@article {pmid42975207,
year = {2026},
author = {Jiang, Y and Duan, X},
title = {Mapping Brain-Computer Interface Research in Depression (1995-2026): A Bibliometric Analysis.},
journal = {Brain and behavior},
volume = {16},
number = {11},
pages = {},
pmid = {42975207},
issn = {2162-3279},
mesh = {*Brain-Computer Interfaces ; Humans ; *Bibliometrics ; *Depressive Disorder/physiopathology ; *Depression/physiopathology ; *Brain Mapping ; Brain/physiopathology ; },
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) have emerged as promising tools for neuromodulation and emotion regulation in depression. This study aimed to systematically map the global research landscape, thematic evolution, and emerging trends of BCI applications in depressive disorders through bibliometric analysis.

METHODS: Data were retrieved from the Web of Science Core Collection and analyzed using the R package bibliometrix, VOSviewer, and CiteSpace. Publication trends, country and institutional contributions, journal impact, author influence, and keyword-based clustering and burst analysis were comprehensively evaluated.

RESULTS: A total of 239 publications authored by 1284 researchers from 239 institutions across 39 countries were identified. The United States and China had the highest publication outputs, while the United Kingdom showed high citation impact. Laureate Institute for Brain Research was the most productive institution and showed the strongest institutional collaboration, while NeuroImage: Clinical showed the highest journal h-index. Masaya Misaki was the leading author according to publication and citation indicators. Keyword analysis identified six major thematic clusters involving neurophysiological and cognitive features, EEG asymmetry and affective regulation, clinical neurofeedback, connectivity and self-regulation, fMRI-based emotion regulation, and psychiatric symptom/network correlates. "Emotion" showed the strongest keyword burst, while recent bursts in "real-time fMRI neurofeedback," "prefrontal cortex," and "emotion regulation" indicated increasing research attention to circuit-level regulation.

CONCLUSION: The field has evolved from EEG-based characterization toward network-level and real-time neurofeedback research. These findings identify emerging research directions while highlighting the need for further methodological standardization and clinical validation.},
}

*Brain-Computer Interfaces
Humans
*Bibliometrics
*Depressive Disorder/physiopathology
*Depression/physiopathology
*Brain Mapping
Brain/physiopathology

@article {pmid42976382,
year = {2026},
author = {Lee, NKK and Barmpas, K and Koliousis, A and Panagakis, Y and Adamos, DA and Laskaris, N and Zafeiriou, S},
title = {A comprehensive review of biosignal foundation models.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aebd1d},
pmid = {42976382},
issn = {1741-2552},
abstract = {Foundation models are emerging as a promising approach to biosignal analysis, offering solutions to common challenges in learning from biosignal data, such as high variability, noise or limited annotations. This survey provides a comprehensive review of the state-of-the-art in this rapidly advancing field. We introduce commonly used biosignal modalities (including EEG, ECG, EMG, EOG and PPG) and discuss their defining characteristics with respect to training large models. We present a structured survey of existing work across unimodal and multimodal settings covering data processing, feature extraction, model architectures, pre-training paradigms and evaluation methods. We identify open challenges such as benchmarking, interpretability and data availability and highlight possible directions for future research. By consolidating our findings from the current literature and opportunities for progress, we aim to guide the development of robust and universal foundation models for biosignals.},
}

@article {pmid42977015,
year = {2026},
author = {Zhu, Y and Xie, SZ and Li, CY and Hu, JX and Li, XM},
title = {A Dynamic Model of Amygdala Circuits in Threat and Reward: Implications for Affective Disorders.},
journal = {Biological psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.biopsych.2026.10.030},
pmid = {42977015},
issn = {1873-2402},
abstract = {Animals must continuously evaluate their surroundings and select adaptive responses to survive in ever-changing environments. Central to this capacity is the assignment of importance to environmental cues, which guides motivated behavior toward rewarding opportunities and away from potential threats. Here we propose a theoretical framework, a dynamic motivational model based on threat and reward imminence, in which motivational states evolve continuously as a function of imminence rather than shifting between discrete stages. Within this framework, three interacting dimensions define the stimulus relevance of an environmental cue: salience, valence, and value. We leverage the amygdala as a substrate to illustrate the circuit mechanisms underlying importance assignment and examine how stimulus imminence drives motivational state transitions to promote adaptive behavior. Applied to affective disorders, it reframes amygdala dysfunction as projection- and circuit-specific stress adaptation rather than uniform regional hyperactivity, providing a common language for comparing major depressive disorder, anxiety disorders, post-traumatic stress disorder, and substance use disorders. By integrating animal circuit studies with human neuroimaging and clinical evidence, this framework positions amygdala-centered networks as candidate biomarkers for biologically informed subtyping and targets for personalized treatment in depression and related affective disorders.},
}

@article {pmid42977063,
year = {2026},
author = {Wang, L and Zhu, Z and Liu, Y and Qu, J and Li, N and Liu, H and Cao, L and Ma, Z and Wu, H and Wang, J},
title = {Comparative Effects of Neurostimulation- and Technology-Augmented Robot-Assisted Training on Upper-Limb Motor Recovery After Stroke: A Systematic Review and Network Meta-Analysis.},
journal = {Archives of physical medicine and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.apmr.2026.10.014},
pmid = {42977063},
issn = {1532-821X},
abstract = {OBJECTIVE: To compare robot-assisted training (RAT) alone with neurostimulation- or technology-augmented RAT for poststroke upper-limb recovery.

DATA SOURCES: PubMed, Embase, Web of Science, Scopus, Cochrane Library, CNKI, and Wanfang were searched through August 1, 2026.

STUDY SELECTION: Randomized trials evaluated RAT alone or RAT combined with virtual reality, a brain-computer interface, low-frequency electrical stimulation (LFES), transcranial direct current stimulation, or repetitive transcranial magnetic stimulation (rTMS).

DATA EXTRACTION: Two reviewers independently selected studies and extracted data; risk of bias and certainty of evidence were assessed with Cochrane and GRADE methods.

DATA SYNTHESIS: Of 103 trials (5,251 participants), 84 (4,277 participants) entered restricted Fugl-Meyer Assessment for Upper Extremity (FMA-UE) Model E. Compared with usual care (UC), mean differences (MDs) were 9.56 points (95% CI, 7.18-11.93) for rTMS plus RAT, 7.52 (95% CI, 4.48-10.57) for LFES plus RAT, and 3.83 (95% CI, 2.69-4.97) for RAT alone. Heterogeneity was substantial (τ=2.79); global inconsistency was not detected (χ²(9)=3.64; P=.934). rTMS plus RAT ranked first (SUCRA, 96.2%), but its probability of being best decreased from 77.8% to 56.0% after predictive uncertainty was incorporated. Signals for stroke stage (P=.030) and total duration (P=.040) did not meet the multiplicity-adjusted threshold (P<.01).

CONCLUSIONS: rTMS plus RAT yielded the largest FMA-UE estimate, followed by LFES plus RAT. Sparse head-to-head evidence, heterogeneity, and very low-to-moderate certainty preclude a definitive hierarchy and warrant comparative trials.},
}

@article {pmid42977519,
year = {2026},
author = {Yang, L and Xiao, Y and Li, J and Li, M and Li, Z and Ma, Y and Yang, L and Shang, Z},
title = {Learning-dependent hippocampal-striatal reorganization during sequential decision-making in pigeons.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1360-26.2026},
pmid = {42977519},
issn = {1529-2401},
abstract = {Adaptive sequential decision-making requires animals to learn how actions shape future states and rewards while adjusting behavioral strategies as experience accumulates. Although the hippocampus is implicated in state-structure representation and the striatum in value learning, how learning strategies evolve from exploratory sampling to efficient exploitation and how hippocampal-striatal interactions reorganize alongside this transition remain unclear. We trained six pigeons of undetermined sex to perform a probabilistic two-step decision task while simultaneously recording behavioral choices and hippocampal-striatal local field potentials. Strategy evolution was tracked using a dynamic reinforcement-learning framework in which model-based, model-free, and weighted model-based/model-free models were fitted across sliding windows. Hippocampal-striatal functional coordination was assessed using phase-locking analysis, and directed predictive influence was quantified using kernel Granger causality. The weighted model best explained choice dynamics, indicating a shift from model-based-like control during early acquisition toward model-free-like control during later stable performance. This behavioral shift was accompanied by two complementary neural changes: hippocampal-striatal phase coordination increased across learning, whereas hippocampus-to-striatum directed predictive influence decreased and covaried positively with model-based-like strategy weight. Direction-ratio analysis showed that hippocampus-to-striatum remained the predominant direction throughout learning, indicating reduced influence rather than directional reversal. After acquisition, hippocampus-to-striatum directed influence was not uniformly reduced but remained relatively preserved during rare, low-value, unrewarded, or less expected events. Together, these findings reveal a learning-dependent reorganization of hippocampal-striatal dynamics, in which increasing functional coordination is accompanied by reduced but condition-dependent hippocampal-to-striatal influence as behavior becomes more stable and efficient.Significance Statement Adaptive learning requires coordinated changes in behavioral strategy and neural interactions as animals progress from exploration to stable performance. Here, we show that pigeons gradually shifted from flexible, model-based-like control toward more stable, model-free-like behavior during sequential learning. In parallel, hippocampal-striatal interactions underwent a complementary reorganization: phase coordination progressively increased, whereas hippocampus-to-striatum directed predictive influence decreased with declining model-based-like control. Importantly, this directed influence remained relatively preserved during rare, low-value, or unrewarded events. These findings link behavioral strategy transition to the dynamic reorganization of hippocampal-striatal interactions, revealing how neural systems may simultaneously support efficient learned behavior while retaining sensitivity to behaviorally informative events.},
}

@article {pmid42977736,
year = {2026},
author = {Sha, L and Fu, Y and Basang, S and Jin, L and Huang, S and Chen, T and Zhang, J and He, M and Zhang, J and Chen, L},
title = {Epilepsy: epidemiology, risk factors, classification, pathophysiology, clinical features, diagnosis and management.},
journal = {Signal transduction and targeted therapy},
volume = {11},
number = {1},
pages = {},
pmid = {42977736},
issn = {2059-3635},
mesh = {Humans ; *Epilepsy/epidemiology/diagnosis/therapy/classification/physiopathology/genetics ; Risk Factors ; Signal Transduction/genetics ; Diet, Ketogenic ; },
abstract = {Epilepsy is one of the most prevalent and disabling neurological disorders, affecting >50 million people worldwide. Beyond recurrent seizures, it is strongly associated with cognitive impairment, psychiatric comorbidities, reduced social participation, and a markedly increased risk of premature death. Although considerable advances in neuroimaging, pharmacology, and surgical techniques have expanded therapeutic possibilities, nearly one-third of patients remain refractory to current treatments, a challenge that highlights the pressing need for deeper mechanistic insights and innovative therapeutic strategies. This review provides an integrated overview of recent progress in epilepsy research and treatment. At the molecular level, we examine how ion channel dysfunction, receptor abnormalities, and disrupted signaling pathways drive hyperexcitability. We highlight insights from genetics and acquired etiologies that illuminate disease heterogeneity. We address prevailing hypotheses of drug resistance and potential biomarkers. We also emphasize the growing recognition of epilepsy's close association with diverse comorbidities, where shared molecular pathways not only deepen our understanding of disease mechanisms but also highlight new therapeutic targets. Finally, we evaluate emerging therapeutic directions, including precision pharmacology, neuromodulation, gene and cell therapies, and metabolic approaches such as the ketogenic diet. By bridging epidemiological perspectives with mechanistic discoveries and innovative therapies, this review underscores how advances in epilepsy research are reshaping the path toward precision medicine and improved patient outcomes.},
}

Humans
*Epilepsy/epidemiology/diagnosis/therapy/classification/physiopathology/genetics
Risk Factors
Signal Transduction/genetics
Diet, Ketogenic

@article {pmid42910645,
year = {2026},
author = {Hu, C and Ruan, Y and Wang, S and Xia, F and Zou, X and Qin, Y and Chao, Y and Fu, J and Shen, Y and Zhou, YD and Zou, C},
title = {Competitive disadvantage masks growth and metabolic phenotypes in Snord116 knockout mice.},
journal = {Clinical science (London, England : 1979)},
volume = {140},
number = {11},
pages = {2567-2581},
doi = {10.1042/CS20260564},
pmid = {42910645},
issn = {1470-8736},
support = {WKJ-ZJ-2409//National Health Commission Scientific Research Fund/ ; CXTD202501031//Zhejiang Clinovation Pride for Children with Hyperlipidemia/ ; CHZJU2025YY002//Children's Hospital of Zhejiang University School of Medicine Pre Research Fund/ ; },
mesh = {Animals ; Female ; Mice, Knockout ; Male ; Phenotype ; Adiposity/genetics ; Mice ; Body Weight/genetics ; *RNA, Small Nucleolar/genetics ; *Prader-Willi Syndrome/genetics/physiopathology/metabolism ; Homozygote ; Eating/genetics ; Disease Models, Animal ; Mice, Inbred C57BL ; Heterozygote ; },
abstract = {Prader-Willi syndrome (PWS) is characterized by early hypotonia and feeding difficulties, followed by hyperphagia, increased adiposity, and metabolic dysfunction. Although Snord116 knockout (KO) mice recapitulate several features of PWS, they generally exhibit reduced body weight and adiposity in contrast with the clinical manifestations in PWS patients. Here, we compared heterozygous breeding, which generates mixed-genotype litters containing wild-type (WT) and KO pups, with isolated homozygous breeding, which generates single-genotype litters. Under heterozygous breeding, KO mice exhibited reduced body weight and adiposity, together with no impairment, or even improved glucose tolerance. In contrast, KO mice from isolated homozygous litters developed increased adiposity and impaired glucose tolerance despite remaining lighter than WT. These effects were sex-dependent, with adiposity and metabolic abnormalities generally more significant in males than in females. Food intake was also influenced by age, sex, and breeding condition. Increased absolute food intake was most evident during the juvenile period in KO mice from isolated homozygous litters, particularly in males, whereas adult KO mice did not show consistent hyperphagia. Cross-fostering, large-litter, and CD1 foster-nursing experiments further demonstrated that early-life nursing conditions modify subsequent growth, adiposity, and metabolism. Moreover, pre-weaning KO pups exhibited reduced spontaneous movement, weaker grip strength, and lower muscle mass, supporting a potential competitive disadvantage during lactation. These findings indicate that early-life nutritional conditions may mask genotype-associated abnormalities, with isolated homozygous breeding representing a suitable model for investigating PWS phenotypes.},
}

Animals
Female
Mice, Knockout
Male
Phenotype
Adiposity/genetics
Mice
Body Weight/genetics
*RNA, Small Nucleolar/genetics
*Prader-Willi Syndrome/genetics/physiopathology/metabolism
Homozygote
Eating/genetics
Disease Models, Animal
Mice, Inbred C57BL
Heterozygote

@article {pmid42969231,
year = {2026},
author = {Yang, X and Li, P and Lv, M and Wang, L and Shan, R and Jiang, Z and Chen, D and Huang, F and Cai, W and Zhao, L and Guo, Z and Chen, X and Cen, Z and Wang, Y and Yang, W and Li, X and Luo, W and Yang, L and Hu, M and Xu, H},
title = {A rare TMEM175 mutation drives Parkinson's disease pathology by disrupting proton gating and lysosomal acidification.},
journal = {The Journal of clinical investigation},
volume = {136},
number = {21},
pages = {},
pmid = {42969231},
issn = {1558-8238},
mesh = {Humans ; *Lysosomes/metabolism/genetics/pathology ; *Parkinson Disease/genetics/metabolism/pathology ; Animals ; Mice ; Male ; *Protons ; *Mutation ; Dopaminergic Neurons/metabolism/pathology ; Female ; *Ion Channel Gating ; Fibroblasts/metabolism/pathology ; Induced Pluripotent Stem Cells/metabolism/pathology ; *Mutation, Missense ; Potassium Channels ; },
abstract = {Transmembrane protein 175 (TMEM175), implicated in Parkinson's disease (PD) risk, has been variably proposed to function as a background K+ "leak" channel or a proton-activated proton channel in lysosomes. Here, we identified the rare TMEM175 variant p.L275R in a patient with early onset PD. Electrophysiological analyses revealed that TMEM175-L275R mutant proteins retain normal lysosomal expression and K+ "leak" activity, but specifically lose proton- and agonist-dependent activation gating. In skin fibroblasts isolated from the patient with PD harboring the monoallelic p.L275R mutation (TMEM175+/L275R), and in derived induced pluripotent stem cells and induced dopaminergic neurons, lysosomal H+ flux, acidification, and degradation were impaired. These lysosomal defects were corrected upon genome editing to produce the isogenic WT allele (+/+Cor), but appeared when the mutant allele was introduced onto a WT background (+/L275RMut). Pathological α-synuclein aggregation was facilitated in patient-derived (+/L275R) and mutation-introduced (+/L275RMut) neurons. Consistent with these genetic findings, pharmacological activation of TMEM175 with small-molecule agonists restored lysosomal functions and reduced α-synuclein aggregation in TMEM175+/L275R neurons. In in-vivo studies, equivalent-site-mutation knock-in mice demonstrated synucleinopathy and PD-like motor phenotypes. Collectively, our work provides genetic and pharmacological evidence that severe loss-of-function mutations may contribute causally to the onset age and pathogenesis of PD and identifies lysosomal proton channels and transporters as potential therapeutic targets for PD intervention.},
}

Humans
*Lysosomes/metabolism/genetics/pathology
*Parkinson Disease/genetics/metabolism/pathology
Animals
Mice
Male
*Protons
*Mutation
Dopaminergic Neurons/metabolism/pathology
Female
*Ion Channel Gating
Fibroblasts/metabolism/pathology
Induced Pluripotent Stem Cells/metabolism/pathology
*Mutation, Missense
Potassium Channels

@article {pmid42969255,
year = {2026},
author = {Meng, Q and Yu, H and He, J and Di, H and Laureys, S and Huang, Y and Xu, M and Ming, D},
title = {Mapping three decades of consciousness research in neuroscience: a scientometric synthesis of intellectual evolution and emerging frontiers.},
journal = {Reviews in the neurosciences},
volume = {},
number = {},
pages = {},
pmid = {42969255},
issn = {2191-0200},
abstract = {Over the past three decades, consciousness research in neuroscience has expanded into a broad and increasingly diverse field. Despite significant progress, a quantitative understanding of how these research areas have collectively evolved has remained limited. In this study, we conducted a large-scale scientometric analysis of consciousness research in neuroscience based on 22,836 publications and 639,975 cited references retrieved from the Web of Science Core Collection spanning 1998-2025. We identified three major research paths: neural correlates of conscious contents and states, internal awareness and self-consciousness, and integrative approaches bridging these two paths. Emerging hotspots included neural mechanisms of consciousness, subjective experience, disorders of consciousness (DoC), and interoception. Despite increasing boundary-spanning research across different areas, bibliometric connections between DoC and other major topics, particularly subjective experience and interoception, remain comparatively sparse. These findings provide a field-level synthesis of consciousness research in neuroscience, offering a quantitative framework for understanding its development and emerging research directions.},
}

@article {pmid42970939,
year = {2026},
author = {Zhang, R and Zhang, Y and Gou, S and He, Q},
title = {Altered brain-state dynamics and associations with dependence severity and affective-somatic burden in cocaine use disorder.},
journal = {Addictive behaviors},
volume = {185},
number = {},
pages = {108888},
doi = {10.1016/j.addbeh.2026.108888},
pmid = {42970939},
issn = {1873-6327},
abstract = {Cocaine use disorder (CUD) is characterized not only by impaired cognitive control but also by pronounced emotion dysregulation. Although resting-state brain dynamics have been linked to adaptive cognitive and emotional functioning, it remains unclear how these dynamics are altered in CUD and whether such alterations relate to dependence severity and affective-somatic symptom burden. We applied Hidden Markov modeling to resting-state fMRI from 58 individuals with CUD and 44 healthy controls to characterize large-scale brain-state dynamics. CUD was associated with reduced global switching, increased occupancy and persistence of a limbic-dominant state, reduced occupancy of a frontoparietal-ventral attention state, and prolonged persistence of a ventral attention-somatomotor state. Transition analyses further identified higher transition probabilities from default mode-dorsal attention and ventral attention-somatomotor configurations toward the limbic-dominant state. Limbic-state occupancy showed positive associations with dependence severity, emotion dysregulation, somatization, and anxiety. Meta-analytic decoding provided functional context for this state, while exploratory spatial comparisons with normative receptor and gene-expression atlases provided molecular context. Together, these findings identify altered resting-state temporal organization in CUD and suggest that limbic-state occupancy is a potential correlate of dependence severity and affective-somatic burden.},
}

@article {pmid42971363,
year = {2026},
author = {Shi, Y and Feng, S and Wu, Y and Wang, Y and Wuzhati, M and Liu, X and Yin, S and Zheng, C and Cheng, Y and Feng, K},
title = {Individualized transcranial temporal interference stimulation (TIS): Research advancements and clinical translation pathways.},
journal = {Journal of neuroscience methods},
volume = {438},
number = {},
pages = {110945},
doi = {10.1016/j.jneumeth.2026.110945},
pmid = {42971363},
issn = {1872-678X},
abstract = {BACKGROUND: Transcranial temporal interference stimulation (TIS) is a promising tool for non-invasive deep brain modulation, yet inter-individual anatomical differences cause substantial electric field (E-field) variability under identical parameters, hindering clinical translation.

OBJECTIVE: We review recent progress (2017-2026) in individualized TIS, focusing on why inter-individual variability occurs and how to address it for clinical applications.

METHODS: We searched PubMed/MEDLINE, Web of Science, Scopus, IEEE Xplore, preprint repositories (bioRxiv, medRxiv, arXiv), and clinical trial registries from June 2017 through August 2026, synthesizing studies across quantitative E-field variability, biophysical mediation mechanisms, computational optimization frameworks, and human trials.

RESULTS: Even with individualized optimization, the focal spot can shift by up to 1.2 cm between subjects. Target modulation depth varies with age and sex, largely due to variations in skull thickness and cerebrospinal fluid volume. For E-field optimization, individualized finite element method (FEM) modeling remains the most accurate approach, whereas faster population-based optimization (17% error margin) is viable only for superficial brain areas. In human translational studies, a clear distinction must be maintained across levels of evidence: while individualized montages targeting the striatum and subthalamic nucleus demonstrate physiological target engagement, behavioral modulation in healthy cohorts, and preliminary acute symptom relief in pilot patient trials, definitive clinical efficacy in large-scale randomized trials (e.g., ongoing nucleus accumbens protocols) and effective active sham protocols remain ongoing challenges.

CONCLUSION: Individualized planning is essential for deep TIS. Future studies should focus on multi-center trials and standardized in vivo closed-loop validation using functional neuroimaging and electrophysiology.},
}

@article {pmid42972338,
year = {2026},
author = {Mender, MJ and Ward, AL and Cubillos, LH and Kelberman, MM and Costello, JT and Temmar, H and Wallace, DM and Lin, ET and Lam, JLW and Willsey, MS and Ganesh Kumar, N and Kung, TA and Patil, PG and Chestek, CA},
title = {Functional electrical stimulation and brain-machine interfaces for simultaneous control of wrist and finger flexion.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {23},
number = {1},
pages = {},
pmid = {42972338},
issn = {1743-0003},
mesh = {Animals ; *Brain-Computer Interfaces ; *Fingers/physiology ; *Wrist/physiology ; Motor Cortex/physiology ; Movement/physiology ; Spinal Cord Injuries/rehabilitation ; Electrodes, Implanted ; Macaca mulatta ; Electric Stimulation ; Biomechanical Phenomena ; },
abstract = {BACKGROUND: Restoring dexterous hand movements in individuals with cervical spinal cord injury requires continuous control of multiple biomechanically linked degrees-of-freedom (DOF). Brain-machine interface (BMI) controlled functional electrical stimulation (FES) is a promising method to restore hand movements. We explored the suitability of restoring movement to two biomechanically linked DOF simultaneously, finger flexion and wrist flexion, using previously introduced FES methods, namely stimulation targeting individual DOFs independently. We then demonstrate a finger and wrist movement BMI that, when combined with the FES system, could restore simultaneous control of wrist and finger flexion.

METHODS: Two monkeys were implanted with intramuscular electrodes in muscles of the hand. Stimulation on these electrodes was used to evoke finger and wrist movements in both monkeys. Additionally, one of these monkeys was implanted with intracortical electrode arrays in the primary motor cortex and used a BMI to control wrist and finger flexion in a virtual hand without FES.

RESULTS: First we demonstrate a monkey using an intracortical BMI to control the wrist and finger flexion in a virtual hand, both before and after the hand is temporarily paralyzed, achieving success rates and acquisition times equivalent to able-bodied control with BMI control after temporary paralysis in two sessions. We then show that intramuscular FES with two predetermined stimulation patterns, one targeting finger flexion and one targeting wrist flexion, can move the monkeys' fingers and wrist across a functional range of motion. Due to the biomechanical coupling of the wrist and fingers, stimulation targeting finger or wrist flexion individually ultimately evokes movements in both DOF. However, we demonstrate that a simple control strategy treating each DOF independently was able to control both DOF simultaneously in a closed-loop task, achieving greater than 80% success rates.

CONCLUSIONS: We outline a method using an artificial brain-to-body interface that could restore continuous wrist and finger movements after spinal cord injury.},
}

Animals
*Brain-Computer Interfaces
*Fingers/physiology
*Wrist/physiology
Motor Cortex/physiology
Movement/physiology
Spinal Cord Injuries/rehabilitation
Electrodes, Implanted
Macaca mulatta
Electric Stimulation
Biomechanical Phenomena

@article {pmid42973849,
year = {2026},
author = {Qiu, S and Zhang, L and Gao, Q},
title = {Effects of combined non-invasive brain stimulation and brain-computer interface on upper extremity motor function after stroke: a systematic review and meta-analysis.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1993570},
pmid = {42973849},
issn = {1664-2295},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; *Upper Extremity/physiopathology ; *Stroke/physiopathology/therapy ; *Transcranial Magnetic Stimulation/methods ; *Transcranial Direct Current Stimulation/methods ; Recovery of Function ; Randomized Controlled Trials as Topic ; },
abstract = {BACKGROUND: Non-invasive brain stimulation (NIBS) and brain-computer interface (BCI) training are both promising approaches for post-stroke upper extremity rehabilitation, and their combination has been hypothesized to produce synergistic effects. However, the efficacy of this combined strategy has not been systematically evaluated.

METHODS: We systematically searched EMBASE, PubMed, Web of Science, and the Cochrane Library from inception to October 2025 for randomized controlled trials evaluating NIBS combined with BCI in stroke rehabilitation. Mean differences (MDs) in pre- to post-intervention change scores and 95% confidence intervals (CIs) were pooled. Prespecified subgroup analyses were conducted to examine potential moderators.

RESULTS: Seven randomized controlled trials were included. Pooled analysis showed that NIBS combined with BCI did not provide superior immediate upper extremity motor recovery compared with BCI alone and was associated with lower FMA-UE improvement (MD = -3.67, 95% CI -5.32 to -2.01; I[2] = 0%). Subgroup analysis suggested that this effect was mainly driven by studies of tDCS plus BCI, while no significant difference was observed for rTMS plus BCI. No significant long-term effect of tDCS plus BCI or stimulation-site difference for rTMS plus BCI was identified. However, the certainty of evidence ranged from moderate to very low, and sensitivity analyses suggested that the findings were unstable.

CONCLUSION: Current evidence does not demonstrate a clear additional benefit of NIBS as an adjunct to BCI for upper extremity rehabilitation after stroke. Given the limited number of studies and the instability of the pooled result in sensitivity analysis, these findings should be interpreted cautiously. More rigorous and adequately powered trials are needed to determine whether optimized stimulation protocols or specific patient subgroups may benefit from this combined strategy.

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251151708.},
}

Humans
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
*Upper Extremity/physiopathology
*Stroke/physiopathology/therapy
*Transcranial Magnetic Stimulation/methods
*Transcranial Direct Current Stimulation/methods
Recovery of Function
Randomized Controlled Trials as Topic

@article {pmid42973952,
year = {2026},
author = {Luo, Z and Yu, L and Wu, M and Gong, X},
title = {The application of brain-computer interface on cognitive function and activities of daily living in stroke patients: a systematic review and meta-analysis.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1862193},
pmid = {42973952},
issn = {1664-2295},
mesh = {Humans ; *Activities of Daily Living ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Stroke/psychology/complications ; *Cognition/physiology ; },
abstract = {OBJECTIVE: Nonmotor dysfunction is a common sequela of stroke, affecting patients across acute, subacute, and chronic recovery stages and significantly impairing their functional independence, psychosocial adjustment, and overall Activities of Daily Living (ADL). Brain-computer interface (BCI) technology has demonstrated effectiveness in motor function rehabilitation. The primary objective of this study was to assess the efficacy of BCI technology on cognitive function (as the primary indicator of nonmotor dysfunction) and ADL in stroke patients through a systematic review and meta-analysis.

METHODS: This review was conducted in accordance with the PRISMA guidelines and registered with PROSPERO (CRD420261453809). Systematic searches were conducted in PubMed, Web of Science, Google Scholar, CNKI, and Wanfang Data up to March 2026. Statistical analyses and the risk of bias were assessed using Review Manager 5.3. The quantitative synthesis focused on the effects of BCI on the cognitive function and ADL of patients with stroke.

RESULTS: The meta-analysis reviewed 11 randomized controlled studies (RCTs) involving a total of 559 participants. Compared with conventional rehabilitation, BCI-based training significantly improved cognitive function (SMD = 0.634, 95% CI: 0.214, 1.055; p = 0.003) and ADL (SMD = 1.198, 95% CI: 0.079, 2.316; p = 0.036). Subgroup analyses suggested exploratory patterns of benefit in certain strata. However, subgroup analysis revealed no significant differences for most comparisons (including scale type, region, sample size, stroke type, BCI type, intervention intensity, and intervention duration; all p > 0.05), except for first-episode stroke in the ADL analysis (p = 0.001).

CONCLUSION: BCI-based training shows preliminary promise for improving cognitive function and ADL in stroke patients. Current evidence remains insufficient to recommend BCI for emotion or speech rehabilitation. However, the GRADE certainty of evidence was rated as low for cognitive function and very low for ADL, indicating that further high-quality research is warranted.

https://www.crd.york.ac.uk/prospero/, CRD420261453809.},
}

Humans
*Activities of Daily Living
*Brain-Computer Interfaces
*Stroke Rehabilitation/methods
*Stroke/psychology/complications
*Cognition/physiology

@article {pmid42319749,
year = {2026},
author = {Hendriks, S and Beckel-Mitchener, AC and Eberwine, J and Farahany, N and Hsu, N and Ngai, J and Grady, C},
title = {Risk-calibrated sharing of human brain data.},
journal = {Brain : a journal of neurology},
volume = {149},
number = {11},
pages = {3708-3721},
pmid = {42319749},
issn = {1460-2156},
support = {CL090125-01//NIH intramural research program/ ; //NIH/ ; //NCCIH/ ; //NEI/ ; //NIA/ ; //NIAAA/ ; //NIBIB/ ; //NICHD/ ; //NIDA/ ; //NIDCD/ ; //NIMH/ ; CL090125-01//NINDS/ ; //U.S. Department of Health and Human Services/ ; },
mesh = {Humans ; *Information Dissemination/ethics/methods ; *Brain/physiology ; },
abstract = {Investigators collecting human brain data are under growing pressure to share data widely to accelerate scientific discovery and improve outcomes in neurological and psychiatric disorders, while at the same time facing increasing concerns about potential misuse of brain data and threats to mental privacy. Several commentators have therefore called for clearer ethical guidance on brain data sharing. This article reviews the bioethics and neuroscience literature on the risks of sharing individual-level human brain data and adds to it using insights from a National Institutes of Health (NIH) workshop and normative analysis. The substantial burden of brain disorders creates a strong ethical imperative to share data. We argue that the risks of sharing brain data are not uniform and depend on both the likelihood of reidentification and the range of inferences that can realistically be drawn from the data given scientific capabilities. Certain types of brain data are more likely to be reidentifiable and to enable sensitive inferences, such as those related to current and future health, behaviour or identity-relevant traits, and thus could be misused to harm individuals or communities. In these cases, brain data might pose greater risks than many, although not all, other forms of biomedical data. At the same time, strategies to mitigate these risks, such as restricting access or reducing data granularity, might limit the scientific value of shared data. We argue that responsible brain data sharing requires calibration of protections to the specific risks associated with sharing a dataset, enabling data sharing that maximizes scientific and clinical value while protecting participants and maintaining trust. To support this, we propose categories of lower-, medium- and higher-risk data based on inferential sensitivity and the likelihood of reidentification or linkage to harm-relevant groups. We suggest safeguards to consider across these risk levels, including data collection and sharing practices that limit access to data, informed consent approaches and data-use governance.},
}

Humans
*Information Dissemination/ethics/methods
*Brain/physiology

@article {pmid42964133,
year = {2026},
author = {Ahmad, N and Nazeer, H and Naseer, N and Obaid Baig, M and Shakil, S and Kai-Yu Tong, },
title = {Offline and simulated-online fNIRS decoding of ankle movements for prospective exo-ankle control: a pilot study in healthy adults and ankle-impaired patients.},
journal = {Frontiers in neuroergonomics},
volume = {7},
number = {},
pages = {1726286},
pmid = {42964133},
issn = {2673-6195},
abstract = {This study developed and evaluated a non-invasive brain-computer interface (BCI) framework that decodes functional near-infrared spectroscopy (fNIRS) signals from the motor cortex to generate offline and simulated-online exo-ankle control commands, as a proof-of-concept step toward future prospective exo-ankle control. fNIRS data were recorded from 20 healthy participants and seven patients with ankle motor impairment using a PsychoPy-based paradigm of rest, dorsiflexion, and plantar flexion. Following the literature, after preprocessing, an evaluation of different combinations of six statistical features and four classifiers, namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), and k-nearest neighbor (kNN) was conducted. Under leave-one-subject-out (LOSO) cross-validation across 20 healthy participants, SVM achieved the mean offline classification accuracy of 72.50% ± 13.8% followed by kNN (65.83% ± 13.5%), LDA (63.75% ± 16.1%), and QDA (60.42% ± 17.7%). A Friedman test indicated a significant overall difference among classifiers (p = 0.0311). Simulated-online implementation used SVM with a sliding window approach with step sizes of 0.5, 1, and 1.5 s under participant-specific leave-one-trial-out validation. The results showed a highest mean accuracy of 62.2% ± 14.1% at a 1.5-s step size. The patient group showed a lower mean accuracy of 57.23% ± 5% using SVM under leave-one-trial-out cross-validation. Together, these offline and simulated-online results establish proof-of-concept feasibility for fNIRS-based exo-ankle control, providing a foundation for future prospective rehabilitation applications.},
}

@article {pmid42964233,
year = {2026},
author = {Shao, ER and Bodenham, RF and Chilongola, J and Cleaveland, S and Maro, VP and Mujaga, B and Mlay, EA and Shirima, LJ and Pyuza, JJ and Njau, J and Lankester, F and Kayaga, R and Rubach, MP and Crump, JA and Tan, CW and Wang, LF and Rostal, MK and Mmbaga, BT},
title = {Comparison of two Crimean-Congo haemorrhagic fever virus serological tests in Tanzanian febrile patients using a Bayesian latent class approach.},
journal = {Frontiers in microbiology},
volume = {17},
number = {},
pages = {1894712},
pmid = {42964233},
issn = {1664-302X},
abstract = {BACKGROUND: Crimean-Congo haemorrhagic fever virus (CCHFV) is a tick-borne zoonotic disease that can cause severe disease in humans, has a high fatality ratio, and is present in many African countries. Accurate diagnosis of CCHF remains difficult. Serological and molecular tests are frequently used to detect exposure or infection, but their accuracy varies with viral strain, infection stage, and population factors. Notably, there is no universally recognised gold standard test for CCHFV serological diagnosis. The reference standard for antibody detection requires neutralisation assays, which must be performed in biosafety level 4 (BSL-4) laboratories. These facilities are scarce and largely unavailable in sub-Saharan Africa, including Tanzania, where CCHFV is endemic. This study aimed to evaluate the diagnostic performance of the IDvet ELISA and Luminex serological assays for detecting prior CCHFV exposure in humans using Bayesian latent class modelling in the absence of a reference standard.

METHODOLOGY: This was a secondary analysis of archived serum samples from a prospective febrile illness surveillance cohort conducted between 2012-2014 in Moshi, Tanzania, at Kilimanjaro Christian Medical Center and Mawenzi Regional Referral Hospital. Bayesian latent class analysis was used to evaluate the performance of IDvet and Luminex assays in detecting CCHFV exposure in the samples collected.

RESULTS: Of the 870 febrile admissions enrolled across both sites, serum samples were available for 427 participants (49%). The median posterior estimate for IDvet sensitivity was 0.893 (95% Bayesian credibility interval (BCI) = 0.555-0.996) and specificity was 0.972 (95% BCI = 0.936-0.996). For the Luminex assay, the median posterior estimate for sensitivity was 0.884 (95% BCI = 0.541, 0.995) and specificity was 0.987 (95% BCI = 0.947, 1.00). The model generated seroprevalence estimates for two subpopulations, livestock owners and non-livestock owners. The livestock owner seroprevalence estimate was 0.027 (95% BCI = 0.002, 0.069), and for non-livestock owners, 0.017 (95% BCI = 0.001, 0.044).

CONCLUSION: In the absence of a reference standard and access to BSL-4 neutralisation testing, both the IDvet ELISA and the Luminex assay demonstrated high diagnostic performance with comparable sensitivity in detecting prior exposure to CCHFV among febrile patients in northern Tanzania. Furthermore, the IDvet may offer operational advantages because of its simplicity and suitability for decentralised laboratories.},
}

@article {pmid42964313,
year = {2026},
author = {de Jong, IP and Sburlea, AI and Valdenegro-Toro, M},
title = {Uncertainty Quantification in Machine Learning for Biosignal Applications - A Review.},
journal = {Journal of healthcare informatics research},
volume = {10},
number = {4},
pages = {1111-1165},
pmid = {42964313},
issn = {2509-4971},
abstract = {Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning predictions. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal-to-noise ratio, and good human interpretability is pivotal for medical applications. To determine how uncertainty estimation can be used for biosignal tasks, we investigate current methods, use cases, applications, evaluations, and uncertainty measures. In this paper, we systematically review the state of the art of applying Uncertainty Quantification to Machine Learning tasks in the biosignal domain. All works from Web of Science, Scopus, IEEE XPlore and PsycINFO that discuss uncertainty in Machine Learning on one of the aforementioned biosignals is included. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain based on the 53 reviewed papers and related literature. We address misconceptions in the field, provide recommendations for future work, and discuss gaps in the literature in relation to diagnostic implementations as well as control for prostheses or brain-computer interfaces. Overall it can be concluded that promising UQ methods are available, but that research is needed on how people and systems may interact with an uncertainty-model in a (clinical) environment.},
}

@article {pmid42964375,
year = {2026},
author = {Zhao, Y and Sun, XT and Shi, WD and Zhu, CZ and Zhang, L},
title = {Rapid estimation of photon measurement density functions using a deep convolutional neural network for functional near-infrared spectroscopy.},
journal = {Neuroimage. Reports},
volume = {6},
number = {4},
pages = {100425},
pmid = {42964375},
issn = {2666-9560},
abstract = {The photon measurement density function (PMDF) plays a fundamental role in both pre-experimental optode arrangement and post-experimental data analysis in functional near-infrared spectroscopy (fNIRS). Conventionally, PMDFs are derived from structural MR images through tissue segmentation and photon propagation modeling (PPM), which are computationally demanding and time-consuming, thereby limiting their practical use. In this study, we propose a novel deep learning-based framework to estimate PMDFs directly from MR images and channel configurations. The proposed method supports flexible source-detector distances and eliminates the need for explicit tissue segmentation and repeated photon simulations. Specifically, a convolutional neural network is trained to predict photon fluence distributions, from which PMDFs are subsequently derived using the adjoint formulation. The trained model is evaluated on channels placed in both trained and unseen scalp regions across commonly used source-detector distances. The results demonstrate that the proposed method achieves PMDF estimations comparable to those obtained from PPM. Overall, this approach significantly reduces computational cost and has the potential to facilitate broader adoption of PMDF-based methods in the fNIRS community.},
}

@article {pmid42964381,
year = {2026},
author = {Wang, F and Lu, X and Deng, Y and Li, Y and Li, X and Huang, C and Ma, M and He, B and Yu, L and Deng, L},
title = {Comparative efficacy of brain-computer interface training with different feedback modalities for post-stroke upper-limb motor recovery: a systematic review and network meta-analysis of randomized controlled trials.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1925602},
pmid = {42964381},
issn = {1662-4548},
abstract = {BACKGROUND: Brain-computer interface (BCI) training may improve post-stroke upper-limb recovery through closed-loop feedback, but the comparative efficacy of different feedback modalities remains uncertain. This study compared BCI training with different feedback modalities for post-stroke upper-limb motor impairment.

METHODS: PubMed, Embase, Web of Science, Cochrane Library, and Scopus were searched from inception through April 2026 for randomized controlled trials of BCI-based upper-limb rehabilitation after stroke. BCI interventions were classified by feedback modality and feedback contingency into multimodal feedback (BCI-MM), robotic or exoskeleton-assisted feedback (BCI-Robot), functional electrical stimulation feedback (BCI-FES), and visual feedback (BCI-visual), while comparator interventions were classified separately, with usual rehabilitation (UR) as the reference. Random-effects network meta-analysis estimated WMDs with 95% CIs. The primary outcome was Fugl-Meyer Assessment for Upper Extremity (FMA-UE); secondary outcomes were the Action Research Arm Test (ARAT) and Wolf Motor Function Test (WMFT). Risk of bias was assessed using the Cochrane Risk of Bias 2 tool, and certainty was assessed using CINeMA.

RESULTS: Twenty-two trials involving 956 patients across subacute, chronic, and mixed post-stroke stages were included. The FMA-UE network contained 11 connected nodes, with no global or local inconsistency. Compared with UR, BCI-Robot showed greater FMA-UE improvement (WMD = 6.28, 95% CI 2.70-9.86), as did non-BCI robotic training (WMD = 4.26, 95% CI 0.42-8.10) and BCI-MM (WMD = 4.03, 95% CI 1.00-7.06). BCI-FES did not differ significantly from UR. BCI-visual had the highest SUCRA value (77.6%), but its comparison with UR was non-significant, with very low certainty of evidence; no BCI feedback modality was superior to another. Secondary outcomes showed greater ARAT improvement with BCI-Robot versus UR, whereas WMFT findings were non-significant. Certainty for the main FMA-UE comparisons was low or very low.

CONCLUSION: BCI-Robot and BCI-MM showed statistically significant FMA-UE benefits versus UR, although certainty was low. As non-BCI robotic training also showed benefit, the BCI-Robot effect cannot be attributed to BCI alone. No BCI feedback modality was statistically superior to another. Further high-quality direct-comparison trials are needed.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420261370474, Unique Identifier: CRD420261370474.},
}

@article {pmid42966015,
year = {2026},
author = {Li, S and Daly, I and Qin, K and Wang, X and Cichocki, A and Jin, J},
title = {Enhanced P300-based brain-computer interface decoding via region-weighted multiscale temporal-spatial representation learning method.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {206},
number = {Pt B},
pages = {109768},
doi = {10.1016/j.neunet.2026.109768},
pmid = {42966015},
issn = {1879-2782},
abstract = {P300-based brain-computer interface (BCI) systems provide a vital communication channel for individuals with severe physical disabilities. While deep learning has achieved significant progress in signal processing, P300 recognition remains hindered by low signal-to-noise ratios and non-stationary dynamics. Existing deep learning approaches exhibit two primary limitations: they rely on fixed-scale temporal filters that fail to capture multi-resolution event-related potential (ERP) dynamics, and they treat electrode channels homogeneously, overlooking the functional modularity of cortical regions and the interaction between local and global network patterns. To address the above issue, we propose the Region-Weighted Multiscale Temporal-Spatial Neural Network (RWMTS-Net). The model integrates three key components: a region partition module that groups physiologically aligned electrodes, a multiscale temporal-spatial module that captures multi-resolution ERP dynamics across functional brain regions, and an adaptive channel attention mechanism that dynamically couples local dynamics with global network features. Evaluated on a public dataset and a self-collected dataset under participant-dependent conditions, RWMTS-Net significantly surpassed state-of-the-art baselines (including EEGNet, ShallowNet, EEGInception, PPNN, and DeepNet). It achieved superior average character recognition accuracies of 98.93% and 91.79% on the respective datasets, along with enhanced information transfer rates (ITRs), higher AUC, and lower performance variance. These results demonstrate that RWMTS-Net effectively resolves key spatio-temporal representation bottlenecks, offering a highly accurate and robust solution for BCI decoding.},
}

@article {pmid42966748,
year = {2026},
author = {Wang, M and Chen, Q and Tang, E and Qi, S and Tang, R and Zhang, J and Zhou, H and Du, L and Chen, H and Hu, S},
title = {State-dependent effects of rTMS on cognitive function in bipolar II disorder: A randomized, double-blind, sham-controlled trial.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {42966748},
issn = {1476-5578},
support = {52407261, 82201675, 82571735, 32441105, 32171046//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising neuromodulatory approach for cognitive improvement in psychiatric disorders. However, whether concurrent cognitive engagement enhances rTMS-related effects remains unclear.

METHODS: In this exploratory, randomized, double-blind, sham-controlled 2 × 2 factorial trial, 96 euthymic patients with bipolar II disorder and subjective cognitive impairment were assigned to active or sham 10-Hz rTMS combined with either a complex or simple cognitive task. Stimulation targeted the left inferior parietal lobule (IPL) using robot-assisted neuronavigation and was delivered in 10 sessions over 5 consecutive days. The main outcomes included changes in working memory capacity and precision, subjective cognitive performance (Perceived Deficits Questionnaire, PDQ), and resting-state fMRI connectivity within hippocampal and IPL networks. Behavioral outcomes were analyzed using likelihood-based intention-to-treat linear mixed-effects models including time, stimulation, task condition, and their interactions. False-discovery-rate correction was applied across behavioral outcomes.

RESULTS: Eighty-three participants completed the trial. In the intention-to-treat analysis including all 96 randomized participants, no stimulation-by-task interaction survived correction for multiple comparisons. Nominal interactions were observed for overall angular error and angular error at set size 2, but both fell short of the corrected significance threshold. The active rTMS and simple cognitive task group showed the most consistent model-estimated within-group improvements across several working-memory outcomes; however, these patterns did not establish superiority over the other factorial conditions. PDQ scores decreased across all four groups, without significant stimulation, task, or interaction effects. Exploratory neuroimaging analyses indicated divergent functional-connectivity changes across intervention conditions.

CONCLUSIONS: This exploratory trial did not provide statistically robust evidence that concurrent complex cognitive engagement enhanced the acute working-memory effects of IPL-targeted 10-Hz rTMS in euthymic bipolar II disorder, suggesting that task type, cognitive load, and stimulation-task coupling warrant further systematic optimization in future neuromodulation studies.

TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT06782763.},
}

@article {pmid42968092,
year = {2026},
author = {Su, W and Ju, J and Gu, M and Wang, X and Liu, S and Yu, J and Mu, D},
title = {Correction: SARS‑CoV‑2 envelope protein triggers depression‑like behaviors and dysosmia via TLR2‑mediated neuroinflammation in mice.},
journal = {Journal of neuroinflammation},
volume = {23},
number = {1},
pages = {},
pmid = {42968092},
issn = {1742-2094},
}

@article {pmid42962226,
year = {2026},
author = {Sarikonda, A and Quraishi, D and Patel, S and Momin, AA and Lan, M and Koeneman, SH and Self, DM and Chen, AY and Ratan, S and Kabani, AS and Prasad, SK and Jallo, J and Heller, J and Sharan, A and Vaccaro, AR and Harrop, J and Sivaganesan, A},
title = {Is the surgeon or hospital a larger cost driver for lumbar fusion? A hierarchical Bayesian approach using time-driven activity-based costing.},
journal = {Journal of neurosurgery. Spine},
volume = {},
number = {},
pages = {1-10},
doi = {10.3171/2026.5.SPINE26125},
pmid = {42962226},
issn = {1547-5646},
abstract = {OBJECTIVE: Considerable variability exists in lumbar fusion outcomes, with prior studies demonstrating significant differences across both surgeons and hospitals. However, similar analyses for cost are lacking, making it unclear whether surgeons or hospitals are larger cost drivers for lumbar fusion, particularly in multihospital health systems. Therefore, the aim of this study was to perform a risk-adjusted time-driven activity-based costing (TDABC) analysis for single-level lumbar fusion to compare overall cost variance between surgeons and hospitals in a multihospital health system.

METHODS: This retrospective cohort study included single-level lumbar fusions performed (2020-2024) at a healthcare network encompassing several quaternary and community hospitals. TDABC was used to calculate the true intraoperative cost (not charges or reimbursement) of each case. Hierarchical Bayesian regression models were fit for total cost, and its primary drivers: supply cost and personnel cost. Random effects were specified for surgeon and hospital site, while fixed covariates included patient- and procedure-specific factors. Markov chain Monte Carlo sampling was performed to generate posterior draws for all model parameters, from which posterior medians and 95% Bayesian credible intervals (BCIs) were reported.

RESULTS: This analysis included 943 lumbar fusions performed by 23 surgeons across 9 hospitals. The mean total cost of surgery was $23,106 ± $10,475, driven by supplies (mean $20,344 ± $10,010) and personnel (mean $2755 ± $1019). Nonoverlapping BCIs demonstrated that between-surgeon variance was greater than between-site variance for both total cost and supply cost. Specifically, surgeon-level variance was 30-times greater than site-level variance for total cost (median 29.8, 95% BCI 1.4-10,756.4) and 53-times greater for supply cost (median 52.5, 95% BCI 2.0-28,374.7). Furthermore, for each 1 standard deviation difference between surgeons, there was a 29.9% (95% BCI 18.2-48.5) increase in total cost and a 35.2% (95% BCI 21.1-57.3) increase in supply cost, whereas comparable differences between sites were associated with only 5.5% (95% BCI 0.3-23.8) and 4.8% (95% BCI 0.2-23.4) increases in cost, respectively. In contrast, surgeon and site contributions were comparable for personnel cost, with overlapping BCIs.

CONCLUSIONS: In cases of single-level lumbar fusions performed at a multihospital health system, variation in cost between surgeons was 30-times greater than variation between hospitals, although residual/unexplained variance was considerable. These hypothesis-generating findings might enhance physician awareness of cost drivers and practice heterogeneity. However, additional research is necessary to identify sources of unexplained variance, as well as to integrate costs with outcomes to determine whether cost variability represents value or waste.},
}

@article {pmid42962808,
year = {2026},
author = {Niu, Q and Nie, Y and Li, X and Li, Y and Quan, Z and Luo, H and Xiao, X and Wang, S},
title = {Differential effects of γ-state-triggered thalamic stimulation on mechanical sensitivity in rats with neuropathic pain.},
journal = {Brain stimulation},
volume = {19},
number = {6},
pages = {103250},
doi = {10.1016/j.brs.2026.103250},
pmid = {42962808},
issn = {1876-4754},
abstract = {OBJECTIVE: To develop and evaluate a thalamic γ-state-triggered closed-loop (CL) deep brain stimulation (DBS) strategy for modulation of neuropathic pain.

METHODS: Neuropathic pain was induced with chronic constriction injury (CCI) in rats. Thalamic local field potentials (LFPs) were used to identify wavelet-packet (WP)-defined gamma (γ)-band states, γ[0] and γ[1], using coefficient magnitudes relative to an adaptive threshold and temporal confirmation. A CL DBS paradigm triggered stimulation contingent on either γ[0] or γ[1] states and was compared with conventional open-loop (OL) 40 Hz and 130 Hz stimulation using behavioral and neurophysiological outcomes.

RESULTS: CCI increased thalamic γ power on days 3, 7 and 14 (raw p = 0.0004, 0.0078 and 0.0162; FDR-adjusted p = 0.0077, 0.0703 and 0.0731) and prolonged γ[1]-state duration (raw p = 0.0283; adjusted p = 0.1417). In the CCI model, γ power was negatively associated with mechanical withdrawal threshold (MWT; raw p = 0.0159; adjusted p = 0.0956). At 130 Hz, mean MWT changes were opposite under the two CL protocols: γ[0] CL increased MWT by 47.07% and γ[1] CL decreased it by 12.06% (raw p = 0.0234 and 0.0391; adjusted p = 0.0469 and 0.0586). The 40 Hz γ[0] CL protocol produced the largest observed mean MWT increase of 81.91% (raw p = 0.0078; adjusted p = 0.0469), 46.02 percentage points above 40 Hz OL. Electrophysiological analyses also showed protocol-associated changes in γ-state dynamics and nominal spectral-power changes.

CONCLUSION: The tested WP-defined γ-state-triggered CL protocols were associated with differing changes in mechanical sensitivity in neuropathic rats. These findings support further evaluation of this approach as a candidate framework for adaptive neuromodulation of neuropathic pain.},
}

@article {pmid42962828,
year = {2026},
author = {Yuan, X and Zhang, L and Zhou, A},
title = {EEG feature-level integration for robust command generation in SSVEP-based brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {438},
number = {},
pages = {110938},
doi = {10.1016/j.jneumeth.2026.110938},
pmid = {42962828},
issn = {1872-678X},
abstract = {BACKGROUND: Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been widely studied for efficient command generation via spatiotemporal information integration and advanced stimulation paradigms. However, users may intentionally or unintentionally suppress SSVEP responses, leading to the SSVEP Blocking Response (SSVEP-BR). This phenomenon, characterized by attenuation or disappearance of SSVEP, has been largely overlooked despite its potential for command generation without additional stimuli.

NEW METHOD: This study aimed to exploit SSVEP-BR to enhance command generation under limited visual stimulation. A novel detection method was proposed based on alpha rhythm sensitivity during transitions between SSVEP and SSVEP-BR states. An EEG feature-level integration strategy was further developed to integrate correlation and alpha rhythm features, enabling effective characterization of their temporal evolution. An asynchronous BCI system was implemented for real-time robotic arm control.

RESULTS: Experiments on 11 offline subjects demonstrated that the proposed method improved SSVEP-BR detection performance. Compared with the previous SSVEP-BR detection framework, average improvements of 17.11%, 8.50%, and 5.44% were achieved in precision, recall, and specificity, respectively. Furthermore, an online robotic grasping experiment was conducted with 5 subjects, where the proposed asynchronous BCI system achieved 8-command generation using two stimulation frequencies.

Unlike prior studies focusing on algorithmic optimization or stimulation design, this work introduced SSVEP-BR as an additional controllable neural response and demonstrated its effectiveness in improving command generation without increasing stimulus complexity.

CONCLUSIONS: Incorporating SSVEP-BR into SSVEP-based BCI frameworks improved flexibility and robustness, enhancing the practicality of asynchronous brain computer interaction systems.},
}

@article {pmid42963327,
year = {2026},
author = {Liao, Y and Sim, MA and Cui, J and Zhang, H and Hilal, S and Chong, EJ and Toh, A and Tan, BY and Venketasubramanian, N and Richards, AM and Xu, X and Tham, YC and Chen, C},
title = {Retinal microvasculature alterations are associated with mild behavioral impairment in Southeast Asian dementia-free older adults.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {22},
number = {10},
pages = {},
pmid = {42963327},
issn = {1552-5279},
support = {KCG/2023/NUSMED//National University of Singapore Yong Loo Lin School of Medicine/ ; NCSP2.0/2023/NUHS/SMA//National University Health System (NUHS) Clinician Scientist Academy/ ; NMRC/CG/NUHS/2010//National Medical Research Council/ ; NMRC/CG/013/2013//National Medical Research Council/ ; MOH-000707//National Medical Research Council/ ; },
mesh = {Humans ; Female ; Male ; Aged ; *Retinal Vessels/pathology/diagnostic imaging ; *Microvessels/pathology/diagnostic imaging ; *Cognitive Dysfunction/pathology/diagnostic imaging ; Neuropsychological Tests ; Magnetic Resonance Imaging ; Aged, 80 and over ; },
abstract = {BACKGROUND: Mild behavioral impairment (MBI) is an important neurobehavioral marker of dementia. This study examined associations between retinal vascular measures, MBI, and its potential neurobiological correlates.

METHODS: Dementia-free older adults from a memory clinic cohort were followed for up to 5 years. MBI was evaluated using the Neuropsychiatric Inventory. Retinal vessel caliber measures were quantified from fundus photographs. Associations of retinal vascular measures with neuroimaging markers of cerebral small vessel disease and plasma proteomic profiles were further explored.

RESULTS: Among 302 participants, reduced arteriole-to-venule ratio (AVR) and narrower arteriole caliber (central retinal artery equivalent) were associated with a greater likelihood of incident MBI. Lower AVR was associated with white matter hyperintensity volume and circulating proteins related to immune function and inflammatory processes.

CONCLUSION: Retinal microvascular alterations are associated with MBI in the pre-dementia stage, with exploratory findings suggesting related vascular and inflammatory biological correlates that warrant further investigation.},
}

Humans
Female
Male
Aged
*Retinal Vessels/pathology/diagnostic imaging
*Microvessels/pathology/diagnostic imaging
*Cognitive Dysfunction/pathology/diagnostic imaging
Neuropsychological Tests
Magnetic Resonance Imaging
Aged, 80 and over

@article {pmid42964054,
year = {2026},
author = {Gao, J and Lin, D and Hu, J},
title = {Governing brain-computer interfaces as embodied autonomous neural systems: an ethical framework for neurorobotic governance in education.},
journal = {Frontiers in neurorobotics},
volume = {20},
number = {},
pages = {1925089},
pmid = {42964054},
issn = {1662-5218},
abstract = {The rapid development of neurorobotic systems, particularly non-invasive brain-computer interfaces (BCIs) deployed in educational contexts, raises urgent ethical questions about child protection and societal impact. Neurorobotic systems acquire biological signals through embodied sensors, process them via brain-inspired algorithms, and actuate physical responses in closed-loop architectures that directly affect vulnerable learners. We propose Neuro-Adaptive Learning Design (NALD) as a mechanism to translate the capabilities of such neurorobotic systems into ethical safeguards across five functional stages (Sense, Analyze, Decide, Intervene, and Evaluate) that characterize embodied autonomous neural systems. Building on Ethics by Design, we introduce Governance by Design (GbD), incorporating ethical mandates into the technical architecture of neurorobotic systems. The Ethical NALD (E-NALD) framework maps 15 concrete safeguards onto NALD stages, grounded in the Belmont Report and operationalized through the UNESCO 2025 Recommendation on the Ethics of Neurotechnology and the UN Convention on the Rights of the Child. By positioning educational BCIs within the broader context of neurorobotics, this study evaluates the potential societal impact of deploying embodied autonomous systems in compulsory learning environments and offers a governance framework that bridges the gap between abstract neuroethics principles and enforceable technical requirements for neurorobotic technologies. The framework addresses the unique vulnerabilities of children as primary users of neurorobotic systems, including neurodivergent learners who may be simultaneously the most likely beneficiaries and the most at-risk populations.},
}

@article {pmid42960440,
year = {2026},
author = {Liu, W and Shuo, L},
title = {Depth-adaptive suppression of subject-dependent interference for robust cross-subject EEG emotion recognition.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {42960440},
issn = {1862-278X},
abstract = {OBJECTIVES: Cross-subject electroencephalography (EEG) emotion recognition is limited by inter-subject variability, which embeds subject-specific nuisance information in learned representations and reduces generalization to unseen individuals.

METHODS: We propose a two-stage layer-adaptive subject-interference suppression framework built on EEG Conformer. In the search stage, differentiable gates and adversarial subject classifiers are attached to hierarchical representations from the patch embedding layer and Transformer blocks to estimate where subject information is most strongly encoded. The top-ranked layers are then fixed and the model is retrained for final emotion classification. A subject-aware supervised contrastive regularizer is further used to improve label-consistent cross-subject representation learning.

RESULTS: Experiments on SEED under strict leave-one-subject-out evaluation show improved performance over representative machine-learning, domain-adaptation, and deep-learning baselines. Auxiliary experiments on SEED-IV indicate adaptability under a more challenging label setting. Layer-wise probing shows that subject-specific information remains decodable across depths but is relatively stronger in shallow and early intermediate representations.

CONCLUSIONS: Selective depth-aware suppression provides a practical and interpretable strategy for robust EEG-based affective brain-computer interfaces.},
}

@article {pmid42952986,
year = {2026},
author = {Paul, S and Bajwa, G},
title = {Federated Spiking GANs with Rényi Differential Privacy for privacy-preserving synthetic EEG generation in brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1909432},
pmid = {42952986},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) data used in brain-computer and brain-machine interfaces (BCI/BMI) contain sensitive, user-specific information, creating privacy risks in consumer applications such as gaming, human augmentation, and virtual reality. Existing methods like encryption and anonymization either fail to protect data during analysis or still expose identifiable attributes. While differential privacy (DP) offers formal protection, it often reduces data utility. The key challenge is to generate synthetic EEG data that protects sensitive attributes while retaining temporal features for downstream use. To address this, we developed a federated spiking generative framework that integrates a spiking neural network (SNN)-based generator, temporally correlated noise, an artificial neural network (ANN)-based discriminator, and Rényi differential privacy (RDP) within a federated learning framework. The SNN-based generator captures temporal characteristics, and the ANN-based discriminator maintains classification accuracy. Federated training prevents centralization of raw client data, while privacy is preserved through controlled perturbation of spike-train and membrane-potential representations, supported by RDP-based privacy accounting. We evaluated our architecture against methods such as RDP-TimeGAN, RDP-CRNN-GAN, RDP-RCGAN, and RDP-Clare-GAN, demonstrating that our approach preserves temporal features and downstream utility while maintaining privacy in synthetic EEG generation. This solution enables privacy-preserving generation of distributed synthetic EEG data for BCI/BMI research and development.},
}

@article {pmid42953001,
year = {2026},
author = {Liu, K and Zhu, W and Xu, R and Wang, W and Yu, Z and Wu, W},
title = {Beyond Static Representations: Learning Electroencephalogram Spatiotemporal Dynamics for Enhanced Emotion Recognition.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0701},
pmid = {42953001},
issn = {2692-7632},
abstract = {Electroencephalogram (EEG)-based emotion recognition plays a pivotal role in brain-computer interfaces and mental health monitoring. However, conventional approaches often neglect the intrinsic spatial correlations among EEG channels and fail to effectively model both global temporal dependencies and the continuity of neural oscillations. To overcome these challenges, we propose the Dynamic Spatial Embedding and Temporal Attention Mamba (DSE-TAM) framework. Our Dynamic Spatial Embedding (DSE) module leverages graph convolutional networks to dynamically capture inter-channel relationships, preserving spatial coherence in EEG representations. Subsequently, the Temporal Attention Mamba module integrates self-attention with state-space models to jointly optimize long-range temporal dependencies and smooth transition dynamics. Extensive experiments on 2 public benchmarks (DEAP and DREAMER) and an in-house CEED dataset demonstrate that DSE-TAM surpasses state-of-the-art methods, achieving accuracies of 79.75% (arousal) and 78.27% (valence) on DEAP, 80.55% (arousal) and 83.91% (valence) on DREAMER, and 73.75% (3-class) on CEED. These results validate the framework's efficacy in advancing EEG-based emotion decoding for brain-computer interfaces. The source code is available at https://github.com/Raizeroko/DSE-TAM.},
}

@article {pmid42953176,
year = {2026},
author = {Spencer, CA},
title = {A two-stage RAG-LLM pipeline for roadmapping neuroscience research in operational environments.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1884892},
pmid = {42953176},
issn = {1662-5161},
abstract = {INTRODUCTION: Neuroscientific research and applications in real world environments are often constrained by limited access, equipment limitations, and high costs. Consequently, robustly selecting constructs to focus effort on is a high-impact task.

METHOD: This study introduces a two-stage large language model (LLM) pipeline that generates research roadmaps from existing real-world data to inform research priorities in the field, given the state of the art. Stage 1 uses retrieval-augmented generation on operational logs to identify candidate psychological, social, and neurocognitive constructs and map their co-occurrence. Stage 2 evaluates how well these constructs are represented in the literature across four dimensions that commonly inform prioritization: ecological validity, neural correlates, biosensor protocols, and brain computer interface readiness, producing an evidence gap matrix. Pipeline evaluation combined human review, deterministic computations, and human-LLM alignment analysis.

RESULTS: Applying the pipeline to Mars Desert Research Station logs, Stage 1 identified 19 constructs. Stage 2 highlighted critical gaps in neuroimaging and measurement protocols, particularly for social domains, thereby identifying potential research priorities and risks.

DISCUSSION: This methodology offers a generalizable starting point for synthesizing operational logs into actionable research roadmaps, but the evaluation showed it still required human scrutiny.},
}

@article {pmid42954022,
year = {2026},
author = {Rustamov, N},
title = {Brain-computer interface technology for chronic pain management.},
journal = {Expert review of neurotherapeutics},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/14737175.2026.2752959},
pmid = {42954022},
issn = {1744-8360},
abstract = {INTRODUCTION: Chronic pain remains inadequately managed by pharmacological and interventional therapies, which carry risks including opioid-related complications and limited long-term efficacy. Brain-computer interfaces (BCI) have emerged as a non-pharmacological therapeutic approach, most commonly implemented using noninvasive modalities such as scalp EEG or fMRI, that aims to target neural activity patterns associated with chronic pain.

AREAS COVERED: This review examines the principles underlying BCI systems and their application in chronic pain, including EEG-based neurofeedback targeting oscillatory activity, infraslow-frequency neurofeedback, a theta-controlled vibrotactile BCI, real-time fMRI-based neurofeedback, and emerging closed-loop neuromodulation systems. Advantages, limitations, and future directions are discussed.

EXPERT OPINION: BCI technology represents a promising, but still early-stage, approach for chronic pain management. Clinical translation will depend less on decoding accuracy than on addressing regulatory and reimbursement barriers, conducting adequately powered, sham-controlled trials, and implementing patient stratification. Convergence with artificial intelligence and closed-loop neuromodulation is promising, but algorithmic sophistication should not be equated with clinical applicability. Wearable platforms raise data privacy and transparency concerns. BCIs are likely to be adjuncts within multimodal pain management, not replacements for existing treatments. Preliminary evidence supports feasibility, but adequately powered, sham-controlled trials are needed to establish treatment-specific and durable efficacy.},
}

@article {pmid42955373,
year = {2026},
author = {Li, T and Liu, X and Meng, T and Liu, S and Ming, D},
title = {Changes in gait function associated with cerebral small vessel disease-related cognitive impairment during dual-task walking: a systematic review and meta-analysis.},
journal = {Journal of neurology},
volume = {273},
number = {11},
pages = {},
pmid = {42955373},
issn = {1432-1459},
support = {62376187//National Natural Science Foundation of China/ ; 24ZXZSSS00330//Natural Science Foundation of Tianjin/ ; },
mesh = {Humans ; *Cerebral Small Vessel Diseases/complications/physiopathology ; *Cognitive Dysfunction/physiopathology/etiology ; Dual-Task Tests ; *Gait Disorders, Neurologic/etiology/physiopathology ; *Walking/physiology ; },
abstract = {Cerebral small vessel disease (CSVD) is a major contributor to vascular cognitive impairment. As gait abnormalities emerge early in the disease course and are closely related to cognitive decline, quantitative gait assessment offers a promising approach for the early detection of CSVD-related cognitive impairment (CSVD-CI). Dual-task (DT) gait assessment increases cognitive demands during walking and may reveal cognitive-motor deficits that are less apparent under single-task (ST) conditions. However, heterogeneity in gait metrics, imaging criteria, and cognitive paradigms has resulted in inconsistent findings, thereby impeding the identification of robust gait markers for CSVD-CI. A systematic search of PubMed, Web of Science, Scopus, and EBSCO identified nine studies reporting DT gait outcomes and eight reporting ST gait outcomes in patients with CSVD-CI, with four studies reporting outcomes under both conditions. Compared with ST conditions, DT gait assessment more effectively discriminated patients with CSVD-CI from cognitively unimpaired controls. Under DT conditions, CSVD-CI patients exhibited significantly slower gait speed (p = 0.014), greater stride time variability (p = 0.027), and higher dual-task costs (p < 0.001), suggesting a slower and less stable gait pattern. Subgroup and sensitivity analyses suggested that CSVD imaging characteristics may contribute to between-study heterogeneity, although these findings remain exploratory. These findings support further investigation of selected DT gait parameters as potential behavioral indicators of CSVD-CI. Further studies in CSVD populations defined using standardized diagnostic and inclusion criteria should evaluate the screening performance and longitudinal predictive value of DT gait parameters, as well as their associations with CI severity.},
}

Humans
*Cerebral Small Vessel Diseases/complications/physiopathology
*Cognitive Dysfunction/physiopathology/etiology
Dual-Task Tests
*Gait Disorders, Neurologic/etiology/physiopathology
*Walking/physiology

@article {pmid42955741,
year = {2026},
author = {Yang, J and Huang, M and Yang, R},
title = {Attention-BrainVerse: A Self-Adaptive Hybrid BCI-VR Interaction Model Driven by Attention.},
journal = {IEEE transactions on visualization and computer graphics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TVCG.2026.3748051},
pmid = {42955741},
issn = {1941-0506},
abstract = {Brain-computer interface (BCI) is an emerging interactive technology that, when integrated with virtual reality (VR), exhibits significant potential for diverse applications. However, widespread adoption of BCI-VR systems remain hindered by challenges such as poor usability, high hardware costs, and a lack of established design guidelines. To address these issues and enhance user experience, this study proposes an attention-driven self-adaptive hybrid BCI-VR interaction model, Attention-BrainVerse. This model innovatively redefines the three core components of BCI-VR interaction: attention-centered input, self-adaptive processing, and feedback-based output. It incorporates our proposed hybrid input modalities, self-adaptive processing with dynamic threshold and dual-factor continuous difficulty adjustment, and output mechanisms based on attention visualization techniques and visual perspectives. To evaluate the proposed interaction model, three rounds of user experiments were conducted. Results demonstrate that, Attention-BrainVerse achieves a user experience comparable to that of traditional VR control methods. The main contribution of this work is the establishment of Attention-BrainVerse as a novel design framework that bridges critical gaps in BCI-VR interaction through its innovative integration of adaptive processing and hybrid input-output mechanisms, ultimately providing both theoretical guidance and practical design pathways for future BCI-VR interaction systems to facilitate the broader adoption of this integrated technology.},
}

@article {pmid42956410,
year = {2026},
author = {Canario, E and Shearer, C and Akcakaya, M and Weber, DJ and Chase, SM and Collinger, J},
title = {Motor cortex-based intracortical BCI performance is robust to changes in attentional load during dual-tasking.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aebb65},
pmid = {42956410},
issn = {1741-2552},
abstract = {High performance intracortical brain-computer interface (iBCI) control has been demonstrated in research settings, but performance can still vary within and between sessions. One potential source of this variability is the change in attentional load that comes from processing naturally occurring distractors such as thoughts, sounds, fatigue, or pain. To improve the consistency of iBCI performance in real-world environments where this sort of multi-tasking is inevitable, we must understand how shifts in attention can impact performance. Approach: Here we examined the effect of attentional load on iBCI performance and movement-related neural activity using a 2D cursor translation + click iBCI task paired with an N-Back working memory task to increase attentional load during dual-task performance. Two participants (P2 and P4) with tetraplegia completed the study while enrolled in a long-term clinical trial of an iBCI device. Common neural correlates of attention (theta and alpha band power) were measured with simultaneously recorded scalp electroencephalography (EEG). Main Results: While the EEG metrics and difficulty ratings suggested increased attentional load during dual-tasking, iBCI performance was quite robust across the various dual-tasking conditions. One participant, P2, experienced a small but significant increase in trial completion time and normalized path length during the mild attentional load condition compared to the moderate load condition. Signal quality differences between the two participants may have impacted the results, as P2 had lower signal quality and was therefore likely more vulnerable to attentional load. P4's higher signal quality likely allowed him to accommodate increased attentional load without a drop in performance. Significance: Overall, iBCI performance appears to be robust to attentional load, but the complex trends observed here reflect a need for continued investigation of BCI use under different cognitive states to elucidate potential challenges and compensatory mechanisms across participants. .},
}

@article {pmid42956414,
year = {2026},
author = {Shin, Y},
title = {Spectral smoothing explains the offline gain from cross-order single-trial MEG movement decoding for non-invasive BCI.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aebb69},
pmid = {42956414},
issn = {1741-2552},
abstract = {Acceleration, velocity, and position can be combined in non-invasive movement decoding, but in this dataset only acceleration is measured; velocity and position are deterministic transforms obtained by integration. We therefore tested whether cross-order decoder fusion improves estimation in position space and whether any apparent gain remains after matching the spectral consequences of those transforms. Approach. We analysed single-trial MEG recordings from nine participants (18 runs; 2,160 trials) using a deterministic five-fold split. The primary outcome was within-trial Pearson correlation in position-residual space after removing trial-invariant trajectory. Acceleration-, velocity-, and position-based routes were fused and compared with same-order bagging, decorrelation-matched controls, and a bandwidth-matched pseudo-fusion of filtered copies of the direct position decoder. We additionally tested implementation sensitivity, residual-space R[2], non-positive-lag and causal pipelines, artefact sensitivity, multi-task architectures, and between-run transfer. Main results. Cross-order fusion exceeded same-order bagging by +0.0389 per-trial r (95% CI [+0.0288, +0.0499]; 9/9 participants). The bandwidth-matched pseudo-fusion exceeded bagging by +0.0424 and reproduced 109.0% of the offline margin, leaving E_cross - E_spec = -0.0035 (95% CI [-0.0105, +0.0032]; upper one-sided 95% bound +0.003). A nested-cross-validated smoothed single decoder likewise matched the ensemble. On residual-space R[2] with a training-fitted gain, the ordering reversed (E_bag -0.099, E_cross -0.120), showing that the correlation gain did not improve amplitude fidelity. With non-positive lags only, bandwidth matching explained 79% of the margin; under a fully causal zero-look-ahead pipeline, a smaller residual persisted and its mechanism was underpowered to resolve. Between-run transfer within the same visit retained only about 18% of within-run accuracy. Significance. In the primary offline regime, cross-order fusion gains can arise from integration-induced spectral smoothing without additional cross-order neural information. A bandwidth-matched surrogate should precede mechanistic interpretation when decoder routes are linked by deterministic transforms. In the causal regime, the advantage remains available online, but its mechanism remains unresolved.},
}

@article {pmid42949615,
year = {2026},
author = {Lavazza, A},
title = {Promises but Also Perils: Why We Should Not Dismiss Mental Surveillance Risks in Neurotechnology Research.},
journal = {The Hastings Center report},
volume = {56},
number = {5},
pages = {28-30},
pmid = {42949615},
issn = {1552-146X},
support = {//Dana Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Privacy ; Confidentiality ; },
abstract = {Speech brain-computer interfaces may restore communication to people who have lost the ability to speak. In the article "Speech Restoration and Mental Surveillance as Conflicting Ethical Concerns in Neural Speech-Decoding Research," Narayan Sankaran and Winston Chiong distinguish systems that decode intended speech from those that might reveal thoughts a person does not wish to communicate. This commentary argues that the surveillance question also extends beyond the device being tested. Research for clinical purposes produces methods, expertise, and infrastructure that could later be adapted for other uses, although present speech interfaces cannot simply be turned into surveillance tools. Mental privacy matters because it protects the space in which people reflect, revise their judgments, and decide what to share. Patients may reasonably accept some privacy risks to communicate, but their choices cannot authorize future uses that affect others. Researchers, funders, and regulators should therefore consider misuse early, involve prospective users in design decisions, and develop safeguards, including international coordination, that preserve access to speech restoration while limiting unwanted neural monitoring.},
}

Humans
*Brain-Computer Interfaces/ethics
*Privacy
Confidentiality

@article {pmid42949653,
year = {2026},
author = {Kong, Y and Zhou, M and Yuan, X and Luo, X and Huang, J and Li, Y and Yang, L and Wang, Y and Sun, X and Sun, L and Song, Y},
title = {Susceptibility to task-irrelevant auditory distractors in relation to visual working memory in children with and without attention-deficit/hyperactivity disorder.},
journal = {Child development},
volume = {},
number = {},
pages = {},
doi = {10.1093/chidev/aacag195},
pmid = {42949653},
issn = {1467-8624},
support = {//Brain Science and Brain-like Intelligence Technology/ ; 2021ZD0200500//National Science and Technology Maior/ ; 32601448//National Natural Science Foundation of China/ ; 32271094//National Natural Science Foundation of China/ ; 82672007//National Natural Science Foundation of China/ ; 82371549//National Natural Science Foundation of China/ ; 23BSH118//National Social Science Foundation of China/ ; },
abstract = {Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder associated with increased distractibility. Previous research has reported that susceptibility to task-irrelevant visual distraction is linked with visual working memory (WM) capacity in adults. Here, we examined how neural susceptibility to task-irrelevant auditory distractors related to visual WM capacity in children with and without ADHD. Across two experiments, electroencephalography (EEG) signals were recorded from 146 Chinese children aged 7-15 (63 with ADHD, 23% girls; collected in 2022-2024), during which they performed visual tasks of varying difficulty with auditory distractors. The mismatch negativity (MMN) amplitude was measured as neural susceptibility to auditory distractors, and a K-score was calculated to assess visual WM capacity. In typically developing (TD) children, those with higher visual WM capacity showed less susceptibility to auditory distractors under high audiovisual competition (r = 0.494), whereas this relation reversed under low competition (r = -0.319). Conversely, in children with ADHD, lower visual WM capacity predicted higher susceptibility under both high (r = 0.442) and low competition (r = 0.326). These findings indicate that susceptibility to task-irrelevant auditory distraction is related to visual WM capacity in children, but the patterns differ between typical and atypical neurodevelopment and are modulated by audiovisual competition.},
}

@article {pmid42950624,
year = {2026},
author = {Mo, F and Xu, Y and Zhao, H and Li, S and Dai, Y and Song, Z},
title = {A synchronized dual-scale platform combining flexible micro-ECoG and skull-screw EEG reveals frequency-dependent cortex-to-skull spectral gain in awake mice.},
journal = {Biosensors & bioelectronics},
volume = {317},
number = {},
pages = {119370},
doi = {10.1016/j.bios.2026.119370},
pmid = {42950624},
issn = {1873-4235},
abstract = {Electroencephalography (EEG) is the most scalable window onto brain dynamics, yet how cortical activity is transformed before reaching extracranial electrodes has largely been inferred from forward models rather than directly measured. We built a dual-scale sensing platform in awake mice that records a 16-channel flexible micro-electrocorticography (micro-ECoG) array and five skull-screw EEG electrodes on a single 30-kHz clock, enabling sample-aligned comparison of cortical and skull-level signals. Across 115 simultaneously recorded ECoG-EEG pairs from three mice, cortex-to-skull spectral gain was not frequency-flat but increased with frequency following a power law, indicating that skull EEG relatively preserves high-frequency cortical activity rather than acting as a simple low-pass copy. This gain was also non-stationary: it shifted across blocks of a fixed-order auditory protocol and was not explained by measured video motion, whereas trial-matched cross-scale coherence depended on the reference transform. With channel number equalized, micro-ECoG retained a larger unique spatial component than skull EEG. These results convert the cortex-to-skull relationship from a static modeling assumption into a measurable, frequency- and state-dependent quantity. For neural sensing, they provide an empirical calibration target for choosing electrode scale, reference, and analysis band and for validating EEG forward models and EEG-to-intracranial translation. More broadly, they indicate that noninvasive biomarkers and brain-computer interface features, particularly in high-frequency bands, should be interpreted through frequency- and state-aware cortex-to-skull mappings.},
}

@article {pmid42951021,
year = {2026},
author = {Zhang, W and Gao, Y and Li, X and Li, M and Liu, Y and Li, J and Chen, S and Liu, S and Ming, D},
title = {Altered Neural Oscillatory Coupling Reveals Cognitive Control Deficits with Hierarchical Characteristics and Emotional Specificity in Major Depressive Disorder.},
journal = {Biological psychiatry. Cognitive neuroscience and neuroimaging},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.bpsc.2026.10.011},
pmid = {42951021},
issn = {2451-9030},
abstract = {BACKGROUND: Cognitive control deficits are a prevalent feature of major depressive disorder (MDD) and are influenced by emotional contexts. However, it remains unclear whether these deficits specifically exist at particular hierarchical levels of cognitive control and whether they are selectively exacerbated under distinct emotional contexts. Furthermore, the neural mechanisms underlying such deficits and their association with depressive symptoms remain to be further elucidated.

METHODS: To fill these gaps, two experiments were conducted. In Experiment 1, 54 MDDs and 41 healthy controls (HCs) performed emotional and non-emotional hierarchical cognitive control tasks. Hierarchical demands were manipulated using low- and high-abstraction rules. Based on hierarchy- and emotion-specific deficits identified in Experiment 1, Experiment 2 (34 MDDs; 28 HCs) introduced rule memory set-size manipulation to test whether the deficit generalized to other hierarchical cognitive control subprocesses. Electroencephalography and eye-tracking were simultaneously recorded. Phase-amplitude coupling (PAC) quantified cross-frequency interactions subserving cognitive control.

RESULTS: Delta-beta PAC was selectively reduced in MDDs compared with HCs during high-abstraction conditions under negative emotional contexts, but not in low-abstraction conditions or other contexts. Under the same conditions, MDDs also failed to show the typical memory-related increase in theta-gamma PAC. Moreover, emotion regulation scores significantly mediated the pathway from PAC abnormalities to depressive severity.

CONCLUSIONS: Our findings indicate that altered neural oscillatory coupling reveals cognitive control deficits in MDD that exhibit both hierarchical characteristics and emotional specificity. These neural abnormalities may contribute to depressive severity through maladaptive emotion regulation strategies, providing a novel biological account of the pathophysiological mechanisms underlying MDD.},
}

@article {pmid42947035,
year = {2026},
author = {Zhu, H and Kang, C and Luo, Y and Huang, X and Koh, D and Mazalan, NS and Cao, Y and Luo, Z},
title = {The application of brain-computer interface combined with neuromodulation for stroke upper-limb rehabilitation: a systematic review and network meta-analysis.},
journal = {BMC neurology},
volume = {26},
number = {1},
pages = {},
pmid = {42947035},
issn = {1471-2377},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; *Upper Extremity/physiopathology ; *Stroke/therapy/physiopathology ; Transcranial Magnetic Stimulation/methods ; Transcranial Direct Current Stimulation/methods ; Electric Stimulation Therapy/methods ; Recovery of Function ; },
abstract = {BACKGROUND: In conventional rehabilitation, upper-limb recovery in patients with stroke is slow and limited. Motor Imagery-Based Brain-Computer Interface (MI-BCI) may promote cortical plasticity by translating brain signals into commands to control external devices; however, the effects of different intervention approaches remain unclear.

METHODS: This study followed the PRISMA 2020 statement and searched PubMed, EBSCO, Web of Science, Scopus, the Cochrane Library, and the China National Knowledge Infrastructure (CNKI) for randomised controlled trials (RCTs) reporting outcomes related to upper-limb function or activities of daily living. Risk of Bias 2 (RoB 2) and Confidence in Network Meta-Analysis (CINeMA) were used to assess risk of bias and confidence in the evidence. Network meta-analysis was conducted using the network and mvmeta modules in Stata 18.0 to assess heterogeneity, network consistency, robustness of the results, and publication bias.

RESULTS: A total of 49 studies involving 2,209 participants and 13 intervention strategies were included. For the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), interventions showing relatively favourable effects included MI-BCI combined with transcranial ultrasound stimulation and functional electrical stimulation (FES) (MD = 17.65, 95% CI: 6.88 to 28.43), MI-BCI combined with repetitive transcranial magnetic stimulation (rTMS) and rehabilitation robotics (ROB) (MD = 14.22, 95% CI: 2.98 to 25.47), and MI-BCI combined with transcranial direct current stimulation (tDCS) and ROB (MD = 13.59, 95% CI: 6.90 to 20.28). For the Action Research Arm Test (ARAT), favourable interventions included MI-BCI combined with ROB and tDCS (MD = 17.26, 95% CI: 5.96 to 28.55) and MI-BCI combined with FES (MD = 14.94, 95% CI: 9.10 to 20.78). For the Wolf Motor Function Test (WMFT), MI-BCI combined with ROB showed the largest effect (MD = 10.72, 95% CI: 9.44 to 17.18). For the Modified Barthel Index (MBI), favorable interventions included MI-BCI combined with tDCS and ROB (MD = 20.40, 95% CI: 11.21 to 29.60), MI-BCI combined with rTMS and ROB (MD = 19.46, 95% CI: 9.33 to 29.59), and MI-BCI combined with intermittent theta-burst stimulation and ROB (MD = 18.24, 95% CI: 5.93 to 30.55). Overall, the network evidence showed no obvious inconsistency; between-study heterogeneity was within an acceptable range, and no clear publication bias was detected.

CONCLUSION: Some non-invasive MI-BCI combined interventions like neuromodulation, ROB, or FES might improve upper-limb function and daily activities in stroke patients. However, rankings don't imply clinical importance. Due to small samples, limited evidence, and differences, these findings need confirmation in larger trials.},
}

Humans
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
*Upper Extremity/physiopathology
*Stroke/therapy/physiopathology
Transcranial Magnetic Stimulation/methods
Transcranial Direct Current Stimulation/methods
Electric Stimulation Therapy/methods
Recovery of Function

@article {pmid42947961,
year = {2026},
author = {Zhang, C},
title = {Applications of functional ultrasound brain-computer interfaces in neurological diseases: research progress based on neurovascular coupling.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1946753},
pmid = {42947961},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) bypass peripheral nerves and muscles to establish direct information pathways between the brain and external devices, providing novel approaches for motor function reconstruction, communication assistance, and neurorehabilitation in patients with neurological disorders. However, conventional non-invasive BCIs relying on modalities such as electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI) exhibit limitations in spatiotemporal resolution, portability, or real-time capability. Functional ultrasound (fUS), an emerging neuroimaging technology based on neurovascular coupling (NVC), detects cerebral blood volume changes at a spatial resolution of approximately 100 μm; its imaging frame interval is 200-500 ms, whereas its effective temporal resolution, limited by the hemodynamic response, is 1-2 s, offering a potential solution to overcome these constraints. This review comprehensively analyzed NVC-based fUS brain-computer interfaces from the dimensions of technical principles, clinical applications, future directions, and challenges. fUS has demonstrated significant clinical value in stroke assessment, epilepsy monitoring, and early identification of neurodegenerative diseases. Emerging technologies such as transcranial focused ultrasound neuromodulation, sonogenetics, and intravascular ultrasound are driving the evolution of fUS from single-signal "acquisition" toward bidirectional "acquisition-modulation" closed-loop systems, which remain at the proof-of-concept stage. However, the hemodynamic response latency relative to neural electrical activity (1-2 s), the yet-unclear biosafety of chronic ultrasound exposure, and insufficient decoding reliability under pathological conditions remain critical bottlenecks requiring urgent resolution. In conclusion, NVC-based fUS brain-computer interfaces provide a novel tool for precision diagnosis and treatment of neurological diseases. Future efforts should focus on conducting multicenter clinical trials and enhancing the robustness of decoding algorithms.},
}

@article {pmid42948201,
year = {2026},
author = {Liu, J and Zhao, X and Zhong, Y and Yin, J and Wu, H and Zhang, T and Yu, Y and Jiang, R and Wang, Y and Chang, C},
title = {Terahertz Photons Ameliorate Traumatic Brain Injury and Reduce Neuroinflammation via the Cyclic Adenosine Monophosphate Signaling Pathway.},
journal = {Research (Washington, D.C.)},
volume = {9},
number = {},
pages = {1468},
pmid = {42948201},
issn = {2639-5274},
abstract = {Traumatic brain injury (TBI) has the highest incidence among common neurological disorders and remains the leading cause of mortality and disability among all kinds of injuries, yet specific therapies for TBI are still unavailable. In the present study, we found that short-term administration of terahertz (THz) photons during the acute phase of TBI ameliorated brain damage and cognitive deficits. THz photons induced a reduced population of microglia and an increased number of anti-inflammatory phenotypes of microglia. In vivo and in vitro validation further revealed that THz photons inhibit inflammasome activation. Blocking adenylyl cyclase type 9 (AC9) in microglia attenuated the beneficial effects of THz irradiation, indicating that THz-irradiation-mediated inhibition of inflammasome activation involves the AC9/cyclic adenosine monophosphate/NLR and pyrin domain-containing protein 3 signaling pathway. Compared with drug therapy, THz photon technology offers noninvasiveness, accurate tissue targeting, and on-demand application and removal. In conclusion, this study established a THz-photon-based therapeutic paradigm, highlighting its potential as a therapy for TBI and potentially other forms of acute brain injury.},
}

@article {pmid42948598,
year = {2026},
author = {Wen, P and Wang, Y and He, Z and Zhu, Z and Chen, C and Wu, K and Fu, X and Zhao, G and Xiong, L and , },
title = {Expert consensus on neuro-musculoskeletal functional reconstruction for post-stroke spastic hemiplegia.},
journal = {Ibrain},
volume = {},
number = {},
pages = {},
pmid = {42948598},
issn = {2769-2795},
abstract = {Persistent motor dysfunction in post-stroke spastic hemiplegia may result from impaired voluntary motor control, abnormal muscle overactivity, fixed structural restriction, muscle force imbalance, or their combination. Advances in brain-computer interfaces (BCIs), peripheral nerve surgery, and tendon and soft-tissue reconstruction have expanded therapeutic options, yet optimal indications and sequencing remain unclear. A multidisciplinary expert panel reviewed current evidence, addressed key clinical questions, and developed consensus recommendations for noninvasive BCI, diagnostic motor nerve block (DNB), selective or hyperselective peripheral nerve surgery, and reconstructive procedures. The consensus emphasizes evaluation of four major components: central voluntary motor impairment, dynamic abnormal muscle overactivity, fixed structural limitations, and muscle force imbalance. Treatment should be guided by the patient's functional goals and the primary factor restricting performance. Task-oriented motor training remains fundamental when voluntary motor control is impaired, with BCI-based closed-loop training as a potential adjunct in selected patients. Focal, functionally significant muscle overactivity caused by abnormal neural drive may be considered for selective or hyperselective peripheral nerve surgery, with DNB assisting patient selection. Fixed structural restrictions may require tendon or soft-tissue procedures, while muscle force imbalance may benefit from tendon transfer or other dynamic reconstruction. When multiple impairments coexist, combined reconstruction followed by targeted motor retraining and comprehensive reassessment may be appropriate. Overall, neuro-musculoskeletal functional reconstruction should follow an individualized pathway linking functional goals, dominant pathophysiology, treatment targets, and outcomes. Given the variable evidence quality, no universal treatment sequence or combination strategy is currently supported.},
}

@article {pmid42948971,
year = {2026},
author = {Zhu, J and Liu, R and Jiang, H and Liu, X and Kong, Z and Wang, L and Xu, W and Wang, Y},
title = {Conducting polymers for implantable neural electrodes in brain-computer interfaces.},
journal = {Journal of materials chemistry. B},
volume = {},
number = {},
pages = {},
doi = {10.1039/d6tb01636g},
pmid = {42948971},
issn = {2050-7518},
abstract = {As core elements of brain-computer interfaces, neural electrodes play a vital role in neural signal recording and stimulation. Conventional metallic electrodes, however, suffer from several inherent drawbacks, including mechanical mismatch with soft neural tissues, high interfacial impedance, and chronic inflammatory responses following implantation. In contrast, conducting polymers (CPs) possess unique comprehensive advantages, rendering them ideal candidate materials for advanced neural electrodes. Based on systematic research, key performance criteria are proposed for next-generation neural electrodes, namely mechanical compliance, electrochemical characteristics, long-term stability, biocompatibility, functional integration, and fabrication feasibility. Guided by this performance evaluation framework, we systematically review recent progress in CP-based neural electrodes, with special focus on CP coatings, nanocomposites, hydrogel composites, all-polymer devices, and biofunctionalized interfaces for optimizing electrode performance. Finally, we summarize the present unresolved limitations and outline future research directions for CP-enabled neural interfaces. This review is expected to provide valuable references and systematic guidance for the innovation and development of next-generation multifunctional neurodevices.},
}

@article {pmid42949596,
year = {2026},
author = {Versalovic, E and Mamede, B and Goering, S and Klein, E},
title = {Recognizing Brain Pioneering as Hard Work: Strategies for Making Research Participation More Meaningful.},
journal = {The Hastings Center report},
volume = {56},
number = {5},
pages = {31-42},
pmid = {42949596},
issn = {1552-146X},
support = {//Dana Foundation/ ; R01MH130457/NH/NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Research Subjects/psychology ; *Persons with Disabilities ; },
abstract = {Brain-computer interface (BCI) research offers novel opportunities for new tools for mobility and communication for those limited by injury or illness. Despite this promise, current BCI research remains largely early stage and without direct therapeutic benefits for the trial participants with motor disabilities who invest extensive time and energy in research trials. In interviews our neuroethics research group conducted, participants in nontherapeutic BCI trials invoked job-like or work-like comparisons as they recounted the demandingness (long commutes, tedious tasks, and tiring hours) and rewards (skill building, satisfaction, and coworker-like social relationships) of the trials. These comparisons, paired with the lack of therapeutic benefit, prompted our reflection on how participation in these long-term, active-engagement studies fits the conceptual framework of meaningful work. Examining trial participants' testimonies through meaningful work's dimensions of excellence, social contribution, community, recognition, and agency reveals useful strategies for ensuring that the extensive time and effort trial participants invest in these trials is recognized, respected, and made meaningful.},
}

Humans
*Brain-Computer Interfaces/ethics
*Research Subjects/psychology
*Persons with Disabilities

@article {pmid42949611,
year = {2026},
author = {Ienca, M},
title = {Two Kinds of Mental Privacy: What Speech Decoding Does and Does Not Threaten.},
journal = {The Hastings Center report},
volume = {56},
number = {5},
pages = {25-27},
pmid = {42949611},
issn = {1552-146X},
support = {01GP2506A//German Federal Ministry of Research, Technology and Space (BMFTR)/ ; //NeuroAI ELSA Observatorium project/ ; 533801653//German Research Foundation (DFG)/ ; //TransforM-Munich Center for Transformative Technologies and Societal Change project/ ; },
mesh = {Humans ; *Speech ; *Privacy ; *Brain-Computer Interfaces/ethics ; },
abstract = {In the article "Speech Restoration and Mental Surveillance as Conflicting Ethical Concerns in Neural Speech-Decoding Research," Narayan Sankaran and Winston Chiong distinguish speech-decoding strategies by whether they read brain signals tied to communicative intent, softening the common equation of neural decoding with mind reading. This commentary situates their distinction within a broader conceptual map of mental privacy, borrowing Noam Chomsky's distinction between the narrow and broad senses of the language faculty. Mental privacy in the narrow sense concerns the decoding of occurrent mental states-thoughts, feelings, intentions-the domain Sankaran and Chiong address. Mental privacy in the broad sense concerns statistical inferences about traits, risks, and dispositions from brain data, which can wrong a person without there being any decoding of their mental state at all. The two senses implicate different harms, different relations to cognitive liberty, and different regulatory logics: intent-based authorization for narrow-sense decoding, purpose limitation and proportionality review for broad-sense inference. Distinguishing them clarifies what current speech-decoding research does, and does not, threaten.},
}

Humans
*Speech
*Privacy
*Brain-Computer Interfaces/ethics

@article {pmid42949614,
year = {2026},
author = {Sankaran, N and Chiong, W},
title = {Speech Restoration & Mental Surveillance as Conflicting Ethical Concerns in Neural Speech-Decoding Research.},
journal = {The Hastings Center report},
volume = {56},
number = {5},
pages = {15-24},
pmid = {42949614},
issn = {1552-146X},
support = {//Dana Foundation/ ; K24AG083117//National Institute on Aging of the National Institutes of Health/ ; R01MH137095//National Institute of Mental Health of the NIH/ ; },
mesh = {Humans ; *Speech ; Brain-Computer Interfaces/ethics ; Ethical Dilemmas ; *Neurosciences/ethics ; *Communication Disorders/rehabilitation ; },
abstract = {The research program of speech decoding-"reading" words directly from brain activity-elicits an unusually wide range of public responses, reflecting a tension between the ethical goal of restoring speech for people with serious communication disabilities and the ethical goal of protecting mental privacy from neurotechnological surveillance. These competing moral perspectives are informed by intrinsic features of the research but also by the broader sociocultural context in which neuroscience is conducted and is communicated to an increasingly interested public. Whereas prior neuroethical literature has looked "downstream" from this research in proposing governance frameworks, in this paper we turn "upstream" to the neuroscience of language to consider how ethical tensions may be informed by the specific neural circuits from which speech is inferred. Our analysis identifies the communicative intent of neural signals as a key feature of some but not all speech-decoding strategies, and we argue that whether a speech BCI decodes linguistic information with or without communicative intent should inform assessments of the ethical implications of the research. At the level of public discourse, we show how such distinctions can inform responsible science communication about future advances in this fast-moving field.},
}

Humans
*Speech
Brain-Computer Interfaces/ethics
Ethical Dilemmas
*Neurosciences/ethics
*Communication Disorders/rehabilitation

@article {pmid42939319,
year = {2026},
author = {Yan, L and Li, T and Zhao, L and Gong, A and Fang, H and Zuo, W and Fu, Y},
title = {Human Factors in Brain-Computer Interfaces: Boundaries, Key Issues, and Methodological Approaches.},
journal = {Brain sciences},
volume = {16},
number = {10},
pages = {},
pmid = {42939319},
issn = {2076-3425},
support = {62376112, 82172058, 62366026//National Natural Science Foundation of China/ ; },
abstract = {The large-scale, real-world deployment of brain-computer interface (BCI) is confronted with human factors bottlenecks, including reliability and usability. While modern human factors engineering has encompassed domains such as cognitive ergonomics, workload, adaptive automation, and human-in-the-loop systems, its theories and methodologies have historically placed their main emphasis on overt behavior and peripheral physiological measures, rather than on central neural activity as a primary control input. For the emerging interaction paradigm that takes central nervous activity as direct input and emphasizes long-term brain-computer co-adaptation, there remains a shortage of targeted conceptual and methodological frameworks. As a conceptual review, this paper discusses the necessity and disciplinary foundations for developing Human Factors Engineering for BCIs as an emerging interdisciplinary research direction. First, grounded in the defining features of BCI interaction-such as the implicit nature of input and brain-computer co-adaptation-it delineates the research boundaries and summarizes the core research objects as user neurocognitive characteristics, brain-computer collaborative adaptation mechanisms, multimodal feedback regulation, and contextual adaptation patterns. Second, it identifies three core scientific questions: individual differences and dynamic adaptation, human factors optimization and fault tolerance, and contextual generalizability principles, with ethics, privacy, and user rights and interests as an overarching dimension. Furthermore, it constructs a methodological system consisting of multimodal measurement and modeling, closed-loop validation, and multidimensional evaluation, and examines the supporting role of artificial intelligence approaches within this framework. Centered on the collaborative adaptation across neural, cognitive, and system levels, Human Factors Engineering for BCIs represents a deepening of the human-centered design philosophy. It provides theoretical reference for user-friendly BCI design and extends the research boundaries of human factors engineering.},
}

@article {pmid42939376,
year = {2026},
author = {Xie, S and Liu, X and Wang, X and Xie, X and Tang, H},
title = {EOG Artifact Removal from EEG Using a Gated WGAN with Multi-Scale and Multi-Domain Feature Extraction.},
journal = {Brain sciences},
volume = {16},
number = {10},
pages = {},
pmid = {42939376},
issn = {2076-3425},
support = {62220106007//National Natural Science Foundation of China/ ; },
abstract = {Background: Electroencephalogram (EEG) signals provide important information for brain-computer interfaces (BCIs) and the diagnosis of neurological disorders but are susceptible to electrooculographic (EOG) artifacts. Traditional methods often rely on multichannel data or relatively long windows, limiting online applicability. Deep learning enables single-channel end-to-end artifact removal, but existing methods may not fully exploit features in the time and frequency domains and may unnecessarily modify relatively clean EEG. Methods: We developed a Wasserstein generative adversarial network (WGAN) integrating reconstruction loss, multi-domain and multi-scale feature extraction, and a gating unit. Reconstruction loss improved waveform reconstruction, yielding a reconstruction-loss-assisted artifact-removal WGAN (rarWGAN). Parallel branches with different kernel sizes extracted multi-scale features, while a multi-domain module combined temporal features with spectral features extracted using complex-valued convolutions. Integrating both modules into rarWGAN produced MDS-rarWGAN. Based on discriminator scores, gating routed inputs classified as contaminated to the generator and retained the others unchanged. Results: On EEGdenoiseNet, MDS-rarWGAN yielded relative root mean square errors (RRMSEs) of 0.189 ± 0.028 and 0.183 ± 0.027 in the time and frequency domains, respectively, with a correlation coefficient of 0.955 ± 0.013. Compared with rarWGAN, RRMSEs decreased by 23.2% and 32.5%, respectively, and the correlation coefficient increased by 1.2%. The gating unit yielded false positive and false negative rates of 1.47% and 0.12%, respectively. For EEG containing artifacts, no statistically significant differences in time domain RRMSE were detected with and without gating (all p > 0.05). On BCI Competition IV 2a, reconstruction metrics measured agreement with a reference derived from independent component analysis (ICA), as artifact-free EEG ground truth was unavailable. Using the same EEGNet classifier, EEG processed by gated-MDS-rarWGAN achieved the highest mean motor imagery classification accuracy among the evaluated deep learning methods. Conclusions: The method suppresses EOG artifacts, reduces unnecessary processing of signals without evident artifacts, and supports motor imagery decoding, offering a feasible preprocessing approach.},
}

@article {pmid42939378,
year = {2026},
author = {Xue, C and Cai, X and Guo, Z and Xu, H and Xie, J and Ji, R and Cao, L and Jia, J},
title = {EEG Features of Cortical Intent for Urination and Defecation in Healthy Aging and Spinal Cord Injury: A Descriptive Feasibility Study for BCI Neurorehabilitation.},
journal = {Brain sciences},
volume = {16},
number = {10},
pages = {},
pmid = {42939378},
issn = {2076-3425},
support = {2018YFC2002301//National Key Research and Development Program Project of China/ ; },
abstract = {Background/Objectives: Systematically elucidating the neural mechanisms of bowel and bladder control is crucial for developing closed-loop brain-computer interface (BCI) neurorehabilitation for patients with spinal cord injury (SCI). This study aims to characterize the spatiotemporal Electroencephalography (EEG) dynamics and compensatory cortical reorganization underlying continence motor intentions across healthy aging and SCI cohorts. Methods: 32-channel EEG signals were collected from healthy young adults, healthy older adults, and SCI patients during motor attempt paradigms of suppressed urination and defecation (SUD) and active urination and defecation (UD). Data were analyzed utilizing event-related spectral perturbation (ERSP) and phase-locking value (PLV) functional connectivity across alpha (8-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz) bands. Results: All cohorts exhibited generally consistent broadband event-related desynchronization (ERD) in the sensorimotor cortex (SMC) during visceral motor attempt. Conversely, the frontal cortex (FC) demonstrated marked frequency-band differentiation. During the SUD task, healthy older adults showed significantly greater SMC beta2 ERD than young adults, whereas SCI patients showed a similar but non-significant trend. Frontal beta2 findings were preliminary and should be interpreted with caution due to potential residual artifacts. Network analysis revealed that physiological aging and SCI are associated with large-scale topological reconfigurations, prominently featuring enhanced long-range frontal functional connectivity to compensate for sensory or peripheral deficits. Conclusions: High-level cortical intentions for continence are preserved after SCI, with frontal high-frequency activity possibly reflecting a compensatory process for sensorimotor disruption. These findings substantiate that visceral intentions exhibit discernible characteristics at the cortical level, establishing a spatiotemporal framework for targeted pelvic nerve modulation via brain-machine interfaces.},
}

@article {pmid42940556,
year = {2026},
author = {Li, Y and Lin, Y and Chen, L},
title = {Epilepsy in Tibetan-Inhabited Plateau Regions of Western China: A Narrative Review of Epidemiology, Uncertainty About Chronic High-Altitude Effects, and Evidence-Informed Healthcare Priorities.},
journal = {Healthcare (Basel, Switzerland)},
volume = {14},
number = {20},
pages = {},
pmid = {42940556},
issn = {2227-9032},
support = {XZ202501ZY0120//Major Special Project of the Science and Technology Projects of Xizang Autonomous Region/ ; 2025NSFTD0027//Sichuan Science and Technology Program/ ; },
abstract = {BACKGROUND/OBJECTIVES: In Tibetan-inhabited plateau regions of western China, remoteness, uneven access to care, and preventable causes complicate epilepsy care. We assessed whether regional evidence supports an independent effect of chronic altitude exposure on epilepsy risk and identified healthcare priorities.

METHODS: We conducted a targeted narrative review of English-language journal articles published through 12 June 2026 (PubMed/MEDLINE, Web of Science Core Collection, citation tracking). Altitude-related evidence was evaluated against four prespecified, review-defined features; core sources underwent structured qualitative appraisal.

RESULTS: Three community surveys with differing case definitions and ascertainment reported prevalence estimates of 2.5-23.5 per 1000; the most recent (Daofu County, 2025) reported 4.4 per 1000 (95% confidence interval 3.7-5.1). No population-based incidence study of long-term residents was identified among the retrieved publications, and no identified resident-population study reported all four features. Regional studies documented parasitic, traumatic, and vascular etiologies, historical treatment gaps of 97-100%, and uneven diagnostic and workforce capacity. Among 127 Daofu participants assessed in detail, 33.1% were classified as receiving nonstandard treatment and 14.2% as untreated; 35.4% and 52.0% screened positive for depressive and anxiety symptoms.

CONCLUSIONS: Current evidence neither establishes nor excludes an independent effect of chronic altitude exposure. It supports prospective evaluation of prevention, treatment continuity, diagnostic and referral services, workforce development, integrated mental health care, and surveillance.},
}

@article {pmid42942690,
year = {2026},
author = {Ji, J and Gao, Y and Liu, J and Tian, M and Wei, Y},
title = {The Development and Evolution of Substrate Materials for Brain-Computer Interfaces.},
journal = {Polymers},
volume = {18},
number = {20},
pages = {},
pmid = {42942690},
issn = {2073-4360},
abstract = {Brain-Computer Interfaces (BCIs) hold tremendous potential for neurological therapeutics and neuroprosthetics; however, their long-term clinical viability is fundamentally limited by chronic interfacial failure. This failure originates from the profound biomechanical mismatch between synthetic microelectrodes and ultra-soft brain tissue, which inevitably triggers persistent mechanical trauma, severe foreign body response (FBR), and insulating glial scarring. In this review, we systematically trace the material and structural evolution of implantable neural interfaces designed to bridge this critical gap. We detail the chronological paradigm shift from rigid inorganic substrates (e.g., silicon) to flexible yet inextensible polymers (e.g., Polyimide, SU-8) for conformal mapping, and subsequently to intrinsically stretchable elastomers (e.g., polydimethylsiloxane polyurethane) that effectively absorb dynamic physiological micro-shear forces. Most importantly, we highlight the emerging frontier of electrospun nanofibrous microelectrodes. By uniquely combining macroscopic tissue-level stretchability with an interconnected, highly permeable 3D topography that mimics the native extracellular matrix (ECM), these biomimetic scaffolds successfully overcome the metabolic limitations of dense elastomeric films. Finally, we discuss current challenges and future perspectives, ranging from scalable high-density manufacturing and active biochemical modulation to fully untethered integration, charting a comprehensive roadmap for next-generation, chronically stable neural interfaces.},
}

@article {pmid42944288,
year = {2026},
author = {Saad Bin Abdul Ghaffar, M and Ertunç, HM and Kaplan, K and Kucuk, K and Yahya, K and Khan, SA},
title = {Bridging Temporal and Cepstral Hemodynamic Intelligence: An Adaptive Multi-Domain Feature Fusion Framework for Multi-Class fNIRS Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {20},
pages = {},
pmid = {42944288},
issn = {1424-8220},
support = {4586//Kocaeli University/ ; },
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; *Hemodynamics/physiology ; Multilayer Perceptrons ; Algorithms ; },
abstract = {Reliable four-class decoding remains challenging for functional near-infrared spectroscopy (fNIRS) brain-computer interfaces (BCIs), because a single feature domain captures only part of the hemodynamic response. This study proposes an Adaptive Multi-Domain Feature Fusion (AMFF) framework that combines six temporal hemodynamic descriptors with ten fNIRS-adapted Mel-frequency cepstral coefficients (MFCCs) and learns sample-dependent weights for the two domains before classification with a multilayer perceptron. The framework was evaluated on an open-access dataset of 29 participants performing left- and right-hand motor imagery, mental arithmetic, and rest, using subject-independent leave-one-subject-out cross-validation. AMFF achieves a mean accuracy of 95.6%, with precision, recall, and F1-score of 94.6%, 95.9%, and 95.3%, respectively, outperforming temporal only (90.7%), cepstral-only (95.0%), and direct concatenation (95.2%) representations, with a modest but consistent gain over direct concatenation across participants. Friedman and Holm-corrected Wilcoxon signed-rank tests confirmed significant differences for all four metrics; AMFF exceeded static concatenation for every participant, although by a small margin (0.2-0.7 percentage points). Most of the gain over temporal descriptors therefore comes from the cepstral domain, with adaptive weighting adding a small but consistent refinement. MFCC parameter-sensitivity, coefficient-ablation, and descriptor-ablation analyses further characterize the contribution of each domain.},
}

Humans
Spectroscopy, Near-Infrared/methods
*Brain-Computer Interfaces
*Hemodynamics/physiology
Multilayer Perceptrons
Algorithms

@article {pmid42944335,
year = {2026},
author = {Higashionna, KK and Meissner, JM and Tarrit, K},
title = {Agentic EEG Pipeline Synthesis Using Large Language Models and the Model Context Protocol.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {20},
pages = {},
pmid = {42944335},
issn = {1424-8220},
support = {MedRes_2025_00010394//Hawaii Community Foundation/ ; },
mesh = {*Electroencephalography/methods ; Large Language Models ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Electroencephalography analysis typically requires multistage processing pipelines for which configuration depends on recording characteristics and the intended analytical objective. Although automated pipelines improve reproducibility, they generally implement predefined processing sequences. We present an agentic framework in which a large language model (LLM) orchestrator synthesizes and executes task-specific EEG processing workflows by selecting, parameterizing, and ordering established signal processing components accessed through Model Context Protocol interfaces. Computational EEG processing and analysis operations are performed using established EEGLAB (version 2026.0.0) implementations running in MATLAB (R2026a), while structured execution results and quality control information are returned to the orchestrator to support adaptation during execution. The framework is evaluated by examining tool-layer execution fidelity, independent pipeline synthesis, and the quality of the resulting EEG output relative to the established DISCOVER-EEG workflow. In an evaluation using eight resting state EEG recordings from four participants, mean epoch retention was 0.845 for the agent-generated pipeline and 0.843 for DISCOVER-EEG, while mean spectral entropy was 4.87 and 4.84 bits, respectively. Mean signal-to-noise ratio was 13.92 dB for the agent-generated pipeline and 11.14 dB for DISCOVER-EEG; however, this difference should be interpreted cautiously because the pipelines used different upper filter boundaries. A secondary analysis examined whether EEG processed using the generated pipeline retained features relevant to Alzheimer's disease versus healthy control classification. These findings demonstrate the feasibility of coupling LLM-based orchestration with EEG processing software to generate executable task-specific workflows with structured execution records and provenance. Further evaluation across larger datasets, independent synthesis runs, and additional EEG paradigms is required in order to establish repeatability and generalizability.},
}

*Electroencephalography/methods
Large Language Models
Humans
*Signal Processing, Computer-Assisted
Algorithms

@article {pmid42944450,
year = {2026},
author = {Memon, PQ and Anderson, CW and Memon, S},
title = {Overt and Covert Speech-State Classification from Tripolar Concentric Ring Electrode EEG: A 4 mm and 10 mm Configuration Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {20},
pages = {},
pmid = {42944450},
issn = {1424-8220},
support = {1R43NS105267-01A1/NH/NIH HHS/United States ; },
mesh = {Humans ; *Electroencephalography/methods ; Electrodes ; *Speech/physiology ; Support Vector Machine ; Signal Processing, Computer-Assisted ; Male ; Brain/physiology ; Female ; Adult ; },
abstract = {Electroencephalography (EEG) speech-state classification is a noninvasive method to identify the brain's overt and covert behavior. The EEG achieved through the scalp is contaminated by artifacts and volume conduction and suffers from poor spatial resolution. We present the first application of small 4 mm tripolar concentric ring electrodes (TCREs) to record the overt and covert EEG signals. In addition, EEG signals were recorded for overt and covert behavior using a 10 mm TCRE as a reference. Data were recorded from 16 participants (4 mm, n=8; 10 mm, n=8). Each participant completed 200 picture-naming trials (100 overt and 100 covert). The analysis includes task-to-baseline root mean square ratios, power spectral density, time frequency representation analysis and participant-wise overt and covert classification using linear discriminant analysis, support vector machines, and EEGNet. The overt speech produced higher task-to-baseline ratios than covert speech. In addition, EEGNet achieved the highest mean accuracy (84.06±15.57%), followed by SVM (81.56±14.23%) and LDA (76.56±15.30%).},
}

Humans
*Electroencephalography/methods
Electrodes
*Speech/physiology
Support Vector Machine
Signal Processing, Computer-Assisted
Male
Brain/physiology
Female
Adult

@article {pmid42945213,
year = {2026},
author = {Mynhier, NA and Gamez, J and Moorse, KP and Pejsa, K and Bari, A and Murray, RM and Andersen, RA},
title = {The compositional encoding of hand-eye coordinated movements for single neurons in the posterior parietal cortex.},
journal = {Cell reports},
volume = {45},
number = {11},
pages = {118204},
doi = {10.1016/j.celrep.2026.118204},
pmid = {42945213},
issn = {2211-1247},
abstract = {Human posterior parietal cortex (PPC) is known to play a role in hand-eye (H-E) coordination, yet the underlying encoding mechanisms remain uncertain. We recorded 412 single neurons across 11 sessions from motor cortex (MC; n = 251) and PPC (n = 161) in a single human participant performing an H-E coordinated center-out task. While MC neurons showed negligible modulation by eye movements, 79% of PPC neurons had neural representations that were additively separable into independent hand- and eye-movement tuning curves. Due to this separability, representations could be separated and additively recomposed while maintaining structural similarity. Consequently, compositional decoders trained solely on single-effector movements could match the performance of decoders trained on coordinated H-E movements (hand: 66% vs. 69%; eye: 34% vs. 36%). These results show that, during simple center-out tasks, MC hand-movement codes are unaffected by eye movements and that compositionality can be used to modularly decode H-E coordinated movements in PPC.},
}

@article {pmid42945585,
year = {2026},
author = {Li, Y and Huang, Y and Chen, W and Jiao, G and Hu, L},
title = {NCEL: Convolution-Enhanced Lightweight Neural Architecture for Robust EEG Signal Decoding in Noninvasive Brain-Computer Interfaces.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/aeb9de},
pmid = {42945585},
issn = {2057-1976},
abstract = {Electroencephalography (EEG) is a cornerstone of brain-computer interfaces (BCIs) and clinical neuro-diagnostics due to its non-invasiveness and high temporal resolution. However, robust EEG decoding remains challenging, particularly for resource-constrained deployments. While lightweight architectures like EEGNet offer favorable parameter efficiency, their reliance on fixed convolution kernels and restricted temporal receptive fields inherently limits their performance in complex, highly variable scenarios. To address these limitations, we propose a Neural Convolution-Enhanced Lightweight network (NCEL) that augments spatiotemporal EEG representations while preserving structural compactness. Specifically, a dynamic neural convolution module (NCM) is designed to learn input-dependent temporal filtering strategies for trial-to-trial variability and session-related distribution shifts.Furthermore, parallel multiscale temporal convolution branches are integrated to concurrently capture short-term local waveform transients and long-interval rhythmic dependencies. Finally, a lightweight channel-attention gating mechanism adaptively recalibrates intermediate representations, amplifying task-relevant neural responses while suppressing noise-dominated channels. Extensive evaluations on five benchmark datasets (BCIC IV-2a, BCIC IV-2b, ERN, SMR, and P300) demonstrate that NCEL achieves a mean classification accuracy of 82.75\% on BCIC IV-2b. Notably, NCEL achieves higher mean performance than the EEGNet reference values, with performance gains of 12.53 percentage points on the P300 dataset and 9.54 percentage points on BCIC IV-2b, while maintaining only $\sim$0.008M parameters. Additional subject-specific session-disjoint evaluations show comparable cross-session performance to EEGNet on BCIC IV-2a and a 1.80-percentage-point mean improvement on BCIC IV-2b. Overall, NCEL provides a parameter-efficient framework for resource-constrained EEG decoding, while broader cross-session generalization requires further validation.},
}

@article {pmid42945587,
year = {2026},
author = {Lu, C and Jie, LL and Ying, X and Jia, Y and Xu, WY},
title = {DSGTANet: A semi-supervised dual-stream graph-temporal adaptation network for cross-subject continuous EEG emotion recognition.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeba18},
pmid = {42945587},
issn = {1741-2552},
abstract = {OBJECTIVE: Continuous emotion recognition using electroencephalogram (EEG) is a critical topic in brain-computer inter-faces (BCIs), with applications in mental disorder regulation and human-computer interaction. However, its practical appli-cation is significantly constrained by several key issues, including the scarcity of emotion labels, inter-subject distribution shifts, and heterogeneous regression noise. To address these issues, this study aims to develop a semi-supervised frame-work for accurate cross-subject continuous EEG emotion recognition.

APPROACH: We propose a novel Dual-Stream Graph-Temporal Adaptation Network (DSGTANet). First, we collect an EEG dataset of six dynamic emotional transitions with continuous labels from 40 participants. Then, DSGTANet is designed to consist of a spatial structural stream, a temporal dynamic stream, and a reliability-driven fusion and alignment module. Specifically, the spatial structural stream learns robust EEG channel-topology features through a learnable graph structure, structural augmentation, and a self-supervised consistency constraint. Meanwhile, the temporal dynamic stream employs a Transformer to capture long-range temporal dependencies in emotion evolution and introduces label-conditioned domain adversarial learning with an exponential mov-ing average (EMA) teacher to enhance pseudo-labeling for unlabeled samples. Furthermore, the reliability-driven module explicitly estimates dual-stream predictive uncertainty via heteroscedastic regression and performs precision-weighted product-of-experts fusion along with reliability-weighted conditional distribution alignment.

MAIN RESULTS: Experimental results on our constructed dataset and the public DEAP dataset show that DSGTANet outperforms state-of-the-art methods in valence and arousal regression, achieving superior accuracy and robustness.

SIGNIFICANCE: These findings highlight the considerable potential of the proposed DSGTANet for real-world EEG-based emotion decoding under conditions of limited labeled data and substantial subject variability.},
}

@article {pmid42945969,
year = {2026},
author = {Wei, Z and Yu, R and Xu, J and Tao, C and Zhang, A and Li, L and Zhang, X and He, J and Yu, J and Xiao, X and Guo, J and Wang, T and Gong, W and Wang, Y and Zhang, CY and Chen, D and Chen, ZP and Ji, C and Chen, X},
title = {Postnatal depression-associated breastmilk extracellular vesicles mediate intergenerational effects on offspring neurodevelopment.},
journal = {Cell metabolism},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cmet.2026.09.023},
pmid = {42945969},
issn = {1932-7420},
abstract = {Postnatal depression (PND) is associated with offspring neurodevelopmental impairments, but the mechanisms remain unclear. Here, we identify an intergenerational pathway whereby maternal psychological state is encoded in breastmilk microRNAs (miRNAs) and transmitted to offspring via small extracellular vesicles (sEVs). Using cross-fostering models, we show that maternal depression disrupts newborn neuron maturation in the offspring hippocampal dentate gyrus (DG), causing cognitive deficits and depressive-like behaviors. Gastric administration of PND-derived breastmilk sEVs to healthy pups recapitulates these phenotypes. Mechanistically, PND-induced miRNAs are packaged into breastmilk sEVs and delivered to the offspring brain, where they suppress mGluR8 in supramammillary nucleus (SuM) neurons projecting to DG. This presynaptic mGluR8 deficiency drives a "high-frequency, low-amplitude" neuronal activation pattern. Inhibiting mammary sEV secretion or sequestering these miRNAs in offspring SuM neurons rescues the deficits. Our findings define a mechanism underlying PND-related offspring neurodevelopmental impairments and suggest a therapeutic target for interrupting the intergenerational transmission of maternal mental disorders.},
}

@article {pmid42874634,
year = {2026},
author = {Guo, L},
title = {Cybertissue neuroprosthetics: a new implantable neuroprosthetics paradigm to achieve seamless lifetime integration.},
journal = {Journal of neural engineering},
volume = {23},
number = {5},
pages = {},
doi = {10.1088/1741-2552/aeb442},
pmid = {42874634},
issn = {1741-2552},
mesh = {Humans ; Animals ; *Neural Prostheses/trends ; *Brain-Computer Interfaces/trends ; *Electrodes, Implanted/trends ; *Robotics/methods/instrumentation/trends ; },
abstract = {Objective.Despite decades of advances in neural interface technology, the long-term instability of implantable neuroprostheses remains a major barrier to achieving reliable lifetime performance. Chronic foreign-body reactions and fibrotic encapsulation progressively degrade the electrode-tissue interface, leading to signal deterioration and eventual device failure. Existing strategies that focus on improving material biocompatibility, reducing mechanical mismatch, or modifying implant surfaces have produced important but largely incremental improvements, while leaving the fundamental challenge unresolved. This perspective paper introduces a fundamentally different paradigm: cybertissue neuroprosthetics (CN).Approach.CN moves beyond the traditional electrode-based neural interface by treating the implant itself as a functional tissue device-composed of living cells and optionally augmented with minimal nanoelectronics, nanomaterials, or genetic modifications-designed to integrate structurally and functionally with the host nervous system to achieve long-term neuromodulation and information exchange. By minimizing or eliminating foreign materials and leveraging the brain's own cellular signaling mechanisms, CN promises to overcome the inherent limitations of current inorganic neuroprosthetic devices and seeks to achieve seamless lifetime integration between neuroprosthetic devices and the nervous system.Main results.This paper introduces the CN concept, unifies a range of emerging research directions under this umbrella, and articulates the core opportunities-from engineered cybercells to transplantable living devices-that collectively define a new interdisciplinary frontier.Significance.We argue that CN represents a foundational shift in neuroprosthetics, with the potential to deliver truly lifetime neural implants that are biologically integrated, self-sustaining, and clinically transformative.},
}

Humans
Animals
*Neural Prostheses/trends
*Brain-Computer Interfaces/trends
*Electrodes, Implanted/trends
*Robotics/methods/instrumentation/trends

@article {pmid42922848,
year = {2026},
author = {Gorenshtein, A and Omar, M and Jia, E and Adiniaev, Y and Daniel, O and Kruskal, J and Ahmed, M and Brook, O and Klang, E and Barash, Y},
title = {Limits of Trial-Adaptive Neural-Language Fusion Across Large Language Models in P300 Brain-Computer Interfaces.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.08.30.26361777},
pmid = {42922848},
abstract = {OBJECTIVE: Published P300-speller fusion schemes fix prior trust regardless of trial reliability; we tested whether a reliability estimate improves on it.

METHODS: We reanalyzed 3,373 archived P300-speller selections from 47 people with ALS (BigP3BCI). A fair, matched-search-space comparison, tuning both a fixed weight and an adaptive policy out-of-fold, was evaluated across 22 evaluable language-model priors up to 46.7B parameters. Two representative priors, GPT-2 and a classical 5-gram, additionally received detailed naive and mechanistic analyses.

RESULTS: No prior's 95% CI favored adaptive fusion under the fair comparison, despite unexploited oracle headroom at every scale. Under GPT-2, the naive comparison was significantly worse for adaptive fusion; both anchors converged to a degenerate or near-degenerate fair-comparison solution. For the representative anchors, three further controllers failed to convert that headroom into benefit; the fixed-fused posterior's output probability outperformed the best controller for flagging errors (2.8- to 3.8-fold enrichment).

CONCLUSION: A tuned fixed weight is a difficult-to-beat default across the tested scale range; reliability estimation gave no deployable adaptive advantage.

SIGNIFICANCE: Adaptive weighting should be validated against a fairly tuned baseline across model families and scales; in this dataset, the fused output's confidence identified high-risk selections better than the tested purpose-built ranker.},
}

@article {pmid42925086,
year = {2026},
author = {Hu, Y and Wang, Q and Sun, K and Li, L and Yin, Z and Liu, Q and Xiao, K and Xiao, X and Liu, H and Wang, D and Li, A and Li, H and Xu, F and Jia, F},
title = {Engineering a low cytotoxicity recombinant rabies virus neurotracer via N-gene recoding.},
journal = {Journal of virology},
volume = {},
number = {},
pages = {e0078326},
doi = {10.1128/jvi.00783-26},
pmid = {42925086},
issn = {1098-5514},
abstract = {UNLABELLED: Viral-vector-based tracers have proven invaluable for mapping neural circuits. Rabies virus (RV)-based retrograde trans-monosynaptic tracers have been widely used for depicting the input neural circuit of targeted neuronal populations. However, the cytotoxicity of the RV tracer limits its application. Here, we employed the synthetic attenuated virus engineering technology to recode the N gene of the RV SAD-B19 strain by codon pair deoptimization to generate attenuated RV tracers. The characteristics of the recoded RV tracers were tested both in vitro and in vivo. The full-length recoded N gene abolished viral rescue, whereas the partial recoded N gene supported successful viral recovery. Compared to its parent, the recoded RV trans-monosynaptic tracer exhibited lower cytotoxicity and labeled more input neurons. It can also robustly express proteins to visualize the neuronal morphology and manipulate the neural circuit activity. Collectively, our work generated a new retrograde trans-monosynaptic tool, which complements existing neural circuit tracers.

IMPORTANCE: Viral vectors play an important role across multiple fields in biomedicine. In the neuroscience community, rabies virus (RV)-based retrograde trans-monosynaptic tracers have been widely used to map the input neural circuits of targeted neuronal populations. However, the inherent cytotoxicity of RV-based tracers limits their broader application. In this study, we utilized synthetic attenuated virus engineering technology to recode the RV N gene, generating an attenuated neurotracer that will significantly benefit future neuroscience research.},
}

@article {pmid42925109,
year = {2026},
author = {Jiang, X and Zhang, L and Li, S and Sun, C and Zhou, Y and Wang, Y and Liu, J and Liu, J and Zhong, L},
title = {Combined neuromodulation techniques via a central-peripheral-central strategy facilitate functional recovery from AIS A to AIS C in a patient with complete spinal cord injury: A case report.},
journal = {The journal of spinal cord medicine},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10790268.2026.2728732},
pmid = {42925109},
issn = {2045-7723},
abstract = {CONTEXT: Complete spinal cord injury (SCI) has traditionally been considered irreversible. However, accumulating evidence suggests that subclinical neural pathways may persist. Multimodal combined neuromodulation targeting brain-spinal-peripheral circuits holds promise to unlock latent neural plasticity, but clinical evidence for its application in patients with clinically complete SCI remains limited.

FINDINGS: A 53-year-old male with chronic complete T9 SCI (AIS A) received a sequential multimodal neuromodulation protocol consisting of spinal cord electrical stimulation (SCS), brain-computer interface (BCI) training, primary motor cortex intermittent theta-burst stimulation (iTBS), and peripheral magnetic stimulation (pMS) of the bilateral iliopsoas muscles, alongside 12 months of conventional rehabilitation. After one year of intervention, his neurological status improved from AIS A to AIS C (T9 to T12). The left lower limb motor score increased from 0 to 6, accompanied by the recovery of partial voluntary movement. Spontaneous defecation and penile erection were restored, and neuropathic pain was fully alleviated (the VAS score decreased from 8 to 0). Electrophysiological assessments revealed partial recovery of lower limb motor conduction and motor evoked potentials (MEPs), indicating possible corticospinal tract remodeling.

CLINICAL RELEVANCE: This single case provides preliminary clinical evidence that combined neuromodulation via central-peripheral-central strategy may be associated with functional recovery in complete SCI. Limitations of the single-case design and the lack of a control group are acknowledged, and the potential influence of spontaneous neurological recovery cannot be excluded. Further multicenter controlled trials are warranted to verify these findings.},
}

@article {pmid42925683,
year = {2026},
author = {Wei, L and Yu, X and Zheng, Y and Zhou, M and Wang, Q and Zhang, Y and Chen, Q and Mu, X and Jiang, H and Ni, P and Deng, W and Guo, W and Qi, X and Li, T},
title = {Discovery of a fluorinated benzoxathiazine AMPA receptor PAM that stabilizes the GluA2 dimer interface and displays antidepressant-like effects.},
journal = {European journal of medicinal chemistry},
volume = {321},
number = {Pt 2},
pages = {119423},
doi = {10.1016/j.ejmech.2026.119423},
pmid = {42925683},
issn = {1768-3254},
abstract = {Positive allosteric modulators (PAMs) of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors hold significant therapeutic promise for neuropsychiatric disorders driven by synaptic dysfunction. Building upon our previously established oxathiazine-dioxide scaffold, we designed and synthesized a novel series of compounds through systematic exploration of the 6-position. Among these compounds, the fluorinated analog C17 was identified as a superior lead candidate. Advanced Gaussian accelerated molecular dynamics (GaMD) simulations demonstrated that C17 stabilizes the GluA2 ligand-binding domain (LBD) dimer more effectively than the clinical benchmark TAK-653. By establishing a robust hydrogen-bonding network, C17 acts as a "molecular glue" to tightly lock the receptor in its canonical activated conformation, thereby preventing transition into the desensitized state. Translating these mechanistic insights into in vivo activity, C17 exhibited potent antidepressant-like efficacy in a chronic social defeat stress mouse model. Electrophysiological characterization confirmed that C17 potentiates (S)-AMPA-induced currents in rat hippocampal neurons without intrinsic agonism. Furthermore, C17 concentration-dependently upregulated brain-derived neurotrophic factor (BDNF) expression. These results identify C17 as an AMPA receptor PAM with a distinct dimer-stabilizing profile, positioning it as a compelling candidate for treating major depressive disorder (MDD).},
}

@article {pmid42926359,
year = {2026},
author = {Sun, M and Ma, X and Wang, X and Bai, B and Wang, J and Wang, H and Wang, Y and Huang, L and Tang, J},
title = {Fabrication Strategies and Performance Optimization Strategies of Flexible Brain-Computer Interface Electrodes.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {},
doi = {10.1002/adhm.71870},
pmid = {42926359},
issn = {2192-2659},
support = {52472100//National Natural Science Foundation of China/ ; },
abstract = {Brain-computer interface (BCI) provides a direct communication pathway between the brain and external devices and has been applied in neurological disease treatment, motor rehabilitation, and human-computer interaction. Electrodes play essential roles in recording and modulating neural signals, and their performance directly affects signal quality, spatial resolution, and long-term stability. Traditional rigid electrodes suffer from mechanical mismatch with brain tissue, which may induce tissue damage and reduce recording stability during long-term implantation. Flexible electrodes offer improved mechanical compatibility and have emerged as promising alternatives. Recent advances in materials science, micro/nanofabrication, and interface engineering have enhanced the electrical, mechanical, and biological performance of flexible BCI electrodes. This review summarizes neural signal characteristics, electrode requirements, fabrication strategies, and performance optimization approaches. The major fabrication methods include microelectromechanical systems (MEMS), printing and transfer, laser processing and thermal drawing, and electrospinning, while deposition and surface modification are discussed as functional enhancement strategies. The review also highlights challenges associated with large-scale manufacturing, long-term stability, and the physical and clinical limitations of invasive, semi-invasive, and noninvasive electrodes. Future directions involving intelligent, wireless, and self-powered systems, together with clinical translation, are discussed to promote the development of flexible electrodes for BCI applications.},
}

@article {pmid42928015,
year = {2026},
author = {Wang, W and Zhang, J and Zhang, J and Yang, Y and Zhang, Y},
title = {Structure-guided development of G protein-biased apelin receptor agonists for cardiovascular disease.},
journal = {Journal of Zhejiang University. Science. B},
volume = {27},
number = {10},
pages = {1059-1065},
pmid = {42928015},
issn = {1862-1783},
support = {32430051 and 32400575//the National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Apelin Receptors/agonists/chemistry ; *Cardiovascular Diseases/drug therapy ; Signal Transduction ; *GTP-Binding Proteins ; Animals ; Ligands ; Drug Design ; Cryoelectron Microscopy ; Protein Conformation ; },
abstract = {Apelin receptor (APLNR) is a crucial G protein-coupled receptor that plays a central role in cardiovascular homeostasis; therefore, it represents a promising therapeutic target for heart failure, myocardial infarction, cardiac hypertrophy, and pulmonary arterial hypertension. However, the activation of APLNR simultaneously engages inhibitory G protein (Gi)-mediated protective signaling and β-arrestin pathways that are associated with pathological cardiac remodeling, thereby imposing significant limitations and challenges for the therapeutic development of conventional balanced agonists. Recent advances in structural biology and pharmacology have provided valuable mechanistic insights into receptor activation and biased signaling. High-resolution cryo-electron microscopy (cryo-EM) structures revealed the conformational basis of signal bias, enabling the rational design of fully G protein-biased agonists that retain cardiovascular benefits while avoiding hypertrophic effects. This review summarizes recent progress in APLNR signaling mechanisms and biased ligand development, highlighting a number of emerging strategies for the precision pharmacological targeting of this receptor.},
}

Humans
*Apelin Receptors/agonists/chemistry
*Cardiovascular Diseases/drug therapy
Signal Transduction
*GTP-Binding Proteins
Animals
Ligands
Drug Design
Cryoelectron Microscopy
Protein Conformation

@article {pmid42930085,
year = {2026},
author = {Althobaiti, M},
title = {Melanin Bias in Cerebral fNIRS: A Monte Carlo Study Toward Equitable Optical Brain Sensing.},
journal = {Biosensors},
volume = {16},
number = {10},
pages = {},
pmid = {42930085},
issn = {2079-6374},
mesh = {*Melanins ; Monte Carlo Method ; Spectroscopy, Near-Infrared ; *Biosensing Techniques ; Humans ; *Brain ; Brain-Computer Interfaces ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is an optical biosensing modality that transduces cortical hemodynamics into a measurable near-infrared signal, underpinning wearable brain monitors and brain-computer interfaces (BCIs). Because epidermal melanin is a strong near-infrared absorber, device sensitivity may depend on skin tone, raising an equity concern for optical neural biosensors. (1) Objective: We quantify, by Monte Carlo simulation, how epidermal melanin biases the cerebral sensitivity of continuous-wave fNIRS biosensors across the Fitzpatrick scale, and evaluate source-wavelength selection as a device-level mitigation. (2) Methods: Using a physically sourced melanin model and a two-stage design-an idealized layered slab for controlled sweeps and the Colin27 anatomical atlas for realistic-geometry confirmation-we computed gray-matter sensitivity for three skin tones, four wavelengths, and seven source-detector separations. (3) Results: Melanin acts as a surface gate that lowers absolute cerebral sensitivity by up to ~12.5-fold (at 690 nm) for dark skin-and by ~5-fold at the 850 nm optimum-while preserving depth selectivity; longer wavelengths reduce the penalty (dark-skin retained sensitivity rising from 8.0% at 690 nm to 29.6% at 940 nm) but never remove it; and 850 nm is a practical optimum for single-wavelength cerebral sensitivity, combining the highest absolute sensitivity with improved equity, whereas 940 nm gains relative equity only as absolute signal collapses through water absorption. Atlas simulations reproduce the 850 nm optimum in realistic anatomy under identical optical assumptions and show that idealized slabs overestimate sensitivity ~50-fold and that sensitivity varies ~19-fold with probe placement. (4) Conclusions: Skin tone and anatomy compound to disadvantage darker-skinned users of optical neural biosensors; wavelength selection and a sourced correction-factor framework offer concrete routes toward equitable device design.},
}

*Melanins
Monte Carlo Method
Spectroscopy, Near-Infrared
*Biosensing Techniques
Humans
*Brain
Brain-Computer Interfaces

@article {pmid42930242,
year = {2026},
author = {Zhu, M and Li, K and Zhu, J and Chen, Y and Cao, H and Xiao, J and Rao, Z},
title = {A Complementary Dual-Branch Receptive Field Network for Cross-Channel-Density Motor Imagery EEG Decoding.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {11},
number = {10},
pages = {},
pmid = {42930242},
issn = {2313-7673},
support = {2022ZD0208900//Brain Science and Brainlike Intelligence Technology-National Science and Technology Major Project/ ; 2018B030339001//Key Laboratory of Guangdong Province/ ; },
abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices, providing a bio-inspired link between humans and artificial systems. However, electroencephalography (EEG)-based motor imagery (MI) decoding continues to pose challenges, due to limited temporal exploitation and insufficient spatio-temporal modeling. To alleviate these limitations, a complementary dual-branch receptive field network (DBRFNet) is proposed for cross-channel-density MI-EEG decoding. Specifically, two complementary branches with distinct temporal receptive fields are designed to capture multi-scale temporal dynamics. Each branch employs a progressive spatio-temporal module integrating temporal, depthwise spatial, and dilated convolutions for modeling short- and long-range temporal dependencies. The features from the two branches are fused using element-wise addition and further encoded for classification. The proposed method is evaluated on three public datasets representing low-, medium-, and high-density EEG configurations. DBRFNet achieves decoding accuracies of 80.48%, 84.37%, and 92.24% under cross-validation analysis, and 87.76%, 80.94%, and 94.64% under hold-out analysis, demonstrating competitive performance compared with state-of-the-art methods. Ablation studies further validate the effectiveness of each component within the proposed architecture. These results indicate that DBRFNet effectively learns discriminative spatio-temporal representations and provides a robust solution for MI-BCI systems.},
}

@article {pmid42930474,
year = {2026},
author = {Mutiari, PD and Dhewayani, IN and Sulistyani, LD and Amir, LR and Boediono, A and Julia, V and Ariawan, D},
title = {A Biodegradable Fish-Derived Gelatin-Chitosan Sponge as a Novel Hemostatic Agent in a Rat Tooth Extraction Model.},
journal = {Marine drugs},
volume = {24},
number = {10},
pages = {},
pmid = {42930474},
issn = {1660-3397},
support = {NKB-97/UN2.RST/HKP.05.00/2024//University of Indonesia/ ; },
mesh = {Animals ; *Hemostatics/pharmacology/chemistry ; *Chitosan/chemistry/pharmacology ; Rats ; *Tooth Extraction/methods ; *Gelatin Sponge, Absorbable/chemistry/pharmacology ; Fishes ; *Gelatin/chemistry ; Rats, Sprague-Dawley ; Male ; Biocompatible Materials/chemistry ; },
abstract = {BACKGROUND: Rapid hemostasis is essential in oral surgery to minimize blood loss, prevent postoperative complications, and promote early wound healing. This study developed freeze-dried fish-derived gelatin-chitosan composite sponges with different polymer compositions (G100, GC75, GC50, and GC25) and regularly investigated the relationships among scaffold microstructure, physicochemical properties, biological performance, and hemostatic efficacy.

METHODS: The sponges were characterized using Fourier transform infrared spectroscopy, scanning electron microscopy, mechanical testing, swelling, and degradation analyses. Biological performance was evaluated through cytocompatibility and antibacterial assays, while hemostatic function was assessed using BCI, BCT, fibrin generation, PT, and PTT.

RESULTS: Among the formulations, the gelatin-rich composite GC75 exhibits an interconnected porous structure, high fluid-absorption capacity, and a distinct degradation profile while maintaining acceptable cytocompatibility. Compared with the other formulations and a commercial gelatin sponge, GC75 demonstrated significantly enhanced hemostatic performance, as evidenced by lower BCI values, shorter BCT, and accelerated fibrin generation (all p < 0.001), while maintaining physiological PT and aPTT values. In a rat tooth extraction model, GC75 significantly reduced bleeding time and achieved hemostatic efficacy comparable to or better than the commercial control.

CONCLUSIONS: These findings demonstrate the potential of gelatin-rich fish-derived gelatin-chitosan composite sponges as biodegradable hemostatic biomaterials for oral surgical applications.},
}

Animals
*Hemostatics/pharmacology/chemistry
*Chitosan/chemistry/pharmacology
Rats
*Tooth Extraction/methods
*Gelatin Sponge, Absorbable/chemistry/pharmacology
Fishes
*Gelatin/chemistry
Rats, Sprague-Dawley
Male
Biocompatible Materials/chemistry

@article {pmid42930953,
year = {2026},
author = {He, J and Lee, H and Yao, X},
title = {Beyond Overlap: Development and Validation of the Inclusion of AI in Self (IAIS) Scale and Its Moderating Role in the Relationship Between AI Identity and AI Threat.},
journal = {Journal of Intelligence},
volume = {14},
number = {10},
pages = {},
pmid = {42930953},
issn = {2079-3200},
support = {32171075//National Natural Science Foundation of China/ ; },
abstract = {Artificial intelligence (AI) is becoming increasingly integrated into people's self-concepts, yet a psychometrically robust multi-item pictorial instrument for assessing the inclusion of AI in self (IAIS) is lacking. Drawing on self-expansion theory and the AI-mediated communication (AI-MC) model, we developed and validated the IAIS scale. The IAIS comprises four pictorial items capturing overlap, distance, centrality, and size, designed as indicators of a single dimension. In Study 1, Chinese university students (N = 195, 56.9% male) were recruited through campus online platforms using convenience sampling. We conducted exploratory and confirmatory factor analyses (EFA/CFA), examined reliability and convergent, discriminant, and criterion-related validity (the last via Steiger's Z tests), and assessed gender invariance with multigroup CFA. In Study 2, the scale was cross-validated in a sample of Chinese employees (N = 513, 38.4% male) recruited through the Credamo online research platform. In Study 3, a mixed student-employee sample (N = 240, 43.3% male) completed a two-wave longitudinal survey with a 15-day interval. The moderating role of IAIS in the relationship between AI identity and AI threat was tested with PROCESS Model 1 (5000 bootstrap resamples). Results supported a replicable single-factor structure, good reliability, and convergent, discriminant, and criterion-related validity. IAIS significantly moderated the association between AI identity and AI threat. We conclude that IAIS is a psychometrically sound instrument for assessing the inclusion of AI in self, and that the negative association between AI identity and AI threat is stronger among individuals who include AI more deeply in the self.},
}

@article {pmid42933029,
year = {2026},
author = {Bakels, S and van de Ruit, M and Jafarian, M},
title = {Accurate EEG-based linear state-space model of cortical dynamics during a passive motor task with exogenous input.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb97c},
pmid = {42933029},
issn = {1741-2552},
abstract = {OBJECTIVE: Revealing the dynamics of the brain in the presence of exogenous inputs is crucial for mechanistic understanding, interventions and regulations. We study state-space mathematical modeling of brain's cortical dynamics using electroencephalography (EEG) data in an experiment with continuous exogenous input.

APPROACH: We use subspace identification techniques to model the brain dynamics in a passive motor task experiment. The EEG data were recorded while participants were seated with their wrist strapped to a haptic manipulator. The manipulator imposed a continuous multisine angular perturbation to the wrist as the exogenous input to the brain.

MAIN RESULT: Using two subspace identification algorithms, we show that linear time-invariant state-space models accurately represent the measurements, in a latent space, assuming that the EEG data are the models' output. The model is verified and validated using data from seven participants. Moreover, we construct linear maps to relate the latent space dynamics to the neural source space. We verify that the findings of our model align with those identified in previous studies.

SIGNIFICANCE: Accurate linear model in the presence of exogenous input provides a pathway for designing parsimonious and simple controllers. In addition, it enables development of online monitoring, connectivity analysis and control relevant to neurorehabilitation, motor learning, and brain-computer interfacing.},
}

@article {pmid42933128,
year = {2026},
author = {Xu, X and Dong, W and Wei, F and Zhuo, L},
title = {REFS: Robust EEG feature selection with incomplete labels for multi-dimensional emotion recognition.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb97e},
pmid = {42933128},
issn = {1741-2552},
abstract = {OBJECTIVE: Compared to single-type features, multi-type electroencephalography (EEG) features provide a multi-level representation for analyzing multi-dimensional emotions. However, the high dimensionality of multi-type EEG features, combined with the relatively small number of high-quality EEG samples, poses challenges such as classifier overfitting and suboptimal real-time performance in the multi-dimensional emotion recognition task. As the number of EEG channels increases, the issue will become increasingly severe. Moreover, practical applications of affective brain-computer interface frequently encounters partial absence of multi-dimensional emotional label data due to diverse emotional self-assessment models, and ambiguity and variability in individual emotion perception.

APPROACH: To address these challenges, this study proposes a novel robust EEG feature selection method for incomplete multi-dimensional emotion recognition. The method leverages adaptive orthogonal latent semantic indexing to reconstruct the multi-dimensional emotional label space through second-order and higher-order relations, which could reduce the negative impact of missing values and outliers on label reconstruction. Simultaneously, it employs least squares regression with graph-based manifold learning regularization and global feature redundancy minimization regularization to enable EEG feature subset selection despite missing information, ultimately achieving robust and fast EEG-based multi-dimensional emotion recognition.

MAIN RESULTS: The proposed method was validated on three multi-dimensional emotional datasets with varying channel configurations, namely DREAMER, DEAP and HDED, under different label missing ratios. Experimental results show that the proposed method consistently outperforms thirteen state-of-the-art feature selection methods in terms of robustness for EEG emotional feature selection across various missing ratios.

SIGNIFICANCE: The proposed method provides a robust and efficient solution for feature selection in multi-dimensional emotion recognition under incomplete EEG label scenarios, delivering reliable performance for practical affective brain-computer interface applications.},
}

@article {pmid42934124,
year = {2026},
author = {Fu, Y and Yan, L and Wang, F and Li, T and Zhao, L and Yang, X and Xu, J and Luo, R},
title = {[Theoretical system of brain-computer interface for basic research].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {5},
pages = {893-901},
doi = {10.7507/1001-5515.202607036},
pmid = {42934124},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Algorithms ; Humans ; *Signal Processing, Computer-Assisted ; Electroencephalography ; *Brain/physiology ; Information Theory ; },
abstract = {Brain-computer interface (BCI) has achieved remarkable progress in brain signal acquisition, decoding algorithms, and related fields. Nevertheless, its underlying theoretical system remains largely fragmented, lacking a systematic characterization of system decodability, performance boundaries, and dynamic constraint mechanisms. To address this gap, this paper formalizes BCI systems as a closed-loop probabilistic information processing model from the perspectives of information theory and statistical decision-making. This paper constructs a foundational theoretical system consisting of scientific hypotheses, neuroscience principles, fundamental theorems, and basic laws, derives four results capable of guiding experimental design, and illustrates the application boundaries with reference to motor imagery BCI paradigms. This theoretical system is expected to provide a unified analytical benchmark for basic BCI research and drive the paradigm shift of BCI from engineering experience-driven algorithm optimization to theory-driven system analysis and design.},
}

*Brain-Computer Interfaces
Algorithms
Humans
*Signal Processing, Computer-Assisted
Electroencephalography
*Brain/physiology
Information Theory

@article {pmid42934128,
year = {2026},
author = {Kong, X and Zhang, P and Chen, J},
title = {[Multi-scale motor imagery electroencephalogram classification framework with dynamic weighted multi-source domain alignment].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {5},
pages = {929-937},
doi = {10.7507/1001-5515.202607017},
pmid = {42934128},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Algorithms ; *Imagination ; Brain/physiology ; },
abstract = {Brain-computer interface (BCI) technology facilitates information exchange between the brain and external devices, offering new approaches to human-computer interaction and medical rehabilitation. However, distribution shifts caused by inter-subject variability in electroencephalogram (EEG) signals limit classification performance and generalization. To address this issue, DWDANet, an EEG classification framework integrating multi-scale spatiotemporal feature modeling with dynamically weighted domain alignment, is developed. Multi-scale convolutions extracted spatiotemporal features, while a Transformer encoder modeled global dependencies. Multi-source domain alignment with dynamic weighting enhanced collaborative modeling across source domains and improved generalization. In within-subject evaluations, DWDANet achieved accuracies of 87.22%, 90.32%, and 68.82% on the BCI IV-2a, BCI IV-2b, and SHU-MI datasets, respectively. In cross-subject experiments, it achieved accuracies of 70.61%, 81.43%, and 64.38% on these datasets, respectively, outperforming other existing methods on all three. These results indicate that DWDANet could provide an effective approach to improving motor imagery EEG classification performance and cross-subject generalization.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Signal Processing, Computer-Assisted
Algorithms
*Imagination
Brain/physiology

@article {pmid42934145,
year = {2026},
author = {Huang, K and Ding, P and Luo, R and Li, T and Zhao, L and Su, L and Xu, J and Fu, Y},
title = {[Mechanisms and outcome evaluation of neurofeedback in post-traumatic stress disorder intervention].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {5},
pages = {1075-1081},
doi = {10.7507/1001-5515.202601036},
pmid = {42934145},
issn = {1001-5515},
mesh = {Humans ; *Neurofeedback/methods ; *Stress Disorders, Post-Traumatic/therapy/physiopathology ; *Brain-Computer Interfaces ; Electroencephalography ; Magnetic Resonance Imaging ; Brain/physiopathology ; },
abstract = {Patients with post-traumatic stress disorder (PTSD) still face challenges such as limited treatment response in some cases and substantial inter-individual variability when receiving existing interventions. Neurofeedback (NFB), as a closed-loop neuromodulation approach based on brain-computer interface (BCI), can guide patients to regulate abnormal neural activity patterns by providing real-time feedback on brain activity features, and therefore shows potential for PTSD rehabilitation. This paper reviews the regulatory mechanisms of NFB on the prefrontal-limbic emotion regulation circuit and the dysregulated states of the default mode network, salience network, and central executive network. It compares three major modalities, including electroencephalography, real-time functional magnetic resonance imaging, and functional near-infrared spectroscopy, in terms of feedback targets, spatiotemporal resolution, and clinical applicability. Based on a multidimensional evaluation framework composed of clinical scales, neurophysiological indicators, and functional recovery indicators, this paper further discusses the clinical positioning of NFB relative to first-line treatments and the potential synergistic mechanisms of its combined application with psychological interventions. Current studies still face limitations such as insufficient control design and a lack of standardized training parameters. Future research should integrate individualized target selection and machine learning-based modeling to promote the standardized application of NFB.},
}

Humans
*Neurofeedback/methods
*Stress Disorders, Post-Traumatic/therapy/physiopathology
*Brain-Computer Interfaces
Electroencephalography
Magnetic Resonance Imaging
Brain/physiopathology

@article {pmid42934232,
year = {2026},
author = {Lai, S and Yu, J and Zhang, L and Wu, M and Peng, G and Zong, G and Ma, H and Yuan, C and Luo, B and Chen, H},
title = {An epigenetic aging-informed dietary pattern is associated with a spectrum of aging-related health outcomes.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {42934232},
issn = {2041-1723},
support = {LQN25H090008//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Epigenesis, Genetic ; *Aging/genetics ; *Diet ; Female ; Male ; Aged ; United Kingdom ; Cohort Studies ; United States ; Aged, 80 and over ; },
abstract = {Diet is associated with biological aging, yet few existing dietary indices have been developed specifically to target biological aging. We develop a data-driven, food-based Empirical Dietary Index for Slower Epigenetic Aging (EDISEA) to identify dietary features predictive of lower GrimAge2 acceleration. Higher EDISEA is associated with 4.17 years slower epigenetic aging in the derivation sample of 1,711 participants (P-value < 0.001) and 3.77 years slower aging in an independent sample of 2,311 participants. Higher EDISEA is consistently associated with lower all-cause mortality (pooled hazard ratio = 0.80, 95% CI 0.76, 0.85) in two US cohorts, and with lower risks of aging-related diseases, functional limitations, and multimorbidity across US and UK aging cohorts. Outcome-wide analyses in a large UK population-based cohort further show broad health associations, supported by molecular and neuroimaging signatures of more favourable vascular, inflammatory, metabolic, and brain structural profiles. EDISEA may provide an epigenetic aging-informed framework for studying dietary strategies relevant to healthy aging.},
}

Humans
*Epigenesis, Genetic
*Aging/genetics
*Diet
Female
Male
Aged
United Kingdom
Cohort Studies
United States
Aged, 80 and over

@article {pmid42934576,
year = {2026},
author = {Li, Y and Fu, Y and Duan, Y and Shi, F and Yang, R and Jin, L and Chen, L},
title = {Prenatal antiseizure medication exposure and offspring structural and neurodevelopmental outcomes: an umbrella review of systematic reviews and meta-analyses.},
journal = {BMC pregnancy and childbirth},
volume = {26},
number = {1},
pages = {},
pmid = {42934576},
issn = {1471-2393},
support = {2024ZDZX0018//Sichuan Science and Technology Program/ ; },
mesh = {Humans ; *Anticonvulsants/adverse effects/therapeutic use ; Pregnancy ; Female ; *Prenatal Exposure Delayed Effects/chemically induced ; Systematic Reviews as Topic ; Meta-Analysis as Topic ; *Neurodevelopmental Disorders/chemically induced ; Valproic Acid/adverse effects ; Epilepsy/drug therapy ; Neurodevelopment ; },
abstract = {BACKGROUND: Review-level evidence on prenatal antiseizure medication (ASM) exposure is difficult to interpret because outcome domains are often synthesized separately, primary studies overlap, and comparator frameworks and certainty vary. We aimed to calibrate evidence across structural and neurodevelopmental outcomes.

METHODS: We conducted an anchor-based umbrella review of systematic reviews and meta-analyses (PROSPERO: CRD420251142541), searching four databases through December 31, 2025. One prespecified anchor review represented each drug-outcome-comparator question; overlapping reviews were not combined into second-order pooled estimates. AMSTAR 2, corrected covered area, GRADE, and directional umbrella-credibility classes were applied, with NS assigned when the 95% confidence interval included the null.

RESULTS: Fourteen reviews were included: nine structural and five neurodevelopmental. All had Low or Critically Low AMSTAR 2 confidence, and ASD/ADHD review overlap was very high (CCA, 28.1%). Valproate showed the most consistent retained pattern, including Class I credibility for combined major congenital anomalies and the most credible retained neurodevelopmental signal for autism spectrum disorder (adjusted hazard ratio, 3.10; 95% CI, 2.24-4.28; Low certainty; Class II). Topiramate showed an oral-cleft signal with Low certainty and Class III credibility. The lamotrigine estimate for major congenital anomalies was NS, whereas the orofacial-cleft estimate excluded the null but had Very Low certainty (OR, 1.42; 95% CI, 1.05-1.92; Class IV). Other retained neurodevelopmental anchors had Very Low certainty and were Class IV or NS; no independent pairwise neurodevelopmental anchor was available for carbamazepine, oxcarbazepine, topiramate, or levetiracetam.

CONCLUSIONS: Review-level evidence was most consistent for valproate and aligned with its established structural and neurodevelopmental risks. NS or Very Low-certainty findings for non-valproate ASMs should not be interpreted as evidence of no risk. Comparative safety requires individualized risk-benefit assessment rather than binary safety classifications.},
}

Humans
*Anticonvulsants/adverse effects/therapeutic use
Pregnancy
Female
*Prenatal Exposure Delayed Effects/chemically induced
Systematic Reviews as Topic
Meta-Analysis as Topic
*Neurodevelopmental Disorders/chemically induced
Valproic Acid/adverse effects
Epilepsy/drug therapy
Neurodevelopment

@article {pmid42934997,
year = {2026},
author = {Liu, Y and Chen, L},
title = {Auditory Rhythm Biases Visual Speed Perception and Shows Tempo-Dependent Signatures During Motion Extrapolation.},
journal = {The European journal of neuroscience},
volume = {64},
number = {8},
pages = {},
doi = {10.1111/ejn.70730},
pmid = {42934997},
issn = {1460-9568},
support = {2021ZD0202600//STI2030-Major Project/ ; //National Natural Science Foundation of China/ ; T2192932//Natural Science Foundation of China/ ; },
mesh = {Humans ; *Motion Perception/physiology ; Male ; Female ; Adult ; *Auditory Perception/physiology ; Young Adult ; Acoustic Stimulation ; *Time Perception/physiology ; *Pitch Perception/physiology ; },
abstract = {Auditory signals can bias visual motion judgements, but how dynamic auditory structure affects variable-speed motion during perception and subsequent extrapolation remains unclear. Across three experiments, we paired changing visual motion with changes in auditory tempo or pitch. In Experiment 1, slow and fast tempi produced robust, opposite shifts in the point of subjective equality for visual speed-change judgements, without reliable evidence for a change in discrimination precision in this paradigm. Experiment 2 revealed a weaker asymmetric pitch effect: Descending pitch shifted judgements toward deceleration, whereas ascending pitch produced no reliable opposite shift. A pooled comparison localized the tempo-pitch difference primarily to the speeding-direction bias rather than psychometric slope. In Experiment 3, the omnibus three-level auditory effect on manual time-to-contact (TTC) was not significant; however, within the two sound-present conditions, accelerating tempo shortened mean manual TTC by approximately 32 ms relative to decelerating tempo. The corresponding primary eye-TTC contrast was small and not significant, although time-resolved eye analyses revealed a transient postocclusion eye-velocity effect together with broader sound-related pupil responses. Leave-one-condition-out model comparison nevertheless showed that the same tempo-by-physical-TTC functional model provided the best held-out prediction for both manual and eye-based estimates. Together, these findings indicate robust auditory-tempo biases in online visual speed judgements and more qualified, tempo-dependent signatures during forward extrapolation after visual information disappears; the absence of a JND effect here should not be generalized to all audiovisual motion tasks.},
}

Humans
*Motion Perception/physiology
Male
Female
Adult
*Auditory Perception/physiology
Young Adult
Acoustic Stimulation
*Time Perception/physiology
*Pitch Perception/physiology

@article {pmid42937202,
year = {2026},
author = {Hawkins, JK},
title = {Nano-totalitarianism: Brain-computer interfaces, fusion databases, and the weaponisation of consciousness.},
journal = {Torture : quarterly journal on rehabilitation of torture victims and prevention of torture},
volume = {36},
number = {2},
pages = {},
doi = {10.7146/torture.v36i2.170024},
pmid = {42937202},
issn = {1997-3322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Consciousness ; *Torture/psychology ; Coercion ; Large Language Models ; },
abstract = {Debate about interrogational torture still returns to the ticking-bomb scenario, despite the weight of evidence that it neither works nor withstands moral scrutiny. The post-9/11 record, from Abu Ghraib to the CIA black sites, already marked a decisive shift toward psychological forms of coercion. The technologies now emerging make a different kind of coercion possible. This paper argues that defence-funded brain-computer interfaces, of which Synchron's Stentrode is the clearest current example, combined with large language models and fusion databases, could support what I call nano-totalitarianism: the convergence of pervasive personal data, neurodata, adaptive AI, and technologically mediated coercion in ways that could penetrate or continuously condition the inner sphere of the person. Operationalised, this is a self-sustaining regime in which a detainee is held in continuous psychological distress through fabricated scenarios, interference with memory, and synthetic social contact. Such a system would turn neuroplasticity into an instrument, laying down durable neural pathways of fear, while automated monitoring assesses compliance in real time. Its lineage runs from MKULTRA's psychochemical experiments to the extraction logic of surveillance capitalism, and its endpoint is a panopticon inside the skull. The essay proceeds anticipatorily, distinguishing throughout between demonstrated current capability, plausible convergence of existing components, and speculative future possibility.},
}

Humans
*Brain-Computer Interfaces
*Consciousness
*Torture/psychology
Coercion
Large Language Models

@article {pmid42938298,
year = {2026},
author = {Zhu, M and Zhang, JF and Hu, SX and Wang, X and Xu, W},
title = {Shared Genetic and Regulatory Architecture of Congenital Heart Disease and Autism Spectrum Disorder Revealed by Cross-Tissue Multi-Omic Integration.},
journal = {Genes},
volume = {17},
number = {10},
pages = {},
pmid = {42938298},
issn = {2073-4425},
support = {92253302//National Natural Science Foundation of China/ ; 2021ZD0203900//National Science and Technology Innovation 2030 Major Project of China/ ; 226-2026-00003//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Autism Spectrum Disorder/genetics ; *Heart Defects, Congenital/genetics ; MEF2 Transcription Factors/genetics ; Genome-Wide Association Study ; Quantitative Trait Loci ; Multiomics ; Genetic Predisposition to Disease ; Machine Learning ; DNA-Binding Proteins/genetics ; },
abstract = {Background: Congenital heart disease (CHD) and autism spectrum disorder (ASD) co-occur more frequently than expected, suggesting shared developmental susceptibility. We aimed to identify cross-tissue molecular candidates linking cardiac and neurodevelopmental abnormalities. Methods: CHD- and ASD-associated genes were curated from MalaCards and SFARI Gene, and their intersection was defined as the CHD- and ASD-associated gene set (CAGES). Shared gene enrichment was assessed against the human protein-coding genome. CHD and ASD genome-wide association data were integrated with heart and brain cis-expression quantitative trait loci using Bayesian colocalization and summary data-based Mendelian randomization with HEIDI testing. Candidates were further evaluated using bulk transcriptomics, six machine learning feature selection methods, independent cohorts, and scTenifoldKnk virtual knockout analyses in developing human heart and cerebral cortex. Results: Seventy-three genes were shared between CHD and ASD. CHD7 showed suggestive colocalization for CHD and ASD, while lower genetically predicted CHD7 expression was associated with higher odds of both disorders, with nonsignificant HEIDI tests. MEF2C was downregulated in tetralogy of Fallot myocardium and selected by all six ASD machine learning methods, with consistently lower expression in ASD. CAGES-derived signatures discriminated independent CHD, ASD cord blood, and cortical cohorts. CHD7 virtual knockout analysis identified MEF2C as a computationally predicted regulatory candidate in the prenatal brain, particularly in late-fetal excitatory neurons and intermediate progenitor cells, with cell-type-specific predicted effects also observed in the developing heart. Conclusions: CHD and ASD share molecular susceptibility involving pleiotropic developmental mechanisms. CHD7 and MEF2C emerge as connected cross-tissue candidates across complementary genetically informed, transcriptomic, and developmental regulatory analyses, highlighting prenatal neuronal populations and developmental windows for investigating the heart-brain axis.},
}

Humans
*Autism Spectrum Disorder/genetics
*Heart Defects, Congenital/genetics
MEF2 Transcription Factors/genetics
Genome-Wide Association Study
Quantitative Trait Loci
Multiomics
Genetic Predisposition to Disease
Machine Learning
DNA-Binding Proteins/genetics

@article {pmid42938443,
year = {2026},
author = {Chong, ZY and Zhang, Z and Chen, X and Xu, W},
title = {Short Video Addiction Risk Transitions Among University Students: A Three-Wave Latent Transition Analysis of Unintentional Procrastination and Self-Concept Clarity.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {16},
number = {10},
pages = {},
pmid = {42938443},
issn = {2076-328X},
abstract = {This study examined changes in short video addiction (SVA) among university students. Three-wave longitudinal data were used to identify relative SVA profiles, estimate transitions among these profiles, and examine the prospective associations of unintentional procrastination and self-concept clarity with subsequent profile membership. A total of 2145 students completed at least one assessment. The primary latent transition analysis included 1135 students with valid SVA data at two or more waves, and the predictor analyses included 679 three-wave completers. Three ordered profiles characterized by lower, moderate, and higher SVA were identified. These profiles represent relative latent classes within the present sample and are not diagnostic categories. Profile membership showed both stability and movement across adjacent waves. In the classification error-adjusted predictor analyses, higher unintentional procrastination was associated with lower odds of membership in the lower profile relative to the higher profile among students initially in the moderate profile. Higher self-concept clarity was associated with greater odds of maintaining lower or moderate profile membership relative to membership in the higher profile. These findings indicate that relative SVA profile membership can change over time and that unintentional procrastination and self-concept clarity show different prospective associations with subsequent profile membership.},
}

@article {pmid42939079,
year = {2026},
author = {Cantillo-Negrete, J and Rodríguez-García, ME and Carrillo-Mora, P and Méndez-Gómez, DD and Carino-Escobar, RI},
title = {Brain-Computer Interfaces for Cognitive Rehabilitation: A Narrative Review.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {10},
pages = {},
pmid = {42939079},
issn = {2306-5354},
abstract = {Cognitive diseases/disorders are a major cause of disability affecting millions of people worldwide due to pathologies or conditions such as Alzheimer's disease, Parkinson's disease, vascular cognitive impairment, and attention deficit/hyperactivity disorder. The effects of these diseases/disorders can range from subjective cognitive complaints to mild cognitive impairment and even dementia that can affect multiple cognitive domains, causing disability and alterations in daily functioning and quality of life. Cognitive training has been researched as a potential therapeutic option for treating cognitive disability. In particular, cognitive training delivered using brain-computer interfaces (BCIs) represents a promising technology for cognitive rehabilitation. For this reason, this work presents a narrative review aimed at bridging the clinical aspects of cognition performance and the technical features of BCI systems. Additionally, it reports on the current advances of BCIs for cognitive rehabilitation in the pathologies/conditions that are the main contributors of cognitive impairment globally. Specifically, Alzheimer's disease, Parkinson's disease, mild cognitive impairment, cerebrovascular diseases, and attention-deficit/hyperactivity disorder were included in the review; other pathologies that can also cause cognitive impairment such as multiple sclerosis, traumatic brain injury, brain tumors, infectious diseases, and psychiatric conditions such as schizophrenia or autism spectrum disorder, or drug or toxin-related causes were outside the scope of the present review. Across the literature, studies have reported that BCI cognitive training can improve cognition performance for mild cognitive impairment, stroke, and attention-deficit/hyperactivity disorder, showcasing its feasibility as a complementary therapy. However, the amount of evidence is still scarce, with few reported randomized controlled clinical trials and all of the applications comprising non-invasive systems using electroencephalography, while invasive or other non-invasive approaches remain largely unassessed. Therefore, further research regarding the design of BCIs for targeting specific cognitive domains is necessary to allow for the future introduction of these types of systems into clinical rehabilitation environments, rather than only demonstrating their feasibility, as has been mainly done to date.},
}

@article {pmid42939144,
year = {2026},
author = {Tang, Z and Li, S and Li, M and Shang, Z},
title = {Locomotor-State Dependence of Neuromodulation During Avian Free Flight.},
journal = {Animals : an open access journal from MDPI},
volume = {16},
number = {20},
pages = {},
pmid = {42939144},
issn = {2076-2615},
support = {62671564//National Natural Science Foundation of China/ ; 62301496//National Natural Science Foundation of China/ ; 2025T180781//China Postdoctoral Science Foundation/ ; 252102311095//Henan Science and Technology Department/ ; 252102210008//Henan Science and Technology Department/ ; },
abstract = {Complex avian locomotor behaviors depend on the coordinated operation of neural processes involved in sensory perception, decision-making, and motor execution. Exogenous neural stimulation can alter motor output by intervening in neural processes, but its effects may depend on the current locomotor state. Here, we investigated the locomotor-state dependence of formatio reticularis medialis mesencephali (FRM) microstimulation in freely flying pigeons. Individualized stimulation parameters were screened under stable laboratory conditions and kept constant during free-flight experiments. Flight-state features before and during stimulation were extracted, and state-response models were constructed to quantify their associations with stimulation-evoked turning responses. Results showed that flight state during stimulation was more closely associated with turning responses than pre-stimulation state, with acceleration during stimulation making the largest contribution. When acceleration during stimulation was above the individual mean, turning-response dispersion decreased by 32.92%. In addition, pre-stimulation angular velocity was positively associated with acceleration during stimulation, indicating that the pre-stimulation locomotor state contains information related to the consistency of subsequent stimulation-evoked responses. These findings demonstrate that the effects of FRM microstimulation are locomotor-state dependent and provide a basis for identifying appropriate neuromodulation timing and improving response stability under complex locomotor conditions.},
}

@article {pmid42939304,
year = {2026},
author = {Zhao, W and Dai, J and Pan, Y and Xiao, S and Xie, T and Li, Y and Yu, Y},
title = {STDR-Net: A Spatio-Temporal Dependency Refinement Network for Motor Imagery EEG Decoding.},
journal = {Brain sciences},
volume = {16},
number = {10},
pages = {},
pmid = {42939304},
issn = {2076-3425},
support = {3502Z202374054 and 3502Z202573102//Natural Science Foundation of Xiamen, China/ ; CJ25020, CYKYPT02, HX25027 and HX25031//Chengyi College, Jimei University/ ; CJ25075, CJ25058, CJ25051 and CJ25052//Education Department of Fujian Province/ ; },
abstract = {Background/Objectives: Motor imagery electroencephalography (MI-EEG) contains weak, non-stationary patterns that evolve across channels and time. Effective decoding requires local sensorimotor structure, trial-wide context, and ordered temporal evolution to be represented without discarding information from earlier processing stages. Methods: We propose STDR-Net, a Spatio-Temporal Dependency Refinement Network that recalibrates convolutional tokens, models their global interactions, restores the original local representation through a cross-stage residual bridge, and refines the fused sequence with a bidirectional long short-term memory network. STDR-Net was evaluated on BCI Competition IV-2a, BCI Competition IV-2b, and the PhysioNet EEG Motor Movement/Imagery dataset against nine reproduced baselines. The PhysioNet experiment used a pooled mixed-participant trial-level split rather than an unseen-participant evaluation. Results: STDR-Net obtained accuracies of 82.69%, 86.99%, and 59.76% and Cohen's kappa values of 0.7692, 0.7398, and 0.46, respectively. It achieved the highest observed mean accuracy on IV-2a and competitive performance on IV-2b and PhysioNet. Bridge-specific, ordering, module, and augmentation ablations supported the proposed pathway on IV-2a, whereas the differences on IV-2b were small and not significant after multiplicity correction. Conclusions: The results support information-preserving dependency refinement as a competitive design for MI-EEG decoding, while also showing that its benefit depends on the dataset and evaluation setting.},
}

@article {pmid42915561,
year = {2026},
author = {Wang, L and Wang, J and Dong, S and Lu, J and Xia, XH and Wang, C},
title = {Biomimetic Nanofluidic Synapse with Programmable DNA Ion Gating for Neuromorphic Sensing and Computing.},
journal = {Journal of the American Chemical Society},
volume = {148},
number = {36},
pages = {39139-39151},
doi = {10.1021/jacs.6c13080},
pmid = {42915561},
issn = {1520-5126},
support = {SKLACLS2619//State Key Laboratory of Analytical Chemistry for Life Sciences/ ; 2025YFC3409400//National Key Research and Development Program of China/ ; 22274076//National Natural Science Foundation of China (NSFC)/ ; 22304084//National Natural Science Foundation of China (NSFC)/ ; BK20233003//Jiangsu Basic Research Center for Synthetic Biology/ ; SKLSIM-F-2024176//State Key Laboratory of Neurology and Oncology Drug Development/ ; SKLSIM-F-2025351//State Key Laboratory of Neurology and Oncology Drug Development/ ; },
mesh = {*DNA/chemistry ; DNA Nanostructures ; *Biomimetic Materials/chemistry ; *Synapses ; *Biomimetics ; *Nanotechnology ; *Biosensing Techniques ; Neural Networks, Computer ; },
abstract = {Biological synapses process information through coupled neurotransmitter and ion fluxes, while conventional electron-based solid-state devices struggle to replicate this chemoelectrical signaling mechanism. Herein, we develop a biomimetic nanofluidic synapse designed for precise and programmable chemical gating. The platform integrates programmable DNA nanochannel (DN) arrays within a phospholipid multilayer assembled in situ on a robust anodic aluminum oxide membrane (termed DPAM), forming a bioinspired ion-gating interface. Crucially, DNA sequence programming enables plug-and-play adaptability for diverse neurotransmitters, while tuning DN dimensions customizes ion transport kinetics. As a prototypical example, glutamate-gated N-methyl-d-aspartate-like signaling is demonstrated to regulate transmembrane Ca2+ flux, successfully emulating fundamental synaptic plasticity. Furthermore, by integrating these synaptic units into an artificial neural network, we demonstrate a chemically encoded handwritten-digit recognition task with an accuracy of 97.1%. This work establishes a versatile platform for neuromorphic systems allow directly processing biochemical signals, holding great promise for brain-machine interfaces and intelligent biosensing.},
}

*DNA/chemistry
DNA Nanostructures
*Biomimetic Materials/chemistry
*Synapses
*Biomimetics
*Nanotechnology
*Biosensing Techniques
Neural Networks, Computer

@article {pmid42919847,
year = {2026},
author = {Gu, Y and Yin, H and Wang, Y and Woon, YQ and Wang, Y and Teng, M and Lu, C and Lu, Z and Chen, J},
title = {Generalizable neural decoding for clinically scalable brain-computer interfaces.},
journal = {npj biomedical innovations},
volume = {3},
number = {1},
pages = {},
pmid = {42919847},
issn = {3005-1444},
support = {625B2053//Natural Science Foundation of China/ ; 62575154//Natural Science Foundation of China/ ; 4254114//Young Scientists Fund of Beijing Natural Science Foundation/ ; },
abstract = {Brain-computer interfaces promise restored communication and movement, yet accuracy achieved under development rarely survives new sessions, users, tasks, or hardware. Generalization, not peak performance, determines clinical scalability. Here we examine generalizable neural decoding from development to deployment, organized by the domain held out at evaluation, target-data access, adaptation strategy, and the clinical conditions under which performance persists, identifying benchmarking standards, prospective validation, and hardware-algorithm co-design as prerequisites for durable benefit.},
}

@article {pmid42920124,
year = {2026},
author = {Feng, Y and Guo, X and Huang, P and Jia, N and Wang, L and Ji, X and Qin, Z and Li, M and Lai, J and Qin, X and Yue, W and Yang, S and Hu, S},
title = {Polygenicity shapes distinct brain patterns and clinical profiles of bipolar disorder.},
journal = {BMC medicine},
volume = {24},
number = {1},
pages = {},
pmid = {42920124},
issn = {1741-7015},
support = {82173585//National Natural Science Foundation of China/ ; 82571735//National Natural Science Foundation of China/ ; 2025C02109, 2024C03098//Key R&D Program of Zhejiang Province/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2025Z156//Ningbo Key Research and Development Program/ ; },
mesh = {Humans ; *Bipolar Disorder/genetics/diagnostic imaging/pathology ; *Brain/diagnostic imaging/pathology ; *Multifactorial Inheritance ; Genetic Predisposition to Disease ; Genetic Risk Score ; Phenotype ; Female ; Polymorphism, Single Nucleotide ; Asian People/genetics ; Male ; },
abstract = {BACKGROUND: Bipolar disorder (BD) manifests both genetic predispositions and brain imaging abnormalities. Genetic analyses provide a powerful approach to disentangling the associations between BD and brain imaging-derived phenotype (IDP). However, in East Asian (EAS) populations, the genetic basis of these neuroimaging changes and their clinical relevance remains underexplored due to limited integrative evidence.

METHODS: We investigated the genetic architecture and clinical relevance of BD and brain IDPs in East Asian populations using individual-level data from the Chinese Bipolar Disorder (CN-BD) cohort (N = 3,920), summary statistics of BD from the Psychiatric Genomics Consortium (PGC) (N = 16,915) and of IDPs from the CHIMGEN Consortium (N = 7,058), and genotype and clinical data from the Chinese Longitudinal and Systematic Study of Bipolar Disorder cohort (CLASS-BD). We employed genetic correlation (GNOVA), multi-trait meta-analysis (MTAG, CPASSOC), and ancestry-specific fine-mapping (MESuSiE) to identify shared genetic loci. Functional annotation, protein association (SMR, BLISS), and spatiotemporal expression trajectory analyses were conducted. Bidirectional Mendelian Randomization (MR) assessed causal relationships, and polygenic risk score (PRS)-PheWAS explored phenome-level associations with clinical traits.

RESULTS: Our multi-trait meta-analysis identified 11 pleiotropic single nucleotide variants (SNVs) implicated in BD-IDP pairs, showing convergent enrichment in lipid metabolism and neurotransmitter biosynthesis pathways. Fine-mapping revealed an EAS-specific causal signal (rs7941324 near RPS27P20), which demonstrated high posterior inclusion probability in EAS but not European ancestries, and colocalized with BD and specific brain alterations. MR revealed 27 IDP-to-BD and 35 BD-to-IDP causal relationships, distinguishing structural changes of the parietal and temporal gyri as markers preceding BD onset and the right precuneus area as a state marker that followed BD onset. Clinically, BD-IDP polygenic risk scores were significantly associated with depressive symptoms, anxiety, and suicide risk. Mediation analysis further indicated that depressive and anxiety symptoms statistically mediated the relationship between BD-IDP PRS and suicide risk. Moreover, PRS-PheWAS identified 135 significant associations with metabolic phenotypes, highlighting a psycho-metabolic nexus in BD.

CONCLUSIONS: Our findings reveal associations between polygenicity and distinct BD brain patterns and clinical profiles. We identified ancestry-specific causal variants and bidirectional BD-IDP relationships. PRS associations with depressive symptoms, anxiety, suicide risk, and metabolic phenotypes highlight precision medicine potential for BD.},
}

Humans
*Bipolar Disorder/genetics/diagnostic imaging/pathology
*Brain/diagnostic imaging/pathology
*Multifactorial Inheritance
Genetic Predisposition to Disease
Genetic Risk Score
Phenotype
Female
Polymorphism, Single Nucleotide
Asian People/genetics
Male

@article {pmid42920311,
year = {2026},
author = {Li, Y and Tan, X and Tan, M and Gao, S},
title = {Modeling the Selective and Lossy Nature of Visual Processing Bridges the Discrepancy in Noninvasive Neural Decoding.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2750029},
doi = {10.1142/S0129065727500298},
pmid = {42920311},
issn = {1793-6462},
abstract = {Noninvasive neural decoding seeks to infer visual stimuli from brain recordings, such as EEG and MEG, thereby providing a computational approach for probing human visual information processing. Current contrastive learning approaches align brain signals with image embeddings from pretrained neural networks. However, these methods often struggle with the intrinsic visual-brain discrepancy arising from systematic representational gaps and noise because brain signals are treated as direct, lossless replicas of visual inputs. To alleviate this mismatch, the proposed framework accounts for the selective and information-compressive characteristics of biological vision. On the visual side, the framework incorporates an adaptive, context-aware visual calibration mechanism, where multilevel blurred representations are dynamically fused under the guidance of structural salience to refine visual features toward the brain's abstraction level. On the brain-signal side, a robust and lightweight neural encoder is used to mitigate inter-channel and temporal variations. Experiments on standard EEG and MEG benchmarks show that the proposed method achieves strong brain-to-image retrieval performance. Our modeling hypothesis, supported by retrieval behavior, suggests that computational representations aligning with salient and structurally meaningful content better match macroscopic neural responses than fine-grained image details. These findings suggest that incorporating the lossy and selective characteristics of human visual processing into computational models is important for advancing noninvasive neural decoding and understanding brain-machine alignment. The source codes are available at SLVP.},
}

@article {pmid42920766,
year = {2026},
author = {Wu, XL and Xia, XQ and Ye, CQ and Wang, YL and Li, XZ and Liu, LX and Hang, FB and Liu, JS and Song, YC and Zhang, Y and Du, XX},
title = {Case Report: An MDT-guided multimodal protocol for large-area stroke plateau - integrating neuromodulation with multimodal assessment.},
journal = {Frontiers in rehabilitation sciences},
volume = {7},
number = {},
pages = {1803123},
pmid = {42920766},
issn = {2673-6861},
abstract = {OBJECTIVE: This case report investigates the feasibility and potential value of an individualized precision rehabilitation protocol for large-area stroke, integrating multimodal neuromodulation under the guidance of a multidisciplinary team (MDT) and dynamic assessment.

METHODS: A 38-year-old male with right hemiplegia and global aphasia following large left hemispheric infarction underwent an 8-month MDT-managed program. A closed-loop "assess-decide-intervene-re-assess" framework was implemented, featuring periodic multimodal evaluations (clinical scales, DTI, fMRI, EEG, 3D gait analysis). The intervention dynamically combined conventional therapy with staged neuromodulation: repetitive Transcranial Magnetic Stimulation (rTMS) for aphasia and intermittent Theta Burst Stimulation (iTBS) for motor disorder, Brain-Computer Interface (BCI)-robotic training and task-oriented Virtual Reality (VR) for upper limb function, and biomechanical correction with custom insoles based on gait analysis.

RESULTS: Notable functional improvements were observed: LOTCA increased by 16 points, CRRCAE subscale improved by 60%-90%, FMA increased from 20 to 56, FMA-B reached 12, and MBI improved from 40 to 91. Neuroimaging revealed increased fractional anisotropy (FA) in the corpus callosum and right corticospinal tract, and elevated ALFF/ReHo in right frontal-temporal areas. Event-related desynchronization (ERD) decreased during BCI training, and gait parameters improved.

CONCLUSION: In this patient with large-area stroke who showed limited response to conventional rehabilitation, an MDT-guided, dynamically adjusted multimodal intervention was temporally associated with concurrent recovery across cognitive, language, motor, and daily living domains. The concomitant neural changes suggest possible trans-hemispheric network reorganization. These observations are hypothesis-generating and, while limited by the single-case design, provide a preliminary rationale for future investigations into precision neurorehabilitation approaches in similar complex cases.

CLINICAL TRIAL REGISTRATION: ChiCTR2300069203.},
}

@article {pmid42921446,
year = {2026},
author = {Yang, S and Sun, Q and Mo, H and Cui, W},
title = {Correction: Perioperative management for invasive brain-computer interface implantation under surgical navigation in a patient with traumatic spinal cord injury: a case report.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1995038},
doi = {10.3389/fnhum.2026.1995038},
pmid = {42921446},
issn = {1662-5161},
abstract = {[This corrects the article DOI: 10.3389/fnhum.2026.1892331.].},
}

@article {pmid42914707,
year = {2026},
author = {Dornan, TN and Dantas, PT and Morgan, DL and Lear, KO and Ingelbrecht, J and Fazeldean, T and Keneally, C and Malik, A and Austin, J and Brookes, JD},
title = {DNA metabarcoding reveals the trophic ecology of Critically Endangered juvenile green sawfish (Pristis zijsron) and sympatric predators.},
journal = {Marine environmental research},
volume = {223},
number = {},
pages = {108466},
doi = {10.1016/j.marenvres.2026.108466},
pmid = {42914707},
issn = {1879-0291},
abstract = {North-western Australia is home to many of Australia's rare and threatened elasmobranchs. Basic ecological data is still lacking for many of these species, including empirical dietary information. DNA metabarcoding was performed on cloacal swabs to obtain the first empirical insights into the trophic ecology of the IUCN Critically Endangered green sawfish (Pristis zijsron). Additionally, qualitative insights were gained into the piscivorous diet of sicklefin lemon sharks (Negaprion acutidens), nervous sharks (Carcharhinus cautus), and blacktip sharks (Carcharhinus tilstoni/C. limbatus). A total of 20 teleosts were identified as prey for green sawfish, with sampling effort covering an estimated 77.9% of their total piscivorous diet. An ontogenetic dietary shift in green sawfish was identified, shifting from glassfish (Ambassis sp.) to hairback herring (Nematalosa come) and sharksuckers (Echeneis naucrates) as individuals grew from ∼800 to 2500 mm total length. Of all co-occurring predators, green sawfish had the greatest piscivorous dietary overlap with sicklefin lemon sharks, and the most common prey among sampled elasmobranch species were hairback herring and giant trevally (Caranx ignobilis). This research vastly expands our understanding of the trophic ecology of green sawfish in Australia, particularly at the critical juvenile age class. The results highlight how the complex intraspecific and interspecific dietary interactions of green sawfish require holistic, ecosystem-based approaches to effectively conserve this highly threatened species.},
}

@article {pmid42913687,
year = {2026},
author = {Kokorin, K and Mu, J and Grayden, DB and John, SE and Yoo, PE},
title = {Decoding attempted handwritten characters based on temporal endovascular neural features.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb82b},
pmid = {42913687},
issn = {1741-2552},
abstract = {We evaluated temporal neural features for motor decoding using a minimally-invasive endovascular brain-computer interfaces (BCI). We hypothesised that relatively long compound movements generate distinct patterns of neural activity that are encoded by temporal features. Approach. A single participant implanted with the Stentrode endovascular BCI attempted to hand write English characters, which were then classified offline and online based on the recorded brain activity. The input to the model was a one-dimensional trajectory of high-gamma power, summed across all channels to isolate the effect of temporal information on decoding performance. Main results. A small number of classes across multiple months was reliably decoded, showing that temporal decoding could identify stable patterns of brain activity associated with each handwriting movement. Across four online sessions, where each session contained 20-30 trials/character and 80-180 rest trials, the best performing (3-class) model achieved a mean rest recall of 87% as well as character recall and precision of 65% and 59%, respectively. These results showed that attempted handwriting can be used in an online multi-class selection BCI and that temporal neural features capture the internal user context. Linear warping of trials to minimise temporal variability, where all trials were modified to have the same duration, led to significantly increased decoding performance compared to classifying raw trials. Significance. The results from our participant showed that decoding brain activity based on the differences between feature trajectories across time can be an effective strategy for discriminating different movements. Therefore, BCIs may be able to use temporal information to reduce their spatial footprint, likely making them less invasive. Overall, our study shows that further investigation of temporal decoding across multiple participants and other recording modalities is warranted. Strategies for automatic temporal alignment are likely to further improve the performance of these systems.},
}

@article {pmid42913696,
year = {2026},
author = {Hart, DM and Polichtchouk, S and Dannhauer, M},
title = {Transformer networks are robust learners of source localization for EEG measurements.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb82c},
pmid = {42913696},
issn = {1741-2552},
abstract = {EEG source localization is an ill-posed inverse problem complicated by measurement noise, electrode dropout from sensor miscalibration, and variability in the number of active sources. Classical methods such as eLORETA and LCMV beamforming impose regularization assumptions that degrade under realistic noise conditions and do not naturally extend to multi-source scenarios. Recent neural network approaches improve localization accuracy but are typically trained and evaluated under idealized conditions that limit their applicability to real-world EEG acquisition. This work aims to develop and evaluate a source localization model that generalizes across the noise levels, source counts, and electrode configurations encountered in clinical and experimental EEG settings. Approach: We present a transformer-based model for single- and multi-source EEG localization. Hundreds of thousands of simulated EEG measurements are generated from known source locations, spanning a range of noise levels, source counts, and electrode dropout configurations, to train and evaluate the model. We establish a systematic evaluation framework that explicitly factors in source region density, signal-to-noise ratio, and electrode availability, and benchmark the transformer against classical (eLORETA, LCMV beamforming) and deep learning baselines across these conditions. We additionally conduct a thorough ablation study on network architecture and training pipeline design. Main results: Across all tested conditions, the transformer consistently outperforms classical and deep learning approaches, with improvements as high as 12\,mm in mean localization error (MLE) for multi-source scenarios under high noise. Additionally, when number of sources is unknown, the transformer can correctly predict the number and location of sources and drastically out perform the threshold-based approach (e.g an F1 Score of 68\% vs 12\% when simulated at 25\% noise). The multiembedding transformers maintains reasonable performance under conditions such as electrode dropout and increased noise. Significance: By explicitly modeling the noise, electrode dropout, and multi-source variability that characterize real EEG acquisition, this work addresses a key gap between benchmark performance and experimental utility in EEG source localization. These results lay the groundwork for more robust localization techniques applicable to real EEG data, with potential relevance to applications such as brain-computer interfaces. The data and code for this work will be made publicly available to support further development and benchmarking in the field.},
}

@article {pmid42909910,
year = {2026},
author = {Zheng, Q and Wen, J and Yuan, W and Qin, J and Duanmu, X and Wu, H and Zhu, Z and Guo, T and Wu, C and Zhou, C and Wu, J and Chen, J and Fang, Y and Zhu, B and Yan, Y and Tian, J and Zhang, B and He, H and Zhang, M and Guan, X and Xu, X},
title = {A clinically interpretable model for Parkinson's disease: insights from complementary MRI modalities.},
journal = {Brain communications},
volume = {8},
number = {5},
pages = {fcag383},
pmid = {42909910},
issn = {2632-1297},
abstract = {Accurate identification of Parkinson's disease may remain challenging in certain clinical scenarios, highlighting the need for a clinical decision-support model based on complementary MRI modalities. The case-control study included 462 participants, split into training [247 Parkinson's disease, 122 normal controls (NC)] and testing (60 Parkinson's disease, 33 NC) cohorts to construct and validate a clinical decision-support model. We employed least absolute shrinkage and selection operator and logistic regression analysis to develop the model utilizing quantitative susceptibility mapping, arterial spin labelling and T1-weighted images. The model was assessed for calibration, discrimination and clinical utility, with individual predicted scores correlated to Parkinson's disease clinical symptoms. The model's performance was also evaluated independently in multiple system atrophy, progressive supranuclear palsy and essential tremor, with DeLong test comparisons. The model was trained in the training cohort (Parkinson's disease: 61.39 ± 8.06 years old, 134 males; NC: 61.29 ± 7.56 years old, 54 males) with an area under the ROC curve (AUC) of 0.842 [95% confidence interval (CI) 0.798-0.883] and 0.771 (95% CI 0.657-0.870) in the testing cohort, whose clinical utility was confirmed. The predicted scores were positively correlated with Unified Parkinson's Disease Rating Scale II (P = 0.034) and III (P = 0.015), while negatively correlated with the Purdue Pegboard total (P = 0.010) and assembly scores (P = 0.007). When discriminating each disease group from NC, the model's performance to identify multiple system atrophy and progressive supranuclear palsy with AUC values of 0.881 and 0.895 was superior than essential tremor with an AUC of 0.709 before false discovery rate correction (multiple system atrophy-NC versus essential tremor-NC: P = 0.041; progressive supranuclear palsy-NC versus essential tremor-NC: P = 0.010). The model based on complementary MRI modalities was an objective clinical decision-support measure for Parkinson's disease with potential applications in other parkinsonisms (multiple system atrophy, progressive supranuclear palsy).},
}

@article {pmid42910633,
year = {2026},
author = {Cai, X and Liu, Y and Sheng, X},
title = {Engineering Bidirectional Neuromodulation: A Review of Neural Excitation and Inhibition Technologies.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {},
doi = {10.1002/adma.75480},
pmid = {42910633},
issn = {1521-4095},
support = {YIRG MOH-001243-00//NMRC Open Fund-Young Individual Research Grant/ ; NRF2021-NRF-ANR004//NRF-ANR Joint Grant Call/ ; 22-4974-A0003//National University of Singapore/ ; 2024Z02ORD001//Tsinghua University/ ; T2425003//National Natural Science Foundation of China/ ; 52272277//National Natural Science Foundation of China/ ; 20253930040//Tsinghua-Toyota Joint Research Fund/ ; },
abstract = {Neuromodulation provides a means to perturb neural activity for investigating nervous system function and treating neurological disorders. Advances in stimulation strategies, molecular tools, functional materials and neural interfaces have progressively expanded the precision and versatility of such interventions. Yet neural activity can require regulation in opposing directions across different cellular, circuit and pathological states, motivating bidirectional neuromodulation that can both enhance and suppress neural activity. In this review, we examine the mechanisms and technologies that enable such bidirectional control across electrical, optical, thermal, mechanical, magnetic and chemical modalities. We compare these approaches in terms of spatiotemporal performance, penetration depth, specificity and safety, and examine the materials, transduction strategies and device architectures used to implement bidirectional neural control. We further discuss the applications of bidirectional neuromodulation in neuroscience research and the treatment of neurological disorders. Finally, we discuss the technical challenges and future directions toward more precise, independent and integrated bidirectional control, including its extension to closed-loop adaptive regulation.},
}

@article {pmid42911760,
year = {2026},
author = {Zhao, G and Feng, Z and Wang, Z},
title = {Adaptive local temporal weighting for short-window EEG-based SSVEP recognition using FB-eTRCA.},
journal = {Science progress},
volume = {109},
number = {4},
pages = {368504261501367},
doi = {10.1177/00368504261501367},
pmid = {42911760},
issn = {2047-7163},
mesh = {Humans ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {To improve short-window SSVEP recognition, we integrated adaptive temporal local weighting (ATLW) with filter-bank ensemble task-related component analysis (FB-eTRCA). ATLW estimates local discriminability from training trials, converts window scores into sample weights, and applies the training-derived weights to training and test trials within each cross-validation fold. Experiments were conducted on two public 40-target SSVEP datasets: the Benchmark dataset containing 35 subjects and six blocks, and the BETA database containing 70 subjects and four blocks. Seven observation windows ranging from 0.2 to 1.4 s were evaluated using subject-specific leave-one-block-out validation, with six folds for the Benchmark dataset and four folds for the BETA database. On the Benchmark dataset, only the 0.2-s accuracy gain remained significant after Holm correction (44.79% vs 44.07%; adjusted p = 0.0300); other windows were nonsignificant. On the Benchmark dataset, ablation analysis showed that adaptive weighting was more stable than fixed temporal weighting. After Holm correction, eTRCA+ATLW significantly outperformed eTRCA at 0.4 s (gain: 0.86 percentage points; adjusted p = 0.0023), but not at 0.2 s, supporting transferability to another TRCA-based decoder. EEGNet and BiLSTM were included as representative CNN and RNN baselines; both performed below the TRCA-family methods under the present subject-specific limited-calibration protocol. On the BETA database, FB-eTRCA+ATLW remained close to FB-eTRCA across all seven observation windows without BETA-specific parameter retuning, but no paired difference was statistically significant. These findings indicate that ATLW is a lightweight temporal refinement that can be applied to more than one TRCA-based decoder. Its measurable benefit may depend on SSVEP signal quality and the amount of available subject-specific calibration data. Accuracy increased with longer windows, whereas the information transfer rate peaked at 0.6 s on the Benchmark dataset. Therefore, no observation window was optimal for both accuracy and communication speed.},
}

Humans
*Electroencephalography/methods
*Evoked Potentials, Visual/physiology
Algorithms
Signal Processing, Computer-Assisted

@article {pmid42911846,
year = {2026},
author = {Shen, Q and Eliseyev, A and Kansara, V and Neves Briard, J and Song, YL and Carmona, J and Gonzales, IJ and Velazquez, A and Klein, AJ and Agarwal, S and Park, S and Ghoshal, S and Roh, D and Connolly, ES and Claassen, J},
title = {Clinically unresponsive patients with acute brain injury activate a brain-computer interface.},
journal = {Brain : a journal of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1093/brain/awag210},
pmid = {42911846},
issn = {1460-2156},
support = {NS112760/NS/NINDS NIH HHS/United States ; NS106014/NS/NINDS NIH HHS/United States ; //Paris Brain Institute America (PBIA)/ ; MP-200963/CAPMC/CIHR/Canada ; //Fondation du Centre hospitalier de l'Université de Montréal/ ; //Power Corporation of Canada Chair in Neuroscience/ ; //Bourse Perras, Perras et Cholette de la Faculté de médecine de l'Université de Montréal/ ; //Royal College of Physicians and Surgeons of Canada/ ; },
abstract = {Brain activation to verbal commands can be detected by EEG in clinically unresponsive patients. These patients with cognitive motor dissociation (CMD) are unable to communicate with the environment despite preserved consciousness. Advanced brain-computer interfaces (BCI) are promising to support motor function in patients with motor neuron disease or spinal cord injury. Our primary objective was to determine if clinically unresponsive patients can control a self-paced, one-way BCI early after acute brain injury. Secondarily, we assessed the association of BCI control with short- and long-term outcomes. We conducted a prospective single-centre cohort study in 41 clinically unresponsive, critically ill and acutely brain injured patients, as well as in a convenience sample of 8 healthy volunteers and 22 conscious critically ill patients. The self-paced BCI analysed EEG in real time and modulated frequency and pitch of a tone that patients controlled by attempting a motor task: patients were provided with instructions 'to make the beep sound faster, keep opening and closing your right hand' alternating with 'to make the beep sound slower, stop opening and closing your right hand'. CMD was detected by applying machine learning to EEG recorded while patients listened to motor commands. Clinical outcomes were recovery of consciousness by hospital discharge, assessed with the Coma Recovery Scale-Revised, and recovery of functional independence at 12 months, defined as a Glasgow Outcome Scale-Extended score of 4 or higher (denoting the ability to function independently for 8 h or more). Additionally, we invited conscious patients as well as family members of conscious and clinically unresponsive patients to complete a survey on perceived utility and challenges associated with the BCI. In this study, 13 clinically unresponsive patients (32%) achieved BCI control and 8 of 41 (20%) were CMD-positive. All healthy volunteers and nine conscious patients (41%) were able to control the BCI. Median signal delay for BCI control was 49.8 [interquartile range (IQR) 39.9-75.6] seconds for clinically unresponsive patients, 17.1 (IQR 7.8-19.9) for conscious patients and 15.5 (IQR 12.9-21.2) for healthy volunteers. In the group of 41 clinically unresponsive patients, 4 of 13 patients with (31%) and 1 of 28 without BCI control (4%) recovered clinical consciousness at hospital discharge [odds ratio (OR) 21.2; 95% confidence interval (CI) 2.3-930.7]. At 12 months, 6 of 13 patients with (46%) and 2 of 28 without BCI control (7%) recovered functional independence (OR 9.9; 95% CI 1.8-81.6). None of the patients that were unable to control the BCI and were CMD-negative recovered. Patients with BCI control had shorter times to recovery of functional independence by 12 months than those who did not (hazards ratio 6.0; 95% CI 1.2-30.6). All respondents of the BCI survey (n = 37) expressed strong support for the potential future use of BCI devices. These results suggest that BCI control is a strong predictor of outcomes following acute brain injury and provide initial evidence that a bedside, EEG-based BCI system might permit bidirectional communication in critically ill unresponsive patients.},
}

@article {pmid42912743,
year = {2026},
author = {Zhu, Y and Shi, Y and Wu, J and Wei, Q},
title = {Decoding motor imagery EEG signals by a deep learning network integrating multiscale filtering and temporal-channel attention mechanisms.},
journal = {PloS one},
volume = {21},
number = {10},
pages = {e0361026},
pmid = {42912743},
issn = {1932-6203},
mesh = {*Deep Learning ; Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Convolutional Neural Networks ; Signal Processing, Computer-Assisted ; },
abstract = {Due to their low signal-to-noise ratio and non-stationarity, accurately classifying EEG signals is still one of the main hindrances that limit the real-world applications of a brain-computer interface (BCI). Deep learning (DL) is a potential method for addressing the problem. However, how to create a suitable DL network for motor imagery (MI)-based BCIs poses severe challenges in the case of insufficient training data. In this paper, a DL network based on multiscale convolutional neural network (CNN) and temporal and channel attention mechanisms is proposed for decoding MI tasks. Temporal and spatial features are extracted by four parallel CNN branches, each of which is followed by a Transformer block for learning the global correlations of local time features. The outputs from the four Transformer blocks are concatenated along channel dimension and then input into a channel attention module for channel fusion. The subsequent feature map is classified by a CNN, which replaces the commonly used fully connected layer for reducing computational complexity. The proposed network was evaluated on three publicly available BCI datasets and compared with five state-of-the-art DL networks. The training data sets are augmented using the approach segmentation and reconstruction. The experimental results demonstrate that the proposed network achieved an average accuracy of 80.21% on Dataset 1 (4-class tasks), 90.19% on Dataset 2 (2-class tasks), and 97.32% on Dataset 3 (4-class tasks), surpassing the strongest baselines FBCNet on Dataset 1, MSFBCNN on Dataset 2 and MSFBCNN on Dataset 3 by 2.59%, 1.27%, and 1.99% respectively. The network effectively decoded the MI-based EEG signals, providing a potential method for BCI applications.},
}

*Deep Learning
Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
Convolutional Neural Networks
Signal Processing, Computer-Assisted