and Zhu, J},
title = {Dataset of chronic intracranial EEG of epilepsy patients via responsive neurostimulation system.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1815732},
pmid = {42182066},
issn = {1662-4548},
}

@article {pmid42183960,
year = {2026},
author = {Chen, LW and Lian, YH and Dong, XL and Luo, KL and Zhan, LQ and Xie, LL and Sun, QK and Lin, W and Gan, SR and Cheng, XP and Ni, J and Chen, XY},
title = {Intermittent Theta-Burst Stimulation (iTBS) Improves Motor Coordination and Modulates Neuroinflammation and Autophagy in SCA3/MJD Mice.},
journal = {Cerebellum (London, England)},
volume = {25},
number = {4},
pages = {},
pmid = {42183960},
issn = {1473-4230},
support = {2023QH1088//Startup Fund for Scientific Research of Fujian Medical University/ ; 82402952//National Natural Science Foundation of China/ ; YXRQN-CXY2025//the Excellence Talent Program of The First Affiliated Hospital of Fujian Medical University/ ; },
mesh = {Animals ; *Machado-Joseph Disease/therapy/physiopathology/pathology/genetics ; *Autophagy/physiology ; Mice ; Mice, Transgenic ; Ataxin-3/genetics/metabolism ; *Transcranial Magnetic Stimulation/methods ; *Neuroinflammatory Diseases/therapy/pathology/physiopathology ; Cerebellum/pathology/metabolism ; *Psychomotor Performance/physiology ; Disease Models, Animal ; },
abstract = {Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is an autosomal dominant neurodegenerative disorder characterized by misfolded ataxin-3 aggregation and neuronal intranuclear inclusions. Its primary symptom is progressive ataxia, progressively restricting daily living activities. While repetitive transcranial magnetic stimulation (rTMS) may alleviate symptoms, the effects and mechanisms of specific rTMS paradigms, particularly intermittent and continuous theta burst stimulation (iTBS/cTBS), remain unclear in SCA3. This study therefore aimed to investigate the impacts of iTBS and cTBS on motor coordination, cerebellar neuroinflammation, and autophagy in SCA3 transgenic mice. Thirty 14-week-old SCA3 transgenic mice were randomly divided into sham, cTBS, and iTBS groups. Cerebellar stimulation was delivered at 30% maximum output (600 pulses/session, once daily, 5 days/week for 2 weeks). Motor coordination was assessed via rotarod and CatWalk gait analysis. Pathological changes were evaluated by measuring ataxin-3 protein and ubiquitin-positive inclusions. Cerebellar neuroinflammation was analyzed using Iba-1, CD206, and a cytokine array, while autophagy was assessed via Beclin-1 and LC3B expression. iTBS significantly improved motor coordination in SCA3 mice, reducing rotarod falls (vs. sham P < 0.001, vs. cTBS P < 0.05) and improving gait symmetry (vs. sham P < 0.05) and regularity index (vs. sham P < 0.01, vs. cTBS P < 0.01). It also alleviated cerebellar pathology, lowering ataxin-3 expression (vs. sham P < 0.01, vs. cTBS P < 0.01) and ubiquitin-positive inclusions (vs. sham P < 0.01, vs. cTBS P < 0.05). While both iTBS and cTBS increased Iba-1-positive cells (P < 0.05 and P < 0.05, respectively, vs. sham), only iTBS raised CD206-positive cells (vs. sham P < 0.05) and downregulated pro-inflammatory cytokines. Furthermore, iTBS activated autophagy, enhancing Beclin-1 (vs. sham P < 0.05) and LC3B expression (vs. sham P < 0.0001, vs. cTBS P < 0.001). iTBS improved motor coordination and alleviated core cerebellar pathology in SCA3 mice. This effect may be mediated through the downregulation of cerebellar neuroinflammation and the activation of autophagy. Furthermore, the therapeutic efficacy of iTBS was superior to that of cTBS across multiple dimensions, demonstrating distinct paradigm specificity.},
}

Animals
*Machado-Joseph Disease/therapy/physiopathology/pathology/genetics
*Autophagy/physiology
Mice
Mice, Transgenic
Ataxin-3/genetics/metabolism
*Transcranial Magnetic Stimulation/methods
*Neuroinflammatory Diseases/therapy/pathology/physiopathology
Cerebellum/pathology/metabolism
*Psychomotor Performance/physiology
Disease Models, Animal

@article {pmid42184169,
year = {2026},
author = {Mathon, B and Mokhtari, K and Galanaud, D and Carpentier, A},
title = {Development and preclinical evaluation of a hybrid stereoelectroencephalographic-laser depth electrode for magnetic resonance imaging-guided interstitial thermal therapy in drug-resistant epilepsy.},
journal = {Epilepsia},
volume = {},
number = {},
pages = {},
doi = {10.1002/epi.70313},
pmid = {42184169},
issn = {1528-1167},
abstract = {OBJECTIVE: This study was undertaken to design and validate a hybrid depth electrode combining stereoelectroencephalographic (sEEG) recording and magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) under real-time magnetic resonance thermometry, to streamline the transition from invasive localization to focal ablation in patients with drug-resistant focal epilepsy.

METHODS: We engineered a magnetic resonance imaging (MRI)-compatible depth probe that integrated intracerebral EEG contacts and a central optical fiber for laser delivery. The contact materials and geometry were optimized to reduce susceptibility artifacts and preserve the proton resonance frequency (PRF) thermometry. Preclinical testing included MRI artifact screening in phantoms, thermal performance testing, PRF thermometry validation against temperature sensors in phantoms and ovine brain, artifact quantification versus clinical depth electrodes, electrophysiologic signal quality assessment before/after thermal stress, and in vivo canine feasibility with serial MRI and histology. MRI compatibility was confirmed for a next generation contact variant.

RESULTS: The optimized contact design produced small MRI artifacts and preserved PRF thermometry outside an approximately 2-mm pericontact exclusion zone. Thermal testing showed localized heating with rapid postlaser decay, modulation by coolant flow, and performance comparable to that of clinical LITT applicators. In dipole-phantom testing, baseline electrophysiological recordings from the new hybrid electrode were comparable to clinical depth-electrode controls, whereas a previously heated hybrid electrode showed increased noise under low-amplitude conditions. In vivo, MRgLITT produced sharply demarcated lesions that scaled with the delivered energy without hemorrhage, edema, midline shift, or device damage. Histological examination revealed coagulative necrosis with a narrow perilesional zone and no carbonization at the contacts.

SIGNIFICANCE: This patented hybrid sEEG-laser electrode supports a "diagnose-model-treat-verify" strategy along a single stereotactic trajectory, enabling sEEG confirmation followed by MRgLITT without a second stereotactic implantation in selected patients. These data support progression to first-in-human evaluation and integration into epilepsy surgery workflows, particularly for MRI-negative focal epilepsies, where minimally invasive strategies are favored.},
}

@article {pmid42184181,
year = {2026},
author = {An, Y and Tong, Y and Wang, W and Su, SW},
title = {Enhancing Brain Signal Generation Through A Hybrid Approach Integrating Reinforcement Learning And Diffusion Models.},
journal = {IEEE transactions on medical imaging},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TMI.2026.3696676},
pmid = {42184181},
issn = {1558-254X},
abstract = {Developing a reliable EEG-based Brain Computer Interface (BCI) system typically requires large and diverse training datasets, but collecting sufficient data remains challenging due to subject fatigue and interindividual variability. To address these limitations, this study proposes a reinforcement learning-enhanced EEG diffusion (RLED) framework for adaptive data augmentation in endogenous EEG tasks, with a focus on motor imagery and emotion recognition. The framework integrates a reinforcement learning mechanism to dynamically regulate the diffusion training process and achieve a flexible balance among temporal, spectral, and class-related features. Experiments on four datasets demonstrate that the proposed method generates high-quality synthetic EEG signals and consistently improves classification performance. These findings show that the proposed RLED framework may serve as a promising tool for EEG data augmentation and generalization in practical BCI applications.},
}

@article {pmid42184467,
year = {2026},
author = {Lu, J and Wang, D and Kong, D and Meng, K and Zhang, R and Wan, H and Chen, M},
title = {Identifying the seizure onset zone with phase-amplitude coupling.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {203},
number = {},
pages = {109151},
doi = {10.1016/j.neunet.2026.109151},
pmid = {42184467},
issn = {1879-2782},
abstract = {Accurate identification of the seizure onset zone (SOZ) is critical for the diagnosis and treatment of drug-resistant epilepsy (DRE). In recent years, although phase-amplitude coupling (PAC) has played an important role in epilepsy-related studies, few investigations have focused on applying PAC methods to SOZ identification. To this end, leveraging the capability of PAC to characterize neural interactions within the brain, this study computes the modulation index (MI) from clinical electrocorticography (ECoG) recordings of DRE patients. Subsequently, a statistical analysis of temporally evolving distributions of MI values across multiple frequency bands is conducted to analyze the differences in MI distribution features between SOZ and non-seizure onset zone (NSOZ) regions. Finally, distribution features of MI values are integrated with machine learning techniques to systematically evaluate the influence of different frequency bands and time windows on SOZ identification performance. The results demonstrate that MI distribution features can achieve accurate SOZ identification, with classification accuracy reaching 90.69%, indicating their potential as biomarkers for SOZ identification.},
}

@article {pmid42184469,
year = {2026},
author = {Zhang, Y and Li, T and Jiang, J and Li, H and Sun, R and Li, Y and Chen, W},
title = {Altered temporal organization of neural response dynamics during attention processing differentiates ADHD subtypes in children.},
journal = {NeuroImage. Clinical},
volume = {50},
number = {},
pages = {104011},
doi = {10.1016/j.nicl.2026.104011},
pmid = {42184469},
issn = {2213-1582},
abstract = {BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) shows marked heterogeneity, and conventional event-related potential (ERP) measures have limited sensitivity to subtype differences. This study examined whether alterations in the temporal organization of neural responses during attentional processing differentiate ADHD subtypes.

METHODS: Children with predominantly inattentive ADHD (ADHD-I), combined-type ADHD (ADHD-C), and typically developing (TD) controls completed an auditory oddball task during electroencephalography. Neural responses were analyzed using time-resolved scalp topographies, low-dimensional neural trajectory analysis, and data-driven neural state modeling. Associations with clinical symptoms were examined.

RESULTS: Both ADHD subtypes showed altered temporal alignment of neural responses relative to TD children, particularly during target processing. Neural trajectories exhibited reduced differentiation between standard and target stimuli, with ADHD-I showing reduced trajectory separation and ADHD-C showing exaggerated but inefficient state excursions. Data-driven analyses further revealed subtype-specific alterations in neural state stability and transitions, which showed exploratory associations with attentional and behavioral impairment.

CONCLUSIONS: ADHD is characterized by disrupted temporal organization of neural responses that is not captured by conventional ERP measures. Subtype-specific neural dynamics provide a mechanistic account of ADHD heterogeneity.},
}

@article {pmid42184818,
year = {2026},
author = {Hennesy, TB and Zander, DA and Kryzer, TJ and Honce, JM and Carlson, ML and Nassiri, AM},
title = {Magnetic Resonance Imaging Artifact Associated With the Oticon Medical Sentio Ti Transcutaneous Bone Conduction Hearing Implant.},
journal = {Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {},
number = {},
pages = {},
doi = {10.1097/MAO.0000000000004949},
pmid = {42184818},
issn = {1537-4505},
abstract = {OBJECTIVE: To evaluate magnetic resonance (MR) imaging artifact and image distortion associated with the Oticon Medical Sentio Ti bone conduction implant (BCI) and identify optimized imaging techniques.

STUDY DESIGN: Cadaveric study.

INTERVENTION: One cadaveric head specimen was unilaterally implanted with Sentio Ti BCI according to the manufacturer's instructions.

MAIN OUTCOME MEASURES: Imaging was performed with a Siemens 1.5 Tesla MR machine on XA60 software before and after implantation. Imaging was performed with both standard and metal mitigation techniques. Image scoring (diagnostic vs. nondiagnostic) and qualitative assessment of anatomic subsites were performed by 2 experienced neuroradiologists.

RESULTS: Image distortion and artifact were noted in all postimplant sequences. For all sequences, imaging of the ipsilateral middle ear, mastoid, and internal auditory canal (IAC) was nondiagnostic. The axial T1 turbo spin echo high bandwidth sequence had the best artifact reduction; however, the ipsilateral temporal bone remained nondiagnostic. Notably, nonecho planar diffusion-weighted imaging (non-EPI DWI) was nondiagnostic for both the ipsilateral temporal bone and the contralateral IAC and middle ear.

CONCLUSIONS: After implantation of the Sentio Ti BCI, imaging of the ipsilateral temporal bone is rendered nondiagnostic on all MR sequences due to artifact despite the use of metal mitigation techniques. Importantly, the non-EPI DWI HASTE sequence, which is used for cholesteatoma surveillance, is nondiagnostic for all ipsilateral and most contralateral temporal bone subsites, making cholesteatoma surveillance challenging with an implant in place. This finding is critical for clinical decision-making, as rehabilitation of conductive hearing loss in the setting of chronic otitis media is among the most common indications for use of a BCI.},
}

@article {pmid42185119,
year = {2026},
author = {Ding, Y and Kosnoff, J and He, B},
title = {A holistic perspective on noninvasive brain-computer interfaces.},
journal = {Trends in neurosciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tins.2026.04.009},
pmid = {42185119},
issn = {1878-108X},
abstract = {Brain-computer interfaces (BCIs) decode neural activity to enable direct communication with external devices. This process consists of three modules: signal acquisition, signal processing, and output translation. While invasive BCIs have demonstrated sophisticated and intuitive capabilities, their reliance on surgical implantation limits widespread use. Noninvasive BCIs, in contrast, are more broadly applicable but have traditionally been constrained by low spatial resolution and suboptimal signal quality. Emerging methodological advances are beginning to overcome these limitations. In this review, we examine recent progress in noninvasive BCIs, focusing on neuromodulation-paired BCIs for signal enhancement, deep neural network-based signal processing approaches, and expanded applications through robotic integration. Together, these parallel developments are driving the emergence of more robust, intuitive, and adaptive BCI systems for human use.},
}

@article {pmid42185310,
year = {2026},
author = {Jian, Y and Jin, S and Liu, P and Zheng, X and Hong, X and Han, Y and Semyanov, A and Duan, S and Tong, X},
title = {GABA signaling in NG2 glia mediates empathy-like behavior under observational social defeat.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-73488-0},
pmid = {42185310},
issn = {2041-1723},
abstract = {Empathy, ranging from emotional contagion to consolation, is central to social cognition. While neural mechanisms of observed pain are well studied, how witnessing trauma affects empathy-related behaviors remains unclear. Using an observational social defeat (OSD) model, we find that OSD-exposed mice display enhanced allogrooming toward defeated conspecifics, indicating increased consolation behavior. Whole-brain cFos mapping and fiber photometry reveal selective activation of medial amygdala (MeA) GABAergic neurons during empathic allogrooming. NG2 glia modulate this behavior via GABA signaling; their specific ablation in the MeA reduces inhibitory synaptic transmission, disinhibiting neighboring GABAergic neurons and increasing allogrooming. Single-cell RNA analysis reveals that GABA signaling originates from Gad1-expressing NG2 glia. Genetic knockout of Gad1 in NG2 glia recapitulates the phenotype. This mechanism requires elevated corticosterone induced by social defeat. Our findings highlight the role of NG2 glia-GABA neuron interactions in promoting prosocial empathy and suggest targeting GABA signaling in NG2 glia as a potential therapeutic strategy for vicarious trauma.},
}

@article {pmid42185473,
year = {2026},
author = {Choi, D and Yip, C and Choi, A and Park, J},
title = {Trust-gated synthetic EEG augmentation reduces performance drops when generalizing to new patients.},
journal = {NPJ digital medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41746-026-02778-0},
pmid = {42185473},
issn = {2398-6352},
support = {2025 NSERC Discovery Grant Bridge Fund//University of Calgary/ ; },
abstract = {Synthetic augmentation can silently harm subject-disjoint EEG generalization. We propose trust-gated augmentation (TGA), a control layer that scores synthetic windows using a teacher trained on real data to ensure label consistency and confidence; only samples above a confidence quantile q are eligible. A fail-closed selector injects synthetic data only if the validation AUROC exceeds the real-only AUROC by a margin; otherwise, it reverts to real-only. In PainMunich chronic-pain EEG (n = 189; 101 chronic pain/88 controls) at 5% subject scarcity, ungated augmentation harmed 56% of paired runs (ΔAUROC < - 0.01), whereas TGA at q = 0.99 reduced harm to 24% with comparable mean AUROC. In BCI IV-2a motor imagery (n = 9) at 25% scarcity, strict gating improved AUROC (0.679 vs. 0.627) and reduced harm (0.16 vs. 0.44). A covariance-manifold audit showed synthetic windows were strongly off-manifold (mean distance ratio 2.39 × 10[4]), motivating explicit governance.},
}

@article {pmid42185513,
year = {2026},
author = {He, M and Sha, L and Tang, G and Pang, J and Jin, L and Fu, Y and Huang, S and Wang, W and Wen, S and Yao, Y and Wei, P and Chen, L},
title = {Towards generalizable seizure monitoring: EpiVLM for cross-environment detection and classification.},
journal = {NPJ digital medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41746-026-02810-3},
pmid = {42185513},
issn = {2398-6352},
support = {JCYJ20220818100213029//Shenzhen Science and Technology Innovation Committee/ ; ZYYC23011//1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University/ ; 2025NSFTD0027//the Supported by Sichuan Science and Technology Program/ ; STI2030-Major Projects+2021ZD0204300//the Ministry of Science and Technology of the People's Republic of China grant/ ; },
abstract = {The translation of automated seizure detection from controlled clinical units to real-world settings is hindered by heterogeneous recording conditions and limited expert monitoring. We introduce EpiVLM, a multimodal vision-language system that combines clinically structured prompts with video reasoning for cross-environment seizure monitoring. Evaluated on a robust and diverse dataset of 232 video recordings from 127 patients, totaling 11,666 expert-annotated segments from two tertiary centers, unconstrained home recordings, and an independent public dataset, EpiVLM recognized five major semiologies with accuracy 0.795-0.947 and sensitivity 0.842-0.957. With prompts and decision thresholds fixed a priori, performance remained consistent across diverse real-world acquisition conditions without site-specific recalibration. In external validation sets, EpiVLM sustained strong recognition while maintaining low video-level false detections (0.47-2.45%) and timely detection (mean onset-to-detection delay <6 s). Compared with standard video deep-learning baselines, EpiVLM achieved superior overall performance. These results support scalable seizure recognition from routine video and motivate prospective evaluation for remote outcome monitoring.},
}

@article {pmid42185690,
year = {2026},
author = {Zhang, J and Zhang, H and Yang, Y},
title = {Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification.},
journal = {Brain informatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40708-026-00308-y},
pmid = {42185690},
issn = {2198-4018},
support = {21A13022003//Humanity and Social Science Foundation of the Ministry of Education of China/ ; LMS26F020033//Natural Science Foundation of Zhejiang Province/ ; },
abstract = {Cross-subject motor imagery classification remains challenging due to EEG data scarcity and inter-subject variability. This study proposes a novel framework integrating generative data augmentation with domain adaptation. First, we employ a diffusion probabilistic model to generate high-fidelity synthetic EEG samples, effectively enriching the training data. Subsequently, we propose the AMSC-DANN architecture, which synergizes an Adaptive Multi-Scale Convolution (AMSC) module for extracting multi-granular features with a Domain Adversarial Neural Network (DANN). This combination enables the model to learn discriminative temporal-spectral representations while simultaneously aligning feature distributions across different subjects. Extensive experiments on BCI Competition IV datasets 2a and 2b demonstrate that our proposed framework outperforms state-of-the-art baselines, validating its effectiveness in enhancing cross-subject generalization.},
}

@article {pmid42172365,
year = {2026},
author = {Cramer, SC and Stein, J and Richards, LG and Hayward, KS},
title = {Advances in Stroke 2026: Recovery and Rehabilitation.},
journal = {Stroke},
volume = {57},
number = {6},
pages = {1792-1795},
doi = {10.1161/STROKEAHA.125.054526},
pmid = {42172365},
issn = {1524-4628},
}

@article {pmid42173506,
year = {2026},
author = {Gong, C and Song, Z and He, Z and Liu, H and Han, R and Li, S and Gao, J and Wei, Y and Wen, D and Xue, T and Xu, Z},
title = {Interfacial Polarization Engineering in MXene-Polymer Nanofibers for High-Output Triboelectric Nanogenerators.},
journal = {Langmuir : the ACS journal of surfaces and colloids},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.langmuir.6c01705},
pmid = {42173506},
issn = {1520-5827},
abstract = {In mechanical energy harvesting and sensing, triboelectric nanogenerators (TENGs) have garnered significant attention for effectively extracting energy from low-frequency, irregular motion and directly transducing it into sensing signals. However, poly(vinylidene fluoride)-based (PVDF-based) TENGs frequently exhibit limitations, including low power density, low output current, and high matched load resistance during operation. Herein, we report a TENG based on MXene, hydroxypropyl methylcellulose (HPMC), and PVDF-HFP composite nanofibers membrane (MHPm) as the negative tribo-layer and Al foil as the counter tribo-layer/electrode for low-frequency mechanical energy harvesting and real-time, ultrasensitive respiratory monitoring. HPMC acts as a synergistic regulator that promotes interfacial interactions among MXene, HPMC, and PVDF-HFP, reduces the coherent stacking scale of MXenes, and facilitates the maintenance of a discrete conductor-polymer-conductor structure, thereby strengthening interfacial polarization and electrical output performance. Under the drive of 70 N and 6 Hz, this device achieves a peak-to-peak (p-p) open-circuit voltage of 1.02 kV, a p-p short-circuit current density of 0.133 A·m[-2], and a peak power density of 27.40 W·m[-2] at a matched load resistance of 20 MΩ, while maintaining a stable current output over 15,000 contact-separation cycles. Moreover, the electrical outputs also provide well-differentiated breathing waveforms, enabling direct self-powered signal acquisition and supporting integrated wearable functionality.},
}

@article {pmid42173881,
year = {2026},
author = {Zheng, JL and Zheng, YX and Chen, K and Wang, S and Liang, JW and Wang, SX and Yang, L and Shi, Y},
title = {Cryo-EM structures of ALECT2 filaments from human renal biopsies.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-73602-2},
pmid = {42173881},
issn = {2041-1723},
support = {2025YFC3409700//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 82371415, 82170724//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2024SSYS0018//Science and Technology Department of Zhejiang Province/ ; 2023-PT310-01//Chinese Academy of Medical Sciences (CAMS)/ ; 2025M782772//China Postdoctoral Science Foundation/ ; },
abstract = {Leukocyte chemotactic factor 2 is a recently identified amyloidogenic protein, whose abnormal aggregation defines a systemic amyloidosis termed ALECT2 amyloidosis. Due to the lack of reliable biomarkers, diagnosis relies primarily on histological demonstration and typing of amyloid deposits in renal biopsies. However, immunohistochemical detection of ALECT2 is often inconsistent, leading to diagnostic uncertainty. The underlying basis remains poorly understood, reflecting our limited knowledge of ALECT2 deposits. Here, using cryo-electron microscopy (cryo-EM), we determined the structures of ALECT2 filaments from renal biopsies of five living patients. Unlike filaments assembled from recombinant proteins in vitro, all 133 residues of mature LECT2 are incorporated into the filament cores, with native disulfide linkages preserved. The filaments consistently adopt the shared six-layered folds in all five patients, indicating a common mechanism of amyloidogenesis. Because all residues are incorporated into the fibril core, epitope accessibility is limited. This can explain variability in immunohistochemical detection and thus highlights the need for conformation-specific antibodies and antibody-independent detection strategies for improving diagnostic accuracy. This biopsy-based workflow not only expands the availability of patient-derived tissue for cryo-EM studies but also demonstrates the potential of cryo-EM as a tool for precise diagnosis of systemic amyloidosis.},
}

@article {pmid42174242,
year = {2026},
author = {Fu, TM and Liu, G and Milkie, DE and Ruan, X and Görlitz, F and Shi, Y and Ferro, V and Divekar, NS and Wang, W and York, HM and Kilic, V and Mueller, M and Liang, Y and Daugird, TA and Gacha-Garay, MJ and Larkin, KA and Adikes, RC and Harrison, N and Shirazinejad, C and Williams, S and Nourse, JL and Sheu, SH and Gao, L and Li, T and Mondal, C and Achour, K and Hercule, W and Stabley, DR and Emmerich, K and Dong, P and Drubin, DG and Liu, ZJ and Mumm, JS and Koyama, M and Killilea, AN and Bravo-Cordero, JJ and Keene, CD and Luo, L and Kirchhausen, T and Pathak, MM and Arumugam, S and Nuñez, JK and Gao, R and Matus, DQ and Martin, BL and Swinburne, IA and Betzig, E and Legant, WR and Upadhyayula, S},
title = {A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms.},
journal = {Nature methods},
volume = {},
number = {},
pages = {},
pmid = {42174242},
issn = {1548-7105},
support = {LDRD 7647437 and 7721359//DOE | LDRD | Lawrence Berkeley National Laboratory (Berkeley Lab)/ ; 1DP2GM136653//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; 1DP2GM136653//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; F32GM133131//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R35GM118149//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; DP2AT010376//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01NS109810//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; T32 CA078207/CA/NCI NIH HHS/United States ; T32EY7143‑22//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R35GM118149//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01OD020376//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01CA244780//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01‑DC005982//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; DP2AT010376//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01NS109810//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01GM121597//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R35GM150290//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; 1R01DC021710//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; 1DP2GM136653//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; P30‑CA196521//U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)/ ; GM130386//U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; },
abstract = {Understanding biological systems requires observing features and processes across vast spatial and temporal scales, spanning nanometers to centimeters and milliseconds to days, often using multiple imaging modalities within complex native microenvironments. Yet, achieving this comprehensive view is challenging because microscopes optimized for specific tasks typically lack versatility due to inherent optical and sample handling tradeoffs, and frequently suffer performance degradation from sample-induced optical aberrations in multicellular contexts. Here, we present Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC), a reconfigurable microscope that integrates multiple advanced imaging techniques including light-sheet, label-free, super-resolution and multiphoton, all equipped with adaptive optics. MOSAIC enables noninvasive imaging of subcellular dynamics in both cultured cells and live multicellular organisms, nanoscale mapping of molecular architectures across millimeter-scale expanded tissues and structural/functional neural imaging within live mice. MOSAIC facilitates correlative studies across biological scales within the same specimen, providing an integrated platform for broad biological investigation.},
}

@article {pmid42167252,
year = {2026},
author = {Feng, X and Le, T and Liu, B and Zhao, H and Su, Y and Li, K and Shao, Y and Liu, J and Li, Z and Deng, G and Cao, K and Zhu, Z and Chen, J and Zeng, L and Han, Y and Yang, H and Yu, YQ and Duan, S and Sun, L},
title = {Slow-wave sleep engages brainstem circuitry to prevent stress-induced anxiety.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2026.04.041},
pmid = {42167252},
issn = {1097-4199},
abstract = {The beneficial effects of sleep on anxiety are established, but the mechanisms remain unclear. We identify a GABAergic circuit from the parafacial zone (PZ) to the lateral parabrachial nucleus (LPB) neurons that project to the oval bed nucleus of the stria terminalis (ovBNST) as a node for slow-wave sleep (SWS)-mediated anxiolysis. Optogenetic activation of PZ GABAergic neurons following social defeat stress induces time-locked SWS and prevents anxiety. Multi-region Ca[2+] recording reveals suppressed activity in LPB and ovBNST during natural and PZ-initiated SWS. The LPB-ovBNST pathway is required to drive wakefulness and anxiety, whereas the LPB-basal forebrain pathway promotes arousal without affecting anxiety. PZ neurons inhibit LPB calcitonin gene-related peptide (CGRP)-expressing neurons, which promote wakefulness and anxiety via ovBNST. This effect specifically requires LPB input to ovBNST corticotropin-releasing hormone (Crh) neurons. Thus, we define a PZ[Vgat]-LPB[CGRP]-ovBNST[Crh] circuit essential for sleep-related anxiolysis, providing a potential therapeutic target for anxiety disorders.},
}

@article {pmid42172342,
year = {2026},
author = {Zheng, L and Pan, L and Fu, X and Wang 王思羽, S and Wu, Y and Xiao, H and Yang, J and Wang 王思雨, S and Yang, L and Wu, X and Pan, F and Yang, H and Chen, G and Wang, H},
title = {The posteroventral part of the medial amygdala nucleus glutamatergic neurons encodes conspecifics' individual identity in rodents.},
journal = {Science advances},
volume = {12},
number = {21},
pages = {eady9830},
doi = {10.1126/sciadv.ady9830},
pmid = {42172342},
issn = {2375-2548},
mesh = {Animals ; Mice ; *Neurons/metabolism/physiology ; Olfactory Bulb/physiology ; Odorants ; Male ; Social Behavior ; *Corticomedial Nuclear Complex/physiology/metabolism/cytology ; Female ; Vesicular Glutamate Transport Protein 2/metabolism ; *Amygdala/physiology ; Smell/physiology ; Behavior, Animal ; Optogenetics ; },
abstract = {The medial amygdala (MeA) processes social olfactory cues, but its precise neural mechanisms remain unclear. We identified the posteroventral MeA (MeApv) as critical for individual conspecific odor discrimination in mice. Exposure to conspecifics or their odors markedly elevates calcium signals and c-Fos expression in MeApv VGluT2-positive neurons. Optogenetic silencing of these neurons or activating Gad2-positive neurons disrupts odor-driven social behaviors, including identity recognition, odor discrimination, and sex discrimination. Social information is directly relayed from the accessory olfactory bulb (AOB) to the MeApv, and acute AOB-MeApv pathway disruption impairs social discrimination. A distinct MeApv VGluT2-positive neuron population encodes individual-specific cues, as revealed by microendoscopic calcium imaging at a single-cell resolution. Selective silencing of these neurons induces deficits in odor-guided social interactions with related conspecifics, confirming the MeApv as a central hub for social information encoding. These findings establish the MeApv's dual necessity and sufficiency in translating olfactory signals into social behavioral responses.},
}

Animals
Mice
*Neurons/metabolism/physiology
Olfactory Bulb/physiology
Odorants
Male
Social Behavior
*Corticomedial Nuclear Complex/physiology/metabolism/cytology
Female
Vesicular Glutamate Transport Protein 2/metabolism
*Amygdala/physiology
Smell/physiology
Behavior, Animal
Optogenetics

@article {pmid42160746,
year = {2026},
author = {Chen, H and Wang, J and Lai, S and Peng, G and Zong, G and Yuan, C and Luo, B},
title = {Smoking Cessation, Weight Change, and Risk of Dementia: A Prospective Cohort Study.},
journal = {Neurology},
volume = {106},
number = {12},
pages = {e218123},
doi = {10.1212/WNL.0000000000218123},
pmid = {42160746},
issn = {1526-632X},
mesh = {Humans ; Female ; *Dementia/epidemiology ; *Smoking Cessation ; Male ; *Weight Gain/physiology ; Prospective Studies ; Aged ; Middle Aged ; Risk Factors ; Cohort Studies ; },
abstract = {BACKGROUND AND OBJECTIVES: Smoking cessation is universally prioritized for the prevention of cardiovascular disease and cancer, but its impact on dementia risk remains uncertain. We aimed to evaluate the associations of smoking cessation and postcessation weight gain with long-term risk of dementia and cognitive trajectories.

METHODS: We conducted a prospective cohort study using data from the US Health and Retirement Study (1995-2020). A total of 32,802 dementia-free adults (mean age 60.5 years [SD 10.7]; 57.1% female) were included. Smoking status and body weight were assessed biennially through structured interviews. The primary outcome was incident dementia identified using the Langa-Weir algorithm, and the secondary outcome was cognitive function measured on a 27-point scale.

RESULTS: Over 25 years of follow-up (median 9.9 years, interquartile range 4.4-16.4 years), 5,868 dementia cases were documented. Compared with current smokers, individuals who quit during follow-up had a lower dementia risk after quitting (hazard ratio 0.84, 95% CI 0.73-0.95), similar to those who had quit before baseline (0.79, 0.72-0.87) and to never smokers (0.75, 0.69-0.83). The benefits of cessation were largely limited to participants with no or modest 2-year postcessation weight gain (≤5 kg). By contrast, the association of quitting accompanied by >10-kg weight gain was not statistically significant (1.33, 0.87-1.82). Restricted cubic spline analysis showed decreasing dementia risk with longer time since quitting, and the risk approached that of never smokers and plateaued at around 7 years after cessation. Cognitive trajectory analyses showed that quitting was associated with long-term slower cognitive decline (slope difference 0.19 points per decade, 95% CI 0.00-0.38) but no transient cognitive change (0.57; 95% CI -0.69 to 1.83), especially among those with minor weight gain (slope difference 0.23 per decade, 95% CI 0.03-0.43).

DISCUSSION: Smoking cessation was associated with a sustained lower dementia risk and slower cognitive decline, comparable to never smokers and those without short-term risk increase. However, postcessation weight gain may attenuate these advantages, highlighting the need for weight management in cessation programs. These findings should be interpreted cautiously, given the potential residual confounding and measurement error.},
}

Humans
Female
*Dementia/epidemiology
*Smoking Cessation
Male
*Weight Gain/physiology
Prospective Studies
Aged
Middle Aged
Risk Factors
Cohort Studies

@article {pmid42161119,
year = {2026},
author = {Pei, X and Sun, M and Chen, R and Wu, M and Wang, X and Xu, D and Li, R and Luo, S and Jin, Y and Tang, YN and Lu, Y and Wang, Q and Ma, L and Hu, B and Mei, Y and Xiao, X and Wei, D and Liu, Y and Song, E},
title = {Dual-frequency-channel integrated bioelectronics for in-sensor decoupling high-dimension neurophysiologic signals.},
journal = {Biosensors & bioelectronics},
volume = {309},
number = {},
pages = {118784},
doi = {10.1016/j.bios.2026.118784},
pmid = {42161119},
issn = {1873-4235},
abstract = {Accurate electrophysiological mapping of biological signals with high spatial and temporal resolution has always been an important requirement to elucidate physiological functions. Herein, we develop a photolithographic organic electrochemical transistor (OECT) matrix with two frequency-dependent channels, which can spatiotemporally map electroneurographic and neurotransmitter signals. The active material can be patterned photolithographically, forming a nanoscale interpenetrating network. The porous structure facilitates fast ion transport, establishing a high-frequency channel to monitor electroneurographic signals; meanwhile enzymatic reaction of glutamate on the surface creates a low-frequency channel to detect neurotransmitter signals, due to the relatively slow diffusion and doping processes. A low detection limit down to 900 zM for glutamate is achieved. During the test, the horseshoe network structure of the OECT array gives the device the ability of conformal contact on the surface of the cerebral cortex, avoiding the motion artifact noise, and the signal-to-noise ratio (SNR) can reach ∼40 dB. The dual-frequency channels efficiently decouple electroneurographic and neurotransmitter signals to avoid signal interference. Finally, the photolithographic matrix images dual-mode neurophysiological patterns in the cerebral cortex of mice, and can dynamically colocalize epileptic focus with high resolution for precise neurosurgical intervention.},
}

@article {pmid42161915,
year = {2026},
author = {Lu, C and Jiang, L and Jia, Q and Jiao, P and Li, S and Sun, S and Luo, J and Wang, M and Cai, X and Wu, Y},
title = {Analysis and dynamic modeling of firing synchronization in electrically interconnected dual-compartment neuronal networks.},
journal = {Microsystems & nanoengineering},
volume = {12},
number = {1},
pages = {},
doi = {10.1038/s41378-026-01309-x},
pmid = {42161915},
issn = {2055-7434},
support = {62121003, T2293730, T2293731,62333020, 62171434, 62471291//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {In vitro cultured neuronal networks offer controllable experimental models for investigating neuronal information processing mechanisms and network plasticity. However, research into synchronization and functional connectivity transitions following physical electrical interconnection between isolated compartments remains elusive. This study presents a microsystem that includes a compartmentalized microchamber neuron chip (CMNC) with programmable electrical interconnection and multichannel electrophysiological recording capabilities. The microsystem is utilized to establish artificial electrical interconnection between dual-compartment neuronal networks (DCNNs). We quantitatively evaluated network functional connectivity throughout control, interconnection, and post-disconnection phases, focusing on three key dimensions: spike timing synchrony, firing activity correlation and phase coherence. The experimental data showed that the electrical interconnection had sustained effects on firing synchrony and phase coherence across the DCNNs. After disconnection, synchrony decreased but remained significantly higher than control levels, suggesting a plastic response of the neuronal networks to the electrical coupling. To bridge experimental observations with mechanistic insights, we developed an Electrical-Interconnection Wilson-Cowan Model (EI-WCM), which quantitatively links physical coupling parameters (K) to network-level integration dynamics. The integrated microsystem and dynamical model presented here provide a stable, controllable platform and approach for studying functional connectivity, synergetic interactions and plasticity of neuronal networks, demonstrating significant potential for applications in brain-computer interfaces and neuronal information processing.},
}

@article {pmid42162083,
year = {2026},
author = {Chaturvedi, S and Ahirwal, MK},
title = {SHAP analysis of an improved EEG-based mental workload classification framework: utilizing data augmentation and explainable AI.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-52330-z},
pmid = {42162083},
issn = {2045-2322},
abstract = {Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research area in brain-computer interface (BCI). Since the EEG signals fluctuate a lot across sessions and individuals, there is a need for a robust classification model that generalizes well for real-world applications. In this work, we used the publicly available dataset "An EEG dataset for cross-session mental workload estimation: passive BCI competition of the Neuroergonomics Conference 2021", and the standard EEGNet model to classify the MWL into three classes (Low, Med, and High). To improve the performance of the model, a synthetic minority oversampling technique (SMOTE) was used by creating synthetic EEG samples, and key hyperparameters (F1, F2, and D) of EEGNet were systematically varied to identify the optimal configuration. Furthermore, Shapley Additive Explanations (SHAP) analysis was performed to identify the most influential EEG channels for model prediction. The proposed approach achieves the highest accuracy of 80.5% and 82.7% without and with SMOTE, respectively. The comparative analysis showed that applying SMOTE resulted in an average performance improvement of approximately 3%. A Wilcoxon signed-rank test confirmed that this improvement was statistically significant (p < 0.05). Finally, the SHAP analysis revealed that the most informative EEG channels were located over the parieto-occipital and temporal regions, which is consistent with established neurophysiological evidence related to MWL processing. The proposed framework improves both performance and explainability in EEG-based MWL classification, representing a systematic integration of SMOTE and SHAP analysis.},
}

@article {pmid42162276,
year = {2026},
author = {Liu, W and Wu, SA and Zhang, BX and Guo, SH and Li, L and Sun, W and Xiong, X and Nan, J and Wu, J and Zeng, L and Li, P and Cai, ZY and Ye, HF and Zhang, S and Nie, S and Li, B and Wu, D and Cheng, P and Qi, X and Fang, D and Chen, W and Zhang, Y and Chen, Q and Yang, ZH and Han, J and Mo, W},
title = {Tau aggregates cause reactivation of transposable DNA elements, leading to Z-RNA-ZBP1-mediated neuronal death.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42162276},
issn = {1546-1726},
abstract = {Once tau aggregates are formed, their neurotoxicity significantly contributes to neuronal death and cognitive decline in tauopathies, with Alzheimer's disease being the most well-known example. Despite its central pathogenic role, however, effective therapeutic strategies targeting the neurotoxicity of tau remain poor. Here we demonstrate the pathogenic role of neuronal cell death in tau-related neurodegeneration (PS19 mouse model). Tau-expressing neurons undergo cell death through Z-DNA-binding protein 1 (ZBP1) activation triggered by endogenous Z-RNAs. These Z-RNAs are derived from reactivated transposable elements that are typically silenced within heterochromatin. Tau aggregates show a strong affinity for H3K9me3-modified chromatin, effectively sequestering these epigenetic marks from heterochromatin protein 1 (HP1), thereby disrupting the condensation of constitutive heterochromatin. Clinically, an inverse correlation between ZBP1 expression levels in excitatory neurons and cognitive performance in individuals with Alzheimer's disease was observed. Importantly, Zbp1 haploinsufficiency significantly ameliorated cognitive deficits in aged (24-month-old) tau-transgenic mice, highlighting the therapeutic potential of ZBP1 inhibition to combat neurodegeneration in tauopathies.},
}

@article {pmid42163088,
year = {2026},
author = {Shah, HA and Khan, A},
title = {Modeling and classifying neuronal activity with a fusion of mathematical and machine learning techniques.},
journal = {BMC bioinformatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12859-026-06487-z},
pmid = {42163088},
issn = {1471-2105},
abstract = {Predicting neuron spike patterns is crucial because spikes are the brain's fundamental language, revealing how information is encoded and transmitted. Such prediction also supports disease diagnosis, brain-machine interfaces, and the control of robotic arms, wheelchairs, and neuromorphic AI design. Yet, simulation techniques often suffer from limited biological realism, numerical instability, and poor generalization across diverse neuronal activity types. These limitations are further compounded by the scarcity of high-quality, labeled datasets that capture the full spectrum of neuronal dynamics, restricting the training and evaluation of machine learning models. To address these challenges, we proposed SpikeNet, a hybrid framework that integrates the Izhikevich neuron model with the Runge-Kutta fourth-order (RK4) algorithm to generate synthetic voltage signals that are both biologically plausible and computationally precise. These signals are then used to train a Bidirectional Long Short-Term Memory (Bi-LSTM) network, which effectively captures long-range temporal dependencies in spike trains. SpikeNet combines accurate simulations with advanced sequence modeling to improve spike pattern classification, providing a scalable solution for reliable data generation and prediction. The proposed model was evaluated on multiple datasets, including single-spike data, multi-label spike data, and the Allen dataset, and demonstrated strong performance across all evaluation metrics.},
}

@article {pmid42164752,
year = {2026},
author = {Bushnell, BD and Boes, N and Cil, A and Farmer, K and Gilot, G and Kassam, H and Khan, A and Miniaci, A and Port, J and Sanchez-Sotelo, J and Schultzel, M and Steinmann, S and Suri, M and Weinstein, D and Wright, M},
title = {Augmentation for rotator cuff repair - clinical use patterns and limited patient access: the American Shoulder and Elbow Surgeons bio-advocacy work group survey.},
journal = {JSES reviews, reports, and techniques},
volume = {6},
number = {3},
pages = {100748},
pmid = {42164752},
issn = {2666-6391},
abstract = {BACKGROUND: Over the last decade, treatment algorithms of rotator cuff pathology have increasingly included various forms of augmentation of rotator cuff repair (RCR). This study aimed to quantify real-world clinical patterns for RCR augmentation and provide consensus statements for clinical practice and payor consideration. It was our hypothesis that augmentation would be popular amongst surgeons, especially for reduction in retear rates, and that a high percentage of respondents would also identify restrictions to access.

MATERIAL AND METHODS: The American Shoulder and Elbow Surgeons Advocacy Committee distributed a 12-question digital survey to all current members of American Shoulder and Elbow Surgeons. The survey evaluated current surgical techniques and augmentation usage, limitations on augmentation access, target patients for augmentation selection, and desired clinical outcomes. Questions were analyzed as either frequency of response or as a rank average with 95% confidence intervals.

RESULTS: The survey was sent to 1,210 surgeons, and 103 surgeons participated in the survey (8.5% response rate). The survey revealed the following: (1) use of RCR augmentation is reported by 76.2% and 85.1% of surgeons for partial-thickness tears (PTT) and full-thickness tears (FTT), respectively. However, 74.5% of surgeons indicate that they have limited or variable access to augmentation options. (2) A bioinductive collagen implant (BCI) is the most preferred form of augmentation for PTT (52.5% of respondents), while both the BCI (45.5%) and human dermal allograft augmentation (45%) are most preferred for FTT.(3) The decision to use augmentation is largely based on positive clinical outcomes (9.4/10) and a defined target patient population (8.4/10), with the most critical outcome being a lower retear rate for both PTT (7/10) and FTT (8/10). (4) For PTT, patient comorbidities (7/10) are of greatest concern and are the most impactful criteria for the decision to use augmentation (6/10). For FTT, poor tendon quality (8.6/10) and increasing tear size (2.9-9.1/10) are of greatest concern, with tear size indicated as the most impactful criteria for selecting augmentation (7.6/10).

CONCLUSION: This expert-opinion survey confirmed the growing popularity of RCR augmentation and the significant limitations in access faced by surgeons and their patients. BCI and human dermal allograft were the most popular augmentation options. Surgeons identify multiple factors as important to decision-making for implant use, including positive clinical outcomes, low retear rates, defined patient populations, patient comorbidities, poor tendon quality, and tear size. Research in this area continues to expand, but additional work on payor approval remains to ensure appropriate access to this technology.},
}

@article {pmid42166269,
year = {2026},
author = {Rao, Z and Lu, Z and Xiao, J and Li, K and Zhu, M and Guan, Z and Chen, Y and Cao, H and Li, Y},
title = {Calibration-Free Online Detection in Wearable Motor Imagery Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3695774},
pmid = {42166269},
issn = {1558-0210},
abstract = {Motor imagery brain-computer interfaces (MI-BCIs) remain challenging for practical use due to their reliance on multi-channel EEG devices and long calibration. To address these limitations, we proposed a wearable system for calibration-free online decoding using a lightweight, few-channel EEG headband, enabling portability, ease of use, and rapid setup. Specifically, we first built a large-scale wearable MI-EEG dataset from 100 healthy subjects to train a subject-independent model. We then developed a CNN-based temporal convolutional network (CTCNet) for online MI detection, which reduced computational complexity while maintaining high decoding performance. Furthermore, we introduced a supervised self-training (SST) strategy that leverages labeled online data and progressively fine-tunes a pre-trained subject-independent model, enabling calibration-free BCI operation without offline calibration. Four online experiments were conducted, involving 25 healthy subjects (Experiments II-IV) and 10 stroke patients (Experiment V). With the SST strategy, the accuracy of the subject-independent model improved from 69 % initially to 81 % after the first update and further increased to 86% after the second update, surpassing the subject-specific model (80%). Stroke patients exhibited a similar improvement trend. Moreover, simulated experiments confirmed the superiority of the subject-independent model compared to training from scratch. These findings demonstrate the effectiveness of the wearable MI-BCI system based on SST and CTCNet for online MI detection and highlight its substantial potential for motor recovery in stroke patients.},
}

@article {pmid42166618,
year = {2026},
author = {Wagner, A and Eisenkolb, VM and Utzschmid, A and Gempt, J and Jacob, SN and Meyer, B},
title = {Chronic Implantation of Planar Microelectrode Arrays as a Brain-Computer Interface: A Technical Note and Operational Workflow.},
journal = {Operative neurosurgery (Hagerstown, Md.)},
volume = {},
number = {},
pages = {},
doi = {10.1227/ons.0000000000002067},
pmid = {42166618},
issn = {2332-4260},
support = {16ME0540//Bundesministerium für Bildung und Forschung/ ; },
abstract = {BACKGROUND AND OBJECTIVES: Chronic implantation of brain-computer interface facilitates stable, high-fidelity neuronal recordings over extended periods of time. Planar microelectrode arrays [Utah arrays (UAs)] are commonly used for intracortical signal acquisition. Here, we describe the surgical workflow for chronic implantation of multiple UAs in 2 patients and report safety and signal-quality outcomes.

METHODS: Two patients (MB, MM) underwent chronic UA implantation within a translational research program. Preoperative planning included magnetic resonance imaging and navigated transcranial magnetic stimulation mapping for localization of functional targets. MB presented with aphasia after a left hemisphere media territory stroke 6 years before implantation and received 4 UAs in speech-related areas. MM presented with tetraparesis after a high level spinal cord injury and received 4 UAs in areas related to grasping functionality, totaling 256 intracortical electrodes for each patient.

RESULTS: The duration of the chronic implantation has currently amounted to 41 months for MB and 4 months for MM. Optimal signal quality has been recorded in MB in 3 of 4 UAs and in MM in all UAs. After 15 months, MB suffered from wound breakdown, necessitating surgical debridement and intravenous antibiotic treatment. Unimpaired signal acquisition resumed after the wound had healed, and no further complications from UA implantation were recorded otherwise.

CONCLUSION: Chronic implantation of UAs across distinct cortical areas is safe. A standardized workflow-combining imaging-based functional navigated transcranial magnetic stimulation mapping, intraoperative neuronavigation, and structured postoperative surveillance-supports reliable, long-term intracortical signal acquisition.},
}

@article {pmid42167136,
year = {2026},
author = {Härmä, V and Palsola, M and Kuusipalo, A and Lindh, E and Melin, M and Nohynek, H},
title = {Lessons from the 2024 avian influenza vaccination campaign in Finland: a qualitative inquiry.},
journal = {Vaccine},
volume = {86},
number = {},
pages = {128736},
doi = {10.1016/j.vaccine.2026.128736},
pmid = {42167136},
issn = {1873-2518},
abstract = {Highly pathogenicity avian influenza H5N1 (HPAI H5N1) viruses cause a continuous threat to wild avian populations. During recent years, spillover to both wild and domestic mammals has occurred with an increasing frequency. As a consequence of the recent developments in the epidemiological situation, the human-animal interface with the risk of human exposure to HPAI H5 has expanded. In 2024, Finland became a global forerunner to offer H5 vaccine to occupational risk groups, specifically fur and poultry workers, following an extensive HPAI H5N1 outbreak in 2023 in fur-farmed minks and foxes. Despite targeted efforts to reach the people at increased risk, only 8,6% of the target population received the first dose and 7,5% completed both doses. To seek a better understanding of the barriers behind low vaccine uptake a Behavioural and Cultural (BCI) insight approach was chosen. A rapid qualitative study was conducted in late 2024 (n = 17), utilising semi-structured interviews with health authorities, industry stakeholders, and risk group representatives in the Ostrobothnia region in Finland. Barriers were identified across three dimensions: (1) logistical failures, including poor timing and difficulties in reaching target groups (2) divergent risk perceptions, where economic livelihood overshadowed personal health risks; and (3) political distrust, stemming from perceived stigmatization by national health authorities. The results will provide vital information for future pre-pandemic communication and implementation strategies and helps to identify key stakeholders and target groups.},
}

@article {pmid42155589,
year = {2026},
author = {Esaian, S and Smith, BA and Oh, J and Espinoza, JC and Vidmar, AP and Goran, MI},
title = {Carbohydrate composition of infant formula and glycemic regulation in early infancy using continuous glucose monitoring: cross-sectional evidence of altered glucose patterns with corn syrup solid-based formulas.},
journal = {The American journal of clinical nutrition},
volume = {},
number = {},
pages = {101325},
doi = {10.1016/j.ajcnut.2026.101325},
pmid = {42155589},
issn = {1938-3207},
abstract = {BACKGROUND: Infant formulas vary widely in carbohydrate composition, yet associations between exposure to nonlactose carbohydrates and glycemic patterns in early infancy remain poorly characterized.

OBJECTIVES: We assessed associations between infant feeding strategy and continuous glucose monitor (CGM)-derived measures of glycemic variability in a cross-sectional observational cohort of infants at 6 mo of age.

METHODS: Forty-five infants (28.0 ± 1.2 wk; 47% female) wore CGMs recording interstitial glucose every 15 min for 3 to 8 d. Feeding strategy was categorized as exclusive human milk, formula containing lactose or corn syrup solids (CSS), or mixed human milk/lactose-based formula. Twenty-eight CGM-derived metrics were computed using the R package iglu. Group differences were tested using Freedman-Lane analysis of covariance with permutation-based post hoc tests; effect sizes (η[2]p) and 95% bootstrap confidence intervals (BCI) were reported for all key comparisons. Exploratory hierarchical clustering (Ward's D2) examined glycemic variability subgroups independent of feeding strategy.

RESULTS: Approximately 46% of CGM-derived metrics differed significantly across feeding strategies, all reflecting contrasts between CSS-based formula and other groups; no metrics differed among human milk, lactose-based formula, or mixed feeding. Compared with human milk, CSS-fed infants were associated with greater glycemic variability and large effect sizes (though the study was powered only to detect large effects), including greater time in hyperglycemia (η[2] = 0.21; 95% BCI = -2.59,2.49), glycemic risk assessment diabetes equation (η[2] = 0.31; 95% BCI = -0.25,0.24), J index (η[2] = 0.24; 95% BCI = -1.07,1.08), and mean amplitude of glycemic excursions (η[2] = 0.40; 95% BCI = -6.14,6.03). Exploratory clustering identified 4 glycemic variability subgroups. One subgroup exhibited broadly elevated glucose variability and included ∼36% of CSS-fed infants, with no representation from other feeding strategies.

CONCLUSIONS: Infant feeding strategy was associated with differences in CGM-derived glycemic variability at 6 mo, driven by greater glucose variability among CSS-fed infants. Human milk and lactose-based formula feeding did not differ. Exploratory analyses identified a subgroup with pronounced glycemic variability that included a subset of CSS-fed infants, highlighting interindividual variability in glycemic response.},
}

@article {pmid42158453,
year = {2026},
author = {Li, Y and Yi, R and Hu, Z},
title = {Brain-computer interface technology for motor rehabilitation in severe stroke: a narrative review.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {14},
number = {},
pages = {1822784},
pmid = {42158453},
issn = {2296-4185},
abstract = {This review examines the application of brain-computer interface (BCI) technology for motor rehabilitation in patients with severe stroke-a population often excluded from conventional therapies due to minimal movement. BCIs establish electronic links between the brain and external devices, enabling motor intention recognition without muscular activity. By pairing neural activation with sensory feedback, these systems promote neuroplasticity and strengthen adaptive motor pathways. Compared with standard therapies, preliminary evidence suggests BCI interventions may facilitate additional motor recovery, though current effect size estimates are limited by small sample sizes, high study heterogeneity, and inherent performance biases. Effective modalities include motor imagery with functional electrical stimulation, robotic-assisted training in virtual environments, and multimodal systems. Despite promising results, challenges persist regarding signal reliability, protocol optimization, patient selection, and cost. Emerging research focuses on integrating artificial intelligence, adaptive closed-loop systems, and portable platforms to enhance clinical feasibility. Interdisciplinary collaboration may help transition BCI technology from experimental use to routine rehabilitation, improving outcomes for severely impaired stroke survivors.},
}

@article {pmid42158584,
year = {2026},
author = {Chaiyanan, C and Phukhachee, T and Iramina, K and Kaewkamnerdpong, B},
title = {Toward practical BCIs: a BMNABC-based feature selection and sensor optimization framework for implicit learning detection from multimodal EEG-fNIRS data.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1778884},
pmid = {42158584},
issn = {1662-5161},
abstract = {Implicit learning is a fundamental cognitive process whose identification is critical for understanding human cognition and developing innovative training methodologies. We propose a generalizable feature selection and sensor optimization framework using simultaneous EEG and fNIRS to identify these events. Our approach leverages a two-stage optimization process driven by a binary multi-neighbor artificial bee colony (BMNABC) algorithm. The BMNABC uses the model's classification accuracy to guide the heuristic search for the most discriminative feature subset. First, the framework prioritizes optimal features from high-dimensional, multimodal data using a normalized weighted sum (NWS) metric. Second, it implements a recursive backward elimination mechanism to reduce the number of sensors for practical brain-computer interface (BCIs) applications. Our results demonstrate that the BMNABC framework successfully identifies a superior feature set, leading to a significant improvement in classification accuracy over using either modality alone. Critically, the selected features provided neurophysiological validation, isolating key biomarkers in the prefrontal cortex. We also show that a sparse yet highly effective sensor configuration can be achieved, maintaining high performance with up to 66% fewer sensors. This work not only provides a data-driven method for detecting implicit learning but also advances the design of more efficient and user-friendly BCI systems.},
}

@article {pmid42151503,
year = {2026},
author = {Xu, JN and Li, JT and Xu, RX and Wang, YF and Gao, HW and He, HT and Guo, H and Liang, Y and Zhu, YD and Li, XW and Yang, JM and Li, XM and Chen, YH and Gao, TM},
title = {The multilevel exploration test, a novel paradigm to measure exploratory behavior in depression animal models and the involvement of the PL-ZI circuit.},
journal = {Acta pharmacologica Sinica},
volume = {},
number = {},
pages = {},
pmid = {42151503},
issn = {1745-7254},
abstract = {Diminished drive is one of the core symptoms of major depressive disorder (MDD) diagnosis, yet its underlying neural mechanisms remain elusive, primarily due to a lack of appropriate animal models. We developed a novel Multilevel Exploration Test (MET) apparatus to evaluate exploratory behavior, which is captured as a dynamic, stage-dependent process involving "search", "attend/investigate", and "approach" phases. We employed fiber photometry to measure real-time dopamine dynamics in the nucleus accumbens. We further combined cFos staining and neural circuit tracing to identify relevant brain regions and circuits, and employed chemogenetics to selectively modulate prelimbic cortex (PL) inputs to zona incerta (ZI). The MET tests were conducted across five depression models, with ketamine administration to evaluate rescue effects. Machine learning algorithms were utilized to analyze MET data and predict individual emotional states (normal, anxiety-like, depression-like). Here, we developed a novel paradigm to assess exploratory behavior, which demonstrates etiological validity, face validity and predictive validity. Depressed mice exhibited reduced motivation for exploration in this paradigm, while stimulation of the PL-ZI circuit not only restored exploratory deficits but also alleviated other depression-like behaviors in these mice. Furthermore, we established a machine learning-based model to predict individual animals' emotional states by integrating data from the new paradigm, achieving a prediction accuracy of over 92%. The MET provides a functional, high-throughput paradigm for dissecting motivation-related pathology. It facilitates the assessment of depressive-like behaviors, enables the prediction of emotional states, and supports the discovery of novel targets for antidepressant development.},
}

@article {pmid42152642,
year = {2026},
author = {Begum, A and Sultana, A and Bin Heyat, MB and Rahman, K and Akhtar, F and Al-Huda, Z and Banu, T and Khaleeq, K and Bian, S and Sawan, M},
title = {Efficacy of Pimpinella anisum L. in Menopausal Women with Psychological Symptoms: A Randomized Controlled Study Integrated with Machine Learning Analysis.},
journal = {Current pharmaceutical design},
volume = {},
number = {},
pages = {},
doi = {10.2174/0113816128433736260209142133},
pmid = {42152642},
issn = {1873-4286},
abstract = {INTRODUCTION: Menopausal women commonly experience psychological symptoms. These symptoms reduce their quality of life. Pimpinella anisum (anise) is an Unani remedy traditionally used for such problems. This study aimed to test the effects of anise on psychological and menopausal symptoms, along with appraising machine learning models in classifying treatment response between the anise and control groups.

METHODS: A total of 60 menopausal women received either 4 g of anise per day or a matched placebo, administered in two divided doses over an eight-week period. Primary outcomes included the Depression, Anxiety, and Stress Scale 21 (DASS-21) in menopausal women. Secondary outcomes included overall Modified Kuppermen Index (MKI), Vaginal Health Index (VHI), and treatment satisfaction (MS-TSQ). Machine learning classifiers, including Gradient Boosting (GB), AdaBoost (AB), K-Nearest Neighbours (KNN), Naive Bayes (NB), and Random Forest (RF), were utilised. Safety was monitored weekly through interviews. Hepatic and renal function were evaluated at baseline and after 12 weeks.

RESULTS: Baseline variables were similar between the two groups. Anise significantly reduced the DASS-21 scores compared to placebo at 8 weeks (p < 0.0001). At 8 weeks, participants receiving anise demonstrated significant improvements across multiple measures. DASS‑21 scores declined markedly compared with placebo (p < 0.0001), with more than 80% reporting no symptoms of depression, anxiety, or stress. MKI and VHI scores also improved significantly in the anise group (p < 0.0001), while the control group showed no notable change. Satisfaction ratings on the MS‑TSQ were high among anise recipients but low in the placebo arm. No adverse effects were observed. In addition, the KNN model achieved outstanding performance, correctly classifying group membership with 99.20% accuracy.

DISCUSSION: Anise demonstrated significant benefits in reducing psychological and menopausal symptoms, with no adverse effects reported, supporting its potential as a safe non‑hormonal therapy. The strong performance of the KNN model additionally exemplifies how machine learning can improve menopausal research by precisely distinguishing treatment responses. Upcoming studies with larger and more varied populations will be important to endorse these findings and to discover long‑term outcomes.

CONCLUSION: This research indicates that anise is a safe and effective alternative for relieving psychological symptoms in menopausal women. The KNN model reliably classified treatment outcomes, signifying that the integration of anise treatment with AI‑based assessment methods could enrich research on menopause care.},
}

@article {pmid42153184,
year = {2026},
author = {Wang, M and Gong, Z and Li, Y and Li, Y and Zheng, Y},
title = {Robust decoding for MI-EEG: a hybrid transformer network using multi-perspective collaborative attention and dynamic hyperbolic tangent.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {93},
pmid = {42153184},
issn = {1871-4080},
abstract = {Deep learning methods have been extensively applied in brain-computer interface (BCI) systems based on motor imagery (MI) for decoding electroencephalogram (EEG) signals. However, most existing hybrid architectures often struggle to effectively eliminate redundant noise in multi-channel signals and lack adaptability to the inherent non-stationarity and distribution drift of EEG signals. This work proposes a novel end-to-end hybrid attention Transformer network (HATNet) for EEG classification. HATNet first employs a convolutional neural network to extract local spatio-temporal features. To overcome the limitations of existing models, it fuses a Collaborative Attention Mechanism for Lightweight Channels, which dynamically recalibrates feature channels through multidimensional pooling strategies, including entropy pooling, to achieve precise spatial noise suppression. Addressing the non-stationary nature of EEG signals, an innovative Dynamic Hyperbolic Tangent module drives the Transformer encoding layer, adapting in real-time to data distribution drifts and significantly enhancing the model's ability to capture individual variations. Furthermore, cross-layer residual fusion pathways deeply integrate global contextual features with raw local spatio-temporal features. To ensure clear scope definition, experiments explicitly distinguish between primary MI tasks and auxiliary motor execution (ME) tasks. HATNet's performance was evaluated on three primary MI benchmark datasets, namely BCIC-IV-2a, BCIC-IV-2b, and the large-scale OpenBMI, as well as one auxiliary ME dataset, HGD. Experimental results demonstrate that HATNet achieves state-of-the-art performance across all analyses. In subject-dependent evaluations, average accuracy rates reached 81.25%, 86.65%, and 69.57% on the three primary MI datasets respectively, and 96.20% on the auxiliary ME dataset. Furthermore, in subject-independent evaluations, it achieved 60.88%, 80.79%, and 76.28% on the MI datasets respectively, alongside 73.95% on the ME dataset. Through multidimensional feature selection and dynamic adaptive modeling, HATNet exhibits superiority and robustness in enhancing both MI and ME decoding performance.},
}

@article {pmid42154699,
year = {2026},
author = {Fu, R and Fang, Y and Xu, F and Hua, C and Hua, C},
title = {SAND: Spectral-Attention Neural Decoding of Hand Kinematics from Low-Frequency EEG Dynamics.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3694935},
pmid = {42154699},
issn = {1558-2531},
abstract = {Brain-Computer Interface (BCI) technology, integrating neuroscience and artificial intelligence, has been widely applied in neural rehabilitation. However, hand kinematics decoding via electroencephalography (EEG) is constrained by limited precision and cross-subject adaptability. This study proposes the Spectral-Attention Neural Decoder (SAND) - a hybrid framework synergizing spectral decomposition and adaptive deep learning for robust 2D/3D trajectory reconstruction. Systematic analysis of the WAY EEG Grasp-and-Lift dataset revealed that hand movement information is primarily encoded in low-frequency EEG bands. Therefore, a dual-branch continuous decoding architecture was developed: (1) a frequency-domain pathway for noise-resistant spectral embedding, and (2) a temporal-attention pathway utilizing transformer networks to capture dynamic neural modulations. Five-fold cross-validation results demonstrate that SAND achieves state-of-the-art performance in hand-trajectory decoding. The Pearson correlation coefficients for the x, y, and z axes reach 0.9595 ± 0.0148, 0.9534 ± 0.0151, and 0.9293 ± 0.0250, respectively, representing an improvement of 0.07-0.13 over baselines. To assess cross-task generalization, we validate SAND on a self-collected dataset, where it attains average correlation coefficients of 0.90 (x-axis) and 0.96 (y-axis) in 2D trajectory reconstruction. The temporal alignment with ground-truth kinematic recordings was validated by remarkable performance in dynamic time warping analysis. These results confirm SAND as an effective solution for precise hand motion decoding advances non-invasive BCI applications.},
}

@article {pmid42154722,
year = {2026},
author = {Hu, C and Liang, S and Li, R and Ong, ST and Xu, H and Zhang, J},
title = {EEGDTF: Time-Frequency Disentangled Diffusion for High-Fidelity EEG Signal Generation.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3694047},
pmid = {42154722},
issn = {2168-2208},
abstract = {EEG signal generation is hindered by challenges such as complex time-frequency structures, the lack of explicit spectral modeling, limited data availability, and limited generalization across subjects and tasks. To address these issues, we propose EEGDTF, a diffusion-based generative framework for synthesizing high-fidelity EEG signals with improved time-frequency modeling. EEGDTF first employs a multi-scale residual encoder to enhance temporal representation learning and training stability. It further introduces a dual-branch encoder-decoder architecture for time-frequency disentanglement: the frequency branch models both periodic and aperiodic components via power spectral parameterization, while the temporal branch captures waveform continuity and long-range dependencies. A frequency-guided cross-attention mechanism integrates both branches effectively. The model is optimized through a joint waveform and spectral loss, enabling stable clean-signal estimation during reverse sampling. Experiments on four benchmark datasets demonstrate that EEGDTF achieves state-of-the-art performance in both time and frequency domains, particularly under cross-subject conditions. These results underscore the model's robustness and generalizability, positioning EEGDTF as a reliable tool for EEG data augmentation and BCI-related applications.},
}

@article {pmid42155274,
year = {2026},
author = {Houshmand, MH and Pishgoo, B},
title = {Real-time emotion recognition based on EEG signals using a hybrid batch-stream architecture.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {202},
number = {},
pages = {109072},
doi = {10.1016/j.neunet.2026.109072},
pmid = {42155274},
issn = {1879-2782},
abstract = {In recent years, emotion recognition using brain-computer interface (BCI) systems has gained substantial attention. Existing models are typically implemented in either offline (batch) or online (streaming) modes. While batch processing approaches generally achieve higher classification accuracy, they are limited by slow processing speed. In contrast, stream processing approaches offer real-time performance but often compromise accuracy. To address this trade-off, we propose a hybrid batch-streaming framework that integrates the strengths of both paradigms while alleviating their individual limitations. The architecture, features a probabilistic intelligent switching mechanism that estimates the reliability of the streaming module based on its historical performance. This reliability measure dynamically determines the probability of selecting outputs from either the batch or streaming unit. The proposed framework is evaluated on three benchmark datasets (DEAP, AMIGOS, and SEED) achieving classification accuracies of 85%, 94%, and 74%, respectively. Also, experiments were conducted to investigate the performance of the switch mechanism and performance of system components against concept drift. Experimental results demonstrate that our method effectively balances classification accuracy and computational efficiency. It is expected that in the future, such hybrid ideas are widely used in feedback - based systems.},
}

@article {pmid42155486,
year = {2026},
author = {Wimmer, M and Elsayed, N and Thomas, BH and Müller-Putz, GR and Veas, EE},
title = {An online brain-computer interface for detecting incongruity in augmented reality applications.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae6ff1},
pmid = {42155486},
issn = {1741-2552},
abstract = {OBJECTIVE: Augmented reality can provide digital information about physical entities presented within its real-world context. However, this information might disagree with the user's expectations due to factual errors in the data or cognitive biases. Such incongruity can impair user experience and undermine trust in the AR system. To address this issue, we propose detecting inconsistencies between physical objects and digital information through hybrid braincomputer interfaces.

APPROACH: We conducted two complementary experiments. First, we implemented a strategy that integrates eye-tracking and brain signals for incongruity detection in an offline study. Subsequently, we assessed our approach in an online study in which participants received immediate feedback on the classification.

MAIN RESULTS: The grand average event-related potentials revealed consistent electroencephalographic responses to incongruent augmentations, specifically a centroparietal N400 effect, across both experiments. We could further distinguish between congruent and incongruent information with an average balanced accuracy of 70 % in the online study.

SIGNIFICANCE: These findings demonstrate the feasibility of detecting incongruity online, allowing for autonomous system adaptation, like presenting information in a more accessible format or providing contextual support.},
}

@article {pmid42144446,
year = {2026},
author = {Liu, JY and Yang, DL and Liu, HY and Xiang, SF and Cao, J and Yang, B and He, QJ and Wang, JH and Shao, XJ and Ying, MD},
title = {Advances in targeted therapies for pediatric tumors.},
journal = {Acta pharmacologica Sinica},
volume = {},
number = {},
pages = {},
pmid = {42144446},
issn = {1745-7254},
abstract = {Pediatric tumors represent a major cause of disease-related mortality in children and exhibit biological features that differ markedly from those of adult cancers. Pediatric malignancies display unique molecular architectures, with lower mutation frequency, higher frequency of chromosomal alterations such as gene rearrangement and amplification, a distinct alteration spectrum marked by dysregulated developmental genes, as well as a characteristic pattern of differentiation blockage. These alterations often arise during developmental windows and sustain tumor dependency, providing unique drug targets for targeted therapy. This review first describes the molecular characteristics and oncogenic drivers of pediatric tumors, as well as the potential mechanisms underlying the formation of oncogenic driver events in these tumors. It subsequently systematically synthesizes recent advances in targeted therapeutic strategies for pediatric tumors, categorizing strategies by disease type and oncogenic driver events, including oncofusion-directed inhibitors, agents targeting amplified or mutated genes, differentiation-inducing approaches, antibody-based therapies, and cellular therapies. We highlight both pediatric-specific drug development and the extrapolation of adult therapies to pediatric patients, while underscoring persistent challenges in clinical translation. This work advocates for a biology-driven framework to accelerate the development of effective targeted therapies for pediatric tumors.},
}

@article {pmid42145810,
year = {2026},
author = {Benachour, A and Syrov, N and Lebedev, M},
title = {Motor imagery affects both cortical and spinal circuitry: a transcranial and trans-spinal magnetic stimulation study.},
journal = {Frontiers in neural circuits},
volume = {20},
number = {},
pages = {1809125},
pmid = {42145810},
issn = {1662-5110},
mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; *Imagination/physiology ; Adult ; *Evoked Potentials, Motor/physiology ; Female ; *Motor Cortex/physiology ; *Spinal Cord/physiology ; Young Adult ; Electromyography ; Muscle, Skeletal/physiology ; Brain-Computer Interfaces ; },
abstract = {INTRODUCTION: Motor imagery (MI), the mental rehearsal of movement without physical execution, is a key technique in brain-computer interfaces (BCIs), known for eliciting cortical modulations similar to those exhibited during real movement. Beyond cortical effects, MI could also modulate spinal cord processing, which offers additional potential for neurorehabilitation in conditions like spinal cord injury (SCI) and stroke, where BCIs are used for therapy.

MATERIAL AND METHODS: To investigate the interactions of MI with both the cortex and the spinal cord, we employed both transcranial magnetic stimulation (TMS) and trans-spinal magnetic stimulation (TSMS) while recording brain and muscle activities.

RESULTS AND CONCLUSION: With proper coil orientation, TSMS elicited lateralized MEPs in ipsilateral forearm muscles at significantly shorter latencies than M1-evoked MEPs, confirming direct spinal cord activation. Importantly, right-hand kinesthetic MI selectively facilitated TSMS-evoked MEPs in the stimulated ipsilateral side only, providing direct evidence that MI modulates spinal cord excitability. Moreover, TSMS-evoked cortical responses were modulated by imagery, demonstrating that MI increases cortical processing of the ascending spinal volley. This within-group demonstration of MI affecting both cortical and spinal circuitry underscores its potential as a powerful strategy for BCI-driven neurorehabilitation, including pairing MI with spinal magnetic stimulation.},
}

Humans
*Transcranial Magnetic Stimulation/methods
Male
*Imagination/physiology
Adult
*Evoked Potentials, Motor/physiology
Female
*Motor Cortex/physiology
*Spinal Cord/physiology
Young Adult
Electromyography
Muscle, Skeletal/physiology
Brain-Computer Interfaces

@article {pmid42147125,
year = {2026},
author = {Bariffi, F},
title = {Mind, machine, and the law: reimagining neurotechnology governance through disability rights.},
journal = {Journal of law and the biosciences},
volume = {13},
number = {1},
pages = {lsag011},
doi = {10.1093/jlb/lsag011},
pmid = {42147125},
issn = {2053-9711},
abstract = {The convergence of neurotechnologies and disability raises urgent questions about autonomy, mental integrity, and legal capacity for persons with disabilities. This article examines the human rights implications of emerging neurotechnologies-from brain-computer interfaces to cognitive monitoring tools-through the lens of the United Nations Convention on the Rights of Persons with Disabilities (CRPD). Drawing on historical abuses under the medical model of disability, it argues that the uncritical deployment of neurotechnologies risks replicating patterns of coercion, paternalism, and exclusion. By advancing a normative framework rooted in the CRPD and the social model of disability, the article proposes legal and ethical safeguards to protect mental privacy, ensure informed consent, and affirm supported decision-making. It calls for regulatory and design paradigms that shift from enhancement and correction to inclusion and empowerment. Ultimately, the article contends that disability rights must be at the center of neurotechnologies governance to prevent ableist harms and foster equitable innovation.},
}

@article {pmid42149465,
year = {2026},
author = {Huang, Y and Zheng, J and Xu, H},
title = {An Immune-Brain Signaling Mediates Sickness-Induced Social Withdrawal.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {42149465},
issn = {1995-8218},
}

@article {pmid42150721,
year = {2026},
author = {Fu, Z and Wu, Z and Wu, X and Jabban, L and Chen, L and Zhang, D},
title = {Stage-Dependent Modulation of High- and Low-Frequency Neural Activity During Motor Imagery based on Stereoelectroencephalography.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122005},
doi = {10.1016/j.neuroimage.2026.122005},
pmid = {42150721},
issn = {1095-9572},
abstract = {Motor imagery (MI) recruits motor networks without overt movement and underpins many brain-computer interface (BCI) paradigms, yet how neural activity is organized across distinct task stages remains unclear. Using stereoelectroencephalography (sEEG) from ten epilepsy patients performing cued limb MI, we compared preparation and imagery stages and quantified trial-wise power changes in low-frequency (8-30 Hz) and high-frequency (60-115 Hz) bands across cortical and subcortical contacts. Low-frequency activity predominantly showed suppression during preparation followed by activation during imagery, consistent with ERD/ERS-like dynamics, whereas high-frequency responses were stronger, observed across a greater number of regions, and additionally showed activation-suppression sequences in a subset of contacts. These findings indicate that neural responses evolve differently across preparation and imagery, reflecting frequency- and region-specific dynamics rather than a uniform task-related response. Modulation in deep structures, including hippocampal subfields, suggests that MI can engage a distributed network beyond canonical sensorimotor areas. These results refine the temporal and spectral characterization of MI and may inform stage-aware BCI feature design and neurorehabilitation.},
}

@article {pmid42141422,
year = {2026},
author = {Zeng, X and Huang, Z and Shen, H and Luo, DY and Jin, T},
title = {Retrospective evaluation of clinical performance of three measurement catheter fixation methods in urodynamic studies.},
journal = {BMC urology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12894-026-02169-3},
pmid = {42141422},
issn = {1471-2490},
abstract = {BACKGROUND: Catheter displacement during urodynamic studies remains a common challenge, potentially introducing artifacts, compromising test accuracy, and decreasing patient comfort. Despite the clinical significance of stable catheter fixation, evidence-based recommendations for optimal fixation techniques are lacking. This study seeks to address this gap by comparing the effectiveness and patient comfort associated with three commonly used catheter fixation methods during urodynamic study.

METHODS: We retrospectively collected data from non-randomized patients who underwent urodynamic studies (UDS) at West China Hospital of Sichuan University between April and June 2023. Patients were selected based on predefined inclusion and exclusion criteria and assigned to one of three catheter fixation methods. The effectiveness of the following fixation techniques was evaluated: waterproof tape fixation (Group 1: catheter secured to the skin with adhesive tape), (2) patient-manual fixation (Group 2: patient holds the catheter manually throughout the procedure), and (3) silk thread fixation (Group 3: catheter secured with silk suture tied and fixed externally).

RESULTS: A total of 168 patients were enrolled in the study, with 56 patients in each group. The median ages for Groups 1, 2, and 3 were 66 (47.25, 76), 67 (61,71), and 66 (48, 76.75) years, respectively. There were no statistically significant differences among the three groups in terms of maximum cystometric capacity (MCC), bladder compliance (BC), maximum flow rate (Qmax), detrusor pressure at Qmax (Pdet.Qmax), bladder contractility index (BCI), or bladder outlet obstruction index (BOOI) (P > 0.05). The overall incidence of catheter displacement was 35.71% in Group 1, 0% in Group 2, and 14.29% in Group 3. Statistically significant differences in Comfort-B scale scores were observed between Group 1 and Group 2, and between Group 2 and Group 3 (P < 0.000). Similarly, visual analogue scale (VAS) scores also showed significant differences between Group 1 and Group 2, and between Group 2 and Group 3 (P < 0.000).

CONCLUSIONS: Our preliminary assessment indicated that the three catheter fixation methods did not significantly influence urodynamic parameters. Notably, patient-manual fixation achieved the lowest catheter displacement rate (0%) but was associated with the highest pain and discomfort scores. In contrast, waterproof tape and silk thread fixation offered better patient comfort but with higher displacement rates. These findings highlight a trade-off between catheter stability and patient comfort, suggesting that fixation method selection should be individualized based on patient characteristics and procedural requireme.

CLINICAL TRIAL NUMBER: Not applicable.},
}

@article {pmid42142489,
year = {2026},
author = {Chen, X and Song, H and Shen, M and Chen, H and Fu, Y},
title = {Sensory reliance in visual working memory across active and passive states.},
journal = {Cognition},
volume = {274},
number = {},
pages = {106587},
doi = {10.1016/j.cognition.2026.106587},
pmid = {42142489},
issn = {1873-7838},
abstract = {Visual working memory (VWM) has been thought to operate in active and passive states, but whether these states differentially engage sensory storage remains debated. The current study aims to delve into this debate further by testing whether increasing the load of active/passive states in VWM affects detection sensitivity to an incoming visual stimulus, a psychophysiological probing method which has been verified to specifically uncover the sensory nature of working memory storage. Across Experiments 1-3, we consistently found that loading either active or passive state impaired visual detection to a similar degree, indicating comparable sensory demands for both states. In Experiment 4, we further validated the manipulation of VWM states by observing dissociative memory-driven attentional bias effect of different states. Experiment 5 showed that information released from VWM no longer impaired visual detection, further confirming the specific role of working memory storage (in either active or passive state) in interfering with concurrent sensory processing. Together, these findings suggest that both active and passive states in VWM engage sensory storage, with comparable functional consequences for ongoing sensory processing.},
}

@article {pmid42143102,
year = {2026},
author = {Siam, MJN and Showrov, TA and Hossain, MS and Ayaan, NS and Bari, SMS and Tariq, F and Mahmud, AA},
title = {Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-50997-y},
pmid = {42143102},
issn = {2045-2322},
abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) enable users to control external devices using EEG signals, offering great potential in assistive and rehabilitation technologies. However, MI recognition remains challenging due to EEG's low signal-to-noise ratio (SNR), inter-subject variability, and complex spatiotemporal patterns. Existing approaches often suffer from limited accuracy, high computational cost, and poor interpretability. In response to these challenges, we present the first comprehensive benchmarking of the publicly available EEG-hand movement (EEG-HM) dataset. Our study aims to establish a standardized performance baseline, guide the selection of optimal models by jointly considering accuracy, prediction time, and explainability, and ultimately accelerate progress in MI-BCI development. We have proposed a two-stage optimization of machine learning models that employs both feature selection and hyperparameter tuning. We exploit five feature selection algorithms for selecting the best set of EEG electrodes and frequency bands, while Bayesian optimization is exploited for machine learning model optimization through hyperparameter tuning. Furthermore, to validate the neurophysiological basis of our model's decisions, we leverage explainable AI (XAI) algorithms-LIME and SHAP-quantifying the contributions of specific EEG electrodes and frequency bands to interpret its decision-making process. Through extensive simulations, the proposed two-stage optimization of the machine learning model demonstrates a superior performance in terms of accuracy, precision, and recall. This method outperforms the existing methods by 21.47% in accuracy with competitive prediction time. Its performance is further evaluated on the PhysioNet MI dataset, achieving a 4.67% accuracy improvement over state-of-the-art methods. Through LIME and SHAP, we provide the local and global explanations for no activity, left-hand, and right-hand imagery movements. Additionally, we analyze how various EEG frequency bands and electrode locations interact during the performance of different motor imagery hand movements.},
}

@article {pmid42143201,
year = {2026},
author = {Osman, YBM and Elsanosi, AHM and Jing, C and Shen, Y and Kuai, H and Wang, S and Ng, MK and Lei, B and Wang, S},
title = {Diffusion models for brain imaging computing: a survey of frameworks and applications.},
journal = {Brain informatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40708-026-00301-5},
pmid = {42143201},
issn = {2198-4018},
abstract = {Advances in brain imaging have generated unprecedented volumes of high-dimensional data, yet extracting meaningful information from complex, noisy, and incomplete brain imaging data remains a significant challenge. Diffusion models (DMs) have introduced a paradigm shift in this field, surpassing traditional generative approaches. This review systematically examines the theoretical foundations of diffusion models, and their practical applications in eight brain imaging computing tasks: registration, super-resolution, cross-modal reconstruction and synthesis, segmentation, classification, brain network analysis, brain-computer interface (BCI) signals augmentation, and BCI decoding. Additionally, we emphasize obstacles that hinder deployment in practice, including computational scalability and sampling inefficiency, limited generalization under domain shift sensitivity, as well as multimodal integration and alignment constraints, while outlining potential future directions that emphasize the convergence of diffusion models with large-scale foundation models, which hold the potential to advance scalable, reliable, and clinically embedded brain imaging solutions. Throughout this review, we aim to establish a roadmap of progress and translational hurdles to guide emerging research and accelerate collaboration spanning DMs, clinical brain imaging, and engineering disciplines.},
}

@article {pmid42143986,
year = {2026},
author = {Sun, Y and Sha, L and Tang, Y and Fu, Y and Duan, Y and Peng, A and Chen, L and Chen, L},
title = {Impaired default mode network connectivity and deviated dorsal-ventral attention networks in catamenial epilepsy.},
journal = {Epilepsy & behavior : E&B},
volume = {182},
number = {},
pages = {111101},
doi = {10.1016/j.yebeh.2026.111101},
pmid = {42143986},
issn = {1525-5069},
abstract = {OBJECTIVES: This study investigated alterations in resting-state brain networks in catamenial epilepsy (CE) and their associations with serum sex hormone levels.

METHODS: First, we constructedfunctional networks based on resting-state fMRI datato compute nodal attributes and identify brain regions exhibiting significant group differences. Subsequently, independent component analysis (ICA) identified important networks and characterized their connectivity patterns. Finally, associations between these network metrics and sex hormone levels were examined.

RESULTS: A total of45 patientswere included in the final analysis, comprising19with CE,26with non-catamenial epilepsy (NCE), and27healthy controls (HC). Nodal efficiency (Ne) differed significantly in key brain regions between patient group and HC group, including the right precentral gyrus (PreCG.R), left and right calcarine cortex (CAL.L and CAL.R), left lingual gyrus (LING.L), right superior parietal gyrus (SPG.R), right inferior parietal lobule (IPL.R) and left precuneus (PCUN.L). While connectivity between the default mode network (DMN) and sensory networks (visual/auditory) was generally weakened in epilepsy patients, CE specifically exhibited a reconfigured attention-network profile: strengthened connectivity between the dorsal attention network (DAN) and auditory network (AN), and weakened connectivity between the ventral attention network (VAN) and visual network (VN). After correction for multiple comparisons, partial correlation analysis controlling for age revealed no statistically significant correlations between sex hormones and brain network metrics.

CONCLUSION: CE patients exhibited decreased Ne in critical regions of the AN, DMN and VN, alongside predominant disruptions in DMN connectivity. These alterations may be partially compensated by increased connectivity in the DAN, giving rise to a unique network pathological pattern. The regulatory effects of sex hormones on brain networks require further confirmation in large-scale longitudinal studies.},
}

@article {pmid42144204,
year = {2026},
author = {Chen, J and Liu, X and Wang, R and Li, J and Xu, Z and Yang, B and He, Q and Yang, X and Yan, H and Luo, P},
title = {Sotorasib induces intestinal epithelial injury through suppression of the cAMP/PKA/CREB signaling axis.},
journal = {Biochemical pharmacology},
volume = {},
number = {},
pages = {118078},
doi = {10.1016/j.bcp.2026.118078},
pmid = {42144204},
issn = {1873-2968},
abstract = {Sotorasib, a first-in-class KRAS G12C inhibitor, has demonstrated substantial clinical efficacy in KRAS G12C-mutant malignancies but is frequently associated with gastrointestinal adverse events, the mechanisms of which remain poorly understood. In this study, we investigated the molecular basis of sotorasib-induced intestinal toxicity using complementary in vitro and an HP-β-CD-optimized in vivo model. Sotorasib treatment disrupted intestinal epithelial homeostasis by promoting apoptosis and suppressing proliferative capacity, resulting in impaired epithelial barrier integrity. Mechanistically, sotorasib markedly suppressed intracellular cAMP signaling in intestinal epithelial cells, as evidenced by reduced protein kinase A (PKA) activity and decreased phosphorylation of the transcription factor CREB. These effects were consistently observed in IEC-6 cells and murine colonic tissues lacking the KRAS G12C mutation, indicating a KRAS-independent mechanism. Pharmacological elevation of intracellular cAMP with forskolin partially restored CREB phosphorylation, attenuated epithelial apoptosis, enhanced proliferative activity, and improved expression of barrier-associated markers. In contrast, in NCI-H358 cells, forskolin increased CREB phosphorylation but failed to rescue sotorasib-induced growth inhibition, highlighting a pronounced cell type-dependent response to sotorasib. Collectively, our findings identify suppression of the cAMP/PKA/CREB signaling axis as a key mechanism underlying sotorasib-induced intestinal epithelial injury and provide mechanistic insight into the tissue-selective gastrointestinal toxicity of KRAS G12C-targeted therapy.},
}

@article {pmid41742719,
year = {2026},
author = {Chowdhury, P and Crichton, CA and Finster, R and Whiting, GL and Bihar, E and Kireev, D},
title = {Skin Conformal Hydrogel Bioelectrodes for High-Fidelity Electrophysiology and Human-Machine Interfaces.},
journal = {Advanced healthcare materials},
volume = {15},
number = {18},
pages = {e05753},
doi = {10.1002/adhm.202505753},
pmid = {41742719},
issn = {2192-2659},
support = {24CDA1049224//American Heart Association/ ; DGE2040434//National Science Foundation Graduate Research Fellowship Program/ ; },
mesh = {Humans ; *Hydrogels/chemistry ; *Skin ; Electrodes ; Polyvinyl Alcohol/chemistry ; Polystyrenes/chemistry ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; *Electrophysiology ; },
abstract = {Bioelectric interfaces used in electrophysiology must be capable of high-quality signal capture, mechanical conformance, and real-time interactivity. This research presents a conformable, reusable, and stretchable hydrogel bioelectrode composed of inkjet-printed PEDOT:PSS with a soft polyvinyl alcohol based substrate. This results in a strong, ion-conductive matrix 100 ± 16 kPa (n = 3) modulus, 660% ± 72% (n = 3) stretchability) and stable impedance (<6.4% drift over 72 h). The hydrogel bioelectrodes maintain <15% resistance drift after 50 strain cycles. The hydrogel bioelectrodes can effectively capture six bioelectrical signals, including heart, brain, muscle, ocular, electrodermal, and sympathetic skin nerve activities with outstanding signal-to-noise (SNR) ratios (up to 70 dB). Brain's alpha activity (8-12 Hz) is clearly detected, confirming the hydrogel bioelectrode's sensitivity to low-amplitude cortical signals. Sympathetic bursts in sympathetic skin nerve activity also show a 21% increase during the Valsalva maneuver, consistent with clinical observations. The hydrogel bioelectrodes also enable real-time human-computer interaction, where a subject-calibrated algorithm converts oculography signals from both eyes into directional drone control commands.},
}

Humans
*Hydrogels/chemistry
*Skin
Electrodes
Polyvinyl Alcohol/chemistry
Polystyrenes/chemistry
Signal-To-Noise Ratio
Brain-Computer Interfaces
*Electrophysiology

@article {pmid42133616,
year = {2026},
author = {Song, H and Zeng, J and Zheng, Y and Huang, H and Wang, H},
title = {Data-driven differentiation analysis of urban high-tech industries: Research on bibliometrics and large language models.},
journal = {PloS one},
volume = {21},
number = {5},
pages = {e0348590},
pmid = {42133616},
issn = {1932-6203},
mesh = {*Bibliometrics ; China ; *Language ; Humans ; Cities ; *Industry ; Artificial Intelligence ; Large Language Models ; },
abstract = {This study examines inter-city heterogeneity in China's high-tech industries from a regional innovation systems (RIS) perspective, with a particular focus on how variations in knowledge production, technological application, and actor configurations are associated with divergent urban innovation trajectories. We compile more than 39,000 publications from the Web of Science (WOS) and nearly 10,000 patent records from the national patent database for the period 2016-2025, covering four representative cities-Wuhan, Chengdu, Hangzhou, and Tianjin-and four technological domains: artificial intelligence (AI), fiber-optic communication (FOC), intelligent connected vehicles (ICV), and storage chips (SC). The study develops an integrated analytical framework combining bibliometric analysis, co-word network modeling, collaboration network mapping, and large language model (LLM)-assisted semantic interpretation. LLMs are employed primarily in keyword cleaning, terminology standardization, and topic identification, improving the consistency and interpretability of textual metadata. Visualizations generated using VOSviewer highlight pronounced inter-city differences in technological portfolios, research priorities, and collaboration structures. The results suggest distinct urban innovation configurations across the four cities. Wuhan exhibits strong positioning in FOC and SC, reflecting a combined industry-academy orientation. Hangzhou shows high frontier intensity in AI and ICV, consistent with an industry-led and digitally driven innovation profile. Chengdu demonstrates substantial academic output but comparatively weaker evidence of technological translation, while Tianjin, despite a smaller overall scale, displays notable specialization in applied domains such as brain-computer interfaces and smart port technologies. Rather than replacing quantitative analysis, LLM-assisted interpretation supports the identification and contextualization of these patterns by enhancing semantic coherence and reducing noise in large-scale textual data. Overall, the proposed framework provides a reproducible and scalable approach for examining regional technological differentiation and is applicable to comparative studies of urban innovation systems across different regions and industrial contexts.},
}

*Bibliometrics
China
*Language
Humans
Cities
*Industry
Artificial Intelligence
Large Language Models

@article {pmid42136985,
year = {2026},
author = {Ameer, OZ},
title = {Autonomic-vascular dysregulation in CKD-associated hypertension: a narrative review with evidence hierarchy.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1808065},
pmid = {42136985},
issn = {1662-4548},
abstract = {Hypertension and chronic kidney disease frequently coexist and mutually accelerate cardiovascular and renal injury. This narrative review prioritizes direct human autonomic phenotyping (Level 1: microneurography, HRV/BRS), human vascular correlates (Level 2: PWV, FMD), and complementary preclinical evidence (Level 3) to elucidate autonomic-vascular mechanisms. Autonomic imbalance, characterized by sympathetic overactivity and reduced parasympathetic restraint, represents a key interface between neural control and vascular pathology in this setting. This narrative review synthesizes experimental and clinical evidence on how the autonomic nervous system shapes vascular function in hypertension and CKD. We outline physiological autonomic control of vascular tone (baroreflex pathways, central networks, brain-kidney communication), characteristic autonomic alterations in hypertension (elevated MSNA, impaired HRV/BRS), and their vascular consequences (endothelial dysfunction, arterial stiffness). We emphasize CKD-specific autonomic drivers (renal afferents, uremic toxins, inflammation) and their translation to exaggerated vascular injury and adverse BP phenotypes. Finally, we discuss pharmacological/device-based strategies targeting autonomic-vascular pathways, highlighting opportunities for neuromodulation, biomarker-guided risk stratification, and individualized treatment. By integrating multidisciplinary evidence, this review frames CKD hypertension as amplified autonomic-vascular injury and positions the autonomic nervous system as a promising therapeutic target.},
}

@article {pmid42139128,
year = {2026},
author = {Hong, J and Rao, P and Wang, W and Najafizadeh, L},
title = {EMBC Special Issue: ChatBCI-Assist: An Intent-Based P300 Speller with A Locally-Deployed LLM and Adaptive Stopping Strategy Enabling Record Online Spelling Performance.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3693965},
pmid = {42139128},
issn = {1558-2531},
abstract = {P300-based speller brain computer interfaces (BCIs) provide promising communication solutions for individuals with severe motor impairments such as those with amyotrophic lateral sclerosis (ALS). However, existing P300 spellers are constrained by slow typing speed and limited efficiency. Here, we present ChatBCI-Assist, an intent-based P300 speller that integrates a locally-deployed large language model (LLM), fine-tuned for the task at hand, with an adaptive stopping strategy for key selection and a graphical user interface (GUI) designed for efficient message composition, to achieve record-level online spelling performance. The LLM, trained on an ALS-specific communication corpus using low-rank adaptation (LoRA), produces context-aware, semantically coherent, and prefix-constrained word and phrase predictions in real time. The proposed GUI supports efficient, user-adaptive message composition, while the adaptive stopping strategy dynamically adjusts stimulus presentation based on each subject's classification performance. Combined with a subject specific stepwise linear discriminant analysis (SWLDA) classifier, ChatBCI-Assist enhances spelling efficiency. Results from online experiments demonstrate that ChatBCI-Assist achieves record performance, with an average information transfer rate (ITR) of 105.2 bits/min, an overall character-level mutual information rate (MIR) of 52.9 bits/min and characters per minute (CPM) of 19.7 in copy-spelling tasks, and 30.7 CPM in semantic spelling tasks. Evaluated using semantic ITR (SITR), a metric proposed to characterize semantic communication efficiency, ChatBCI-Assist achieved SITR of 147.1 bits/min. User experience evaluations further confirm reduced workload and higher usability from LLM-based semantic spelling configurations, compared to traditional copy-spelling paradigms (dictionary or LLM). This work demonstrates that integrating locally-adapted LLMs with intent driven design and subject-specific decoding optimization can substantially improve the speed, efficiency, and user experience of BCI-based communication systems.},
}

@article {pmid42140229,
year = {2026},
author = {Lu, J and Meng, K and Zhou, Z and Li, L},
title = {The application of the unscented Kalman filter in epilepsy research: a review.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae6e46},
pmid = {42140229},
issn = {2057-1976},
abstract = {Epilepsy is a complex neurological disorder characterized by nonlinear dynamic interactions among multiple brain regions. The Unscented Kalman Filter (UKF), a high-precision algorithm for nonlinear state and parameter estimation, has recently gained prominence in epilepsy research as it infers latent physiological parameters from electrophysiological signals to reveal the underlying seizure mechanisms. This review provides a comprehensive overview of recent progress in applying UKF to epileptic dynamics modeling and signal analysis, focusing on three major aspects: parameter estimation and model optimization based on neural computational models, seizure detection and prediction, and closed-loop control for seizure intervention. Studies have demonstrated that UKF can robustly reconstruct neuronal dynamics under noise and nonstationary conditions, providing real-time tracking of seizure evolution and contributing to a unified framework that integrates modeling, signal interpretation, and intervention. Despite these advances, important challenges remain, including noise covariance selection, high-dimensional parameter estimation, large-scale network modeling, and limited clinical validation. Future research should focus on adaptive mechanisms, improved multi-parameter estimation, and broader validation using multimodal data and real-patient cohorts. Overall, UKF has shown considerable promise as a model-based framework for epilepsy research and, more broadly, as an interpretable engineering approach for latent neural-state estimation from noisy physiological signals, although broader clinical evidence and further methodological refinement are still required before it can be considered a clinically mature framework.},
}

@article {pmid42141222,
year = {2026},
author = {Ye, Z and Ding, J and Cheng, C and Yu, H and Xu, F and Sun, Q and Yang, H and Hua, T and Wang, H},
title = {Visual Perception and Gamma Oscillations in Cat V1 are Dynamically Correlated in Contrast Sensitivity Functions.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {42141222},
issn = {1995-8218},
abstract = {Local field potentials (LFPs) encode visual information through power variations across multiple frequencies. However, the mechanism through which LFPs encode visual contrast sensitivity during visual perception remains unclear. Herein, we developed a method to decode visual perception levels using LFPs and found that gamma oscillations exhibited the best performance in the detection of visual contrast. Furthermore, gamma power and theta-gamma phase amplitude coupling employed different strategies to code contrast sensitivity. Subsequently, suppressing the top-down influence from area 21a lowered both behavioral and gamma power-measured contrast sensitivity across the same spatial frequencies. Model analysis revealed that gamma oscillations modulated contrast-tuning responses via a contrast gain mechanism and were involved in the external noise exclusion mechanism through a top-down influence. Our findings reveal a link between gamma oscillations and visual contrast sensitivity and demonstrate that a reduction in gamma oscillation power through the suppression of top-down influences impairs perception of visual contrast.},
}

@article {pmid42129136,
year = {2026},
author = {Huo, Y and Huang, W and Liu, Z and Chen, H and Bai, T and Wang, Y and Wang, K and Zhang, D and Cheng, J and Sun, Y and Ma, G and Zhao, C and Zhang, Z and Shu, N},
title = {Functional system-specific brain aging across the Alzheimer's disease continuum.},
journal = {Translational psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41398-026-04081-8},
pmid = {42129136},
issn = {2158-3188},
support = {82301608, 32271145, 81871425, 210510238//National Natural Science Foundation of China (National Science Foundation of China)/ ; L252087//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; },
abstract = {Accelerated brain aging is implicated in Alzheimer's disease (AD). However, the spatial heterogeneity of brain aging patterns across different functional systems along the AD continuum remains largely unexplored. We developed functional system-specific brain age models derived from structural magnetic resonance imaging in a healthy adult cohort (n = 22,672) and applied them to 1478 participants across the AD continuum. Using up to 6 years of retrospective longitudinal data before clinical AD conversion, we quantified predicted age differences (PADs) and their change rates, characterized heterogeneous brain aging trajectories, and examined their associations with AD biomarkers, cognitive performance, and clinical progression. Progressive mild cognitive impairment (MCI) individuals showed early PAD deviations in the default mode network and accelerated changes in attention and control networks. System-wise PAD dynamics mediated the effects of AD-related biomarkers on cognitive decline. Integrating PAD features can improve predictive accuracy of MCI-to-AD conversion (AUC = 0.95). Functional system-specific PADs can be sensitive biomarkers for early detection and monitoring of individualized AD risk.},
}

@article {pmid42130383,
year = {2026},
author = {Meng, L and Bell, JM and Barbre, K and Guthrie, S and Mason, L and Massey, J and Wiegand, R and Rowe, T and Sillions Prosper, M and Woods, A and Qureshi, I and Reses, H and Stuckey, M and Kuhar, D and Lindley, M and Hernandez-Romieu, AC and Benin, A},
title = {Using a longitudinal k-means clustering method to explore nursing home factors associated with SARS-CoV-2 infection peak and resilience to a COVID-19 surge.},
journal = {Infection control and hospital epidemiology},
volume = {},
number = {},
pages = {1-7},
doi = {10.1017/ice.2026.10406},
pmid = {42130383},
issn = {1559-6834},
abstract = {OBJECTIVE: Nursing home residents have been disproportionately impacted by respiratory virus-related morbidity and mortality due to inherent vulnerability and communal living environments. This study aims to identify nursing homes with higher infection rates during a period of intense SARS-CoV-2 transmission and explore facility-level characteristics potentially associated with infection surges.

DESIGN: A longitudinal k-means clustering approach followed by exploratory regression analyses.

SETTING: U.S. Nursing homes reporting to the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN).

METHODS: A longitudinal k-means method (kmlShape) classified the facilities based on their weekly SARS-CoV-2 incidence rate epidemic curve, identifying two categories (low vs high infection peak) based on the magnitude of infection peaks. A logistic regression model with bootstrapping was developed to assess facility characteristics associated with higher SARS-CoV-2 infection surges.

RESULTS: Among 11,990 nursing homes analyzed, 9,058 were classified as having a low infection peak, while 2,932 had a high infection peak. Nursing homes that are for-profit (OR = 1.570, 95% bootstrap confidence interval [BCI] 1.441-1.807), with high staff turnover (OR = 1.292, 95% BCI 1.154-1.451), or located in areas with higher social vulnerability (OR = 1.457, 95% BCI 1.239-1.880) were more likely to experience high infection peaks. Nursing homes with higher residents' vaccination coverage (OR = .321, 95% BCI .248-.380) and located in urban areas were less likely to experience high infection peaks.

CONCLUSIONS: The facility-level characteristics associated with lower SARS-CoV-2 infection peaks may indicate resiliency and help evaluate the capacity of nursing homes to endure stressors such as respiratory viruses and other communicable illnesses.},
}

@article {pmid42131835,
year = {2026},
author = {Qian, L and Shi, Y and Liu, W and Yao, Q},
title = {Muscle energy techniques for post-stroke spasticity: mechanisms and clinical applications.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1773854},
pmid = {42131835},
issn = {1664-2295},
abstract = {Spasticity is a common and disabling complication after stroke, often leading to progressive joint stiffness, restricted movement, and reduced functional independence. Current management strategies for post-stroke spasticity (PSS) are limited by inconsistent efficacy and a lack of standardized protocols. Muscle energy techniques (MET) have emerged as a promising non-invasive approach, though their mechanisms and clinical value in PSS remain poorly understood. This review summarizes available evidence on MET for PSS based on systematic searches of PubMed, Web of Science, CNKI, and WanFang up to November 2025. MET may alleviate PSS through two main routes, namely inhibiting spinal and cortical motor neuron excitability and modulating pain pathways, though the evidence for these mechanisms remains limited and comes mainly from experimental studies. Key clinical studies indicate that MET can reduce muscle tone, improve range of motion, and enhance functional outcomes, with particularly notable effects on upper limb spasticity. However, heterogeneity in treatment protocols and a shortage of high-quality trials limit the strength of current conclusions. We further discussed critical limitations, including the reliance on active patient participation, which may preclude its use in persons with stroke with significant cognitive or motor deficits. Future directions include standardizing treatment protocols and integrating MET with emerging technologies such as biofeedback and brain-computer interfaces. This review offers a mechanistic and clinical framework to support the evidence-based integration of MET into PSS rehabilitation.},
}

@article {pmid42132445,
year = {2026},
author = {Mokienko, O and Zisman, M and Bobrov, P and Ustinova, K},
title = {Brain-Computer Interfaces for Gait Rehabilitation After Stroke: A Scoping Review.},
journal = {American journal of physical medicine & rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1097/PHM.0000000000002943},
pmid = {42132445},
issn = {1537-7385},
abstract = {Brain-computer interfaces (BCIs) represent a promising technology for restoring lower limb motor functions and gait after stroke. The application of BCIs in this field is supported by a limited number of studies. The objective of the review was to systematically and critically evaluate the current evidence on the BCIs use for lower limb function rehabilitation in stroke patients. A systematic literature search was conducted in PubMed, Scopus, and Web of Science databases. The inclusion criteria were as follows: studies involving adult patients with post-stroke hemiparesis; implementation of non-invasive BCIs specifically targeting lower limb rehabilitation; detailed reporting of training protocols. Quality assessment was conducted using the National Institutes of Health Study Quality Assessment Tools. Twenty-two studies were included in the analysis with the following results. Electroencephalography-based BCIs integrated with functional electrical stimulation of muscles (EEG-BCI-FES) represent the most extensively investigated technology in this field, with efficacy and safety demonstrated in three randomized controlled trials (RCTs) and one non-RCT. BCI systems integrated with mechanical devices have been less studied, with evidence from two RCTs, while systems with only visual feedback have also been evaluated in two RCTs. For BCIs with exoskeletons, only technical feasibility has been demonstrated. Further research is needed to optimize training protocols and study long-term effects of using BCI for rehabilitation.},
}

@article {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.},
}