@article {pmid42645039,
year = {2026},
author = {Zhang, R and Zhong, M and Ma, C and Xiao, Z and Zhang, Y and Liu, C},
title = {GLSTNet: A Global-Local Spatial Relations and Temporal Dynamics Network for EEG-Based Emotion Recognition.},
journal = {Biosensors},
volume = {16},
number = {8},
pages = {},
pmid = {42645039},
issn = {2079-6374},
support = {62171123//National Natural Science Foundation of China/ ; 1132932502//Jiangsu University of Science and Technology/ ; 1132932304//Jiangsu University of Science and Technology/ ; 2023YFC3603600//the National Key Research and Development Program of China/ ; },
mesh = {Humans ; *Electroencephalography ; *Emotions ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Electroencephalography (EEG)-based emotion recognition is an important biosensing technique for affective brain-computer interfaces (BCIs), mental-state assessment, and physiological monitoring. Existing methods often rely on a single spectral descriptor or regular two-dimensional brain maps, which makes it difficult to jointly model local spatial representations, global spatial relations, and temporal dynamics. This paper proposes GLSTNet, a global-local spatial relations and temporal dynamics network for EEG emotion recognition. EEG trials are divided into short windows, from which multi-band spectral features are extracted and arranged into compact spatial maps. The local spatial encoder (LSE) learns local spatial and spatial-spectral representations from these compact multi-band spatial maps. The global spatial-relation encoder (GSRE) models long-range spatial relations between non-adjacent electrodes using a Pearson correlation prior and a learnable residual adjacency matrix. After local and global representations are integrated through gated fusion, the temporal dynamics encoder (TDE) models consecutive EEG windows using a gated recurrent unit with temporal attention. Comprehensive validation is conducted on two public EEG emotion datasets, the Database for Emotion Analysis using Physiological Signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). In the subject-dependence setting, GLSTNet achieves 93.50 ± 3.22% accuracy for valence and 93.79 ± 3.64% accuracy for arousal on DEAP, and 92.48 ± 3.30% accuracy on SEED. In the subject-independence setting with target-subject calibration, GLSTNet obtains 75.61 ± 6.19% and 79.57 ± 5.99% accuracy for DEAP valence and arousal, respectively, and 88.22 ± 4.70% accuracy on SEED. These results indicate that integrating global-local spatial relations with temporal dynamics provides an effective representation strategy for EEG-based emotion recognition.},
}

Humans
*Electroencephalography
*Emotions
Brain-Computer Interfaces
Signal Processing, Computer-Assisted
Algorithms

@article {pmid42645052,
year = {2026},
author = {Huang, D and Zhang, X and Li, Y and Wu, J and Yue, Y},
title = {MSA-CNN: A Multi-Scale Attention Convolutional Neural Network for fNIRS-Based Emotion Recognition.},
journal = {Biosensors},
volume = {16},
number = {8},
pages = {},
pmid = {42645052},
issn = {2079-6374},
mesh = {Spectroscopy, Near-Infrared ; Convolutional Neural Networks ; Humans ; *Emotions ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in affective brain-computer interface research due to its non-invasive nature, portability, and robustness to motion artifacts. However, substantial inter-subject variability in neural responses remains a major challenge for subject-independent emotion recognition. To address this issue, this work presents an effective integration of multi-scale temporal convolution and dual-attention mechanisms for subject-independent fNIRS emotion recognition evaluated under the leave-one-subject-out protocol within a single dataset. The proposed framework employs multi-scale temporal convolutions to capture hemodynamic characteristics at different temporal resolutions and incorporates channel and temporal attention mechanisms to adaptively emphasize informative brain regions and critical temporal segments. Experiments were conducted on both a self-collected fNIRS emotion dataset and the publicly available ENTER dataset using the Leave-One-Subject-Out (LOSO) evaluation protocol. On the self-collected dataset, MSA-CNN achieved an accuracy of 65.06 ± 7.10% with an F1-score of 0.605. On the ENTER dataset, the proposed model obtained an accuracy of 68.91% and an F1-score of 0.621, outperforming conventional machine learning approaches and several representative deep learning baselines. Ablation studies further demonstrated the positive contributions of both the multi-scale convolutional structure and the dual-attention mechanism. Experimental results on both the self-collected and ENTER datasets demonstrate that the proposed MSA-CNN achieves competitive emotion recognition performance under the LOSO protocol. Class-wise evaluation using precision, recall, and the F1-score further provides a comprehensive assessment of the model's classification behavior. These results indicate the effectiveness of the proposed framework for cross-subject fNIRS-based emotion recognition under the current experimental settings. The results indicate that multi-scale temporal feature learning combined with attention mechanisms can effectively enhance fNIRS-based emotion recognition performance and provides a promising framework for within-dataset cross-subject evaluation in fNIRS-based emotion recognition. Future work will focus on expanding the subject population, conducting cross-dataset train-test evaluations, and incorporating multimodal neural signals to further improve robustness and generalization.},
}

Spectroscopy, Near-Infrared
Convolutional Neural Networks
Humans
*Emotions

@article {pmid42645055,
year = {2026},
author = {Kim, JS and Choi, SI and Hwang, HJ and Han, CH},
title = {Deep Learning for Ear-EEG-Based Brain-Computer Interface: A Systematic Comparison and Design Insights.},
journal = {Biosensors},
volume = {16},
number = {8},
pages = {},
pmid = {42645055},
issn = {2079-6374},
support = {No. RS-2025-25419004//National Research Foundation of Korea/ ; No. RS-2024-00397674//National Research Foundation of Korea/ ; IITP-2026-RS-2023-00258971//Ministry of Science and ICT/ ; },
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography ; Humans ; *Ear ; Neural Networks, Computer ; Signal-To-Noise Ratio ; },
abstract = {Electroencephalography (EEG) measured inside or around ears, called ear-EEG, provides a practical measurement modality for daily brain-computer interface (BCI) applications. However, reliable decoding of mental imagery remains challenging due to the limited number of channels, low signal-to-noise ratio (SNR), and substantial inter- and intra-subject variability inherent to ear-EEG. Addressing these constraints requires advanced decoding strategies specifically optimized for this signal domain. In this study, we retrospectively analyze the ear-EEG dataset of a previous study in which a real-time endogenous BCI was evaluated using conventional machine learning. Specifically, we present an offline benchmark of 23 deep neural network architectures originally developed for scalp-EEG, which were adapted to ear-EEG and evaluated under an identical validation framework. To the best of our knowledge, this is the first systematic comparison of this breadth for ear-EEG-based mental-task classification. Beyond conventional performance comparison, we identify the optimal architecture by jointly considering statistical significance and a performance-cost trade-off, incorporating classification accuracy, parameter count, and measured computational cost. Our results demonstrate that FBLightConvNet achieves the highest classification accuracy among all evaluated models and outperforms common spatial pattern-linear discriminant analysis (CSP-LDA), a widely adopted and robust conventional baseline, on all three recording days, with the difference reaching statistical significance on Days 2 and 3. Notably, many state-of-the-art scalp-EEG models fail to generalize effectively to ear-EEG, highlighting the importance of architecture selection in this domain. These findings identify the best-performing architecture in this setting and indicate which architectural characteristics support effective ear-EEG decoding. Ultimately, this study offers practical design insights and a reproducible benchmarking framework for developing lightweight and high-performance deep learning models, which we hope will support future efforts toward real-world ear-EEG-based BCI systems.},
}

*Brain-Computer Interfaces
*Deep Learning
*Electroencephalography
Humans
*Ear
Neural Networks, Computer
Signal-To-Noise Ratio

@article {pmid42645069,
year = {2026},
author = {Zhang, Y and Shao, Q and Yang, H and Ren, X and Peng, X},
title = {DMG-GCN: A Dynamic Microstate-Guided Graph Convolutional Network for EEG Cognitive Workload Decoding in Air Traffic Control.},
journal = {Biosensors},
volume = {16},
number = {8},
pages = {},
pmid = {42645069},
issn = {2079-6374},
support = {2024YFC3014400//National Ministry of Science and Technology/ ; U2233208//National Natural Science Foundation of China/ ; },
mesh = {*Electroencephalography ; Graph Neural Networks ; Humans ; Brain-Computer Interfaces ; *Cognition ; *Aviation ; Workload ; Algorithms ; },
abstract = {Complex inter-subject variability induces severe distribution shifts in the physiological features of electroencephalography (EEG) for air traffic controllers (ATCOs). These inter-subject shifts limit the generalization and interpretability of passive brain-computer interfaces (pBCIs) during cognitive workload decoding. To address this, a Dynamic Microstate-Guided Graph Convolutional Network (DMG-GCN) is proposed for robust cross-subject workload recognition. This approach utilizes a Dynamic Selective Kernel Temporal Convolutional Block (DSK-TCB) to adaptively extract multi-scale temporal-spectral dynamics, while concurrently constructing a time-evolving adjacency matrix via a Microstate-Guided Dynamic Graph Block (MG-DGB) to disentangle topological sub-networks. A spatiotemporal graph convolution module then aggregates these representations, and a temporal self-attention mechanism focuses on task-critical transition moments. Extensive experiments on simulated multi-level air traffic control tasks demonstrate that the proposed model achieves an overall average accuracy of 80.30% and an average F1-score of 78.63% in cross-subject evaluations, significantly outperforming state-of-the-art baselines. Moreover, an exploratory interpretability analysis suggests that the extracted topological sub-networks exhibit spatial patterns consistent with specific brain network reorganizations, which encompass the transition from global distributed monitoring to temporal multimodal integration and parietal-occipital parallel processing during workload regulation. The framework provides a robust and analytically transparent pBCI solution for adaptive automation in modern aviation.},
}

*Electroencephalography
Graph Neural Networks
Humans
Brain-Computer Interfaces
*Cognition
*Aviation
Workload
Algorithms

@article {pmid42648317,
year = {2026},
author = {Hu, J and Gao, Z and Hao, Y and Xie, S and Li, X and Cai, Q and An, J},
title = {MTGNet: A task-oriented and spectrally guided framework for EEG denoising.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9ef1},
pmid = {42648317},
issn = {1741-2552},
abstract = {Electroencephalography (EEG) is widely used in brain-computer interfaces (BCIs), but its microvolt-level signals are easily contaminated by electromyography (EMG), electrooculography (EOG), and mixed physiological artifacts. This study develops an EEG denoising framework that suppresses artifacts while preserving information used by downstream biomedical artificial intelligence (AI) tasks. Approach. We propose MTGNet, a task-oriented and spectrally guided EEG denoising framework. MTGNet combines Low-Rank Adaptation (LoRA)-based Task-Aware Consistency Regularization (TACR), a spectrally aware Guidance Network, and parallel Mamba-Transformer backbone. A pretrained 11.97M-parameter backbone learns to preserve intrinsic EEG characteristics from paired noisy-clean data, while 0.33M LoRA parameters enable task-specific adaptation without requiring paired clean EEG references. Main results. On EEGDenoiseNet, MTGNet reduces spectral relative root-mean-square error (S-RRMSE) by over 18.9%, 31.5%, and 14.0% for EMG, EOG, and hybrid artifacts, respectively (p<0.001). On a real-world fatigue EEG dataset, it improves classification accuracy by 6.20-6.69 percentage points compared with unprocessed inputs (p<0.05). Ablation, cross-classifier, and cross-dataset analyses validate the proposed components and support the transferability of MTGNet across the evaluated settings. Significance. The proposed framework provides a practical approach to task-aware EEG denoising, while future work should further validate its applicability across real-world EEG settings involving diverse tasks, artifact types, and acquisition conditions.},
}

@article {pmid42648341,
year = {2026},
author = {Sawyer, A and Welle, CG and French, J and Kelemen, J and Bashford, L and Kramer, DR and McMullen, D and Franklin, R and Angle, MR and Herrmann, D and Oxley, T and Dister, J and Chetty, N and Radcliffe, EM and Kowalczyk, J and Bose, R and Farooqui, J and Hochberg, LR},
title = {Brain-computer interface clinical trial design considerations and clinical outcome assessments in pivotal studies: a summary of the 11th BCI society meeting 2025 workshop.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9eee},
pmid = {42648341},
issn = {1741-2552},
abstract = {The Brain Computer Interface (BCI) Society Meeting 2025 held a workshop in collaboration with the Implantable BCI Collaborative Community (iBCI-CC) to discuss the selection, development and validation of clinical trial outcome assessments (COAs) for pivotal studies of BCIs. The iBCI-CC Clinical Study Endpoints Workgroup aims to build consensus on a COA framework that can meet the demands of emerging iBCI trials. Approach: The workshop brought together diverse stakeholders from the iBCI-CC community and beyond, including industry, academic and government institutions to engage in pre-competitive collaborative discussion. Main results: Through presentations, panel discussions and breakout groups, workshop participants highlighted meaningful aspects of health (MAHs) relevant to iBCI users, clarified the need to define corresponding concepts of interest (COIs) and to identify or adapt clinical outcome assessments (COAs) capable of capturing both functional impact and real-world use. Key challenges highlighted during the workshop are patient heterogeneity, the lack of widely validated and fit-for-purpose COAs, and the need to capture meaningful outcomes in home and daily-life environments. Lessons drawn from trials such as ADAPT-PD underscore the value of capturing device performance in home and daily-life settings, highlighting the need for context-sensitive and flexible metrics that reflect patient priorities. Significance: This workshop lays a foundation for the iBCI-CC Clinical Study Endpoints Workgroup to establish a transparent process for identifying MAHs and COIs that are suitable for specific iBCI technologies, patient-informed, and conducive to regulatory approval and reimbursement. By combining rigorous, quantitative assessment with patient-informed goals, iBCI clinical trials can advance toward safe, effective, and accessible neurotechnologies that enhance how a person with a severe motor impairment feels, functions and survives. .},
}

@article {pmid42649661,
year = {2026},
author = {Yan, C and Zhao, Y and Zhou, W and Liu, G},
title = {A Learnable Entropy-Power Scaling Transform with a Radial Basis Function Network for Electroencephalography-Based Familiar and Unfamiliar Face Classification.},
journal = {Entropy (Basel, Switzerland)},
volume = {28},
number = {8},
pages = {},
pmid = {42649661},
issn = {1099-4300},
support = {No. 62401342//National Natural Science Foundation of China/ ; No. ZR2024QF092, ZR2024LZH007, and ZR2025ZD24//Natural Science Foundation of Shandong Province/ ; No. 2025A1515011826 and 2026A1515010768//Guangdong Basic and Applied Basic Research Foundation/ ; No. JCYJ20250604124702003//Shenzhen Fundamental Research Program/ ; No. 2024YFC2418300 and 2024YFC2418303//National Key Research and Development Program of China/ ; No. SCCl2025YB02//Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education/ ; },
abstract = {Familiar and unfamiliar face recognition is an important cognitive process with potential applications in brain-computer interface (BCI) systems and neurocognitive assessment. Classification of familiar and unfamiliar faces based on electroencephalography (EEG) remains challenging because discriminative information is distributed across multiple frequency bands, temporal windows, and scalp channels. This study proposes LEPST-RBFNet, which combines a Learnable Entropy-Power Scaling Transform (LEPST) and a radial basis function (RBF) network for EEG-based familiar and unfamiliar face classification. The model first segments the EEG into multi-scale time-frequency segments and then extracts local standard-deviation features. After that, a learnable entropy-power-inspired scaling transformation is applied using the LEPST module to obtain adaptive local time-frequency EEG entropy features. The transformed features are classified by an RBF prototype module with learnable centers and an adaptive kernel-width parameter. Experiments using a five-fold leave-one-block-out validation protocol show that LEPST-RBFNet achieves a superior average classification accuracy of 73.60%. Ablation and visualization analyses further indicate that the proposed model provides a competitive and interpretable framework for EEG-based familiar and unfamiliar face recognition.},
}

@article {pmid42649737,
year = {2026},
author = {Wu, W and Tang, J and Wang, Q and Yang, J},
title = {Optimizing Transcutaneous Electrical Stimulation Based on Frequency-Dependent Tissue Modeling and Signal Analysis.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {8},
pages = {},
doi = {10.3390/bioengineering13080847},
pmid = {42649737},
issn = {2306-5354},
support = {KJQN202302808, KJQN202402841//Chongqing Education Commission of China/ ; K23ZG3030043//Chongqing Municipal Education Science Planning Project/ ; ygz2021101//Chongqing Medical and Pharmaceutical College/ ; },
abstract = {Transcutaneous electrical stimulation (TES) is limited by cutaneous discomfort caused by unavoidable activation of superficial sensory nerves during current delivery to deep targets. While psychophysical studies have empirically identified waveforms that reduce skin sensation, existing computational models typically employ quasi-static approximations that neglect the pronounced dielectric dispersion of biological tissues, leaving the biophysical mechanisms poorly understood. We developed a finite-element model of the human forearm incorporating the frequency-dependent dielectric properties of skin, fat, muscle, bone, and nerve tissues (DC to 1 MHz), coupled with a linear time-invariant signal-processing framework based on the system transfer function H(f). The model quantitatively reproduced the waveform-dependent sensation trends reported by Hsu et al. We introduced a penetration ratio to quantify deep-to-superficial nerve activation and found that all time-varying waveforms exhibit lower penetration than direct current (DC), revealing a skin-effect-like behavior of electrical current in biological tissues. Moreover, deep and superficial nerve activations co-varied under waveform parameter changes, indicating that waveform optimization alone cannot improve depth selectivity. Electrode spatial configuration was shown to offer a complementary strategy: positioning the active electrode close to the target muscle nerve while avoiding superficial cutaneous nerves, combined with sufficient transverse spacing, enhances depth selectivity. This work bridges psychophysical observations with tissue electrophysiology and provides a computational tool for waveform and electrode optimization in TES.},
}

@article {pmid42651174,
year = {2026},
author = {Tsai, PC and Tang, KT and Akpan, A and Lakany, H},
title = {Feasibility of Gamified EEG Neurofeedback Combined with Adaptive Working-Memory Training in Older Adults: A Pilot Study.},
journal = {Brain sciences},
volume = {16},
number = {8},
pages = {},
doi = {10.3390/brainsci16080865},
pmid = {42651174},
issn = {2076-3425},
support = {//University of Liverpool/ ; },
abstract = {BACKGROUND: Falls among older adults are linked to central nervous system deterioration and executive function deficits.

OBJECTIVES: This study evaluates the feasibility of a gamified EEG-based neurofeedback intervention combined with adaptive working-memory training, targeting neural processes associated with attentional inhibition and working memory in older adults, and characterises, as exploratory secondary outcomes, the oscillatory and functional measures that change over the training period.

METHODS: Twenty healthy older adults (65.0±3.3 years) completed a 24-session longitudinal training programme. The intervention combined real-time alpha-band neurofeedback (NF), targeting left-frontal alpha activity associated with inhibitory control, with an adaptive N-back task engaging theta-band working memory processes. Behavioural outcomes included the Colour Trail Making Test A and B (CTMT-A/B), the Berg Balance Scale short form (BBS-3P), and the four-item Dynamic Gait Index (DGI-4). EEG was recorded at the Early, Middle, and Later stages to characterise training-related neural and behavioural changes.

RESULTS: The programme proved highly deliverable: adherence was 100% across all 24 sessions, no session was terminated early, and no severe adverse events occurred. Participants acquired control of the trained signal, with left-frontal alpha power rising across training at the neurofeedback target site, and frontal theta increased bilaterally under adaptive working memory load, consistent with the reduced hemispheric asymmetry characteristic of this age group. Scores changed in the direction of improvement on all four behavioural measures; however, functional changes were small, and cognitive changes cannot be separated from repeated-testing effects. Of sixteen candidate EEG-behaviour associations, four showed moderate-to-large participant-level coefficients and were retained as candidate associations: right-frontal alpha with balance (r=-0.64) and with gait adaptability (r=-0.48), and bilateral frontal theta with processing speed (r=-0.53 and -0.51). Several associations that appeared strong when observations were pooled across stages did not survive participant-level modelling, indicating that repeated-measures methods are needed in this literature. All associations are exploratory, uncorrected for multiplicity, and reported with effect sizes and confidence intervals.

CONCLUSIONS: The gamified neurofeedback and working-memory training programme was feasible, well tolerated, and fully adhered to in a supervised experimental setting by community-dwelling older adults and was accompanied by measurable modulation of the targeted frontal rhythms. This study delivers a shortlist of candidate EEG features, with the effect-size estimates needed to power a confirmatory trial. Because the design is single-arm, both functional scales approached ceiling, and the same test forms were repeated at each stage, practice effects cannot be separated from intervention effects, and these associations are hypothesis-generating; a sham-controlled trial in a fall-prone population is the appropriate next step.},
}

@article {pmid42651178,
year = {2026},
author = {Xu, Y and Otsuka, S and Nakagawa, S},
title = {Diffusion Inverse Filtering: Enhancing Functional Connectivity-Based Pattern Recognition by Counteracting Spatial Smoothing.},
journal = {Brain sciences},
volume = {16},
number = {8},
pages = {},
doi = {10.3390/brainsci16080869},
pmid = {42651178},
issn = {2076-3425},
support = {JP24K03260//Japan Society for the Promotion of Science/ ; JPMJSP2109//Japan Science and Technology Agency/ ; },
abstract = {Background/Objectives: This study proposes Diffusion Inverse Filtering (DIF), a spatially informed transformation designed to counteract spatial smoothing in functional-connectivity representations (i.e., functional networks) and thereby enhance their discriminative power for pattern recognition. Spatial smoothing in electroencephalography (EEG) signals and derived features, such as functional connectivity, is largely attributed to volume conduction. Functional connectivity has been increasingly used in brain-computer interface (BCI) studies; however, this spatial smoothing can introduce spurious connections and distort functional-connectivity patterns. Methods: DIF approximates spatial smoothing in functional connectivity, which is potentially associated with volume conduction, as a diffusion-like process and applies a regularized inverse operation to transform the observed functional networks into networks with enhanced discriminative representations. The effectiveness of DIF in enhancing the discriminative power of functional-connectivity representations in pattern recognition was evaluated using emotion recognition as the paradigm task, which is a critical component of BCI systems. Experimental results show that DIF generally improves emotion-recognition performance relative to the originally observed functional networks under electrode-sparsification conditions, with the most consistent improvements observed for Pearson correlation coefficient (PCC) estimation. Both signal-level processing, which operates on EEG signals before functional-connectivity estimation, and function-al-connectivity-level transformations, including graph signal processing (GSP)-based filtering applied after functional-connectivity estimation, were included as comparators. Under this framework, DIF is also considered a functional-connectivity-level transformation, but does not rely on a GSP framework. Results: The performance of DIF demonstrates the potential of functional-connectivity-level transformation as a complement or alternative to signal-level processing for enhancing connectivity-based pattern recognition. Overall, DIF improves classification performance and offers strong compatibility with modern functional-connectivity-based BCI pipelines.},
}

@article {pmid42651451,
year = {2026},
author = {Chen, Y and Geng, F and Gong, M and Zhou, H and Hu, Y},
title = {A Mouse-Tracking Paradigm for Value-Driven Attentional Capture.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {16},
number = {8},
pages = {},
doi = {10.3390/bs16081273},
pmid = {42651451},
issn = {2076-328X},
support = {81971245//National Natural Science Foundation of China/ ; 82471511//National Natural Science Foundation of China/ ; },
abstract = {Reward learning is fundamental to adaptive behavior and exhibits substantial individual variability linked to addiction and other psychiatric conditions. Traditional value-driven attentional capture (VDAC) paradigms rely mainly on reaction time (RT) and accuracy, but it remains unknown whether engaging motor control processes could enhance sensitivity for detecting individual differences in reward-induced attentional biases. We developed a novel mouse-tracking VDAC paradigm that engages motor control processes. Two independent samples (n1 = 45, n2 = 59) completed the task and a battery of reward-related psychological scales (reward sensitivity, impulsivity, self-control, digital addiction tendency). A new index, maximum deviation (MD) of mouse trajectories, quantified how reward history biases attention and motor response. The paradigm replicated the classic RT-based VDAC effect. Trajectory-based measures revealed bidirectional effects: high-value distractors increased attraction and reduced active avoidance. The relative angle modulated VDAC strength, with the largest effect at 120°. ΔMD (high-value vs. absent) showed significant positive correlations with reward sensitivity, impulsivity, and addiction tendency, and negative correlations with self-control in both samples, whereas ΔRT showed no significant correlations. The consistent pattern across two independent samples suggests that mouse-tracking provides a complementary, process-sensitive index for detecting individual differences in reward-driven attention. This paradigm may serve as a useful tool for investigating cognitive markers of vulnerability to reward-related disorders, including addiction.},
}

@article {pmid42651598,
year = {2026},
author = {Pan, X and Shi, Z and Guo, X and Liu, X and Su, Y},
title = {Developmental Patterns in Cooperative Contexts: How Children Integrate Social Strategy and Resource Endowment in Third-Party Responses.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {16},
number = {8},
pages = {},
doi = {10.3390/bs16081422},
pmid = {42651598},
issn = {2076-328X},
support = {32371111//National Natural Science Foundation of China/ ; 32071075//National Natural Science Foundation of China/ ; },
abstract = {This cross-sectional study examined how children integrated social strategy and initial resource endowment when responding to others as third-party observers in a cooperative context. Using an age-appropriate collective dilemma game, 141 Chinese children observed fictional peers choosing among cooperation, self-reliance, and free-riding strategies under conditions of high or low resource endowment, yielding a 3 (strategy) × 2 (resource endowment) experimental design. Children responded to each target through punishment, social evaluation, and partner choice. Results showed a strong preference for high-resource individuals among younger children (ages 5-6), which diminished with age, suggesting a developmental shift away from reliance on external cues such as resource. Across all age groups, children consistently responded most negatively to free riders, regardless of resource endowment, indicating their moral sensitivity to exploitative intent and fairness violations. Importantly, self-reliant individuals with high resources were viewed more favorably than those with low resources, indicating that children adjust their responses based on social strategy and resource endowment. These findings suggest two key developmental patterns: (1) an age-related decline in resource-based preferences and (2) early-emerging ability that enables children to weigh contextual cues when responding to non-cooperative behaviors. The study highlights age-related patterns in children's third-party responses, reflecting their sensitivity to both social strategy and situational information in cooperative contexts.},
}

@article {pmid42651898,
year = {2026},
author = {Wu, P and Peng, Y and Zhu, J and He, Q and Wang, J and Niu, X and Wang, S and Wang, Z and Shi, L},
title = {Categorical Representation of Numerosity in the Pigeon Entopallium: Coding Format and Temporal Dynamics.},
journal = {Animals : an open access journal from MDPI},
volume = {16},
number = {16},
pages = {},
doi = {10.3390/ani16162494},
pmid = {42651898},
issn = {2076-2615},
support = {62206253//National Natural Science Foundation of China/ ; 2024M752934//China Postdoctoral Science Foundation/ ; 262102211044//Henan Science and Technology Department/ ; },
abstract = {Numerosity perception is an evolutionarily conserved ability observed across diverse taxa, from insects and fish to birds and primates. Unlike traditional visual categories, numerosity possesses an intrinsic metric structure, making it well suited for quantitatively investigating how categorical representations emerge along the visual hierarchy. While numerical representations are well characterized in high-level associative areas of non-human primates and avian species, neuronal processing of numerosity in upstream regions remains largely unexplored. To address this gap, we recorded single-unit activity in the pigeon entopallium during a delayed match-to-numerosity task. We found a subset of neurons that encoded numerosity independently of the non-numerical feature controlled in the corresponding recording session, exhibiting quasi-monotonic increasing (QMI) or decreasing (QMD) response profiles. Compared with QMI neurons, QMD neurons exhibited a markedly later coding window and stronger category discriminability. Moreover, in both neuronal populations, the normalized response functions and category discriminability were better described by compressed numerosity scales than by a linear scale, with the logarithmic scale showing a modest overall advantage. These findings help refine current theoretical frameworks for how numerosity information is extracted and represented along the avian visual processing hierarchy, providing a comparative basis for cross-species research.},
}

@article {pmid42651974,
year = {2026},
author = {Yang, L and Guo, X and Tao, A and Li, Z},
title = {Hippocampal Local Field Potentials Encode Continuous Flight Speed in Homing Pigeons via Complementary Gamma and Theta Signatures.},
journal = {Animals : an open access journal from MDPI},
volume = {16},
number = {16},
pages = {},
doi = {10.3390/ani16162569},
pmid = {42651974},
issn = {2076-2615},
abstract = {Although the role of the mammalian hippocampus in representing locomotor speed has been widely investigated, how the avian hippocampus represents continuous flight speed under free-flight conditions in the outdoor environment remains unclear. In this study, we used homing pigeons as a model system and synchronously recorded hippocampal formation (HF) local field potentials (LFPs), global positioning system (GPS) trajectories, and inertial measurement unit (IMU) data during natural homing flights. We aimed to determine whether and how the avian HF encodes flight speed. Flight-speed-related neural features were extracted from both frequency-domain and time-domain signals, including the 50-70 Hz power spectral density (PSD) ratio and theta-demodulated amplitude (DAmp). We then constructed models for discrete flight-speed state decoding and continuous flight-speed prediction. The results showed that the 50-70 Hz PSD ratio in the HF was significantly negatively correlated with flight speed, whereas DAmp was significantly positively correlated with flight speed. Both features exhibited consistent speed-related trends across different spatial release sites. Support vector machine (SVM)-based classification showed that PSD, DAmp, and their combined features could effectively decode four flight-speed states, including non-flight, low-speed, medium-speed, and high-speed states, with the combined features achieving the best performance. Further Gaussian process regression (GPR) analysis demonstrated that the combined features predicted continuous flight speed more accurately than either single feature. These findings provide evidence that the avian hippocampal formation encodes continuous flight speed during natural navigation through the complementary integration of frequency-domain and time-domain features, extending the known role of the avian hippocampal formation from static spatial mapping to dynamic self-motion representation.},
}

@article {pmid42638125,
year = {2026},
author = {Zhang, H and Xian, R and Zhang, Y and Liu, X and Yang, G and Chen, G},
title = {Brain-computer interface for upper limb functional recovery post-stroke: a meta-analysis of improvement in upper limb motor function and changes in neurophysiological markers.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {23},
number = {1},
pages = {},
pmid = {42638125},
issn = {1743-0003},
mesh = {Humans ; *Upper Extremity/physiopathology ; *Recovery of Function/physiology ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Stroke/physiopathology ; },
abstract = {BACKGROUND: Brain-Computer Interface (BCI) has emerged as a promising intervention, facilitating recovery of upper limb motor function, enhancing associated neurophysiological markers, and improving activities of daily living (ADL) performance, which is measured using the Modified Barthel Index (MBI). Nevertheless, most existing research has centered on individual outcome domains, and thus critical questions remain unanswered. These questions include heterogeneity in treatment efficacy across different post-stroke phases, in addition to the consistency of BCI's effects across specific functional assessment scales, such as the Fugl-Meyer Upper Extremity Scale (FMA-UE) and the Action Research Arm Test (ARAT)-and on neurophysiological markers.

OBJECTIVE: To perform a systematic review and meta-analysis of the effects of BCI-mediated rehabilitation versus conventional rehabilitation on upper limb motor function, functional activities, neurophysiological markers, and MBI performance in patients with stroke.

METHODS: Systematic searches were performed in 7 databases for this study: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wan Fang Data, and China Biology Medicine Database (CBM). Inclusion criteria were randomized controlled trials (RCTs) comparing BCI-mediated rehabilitation with conventional rehabilitation in patients with upper limb dysfunction following stroke. The primary outcome measure was FMA-UE scores. Secondary outcome measures included functional activity levels as assessed by ARAT, neurophysiological markers (e.g., motor evoked potentials, MEPs, electroencephalography, EEG indices), and MBI scores. The methodological quality of the included studies was assessed using the validated evidence-based Cochrane Collaboration's RoB 2.0. In the meta-analysis, if the results of the heterogeneity test were less than 25%, the fixed-effect model was employed; otherwise, the random-effects model was used. For each outcome measure, the mean difference (MD) or standardized mean difference (SMD), together with their corresponding 95% confidence intervals (95% CI), were calculated.

RESULTS: After screening against the inclusion and exclusion criteria, 16 eligible randomized controlled trials (RCTs) were ultimately included, encompassing a total of 1761 patients. Compared with conventional rehabilitation interventions, BCI rehabilitation interventions exert a certain effect on improving patients 'upper limb motor function (SMD = 0.49, 95%CI;0.26-0.73, p = 0.037).Additionally, significant improvements were observed in ARAT scores (SMD = 0.53, 95% CI: 0.14-0.91, p = 0.035),neurophysiological markers(SMD = 0.53, 95% CI;0.08-0.98, p < 0.05)and MBI scores(SMD = 0.85, 95% CI;0.55-1.14, p < 0.001).Subgroup analyses of BCI-mediated rehabilitation interventions for upper limb motor function recovery in stroke patients demonstrated certain subgroup-specific disparities in the magnitude of therapeutic effects across different subgroups. With respect to stroke staging, BCI-mediated rehabilitation interventions conferred superior efficacy for motor function recovery in patients with subacute stroke, a finding plausibly attributable to the temporal course of post-stroke neural remodeling. Subgroup analyses stratified by intervention dosage demonstrated that both 10-15 and 20-30 sessions of BCI-mediated rehabilitation interventions resulted in clinically meaningful improvements in patients' upper limb function. No statistically significant difference was detected between these two treatment regimens, yet the 20-30-session protocol was associated with a relatively larger effect size. Furthermore, subgroup analyses stratified by intervention modality demonstrated that no statistically significant differences emerged across the distinct intervention approaches, and this observation plausibly suggests the rehabilitative benefits of divergent BCI paradigms in patients are uniformly mediated through neural circuit remodeling.

CONCLUSION: BCI emerges as a promising therapeutic modality for stroke rehabilitation, delivering substantial, statistically significant improvements across multiple key outcomes, including FMA-UE, ARAT, neurophysiological markers, and MBI scores with consistent statistical significance observed across all aforementioned domains.},
}

Humans
*Upper Extremity/physiopathology
*Recovery of Function/physiology
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
Stroke/physiopathology

@article {pmid42640039,
year = {2026},
author = {Peng, W and Yang, Y and Wang, J and Ren, X and Wang, D},
title = {Application of non-invasive electroencephalogram-based brain-computer interfaces in post-stroke hand function rehabilitation.},
journal = {Topics in stroke rehabilitation},
volume = {},
number = {},
pages = {1-17},
doi = {10.1080/10749357.2026.2723408},
pmid = {42640039},
issn = {1945-5119},
abstract = {BACKGROUND: Post-stroke hand dysfunction severely limits patients' independence, and conventional rehabilitation often fails those without active movement. Noninvasive EEG-based brain-computer interface (BCI) technology addresses this by creating a closed-loop feedback system rooted in Hebbian learning principles. This system decodes rhythmic signals from the sensorimotor cortex during imagined hand movements in real-time. The decoded intention is then translated into commands to drive exoskeletons, functional electrical stimulation (FES), or virtual reality (VR) devices, thereby moving the affected limb. This process strengthens or remodels damaged neural pathways, promoting motor recovery.

METHODS: This article systematically outlines the neurophysiological basis of EEG-BCI and three major rehabilitation paradigms: motor imagery with physical feedback, motor imagery with virtual/multisensory feedback, and the steady-state visual evoked potential (SSVEP)-driven paradigm.

RESULTS: Studies confirm these approaches can improve upper limb function, showing significant potential. However, widespread clinical use faces challenges like low signal-to-noise ratios, significant individual variability, and "BCI blindness."

CONCLUSIONS: Future work should focus on improving decoding algorithms, developing more user-friendly devices, deepening mechanistic understanding, and establishing standardized clinical assessments. This review aims to offer valuable guidance for subsequent research.},
}

@article {pmid42640770,
year = {2026},
author = {Pham, HM and Pham, TM and Nguyen, TH and Nguyen, LST and Nguyen, VK and Nguyen, LHK and Vo, QTN and Ha, HTT and Quan, TT},
title = {Towards Balanced Bias-Variance With SincDualFormer: A Dual-Scale Sinc-Filterbank Transformer Model With SR-BandMix Augmentation for Motor Imagery BCI.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3727259},
pmid = {42640770},
issn = {2168-2208},
abstract = {Motor imagery electroencephalography (MI-EEG) decoding remains challenging because of its low signal-to-noise ratio, substantial inter-subject variability, and limited training data. This study proposes SincDualFormer, a dual-rhythm architecture that combines physiologically constrained Sinc filtering and unconstrained temporal convolution within parallel mu and beta-aligned branches. The two complementary representations are independently recalibrated by BandSE and projected through depthwise spatial convolutions before pointwise fusion. A multi-scale Inception-TCN and a lightweight Transformer encoder are then employed to model local temporal patterns and long-range dependencies, respectively. We further introduce SR-BandMix, a joint time-frequency augmentation strategy that integrates same-class Segmentation-Reconstruction with fine-grained spectral mixing over multiple sub-bands within the MI-related 8-30 Hz range. Experiments on two public benchmarks and one private dataset show that SincDualFormer achieves average accuracies of 82.14%, 87.93%, and 58.50% on BCI Competition IV-2a, BCI Competition IV-2b, and the HCMIU MI Hand-Binary dataset, respectively. It achieves the highest average accuracy among the evaluated methods on all three datasets under their corresponding evaluation protocols. Across representative backbone architectures, SR-BandMix generally improves or maintains competitive performance relative to Segmentation-Reconstruction, BandMix, and no augmentation. In particular, SincDualFormer consistently achieves its best performance with SR-BandMix on both public benchmarks. Ablation, bias-variance, and visualization analyses further demonstrate the complementary contributions of rhythm-constrained filtering, data-driven temporal modeling, and joint time-frequency augmentation to robust MI-EEG decoding.},
}

@article {pmid42640816,
year = {2026},
author = {Park, N and Shin, Y and Kang, J and Lee, YE and Lee, A},
title = {Wireless Electroencephalography in Research on Children With Developmental Disabilities: Scoping Review.},
journal = {Journal of medical Internet research},
volume = {28},
number = {},
pages = {e84707},
pmid = {42640816},
issn = {1438-8871},
mesh = {Humans ; Child ; *Electroencephalography/instrumentation/methods ; *Developmental Disabilities/physiopathology ; *Wireless Technology ; },
abstract = {BACKGROUND: Wireless electroencephalography (EEG) systems offer practical advantages over conventional wired devices in the assessment of children with developmental disabilities (DDs), including enhanced portability, reduced participant burden, and ease of use. However, how these systems have been applied across diverse DD populations, research purposes, and clinical contexts remains unclear.

OBJECTIVE: This scoping review aimed to map available evidence on wireless EEG applications in children with DDs, characterize device specifications by application purpose, identify neurobehavioral challenges and corresponding methodological solutions, and assess data quality-related reporting practices.

METHODS: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews), PRISMA-S (PRISMA Statement for Reporting Literature Searches in Systematic Reviews), and the population, concept, and context framework: population, children aged<19 years with DDs; concept, studies using wireless EEG devices for data collection; and context, all research and clinical settings. A systematic search was conducted across 7 databases (PubMed, Embase, IEEE Xplore, Web of Science, CINAHL, PsycINFO, and Scopus) from their inception through December 2025. Screening was performed independently by 4 reviewers. Data on study characteristics, device specifications, neurobehavioral recording challenges, and data quality reporting were extracted and synthesized descriptively, including cross-tabulation of devices by application purpose.

RESULTS: Of 594 identified records, 64 studies enrolling 3103 participants met the inclusion criteria. Studies were published between 2005 and 2025, with an increasing trend in both publications and sample sizes. Attention-deficit/hyperactivity disorder (38/64, 59.4%) and autism spectrum disorder (20/64, 31.3%) were the most frequently studied conditions. Primary application domains were biomarker-driven assessment and diagnosis (33/64, 51.6%), brain-computer interface (BCI) technology (18/64, 28.1%), intervention evaluation (8/64, 12.5%), and task or state monitoring (5/64, 7.8%). Across 65 study-device pairs, consumer-grade devices predominated (31/65, 47.7%), followed by research-use-only (19/65, 29.2%) and medical devices (15/65, 23.1%). Purpose-driven patterns emerged: BCI studies favored low-channel, dry-electrode, consumer-grade devices, whereas biomarker-driven and intervention studies used higher channel counts and greater signal fidelity. Recurring neurobehavioral challenges (eg, inattention, sensory hypersensitivity, and motor impairment) were addressed through rapid, low-preparation electrode setups, child-friendly device designs, and adapted recording protocols such as home-based or caregiver-mediated sessions. Data quality-related reporting was substantially incomplete: 85.9% (55/64) did not report validation against a wired EEG system, 79.7% (51/64) did not specify impedance thresholds, and 12.5% (8/64) described no artifact handling.

CONCLUSIONS: This scoping review is the first to comprehensively map wireless EEG research in children across a broad spectrum of DDs-integrating diagnosis, study context, and device characteristics-rather than focusing on a single condition or purpose. This review highlights critical gaps in data quality-related reporting that limit the interpretability and comparability of current findings. Future studies should prioritize rigorous validation within DD cohorts and the development of population-specific guidelines for device selection, signal quality assurance, and reporting transparency.},
}

Humans
Child
*Electroencephalography/instrumentation/methods
*Developmental Disabilities/physiopathology
*Wireless Technology

@article {pmid42641774,
year = {2026},
author = {Wu, X and Zhu, Y and Du, S and Chen, Q and Chen, F and Xu, X and Long, C and Ge, D and Lei, Y and Bai, Y and Liu, D and Chen, Y and Fan, S and Yang, H and Zhu, Z and Jiang, Z and Li, Q and Lu, J and Zhang, B},
title = {Altered Olfactory Adaptation of Primary Olfactory Cortex-Hippocampus-Parietal Lobe in Alzheimer's Disease Continuum: An Olfactory Task fMRI Study.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122183},
doi = {10.1016/j.neuroimage.2026.122183},
pmid = {42641774},
issn = {1095-9572},
abstract = {Olfactory adaptation, the progressive reduction of neural responses to repeated odor stimulation, is closely linked to cognitive function and is altered in Alzheimer's disease (AD). However, its evolution across biomarker-defined stages and relationship with plasma p-tau217 remain unclear. We studied 168 participants classified by plasma p-tau217: cognitively normal (p-tau217-NC, n = 37; p-tau217+NC, n = 8), subjective cognitive decline (p-tau217-SCD, n = 57; p-tau217+SCD, n = 16), and mild cognitive impairment (p-tau217-MCI, n = 40; p-tau217+MCI, n = 10). Odor-induced fMRI with four concentrations (0.032%, 0.1%, 0.32%, 1.0%) presented in a fixed ascending order, although concentration effects could not be fully separated from time-related factors and other confounders, to assess activation in the primary olfactory cortex (POC), hippocampus (HP), and parietal lobe (PL). Receiver operating characteristic (ROC) analyses were performed using logistic regression models. In NC groups, adaptation emerged at 0.1% and 0.32% odor conditions. In p-tau217-SCD, POC adaptation was delayed to 1.0% odor condition, HP was largely preserved, and PL was dysregulated. p-tau217+SCD and MCI groups showed delayed and dysregulated adaptation across all regions. Odor adptations were associated with plasma p-tau217 levels and olfactory memory (p_unc < 0.05, p_FDR > 0.05). Furthermore, plasma p-tau217 partially mediated the relationship between adaptation-related alterations and olfactory memory. ROC analyses indicated that olfactory adaptation may distinguished individuals across disease stages, require further confirmation in independent cohorts. These findings reveal that impaired olfactory adaptation may represent an early signature associated with AD continuum, particularly in SCD and p-tau217-positive stages.},
}

@article {pmid42642203,
year = {2026},
author = {Deng, Q and Wu, K and Xie, L and Zhang, L and Wang, H and Zheng, X},
title = {[Emerging role of brain-computer interface in orthopedic rehabilitation and motor function restoration].},
journal = {Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery},
volume = {40},
number = {8},
pages = {1314-1319},
doi = {10.7507/1002-1892.202602068},
pmid = {42642203},
issn = {1002-1892},
mesh = {*Brain-Computer Interfaces ; Humans ; Recovery of Function ; Neuronal Plasticity ; },
abstract = {OBJECTIVE: To review the research progress and application prospects of brain-computer interface (BCI) in orthopedic rehabilitation and motor function restoration.

METHODS: Through literature analysis, the neurophysiological mechanisms of BCI in improving central motor drive deficits, technical paradigms, and clinical evidence in quadriceps inhibition after anterior cruciate ligament reconstruction and other orthopedic conditions were summarized, and the challenges in clinical translation were discussed.

RESULTS: BCI decodes movement-related neural signals and establishes closed-loop feedback between central and peripheral systems, which can enhance corticospinal tract excitability, induce neuroplasticity, and alleviate postoperative muscle inhibition and dysfunction caused by insufficient central motor drive. Available evidence indicates that motor imagery-based BCI training can improve quadriceps voluntary activation rate and reduce muscle strength loss after surgery, showing potential intervention value in other orthopedic conditions.

CONCLUSION: BCI holds promise as an important adjunct to conventional orthopedic rehabilitation, particularly for patients with central motor drive deficits. However, its clinical translation still faces challenges including insufficient mechanistic evidence, high patient heterogeneity, and lack of standardized training protocols, which represent key directions for future research.},
}

*Brain-Computer Interfaces
Humans
Recovery of Function
Neuronal Plasticity

@article {pmid42642419,
year = {2026},
author = {Zheng, Y and Liang, J and Li, Z and Liu, Y and Chu, X and Zhang, S and Wang, W and Bao, J and Liu, B and Ma, C and Yin, G and Deng, J and Chi, W and Meng, L and Shi, Y},
title = {Cryo-EM structures of biopsy-derived TTR fibrils in hereditary transthyretin amyloidosis.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {42642419},
issn = {2041-1723},
support = {2025YFC3409700, 2024YFC3406200//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 82371415, 82230031, 32350018//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)/ ; 2024IR12//Peking University (PKU)/ ; 2024A1515011296//Guangdong Science and Technology Department (Science and Technology Department, Guangdong Province)/ ; },
mesh = {Humans ; *Prealbumin/genetics/metabolism/ultrastructure/chemistry ; *Cryoelectron Microscopy/methods ; *Amyloid/ultrastructure/metabolism/genetics/chemistry ; *Amyloid Neuropathies, Familial/genetics/pathology/metabolism ; Mutation ; Biopsy ; Muscle, Skeletal/pathology/metabolism/ultrastructure ; Female ; Male ; },
abstract = {Hereditary transthyretin amyloidosis (ATTRv) is a fatal autosomal dominant disease characterized by systemic deposition of transthyretin (TTR) amyloid fibrils, leading to progressive neuropathy and cardiomyopathy. More than 130 pathogenic mutations in the TTR gene have been identified, but their roles in TTR fibril formation and disease pathogenesis remain unclear. Here, using cryo-electron microscopy (cryo-EM), we present nineteen high-resolution TTR fibril structures (1.9-3.4 Å) from gastrocnemius muscle biopsies and vitreous humor of ten living ATTRv patients carrying nine distinct heterozygous mutations. Deep-learning-based analysis of cryo-EM densities enables semi-quantitative assessment of mutant or wild-type dominance within fibrils. These compositional profiles, combined with their structures, suggest an association between disease onset and the TTR species (wild-type or mutant) that primarily initiates amyloid formation. This biopsy-based workflow broadens access to patient tissue for amyloid structural studies, enabling systematic investigation of heterogeneous hereditary amyloidoses and the role of mutations in amyloid formation.},
}

Humans
*Prealbumin/genetics/metabolism/ultrastructure/chemistry
*Cryoelectron Microscopy/methods
*Amyloid/ultrastructure/metabolism/genetics/chemistry
*Amyloid Neuropathies, Familial/genetics/pathology/metabolism
Mutation
Biopsy
Muscle, Skeletal/pathology/metabolism/ultrastructure
Female
Male

@article {pmid42642612,
year = {2026},
author = {Griebsch, I and Shrestha, S and Gotay, CC and Gore, JL and Curtis, F and Bottomley, A},
title = {Patient-Reported Outcome Instruments in Non-Muscle-Invasive Bladder Cancer (NMIBC): A Systematic Review.},
journal = {Oncology and therapy},
volume = {},
number = {},
pages = {},
pmid = {42642612},
issn = {2366-1089},
abstract = {INTRODUCTION: Non-muscle-invasive bladder cancer (NMIBC) is a chronic and recurrent condition requiring repeated intravesical treatments and lifelong surveillance, imposing a substantial burden on patients. Patient-reported outcomes (PROs) are increasingly required by regulatory bodies and HTA bodies to capture treatment impact from the patient perspective, yet no consensus on the optimal PRO instruments for use in NMIBC trials exists. This systematic review evaluated PRO instruments used in NMIBC and appraised their psychometric evidence using a structured evidence-maturity framework informed by the consensus-based standards for the selection of health measurement instruments (COSMIN) methodology.

METHODS: Six electronic databases (MEDLINE, CENTRAL, Embase, Scopus, Web of Science, and APA PsycINFO) were searched from January 2000 to 31 December 2024. Studies reporting the development, validation, or application of PRO instruments in adult NMIBC populations were eligible. Eligible instruments were required to be multi-item measures with psychometric validation, evidence of interpretability, or documented use in clinical trials. No language restrictions were applied. Conference abstracts and grey literature without peer-reviewed full texts were excluded. Data were extracted using Covidence, synthesized by instrument, and mapped against an NMIBC conceptual model. Evidence maturity was classified as tier 1 (well-established evidence base), tier 2 (moderate evidence base or context-specific validation), or tier 3 (preliminary evidence or under active development), on the basis of prespecified criteria. The COSMIN risk of bias checklist was used to appraise the methodological quality of included measurement property studies. Single-item measures were excluded from the main synthesis but are discussed in the context of future use.

RESULTS: A total of 39 studies covering eight PRO instruments met the inclusion criteria. The European organization for research and treatment of cancer quality of life questionnaire-non-muscle-invasive bladder cancer 24-item module (EORTC QLQ-NMIBC24) and bladder cancer index (BCI) demonstrated strong validity, reliability, and responsiveness in NMIBC, covering urinary, sexual, and recurrence-related domains, and were classified as tier 1 (well-established evidence base). The European organisation for the research and treatment of cancer quality of life questionnaire core 30 (EORTC QLQ-C30), functional assessment of cancer therapy-general (FACT-G), and FACT-Bladder were classified as tier 2, having been validated across broader cancer populations but with limited NMIBC-specific evidence. The NMIBC-symptom index (SI), PRO-common terminology criteria for adverse events (CTCAE), and M.D. Anderson symptom inventory (MDASI) were classified as tier 3, as conceptually relevant instruments with promising early validation but insufficient NMIBC-specific psychometric evidence for regulatory-grade use. A new structured psychometric comparison facilitates direct cross-instrument comparison of internal consistency, test-retest reliability, construct validity, responsiveness, and minimally important difference estimates. Conceptual mapping revealed persistent gaps in the coverage of recurrence anxiety, cumulative treatment burden, and long-term survivorship domains. Single-item burden measures (FACT-GP5 and EORTC item library Q168) may serve as pragmatic complements to multi-item instruments in regulatory submissions.

CONCLUSION: On the basis of the available evidence, the EORTC QLQ-NMIBC24 and BCI currently provide the strongest psychometric foundation for use as primary PRO instruments in NMIBC clinical trials, though this recommendation should be interpreted in light of the methodological limitations noted above. Core measures (QLQ-C30 and FACT-G) remain valuable when used in combination with disease-specific modules. Persistent gaps in the measurement of recurrence anxiety and treatment burden highlight the need for complementary instruments and targeted single items to achieve fully regulatory-aligned PRO assessment in future NMIBC trials. TRIAL REGISTRATION: This systematic review was registered in International Prospective Register of Systematic Reviews (PROSPERO) [CRD420251076486].},
}

@article {pmid42643271,
year = {2026},
author = {He, Q and Huang, CB and Chen, J and Thompson, B},
title = {Editorial: Advances in perceptual learning: new directions, techniques, and applications.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1935934},
pmid = {42643271},
issn = {1662-4548},
}

@article {pmid42643301,
year = {2026},
author = {Chandra, A and Ramaprasad, A and Rangaswamy, M},
title = {Ethics of brain-computer interface in mental healthcare.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1846283},
pmid = {42643301},
issn = {1662-5161},
abstract = {Invasive, semi-invasive, and non-invasive brain-computer interfaces (BCI) are playing an increasingly important role in mental healthcare. They play a central role in all the stages of neuro-behavioural interventions, both by the care providers and the caretakers of a patient. The novelty of the technology and its rapid development create complex ethical challenges that must be balanced against the potential gains in efficiency and effectiveness of using the technology in mental healthcare. The paper presents an Ontology of Ethics of Brain-Computer Interface in Mental Healthcare, as a roadmap to guide to balance the ethics, effectiveness, and efficiency of the use of the technology. Like a 'Google Map' the ontology presents all the pathways by which the three issues can affect neuro-behavioural interventions using BCI technologies by the variety of agents that deliver mental healthcare. Using the ontology one can determine (a) the desired ethical pathways and reinforce them, (b) the undesired/unethical pathways and redirect them, and (c) discover novel ethical pathways and explore them.},
}

@article {pmid42643628,
year = {2026},
author = {Zhang, Y and Qian, K and Coyle, D and Dangana, M and Metcalfe, B},
title = {Closed-loop EEG-based neurofeedback and brain-computer interface interventions for mental health: a review.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1914165},
pmid = {42643628},
issn = {1662-4548},
abstract = {Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been widely explored for detecting and monitoring mental health-related states, with many existing studies focusing on identification and classification. Such approaches are primarily observational and provide limited support for intervention. Closed-loop EEG-based BCIs address this limitation by incorporating real-time feedback to observe neural activity. A key paradigm within this context is neurofeedback, in which users learn to modulate their own brain activity, with the aim of supporting improvements in mental states and in psychological functioning. In this work, we conducted a review of closed-loop EEG-based BCI interventions for mental health published since 2021, guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) reporting principles. A structured search was conducted across three databases (Scopus, Web of Science, and PubMed), yielding 1,101 records, of which 25 studies met the inclusion criteria. Advancements and observations were summarized across four categories: application, paradigm design and feedback mechanisms, signal processing and machine learning methods, and performance metrics and outcomes. In addition, this review discusses considerations related to signal processing and machine learning (ML), user interface design, and regulatory mechanisms across BCI interventions. Finally, potential directions for future research are outlined, including multimodal BCIs, domain adaptation, the integration of generative AI for BCI-based therapeutic interventions, and home-based deployment. Overall, current findings support the technical feasibility and emerging therapeutic potential of closed-loop EEG-based neurofeedback and BCI interventions in mental health applications, but the evidence base remains preliminary, as many studies were small pilot or feasibility studies.},
}

@article {pmid42644107,
year = {2026},
author = {Li, D and Deng, S and Li, Y and Zhang, Z and Nan, J and Yu, C and Yue, L and Yang, C and Zhang, H},
title = {An ERP dataset for multi-information identity authentication.},
journal = {Data in brief},
volume = {68},
number = {},
pages = {113148},
pmid = {42644107},
issn = {2352-3409},
abstract = {Conventional biometric authentication methods, such as fingerprint recognition, facial recognition, and voiceprint recognition, are vulnerable to spoofing, coercion, and data leakage. Electroencephalography (EEG), owing to its resistance to replication and involuntary nature, has shown considerable potential in secure biometric authentication, particularly in brain-computer interface (BCI) systems based on rapid serial visual presentation (RSVP). However, authentication relying solely on facial information remains limited by the single-dimensional nature of the authentication cue, insufficient resistance to attacks, and limited robustness in practical applications. Therefore, this study constructs, for the first time, a BIDS-compliant RSVP-EEG dataset for multi-information identity authentication, incorporating three types of authentication factors: target faces, target names, and verification images. Data were collected from 32 healthy participants, each of whom completed two experimental sessions for all three authentication tasks with an interval of >24 h. Identity-related and identity-unrelated stimuli were presented at a frequency of 10 Hz to elicit prominent recognition-related neural responses, and EEG signals were recorded using an eight-channel portable EEG system covering frontal and occipital regions. The proposed experimental design integrates multiple types and hierarchical levels of identity-related information, providing a solid data foundation for ERP analysis, RSVP-based cognitive experimental research, and the development of multi-information identity authentication systems.},
}

@article {pmid42644538,
year = {2026},
author = {Hu, Z and Zhuang, J and Wang, J and Jin, X and Fang, L and Hu, J},
title = {Body roundness index, frailty, and prevalent self-reported stroke: An exploratory cross-sectional decomposition analysis.},
journal = {Annals of the Academy of Medicine, Singapore},
volume = {},
number = {},
pages = {},
doi = {10.47102/annals-acadmedsg.2025411},
pmid = {42644538},
issn = {2972-4066},
abstract = {INTRODUCTION: Prior NHANES studies have reported nonlinear associations of the body roundness index (BRI) with prevalent stroke and frailty. The authors updated these observations using NHANES 1999-2023 and examined the additional contribution of frailty to the BRI-prevalent self-reported stroke association.

METHOD: This cross-sectional study included 36,324 adults. Stroke was defined by self-reported physician diagnosis. Survey-weighted logistic regression and restricted cubic splines were used to assess BRI associations. Expanded models were fitted with and without the frailty index (FI). A post hoc 2-piecewise model estimated a potential BRI breakpoint using 1000 bootstrap resamples. Incremental discrimination was evaluated across nested age-sex, age-sex-BRI, and age-sex-BRI-FI models. Analyses were interpreted as cross-sectional associations.

RESULTS: In the primary adjusted model, higher BRI was associated with prevalent self-reported stroke (odds ratio [OR]=1.08, 95% confidence interval [CI] 1.05-1.12). The spline showed increasing odds across lower-to-middle BRI values, followed by attenuation at higher values. The exploratory breakpoint was 6.34 (bootstrap 95% CI 6.14-7.65). In expanded sensitivity analyses, the BRI estimate was attenuated without FI (OR=1.01, 95% CI 0.98-1.04) and became inverse after FI was included (OR=0.91, 95% CI 0.88-0.95). FI remained strongly associated with prevalent self-reported stroke (OR=2.38 per 0.1-unit increase, 95% CI 2.23-2.54). Adding BRI to age and sex increased area under the curve (AUC) from 0.763 to 0.769, while adding FI increased AUC to 0.868.

CONCLUSION: BRI showed a nonlinear, model-dependent association with prevalent self-reported stroke, and frailty provided additional cross-sectional information. These findings support prospective evaluation of BRI and frailty as complementary markers in stroke-related assessment.},
}

@article {pmid42635515,
year = {2026},
author = {Li, H and Dang, W and Liu, L and Du, P and Cui, X and Hao, Y and Hu, J and Han, K and Wang, X and Gao, Z},
title = {Graph convolution neural network channel selection with attention for motor imagery EEG decoding.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {36},
number = {8},
pages = {},
doi = {10.1063/5.0319599},
pmid = {42635515},
issn = {1089-7682},
mesh = {*Graph Neural Networks ; Humans ; *Electroencephalography/methods ; Brain-Computer Interfaces ; *Attention ; *Imagination/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Convolutional Neural Networks ; },
abstract = {Accurate decoding of motor imagery electroencephalography (MI-EEG) signals is critical for practical brain-computer interface (BCI) systems. However, conventional approaches typically rely on dense multi-channel recordings, which not only introduce data redundancy but may also incorporate noise, thereby hindering real-world deployment. To address this challenge, we propose a graph neural network-based co-optimization framework that simultaneously performs channel selection and MI classification. The framework comprises two core components: one is the Key Channel Locator (KCL), which models EEG electrodes as graph nodes and identifies a subject-specific, fixed-size subset of informative channels through a dual-perspective evaluation that integrates graph convolutional topology with self-attention-derived feature importance, and the other is the UniEEG-Net, which efficiently decodes MI tasks from the selected channels using multi-scale temporal convolutions, depthwise separable spatial projection, and a self-attention mechanism. We extensively validate the proposed method on three datasets, including BCI Competition IV 2a, High Gamma, and a newly collected dataset. Experimental results demonstrate that our approach achieves performance comparable to that obtained with all channels while using significantly fewer electrodes. Moreover, UniEEG-Net exhibits classification accuracy surpassing current state-of-the-art models. The entire system is thus well-suited for real-world BCI applications, particularly in neurorehabilitation.},
}

*Graph Neural Networks
Humans
*Electroencephalography/methods
Brain-Computer Interfaces
*Attention
*Imagination/physiology
Algorithms
Signal Processing, Computer-Assisted
Convolutional Neural Networks

@article {pmid42635627,
year = {2026},
author = {Pitt, KM and Thiessen, A and Fowler, G and Butler, E},
title = {Engaging Children in Brain-Computer Interface (BCI) Development: Utilizing an Interactive iPad Tool to Capture Picture-Based P300-Based BCI Augmentative and Alternative Communication Design Preferences.},
journal = {American journal of speech-language pathology},
volume = {},
number = {},
pages = {1-13},
doi = {10.1044/2026_AJSLP-26-00032},
pmid = {42635627},
issn = {1558-9110},
abstract = {INTRODUCTION: P300-based brain-computer interface augmentative and alternative communication (P300-BCI-AAC) systems show promise for supporting communication for children with severe speech and physical impairments. However, little is known about how children prefer these displays to be designed. This study examined children's design choices and the rationales guiding those choices to inform the development of user-centered, pediatric BCI-AAC displays.

METHOD: Thirty-eight typically developing children (8-12 years of age) used a P300-BCI-AAC design application to create their preferred picture-based interface. Participants selected features such as color, animation, and overlays. Semistructured interviews followed to capture reasons for their decisions.

RESULTS: Two themes guided design decisions: (a) supporting visual distinction to make items more recognizable and (b) personal factors influencing design choices. Most children selected preferred colors; animation effects, especially zooming animations; and sound cues. Animation was frequently chosen to help with visual accessibility and ease of target location and to support general personalization, whereas changes in background colors were commonly used to support the incorporation of preferred colors, and video GIFs and picture overlays commonly supported the incorporation of personal relevance. Some participants emphasized symbol visibility or reduced intensity.

CONCLUSIONS: Findings underscore the preliminary importance of motion, color, personalization, and visual clarity in pediatric BCI-AAC interfaces. These insights provide a foundation for developing customizable, engaging P300-BCI-AAC systems and highlight the need for research involving children who use AAC in daily life. Research incorporating individuals with motor difficulties is warranted to refine and extend conclusions.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.33289134.},
}

@article {pmid42636119,
year = {2026},
author = {Yang, Y and Sun, C and Lyu, R and Wang, Z and Chen, X and Lin, CT and Jung, TP and Wan, F},
title = {Adaptive Class-wise Multicentric Prototype Source-Free Domain Adaptation for Privacy-Preserving BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3726669},
pmid = {42636119},
issn = {2168-2208},
abstract = {Recently, conventional domain adaptation (DA) methods have demonstrated promising performance in cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). However, these methods require direct access to labeled source subject data, potentially compromising biometric privacy. Source-free domain adaptation (SFDA) addresses this issue by leveraging pre-trained source models with prototype-based pseudo-labeling, thereby eliminating the need for source data access. Nevertheless, current SFDA methods rely on oversimplified representations that fail to adequately capture EEG dynamics, resulting in two critical drawbacks: (1) oversimplified representations, using single centroids, fail to adequately capture complex neural manifolds, leading to intra-class collapse; (2) inadequate feature discrimination leads to error propagation in pseudo-labeling. To overcome these challenges, we propose the Adaptive Class-wise Multicentric Prototype-based SFDA (ACMP-SFDA) framework, which improves performance in new subjects while safeguarding personal privacy. Specifically, ACMP-SFDA dynamically constructs multiple prototypes per class to capture non-stationary EEG dynamics. Besides, we integrate semantic contrastive learning to improve inter-class discriminability while preserving the intrinsic structure of intra-class neural manifolds. Extensive experiments conducted across two BCI paradigms (motor imagery and affective BCI) demonstrate that ACMP-SFDA outperforms state-of-the-art methods, achieving 1.36$\%$, 1.96$\%$, and 1.94$\%$ accuracy improvements on MI2014001, MI2015001, and SEED, respectively, in cross-subject tasks.},
}

@article {pmid42636120,
year = {2026},
author = {Liu, H and Liu, G and Zhang, Y and Shi, Y and Zhang, Z and Zhang, D},
title = {TASTE: Trait-like and State-like Two-stream Framework for EEG Imputation.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3726783},
pmid = {42636120},
issn = {2168-2208},
abstract = {Electroencephalography (EEG) is a core sensing modality for brain-computer interfaces, yet real-world recordings often contain missing values due to motion artifacts, electrode issues, and device or channel failures. Unhandled missingness can bias analysis and degrade downstream decoding. Existing deep imputation methods typically formulate EEG recovery as a generic spatiotemporal completion problem and thus underutilize EEG-specific priors. Motivated by neurophysiological evidence, we characterize EEG signals with complementary trait-like regularities (reproducible temporal/spatial patterns) and state-like variations (acquisition noise and informative nonstationary dynamics). Based on this view, we propose TASTE, a Trait-like And State-like Two-stream framework for EEG imputation. TASTE integrates (i) a Multi-View Trait Modeling module that learns persistent temporal, spatial, and spatio-temporal priors via a tri-branch learnable codebook with squeeze-and-excitation fusion, and (ii) a State-Aware Reconstruction module that performs noise-robust feature-space completion and preserves meaningful nonstationarity using de-stationary attention. We evaluate TASTE on five public EEG benchmarks spanning diverse tasks. TASTE consistently improves imputation fidelity and downstream classification, achieving the best performance in most settings and delivering average MAE/MSE gains of 22.22%/28.60% over strong baselines. Source code is available at https://github.com/XJTU-EEG/TASTE.},
}

@article {pmid42637065,
year = {2026},
author = {Zhang, H and Chen, G and Wang, X and Wang, P and Guo, R and Ji, S and He, J and Li, M and He, F and Zhang, Y and Jian, X and Xu, M and Ming, D},
title = {Transcranial Acoustoelectric Brain Imaging with High Spatiotemporal Resolution.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122179},
doi = {10.1016/j.neuroimage.2026.122179},
pmid = {42637065},
issn = {1095-9572},
abstract = {Non-invasive neuroimaging has long faced the inherent trade-off between spatial and temporal resolution. Acoustoelectric brain imaging (ABI) holds promise to bridge this gap by combining the spatial precision of focused ultrasound (millimeter-level) with the temporal resolution of electroencephalography (EEG) signals (millisecond-level). However, its transcranial application remains fundamentally challenged by skull-induced wavefront aberration and attenuation. Here, we developed a full ABI system featuring a custom 128-element ultrasound phased array. Our system integrates developed algorithms for transcranial phase and amplitude correction, which were experimentally validated through an ex vivo human skull. We demonstrate that our system enables precise intracranial focus steering (lateral error ≤ 0.2 mm), accurate source localization (error ≤ 0.8 mm), and high-fidelity waveform reconstruction (correlation coefficient > 0.84). This work addresses the fundamental challenge of skull-induced imaging quality degradation in ABI, providing algorithmic and systemic foundations for advancing non-invasive neuroimaging techniques.},
}

@article {pmid42637274,
year = {2026},
author = {Lima, TA and Malaquias, JB and Zanuncio, JC and Di Stefano, JG and Silva, CADD},
title = {A low-cost alternative to Parafilm for cotton boll weevil larval encapsulation and parasitism by Jaliscoa grandis (Hymenoptera: Pteromalidae).},
journal = {Journal of economic entomology},
volume = {},
number = {},
pages = {},
doi = {10.1093/jee/toag241},
pmid = {42637274},
issn = {1938-291X},
support = {001//Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/ ; 88887.838277/2023-00//Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/ ; 20.24.00.007.00.00//Better Cotton Initiative (BCI) for financial support of the "Embrapa Cotton"/ ; },
abstract = {Jaliscoa grandis (Burks) (Hymenoptera: Pteromalidae), one of the main parasitoids of the cotton boll weevil, Anthonomus grandis grandis Boheman (Coleoptera: Curculionidae), is mass-reared using larvae of this insect encapsulated in Parafilm, an efficient synthetic material; however, its high cost highlights the need to evaluate alternative films for this process. Three experiments were conducted. The first aimed to analyze the oviposition behavior of J. grandis in cotton squares with cotton boll weevil larvae and in cotton squares containing larvae encapsulated in different substrates. The second evaluated parasitism by J. grandis in larvae encapsulated in EVA cells with different thicknesses and types of paper covering them, compared to Parafilm. The third experiment analyzed parasitism using the alternative film and the production costs of this material for laboratory use. The numbers of perforations, eggs laid, progeny emergence, and mortality of cotton boll weevil larvae caused by the parasitoid J. grandis were similar in host larvae encapsulated with Parafilm or with EVA coated with tissue paper or paper towel. The operational and economic advantages of these alternative films, without reducing parasitism or the development of J. grandis progeny, reinforce their potential for use in augmentative biological control programs against the cotton boll weevil.},
}

@article {pmid42586149,
year = {2026},
author = {Nan, J and Yang, X and Jiang, H and Hong, C and Yang, J and Bai, Y and Wu, J and Ni, G},
title = {A multi-feature resting-state EEG framework for candidate EEG feature discovery in central vertigo.},
journal = {Journal of neural engineering},
volume = {23},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ae98b9},
pmid = {42586149},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Female ; *Vertigo/physiopathology/diagnosis/etiology ; Male ; Stroke/physiopathology/complications/diagnosis ; Middle Aged ; *Rest/physiology ; Aged ; Machine Learning ; },
abstract = {Objective. Central vertigo (CV) lacks objective electrophysiological measures for severity assessment and rehabilitation monitoring. We aimed to characterize multiscale resting-state EEG alterations and identify clinically interpretable candidate features in stroke-related CV.Approach. Resting-state EEG was analyzed in 50 patients with stroke-related CV (31 moderate, 19 severe) and 31 age-matched healthy controls. The framework integrated relative spectral power, cross-frequency coupling, PLV-based sensor-level phase synchrony, graph metrics, machine-learning feature ranking, and associations with balance confidence and dizziness severity.Main results. Severe CV showed widespread relative delta-power reductions of 28.7%-29.4% versus controls. Post hoc analysis showed lower global absolute delta power in severe CV than controls (Tukeyp= 0.0227; rank-based false-discovery-rate (FDR)q= 0.0459), although the absolute-power effect was less spatially extensive. Both patient groups showed reduced delta-theta and delta-beta amplitude-amplitude coupling (AAC), enhanced delta-alpha PPC, and theta-band increases in PLV-derived node degree, clustering, and global efficiency; local efficiency increased only in SV. Delta-beta AAC ranked highest across machine-learning methods and correlated moderately with balance confidence (ρ= 0.470,p= 0.001) and dizziness severity (ρ= - 0.472,p= 0.001). Zero-lag-robust measures showed the same theta ordering but were nonsignificant after FDR correction and did not establish volume-conduction-independent topology, supporting cautious PLV interpretation.Significance. Stroke-related CV involves coordinated alterations across oscillatory, cross-frequency, and sensor-level network measures. This interpretable framework identifies candidate EEG features for objective characterization that require external and longitudinal validation before clinical use.},
}

Humans
*Electroencephalography/methods
Female
*Vertigo/physiopathology/diagnosis/etiology
Male
Stroke/physiopathology/complications/diagnosis
Middle Aged
*Rest/physiology
Aged
Machine Learning

@article {pmid42627726,
year = {2026},
author = {Muñoz, JM},
title = {Neurotechnological Natives and the Neurotechnology Shift: A Research Agenda for the Brain-as-Interface Era.},
journal = {Annals of the New York Academy of Sciences},
volume = {1562},
number = {1},
pages = {e70381},
pmid = {42627726},
issn = {1749-6632},
mesh = {Humans ; *Brain/physiology ; *Neurosciences/trends/methods ; Cognition/physiology ; *Brain-Computer Interfaces/trends ; Research/trends ; },
abstract = {Neurotechnologies are increasingly moving beyond clinical and laboratory environments into a growing range of everyday contexts, including consumer markets, workplaces, and educational settings. This article presents the concept of the neurotechnology shift, understood as a technoscientific, cognitive, and sociocultural transformation through which neurotechnologies progressively expand into daily life, turning the brain itself into a shared interface between biological cognition and digital systems. This condition may eventually give rise to neurotechnological natives, a concept that is introduced here as a heuristic hypothesis intended to guide interdisciplinary research. Rather than predicting the emergence of a homogeneous generation shaped by neurotechnologies, the concept functions as an analytical tool to structure inquiry into the implications of brain-technology interaction in everyday contexts. To this end, the article outlines a research agenda organized across three dimensions of the neurotechnology shift: technoscientific infrastructures enabling scalable brain-technology interaction and neurodata ecosystems; cognitive implications related to neural plasticity, identity, impulse control, and language processes; and sociocultural transformations concerning governance, inequality and access, and collective imaginaries. Understanding the dynamics and bidirectional interactions across these three dimensions is essential for anticipating the ethical, legal, and societal implications of the emerging brain-as-interface era.},
}

Humans
*Brain/physiology
*Neurosciences/trends/methods
Cognition/physiology
*Brain-Computer Interfaces/trends
Research/trends

@article {pmid42627741,
year = {2026},
author = {Nair, K and Cecotti, H},
title = {Multiple-Classifier Binary Convolutional Siamese Networks for Code-Modulated Visual Evoked Potential Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3726071},
pmid = {42627741},
issn = {1558-2531},
abstract = {OBJECTIVE: Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (c-VEPs) using electroencephalography (EEG) signals require robust classification algorithms. It is unclear whether the best approach is to use a similarity measure or to follow a discriminant method.

METHODS: We propose a multiple-classifier binary convolutional Siamese (MCBCS) network for single-trial c-VEP decoding, in which the multi-class recognition problem is decomposed into a set of class-specific binary similarity-learning tasks. The proposed MCBCS framework is systematically compared against a single multi-class Siamese network, convolutional neural networks for 63-bit m-sequence reconstruction and direct classification, and conventional correlation-based and canonical correlation analysis approaches. The study also investigates distance-based decoding strategies and the effect of temporal data augmentation with small to medium time shifts.

RESULTS: Experimental results on EEG data from 13 subjects demonstrate that the MCBCS architecture consistently outperforms other tested methods under within-subject evaluation, with a mean single-trial accuracy of 96.89%. However, the MCBCS approach achieves 96.17% under a leave-one-subject-out protocol, while EEGNet achieves 96.79%. Finally, the Wasserstein Distance (WD$_{1}$
) achieved the highest accuracy (93.88%) among the distance metrics.

CONCLUSION: The multiple-classifier convolutional binary Siamese network achieved the highest overall performance.

SIGNIFICANCE: The results highlight the effectiveness of class-specific similarity learning for robust compared to direct discriminant approaches.},
}

@article {pmid42627854,
year = {2026},
author = {Danayi, A and Soltanian-Zadeh, H},
title = {Improved motor imagery BCI performance via task-unaware compression in the BELT Bayesian Edge-Cloud architecture.},
journal = {PloS one},
volume = {21},
number = {8},
pages = {e0354976},
pmid = {42627854},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Bayes Theorem ; Humans ; Data Compression/methods ; Algorithms ; Signal Processing, Computer-Assisted ; Compression Algorithms ; Electroencephalography ; },
abstract = {Brain-computer interface (BCI) systems have advanced with deep learning, but they are still limited by designs tied to specific applications, poor scalability, weak portability, the need for user-specific adaptation, and privacy concerns. We present BELT, a modular Bayesian Edge-Cloud architecture based on three principles: (i) Bayesian priors and posteriors to balance generalization and subject-specific learning, (ii) lightweight classifiers suitable for embedded devices, and (iii) task-aware compression to reduce bandwidth and improve privacy in edge-cloud communication. To show feasibility, we implement BELT-lite as an instantiation of BELT, a lightweight version built only from linear time-invariant operations, making it directly compatible with digital signal processing hardware. Using the BCI Competition IV-2a and IV-2b motor imagery datasets (18 subjects total, ten-fold cross-validation), BELT-lite achieved strong posterior performance after subject-specific fine-tuning: mean accuracy of 87.9%±6.8% on Dataset B and 80.6%±8.6% on Dataset A with data augmentation. After adaptation, four subjects from Dataset B and two from Dataset A exceeded 90% accuracy. On ARM Cortex-A7 hardware, BELT-lite achieved a mean latency of 6.75 ms per sample, significantly faster than EEGNet's 8.36 ms (p < 10-17)-a 21% speed improvement-at the cost of a modest but statistically significant accuracy reduction of approximately 2.7 percentage points compared to EEGNet. Network Tuning Blocks allowed partial parameter freezing: classifier-only fine-tuning incurred a modest 2-5% accuracy drop while substantially reducing training cost. Compression via the task-unaware autoencoder reduced data size by 3.3× while maintaining high accuracy: prior-model performance stayed within ≈1% of the uncompressed baseline (with slight improvements in some configurations), full posterior fine-tuning showed a ≈1% drop, and classifier-only fine-tuning incurred a ≈3% drop-an acceptable trade-off for privacy-preserving edge-cloud communication, where only a compressed latent representation is transmitted instead of raw EEG. Notably, this task-unaware autoencoder (trained solely to reconstruct the input) consistently outperformed autoencoders that also incorporated classification objectives (task-aware or task-only), providing the best accuracy-compression trade-off across all fine-tuning scenarios. These findings show that BELT provides a principled design for modular and scalable BCIs, while BELT-lite demonstrates that the approach supports accurate, efficient, and portable implementations. Together, they point toward BCI systems that are more practical, mass-producible, and privacy-aware, enabling wider use in real-world settings.},
}

*Brain-Computer Interfaces
Bayes Theorem
Humans
Data Compression/methods
Algorithms
Signal Processing, Computer-Assisted
Compression Algorithms
Electroencephalography

@article {pmid42629376,
year = {2026},
author = {Priori, S and Ricci, P and Consoli, D and Micheli, A and Merlini, A and Andriulli, FP},
title = {Correction: A visual imagery paradigm for BCI strategies using imagined flickering patterns.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
doi = {10.1038/s41598-026-67580-0},
pmid = {42629376},
issn = {2045-2322},
}

@article {pmid42631357,
year = {2026},
author = {Ciaglia, T and Carleo, G and Yang, Z and Matteo, FD and De Rosa, F and Smaldone, G and Gao, ZB and D'Alì, M and Turcio, R and Ma, D and Sarno, VD and Su, N and Giofrè, SV and Pepe, G and Campiglia, P and Bertamino, A and Miceli, F and Guo, J and Iraci, N and Ostacolo, C and Taglialatela, M},
title = {Molecular Basis for Activation to Inhibition Switching in Kv7.2 Channel Modulators.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {},
number = {},
pages = {e7948330},
doi = {10.1002/anie.7948330},
pmid = {42631357},
issn = {1521-3773},
support = {CUP E63C22002170007//European Union-Next Generation EU, Mission 4, Component 2/ ; //Ministero della Salute/ ; //Regione Campania/ ; //European Rare Disease Research alliance/ ; 32371204//National Natural Science Foundation of China/ ; 32421001//National Natural Science Foundation of China/ ; 2025YFC3409700//National Key R&D Program of China/ ; //MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University/ ; //K.C. Wong Education Foundation/ ; },
abstract = {Kv7 voltage-gated potassium channels play a critical role in controlling electrical properties of excitable tissues. Neuronally-expressed Kv7 channels are involved in both common and rare neuropsychiatric disorders, ranging from epilepsy to depression and neurodegenerative diseases; thus, they represent attractive therapeutic targets. However, no Kv7 modulator is currently available for clinical use. Improved knowledge of the functional and structural determinants responsible for ligand-induced Kv7 channel modulation is likely to fill this gap. In the present work, we describe the cryo-electron microscopy structures of human Kv7.2 channels in complex with two retigabine analogues: compound 60 (c60), which we previously described as a Kv7 activator with improved pharmacokinetic and pharmacodynamic properties, and the newly designed compound 106 (c106), which acts as a potent Kv7 blocker. Although both compounds occupy the same pocket at the S5-S6 interface in the pore domain, docking and molecular dynamics simulations and electrophysiological experiments revealed that the opposite functional behavior is due to their differential interaction, involving the L307 residue. Thus, we herein provide novel mechanistic insights into the molecular mechanisms governing Kv7 channel modulation by exogenous ligands which may prove useful to target Kv7 channels with more potent and selective modulators.},
}

@article {pmid42631788,
year = {2026},
author = {Sivan, V and Alam, Z and Polavarapu, H and Manivel, S and Kumar, RP and O'Malley, GR and Ruzicka, F and Patel, NV},
title = {Brain-spine interface: an exploration of a potential future treatment for spinal cord injury.},
journal = {Neurosurgical review},
volume = {49},
number = {1},
pages = {},
pmid = {42631788},
issn = {1437-2320},
mesh = {Humans ; *Spinal Cord Injuries/therapy/surgery/physiopathology ; Animals ; Neuronal Plasticity/physiology ; Recovery of Function/physiology ; *Brain/surgery ; Brain-Computer Interfaces ; },
abstract = {Spinal cord injuries (SCIs) profoundly impact millions globally, leading to loss of motor and sensory functions below the injury site. Brain-spine interfaces (BSIs) represent an early-stage neuroprosthetic strategy that attempts to restore functional communication between cortical motor-intention signals and spinal sensorimotor circuits below the level of injury. Although early preclinical and highly selected clinical studies have shown encouraging motor outcomes, the evidence remains preliminary, and routine clinical use is limited by questions regarding safety, durability, patient selection, accessibility, and long-term functional benefit. BSI approaches are based on the observation that residual spinal pathways and sensorimotor circuits may remain partially responsive to neuromodulation even after injury. Along the way, technological advancements have significantly bolstered SCI treatment strategies, ranging from surgical interventions to regenerative therapies. Approaches such as neurostimulation and biomaterial-based strategies have shown potential in experimental and early translational settings, although their clinical efficacy and generalizability remain incompletely established. Furthermore, exploring neuroplasticity and the body's intrinsic ability to reorganize neural connections post-injury underscores the potential for spontaneous recovery in certain cases. However, integrating BSIs into clinical practice faces substantial hurdles, including technical challenges, ethical considerations, and the need for specialized training for healthcare providers. Despite these obstacles, BSIs and other novel treatments may have potential to improve the quality of life for SCI patients, although further clinical investigation is needed to establish their safety, efficacy, and generalizability. This review catalogs recent conceptual and technological developments contributing to the emergence of BSI.},
}

Humans
*Spinal Cord Injuries/therapy/surgery/physiopathology
Animals
Neuronal Plasticity/physiology
Recovery of Function/physiology
*Brain/surgery
Brain-Computer Interfaces

@article {pmid42625007,
year = {2026},
author = {Fares, H and Ronchini, M and Zamani, M and Farkhani, H and Chiappalone, M and Neftci, E and Moradi, F},
title = {Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.},
journal = {npj biomedical innovations},
volume = {3},
number = {1},
pages = {},
pmid = {42625007},
issn = {3005-1444},
support = {R402-2022-1413//Lundbeck Foundation/ ; No 767092//European Union's Horizon 2020 research and innovation program/ ; },
abstract = {Neurological disorders pose a growing global health burden, motivating advances in neural interfacing and artificial intelligence (AI). This review surveys state-of-the-art approaches for neural recording, stimulation and signal decoding and encoding across brain-computer interfaces, neuroprosthetics, and neuromodulation systems aimed at restoring function and treating neurological disorders. It highlights recent progress in AI, particularly neuromorphic computing and spiking neural networks (SNNs), and introduces Brain-Inspired Brain-Computer Interfaces (BI-BCIs) as a unifying framework for low-power, closed-loop, and miniaturized neuromorphic neurotechnologies.},
}

@article {pmid42625783,
year = {2026},
author = {Gu, W and Daly, I and He, X and Li, S and Lau, AT and Wang, X and Cichocki, A and Chen, Y and Jin, J},
title = {EFS-NET: EEG-fNIRS multi-scale fusion network based on spatial calibration.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {160},
pmid = {42625783},
issn = {1871-4080},
abstract = {Hybrid brain-computer interfaces (hBCIs) integrate multiple neuroimaging modalities and utilize their complementary information to address the inherent limitations of single-modality neural signal decoding. For electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) hybrid BCIs, advanced fusion algorithms are crucial to fully exploit the superior spatial localization capability of fNIRS and the millisecond-level temporal resolution of EEG. This work proposes an end-to-end spatial calibration-based multi-scale EEG-fNIRS fusion network named EFS-Net, which organically integrates EEG and fNIRS signals through a multi-scale spatio-temporal fusion architecture. The network consists of three complementary functional branches: a multi-scale temporal convolution branch for capturing rapidly changing cortical electrophysiological features of EEG, an EEG spatial branch for constructing latency-compensated cortical topographies to adapt to the delayed hemodynamic response of fNIRS, and a spatially calibrated fNIRS spatial branch for dynamically fusing spatial feature maps with EEG counterparts to generate temporally aligned and spatially enhanced neural representations. This three-branch fusion structure constructs abundant spatio-temporal feature embeddings and improves the discriminability of neural features. Evaluated on two public datasets including Word Generation (WG) and Mental Arithmetic (MA) with a rigorous subject-specific leave-one-session-out cross-validation protocol, EFS-Net achieves classification accuracies of 77.71 ± 8.23% and 81.69 ± 9.49% on the WG and MA datasets respectively, which surpasses state-of-the-art unimodal algorithms and traditional fusion models. Visualization results demonstrate that the designed alignment strategy can restore realistic cortical spatial distribution characteristics, providing a feasible solution for personalized neural signal decoding in hybrid BCIs.},
}

@article {pmid42626977,
year = {2026},
author = {Qin, Y and Li, M and Li, Y and Ma, M and Xu, J and Lu, Y and Shi, X and Cui, G and Zhao, H and Yang, K},
title = {Brain-computer interface training for motor recovery after stroke.},
journal = {The Cochrane database of systematic reviews},
volume = {8},
number = {8},
pages = {CD015065},
pmid = {42626977},
issn = {1469-493X},
mesh = {Humans ; Randomized Controlled Trials as Topic ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; *Recovery of Function ; Activities of Daily Living ; Stroke/complications ; Bias ; Adult ; Muscle Strength ; },
abstract = {RATIONALE: Stroke is one of the leading causes of death and disability worldwide. Motor dysfunction is a highly prevalent and disabling consequence of stroke. Brain-computer interface (BCI) training has emerged as a promising neurorehabilitative strategy that utilises closed-loop feedback to promote targeted neural plasticity and motor recovery. However, current clinical evidence remains fragmented.

OBJECTIVES: To assess the effects of brain-computer interface training for motor recovery in people after stroke.

SEARCH METHODS: We searched the Cochrane Stroke Group's Specialised Register, CENTRAL, MEDLINE, Embase, 11 other databases, trial registries, reference lists, and Google Scholar up to 27 October 2025, without language or time restrictions.

ELIGIBILITY CRITERIA: We included randomised controlled trials (RCTs) involving adults with stroke and motor dysfunction. We compared BCI training versus conventional rehabilitation, sham-BCI, or other active non-BCI interventions.

OUTCOMES: Critical outcomes were motor function (upper and lower extremity), activities of daily living (ADL), and adverse events. Important outcomes included measures of balance, muscle strength, spasticity, and neurological function.

RISK OF BIAS: Four review authors independently assessed risk of bias using the Cochrane risk of bias (RoB 1) tool.

SYNTHESIS METHODS: We pooled data using random-effects models, calculating risk ratios (RRs) and mean differences (MDs) or standardised mean differences (SMDs). We calculated 95% confidence intervals (CIs) using the Hartung-Knapp-Sidik-Jonkman (HKSJ) method. We calculated 95% prediction intervals when at least five studies were available. Heterogeneity was assessed using the I[2] statistic and evidence certainty was evaluated using the GRADE approach.

INCLUDED STUDIES: We included 43 RCTs involving a total of 1628 participants. The trials were conducted in 11 countries across hospital, rehabilitation unit, or outpatient-clinic settings. The trials primarily recruited participants with both ischaemic and haemorrhagic stroke, mostly in the subacute and chronic phases. The interventions predominantly utilised EEG-based motor imagery - BCI combined with robotic systems, functional electrical stimulation, or neurofeedback.

SYNTHESIS OF RESULTS: Most studies were at low risk of bias for incomplete outcome data and blinding of assessors, but at high or unclear risk for participant blinding and allocation concealment. Overall, the certainty of evidence was low to very low, downgraded primarily for these risk of bias concerns, severe imprecision (due to small sample sizes), and potential publication bias. BCI training versus conventional therapy BCI training may slightly improve upper extremity motor function (MD 4.67, 95% CI 2.25 to 7.09; 10 studies, 611 participants; low-certainty evidence). It is uncertain whether BCI training improves ADL due to very low-certainty evidence. It may result in little to no difference in lower extremity motor function (MD 2.62, 95% CI 2.17 to 3.07; 1 study, 64 participants; low-certainty evidence) and the risk of adverse events (RR 1.11, 95% CI 0.72 to 1.69; 7 studies, 624 participants; low-certainty evidence). BCI training may result in little to no difference in upper extremity spasticity compared to conventional therapy (MD -0.04, 95% CI -0.24 to 0.16; 1 study, 296 participants; low-certainty evidence). No studies reported information on balance, muscle strength, and neurological function for this comparison. BCI training versus active controls It is uncertain whether BCI training improves upper extremity motor function (SMD 0.58, 95% CI 0.23 to 0.92; 14 studies, 331 participants; very low-certainty evidence) and ADL (MD 8.52, 95% CI 1.76 to 15.29; 5 studies, 163 participants; very low-certainty evidence). It may result in little to no difference in lower extremity motor function (MD 2.46, 95% CI 0.71 to 4.21; 4 studies, 130 participants; low-certainty evidence). Furthermore, it is uncertain whether it affects adverse events (RR 0.72, 95% CI 0.23 to 2.32; 9 studies, 234 participants; very low-certainty evidence). BCI training may improve balance (MD 3.25, 95% CI 1.07 to 5.43; 6 studies, 165 participants; low-certainty evidence). It is uncertain whether it improves neurological function or muscle strength due to very low-certainty evidence. It may result in little to no difference in spasticity (low-certainty evidence). BCI training versus sham-BCI training The evidence is uncertain about the effect of BCI training on upper extremity motor function (SMD 0.22, 95% CI -0.08 to 0.52; 9 studies, 279 participants; low-certainty evidence), lower extremity motor function (MD 0.55, 95% CI -5.88 to 6.97; 3 studies, 106 participants; low-certainty evidence), and ADL (MD 6.64, 95% CI -9.95 to 23.23; 1 study, 28 participants; low-certainty evidence). It is uncertain whether BCI training increases the risk of adverse events compared to sham-BCI training (RR 1.35, 95% CI 0.32 to 5.72; 7 studies, 205 participants; very low-certainty evidence). BCI training may result in little to no difference in balance (MD 1.28, 95% CI -0.16 to 2.72; 1 study, 28 participants; low-certainty evidence). It is uncertain whether it improves muscle strength on the wrist extensor (MD 0.70, 95% CI 0.33 to 1.08; 2 studies, 51 participants), spasticity (SMD 0.30, 95% CI -1.00 to 1.61; 3 studies, 82 participants), and neurological function (MD 2.60, 95% CI -3.35 to 8.55; 1 study, 27 participants), all based on very low-certainty evidence.

AUTHORS' CONCLUSIONS: Compared with conventional therapy, BCI training may slightly improve upper extremity motor function, with little to no difference in lower extremity function. ADL effects are very uncertain. Compared with active controls, it may improve balance, with little to no difference in lower extremity function. Effects on upper extremity function and ADL are very uncertain. Compared with sham-BCI, BCI training may result in little to no difference in motor function or ADL. Regarding adverse events, it may result in little to no difference versus conventional therapy, but remains uncertain across other comparisons. The certainty of the evidence ranged from low to very low across all comparisons. These findings were primarily limited by high risk of bias, small sample sizes, heterogeneity, and possible publication bias. Future larger, adequately powered, and methodologically rigorous RCTs, particularly those utilising sham-BCI controls and standardised outcome measures, are needed to determine the specific therapeutic benefits of BCI training and to adequately assess potential harms.

FUNDING: This Cochrane Review had no dedicated funding.

REGISTRATION: Protocol (2022) DOI: 10.1002/14651858.CD015065.},
}

Humans
Randomized Controlled Trials as Topic
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
*Recovery of Function
Activities of Daily Living
Stroke/complications
Bias
Adult
Muscle Strength

@article {pmid42616624,
year = {2026},
author = {Li, S and Ouyang, J and Cui, Z and Wang, Z and Jia, T and Wan, F and Wu, D},
title = {Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3725326},
pmid = {42616624},
issn = {2168-2208},
abstract = {Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints. While test-time adaptation (TTA) mitigates distribution shifts under online data streams without per-use calibration sessions, existing TTA approaches heavily rely on explicitly defined loss objectives that require backpropagation for updating model parameters, which incurs computational overhead, privacy risks, and sensitivity to noisy data streams. This paper proposes Backpropagation-Free Transformations (BFT), a TTA approach for EEG decoding that avoids these issues. BFT applies multiple sample-wise transformations, based on knowledge-guided augmentations or structured feature masking, to each test trial, producing multiple predictions for a single test sample using only forward passes. A learning-to-rank module, trained on source data, estimates the reliability of each transformed prediction, so that a weighted aggregation suppresses prediction uncertainty during online inference, with theoretical justification. Extensive experiments on five EEG datasets, covering motor imagery classification and driver drowsiness regression, demonstrate the effectiveness, versatility, robustness, and efficiency of BFT. This research enables lightweight plugand- play BCIs on resource-constrained devices, broadening the real-world deployment of EEG-based BCIs.},
}

@article {pmid42617634,
year = {2026},
author = {Li, Z and Wang, F and Lu, H and Luo, R and Li, TW and Zhao, L and Fu, Y},
title = {DRDNet: A dual-view representation decoupling network for handwriting imagery EEG classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9bef},
pmid = {42617634},
issn = {1741-2552},
abstract = {OBJECTIVE: Handwriting imagery (HI) based on electroencephalography (EEG) offers a non-invasive route to text input and complex intention expression for brain-computer interfaces (BCIs). However, HI EEG decoding is challenged by low signal-to-noise ratios, non-stationarity, cross-session distribution shifts, and the coexistence of continuous temporal trends and local high-response patterns.

APPROACH: We propose a Dual-View Representation Decoupling Network (DRDNet) for within-subject crosssession HI EEG classification. DRDNet first constructs two complementary temporal views from spatial EEG features using average and max pooling, corresponding to smooth trend-oriented and salient response-oriented representations. These views are then modeled by a bidirectional Mamba encoder and a Transformer encoder, respectively, and adaptively integrated through a time-step-level dynamic fusion mechanism followed by long short-term memory based temporal aggregation. The method is evaluated on two tasks from a public HI EEG dataset: Chinese character stroke handwriting imagery (CCSHI) and pinyin single-vowel handwriting imagery (SVHI).

MAIN RESULTS: DRDNet achieved average accuracies of 67.74% and 62.51%, with Cohen's kappa scores of 0.5968 and 0.5502, on CCSHI and SVHI, respectively. It outperformed seven representative EEG decoding baselines under the same crosssession protocol. Confusion matrices, feature visualization, ablation studies, structural variant analysis, and complexity evaluation further showed improved feature separability and a favorable balance between decoding performance and computational efficiency.

SIGNIFICANCE: The results indicate that decoupling HI EEG into complementary temporal views and matching them with heterogeneous temporal encoders provides an effective representation learning strategy for robust non-invasive handwriting BCI decoding.},
}

@article {pmid42619707,
year = {2026},
author = {Fogg, ZM and Card, NS and Wairagkar, M and Srinivasan, A and Singer-Clark, T and Hou, X and Okorokova, E and Peracha, H and Iacobacci, C and Brailow, T and Jude, J and Levi-Aharoni, H and Le, T and Mifsud, D and Deevi, PI and Nason-Tomaszewski, SR and Pritchard, AL and Zhang, Y and Richards, B and Bechefsky, P and Hochberg, LR and Williams, Z and Shahlaie, K and AuYong, N and Rubin, DB and Pandarinath, C and Brandman, DM and Stavisky, SD},
title = {A generalizable speech neuroprosthesis.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.07.23.739430},
pmid = {42619707},
issn = {2692-8205},
abstract = {Intracortical brain-computer interfaces (BCIs) can restore communication to people with vocal tract paralysis by decoding cortical activity during attempted speech into text. State-of-the-art systems pairing neural-to-phoneme decoders with phoneme-to-word language models have achieved word error rates (WERs) as low as 1%, but only after collecting thousands of sentences of training data. Shortening the data collection process would facilitate scaling this new technology by reducing the time from device implant to high-accuracy communication. Here we introduce a transformer-based decoder model trained jointly across six intracortical speech BCI participants. For every participant - regardless of sex, disease etiology, or attempted speaking strategy - a multi-user model decoded speech more accurately (over 50% lower relative WER on average) than models trained on individual users' data. Notably, the multi-user model could be finetuned on fewer than 200 sentences from a held-out user to achieve a WER below 7%. These results reveal how to pool intracortical data across people to yield more accurate, generalizable, and rapidly-deployable decoding models.},
}

@article {pmid42620125,
year = {2026},
author = {Lyu, D and Allen, L and Pantis, S and Karagoz, EG and Quabs, J and Parvizi, J},
title = {Causal Mapping of Bodily Awareness and Mesoscale Circuit Organization in the Human Cingulate and Precuneus.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {42620125},
issn = {2693-5015},
abstract = {How the human brain generates subjective bodily experience remains a fundamental question in cognitive neuroscience. The cingulate cortex and precuneus (CC/PCu) have been implicated in bodily awareness and self-related processing, yet the functional architecture and causal relevance of these regions and the circuit mechanisms supporting conscious bodily states remain unclear. Here, we combined intracranial electrical stimulation, first-person reports, causal electrophysiological connectivity mapping and large-scale functional network analyses to investigate the human CC/PCu in 63 individuals. Across 660 stimulation sites, we identified a mesoscale functional mosaic in which the stimulation of neighboring cortical populations, separated by millimeters, gave rise to categorically distinct subjective experiences, ranging from localized sensorimotor sensations to complex integrated bodily states. These phenomenological differences were not explained by anatomical location alone, but by distinct causal connectivity profiles. Sensorimotor-responsive sites preferentially exhibited divergent outgoing connectivity, whereas complex bodily sites showed convergent incoming connectivity from distributed networks. Among these pathways, connectivity between the CC/PCu and posterior insular cortex emerged as a critical determinant of whether local stimulation produced a reportable bodily experience: millimeters away, neighbouring sites lacking this connectivity remained silent. We further identified a right-lateralized network architecture supporting bodily experience across both neuroimaging and electrophysiological connectivity measures. Together, these findings reveal that conscious bodily states may be rooted in fine-grained mesoscale circuits embedded within large-scale brain networks, demonstrating that connectivity architecture, rather than anatomical location alone, determines the capacity of human cortical sites to be involved in conscious and subjective bodily awareness.},
}

@article {pmid42620316,
year = {2026},
author = {He, B and Weng, Y and Luo, P and Xu, Z and Yan, H and Yang, B and He, Q and Lu, J and Yang, X},
title = {Exploring the potential of natural products in treating alopecia areata.},
journal = {Journal of pharmaceutical analysis},
volume = {16},
number = {8},
pages = {101539},
pmid = {42620316},
issn = {2214-0883},
abstract = {Alopecia areata (AA) is an autoimmune disorder characterized by sudden hair loss, affecting millions worldwide. Conventional synthetic drug treatments are often limited by side effects, driving interest in natural alternatives. This review highlights terpenoids, flavonoids, and polyphenols as promising natural compounds that effectively promote hair regrowth in AA. These phytochemicals exert therapeutic effects primarily by activating pro-anagenic signaling pathways, notably the Wnt/β-catenin and phosphoinositide 3-kinase-protein kinase B (PI3K-Akt) pathways, while inhibiting catagen-associated pathways such as transforming growth factor-β (TGF-β), and modulating immune dysregulation through the suppression of Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling. This review has detailed the mechanisms through which these compounds support hair follicle regeneration, restore immune balance, and reduce inflammation. Collectively, the evidence highlights the significant therapeutic potential of natural products for AA, underscoring the need for further translational research.},
}

@article {pmid42622537,
year = {2026},
author = {Rahman, MKM and Shuvo, HMT},
title = {MI recognition by subject specific localised frequency fusion.},
journal = {Journal of medical engineering & technology},
volume = {},
number = {},
pages = {1-19},
doi = {10.1080/03091902.2026.2720510},
pmid = {42622537},
issn = {1464-522X},
abstract = {EEG-based motor imagery (MI) discrimination is widely utilised in real-life applications, such as brain-computer interfaces (BCIs), due to its non-invasive and comparatively cost-effective nature. However, traditional BCI systems typically rely on a uniform, broad frequency band or a standardised set of sub-bands to extract features. Consequently, they fail to account for distinct physiological variability across subjects, leading to significant classification performance degradation. To address this limitation, we propose a novel framework named subject-specific localised frequency fusion (SSLFF). The proposed method systematically searches for and selects optimal frequency sub-bands , while dynamically merging complementary spectral bands. In addition to this subject-specific frequency localisation, the framework integrates a time-localised feature extraction process. Unlike traditional approaches, the proposed method can operate effectively over a broader master frequency band without experiencing performance degradation, as it automatically isolates the optimal spectral parameters for each subject. Overall, the proposed framework achieves a statistically significant improvement in classification accuracy compared to alternative traditional methods, while maintaining exceptional robustness against variations in system parameters. In short, SSLFF try to address the fact that the learning of human brain varies from person to person and it incorporates subject specific tuning not only in the spatial filter and classifier, but also in frequency band selection and localised feature extraction, leading to a superior classification performance.},
}

@article {pmid42623205,
year = {2026},
author = {Wang, F and Zhang, J and Yang, X and Pan, J and Huang, H and He, Y},
title = {An Auditory BCI System Based on Stimulus-Related Semantic Backgrounds for Consciousness Detection.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3718066},
pmid = {42623205},
issn = {1558-2531},
abstract = {Brain-computer interfaces (BCIs) hold significant promise in medical applications, particularly for detecting consciousness in patients with disorders of consciousness (DoC). However, conventional BCIs often rely on visual stimuli, which limits their accessibility for visually impaired patients. To expand the applicability of BCIs to a wider range of patients, this study introduces an advanced auditory BCI system. The system incorporates an auditory paradigm utilizing stimulus-related semantic backgrounds and an improved EEG-Inception prototype network with ECA (ECAEI-ProNet). To validate the effectiveness of the proposed system, Experiment 1 was conducted to compare it against three control conditions: Condition 1, which included related backgrounds and stimuli; Condition 2, which included unrelated backgrounds and stimuli; and Condition 3, which presented no background. The results demonstrated that utilizing stimulus-related semantic backgrounds significantly improved BCI classification performance while eliciting event-related potentials (ERP) patterns associated with semantic consistency in subjects. Additionally, the two improvements made to the ECAEI-ProNet model, building upon EEG-Inception, enhanced the model's classification performance. Our proposed paradigm and classification model achieved online accuracies of 88.8$\pm$ 9.1%. To evaluate the clinical feasibility of the proposed BCI system, we applied it to 17 patients with DoC in Experiment 2. Results indicated that seven patients achieved online accuracy significantly above the chance level ($>$64%). Among them, the three highest-performing patients (P3, P5, and P12) showed improvements in both CRS-R scores and clinical diagnosis at the three-month follow-up. These findings suggest that the proposed auditory BCI system may offer preliminary information relevant to residual consciousness-related processing in some patients with DoC.},
}

@article {pmid42567194,
year = {2026},
author = {Fu, Z and Gong, Z and Shen, Z and Yao, S and Ding, X and Chen, Y},
title = {Slide-DML: sliding-window-based estimation of heterogeneous treatment effects.},
journal = {Physiological measurement},
volume = {47},
number = {8},
pages = {},
doi = {10.1088/1361-6579/ae9713},
pmid = {42567194},
issn = {1361-6579},
mesh = {Treatment Effect Heterogeneity ; *Machine Learning ; Humans ; },
abstract = {Objective.Heterogeneous treatment effect (HTE) estimation is essential for understanding individual differences in physiological responses and supporting personalized healthcare. However, existing non-parametric HTE estimation methods often rely on complex partition strategies and may have limited interpretability when applied to physiological measurements with continuous variations and non-uniform data distributions. This study aims to develop an adaptive and interpretable framework for HTE estimation in physiological measurement systems.Approach.We propose a sliding-window-based double machine learning framework (Slide-DML) for non-parametric HTE estimation. Slide-DML adaptively constructs quasi-homogeneous local windows based on treatment effect variation and estimates local linear HTE within each selected window. The local estimates are subsequently aggregated using adaptive weighting to obtain a smooth global HTE function while preserving interpretability.Main results.The performance of Slide-DML was evaluated using synthetic data, semi-synthetic clinical data, and real physiological measurements. In synthetic experiments, Slide-DML achieved a mean squared error (MSE) of 0.006, outperforming existing machine learning-based HTE estimation methods. In semi-synthetic experiments, Slide-DML achieved a root MSE of 3.526, demonstrating superior performance compared with both machine learning-based and deep learning-based approaches. Experiments on real photoplethysmogram and electrocardiogram data further showed that the estimated treatment effect curves were consistent with established cardiovascular knowledge and provided improved interpretability.Significance.Slide-DML provides an effective and interpretable approach for estimating HTE in physiological measurement systems. By capturing continuous variations in physiological states, the proposed framework may facilitate individualized analysis of cardiovascular responses and support personalized healthcare applications, such as cuffless blood pressure monitoring using wearable physiological devices.},
}

Treatment Effect Heterogeneity
*Machine Learning
Humans

@article {pmid42612453,
year = {2026},
author = {Zuo, X and Sun, X and Cong, L and Wang, H and Liu, S and Zhang, XD},
title = {Flexible bioelectronic electrodes: From engineering strategies to cross-domain biomedical applications.},
journal = {Biosensors & bioelectronics},
volume = {313},
number = {},
pages = {119131},
doi = {10.1016/j.bios.2026.119131},
pmid = {42612453},
issn = {1873-4235},
abstract = {Flexible electrodes have emerged as key bioelectronic interfaces for recording, modulation, and sensing in complex biological environments. Their mechanical compliance, conformal contact, and tunable electrochemical properties enable effective coupling between soft biological tissues and rigid electronic systems. However, the fabrication strategies and interface requirements governing flexible electrode performance differ substantially across biological domains, and this cross-domain design landscape has not been systematically addressed. This review comprehensively examines preparation and optimization strategies for flexible electrodes, covering structure-driven designs, material selection, and surface functionalization approaches targeting charge transfer, selectivity, and biostability. We further survey applications across four biomedical domains: brain interfaces, peripheral nerve interfaces, gastrointestinal tract, and wearable health monitoring, spanning epidermal, visceral, and neural environments in both in vitro and in vivo settings. By mapping design strategies onto diverse biological interface requirements, this review provides a cross-domain reference framework to guide application-specific electrode design. Remaining challenges in scalable fabrication, chronic biointegration, closed-loop operation, and clinical translation are critically discussed, with future directions emphasizing convergence across neuroscience, materials science, and intelligent bioelectronics.},
}

@article {pmid42614398,
year = {2026},
author = {Yu, Z and Cao, S and Zhang, G and Feng, P},
title = {Feedback-regulated dual-role memory consolidation for continual learning: a stability-plasticity framework inspired by hippocampo-cortical systems consolidation.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {159},
pmid = {42614398},
issn = {1871-4080},
abstract = {Artificial agents trained on non-stationary task streams often acquire new categories at the cost of older representations. Systems-level accounts of hippocampo-cortical consolidation suggest that stored traces can support later learning in different ways and that consolidation can depend on both new learning and the vulnerability of established knowledge. Motivated by these functional principles, we propose dual-role memory consolidation learning (DRMCL) for class-incremental learning with a bounded replay buffer. DRMCL stores prototype-preserving core samples and decision-sensitive boundary samples, then combines both roles with replay, logit distillation, and task-level feedback control. On Split CIFAR-10 with a pretrained ResNet18 backbone, the clearest benefit occurs when memory is scarce. With 1000 stored samples, average accuracy increases from 0.4823 for Dark Experience Replay++ (DER++) to 0.6905, while forgetting decreases from 0.5734 to 0.2650. At medium and high memory, DRMCL usually lowers forgetting, although DER++ can achieve higher raw accuracy. Ablations show that feedback-regulated replay and distillation account for most of the retention effect, while the core/boundary organization makes the selected memory easier to inspect. An electroencephalography (EEG) brain-computer interface (BCI) feasibility study on BCI Competition IV 2a provides a cross-domain check. In a five-seed subject-1 experiment, DRMCL remains close to ER and DER++ rather than separating from them, and session spectral/coherence shifts vary across subjects. The results support DRMCL as a stability-oriented computational framework; they do not establish a circuit-level model of hippocampo-cortical consolidation or a new EEG decoding benchmark.},
}

@article {pmid42615121,
year = {2026},
author = {Rahman, MH and Kadri, HJ and Ahmed, F and Nuruzzaman, M and Mondal, MIH},
title = {Fabrication & characterization of neem oil-loaded hydrophobic PVA film for rapid hemostasis and minimizing blood loss in wound dressing.},
journal = {Biomedical materials (Bristol, England)},
volume = {},
number = {},
pages = {},
doi = {10.1088/1748-605X/ae9b57},
pmid = {42615121},
issn = {1748-605X},
abstract = {The objective of this investigation is to develop a dressing film with a blood-repellent surface to improve hemostatic performance while minimizing excessive blood loss from absorption and potentially reducing clot-dressing adhesion. As a result, a modified technique was utilized to fabricate a hydrophobic film made of PVA and neem seed oil. The developed film underwent multiple physicochemical studies to determine its structure and properties. The films exhibited increasing hydrophobicity with neem oil incorporation, as evidenced by water contact angle values rising from 47.8° (pure PVA) to 93.56° (PVA with 4% NSO), which suggests a potential reduction in adhesion risk. Kirby-Bauer agar diffusion assays demonstrated inhibition zones of up to 14 ± 0.1 mm against Pseudomonas aeruginosa, indicating preliminary antibacterial activity. Hemostatic evaluation revealed enhanced red blood cell and platelet adhesion, with the optimized PVA/NSO (A4) film exhibiting a blood clotting index (BCI) approximately 28% lower than that of the gauze control, indicating enhanced in vitro clotting efficiency compared with the gauze control. Qualitative assessment demonstrated that the films maintained good flexibility and structural integrity under bending, twisting, and stretching. Biodegradation studies showed weight loss of 25.7% for PVA/NSO films after 28 days, confirming environmental compatibility. Collectively, these results demonstrate that PVA/NSO composite films possess hydrophobicity, antibacterial activity, mechanical stability, and superior hemostatic performance, making them promising candidates for wound dressing and rapid hemostasis applications.},
}

@article {pmid42615139,
year = {2026},
author = {Conte, G and Borghi, F and Lazzarini, C and Piazzoni, C and Konstantoulaki, A and Fabbri, R and Caprini, M and Milani, P and Benfenati, V},
title = {Nanostructured Zirconia Thin Films as a Neurogliomorphic Interface for Neural Cells of Central and Peripheral Nervous Systems.},
journal = {ACS applied bio materials},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsabm.6c01280},
pmid = {42615139},
issn = {2576-6422},
support = {ASTROSENSE FA9550-25-1-0001//Air Force Office of Scientific Research/ ; ASTROTALK - FA9550-23-1-0736//Air Force Office of Scientific Research/ ; W911NF2520009_ ASTROCLUSTER//Army Research Office/ ; CUP: E29I25000730007//Regione Lombardia/ ; ECS_00000033_ECOSISTER//Ministero dell'Università e della Ricerca/ ; },
abstract = {Recent advances in neuroscience have highlighted the central role of glial cells, particularly astrocytes, in regulating neural network activity through calcium-dependent neuron-glia communication. In parallel, nanostructured cluster-assembled materials have emerged as promising candidates for developing brain-machine interfaces because of their biomimetic morphology, mechanotransductive properties, and neuromorphic behavior. Among these, nanostructured zirconium oxide (ns-ZrOx) thin films have recently demonstrated memristive and signal-processing capabilities compatible with biohybrid neural systems, yet their interaction with heterogeneous neuroglial networks remains poorly understood. Here, we investigate the biocompatibility and functional effects of ns-ZrOx interfaces on primary astrocytes and dorsal root ganglion (DRG) neuron-glia co-cultures, comparing nanostructured and flat zirconia substrates. Both substrates supported cellular adhesion, survival, and differentiation. However, ns-ZrOx selectively enhanced glial calcium signaling, increasing transient amplitude and accelerating response kinetics in both central and peripheral glial populations. Our findings identify ns-ZrOx as an active neurogliomorphic interface capable of modulating neuron-glia communication through nanoscale material properties. By bridging glial physiology with neuromorphic nanomaterials, this work supports the development of hybrid bioelectronic platforms integrating living neural networks with adaptive functional materials for brain-inspired computing and advanced neural interfaces.},
}

@article {pmid42611651,
year = {2026},
author = {Wang, Z and Liu, Y and Huang, S and Wu, W and Huang, H and Li, Z and Zhang, H and Guo, J and Xu, M and Ming, D},
title = {ERD Full-process Longitudinal Trend and Pre-post Motor Recovery Under BCI-controlled Sixth-finger Neurofeedback Intervention in Stroke Patients: an Exploratory Single-arm Study.},
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.2026.3724844},
pmid = {42611651},
issn = {1558-0210},
abstract = {Motor imagery-based brain-computer interface (MI-BCI) is used in stroke rehabilitation to match brain activity with contingent feedback to establish closed-loop pathways and provide a measure of neuroplasticity changes in patients. However, most studies assessed neural function only at pre- and post-train, thereby longitudinal trends of neural patterns and mechanisms during full-process of intervention remain unclear. Fourteen stroke patients were recruited to receive a total of 8-session (2-week) MI-BCI-controlled "sixth-finger" intervention. Resting-state electroencephalography (EEG) and clinical scales, including the Fugl-Meyer Assessment (FMA-UE) and Barthel Index (BI), were evaluated pre- and post-train. Furthermore, MI tasks EEG signals throughout the full-process of intervention were tracked to reflect the longitudinal continuous trends of neural activity. EEG longitudinal trend shows two phases over full-process of intervention: event-related desynchronization (ERD) gradually increased in the first week of training, weakened and focused on the contralateral sensorimotor area in the second week, and showed a significant correlation over sessions. And resting-state functional connectivity increased after intervention. Motor function improved significantly from pre- to post-train by clinical metrics, with + 7.9 in FMA-UE and + 7.1 in BI. More than half of patients (9/14) reached the minimally clinically important difference (MCID) of 6.6 points change for FMA-UE after therapy. Meanwhile, the improvement of motor function is associated with the enhancement of resting-state functional connectivity. This work reveals longitudinal trend of neural patterns over full-process of intervention and its correlation with motor recovery, providing crucial evidence for understanding the mechanisms of neuroplasticity in stroke rehabilitation.},
}

@article {pmid42611663,
year = {2026},
author = {Wu, H and Zhang, G and Liao, J and Zhang, J and Zheng, WS and Long, J},
title = {Calibration-free Plug-and-Play EEG-based BCIs.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TPAMI.2026.3725079},
pmid = {42611663},
issn = {1939-3539},
abstract = {Calibration-free plug-and-play operation is critical for real-world EEG-based brain-computer interfaces (BCIs), yet existing methods demand subject-specific calibration or batch processing of target data. To overcome this, we propose Local Online Transfer Learning (LOTL), a novel online transfer learning method enabling effective and immediate response without calibration. Specifically, for each domain (subject), LOTL generates a global hyperplane and multiple local hyperplanes by considering the nonlinear separation problem of samples. During online processing, LOTL attempts to update the current model by retaining the new model close to the current source models and the target model while imposing a margin separation on the latest samples. This is achieved by minimizing the differences between the current and new global and local hyperplanes of the source and target domains, making the learned global and local hyperplanes of the source and target domains transferable. Crucially, LOTL works in a one-pass online manner, using each arriving sample only once without storing any historical samples. We derive a closed-form solution for effective model updates and establish theoretical guarantees mathematically, including mistake bound and convergence analyses. Extensive experiments on four EEG datasets (motor imagery and emotion analysis) and a real-world BCI system provide encouraging evidence for the effectiveness of the proposed method, suggesting the potential of LOTL to facilitate the deployment of BCI applications under conditions where the source and target domains share a consistent channel configuration.},
}

@article {pmid42612138,
year = {2026},
author = {Li, K and Shen, Z and Lu, H and Fang, X},
title = {Sleep Deprivation, Decision Fatigue, and Clinical Performance in Medical Training: A Narrative Review and Perspective on Neurophysiological Monitoring.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {233},
pages = {},
doi = {10.3791/71627},
pmid = {42612138},
issn = {1940-087X},
mesh = {*Sleep Deprivation/physiopathology ; Humans ; *Fatigue/physiopathology ; *Decision Making/physiology ; *Neurophysiological Monitoring/methods ; Electroencephalography/methods ; *Clinical Competence ; *Education, Medical/methods ; Spectroscopy, Near-Infrared/methods ; },
abstract = {Sleep deprivation and decision fatigue are important but often under-recognized determinants of daytime clinical performance. In medical training environments, these state-dependent factors may impair attention, executive control, and decision-making, complicating the interpretation of observed competence during wakefulness. This narrative review and perspective synthesizes evidence from sleep science, neurophysiology, psychology, and medical education to examine how sleep loss and decision fatigue shape clinical performance and how non-invasive monitoring approaches may help contextualize this variability. Attention is given to electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as candidate research tools for characterizing fatigue-related changes in cognitive workload and vigilance. The reviewed literature supports strong links between sleep loss, fatigue, and impaired neurocognitive functioning, but the use of real-time neurophysiological monitoring for actionable readiness assessment in medical training remains conceptual and requires empirical validation. We propose the CBME-BCI-Fatigue framework as a hypothesis-generating perspective model for integrating physiological state information with competency-based assessment, while emphasizing limitations related to construct validity, incremental value, individual variability, feasibility, and governance. This framework may inform future simulation-based and low-stakes research on fatigue-aware feedback and learner support in sleep-vulnerable clinical training environments.},
}

*Sleep Deprivation/physiopathology
Humans
*Fatigue/physiopathology
*Decision Making/physiology
*Neurophysiological Monitoring/methods
Electroencephalography/methods
*Clinical Competence
*Education, Medical/methods
Spectroscopy, Near-Infrared/methods

@article {pmid42612169,
year = {2026},
author = {Wang, J and Zhang, Y and Van Cang, MP and Ding, N and Morillon, B and Doelling, KB},
title = {Music Is Quasi-Rhythmic Too: Comparing Bandwidth in Speech and Music Acoustic Modulation Spectra.},
journal = {Annals of the New York Academy of Sciences},
volume = {1562},
number = {1},
pages = {e70380},
pmid = {42612169},
issn = {1749-6632},
support = {//Fundamental Research Funds for the Central Universities/ ; //Fondation Fyssen/ ; //Fondation Pour l'Audition/ ; //French government/ ; //European Union/ ; },
mesh = {Humans ; *Music ; *Periodicity ; *Speech/physiology ; *Speech Perception/physiology ; *Auditory Perception/physiology ; *Acoustics ; Acoustic Stimulation/methods ; },
abstract = {Speech and music both unfold over time. However, these dynamics are perceived quite differently: music as rhythmic, and speech as quasi-rhythmic. Here, we aim to identify whether this distinction between rhythm and quasi-rhythm is sourced from the raw acoustic waveform by analyzing the modulation spectrum of the acoustic envelope. We analyzed several large corpora on three scales: the coarsest scale containing recordings of each corpus, the intermediate scale of individual speakers/songs, and the finest scale of individual sentences or short musical segments. We confirm previous findings that speech and music modulation spectra differ in their center frequency, which reflects how quickly the sound envelope fluctuates, but find that the modulation bandwidth, which captures how periodic the speech/music envelope was, is comparable for music and speech. Furthermore, the bandwidth is largely preserved across the three scales, indicating that the corpus-level temporal irregularity is dominated by the irregularity within a few seconds of speech/music recordings. These results demonstrate that the perceived rhythmicity of music may not directly reflect the temporal periodicity present in its acoustic structure.},
}

Humans
*Music
*Periodicity
*Speech/physiology
*Speech Perception/physiology
*Auditory Perception/physiology
*Acoustics
Acoustic Stimulation/methods

@article {pmid42612357,
year = {2026},
author = {Xiao, X and Sheng, X and Li, S and Chen, X and Zhang, Z and Si, K and Gong, W},
title = {Dual-mode thermo-responsive microneedle liposome patch for adaptive transdermal delivery in postherpetic neuralgia.},
journal = {Biomaterials advances},
volume = {189},
number = {},
pages = {215121},
doi = {10.1016/j.bioadv.2026.215121},
pmid = {42612357},
issn = {2772-9508},
abstract = {Postherpetic neuralgia (PHN) presents as persistent background pain accompanied by unpredictable breakthrough episodes. Current topical therapies are poorly suited to this fluctuating pain pattern because they provide limited transdermal penetration and static drug release. Here, we developed a thermo-responsive microneedle platform integrating sustained local lidocaine delivery with externally triggered accelerated release and evaluated its material characteristics, temperature-dependent release behavior, transdermal delivery performance, and in vivo pharmacokinetic behavior. The system consists of a gelatin and poly (ethylene glycol) diacrylate microneedle matrix, lidocaine-loaded liposomes, and a flexible pullulan backing layer. The patch demonstrated adequate mechanical strength for skin insertion, exceeding 0.1 N per needle, achieved an insertion efficiency above 93%, and delivered cargo to a depth of approximately 75 μm near the epidermal-dermal interface. Compared with a conventional topical patch, the microneedle liposome configuration enhanced transdermal permeation and prolonged drug retention in the skin for up to 48 h, enabling both rapid initial delivery and sustained local availability. The system exhibited a dual-mode release profile, with sustained release at physiological skin temperature (33 °C) and accelerated release under mild heating (40 °C), allowing externally triggered control of release kinetics. The flexible backing maintained conformal adhesion under dynamic deformation, and skin evaluation indicated minimal barrier disruption with only mild, transient erythema in human subjects. These findings demonstrate the feasibility of the system as a controlled local delivery platform and support further evaluation of the system in disease-relevant PHN models.},
}

@article {pmid42484995,
year = {2026},
author = {Tahmasebi, G and Vargas, S and Kuete, CF and Haghighi, P and Solis, E and Massaquoi, A and Pancrazio, JJ},
title = {Sigmoidal decoding from distinct M1 spiking populations and LFP band power for locomotion speed in mice.},
journal = {Journal of neurophysiology},
volume = {136},
number = {2},
pages = {807-823},
doi = {10.1152/jn.00112.2026},
pmid = {42484995},
issn = {1522-1598},
support = {R01NS131502//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
mesh = {Animals ; Mice ; *Motor Cortex/physiology/cytology ; *Action Potentials/physiology ; Local Field Potential Measurement ; Male ; *Locomotion/physiology ; *Neurons/physiology ; Models, Neurological ; Mice, Inbred C57BL ; Female ; },
abstract = {The extent to which the primary motor cortex (M1) encodes locomotion speed is relatively unexplored, with some studies suggesting linear or gain-modulated tuning. Here, we show that there are neurons in the mouse M1 where spiking relates to locomotion speed in a manner consistent with a sigmoidal state-transition model implemented by two functionally distinct neural populations. In addition, sigmoidal framework extends to local field potential (LFP) band power, enabling a direct within-animal comparison of spiking and LFP-based speed encoding. We recorded extracellular activity (5,889 single units, 384 channels) using chronic 32-channel laminar arrays in eight mice locomoting on a motorized treadmill over 8 wk, with actual speed tracked via DeepLabCut. Unsupervised clustering of temporal firing rate profiles identified two groups: speed-positively related (70.8%) and speed-inversely related (29.2%) units. Sigmoidal models of speed-positively related rate tuning significantly outperformed linear and quadratic alternatives for both populations, and the two clusters shared a common speed threshold (∼2.3 m/min) consistent with a shared subcortical locomotor gate. When exploring decoding, the minority speed-inversely related population demonstrated significantly higher decoding accuracy via inverse-sigmoid transformation compared with the larger speed-positively related population or all units combined, an advantage that generalized across animals via leave-one-animal-out cross-validation. LFP band power also exhibited sigmoidal tuning relative to locomotion speed, but decoded speed with lower fidelity. These findings suggest a push-pull sigmoidal architecture for spiking-based speed representation in M1 and demonstrate that LFP provides a complementary and stable signal for coarser speed estimation.NEW & NOTEWORTHY This study reveals that locomotion speed is encoded in mouse primary motor cortex through a sigmoidal state-transition mechanism carried by two functionally distinct spiking populations with a shared speed threshold. Using high-density laminar recordings and markerless motion tracking, we show that this framework extends to local field potentials and enables accurate, generalizable decoding. These findings establish a push-pull sigmoidal architecture and highlight implications for stable, calibration-light brain-machine interface design.},
}

Animals
Mice
*Motor Cortex/physiology/cytology
*Action Potentials/physiology
Local Field Potential Measurement
Male
*Locomotion/physiology
*Neurons/physiology
Models, Neurological
Mice, Inbred C57BL
Female

@article {pmid42603660,
year = {2026},
author = {Pang, Y and Zhang, Z and Wang, X and Pang, J and Zhao, S and Gao, L and Zhang, R and Li, Z and Wang, J},
title = {Brain connectivity mediates the association between childhood maltreatment and personality in young adults.},
journal = {Progress in neuro-psychopharmacology & biological psychiatry},
volume = {149},
number = {},
pages = {111897},
doi = {10.1016/j.pnpbp.2026.111897},
pmid = {42603660},
issn = {1878-4216},
abstract = {Childhood maltreatment (CM) is widely acknowledged to have a lasting impact on mental health. However, the neurobiological underpinnings of CM and its long-term effects on personality remain unclear. This study aims to identify the functional connectivity (FC) basis of CM through a data-driven analytic strategy, and to examine the association among CM, FC, and personality. A total of 332 young adults (aged 17-24 years) with resting functional magnetic resonance imaging scans were included. CM level was assessed using the Childhood Trauma Questionnaire and personality was measured with the Temperament and Character Inventory. Multivariate distance matrix regression (MDMR) was performed to identify key regions where inter-individual FC variations were linked to CM at the voxel level, and a follow-up seed-based FC analysis was conducted to elucidate specific connectivity patterns of these key regions. MDMR identified four regions in the inferior parietal lobule (IPL), calcarine sulcus (CAL), middle occipital gyrus, and middle temporal gyrus. Specifically, follow-up analysis showed that the FC between IPL and sensorimotor network/theory of mind network, and between CAL and salience network were negatively associated with CM. Moreover, CM was negatively associated with reward dependence and cooperativeness, and those identified FCs mediated the relationship between CM and cooperativeness. Our results emphasize that CM has an enduring influence on the integration of sensory and social-cognitive processing, revealing the neurobiological consequences of CM and providing a new framework for understanding how CM leads to maladaptive personality.},
}

@article {pmid42603816,
year = {2026},
author = {Rühl, P and Hussein, RA and Reuter, S and Frahnert, KS and Nair, AG and Mrowka, R and Schönherr, R and Heinemann, SH},
title = {Sub-millivolt voltage imaging reveals gap junction-mediated bioelectric contact inhibition.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {42603816},
issn = {2041-1723},
support = {705944SH//Simmons Family Foundation/ ; 705944SH//Simmons Family Foundation/ ; 91819480//Deutscher Akademischer Austauschdienst (German Academic Exchange Service)/ ; },
mesh = {*Gap Junctions/physiology/metabolism ; Animals ; *Membrane Potentials/physiology ; *Contact Inhibition/physiology ; Humans ; },
abstract = {Sub-millivolt membrane potential (Vm) dynamics in multicellular non-excitable networks have remained largely inaccessible due to insufficiently sensitive imaging tools. Here, we introduce rEstus2s, a next-generation genetically encoded voltage indicator that overcomes this barrier by enabling high-resolution Vm imaging. Using rEstus2s, we uncover bioelectric contact inhibition (BCI), a biophysical principle in which gap junction coupling passively stabilizes Vm by suppressing electrical volatility. We show that Vm variance scales inversely with network size (1/n), reflecting a transition from stochastic single-cell behavior to collective electrical stability. Ca[2+]-activated oncogenic ion channels, including ANO1 and KCa3.1, drive pronounced electrical volatility in isolated cells, but BCI effectively attenuates this volatility in electrically coupled networks. Disruption of gap junction coupling abolishes BCI and restores high electrical volatility. These findings establish a unifying framework for how multicellular systems maintain electrical homeostasis and reveal gap junction coupling as a key determinant of bioelectric stability in health and disease.},
}

*Gap Junctions/physiology/metabolism
Animals
*Membrane Potentials/physiology
*Contact Inhibition/physiology
Humans

@article {pmid42603854,
year = {2026},
author = {A, S and I, B and M, W and N, R and M J, V and D, P},
title = {A Unified Framework of Functional Clinical Outcomes for Implantable Motor Brain-Computer Interfaces: From Digital Motor Outputs to Critical Digital Activities.},
journal = {Neurorehabilitation and neural repair},
volume = {},
number = {},
pages = {15459683261477076},
doi = {10.1177/15459683261477076},
pmid = {42603854},
issn = {1552-6844},
abstract = {Implantable brain-computer interfaces (iBCIs) are at a pivotal inflection point. Multiple commercial ventures are advancing toward regulatory approval, and the challenge facing the field is transitioning from demonstrating safety and feasibility to achieving regulatory approval, reimbursement by payors, and sustainable clinical integration. Central to this challenge is the urgent need for robust, valid, and reliable clinical outcome assessments (COAs) that satisfy both regulatory and payor requirements and meaningfully reflect the lived experiences of people with severe motor impairment. Without such measures, iBCIs risk stalling in the transition from early feasibility to reimbursable therapy.},
}

@article {pmid42606820,
year = {2026},
author = {Zorrilla-Muñoz, V and Lillo-Navarro, C and Blanco-Ivorra, A and Catalan, JM and Martinez-Pascual, D and Garcia-Aracil, N},
title = {Gender differences in technological and health status perception and its impact on daily life activities in persons affected by stroke.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/17483107.2026.2717026},
pmid = {42606820},
issn = {1748-3115},
abstract = {BACKGROUND/OBJECTIVE: To explore gender-segmented perceptions of improvement through technological devices among stroke survivors, focusing on how these devices influence self-perceived health status and daily life activities.

DESIGN: Cross-sectional analysis of survey data.

SETTING: Community-based data collected from a national disability survey conducted in 2020.

PARTICIPANTS: A sample of 971 individuals (464 men, 507 women) aged 18 years and older, diagnosed with stroke and disability resulting from stroke or another condition.

INTERVENTIONS: Not applicable.

MAIN OUTCOME MEASURES: Self-reported perception of improvement in daily activities (mobility, self-care, domestic life) associated with the use of technological devices, segmented by gender. The primary outcome is based on a single self-reported item.

RESULTS: Participants reporting greater difficulties in daily activities, including mobility, self-care, and domestic life, exhibited a lower perception of improvement through the use of technological devices. Older women, in particular, reported additional barriers to technology adoption.

CONCLUSION: The findings underscore the need for inclusive, gender-sensitive, and personalised technological solutions in stroke rehabilitation. Current technologies show potential for enhancing daily functionality and mobility but have limited perceived benefits among stroke survivors, particularly older women facing adoption challenges.},
}

@article {pmid42606964,
year = {2026},
author = {He, K and Yang, R and Li, C and Cai, Y and Cheng, H and Jin, Z and Li, D and Zheng, F and Zou, P and Lyu, B and Gao, JH},
title = {Towards Wearable High-Density MEG: A Compact, Low-Power, and Scalable OPM-MEG System with Automated Control.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3724753},
pmid = {42606964},
issn = {1558-2531},
abstract = {OBJECTIVE/BACKGROUND: Optically pumped magnetometer-based magnetoencephalography (OPM-MEG) is advancing toward wearable and high-density sensor configurations, posing significant engineering challenges in miniaturization, thermal management, and scalable integration. Here, we present a high-performance wearable OPM-MEG system that addresses these challenges.

METHODS: The system was developed through a system-level co-design of the sensor head and electronic control units (ECUs) with a fully automated control workflow. To reduce thermal dissipation and lower scalp temperature, we implemented a thermally optimized suspended vapor-cell module.

RESULTS: This design achieves a miniaturized sensor head (12 × 16.5 × 22.5 mm3) and ECU footprint (34 × 28 mm2), with a total per-sensor power consumption of 3.5 W (0.7 W allocated to the sensor head). Crucially, the sensor maintains high sensitivity required for detecting ultraweak brain magnetic fields, exhibiting single-axis sensitivity < 7 fT/$\sqrt$ Hz and dual-axis sensitivity < 10 fT/$\sqrt$ Hz, with a bandwidth of 130 Hz. Inter-sensor crosstalk and intra-sensor cross-axis projection error (CAPE) are suppressed to lower than 2%, addressing the critical challenge of signal interference in high-density sensor arrays. Phantom experiments demonstrate accurate source localization with a mean error of  1 mm, while human recordings provide further validation of stable and high-fidelity performance in wearable OPM-MEG settings.

CONCLUSION: Collectively, this work establishes a scalable, instrument-level framework for the design, automated operation, and quantitative end-to-end evaluation of high-density wearable OPM-MEG systems.

SIGNIFICANCE: This work provides a scalable foundation for high-density wearable OPM-MEG, enabling mobile brain measurements for brain-computer interfaces, cognitive neuroscience, and future translational clinical neuroimaging applications.},
}

@article {pmid42607563,
year = {2026},
author = {Liu, X and Li, X and Quan, H and Yu, Z and Ji, X and Li, P and Zhou, W and Meng, L},
title = {Acoustic separation of cells and bacteria in open sessile droplets.},
journal = {Ultrasonics},
volume = {169},
number = {},
pages = {108270},
doi = {10.1016/j.ultras.2026.108270},
pmid = {42607563},
issn = {1874-9968},
abstract = {Efficient and non-destructive separation of cells and pathogenic microorganisms is a core technical bottleneck in clinical point-of-care diagnostics. Traditional separation methods mostly rely on microchannel structures, external fluid actuation, or biochemical labeling, making it difficult to synergistically optimize separation efficiency, purity, cell viability, and system portability. To address this challenge, this study proposes a label-free separation platform based on traveling surface acoustic wave (TSAW) in an open sessile droplet. The platform innovatively places a droplet of only 2 μL asymmetrically at the edge of the acoustic aperture of an interdigital transducer (IDT), with approximately one-third of the droplet area lying within the acoustic propagation path. By exploiting the asymmetric leakage of TSAW at the solid-liquid interface, a gradient-distributed acoustic streaming vortex field is induced inside the droplet, enabling rapid separation of cell-bacteria systems without channels, pumps, or labels. To elucidate the separation mechanism, a multiphysics coupling model was established and a size-dependent force-balance criterion was established to analyze the motion behaviors of particles of different sizes. Experimental results showed that under 17.5 MHz TSAW excitation, human breast cancer cells (MCF-7, ∼20 μm in diameter) and Escherichia coli (E. coli, 2-3 μm) were successfully separated. The smaller bacteria were driven by the streaming-induced drag force and thus enriched at the droplet periphery, whereas the larger cells were dominated by the acoustic radiation force (ARF) and concentrated at the droplet center. A separation efficiency of 91.2 ± 1.2 % was achieved, and the cells retained high viability after separation. The platform requires only 2 μL of sample, combines simple operation, rapid separation, non-invasiveness, and portability, and can provide a lightweight, label-free paradigm for biological sample pretreatment in application scenarios, such as point-of-care screening of bloodstream infections, circulating tumor cell enrichment, and environmental microbial detection.},
}

@article {pmid42608413,
year = {2026},
author = {Kostulin, DV and Shaposhnikov, PD and Ekizyan, AK and Shevchenko, IG and Shaposhnikov, DG and Shcherban, IV and Kiroy, VN},
title = {EEG-based brain-computer interface (BCI) dataset for directional word recognition.},
journal = {Scientific data},
volume = {13},
number = {1},
pages = {},
pmid = {42608413},
issn = {2052-4463},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; Electromyography ; Machine Learning ; },
abstract = {We present an EEG dataset recorded from 22 neurologically healthy volunteers (12 native Russian speakers and 10 native Spanish speakers) during overt and covert articulation of six spatial-direction words. Monopolar EEG signals were acquired from 38 electrodes positioned according to the international 10-10 system using a Neurovisor-BMM-52 (NVX) amplifier at 500 Hz. In a subset of participants, electromyography (EMG) was simultaneously recorded from the masseter muscle and laryngeal region to exploratorily characterize articulatory muscle activation. Exploratory spectral and coherence analyses, together with classification using standard machine learning methods (Random Forest, SVM, LDA), confirm the presence of condition-specific neural activity distinguishable by standard classifiers (best accuracy 78 ± 4%). The dataset is intended to support the development and benchmarking of algorithms for inner speech recognition in brain-computer interface applications.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography
Electromyography
Machine Learning

@article {pmid42608765,
year = {2026},
author = {Zhao, D and Xie, H and Zhang, H and Han, Q and Yang, Y and Li, Q and Liu, X and Zhang, L and Huang, X and Yang, H},
title = {Post-stroke limb motor dysfunction and functional recovery: A bibliometric analysis of research trends and hotspots.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-26-00551},
pmid = {42608765},
issn = {1673-5374},
abstract = {Post-stroke motor dysfunction severely affects patients' quality of life and independence. Although post-stroke rehabilitation has been widely studied, no detailed analysis of emerging trends in limb motor dysfunction and functional recovery has been performed. To address this gap, our research team conducted a bibliometric analysis of the research landscape, evolutionary path, and interdisciplinary convergence of hotspots in the field of post-stroke limb motor dysfunction and functional recovery from 2016 to 2025. A total of 3173 articles from the Web of Science Core Collection database were included in the bibliometric analysis, revealing that this research field is in a period of rapid expansion. Keyword analysis indicated that emerging technology-assisted rehabilitation has become the dominant trend. Currently, robot-assisted training and virtual reality are the two major technological keywords, and most related studies focus on upper limb rehabilitation. Burst literature analysis revealed that research hotspots in this field have gone through three phases: the early phase (2014-2016): evidence synthesis and clinical guideline development; the middle phase (2017-2019): technological innovation (robotics and virtual reality) and standardized assessment; and the recent phase (2020 to present): neuromodulation (brain-computer interface and non-invasive brain stimulation), global disease burden, and public health policy. Analysis of the highly cited literature revealed the evolutionary path from evidence-based foundations to technological advancement and, finally, to public health applications. Highly cited studies have focused on comparative efficacy, robot-assisted training, brain-computer interfaces, vagus nerve stimulation, and the prediction of rehabilitation outcomes, which form the current mainstream research directions. Upper limb dysfunction has become the core of current research due to its significant impact on daily living and the maturity of plasticity models, whereas research on lower limb recovery is relatively underdeveloped. It should be noted that surgical interventions, such as contralateral C7 nerve crossover, selective neurotomy, and tendon transfer, offer unique therapeutic value for patients with post-stroke chronic spasticity and motor imbalance; however, surgical interventions are still a niche area within the overall field of research and hold great potential for interdisciplinary integration. Taken together, the data above indicate that research on post-stroke motor disorders and functional recovery is under rapid development and that the research focus has shifted from the integration of evidence-based medicine to the deep integration of neuromodulation and intelligent rehabilitation. Surgical interventions, as important adjuncts, have unique clinical value in specific patient populations. In the future, interdisciplinary collaboration among rehabilitation medicine, neurosurgery, and orthopedics should be strengthened to increase the integration of multimodal data and the development of precision rehabilitation strategies, thereby comprehensively improving therapeutic outcomes for motor function recovery in stroke patients.},
}

@article {pmid42609229,
year = {2026},
author = {Lou, W and He, T and Li, G},
title = {The lingering effects of perceived daily negative workplace gossip on next-day work alienation in China: a dual-pathway perspective of rumination and emotion.},
journal = {Frontiers in psychology},
volume = {17},
number = {},
pages = {1824972},
pmid = {42609229},
issn = {1664-1078},
abstract = {INTRODUCTION: Existing research has predominantly confined its scope to the immediate, detrimental consequences of daily negative workplace gossip, largely overlooking its lingering effects. To address this critical limitation, we present a dual-pathway model grounded in the cognitive theory of rumination. By conceptualizing negative gossip as a signal of interpersonal "goal failure," we suggest its effects are more nuanced and enduring than previously assumed.

METHODS: We examined how negative gossip triggers distinct overnight responses through a multi-method approach. Study 1 employed an online recall experiment with 132 employees to establish the causal effect of negative gossip on two distinct forms of post-work information processing: problem-solving pondering and affective rumination. Building upon this, Study 2 utilized a daily diary design with 139 employees over 9 days to dynamically examine the lagged effects of these cognitive processes.

RESULTS: Study 1 demonstrated that perceived negative gossip causes employees to engage in both problem-solving pondering and affective rumination. Multilevel analyses in Study 2 revealed that these lingering effects unfold in opposing directions: problem-solving pondering at night fosters strong emotion the next morning, which subsequently reduces work alienation. Conversely, affective rumination precipitates distress emotion the next morning, thereby exacerbating work alienation.

DISCUSSION: These findings offer critical insights into the enduring and dual-edged nature of workplace gossip. By identifying the sequential cognitive-affective chains during and after non-work time, this study expands theoretical understandings of how daily gossip shapes next-day work experiences through both maladaptive strain and constructive mobilization.},
}

@article {pmid42610205,
year = {2026},
author = {Xu, F and Sun, Y and Lun, Z and Zhao, P and Zhang, Z and Zhang, C and Zhang, A and Feng, C and Zhang, Y and Jung, TP and Leng, J},
title = {Enhancing Motor Imagery Decoding for Stroke Patients Using Data Augmentation and Transfer Learning.},
journal = {International journal of neural systems},
volume = {36},
number = {13},
pages = {2650051},
doi = {10.1142/S0129065726500516},
pmid = {42610205},
issn = {1793-6462},
mesh = {Humans ; *Electroencephalography/methods ; Generative Adversarial Networks ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; *Imagination/physiology ; *Stroke Rehabilitation/methods ; Generative Artificial Intelligence ; Female ; Male ; *Motor Activity/physiology ; },
abstract = {This study proposes an innovative WalkBCI real-time motor imagery brain-computer interface system to solve the problems of time-consuming model calibration and low EEG decoding accuracy in stroke patients because of individual differences. WalkBCI integrates generative adversarial networks and transfer-learning techniques to generate motor imagery feature data via RM-GAN using the resting data of target subjects, which shortens the time of calibration data acquisition and reduces patient fatigue. Meanwhile, the system enhances classification performance by combining source domain data during transfer learning optimization. The system is evaluated in both offline and online experiments to assess its practicality and stability. The study results show that WalkBCI outperformed traditional methods on the stroke patient dataset. In the offline experiments, the model's accuracy, precision, recall, and F 1-score of the model increased by 3.3%, 2.6%, 3.8%, and 3.4%, respectively. In online experiments, the system maintained stable classification performance with a maximum accuracy of 72.5%, while reducing calibration time by 58%. WalkBCI effectively lowered subjects' fatigue, and the comparison experiments revealed average reductions of 7.5 points in FS-14 scores and 74.05 in frontal theta/beta values. This study offers an efficient and stable solution for real-time EEG decoding, particularly well-suited for motor-imagery tasks in stroke patients.},
}

Humans
*Electroencephalography/methods
Generative Adversarial Networks
*Brain-Computer Interfaces
*Stroke/physiopathology
*Imagination/physiology
*Stroke Rehabilitation/methods
Generative Artificial Intelligence
Female
Male
*Motor Activity/physiology

@article {pmid42611216,
year = {2026},
author = {Zhang, R and Jia, Y and Ren, Z and Xu, H and Zheng, D and Yao, Q and Wang, M and Bai, R and Gao, Y},
title = {Enzyme-Instructed Supramolecular Assemblies Occlude Extrinsic Aquaporin-4 via Multivalent Effect for Sensitized Glioma Chemotherapy.},
journal = {ACS nano},
volume = {20},
number = {32},
pages = {22505-22516},
doi = {10.1021/acsnano.6c03807},
pmid = {42611216},
issn = {1936-086X},
support = {32301103//National Natural Science Foundation of China/ ; 32471398//National Natural Science Foundation of China/ ; 52221006//National Natural Science Foundation of China/ ; Z250013//Natural Science Foundation of Beijing Municipality/ ; buctrc202308//Fundamental Research Funds for the Central Universities/ ; buctrc202340//Fundamental Research Funds for the Central Universities/ ; 2025KF-09//State Key Laboratory of Biopharmaceutical Preparation and Delivery/ ; },
mesh = {*Glioma/drug therapy/metabolism/pathology ; Animals ; *Aquaporin 4/metabolism/antagonists & inhibitors ; Humans ; Mice ; Temozolomide/pharmacology ; Cell Line, Tumor ; *Antineoplastic Agents/pharmacology/chemistry ; *Brain Neoplasms/drug therapy/metabolism/pathology ; *Peptides/chemistry/pharmacology ; },
abstract = {Aquaporin-4 (AQP4) proteins serve as a potential therapeutic target for glioma treatment while efficient small molecular inhibitors remain undiscovered. TGN-020 exhibits only transient inhibitory effects and lacks cell specificity. Stemmed from the solvent-exposed region of TGN-020, here we synthesized TGN-020/peptide conjugates which can self-assemble upon dephosphorylation. These conjugates selectively formed supramolecular nanofibers on the plasma membrane of ALP+/AQP4+ glioma cells, which bind to AQP4 in a multivalent manner. The AQP4-nanofiber interaction turned out to be a sustained inhibition and impeded the water exchange. The occlusion of AQP4 enhanced the sensitivity of glioma xenografts to temozolomide treatment in a subcutaneous mouse model. We envision that protein-assembly interactions represent a promising strategy for enabling multivalent binding to proteins of interest, which lead to the sustained regulation of protein function for potential biomedical applications.},
}

*Glioma/drug therapy/metabolism/pathology
Animals
*Aquaporin 4/metabolism/antagonists & inhibitors
Humans
Mice
Temozolomide/pharmacology
Cell Line, Tumor
*Antineoplastic Agents/pharmacology/chemistry
*Brain Neoplasms/drug therapy/metabolism/pathology
*Peptides/chemistry/pharmacology

@article {pmid42602063,
year = {2026},
author = {He, M and Usman, L and Charkhkar, H and Cao, R},
title = {Functional ultrasound imaging in preclinical brain stimulation: from multimodal readouts to closed-loop neuromodulation.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1897417},
pmid = {42602063},
issn = {1662-4548},
support = {R00 EB034298/EB/NIBIB NIH HHS/United States ; R01 EB037413/EB/NIBIB NIH HHS/United States ; UM1 TR004528/TR/NCATS NIH HHS/United States ; },
abstract = {Understanding how brain stimulation engages neural circuits requires readouts that combine rapid monitoring, access to deep structures, and high spatiotemporal resolution. Few existing modalities can provide this combination. This minireview summarizes recent advances in the application of functional ultrasound (fUS) imaging to preclinical brain stimulation, with an emphasis on its value as an advanced functional imaging modality across different stimulation paradigms. Building on ultrafast ultrasound, fUS captures cerebral hemodynamic changes associated with neural activity, enabling near-real-time hemodynamic monitoring with high spatiotemporal resolution. We survey the applications of fUS across optogenetic stimulation, deep brain stimulation (DBS), and focused ultrasound (FUS) stimulation, covering its role in mapping stimulation-evoked network responses and characterizing parameter-dependent neuromodulation effects. We further argue that the unique compatibility between fUS as a readout and FUS as an effector points toward a fully integrated read-write closed-loop neuromodulation framework, in which fUS continuously monitors brain hemodynamic states and feeds these signals back into an adaptive controller to deliver FUS. Together, these directions position fUS as an effective and promising tool for examining the mechanisms of brain function and its responses to stimuli and for developing precise, adaptive neuromodulation strategies.},
}

@article {pmid42602694,
year = {2026},
author = {Tan, Q and Zhang, X and Jia, K and Gong, M},
title = {Associative learning of social interaction alters attention to human faces.},
journal = {iScience},
volume = {29},
number = {8},
pages = {117081},
pmid = {42602694},
issn = {2589-0042},
abstract = {Beyond current goals and physical salience, selection history reflects how past experience automatically biases visual attention. While existing frameworks characterize how we learn to prioritize non-social regularities such as reward and spatial probability, whether selection history can incorporate abstract social structures governing interactions between others remains unknown. Across four experiments (N = 144), we examined whether observed social configurations produce lasting attentional biases toward individual agents. Participants first viewed pairs of human faces arranged in interacting (face-to-face) or non-interacting (back-to-back) configurations. In a subsequent cueing task, faces previously shown interacting captured attention more strongly than those shown non-interacting. This effect was abolished by face inversion and absent for non-social directional stimuli (arrows, animal faces), ruling out contributions from low-level visual features or general directional cueing. Together, these findings indicate that social interaction history induces lasting changes in the priority map, endowing faces with learned attributes that guide selection in social environments.},
}

@article {pmid42603080,
year = {2026},
author = {Ordóñez-Ordóñez, LE and Caraballo-Arias, JA and Díaz-Badillo, H and Medina-Bravo, PA and Macias-Tolosa, C and Medina-Parra, JA and Angulo-Martínez, ES and Hernández, EC},
title = {Linear Versus Curved Incision for Transcutaneous Bone Conduction Implants: Comparative Scar Assessment.},
journal = {International archives of otorhinolaryngology},
volume = {30},
number = {3},
pages = {1-11},
pmid = {42603080},
issn = {1809-9777},
abstract = {INTRODUCTION: In some patients with conductive or mixed hearing loss, or single-sided deafness, bone conduction implants (BCIs) offer an effective treatment; and scar appearance is a relevant factor in the overall outcome.

OBJECTIVE: To compare scar appearance and alopecia between linear and curved scalp incisions in patients undergoing transcutaneous BCIs surgery.

METHODS: An observational, cross-sectional study was conducted at a tertiary referral center in patients who underwent transcutaneous BCI. Postoperative follow-up period was between 6m to 3y. Scar evaluation was performed using the Patient and Observer Scar Assessment Scale (POSAS) and the Vancouver Scar Scale (VSS). Alopecia was defined as absence of hair follicles within 2 mm of the incision line. Statistical comparisons between linear and curved incisions groups were conducted, as well as multivariate analysis with logistic regression models.

RESULTS: Eighty-four cases were analyzed: 28 underwent linear incisions and 56 underwent curved incisions. Patients reported significantly better scar appearance with linear incisions in POSAS, p  = 0.035. Alopecia occurred less frequently among patients who received the Bonebridge implant (OR = 0.113; 95% CI: 0.027-0.478; p  = 0.003). No statistically significant differences were found in observer assessment of POSAS or VSS scores.

CONCLUSION: Linear incisions may offer advantages in scar appearance with transcutaneous BCIs. Certain scar characteristics may also be influenced by the type of device and the surgical approach required for its placement, making it challenging to isolate this effect from that attributed solely to type of incision. These results highlight the need to consider both cosmetic factors and auditory outcomes in surgical planning.},
}

@article {pmid42601216,
year = {2026},
author = {Cao 曹盛浩, S and Tian 田凯茜, K and Yu 余山, S},
title = {Localized Tuning Fields for 3D Hand Position in the Primary Motor Cortex and Premotor Cortex of Macaques.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1773-25.2026},
pmid = {42601216},
issn = {1529-2401},
abstract = {A central question in motor neuroscience is how the brain represents the state of the limbs to guide volitional movements. While the primate motor cortex is known to encode movement kinematics, such as velocity and direction, whether it also maintains a direct and explicit representation of hand position in 3D space remains debated. To address this, we recorded the activity of single neurons in the primary motor cortex (M1) and dorsal premotor cortex (PMd) of two male rhesus macaques performing a naturalistic, self-paced 3D reach-and-grasp task. We found significant populations of neurons in both M1 (36.2%) and PMd (21.3%) that are robustly tuned to the instantaneous 3D position of the hand. In these neurons, the tuning for hand position-characterized by localized, elongated fields-coexists with tunings for other kinematic variables, reflecting the principle of mixed selectivity. Critically, the spatial organization of these representations differs between the two areas: M1 fields are systematically oriented along cardinal axes and exhibit multi-scale spatial clustering, whereas PMd fields are more randomly organized. Furthermore, a small subset of these hand position-tuned cells is sufficient to decode the hand's 3D trajectory with high fidelity. Our findings demonstrate that an explicit and functionally organized representation of 3D hand position is a fundamental component of primate motor cortex, complementing dynamic motor signals to support high-fidelity motor control.Significance Statement To guide skilled actions, the brain must track the hand's location. While the motor cortex is known for controlling movement commands, we reveal it also creates an explicit, highly organized 3D map of hand position. This neural representation is systematically structured, differing between primary and premotor areas. This discovery reshapes our understanding of motor control, showing the brain merges spatial information ("where") with motor commands ("how") of the hand in the same areas. These insights are crucial for creating more effective brain-computer interfaces for individuals with paralysis.},
}

@article {pmid42601605,
year = {2026},
author = {Sosnoski, O and Cloyd, J and Briggs, B},
title = {Obstructive shock from delayed blunt cardiac injury after chest trauma.},
journal = {International journal of emergency medicine},
volume = {19},
number = {1},
pages = {},
pmid = {42601605},
issn = {1865-1372},
abstract = {BACKGROUND: Blunt cardiac injury (BCI) is a rare but serious presentation in emergency departments (EDs) nationwide. These injuries arise in trauma and may be accompanied by confounding injuries that complicate the clinical picture. Timely recognition is crucial, as BCI may lead to complications such as pericarditis or life-threatening cardiac tamponade. Notably, there are no current guidelines on approaches in evaluating BCI.[1] CASE PRESENTATION: We describe a patient who presents one week after a motor vehicle accident (MVA) with delayed BCI manifesting as pericarditis. After an extensive literature search, we found only one similar case-highlighting limitations in the current discussion. Importantly, our case is unique because the patient was initially unable to provide a medical history, and point of care ultrasound (POCUS) was pivotal in the initial evaluation.

CONCLUSIONS: BCI remains challenging due to variable presentation and potential for delayed symptoms, as seen in this case. This case underscores the importance of maintaining a high index of suspicion for BCI, particularly in patients with chest trauma who delay seeking care or present with limited history. Further, early intervention is critical in preventing life-threatening sequelae. Utilizing a combination of physical examination, imaging, and laboratory findings can guide clinicians in accurate diagnosis of BCI. Our report contributes to a limited body of literature by documenting a delayed pericardial complication one week post-MVA, highlighting the need for broader awareness and more structured diagnostic pathways for BCI.},
}

@article {pmid42594553,
year = {2026},
author = {Zhang, Y and Piao, Q and Yang, Y and Boland, JE and Du, Y and Wang, Y and Han, M},
title = {When Chinese verb transitivity meets wrong syntactic category.},
journal = {Brain and language},
volume = {281},
number = {},
pages = {105820},
doi = {10.1016/j.bandl.2026.105820},
pmid = {42594553},
issn = {1090-2155},
abstract = {Two experiments using the same stimuli were performed to examine whether verb transitivity processing proceeds even when phrase structure building based on syntactic categories (noun, verb, etc.) fails during Chinese sentence reading. The sentences contained (a) no violations, (b) transitivity violations, (c) syntactic category violations, or (d) combined syntactic category and transitivity violations. Event-related brain potentials were recorded in Experiment 1, in which participants performed a sentence acceptability task. Transitivity violations elicited an N400 effect in an early (200-450-ms) time window, no matter whether there was a simultaneous syntactic category violation or not. This finding suggests an independence of transitivity processing from local phrase structure building using syntactic category information. In Experiment 2, participants were asked to make speeded responses upon detecting an anomaly. The detection latencies were longer for the transitivity than for the syntactic category violations, which reduces the possibility that the independence of transitivity is due to the relative temporal point at which syntactic category and transitivity violations are detected. Overall, these results reveal a functional independence of transitivity from syntactic category, at least for Chinese.},
}

@article {pmid42595662,
year = {2026},
author = {Qian, S and Lin, F and Zhou, Q and Liu, S and Zhao, W and Zhao, Q},
title = {Flexible radio-frequency carbon nanotube transistors operating in the sub-THz regime.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2026.08.008},
pmid = {42595662},
issn = {2095-9281},
}

@article {pmid42599179,
year = {2026},
author = {Tang, A and Chen, Y and Ding, K and Zhang, J and Xu, L and Chen, W and Hu, S and Lai, J},
title = {IL-1β pathway-dependent regulation of glutamate receptor activity by gut microbiota in bipolar depression.},
journal = {Journal of Zhejiang University. Science. B},
volume = {27},
number = {8},
pages = {888-905},
pmid = {42599179},
issn = {1862-1783},
support = {2023YFC2506200 and 2023YFC2506203//the National Key Research and Development Program of China/ ; 82571735 and 82471542//the National Natural Science Foundation of China/ ; 2024C03098 and 2025C02109//the Key R&D Program of Zhejiang Province/ ; JNL-2023001B//the Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2023ZFJH01-01 and 2024ZFJH01-01//the Fundamental Research Funds for the Central Universities/ ; 2023316//the Major Science and Technology Plan Guidance Project of Xiaoshan District/ ; },
mesh = {Animals ; Male ; *Interleukin-1beta/physiology/metabolism ; *Bipolar Disorder/microbiology/metabolism ; Mice, Inbred C57BL ; *Gastrointestinal Microbiome ; Mice ; *Receptors, Glutamate/physiology/metabolism ; Signal Transduction ; Fecal Microbiota Transplantation ; Disease Models, Animal ; Receptors, N-Methyl-D-Aspartate/genetics/metabolism ; Microglia ; Interleukin 1 Receptor Antagonist Protein/pharmacology ; },
abstract = {OBJECTIVES: Neuroinflammation may disrupt neurotransmitter signaling. This study investigated whether gut microbiota-induced neuroinflammation can regulate glutamate pathways in bipolar disorder (BD).

METHODS: Fecal microbiota transplantation (FMT) was performed to observe behavioral changes in the antibiotic-treated C57BL/6J male mouse model of bipolar depression. Gut microbial structure, circulating, and prefrontal levels of inflammatory factors, microglial activation, and transcription levels of N-methyl- d-aspartate receptor (NMDAR) and α-amino-3-hydroxy-5-methyl-4 isoxazole receptor (AMPAR) genes were measured in the "BD" and control mice. Furthermore, the effects of interleukin-1 (IL-1) receptor antagonist (IL-1RA) on the glutamate pathways were assessed.

RESULTS: Compared with the control mice, "BD" mice displayed depression-like behaviors, with a lower diversity of gut bacteria and a decreased abundance of certain species. In addition, "BD" mice showed increased levels of inflammatory factors (e.g., IL-1β) in the serum and prefrontal cortex, microglial activation, and changes in the messenger RNA (mRNA) levels of NMDAR and AMPAR. Treatment with IL-1RA partially reversed the behavioral patterns, neuroinflammation, and transcription levels of glutamate receptors.

CONCLUSIONS: The findings suggest that gut microbiota may influence glutamate receptor gene expression via an IL-1β-dependent pathway in a mouse model of BD, potentially contributing to neuroinflammatory mechanisms relevant to this disorder.},
}

Animals
Male
*Interleukin-1beta/physiology/metabolism
*Bipolar Disorder/microbiology/metabolism
Mice, Inbred C57BL
*Gastrointestinal Microbiome
Mice
*Receptors, Glutamate/physiology/metabolism
Signal Transduction
Fecal Microbiota Transplantation
Disease Models, Animal
Receptors, N-Methyl-D-Aspartate/genetics/metabolism
Microglia
Interleukin 1 Receptor Antagonist Protein/pharmacology

@article {pmid42599861,
year = {2026},
author = {Wang, Y and Wei, W and Wang, K and Zhao, K and Qiu, S and He, H},
title = {A Multi-Scale Adaptive EEG Feature Selection and Weighting Method for Mental Workload Estimation in Rapid Serial Visual Presentation.},
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.2026.3723771},
pmid = {42599861},
issn = {1558-0210},
abstract = {Accurate mental workload estimation is crucial for enhancing performance and reliability in Rapid Serial Visual Presentation- based Brain-Computer Interfaces (RSVP-BCIs). However, existing (Electroencephalography)EEG-based methods suffer from high-dimensional data, feature redundancy, and inflexible feature representations, limiting their accuracy and generalization. In this work, we designed an RSVP-based aircraft target detection task with varying presentation rates to induce workload, collecting behavioral, subjective, and high-density EEG data. And, we propose a Multi-scale Adaptive Feature selection and Augmented Weighting (MAFA) framework, combining adaptive channel selection with multi-scale feature compression and weighting for mental workload classification. First, an adaptive channel selection model is developed to assess channel importance through Ridge Regression and automatically determine the optimal subset of electrodes through Kneedle-based knee point detection for reducing spatial redundancy. Second, to highlight discriminative features, multi-scale features extracted from EEG are reweighted with averaged coefficients vector derived from L1-regularized multinomial logistic regression. Experimental results indicate that our multi-rate RSVP paradigm can elicit distinct workload levels with significant differences in EEG features across these levels. Our method achieves higher classification accuracy than comparison approaches. The visualization of EEG features weights reveals that the proposed MAFA framework can adaptively pay attention to features in posterior regions dominated by theta and alpha bands, which is consistent with the analysis of brain patterns in workload. These results demonstrate the feasibility and interpretability of our proposed MAFA for mental workload estimation, highlighting its potential application in RSVP-BCI systems.},
}

@article {pmid42600436,
year = {2026},
author = {Du, L and Briki, M and Buclin, T and Guidi, M and Carrara, S and Thoma, Y},
title = {ETODA: Automatic three-dimensional error tolerance grid generation for dosage adaptation in precision medicine.},
journal = {Computer methods and programs in biomedicine},
volume = {286},
number = {},
pages = {109597},
doi = {10.1016/j.cmpb.2026.109597},
pmid = {42600436},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Model-informed precision dosing (MIPD) relies on drug concentration measurements to individualize dosage regimens, yet emerging point-of-care (POC) technologies may introduce substantial measurement uncertainty compared with conventional laboratory assays. Existing frameworks lack systematic tools to evaluate how such inaccuracies propagate to dosage adaptation decisions and affect therapeutic outcomes across different sampling times. We propose ETODA (error tolerance of dosage adaptation), a computational framework that constructs automatic three-dimensional error-tolerance grids to quantify the robustness of dosage decisions under measurement uncertainty.

METHODS: By integrating population pharmacokinetic (popPK) models, patient-specific covariates, Bayesian posterior estimation, and Monte Carlo simulations, ETODA maps the relationship between measured and true drug concentrations, sampling time, and resulting therapeutic risk. The framework was applied to imatinib and vancomycin as model drugs with distinct therapeutic targets and dosing strategies, using 50 × 50 grids from simulated steady-state concentration ranges.

RESULTS: The grids revealed drug-specific responses to discrepancies between measured and true concentrations, and highlighted the influence of sampling time on therapeutic classification. For imatinib, peak sampling at 4 h yielded the highest distribution-weighted therapeutic-target classification percentage (52.24%), while the distribution-weighted percentage of grid points in the inefficacy alarm range increased to 53.47% at 24 h. For vancomycin, AUC/MIC-based monitoring showed higher distribution-weighted therapeutic-target classification percentages and lower distribution-weighted alarm-level classification percentages than trough-based monitoring across the evaluated sampling times.

CONCLUSIONS: ETODA provides a robustness-evaluation layer that operates on top of existing popPK and MIPD workflows, mapping measurement error onto downstream dose decisions and therapeutic-risk classifications. It thereby supports more robust MIPD and informs the design of POC monitoring technologies for safer, more effective precision dosing.},
}

@article {pmid42591065,
year = {2026},
author = {Zhu, Z and Schaffer, N and Yang, X},
title = {Neuromorphic Devices and Computing for Sensing, Memory, and Control.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e77116},
pmid = {42591065},
issn = {2198-3844},
support = {//Johns Hopkins University Startup Fund/ ; //Maryland Stem Cell Research Fund Launch Program/ ; },
abstract = {Neuromorphic devices are bioinspired electronic systems that mimic key structures and functions of the nervous system, enabling integration and communication between living tissues and machines. This review examines how neuromorphic devices and computing are designed to emulate the structure, organization, and function of the nervous system. For neuromorphic devices, we first describe strategies that mimic subcellular neural functions. We then highlight how device architecture recapitulates biological topology from subcellular components to brain networks. We next summarize how neuromorphic devices emulate sensory and sensorimotor (sensory-modulation) neural circuits. For neuromorphic computing, we review recent advances in artificial and biological neuromorphic computing, including spiking neural networks, bioinspired learning algorithms, and applications. We also discuss emerging biohybrid intelligence systems leveraging two- and three-dimensional biological networks as computational units. Finally, we outline key challenges, potential milestones, and future directions.},
}

@article {pmid42591285,
year = {2026},
author = {Li, Y and Deng, Y and Deng, L and Lu, X and Huang, C and Chen, X and Liu, Y and Chen, P and Tang, M and Wang, F},
title = {Different feedback modes of brain-computer interface training for upper limb motor function after stroke: a protocol for a systematic review and network meta-analysis.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1865968},
pmid = {42591285},
issn = {1664-2295},
abstract = {BACKGROUND: Upper limb motor impairment after stroke is a major contributor to long-term disability and can have lasting effects on patients' activities of daily living, social participation, and quality of life. Brain-computer interface (BCI) training establishes a closed-loop training process that links central nervous system activity, external feedback, and sensory reafferent input, and is considered a promising approach to facilitating neuroplastic reorganization and motor recovery. Existing studies on upper-limb rehabilitation after stroke have investigated several BCI feedback modes. However, the relative efficacy of these feedback modes has not been systematically compared across available randomized controlled trials.

METHODS: This study protocol has been registered in PROSPERO with registration number CRD420261370474 and will be conducted in accordance with the PRISMA-P statement and the PRISMA-NMA extension. PubMed, Embase, Web of Science, the Cochrane Library, and Scopus will be systematically searched from inception to March 2026. Only parallel-group randomized controlled trials will be eligible for inclusion. Participants will be patients with post-stroke upper-limb motor impairment. The experimental interventions will consist of BCI training with different feedback modes, and intervention nodes will be defined according to specific feedback categories, including functional electrical stimulation, robot- or exoskeleton-assisted feedback, visual feedback, virtual reality feedback, and combined multimodal feedback. Control groups will include conventional rehabilitation, sham BCI, no additional intervention, and other active interventions. The primary outcome will be the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) score. The secondary outcomes will be the Action Research Arm Test (ARAT) score and the Wolf Motor Function Test (WMFT) score. Two reviewers will independently perform study selection, data extraction, and risk-of-bias assessment, and any disagreements will be resolved through discussion or adjudication by a third reviewer. If the evidence network is sufficiently connected, a network meta-analysis will be performed to compare the relative efficacy across different intervention nodes. Local inconsistency will be assessed using the node-splitting method, and the surface under the cumulative ranking curve (SUCRA) will be used to rank different feedback modes according to efficacy.

CONCLUSION: This protocol specifies the methods for comparing different BCI feedback modes in post-stroke upper-limb rehabilitation and provides a transparent framework for the planned systematic review and network meta-analysis.

https://www.crd.york.ac.uk/prospero/, identifier CRD420261370474.},
}

@article {pmid42591358,
year = {2026},
author = {Yang, X and Tian, H and Li, Y and Zhang, J and Li, S and Pan, G},
title = {Brain-CLIPLM: semantic compression for EEG-to-text decoding.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1899770},
pmid = {42591358},
issn = {1662-4548},
abstract = {Decoding natural language from non-invasive electroencephalography (EEG) remains constrained by low signal-to-noise ratio and limited information bandwidth. This raises a central question: can sentence-level language be reliably recovered from such signals? Under realistic information constraints, this direct-recovery assumption may be too strong. We introduce a semantic compression hypothesis: non-invasive EEG may preserve recoverable semantic anchors rather than the full lexical-syntactic form of a sentence. From this perspective, direct sentence reconstruction is overly fine-grained relative to the recoverable information scale of EEG. To address this mismatch, we propose Brain-CLIPLM, a two-stage framework that decomposes EEG-to-text decoding into semantic-anchor recovery and anchor-guided sentence reconstruction. Stage 1 uses contrastive learning to align word-level EEG evidence with a fixed keyword vocabulary and recover ordered semantic anchors. Stage 2 uses a retrieval-grounded large language model with chain-of-thought reasoning prompts to reconstruct sentence meaning from these anchors, following a granularity matching principle that aligns decoding complexity with the recoverable neural information scale. On the combined Zurich Cognitive Language Processing (ZuCo) benchmark, Brain-CLIPLM achieves 67.6% Top-5 and 85.0% Top-25 sentence retrieval accuracy, with the strongest performance at intermediate anchor granularity. Control analyses show that EEG-derived anchors carry sentence-specific information beyond language-model priors. Within the constrained ZuCo sentence pool and fixed keyword-vocabulary settings, these findings suggest that EEG-to-text decoding is better framed as recovering compressed semantic content before anchor-guided sentence reconstruction.},
}

@article {pmid42592748,
year = {2026},
author = {Zeng, N and Zhou, D and Zhou, J and Su, Y and Deng, G and Zhang, J and Liu, X and Aleman, A and Xu, P and Luo, Y and Goerlich, KS},
title = {Computational modelling of effort-based decision-making in depression: The role of apathy and anhedonia in adolescent girls.},
journal = {The British journal of clinical psychology},
volume = {},
number = {},
pages = {},
doi = {10.1111/bjc.70086},
pmid = {42592748},
issn = {0144-6657},
support = {32371104//National Natural Science Foundation of China/ ; 2025SHIBS0003//Shenzhen-Hong Kong Institute of Brain Science Shenzhen Fundamental Research Institutions/ ; 2243300005//Fundamental Research Funds for the Central Universities/ ; PT-2024-06B//National Human Genetic Resources Sharing Service Platform/ ; 2005DKA21300//National Human Genetic Resources Sharing Service Platform/ ; },
abstract = {BACKGROUND: Adolescence is a period of heightened vulnerability to depression, particularly in females. Motivational deficits are a hallmark of depression, yet the cognitive mechanisms underlying impaired effort-based decision-making (EBDM) in female adolescents remain poorly understood. Computational psychiatry addresses this by fitting mathematical models to behavioural data and extracting latent parameters, offering a more mechanistic view.

METHODS: This study used computational modelling to investigate the distinct effects of apathy and anhedonia on effort-based decision-making in adolescent girls with depression. Thirty-two adolescent girls with depression and 33 matched healthy controls performed a physical effort-based decision-making task. Participants accepted or rejected to exert different levels of physical effort to obtain different magnitudes of rewards or to avoid loss. Group differences were analysed utilizing a hierarchical Bayesian discounting model.

RESULTS: The depression group exhibited a significantly reduced willingness to exert effort to obtain rewards, reflected by a lower accept rate and longer response time compared to the healthy control group. In line with this, the discounting model showed that the depression group had a higher effort sensitivity. Moreover, in the healthy control group, higher apathy levels were associated with a lower accept rate, whereas no such associations were observed for anhedonia; in the depression group, no significant associations were found for either apathy or anhedonia.

CONCLUSIONS: This study identifies a computational signature of motivational deficits in female adolescent depression, characterized by oversensitivity to effort expenditure and modulated by apathy severity. By focusing on female adolescents, our findings highlight sex-specific mechanisms underpinning vulnerability to depression.},
}

@article {pmid42582168,
year = {2026},
author = {Letner, JG and Lam, JLW and Copenhaver, MG and Barrow, M and Patel, PR and Richie, JM and Lee, J and Kim, HS and Cai, D and Weiland, JD and Phillips, J and Blaauw, D and Chestek, CA},
title = {A method for efficient, rapid, and minimally invasive implantation of individual non-functional motes with penetrating subcellular-diameter carbon fiber electrodes into rat cortex.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1848862},
pmid = {42582168},
issn = {1662-4548},
abstract = {Distributed arrays of wireless neural interfacing chips with 1-2 channels each, known as "neural dust," could enhance brain machine interfaces (BMIs) by removing wired connections through the scalp and increasing biocompatibility with their submillimeter size. Although several neural dust designs have emerged, currently reported procedures for implanting them in batches place the chips directly inside the brain, which can damage or displace large numbers of neurons. Therefore, a procedure for safely implanting neural dust in batches such that only ultrasmall microwire elements enter the brain is needed. Here, we demonstrate the feasibility of implanting batches of wireless motes that rest on the cortical surface and reach 1 mm brain depths via penetrating carbon fiber electrodes (6.8-8.4 μm diameter) without employing disruptive insertion shuttles. To simulate their implantation, we assembled over 230 mechanically-equivalent carbon fiber motes and affixed them to insertion tools with polyethylene glycol (PEG), a quickly dissolvable and biocompatible material. Then, we implanted batches into rat cortex in vivo and evaluated insertion success and their arrangement on the brain surface. When positioning motes for insertion, we discovered that they readily aggregated in molten PEG such that average array pitches were 5% longer than an individual mote's dimensions (240 × 240 μm). Overall, 187/214 (87%) motes tightly-packed in 4 × 4 (N = 4) and 5 × 5 (N = 6) square grid configurations successfully inserted into rat cortex. After implantation, measurements of how much motes tilted (22 ± 9°, X̄ ± S) and had been displaced from their original positions were smaller than those measured in the literature for devices implanted inside the brain. Collectively, these data establish the mechanical viability of assembling and safely implanting motes with ultrasmall electrodes and epicortically-situated chips, motivating the use of arrays with similar geometries in future BMIs.},
}

@article {pmid42582324,
year = {2026},
author = {Wang, J and Bi, L and Fei, W and Wei, Y},
title = {Bimanual attempts improving EEG-based hand movement decoding in stroke patients with hemiplegia.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1890774},
pmid = {42582324},
issn = {1662-5161},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on electroencephalography (EEG) signals have been applied to improve active hand motor function rehabilitation of stroke patients. Besides, the bilateral arm rehabilitation training can effectively promote the recovery of the unilateral affected hand. However, existing studies on BCIs for stroke patients mainly focus on decoding the unilateral affected hand without effectively utilizing the unaffected hand.

OBJECTIVE: In this paper, we studied the neural signatures and decoding of bimanual and unimanual motor attempts of hemiplegic stroke patients from EEG signals to explore the neural signature differences between bimanual and unimanual movements of patients and whether the differences can improve movement decoding accuracy. Methods: We designed the experimental paradigm of unimanual and bimanual opening and closing motor attempts. Furthermore, we compared the neural signature differences using movement-related cortical potential (MRCP) and event-related desynchronization (ERD).

RESULTS: Experimental results from hemiplegic stroke patients indicated that greater MRCP activation was found during bimanual movements than unimanual movements. Although the event-related spectral perturbation (ERSP) varied among patients, it mainly appeared in the [8-30] Hz frequency band, and the ERD activation of bimanual movements was greater than that of unimanual movements in specific frequency bands and brain regions for each patient. The average decoding accuracy of bimanual movements was higher than that of unimanual movements by 4% to 12%.

DISCUSSION: This work can potentially advance BCI-based active rehabilitation of hand motor function of stroke patients.},
}

@article {pmid42582409,
year = {2026},
author = {Zhuo, L and Xiong, R and Zhang, T and Wei, X and Zhang, J and Jin, Z and Li, L},
title = {Sex differences in connectivity-based prediction of working memory and its neural organization.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {156},
pmid = {42582409},
issn = {1871-4080},
abstract = {UNLABELLED: Working memory (WM) depends on coordinated interactions among distributed brain systems. Although sex differences in WM have been widely studied, most previous work has focused on group-level activation differences. As a result, it remains unclear whether males and females can be characterized by distinct functional connectivity-based models of WM performance. We analyzed 622 participants (311 females and 311 males) from the Human Connectome Project who were retained after data quality and completeness screening, with the male and female groups matched on relevant covariates and showing no significant between-group differences. Using n-back task functional magnetic resonance imaging data and corresponding behavioral measures, we constructed connectome-based predictive models of WM performance separately in females and males. WM performance could be predicted in both sexes. However, cross-sex validation showed that the female-trained model significantly predicted male WM performance, whereas the male-trained model did not significantly predict female WM performance. To examine factors associated with this asymmetry, we analyzed inter-subject consistency within each sex group. Females showed greater within-sex neural dynamic consistency than males across multiple brain regions, and subsampling analyses further linked higher inter-subject consistency to greater predictive feature stability and better cross-sex prediction performance. Importantly, sex differences extended beyond model performance to the predictive connectivity patterns themselves, mainly involving higher-order cognitive control and lower-level visual systems. Functional connectivity gradient analyses provided convergent support for this pattern. Together, these findings deepen our understanding of WM network mechanisms and provide a basis for understanding sex-related differences in WM impairment and for exploring potential interventions.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10525-0.},
}

@article {pmid42584395,
year = {2026},
author = {Ni, P and Zhang, Q and Jiang, Y and Yao, H and Zhou, C and Tian, X and Gao, S and Zhao, J and Ren, X and Xu, N and Fu, M and Zhao, L and Qi, X and Yu, X and Guo, W and Chung, S and Guo, F and Li, T},
title = {Gestational Hypoxia Disrupts Medial Ganglionic Eminence Progenitor Dynamics and Interneuron Development in Schizophrenia.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e76936},
pmid = {42584395},
issn = {2198-3844},
support = {82571737//National Natural Science Foundation of China/ ; 82230046//National Natural Science Foundation of China/ ; 2025C02112//Key R&D Program of Zhejiang/ ; 20241203A14//the Key R&D by Hangzhou Science and Technology Bureau/ ; 2024WJC035//Hangzhou Special Project for Biomedical and Health Industry/ ; 2024ZY01010//Zhejiang Key Laboratory of Clinical and Basic Research for Psychiatric Diseases/ ; 2024E10107//Zhejiang Key Laboratory of Clinical and Basic Research for Psychiatric Diseases/ ; 2025HZGF10//The Construction Fund of Medical Disciplines of Hangzhou/ ; MH133205/MH/NIMH NIH HHS/United States ; MH131610/MH/NIMH NIH HHS/United States ; },
abstract = {Schizophrenia (SCZ) is a neurodevelopmental disorder characterized by heterogeneous symptoms and multifactorial etiologies. Medial ganglionic eminence (MGE) spheroids generated from first-episode schizophrenia (FES) patients revealed accelerated neurodevelopmental trajectories and enhanced hypoxia responses via single-cell transcriptomics. Notably, FES patient-derived MGE spheroids exhibited defective interneuron migration, disrupted synaptic ultrastructure, and diminished network synchronization. To establish causal links, the gestational hypoxia mouse model recapitulated key pathologies, including reduced progenitor proliferation, abbreviated cell cycles, mismatched interneuron subtypes, and schizophrenia-like behavioral deficits in offspring. Critically, maternal administration of N-acetylcysteine (NAC) restored redox homeostasis and rescued both cellular and behavioral phenotypes. Collectively, these results demonstrate that developmental redox disruption directly impairs GABAergic circuit assembly, while supporting targeted antioxidant pharmacotherapy during gestation as a translatable strategy to mitigate neurodevelopmental risk.},
}

@article {pmid42586058,
year = {2026},
author = {Jin, S and Liu, Y and Wang, T and Shan, X and Liu, J and Zhu, S and Shen, T and Liang, Y and Wei, G and Cao, Y and Li, X and Duan, S and Hu, H and Fan, Z},
title = {Status-dependent competition engagement via mesocortical pathway.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2026.07.020},
pmid = {42586058},
issn = {1097-4199},
abstract = {The decision to engage in competition, enabling individuals to acquire resources with minimal risk and energetic cost, is based on prior outcomes. Yet the neural mechanism underlying this decision-making process remains unclear. Here, we developed a win maze, allowing mice to voluntarily decide whether to re-engage in competition following an outcome. Dominant males showed a stronger preference for competition engagement than subordinate males. Mechanistically, dorsomedial prefrontal cortex (dmPFC) population activity exhibited rotational dynamics during the task, with the competition process driving an enlarged loop trajectory, and remained within the competition-related latent space during consecutive win trials. Meanwhile, winning outcomes evoked dmPFC dopamine release, which was associated with competition re-engagement. Consistently, inhibiting the ventral tegmental area (VTA)[DA] → dmPFC pathway or dmPFC dopamine D1 receptor-positive (D1R[+]) neurons during winning outcomes abolished re-engagement in dominant males, whereas activating the VTA[DA] → dmPFC pathway enhanced engagement in subordinate males. Together, these findings reveal a status-dependent dopamine mechanism that translates competitive outcomes into future engagement decisions.},
}

@article {pmid42586601,
year = {2026},
author = {Chai, X and Zheng, Z and Wang, N and He, Q and Jia, Y and Wan, K and Zhu, S and Zhang, B and Niu, H and Zhai, Y and Zhao, J and Yang, Y},
title = {Real-world clinical characteristics of motor disorders for personalised brain-computer interfaces: phenotypic insights from a specialised clinic.},
journal = {Stroke and vascular neurology},
volume = {},
number = {},
pages = {},
doi = {10.1136/svn-2026-005290},
pmid = {42586601},
issn = {2059-8696},
abstract = {Motor dysfunction caused by neurological disorders such as stroke, spinal cord injury and amyotrophic lateral sclerosis has become a major global public health issue. Conventional rehabilitation approaches yield limited efficacy, highlighting the urgent need for innovative therapies. Brain-computer interface (BCI) technology offers a promising avenue for motor function restoration by decoding motor intentions and driving external devices or providing sensory feedback. However, current BCI development has predominantly emphasised technical performance metrics, lacking systematic investigation into the clinical characteristics of real-world patient populations. This disconnect between technological advancement and genuine clinical demands persists because existing studies often recruit idealised subjects while neglecting prevalent conditions like stroke and lack standardised assessments of clinically meaningful outcomes. Consequently, system designs frequently fail to align with patient-specific profiles. To address this gap, our study established a prospective BCI evaluation outpatient cohort, comprehensively collecting data from patients presenting with motor dysfunction throughout 2025. Among the total of 1641 patients with motor dysfunction, the majority were patients with chronic-phase stroke (1087, 66.24%). The cohort was primarily middle-aged, with a mean age of 45.93±11.23 years. The study further detailed patterns of muscle strength, joint range of motion and sensory impairments. The goal is to construct the first clinical profile characteristics to inform precise patient selection and guide personalised system design.},
}

@article {pmid42589408,
year = {2026},
author = {He, Z and Ma, J and Liu, Y and Du, Z},
title = {Molecular Mechanisms of Foreign Body Responses to Neural Electrodes and Surface Biofunctionalization Strategies for Interface Modulation.},
journal = {International journal of molecular sciences},
volume = {27},
number = {15},
pages = {},
doi = {10.3390/ijms27156752},
pmid = {42589408},
issn = {1422-0067},
support = {20222D0209800//Ministry of Science and Technology of the People's Republic of China/ ; },
mesh = {Humans ; Animals ; *Foreign-Body Reaction/etiology/metabolism/pathology ; *Electrodes, Implanted/adverse effects ; *Brain-Computer Interfaces ; Surface Properties ; },
abstract = {Long-term implantable neural electrodes underpin brain-machine interfaces, deep brain stimulation, epilepsy monitoring, and closed-loop neuromodulation. Following chronic implantation, however, the foreign body response (FBR) at the electrode-tissue interface remains a major constraint on long-term performance, as reflected by increased interfacial impedance, lower signal-to-noise ratios, fewer resolvable units, and higher stimulation thresholds. This deterioration arises from interrelated events that include implantation injury, protein adsorption, blood-brain barrier disruption, complement activation, glial reactivity, oxidative stress, glial scar formation, and neuronal loss. It cannot be attributed solely to material ageing or encapsulation failure. This review examines the molecular mechanisms of neural-electrode FBR and relates them to surface-biofunctionalization strategies, including antifouling coatings, bioactive ligands, immobilized neurotrophic factors, drug-eluting electrodes, and emerging immunomodulatory interfaces. Establishing mechanistic links among molecular events, material interfaces, and functionalization strategies may guide the rational design of durable neural electrodes.},
}

Humans
Animals
*Foreign-Body Reaction/etiology/metabolism/pathology
*Electrodes, Implanted/adverse effects
*Brain-Computer Interfaces
Surface Properties

@article {pmid42589793,
year = {2026},
author = {Teixeira, HC and Oliveira, RC and Araújo, A and Guedes, JF},
title = {Tailoring Matrix Toughness for High-Performance Composites in Cryogenic Applications.},
journal = {Polymers},
volume = {18},
number = {15},
pages = {},
doi = {10.3390/polym18151864},
pmid = {42589793},
issn = {2073-4360},
abstract = {The rapid expansion of space exploration has increased the demand for lightweight structural materials capable of maintaining performance under extreme thermal conditions. Carbon fibre-reinforced polymers (CFRPs) offer high specific strength and low density; however, their application in cryogenic environments remains challenging due to the brittleness of epoxy matrices, which are susceptible to cracking at low temperatures and under thermal cycling. In this work, two strategies were investigated to improve the damage tolerance of epoxy nanocomposites: (i) the use of a biscitraconimide-based (BCI) resin and (ii) the incorporation of methyl methacrylate-butadiene-styrene (MBS) core-shell particles. Low additive contents were evaluated to identify formulations compatible with prepreg manufacturing. The incorporation of 2 wt.% of MBS core-shell particles significantly improved the impact resistance of the nanocomposites and was selected for CFRP laminate production. When applied to CFRPs, the modified matrix maintained the overall tensile behaviour while increasing the interlaminar fracture toughness by 102% and 122% at room (RT) and cryogenic temperatures (CT), respectively. These findings demonstrate that matrix modification using low-content toughening is an effective strategy to enhance the cryogenic performance of CFRPs, contributing to the development of lighter and more resilient composite structures for next-generation space systems.},
}

@article {pmid42589863,
year = {2026},
author = {Aktepe, OH and Ekin, O and Butun, O and Izgor, RB and Karaoglu, A and Karabulut, B and Yalcin, S},
title = {Prognostic Value of the Vitamin B12/C-Reactive Protein Index in Resected Stage II-III Cutaneous Melanoma: Comparison with Its Individual Components.},
journal = {Journal of clinical medicine},
volume = {15},
number = {15},
pages = {},
doi = {10.3390/jcm15155759},
pmid = {42589863},
issn = {2077-0383},
abstract = {Background: Recurrence risk in completely resected stage II-III cutaneous melanoma remains incompletely defined by conventional staging alone. This study evaluated whether the baseline vitamin B12 (VB12)/C-reactive protein (CRP) index (BCI), a simple biomarker reflecting metabolic and inflammatory status, is associated with recurrence-free survival (RFS). Methods: This retrospective study included 136 patients. RFS was analyzed using Kaplan-Meier curves and Cox proportional hazards models. Receiver operating characteristic (ROC) analysis was used to determine the optimal BCI cut-off for recurrence, and a median-based cut-off was additionally examined as a sensitivity analysis. The natural logarithms (ln) of CRP, VB12, and BCI were standardized and compared in adjusted Cox models. Results: During a median follow-up of 62.0 months, recurrence occurred in 49 patients (36.0%). ROC analysis yielded an area under the curve of 0.66 (95% confidence interval [CI]: 0.57-0.76; p = 0.002), and the optimal BCI cut-off was 3386, with 65% sensitivity and 67% specificity. Patients with high BCI had significantly shorter median RFS than those with low BCI (63.5 months vs. not reached; p < 0.001). In multivariable analysis using the ROC-derived cut-off, higher Breslow thickness (hazard ratio [HR]: 1.10, 95% CI: 1.03-1.17; p = 0.002), stage III disease (HR: 2.37, 95% CI: 1.06-5.30; p = 0.035), and high BCI (HR: 2.39, 95% CI: 1.30-4.38; p = 0.005) remained independently associated with shorter RFS. The association remained significant using the median BCI cut-off (HR: 2.22, 95% CI: 1.21-4.07; p = 0.010). In head-to-head adjusted models, standardized ln(CRP) (HR per 1-standard-deviation [SD] increase: 1.57, 95% CI: 1.17-2.11; p = 0.002) and ln(BCI) (HR per 1-SD increase: 1.54, 95% CI: 1.16-2.04; p = 0.003) were associated with shorter RFS, whereas ln(VB12) was not (HR per 1-SD increase: 1.01, 95% CI: 0.80-1.28; p = 0.894). The CRP- and BCI-based models showed similar model fit. Conclusions: Higher baseline BCI was associated with shorter RFS after adjustment for clinicopathological factors in patients with resected stage II-III cutaneous melanoma. However, its prognostic performance was comparable to that of CRP alone, and its incremental value over CRP was not established. These findings are exploratory and require external validation before clinical application.},
}

@article {pmid42590746,
year = {2026},
author = {Bouyam, C and Siribunyaphat, N and Aung, ST and Punsawad, Y},
title = {Electroencephalography-Based Emotion Recognition Using Auditory Stimulation for Affective Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {15},
pages = {},
doi = {10.3390/s26154971},
pmid = {42590746},
issn = {1424-8220},
support = {CGS-RF-2025/01//Walailak University/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Emotions/physiology ; *Acoustic Stimulation/methods ; Female ; Male ; Adult ; Wavelet Analysis ; Machine Learning ; },
abstract = {Although electroencephalography (EEG)-based emotion recognition is a promising approach for affective brain-computer interface (BCI) applications, substantial inter-subject variability continues to limit its generalizability. This study proposed an EEG-based framework to recognize emotions within a valence-arousal model using auditory stimulation. Predefined emotional states were established using validated affective video clips and subsequently evaluated through EEG responses elicited by instrumental melodies. Three EEG features, which include discrete wavelet transform (DWT), functional connectivity (FC), and effective connectivity (EC), together with their combined feature set, were systematically evaluated using five machine learning classifiers. Performance was assessed under subject-dependent, subject-independent (leave-one-subject-out, LOSO), and few-shot subject-adaptation protocols. The results showed that DWT achieved the highest subject-dependent classification accuracy (0.88), followed by the combined feature set (0.84). In contrast, the subject-independent LOSO evaluation yielded near-chance performance across all feature domains (0.23-0.30), highlighting substantial inter-subject variability. Few-shot subject adaptation using 25-75% subject-specific calibration data substantially improved subject-independent performance, with the highest accuracy of 0.77 achieved by FC at 75% calibration. In conclusion, these findings demonstrate the feasibility of EEG-based emotion recognition using auditory stimulation under predefined affective conditions and provide a foundation for the future development of personalized affective brain-computer interface systems.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
*Emotions/physiology
*Acoustic Stimulation/methods
Female
Male
Adult
Wavelet Analysis
Machine Learning

@article {pmid42579524,
year = {2026},
author = {Zhou, J and Xu, S and Ye, L and Wang, Z and Li, H and Chen, X and Chen, R and Yang, Y and Song, C and Hu, L and Miao, P and Wang, S and Ding, Y and Shen, C and Guo, Y and Chen, C and Wang, S},
title = {Clinical subtypes and sleep-wake evolution pattern in epilepsy manifesting as sleep-related seizures.},
journal = {Epilepsia},
volume = {},
number = {},
pages = {},
doi = {10.1002/epi.70447},
pmid = {42579524},
issn = {1528-1167},
support = {82301636//National Natural Science Foundation of China/ ; 82471469//National Natural Science Foundation of China/ ; LD24H090003//Natural Science Foundation of Zhejiang Province/ ; },
abstract = {OBJECTIVE: Epilepsy manifesting as sleep-related seizures (EpSRS) represents a common but heterogeneous group. This study aimed to identify distinct clinical subtypes of EpSRS and explore alterations in sleep-wake seizure rhythms over the disease course.

METHODS: EpSRS is defined by seizures occurring ≥80% during sleep. We conducted a cross-sectional study of 550 patients with EpSRS. Patients were classified as current EpSRS if ≥80% of their seizures occurred during sleep over the past year, or as previous EpSRS if they had a prior history of EpSRS but no longer met this 80% threshold during the past year. A two-step cluster analysis was applied to the current EpSRS cohort, and disease trajectories were evaluated across both cohorts.

RESULTS: Cluster analysis classified the current EpSRS cohort (n = 447) into two distinct subtypes. The sleep tonic-clonic seizure (TCS) subtype (n = 218) featured later onset (18.0 years), infrequent (<1 seizure/month, 73.9%) TCSs (99.5%), an unknown etiology (78.0%), and a favorable drug response (2.8% drug-resistant epilepsy [DRE]). The sleep non-TCS subtype (n = 229) featured early onset (11.0 years), frequent (≥1 seizure/month, 52.0%) focal preserved/impaired consciousness seizures (FPCSs/FICSs, 96.9%), an association with etiology of malformations of cortical development (27.9%), and a high rate of DRE (62.9%). Furthermore, an evolution pattern was found in the previous EpSRS cohort (n = 103), marked by a transition from the sleep TCS subtype into awake FPCS/FICS pattern (52.5% were diagnosed as temporal lobe epilepsy), with the DRE proportion surging from 5.2% to 39.0%. This phenotypic evolution could be predicted by later age at epilepsy onset, female sex, and a history of febrile seizures.

SIGNIFICANCE: This study establishes a practical framework for classifying EpSRS, which is useful for predicting prognosis and evaluating etiology. It should be noted that among patients with the sleep TCS subtype, a transition into an awake FPCS/FICS pattern is associated with an increased risk of DRE.},
}

@article {pmid42579561,
year = {2026},
author = {Yang, J and Tang, BJ and Li, JY and Xie, SL and Duan, S and Chen, J and Zhu, S and Xu, ZZ},
title = {VlPAG/DRN Microglia Drive Neuropathic Pain-Induced Depression via a Defined Neuroimmune Axis.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e77071},
pmid = {42579561},
issn = {2198-3844},
support = {2021ZD0202703//Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project/ ; 82301397//National Natural Science Foundation of China/ ; 82171206//National Natural Science Foundation of China/ ; 32571181//National Natural Science Foundation of China/ ; LZ25H090001//Zhejiang Provincial Natural Science Foundation of China/ ; 2025J032//Natural Science Foundation of Ningbo Municipality/ ; 2023ZFJH01-01//Fundamental Research Funds for Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for Central Universities/ ; 226-2022-00227//Fundamental Research Funds for Central Universities/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 04202508016//Nanhu Brain-Computer Interface Institute/ ; //MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University/ ; PPXK2024-05//Ningbo Medical and Health Brand Discipline/ ; },
abstract = {Neuropathic pain is frequently comorbid with anxiety and depression, yet the mechanisms linking immune signaling to affective brain circuits remain poorly understood. Here, we identify a neuroimmune circuit in which peripheral nerve injury activates microglia in the midbrain ventrolateral periaqueductal gray/dorsal raphe (vlPAG/DRN), triggering an NLRP3-IL-1β-dependent inflammatory cascade. Direct optogenetic or chemogenetic activation of vlPAG/DRN microglia is sufficient to drive negative affective behaviors. We show that local VGLUT2[+] glutamatergic neurons (vlPAG/DRN[Glu]) are the principal IL-1R1-expressing targets; IL-1β activates these neurons to drive anxiety- and depression-like states. Conversely, microglia-specific Nlrp3 deletion or local IL-1R1 blockade prevents neuropathic pain-induced affective deficits. Furthermore, circuit mapping and functional manipulation reveal an excitatory vlPAG/DRN[Glu] to the bed nucleus of the stria terminalis (BNST[GABA]) pathway that is both sufficient to induce and required to maintain the affective component of neuropathic pain. Together, our findings delineate a microglia-vlPAG/DRN[Glu]-BNST[GABA] axis that translates peripheral injury into maladaptive emotional states, revealing a discrete neuroimmune circuit substrate for mood comorbidity in chronic pain.},
}

@article {pmid42580195,
year = {2026},
author = {K, ND and G, S},
title = {Structural control and resilience in the oxytocin molecular graph: A graph-theoretic framework for critical atom and bond identification with comparative validation across cyclic peptides.},
journal = {Journal of molecular graphics & modelling},
volume = {148},
number = {},
pages = {109538},
doi = {10.1016/j.jmgm.2026.109538},
pmid = {42580195},
issn = {1873-4243},
abstract = {Graph-theoretic analysis provides a rigorous mathematical framework for investigating molecular architecture; however most existing studies primarily rely on conventional topological indices and centrality measures that characterize connectivity without explicitly quantifying structural control, redundancy, vulnerability or resilience. This study develops a unified graph-theoretic framework for resilience-oriented analysis of cyclic peptide molecular graphs through a collection of novel connectivity-based descriptors. Oxytocin is selected as the principal case study because of its well-defined cyclic architecture and conserved disulfide bridge, four additional cyclic peptides: Vasopressin, Desmopressin, Octreotide and Somatostatin are analyzed to validate the robustness, discriminative capability and general applicability of the proposed methodology. Hydrogen-suppressed molecular graphs were constructed from experimentally established molecular structures. In addition to classical graph-theoretic measures including degree, betweenness, closeness, eigenvector centralities and network efficiency, the proposed framework introduces the Enhanced Structural Control Index (ESCI), Bond Criticality Index (BCI), Weighted Bond Criticality Index (WBCI), Disulfide Structural Redundancy Index (DSRI), Atom Vulnerability Spectrum (AVS) and Molecular Resilience Ratio (MRR). Structural robustness was further investigated by comparing targeted perturbations performed through sequential removal of the five highest-ranked AVS atoms with random vertex deletions. The oxytocin molecular graph contains 69 vertices, 71 edges and a cyclomatic number of three revealing strong dependence on a limited set of articulation points and bridge edges. AVS exhibits a strong correlation with betweenness centrality (Pearson correlation >0.91 across all investigated peptides) while providing complementary efficiency-based vulnerability information beyond conventional centrality measures. Comparative validation across five cyclic peptide molecular graphs demonstrates that the proposed descriptors consistently distinguish structural control, bond criticality, redundancy, vulnerability and resilience. The proposed framework establishes a mathematically interpretable, computationally efficient and broadly applicable methodology for graph-theoretic analysis of cyclic peptide molecular graphs with potential extensions to larger peptide systems and related biomolecular networks.},
}

@article {pmid42580226,
year = {2026},
author = {Jiang, N and Xue, Y and Peng, Y and Liu, M and Hu, J and Wang, Q and Yuan, Q and Chen, C and Yu, Y and Huang, L and Hsia, KJ and Lin, H and Wang, P and Zhuang, L},
title = {Biohybrid organoid-robot sensing for olfaction intelligence.},
journal = {Biosensors & bioelectronics},
volume = {313},
number = {},
pages = {119107},
doi = {10.1016/j.bios.2026.119107},
pmid = {42580226},
issn = {1873-4235},
abstract = {Owing to the remarkable advancements in artificial intelligence (AI), the sensory modalities in artificial systems that characterize human embodiment have received significant attention. However, olfaction remains largely absent in artificial systems, primarily due to several technological challenges. In this study, we aim to advance biomimetic olfactory processing by developing a biohybrid organoid-robot (BOR) system. This system integrates an olfactory organoid-based bioelectronic nose, machine learning (ML) decoders, and an odor-triggered robotic platform. By harnessing the sensitivity and specificity inherent in biological olfactory systems, organoid-based bioelectronic noses present a distinct advantage over traditional electronic noses, facilitating the detection of a wide spectrum of odors at low concentrations with rapid response times. Real-time ML-powered decoding of sensing signals triggers predefined actions in the robotic system, thereby establishing a perception-interpretation-actuation loop that enables the BOR system to detect environmental olfactory cues and execute corresponding physical actions. The research presented herein advances the field towards realizing the sense of smell in systems with truly embodied intelligence.},
}