@article {pmid41335287,
year = {2025},
author = {Hereth, B and de Boisboissel, G and Bricknell, MC and Brincker, M and Casebeer, W and Davidovic, J and Davis, J and Earl, J and Eisikovits, N and Feldman, D and Garcia, LF and Gilbert, F and Guérin, V and Henschke, A and Hughes, J and Lambert, D and Latheef, S and Moreno, JD and Peebles, IS and T Pham, M and Pindyck, S and Rudyak, I and Shinomiya, N and Shortland, ND and Sparrow, R and Stramondo, J and Tabouy, L and Tubig, P and Whetham, D and Evans, NG},
title = {Horizon Scan of Emerging Issues at the Intersection of National Security, Artificial Intelligence, and Human Performance Enhancement.},
journal = {Science and engineering ethics},
volume = {32},
number = {1},
pages = {3},
pmid = {41335287},
issn = {1471-5546},
mesh = {Humans ; *Artificial Intelligence/ethics/trends ; Brain-Computer Interfaces ; Military Personnel ; *Security Measures ; Politics ; Informed Consent ; *Biomedical Enhancement ; },
abstract = {Horizon scanning is intended to identify opportunities and threats associated with technology, regulatory, and social change. Here, we report the results of a new horizon scan based on inputs of an international group of 33 participants, focusing on future issues arising from the military use of artificial intelligence (AI) for augmenting human performance. The final list of 12 issues includes topics spanning from the political (educating and training individuals to accept and work with AI), to the regulatory (issues of consent to human-AI teaming and hybridization), to security (the hackability of neural devices that connect to AI), to philosophical (the nature and phenomenology of brain-to-brain interfaces). The early identification of such issues is relevant to researchers, policymakers, military practitioners, and the wider public.},
}

Humans
*Artificial Intelligence/ethics/trends
Brain-Computer Interfaces
Military Personnel
*Security Measures
Politics
Informed Consent
*Biomedical Enhancement

@article {pmid42348373,
year = {2026},
author = {Perley, AS and Coleman, TP},
title = {A Dynamic Mutual Information Measure of Phase-Amplitude Coupling with Uncertainty Quantification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3705664},
pmid = {42348373},
issn = {1558-2531},
abstract = {Phase-amplitude coupling (PAC)-in which the phase of a low-frequency rhythm modulates the amplitude of a higher-frequency oscillation-is widely observed across the brain and has been linked to cognition as well as neurological disorders including Parkinson's disease, epilepsy, and depression. Standard PAC metrics typically aggregate over long windows and return a single summary statistic, obscuring transient structure. Although recent work pursues time-resolved PAC via windowed analyses, these methods often fail to capture fast, sample-level fluctuations in coupling strength. To overcome these limitations, we introduced a dynamic state space model with a latent Gauss state space model for regression weights and a Gamma generalized linear model for measurements. Upon estimation, a mutual information measure of PAC at any time point provides our dynamic PAC formulation. Our approach yields interpretable, smoothly evolving PAC trajectories and allows multiple trials to be incorporated in a unified probabilistic framework. Our key contribution in this work is to extend this paradigm into a unified modeling framework by developing methodology for hyperparameter tuning, multi-trial PAC estimation and uncertainty quantification. To tune key hyperparameters, we introduce an expectation- maximization (EM) algorithm that uses a Laplace approximated posterior to perform tractable updates. Furthermore, we develop the ability of our model to accommodate multi-trial analyses that are ubiquitous in neuroscience, and demonstrate the ability to detect when PAC phenomena are repeatable. We further describe a Bayesian uncertainty-quantification procedure based on the Laplace approximation, enabling computation of credible intervals for every PAC trajectory and offering an explicit measure of confidence in dynamic estimates. Using synthetic data with ground-truth time-varying coupling, we show that the proposed method more accurately tracks rapid changes and discriminates coupled from uncoupled periods. Applied to human sleep EEG, the approach reliably detects PAC during spindle events-highlighting its potential relevance for biomarkers of neurophysiological disorders, including Alzheimer's disease. Overall, this dynamic PAC framework provides a flexible, statistically grounded tool for basic and clinical neuroscience, and may support future applications in adaptive neurostimulation and real-time brain-computer interfaces.},
}

@article {pmid42348934,
year = {2026},
author = {Gangolu, R and Kadambari, KV},
title = {LEGEND: Lorentzian electro-modal graph encoder for neural decoding for SCI rehabilitation.},
journal = {Computers in biology and medicine},
volume = {213},
number = {},
pages = {111830},
doi = {10.1016/j.compbiomed.2026.111830},
pmid = {42348934},
issn = {1879-0534},
abstract = {Spinal cord injury (SCI) severs voluntary motor control between cortical intent (electroencephalography, EEG, above the lesion) and peripheral muscle activity (electromyography, EMG, below it), leaving spinal circuit signals (electrospinography, ESG) as the critical intermediate. A viable neural bypass must jointly model all three tiers of the motor hierarchy. We introduce LEGEND (Lorentzian Electro-modal Graph Encoder for Neural Decoding), a two-stage architecture that encodes EEG/ESG/EMG in Lorentz hyperboloid space via a shared TriModalLorentzNet, connects the 51 channel nodes through a signed tri-layer Phase-Locking Value (PLV) graph, and refines embeddings with a class-prototype-conditioned HyperbolicGraphAttentionHead. On the Steele dataset Steele et al. (2023) [1], the only public simultaneous tri-modal motor corpus under strict leave-one-subject-out (LOSO) evaluation, LEGEND achieves 56.51%±12.27%, a +23.4 pp gain over EEGNet (33.11%) and +22.5 pp over ShallowConvNet (33.97%) (p<0.01, Wilcoxon), while outperforming three recent graph-neural-network baselines adapted to the same tri-modal 51-channel input: BrainTopoGCN (35.84%), EEG-GLT-Net (30.63%), and SAMGCN (44.82%), all p≤0.007. An Input-GradCAM extractor identifies class-discriminative EEG→ESG→EMG functional chains (mean pairwise top-5 Jaccard = 0.207). Evaluating the EEG encoder alone on standard motor-imagery benchmarks: we provide the strict cross-subject LOSO evaluation on BCI-IV-2a (9 subjects, 4-class motor imagery, MI), where LEGEND achieves 42.9%±7.0%, outperforming EEGNet-LOSO (35.1%), ShallowConvNet-LOSO (37.8%), and all three graph baselines reimplemented under the same protocol: BrainTopoGCN (38.8%), EEG-GLT-Net (25.2%), and SAMGCN (28.9%). On PhysioNet (109 subjects, binary MI), 80.50%±10.48% LOSO, competitive with EEG-Conformer (81.3%) despite the stricter protocol. Taken together, these results demonstrate that Lorentzian hyperbolic representations are broadly beneficial across EEG-only and tri-modal brain-computer interface (BCI) paradigms, and most importantly provide the validated computational foundation for a neural bypass targeting spinal cord injury rehabilitation.},
}

@article {pmid42349111,
year = {2026},
author = {Zhang, JJ and Lin, R and Zeng, RR and Tang, M and Chan, SSY and Zhang, BB and Sun, R and Kranz, GS and Lau, SCL and Lührs, M and Fong, KNK and Mathiak, K and Mehler, DMA and Lau, GKK},
title = {Using a functional near-infrared spectroscopy-guided brain-computer interface to facilitate observational imitation after stroke.},
journal = {Annals of physical and rehabilitation medicine},
volume = {69},
number = {6},
pages = {102149},
doi = {10.1016/j.rehab.2026.102149},
pmid = {42349111},
issn = {1877-0665},
abstract = {BACKGROUND: A brain-computer interface (BCI) shows promise for facilitating motor imagery (MI) during observational imitation motor relearning of the upper extremity in people after stroke.

OBJECTIVE: To investigate the efficacy and mechanisms of a functional near-infrared spectroscopy (fNIRS)-based BCI in augmenting MI during observational imitation for poststroke upper extremity rehabilitation.

METHODS: A randomized trial was conducted among participants after stroke. In the real fNIRS-BCI group, participants engaged in kinesthetic MI. When the activation level over the corticomotor areas recorded by fNIRS surpassed a predefined threshold, an instructional video showing the target movement was triggered, and participants were instructed to observe and imitate the movement. The sham group received feedback at constant intervals without being contingent on individual brain signals. Upper extremity motor tests and mirror visual feedback (MVF)-induced sensorimotor event-related desynchronization (ERD) were assessed before and after intervention. MI-induced oxygenated hemoglobin (HbO) concentrations were extracted from participants receiving fNIRS-BCI.

RESULTS: Forty-four participants were enrolled. Observational imitation with or without BCI was effective in enhancing upper extremity function. However, there were no between-group differences in upper extremity motor improvement. fNIRS-BCI-driven observational imitation significantly enhanced MVF-induced beta sensorimotor ERD bilaterally more than sham BCI did. In participants receiving fNIRS-BCI, the capacity to upregulate MI-induced HbO over the ipsilesional sensorimotor cortex and supplementary motor area was significantly enhanced post-intervention.

CONCLUSION: fNIRS-BCI shows promise for monitoring real-time brain activity during rehabilitation and enhancing the participants' ability to upregulate corticomotor activity through neurofeedback; however, it did not yield superior benefits in upper extremity measures. fNIRS-BCI may improve brain responsiveness to visual feedback after stroke. Future research should determine how these neurophysiological effects can be translated into better clinical outcomes.

TRIAL REGISTRATION: NCT06503484.},
}

@article {pmid42349602,
year = {2026},
author = {Pan, Y and Peng, J and Li, X and Jin, X and Yun, L and Chen, Z},
title = {Adaptive reconfiguration of prefrontal networks during prolonged cognitive interference: Evidence from fNIRS.},
journal = {Brain research},
volume = {},
number = {},
pages = {150445},
doi = {10.1016/j.brainres.2026.150445},
pmid = {42349602},
issn = {1872-6240},
abstract = {In the era of information overload, understanding the brain's adaptive responses to prolonged cognitive tasks is critical. This study investigates the neural compensatory mechanisms that sustain cognitive performance under mental fatigue, offering insights into dynamic resource allocation and practical applications in high-demand settings. Twenty healthy participants performed a Stroop-based cognitive interference task while prefrontal hemodynamic activity was monitored using functional near-infrared spectroscopy (fNIRS). Subjective fatigue was assessed via the Multidimensional Fatigue Inventory (MFI-20), NASA Task Load Index (NASA-TLX), and Visual Analogue Scale (VAS). Behavioral performance (reaction time and accuracy) was recorded simultaneously. Neural activation was analyzed using a Generalized Linear Model (GLM), and functional connectivity alongside network topology metrics (global efficiency, clustering coefficient) were evaluated. Results show that subjective fatigue increased significantly post-task (MFI-20, p < 0.05), with progressive rise in VAS scores. Behaviorally, reaction times decreased while accuracy remained stable, indicating a speed-accuracy trade-off. fNIRS revealed marked activation changes in specific prefrontal regions (e.g., CH1, CH7), with overall activation shifting from positive to negative. This pattern may reflect time-dependent modulation of task-evoked activation and could be associated with multiple factors, including fatigue-related changes in engagement, habituation effects, or resource-related processes. In addition, fatigue accumulation was accompanied by increased functional connectivity between the frontal eye fields (FEF) and dorsolateral prefrontal cortex (DLPFC) (F = 4.61, p = 0.008), as well as between the frontopolar area (FPA) and DLPFC (F = 3.74, p = 0.020). Global efficiency (F = 0.169, p = 0.022) and clustering coefficient (F = 0.177, p = 0.008) also showed significant increases across task progression.Together, these findings may indicate time-dependent modulation of prefrontal network organization during prolonged cognitive interference tasks. Rather than reflecting a single mechanism, these changes could be associated with dynamic adjustments in functional coordination under sustained task demands. The present findings may provide preliminary neurophysiological evidence relevant to neuroergonomics, brain-computer interfaces, and cognitive workload management.},
}

@article {pmid42351204,
year = {2026},
author = {Sun, H and Ding, M and Shan, X and Xie, S and Chang, D and Zuo, N and Cai, Z},
title = {Decoding motor imagery related to major mimetic muscles from electroencephalography.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-02065-9},
pmid = {42351204},
issn = {1743-0003},
support = {PKUSS-2023CRF102//Peking University School and Hospital of Stomatology/ ; 2022-2-4102//Beijing Municipal Health Commission/ ; },
abstract = {BACKGROUND: Functional and aesthetic deficits in individuals with facial nerve paralysis (FNP) significantly impair their quality of life. By decoding motor intentions and controlling rehabilitation devices, motor imagery (MI)-based brain-computer interfaces can improve outcomes in people with peripheral paralysis. However, the electroencephalography (EEG) features underlying different facial MIs and their decodability remain unclear. This study aims to investigate the feasibility of decoding facial MIs related to major mimetic muscles and to explore suitable decoding strategies.

METHODS: Following a preliminary comparison between block and event-related designs, 20 healthy participants performed four types of facial MIs (eyebrow raising, eye closing, lip puckering and grinning) in two modalities: kinesthetic and visual, from which event-related desynchronization/synchronization (ERD/S) features were extracted using time-frequency analysis. A deep learning model was then developed for both within-subject and cross-subject decoding and benchmarked against three mainstream EEG decoding models (EEGNet, DeepConvNet, and ShallowConvNet). Finally, the proposed decoding strategy was further evaluated on EEG data from six individuals with FNP.

RESULTS: Facial MIs induced prominent low-frequency ERD in the left prefrontal and right central-temporal regions, co-occurring with shorter and weaker ERS in higher frequencies. Regarding MI decoding, the model outperformed the baselines and achieved the highest average accuracy of 85.17% in within-subject classification of kinesthetic MI, with EEG features from the left frontal and parietal regions contributing more to decoding. Using this strategy, evaluation on patients yielded an average accuracy of 83.81% with halved training data, remaining at 76.46% after further channel reduction.

CONCLUSION: This study demonstrated that major mimetic muscle-related MIs can be accurately recognized from EEG using deep learning, with within-subject classification of kinesthetic MI representing the most effective decoding strategy among the evaluated conditions.},
}

@article {pmid42351857,
year = {2026},
author = {Muksimova, S and Iskhakova, N and Cho, YI},
title = {Unified Temporal-Spectral-Spatial Modeling for Robust and Generalizable Motor Imagery Brain-Computer Interfaces.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {6},
pages = {},
doi = {10.3390/bioengineering13060612},
pmid = {42351857},
issn = {2306-5354},
abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) have led to great interest as a result of their potential use in neurorehabilitation, assistive robotics, and human-computer interaction. However, decoding electroencephalographic (EEG) signals with high accuracy continues to be a difficult task due to the weak signal-to-noise ratio, differences among subjects, and the complicated temporal-spectral-spatial neural dynamics. Deep learning methods recently developed, such as convolutional neural networks, recurrent architectures, graph neural networks, and adversarial transfer learning, have enhanced MI decoding performance, yet many models are still concentrating on a single representation domain or they need costly adaptation phases in terms of computation. To tackle these shortcomings, we present NeuroCrossNet, a unified tri-modal deep learning model that is able to learn the temporal, spectral, and spatial EEG features jointly for robust and calibration-free MI decoding. The suggested network combines a Temporal HyperMixer Block for capturing long-range temporal dependencies, a wavelet transformer for learning localized time-frequency representation, and a Graph Attention Network for EEG topology-aware spatial reasoning. Additionally, a Dynamic Residual Attention Gate (DRAG) has been developed to adaptively merge heterogeneous feature streams, and a compact subject-aware normalization (SAN) method enhances cross-subject generalization without the use of labeled target-domain calibration data. Our proposed model was tested following the rigorous leave-one-subject-out (LOSO) approach on BCI Competition IV-2a and High-Gamma datasets. NeuroCrossNet reached a classification accuracy of 91.30%, surpassing several strong benchmark methods, including CNN-LSTM, EEGNet, DeepConvNet, spectral CNN, and graph-based EEG decoding frameworks. Furthermore, a large number of ablation studies reveal that the integration of temporally, spectrally, and spatially complementary representations considerably boosts robustness and inter-subject consistency.},
}

@article {pmid42352561,
year = {2026},
author = {Hu, S and Wang, F and Gao, X and Zhi, Y and Kim, D},
title = {Effects of Brain-Computer Interface-Controlled Hand Robot Training on Post-Stroke Recovery of Upper Limb Motor Functions: A Meta-Analysis of Dose-Matched Randomized Controlled Trials.},
journal = {Brain sciences},
volume = {16},
number = {6},
pages = {},
doi = {10.3390/brainsci16060552},
pmid = {42352561},
issn = {2076-3425},
abstract = {Objective: To systematically evaluate the rehabilitation effect of brain-computer interface (BCI)-controlled hand robot training on post-stroke motor functions, especially upper limb functions. Methods: PubMed, Embase, Web of Science, Cochrane Library, CNKI, SinoMed, WanFang Data, and VIP Database were searched from inception to 13 March 2026. Randomized controlled trials (RCTs) with dose-matched designs were included, where the test group underwent BCI-controlled hand robot training and the control group received either pure hand robot training or routine rehabilitation. Meta-analysis was performed on RevMan 5.4. Results: Totally 11 RCTs involving 380 patients were included. Compared with hand robot training alone, BCI-controlled hand robot training significantly improved Fugl-Meyer Assessment for Upper Extremity (FMA-UE) scores (MD = 4.87, 95% CI: 1.04 to 8.69) and FMA-UE proximal scores (MD = 4.44, 95% CI: 0.15 to 8.74), and significantly reduced finger flexor spasticity (MD = -0.44, 95% CI: -0.68 to -0.21), but showed no significant difference in distal upper limb motor function or Action Research Arm Test (ARAT) scores. Compared with routine rehabilitation, BCI-controlled hand robot training significantly improved FMA-UE scores (MD = 6.55, 95% CI: 3.49 to 9.61). Conclusions: BCI-controlled hand robot training can effectively improve overall upper limb and proximal motor function after stroke and alleviate finger flexor spasticity, but the evidence for distal hand function and long-term efficacy remains limited.},
}

@article {pmid42352598,
year = {2026},
author = {He, J and Mao, T and Sun, Y and Guan, L},
title = {Application of Brain-Computer Interface Technology in Vascular Cognitive Impairment: A Systematic Review.},
journal = {Brain sciences},
volume = {16},
number = {6},
pages = {},
doi = {10.3390/brainsci16060589},
pmid = {42352598},
issn = {2076-3425},
support = {82271516//National Natural Science Foundation of China/ ; 81801187//National Natural Science Foundation of China/ ; },
abstract = {Background: Vascular cognitive impairment (VCI) is a common consequence of cerebrovascular diseases and significantly affects multiple cognitive domains. Brain-computer interface (BCI) technology has emerged as a promising tool for cognitive assessment and rehabilitation in patients with VCI. This systematic review examined the current applications of BCI technology for cognitive function in patients with VCI. Methods: In accordance with the PRISMA 2020 guidelines, we searched Medline, PubMed, Web of Science, Embase, and the Cochrane Central Register of Controlled Trials. We included studies published between January 2000 and March 2026 that evaluate BCI for cognitive function in patients with VCI. Results: A total of 30 studies were included in the review. The participants comprised 696 stroke patients, 71 patients with early cerebral microangiopathy and 128 patients with VCI and no dementia. In patients with VCI, BCI interventions combined with other technologies (e.g., exoskeleton, virtual reality, functional electrical stimulation, acupuncture, or game-based cognitive training) appeared more effective for cognitive rehabilitation than BCI alone. Attention was the most consistently improved domain among the studies reviewed. Global cognitive function also improved in many studies, though not uniformly. Memory, executive function, and language outcomes varied depending on factors such as intervention protocols, training duration, and assessment tools. Conclusions: BCI is a promising tool for cognitive assessment and rehabilitation in patients with VCI. However, substantial heterogeneity across studies limits the conclusions. Future large-scale, well-designed randomized controlled trials with standardized outcome measures are needed to validate the efficacy of BCI technology and to explore its underlying mechanisms.},
}

@article {pmid42352601,
year = {2026},
author = {Chen, Y and Sun, W and Meng, M},
title = {Self-Rectifying Integrate-and-Fire Neuron and Collaborative Trim Training Framework for SNN-Based EEG Motor Imagery Classification.},
journal = {Brain sciences},
volume = {16},
number = {6},
pages = {},
doi = {10.3390/brainsci16060592},
pmid = {42352601},
issn = {2076-3425},
support = {62271181//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Spiking neural networks (SNNs) have attracted significant attention in the field of brain-computer interfaces owing to their distinctive biological plausibility and energy efficiency advantages. However, the discrete nature of spikes renders gradient-based differentiation infeasible, making it difficult to directly obtain well-trained SNNs. A common approach is to transfer the weights from artificial neural networks (ANNs) to SNNs. However, this process introduces conversion errors that pose significant challenges.

METHODS: To address these challenges, we propose the self-rectifying integrate-and-fire (SRIF) neuron, which employs negative spikes to reduce asynchronism error and rectification spikes to diminish clipping error. Concomitantly, we propose a collaborative trim (CT) training framework that introduces a quantized network to perceive the weights and results of SNNs, which can further improve performance.

RESULT: The proposed training methodology enables SNNs to achieve performance metrics comparable to those of ANNs in EEG-based motor imagery (MI) classification.

CONCLUSIONS: Experimental results demonstrate that our method not only preserves the superior classification performance of ANNs but also leverages the superior energy efficiency and lower computational complexity of SNNs.},
}

@article {pmid42352658,
year = {2026},
author = {Xu, Y and Otsuka, S and Nakagawa, S},
title = {Enhancing EEG-Based Brain Pattern Recognition Through Functional-Network-Level Volume Conduction Mitigation: Spatially Informed Decay Modeling-Residual Correction.},
journal = {Brain sciences},
volume = {16},
number = {6},
pages = {},
doi = {10.3390/brainsci16060649},
pmid = {42352658},
issn = {2076-3425},
support = {JP24K03260//Japan Society for the Promotion of Science/ ; JPMJSP2109//Japan Science and Technology Agency/ ; },
abstract = {Background/Objectives: Advancements in neuroscience and machine learning have increasingly enabled brain pattern recognition based on bio-signal measurements, such as electroencephalography (EEG). These developments support next-generation technologies, including brain-computer interfaces (BCIs) and AI-assisted systems. However, volume conduction (VC) effects remain a major source of contamination in EEG recordings, affecting both univariate analyses and functional connectivity estimation. Methods: In this work, we propose a VC mitigation method that explicitly models and suppresses VC components in the observed functional networks. Specifically, the observed functional network is decomposed into a matrix capturing only VC-related components (i.e., components attributed to volume conduction) and a residual matrix, where the residual is regarded as a proxy for a VC-mitigated functional network that better reflects the underlying functional interactions. The VC component matrix is modeled using a decay function parameterized by the inter-electrode distance matrix, capturing the dominant spatial bias induced by VC. To estimate these parameters, we introduce supervised channel importance, quantified as the mutual information between experimental labels and channel signals, as a proxy for task-relevant neural activity. The parameters are optimized such that the unsupervised node importance derived from the VC-mitigated functional network, defined as the average node strength, aligns with the supervised channel importance. Results: Evaluation results using a deep-learning framework demonstrate that, compared with the observed functional network, the VC-mitigated functional network improves classification performance in brain pattern recognition tasks.},
}

@article {pmid42354728,
year = {2026},
author = {Zhang, Y and Meng, X and Lu, C and He, Y and Liang, X},
title = {Miniaturized Wearable System for Multimodal EEG/ECG/EMG Sensing and Real-Time Physiological Monitoring.},
journal = {Micromachines},
volume = {17},
number = {6},
pages = {},
doi = {10.3390/mi17060697},
pmid = {42354728},
issn = {2072-666X},
support = {52403174, 52130302, 22475048, 22305042//National Natural Science Foundation of China/ ; 22A0138//Hunan Provincial Department of Education/ ; 2025A0505010014, 2020A1515110288, 2025A1515011154//Natural Science Foundation of Guangdong Province/ ; S2023JJQNJJ1176, 2023JJ40655, 2025JJ40038//Natural Science Foundation of Hunan Province/ ; RCBS20210609103713046, JCYJ20250604191212016//Shenzhen Science and Technology Program/ ; CAAS-ASTIP-IBFC//Chinese Academy of Agricultural Sciences/ ; //Postdoctoral Research Start-up Funds of Dapeng New District and Shenzhen City/ ; },
abstract = {Real-time physiological state awareness is central to next-generation wearable computing, yet most existing electrophysiological signal acquisition platforms remain limited to single-modality sensing, high component cost, or bulky form factors that hinder everyday deployment. Here, we present a compact, low-cost wearable platform for simultaneous electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) acquisition. The system integrates an analog front-end, a microcontroller, and a Bluetooth wireless link on a compact single-board platform (5.6 × 3.8 cm, approximately 12.8 g with the selected lithium-polymer battery installed), with an estimated bill-of-materials cost of 67.40 USD. Experimental validation across three healthy subjects, with the ECG channel additionally benchmarked against a commercial clinical-grade ambulatory ECG recorder, demonstrates that the platform captures ECG waveforms with recognizable P-QRS-T morphology under controlled recording conditions, supports reliable R-peak detection and heart rate estimation, records stable resting-state EEG spectral features, and distinguishes EMG activation from resting baseline in both time-domain amplitude and time-frequency structure. Leveraging the real-time wireless data link between the wearable hardware and a PC-hosted MATLAB environment, we further explore application-oriented signal processing scenarios. As an offline algorithm-pipeline compatibility demonstration, a CNN-based seizure detection pipeline is applied to the Bonn EEG benchmark for five-class epileptic state classification, achieving 86.60% mean classification accuracy. The proposed system offers a scalable and affordable foundation for wearable human-state-aware interaction, with potential applications in clinical monitoring, rehabilitation, and brain-computer interfaces.},
}

@article {pmid42355337,
year = {2026},
author = {Yang, K and Wu, X and Chen, Y and Ma, W and Lin, Y and Ma, X and Zhang, J},
title = {Cold Tolerance and Differential Expression of Cuticular Protein Genes in Sungaya inexpectata Zompro, 1996 (Insecta: Phasmatodea).},
journal = {Insects},
volume = {17},
number = {6},
pages = {},
doi = {10.3390/insects17060604},
pmid = {42355337},
issn = {2075-4450},
abstract = {Sungaya inexpectata is a tropical stick insect endemic to the Philippines, providing a useful system for investigating cold responses in tropical ectotherms. In this study, we exposed individuals to low temperature (8 °C) and normal temperature (25 °C) and characterized their transcriptomic responses. A total of 1656 differentially expressed genes were identified, including those involved in energy metabolism, cuticular proteins (CPs), and heat shock proteins. Since CP-related genes were notably enriched, we focused on this family. qPCR assessment provided preliminary expression profiles for selected candidate CP genes. Using comparative transcriptomics with eight New Zealand alpine stick insect species, we reconstructed the phylogeny of major CP families and annotated their conserved domains. Clade analysis revealed significant positive selection in the CPAP3-3 gene. In summary, this study reveals the transcriptional response of cuticular protein genes in S. inexpectata under cold exposure at 8 °C. These findings provide preliminary transcriptional data for understanding how this species responds to low temperature.},
}

@article {pmid42356668,
year = {2026},
author = {Guo, J and Pan, X and Mi, N and Zhang, J and Huyan, T},
title = {Personalized Adaptive Gabor Filtering with Three-Stage Semi-Supervised Domain-Adversarial Learning for Cross-Subject SSVEP Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {12},
pages = {},
doi = {10.3390/s26123694},
pmid = {42356668},
issn = {1424-8220},
support = {2025B-215//Innovation Fund Project for University Teachers in Gansu Province/ ; 25YFGM002//Key Research and Development Program of Gansu Province-Industrial Project/ ; 2026QB-096//Scientific Research Projects of Higher Education Institutions in Gansu Province/ ; 2025JY1005//Qingyang City Major Science and Technology Project-Industrial Field Project/ ; XYBYZK2510//Doctoral Fund Project of Longdong University/ ; LYJYJX2026A45//Longdong University Higher Education Teaching Research 2026 Key Project/ ; HXZK2547//Crossing Research Project of Longdong University/ ; },
mesh = {*Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; Humans ; Neural Networks, Computer ; Algorithms ; Electroencephalography ; Transfer Machine Learning ; Adaptive Algorithms ; Calibration ; },
abstract = {Improving the decoding accuracy and information transfer rate (ITR) of steady-state visual evoked potential brain-computer interface (SSVEP-BCI) systems, while enhancing cross-subject generalization and reducing calibration cost, is essential for practical deployment. This study proposes an end-to-end framework that integrates adaptive filtering with semi-supervised domain adaptation. The framework incorporates a Gabor adaptive filter bank (G-AFB) to optimize time-frequency representations and extract features matched to individual neural responses. It also introduces a three-stage semi-supervised domain-adversarial neural network (TriS-DANN), which combines unsupervised pre-alignment and supervised fine-tuning to align cross-subject feature distributions and enable lightweight calibration. On the 1.0 s public benchmark dataset, G-AFB-tCNN achieved 89.13% accuracy, a 4.63 percentage-point improvement over its conventional filter-bank counterpart. On the 0.4 s in-house dataset, G-AFB-tCNN achieved 91.85% accuracy, a 3.22 percentage-point improvement over the conventional fixed filter bank. In transfer learning, TriS-DANN reached 86.60% accuracy using 0.4 s segments extracted from the stimulation period and only 23.07% of the available target-domain training/calibration trials, demonstrating higher efficiency and stability than conventional fine-tuning. These results support the proposed framework as a feasible route toward reliable, low-calibration SSVEP-BCI systems.},
}

*Evoked Potentials, Visual/physiology
*Brain-Computer Interfaces
Humans
Neural Networks, Computer
Algorithms
Electroencephalography
Transfer Machine Learning
Adaptive Algorithms
Calibration

@article {pmid42356699,
year = {2026},
author = {Liu, Y and Hu, Q and Wang, X and Herath, D and Wang, M},
title = {Rethinking Brain-Computer Interfaces for Soft Robotic Systems: A Unified Framework and Perspective.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {12},
pages = {},
doi = {10.3390/s26123726},
pmid = {42356699},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; *Robotics/methods ; Humans ; Soft Computing ; Intelligent Systems ; },
abstract = {Soft robotics enables inherently safe, compliant interaction, yet integrating brain-computer interfaces (BCIs) remains hindered by a fundamental mismatch: BCIs typically output low-bandwidth, discrete commands, whereas soft robots possess high-dimensional, nonlinear dynamics. In this position paper, we argue that BCI-soft robot integration must move beyond direct decoder-to-actuator mapping. We propose a unified, application-oriented compatibility framework that structurally decouples hierarchical control and formally allocates authority between human neural input and local soft robotic autonomy. Crucially, we introduce verifiable, quantitative design principles that define integration as a matching problem across neural bandwidth, update frequency, latency tolerance, and control dimensionality. Through these testable hypotheses, we demonstrate that active, reactive, and passive BCIs serve distinct, complementary roles. We conclude that shared-control strategies-where the BCI provides high-level intent, target selection, or user-state feedback, while the soft robot manages low-level physical execution and interaction-offer the most practical pathway forward. We argue that future progress depends on the co-design of paradigm, decoding, control, and embodiment for neuro-adaptive and human-centred soft robotic systems.},
}

*Brain-Computer Interfaces
*Robotics/methods
Humans
Soft Computing
Intelligent Systems

@article {pmid42356896,
year = {2026},
author = {Bibbò, L and Laganà, F and Pullano, SA and Angiulli, G},
title = {Multimodal EEG-EMG and FEM-Based Adaptive Control of Passive Upper-Limb Exoskeletons.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {12},
pages = {},
doi = {10.3390/s26123924},
pmid = {42356896},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Electromyography/methods ; *Exoskeleton Device ; Finite Element Analysis ; *Upper Extremity/physiology ; Male ; Robotics ; Biomechanical Phenomena ; Adult ; Neural Networks, Computer ; },
abstract = {Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG-EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN-LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user's physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque-angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device's intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain-machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human-robot applications.},
}

Humans
*Electroencephalography/methods
*Electromyography/methods
*Exoskeleton Device
Finite Element Analysis
*Upper Extremity/physiology
Male
Robotics
Biomechanical Phenomena
Adult
Neural Networks, Computer

@article {pmid42358667,
year = {2026},
author = {Rabbani, H and Das, I and Mandal, AS and Nelson, T and Hadar, P and Hsueh, B and Ciordia, R and Coughlin, BF and Peng, E and Cleary, DR and Collins, KL and Raslan, AMT and Williams, ZM and Choi, BD and Cosgrove, GR and Dayeh, SA and Jones, PS and Tiwana, HK and Dunn, GP and Richardson, RM and Bi, WL and Tobochnik, S and Cash, SS and Cahill, DP and Paulk, AC},
title = {A modular and flexible pipeline for intraoperative electrode reconstruction and localization in patients with brain lesions.},
journal = {Frontiers in neural circuits},
volume = {20},
number = {},
pages = {1814667},
pmid = {42358667},
issn = {1662-5110},
mesh = {Humans ; Magnetic Resonance Imaging/methods ; *Intraoperative Neurophysiological Monitoring/methods ; *Electrodes, Implanted ; *Electrocorticography/methods ; *Brain Mapping/methods ; Brain Neoplasms/surgery ; *Image Processing, Computer-Assisted/methods ; Female ; *Brain/surgery/diagnostic imaging ; },
abstract = {Intraoperative intracranial electrophysiological recordings provide unique access to human cortical dynamics but remain difficult to translate across patients due to inconsistent localization of transient surface electrodes. Unlike chronic implantations, intraoperative electrodes are placed transiently, rarely visible on imaging, and often inconsistently documented. We present an open-source imaging pipeline, ALIGNER (Advanced Localization and Imaging Guidance for Neurosurgical Electrode Recording), designed to reconstruct intraoperative surface electrode array placements and quantitatively map neural activity to individualized anatomical and pathological substrates. By enabling anatomical localization of these electrodes, this framework supports systematic analysis of spatial gradients in neural activity relative to pathological tissue. We developed a multimodal reconstruction framework integrating pre- and postoperative MRI and CT, cortical surface modeling, semi-automated pathology segmentation, intraoperative photographs or videos when available, and physics-based electrode modeling. To improve robustness in cases with distorted anatomy, artificial intelligence tools such as SynthSR were used to enable reliable cortical surface reconstruction prior to FreeSurfer processing. A monocular depth-estimation network was incorporated to constrain electrode placement in conjunction with Blender cloth-physics simulation when photographic images were available, while atlas- and note-guided inference supported reconstruction otherwise. The pipeline was applied to 38 neurosurgical patients across drug-resistant epilepsy resection (n = 24), malformation (n = 1), brain tumor (n = 11), and deep brain stimulation (n = 2) cases, achieving some type of reconstruction and electrode localization in all participants. By exporting electrode coordinates for quantitative spatial analyses, including distance-based mapping relative to lesions and resection cavities, ALIGNER enables anatomically grounded and reproducible analysis of intraoperative electrophysiology. This open-source framework provides foundational infrastructure for cancer neuroscience studies of tumor-neuron interactions and establishes a scalable platform for future neurostimulation, implantable neurodevice, and brain-computer interface applications requiring precise anatomical localization.},
}

Humans
Magnetic Resonance Imaging/methods
*Intraoperative Neurophysiological Monitoring/methods
*Electrodes, Implanted
*Electrocorticography/methods
*Brain Mapping/methods
Brain Neoplasms/surgery
*Image Processing, Computer-Assisted/methods
Female
*Brain/surgery/diagnostic imaging

@article {pmid42359044,
year = {2026},
author = {Solé-Casals, J and Ahn, S and He, B},
title = {Editorial: Deep learning in brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1888360},
doi = {10.3389/fnhum.2026.1888360},
pmid = {42359044},
issn = {1662-5161},
}

@article {pmid42359634,
year = {2026},
author = {Zhang, K and Chen, S and Zheng, M and Suo, Y and Yu, J and Zhang, X},
title = {Computational Simulation and Experimental Validation of Electric Field Distribution Patterns in TTFields Therapy for Lung Cancer.},
journal = {Bioelectromagnetics},
volume = {47},
number = {5},
pages = {e70064},
doi = {10.1002/bem.70064},
pmid = {42359634},
issn = {1521-186X},
support = {LR23E070001//Natural Science Foundation of Zhejiang Province/ ; },
mesh = {*Lung Neoplasms/therapy ; Animals ; *Computer Simulation ; Female ; Male ; Humans ; *Electric Stimulation Therapy/methods/instrumentation ; Mice ; Electricity ; },
abstract = {Tumor treating fields (TTFields) is a non-invasive therapeutic technology that disrupts mitotic division via intermediate-frequency alternating electric fields. For non-small cell lung cancer (NSCLC) at 150 kHz, complex thoracic anatomy and heterogeneous tissue properties often hinder the attainment of the therapeutic threshold (≥ 1 V/cm). To overcome this, high-fidelity Duke (male) and Ella (female) anatomical models were employed for full-wave simulations. The coordinated deployment of orthogonal transducer arrays (AP-20, LR-20, LR-13) with sex-specific tuning substantially enhanced electric-field coverage in the lower and lateral lung regions. Furthermore, modifying lung dielectric parameters by 20% demonstrated that these configurations maintain stable therapeutic coverage, exhibiting robustness against potential physiological or pathological variations. To provide an experimental foundation, in vivo murine measurements were conducted. Rather than attempting to replicate deep spatial complexities of the human body, these experiments served as a translational bridge to validate macroscopic voltage transfer efficiency and system-level losses. By introducing a physically derived correction factor (Roi) to account for voltage delivery drops, statistical analyses confirmed a high agreement between simulated and in vivo datasets, verifying the reliability of the computational framework. Regarding safety, the computed electrode-skin current density remained strictly below 31 mA/cm[2], which, alongside built-in clinical hardware temperature limits, effectively mitigates the risk of thermal and stimulation-induced injuries. Ultimately, this optimized strategy provides a complementary, independent physical modality that integrates bioelectromagnetic modeling with preclinical validation, offering a reliable theoretical reference to facilitate individualized NSCLC treatment planning.},
}

*Lung Neoplasms/therapy
Animals
*Computer Simulation
Female
Male
Humans
*Electric Stimulation Therapy/methods/instrumentation
Mice
Electricity

@article {pmid42360145,
year = {2026},
author = {Ji, Y and Li, T and Lei, G and Luo, H and Guo, X and Xiang, J and Shi, T and Liu, W and Zhang, Y and Liao, X and Zhao, S and Wu, J and Zhang, W and Liu, W and He, C and Chen, S and Wu, T and Ma, K},
title = {Closed-Loop Decoding and Intervention of Pain: A Novel BMI Strategy Integrating θ-Band Detection and Mechano-Electro-Biological Coupled Hydrogels.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e76269},
doi = {10.1002/advs.76269},
pmid = {42360145},
issn = {2198-3844},
support = {82371224//National Natural Science Foundation of China/ ; 32271412//National Natural Science Foundation of China/ ; 32401133//National Natural Science Foundation of China/ ; 32071350//National Natural Science Foundation of China/ ; 82401432//National Natural Science Foundation of China/ ; 82272486//National Natural Science Foundation of China/ ; 82572771//National Natural Science Foundation of China/ ; 24YF2701400//Shanghai Municipal Commission of Science and Technology/ ; JYZZ289//Fundamental research program funding of Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine/ ; 20230103//Major Project of Shanghai Jiao Tong University's "STAR" Program/ ; },
abstract = {Neuropathic pain (NP) lacks objective biomarkers and effective therapies. We developed a closed-loop brain-machine interface (BMI) using a novel hydrogel electrode for integrated NP management. Clinically, we identified enhanced prefrontal θ-band (4-8 Hz) activity as a specific NP biomarker. To achieve stable long-term recording, we engineered a PFAPT hydrogel with superior biocompatibility, anti-swelling, and low impedance. In rats, it enabled stable electrocorticography (ECoG) acquisition for 28 d, outperforming screw electrodes, and validated θ-band alterations in NP. Incorporated into a closed-loop BMI, the hydrogel delivered θ-triggered cortical stimulation, which increased pain thresholds and alleviated anxiety- and depression-like behaviors in NP rats. Spatial transcriptomics revealed that stimulation bidirectionally regulated inflammation, central sensitization, and affective pathways. This work presents a hydrogel-based closed-loop BMI as a precise diagnostic and therapeutic platform for NP.},
}

@article {pmid41627542,
year = {2026},
author = {Villa-Muñoz, MC and Blázquez-Moreno, MC and Meza-Montenegro, MM and Lara-Reséndiz, RA and Ortega-Rubio, A and Zenteno-Savín, T},
title = {Biochemical responses in tail tissue of the insectivorous lizard Uta stansburiana in agricultural areas.},
journal = {Ecotoxicology (London, England)},
volume = {35},
number = {2},
pages = {41},
pmid = {41627542},
issn = {1573-3017},
abstract = {Agricultural expansion and pesticide use pose significant threats to wildlife. Reptiles are often overlooked in ecotoxicological studies despite their ecological importance. This study assessed the biochemical responses in the side-blotched lizard (Uta stansburiana) by analyzing tail tissue across three agricultural sites (C1: agave and citrus crops, C2: corn and alfalfa, C3: vegetables) and a reference site in La Paz, Baja California Sur, Mexico. Soil samples were analyzed for organochlorine pesticide concentrations. A total of 15 individuals per site were captured (55 adults and 5 juveniles). Morphometric parameters showed that adult males and females from C2 were significantly larger and heavier than those from C1 and C3, and body condition index (BCI) was significantly higher in adult males from C2. Tail tissue was used to measure glutathione S-transferase (GST) activity, acetylcholinesterase (AChE) activity, superoxide anion (O2•−) production rate, and protein carbonyl concentrations. Results revealed the presence of the pp’-DDT metabolite pp’-DDE in an agricultural site (C1), indicating historical contamination. GST activity was significantly higher in the agricultural areas compared to the reference site, suggesting a detoxification response. No significant differences were found in AChE activity or O2•− production rate between sites. Protein carbonyl levels were higher in males than in females, reflecting sex-specific oxidative damage. This study supports the utility of tail tissue as a minimally invasive method, emphasizes the importance of considering sex-specific responses in ecotoxicology, and highlights the need for improved pesticide monitoring and regulation in agricultural landscapes.},
}

@article {pmid41894135,
year = {2026},
author = {Barrio-Pujante, A and Blasco, L and Bleriot, I and Fernández-García, L and Ibarguren-Quiles, C and Arman, L and Aracil, B and López-Cortes, LE and Menéndez-Rodríguez, O and Fernández-Grela, P and Vázquez, MP and Tomás, M},
title = {Auxin (Indole-3-acetic acid) modulation of quorum sensing enhances phage susceptibility in Klebsiella pneumoniae.},
journal = {European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology},
volume = {},
number = {},
pages = {},
pmid = {41894135},
issn = {1435-4373},
abstract = {PURPOSE: Klebsiella pneumoniae is a multidrug-resistant (MDR) bacterium that has emerged as a major global public health threat. The decreasing effectiveness of conventional antibiotics has renewed interest in bacteriophage therapy as a promising alternative antibacterial strategy. However, the rapid emergence of phage resistance remains a critical limitation. This study aimed to investigate whether indole-3-acetic acid (IAA) can modulate bacterial quorum sensing (QS) and enhance phage therapy by modulating the QS regulator SdiA. METHODS: Experiments were performed using the lytic bacteriophage VAC7 and a clinical K. pneumoniae ST16-OXA48 strain. The minimum inhibitory concentration (MIC) of IAA was determined, and sub-inhibitory concentrations were used to assess QS gene expression by RT-qPCR, phage infection dynamics, and bacterial proteomic responses. RESULTS: RT-qPCR analysis demonstrated that IAA significantly reduced the expression of the QS regulator gene sdiA while increasing luxS expression. Phage infection assays showed enhanced bactericidal activity of VAC7 against the ST16-OXA48 clinical isolate in the presence of IAA. Proteomic analysis revealed a reduced abundance of several bacterial phage defense proteins in the presence of IAA, including GmrSD restriction endonuclease, bacteriophage control infection (BCI), mRNA interferase PemK, and abortive infection protein. Proteins associated with phage receptors, such as LamB, OmpK36, OmpC, and FhuA, were detected under all conditions but reduced when the phage is present. The detection of multiple phage structural, functional and anti-phage defense system proteins was consistent with active phage replication. CONCLUSIONS: IAA, a plant-derived auxin, modulates bacterial QS and SdiA, and reduces phage resistance mechanisms in K. pneumoniae. These findings highlight the potential of IAA, and possibly other auxins, as adjuvants in phage therapy and as versatile components of combination antimicrobial strategies.},
}

@article {pmid42339284,
year = {2026},
author = {Lim, S and Dong, SY and Jung, TP},
title = {Prefrontal fNIRS hemodynamic correlates of attentional load during rapid serial visual presentation tasks.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1843879},
pmid = {42339284},
issn = {1662-5161},
abstract = {BACKGROUND: Despite the growing interest in functional near-infrared spectroscopy (fNIRS) for practical brain-computer interface (BCI) applications, prefrontal hemodynamic responses during rapid serial visual presentation (RSVP) tasks remain poorly characterized, even though these tasks demand sustained attentional engagement under fast-paced visual streams.

METHODS: We examined prefrontal cortex (PFC) activation and functional connectivity as indices of attentional monitoring during an RSVP task using a 15-channel prefrontal fNIRS device in 50 participants. Trials either contained one target image among nontargets or consisted entirely of nontarget images. Eleven statistical activation features from oxygenated (HbO) and deoxygenated (HbR) hemoglobin changes, and Fisher's r-to-z transformed inter-channel connectivity values were compared between conditions using paired-samples t-tests with false discovery rate correction. Response time and exploratory correlations between behavioral latency and hemodynamic features were also analyzed.

RESULTS: The nontarget condition showed slightly higher HbO activation, mainly in amplitude-related features such as max and mean, suggesting increased sustained processing demands when target absence had to be confirmed. In contrast, HbR differences were more strongly characterized by distributional and transient-related features, including kurtosis, skewness, and peak-related features, suggesting complementary HbO and HbR sensitivity to task conditions. Connectivity analysis revealed condition-dependent inter-channel coupling patterns, with generally stronger HbO connectivity in the nontarget condition and a partially different HbR pattern. Response times were significantly longer in the nontarget condition and were more closely associated with temporal and distributional hemodynamic features than with amplitude-based features.

CONCLUSION: These findings provide an initial exploratory characterization of condition-dependent prefrontal activation and connectivity differences during RSVP tasks, highlighting the potential of fNIRS as a practical tool for attentional monitoring and informing future multimodal neuroimaging approaches.},
}

@article {pmid42339732,
year = {2026},
author = {Zhu, JC and Zhu, MJ and Wu, P and He, QZ and Wang, JT and Niu, XK and Wang, ZZ},
title = {Neural representation of object category and viewpoint in the entopallium of pigeons.},
journal = {Zoological research},
volume = {47},
number = {3},
pages = {960-976},
doi = {10.24272/j.issn.2095-8137.2025.581},
pmid = {42339732},
issn = {2095-8137},
mesh = {Animals ; *Columbidae/physiology ; *Neurons/physiology ; *Visual Pathways/physiology ; *Visual Perception/physiology ; },
abstract = {Object recognition depends on the ability of the visual system to preserve stable category assignment despite variation in viewpoint. Although the mechanisms encoding object category and viewpoint have been extensively described in the primate ventral visual pathway, it remains unclear how the avian brain, which does not contain a layered cortical structure, carries out similarly complex visual computations. Here, we systematically investigated how neurons in the pigeon entopallium (ENTO), the terminal station of the tectofugal visual pathway, represent object identity and viewpoint. Large-scale electrophysiological recordings showed that ENTO neurons displayed moderate category selectivity and strong continuity in viewpoint tuning, and a subset of neurons expressed both tuning properties. At the population level, neural responses formed organized representational manifolds that supported categorical separation and viewpoint continuity at the same time. Moreover, ENTO neurons were strongly sensitive to color, and subpopulation analyses indicated that object representations in the ENTO were not confined to one processing level but jointly included low-level visual features (e.g., color and shape) and higher-level semantic information. These results suggest that the avian visual system can construct complex object representations. During this process, the ENTO may establish functionally specific neural representations through distributed coding based on sparse combinations of distinct neuronal subpopulations.},
}

Animals
*Columbidae/physiology
*Neurons/physiology
*Visual Pathways/physiology
*Visual Perception/physiology

@article {pmid42340555,
year = {2026},
author = {Kazmi, J and Ahmad, W and Naqi, M and Abbas, Y and Abbas, A and Wang, P and Mohamed, MA and Rosei, F and Wang, ZM and Song, H},
title = {2D Materials Powering Neuromorphic Intelligence.},
journal = {Nano-micro letters},
volume = {18},
number = {1},
pages = {},
pmid = {42340555},
issn = {2150-5551},
abstract = {The exponential demand for energy-efficient and adaptive computing architectures drives the evolution of artificial intelligence (AI) and machine learning (ML). Neuromorphic computing, inspired by biological neural networks, overcomes the limitations of traditional von Neumann architectures, including high energy consumption and limited scalability. The introduction of two-dimensional (2D) materials, such as transition metal dichalcogenides, hexagonal boron nitride, black phosphorus, and tellurene, enables neuromorphic devices with unprecedented control over electronic and optoelectronic properties. These materials exhibit atomic-scale thickness, high carrier mobility, and tunable bandgaps, facilitating synaptic behaviours such as spike-timing-dependent plasticity and paired-pulse facilitation. This review describes the integration of 2D materials into neuromorphic systems, highlighting applications in wearable electronics, brain-machine interfaces, and quantum neuromorphic platforms. In wearable and edge computing, 2D-based devices enable localized, ultra-low-power data processing. In brain-machine interfaces, they enhance signal transduction and neural interfacing. Quantum effects in 2D materials further enable hybrid quantum-classical neuromorphic architectures for high-dimensional computational tasks. Despite significant advances, challenges in reproducibility, scalability, and stability remain. Addressing these limitations through innovations in synthesis and defect passivation is essential for practical application. This review underscores the transformative potential of 2D-material-based neuromorphic computing for energy-efficient AI. Integration of 2D materials into neuromorphic computing architectures offers a promising pathway toward energy-efficient and adaptive systems that bridge biological learning mechanisms with machine intelligence.},
}

@article {pmid42341014,
year = {2026},
author = {Kang, Y and Dousty, M and Khodami, F and Sejdić, E},
title = {Decoding visual object recognition from EEG signals.},
journal = {PloS one},
volume = {21},
number = {6},
pages = {e0351872},
pmid = {42341014},
issn = {1932-6203},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Pattern Recognition, Visual/physiology ; Brain Mapping/methods ; Random Forest ; },
abstract = {Brain-computer interfaces (BCIs) and clinical EEG require compact and interpretable decoders, yet scalp sensors mix cortical signals and blur frequency-specific activity. Identifying which cortical regions and features carry discriminative visual information enables efficient, anatomically grounded object recognition decoding. This study localizes the cortical sources of informative EEG signals and identifies compact, mechanism-guided features that are most efficient given fixed data or compute budgets. To address this, we construct a source-space decoding pipeline that projects sensor signals onto anatomically defined cortical regions. Trial-wise activity is summarized within regions of interest (ROIs), and four feature families are extracted from each ROI: band-limited power (delta-gamma), line length (LL) for transient activity, temporal morphology, and couplings reflecting coordination between regions. Per-participant Random Forest (RF) classifiers are trained, and generality is quantified as consistency and ROI importance rankings across participants. A low-dimensional representation based on line length yields the strongest overall performance, while temporal morphology and coupling features contribute less under short RSVP (Rapid Serial Visual Presentation) trials. Relative to the EEG-ImageNet sensor-space baseline (310 features), the 24-ROI LL-only stack shows higher reported mean accuracy while using 92% fewer features (24 features), while a finer-grained, extended visual-pathway ROI set shows higher reported mean accuracy while using 84% fewer features (50 features). Adding a small, anatomically constrained high-[Formula: see text] block produces near-tied performance rather than a consistent improvement. These findings indicate that, for single-trial 0.5 s RSVP decoding, most discriminative information is captured by simple time-domain structure in anatomically defined ROIs. High-[Formula: see text] power remains a useful reference feature family, but its incremental value is limited once LL is included. By grounding features in neuro-informed regions, this approach compares favorably, at the level of reported mean accuracy, with the sensor-space baseline while providing clear anatomical attribution at substantially lower dimensionality, supporting lightweight and interpretable EEG decoding.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
*Pattern Recognition, Visual/physiology
Brain Mapping/methods
Random Forest

@article {pmid42342719,
year = {2026},
author = {Chang, H and Li, J and Kang, D and Long, F and Zhu, R and Ye, J and Li, L},
title = {Study on exploring the relationships between physiological indicators in near-death experiences by drawing on in-mold electronics and node displacement concepts in brain-computer interface signal transmission.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-49016-x},
pmid = {42342719},
issn = {2045-2322},
support = {(2024A0505050013)//the 2023 Guangdong Science and Technology Program/ ; },
abstract = {The association between near-death experiences (NDEs) and physiological indicators remains an unsolved mystery, which hinders in-depth understanding of the essence of consciousness and life processes. Traditional single-indicator analysis methods have limitations, and a comprehensive multi-modal research approach is urgently needed to advance relevant explorations in the fields of medicine, neuroscience, and philosophy. Multi-modal physiological monitoring data, including electroencephalogram (EEG) and electrocardiogram (ECG), from critical care settings (ICU, emergency department) were integrated. Signal analysis was conducted by drawing analogies from the concepts of in-mold electronics (IME) and injection molding node displacement. Latin Hypercube Sampling (LHS) was used to collect injection molding parameters, and the Multi-Strategy Differential Evolution (MSDE) algorithm (incorporating elite-sharing, perturbation-backtracking, and adaptive-tuning strategies) was combined to optimize the injection molding process of the brain-computer interface (BCI). A node displacement prediction model was constructed through Moldex3D simulation and Kriging interpolation. During the out-of-body sensation phase of NDEs, EEG showed an increase in gamma waves and a decrease in alpha waves, while ECG exhibited arrhythmia, confirming the coordinated changes between the brain and the heart. In BCI manufacturing, the MSDE algorithm reduced the average node displacement from 0.289 mm to 0.021 mm (with an optimization rate of 92.73%), the volume shrinkage rate from 10.162% to 6.39%, and the optimized voltage difference from 5.78 V to 0.42 V, which was consistent with the improvement in displacement. Multi-dimensional analysis is crucial for decoding the mechanism of NDEs. The optimized BCI hardware enables accurate collection of NDE-related physiological signals, providing scientific support for end-of-life care, optimization of resuscitation protocols, and consciousness research, while also building a cross-disciplinary bridge between engineering and life sciences.},
}

@article {pmid42343581,
year = {2026},
author = {Tang, H and He, S and Ye, M and Wang, C},
title = {Mechanical Tuning of Intrinsic Chirality in a Bilayer Electromagnetic Metamaterial via Out-of-Plane Rotation.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.6c03047},
pmid = {42343581},
issn = {1944-8252},
abstract = {Mechanically tunable chiral metamaterials provide an effective platform for reconfigurable polarization control of electromagnetic waves. Here, we propose a bilayer electromagnetic metamaterial with intrinsic chirality in its undeformed state. Under mechanical stretching, out-of-plane rotation of the resonators enables continuous suppression of circular dichroism (CD). This mechanical rotation reconfigures the relative alignment of effective electric and magnetic dipoles, thereby progressively weakening the chiral coupling. Experimental and numerical results confirm reversible CD modulation across two resonant modes, with a tunable range from -0.77 to near zero. Furthermore, under oblique incidence, the combined effect of the incident angle and mechanical rotation allows enhanced control over polarization azimuth rotation and ellipticity. This work offers a mechanically reconfigurable route for modulating intrinsic chirality in bilayer electromagnetic metamaterials and expands the design space for tunable chiral electromagnetic devices.},
}

@article {pmid42343704,
year = {2026},
author = {Lin, F and Qian, S and Xie, L and Zhou, C and Liu, S and Zhao, W and Zhao, Q},
title = {High-Conductivity Flexible MXene Electromagnetic Films for Radio-Frequency Antennas.},
journal = {Nano letters},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.nanolett.6c02011},
pmid = {42343704},
issn = {1530-6992},
abstract = {With the advancement of the Internet of Everything (IoE), flexible electromagnetics has emerged to enable adjustable electromagnetic performance under mechanical deformation. As key electromagnetic devices, flexible radio-frequency (RF) antennas enable reliable wireless communication, which requires conductive materials that combine mechanical flexibility with high electrical conductivity to minimize electromagnetic loss. However, maintaining efficient electron transport while accommodating deformation remains a major challenge. MXene emerges as a promising electromagnetic material because its atomic-thin layers can slide under bending to reduce strain, and its high in-plane conductivity enables efficient electron transport. Nevertheless, the monolayer defects or inefficient interlayer charge transport in MXene films increases the effective skin depth and weakens surface current confinement, thereby increasing electromagnetic transmission loss with reduced radiation efficiency. This review summarizes recent advances in high-conductivity (≥10,000 S cm[-1]) MXene electromagnetic films and outlines the challenges and prospects for their application in flexible RF antennas.},
}

@article {pmid42343739,
year = {2026},
author = {Zhu, H and Zhang, X and Chen, T and Lim, Z and Saeed, S and Tong, J and Shen, Y},
title = {Bridging Three Decades: Global Self-Harm Trends From 1990-2021 and Projections to 2040.},
journal = {Actas espanolas de psiquiatria},
volume = {54},
number = {3},
pages = {644-656},
doi = {10.62641/aep.v54i3.2111},
pmid = {42343739},
issn = {1578-2735},
mesh = {*Self-Injurious Behavior/epidemiology ; Humans ; *Global Health ; Forecasting ; Female ; Male ; Prevalence ; Incidence ; Time Factors ; Socioeconomic Disparities in Health ; Disability-Adjusted Life Years ; },
abstract = {BACKGROUND: Self-harm, which includes both nonsuicidal self-injury and suicidal behaviors, poses a major global public health challenge. This study provides a comprehensive analysis of trends in self-harm worldwide, the socioeconomic disparities associated with it, and future projections, using data from the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2021.

METHODS: Self-harm data were extracted from GBD 2021, including incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years(DALYs) for 204 countries and territories from 1990 to 2021. Age-standardized rates and estimated annual percentage change (EAPC) were calculated. Inequality was assessed using the Slope Index of Inequality (SII) and Concentration Index (CI). Autoregressive Integrated Moving Average (ARIMA) models were employed to generate projections of self-harm burden from 2022 to 2040.

RESULTS: The global burden of self-harm is projected to change substantially by 2040, with deaths estimated to increase to 829,853 (95% Uncertainty Interval (UI), 262,233-1,397,474) and prevalence projected to rise to 35,863,341 (95% UI, 8,079,108-63,647,574) cases (representing a 131.9% increase from the 2021 baseline of 15,467,153 cases). From 1990 to 2021, age-standardized rates of self-harm demonstrated decreasing trends globally and across sociodemographic index (SDI) levels, with the largest declines observed in high-middle SDI countries. Gender disparities were evident, with more pronounced decreases in females. Inequalities in DALYs due to self-harm decreased over time but remained higher among females in lower-SDI populations.

CONCLUSIONS: Despite decreasing age-standardized rates, the global burden of self-harm is projected to increase substantially by 2040, with driven by increasing incidence and prevalence in incidence and prevalence. Inequities persist, particularly among females in lower-SDI populations. Implementation of targeted prevention and intervention strategies, strengthening of mental health systems, and addressing social determinants of health are imperative to reduce the growing burden of self-harm worldwide.},
}

*Self-Injurious Behavior/epidemiology
Humans
*Global Health
Forecasting
Female
Male
Prevalence
Incidence
Time Factors
Socioeconomic Disparities in Health
Disability-Adjusted Life Years

@article {pmid42345666,
year = {2026},
author = {Yuan, J and Zhang, R and Zhao, Y and Zhou, W and Tian, L and Liu, G},
title = {PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {11},
number = {6},
pages = {},
pmid = {42345666},
issn = {2313-7673},
support = {62401342, 62271291//National Natural Science Foundation of China/ ; 2025A1515011826//the Guangdong Basic and Applied Basic Research Foundation/ ; ZR2020LZH009//the Key Program of Natural Science Foundation of Shandong Province/ ; GJHZ20220913142607013//the Shenzhen Science and Technology Program/ ; ZR2024QF092//the Natural Science Foundation of Shandong Province/ ; SCCl2025YB02//the Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education/ ; },
abstract = {Motor imagery brain-computer interfaces (MI-BCIs) have important applications in neurorehabilitation, assistive communication, and non-muscular human-machine interaction. From a bionic neural-interfacing perspective, MI-BCI decoding provides a computational bridge between biological motor intention and external machine control. However, reliable motor imagery electroencephalography (MI-EEG) classification remains challenging due to the highly non-stationary features of MI-EEG and limited interpretability. In this work, we propose PG-MCTFormer, a prior-guided multi-scale convolutional Transformer for MI-EEG classification that integrates rhythm-aware temporal filtering, dual-scale spatial modeling, and contextual decoding within a unified architecture. We evaluated the model on the publicly available BCI Competition IV 2a dataset, achieving 85.08% average accuracy and a Cohen's kappa of 0.80, with significant performance improvement over the traditional methods. Comprehensive multi-view interpretability analyses in the frequency, temporal, and spatial domains further show that the learned filters remain aligned with canonical MI-related bands, discriminative evidence concentrates in the middle-to-late imagery interval, and the spatial prior is refined into subject-adaptive sensorimotor topographic patterns. These results indicate that explicit neurophysiological priors can improve both the robustness and the interpretability of MI-EEG decoders for biomimetic neural-interface applications.},
}

@article {pmid42345668,
year = {2026},
author = {Luo, J and Zhu, M and Xiao, Y and Long, Y and Zhang, X and Cao, H and Atai, J and Xiao, J and Chen, X},
title = {An Immersive P300 Brain-Computer Interface Based on 3D Morphological Stimuli and Self-Adaptive Bayesian Linear Discriminant Analysis.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {11},
number = {6},
pages = {},
pmid = {42345668},
issn = {2313-7673},
support = {Grant 21A0494//Education Department of Hunan Province/ ; 2022JJ50314//Department of Science and Technology of Hunan Province/ ; },
abstract = {Conventional P300-based brain-computer interfaces (BCIs) commonly rely on two-dimensional (2D) visual flashing, which may induce visual fatigue and limit immersion, thereby restricting long-term usability and system performance. To address these limitations, this study proposes an immersive P300-BCI framework integrating a three-dimensional morphological stimulation paradigm, termed 3D-Morph, with self-adaptive Bayesian linear discriminant analysis (SA-BLDA). Instead of using color or luminance flickering, the proposed paradigm employs dynamic 2D-to-3D morphological transformations of virtual objects in a virtual reality environment to enhance target-related event-related potentials while preserving visual immersion. SA-BLDA further adjusts the number of stimulation rounds according to classification confidence to balance accuracy and interaction efficiency. Experiments with 24 participants showed that the proposed system outperformed the conventional 2D paradigm. In offline analysis, the proposed method achieved an average classification accuracy of 94.17% and an information transfer rate (ITR) of 25.50 bits/min, significantly outperforming the 2D paradigm (87.29% accuracy, 22.75 bits/min ITR, both p<0.001, Cohen's d≥1.22). In online experiments, the 3D-Morph paradigm achieved an average accuracy of 91.46% and an ITR of 37.23 bits/min, compared with 83.96% and 28.74 bits/min for the conventional 2D paradigm (both p<0.01, Cohen's d≥1.14). The average response time was reduced by 0.46 s (p<0.01, Cohen's d=0.78), and the processing time per stimulation round (PT) of SA-BLDA was significantly reduced from 48.54±10.47 ms in the 2D paradigm to 26.40±9.41 ms in the 3D-Morph paradigm (p<0.01, Cohen's d=2.34), corresponding to a 45.61% reduction in computational time per round. NASA-TLX evaluations indicated a significantly lower subjective workload across all dimensions (all p<0.05, Cohen's d≥0.76). These results demonstrate that combining 3D-Morph stimulation with SA-BLDA can significantly improve classification performance, interaction efficiency, and user experience, providing a feasible framework for immersive and practical P300-BCI applications.},
}

@article {pmid42345883,
year = {2026},
author = {Li, X and Han, X and Han, Q and He, X and Huang, Y and Liu, A},
title = {Advancements in Nanomaterial-Based Biosensors for Neuropsychiatric and Neurodegenerative Diagnostics: From Biomarker Discovery to Clinical Translation.},
journal = {Biosensors},
volume = {16},
number = {6},
pages = {},
pmid = {42345883},
issn = {2079-6374},
support = {82401622//National Natural Science Foundation of China/ ; ZR2023QH131//Natural Science Foundation of Shandong Province/ ; },
mesh = {*Biosensing Techniques ; Humans ; Biomarkers/analysis ; *Neurodegenerative Diseases/diagnosis ; *Nanostructures ; *Mental Disorders/diagnosis ; },
abstract = {Nanobiosensors, with their unique physicochemical properties, are transformative tools for diagnosing and monitoring neurodegenerative diseases and mental disorders. This article systematically reviews the latest progress of nanomaterial systems and integrated sensing modalities in neurological disease diagnosis. First, we clarify the multiple functional roles of nanomaterials in biosensors, including signal amplification, interface optimization, and spatial positioning, and compare the applicable scenarios of various sensing principles based on different nanomaterials. Second, we evaluate the design and integration strategies of molecular recognition elements (antibodies, nucleic acid aptamers, molecularly imprinted polymers, and CRISPR-Cas systems) and discuss their synergistic integration mechanisms for improving detection performance. In terms of detection targets, we focus on three applications: high-sensitivity quantification of established protein biomarkers, real-time monitoring of dynamic neurochemicals (dopamine, serotonin, glutamate), and emerging liquid biopsy targets such as exosomal cargo and circulating microRNAs. Finally, to address the core challenges of biofouling, sensitivity-selectivity trade-offs, and multiplex detection in complex matrices, we propose three breakthrough directions for next-generation diagnostics: deep integration of multimodal and multiplexing platforms, closed-loop chemical brain-computer interfaces (cBCIs), and AI-driven predictive diagnostic models, collectively enabling a transition from passive detection to active sensing and intervention for precise, rapid, and non-invasive neurological disease management.},
}

*Biosensing Techniques
Humans
Biomarkers/analysis
*Neurodegenerative Diseases/diagnosis
*Nanostructures
*Mental Disorders/diagnosis

@article {pmid42346323,
year = {2026},
author = {Ran, T and Guo, L and Xu, H},
title = {Cognitive Mechanisms of Referential Ambiguity Resolution in L2 Russian by Chinese Learners: Evidence from Eye-Tracking.},
journal = {Journal of eye movement research},
volume = {19},
number = {3},
pages = {},
pmid = {42346323},
issn = {1995-8692},
support = {2021KFKT001//Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
abstract = {A central question in second language (L2) sentence processing concerns how learners resolve referential ambiguity in real time, particularly when cues from their first language (L1) conflict with those of the target language. Given the substantial typological distance between Chinese (an analytic language) and Russian (a highly inflectional language), this study employs eye-tracking methodology to investigate the developmental trajectory of the cognitive mechanisms in referential ambiguity resolution among Chinese learners of Russian. The results revealed proficiency-related differences in ambiguity processing. Both proficiency groups showed increased processing difficulty when encountering ambiguous pronouns, indicating that referential ambiguity imposed a measurable online cost. High-proficiency learners read more efficiently overall, whereas low-proficiency learners showed a stronger first-mention anchoring pattern. These findings suggest that increasing L2 proficiency is associated with changes in processing efficiency and cue weighting during referential resolution. Notably, even high-proficiency learners did not categorically rely on Russian gender agreement to resolve reference in the morphologically disambiguated condition, suggesting that the integration of morphosyntactic cues into real-time reference resolution remains effortful at advanced proficiency. The study contributes eye-tracking evidence on how Chinese-speaking learners manage referential ambiguity in a morphologically rich L2.},
}

@article {pmid42347126,
year = {2026},
author = {Evancho, A and Dawson, J and Walker, HC and Ballmann, CG and Tyler, WJ},
title = {Auricular Vagus Nerve Stimulation Combined with Physical Therapy for Individuals with Parkinson's Disease: A Pilot Randomized Sham-Controlled Trial.},
journal = {Neurology international},
volume = {18},
number = {6},
pages = {},
doi = {10.3390/neurolint18060118},
pmid = {42347126},
issn = {2035-8385},
support = {N/A//University of Alabama at Birmingham/ ; },
abstract = {Background: Both neuromodulation and physical therapy have been shown to mitigate motor and non-motor symptoms of Parkinson's disease. To date, no studies have examined the integration of transcutaneous auricular vagus nerve stimulation (taVNS) with physical therapy approaches for improving Parkinsonian symptoms. The purpose of this study was to investigate the safety, tolerability, and feasibility of combining taVNS with physical therapy to enhance the therapeutic benefits of exercise as medicine in a clinical setting. Methods: Participants were randomly assigned to receive active or sham bilateral taVNS in combination with PT for 12 visits over 6 weeks. Safety, tolerability, and feasibility outcomes were primary. Secondly, exploratory analyses of changes in cardiovascular and motor function over time were also performed. Results: Overall, taVNS was safe and well-tolerated prior to PT. Cardiovascular analyses suggest that active taVNS may augment HR response to exercise compared to sham. For motor outcomes, both groups showed significant overall improvements; however, no significant between-group differences were found. Conclusions: The preliminary results obtained in this pilot trial confirm that taVNS combined with physical therapy for individuals with PD is safe and feasible. The exploratory cardiovascular and motor findings support the need for larger, adequately powered clinical trials investigating the integration of taVNS into PT and exercise methods for improving PD symptomology. Trial registration: ClinicalTrials.gov NCT05871151.},
}

@article {pmid42347152,
year = {2026},
author = {Tan, TJY and Zhang, Z and Ang, KK and Ang, J},
title = {Application of Filter Bank to Improve Fatigue Monitoring in Wearable EEG-Based Brain-Computer Interface.},
journal = {NeuroSci},
volume = {7},
number = {3},
pages = {},
doi = {10.3390/neurosci7030064},
pmid = {42347152},
issn = {2673-4087},
abstract = {Fatigue monitoring and detection are crucial for improving efficiency and safety due to their influence on reducing cognitive and physical performance that may result in safety-related incidents. This paper proposes a filter bank-based approach that decomposes electroencephalography (EEG) signals into delta, theta, alpha, beta, and gamma sub-bands for feature extraction to enhance fatigue detection using a wearable EEG-based brain-computer interface (BCI). The study utilized a publicly available EEG dataset from 40 participants collected with a dry-EEG headband while performing two cognitive tasks: a Cognitive Vigilance Task (CVT) and a Multi-Modal Integration Task (MMIT). The data was previously investigated for stress detection on the MMIT. In this study, we investigate fatigue detection on the CVT. Subjects who were not fatigued post-CVT were iteratively removed. Two models were trained with five models to classify the fatigued state from the non-fatigued state, one using features extracted from a broadband filter approach and the other from the proposed filter bank approach. Leave-one-subject-out cross-validation yielded accuracies of 75.8% ± 10.4% (95% confidence interval) from the broadband filter approach, and 86.4% ± 8.3% (95% confidence interval) from the proposed filter bank approach, yielding an overall increase of 10.6%. These results demonstrate the potential of filter bank-based feature extraction for fatigue detection in wearable EEG-based BCI systems.},
}

@article {pmid42333730,
year = {2026},
author = {Chesnel, C and Turmel, N and Teng, M and Blouet, E and Amarenco, G and Hentzen, C},
title = {Urodynamic Voiding Patterns in Multiple Sclerosis.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.70348},
pmid = {42333730},
issn = {1520-6777},
abstract = {AIMS: This study aimed to describe urodynamic voiding patterns in patients with MS (PwMS) using standardized assessments, and to compare the performance of the available nomograms and indices for obstruction and bladder contractility.

METHODS: PwMS and lower urinary tract symptoms underwent cystometry and pressure flow studies. Fluoroscopy findings were collected when available. Bladder contractility was assessed using the following parameters: bladder voiding efficiency (BVE), bladder contractility index (BCI), Watts factor (WF) for men, Projected Isovolumetric Pressure 1 (PIP1), the Valentini-Besson-Nelson (VBN) parameter k, and the urodynamic cut-off proposed by Gammie et al. for women. Obstruction was assessed using the bladder outlet obstruction index (BOOI) in men, and BOOIf in women. Agreement between the diagnosis of detrusor underactivity (DUA) and obstruction according to each parameter was assessed with weight kappa.

RESULTS: Ninety-seven PwMS were included (mean age 48.4 ± 10.5, 58 [60%] women, mean EDSS 3.9 ± 1.8). Sphincter dysfunction was observed in 68 patients (70%), most frequently detrusor sphincter dyssynergia (DSD) in 32 (33%), followed by non-relaxing urethral sphincter in 29 (30%), and delayed relaxation of the urethral sphincter in 7 (7%) PwMS. DUA was found in 22 PwMS (23%). All indices demonstrated low to moderate agreement with the diagnosis of DUA and obstruction in this cohort.

CONCLUSION: Abnormal voiding patterns are common in PwMS, especially sphincter dysfunction. None of the developed indices for obstruction and DUA appear highly relevant in this specific neurogenic population.},
}

@article {pmid42334649,
year = {2026},
author = {Niu, M and Ren, S and Lin, C and Wang, Q and Yin, Y and Guo, H and Fu, Y},
title = {Mapping Whole-Brain Nonlinear Structure-Function Dynamics in Aging via Neural Granger Causality.},
journal = {Brain topography},
volume = {39},
number = {5},
pages = {},
pmid = {42334649},
issn = {1573-6792},
support = {25YFWA005//u Fu, Gansu Provincial Key Research and Development Program/ ; BMI2400002//Yu Fu, Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; },
mesh = {Humans ; *Aging/physiology ; Female ; Male ; Middle Aged ; *Brain/physiology/diagnostic imaging/anatomy & histology ; Magnetic Resonance Imaging ; Aged ; Nonlinear Dynamics ; *Brain Mapping/methods ; Adult ; Young Adult ; Neural Pathways/physiology/diagnostic imaging ; },
abstract = {Brain aging is characterized by complex alterations in both anatomical structure and neural function. While the interdependence between structural connectivity (SC) and functional connectivity (FC) is well-established, the patterns of structural-functional coupling (SFC) during aging remain largely unexplored, despite being crucial for elucidating the neural mechanisms of age-related changes. Moreover, traditional resting-state fMRI studies have predominantly focused on linear correlations, often overlooking nonlinear causal interactions that may play a pivotal role in the aging brain. To address this, we employed a Nonlinear Granger Causality (NGC) model to investigate SFC at the whole-brain level. The study included 227 healthy participants, stratified into a young group (20-35 years, [Formula: see text]) and an older group (59-77 years, [Formula: see text]), with further subgrouping by sex. We analyzed SFC from both static and dynamic perspectives at regional and subnetwork levels. Our results demonstrated that the young group exhibited significantly stronger NGC-based SFC compared to the sex-matched older group. Additionally, males displayed a higher proportion of strong SFC connections than age-matched females. Notably, a widespread age-related decline in nonlinear causal coupling was observed across both regional and subnetwork scales, particularly within networks governing cognitive control and attention. Furthermore, dynamic analyses across sliding windows confirmed the persistence of these aging patterns throughout the scanning duration, despite increased temporal variability observed in the elderly. This study underscores the importance of incorporating nonlinear causal relationships into brain network research, as this approach offers deeper insights into the potential mechanisms underlying age-related cognitive decline and neurodegenerative processes.},
}

Humans
*Aging/physiology
Female
Male
Middle Aged
*Brain/physiology/diagnostic imaging/anatomy & histology
Magnetic Resonance Imaging
Aged
Nonlinear Dynamics
*Brain Mapping/methods
Adult
Young Adult
Neural Pathways/physiology/diagnostic imaging

@article {pmid42334958,
year = {2026},
author = {Zhang, W and Li, X and Li, Z and Chen, P and Zhang, B and Liu, X and Liu, S and Ming, D},
title = {Emotional Information Recruits Specific Neural Dynamics to Support Hierarchical Cognitive Control.},
journal = {Social cognitive and affective neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1093/scan/nsag051},
pmid = {42334958},
issn = {1749-5024},
abstract = {Hierarchical cognitive control supports flexible behavior guided by abstract goals; however, how emotional information modulates the underlying neural dynamics across hierarchy levels remains unclear. To fill this gap, we designed emotional and non-emotional hierarchical cognitive control paradigms, with hierarchical demands manipulated by varying abstraction levels of task rules. Forty-one participants performed the experiment, with electroencephalography (EEG) and eye-tracking data simultaneously recorded. By aligning EEG with eye-tracking data through time-locked analyses, we characterized oscillatory dynamics of each hierarchy level, integrating single-frequency (power spectral density, PSD) and cross-frequency (phase-amplitude coupling, PAC) features. Results showed that increasing hierarchical demands enhanced PSD across multiple frequency bands in both emotional and non-emotional contexts. Both contexts also exhibited enhanced narrowband delta-beta PAC with increasing abstraction. Importantly, emotional hierarchical demands additionally elicited PAC patterns beyond those in non-emotional contexts, such as delta-alpha and alpha-beta couplings. Topographical clustering revealed that emotion-related PAC activity predominated in fronto-parietal and temporo-occipital regions. These findings suggest that while the brain utilizes general neural dynamics for processing abstract goals, emotional demands recruit additional neural resources to support it. Beyond elucidating how the brain integrates affect with abstract cognitive events, this study offers potential neurodynamic signatures and intervention targets for affective and cognitive disorders.},
}

@article {pmid42335523,
year = {2026},
author = {Zheng, F and Yu, T and Salim Ahmad, I and Zhao, X and Yuan, B and Wang, F and Lin, R and Liao, J and Wang, H and Wu, B and Hu, Z},
title = {Research of Tuberous Sclerosis Complex (TSC)-Associated Neuropsychiatric Disorders (TAND) in China.},
journal = {Pediatric neurology},
volume = {181},
number = {},
pages = {148-157},
doi = {10.1016/j.pediatrneurol.2026.05.013},
pmid = {42335523},
issn = {1873-5150},
abstract = {BACKGROUND: Tuberous sclerosis complex (TSC) is a multisystem genetic disorder. Neuropsychiatric manifestations (TSC-associated neuropsychiatric disorders [TAND]) are nearly universal and a key quality of life determinant but often overlooked.

OBJECTIVE: To adapt the validated TAND Checklist to Chinese, describe TAND profiles across age groups in Chinese TSC patients, and compare severity between TSC1 and TSC2 genotypes.

METHODS: Cross-sectional study of 311 consecutive patients meeting 2012 International TSC Consensus criteria. Data were collected March-December 2024 at two Chinese tertiary centers.

MAIN OUTCOMES AND MEASURES: TAND prevalence and profiles across six domains using the adapted Chinese TAND Checklist. Intellectual disability defined as IQ < 70.

RESULTS: No sex differences in TAND prevalence were found. Patients averaged eight behavioral problems; most common were mood swings (67.8%), language impairment/delay (67.5%), and inattention (64.6%). Of 152 with formal IQ testing, 68.4% had intellectual disability. Autism spectrum disorder (22.2%) was the most prevalent psychiatric diagnosis. School-aged children had prominent academic difficulties, especially in mathematics (64.3%). Top concerns were memory problems (56.3%) and family stress (52.7%). Children had more language, self-care, and attention problems and adults had more anxiety and depression. TAND features differed significantly between TSC1 and TSC2 genotypes (P < 0.05).

CONCLUSIONS: TAND was pervasive. The adapted Chinese TAND Checklist enabled systematic assessment. TSC2 variants were associated with more severe neuropsychiatric phenotypes. Routine TAND screening and genotype-aware, personalized management are warranted.},
}

@article {pmid42335929,
year = {2026},
author = {de Miguel Gomez, A and Totah, N and Maoz, U},
title = {Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae80fd},
pmid = {42335929},
issn = {1741-2552},
abstract = {OBJECTIVE: Accurate and reliable neural decoding of locomotion holds promise for advancing clinical applications such as rehabilitation and prosthetic control, as well as for understanding neural correlates of action. Recent studies have demonstrated successful decoding of locomotion kinematics across species in motorized treadmill settings. However, efforts to decode locomotion speed directly and continuously in more natural contexts-where pace is self-selected rather than externally imposed-are scarce, and those that exist generally achieve only modest accuracy and require intracranial implants. Here, we aim to decode self-paced locomotion speed non-invasively and continuously using cortex-wide EEG recordings from rats.

APPROACH: We introduce an asynchronous brain-computer interface (BCI) that processes a continuous stream of 32-electrode skull-surface EEG recordings (0.01-45 Hz) to decode instantaneous speed readouts from a non-motorized treadmill during self-paced locomotion in head-fixed rats. Using recurrent neural networks and a large dataset comprising over 133 h of recordings, we trained decoders to map ongoing EEG activity to treadmill speed.

MAIN RESULTS: Our decoding methodology achieves a correlation of 0.88 (R² = 0.78) for speed, primarily driven by visual cortex electrodes and low-frequency (< 8 Hz) oscillations. Moreover, pre-training on a single recording session permitted decoding on other sessions from the same rat, suggesting the presence of uniform neural signatures of locomotion that generalize across sessions but fail to transfer across animals. Finally, we found that cortical states not only carry precise information about the current speed, but also about future and past dynamics, extending up to 1000 ms.

SIGNIFICANCE: These findings demonstrate that self-paced locomotion speed can be decoded accurately and continuously from non-invasive, cortex-wide EEG. Our approach may provide a useful framework for developing high-performing, non-invasive BCI systems for locomotion and contribute to understanding distributed neural representations of action dynamics.},
}

@article {pmid42337555,
year = {2026},
author = {Zhao, Y and Lin, Y},
title = {Consent in flux: when brain‑computer interface embodiment undermines informed consent.},
journal = {BMC medical ethics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12910-026-01540-1},
pmid = {42337555},
issn = {1472-6939},
support = {HNSK (YB) 25‑86//Hainan Provincial Association of Social Sciences/ ; },
abstract = {BACKGROUND: In March 2026, China approved the first invasive brain‑computer interface (BCI) for clinical use globally. While this milestone accelerates neurotechnology translation, it exposes a neglected ethical problem: in some long-term invasive BCI cases, participants who initially consent to later device explantation may subsequently develop functional dependence on, and phenomenological incorporation of, the device. This paper examines whether such post-implantation integration can weaken the continuing moral authority of initial consent to explantation.

METHODS: This paper provides a normative analysis grounded in established bioethics concepts. We draw on theories of extended cognition and transformative experience, integrate findings from qualitative empirical studies of BCI user phenomenology, and engage with the neurorights discourse and Chinese regulatory developments. The analysis focuses on the moral authority of initial consent where long-term device integration may alter the participant's embodied agency, with particular attention to the implications for interpreting the right to withdraw from research.

RESULTS: Transformative experience alone is not sufficient to distinguish implantable BCIs from other medical interventions that may also alter self-understanding. The distinctive diachronic consent problem arises when ex ante experiential ignorance is combined with extended cognition, embodied agency, functional dependence, and the possibility that explantation would remove a device experienced as part of the user's agential system. We identify a specific governance gap in China's current ethical guidelines and propose three interlocking reforms: adopting a dynamic consent model with periodic re‑consent encounters, establishing ongoing ethical review for implanted participants, and decoupling withdrawal from research from consent to explantation. An ethical algorithm to guide explantation decisions is presented.

CONCLUSIONS: Informed consent for long-term invasive BCI use should be reconceptualised as an ongoing process where there is evidence of functional dependence or phenomenological incorporation. Initial consent remains important, but it should not be treated as conclusively authorising later explantation without post-integration reassessment.},
}

@article {pmid42338375,
year = {2026},
author = {Wang, Y and Jiang, X and Cui, L and Cao, X and Shi, R and Wang, Y and Liu, K and Zhang, C and Zhu, J},
title = {[Research and Countermeasure Analysis on the Classification of Brain-Computer Interface Rehabilitation Medical Devices].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {50},
number = {3},
pages = {334-341},
doi = {10.12455/j.issn.1671-7104.250541},
pmid = {42338375},
issn = {1671-7104},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; },
abstract = {OBJECTIVE: To explore the current management status and challenges of brain-computer interface (BCI) rehabilitation medical devices. The existing classification system in China faces several problems, including a single classification dimension, insufficient risk adaptation, and having difficulties in coping with technological iteration. This study aims to propose scientific classification ideas and promote the healthy and orderly development of such devices in the medical field.

METHODS: By sorting out the regulatory experience of the United States, the European Union and Japan, combining the current situation of BCI technology research and classification management in China, this study analyzes the technical characteristics of BCI products and the current status of "ambiguous definition and classification, and pending update of regulations and standards". On this basis, a three-dimensional classification model based on "function, risk and invasiveness" is constructed.

RESULTS: The core function of these products is defined as "collecting electroencephalographic signals to identify patients' intentions for rehabilitation training or adjuvant therapy". Subdivisions such as "invasive/non-invasive" and "rehabilitation training/augmentative and alternative communication/mental illness treatment" are achieved. For example, multi-functional devices for stroke patients are classified as "medium risk". International practices are integrated to overcome the limitations of traditional classification, providing a path for China to learn from international ideas. Space is reserved for technological iteration, such as new electrodes and artificial intelligence algorithms.

CONCLUSION: By clarifying functional purposes and risks, the proposed classification idea provides a basis for supervision. It can be optimized with the expansion of technology and scenarios, and it helps make the classification of BCI products more scientific .},
}

*Brain-Computer Interfaces
Electroencephalography
Humans

@article {pmid42161290,
year = {2026},
author = {Yi, Y and Tian, Y and Xu, Y and Yang, B},
title = {HRGNN: hierarchical region-aware graph neural network for interpretable EEG-Based emotion recognition.},
journal = {Journal of neural engineering},
volume = {23},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ae70a9},
pmid = {42161290},
issn = {1741-2552},
mesh = {*Graph Neural Networks ; Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain/physiology ; *Pattern Recognition, Automated/methods ; },
abstract = {Objective.Electroencephalography (EEG)-based emotion recognition has increasingly adopted graph neural networks (GNNs) to model functional connectivity. However, most existing approaches operate at the electrode level without explicitly incorporating functional brain region priors, leading to limited granularity in regional representation. Furthermore, conventional GNN architectures often suffer from over-smoothing, and global pooling strategies may obscure critical region-specific information. This study aims to develop a region-aware hierarchical graph framework that better aligns with neurophysiological organization while enhancing discriminative representation and interpretability.Approach.We propose a Hierarchical region-aware GNN (HRGNN). First, a brain region embedding module integrates anatomical partition priors to transform whole-brain EEG signals into structured regional subgraphs. A region-aware graph encoder is then employed to capture multi-scale intra- and inter-regional interactions through regional aggregation and hierarchical pooling. To further enhance discriminative power, a dynamic routing-based mixture-of-experts module adaptively fuses regional representations by assigning higher weights to emotionally salient brain regions.Main results.Extensive evaluations across eight publicly available EEG emotion recognition datasets demonstrate that HRGNN consistently outperforms state-of-the-art methods in both within-subject and cross-subject settings. The model achieves improved classification accuracy and robustness while mitigating over-smoothing effects. Visualization analyses reveal region-specific contribution patterns that are consistent with established neuroscientific findings on functional brain interactions during emotional processing.Significance.By integrating hierarchical region-aware modeling with adaptive expert fusion, HRGNN bridges graph-based learning and neurophysiological priors. The proposed framework improves performance, interpretability, and cross-subject generalization, offering a principled approach for EEG-based affective brain-computer interfaces and neuroengineering applications.},
}

*Graph Neural Networks
Humans
*Electroencephalography/methods
*Emotions/physiology
*Brain/physiology
*Pattern Recognition, Automated/methods

@article {pmid42330987,
year = {2026},
author = {Gu, J and Cai, Q and Wang, H},
title = {Decoding imagined Chinese speech: A capsule neural network based on bidirectional knowledge transfer for hierarchical multi-label classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae805f},
pmid = {42330987},
issn = {1741-2552},
abstract = {OBJECTIVE: Speech imagination is one of the most important research directions in the field of brain-computer interfaces. However, there is insufficient research on silent brain-computer interfaces based on the Chinese stimulating materials. An experimental paradigm for Chinese speech imagery, which features a distinctive initial-and-final structure, is designed in this paper. According to the characteristics of vocalization and structure, the collected EEG data can be organized into a multi-level tree structure. Compared to conventional multi-label classification, our paper aims to study how to effectively utilize hierarchical structural information in the multi-granularity hierarchical classification tasks.

APPROACH: We propose a hierarchical capsule network based on bidirectional knowledge transfer by using multi-band feature matrix, which is tailor-made for the phonological structure of Mandarin Chinese. The adoption of capsule network as the primary architecture is mainly due to the dynamic routing mechanism that can naturally model hierarchical relationships in the syllable hierarchy. In addition, we introduce the bidirectional knowledge transfer strategy to further improve the classical dynamic routing. Specifically, features from coarse-grained levels are added to fine-grained levels to fully utilize the dependency information between levels. In order to mitigate error propagation in the forward learning process, we also employ reverse knowledge transfer constrained via soft labels.

MAIN RESULTS: The hierarchical classification results and ablation experiments both demonstrate the effectiveness of our proposed algorithm. The highest recognition rates for each layer reach 90.86%, 73.69%, and 69.45%, respectively.

SIGNIFICANCE: This article offers a novel perspective for decoding hierarchical Chinese silent BCI paradigms. Our study not only reveals the potential of linguistic domain knowledge in guiding neural network architectures for task-specific applications, but also provides a robust foundation for future individual phoneme classification.},
}

@article {pmid42331292,
year = {2026},
author = {Balilti-Turgeman, L and Shalvi, N and Pinchasof, O and Geva, N and Deitch, D and Rubin, A and Ziv, Y},
title = {BAMBI: A Ca[2+] Imaging-Based Brain-Computer Interface for Longitudinal Neuronal Tracking in Freely Behaving Mice.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110832},
doi = {10.1016/j.jneumeth.2026.110832},
pmid = {42331292},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) are powerful tools for investigating neuronal dynamics underlying cognitive processes. However, BCIs commonly rely on electrophysiological techniques, which are limited in their ability to track populations of the same neurons over long periods, constraining their utility for studying long-term neuronal processes.

NEW METHOD: To address this, we developed a BCI system, BAMBI (BCI Applying Mini-microscopes and Behavioral Imaging), which provides feedback based on real-time Ca²⁺ imaging that tracks neuronal activity over extended periods in freely behaving mice.

RESULTS: By maintaining a stable alignment of the same field of view over multiple sessions, BAMBI can reliably track the same neurons over multiple days without requiring retraining. We validated BAMBI using distinct behavioral paradigms in head-fixed and freely moving conditions and demonstrated that mice can reliably modulate specific hippocampal neurons to obtain rewards over multiple days.

Previous Ca[2+] imaging based BCI applications have been largely limited to head-fixed conditions or lacked in-vivo proof-of-concept and have not demonstrated stable BCI performance across days.

CONCLUSIONS: Our results establish Ca²⁺ imaging-based BCI as a tool for longitudinal studies of neuronal population dynamics over extended timescales.},
}

@article {pmid42331791,
year = {2026},
author = {Chen, Q and Li, C and Wang, Z and Ju, Z and Song, J and Lin, H and Tang, H and Guo, C and Ma, Y and Cao, X and Zhao, D},
title = {Broadband opto-thermal camouflage and infrared encrypted communication via inverse design.},
journal = {Light, science & applications},
volume = {15},
number = {1},
pages = {},
pmid = {42331791},
issn = {2047-7538},
support = {No.52276178//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Multispectral detection technologies spanning optical and thermal bands pose a severe threat to military camouflage while simultaneously unlocking new opportunities for covert communication. However, smart materials capable of both countering these threats and exploiting these bands for communication are still lacking. Here, using a Bayesian-optimization-based inverse-design strategy, we propose an opto-thermally decoupled photonic structure. It features broadband optical camouflage across the 0.38-2.5 μm range, encompassing the visible, near-infrared, and short-wave infrared bands (including the 1.55 μm laser wavelength), with a tunable structural-color palette that covers 66% of the CMYK gamut. Crucially, while these colors are independently tunable, the structure modulates radiance in the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands for dynamic thermal camouflage, achieving consistent MWIR/LWIR emissivity switching between 0.41 ± 0.04/0.90 ± 0.01 and 0.93 ± 0.03/0.45 ± 0.01 driven by the vanadium dioxide (VO2) phase transition. Beyond camouflage, by precisely regulating the temperature, we exploit the differential MWIR/LWIR thermal signatures generated by the continuous phase evolution of VO2 to encode information for infrared encrypted communication. We experimentally demonstrate the structure's dual capabilities for broadband opto-thermal concealment and covert communication. This work integrates multispectral camouflage and covert communication within a single platform, offering a new design strategy for next-generation military smart materials.},
}

@article {pmid42331974,
year = {2026},
author = {Kelly, BC and Brewer, SC and Schwartz, JD and Silver, RM and Holmes, HA and Hanson, HA and Doherty, JA and Debbink, MP},
title = {Linking mixtures of air pollution exposures and preterm birth with a self-organizing map.},
journal = {Journal of exposure science & environmental epidemiology},
volume = {},
number = {},
pages = {},
pmid = {42331974},
issn = {1559-064X},
abstract = {BACKGROUND: Exposure to nitrogen dioxide (NO2), ozone (O3), fine particulate matter (PM2.5), and heat has previously been associated with preterm birth. However, individuals are often exposed to a mixture of pollutants, which may exacerbate health effects. Unsupervised neural networks are highly suitable for characterizing high-dimensional exposures, but few studies of environmental mixtures have utilized these algorithms.

OBJECTIVES: To implement a novel epidemiologic machine learning framework for linking high-dimensional mixture exposures and health outcomes, with an application in preterm birth and residential exposure to PM2.5, O3, NO2, and temperature.

METHODS: Data comes from a retrospective cohort of 44,874 individuals living in Utah who gave birth for the first time between 2013 and 2016. Fine-scale air pollution estimates were used to create a high-dimensional self-organizing map (a type of unsupervised neural network) of exposure mixtures. We used cluster analysis to group similar weekly exposure mixtures. Pregnancies were linked to the clusters, and Bayesian mixed-effects logistic regression was used to estimate odds ratios at each gestational week with false discovery rate (FDR) multiple comparison adjustments.

RESULTS: Exposure to certain mixtures at critical windows was associated with a higher risk of preterm birth. In particular, maternal exposure to Cluster 10 (a high O3 and PM2.5 mixture) in weeks 9-14 was associated with up to 53% greater risk of preterm birth, peaking in week 11 (ORweek11: 1.53, 95% BCI [1.12, 2.08], Cr = 99.7%). Repeated exposure for this entire period (weeks 9-14) was associated with 2.8-times greater risk of preterm birth (OR: 2.81, 95% BCI [1.99, 3.96], Cr = 100%).

SIGNIFICANCE: Using a novel machine learning approach, we identified several patterns of exposure to mixtures, primarily composed of O3 and PM2.5, which may be associated with preterm birth at critical windows. The proposed framework reduces complexity while preserving time-varying exposures.

IMPACT: Exposure to air pollution mixtures at critical times during pregnancy may have synergistic effects on the risk of preterm birth, and few studies have examined these relationships using neural networks. We propose an epidemiologic machine learning framework for dimension reduction, which assigns exposure types to individuals over time and allows for estimation of odds ratios of the outcome of interest. Using this method, we describe patterns of exposure to mixtures of NO2, O3, PM2.5, and heat and estimate their effects on preterm birth.},
}

@article {pmid42332720,
year = {2026},
author = {Sun, J and Sun, X and Xia, M and Xiao, Q and Ma, K and Liao, Z and Mo, G and Li, X and Lai, L and Huang, H and Cui, C and Li, P and Duan, X and Xiao, J},
title = {pH-modulating binary hydrogen-bonded organic frameworks accelerate diabetic wound healing.},
journal = {Journal of nanobiotechnology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12951-026-04701-x},
pmid = {42332720},
issn = {1477-3155},
support = {GRF 15304125//Research Grants Council of the Hong Kong Special Administrative Region/ ; A2025235//Medical Science and Technology Research Foundation of Guangdong Province/ ; 2022A1515110168//Basic and Applied Basic Research Foundation of Guangdong Province/ ; 82272153//National Natural Science Foundation of China/ ; 0720240217//Guangdong Special Support Plan/ ; 2024A1515012721//Natural Science Foundation of Guangdong Province/ ; 2025A04J7214//Science and Technology Projects of Guangzhou/ ; GSP030221102//Starting Grants from Zhujiang Hospital of Southern Medical University/ ; },
abstract = {Diabetic foot ulcers have become an increasingly serious clinical problem, in which wound pH dysregulation and infection lead to impaired healing. To improve this situation, we propose a fabric coated with a binary hydrogen-bonded organic framework (SMU-8@W) to enhance wound healing efficiency. Specifically, the hydrogen-bonded organic framework (SMU-8) not only suppresses wound infection through its high photodynamic activity but also guides a "near-neutral-weakly acidic-near-neutral" pH transition at the wound site, thereby modulating the wound microenvironment. Moreover, SMU-8@W exhibits favorable hemostatic performance (BCI = 21.19 ± 2.52%), moisture permeability (5874.20 ± 196.03 g·m[-2]·d[-1]), biosafety, and long-term stability, effectively managing wound exudate and alleviating inflammation, thereby accelerating wound healing. In summary, SMU-8@W offers a pH-modulating, infection-suppressing, angiogenesis-promoting, and collagen-enhancing therapeutic strategy for diabetic foot ulcers.},
}

@article {pmid42332942,
year = {2026},
author = {Myong, Y and Kim, E and Shin, G and Oh, E and Oh, BM},
title = {Reconnecting Minds to the World: Patient Perspectives on Brain Computer Interface After High Cervical Spinal Cord Injury.},
journal = {Annals of rehabilitation medicine},
volume = {},
number = {},
pages = {},
doi = {10.5535/arm.260018},
pmid = {42332942},
issn = {2234-0645},
abstract = {OBJECTIVE: : To explore the perspectives of individuals with high cervical spinal cord injury (C-SCI) regarding expectations, concerns, and desired applications of brain-computer interface (BCI).

METHODS: : A structured focus group interview was conducted with four individuals with chronic high cervical spinal cord injury, all with neurological levels of C4 or above. Pre- and post-interview questionnaires were administered to assess expectations, concerns, and acceptance of BCI before and after discussion. The interview was facilitated by experts in rehabilitation medicine and biomedical engineering, and qualitative data were analyzed inductively to identify key themes.

RESULTS: : Five major themes emerged: digital accessibility, offline physical activity, social interactions and psychological health, usability, and acceptance. Participants expressed strong interest in using BCI to improve digital independence, particularly for messaging, online banking, internet use, and smart home control. They also viewed BCI as a potential tool to enhance autonomy in daily activities and reduce reliance on caregivers. Most participants were open to training and, in some cases, invasive procedures; however, concerns regarding surgical safety, device maintenance, reliability, and practical usability were frequently raised. Views on the emotional and social impact of BCI varied across individuals. Questionnaire responses showed increased willingness to undergo invasive procedures after the focus group interview, while expected functional outcomes became more realistic, suggesting that structured group discussion may have shaped participants' understanding of BCI.

CONCLUSION: : BCI was perceived as a promising pathway to greater independence, participation, and autonomy among individuals with high C-SCI. These findings emphasize user-centered design and demonstrate how lived experience can guide assistive neurotechnology development in rehabilitation research.},
}

@article {pmid42320451,
year = {2026},
author = {Li, J and Yang, Y and Huang, Y and Xu, K and Chen, Y and Yu, L and Yao, Z and Fu, Y},
title = {Reconstructing shared visual experiences from human brain activity across individuals.},
journal = {Medical image analysis},
volume = {113},
number = {},
pages = {104157},
doi = {10.1016/j.media.2026.104157},
pmid = {42320451},
issn = {1361-8423},
abstract = {Reconstructing visual experiences from brain activity promises to strengthen brain-computer interfaces and our fundamental understanding of perception. However, current deep learning approaches for functional magnetic resonance imaging (fMRI)-based image synthesis are often person-specific, requiring substantial data to adapt to new individuals, thus limiting their scalability and translational potential. Here, we present MindShow, a unified generative framework for shared-subject fMRI-to-image reconstruction under a cohort-level training setting. The core of MindShow is a Hierarchically-Conditioned Mixture-of-Experts (HiCo-MoE) encoder that disentangles population-shared latent representations from subject-specific neural characteristics, enabling data-efficient target-subject adaptation under limited calibration data. These representations are then processed by our Gated Perceiver Bottleneck (GPB), a gated Perceiver-style tokenization interface that resolves multi-scale representational misalignment by adaptively mapping the fMRI features into distinct, fixed-size image and text latent tokens. To improve semantic and structural consistency, we introduce a multi-granular optimal transport loss (MOT-Align), which regularizes sample- and token-level distributional alignment between brain-derived features and the latent space of a pretrained vision-language model. When guided by these aligned embeddings, a frozen diffusion model synthesizes images that aim to preserve the semantic content and coarse layout of the perceived content. MindShow improves high-level reconstruction metrics while maintaining competitive structural fidelity, representing a methodological step toward scalable shared-subject neural decoding. All implementation code is available on GitHub: https://github.com/AI-NMI/MindShow.},
}

@article {pmid42322017,
year = {2026},
author = {Pavlyk, I and Field, G and Young, M and Carpentier, J and Szlosarek, EA and O'Keeffe-Brown, MR and Crook, T and Syed, N and Bomalaski, JS and Chan, PY and Szlosarek, PW},
title = {Pegargiminase Suppresses the Fanconi Anemia Pathway and Promotes Melphalan-Induced DNA Double-Strand Breaks in Uveal Melanoma.},
journal = {Pigment cell & melanoma research},
volume = {39},
number = {4},
pages = {e70104},
pmid = {42322017},
issn = {1755-148X},
support = {MIMR1A3S//Polaris Pharmaceuticals/ ; },
mesh = {Humans ; *Melanoma/pathology/genetics/drug therapy/metabolism ; *Melphalan/pharmacology ; *Uveal Neoplasms/pathology/genetics/drug therapy/metabolism ; *Polyethylene Glycols/pharmacology ; *DNA Breaks, Double-Stranded/drug effects ; Uveal Melanoma ; Cell Line, Tumor ; *Fanconi Anemia/pathology/metabolism/genetics ; Cell Proliferation/drug effects ; *Signal Transduction/drug effects ; Gene Expression Regulation, Neoplastic/drug effects ; Hydrolases ; },
abstract = {Uveal melanoma is a hard-to-treat arginine-dependent cancer secondary to argininosuccinate synthetase 1 (ASS1) loss with half of patients succumbing to liver-dominant metastases. Arginine deprivation with pegargiminase is a novel antimetabolite strategy for patients with uveal melanoma. We investigated the preclinical rationale for combining pegargiminase with melphalan, an alkylating agent approved recently for the treatment of hepatic-centric disease. Drug sensitivity of ASS1-deficient uveal melanoma cell lines was performed in 2D culture using proliferation and cytotoxicity assays, with analysis of cell death, cell cycle, DNA double-strand breaks, and interrogation of the molecular mechanism of action by RNA-seq. ADI-PEG20 and melphalan suppressed uveal melanoma cell line proliferation and triggered cytotoxicity, effects which were enhanced with the drug combination. ADI-PEG20 downregulated multiple genes of the Fanconi anemia pathway and synergized with melphalan to increase DNA double-strand breaks. Melphalan and pegargiminase is a rational new drug combination that warrants clinical testing in uveal melanoma.},
}

Humans
*Melanoma/pathology/genetics/drug therapy/metabolism
*Melphalan/pharmacology
*Uveal Neoplasms/pathology/genetics/drug therapy/metabolism
*Polyethylene Glycols/pharmacology
*DNA Breaks, Double-Stranded/drug effects
Uveal Melanoma
Cell Line, Tumor
*Fanconi Anemia/pathology/metabolism/genetics
Cell Proliferation/drug effects
*Signal Transduction/drug effects
Gene Expression Regulation, Neoplastic/drug effects
Hydrolases

@article {pmid42323786,
year = {2026},
author = {Khanam, T and Siuly, S and Wang, K and Whittaker, F and Wang, H},
title = {Evaluating multi-level membership inference risk in federated EEG learning.},
journal = {Brain informatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40708-026-00313-1},
pmid = {42323786},
issn = {2198-4018},
abstract = {Electroencephalography (EEG) records electrical brain activity from the scalp and is widely used in brain-computer interface (BCI) systems for communication, and assistive technologies. EEG is widely used in motor-imagery (MI) based BCIs, where neural recordings contain highly individual and potentially sensitive information. In this regard, federated learning (FL) is a prominent privacy-enhancing approach which enables collaborative model training without centralising raw signals. However, recent work has shown that FL models still leak private information through membership inference attacks (MIAs). Most existing studies examine only single attack type, so it remains unclear how multiple MIAs together expose different layers of privacy risk in FL-based EEG systems. To address this gap, this study develops a federated MI-EEG classification framework and evaluates privacy leakage across four complementary MIAs: record-level, feature-level, gradient-level, and client-identity inference. Two neural networks were trained using per-subject FL, and differential privacy (DP) with epsilon (ε) ∈ {1, 5, 10}
was applied to client updates. Results showed that standard FL alone provides limited intrinsic protection, while adding DP substantially reduces attack success particularly for gradient and identity-level attacks. Strong privacy settings (ε = 1) offered the greatest leakage reduction but degraded classification accuracy, whereas a moderate privacy budget (ε = 5) achieved the most favourable privacy-utility balance. Overall, the findings demonstrate that FL alone is insufficient as a privacy safeguard for EEG-BCI systems. Explicit privacy mechanisms such as DP are required to mitigate multi-level leakage, supporting the design of trustworthy and secure neural-learning technologies.},
}

@article {pmid42325017,
year = {2026},
author = {Shibamori, K and Kyoda, Y and Shinkai, N and Nofuji, S and Yorozuya, W and Okabe, K and Hashimoto, K and Kobayashi, K and Tanaka, T and Masumori, N},
title = {Differences in Urodynamic Findings of Robot-Assisted Radical Prostatectomy Before and 2 Years After Surgery.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.70358},
pmid = {42325017},
issn = {1520-6777},
abstract = {BACKGROUND: Numerous studies have investigated changes in urinary function following robot-assisted radical prostatectomy (RARP); however, most studies have primarily focused on identifying predictors of postoperative urinary incontinence. This study aimed to evaluate bladder function and lower urinary tract symptoms through urodynamics examined before and 2 years after RARP.

METHODS: This single-center, retrospective study included patients who underwent RARP for prostate cancer between April 2014 and April 2022. All participants who met our criteria completed questionnaires and underwent uroflowmetry (UFM) and pressure-flow study (PFS) both preoperatively and 2 years postoperatively.

RESULTS: Of the 376 patients, 141 were included in the analysis. Their median age was 68 years. The median prostate volume (PV) was 34.5 mL, and the median PSA level was 6.17 ng/mL. Postoperative evaluations revealed significant reductions in International Prostate Symptom Score (IPSS) items Q1, Q3, Q5, and the total score, while overactive bladder symptom score (OABSS) item Q4 increased significantly. UFM findings demonstrated significant improvements in maximum and average flow rates, flow time, and residual urine volume. PFS results showed significant improvements in voiding phase parameters and the bladder outlet obstruction (BOO) index. However, the bladder contractility index (BCI) significantly declined. Higher preoperative BCI and BOOI, advanced age, and smaller prostate volume were identified as independent risk factors for greater postoperative decline in BCI. Patients with 3-4 risk factors exhibited a significantly greater decline in BCI than those with 0-2 risk factors (median changes in BCI: -28.8 [IQR, -43.8 to -10.6] vs. 2.1 [IQR, -23.7 to 16.2], p < 0.001).

CONCLUSIONS: RARP was associated with improving urinary symptoms and objective urodynamic parameters, including UFM and PFS outcomes. Nevertheless, a decline in the BCI was observed at 2 years postoperatively among older patients. These findings suggest that increased age at the time of surgery might be associated with postoperative deterioration of detrusor function, although causality remains unclear.},
}

@article {pmid42325271,
year = {2026},
author = {Liang, C and Jiang, W and Chen, J and Turner, JA and Calhoun, VD and Abbott, CC and Jiang, R and Fu, Z and Wu, L and Wang, X and Qi, S and Yuan, Y},
title = {Longitudinally altered default mode network and insula multimodal brain pattern in end-stage renal disease during sustained hemodialysis treatment.},
journal = {iScience},
volume = {29},
number = {6},
pages = {116008},
pmid = {42325271},
issn = {2589-0042},
abstract = {Hemodialysis (HD) is the predominant treatment for end-stage renal disease (ESRD). Despite the efficacy of HD, the neurobiological underpinnings underlying high-risk complications remain unclear. In this study, using unsupervised fusion of functional and structural MRI, we identified a longitudinally altered default mode network (DMN)-insula pattern in ESRD receiving HD over 1-year follow-up (n = 39). This pattern was associated with cognition, and its related genes were enriched in biological processes involving DNA damage and repair, energy metabolism, and cellular activation. The baseline DMN-insula pattern demonstrated potential predictive value for follow-up cognition in ESRD. More importantly, these brain-cognition associations were validated in independent high-risk complications cohorts, including major depressive disorder (n = 60), mild cognitive impairment (n = 291), and Alzheimer's disease (n = 77) by extracting the corresponding brain features and assessing their correlations with cognition. Collectively, this study may help researchers better understand the underlying mechanisms of ESRD receiving HD from a multimodal neuroimaging and molecular perspective.},
}

@article {pmid42328775,
year = {2026},
author = {Cubillos, LH and Kelberman, MM and Mender, MJ and Hite, A and Temmar, H and Willsey, M and Kumar, NG and Kung, TA and Patil, PG and Chestek, C and Krishnan, C},
title = {Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance.},
journal = {Restorative neurology and neuroscience},
volume = {},
number = {},
pages = {9226028261457824},
doi = {10.1177/09226028261457824},
pmid = {42328775},
issn = {1878-3627},
abstract = {Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method but currently struggle with higher-DoF movements: something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-invasive applications. However, their effectiveness in improving decoding and generalizability for implanted recordings across varied tasks is unclear. Here, we evaluated whether brain and muscle synergies can enhance iBMI performance in non-human primates performing a two-DoF finger task. Specifically, we tested if PCA, dPCA, and NMF could compress and denoise brain and muscle data and improve decoder generalization across tasks. Our results showed that while all methods effectively compressed data with minimal loss in decoding accuracy, none improved performance through denoising in our datasets. Additionally, none of the methods enhanced generalization across tasks. These findings suggest that while dimensionality reduction can aid data compression, extracting synergies alone did not provide an advantageous or cleaner control space for linear decoding in our study. Further research with larger sample sizes and more channels in muscle recordings is required to determine whether synergies can be leveraged as an optimal control framework or if alternative approaches are required to enhance decoder robustness in iBMI applications.},
}

@article {pmid42329899,
year = {2026},
author = {Ng, KKW and Birznieks, I and Vickery, RM},
title = {Vibrotactile intensity perception: Predominant influence of afferents recruited remote from stimulus site.},
journal = {The Journal of physiology},
volume = {},
number = {},
pages = {},
doi = {10.1113/JP290236},
pmid = {42329899},
issn = {1469-7793},
support = {APP1028284//National Health and Medical Research Council/ ; 13/RC/2073_P2//Research Ireland/ ; 101081457//HORIZON EUROPE Marie Sklodowska-Curie Actions/ ; DP230100048//Australian Research Council/ ; DP200100630//Australian Research Council/ ; },
abstract = {Sinusoidal vibratory stimuli are frequently used to study human sensory perception but have the limitation that changes in vibration frequency are accompanied by changes in the number and type of activated mechanoreceptive afferents. Here we used trains of brief mechanical pulses to investigate the neural coding of vibrotactile perceived intensity by grouping pulses into bursts. These pulse trains evoke the same perceived frequency, determined by the interval between bursts, as we have previously demonstrated, and held constant across conditions. Subjects rated the perceived intensity using a magnitude estimation task for stimuli varying in the number of pulses per burst (up to four) and stimulation amplitude (5-150 µm). In marked contrast to our previous findings using electrical stimulation, increasing the number of pulses per burst had only a minimal and inconsistent effect on perceived intensity. To explain this we simulated the responses of the afferent population across the hand using the TouchSim computational model. The model revealed that increasing pulse number, without changing amplitude, produced only a modest increase in total population spike count. This occurred because the population response was dominated by large numbers of afferents remote from the stimulation site, many of which failed to respond reliably to each pulse within a burst. In contrast increasing stimulus amplitude enhanced spatial recruitment, leading to greater population spike counts and increased perceived intensity. Together these results highlight the importance of both temporal and spatial summation in shaping tactile intensity perception and argue against a 'hot zone' model of intensity encoding. KEY POINTS: Electrical stimulation of the finger has been shown to change perceived intensity when varying the number of pulses within a stimulus burst, indicating that touch-sensitive nerve fibres encode intensity through the number of impulses they generate within bursts of activity. Here we used mechanical pulses applied to the skin to induce similar bursts of activity. In contrast to expectations, increasing the number of pulses within a burst did not consistently increase perceived intensity. We simulated the neural responses of the entire population of tactile nerve fibres in the hand, revealing that the burst pattern had only a small effect on nerve activity, which was dominated by large numbers of remote fibres that did not reliably follow bursts. These findings argue against a 'hot zone' model, where intensity is determined by nerve activity near the stimulus site, and instead suggest that the number of active fibres and overall activity are most significant.},
}

@article {pmid42329949,
year = {2026},
author = {Feng, L and Xu, B and Duan, L and Jia, S and Jia, Z and Ni, W},
title = {A New Dual-Attention Multi-Node Fusion Network for EEG-fNIRS Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3706103},
pmid = {42329949},
issn = {2168-2208},
abstract = {Brain-computer interface (BCI) based on motor imagery (MI) can realize the direct control of external devices by decoding different signals. The decoding of MI based on electroencephalogram (EEG) suffers from low spatial resolution and is susceptible to noise. Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention as a complementary modality. There have been attempts to fuse the two types of signals, but their spatio-temporal characteristics have not been fully explored. We propose a new multimodal EEG-fNIRS fusion MI classification and recognition model based on a dual attention mechanism. The model comprises two feature extraction branches and a central fusion network. We set two fusion layers in the central fusion network to exploit the spatio-temporal features of EEG and fNIRS. To reduce redundancy and mine correlation characteristics of multiple sensors, the features are fused in the filter dimension to prevent adverse effects between signals during fusion, thereby enabling the deep network to learn cross modal correlations while reducing mutual interference. The method is evaluated on two multimodal datasets. Experiments show that DAMFNet outperforms STA-Net and M2NN by 4.49% and 2.88% on Dataset1, respectively, and shows competitive performance on Dataset2. The code is available at https://github.com/useflf/DAMFNet.},
}

@article {pmid42329951,
year = {2026},
author = {Liu, Q and Pan, Y and Liu, Q and Zhang, L and Huang, G and Chen, X and Liu, Y and Li, F and Xu, P and Liang, Z},
title = {DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3705860},
pmid = {42329951},
issn = {2168-2208},
abstract = {Affective brain-computer interfaces (aBCIs) are increasingly recognized for their potential in monitoring and interpreting emotional states through electroencephalography (EEG) signals. Current EEG-based emotion recognition methods perform well with short segments of EEG data (known as segment-based emotion analysis). However, these methods encounter significant challenges in real-life scenarios where emotional states evolve over extended periods. To address this issue, we propose a novel Dual Attentive (DuA) transformer framework for long-term continuous EEG emotion analysis. Unlike segment-based approaches, our model processes an entire EEG trial as a whole, identifying emotions at the trial level, referred to as trial-based emotion analysis. This framework is designed to adapt to varying signal lengths, providing a substantial advantage over traditional methods. The DuA transformer incorporates three key modules: the spatial-spectral network module, the temporal network module, and the transfer learning module. The spatial-spectral network module simultaneously captures spatial and spectral information from EEG signals, while the temporal network module detects temporal dependencies within long-term EEG data. The transfer learning module enhances the model's adaptability across different subjects and conditions. To verify the effectiveness of the proposed DuA transformer, we construct a long-term continuous EEG emotion database and extensively evaluate our model using the self-constructed database along with two benchmark EEG emotion databases. On the basis of the trial-based leave-one-subject-out cross-subject cross-validation protocol, our experimental results demonstrate that DuA significantly outperforms existing methods in long-term continuous EEG emotion analysis, with an average improvement of 2.8%. The DuA transformer's ability to adapt to varying signal lengths and its superior performance across diverse subjects and conditions highlight its potential for real-world applications, enhancing the overall user experience and efficacy of aBCI systems.},
}

@article {pmid42330351,
year = {2026},
author = {Emerick, T and Vorenkamp, KE and Francio, VT and Srinivasan, S and Singla, P and Castellanos, J and Ahadian, FM and Chen, Z and Gianlorenco, AL and Sarno, D and Sehgal, N and Murthy, N and Barreveld, AM},
title = {AAPM Consensus Guidelines on Neuromodulation Technologies and Neurocomputer Interfaces for Pain Management and Functional Recovery.},
journal = {Pain medicine (Malden, Mass.)},
volume = {},
number = {},
pages = {},
doi = {10.1093/pm/pnag076},
pmid = {42330351},
issn = {1526-4637},
abstract = {OBJECTIVE: To provide an evidence-based framework for healthcare professionals to use neuromodulation technologies to restore neuromuscular function and relieve pain.

METHODS: An expert panel, convened by the American Academy of Pain Medicine Foundation, conducted a literature review of English-language studies published between 2015 and 2025 using PubMed, the Cochrane Library, Web of Science, and Scopus (detailed in Supplement 2). The panel screened abstracts, extracted key data, and evaluated evidence quality using a modified United States Preventive Services Task Force criteria. A Delphi process was used to achieve expert consensus on clinical recommendations for various neuromodulation technologies: Artificial intelligence-guided and robotic rehabilitation systems, virtual/augmented reality interfaces, brain-computer interfaces, electrical nerve stimulation (encompassing peripheral nerve stimulation transcutaneous electrical stimulation), vagus nerve stimulation, multifidus neurostimulation, surgery (eg,, regenerative peripheral nerve interface), scrambler therapy, spinal cord stimulation for motor restoration, and transcranial magnetic stimulation.

RESULTS: The panel provided clinical recommendations and discussed mechanisms of action, evidence, and clinical considerations for each intervention. Evidence for these technologies is evolving, with some showing promising results in areas like improving upper limb function post-stroke, improving functional spine-related outcomes, and reducing chronic pain.

CONCLUSIONS: Neuromodulation technologies offer a promising approach for neuromuscular restoration, focusing on interventions that promote functional recovery rather than solely providing symptomatic care. Areas for future research include more high-quality, large-scale studies with consistent outcome measures.},
}

@article {pmid42330680,
year = {2026},
author = {Bai, Y and Jiang, S and Yang, J and Hong, C and Zhao, W and Ni, G},
title = {Dynamic neural oscillations underpin audiovisual gain from visual lip cues in speech noise.},
journal = {Hearing research},
volume = {479},
number = {},
pages = {109703},
doi = {10.1016/j.heares.2026.109703},
pmid = {42330680},
issn = {1878-5891},
abstract = {Speech noise substantially impairs speech perception, whereas congruent lip movements provide effective visual support when auditory input is degraded. Using natural continuous Mandarin sentences, the present study recorded behavioral and electroencephalographic (EEG) data under speech noise conditions with different signal-to-noise ratios (SNR) to examine the dynamic neural mechanisms by which visual lip information facilitates speech perception. The results showed that speech noise significantly reduced speech recognition accuracy and weakened δ band activity and functional connectivity in the superior temporal sulcus/posterior superior temporal gyrus (STS/pSTG) and auditory-related regions. Congruent lip movements significantly improved speech recognition in noise and enhanced δ/θ band activity in the STS/pSTG during the early stage after target speech onset. Further analyses showed that, under low-SNR conditions, congruent lip movements increased 11∼15 Hz functional connectivity between the STS/pSTG and motor-related regions. These findings suggest that visual lip information may first facilitate early audiovisual integration by modulating low-frequency neural oscillations and may further recruit sensorimotor networks under difficult listening conditions to provide compensatory support for speech perception in noise.},
}

@article {pmid42330981,
year = {2026},
author = {Yao, Z and Zhou, J and Li, J and Bai, Z and Ji, H and Liu, L and Jin, L},
title = {EEG-EMG spatiotemporal cross-attention fusion network for functional upper-limb movement classification.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae802c},
pmid = {42330981},
issn = {2057-1976},
abstract = {OBJECTIVE: Electroencephalography (EEG) and electromyography (EMG) are widely used for decoding motor intentions, yet unimodal approaches often suffer from low robustness and limited representational information. EEG-EMG hybrid brain-computer interfaces (BCI) can bridge cortical intention and muscular execution, but challenges remain in signal alignment, fusion modeling, and clinical generalization.

APPROACH: To address these issues, we propose STCAFusion, a spatiotemporal cross-attention framework that integrates EEG and EMG through multi-band based dual-branch convolutional encoders and parallel temporal and spatial cross-attention modules. This design enables detailed modeling of inter-modal correlations across both time and space. We evaluate STCAFusion on a newly collected dataset of synchronous EEG-EMG recordings from 12 subjects, where the data were acquired under two paradigms (Reaching and Lifting) designed from daily functional upper-limb activities to emphasize directional and strength control.

MAIN RESULTS: With leave-one-run-out cross-validation, STCAFusion achieves average accuracies of 84.15% and 95.22% in the two paradigms, outperforming the strongest competing EEG-EMG fusion baselines by 3.4% in the Reaching paradigm and 1.8% in the Lifting paradigm. Visualization of learned attention weights further reveals meaningful spatiotemporal EEG-EMG coupling patterns, offering insights into neural-muscular coordination patterns relevant to rehabilitation-oriented BCI design.

SIGNIFICANCE: These results highlight the potential of cross-attention-based multimodal physiological signal fusion in building reliable hybrid BCI and wearable devices for upper-limb control and rehabilitation.},
}

@article {pmid42320271,
year = {2026},
author = {Solano-Suarez, KG and Arango-Sabogal, JC and Roy, JP and Molgat, E and Gagnon, CA and Buczinski, S and Dufour, S},
title = {Evaluation of three bulk tank milk enzyme-linked immunosorbent assays to estimate within-herd prevalence of bovine leukosis virus in dairy farms.},
journal = {Preventive veterinary medicine},
volume = {254},
number = {},
pages = {106939},
doi = {10.1016/j.prevetmed.2026.106939},
pmid = {42320271},
issn = {1873-1716},
abstract = {The aim of this study was to estimate the bovine leukosis virus (BLV) herd-level and within-herd prevalence (WHP) in dairy herds and to assess the ability of three bulk tank milk (BTM) ELISA-Ab tests (SVANOVIR, Bovichek, and IDEXX) to predict the BLV WHP. A cross-sectional study was performed on a convenience sample of 93 dairy herds from Québec, Canada, where individual milk samples from all lactating cows and a BTM sample were collected. Individual milk ELISA-Ab results (n = 7612 cows) from a previously validated kit were incorporated into a two-stage hierarchical Bayesian latent class model to estimate BLV herd-level prevalence and WHP. To mitigate potential test saturation, we evaluated the three BTM ELISA-Ab tests with and without sample dilutions. Adjusted BTM ELISA-Ab optical density values were linked to the WHP estimates using a zero-inflated beta regression, and dilution-specific predictive models were compared. The herd-level BLV prevalence was estimated at 87% (95% Bayesian credible interval [BCI]: 78, 93), and BLV WHP ranged from 0% to 90%. The best predictive model-dilution combinations were SVANOVIR 1/10 (nonmonotonic curvilinear), Bovichek 1/5 (linear), and IDEXX 1/50 (nonmonotonic curvilinear). Across tests, optical density values increased with WHP, consistent with higher antibody concentrations in bulk milk. BLV remains highly prevalent at the herd level, with substantial variability in WHP among positive herds. These results indicate that BLV WHP can be reliably estimated from a single BTM sample, reducing the need for individual cow testing.},
}

@article {pmid42319942,
year = {2026},
author = {He, X and Chamberlin, M and Chen, Z and Li, J and Gao, Y and Guo, F and Ma, Y and Wei, X and Luo, X and Zhang, W and Qu, J and Wei, D and Zhu, G and Wang, F and Zhang, H and Yu, X and Chen, X and Yang, Y and Shi, P},
title = {Electronics with switchable flexibility for 3D conforming neural interfaces.},
journal = {Science advances},
volume = {12},
number = {25},
pages = {eaee2752},
doi = {10.1126/sciadv.aee2752},
pmid = {42319942},
issn = {2375-2548},
mesh = {Animals ; Alloys/chemistry ; *Electronics ; *Brain-Computer Interfaces ; },
abstract = {The intricate cortical folds of large primates physically restrict access to substantial portions of neural information via interface devices. Here, we develop a bioelectronic system, sFlex-Fold, with switchable flexibility, representing the neural interface capable of nondestructive three-dimensional (3D) access to both cortical gyri and sulci, providing large-area, nonpenetrative deep tissue coverage. sFlex-Fold is based on an artificial intelligence (AI)-designed liquid metal alloy (LM-alloy), leveraging the phase change of the tailor-made LM-alloy to create neural interfacing electronics with tunable mechanical response to temperatures ranging from 25° to 37°C. The LM-alloy can be patterned into arbitrary circuit layouts with an ~10-micrometer resolution. The flexibility switching happens at the LM melting point, fine-tuned to 36.2°C, upon in vivo tissue contact, causing a three-order-of-magnitude reduction in the effective modulus of the implanted device. As a result, sFlex-Fold has the unique advantages of both a rigid and flexible state and can be morphed into complex, folded, 3D shapes. This enables nondestructive in vivo implantation into deep cortical sulci while maintaining large coverage (>80 square centimeters) over curved brain surfaces with tissue-matching mechanical compliance. Such 3D structural and mechanical mimicking enables high-quality electrical interfacing as quantitatively assessed using rodent and porcine models.},
}

Animals
Alloys/chemistry
*Electronics
*Brain-Computer Interfaces

@article {pmid42311456,
year = {2026},
author = {Chen, Y and Jiang, Y and Wang, X and Zeng, M and Cui, J},
title = {Advances in mechanisms of neuroplasticity induced by multimodal closed-loop brain-computer interfaces after stroke.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1828191},
pmid = {42311456},
issn = {1662-5161},
abstract = {Post-stroke motor dysfunction is one of the leading causes of acquired disability worldwide. The induction and maintenance of neuroplasticity constitute the core mechanisms underlying motor function recovery. Conventional open-loop brain-computer interfaces (BCIs) lack real-time closed-loop feedback and are therefore unable to reliably activate the "temporal contingency" principle required by Hebbian synaptic remodeling, resulting in limited rehabilitation efficacy. Multimodal closed-loop BCIs integrate motor intent decoding, functional electrical stimulation (FES), virtual reality (VR), and exoskeleton-mediated proprioceptive feedback to construct a complete sensorimotor closed-loop circuit. These systems can precisely induce activity-dependent synaptic plasticity, facilitate cortical reorganization, and ameliorate interhemispheric inhibitory imbalance. The present review systematically examines the theoretical foundations of neuroplasticity induction by multimodal closed-loop BCIs following stroke, the constituent system components, electrophysiological and neuroimaging evidence, and the key factors modulating neuroplasticity induction efficacy. Future directions toward personalized adaptive closed-loop systems and long-term home-based rehabilitation are discussed. This review integrates converging evidence from electroencephalography, functional magnetic resonance imaging, transcranial magnetic stimulation, and randomized controlled trials to establish a comprehensive mechanistic framework for multimodal BCI-mediated neuroplasticity, and provides reference for both basic research and clinical translation in this field.},
}

@article {pmid42314246,
year = {2026},
author = {Mohan, A and Anand, RS},
title = {Dynamic wavelet-based augmentation for enhanced EEG-based imagined speech classification.},
journal = {Computers in biology and medicine},
volume = {213},
number = {},
pages = {111810},
doi = {10.1016/j.compbiomed.2026.111810},
pmid = {42314246},
issn = {1879-0534},
abstract = {Brain-computer interfaces allow direct communication between the brain and external devices. They have real potential for assistive technologies and neurorehabilitation. Among the various paradigms, imagined speech decoding aims to translate silent mental speech into actionable outputs. Electroencephalography is used for imagined speech decoding due to its non-invasive nature, high temporal resolution, portability, and affordability compared to other neuroimaging modalities. The noisy and non-stationary characteristics of electroencephalography signals pose challenges for reliable classification. This study proposes a dynamic wavelet basis selection augmentation method. For each electroencephalography epoch, it adaptively chooses the most informative wavelet basis by minimizing wavelet entropy. After basis selection, data augmentation is performed by injecting Gaussian noise into the corresponding wavelet coefficients. The approach helps models handle the noise and differences in electroencephalography signals better than fixed methods. The augmented signals are classified using a convolutional neural network with channel-wise excitation mechanisms to enhance discriminative feature learning. Primary performance evaluation is conducted in an intra-subject setting using a hold-out validation protocol with trial-level separation. Additional verification is performed using cross-validation with 3, 5, and 7 folds to assess robustness. The imagined speech electroencephalography dataset comprises 32 channels, 8 stimuli, and recordings from 10 participants. The highest classification accuracy of up to 98% is achieved for the words-vowels experimental combination, with a Cohen's kappa value of 0.95. Comparatively lower performance is observed in the full eight-class classification. The proposed approach outperforms conventional augmentation strategies and static wavelet-based approaches.},
}

@article {pmid42314707,
year = {2026},
author = {Darie, R and Parker, SR and Calvert, JS and Tiwari, E and Abdelrahman, N and Syed, S and Shaaya, E and Fridley, JS and Merlo, MW and Halpern, I and Borton, DA},
title = {A modular, high-bandwidth, bidirectional implantable device for neural interrogation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7f52},
pmid = {42314707},
issn = {1741-2552},
abstract = {OBJECTIVE: Modern neuroelectronic interfaces have shown great potential to diagnose conditions, address neurological dysfunction, and advance neuroscientific knowledge. However, neural interface systems today require tethered connections that restrict mobility, prevent testing across ecological contexts, and inhibit clinical translation to at-home use. Fully implantable commercial systems have previously been developed, but exhibit significant constraints, including limited modularity, low bandwidth, or unidirectional communication. We aimed to close this gap by developing a neuroelectronic interface that can be deployed flexibly with a variety of third-party neural probes.

APPROACH: We have developed the Modular Bionic Interface (MBI), a system composed of a fully implantable device and a worn unit for high-bandwidth, bidirectional interfacing with the nervous system. The MBI can record high fidelity electrophysiological signals and deliver spatiotemporally modulated electrical stimulation for clinical and research purposes through flexible interaction with third party implantable devices.

MAIN RESULTS: We performed benchtop evaluation to validate the recording and stimulation capabilities of the MBI across a diverse range of inputs and outputs. We then evaluated the MBI system in vivo through chronic implantation within a sheep, where results were stable for the length of evaluation, over six months. While connected to an actively powered, third-party high-resolution spinal cord stimulation electrode array, the MBI system was able to deliver stimulation to evoke lower extremity motor responses and record spinal compound action potentials evoked by peripheral nerve and spinal stimulation.

SIGNIFICANCE: We demonstrate a fully implantable system with a small footprint capable of high-resolution, bi-directional communication with the nervous system via modular connections to third-party devices. We expect that modular devices will further our ability to treat complex neurological disease and injury.},
}

@article {pmid42317101,
year = {2026},
author = {Ren, H and Liu, Y and Huang, Y and Tang, Y and Xiao, L and Wu, Y and Liu, S and Yin, Y and Ma, Q and Dai, M and Tao, S and Xie, M and Li, M and Li, T and Wang, Q},
title = {Schizophrenia and bipolar disorder: a comparative analysis of genetic and brain network connectivity.},
journal = {Psychological medicine},
volume = {56},
number = {},
pages = {e202},
doi = {10.1017/S0033291726104413},
pmid = {42317101},
issn = {1469-8978},
support = {82571712//National Natural Science Foundation of China/ ; 82230046//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Bipolar Disorder/genetics/physiopathology/diagnostic imaging ; *Schizophrenia/genetics/physiopathology/diagnostic imaging ; Genome-Wide Association Study ; Magnetic Resonance Imaging ; *Brain/physiopathology/diagnostic imaging ; Female ; Male ; *Nerve Net/physiopathology/diagnostic imaging ; Middle Aged ; Default Mode Network/physiopathology/diagnostic imaging ; Connectome ; UK Biobank ; Genetic Predisposition to Disease ; },
abstract = {BACKGROUND: Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions with overlapping clinical presentations, genetic risk factors, and brain network dysfunction. Whether alterations in large-scale intrinsic brain networks reflect shared or disorder-specific genetic influences remains poorly understood. Clarifying this distinction is essential for refining etiological models and improving diagnostic precision.

METHODS: Genome-wide inferred statistics (GWIS) were applied to decompose the genetic architecture of SCZ and BD into shared and unique components. Using resting-state network (RSN) data from the UK Biobank, functional connectivity (FC) and structural connectivity (SC) were extracted as neuroimaging phenotypes. Causal inference approaches were subsequently employed to infer potential directional relationships between brain network connectivity and each disorder.

RESULTS: Analyses revealed both common and distinct patterns of brain network connectivity associated with SCZ and BD. Notably, SC within the default mode network (DMN) exhibited opposing effects across the two disorders, suggesting divergent structural underpinnings despite clinical overlap. Additionally, SC within the limbic network (LN) and frontotemporal control network demonstrated potential causal relationships with both conditions, implicating these circuits astransdiagnostic neural substrates.

CONCLUSION: These findings illuminate the shared and disorder-specific genetic and neural architecture underlying SCZ and BD. Integrating genome-wide genetic methods with large-scale neuroimaging data offers a powerful framework for disentangling psychiatric comorbidity and may inform more targeted diagnostic criteria and individualized treatment strategies.},
}

Humans
*Bipolar Disorder/genetics/physiopathology/diagnostic imaging
*Schizophrenia/genetics/physiopathology/diagnostic imaging
Genome-Wide Association Study
Magnetic Resonance Imaging
*Brain/physiopathology/diagnostic imaging
Female
Male
*Nerve Net/physiopathology/diagnostic imaging
Middle Aged
Default Mode Network/physiopathology/diagnostic imaging
Connectome
UK Biobank
Genetic Predisposition to Disease

@article {pmid42317238,
year = {2026},
author = {Song, Z and Zhang, X},
title = {A weighted multi-scale attention-enhanced temporal convolutional network for motor imagery EEG decoding in brain-computer interfaces.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {14},
number = {},
pages = {1842610},
pmid = {42317238},
issn = {2296-4185},
abstract = {Accurate decoding of motor imagery electroencephalogram signals plays a critical role in brain-computer interfaces for neurorehabilitation and assistive technologies. However, existing multi-scale temporal methods often overlook scale-specific importance and fail to jointly capture transient and long-term neural dynamics, we propose a Weighted Multi-scale Attention-enhanced Temporal Convolutional Network (WMA-TCNet). The model employs parallel multi-scale temporal convolutions to capture neural patterns associated with distinct EEG rhythms. A global-aware scale attention mechanism adaptively weights each branch to emphasize task-relevant temporal information. A weighted Channel-Preserving Prior Path is introduced to maintain channel-wise dependencies and enhance spatial modeling stability across cortical regions. In addition, a temporal attention-guided TCN jointly captures local and long-range temporal dependencies. Experiments on BCI Competition IV 2a and 2b datasets show that WMA-TCNet achieves accuracies of 85.8% and 90.0% in subject-dependent settings, and 68.6% and 79.5% in cross-subject scenarios. These results demonstrate improved decoding performance and robustness, while providing a biologically meaningful framework for modeling multi-scale neural dynamics, with potential applications in brain-computer interfaces and neurorehabilitation.},
}

@article {pmid42317384,
year = {2026},
author = {Huang, X and Xu, J and Chen, L},
title = {Two decades of AI-driven motion capture in rehabilitation: Mapping research networks, thematic hotspots, and future trajectories.},
journal = {Digital health},
volume = {12},
number = {},
pages = {20552076261462645},
pmid = {42317384},
issn = {2055-2076},
abstract = {BACKGROUND: Motion capture technology integration in artificial intelligence (AI)-driven rehabilitation represents a rapidly expanding interdisciplinary field with significant potential for advancing movement analysis and motor recovery. A comprehensive bibliometric mapping of this domain is currently lacking, limiting systematic understanding of its development trajectory and key contributors.

OBJECTIVE: To provide a WoS-indexed bibliometric analysis of AI applications in motion capture for rehabilitation, identifying research trends, collaboration networks, key contributors, and emerging research frontiers from 2004 to 2023.

METHODS: A total of 3,500 relevant publications indexed in the Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) databases were retrieved and analyzed. Bibliometric and visualization analyses were performed using VOSviewer (version 1.6.19) and CiteSpace (version 6.2.4) to map collaboration networks, co-citation relationships, and keyword co-occurrence patterns.

RESULTS: Annual publication output demonstrated consistent growth from 2004 to 2023, with cumulative publications exceeding 3,500. The United States (1,014 publications) and China (722 publications) dominated research output, although collaboration patterns differed substantially. The University of Chinese Academy of Sciences led institutional contributions (52 publications). Keyword clustering revealed prominent research themes centered on brain-computer interfaces, machine learning, EEG-based signal processing, and real-time rehabilitation feedback systems. Temporal analysis demonstrated a paradigm shift from fundamental neurophysiological investigations toward computationally-driven and AI-integrated rehabilitation frameworks.

CONCLUSIONS: This bibliometric analysis provides a WoS-indexed mapping of AI-driven motion capture research in rehabilitation. The identified research hotspots and collaboration patterns offer a foundational reference for future investigations, despite limitations related to database coverage and language scope. Continued interdisciplinary collaboration and standardized methodological frameworks are essential to accelerate clinical translation.},
}

@article {pmid42317731,
year = {2026},
author = {Zhang, W and Xiong, B and Kang, Y and Gao, Y and Yang, R and Wang, Q and Wang, W},
title = {Preliminary clinical and electrophysiological findings of NAc/ALIC deep brain stimulation strategy for treatment-resistant depression.},
journal = {iScience},
volume = {29},
number = {6},
pages = {116032},
pmid = {42317731},
issn = {2589-0042},
abstract = {Effective therapeutic options for treatment-resistant depression remain limited. This study investigated the clinical and electrophysiological effects of dual-target deep brain stimulation of the nucleus accumbens and anterior limb of the internal capsule. In a small patient cohort, the intervention was associated with reduced depressive symptoms and improved cognitive performance. Concurrent electrophysiological recordings revealed that clinical improvement correlated with elevated gamma oscillations in the stimulated target and increased theta-band power in frontal-limbic regions. These integrated findings provide a preliminary framework for understanding therapeutic mechanisms and suggest potential biomarkers for optimizing deep brain stimulation therapy in treatment-resistant depression.},
}

@article {pmid42318605,
year = {2026},
author = {Tor, A and Clarke, SE and Bray, IE and Nuyujukian, P and , },
title = {Material damage to multielectrode arrays after electrolytic lesioning is insignificant.},
journal = {eLife},
volume = {14},
number = {},
pages = {},
doi = {10.7554/eLife.106452},
pmid = {42318605},
issn = {2050-084X},
support = {National Defense Science and Engineering Graduate//United States Department of Defense/ ; 1828993//National Science Foundation/ ; Dean's Postdoctoral Fellowship//Stanford School of Medicine/ ; 828653//American Heart Association/ ; Graduate Research Fellowship Program 1656518//National Science Foundation/ ; R01NS123517/NH/NIH HHS/United States ; R01NS130789/NH/NIH HHS/United States ; U19NS118284/NH/NIH HHS/United States ; ECCS-2026822//National Science Foundation/ ; },
mesh = {Animals ; *Electrodes, Implanted ; Microscopy, Electron, Scanning ; *Electrolysis ; *Motor Cortex/physiology ; Neurons/physiology ; },
abstract = {The quality of stable long-term recordings from chronically implanted electrode arrays is essential for experimental neuroscience and brain-computer interfaces. This work uses scanning electron microscopy (SEM) to image and analyze eight 96-channel Utah arrays previously implanted in motor cortical regions of four subjects (subject H = 2242 days implanted, F = 1875, U = 2680, C = 594), providing important contributions to a growing body of long-term implant research leveraging this imaging technology. Four of these arrays have been used in electrolytic lesioning experiments (H = 10 lesions, F = 1, U = 4, C = 1), a recently developed electrolytic perturbation technique demonstrated compatible with continued neuroelectrophysiology using small direct currents. Previously, our group showed that electrolytic lesioning can be used as a technique to create regions of controlled neuron loss without significantly changing recording quality (Bray, Clarke et al., 2024). Here, by surveying physical damage such as biological debris and material deterioration, we show that electrolytic lesioning causes no statistically significant material damage to the implanted electrode arrays. In addition to surveying physical damage, such as biological debris and material deterioration, this work also analyzes whether electrolytic lesioning created damage beyond what is typical for these arrays. These findings also indicate that there are no statistically significant differences between the damage observed on normal electrodes versus those used for electrolytic lesioning, yielding no evidence that electrolytic lesioning significantly affects the material quality of chronically implanted electrode arrays. Finally, this work also includes the largest collection of single-electrode SEM images for previously implanted multielectrode Utah arrays, spanning 11 different intact arrays and one broken array. As the clinical relevance of chronically implanted electrodes with single-neuron resolution continues to grow, these images may be used to provide the foundation for a larger public database and inform further electrode design and analyses.},
}

Animals
*Electrodes, Implanted
Microscopy, Electron, Scanning
*Electrolysis
*Motor Cortex/physiology
Neurons/physiology

@article {pmid42319573,
year = {2026},
author = {Zhang, Q and Qiao, Y and Li, H and Zeng, J and Chen, J and Zhou, H and Hu, Y and Luo, J},
title = {Male-Biased Social Deficits in Chd8[+/R2219*] Mouse Model of Autism Linked to Hippocampal Abnormalities.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {42319573},
issn = {1995-8218},
abstract = {Autism spectrum disorder (ASD) is a neurodevelopmental condition with a 4:1 male bias, yet the biological basis for this sex difference remains poorly understood. Heterozygous mutations in the chromatin remodeler CHD8 confer high risk for ASD, with phenotypes influenced by sex and genetic background. Here, we generated Chd8[+/R2219*] mice carrying a CRISPR-Cas9-generated mutation orthologous to a human variant. These mice recapitulated core ASD features, including macrocephaly and autistic-like behaviors. Notably, social deficits showed a male preponderance, directly mirroring the human sex bias. Structural magnetic resonance imaging (MRI) confirmed whole-brain enlargement in mutants, with voxel-based morphometry identifying bilateral hippocampal expansion. Crucially, hippocampal volume correlated with social deficit severity exclusively in male mutants. Functional connectivity analyses revealed disrupted hippocampal networks, and connectivity patterns within socially relevant circuits predicted behavioral outcomes. Together, our findings establish this model as exhibiting pronounced sexual dimorphism and implicate aberrant hippocampal structure and connectivity as key neural correlates of male-biased social deficits.},
}

@article {pmid42309989,
year = {2026},
author = {Di Liberto, GM and Ip, EYJ},
title = {Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?.},
journal = {The European journal of neuroscience},
volume = {63},
number = {12},
pages = {e70496},
doi = {10.1111/ejn.70496},
pmid = {42309989},
issn = {1460-9568},
support = {13/RC/2106_P2//Research Ireland/ ; 18/CRT/6224//Research Ireland/ ; },
mesh = {Humans ; *Speech/physiology ; *Speech Perception/physiology ; *Brain/physiology ; Brain Mapping/methods ; *Neurophysiology/methods ; },
abstract = {Understanding the neural basis of speech communication is essential for uncovering how sounds are translated into meaning, how that changes with development, ageing and speech-related deficits, as well as contributing to brain-computer interfaces research. While traditional neurophysiological studies have relied on simplified, controlled paradigms, recent advances have shifted the field towards more ecologically valid approaches. Here, we describe the evolving landscape of experimental designs in speech neurophysiology, from discrete to continuous stimuli and from socially isolated listening to dynamic, multiagent communication. Realistic paradigms in that space challenge conventional methods, offering richer insights into neural encoding, functional brain mapping and neural entrainment. At the same time, they introduce significant analytical and technical complexities, particularly when incorporating social interaction. By synthesising findings across studies, we highlight how these ecologically valid speech paradigms have been contributing to refining theories of language processing and open new avenues for research. In doing so, this review critically evaluates of whether the move towards realism in speech neurophysiology represents a technological trend or a transformative leap in understanding the neural underpinnings of speech communication.},
}

Humans
*Speech/physiology
*Speech Perception/physiology
*Brain/physiology
Brain Mapping/methods
*Neurophysiology/methods

@article {pmid42310346,
year = {2026},
author = {Yeom, HG and Choi, WS and An, KM},
title = {A novel transformer architecture for EEG decoding and neuroscientific analysis.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-56405-9},
pmid = {42310346},
issn = {2045-2322},
support = {K207700005//Chosun University/ ; },
abstract = {Deep learning has significantly advanced brain-computer interface (BCI) technology. However, most deep learning models operate as black boxes, limiting their clinical applicability and scientific interpretability. This lack of transparency makes it difficult to determine whether predictions are driven by genuine neural activity or artifacts. To address this limitation, we propose Analformer, a novel Transformer-based architecture designed to achieve both high predictive performance and neuroscientific interpretability. The core component of Analformer is an Analytical Patch Embedding module, which employs fixed, non-trainable Morlet wavelet kernels to extract explainable spatio-temporal-frequency features from raw EEG signals. This structure enables standard neurophysiological analyses-including time-frequency analysis, topography, and F-value time-frequency (FTF) analysis-to be derived directly from the model's internal representations. Furthermore, by analyzing attention weights over these interpretable features, Analformer provides attention-based connectivity that may reflect functional relationships between brain regions. We evaluated Analformer on two large public datasets covering three representative BCI paradigms: Motor Imagery (MI), event-related potentials (ERP), and steady-state visually evoked potentials (SSVEP). Experimental results demonstrate that Analformer achieves competitive performance across all paradigms while producing analysis outputs consistent with established neuroscientific findings. These results suggest that Analformer provides a unified framework that bridges high-performance BCI decoding with interpretable, data-driven scientific analysis.},
}

@article {pmid42310450,
year = {2026},
author = {Deo, DR and Okorokova, EV and Pritchard, AL and Hahn, NV and Card, NS and Nason-Tomaszewski, SR and Jude, J and Hosman, T and Choi, EY and Qiu, D and Meng, Y and Wairagkar, M and Nicolas, C and Kamdar, FB and Iacobacci, C and Acosta, A and Hochberg, LR and Cash, SS and Williams, ZM and Rubin, DB and Brandman, DM and Stavisky, SD and AuYong, N and Pandarinath, C and Downey, JE and Bensmaia, SJ and Henderson, JM and Willett, FR},
title = {A mosaic of whole-body representations on the human precentral gyrus.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {42310450},
issn = {1476-4687},
abstract = {Understanding how the body is represented in the motor cortex is key to understanding how the brain controls movement. Although the motor cortex has been mapped in animal models at a fine scale[1-10], characterization in humans remains primarily limited to low-resolution recording[11-16] and stimulation techniques[17-20]. Here we created a comprehensive map of the human motor cortex at single-neuron resolution, spanning microelectrode array recordings from 20 arrays across 8 individuals with paralysis from spinal cord injury, amyotrophic lateral sclerosis or brainstem stroke, all enrolled in brain-computer interface clinical trials. These arrays broadly sample the crown of the precentral gyrus (PCG; thought to be composed largely of the premotor cortex (Brodmann area 6)). We found that body parts were highly intermixed, such that the entire body was represented in all sampled locations of the PCG, although the relative strength of body parts was roughly consistent with the motor homunculus[17,18]. We also found two speech-preferential areas with a broadly tuned, orofacial-dominant area in between them. Throughout the PCG, movement representations of the four limbs were interlinked, with homologous movements of different limbs (for example, toe curl and hand close) having correlated representations. These data provide evidence consistent with an intermixed, interrelated and behaviour-centred organization of the motor cortex[3,21]. The resulting map also provides important targeting information for brain-computer interfaces that seek to restore motor function.},
}

@article {pmid42311454,
year = {2026},
author = {D'Aquino, A and Schack, T},
title = {Bridging cognition and control through passive eye movement integration in motor imagery brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1849674},
pmid = {42311454},
issn = {1662-5161},
abstract = {Motor Imagery (MI) Brain-Computer Interfaces (BCIs) represent a promising technology for neurorehabilitation and assistive control. However, the clinical viability of these systems is frequently hindered by the inherent limitations of electroencephalography (EEG) with regard to its low signal-to-noise ratio (SNR), non-stationarity, and high inter-subject variability. Standard decoding methods often fail to capture the complexity of user intention leading to unreliable performance and user frustration. This review proposes a solution to these challenges by advocating for the integration of passive eye movements (EM) as a complementary data stream. The theoretical rationale for this approach rests on the neurocognitive principle of functional equivalence. Because imagined actions recruit similar visuomotor networks to those used in physical execution, EM constitute a robust correlate of the underlying neural simulation. We distinguish this approach from conventional hybrid systems that use gaze coordinates for active control. Instead, we argue for a framework of passive monitoring where oculomotor metrics, including pupil diameter, fixation patterns, and saccadic dynamics, serve as a continuous window into the user's cognitive state. We synthesize evidence demonstrating that these passive signals can reliably index cognitive load, attentional allocation, and covert motor planning. By fusing these behavioral metrics with EEG, a BCI can disambiguate uncertain neural patterns and verify user intent without imposing additional task demands. Furthermore, we discuss how this multimodal integration enables the development of adaptive classifiers that respond to fluctuations in user fatigue and engagement. Bridging the gap between cognition and control through passive EM monitoring offers a pathway to create BCI systems that are intrinsically responsive to the user's internal state.},
}

@article {pmid42135191,
year = {2026},
author = {Carleton, M and Oesch, NW},
title = {Inhibitory Circuits Can Restore OFF Pathway Responses in Retinal Prostheses.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {46},
number = {24},
pages = {},
doi = {10.1523/JNEUROSCI.0083-26.2026},
pmid = {42135191},
issn = {1529-2401},
mesh = {Animals ; *Visual Prosthesis ; Mice ; Male ; *Retinal Ganglion Cells/physiology ; *Retinitis Pigmentosa/physiopathology/therapy ; Female ; *Visual Pathways/physiology ; *Neural Inhibition/physiology ; Electric Stimulation ; Amacrine Cells/physiology ; Mice, Inbred C57BL ; Photic Stimulation ; },
abstract = {One of the first steps in processing visual information is to split the light signal captured by photoreceptors into complementary ON and OFF pathways, which separately encode increases and decreases in luminance. In blind patients with retinal degeneration, optoelectronic prostheses can successfully activate the ON pathway and evoke bright percepts; however, patients do not perceive dark features. This indicates that the OFF pathway is not being activated by existing prosthetics. To quantify OFF pathway deficits, we stimulated retinal ganglion cells (RGCs) in a mouse model of retinitis pigmentosa of either sex with electrical stimulation mimicking retinal prosthetic activation and recorded their voltage responses using whole-cell recording. We found that most OFF RGCs respond with incorrect ON responses, except for one specific subtype of RGC, the OFFα, which retained correct OFF-type responses following the termination of stimulation. We found that these preserved OFF responses were driven by postinhibitory rebound excitation, mediated by hyperpolarization-activated cyclic nucleotide-gated channels. Using a combinatorial genetic approach to achieve chemogenetic control, we identified AII amacrine cells as the presynaptic source driving these electrically evoked OFF responses. These insights into how the OFF pathway responds to artificial stimulation suggest new opportunities to improve prosthetic vision restoration through tuning of stimulation parameters.},
}

Animals
*Visual Prosthesis
Mice
Male
*Retinal Ganglion Cells/physiology
*Retinitis Pigmentosa/physiopathology/therapy
Female
*Visual Pathways/physiology
*Neural Inhibition/physiology
Electric Stimulation
Amacrine Cells/physiology
Mice, Inbred C57BL
Photic Stimulation

@article {pmid42303138,
year = {2026},
author = {Gao, X and Lai, Y and Hu, W and Wang, L and Liu, Y and Liao, J},
title = {Spatial variations and risk factors of multimorbidity in China: A population-based spatial modelling study.},
journal = {American journal of preventive medicine},
volume = {},
number = {},
pages = {108481},
doi = {10.1016/j.amepre.2026.108481},
pmid = {42303138},
issn = {1873-2607},
abstract = {INTRODUCTION: Multimorbidity burden is likely to vary across China, but relevant evidence is insufficient. The extent to which individual and provincial factors may affect spatial variations of multimorbidity has not been fully examined. This study aims to estimate the provincial multimorbidity burden among Chinese aged 45 and older, identifying which risk factors remain constant and which vary across China.

METHODS: This study included 18,561 adults aged 45 and older from the China Health and Retirement Longitudinal Study in 2020. A Bayesian spatial varying coefficients model was adopted to estimate the multimorbidity burden and 95% Bayesian credible intervals, using the Chinese Multimorbidity-Weighted Index (CMWI) as a measurement. Partial correlation coefficients between covariates and CMWI in each region were calculated to investigate the need for varying coefficients. Spatial autocorrelation analyses were used to identify clusters of high and low multimorbidity burden.

RESULTS: The estimated CMWI across the 27 provinces in China ranged from 1.76 (95% BCI: 1.64, 1.89) to 4.42 (95% BCI: 4.16, 4.70). High multimorbidity burden areas were clustered in North and Northeast China, while areas with relatively low burden were in southern China. The top three provinces by median CMWI estimates were Neimenggu, Heilongjiang, and Jilin, whereas Guangdong, Zhejiang, and Beijing were among the lowest CMWI estimates. The effect of age and sex showed spatial variation across China, while other risk factors showed fixed effects.

CONCLUSIONS: The burden of multimorbidity varies across China and not all risk factors associated with multimorbidity are consistent across regions, providing valuable insights for chronic disease management.},
}

@article {pmid42303715,
year = {2026},
author = {Zhou, J and Li, W and Luo, S and Chen, K and Xu, S and Liu, Q and Chen, H and Liao, W and Li, J},
title = {Mapping the functional connectome between grey matter and white matter.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-10483-7},
pmid = {42303715},
issn = {2399-3642},
support = {62473082, 82121003, and 62036003//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Brain white matter (WM) has traditionally been viewed as a passive conduit for neural transmission. However, evidence of blood oxygen level-dependent (BOLD) signals measured from the WM suggests its active participation in grey matter (GM) functional networks. Using 7-Tesla functional MRI (fMRI) data, we constructed a GM-WM functional connectome. We found that GM-WM functional architecture follows the unimodal-transmodal hierarchy of GM and is shaped by distributions of neurotransmitter receptors. Distinct WM networks exhibit unique connectivity profiles with GM, reflecting their roles in specific cognitive domains. Individual variations in this connectome correlated with cognitive performance. Notably, compared with the traditional GM-GM functional connectome, the GM-WM functional connectome shows stronger associations with brain disorders, suggesting greater diagnostic sensitivity as a neuromarker. These findings are replicated in a 3-Tesla fMRI cohort. Our work establishes WM as an integral component of the brain's functional architecture, contributing to hierarchical architecture and supporting higher-order cognition.},
}

@article {pmid42303998,
year = {2026},
author = {Zhang, H and Zhang, D and Wu, J and Li, Y and Lu, J},
title = {A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07619-z},
pmid = {42303998},
issn = {2052-4463},
support = {32371146//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371154//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128-256-channel subdural grids and synchronized with the audio; ECoG signals were down-sampled to 400 Hz, filtered in the high-gamma range (70-150 Hz), and used to derive high-gamma amplitude. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including high-gamma amplitude; word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.},
}

@article {pmid42304912,
year = {2026},
author = {Gong, L},
title = {Neuroimaging Mechanisms and Neuromodulation Strategies for Neuropsychiatric Disorders.},
journal = {Current neuropharmacology},
volume = {},
number = {},
pages = {},
doi = {10.2174/011570159X512508260603044250},
pmid = {42304912},
issn = {1875-6190},
}

@article {pmid42305049,
year = {2026},
author = {Li, C and Hu, R},
title = {Caught the 'Catch' of midnolin: structural basis for broad substrate specificity in ubiquitin-independent proteasomal degradation.},
journal = {Acta biochimica et biophysica Sinica},
volume = {58},
number = {6},
pages = {1431-1432},
doi = {10.3724/abbs.2026006},
pmid = {42305049},
issn = {1745-7270},
}

@article {pmid42305781,
year = {2026},
author = {Wang, J and Xu, G and Du, C and Li, Z and Li, H and Chen, S and Han, C and Zhang, S},
title = {Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1851006},
pmid = {42305781},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor imagery (MI) is one of the most widely used paradigms in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). In recent years, deep learning and transfer learning techniques have been increasingly adopted to further improve MI-EEG decoding performance, thereby facilitating the practical deployment of BCIs. In transfer learning, the similarity between the source and target domains is a critical factor influencing its effectiveness. Given the analogous cortical activation patterns observed in MI and motor execution (ME) tasks, cross-task transfer learning from ME to MI presents a promising yet underexplored direction.

METHODS: To tackle the underexplored problem of cross-task transfer learning from ME to MI, we propose a domain-aware domain-class adaptation network (DDCA Net), which consists of a domain-shared feature extractor, two classifiers, and two domain-specific feature re-weighting blocks. Domain-level alignment is achieved by minimizing the maximum mean discrepancy between source and target feature distributions, while domain-specific feature re-weighting preserves discriminative characteristics unique to each task. In addition, a bi-classifier adversarial learning framework is employed to encourage consistency of decision boundaries across domains, thereby enabling implicit class-level alignment.

RESULTS: Extensive experiments were conducted on a public dataset with over 100 subjects under varying proportions of target-domain training samples. When 80% of target-domain samples are used for training, the proposed DDCA Net significantly outperforms the within-task baseline, achieving a 7.71% improvement in classification accuracy and converting approximately 80% of previously BCI-illiterate subjects into BCI-literate users.

DISCUSSION: To the best of our knowledge, this is the first work to verify the feasibility of applying domain adaptation for cross-task transfer learning in MI-EEG classification. The findings of this study provide new insights for integrating ME and MI in advanced BCIs.},
}

@article {pmid42305825,
year = {2024},
author = {DeRoo, SC and George, I},
title = {Aortic dissection after transcatheter aortic valve replacement.},
journal = {JTCVS structural and endovascular},
volume = {1-2},
number = {},
pages = {100009},
pmid = {42305825},
issn = {2950-6050},
}

@article {pmid42306198,
year = {2026},
author = {Li, X and Zhang, S and Song, Y and Zhang, S and Chen, X and Wang, Y and Gao, X},
title = {Text Sequence Stimulation for High-Speed and Comfortable SSVEP-BCI.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0612},
pmid = {42306198},
issn = {2692-7632},
abstract = {Steady-state visual evoked potential brain-computer interfaces offer a high-speed communication channel. However, traditional steady-state visual evoked potential paradigms often rely on strong flickering visual stimulation, which can lead to substantial visual fatigue. Moreover, the electroencephalography responses evoked by brightness flicker are spatially constrained and are primarily associated with occipital visual processing. This study presents a novel text sequence stimulation paradigm that combines periodic visual stimulation with orthographic information and elicits distinct occipital and occipitotemporal scalp response patterns relative to conventional brightness flicker. Frequency-sweep experiments were conducted to investigate the temporal, spatial, and spectral characteristics of the evoked responses. A comparison experiment further showed that text sequence stimulation is less sensitive to variations in stimulus size and luminance than conventional brightness flicker. Based on these findings, a 40-target speller was developed and validated through online experiments. The proposed paradigm achieved an information transfer rate of 235.12 ± 30.12 bits/min while significantly improving user comfort, as confirmed by questionnaire evaluations. These results suggest that text sequence stimulation offers a practical design direction for high-speed and more comfortable visual brain-computer interface.},
}

@article {pmid42306266,
year = {2026},
author = {Tagliafierro, M and Hassanabad, AF and Nickles, J and Sevensky, R and Geirsson, A and George, I and Takayama, H and Argenziano, M and Pirelli, L},
title = {A decade of cardiac surgery after transcatheter aortic valve replacement: Short-term clinical outcomes at a high-volume center.},
journal = {JTCVS structural and endovascular},
volume = {10},
number = {},
pages = {100128},
pmid = {42306266},
issn = {2950-6050},
abstract = {BACKGROUND: The growing adoption of transcatheter aortic valve replacement (TAVR) in younger and lower-risk patients has increased the number of patients with other cardiac diseases requiring surgical intervention. Such surgeries pose unique technical challenges due to the presence of the TAVR, often requiring explantation. Despite increasing clinical relevance, outcomes of these operations remain poorly characterized. We sought to assess the incidence, indications, and short-term results of cardiac surgery following TAVR at a high-volume institution.

METHODS: This was a retrospective single-center analysis of patients undergoing any cardiac surgery post-TAVR between 2015 and 2024. Primary endpoints were perioperative all-cause mortality and stroke; secondary endpoints included cardiopulmonary bypass and cross-clamp times, as well as in-hospital and 30-day outcomes.

RESULTS: Among 10,898 surgeries, 61 (0.5%) involved patients with prior TAVR (median age, 72 years; 59% male), including 85% with hypertension, 28% with diabetes, 43% with chronic lung disease, and 15% with cerebrovascular disease. The median time between TAVR and surgery was 20 months, and 57% of the surgeries were urgent or emergent/salvage procedures. Major indications included TAVR dysfunction (28%), infective endocarditis (26%), and aortic pathology (13%). Common procedures were TAVR explant and surgical aortic valve replacement (n = 49), mitral surgery (n = 19), aortic root/arch surgery (n = 12), and multivessel coronary artery bypass grafting (n = 5). The median aortic cross-clamp and cardiopulmonary bypass times were 121 minutes and 160 minutes, respectively. The mortality rate was 13%. Other outcomes included stroke (3%), prolonged ventilation (31%), tracheostomy (7%), de novo dialysis (8%), need for postoperative blood products (61%), cardiac reintervention (10%), discharge to rehabilitation facility (34%), and readmission (13%).

CONCLUSIONS: Cardiac surgery post-TAVR is uncommon and associated with significant morbidity and mortality. Prosthesis dysfunction and endocarditis are the leading indications, and TAVR explant remains a common although highly morbid intervention.},
}

@article {pmid42308156,
year = {2026},
author = {Hui, W and Wang, Y and Wei, M and Yu, S and Zhou, Y and Ka-Meng, L and Yang, N and Zhu, Z},
title = {Cells on IC Chip.},
journal = {ACS sensors},
volume = {},
number = {},
pages = {},
doi = {10.1021/acssensors.6c00959},
pmid = {42308156},
issn = {2379-3694},
abstract = {Cellular signals are essential for sensing the microenvironment and coordinating physiological functions. Their multimodal nature, encompassing electrophysiological, chemical, mechanical, and optical components, helps define cellular functional states and fate. High-resolution spatiotemporal analysis of these weak, dynamic, and heterogeneous signals is critical for elucidating fundamental life processes, uncovering disease mechanisms, and advancing precision medicine. Recent advances in integrated circuit (IC) technology, particularly the co-integration of complementary metal-oxide-semiconductor (CMOS) and micro-electro-mechanical systems (MEMS), have enabled unprecedented capabilities for cellular signal analysis, driving a transition from conventional instruments and microfluidic platforms to chip-level cellular analysis. This review summarizes the key technological foundations, multimodal sensing mechanisms, and emerging applications of IC technology for cellular signal analysis. It explores the core principles of high-sensitivity, long-term stable signal acquisition, focusing on bioelectronic interfaces, biocompatible packaging, and low-noise signal processing. It also reviews breakthroughs in microelectrode arrays, field-effect transistors, CMOS image sensors, MEMS sensors, and multi-parameter chemical sensing chips for single-cell and population-level detection. The review highlights applications in drug screening, clinical diagnostics, single-cell analysis, and brain-computer interfaces. Finally, it addresses challenges such as biocompatibility, crosstalk suppression, and energy efficiency, while outlining future directions in material innovation, three-dimensional integration, and brain-inspired computing.},
}

@article {pmid42308306,
year = {2026},
author = {Sun, R and Wang, W and Liu, W and Yang, M and Zhang, D and Zou, Z and Yang, K and Gao, C and She, J and Zhang, Z and Hu, Z and Hu, F and Jiang, W and Shu, K and Xie, M and Tang, Z and Zhang, L and Yu, C and Luo, Z},
title = {Untethered thin-film neurostimulator wrapped around tiny nerve trunks for wireless neuromodulation.},
journal = {Science advances},
volume = {12},
number = {25},
pages = {eaec9247},
doi = {10.1126/sciadv.aec9247},
pmid = {42308306},
issn = {2375-2548},
mesh = {Animals ; Rats ; *Wireless Technology/instrumentation ; *Vagus Nerve Stimulation/instrumentation/methods ; Vagus Nerve ; *Implantable Neurostimulators ; Disease Models, Animal ; Cytokines/metabolism ; },
abstract = {Electrical stimulation of tiny peripheral nerve trunks for near-organ neuromodulation enables precise electroceutical therapy for refractory diseases, but long-term stable neuromodulation of these delicate, fragile nerve trunks remains an engineering challenge. Here, we report the development of NeuroWrap Ultrasonic Stimulator (NWUS), an untethered, self-adhesive thin-film neurostimulator capable of conformally wrapping around tiny nerve trunks. The untethered architecture can help avoid the damage or rupture of tiny nerves due to uncontrollable micromotion of lead wires during bodily movements. The NWUS is an ultrasound-responsive acoustoelectric converter with optimized impedance matching, which enables highly effective wireless electrical stimulation. The electroceutical application of NWUS was further illustrated in chronic vagus nerve stimulation for immunomodulation therapy in a rat model. The wireless neuromodulation therapy of rat experimental autoimmune myocarditis was proven to restore left ventricular function, suppress proinflammatory cytokine expression as well as macrophage infiltration within cardiac tissue, and promote regulatory T cell recruitment along with increased anti-inflammatory cytokine levels.},
}

Animals
Rats
*Wireless Technology/instrumentation
*Vagus Nerve Stimulation/instrumentation/methods
Vagus Nerve
*Implantable Neurostimulators
Disease Models, Animal
Cytokines/metabolism

@article {pmid42309008,
year = {2026},
author = {Xin, Q and Hu, H},
title = {Two circuits, one stress: Dissecting the neural logic of comorbid fear and anhedonia.},
journal = {Neuron},
volume = {114},
number = {12},
pages = {2081-2083},
doi = {10.1016/j.neuron.2026.05.003},
pmid = {42309008},
issn = {1097-4199},
mesh = {*Fear/physiology/psychology ; Animals ; *Anhedonia/physiology ; Humans ; *Stress, Psychological/physiopathology ; *Prefrontal Cortex/physiology ; Neural Pathways/physiology ; Interneurons/physiology ; },
abstract = {In this issue of Neuron, Li and colleagues[1] unveil a circuit-based framework in which parallel insula-prefrontal circuits independently govern stress-induced social fear and novelty preference deficits, and lateral inhibition between these circuits via local parvalbumin interneurons drives their comorbidity.},
}

*Fear/physiology/psychology
Animals
*Anhedonia/physiology
Humans
*Stress, Psychological/physiopathology
*Prefrontal Cortex/physiology
Neural Pathways/physiology
Interneurons/physiology

@article {pmid42309130,
year = {2026},
author = {Patel, RJ and Bryson, B and Carlson, T and Demosthenous, A and Jiang, D},
title = {Stability and neurophysiological validity of graph connectivity features for non-stationary motor imagery BCIs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7ead},
pmid = {42309130},
issn = {1741-2552},
abstract = {Motor imagery (MI) Electroencephalography (EEG) Brain-computer interfaces (BCI) degrade under longitudinal non-stationarity, especially in amyotrophic lateral sclerosis (ALS). Functional connectivity (FC) has been proposed as an alternative feature space, but it remains unclear which FC estimators yield stable, class-informative features across sessions. Approach: Using a multi-session ALS EEG dataset, we computed a broad family of FC estimators per trial to form weighted graphs. We extracted edge weights and node strength features, and quantified (i) feature reproducibility and (ii) LH-RH separability using coefficient of variation and symmetric Kullback-Leibler divergence, respectively. We assessed neurophysiological plausibility via spatial topographies, distance-dependence controls, and evaluated selected feature sets in a strictly temporal cross-session decoding protocol against Common Spatial Patterns, Band Power and Riemannian Methods. Main Results: Coherence-based estimators, particularly magnitude-squared coherence, most consistently produced features exhibiting favourable reproducibility-separability trade-offs across subjects. Node-strength discriminability maps showed lateralised sensorimotor structure consistent with known MI physiology. In temporal generalisation, Magnitude Squared Coherence derived features achieved more consistent test performance than baseline methods for most subjects. Significance: Joint reproducibility-separability profiling provides a principled way to select FC feature spaces for longitudinal MI-BCIs and suggests coherence-based connectivity is a stronger sensor-space candidate under drift.},
}

@article {pmid42309345,
year = {2026},
author = {Tozzi, A},
title = {Embedding EEG trajectories in a Möbius-like manifold: An exploratory study.},
journal = {Neuroscience letters},
volume = {},
number = {},
pages = {138665},
doi = {10.1016/j.neulet.2026.138665},
pmid = {42309345},
issn = {1872-7972},
abstract = {Time-frequency decompositions and nonlinear dynamical methods analyze electroencephalographic (EEG) signals as time series evolving in Euclidean state spaces. We explore an alternative representation of EEG dynamics in which neural activity evolves within a Möbius-like state space. While conventional amplitude-phase descriptions represent oscillatory activity within a cylindrical state space, we additionally consider that a shift of half an oscillatory cycle reverses the sign of the waveform, transforming positive amplitudes into negative ones and vice versa. This symmetry introduces a twist into the cylindrical representation, yielding a non-orientable topology analogous to a Möbius strip in which EEG activity evolves as a continuous cyclic trajectory. Using normalized signal amplitude and instantaneous phase derived from the Hilbert transform, we reconstructed three-dimensional trajectories from EEG recordings of a healthy young adult. Our Möbius-like approach describes the geometry of the embedded EEG trajectory in terms of cyclic evolution, phase-dependent symmetry, winding number and torsion. The winding number quantifies cumulative oscillatory phase progression by measuring the number of rotations performed by the trajectory around the manifold, whereas torsion captures local changes in amplitude-phase organization by characterizing how strongly the trajectory twists in three-dimensional space. Together, these descriptors provide complementary assessment of global and local neural dynamics that are not represented by conventional EEG measures based solely on temporal, spectral or statistical properties. Potential applications include characterization of physiological and pathological brain activity, trajectory-based EEG feature extraction, integration with brain-computer interface approaches and comparative analysis of neural dynamics across cognitive and behavioral conditions.},
}

@article {pmid42309710,
year = {2026},
author = {Sun, H and Wang, R and Karako, K and Song, P and He, J},
title = {Clinical translation and accessibility of brain-computer interfaces: From technology development to clinical application.},
journal = {Bioscience trends},
volume = {},
number = {},
pages = {},
doi = {10.5582/bst.2026.01118},
pmid = {42309710},
issn = {1881-7823},
abstract = {Brain-computer interface (BCI) technology establishes a direct communication pathway between neural activity and external devices. Driven by advances in neuroscience, artificial intelligence (AI), neural signal acquisition, decoding algorithms, and implantable system design, BCIs have progressed rapidly from experimental prototypes toward clinically relevant neurotechnologies. However, the translation of these technical advances into routine clinical practice and equitable real-world access remains substantially slower than technological innovation. This review summarizes the major technological pathways of BCIs and their clinical applications, and it then examines BCI development from the perspective of clinical translation and accessibility. We focus on key barriers across the translational chain, including long-term technical stability, quality of clinical evidence, evaluation standards, reimbursement mechanisms, health-economic evidence, and the feasibility of implementation in real-world healthcare settings. We argue that the central challenge in BCI development has shifted from improving technical performance alone to building the translational infrastructure required for safe, effective, affordable, and sustainable clinical integration.},
}

@article {pmid42309711,
year = {2026},
author = {Asakawa, T},
title = {Brain-computer interfaces: A lifeline for paralysis or a Pandora's box for humanity?.},
journal = {Bioscience trends},
volume = {},
number = {},
pages = {},
doi = {10.5582/bst.2026.01142},
pmid = {42309711},
issn = {1881-7823},
abstract = {Recent advances in computerized technologies, neuroscience, and materials and engineering have transformed brain‑computer interfaces (BCIs) from conventional unidirectional signal recording systems (brain-to-device) to bidirectional closed-loop neuromodulation systems (brain-device-brain). BCI-based devices enable direct information exchange between the human central nervous system and external electronic devices, and they are widely used in scenarios such as rehabilitation of patients with dyskinesia or enhancement of the self-care ability of disabled individuals. This editorial discusses the rapidly evolving field of BCIs, highlighting both their transformative potential to restore neurological function and the emerging ethical concerns associated with neural data access, cognitive enhancement, and human autonomy. The academic consensus and future translational prospects are also discussed. This article attempts to provide insightful, balanced, and critical viewpoints to help BCI-related research. Indeed, the future of BCIs will depend not only on technological innovation but also on society's ability to establish robust ethical and regulatory frameworks. Whether BCIs become a lifeline for millions of patients or a source of new societal risks will be determined by the choices made today.},
}

@article {pmid42296267,
year = {2026},
author = {Banta, AR and Fedorov, D and Altilia, A and Malliaras, GG and Barone, DG},
title = {Brain-computer interfaces and neuroprosthetics in the next era of neurosurgery.},
journal = {British journal of neurosurgery},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/02688697.2026.2682817},
pmid = {42296267},
issn = {1360-046X},
abstract = {PURPOSE: Brain-computer interfaces (BCIs) and neuroprosthetic systems are rapidly advancing from experimental concepts to clinically meaningful technologies capable of restoring communication, movement, sensation, and therapeutic neuromodulation. This review examines the current state of BCI and neuroprosthetic technologies, their neurosurgical applications, emerging frontiers, and the evolving role of neurosurgeons in their clinical translation.

MATERIALS AND METHODS: A narrative review of the contemporary literature was performed, focusing on neural signal acquisition technologies, including intracortical microelectrode arrays, electrocorticography, depth electrodes, and endovascular recording systems. The review also evaluates advances in neural decoding algorithms, closed-loop stimulation paradigms, neuroprosthetic applications, long-term implant stability, cognitive and affective BCIs, and ethical and regulatory considerations relevant to neurosurgical practice.

RESULTS: Recent developments in neural interface design, implantable electronics, adaptive decoding algorithms, and closed-loop neuromodulation have enabled substantial progress in motor restoration, sensory feedback, speech decoding, and therapeutic neuromodulation. Intracortical and minimally invasive recording systems have expanded the range of achievable clinical applications, while adaptive deep brain stimulation and responsive neurostimulation demonstrate the growing importance of closed-loop approaches. Key challenges remain, including foreign body reactions, long-term signal instability, neural signal drift, and ethical concerns related to cognitive applications, privacy, and data security.

CONCLUSION: BCIs and neuroprosthetics are transforming the neurosurgical landscape by providing new opportunities to restore lost neurological function and deliver personalized neuromodulation therapies. Continued advances in biological integration, system adaptivity, and cognitive applications are expected to accelerate clinical adoption. As these technologies mature, neurosurgeons will play a central role in implantation, long-term management, and the responsible clinical translation of neural interface technologies.},
}

@article {pmid42297203,
year = {2026},
author = {Jin, Z and Liu, C and Liu, G and Feng, G and Li, J and Wu, Y and Jia, H and Keefe, DL and Liu, L},
title = {An Asynchronous Production Line of Meiotic Prophase I in the Mouse Fetal Ovary.},
journal = {Experimental cell research},
volume = {},
number = {},
pages = {115099},
doi = {10.1016/j.yexcr.2026.115099},
pmid = {42297203},
issn = {1090-2422},
abstract = {The initiation of meiosis in the female germline of mammals is a gradual process, but there is currently no clear quantitative framework for determining the precise timing of its onset. Here, we attempt to standardize the description of meiotic entry timing through a systematic, quantitative analysis of meiotic entry and progression in the mouse fetal ovary. Using dynamic expression profiling of key regulators Stra8, Sycp1, and Sycp3 alongside proliferation markers, we demonstrate that germ cells enter meiosis asynchronously and continuously between embryonic days E12.5 and E16.5. During this extended period, mitotic proliferation persists, indicating that germ cells are progressively recruited into the meiotic pathway rather than halting division simultaneously. Homologous chromosome synapsis, marked by Sycp1/Sycp3 co-localization, initiates at E14.5 and is completed prenatally by E18.5. Using stage-composition data, we constructed a continuous-time Markov chain model to infer a population-level meiotic stage clock. This model estimates approximately conserved population-level effective intervals from the modeled early-prophase L compartment to pachytene-stage synapsis (∼72 h) and to the late-prophase/dictyate-associated D-state transition (∼91 h) across modeled cohort-start times. Our findings refine the conventional view by quantitatively defining the extended window of meiotic entry and subsequent progression through prophase I.},
}

@article {pmid42297978,
year = {2026},
author = {Card, NS and Singer-Clark, T and Peracha, H and Iacobacci, C and Hou, X and Wairagkar, M and Fogg, Z and Offenberg, EC and Hochberg, LR and Stavisky, SD and Brandman, DM},
title = {Long-term independent use of an intracortical brain-computer interface for speech and cursor control.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
pmid = {42297978},
issn = {1546-170X},
support = {N/A//Burroughs Wellcome Fund (BWF)/ ; N/A//Burroughs Wellcome Fund (BWF)/ ; N/A//Achievement Rewards for College Scientists Foundation (ARCS Foundation)/ ; A2295-R//VHA Office of Research and Development | Rehabilitation Research and Development Service (Rehabilitation Research & Development Service)/ ; 1DP2DC021055//U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD)/ ; AL220043//United States Department of Defense | Office of the Secretary of Defense (OSD)/ ; 23-SGP-652//Amyotrophic Lateral Sclerosis Association (ALS Association)/ ; },
abstract = {Brain-computer interfaces (BCIs) can provide naturalistic communication and digital access to people with severe paralysis by decoding neural activity associated with attempted speech and movement. Recent work has demonstrated highly accurate intracortical BCIs for speech and cursor control, but two critical capabilities needed for practical viability were unmet: independent at-home operation without researcher assistance and reliable long-term performance supporting accurate speech and cursor decoding. Here we demonstrate the independent and near-daily use of a multimodal BCI with novel brain-to-text speech and computer cursor decoders by a man with paralysis and severe dysarthria due to amyotrophic lateral sclerosis. Over nearly 2 years, the participant used the BCI for more than 3,800 h at home with no researchers present to maintain rich interpersonal communication with his family and friends, independently control his personal computer and sustain full-time employment-despite being paralyzed. He communicated 183,060 sentences-totaling 1,960,163 words-at an average rate of 56 words per minute. He labeled 92% of sentences as being decoded at least mostly correctly. In formal quantifications of performance where he was asked to say words presented on a screen, attempted speech was consistently decoded with more than 99% word accuracy (125,000 word vocabulary). The participant also used the speech BCI as keyboard input and the cursor BCI as mouse input to control his personal computer, enabling him to send text messages and emails and to browse the internet. These results demonstrate that intracortical BCIs have the potential to support independent use in the home, marking a critical step toward practical assistive technology for people with severe motor impairment.},
}

@article {pmid42300820,
year = {2026},
author = {Nazif, TM and Simonato, M and Makkar, RR and Thourani, VH and Desai, ND and Babaliaros, V and Greason, K and Rovin, J and Waxman, S and Davidson, C and Kereiakes, DJ and Gupta, A and Satler, L and Schwartz, R and Kapadia, S and Wong, SC and Smalling, RW and Ghani, M and Teirstein, P and George, I and Potluri, S and Szerlip, M and Xu, K and Cohen, DJ and Sharma, RP and Pibarot, P and Hahn, RT and Mack, MJ and Leon, MB and , },
title = {10-Year Outcomes of SAPIEN 3 Transcatheter Aortic Valve Replacement or Surgery in Intermediate-Risk Patients.},
journal = {Journal of the American College of Cardiology},
volume = {87},
number = {23},
pages = {3296-3308},
doi = {10.1016/j.jacc.2026.03.170},
pmid = {42300820},
issn = {1558-3597},
mesh = {Humans ; *Transcatheter Aortic Valve Replacement/methods/mortality ; Female ; *Aortic Valve Stenosis/surgery/mortality/diagnosis ; Male ; Treatment Outcome ; Registries ; Prospective Studies ; Aged, 80 and over ; Follow-Up Studies ; *Heart Valve Prosthesis ; Risk Assessment ; Aged ; Time Factors ; Risk Factors ; Postoperative Complications ; *Aortic Valve/surgery/diagnostic imaging ; },
abstract = {BACKGROUND: Transcatheter aortic valve replacement (TAVR) is an alternative to surgical aortic valve replacement for patients with symptomatic severe aortic stenosis. However, long-term outcomes data are lacking for TAVR, particularly with newer-generation transcatheter heart valves.

OBJECTIVES: The purpose of this study was to compare 10-year outcomes of intermediate-risk patients who underwent TAVR with the third-generation, balloon-expandable SAPIEN 3 valve in the PARTNER 2 SAPIEN 3 Intermediate-risk Registry (P2S3i) with those who underwent surgery in the PARTNER 2A (P2A) randomized trial.

METHODS: Intermediate-risk patients were enrolled in the P2A trial from 2011 through 2013 and in the P2S3i registry in 2014. These prospective, multicenter studies used the same eligibility criteria and stratified patients based on suitability for transfemoral or transthoracic (transapical/transaortic) access. Ten-year outcomes were evaluated, including all-cause mortality, aortic valve reintervention, and core laboratory-adjudicated echocardiographic outcomes. Patient reconsent was required at 5 years for extended 10-year follow-up, and vital status sweeps were implemented to improve data completeness for all-cause mortality. To account for potential baseline differences and reduce confounding, P2S3i TAVR patients were propensity score-matched 1:1 to P2A surgical patients.

RESULTS: Among 2,005 patients who received a valve, 1,069 underwent TAVR in P2S3i and 936 underwent surgery in P2A. After propensity score matching (N = 783 patients in each group), baseline characteristics were similar between groups: mean age was approximately 82 years, 43% were female, and mean Society of Thoracic Surgeons score was 5.5%. At 10 years, all-cause mortality rate was 83.4% after TAVR and 82.3% after surgery, respectively (HR: 1.01 [95% CI: 0.91-1.13]; P = 0.82). Aortic valve reintervention rates adjusted for competing mortality were 2.0% for TAVR and 1.9% for surgery (P = 0.47). Among 32 TAVR and 30 surgical patients with available echocardiographic data at 10 years, mean gradients were 11.0 mm Hg and 12.6 mm Hg, respectively.

CONCLUSIONS: At 10 years, TAVR with the SAPIEN 3 valve and surgery resulted in similar rates of mortality and aortic valve reintervention, and similar hemodynamics in intermediate-risk patients with symptomatic severe aortic stenosis. This analysis highlights challenges associated with extended long-term follow-up of clinical trials, including differential loss to follow-up and the competing risk of mortality in elderly populations. (PARTNER 2A Trial; NCT01314313; PARTNER 2 SAPIEN 3 Intermediate-Risk Registry; NCT03222128).},
}

Humans
*Transcatheter Aortic Valve Replacement/methods/mortality
Female
*Aortic Valve Stenosis/surgery/mortality/diagnosis
Male
Treatment Outcome
Registries
Prospective Studies
Aged, 80 and over
Follow-Up Studies
*Heart Valve Prosthesis
Risk Assessment
Aged
Time Factors
Risk Factors
Postoperative Complications
*Aortic Valve/surgery/diagnostic imaging

@article {pmid42300996,
year = {2026},
author = {Tao, Y and Zhang, F and Zhu, D and Zhang, S and Ma, L},
title = {Engineered Injectable Coaxial Supramolecular Hydrogel for a Minimally Invasive Neural Electrode.},
journal = {ACS applied bio materials},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsabm.6c00499},
pmid = {42300996},
issn = {2576-6422},
abstract = {Implantable neural electrodes with long-term stability remain a central challenge for brain-computer interfaces due to the severe mechanical mismatch between rigid electrodes and soft neural tissue, which triggers chronic inflammation and signal degradation. Herein, we report an injectable coaxial supramolecular hydrogel electrode based on dynamic host-guest interactions between β-cyclodextrin and adamantane, combined with silver nanowire incorporation for enhanced electrical conductivity. The resulting hydrogel exhibits shear-thinning and rapid self-recovery behavior, enabling minimally invasive injection and in situ formation of soft, cylindrical neural electrodes without auxiliary insertion devices. By tuning the supramolecular crosslinker density, the hydrogel achieves tissue-matched mechanical properties comparable to those of brain tissue, effectively mitigating a mechanical mismatch at the electrode-tissue interface. The incorporation of silver nanowires establishes a percolated conductive network, leading to low impedance and stable electrochemical performance. In vivo implantation demonstrates stable impedance and reliable neural signal recording over 14 days. Furthermore, the hydrogel electrodes successfully capture stimulus-evoked neural responses and pathological epileptic activity in a rat model. This work provides a versatile strategy for constructing injectable, mechanically compliant, and electrically robust neural electrodes, offering opportunities for next-generation soft neural interfaces.},
}

@article {pmid42301062,
year = {2026},
author = {Pappas, S},
title = {Xiao Yang: Improving brain-machine interfaces to make them more resilient.},
journal = {Scientific American},
volume = {335},
number = {1},
pages = {58},
doi = {10.1038/scientificamerican072026-2AGY9Pv9CG9vZKawSKhyyi},
pmid = {42301062},
issn = {0036-8733},
}