@article {pmid42395509,
year = {2026},
author = {Xuan, D and Burk, DC and Bartolo, R and Li, X and Averbeck, BB and Tang, H},
title = {Long-term Learning Induces Plastic Changes in Frontostriatal Circuits.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {42395509},
issn = {2692-8205},
abstract = {Neural activity in frontal-striatal circuits underlies reinforcement learning. Traditional theories suggest that reinforcement signals, which drive learning, strengthen connections within the basal ganglia. This strengthening is believed to shift information processing from cortical regions to subcortical regions as learning becomes established over time. To examine this hypothesis, we trained macaques to associate multiple sets of images with their values. Selecting different images led to either an increase (+2, +1) or a decrease (-1, -2) in the number of tokens, which subsequently determined the amount of juice reward the macaques received. We simultaneously recorded neuronal activity from orbitofrontal cortex, ventral striatum, amygdala, and dorsomedial thalamic nucleus, analyzing the dynamic changes in these brain regions during both the initial learning and overlearned stages. The results indicated that as learning progressed from the initial stage to the overlearned stage, information processing shifted from the ventral striatum to the orbitofrontal cortex, corresponding to the abstraction from stimulus value to state value. This finding challenges traditional theories and provides a new perspective on the neural circuit mechanisms of learning.},
}

@article {pmid42433591,
year = {2026},
author = {Li, T and Li, M and Sun, R and Feng, J},
title = {DyAMNet: dynamic adversarial and contrastive network for EEG biometrics.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1815191},
pmid = {42433591},
issn = {1662-4548},
abstract = {INTRODUCTION: Electroencephalogram (EEG)-based biometric recognition for brain-computer interfaces faces challenges from domain shifts, temporal nonstationarity, and limited scalability.

METHODS: To address these issues, we present DyAMNet, a framework that combines EEG microstate analysis with a hybrid attention mechanism. DyAMNet employs dynamic loss balancing to improve generalization and constructs a domain-invariant feature space that supports user expansion without catastrophic forgetting. We evaluated the model on three benchmark datasets (DEAP, THU-EP, and SEED).

RESULTS: The framework attains 87.2% accuracy in cross-dataset recognition and retains 84.0% accuracy when incrementally scaling to 60 users. The system also tolerates physiological artifacts and intersession signal drift, outperforming state-of-the-art models.

DISCUSSION: These findings show that dynamic adversarial training coupled with contrastive feature learning reduces brain-signal variability and preserves scalability. The work establishes a robust basis for feasible identity authentication and supports deploying brain-computer interfaces in clinical and everyday settings. The code is available at: https://github.com/cangtianhaoxue/DyAMNet.git.},
}

@article {pmid42435253,
year = {2026},
author = {Bobier, C and Hurst, DJ},
title = {Post-trial obligations and participant enrollment in brain pioneering research: should we expand inclusion criteria?.},
journal = {Medicine, health care, and philosophy},
volume = {},
number = {},
pages = {},
pmid = {42435253},
issn = {1572-8633},
abstract = {Brain pioneering research investigates the clinical utility of implantable neural devices that acquire, process, and translate brain signals to enable individuals to produce actions or effects. There is a scholarly consensus that researchers, funders, and organizations have post-trial obligations to provide participants continued access to and support for implanted neural devices that benefit them. In this paper, we examine the case for post-trial obligations by critically assessing the current limitation that participation in brain-pioneering research be restricted to individuals with debilitating conditions who lack clinically acceptable or proportionate therapeutic alternatives. We consider the possibility of broadening eligibility to include anyone who meets the study's clinical and safety criteria regardless of therapeutic alternatives. Expanding inclusion criteria in this way may help address concerns about the practical feasibility of meeting post-trial obligations and obtaining valid informed consent, while affirming the equal moral respect owed to all prospective participants.},
}

@article {pmid42435777,
year = {2026},
author = {Gu, L and Han, H and Wang, H and Xiong, X},
title = {Event-related Potential Dynamics of Unilateral Lower Limb Movement with Functional Connectivity Analysis.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110864},
doi = {10.1016/j.jneumeth.2026.110864},
pmid = {42435777},
issn = {1872-678X},
abstract = {BACKGROUND: Locomotor lateralization represents a general evolutionary trait in primates and is particularly well documented in human upper limb movements. Whether a corresponding lateralization pattern exists in lower limb movements, however, has remained largely unexplored.

NEW METHOD: To address this question, electroencephalographic (EEG) signals were recorded from 15 healthy volunteers while they performed instructed foot‑lift tasks under both motor execution (ME) and motor imagery (MI) conditions. The study characterized the frequency‑domain network representation of lower limb movement by analyzing event‑related potential (ERP) components in conjunction with functional connectivity measures.

RESULTS: The results revealed that unilateral motor imagery of the lower limb elicits functionally opposing patterns, manifested as distinct alterations in the spatial distribution of lateralized neural activities across hemispheres.

By systematically comparing motor imagery and motor execution within the same paradigm, the study provides empirical evidence that the laterality hypothesis previously established for upper limb tasks extends universally to unilateral lower limb movements, thereby advancing beyond descriptive lateralization accounts.

CONCLUSIONS: These findings confirm that lower limb movements exhibit robust lateralization patterns, and the observed similarity between actual and imagined movements suggests a common neural substrate, providing insights into the neural network organization that may inform future brain computer interface research.},
}

@article {pmid42436348,
year = {2026},
author = {Li, C and Hasegawa, I and Tanigawa, H},
title = {Gamma oscillations provide a stable geometric scaffold for color representation in primate inferior temporal cortex.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-10652-8},
pmid = {42436348},
issn = {2399-3642},
support = {31872776//National Natural Science Foundation of China (National Science Foundation of China)/ ; 23H00413//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; JP26wm0625205//Japan Agency for Medical Research and Development (AMED)/ ; },
abstract = {To bridge the gap between neural geometry and oscillatory dynamics, we analyzed gamma-band oscillations in macaque inferior temporal cortex using large-scale electrocorticography. We identified an "oscillatory manifold"-a stable, low-dimensional geometric structure embedded within oscillatory waveforms-that encodes color information. Within this manifold, trajectories for different colors were segregated via color-specific amplitude modulations of a shared oscillatory carrier. While the absolute spatial separation of these trajectories rapidly decayed following an initial stimulus-locked transient, their scale-invariant topological shape was conserved. Although stimulus shape was decodable from gamma-band power, it lacked the stable geometric structure observed for color. This contrast confirms that the oscillatory manifold is a specific representational format rather than a generic byproduct of neural activation. Consequently, gamma oscillations provide a dynamic "geometric scaffold" safeguarding perceptual fidelity against energy fluctuations. This framework unifies representational geometry with oscillatory dynamics, offering a feature-specific perspective on how the cortex stabilizes sensory information.},
}

@article {pmid42437188,
year = {2026},
author = {Li, W and Zhang, S and Zhang, B and Qian, J and Lo, V and Su, M and Li, X and Chen, Y and Li, Y and Sun, J and Gong, Y and Guo, T},
title = {Effects of Transcutaneous Auricular Vagus Nerve Stimulation and Transcutaneous Electrical Acupoint Stimulation on Peripheral Inflammatory Factors in Patients with Negative Symptoms of Schizophrenia: A 2×2 Factorial Design Protocol.},
journal = {Journal of inflammation research},
volume = {19},
number = {},
pages = {616229},
pmid = {42437188},
issn = {1178-7031},
abstract = {BACKGROUND: Negative Symptoms of Schizophrenia (NSS) are the primary contributors to poor prognosis in schizophrenia (SCZ), and immune-inflammatory mechanisms play a pivotal role in their pathogenesis. As non-invasive neuromodulation techniques, transcutaneous auricular vagus nerve stimulation (taVNS), and transcutaneous electrical acupoint stimulation (TEAS) have been demonstrated to modulate peripheral inflammation levels in patients with SCZ.

PURPOSE: This study aims to investigate the independent and synergistic effects of taVNS and TEAS on modulating peripheral inflammatory factors and ameliorating negative symptoms in patients with NSS.

PATIENTS AND METHODS: This study employs a single-blind, randomized, sham-controlled, 2×2 factorial design. A total of 108 participants will be randomly allocated in a 1:1:1:1 ratio to four groups: taVNS plus TEAS, active taVNS plus TEAS, sham taVNS plus TEAS, and sham taVNS plus sham TEAS. The interventions will be administered for 30 minutes per session on alternate days for 4 weeks, followed by a 4-week follow-up period. The primary outcome is the change from baseline in peripheral inflammatory cytokine levels at weeks 4 and 8.

RESULTS: Recruitment is ongoing.

CONCLUSION: The study protocol aims to investigate the pre- and post-treatment changes in peripheral inflammatory cytokines among patients with NSS, with a specific focus on the correlation between symptom severity and alterations in inflammatory levels, thereby providing a biological rationale to guide clinical treatment.},
}

@article {pmid42437620,
year = {2026},
author = {Gao, A and Li, W and Cui, Y and Li, K and Wang, M and Wu, T and Bai, J and Yan, C and Wu, J and Zhang, Y},
title = {Alterations in the coupling between glymphatic function and cortical morphology in children with short stature.},
journal = {Brain research},
volume = {},
number = {},
pages = {150466},
doi = {10.1016/j.brainres.2026.150466},
pmid = {42437620},
issn = {1872-6240},
abstract = {OBJECTIVE: To investigate the functional changes in the glymphatic system and cortical morphological features in children with growth hormone deficiency (GHD) and idiopathic short stature (ISS).

METHODS: In this prospective study, we recruited 28 children with GHD, 89 children with ISS, and 35 age- and sex-matched typically developing (TD) children. The glymphatic system was evaluated using diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index, preprocessed with FMRIB Software Library. Cortical morphological features (sulcal depth, cortical curvature, and cortical thickness) were extracted from three-dimensional T1- weighted magnetization prepared rapid gradient echo (3D-T1 MPRAGE) imaging, preprocessed with fMRIPrep (v1.5.3), and surface reconstruction was performed using FreeSurfer (version 6.0.0). One-way analysis of variance was used for comparisons between multiple groups. Independent-samples t-test and Pearson's correlation analyses were performed, with Bonferroni or false discovery rate corrected-p value.

RESULTS: In comparison with the TD group, the GHD group showed significantly lower DTI-ALPS indexes (p < 0.05). The left-hemisphere and mean DTI-ALPS indexes were significantly lower in the ISS group than in the TD group (p < 0.05). Children with ISS had smaller brain volumes than TD children (1429.07 ± 128.14 vs. 1489.96 ± 122.72; p < 0.05), whereas the brain volume in children with GHD (1467.16 ± 117.28) was not significantly different from that in TD children (p > 0.05). The ISS and GHD groups showed no significant differences in brain volume. With age-related growth, the children with ISS showed more cortical-area changes in cortical curvature, sulcal depth, and thickness than those in the GHD and TD groups. Cortical morphological features in ISS were intermediate between the GHD and TD groups. Sulcal depth correlated with DTI-ALPS indexes primarily in the peripheral regions of the central sulci in the ISS and TD groups, as well as in the combined cohort. In children with GHD, no significant correlations were observed between the DTI-ALPS indexes and cortical morphological features.

CONCLUSIONS: Glymphatic function and development failed in children with short stature. Notably, the coupling between glymphatic function and cortical morphology was absent in children with GHD and enhanced in children with ISS. These findings indicate that the CNS developmental patterns differ between children with GHD and those with ISS and imply that the ISS is not an appropriate control group for GHD in CNS research.},
}

@article {pmid42437853,
year = {2026},
author = {Nizinski, J and Dzianach, M and Bushnell, B and Lubiatowski, P},
title = {Bioinductive collagen implant augmentation after arthroscopic repair of rotator cuff tears with incomplete footprint coverage: a prospective clinical and morphological study.},
journal = {International orthopaedics},
volume = {},
number = {},
pages = {},
pmid = {42437853},
issn = {1432-5195},
abstract = {PURPOSE: The aim of this study was to evaluate the outcomes of bioinductive collagen implant (BCI) augmentation after arthroscopic repair of rotator cuff tear with incomplete footprint coverage.

METHODS: This was a prospective single-centre, single-surgeon case series. 27 patients with posterosuperior tears (10 -isolated SST, 14 SST with IST) were enrolled with 24 patients completing the entire follow-up. All patients were treated using arthroscopic double row technique with BCI augmentation. BCI was applied when complete tendon repair was possible, but footprint coverage was incomplete. Patients were assessed preoperatively, then at four, six and 12-months postoperatively. Each evaluation included a clinical assessment using shoulder specific outcome measures (ASES, Simple Shoulder Test, UCLA, Constant), isokinetic strength testing, and tendon healing assessment via magnetic resonance imaging (MRI).

RESULTS: Retear rate for isolated SST was 30%. For massive rotator cuff tears (SST + IST), retear rate of SST was 42% and IST was 14%. Six SST retears were type 1 and three were type 2. In ten patients who healed, complete footprint coverage was observed on MRI despite an intraoperative defect in footprint coverage. PROMs and range of motion improved significantly throughout follow-up. There were no significant differences in final clinical scores between SST tears and SST + IST tears. However, patients with isolated SST tear had better strength parameters (Peak Torque/Body Weight and Total Work deficit).

CONCLUSIONS: Arthroscopic repair of "difficult" rotator cuff tears of poor tissue quality with the use of bioinductive implant is associated with significant clinical, biomechanical and radiological improvements. IST retear was very low despite risk factors. Patients with SST and massive SST + IST rotator cuff tears had similar clinical results but massive had less shoulder strength and endurance. The BCI appears to reduce the incidence of type 2 retears and may promote tendon regeneration.},
}

@article {pmid42438682,
year = {2026},
author = {Wu, D and Tang, M and Liu, S},
title = {HADANet: hybrid attentive domain adaptation for cross-subject motor imagery EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {137},
pmid = {42438682},
issn = {1871-4080},
abstract = {Motor imagery (MI) based brain-computer interfaces (BCIs) enable direct decoding of human motor intentions from neural activity. Electroencephalography (EEG) is widely used for MI decoding due to its non-invasive nature and high temporal resolution. However, large inter-subject variability in EEG signals leads to significant distribution discrepancies across subjects, which limits the generalization ability of existing decoding models. To address this issue, we propose a Hybrid Attentive Domain Adaptation Network (HADANet) for cross-subject motor imagery EEG decoding. The proposed framework employs a hierarchical convolutional feature extractor to capture complementary temporal and spectral characteristics of EEG signals. A hybrid attention mechanism further enhances discriminative spatial and channel-wise neural representations. In addition, a hybrid domain adaptation strategy combining adversarial learning and multi-kernel maximum mean discrepancy (MMD) alignment is introduced to reduce inter-subject distribution discrepancies and learn domain-invariant features. Experiments on the PhysioNet and Cho motor imagery datasets demonstrate that HADANet achieves competitive performance compared with several state-of-the-art methods, obtaining average accuracies of 82.85% and 85.87%, respectively. The results demonstrate that our framework effectively models motor imagery-related neural patterns and improves cross-subject generalization for practical BCI systems. the code in public https://github.com/curiouspeople/HADANet-.},
}

@article {pmid42431083,
year = {2026},
author = {Xu, C and Song, Y and Liao, Z and Zhang, H and Wang, Q and Zheng, Q},
title = {UMind: A unified multitask network for zero-shot M/EEG visual decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {205},
number = {Pt A},
pages = {109314},
doi = {10.1016/j.neunet.2026.109314},
pmid = {42431083},
issn = {1879-2782},
abstract = {Decoding visual information from time-resolved brain recordings, such as EEG and MEG, plays a pivotal role in real-time brain-computer interfaces. However, existing approaches primarily focus on direct brain-image feature alignment and are limited to single-task frameworks or task-specific models. In this paper, we propose a Unified MultItask Network for zero-shot M/EEG visual Decoding (referred to UMind), including visual stimulus retrieval, classification, and reconstruction within a shared representation space. Our method learns robust neural-visual and semantic representations through multimodal alignment with both image and text modalities. The integration of both coarse and fine-grained texts enhances the extraction of these neural representations, enabling more detailed semantic and visual decoding. These representations then serve as dual conditional inputs to a pre-trained diffusion model, guiding visual reconstruction from both visual and semantic perspectives. Extensive evaluations on MEG and EEG datasets demonstrate the effectiveness, robustness, and biological plausibility of our approach in capturing spatiotemporal neural dynamics. Our approach sets a multitask pipeline for brain visual decoding, highlighting the synergy of semantic information in visual feature extraction. The code is available at https://github.com/xuchengjian632/UMind.},
}

@article {pmid42431561,
year = {2026},
author = {Tsoneva, T and Desain, P and Garcia-Molina, G and Thielen, J},
title = {The steady-state visual evoked potential (SSVEP): A review of applications in cognitive and clinical neuroscience and neural engineering.},
journal = {NeuroImage},
volume = {338},
number = {},
pages = {122095},
doi = {10.1016/j.neuroimage.2026.122095},
pmid = {42431561},
issn = {1095-9572},
abstract = {The steady-state visual evoked potential (SSVEP), the brain's oscillatory response to repetitive visual stimulation (RVS), has emerged as a powerful tool in neuroscience with wide-ranging applications in multiple disciplines. This review provides a scoping, narrative roadmap of SSVEP applications organized into three primary domains: fundamental research in vision and cognition, clinical neuroscience, and neural engineering. Although these fields differ in focus, they often converge in their use of similar research questions, stimulation paradigms, analysis techniques, and application scenarios. At the same time, specialization may have created knowledge silos that limit cross-disciplinary transfer of methods and insights. By bridging findings from seemingly disparate domains, this review highlights the versatility of SSVEPs in investigating neural mechanisms, supporting diagnosis and treatment of neurological and psychiatric conditions, and advancing brain-computer interface technology. We conclude with cross-field insights on how stimulus and analysis choices affect interpretation and usability, and we outline directions for improving the comparability and transferability of SSVEP research and applications.},
}

@article {pmid42432713,
year = {2026},
author = {Ivanov, N and Chau, T},
title = {Exploration-based feedback for BCI training: a case study with an adolescent with paraplegia.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-02070-y},
pmid = {42432713},
issn = {1743-0003},
abstract = {PURPOSE: Brain-computer interface (BCI) inefficiency limits clinical utilization of BCIs, as many users struggle to produce consistent machine-recognizable electroencephalography (EEG) patterns for reliable control. While training can improve BCI performance for most users, the required duration and intensity may hinder BCI accessibility. Prior BCI and motor learning research suggests that feedback enabling efficient exploration of different task strategies may enhance training.

METHODS: An eight-session BCI training case study was completed with an adolescent participant with paraplegia. To support training, a novel feedback system that visualized EEG signal pattern states identified via K-means clustering within the EEG covariance space. Unlike common classifier feedback, this interface presented the EEG signal patterns produced throughout each trial, allowing the participant to explore strategies that yielded task-specific pattern states.

RESULTS: The participant was initially a low-performing user and showed little progress across the first five sessions. After transitioning to a simplified feedback mode emphasizing deviation from resting state patterns in the sixth session, the participant displayed significant improvement in task-related physiological signal discriminability. Post-training analysis, however, revealed that this improvement was partially attributable to electromyography (EMG) activity from cranial muscles.

CONCLUSION: Although the observed gains were not solely attributable to neuro-cortical signal modulation, the case study highlights the potential of simplified feedback to support task exploration in low-performing users and presents potential implications for hybrid EEG-EMG BCIs for relevant clinical populations.},
}

@article {pmid42427541,
year = {2026},
author = {Hashimoto, H and Jude, JJ and Levi-Aharoni, H and Williams, ZM and Simeral, JD and Hochberg, LR and Rubin, DB},
title = {Learning-related population dynamics in right and left dorsal premotor cortex during typing skill acquisition.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.07.02.736059},
pmid = {42427541},
issn = {2692-8205},
abstract = {Advances in intracortical brain-computer interface (BCI) technology have enabled increasingly sophisticated communication paradigms, including for decoding intended speech and touch typing. However, the methods by which intracortical neural population dynamics are engaged during practice-related skill acquisition in humans remain poorly understood. Here, we examined learning-related changes in neural activity during motor skill acquisition in a right-handed BCI clinical trial participant with tetraplegia, with intracortical microelectrode arrays placed in the bilateral dorsal precentral gyri (Brodmann area 6d), who learned how to type using a BCI-enabled typing interface. While decoder performance remained stable across sessions, typing speed improved with practice, indicating practice-related skill acquisition. Over weeks, low-dimensional neural population activity became progressively more compact, and this compaction was strongly associated with faster typing, independent of decoder accuracy. Although this compaction was observed bilaterally in 6d, firing-rate modulation and cross-session generalization were selectively enhanced in left 6d. Moreover, neural population changes across sessions were largely accounted for by canonical correlation analysis in right 6d, but only partially accounted for in left 6d. Together, these findings demonstrate that human intracortical neuro-motor skill acquisition related to intended typing engages shared bilateral population-level dynamics, with additional learning-related changes selectively expressed in dominant dorsal premotor cortex.},
}

@article {pmid42428047,
year = {2026},
author = {Zhang, F and Chai, B and Wu, Y and Siok, WT and Wang, N},
title = {Linguistics and human brain: a perspective of computational neuroscience.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {127},
pmid = {42428047},
issn = {1871-4080},
abstract = {Elucidating the language-brain relationship requires bridging the methodological gap between linguistics' abstract theoretical frameworks and neuroscience's empirical neural data. As an interdisciplinary cornerstone, computational neuroscience formalizes language's hierarchical and dynamic structures into testable neural representation models through modeling, simulation, and data analysis, enabling computational dialogue between linguistic hypotheses and neural mechanisms. Recent advances in deep learning, particularly large language models (LLMs), have further advanced this inquiry: their high-dimensional representational spaces provide a new scale for probing the neural basis of linguistic processing, the model-brain alignment framework offers a principled approach to evaluating the biological plausibility of language-related theories, provided that representational correspondence is interpreted together with behavioral, temporal, causal, and biological constraints. This review synthesizes interdisciplinary progress from a computational neuroscience perspective. First, it outlines the core connotations of major linguistic frameworks (generative grammar, functional linguistics, and cognitive linguistics), their cross-cultural and evolutionary characteristics, and key challenges for neural alignment, including limited quantitative mechanisms, poor accessibility of abstract constructs to neural measures, and insufficient treatment of dynamics and plasticity. Second, it introduces the methodological foundations of linguistics-neuroscience dialogue, focusing on four technical pillars: neural activity measurement (e.g., fMRI, EEG, MEG, fNIRS, ECoG, SEEG), linguistic numerical representation, the evolution of language models from statistical approaches to LLMs, and neural coding frameworks that link model representations to brain signals, illustrated with a model-brain alignment case study. Third, it summarizes major findings, ranging from early computational insights into predictability and structural processing to recent LLM-driven progress in cross-modal interaction, inter-brain coupling, hierarchical computation, learning strategy sensitivity, and language plasticity. Finally, the review discusses current limitations-including functional alignment without structural homology, constraints on real-time validation, biased research coverage, and narrow evaluation metrics-and proposes future directions, such as exploring whether spiking neural network-based language models can improve biological plausibility in settings requiring temporally precise and event-driven neural modeling, developing cognitive-level alignment frameworks integrating memory, causality, and metacognition, and extending clinical applications. In summary, this work aims to advance a comprehensive, mechanistic understanding of the language-brain relationship and promote computational neuroscience as a generative theoretical framework for testable neuro-computational accounts of language.},
}

@article {pmid42428129,
year = {2026},
author = {Raemaekers, M and Geukes, SH and Aarnoutse, EJ and Branco, MP and Freudenburg, ZV and Schippers, A and Crone, NE and Leinders, S and Berezutskaya, J and Ramsey, NF and Vansteensel, MJ},
title = {Association between motor cortex grey matter loss and inability to control an ECoG-based implanted Brain-Computer Interface in ALS.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.06.23.26355654},
pmid = {42428129},
abstract = {BACKGROUND: The field of implantable Brain-Computer Interfaces (iBCIs) is rapidly advancing, with individuals with amyotrophic lateral sclerosis (ALS) as key beneficiaries. However, ALS-related cortical degeneration may impair iBCI effectiveness. This study investigated whether structural magnetic resonance imaging (MRI) and functional MRI (fMRI) metrics are associated with the quality of electrocorticography (ECoG) signals critical for iBCI use.

METHODS: Six late-stage ALS participants and 76 controls underwent T1-weighted structural MRI and task-based fMRI during right-hand movement or attempts thereof. ECoG data of ALS participants was benchmarked using ECoG data acquired in epilepsy patients. Grey matter thickness in the sensorimotor cortex and fMRI activation in the motor-hand area were measured.

RESULTS: Four ALS participants showed >0.4 mm thinning in the precentral gyrus, while the postcentral gyrus was spared. ECoG signal quality was significantly associated with precentral grey matter thickness, but not with fMRI activity.

CONCLUSIONS: These findings suggest that presurgical assessment of precentral grey matter thickness could potentially prove useful for iBCI candidate selection in advanced ALS.

PLAIN LANGUAGE SUMMARY: People with amyotrophic lateral sclerosis (ALS) can lose the ability to move and speak, but their thinking often remains intact. Implantable brain-computer interfaces (iBCIs) can help by translating brain signals into commands for communication devices. However, ALS damages the motor cortex, which may reduce the quality of these signals. In this study, we examined brain scans and electrical recordings from six people with advanced ALS. We found that thinning of the motor cortex was linked to weaker brain signals needed for iBCI control, while functional MRI activity was less predictive. This suggests that measuring motor cortex thickness before surgery could help identify who will benefit most from an iBCI, improving treatment decisions and future clinical trials.

TWO SENTENCE SUMMARY: We examine presurgical MRI/fMRI and ECoG recordings from people with advanced ALS receiving implanted brain-computer interfaces. Motor cortex thinning is associated with poorer ECoG signal quality, suggesting cortical thickness may help identify candidates likely to benefit.},
}

@article {pmid42428243,
year = {2026},
author = {Khanam, T and Siuly, S and Wang, K and Wang, H},
title = {A novel deep learning approach for privacy-preserving encoded EEG-based brain-computer interfaces with clinical LLM applications.},
journal = {Health information science and systems},
volume = {14},
number = {1},
pages = {73},
pmid = {42428243},
issn = {2047-2501},
abstract = {PURPOSE: The rise of large language models (LLMs) such as GPT-4 and DeepSeek has transformed healthcare information processing by enabling natural language-based clinical reasoning. However, the integration of LLMs with privacy-sensitive biomedical signals, particularly electroencephalogram (EEG) data used in brain-computer interface (BCI) systems, remains underexplored. EEG signals, especially during motor imagery (MI) tasks, are critical for assistive neurotechnologies but pose significant privacy risks due to their capacity to reveal cognitive and medical information. Traditional encryption techniques often distort signal structure or require decryption with additional noise, compromising classification performance and real-time usability.

METHODS: To address this gap, we propose a deep denoising structure-preserving neural encoding network (DSNet) that enables accurate classification of privacy-preserving encoded EEG representations without requiring decryption. EEG features were extracted using common spatial pattern (CSP) and transformed into privacy-preserving encoded representations while preserving their statistical structure. Here, encoding refers to a non-reversible neural transformation designed for privacy preservation rather than a formal cryptographic guarantee. Two deep learning architectures, a feedforward neural network (NN) and a recurrent neural network (RNN), were evaluated for classification in the encoded feature space. Furthermore, we integrated an LLM (GPT-4) to generate clinical-style summaries based on model outputs, enhancing interpretability for clinician review and potential clinical support use.

RESULTS AND CONCLUSION: Using publicly available datasets, DSNet-NN achieved over 87% accuracy for every subject, outperforming both the RNN variant and baseline models. It also demonstrated resilience to simulated privacy attacks. LLM-generated reports provided clinician-friendly interpretations of MI predictions, supporting potential real-world applicability. This study introduces an AI framework that bridges privacy-preserving EEG decoding with LLM-based clinical reasoning, offering a practical solution for privacy-preserving neurorehabilitation and digital health systems.},
}

@article {pmid42428961,
year = {2026},
author = {Kleih, SC},
title = {MotiVE BCI: motivation models including valence and expectancy in brain-computer interface use.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1681683},
pmid = {42428961},
issn = {1662-5161},
abstract = {In this work, a theoretical framework addressing the role of motivation in brain-computer interface (BCI) was developed. The aim was to present theory-based versions of motivation models for BCI use that can serve as a foundation for hypothesis generation and experimental testing. As a synthesis of the existing literature on the role of motivation in BCI use, and grounded in a predominantly psychological theoretical background, the P300 MotiVE model and the sensorimotor rhythm (SMR) MotiVE model were introduced. To the best of my knowledge, the MotiVE models represent the first models based on psychological theories to explicitly target motivation and its subcomponents as factors influencing BCI performance. However, the underlying assumptions require empirical validation, and the practical utility of these models remains to be demonstrated. Further development may also be necessary to accommodate different types of BCI systems.},
}

@article {pmid42430410,
year = {2026},
author = {Zhao, Y and Stealey, HM and Lu, HY and Contreras-Hernandez, E and Chang, YJ and Tobler, PN and Santacruz, SR},
title = {Distinct roles of neuronal phenotypes during neurofeedback adaptation.},
journal = {PloS one},
volume = {21},
number = {7},
pages = {e0351053},
doi = {10.1371/journal.pone.0351053},
pmid = {42430410},
issn = {1932-6203},
mesh = {Animals ; *Neurofeedback/physiology ; Macaca mulatta ; *Neurons/physiology ; *Adaptation, Physiological/physiology ; Phenotype ; Learning/physiology ; Brain-Computer Interfaces ; Male ; },
abstract = {Learning adaptation allows the brain to refine motor patterns in response to changing environments rapidly. While population-level neural dynamics and single-neuron activity in motor learning have been widely studied, the contributions of individual neuron types remain poorly understood. Here, we employed a brain-machine interface (BMI) task with perturbations of varying difficulty to investigate single-neuron dynamics underlying neurofeedback adaptation in two rhesus macaques. Cortical neurons were classified based on waveform shape into narrow waveform (NW) and broad waveform (BW) categories, representing putative inhibitory interneurons and excitatory pyramidal neurons, respectively. Compared to BW neurons, NW neurons were more active and more strongly involved in the learning process. Moreover, task difficulty modulated neural responsiveness and coordination within both neuron groups, highlighting differential neuron engagement during neurofeedback adaptation. Our findings provide novel insights into single-neuron mechanisms underlying neurofeedback adaptation and emphasize the distinct functional roles of neuronal phenotypes in rapid learning processes.},
}

Animals
*Neurofeedback/physiology
Macaca mulatta
*Neurons/physiology
*Adaptation, Physiological/physiology
Phenotype
Learning/physiology
Brain-Computer Interfaces
Male

@article {pmid42430721,
year = {2026},
author = {Rao, S and Chen, X and Deng, G and Xie, J and Jiang, T and Li, T and Zhang, Y and Jiang, H},
title = {Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.},
journal = {JMIR medical informatics},
volume = {14},
number = {},
pages = {e91249},
doi = {10.2196/91249},
pmid = {42430721},
issn = {2291-9694},
mesh = {*Natural Language Processing ; Humans ; *Electronic Health Records ; Large Language Models ; *Psychiatry/methods ; *Mental Disorders/diagnosis ; },
abstract = {BACKGROUND: Psychiatric clinical notes in electronic health records (EHRs) provide rich longitudinal information that can support clinical decision-making. Using historical medical data can enable earlier identification of mental illness, better characterization of disease trajectories, and more personalized treatment planning. Natural language processing (NLP) transforms these unstructured notes into analyzable representations for research and care.

OBJECTIVE: This study aims to systematically summarize NLP methodologies for psychiatric clinical notes, compare major modeling paradigms and application areas, and highlight emerging large language model (LLM) trends, key challenges, and future research directions.

METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a literature search was conducted for articles on NLP methods based on psychiatric clinical notes published from January 2021 to December 2025 in Ovid MEDLINE, Ovid EMBASE, PubMed, Scopus, Web of Science, the ACM Digital Library, and ScienceDirect. This scoping review analyzed NLP methods applied to psychiatric clinical notes, focusing on major trends, identifying suitable features for traditional machine learning (ML)-based models, applications of pretrained language models (PLMs), and key challenges. Approaches were categorized as rule-based, traditional ML, hybrid, deep learning (DL), and LLM-based methods across information extraction and text classification tasks.

RESULTS: In total, 101 studies were eligible for inclusion. Rule-based methods (n=36) and hybrid approaches (n=34) remained the most widely used techniques, largely favored for their interpretability in handling nuanced, subjective clinical notes. These were followed by DL (n=15), traditional ML (n=10), and LLM-based approaches (n=6). Traditional ML studies relied heavily on engineered features, which could be grouped into 5 broad categories: domain knowledge features, lexical and statistical features, vector-based semantic features, emotion-related features, and temporal features. PLMs improved performance mainly through domain adaptation and task-specific fine-tuning, enhancing the handling of psychiatric language, medical terminology, and clinical note structure. LLM-based studies, although still limited in number, indicated a growing shift toward generative and reasoning-based applications.

CONCLUSIONS: Hybrid NLP approaches remain dominant, combining domain rules with ML for extraction and classification. DL approaches continue to advance, with domain adaptation supporting medical terminology and clinical semantics. LLMs may further automate complex workflows via zero-shot capabilities and reasoning, alongside growing interest in temporal modeling and multimodal integration. Key future needs include improved generalizability across institutions, privacy protection, and careful attention to ethical implications in clinical deployment.},
}

*Natural Language Processing
Humans
*Electronic Health Records
Large Language Models
*Psychiatry/methods
*Mental Disorders/diagnosis

@article {pmid42415606,
year = {2026},
author = {So, SW and Lee, H and Lee, G and Cho, SY and Jeong, SJ and Oh, SJ},
title = {Effect of Urodynamic Bladder Outlet Obstruction on Bladder Trabeculation Grade in Patients With Benign Prostatic Hyperplasia: Retrospective Patient Cohort Study.},
journal = {International neurourology journal},
volume = {30},
number = {2},
pages = {127-136},
pmid = {42415606},
issn = {2093-4777},
abstract = {PURPOSE: This study aimed to evaluate the correlation between bladder outlet obstruction (BOO) and bladder trabeculation in patients with benign prostatic hyperplasia (BPH).

METHODS: We analyzed data from consecutive BPH patients from July 2014 to June 2024, who underwent urodynamic study (UDS) and cystourethroscopy. The results of free uroflowmetry, filling cystometry, and pressure-flow study were analyzed to evaluate functional parameters. For anatomical parameters, bladder trabeculation grade, lateral lobe protrusion of prostate, and bladder neck elevation (BNE) measured in cystourethroscopy were used. BOO was defined as the BOO index of 40 or higher in UDS. Bladder trabeculation was graded using our previous studies.

RESULTS: Among total of 1,452 BPH patients, 1,028 patients had trabeculation on cystoscopy. Age, serum prostate-specific antigen, postvoid residual, total prostate volume, transition zone volume, terminal type detrusor overactivity, detrusor pressure at the maximal flow rate, bladder contractility index (BCI), and BOO index increased according to increase in bladder trabeculation. Multivariable logistic analysis showed that bladder trabeculation was significantly associated with age (odds ratio [OR], 1.05; P<0.001), kissing sign (OR, 1.55; P=0.007), BNE (OR, 1.76; P<0.001), detrusor overactivity (OR, 1.88; P=0.002), BCI (OR, 1.01; P=0.037), BOO (OR, 2.27; P<0.001). BOO had the greatest correlation with bladder trabeculation. In addition, BOO index showed a positive correlation (r=0.39, P<0.001) with bladder trabeculation. BOO index well distinguished between moderate trabeculation of grade 2 or higher in receiver operating characteristic analysis (area under curve=0.72, P<0.001).

CONCLUSION: Our results showed that the severity of BOO is positively associated with the severity of bladder trabeculation.},
}

@article {pmid42418149,
year = {2026},
author = {Srimadumathi, V and Reddy, MR},
title = {CNN models in time-frequency domain for identification of motor imagery tasks from EEG signals.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {42418149},
issn = {2662-4737},
abstract = {The Motor imagery (MI) based brain computer interface (BCI) system provides a way for the people suffering from motor impairments to communicate to the external world. This work proposes compact convolutional neural network (CNN) models with single convolutional layer, for effectively classifying the left and right hand MI tasks using the EEG data from only two channels. The Complex Morlet Wavelets (CMW) are used here to extract high-resolution time and frequency domain features from MI EEG signal. These time-frequency representations (TFR) serve as inputs to three proposed CNN models namely the time domain CNN (TD-CNN), the frequency domain CNN (FD-CNN) and the time-frequency domain CNN (TF-CNN) models, which perform a convolution along the time, frequency and time-frequency domain features of the data respectively. The developed models have been evaluated on the BCI Competition 4 dataset 2a using the subject-dependent and subject-independent validation strategies. The TF-CNN model has outperformed the TD-CNN and FD-CNN models, by giving a classification accuracy of 85.83% and 77.6% for the subject dependent and independent validations respectively. The results show that the proposed models have given a better performance than the state-of-the art methods and the existing CNN models with complex network architectures.},
}

@article {pmid42420241,
year = {2026},
author = {Jang, Y and Park, B and Choi, J and Jin, DY and Kim, EH and Jang, H and Lee, JH and Ko, E and Shin, D and Kim, K and Lee, W and Lee, A and Lee, MC and Sun, IC and Yoon, HY and Lee, S and Kim, SH and Park, J and Kim, K and Kim, JS and Yang, Y and Shim, MK},
title = {Switching tumor-derived extracellular vesicles off and on via targeted proteolysis to shift toward immunogenic phenotypes.},
journal = {Signal transduction and targeted therapy},
volume = {11},
number = {1},
pages = {},
pmid = {42420241},
issn = {2059-3635},
support = {Intramural Research Program//Korea Institute of Science and Technology (KIST)/ ; },
mesh = {Humans ; *Extracellular Vesicles/immunology/genetics ; Animals ; *Photochemotherapy ; Female ; Proteolysis Targeting Chimera ; Proteolysis ; },
abstract = {Despite compelling evidence that tumor-derived extracellular vesicles (TEVs) exhibit either pro- or antitumorigenic phenotypes, pharmacological efforts have focused primarily on their indiscriminate suppression. Here, we propose a strategy of "switching TEVs off and on" to redirect them toward an immunogenic phenotype. Designed as a nanoproteolysis-targeting chimera (Nano-PROTAC) for TEV reprogramming, EVOTAC is composed of tripartite building blocks that integrate a PROTAC and a photosensitizer via a cancer biomarker-responsive cleavable linker and spontaneously self-assemble into supramolecular nanostructures. Upon biomarker-guided activation preferentially in tumors over normal tissues, EVOTAC initially eliminates TEVs by selectively degrading intracellular proteins involved in extracellular vesicle (EV) biogenesis. Subsequent localized laser irradiation reactivates EV generation, prompting tumor cells to predominantly produce immunogenic TEVs in response to photodynamic therapy (PDT). TEVs generated through this switching-off-and-on strategy independently exert pleiotropic effects by inhibiting tumor growth, migration, and metastasis while increasing mature dendritic cells and cytotoxic T lymphocytes in lymphoid organs and tumor tissues. This TEV-toggling process, therefore, significantly enhances both innate and adaptive immune responses to photoimmunotherapy, which leads to a complete regression of triple-negative breast cancer (TNBC) and prevents metastasis and recurrence. Our study highlights the potential of this therapeutic approach for precise TEV modulation and encourages further exploration, adding new breadth to the growing list of EV-targeting cancer immunotherapy concepts.},
}

Humans
*Extracellular Vesicles/immunology/genetics
Animals
*Photochemotherapy
Female
Proteolysis Targeting Chimera
Proteolysis

@article {pmid42422209,
year = {2026},
author = {Cheng, Y and Guo, X and Dong, L and Deng, Q and Qiu, M and Luo, Z},
title = {Advancing stroke rehabilitation: the potential and challenges of closed-loop brain-computer interface technology.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1861673},
pmid = {42422209},
issn = {1664-2295},
abstract = {BACKGROUND: Stroke is one of the leading causes of long-term disability in older worldwide. As an emerging neuromodulation intervention, closed-loop brain-computer interfaces (BCIs) aim to promote the reconstruction of the damaged cortex through real-time feedback mechanisms. This study aims to systematically review the latest clinical advancements, neural mechanisms, and challenges of closed-loop BCIs in post-stroke rehabilitation.

METHODS: Following the PRISMA guidelines, this study systematically searched databases including PubMed, Web of Science, Cochrane Library, Embase, Scopus, and IEEE Xplore. Given the high methodological heterogeneity in intervention paradigms and outcome measures across different studies, a qualitative synthesis strategy was employed. The minimal clinically important difference (MCID) was introduced to evaluate the substantive clinical benefits of various interventions. Ultimately, 42 original studies meeting the strict definition of closed-loop systems were included.

RESULTS: Closed-loop BCI technology demonstrates multi-dimensional application potential in stroke rehabilitation. In motor rehabilitation, BCIs combined with external actuators (e.g., robotics, FES) promote interhemispheric functional rebalancing and corticospinal tract remodeling. In the cognitive domain, although neurofeedback has shown initial efficacy in improving specific executive functions and attention, the current evidence exhibits high heterogeneity and requires cautious interpretation. Regarding safety, adverse reactions to non-invasive devices primarily manifest as mild fatigue; for invasive systems, the incidence of device-related adverse events is approximately 5.6 per 1,000 device-days, indicating overall controllable safety.

CONCLUSION: Closed-loop BCIs provide a promising novel neuromodulation strategy for stroke rehabilitation. Future validation of their efficacy and acceleration of clinical translation will rely on multicenter randomized controlled trials (RCTs), standardized core outcome sets (COS), and deep integration with artificial intelligence (AI).},
}

@article {pmid42422218,
year = {2026},
author = {Jin, W and Niu, X and Liu, Y and Zhu, Z and Gao, Z and Wang, S and Meng, D and Zhu, X and Wang, H and Wang, L and Xu, G and Mao, Y},
title = {Closed-loop motor imagery brain-computer interface-assisted training for upper limb rehabilitation after subacute stroke: clinical and electroencephalographic outcomes from a randomized pilot trial.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1880696},
pmid = {42422218},
issn = {1664-2295},
abstract = {BACKGROUND: Closed-loop motor imagery brain-computer interface (MI-BCI) training may support post-stroke upper-limb rehabilitation by coupling motor intention with contingent multisensory feedback. This randomized pilot trial examined its feasibility, safety, short-term clinical effects, and exploratory EEG correlates in patients with subacute stroke.

METHODS: In this single-center, assessor-blinded, two-arm pilot trial, 40 patients with first-ever subcortical stroke in the subacute phase were randomized 1:1 to a BCI group or an active control group after a 2-day motor imagery familiarization phase. Both groups received routine medical management, standardized conventional rehabilitation, and dose-matched motor imagery-based hand training for 4 weeks. The BCI group received EEG-contingent closed-loop MI-BCI-assisted training with a soft rehabilitation glove, whereas the control group received non-EEG-contingent glove-assisted motor imagery training under matched training duration, task instructions, device exposure, and multisensory feedback. The primary outcome was the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE). Secondary outcomes included the Action Research Arm Test (ARAT) and Modified Barthel Index (MBI). Exploratory EEG outcomes included FFT%α and FFT%β during motor imagery. Clinical and EEG outcomes were analyzed using baseline-adjusted ANCOVA models, with week-4 values as dependent variables and corresponding baseline values as covariates.

RESULTS: All randomized participants completed the 4-week assessment. In baseline-adjusted ANCOVA models, the BCI group showed higher week-4 scores than the control group for FMA-UE (adjusted mean difference, 13.40 points; 95% CI, 10.71-16.08; p < 0.001), ARAT (7.31 points; 95% CI, 4.55-10.07; p < 0.001), and MBI (12.21 points; 95% CI, 8.55-15.87; p < 0.001). Exploratory EEG analyses also showed higher week-4 FFT%α and FFT%β in the BCI group, with adjusted mean differences of 6.78 percentage points (95% CI, 5.22-8.34; p < 0.001) and 3.95 percentage points (95% CI, 2.53-5.36; p < 0.001), respectively. No serious adverse events occurred.

CONCLUSION: Closed-loop MI-BCI-assisted training was feasible and well tolerated in selected patients with subacute stroke. The observed short-term improvements in upper-limb impairment and activity capacity provide preliminary signals of potential benefit beyond dose-matched non-EEG-contingent feedback training. Exploratory EEG findings suggest task-related modulation of alpha- and beta-band sensorimotor rhythmic activity, but should be interpreted as hypothesis-generating rather than confirmatory evidence of neural reorganization. Larger multicenter trials with longer follow-up, rigorous neurophysiological analyses, and real-world upper-limb use outcomes are needed.

CLINICAL TRIAL REGISTRATION: ChiCTR2400083992. https://www.chictr.org.cn/showproj.html?proj=229529.},
}

@article {pmid42424745,
year = {2026},
author = {Feng, X and Bao, X and Huang, H and Wang, Z and Chen, Q and Wen, Z and Li, J and Cai, Y and Huang, Q and Li, Y},
title = {Spatiotemporal neurodynamic mapping of tinnitus from pre-sleep through sleep cycles.},
journal = {Sleep medicine},
volume = {147},
number = {},
pages = {109102},
doi = {10.1016/j.sleep.2026.109102},
pmid = {42424745},
issn = {1878-5506},
abstract = {Tinnitus manifests as phantom sounds arising from hyperactivity within the auditory pathway, significantly degrading sleep quality. However, the precise mechanisms by which aberrant neural network activation disrupts sleep onset and the extent to which this disruption persists across subsequent sleep stages in tinnitus patients remain largely unknown. In this study, we collected scalp electroencephalogram (EEG) data from 52 tinnitus patients and 52 age- and sex-matched controls throughout the entire sleep process. Based on the hypothesis that cortical hyperarousal is a potential core mechanism underlying sleep disruption in tinnitus, we employed a baseline-correction analysis approach to generate a time- (state-) contingent hyperarousal metric. This aimed to identify abnormal cortical neurodynamics during pre-sleep eyes-closed (EC) relaxation and subsequent sleep stages. The results unveiled a distinctive hierarchical neurodynamic pattern: Patients exhibited typical hyperarousal in the temporal regions during EC relaxation; this hyperarousal redistributed toward the prefrontal cortex across sleep stages and extended to broader parietal-occipital regions during rapid eye movement (REM) sleep. Furthermore, we elucidated the neural correlates underlying clinical behavioral abnormalities related to sleep-onset and maintenance difficulties in tinnitus patients. Additionally, hyperarousal-associated neural patterns in tinnitus patients were consistently validated by two supplementary metrics throughout the sleep cycle. Overall, these findings on neurodynamic spatiotemporal patterns are likely attributable to significant changes in regional activation and inhibition during brain state transitions. These insights not only deepen our understanding of the pathological neural network mechanisms behind sleep-related tinnitus but also offer mechanistic guidance for interventions targeting the wake-sleep continuum in tinnitus management.},
}

@article {pmid42425186,
year = {2026},
author = {Gao, YY and Zhang, J and Xu, S and Zheng, W and Pan, Y and Li, X},
title = {Prefrontal multiscale entropy and state transitions distinguish large language model-assisted from search-assisted learning.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122123},
doi = {10.1016/j.neuroimage.2026.122123},
pmid = {42425186},
issn = {1095-9572},
abstract = {Learning is fundamental to human development and relies critically on cognitive processing. Increasingly, individuals use external tools, particularly search engines and large language models (LLMs), to enhance learning. Although both tools can improve learning outcomes, they may shape neurocognitive processing through distinct pathways. The present study examined prefrontal neural dynamics, using multiscale entropy and latent state transitions, across three conditions: LLM-assisted, Search-assisted, and an Unassisted condition. Participants completed an identical cognitive test while prefrontal hemodynamic activity was recorded using functional near-infrared spectroscopy (fNIRS). Multiscale entropy was used to quantify the multiscale temporal irregularity in prefrontal signals, whereas Gaussian Hidden Markov Model (GHMM)-derived transition ratios and dwell times were used to characterize latent prefrontal state transitions. Both LLM-assisted and Search-assisted conditions improved test performance relative to the Unassisted condition. However, they exhibited dissociable neural patterns. Compared with search assistance, LLM assistance was associated with lower entropy, fewer state transitions, and longer dwell times. These findings indicate that comparable behavioral performance may be supported by distinct patterns of prefrontal signal complexity and latent state transitions. More broadly, these findings may highlight the potential importance of considering tool-specific cognitive processing dynamics when selecting and integrating external tools in educational settings.},
}

@article {pmid42413890,
year = {2026},
author = {Jin, J and Hu, Y and Wang, Z and Pan, Y and Ma, B and Dong, B and Dong, J and Pei, G},
title = {Brain-Inspired Large Model Mindreading.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122108},
doi = {10.1016/j.neuroimage.2026.122108},
pmid = {42413890},
issn = {1095-9572},
abstract = {Multimodal large language models (MLLMs) demonstrate significant limitations in visual Theory of Mind (ToM) abilities compared to humans. Investigating the neural mechanisms underlying human mind-reading not only addresses critical gaps in visual ToM research but also provides valuable insights for MLLM optimization. Using fMRI data from 83 participants, we systematically compared neural processing patterns between two conditions in visual ToM tasks: MLLM-incorrect/human-correct (MLLMI) and mutually correct (MLLMC). The questionnaire results indicated that, compared with the MLLMC condition, human confidence was significantly lower under the MLLMI condition, and expectations for MLLM performance were also lower. Natural language analysis revealed that, relative to MLLM responses, human responses were more closely aligned with the question context, more concise, and exhibited greater certainty. Neuroimaging results indicated significantly stronger activation in bilateral precuneus and middle temporal gyrus in MLLMI. Furthermore, we observed enhanced functional connectivity in networks associated with task coordination and attention allocation. Leveraging these neural signatures, we constructed multiple prediction models for decoding the two conditions (MLLMI vs. MLLMC), among which the 2-layer Transformer model achieved the highest classification accuracy of 78.6%. Extending these findings, we propose the Knowledge-Thinking-Adaptation (KTA) framework, which integrates memory retrieval, divergent thinking, and multi-level attention mechanisms to provide a potential roadmap for future work in developing AI systems with human-like visual ToM capabilities.},
}

@article {pmid42414143,
year = {2026},
author = {Mathon, B},
title = {Early brain biopsy in neurological diseases of unknown etiology: Moving the diagnostic clock forward, not skipping the work-up.},
journal = {European journal of internal medicine},
volume = {},
number = {},
pages = {107058},
doi = {10.1016/j.ejim.2026.107058},
pmid = {42414143},
issn = {1879-0828},
}

@article {pmid42414692,
year = {2026},
author = {Thomas Han, N and Steinborn, MB and Cao, L},
title = {Decoding time from space: A review of the complication clock and its representation of temporal experience.},
journal = {Psychonomic bulletin & review},
volume = {33},
number = {6},
pages = {},
pmid = {42414692},
issn = {1531-5320},
support = {226-2024-00207//Fundamental Research Funds for the Central Universities/ ; 2023M733124//China Postdoctoral Science Foundation/ ; YJ20220315//China Postdoctoral Science Foundation/ ; 32271078//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Time Perception/physiology ; *Space Perception/physiology ; Sense of Agency ; },
abstract = {The complication clock, originally introduced by Wilhelm Wundt, remains a pivotal method in experimental psychology for probing the subjective timing of events. By localising the position of a moving pointer, one can objectively measure when someone perceives an event to have occurred. The method therefore maps temporal judgments via a spatial representation of time on the basis of a moving pointer. Although it provides a unique tool for capturing otherwise unobservable phenomena, the method also raises critical conceptual and methodological challenges. In this article, we provide a historical account of the research, from early complication experiments on sensory processing through Libet's (Libet et al., 1982) repurposing for volition research to present-day investigations of sense of agency, affect, and perceptual awareness. We then discuss the measurement structure of the clock, illustrating how the spatial nature of the clock introduces systematic distortions to time reports and examining conditions under which these distortions can be identified and controlled. We further show that the method carries an implicit commitment to serial-discrete temporal order, constraining the range of cognitive phenomena that the clock is able to investigate. These analyses can help to inspire greater methodological and conceptual sensitivity for future investigations into our subjective timing of events.},
}

Humans
*Time Perception/physiology
*Space Perception/physiology
Sense of Agency

@article {pmid42415272,
year = {2026},
author = {Guo, B and Yan, K and Liu, Z and Deng, Y and Fu, S and Zhao, W and Xu, J and Jiang, C and Tao, R and Chen, X and Wang, M and Mao, T and Rao, H},
title = {Diurnal Variations and Test-Retest Reliability of Resting-State Functional MRI Metrics.},
journal = {Human brain mapping},
volume = {47},
number = {10},
pages = {e70590},
pmid = {42415272},
issn = {1097-0193},
support = {2021ZD0200500//Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project/ ; 32441108//National Natural Science Foundation of China/ ; 32200889//National Natural Science Foundation of China/ ; 2021114002//Shanghai International Studies University Research Projects/ ; //Open Research Fund of the State Key Laboratory of Cognitive Science and Mental Health/ ; LQN25G020003//Zhejiang Provincial Natural Science Foundation of China/ ; 2024QN071//Major Humanities and Social Research Projects in Zhejiang higher education institutions/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods/standards ; *Circadian Rhythm/physiology ; Reproducibility of Results ; Young Adult ; *Brain/physiology/diagnostic imaging ; Male ; Female ; Adult ; Rest ; Brain Mapping/methods ; Image Processing, Computer-Assisted ; },
abstract = {Resting-state fMRI (rs-fMRI) is widely used to assess intrinsic brain activity, yet concerns about its test-retest reliability and reproducibility persist. Circadian rhythms strongly influence brain physiology, but their impact on rs-fMRI reliability remains poorly understood. In this study, we scanned 39 healthy young adults six times within a single day (08:00-20:00) under standardized conditions. For each session, we computed four common rs-fMRI metrics, including amplitude of low-frequency fluctuations (ALFF), wavelet-transformed ALFF (wALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo), and assessed reliability using intraclass correlation coefficients (ICCs). ReHo showed relatively higher and more stable reliability across sessions, whereas amplitude-based metrics, particularly fALFF, exhibited greater diurnal variation. Both network-level and region-specific analyses revealed low reliability in the limbic and subcortical structures, with a mid-morning dip at 10:00. Moreover, ICCs for ALFF, wALFF, and fALFF declined with increasing inter-scan intervals, whereas ReHo remained robust. These findings demonstrate diurnal fluctuations in rs-fMRI reliability, with different metrics exhibiting distinct temporal stability profiles. We recommend that scan timing and circadian influences should be explicitly considered in the design, analysis, and interpretation of future rs-fMRI studies.},
}

Humans
*Magnetic Resonance Imaging/methods/standards
*Circadian Rhythm/physiology
Reproducibility of Results
Young Adult
*Brain/physiology/diagnostic imaging
Male
Female
Adult
Rest
Brain Mapping/methods
Image Processing, Computer-Assisted

@article {pmid42412122,
year = {2026},
author = {Schüller, A and Jehn, C and Stegmaier, M and Riegel, J and Reichenbach, T},
title = {Deep Learning Reveals Cross-Modal Neural Representations of Auditory and Visual Mental Imagery in MEG.},
journal = {Journal of neurophysiology},
volume = {},
number = {},
pages = {},
doi = {10.1152/jn.00563.2025},
pmid = {42412122},
issn = {1522-1598},
support = {523344822//Deutsche Forschungsgemeinschaft (DFG)/ ; 03ZU1210FB//German Federal Ministry of Education and Research/ ; },
abstract = {Mental imagery provides a unique window into the brain's ability to internally simulate sensory experiences, offering valuable insights for both cognitive neuroscience and brain-computer interface (BCI) research. This study examined the neural representations of imagined auditory and visual stimuli using magnetoencephalography (MEG) and assessed the ability of machine learning models to decode these mental processes. MEG data were recorded from 18 right-handed participants during auditory and visual imagery tasks and source-reconstructed within modality-specific cortical regions of interest. We compared a convolutional neural network (CNN) and a linear logistic regression model within a subject-specific classification framework. Both approaches achieved above-chance decoding accuracies, with the CNN outperforming the linear model in both tasks, yielding a mean decoding accuracy of > 70% for the visual imagery task. Notably, the CNN achieved significant decoding performance even when trained on non-task-relevant cortical regions, indicating that imagined stimuli are represented in distributed and partially overlapping neural networks across modalities. This cross-modal decoding capability highlights the potential of deep learning models to capture complex, multimodal neural patterns and suggests that future brain-computer interfaces could benefit from integrating auditory and visual information. These findings advance our understanding of cross-modal mental imagery and point toward more flexible and personalized approaches in BCI design.},
}

@article {pmid42412255,
year = {2026},
author = {Chen, W and He, X and Zheng, J and Chen, S and Tian, X},
title = {Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance.},
journal = {Journal of medical systems},
volume = {50},
number = {1},
pages = {},
pmid = {42412255},
issn = {1573-689X},
support = {QD2025017//Scientific Research Foundation of Hang Zhou City University/ ; },
mesh = {Humans ; *Polysomnography/methods ; *Sleep Stages/physiology ; *Signal Processing, Computer-Assisted ; Machine Learning ; Algorithms ; },
abstract = {Cardiorespiratory-based methods offer promising alternatives to traditional PSG for longitudinal sleep monitoring, holding significant systemic medical value for scalable sleep health management. This systematic review synthesizes methodological frameworks and performance outcomes of automatic sleep staging using cardiorespiratory signals. Four databases were searched and a total of 35 studies published since 2010 were identified. The analysis revealed that cardiorespiratory signal-based sleep staging achieved a practically meaningful accuracy of 70%, with no significant performance differences observed among signal modalities (cardiac signals, cardiorespiratory signals, or cardiac/cardiorespiratory signals combined with other non-EEG modalities) or between modeling algorithms (traditional machine learning vs. deep learning). However, we identified significant methodological heterogeneity and several critical model failure modes that hinder clinical translation, including the widespread lack of external validation, consistently poor classification of the N1 sleep stage, and limited generalization across diverse patient populations. To realize the technology's potential, future research must establish consensus-driven methodological guidelines and rigorously validate algorithms on large, demographically and clinically diverse datasets. These advances are essential for integrating cardiorespiratory-based sleep staging into healthcare systems as a scalable tool for population-level screening, longitudinal monitoring, and tiered clinical decision support.},
}

Humans
*Polysomnography/methods
*Sleep Stages/physiology
*Signal Processing, Computer-Assisted
Machine Learning
Algorithms

@article {pmid42413862,
year = {2026},
author = {Amos, TJ and Han, W and Sun, R and Jin, Z and Zhang, J and Li, L},
title = {Shared and Culture-Specific Brain Networks for Emotional Facial Discrimination: Evidence from Predictive Modeling.},
journal = {Neuropsychologia},
volume = {},
number = {},
pages = {109541},
doi = {10.1016/j.neuropsychologia.2026.109541},
pmid = {42413862},
issn = {1873-3514},
abstract = {Whether emotional facial expressions are perceived universally or are culture-specific has been a topic of debate in affective neuroscience. To address this, we employed connectome-based predictive modeling (CPM) to analyse whole-brain connectivity data from an emotional face discrimination task, examining cultural influences on emotional facial perception in White Americans and Han Chinese. We demonstrated that individual differences in emotional facial discrimination could be predicted in both groups. However, there was limited overlap in the network predictors between groups, with the motor regions, inferior semi-lunar lobule, and culmen emerging as shared hubs at both the network and node levels. Group-specific patterns were also observed. In the Chinese group, unique predictive nodes included the inferior occipital gyrus, cingulate gyrus, and cerebellar tonsil, with dominant contributions arising from cerebellar-motor interactions. In contrast, the White American group showed distinct involvement of the superior temporal gyrus, nodule, and culmen, with primary predictive contributions driven by the motor network. Notably, the predictive model trained on White Americans showed success in generalizing to Han Chinese individuals, whereas the reverse was not observed. These differences may reflect cultural differences in the functional relevance of core predictive networks. Classification analyses validated the functional importance of CPM-identified nodes, showing that activity in these regions distinguished cultural groups in their responses to emotional faces and baseline conditions. These findings offer unique insights into the neural mechanisms by which culture influences emotional facial perception, underscoring the importance of predictive modeling in cultural neuroscience.},
}

@article {pmid42401540,
year = {2026},
author = {Baratin, C and Pessiglione, M and Kahane, P and Robin, A and Minotti, L and Becq, GJC and Bastin, J},
title = {Closed-loop readout of anterior insula high-gamma activity steers value-based decisions.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-75265-5},
pmid = {42401540},
issn = {2041-1723},
abstract = {The decision-making field has long attempted to understand the origins of human choice variability. A potential source of variability lies in spontaneous fluctuations of ongoing neural activity, which may influence behavior across several cognitive domains. Here, we developed a closed-loop intracranial brain-computer interface that detects transient fluctuations of broadband gamma activity (BGA, 70-150 Hz) to trigger stimulus presentations contingent on high or low neural states. Using this approach, we examined how spontaneous activity in the anterior insula influences accept/reject decisions involving multi-attribute offers combining pleasant and unpleasant components. Offers preceded by high BGA in the anterior insula were associated with a transient post-offer suppression in anterior insula activity, which biased decisions toward accepting an unpleasant item in exchange for a pleasant one in hypothetical multi-attribute choices. These findings demonstrate that sub-second endogenous neural fluctuations directly modulate choice behavior, challenging current neuro-computational models by emphasizing the critical role of intrinsic brain states in shaping decision variability.},
}

@article {pmid42403927,
year = {2026},
author = {Yao, W and Li, Y and Meng, Y and Pan, X and Sun, Z and Gao, Y and Zhu, X},
title = {Multifunctional material platforms for neural interfaces: active orchestration of dynamic foreign body response across implantation lifetimes.},
journal = {Bioactive materials},
volume = {66},
number = {},
pages = {139-175},
pmid = {42403927},
issn = {2452-199X},
abstract = {The sustained reliability of invasive brain-computer interface (BCI) electrodes is fundamentally constrained by progressive interface destabilization, a process driven by the dynamic foreign body response (FBR). Given the intricate, time-dependent evolution of the FBR, the establishment of long-term stable neural interfaces necessitates the deployment of sophisticated material architectures capable of intercepting core regulatory mechanisms across distinct pathological phases. This review synthesizes bio-inspired and functional material design strategies, systematically examining their capacity to actively modulate the FBR in a stage-specific manner. Specifically, these approaches are engineered to attenuate acute inflammatory cascades, which is hypothesized to impede detrimental glial scarring-while establishing robust biological barriers resilient to chronic biofouling and infection. Furthermore, by mitigating material degradation and micromotion-induced fretting, these strategies are associated with preserved the functional integrity of the interface over extended periods. By consolidating the theoretical principles, recent advancements, and persisting challenges associated with these material paradigms, this work aims to delineate a forward-looking framework for the development of ultra-durable BCI electrodes, thereby accelerating the clinical translation of neural interface technologies.},
}

@article {pmid42404120,
year = {2026},
author = {Xia, X and Kang, X and Jia, L and Wang, Q and Zhang, L and Zhang, R and Wang, Y and Wu, X and Chen, X and Liu, L},
title = {Intermittent theta burst stimulation enhances the efficacy of brain-computer interface in upper limb rehabilitation post-stroke.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1839697},
pmid = {42404120},
issn = {1664-2295},
abstract = {BACKGROUND: "BCI illiteracy," characterized by insufficient μ-rhythm Event-Related Desynchronization (ERD) in approximately 40% of stroke patients, limits the efficacy of Brain-Computer Interface (BCI) training. Intermittent Theta Burst Stimulation (iTBS) can modulate cortical excitability. We hypothesized that sequential application of iTBS over the affected primary motor cortex (M1) before BCI training may enhance cortical activation, improve BCI decoding efficiency, and thereby promote upper limb motor recovery after stroke.

METHODS: This exploratory single-center randomized controlled trial (RCT) enrolled 18 subacute stroke patients, randomized to: BCI group (conventional rehab + BCI training) or iTBS + BCI group (conventional rehab + iTBS applied to the affected M1 cortex followed sequentially by BCI training). Interventions occurred 10 times over 2 weeks. Primary outcome: Fugl-Meyer Assessment - Upper Extremity (FMA-UE) score. Secondary outcomes: Modified Barthel Index (MBI), BCI task accuracy (BCI-TA). Mechanistic measures: sensorimotor cortex ERD and Laterality Index (LI).

RESULTS: In this exploratory study, FMA-UE improvement was greater in the iTBS + BCI group, with significant differences at week 4 (Z = 2.569, p = 0.008). iTBS + BCI group showed a greater BCI-TA increase (87.22 ± 10.83% vs. 68.24 ± 5.75%, p = 0.041), which correlated negatively with attention improvement (Schulte test time reduction; r = -0.796, p < 0.001). Only the iTBS+BCI group demonstrated deeper ERD over the affected sensorimotor cortex (C4; p = 0.001) and a shift in LI towards the affected side (p = 0.017) during affected hand motor imagery.

CONCLUSION: This exploratory study suggests that sequential iTBS combined with BCI may have potential benefits in enhancing upper limb function in stroke patients. It boosts affected cortical excitability, improves BCI decoding efficiency, and remodels motor network activation, offering a new strategy to overcome "BCI illiteracy."

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/showproj.html?proj=173657, Identifier: ChiCTR2300069203.},
}

@article {pmid42409211,
year = {2026},
author = {Yan, T and Wei, Y and Geng, F and Chen, S and Zhou, H and Hu, Y},
title = {EEG oscillatory correlates of meditation practice: a systematic review and exploratory meta-analysis.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2026.07.012},
pmid = {42409211},
issn = {1873-7544},
abstract = {Meditation practice has been associated with changes in EEG oscillatory activity, although findings across studies remain heterogeneous. This systematic review and meta-analysis examined frequency-specific EEG patterns associated with meditation practice. Frequency-specific random-effects meta-analyses and multilevel nested mixed-effects meta-regression models were used to account for the non-independence of multiple effect sizes within studies. Significant positive pooled effects were observed for alpha, beta, and gamma oscillations, whereas no significant pooled effect was observed for theta oscillations. The primary multilevel meta-regression identified measurement state as a significant moderator, with larger effects observed for Meditation relative to Rest. However, a sensitivity analysis excluding Interaction category (Group × State interaction effects) effect sizes indicated that this finding was not robust and should be interpreted cautiously. In contrast, meditation type, practitioner expertise, and EEG frequency band were not significant moderators. Exploratory analyses indicated a modest positive association between practice duration and theta-band effect sizes; however, this finding should be interpreted cautiously given the uneven distribution of practice-duration data and the predominance of cross-sectional evidence. Overall, the findings suggest substantial heterogeneity in meditation-related EEG effects, highlighting the influence of methodological differences across studies and the need for cautious interpretation of apparent measurement-state effects. More standardized longitudinal and experimental studies are needed to clarify the temporal and practice-related dynamics of EEG oscillatory changes.},
}

@article {pmid42409634,
year = {2026},
author = {Lyu, Y and Liu, X and Cui, H and Wang, N and Zhu, Q and Ding, X},
title = {Experiences and coping with financial toxicity among older cancer patients and caregivers: A qualitative study.},
journal = {Nursing ethics},
volume = {},
number = {},
pages = {9697330261465868},
doi = {10.1177/09697330261465868},
pmid = {42409634},
issn = {1477-0989},
abstract = {BackgroundFinancial toxicity imposes a heavy burden on older cancer patients and their families. In Confucian societies, cultural norms fundamentally shape how financial burden is experienced, communicated, and managed-caregivers feel duty-bound to bear treatment costs, while older patients often conceal financial concerns to avoid burdening their families. This renders financial toxicity a dyadic, relational phenomenon rather than a purely individual economic stressor. Yet how patients and caregivers together experience and cope with this culturally embedded stress remains poorly understood.AimsTo investigate the experiences and coping mechanisms of older cancer patients and their caregivers regarding financial toxicity from a dyadic perspective.DesignA descriptive qualitative study was conducted from May to August 2025 with 12 purposively sampled older cancer patient-caregiver dyads from two tertiary cancer hospitals, using semi-structured, in-depth face-to-face interviews. Data were analyzed following Braun and Clarke's thematic data analysis guide.Ethical ConsiderationsThe study protocol was approved by the ethics committee and adhered to ethical principles.FindingsFour themes comprising ten sub-themes were extracted and organized into two overarching domains. Regarding the experience of financial toxicity, two themes emerged: (1) ethical dilemmas and relational strains as the double-edged sword of familial obligation; (2) survival erosion and family resilience while negotiating the multidimensional impact. Regarding coping mechanisms, two themes emerged: (3) familial survival logic of resilience and adaptation; (4) familial praxis logic in navigating resource allocation.ConclusionRooted in traditional Chinese family culture, where Confucian ethics predominate, financial toxicity imposes a shared burden on patients and caregivers, creating a family-level crisis. Healthcare providers should recognize its profound impact on both caregivers and families. Given the confluence of rapid population aging, family-centered care expectations, and insurance gaps in China, targeted interventions should be developed through a multi-tiered approach, helping cancer-affected families mitigate financial toxicity and improve quality of life.},
}

@article {pmid42411602,
year = {2026},
author = {Yoon, C and Lee, Y and Yim, G and Lee, SW and Lee, W and Park, YG and Park, S},
title = {Nongenetic in Vivo Bimodal Neuromodulation via Photothermal Gold Nanorods and a Multifunctional Fiber Neural Probe.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.6c02201},
pmid = {42411602},
issn = {1936-086X},
abstract = {Neuromodulation is central to both fundamental neuroscience and the development of next-generation brain-computer interfaces (BCIs). However, most cell-type-specific neuromodulation strategies rely on genetic approaches such as optogenetics, which, despite their high spatiotemporal precision, can perturb intrinsic neuronal properties and raise concerns regarding off-target effects and gene-expression efficiency, thereby limiting clinical translation. Moreover, achieving true bimodal neuromodulation remains challenging, as single-gene expression typically enables either inhibition or excitation, restricting applications to one-way perturbations rather than bimodal control of neural activity. Here, we establish a nongenetic bimodal neuromodulation platform by integrating cholesterol-functionalized gold nanorods (GNR-CLS) with a multifunctional fiber-based neural (MFN) probe for localized photothermal stimulation and validate its functionality in the mouse brain. The MFN probe combines microfluidic delivery, near-infrared (NIR) light transmission, and electrophysiological recording within a single flexible fiber, enabling submillimeter colocalization of nanoparticles and optical stimuli with electrophysiological verification of photothermal neuromodulation. Using this platform, we demonstrate in vivo bimodal neuromodulation with both inhibitory and excitatory neuronal responses. Specifically, continuous NIR irradiation suppresses spontaneous firing of GNR-CLS-treated CA1 neurons via activation of thermosensitive inhibitory ion channels, whereas high-intensity NIR pulses delivered to the medial entorhinal cortex elicit spiking activity in the downstream dentate gyrus by transient modulation of membrane capacitance. Neuronal responses are governed by optical pulse parameters, with pulse width and frequency dictating a reversible transition of inhibitory and excitatory neuromodulation. Together, these results demonstrate a fully nongenetic approach to bimodal neuromodulation, enabling both excitatory and inhibitory neuronal control through optical parameter tuning alone.},
}

@article {pmid42385998,
year = {2026},
author = {Li, W and Ngetich, RK and Zhang, Q and Zhang, J and Jin, Z and Li, L},
title = {The role of inferior frontal gyrus in emotion regulation: Evidence from fMRI and tDCS investigation.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {227},
number = {},
pages = {113438},
doi = {10.1016/j.ijpsycho.2026.113438},
pmid = {42385998},
issn = {1872-7697},
abstract = {Previous neuroimaging studies indicate that the inferior frontal gyrus (IFG) may be associated with emotion regulation through the use of cognitive reappraisal strategy. However, whether there is a causal role of IFG in reappraisal-based regulation of general negative emotions remains unclear. Therefore, the present study employed a two-study, progressive research framework combining functional magnetic resonance imaging (fMRI) and transcranial direct current stimulation (tDCS) to investigate the critical role of the IFG in reappraisal-based emotion regulation. In Study 1 (fMRI experiment), thirty-two participants completed an emotion regulation task during scanning. Brain activations were compared between reappraisal condition and view condition to identify neural correlates of emotion regulation. Whole-brain family-wise error (FWE p < 0.05) correction revealed significant activation in the bilateral IFG and other frontal-temporal regions during the use of reappraisal strategy. In Study 2 (tDCS experiment), building on the fMRI findings, twenty participants were recruited to complete the emotion regulation task while receiving counterbalanced active or sham anodal tDCS over the right IFG (one week apart), to observe potential changes in regulation effect when using the reappraisal strategy. Results showed that active tDCS significantly enhanced the regulation effect of reappraisal compared to sham stimulation. Collectively, these findings provide converging evidence for a critical role of the right IFG in reappraisal-based down-regulation of general negative emotions. This may have potential implications for clinical interventions, particularly in psychiatric conditions associated with emotion regulation deficits.},
}

@article {pmid42399230,
year = {2026},
author = {Wang, J and Xue, H and Li, J and Li, Y and Li, R and Mei, Y and Song, E},
title = {Active devices and systems for closed-loop neuromodulation.},
journal = {Microsystems & nanoengineering},
volume = {12},
number = {1},
pages = {},
pmid = {42399230},
issn = {2055-7434},
support = {62574057//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62304044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62204057//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2018SHZDZX01//Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission)/ ; },
abstract = {Neuromodulation has become a central topic in neuroscience and biomedical engineering, as it provides powerful means to interrogate and regulate neural activity and offers promising therapeutic strategies for a wide range of neurological disorders. Conventional neuromodulation approaches predominantly rely on electrode-based electrical stimulation or remote physical stimuli, including optical, chemical, magnetic, and ultrasound-based methods, to influence neuronal excitability and neural circuit dynamics. Recent advances in neuromodulation involve microsystem engineering, nanotechnology, and genetically enabled techniques such as optogenetics. They have achieved increasingly precise and versatile control over neural systems with high spatial and temporal resolution. Owing to their intrinsic capability for integrated sensing, signal amplification, and adaptive regulation, active devices are particularly well suited for system-level implementations of neuromodulation. This review summarizes recent advances in active devices for neuromodulation, with a particular emphasis on their functional roles in neural regulation. By discussing different material platforms and device architectures, this review further provides insights into the rational design of next-generation neural interface systems.},
}

@article {pmid42399467,
year = {2026},
author = {Qin, J and Cai, C and Shan, M and Zhou, S and Shen, Q and Zhu, T and Zhao, M and Mei, Y and Ji, F and Shen, DD and Zang, SK and Zhang, H and Xu, H and Yang, M and Wang, WW and Xiao, R and Yang, B and Mao, C and Shao, Z and Wu, H and Lu, Q and Zhang, Y},
title = {Atypical signaling, ligand recognition and selective agonist discovery of complement receptor C5aR2.},
journal = {Cell research},
volume = {},
number = {},
pages = {},
pmid = {42399467},
issn = {1748-7838},
abstract = {C5a, the most potent anaphylatoxin in the complement system, exerts its effects through the canonical G protein-coupled receptor C5aR1 and the arrestin-coupled receptor C5aR2. Despite the critical role of C5aR2 in immunomodulation, the molecular mechanisms underlying its biased signaling, ligand recognition, and associated pathophysiology remain poorly understood. Here, we report cryo-electron microscopy structures of β-arrestin 1-bound C5aR2 and C5aR1 stimulated by C5a or its metabolite C5a[desArg]. By combining structural analysis with functional assays, we identified the key structural determinants that prevent G protein coupling and confer intrinsic bias toward β-arrestins. Comparative analysis elucidated the distinct ligand recognition mechanism of C5aR2 and explained the retained affinity of C5a[desArg] for C5aR2. These findings guided the rational design of ZQ105, a highly selective C5aR2 agonist. Leveraging ZQ105 as a chemical probe, functional studies revealed that selective C5aR2 activation induces distinct pro-inflammatory responses and receptor internalization in neutrophils. This study provides novel structural insights into transducer engagement and ligand recognition by C5aR2, yielding a valuable pharmacological tool for exploring C5aR2-related pathophysiological processes.},
}

@article {pmid42400035,
year = {2026},
author = {Luo, Y and Fan, J and Zang, Y},
title = {Brief digital narrative intervention for adolescent depression, anxiety, and insomnia during academic stress: a cluster randomized controlled trial.},
journal = {BMC medicine},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12916-026-05047-9},
pmid = {42400035},
issn = {1741-7015},
support = {QY24067//Beijing Natural Science Foundation, China/ ; 32371139, 32000776//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: High-stakes examinations represent a significant but underrecognized threat to adolescent mental health, contributing to elevated symptoms of depression, anxiety, and insomnia. Despite the global scale of this problem, effective interventions during these critical periods remain scarce, largely due to implementation barriers in both clinical and educational settings. Scalable, low-resource solutions are urgently needed to address this mental health gap in adolescent care.

METHODS: In a cluster randomized controlled trial (ChiCTR2200058881, N = 587), we examined the efficacy of the Guided Narrative Technique (GNT), a brief digital writing intervention, compared to a neutral writing group (NWG). Adolescents preparing for China's College Entrance Examination within 100 days (Mage = 18.23, SDage = 0.60) were assigned to GNT (n = 290) or NWG (n = 297) through class-level cluster randomization and completed three consecutive 20-minute daily sessions. The primary outcome was test anxiety, assessed across the intervention and follow-up period. Secondary outcomes were depression, general anxiety, and insomnia, assessed at baseline, post-intervention, and 15-day follow-up.

RESULTS: For the primary outcome, GNT did not produce significantly greater reductions in overall test anxiety than NWG in the full sample. However, GNT was associated with greater reductions on the TAI worry subscale, representing the cognitive component of test anxiety (d = 0.18, 95% CI [0.01, 0.36]) in exploratory subgroup analyses among adolescents with elevated baseline test anxiety. For secondary outcomes, compared with NWG, GNT resulted in significantly greater reductions in depression (d = 0.35, 95% CI [0.16, 0.54]), general anxiety (d = 0.37, 95% CI [0.18, 0.56]), and insomnia (d = 0.23, 95% CI [0.04, 0.42]) during the intervention, with between-group differences also observed for depression and general anxiety at follow-up.

CONCLUSIONS: GNT did not significantly reduce overall test anxiety, but showed preliminary benefits for depression, general anxiety, insomnia, and the worry component among adolescents with high baseline anxiety, warranting further evaluation in adequately powered trials.

TRIAL REGISTRATION: The study was registered as ChiCTR2200058881.},
}

@article {pmid42401151,
year = {2026},
author = {Wang, M and Shang, S and Xu, Y and Zhou, C and You, X and Jiang, H and Fan, X and Zhou, X and Wang, W and Zhang, W and Wang, X and Wang, L and Zhang, S and Ji, B and Wang, G and Liu, J},
title = {Three-dimensional helical integration of high-density linear microelectrode arrays and their cross-tissue applications.},
journal = {Biosensors & bioelectronics},
volume = {311},
number = {},
pages = {118987},
doi = {10.1016/j.bios.2026.118987},
pmid = {42401151},
issn = {1873-4235},
abstract = {Implantable neural microelectrodes are the core components enabling high spatiotemporal resolution neural signal recording and stimulation in brain-computer interfaces (BCIs). However, current technologies still face challenges in achieving high-throughput recording, precise implantation, and long-term stability. In this work, we present a high-throughput three-dimensional (3D) helical stretchable neural probe, fabricated via planar electrode micro-fabrication technology followed by thermally driven helical shaping. The main innovations are reflected in the following: First, through the helical deformation, it is possible to simultaneously achieve cross-tissue recording on cortical surface, deep brain, and inside blood vessels. Secondly, the helical structure can expand the wiring space of the electrodes into three dimensions, achieving high spatial resolution and good mechanical compatibility with the tissue. Interface mechanics simulations indicate that the helical structure effectively mitigates strain induced by brain micromotion. Electrochemical modification significantly reduces interface impedance and enhances charge storage capacity (CSC), while cyclic stretching tests confirm stable electrochemical performance under repeated high-strain conditions. Trans-tissue in vivo experiments further validate the probe's versatility: flexible planar MEAs successfully recorded high-quality subcutaneous electromyography (EMG) signals in mice; the helical probe captured single-unit activity in the deep brain of mice with long-term recording stability; and 1024-channel high-throughput signal acquisition was achieved in the pig cerebral cortex. This technology enables high-throughput, stretchable, and cross-scale long-term stable neural recording, providing a versatile tool for next-generation BCIs and clinical neuromonitoring.},
}

@article {pmid42392342,
year = {2026},
author = {Zhang, S and Mo, L and Fang, F and Mo, C},
title = {Spatial frequency channels implement a mental ruler in spatial vision.},
journal = {NeuroImage},
volume = {338},
number = {},
pages = {122105},
doi = {10.1016/j.neuroimage.2026.122105},
pmid = {42392342},
issn = {1095-9572},
abstract = {Perception of basic spatial properties (e.g., size, separation) varies with visual context, indicating a rescaling process in spatial vision. Previous findings have suggested that this rescaling is supported by an adjustable "mental ruler", an internal metric that can be flexibly changed by context. However, the neural implementation of this putative mental ruler remains unknown. We hypothesized that this mental ruler is represented by multiple spatial frequency (SF) channels with different tunings. In this account, the relative weighting of different SF channels sets the unit length of the mental ruler. Up-weighting of high-SF channels drives the concentration of neuronal receptive fields in early visual cortex, leading to a shorter unit length (a finer division) and perceptual inflation. Conversely, up-weighting of low-SF channels produces a longer unit length (a coarser division) and perceptual compression. Consistent with this account, we found that modulating the relative contribution of the high- and low-SF channels is coupled with a systematic distortion in perceived separation, a fundamental spatial property, and a global displacement of population receptive fields (pRFs) in primary visual cortex. Computational modeling further demonstrated that the perceptual distortion and the pRF displacements were quantitatively linked through SF channel modulation. Together, these results provide converging evidence for the neural implementation of an adjustable mental ruler and suggest a rescaling mechanism through which the visual system dynamically calibrates perceived spatial properties across different pictorial, image-based contexts.},
}

@article {pmid42395922,
year = {2026},
author = {Shaji, A and Kruthika, SL and Prakash, C and Abinaya, S},
title = {EEG-based cognition-aware task classification and scheduling using enhanced fuzzy transition modeling.},
journal = {Frontiers in artificial intelligence},
volume = {9},
number = {},
pages = {1862612},
pmid = {42395922},
issn = {2624-8212},
abstract = {The cognitive state modeling (CSM) problem is typically formulated as a classification problem, limiting the application of the CSM for adaptive real world applications, where the desired outputs are cognitive states to be desired and the inferred ones have to be used for decision making. While conventional methods classify states of the brain, they have not yet been able to connect the class to the task level. To address this, this paper suggests a neurosymbolic model of cognition as a continual latent process rather than an incremental labelling process. It includes a Pseudo Task based Neural State Encoder (PNSE) to encode EEG windows into a structured hyperspherical embedding space, a Neural Transition Graph Network (NTGN) to learn the relationships between cognitive states and tasks, and a Temporal Pseudo-Task Boundary Model (TPBM) to capture the temporal evolution of cognitive states. The neurosymbolic decision layer is used to produce a single scheduling metric using a neural compatibility score, a probabilistic transition measure and a symbolic fuzzy membership, while a fuzzy inference engine is used to categorize candidate task classes with fuzzy membership grades. The framework was tested on a multi-session multi-task EEG cognitive dataset (COG-BCI) using a protocol that was subject independent. The Silhouette Score, Hit Rate, Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR) achieved experimental results of 73.7%, 71.43%, 91.58% and 76.67%, respectively, in the fuzzy membership space. Moreover, the proposed system had a precision of 81.1%, a recall of 83.4% and achieved an accuracy of 83.47% and an F1 score of 82.7%. The outcomes illustrate the possibility of getting cognitive modelling from EEG data to enable active recognition of cognitive states, and the inference and scheduling of uncertain tasks. The proposed framework provides a tractable, temporally unified and cognitively flexible foundation for future decision-support systems that would benefit from both the interpretability and adaptability of neural representation learning and symbolic reasoning and temporal modelling.},
}

@article {pmid42396973,
year = {2026},
author = {Salimian, S and Grier, H and Kaufman, MT},
title = {Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex.},
journal = {eLife},
volume = {14},
number = {},
pages = {},
pmid = {42396973},
issn = {2050-084X},
support = {NCS 1835390//U.S. National Science Foundation/ ; R01 NS121535/NS/NINDS NIH HHS/United States ; 876393SPI//Simons Foundation/ ; DMS-2235451//U.S. National Science Foundation/ ; MP-TMPS-00005320//Simons Foundation/ ; T32 NS121763/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Sensorimotor Cortex/physiology ; Mice ; *Neurons/physiology ; Brain Mapping ; Male ; Mice, Inbred C57BL ; },
abstract = {Cortical control of movement is a distributed computation spanning multiple densely interconnected regions. Although we have rich anatomical atlases and a coarse understanding of how function maps to areas and subregions, we lack a detailed account of how behaviorally relevant activity is organized across the cortical sheet. Here, we trained head-fixed mice to perform a 15-target reach-to-grasp task while we performed cellular-resolution, two-photon calcium imaging across five regions of sensorimotor cortex (>39,000 layer 2/3 neurons). We characterized each neuron's trial-averaged peri-event activity with interpretable metrics and mapped these response properties across areas, revealing large-scale spatial structure. Neuronal response profiles often shifted abruptly at anatomical borders: motor areas showed sharper tuning and more linear relationships with target location, whereas somatosensory areas displayed more heterogeneous response patterns. Neural response properties also differed according to somatotopic representation. Nonlinear dimensionality reduction of the neural feature matrix revealed that areas varied in their average response profiles, but that areas did not have well-separated feature distributions; instead, each area contained subpopulations. Neurons in each subpopulation had characteristic response profiles and were distributed across multiple cortical areas. The spatial distributions of the subpopulations overlapped, with neurons from different subpopulations salt-and-pepper intermingled in the overlap zones. Together, these results describe novel activity structure across sensorimotor cortex and identify several distinct but spatially overlapping subpopulations with characteristic activity patterns during reach-to-grasp behavior.},
}

Animals
*Sensorimotor Cortex/physiology
Mice
*Neurons/physiology
Brain Mapping
Male
Mice, Inbred C57BL

@article {pmid42398529,
year = {2026},
author = {Kurmanavičiūtė, D and Makkonen, M and Zubarev, I and Kahilakoski, OP and Parkkonen, L},
title = {Overcoming brain non-stationarity: Adaptive RLS classification for stable BCIs based on auditory evoked potentials.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae863e},
pmid = {42398529},
issn = {1741-2552},
abstract = {Brain-computer interfaces (BCIs) based on selective auditory attention aim to restore communication by decoding selective attention from auditory evoked potentials. Clinical translation of such BCIs requires maintaining sufficient decoding performance despite brain-state non-stationarity. Approach: We compared classifiers across four evaluation settings: offline baseline classifiers using shuffled 5-fold cross-validation; a causal classifier using a chronological 20%/80% calibration/test split; simulated real-time deployment with a static classifier calibrated on 20 trials; and simulated real-time deployment with an adaptive recursive least squares (RLS) classifier, evaluated within-subject and in a leave-one-subject-out (LOSO) setting. The analysis used 62-channel electroencephalography recorded from 25 healthy adults (18 retained after artifact rejection). Main results: The best offline baseline classifier, logistic regression with point-to-point features, achieved a mean ROC AUC of 0.75 and an estimated information transfer rate (ITR) of 2.46 bits/min, derived from ROC AUC via a conservative heuristic. Under causal application, performance decreased to ROC AUC = 0.63 and ITR = 0.68 bits/min. In simulated real-time deployment, static classification dropped further to ROC AUC = 0.51, whereas adaptive RLS improved ROC AUC to 0.68 and ITR from 0.14 bits/min to 1.42 bits/min (p < 0.001, Cohen's d > 1.49). In the LOSO setting, RLS achieved ROC AUC = 0.57 and ITR = 0.86 bits/min. The LOSO result further suggests that zero-calibration deployment is feasible, with personalization occurring trial-by-trial. Significance: Brain-state non-stationarity is a major driver of performance decline in auditory BCIs. Lightweight adaptive recalibration substantially restores real-time performance and supports the translational potential of ERP-based communication paradigms.},
}

@article {pmid42398887,
year = {2026},
author = {Zhang, Q and Zhang, D and Alimu, G and Bishal, G and Li, W and Zhang, J and Jin, Z and Li, L},
title = {The timing of visual selective attention in fronto-parietal network: TMS behavioral and brain structural evidence.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2026.07.011},
pmid = {42398887},
issn = {1873-7544},
abstract = {Neuronal activation within the fronto-parietal network (FPN) exhibits distinct time windows during bottom-up and top-down attention. Previous studies have shown that transcranial magnetic stimulation (TMS) applied to the FPN can have inhibitory effects on attention performance. However, whether the timing of TMS over the FPN differentially inhibits bottom-up and top-down behaviors requires further investigation. Here, we examined how the timing of TMS delivery to (FPN) nodes affects visual selective attention. The single-pulse TMS was applied to the right dorsolateral prefrontal cortex (rDLPFC) and right superior parietal lobule (rSPL) in both active and sham groups, with different timings (early: 33 ms, 50 ms, 66 ms, 83 ms; late: 216 ms, 233 ms, 250 ms, 266 ms) of TMS pulses after stimulus onset. Behavioral results showed that late TMS over the rDLPFC impaired top-down attention by decreasing accuracy and prolonged reaction times (RTs). Late TMS over the rSPL enhanced top-down attention by increasing accuracy and reducing the RT/Accuracy index. Late TMS over the rDLPFC and rSPL respectively enhanced and reduced the cognitive load difference between bottom-up and top-down attention. Voxel-based morphometry further revealed that RTs in the active group were correlated with gray matter volume (GMV) in the fronto-parietal cortex. Predictive analysis confirmed the stability of the associations between regional GMV and attention. These findings provide causal behavioral evidence that the FPN contributes to visual selective attention during the late time window, and the brain structure results further support the relationship between fronto-parietal structure and the behavioral regulation of visual selective attention.},
}

@article {pmid42389495,
year = {2025},
author = {Temmar, H and Wang, Y and Gill, N and Mellon, NB and Liu, C and Cubillos, LH and Parsons, RI and Costello, JT and Ceradini, M and Kelberman, MM and Mender, MJ and Hite, AI and Wallace, DM and Nason-Tomaszewski, SR and Willsey, MS and Patil, PG and Draelos, AW and Chestek, CA},
title = {Long-term Intracortical Neural activity and Kinematics (LINK): An intracortical neural dataset for chronic brain-machine interfaces, neuroscience, and machine learning.},
journal = {Advances in neural information processing systems},
volume = {38},
number = {},
pages = {},
pmid = {42389495},
issn = {1049-5258},
abstract = {Intracortical brain-machine interfaces (iBMIs) have enabled movement and speech in people living with paralysis by using neural data to decode behaviors in real-time. However, intracortical neural recordings exhibit significant instabilities over time, which poses problems for iBMIs, neuroscience, and machine learning. For iBMIs, neural instabilities require frequent decoder recalibration to maintain high performance, a critical bottleneck for real-world translation. Several approaches have been developed to address this issue, and the field has recognized the need for standardized datasets on which to compare them, but no standard dataset exists for evaluation over year-long timescales. In neuroscience, a growing body of research attempts to elucidate the latent computations performed by populations of neurons. Nonstationarity in neural recordings imposes significant challenges to the design of these studies, so a dataset containing recordings over large time spans would improve methods to account for instabilities. In machine learning, continuous domain adaptation of temporal data is an area of active research, and a dataset containing shift distributions on long time scales would be beneficial to researchers. To address these gaps, we present the LINK Dataset (Long-term Intracortical Neural activity and Kinematics), which contains intracortical spiking activity and kinematic data from 312 sessions of a non-human primate performing a dexterous, 2 degree-of-freedom finger movement task, spanning 1,242 days. We also present longitudinal analyses of the dataset's neural spiking activity and its relationship to kinematics, as well as overall decoding performance using linear and neural network models. The LINK dataset and code are freely available to the public through the dataset website (https://chesteklab.github.io/LINK_dataset/).},
}

@article {pmid42392141,
year = {2026},
author = {Boccato, T and Olak, M and Ferrante, M},
title = {Cross-subject decoding of human neural data for speech Brain Computer Interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae8576},
pmid = {42392141},
issn = {1741-2552},
abstract = {Objective: Brain-to-text systems have recently achieved impressive performance when trained on single-participant data, but remain limited by uninvestigated cross-subject generalization. Approach:We present the first neural-to-phoneme decoder trained jointly on the two largest intracortical speech datasets (Willett et al. 2023; Card et al. 2024), introducing day- and dataset-specific affine transforms to align neural activity into a shared space. Additionally, a hierarchical GRU decoder with intermediate CTC supervision and feedback connections is designed to address the conditional-independence assumption of standard CTC loss. Main Results:Our model matches or outperforms within-subject baselines while being trained across participants, and adapts to unseen subjects using only a linear transform or brief fine-tuning. On an independent inner-speech dataset (Kunz et al. 2025), our approach shows some initial evidence of generalization, by training only subject-, day-specific transforms. Significance:These results demonstrate the feasibility of cross-subject pretraining as a promising direction toward more scalable speech BCIs.},
}

@article {pmid42392147,
year = {2026},
author = {Sato, Y},
title = {Unveiling subject-specific causal latency in motor imagery: a physiologically transparent BCI via Riemannian tangent space fusion.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae857a},
pmid = {42392147},
issn = {1741-2552},
abstract = {While deep learning has improved motor imagery (MI) brain-computer interfaces (BCIs), its "black-box" nature lacks physiological interpretability. Building upon our previous findings that cortical state transitions are governed by non-linear network dynamics, this study aims to elucidate subject-specific functional network delays during MI and propose a physiologically transparent BCI architecture incorporating these functional network temporal delays. Approach: We analyzed 4-class MI EEG data (sensorimotor μ and β rhythms, 8-30 Hz) from the full cohort of 109 subjects in the PhysioNet dataset. To effectively mitigate instantaneous volume conduction effects, we utilized partial correlation-based True Transfer Entropy (True-TE) to extract the optimal functional causal latency (τopt) of information between the supplementary motor area and the primary motor cortex. We then proposed a Tangent Space Fusion (TSF-PDER) framework, independently projecting the current and delayed spatial covariance matrices into the Riemannian tangent space before fusion to prevent topological degradation. Main results: Under a strict, leakage-free nested cross-validation where τopt was estimated exclusively within the training folds, the extracted personalized latencies exhibited a wide functional distribution (median: 374.0 ms). Incorporating TSF-PDER significantly outperformed the spatial-only Riemannian baseline (mean accuracy: 47.24% vs. 45.70%, Wilcoxon signed-rank p = 1.577e-04), while a deep learning baseline (EEGNet) achieved only 28.53% under strictly limited data conditions. Furthermore, bidirectional control analysis revealed significantly stronger feedback information flow than feedforward flow. External validation on the BCI Competition IV-2a dataset demonstrated consistent improvements, with TSF-PDER achieving an average accuracy of 61.92% (vs. baseline 59.07%). Significance: MI execution involves personalized, long-range functional network loops. Fusing these personalized functional delays within the Riemannian tangent space provides a robust decoding boundary without topological degradation. Consequently, TSF-PDER offers a computationally lightweight proof-of-concept for an interpretable BCI, paving the way for personalized neurorehabilitation tailored to patient-specific cortical network dynamics.},
}

@article {pmid42392151,
year = {2026},
author = {Fei, SW and Chen, Y and Chen, JL},
title = {Dynamic functional graph-Laplacian priors integrated with optimization for EEG source localization.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae857e},
pmid = {42392151},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalography (EEG) source localization is an ill-posed inverse problem in which conventional methods often rely on static anatomical or smoothness assumptions and may neglect task-related dynamic functional interactions. This study aims to develop a dynamic graph-regularized EEG source localization framework that incorporates time-varying functional connectivity directly into inverse reconstruction and improves source-space motor imagery decoding.

APPROACH: We propose DynaGraph-alternating direction method of multipliers (DG-ADMM), a source localization framework that combines linearly constrained minimum variance beamforming, region-of-interest-level dimensionality reduction, dynamic phase synchronization analysis, graph-Laplacian regularization, and efficient optimization. Initial source estimates are obtained using a linearly constrained minimum variance beamformer. Region-level source signals are then extracted using principal component analysis and sliding-window phase-locking values with surrogate-based statistical testing are used to construct reliable dynamic functional graphs. The resulting graph Laplacian is mapped back to source space and embedded as a structured prior in an alternating direction method of multipliers optimization problem.

MAIN RESULTS: Experiments on the MNE sample dataset showed that DG-ADMM produced spatially concentrated and physiologically plausible source patterns. On the PhysioNet motor imagery dataset, the proposed framework achieved a binary left-versus-right motor imagery classification accuracy of 93.52%, outperforming representative deep learning baselines. In a 320-dataset synthetic benchmark covering single-source, double-source, correlated-source, and dynamic-source conditions at two signal-to-noise ratios, DG-ADMM achieved the lowest mean center-of-mass localization error in five of eight conditions and showed its clearest advantage for dynamic sources.

SIGNIFICANCE: The results demonstrate that dynamic functional connectivity can serve as an informative graph-structured prior for EEG inverse reconstruction. DG-ADMM provides an interpretable and computationally feasible strategy for improving spatial focus, temporal consistency, and source-space decoding performance in EEG-based brain-computer interfaces.},
}

@article {pmid42393084,
year = {2026},
author = {Jia, T and Yang, X and McGeady, C and Li, Y and Lin, J and Ho, KS and Pan, F and Ji, L and Li, C and Farina, D},
title = {Concurrent control of natural and robotic limbs through a tactile-encoded brain-computer interface.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-75213-3},
pmid = {42393084},
issn = {2041-1723},
abstract = {Brain-computer interfaces (BCIs) promise to extend human movement capabilities by enabling direct neural control of supernumerary effectors, yet integrating augmented commands with multiple degrees of freedom without disrupting natural movement remains a key challenge. Here, we propose a tactile-encoded BCI that leverages sensory afferents through a tactile-evoked P300 paradigm, allowing reliable decoding of supernumerary motor intentions even when superimposed with voluntary actions. The interface was evaluated in a multi-day experiment comprising a single motor recognition task to validate baseline BCI performance and a dual-task paradigm to assess the potential influence between the BCI and natural human movement. The interface achieved real-time and reliable decoding of four supernumerary degrees of freedom, with significant performance improvements after three days of training. After training, performance did not differ significantly between the single-task and dual-task conditions, and natural movement remained unimpaired during concurrent supernumerary control. Lastly, the interface was deployed in a movement augmentation task, demonstrating its ability to command two supernumerary robotic arms for functional assistance during bimanual tasks. These results establish a neural interface paradigm for movement augmentation through stimulation of sensory afferents, expanding motor degrees of freedom without impairing natural movement.},
}

@article {pmid42393153,
year = {2026},
author = {Wu, D},
title = {Riemannian manifold dynamic attention fusion network for motor imagery EEG decoding.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-58874-4},
pmid = {42393153},
issn = {2045-2322},
abstract = {A notable challenge encountered by motor imagery decoding algorithms utilizing electroencephalography (EEG) signals is the substantial redundancy and inadequate geometric representation of spatiotemporal features, which stem from the volume conduction effects inherent to the human head. This phenomenon can obscure essential information regarding motor intentions with noise or non-discriminative features. Although traditional decoding models have sought to alleviate redundancy through shallow attention mechanisms or feature selection in Euclidean space, they frequently neglect the intrinsic manifold geometric properties of EEG signals, such as the positive definiteness of covariance matrices. Additionally, static attention weights are often insufficient in dynamically capturing the cross-domain dependencies between spatiotemporal and spectral features. To address these limitations, we propose a novel spatiotemporal dynamic attention fusion network grounded in Riemannian manifolds (ST-MA-SENet) for EEG motor imagery decoding. ST-MA-SENet adeptly assesses the spatiotemporal correlations among EEG features in both Euclidean and Riemannian spaces from a comprehensive perspective, thereby facilitating the selection of a distinctive and effective EEG fusion feature for motor imagery recognition. To evaluate the efficacy of ST-MA-SENet, we conducted experiments utilizing three motor imagery datasets (BCI IV 2a, BCI IV 2b, HGD), and the results demonstrate that ST-MA-SENet represents a highly promising approach for EEG signal decoding.},
}

@article {pmid42394533,
year = {2026},
author = {Shang, Z and Wang, Y and Li, M and Jin, F and Yang, L and Li, Z and Yue, C},
title = {Interaction between dynamic reinforcement learning and working memory of pigeon: A comparative modeling study.},
journal = {The Journal of experimental biology},
volume = {},
number = {},
pages = {},
doi = {10.1242/jeb.252383},
pmid = {42394533},
issn = {1477-9145},
support = {62301496//National Natural Science Foundation of China/ ; 2025T180781//China Postdoctoral Science Foundation/ ; GZC20232447//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; 252102311095//Key Scientific and Technological Projects of Henan Province/ ; 252102210008//Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {In animal decision-making research, reinforcement learning (RL) and working memory (WM) are regarded as two fundamental cognitive mechanisms, corresponding respectively to the accumulation of reward-based experience and the rapid utilization of recent information. This study focuses on the decision-making behavior of pigeons in low- and high-difficulty probabilistic choice tasks. Based on behavioral data from five pigeons across two types of tasks, we constructed three computational models: a value-updating Rescorla-Wagner (RW) model, a limited-capacity working memory (WM) model, and a dual-system Rescorla-Wagner and Working Memory (RWWM) model with dynamic weighting. These models were used to investigate the cognitive mechanisms underlying decision-making and their dynamic characteristics under varying task demands. Results revealed that pigeons continually adjusted their learning strategies in dynamic environments: working memory exerted a stronger influence during the early stages of learning, facilitating rapid adaptation to changing contingencies, while reinforcement learning became increasingly dominant in later stages or in more complex tasks, supporting the gradual accumulation of long-term value. Further analyses showed that in low-difficulty tasks, pigeons quickly and stably selected the option associated with the highest reward probability, consistent with predictions from the RW model. In contrast, in high-difficulty tasks, some individuals exhibited recent reward-sensitive behavior patterns more aligned with WM-based mechanisms. This study provides both computational and empirical evidence for understanding how animals flexibly deploy cognitive strategies under different learning contexts.},
}

@article {pmid42394952,
year = {2025},
author = {Jung, T and Zeng, N and Fabbri, JD and Eichler, G and Li, Z and Zabeh, E and Das, A and Willeke, K and Wingel, KE and Dubey, A and Huq, R and Sharma, M and Hu, Y and Ramakrishnan, G and Tien, K and Mantovani, P and Parihar, A and Yin, H and Oswalt, D and Misdorp, A and Uguz, I and Shinn, T and Rodriguez, GJ and Nealley, C and van der Molen, T and Sanborn, S and Gonzales, I and Roukes, M and Knecht, J and Kosik, KS and Yoshor, D and Canoll, P and Spinazzi, E and Carloni, LP and Pesaran, B and Patel, S and Jacobs, J and Youngerman, B and Cotton, RJ and Tolias, A and Shepard, KL},
title = {A wireless subdural-contained brain-computer interface with 65,536 electrodes and 1,024 channels.},
journal = {Nature electronics},
volume = {8},
number = {12},
pages = {1272-1288},
pmid = {42394952},
issn = {2520-1131},
abstract = {Electrocorticography uses non-penetrating electrodes embedded in flexible substrates to record electrical activity from the surface of the brain. To use the technology to develop minimally invasive, high-bandwidth brain-computer interfaces, it will be necessary to improve the number of recording channels and the scalability of devices, which could be achieved by merging electrodes and electronics onto a single substrate. Here we report a 50-μm-thick, mechanically flexible micro-electrocorticography brain-computer interface that integrates a 256 × 256 array of electrodes, signal processing, data telemetry and wireless powering on a single complementary metal-oxide-semiconductor substrate. The device contains 65,536 recording electrodes, from which we can simultaneously record a selectable subset of up to 1,024 channels at a given time. Our chip is wirelessly powered, and when implanted below the dura, it can communicate bidirectionally with an external relay station outside the body. We show that the device can provide chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from the somatosensory, motor and visual cortices, decoding brain signals at high spatiotemporal resolution.},
}

@article {pmid41874311,
year = {2026},
author = {Alvaro-Espinosa, L and Marquez-Galera, A and Priego, N and García-Calvo, V and Perea-García, M and Hernandez-Oliver, C and Retana, D and Sanchez, O and de Pablos-Aragoneses, A and García-Gómez, P and Graña-Castro, O and Lapuente-Santana, Ó and Serrano-Ron, L and Al-Shahrour, F and Cayuela López, A and Peset, I and Megías, D and Ola, M and Varešlija, D and Young, LS and Martí-Mateos, Y and Enríquez, JA and Hernández-Encinas, E and Blanco-Aparicio, C and Soengas, MS and Bernhagen, J and Antón-Fernández, A and Ávila, J and Marchena, MA and Torres, M and de Castro, F and Márquez-Ropero, M and Sierra, A and Lopez-Atalaya, JP and , and Valiente, M},
title = {MIF-Induced CD74+ Microglia and Macrophages Promote Progression of Brain Metastasis and Are Clinically Relevant across Central Nervous System Disorders.},
journal = {Cancer research},
volume = {86},
number = {13},
pages = {3249-3269},
pmid = {41874311},
issn = {1538-7445},
support = {201906-30-31-32//Fundació la Marató de TV3 (Fundació la Marató)/ ; SAF2017-89643-R//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; SAF2014-57243-R//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; SAF2015-62547-ERC//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; PID2022-143110OB-I00 and RED2024-153909-E//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; 828972//H2020 Future and Emerging Technologies (FET)/ ; 498103//Melanoma Research Alliance (MRA)/ ; 54545//Cancer Research Institute (CRI)/ ; LABAE19002VALI//Fundación Científica Asociación Española Contra el Cáncer (AECC)/ ; PRYCO234528VALI//Fundación Científica Asociación Española Contra el Cáncer (AECC)/ ; TRNSC213878VALI//Fundación Científica Asociación Española Contra el Cáncer (AECC)/ ; 864759//HORIZON EUROPE European Research Council (ERC)/ ; TRANSCAN2021-203//Transcan (Transcan-3)/ ; AC20/00114//Instituto de Salud Carlos III (ISCIII)/ ; HR23-00051//"la Caixa" Foundation ("la Caixa")/ ; RYC-2013-13365//Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España (MINECO)/ ; RTI2018-102260-B-I00//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; PROMETEO/2020/007//Generalitat Valenciana (GVA)/ ; CIVP20S10662//Fundación Ramón Areces (FRA)/ ; CIVP19S8163//Fundación Ramón Areces (FRA)/ ; RTI2018-099267-B-I00 and PID2022-136698OB-I00//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; IT1473-22//Eusko Jaurlaritza (Gobierno Vasco)/ ; AARG-NTF-24-1304352//Alzheimer's Association (AA)/ ; 18239A01//Breast Cancer Ireland (BCI)/ ; 19/FFP/6443//Research Ireland (Researchirel)/ ; 23/SPP/11783//Research Ireland (Researchirel)/ ; 20/FFP-P/8597//Research Ireland (Researchirel)/ ; 2021JulyPCC1460//Breast Cancer Now (BCN)/ ; 2019AugSF1310//Breast Cancer Now (BCN)/ ; RTI2018-099357-B-I00//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; PID2021-1279880B//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; and TED2021-131611B-I00//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; CB16/10/00282//Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES)/ ; 17CVD04//Fondation Leducq (Leducq Foundation)/ ; LCF/BQ/DI17/11620028//"la Caixa" Foundation ("la Caixa")/ ; LCF/BQ/DI19/11730044//"la Caixa" Foundation ("la Caixa")/ ; POSTD19016PRIE//Fundación Científica Asociación Española Contra el Cáncer (AECC)/ ; BES-2017-081995//Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España (MINECO)/ ; PRE2018-083478//Ministerio de Ciencia, Innovación y Universidades (MCIU)/ ; 4053//European Molecular Biology Organization (EMBO)/ ; CIVP19A5917//Fundación Ramón Areces (FRA)/ ; },
mesh = {*Microglia/metabolism/pathology/immunology ; *Antigens, Differentiation, B-Lymphocyte/metabolism/genetics ; *Brain Neoplasms/secondary/metabolism/pathology/genetics ; *Histocompatibility Antigens Class II/metabolism/genetics ; Animals ; Humans ; *Macrophages/metabolism/immunology/pathology ; Disease Progression ; *Macrophage Migration-Inhibitory Factors/metabolism ; *Intramolecular Oxidoreductases/metabolism/genetics ; Mice ; *Central Nervous System Diseases/pathology/metabolism ; Tumor Microenvironment ; Cell Line, Tumor ; },
abstract = {UNLABELLED: The upregulation of CD74, a chaperone involved in MHC-II antigen processing, has been mostly interpreted as indicative of antigen presentation in multiple brain disorders. However, CD74 expression has also been described in cancer cells across multiple tumor types and in the tumor microenvironment, notably in glioma. In this study, we found that the presence of CD74+ microglia/macrophages, which was induced by increased levels of interferon γ in brains affected by metastases, did not relate to its canonical pathway. Instead, the alternative function of CD74 as a cytokine receptor was pivotal. Proliferating cancer cells produced high levels of the ligand migration inhibitory factor (MIF) that bound the CD74 receptor and induced its translocation to the nucleus where it activated an NF-κB-dependent program that promoted metastatic progression. In patients, a CD74 signature was associated with more aggressive progression of brain metastatic disease, although it had no clinical correlation with the matched primary tumor. Interestingly, a pan-disease noncanonical and clinically relevant signature derived from the CD74+ myeloid population was identified that occurred in additional brain disorders, including Alzheimer's disease and multiple sclerosis. The brain-penetrant drug ibudilast, which prevents the binding of MIF to CD74, decreased brain metastasis in experimental models in vivo and in patient-derived organotypic cultures ex vivo in a primary tumor-agnostic manner. These findings suggest that MIF/CD74-induced reprogramming of myeloid cells in brain disorders is a vulnerability that could be exploited therapeutically against brain metastases and possibly other brain disorders.

SIGNIFICANCE: A reprogrammable subset of CD74+ microglia/macrophages is a shared population with translational relevance across neurologic diseases that drives pathology in brain metastases. See related commentary by Lee and Kang, p. 3103.},
}

*Microglia/metabolism/pathology/immunology
*Antigens, Differentiation, B-Lymphocyte/metabolism/genetics
*Brain Neoplasms/secondary/metabolism/pathology/genetics
*Histocompatibility Antigens Class II/metabolism/genetics
Animals
Humans
*Macrophages/metabolism/immunology/pathology
Disease Progression
*Macrophage Migration-Inhibitory Factors/metabolism
*Intramolecular Oxidoreductases/metabolism/genetics
Mice
*Central Nervous System Diseases/pathology/metabolism
Tumor Microenvironment
Cell Line, Tumor

@article {pmid42384509,
year = {2026},
author = {Wang, Q and Yang, L and Song, J and Bai, Y and Du, J},
title = {HEDN: A Hard-Easy Dual Network with Source Reliability Assessment for Cross-Subject EEG Emotion Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3708734},
pmid = {42384509},
issn = {2168-2208},
abstract = {Cross-subject electroencephalography (EEG) emotion recognition is essential for real-time monitoring of cognitive and affective states in brain-computer interface (BCI) and wearable health applications, but substantial inter-subject variability poses a major challenge. Multi-Source Domain Adaptation (MSDA) offers a potential solution, but existing MSDA frameworks typically assume equal source quality, leading to negative transfer from low-reliability domains and prohibitive computational overhead due to multi-branch model designs. To address these limitations, we propose the Hard-Easy Dual Network (HEDN), a lightweight reliability-aware MSDA framework. HEDN introduces a novel Source Reliability Assessment (SRA) mechanism that dynamically evaluates the structural integrity of each source domain during training. Based on this assessment, sources are routed to two specialized branches: an Easy Network that exploits high-quality sources to construct fine-grained, structure-aware proto types for reliable pseudo-label generation, and a Hard Net work that improves the discriminability of low-reliability sources while regularizing source-target alignment. Furthermore, a cross-network consistency loss aligns pre dictions between branches to preserve semantic coherence. Extensive experiments conducted on SEED, SEED IV, and DEAP datasets demonstrate that HEDN achieves highly competitive performance compared with state-of the-art methods under cross-subject evaluation protocols while reducing adaptation complexity. The source code is available at https://github.com/qwangwl/HEDN.},
}

@article {pmid42384796,
year = {2026},
author = {Hu, H and Guo, D and Pu, Y and Abuduaini, Y and Wang, X and Francks, C and Thompson, PM and Kong, XZ},
title = {Variations of global brain asymmetry are associated with aging and related diseases.},
journal = {Science advances},
volume = {12},
number = {27},
pages = {eadu9309},
pmid = {42384796},
issn = {2375-2548},
mesh = {Humans ; *Aging/pathology ; *Brain/physiopathology/pathology/diagnostic imaging ; Female ; *Functional Laterality ; *Alzheimer Disease/physiopathology/diagnostic imaging/genetics/pathology ; Male ; Magnetic Resonance Imaging ; *Parkinson Disease/physiopathology/diagnostic imaging/genetics/pathology ; Aged ; Cognition ; Machine Learning ; },
abstract = {Lateralization is a hallmark of brain organization, yet the structural basis underlying this phenomenon remains a critical, unresolved question in cognitive and systems neuroscience. In this study, we applied multivariate machine learning techniques to investigate variations of global brain asymmetry and their associations with cognitive functions, aging, and aging-related diseases, using large-scale datasets. Our findings revealed substantial and previously unknown structural differences between the hemispheres, and established key associations between structural asymmetries and lateralized functions. At the population level, we identified unique aging trajectories of hemispheric differences and uncovered diagnosis-specific variations in patients with Alzheimer's and Parkinson's disease, and in APOE ε4 carriers at genetic risk. Notably, we identified a "left hemi-aging" pattern that challenges the conventional "right hemi-aging" model. Together, these results advance our understanding of functional lateralization in the human brain and highlight the potential of global brain asymmetry as a biomarker for brain aging and related diseases.},
}

Humans
*Aging/pathology
*Brain/physiopathology/pathology/diagnostic imaging
Female
*Functional Laterality
*Alzheimer Disease/physiopathology/diagnostic imaging/genetics/pathology
Male
Magnetic Resonance Imaging
*Parkinson Disease/physiopathology/diagnostic imaging/genetics/pathology
Aged
Cognition
Machine Learning

@article {pmid42385308,
year = {2026},
author = {Dang, T},
title = {Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.},
journal = {Computers in biology and medicine},
volume = {213},
number = {},
pages = {111836},
doi = {10.1016/j.compbiomed.2026.111836},
pmid = {42385308},
issn = {1879-0534},
abstract = {The human brain maintains functional stability under changing conditions through interacting processes that include synaptic plasticity, homeostatic regulation, adaptive connectivity, and oscillatory dynamics. By contrast, electroencephalographic (EEG) recordings are highly non-stationary across sessions, individuals, and recording conditions. The resulting distribution shifts can impair model generalization and undermine the long-term reliability of brain-computer interface (BCI) systems. Machine learning (ML) and transfer learning (TL) approaches have improved cross-session and cross-subject decoding, but many depend on data-driven adaptation, often require recalibration, and do not explicitly model biological processes associated with neural adaptation and stability. This perspective-driven review examines how bio-inspired mechanisms, including synaptic plasticity, homeostatic regulation, neural oscillations, and spiking representations, could inform EEG models that are more robust to non-stationarity. This review synthesizes recent advances, critically compares bio-inspired methods with conventional ML and TL paradigms, and considers hybrid designs in which biologically grounded mechanisms complement artificial neural networks. To support clearer evaluation, the paper introduces operational definitions of bio-inspired, bio-plausible, and bio-realistic modeling; maps biological mechanisms to mathematical descriptions and computational modules; identifies evidence gaps and mechanism-specific limitations; and proposes a minimum specification for continual EEG benchmarks. Because direct EEG evidence remains limited for many proposed mechanisms, the review distinguishes empirically supported findings from hypotheses and future research directions. Together, this framework provides a testable roadmap for developing and evaluating adaptive EEG learning systems under realistic non-stationary conditions.},
}

@article {pmid42386847,
year = {2026},
author = {Li, A and Ruotsalo, T and Ang, JH and Spapé, MM},
title = {BrainPrompting: reconstructing facial memory by brain-based generative AI interaction.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-59737-8},
pmid = {42386847},
issn = {2045-2322},
support = {SRG2023-00056-ICI, MYRG-GRG2024-00023-ICI, MYRG-CRG2024-00040-ICI//Research Services and Knowledge Transfer Office, University of Macau/ ; },
abstract = {Generative artificial intelligence (GenAI) has transformed image synthesis, yet using these models to construct images that accurately represent a human mental image remains a challenge. Current "prompting" methods rely on explicit verbal descriptions, which can fail to capture the complex nuances of a mental representation. BrainPrompting is a neuroadaptive method that utilises implicit brain activity to navigate the latent space of a generative model. Candidate stimuli generated from a generative neural network model are presented to 'query' users, who 'reply' via implicit, EEG-based recognition signals. Previous work within this field demonstrated the capability of generating images matching mental categories and visualising individual differences, yet it remains uncertain how well generated images match human representations. To investigate, a "police line-up" experiment was conducted in which target faces were kept in mind while a stream of faces similar or dissimilar to the target were shown. Faces that were reconstructed using BrainPrompting were shown as closely resembling human memory, demonstrating strong alignment between the neural network model and mental representation. By showing that this accuracy increases as a function of aggregating neural signals across multiple users, we present evidence for an above-individual level of accuracy.},
}

@article {pmid42387104,
year = {2026},
author = {Feng, Y and Guo, X and Huang, P and Jia, N and Hu, S and Yang, S},
title = {Cross-ancestry pleiotropic analysis of imaging-derived phenotypes enhances risk stratification of depression.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {42387104},
issn = {1476-5578},
abstract = {Depression arises from dynamic interactions among genetic predisposition, brain alterations, and environmental stressors. Despite genome-wide association studies (GWAS) identifying risk loci, the mechanisms translating genetic variation into brain changes remain elusive. Imaging-derived phenotypes (IDPs) were the intermediate traits linking genetic architecture to neural circuit dysfunction. Here, we collected large-scale GWAS summary statistics of depression and IDPs across European (EUR; N = 1,293,933 and 33,224, respectively) and East Asian (EAS; N = 82,874 and 7058, respectively). In the multiple-trait analysis between depression and IDPs, we clarified their genetic correlation through MTAG, identified the pleiotropic single nucleotide variants (SNVs) and genes with functional insight, and established the causal relationship through Mendelian randomization via TwoSampleMR in EUR and EAS ancestry, respectively. To discern the heterogeneous genetic drivers, we selected independent SNVs from the multiple-trait analyses to perform unsupervised clustering. Six clusters delineated distinct biological pathways for metabolic regulation, neurotransmitter dynamics, and neuroimmune interactions, with tissue/cell type specificity through MAGMA. Finally, we dissected relationships between depression and polygenic risk score, IDPs, and modifiable lifestyle factors, and introduced a machine learning framework to refine risk stratification (N = 16,166). Our study advanced the understanding of the multiscale etiology of depression while providing dynamic depression risk stratification for precision prevention.},
}

@article {pmid42387333,
year = {2026},
author = {Guo, Y and Liu, W and Bu, W and Wang, R and Su, D and Li, H},
title = {A preliminary analysis of the inflammatory protein landscape in the CSF of mid- to late-stage Parkinson's disease: associations with motor severity and subtypes.},
journal = {BMC neurology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12883-026-05109-8},
pmid = {42387333},
issn = {1471-2377},
abstract = {PURPOSE: Parkinson's disease (PD) is a progressive neurodegenerative disorder in which neuroinflammation is recognized as a contributor to clinical progression. This study aimed to characterize the cerebrospinal fluid (CSF) inflammatory profile in mid- to late-stage PD patients and identify specific inflammatory proteins with potential clinical relevance to motor symptoms and disease severity.

METHODS: In this preliminary retrospective cross-sectional study, CSF samples were obtained from 25 patients with mid- to late-stage PD undergoing evaluation for deep brain stimulation (DBS) (mean disease duration: 10.24 ± 4.65 years) and 15 non-PD controls (essential tremor or dystonia) undergoing identical surgical procedures. The levels of 92 inflammation-related proteins were quantified using the Olink proximity extension assay (PEA). Based on the identified differentially expressed proteins (DEPs), we next performed preliminary exploratory comparisons of inflammatory profiles between the postural instability and gait difficulty (PIGD, n = 10) and tremor-dominant (TD, n = 10) PD subtypes. Additionally, preliminary correlation analyses were performed between the DEPs and Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part-III (MDS-UPDRS-III) scores to generate preliminary observations with limited clinical inference.

RESULTS: Using the Olink platform, 28 DEPs were identified between the PD and non-PD groups (p < 0.05). Subsequent protein-protein interaction network analysis identified IFN-γ as the central hub. Exploratory descriptive analyses of TD and PIGD subgroups are provided in Additional file 1: Supplementary Figure S1. Among all DEPs, IL-10RB (r = 0.440, 95% CI [0.054, 0.711], p = 0.028), CD8A (r = 0.415, 95% CI [0.024, 0.696], p = 0.039), and CXCL9 (r = 0.414, 95% CI [0.023, 0.696], p = 0.040) showed moderate correlations with MDS-UPDRS-III scores.

CONCLUSION: In profiling 92 CSF inflammation-related proteins from 25 advanced PD patients (Hoehn-Yahr 3-4) and 11 controls, we identified 28 upregulated DEPs. IFN-γ emerged as a topologically connected hub in protein-protein interaction networks, and three proteins (IL-10RB, CD8A, CXCL9) showed moderate correlations with motor scores. As cross-sectional descriptive observations, these findings establish a preliminary framework to generate hypotheses for future mechanistic validation and biomarker discovery, while no functional causality can be inferred from the present results.},
}

@article {pmid42389048,
year = {2026},
author = {S V, A},
title = {Olfactory Perception and Neural Rhythms: A Simulation-Based EEG Analysis Using Power Spectral Density FeaturesOlfactory perception and neural rhythms: a simulation-based eeg analysis using power spectral density features.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {124},
pmid = {42389048},
issn = {1871-4080},
abstract = {The human ability to smell functions as a critical cognitive function because it enables people to detect their surroundings while experiencing feelings and recalling memories and making choices. Researchers face difficulties when they use electroencephalography (EEG) to study how the brain responds to smells because olfactory brain signals produce low signal-to-noise ratios and different people show different response patterns and researchers lack established olfactory EEG databases for their studies. The study proposes a simulation-based framework which enables researchers to study olfactory EEG signals through power spectral density (PSD) analysis. The research team created a simulated olfactory EEG dataset which simulated the responses of fifty virtual participants who experienced two distinct odor categories of pleasant rose and unpleasant rotten at three different concentration levels of low medium and high to create six separate olfactory conditions. The simulated EEG signals included 45 channels which recorded data at a 256 Hz sampling rate. Welch's method estimated PSD features for five canonical EEG frequency bands which included delta theta alpha beta and gamma after the data underwent band-pass filtering at the 0.5-70 Hz range. The researchers used Stratified 10-fold cross-validation to evaluate the band's characteristics which they had developed as training data for their multiclass support vector machine (SVM) classification model. The PSD-based features demonstrated their ability to distinguish between different olfactory conditions in controlled tests which showed the system's classification accuracy of 99.67% and macro-averaged F1-score of 0.99. The research provides a methodological validation platform which enables scientists to conduct reproducible olfactory EEG studies through their complete pipeline of interpretation. The proposed framework establishes the essential foundations for subsequent research which will assess and develop these techniques through actual human olfactory EEG data in cognitive neuroscience studies.},
}

@article {pmid42175522,
year = {2026},
author = {Farina, D and Ferrante, L and Yang, X},
title = {From mixed signals to neural code: compensating volume conduction for noninvasive generic neural interfaces.},
journal = {Expert review of medical devices},
volume = {23},
number = {7},
pages = {749-753},
doi = {10.1080/17434440.2026.2679688},
pmid = {42175522},
issn = {1745-2422},
mesh = {Humans ; *Brain-Computer Interfaces ; Animals ; *Signal Processing, Computer-Assisted ; Models, Neurological ; },
abstract = {INTRODUCTION: Noninvasive neural interfaces promise scalable access to neural information without the risks of implanted sensors, but their fundamental limitation is the transformation imposed by the volume conductor between neural sources and sensors. Biological tissues spatially and temporally filter, mix, and disperse neural activity, such that recorded signals (e.g. EEG, ENG, surface EMG) primarily reflect convolution with tissue-dependent impulse responses rather than the underlying neural information.

AREA COVERED: We frame this challenge using a generic convolutive model in which neural sources are observed through volume-conductor filters and noise. Two complementary strategies are discussed: direct compensation, which seeks to separate and recover subsets of neural sources through deconvolution methods, and indirect compensation, which learns representations that are invariant to volume-conductor variability from large, diverse datasets.

EXPERT OPINION: We argue that progress in noninvasive interfacing will depend on explicit recognition and compensation of the volume conductor effect, either directly or indirectly. Together, these strategies point toward noninvasive neural interfaces that can scale beyond subject-specific calibration by isolating neural information from tissue-dependent distortions.},
}

Humans
*Brain-Computer Interfaces
Animals
*Signal Processing, Computer-Assisted
Models, Neurological

@article {pmid42374156,
year = {2026},
author = {Lévy, J and Zhang, M and Pinet, S and Rapin, J and Banville, H and d'Ascoli, S and King, JR},
title = {Noninvasive decoding of typed sentences from human brain activity.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42374156},
issn = {1546-1726},
abstract = {Modern neuroprostheses can now restore communication in patients who have lost the ability to speak or move. However, implanting these invasive devices comes with risks inherent to neurosurgery. Here we introduce a noninvasive method to decode the production of sentences from brain activity and demonstrate its efficacy in a cohort of 35 healthy volunteers. For this, we present Brain2Qwerty, a new deep learning architecture trained to decode sentences from either electro- or magnetoencephalography, while participants typed briefly memorized sentences on a QWERTY keyboard. With magnetoencephalography, Brain2Qwerty reaches, on average, a character error rate of 29% and substantially outperforms electroencephalography (character error rate: 65%). For the best participants, the model achieves a character error rate of 18%, and can perfectly decode a variety of sentences outside of the training set. Overall, these results narrow the gap between invasive and noninvasive methods and thus open the path for developing safe brain-computer interfaces for noncommunicating patients.},
}

@article {pmid42378055,
year = {2026},
author = {Wang, D and Zhou, H and Chen, X and Long, Q and Hu, Y and Potenza, MN and Fu, J},
title = {Dynamic interplay between food addiction, psychological and behavioral factors, and weight-related measures: A longitudinal network analysis in developing youth.},
journal = {Journal of behavioral addictions},
volume = {},
number = {},
pages = {},
doi = {10.1556/2006.2025.00141},
pmid = {42378055},
issn = {2063-5303},
abstract = {BACKGROUND AND AIMS: Food addiction (FA) likely contributes to obesity within a complex system of psychological, behavioral, and physiological factors. However, interactions among these factors remain incompletely understood. This study used a network approach to identify interrelationships among FA, eating motives, lifestyle habits, and weight indicators in a developmental youth cohort.

METHODS: A longitudinal panel study was conducted in Eastern China. Youth aged >8 years (mean ages 13-14 years) completed survey and anthropometric measurements. The Chinese version of the dimensional Yale Food Addiction Scale for Children 2.0 (dYFAS-C 2.0), Kids Palatable Food Eating Motive Scale (K-PEMS) and Mindful Eating Scale for Children (MEQ-C) were used to measure addictive eating motives and behaviors. Semi-quantitative food intake, physical activity and sleep duration were assessed, and height and weight were measured by trained researchers to calculate a Body Mass Index Z score (BMIZ). Body composition measurement utilized bioelectrical impedance for detecting subcutaneous fat content (FC) and a visceral fat level (VFL). Network analysis, including cross-sectional network estimation at each time point, longitudinal network comparison, and cross-lagged panel network (CLPN) modeling, was used to examine the centrality, connectivity, and temporal relationships among FA and key variables.

RESULTS: Among the 2680 participants enrolled, 2054 completed both waves of surveys (retention rate: 76.7%; 49.5% girls). In the baseline (T1) cross-sectional network, FA showed the highest closeness and betweenness and was linked to the weight-related subnetwork (BMIZ, FC, and VFL) primarily through VFL. The follow-up (T2) cross-sectional network showed a broadly similar overall pattern. Longitudinal comparison suggested generally stable centrality patterns across time, with lifestyle-related factors showing relatively greater prominence at T2. In the CLPN, the strongest directional paths primarily extended from FA to later eating motives, whereas direct longitudinal paths from FA to weight indicators were not observed. FA and mindful eating exhibited bidirectional relationships, and mindful eating was also negatively associated with subsequent reward-based eating motives.

CONCLUSION: FA may represent an important intervention target within the broader obesity-related psychobehavioral system in youth, particularly in relation to eating motives and reward-driven eating processes. Interventions that address FA together with mindful eating and modifiable lifestyle factors may offer a more comprehensive approach to youth weight-related health.},
}

@article {pmid42379192,
year = {2026},
author = {Foli, C and Conlan, EC and Memberg, WD and Bhat, P and Graczyk, EL and Johnson, TR and Taylor, DM and Herring, EZ and Sweet, JA and Ajiboye, AB},
title = {Neural mechanisms of mixed speech and grasp representation in sensorimotor cortices.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae847b},
pmid = {42379192},
issn = {1741-2552},
abstract = {Recent brain-machine interface (BMI) studies have challenged traditional views of functional specialization in human motor cortices, suggesting that regions associated with hand movement also support speech. However, the extent of this dual functionality as well as the neural mechanisms underlying it are unclear. This study aims to determine whether mixed speech and grasp representation generalizes across a wide range of cortical regions and if so, to identify the population-level mechanisms supporting such mixed selectivity. Approach. We analyzed intracortical neural activity from seven brain regions (spanning motor, premotor, somatosensory and parietal cortices) while two human participants with tetraplegia completed speech and hand movement tasks. We assessed the population-level encoding of the two tasks using offline neural decoding, functional connectivity and subspace analyses. Main results. Robust grasp decoding was evident across all regions, along with reliable discrete-word decoding during both silent and vocalized speech. Within each region, overlapping neural populations contributed to both tasks, indicating mixed selectivity. However, these populations reconfigured their functional connectivity between tasks, organizing neural activity into non-overlapping speech and grasp subspaces. Significance. Our results indicate that mixed selectivity for speech and grasping generalizes across distributed sensorimotor cortical networks. Additionally, our findings support the development of multi-functional BMIs capable of decoding both speech and grasping from the same implant and highlight ventral premotor area 6r as a novel target. .},
}

@article {pmid42379632,
year = {2026},
author = {Huang, Y and Li, M and Shang, Z and Yang, L},
title = {Individual detachment-reintegration events in homing pigeon flocks and the dominance of directional adjustment in their kinematic features.},
journal = {Journal of the Royal Society, Interface},
volume = {23},
number = {240},
pages = {},
doi = {10.1098/rsif.2026.0296},
pmid = {42379632},
issn = {1742-5662},
support = {2025T180781//China Postdoctoral Science Foundation/ ; 62301496//National Natural Science Foundation of China/ ; GZC20232447//Postdoctoral Fellowship Program of the China Postdoctoral Science Foundation/ ; 252102311095//Key Scientific and Technological Projects of Henan Province/ ; 252102210008//Key Scientific and Technological Projects of Henan Province/ ; 26A416004//Key Scientific Research Project of Higher Education Institutions in Henan Province/ ; 20250662A//Technology Development Project of the Affiliated Encephalopathy Hospital of Zhengzhou University/ ; },
mesh = {Animals ; *Columbidae/physiology ; Biomechanical Phenomena ; *Flight, Animal/physiology ; *Homing Behavior/physiology ; *Models, Biological ; },
abstract = {Individual detachment-reintegration processes offer a useful window into brief non-steady reorganization in collective flight, but whether such events can be identified objectively across flight trials remains unclear. Using high-resolution three-dimensional trajectory data from homing pigeon flocks, we first tested whether nearest-neighbour distance distributions contained a separable second spatial scale. Only in trials that passed this test did we define a trial-specific event threshold and extract validated events using temporal continuity screening and reintegration-stability verification. Of four homing-flight trials, only two satisfied the separability criterion, yielding 17 validated events. Within these events, directional adjustment provided a more consistent kinematic signature than speed difference: median lateral steering ratios Rsteer were 0.674 and 0.715, and median acceleration-direction deviation angles ∠aout were 93.27∘ and 90.15∘, whereas speed difference Δv showed no consistent unidirectional change across trials. Comparisons with matched non-event baselines and robustness analyses further indicated that event specificity was expressed primarily as enhanced directional adjustment.},
}

Animals
*Columbidae/physiology
Biomechanical Phenomena
*Flight, Animal/physiology
*Homing Behavior/physiology
*Models, Biological

@article {pmid42380266,
year = {2026},
author = {Monteza-Moreno, CM and Giacalone, J and Grote, MN and Dent, DH and Willis, G and Crofoot, MC},
title = {Mammals tolerate harmless human presence: Lessons from COVID-19 lockdown on Barro Colorado Island, Panamá.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {42380266},
issn = {2045-2322},
mesh = {Animals ; Humans ; Panama/epidemiology ; *COVID-19/prevention & control/epidemiology/virology ; *Mammals/physiology ; Forests ; SARS-CoV-2/isolation & purification ; Human Activities ; },
abstract = {Human presence in protected forests impacts wildlife, but investigating such impacts is challenging because it is rare to isolate human presence from other anthropogenic factors. The COVID-19 lockdowns in 2020 provided a quasi-natural experiment that reduced human activity on Barro Colorado Island, a tropical forest isolated from most human footprints in Panamá. We used trail-based camera trap data from mammal species to compare a lockdown period (April-July 2020) versus non-lockdown (2019). For all observed species, we tested the hypotheses that human presence impacts activity level for 14 species, and for focal species we also tested diel activity, predator-prey dynamics, group cohesiveness, scent-marking, foraging, and vigilance. To assess lockdown effect, we analyzed our data using negative binomial, logistic and recurrent event analysis, and we contrasted null and alternative models. We also estimated diel activity patterns and used confidence intervals to examine lockdown effects. Based on camera trap observations, human presence on BCI forest was 9 times lower, while 16.2 times lower based on safety book records. Results showed no significant changes in activity level (rate of events) and diel activity for any species; in foraging duration of agouti, collared-peccary, red-brocket deer, and white-nosed coati; in predator-prey dynamic between agouti and ocelot; and in scent-marking of agouti. However, group cohesiveness and vigilance of white-nosed coati and collared-peccary were higher during lockdown. Overall, under lockdown, animal activity and diel activity patterns remained unchanged, although agouti, peccary, coati, and ocelot's diel activity slightly increased during typical human-active hours. Our results indicate that mammals, living on a managed forest with low anthropogenic impact and disturbance, can tolerate non-consumptive human presence.},
}

Animals
Humans
Panama/epidemiology
*COVID-19/prevention & control/epidemiology/virology
*Mammals/physiology
Forests
SARS-CoV-2/isolation & purification
Human Activities

@article {pmid42380885,
year = {2026},
author = {Ryan, C and White, C and Lennon, O},
title = {The classification of walking and phases of gait using EEG: a scoping review.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-02076-6},
pmid = {42380885},
issn = {1743-0003},
abstract = {Brain-computer interfaces (BCIs) have the potential to optimise robotic-assisted gait rehabilitation by closing the loop between user and device. Developing such systems requires reliable methods for classifying walking and its component phases to enable real-time feedback. However, research into electroencephalography (EEG)-based gait classification remains limited, with no consensus on optimal methodological or classification approaches. This scoping review aimed to identify (i) gait states and phases classified to date, (ii) methodological processes employed in gait and gait phase classification and (iii) classification systems used. A pre-registered scoping review was conducted. SCOPUS, EMBASE, PubMed and Web of Science were searched. Studies investigating EEG-based gait or gait phase classification, with or without robotic assistance were included. From 15,915 unique studies, 62 were included. Ten studies classified gait phases, with most limited to two phases (n = 5). The majority involved treadmill walking (n = 8), with only two investigating overground walking. Only one study addressed gait phase classification (swing versus stance) during robotic-assisted gait training (RAGT), achieving a maximum accuracy of 83.06%. No studies investigated gait phase classification in a neurological population. Almost all gait phase studies used offline analysis (n = 9). One study performed online analysis, achieving an accuracy of 82.3% for swing versus stance classification. The remaining studies classified gait against other forms of movement. Sensorimotor area electrodes were most frequently used for classification and sensorimotor regions consistently exhibited gait-related discriminative EEG features. Collation of methodological processes identified little consensus within the field. Furthermore, classification accuracy was variable. This scoping review summarises EEG-based gait classification systems, highlighting the need for methodological consensus. Knowledge gaps include the classification of more than two gait phases during free overground walking and limited RAGT-based studies. Notably, gait phase classification research in neurological populations is entirely lacking. Addressing these gaps is critical to advance BCIs for gait rehabilitation.},
}

@article {pmid42381342,
year = {2026},
author = {Vale, L and Charrua, A and Martins-Silva, C and Albino-Teixeira, A and Cruz, F and Antunes-Lopes, T},
title = {Selected Patients with Benign Prostatic Enlargement and Detrusor Underactivity may Benefit from Prostate Surgery: An Exploratory Study.},
journal = {Urology research & practice},
volume = {52},
number = {},
pages = {1-5},
doi = {10.5152/tud.2026.26005},
pmid = {42381342},
issn = {2980-1478},
abstract = {OBJECTIVE: To evaluate non-invasive biomarkers of detrusor underactivity (DU) in men with benign prostatic enlargement (BPE) and their ability to predict clinical and urodynamic improvement following prostate surgery. Particular attention was given to urinary adenosine triphosphate (ATP) levels, bladder contractility index (BCI), and bladder voiding efficiency (BVE).

METHODS: This exploratory study included 24 men with BPE undergoing prostate surgery. Based on pre-operative pressure-flow studies (P/F studies), patients were divided into DU (BCI < 100) and non-DU (BCI ≥ 100) groups. Urinary ATP levels were compared among DU patients, non-DU patients, and age-matched healthy male volunteers. Clinical and urodynamic parameters, including BCI and BVE, were assessed before and 1 year after surgery.

RESULTS: Thirteen patients were classified as DU, and 11 as non-DU. Median urinary ATP levels did not differ between groups. One year after surgery, DU patients presented a significant improvement in International Prostate Symptom Score, quality of life, Qmax, and post-void residual volume. However, median BCI did not increase after surgery. Apart from BCI, all other parameters were similar between groups after surgery. In this cohort, all DU patients with a pre-operative BVE > 40% achieved surgical success, compared to <50% of those with BVE < 40%.

CONCLUSION: Urinary ATP does not appear to be a useful DU biomarker in men with BPE. Although prostate surgery did not significantly improve detrusor function 1 year after surgery, most DU patients experienced clinical improvement. Pre-operative BVE seems a simple non-invasive tool to identify DU patients more likely to benefit from surgery. Cite this article as: Vale L, Charrua A, Silva CM, Teixeira AA, Cruz F, Antunes-Lopes T. Selected patients with benign prostatic enlargement and detrusor underactivity may benefit from prostate surgery: an exploratory study. Urol Res Pract. 2026, 52, 0005, doi: 10.5152/tud.2026.26005.},
}

@article {pmid42381831,
year = {2026},
author = {Liu, J and Liu, L and Chen, H and Xuan, S and Leng, X},
title = {Advances in the Application of Brain-Computer Interface-Based Neurofeedback Training in the Rehabilitation of Patients with Major Depressive Disorder.},
journal = {Neuropsychiatric disease and treatment},
volume = {22},
number = {},
pages = {609788},
pmid = {42381831},
issn = {1176-6328},
abstract = {Major depressive disorder is a highly prevalent affective disorder worldwide, and existing pharmacological and psychological treatments continue to demonstrate notable limitations in terms of therapeutic stability, adverse effects, and relapse prevention. Brain-computer interface-based neurofeedback training (BCI‑NFT) guides patients to actively regulate abnormal neural functional states through real‑time feedback of brain activity signals, thereby providing a precise interventional pathway that acts directly at the level of neural function. This narrative review examines the theoretical foundations, neural mechanisms, and clinical application modalities of BCI‑NFT in depression rehabilitation, encompassing advances in both non‑invasive and invasive neurofeedback technologies, diverse combined intervention paradigms, and the application prospects of a concurrent intervention approach integrating wearable BCI technology with aerobic exercise at the interdisciplinary intersection of sports neuroscience and psychiatric rehabilitation medicine. Preliminary evidence suggests that BCI‑NFT may facilitate neural functional recovery in patients with major depressive disorder through three primary mechanisms: remodeling of emotion‑regulation‑related brain regions, correction of aberrant EEG activity patterns, and improvement of large‑scale brain network connectivity; however, given the sample sizes and methodological heterogeneity of existing studies, these conclusions still require further validation through large-scale randomized controlled trials. Nevertheless, the widespread clinical implementation of BCI‑NFT remains constrained by a lack of standardized training protocols, insufficient clarity regarding suitable patient populations, and a paucity of large‑sample clinical data. Future research should, within a precision psychiatry framework and through the integration of multimodal neuroimaging and large‑scale randomized controlled trials, further advance the standardized clinical translation of BCI‑NFT.},
}

@article {pmid42382696,
year = {2026},
author = {Feng, Y and Zhang, W and Chen, J and Wu, H and Wu, L and Qiu, Y and Deng, X and Zhu, C and Chen, Y and Zhao, Z and Wang, C and Zhang, X},
title = {Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.},
journal = {Exploration (Beijing, China)},
volume = {6},
number = {3},
pages = {20250452},
pmid = {42382696},
issn = {2766-2098},
abstract = {Brain-computer interface (BCI) technology has emerged as a crucial interdisciplinary advancement in the field of neuropsychiatric disease treatment. With the global rise in the prevalence of neurological and psychiatric disorders, which impose a substantial burden on society, BCI offers a novel approach. Since the discovery of bioelectric phenomena in the 19th century, various classification frameworks have been developed based on signal paradigms, invasiveness, and feedback mechanisms. BCI applications span multiple disease areas. In movement disorders, it aids in restoring motor function through prosthetic control, functional electrical stimulation, and brain stimulation-based therapies. For patients with communication barriers, it enables alternative communication methods and speech-related neural signal decoding. In psychiatric conditions, BCI shows growing potential in both diagnosis and treatment, particularly in conditions like autism and depression. Despite significant progress, BCI faces challenges. The long-term biocompatibility of electrodes and the resolution of neural signals remain to be improved. To address these limitations, research on new electrode materials, such as carbon nanomaterials and composites, is ongoing. Emerging BCI technologies, including endovascular BCI and optogenetics BCI, present new possibilities. The integration of multimodal technologies and artificial intelligence in BCI systems is expected to enhance performance and enable more personalized treatment. Overall, BCI technology holds great promise for improving the quality of life of patients with neuropsychiatric disorders and driving innovation in the medical and neuroscience fields.},
}

@article {pmid42370488,
year = {2026},
author = {Peng, J and Li, X and Li, W and Gao, C and Ma, C and Zou, Z and Yang, Y and Liu, B and Luo, Z and Wang, X},
title = {Injectable Antifouling Adhesive Hydrogel Enables Robust Neural Interfaces for Stable ECoG Recording.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e71397},
doi = {10.1002/adhm.71397},
pmid = {42370488},
issn = {2192-2659},
support = {32471387//National Natural Science Foundation of China/ ; },
abstract = {Micro-electrocorticography (micro-ECoG) provides high-spatiotemporal-resolution cortical sensing for brain-computer interfaces, but stable subdural recording remains difficult because dural opening disrupts barrier integrity, cortical micromotion weakens device-tissue coupling, and biofouling triggers foreign body responses (FBR) that isolate the array. Here, we present an injectable, in situ-forming multifunctional hydrogel designed to treat these failure modes as one integrated interface problem rather than three separate ones. The hydrogel combines dopamine-grafted sodium alginate (SA-DA) and branched poly(ethylene imine) (PEI) in a charge-balanced pseudozwitterionic macromolecular network that resists nonspecific protein adsorption while providing catechol-mediated wet adhesion. Through dual macromolecular crosslinking, the hydrogel undergoes rapid gelation under surgically compatible conditions, conformally seals dural defects, fills interfacial gaps, and provides wet tissue adhesion without relying on diffusible small-molecule monomers. When integrated with a 128-channel flexible micro-ECoG mesh array, this platform reduced glial activation and fibrotic encapsulation and preserved stable, high-fidelity cortical recordings over a 3-week early-chronic period. More broadly, this study establishes a design principle for sustained soft bioelectronics: long-term function can be improved by co-engineering barrier restoration, interfacial adhesion, and antifouling protection within a single interface material.},
}

@article {pmid42371930,
year = {2026},
author = {Lu, HY and Stealey, HM and Zhao, Y and Barnett, CR and Contreras-Hernandez, E and Santacruz, SR},
title = {Long-term neuron tracking reveals balance of stability and plasticity in functional properties.},
journal = {PloS one},
volume = {21},
number = {6},
pages = {e0321830},
pmid = {42371930},
issn = {1932-6203},
mesh = {Animals ; *Neurons/physiology ; *Neuronal Plasticity/physiology ; Action Potentials/physiology ; Microelectrodes ; },
abstract = {Neural stability is essential for executing learned motor behaviors while plasticity provides the flexibility needed to adapt to new tasks and environments. Although low-dimensional neural population dynamics exhibit long-term stability, the extent to which individual neurons retain their functional properties over time and balance the need for both stability and plasticity remains an open question. Tracking individual neurons across multiple recording sessions is crucial to addressing this question, yet conventional methods face challenges such as electrode drift, waveform variability, and large inter-electrode distances that limit the number of channels a neuron is observed on. Here, we introduce a waveform-based neuron tracking method optimized for standard microelectrode arrays, enabling the identification of the same neurons across sessions without relying on spatial overlap, a strategy commonly leveraged with high-density electrode arrays. We apply this method to assess the longitudinal stability of multiple neural properties, including firing rates, inter-spike intervals, tuning properties, and spike-field interactions. Our findings reveal that while spike waveform properties remain stable, certain functional properties such as ISI and tuning can exhibit gradual shifts, suggesting a balance between neural stability and plasticity. Understanding the persistence of individual neural signals provides insight into learning and adaptation while advancing the study of neural stability and plasticity over extended timescales. Beyond basic neuroscience, this framework has potential to enhance the long-term reliability of brain-machine interfaces and closed-loop deep brain stimulation systems that rely on chronic neural sensing.},
}

Animals
*Neurons/physiology
*Neuronal Plasticity/physiology
Action Potentials/physiology
Microelectrodes

@article {pmid42372761,
year = {2026},
author = {Redmond, P and Fleury, A and Ward, T},
title = {VEOS: Vision-based vertical electrooculography inference from monocular periocular video for ocular artefact suppression in EEG.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae83bb},
pmid = {42372761},
issn = {2057-1976},
abstract = {Ocular artefacts from blinks and eye movements remain a persistent obstacle to reliable electroencephalography (EEG), particularly when dedicated electrooculography (EOG) electrodes are unavailable or undesirable. We investigate whether monocular periocular video can provide a vertical EOG (VEOG)-like surrogate for ocular artefact suppression in EEG. Approach. We present VEOS, a video-to-VEOG inference pipeline based on monocular periocular tracking, canthus-defined geometric normalisation, engineered eyelid and iris features, and temporal modelling with a temporal convolutional network (TCN). The inferred VEOG is used as an auxiliary reference in blink-window lagged ridge-regression subtraction of ocular artefacts from EEG. Evaluation used leave-one-subject-out validation on two multimodal datasets: an in-house development dataset (VEOS-Dev; 5 participants with synchronised EEG, EOG and 120 Hz video) and the public Eye-BCI dataset (31 subjects, 63 sessions; high-speed video, EEG and ocular measurements). For centred-window models, a ±ℓ boundary margin prevented temporal leakage between training, validation and test data. Main results. On VEOS-Dev, VEOS achieves median Pearson correlation r = 0.81 to ground-truth VEOG and median blink-onset timing error of 18 ms. On Eye-BCI, the video-only model achieves median r = 0.74, outperforming an eye-tracker baseline. For blink-window cleaning, VEOS suppresses approximately 50% of blink peak-to-peak amplitude on frontal EEG channels, compared with 66% for true VEOG. Cleaning preserves task-relevant EEG structure, including motor imagery mu/beta rhythms, SSVEP spectral signal-to-noise ratio, and P300 morphology. At the participant level, VEOS yields a modest improvement in motor imagery, leaves SSVEP unchanged, and approaches true-VEOG cleaning for P300 spelling (90% vs 91%). Significance. These results show that camera-derived VEOG can act as a useful ocular reference for EEG artefact suppression without additional periocular electrodes. The validation was conducted on controlled recordings and should be interpreted as a best-case demonstration rather than a full validation in unconstrained wearable settings.},
}

@article {pmid42373633,
year = {2026},
author = {Wu, C and You, J and Sheng, T and Li, GF and Zhang, C and Liu, L and Xu, LZ and Xiong, W and Yang, F and Yang, W and Qiu, WB and Zheng, HR and Li, XY},
title = {TRPC4/TRPC5 are critical for neuronal modulation by transcranial focused ultrasound in retrosplenial cortex in male mice.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-74779-2},
pmid = {42373633},
issn = {2041-1723},
support = {82101250//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Transcranial focused ultrasound (tFUS) enables non-invasive neuromodulation, yet its underlying molecular mechanisms remain largely elusive. Here, we show that transient receptor potential canonical 4 (TRPC4) and transient receptor potential canonical 5 (TRPC5) channels are critical mediators of tFUS-induced neuronal modulation in the mouse brain. Applying tFUS to the retrosplenial cortex (RSC) in male mice desensitizes mechanical and thermal sensitivity while robustly elicits early growth response 1 (Egr1) expression. Inhibiting these tFUS-induced Egr1 ensembles blocks the somatic sensory effects. Transcriptomic analysis identifies Trpc4 enrichment in tFUS-activated Egr1-positive cells. Both pharmacological inhibition and genetic knockdown of TRPC4 abolish tFUS-mediated sensory modulation. Targeted knockdown further demonstrates that the highly homologous TRPC5 plays a comparable role. In situ proximity ligation assay, co-immunoprecipitation, and live-cell calcium imaging confirm that TRPC4 and TRPC5 form a protein complex in the RSC that facilitates the tFUS response. These findings establish TRPC4/TRPC5 as essential molecular components for tFUS neuromodulation.},
}

@article {pmid42363813,
year = {2026},
author = {Chen, C and Xia, L and Zhuang, J and Qian, Q and Ji, H and Li, J},
title = {Cross-Subject Event-Related Potential Classification via Multi-View Based Contrastive Learning.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {21580014261462127},
doi = {10.1177/21580014261462127},
pmid = {42363813},
issn = {2158-0022},
abstract = {BACKGROUND: Event-related potentials (ERPs) provide implicit feedback and error-correction signals that are valuable for brain-computer interfaces (BCIs). However, models trained on source-domain subject data are vulnerable to inter-subject variability and acquisition noise, which substantially degrades generalization to unseen subjects.

OBJECTIVE: We propose a multi-view contrastive learning domain generalization (MVCLDG) method to improve cross-subject generalization in ERP recognition by jointly exploiting discriminative feature extraction and domain-invariant representation learning.

METHODS: MVCLDG employs a multi-view feature-extraction module that fuses raw electroencephalography with phase information derived from the Hilbert transform via multi-scale inception blocks, thereby capturing both amplitude and phase features. The model then applies domain-alignment and contrastive-learning constraints to reduce distributional discrepancy across domains, compact within-class representations, and enlarge between-class separability. The approach was evaluated on a public Error-Related Negativity (ERN) dataset and a self-collected semantic-syntactic violation dataset; performance was assessed in cross-subject settings, and ablation and visualization analyses were conducted to probe the contributions of components and neurophysiological interpretability.

RESULTS: MVCLDG outperformed baseline and representative domain generalization methods in cross-subject ERP recognition without requiring additional target-domain adaptation. Ablation experiments confirmed the effectiveness of each component. Eigen-Class Activation Maps visualizations indicate consistency between the model-attended electrodes and known neurophysiological scalp patterns, supporting both the model's generalization mechanism and its biological interpretability.

CONCLUSIONS: MVCLDG offers an effective strategy for integrating phase-aware multi-view feature mining with contrastive domain generalization, yielding improved and interpretable cross-subject ERP recognition. The method advances the feasibility of ERP-based closed-loop BCIs that generalize across users.},
}

@article {pmid42364823,
year = {2026},
author = {Zhu, Y and Li, M and Wang, Z and Gu, X},
title = {Motor neurons: From developmental biology and plasticity to injury classification, regenerative mechanisms, and therapeutic strategies.},
journal = {Neurobiology of disease},
volume = {},
number = {},
pages = {107511},
doi = {10.1016/j.nbd.2026.107511},
pmid = {42364823},
issn = {1095-953X},
abstract = {Within the central nervous system (CNS), motor neurons constitute the principal and highly specialized functional units responsible for the precise regulation of somatic motor activity. Their developmental processes and plasticity mechanisms directly underpin the establishment and maintenance of neural circuits. This review offers a focused overview of the developmental processes and functional characteristics of motor neurons, while clarifying the definition of motor neuron plasticity. It further elucidates the intricate interplay between plastic alterations and the onset of injury, whereby aberrant plasticity acts as both a critical determinant in motor neuron injury and an accelerator of motor function deterioration. Building on these insights, the review constructs a multidimensional classification framework of motor neuron injury and further elaborates on the core molecular mechanisms of motor neuron regeneration, including signaling pathway regulation, epigenetic modifications, and maintenance of microenvironmental homeostasis. Conclusively, it summarizes the existing therapeutic strategies for motor neuron injury-related disorders, such as targeted gene therapy, cell replacement therapy, and neuromodulation technology, while dissecting the intervention mechanisms and limitations of each strategy. Although extensive studies have separately investigated various aspects of motor neurons, this review establishes a comprehensive framework that integrates findings from developmental mechanisms to therapeutic strategies and provides a comprehensive theoretical reference for future research on precision therapeutics, and facilitates bridging preclinical research and clinical translation.},
}

@article {pmid42366446,
year = {2026},
author = {Yang, X and Wang, L and Yang, J and Zheng, C and Ming, D},
title = {[Research progress on the neuromodulation targets in stroke rehabilitation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {3},
pages = {615-624},
pmid = {42366446},
issn = {1001-5515},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Transcranial Magnetic Stimulation/methods ; *Transcranial Direct Current Stimulation ; Stroke/physiopathology/therapy ; Sensorimotor Cortex/physiopathology ; Vagus Nerve ; Cerebellum/physiopathology ; },
abstract = {Recent years have witnessed significant advances in neuromodulation techniques for stroke rehabilitation, especially in ameliorating motor deficits, positioning them as a key focus in both research and clinical practice. The selection of stimulation targets is crucial, as different sites engage distinct neural mechanisms and yield varied therapeutic outcomes. This review systematically synthesizes evidence from neuromodulation studies that target key regions, including the cerebral hemispheres, sensorimotor cortex, cerebellum, and vagus nerve. By analyzing the stimulation protocols, therapeutic effects, and optimal parameters associated with each target, we aim to provide a theoretical foundation and practical guidance for refining neuromodulation strategies in stroke rehabilitation.},
}

Humans
*Stroke Rehabilitation/methods
*Transcranial Magnetic Stimulation/methods
*Transcranial Direct Current Stimulation
Stroke/physiopathology/therapy
Sensorimotor Cortex/physiopathology
Vagus Nerve
Cerebellum/physiopathology

@article {pmid42366447,
year = {2026},
author = {Li, X and Yuan, Y and Chen, P and Liu, Z and Li, K and Teng, H},
title = {[Application and perspective of novel auditory intervention paradigms based on verbal and nonverbal stimuli for severe traumatic brain injury].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {3},
pages = {625-632},
pmid = {42366447},
issn = {1001-5515},
mesh = {Humans ; *Brain Injuries, Traumatic/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; *Acoustic Stimulation ; Consciousness ; },
abstract = {Patients with severe traumatic brain injury (TBI) often experience disorders of consciousness (DoC) and motor impairments, which pose challenges to the implementation of conventional intervention approaches. As one of the relatively preserved sensory pathways in these patients, the auditory system holds considerable potential for assessing consciousness and facilitating its recovery. Therefore, this review systematically examines the mechanisms of verbal and nonverbal stimuli in consciousness recovery, focusing on analyzing the synergistic effects and limitations of language stimuli in emotional arousal and semantic integration. Based on this, a complementary mechanism leveraging the respective strengths of both approaches has been explored to establish a new intervention paradigm. Furthermore, integrating advancements in brain-computer interface (BCI) technology, it proposes a closed-loop intervention approach that combines multi-type auditory stimuli with real-time neural feedback. This framework emphasizes individual differences and dynamic regulation, providing theoretical foundations and developmental directions for constructing precision-oriented, mechanism-driven auditory intervention paradigms for severe TBI.},
}

Humans
*Brain Injuries, Traumatic/rehabilitation/physiopathology
*Brain-Computer Interfaces
*Acoustic Stimulation
Consciousness

@article {pmid42366448,
year = {2026},
author = {Hu, X and Gong, A and Shi, X and Gong, B and Li, S and Shi, T and Fu, Y},
title = {[Research on auditory neurofeedback technology and its multi-disciplinary applications].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {3},
pages = {633-643},
pmid = {42366448},
issn = {1001-5515},
mesh = {Humans ; *Neurofeedback/methods ; Electroencephalography ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; *Brain/physiology ; Acoustic Stimulation ; },
abstract = {Auditory neurofeedback (ANF) utilizes sound to map brain activity in real-time, guiding individuals to self-regulate neural states via operant conditioning. Compared to visual neurofeedback, ANF offers distinct advantages in millisecond-level response speed and non-visual dependency, significantly enhancing ecological validity and reducing visual fatigue. This paper reviews the physiological basis, signal processing workflows, and three typical experimental paradigms of ANF: threshold regulation, parameter modulation, and state-dependent triggering. Furthermore, it discusses current applications in neurorehabilitation, psychiatric treatment, sports medicine, and auditory cognitive screening. Finally, the paper analyzes challenges regarding mechanistic evidence and parameter standardization, and prospects future trends such as ear electroencephalography-based portable design and multimodal fusion, providing theoretical insights for clinical translation.},
}

Humans
*Neurofeedback/methods
Electroencephalography
Brain-Computer Interfaces
Signal Processing, Computer-Assisted
*Brain/physiology
Acoustic Stimulation

@article {pmid42366792,
year = {2026},
author = {Ding, H and Zou, L and Ma, L and Wen, Z and Hu, Y and Zhong, L and Sun, R and Zhou, B and Zhong, D and Wu, H and Lv, X and Liu, Q and Jiang, B and Wang, H and Cao, J and Yu, L and Qiu, S and Yan, M},
title = {A descending posterior insular pathway drives sensory hypersensitivity in neuropathic pain.},
journal = {Brain : a journal of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1093/brain/awag221},
pmid = {42366792},
issn = {1460-2156},
abstract = {Descending cortical control plays a critical role in shaping neuropathic pain. However, how specific circuits within the insular cortex (IC), a critical cortical hub that integrates the sensory and affective dimensions of pain, contribute to this process remains poorly understood. Here, we show that posterior IC glutamatergic (pICGlu) neurons are selectively and contralaterally activated in a chronic constriction injury (CCI) model and bidirectionally regulate pain sensitivity, whereas anterior IC glutamatergic (aICGlu) neurons preferentially mediate CCI-induced anxiety-like behaviors. Circuit mapping revealed that pICGlu neurons project to rostral ventromedial medulla GABAergic (RVMGABA) neurons, particularly a Slc32a1+/Penk- subpopulation, which in turn innervate spinal dorsal horn GABAergic (DHGABA) interneurons. Consistent with this organization, pathway-specific manipulations demonstrated that activation of the pIC-RVM-DH circuit enhances nociceptive hypersensitivity, whereas its inhibition alleviates CCI-induced allodynia. Together, these findings identify a previously uncharacterized pICGlu-RVMGABA-DHGABA circuit that mediates the sensory dimension of neuropathic pain and provides a circuit-level framework for descending cortical control of pain hypersensitivity.},
}

@article {pmid42367738,
year = {2026},
author = {Xie, Y and Han, B and Tao, X and Song, F and Zhao, C and Delpire, E and Li, J and Wu, S and Guo, J},
title = {Structural basis for the ion selectivity of potassium-chloride cotransporter KCC4 revealed by cryo-EM titration.},
journal = {Biophysics reports},
volume = {12},
number = {3},
pages = {193-206},
pmid = {42367738},
issn = {2364-3420},
abstract = {Potassium-chloride cotransporters KCCs mediate the coupled, electroneutral cotransport of K[+] and Cl[-] across the membrane and are involved in important physiological processes such as cell volume regulation and γ-aminobutyric acid (GABA) and glycine-mediated inhibitory neurotransmission. Although structures of KCCs have been reported, the identification of ions bound in KCCs awaits experimental studies. Here using the cryo-electron microscopy (cryo-EM) titration methods, we present six structures of human KCC4 in different ion conditions at 2.38-2.58 Å resolutions. These structures, along with molecular dynamic simulations, allow us to assign one K[+] and two Cl[-] ions in the substrate-binding pocket. The K[+] at S1 and Cl[-] at the S2 site are tightly coupled in the binding and dissociation, suggesting that the Cl[-] at S2 but not at S3 is the cotransported one. The S1 site provides coordination that largely matches the K[+] dehydration radius and therefore displays higher selectivity to K[+] over Na[+]. This study establishes the structural basis for the K[+] selectivity of KCCs by the cryo-EM titration.},
}

@article {pmid42367869,
year = {2026},
author = {Ding, Y and Karrenbach, M and Johnson, Z and Wang, H and Zhang, J and Wittenberg, GF and He, B},
title = {Adaptive Neural Reorganization Enables Real-Time Finger-Level Robotic Control in BCI-Naïve Stroke Survivors.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.06.15.732267},
pmid = {42367869},
issn = {2692-8205},
abstract = {Restoring hand function remains a major challenge for individuals with motor impairments following stroke. Noninvasive brain-computer interfaces (BCIs) aim to address this problem by translating neural signals into robotic assistance; however, control of individual fingers has not been demonstrated in BCI-naïve populations. In this study, we investigated whether individuals with stroke and no prior BCI experience could achieve finger-level robotic control using motor imagery. Nine stroke-affected participants performed real-time BCI tasks to control a robotic hand through imagined finger movements decoded from electroencephalography. On average, participants achieved decoding accuracies of 84% for two-finger tasks and 61% for three-finger tasks, demonstrating reliable control at the level of individual fingers. These results indicate that discriminable neural signals for fine motor control persist after stroke and can be leveraged using data-driven deep learning decoders. Sensor-level and source-level electrophysiological analyses further reveal patterns of stroke-related neural reorganization. Overall, these findings support the potential of noninvasive, finger-level BCIs for post-stroke robotic assistance.},
}

@article {pmid42368020,
year = {2026},
author = {Canario, E and Shearer, C and Akcakaya, M and Weber, D and Chase, S and Collinger, JL},
title = {Intracortical BCI Performance is Robust to Changes in Attentional Load During Dual-Tasking.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.06.16.732398},
pmid = {42368020},
issn = {2692-8205},
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. 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 (NCT1894802). Common neural correlates of attention (theta and alpha band power) were measured with simultaneously recorded scalp electroencephalography (EEG). While the EEG recordings 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. 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. 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 {pmid42368409,
year = {2026},
author = {Ahmed, SG and Ambali Parambil, MM and Damseh, R and Bouktif, S and Alnajjar, F and Belkacem, AN},
title = {Transfer learning for EEG-based BCIs: a comparative evaluation and optimization of data alignment methods.},
journal = {Frontiers in systems neuroscience},
volume = {20},
number = {},
pages = {1840121},
pmid = {42368409},
issn = {1662-5137},
abstract = {BACKGROUND: This paper addresses a critical challenge in developing practical EEG-based brain-computer interfaces (BCIs): enhancing cross-subject generalization by mitigating individual differences in brain signals. How can we effectively leverage data from existing subjects to improve performance for a new user with minimal subject-specific calibration?

METHODS: We systematically compare and optimize three prominent data alignment techniques, Riemannian Procrustes Analysis (RPA), Euclidean Alignment (EA), and Correlation Alignment (CORAL), designed to transform EEG data from multiple source subjects and a target subject into a common representation space, mitigating variability.

EVALUATION: We employed leave-one-subject-out cross-validation (LOSO-CV) framework on EEG-based attention decoding data to empirically evaluate the effectiveness of each alignment method compared to a baseline condition with no alignment. Key parameters, specifically the regularization parameter α for EA, were optimized to maximize cross-subject transfer performance.

RESULTS: The study demonstrates that alignment methods improve classification accuracy compared to the baseline. Notably, EA evaluated at α = 100 the scaling value at which the largest fraction of subjects attained their best accuracy in our parameter sweep yielded the largest mean improvement, increasing classification accuracy by 3.44% over the no alignment baseline (paired t(17)≈2.48, p≈0.024; Cohen's d z ≈0.59; 95% confidence interval for the mean improvement [0.52%, 6.36%]). Because this α value was identified from the same sweep that produced the per-subject accuracies, this estimate together with the per-subject "best-parameter" results should be interpreted as an oracle sensitivity-analysis upper bound on subject-specific tuning rather than as a leakage-free LOSO estimate. While optimized EA showed the best mean performance, the analysis also demonstrated subject-specific differences in the most ideal alignment strategy.

CONCLUSION: This comparison framework quantifies the benefits of different alignment approaches and highlights the valuable contribution of parameter optimization, particularly for EA.

SIGNIFICANCE: These results indicate the potential of optimized alignment techniques, EA in particular, to significantly enhance cross-subject transfer learning in EEG-based BCIs. This has practical ramifications for methodology selection and tuning, and maps a path toward more robust and generalizable BCI systems requiring less subject-specific calibration for real-world applications.},
}

@article {pmid42368477,
year = {2026},
author = {Chen, Z and Zhang, L and Chen, G and Liu, H and Wang, Z and Zhao, X and Guo, S and Zhao, T and Sun, M and Liang, W and Qin, L and Zhang, M and Liu, L and Wang, W},
title = {Cyborg-swarm cooperation and game via affective-based brain-machine interface.},
journal = {National science review},
volume = {13},
number = {13},
pages = {nwag313},
pmid = {42368477},
issn = {2053-714X},
abstract = {The integration of biological organisms with robotic systems has enabled hybrid cyborg platforms that combine biological sensory agility with electromechanical precision. However, existing cyborg systems predominantly rely on unidirectional stimulus-driven control, treating animals as bio-actuators while neglecting their intrinsic cognitive states. To bridge this gap, we present a closed-loop cyborg-swarm architecture that utilizes the animal's internal affective state (fear) as a high-level trigger to modulate robotic swarm strategies. Specifically, we developed a lightweight, real-time wireless brain-machine interface (BMI) to record local field potentials from the mouse basolateral amygdala. To ensure robust decoding in freely moving subjects, we implemented a dual-threshold detection algorithm that identifies fear states based on elevated [Formula: see text]-band power (15-30 Hz) and suppressed high-frequency noise, effectively rejecting motion artifacts. This decoded intent drives a dual-mode control framework: under baseline conditions, the system operates in a proportional-integral-derivative (PID)-based Exploration Mode; upon detection of fear, it autonomously switches to an Interaction Mode governed by Multi-Agent Deep Deterministic Policy Gradient. In this mode, a heterogeneous robotic swarm (comprising a MouseBot and an ally micro aerial vehicle (MAV)) executes coordinated adversarial defense strategies against an enemy MAV. Experimental results in a search-interference game demonstrate that biological affective signals can successfully trigger millisecond-level control authority switching, enabling the emergence of complex bio-machine cooperative behaviors. This work marks a paradigm shift from physical-level interaction to cognitive-level bio-hybrid cooperation, validating a scalable framework for emotion-modulated cyborg swarms.},
}

@article {pmid42368832,
year = {2026},
author = {Qin, G and Huang, J and Mi, P and Liu, Z and Yang, Y},
title = {MSCANet: a cross-attention-based multi-scale convolutional fusion neural network for EEG motor imagery classification.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {118},
pmid = {42368832},
issn = {1871-4080},
abstract = {UNLABELLED: Brain-computer interfaces (BCIs) utilize physiological neural signals from the brain to control external devices, offering significant potential to restore normal life in patients with brain injuries or motor impairments. However, electroencephalogram (EEG) signals are inherently non-stationary, possess low signal-to-noise ratios, and exhibit inter-subject variability, posing substantial decoding challenges. To effectively integrate multi-scale spatiotemporal features, this study proposes a cross-attention-based multi-scale convolutional fusion neural network (MSCANet) that integrates local and global features while capturing temporal dependencies across multiple scales. Specifically, MSCANet first employs a multi-scale spatio-temporal convolutional module to extract localized spatio-temporal information from variable-sized windows within individual frequency bands. Subsequently, channel and spatial attention mechanisms are incorporated to enhance discriminative feature representation by prioritizing salient information. A temporal convolution module with multi-level residual connections then preliminarily captures both short- and long-term dependencies. Finally, cross-attention mechanisms further capture temporal correlations and fuse features across frequency bands before classification via fully connected layers. In subject-dependent experiments, MSCANet achieved classification accuracies of 82.06% and 87.45%, with kappa values of 0.76 and 0.76 on the BCI IV-2a and BCI IV-2b Datasets, respectively. The proposed method outperforms several comparative models and demonstrates promising potential for BCI applications.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10485-5.},
}

@article {pmid42368901,
year = {2026},
author = {Pwint, MY and Shi, D and Cui, XT},
title = {Buckling-Resistant and Trace-Stacked (BRATS) Design Enables Aid-Free Implantation of Flexible Multielectrode Array with Minimized Inflammatory Tissue Response.},
journal = {Advanced functional materials},
volume = {36},
number = {4},
pages = {},
pmid = {42368901},
issn = {1616-301X},
abstract = {Intracortical microelectrode arrays (MEAs) are vital tools for brain-machine interface applications and basic neuroscience research, with the potential to advance treatments for neurological disorders and enhance the understanding of the nervous system. However, implanting intracortical electrodes can damage native tissue along the insertion path and trigger inflammatory responses characterized by neuronal loss and glial activation. While flexible electrodes reduce some of these adverse effects compared to rigid counterparts, their mechanical compliance often leads to buckling during insertion, necessitating the use of insertion aids such as stiff shuttles or dissolvable coatings. These aids, however, introduce additional complexity and can cause further tissue damage. In this work, microfabricated polyimide MEAs featuring a buckling-resistant, trace-stacked (BRATS) design are presented. It is demonstrated that BRATS MEAs can penetrate agarose brain phantoms and the rat cerebral cortex without the need for insertion aids. Once implanted, BRATS MEAs established a stable and functional electrical interface with the brain, enabling high-fidelity, single-unit electrophysiological recordings. Compared to conventional flexible MEAs inserted with a stiff shuttle, BRATS MEAs elicited significantly lower inflammatory responses and preserved a higher density of neurons near the implant site one week post-implantation.},
}

@article {pmid42370056,
year = {2026},
author = {Brown, AI and Goering, S and Klein, E},
title = {Research participants as 'pioneers'? Exploring how neurotechnology research is adapting the rhetoric of scientific risk-taking, exploration and trailblazing.},
journal = {Science as culture},
volume = {},
number = {},
pages = {},
pmid = {42370056},
issn = {0950-5431},
abstract = {The theme of pioneers charting new frontiers has long been a staple of scientific and technological discourse in the United States. Scientists, engineers and entrepreneurs perceived as doing groundbreaking work are lauded as pioneers in their fields. In recent years, the application of the pioneer label has expanded to include research participants in clinical trials for neurotechnologies such as Neuralink or other brain-computer interface (BCI) devices: 'BCI pioneers.' What might explain this new popular usage of the pioneer label? American science policy in the mid twentieth century drew on the mythos of the American frontier as a way of mobilizing public interest in scientific and technological innovation on a mass scale. One such way of mobilizing interest was to use frontier rhetoric to attribute novelty to scientific undertakings and technological developments: dubbed the new frontiers. A less explored aspect of this history is the application of the pioneer label to patients and research participants in scientific, mostly biomedical, studies. Through the use of frontier rhetoric, articulations of novelty can be attributed to patients and participants, and their participatory practices, including being the first to undergo a new medical procedure, as well as being explorers and trailblazers in their own right. Such rhetoric may be understood as mobilizing support for research participants by acknowledging their bravery, altruism and contributions to science, with implications for participatory science and scientific and technological innovation.},
}

@article {pmid42362449,
year = {2026},
author = {Pan, Y and Cheng, X and Dumas, G and Dikker, S},
title = {Multi-brain neurofeedback: what are we training for?.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tics.2026.05.007},
pmid = {42362449},
issn = {1879-307X},
abstract = {Multi-brain neurofeedback offers new possibilities for guiding social interaction by capturing and modulating interpersonal neural dynamics in real time. We propose a hierarchical framework where neurofeedback targets shared sensory dynamics (signal), socio-cognitive processes (functional), or social outcomes (system). We highlight key methodological challenges and potential real-world therapeutic and pedagogical applications.},
}

@article {pmid42362714,
year = {2026},
author = {Yin, F and Guan, C and Aflalo, T and Gamez, J and Pejsa, K and Rosario, E and Liu, C and Bari, A and Andersen, RA},
title = {Duration-modulated neural population dynamics in humans during BMI controls.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-10369-8},
pmid = {42362714},
issn = {2399-3642},
abstract = {The motor cortex (MC) is often described as an autonomous dynamical system during movement execution. In an autonomous dynamical system, flexible movement generation depends on reconfiguring the initial conditions, which then unwind along known dynamics. An open question is whether these dynamics govern MC activity during brain-machine interface (BMI) control. We investigate MC activity during BMI cursor movements of multiple durations, ranging from hundreds of milliseconds to sustained over seconds. These durations are chosen to cover the range of movement durations necessary to control modern BMIs under varying precision levels. Movements share their MC initial condition with movements of different durations in the same direction. Long-duration movements sustain MC activity in a low-velocity steady state until each movement goal is reached. The difference across durations in MC population dynamics may be attributed to external inputs. Our results highlight the role of sustained inputs to MC during movement.},
}

@article {pmid42362919,
year = {2026},
author = {Lu, R and Luo, J and Zhong, SH and Gao, T},
title = {An Upper-Limb Motor Imagery EEG Dataset of Chronic Stroke Patients.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07742-x},
pmid = {42362919},
issn = {2052-4463},
support = {2025ZD0218900//Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 2025A1515012154//Guangdong Basic and Applied Basic Research Foundation/ ; JCYJ20250604181605008//The Science and Technology Innovation Commission of Shenzhen/ ; 62472291//The National Natural Science Foundation of China/ ; },
abstract = {Motor imagery (MI)-based brain-computer interface (BCI) systems offer a promising approach for post-stroke motor rehabilitation. However, their clinical translation is limited by the scarcity of large, clinically relevant electroencephalography (EEG) datasets. This study presents the HS Stroke dataset, consisting of 57,902 left- and right-hand MI EEG trials collected across 278 sessions from 14 chronic stroke patients, along with comprehensive clinical assessments. Under the cross-trial evaluation setting, validation results show that state-of-the-art deep learning models achieve up to 82.65% accuracy in MI classification, confirming the quality and discriminability of the collected EEG signals. Regression analyses further suggest that MI-related EEG features are predictive of individual Fugl-Meyer Assessment scores, highlighting their potential as non-invasive markers of motor recovery. The HS Stroke dataset is expected to support the development of MI-BCI decoding methods and facilitate research in post-stroke rehabilitation.},
}

@article {pmid42361868,
year = {2026},
author = {Ocoko, MYM and Kasthuri, N and Lugo, I and Hanzlicek, B and Wang, J and Duncan, J and Wang, H and Capadona, JR and Hamedani, HA and Hess-Dunning, A},
title = {Nanostructured Coatings on Soft-Polymer Based Neural Probes for Addressing Neuroinflammation.},
journal = {Acta biomaterialia},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.actbio.2026.06.052},
pmid = {42361868},
issn = {1878-7568},
abstract = {Intracortical microelectrodes (IMEs) record neural activity with single- and multi-unit resolution and interface with brain-machine interfaces (BMIs) to control assistive devices. Long-term performance, however, remains limited by neuroinflammatory responses that progressively degrade signal quality. Multiple strategies have been explored to improve tissue-device integration, including mechanically compliant substrates to reduce strain, surface coatings to modulate cell-material interactions, and local delivery of anti-inflammatory therapeutics. Here, we combined approaches by transferring dexamethasone-loaded titania nanotube arrays (TNAs) onto a mechanically adaptive polymer nanocomposite (NC) substrate. To isolate the effects of mechanical compliance, nanostructured surfaces, and sustained local dexamethasone delivery, four implant types-silicon, NC, TNA-NC Empty, and TNA-NC DEX-were evaluated in a mouse model (n = 10 per group) at 2- and 4-week time points using a targeted 154-gene neuroinflammatory panel. At 2 weeks post-implantation, gene expression profiles were broadly similar across all implant types, reflecting a conserved acute injury response at early time points. By 4 weeks, expression patterns diverged, indicating a material-dependent tissue response over time. At this later time point, NC-based implants (NC, TNA-NC Empty, and TNA-NC DEX) exhibited fewer differentially expressed neuroinflammatory genes relative to rigid silicon implants. Notably, TNA-NC Empty implants demonstrated further improvements compared to both NC and TNA-NC DEX groups. The lack of improvement in the DEX group suggests that TNA-mediated dexamethasone delivery requires optimization. Nevertheless, the findings indicate the addition of the TNA layer to a soft, compliant material produces synergistic effects that promote the resolution of the neuroinflammatory response at 4 weeks. STATEMENT OF SIGNIFICANCE: Intracortical neural interfaces fail over time due to persistent neuroinflammation driven by mechanical mismatch, cell-material interactions, and immune activation. Prior studies have shown that mechanically compliant materials reduce strain at the tissue-device interface but do not fully resolve inflammation. We addressed this limitation by integrating anti-inflammatory and antimicrobial titania nanotube arrays (TNAs) onto a mechanically adaptive polymer nanocomposite substrate and evaluating the biological response using transcriptomic analysis. Comparisons across stiff silicon, a compliant control, and unloaded and drug-loaded TNA coatings demonstrate that combining mechanical compliance with nanoscale surface bioactivity reduces inflammation-related gene expression while increasing enrichment of tissue repair and developmental pathways. Our findings reveal the synergistic effects of materials and support the development of multi-material implant architectures.},
}

@article {pmid42362313,
year = {2026},
author = {Wang, MJ and Kung, TA and Cederna, PS and Kemp, SWP},
title = {Regenerative Peripheral Nerve Interface and the Future of Intuitive Control.},
journal = {Hand clinics},
volume = {42},
number = {3},
pages = {237-248},
doi = {10.1016/j.hcl.2026.03.009},
pmid = {42362313},
issn = {1558-1969},
mesh = {Humans ; *Nerve Regeneration/physiology ; *Peripheral Nerves/physiology/surgery ; Animals ; *Artificial Limbs ; Peripheral Nerve Injuries ; },
abstract = {This article explores the regenerative peripheral nerve interface (RPNI) as an innovative solution for intuitive prosthetic control. Traditional control methods (myoelectric, brain-computer interfaces, and peripheral nerve interfaces) each have their own unique limitations. RPNI surgery involves implanting severed peripheral nerves into free skeletal muscle grafts to amplify action potentials and provide long-term signal stability. Preclinical studies in rats and nonhuman primates demonstrated the effectiveness of the RPNI in neural signal transduction, neuroma prevention, and long-term stability. Early human trials confirm its viability for volitional prosthetic control.},
}

Humans
*Nerve Regeneration/physiology
*Peripheral Nerves/physiology/surgery
Animals
*Artificial Limbs
Peripheral Nerve Injuries

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

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

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