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

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

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

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

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

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

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

@article {pmid42580336,
year = {2026},
author = {Bougou, V and Gamez, J and Rosario, ER and Liu, C and Pejsa, K and Bari, A and Andersen, RA},
title = {Hierarchical and context-dependent encoding of actions in human posterior parietal and motor cortex.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2026.07.032},
pmid = {42580336},
issn = {1097-4172},
abstract = {Action understanding requires internal models that link visual input to motor goals. In monkeys, mirror neurons have been proposed to support this process through motor resonance during observation, yet single-unit evidence in humans remains scarce. We recorded neural activity from the motor cortex (MC) and the superior parietal lobule (SPL) in two tetraplegic participants implanted with Utah arrays while they intended or observed hand actions. MC strongly encoded intention but showed weak responses during observation, evident primarily at the population level. In contrast, SPL supported shared representations across intended and observed actions at both the single-unit and population levels. When incongruent instructed and observed actions occurred simultaneously, SPL encoded the observed actions only when they were behaviorally relevant, whereas MC remained intention dominant. Our results identify a context-dependent gating mechanism in SPL and suggest a hierarchical organization in which MC maintains intention-specific codes while SPL flexibly links observed actions with internal goals.},
}

@article {pmid42580347,
year = {2026},
author = {Rigotti-Thompson, M and Nason-Tomaszewski, SR and Bechefsky, PH and Acosta, A and Hahn, NV and Avansino, DT and Richards, BA and Nicolas, C and Ali, YH and Henderson, JM and Hochberg, LR and AuYong, N and Pandarinath, C},
title = {Preparatory encoding of intended movement in the human motor cortex and implications for brain-computer interfaces.},
journal = {Current biology : CB},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cub.2026.07.040},
pmid = {42580347},
issn = {1879-0445},
abstract = {Over the course of a voluntary movement, motor cortical activity exhibits a transition from preparation to execution, with markedly different activity across these phases. Preparatory activity in particular might be used to improve brain-computer interfaces (BCIs) that harness brain activity to control external assistive devices, for example by anticipating a user's intended movement trajectory for quick and fluid performance. However, to leverage preparatory activity for clinical BCIs, we must first understand which features of upcoming movements are encoded by preparatory activity in humans. In this work, we collected intracortical recordings from 3 research participants in the BrainGate2 clinical trial to investigate whether diverse features of movement, such as direction, curvature, and distance, are encoded by preparatory activity in the human motor cortex. We first show that preparatory activity is tuned to the direction of upcoming movements, and this tuning is largely preserved across movements with different effectors. Further investigation demonstrated that this preparatory activity is also informative of initial and target directions of curved movement trajectories and encodes, to a weaker extent, either movement distance or speed. Finally, we present an online control paradigm that leverages preparatory activity to predict movements toward intended directions in advance, yielding rapid, user-initiated control of a computer cursor by human participants. Altogether, these results demonstrate that preparatory activity in the human motor cortex encodes rich features of upcoming movement, highlighting its potential use for high-performance BCI applications.},
}

@article {pmid42581396,
year = {2026},
author = {Li, M and Wu, Z and Lu, X and Zalesky, A and Cheng, X and Zhang, Y and Cash, RFH and Jin, F and Jia, S and Xiao, Y and Zhong, G and Zhang, X and Yue, W and Yang, Z and Jiang, T and Yan, H},
title = {Individualized Connectivity-Guided Versus Conventional Targeting of Accelerated Theta-Burst Stimulation in Depression: A Randomized, Double-Blind, Parallel-Design Trial.},
journal = {The American journal of psychiatry},
volume = {},
number = {},
pages = {appiajp20251084},
doi = {10.1176/appi.ajp.20251084},
pmid = {42581396},
issn = {1535-7228},
abstract = {OBJECTIVE: Scalp-based repetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex (DLPFC) does not account for interindividual variability. This trial was designed to determine whether individualized connectivity-guided intermittent theta-burst stimulation (iTBS) improves antidepressant outcomes compared with 5-cm targeting.

METHODS: In this single-center, randomized, double-blind, three-arm trial (February 2023-March 2025), adults ages 18-65 years with major depressive disorder or bipolar II depression (≥1 antidepressant failure) were randomly assigned to receive 20 sessions of iTBS over 2 weeks using robotic neuronavigation with one of three targeting strategies: 5-cm rule, functional connectivity (FC)-guided targeting showing negative resting-state connectivity with the subgenual anterior cingulate cortex (sgACC), or structural connectivity (SC)-guided sites defined by probabilistic tractography to the sgACC. The primary outcome was percentage reduction in 17-item Hamilton Depression Rating Scale (HAM-D) score at week 2. Secondary outcomes included HAM-D score reduction at weeks 6 and 12, response or remission, self-reported symptoms, performance on a cognitive battery, and adverse events.

RESULTS: Of 123 randomized participants, 119 were included in the modified intention-to-treat analyses (5-cm, N=40; SC-guided, N=39; FC-guided, N=40). SC-guided iTBS produced significantly greater HAM-D score reduction than the 5-cm rule at week 2 (least squares mean difference [LSMD]=8.79 percentage points, 95% CI=2.19, 15.40; Cohen's d=0.70). At week 6, both SC-guided and FC-guided groups showed significantly greater improvement than the 5-cm group (SC-guided LSMD=12.87 percentage points, 95% CI=6.21, 19.54; Cohen's d=1.03; and FC-guided LSMD=9.41 percentage points, 95% CI=2.84, 15.98; Cohen's d=0.75). By week 12, between-group differences were no longer significant. Adverse events were comparable across groups, with no seizures or mania.

CONCLUSIONS: SC-guided iTBS produced promising preliminary evidence of improved antidepressant response compared with 5-cm targeting, supporting sgACC-based connectivity-guided precision neuromodulation in depression.},
}

@article {pmid42570994,
year = {2026},
author = {Cai, Y and Lu, Y and Gao, Z},
title = {Brain-Heart Crosstalk: Unveiling the Neural-Immune Contribution.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {42570994},
issn = {1995-8218},
}

@article {pmid42572548,
year = {2026},
author = {Zhou, J and Hu, D and Wu, M and Hong, Z and Li, R and Chen, Q and Mi, X and Zhang, J},
title = {Review of brain-computer interface technology in ophthalmology: Current status, challenges and future directions.},
journal = {Advances in ophthalmology practice and research},
volume = {6},
number = {3},
pages = {247-256},
pmid = {42572548},
issn = {2667-3762},
abstract = {BACKGROUND: Visual impairment is a major global public health issue. Irreversible blindness caused by end-stage outer retinal diseases, optic nerve injuries, and other conditions remains refractory to conventional therapies. Brain-Computer Interface (BCI) technology, which establishes a direct communication pathway between the brain and external devices, has emerged as a promising interdisciplinary strategy for ophthalmic diagnosis, functional assessment, and artificial visual restoration.

MAIN TEXT: This review summarizes recent advances in BCI technology for ophthalmic applications. In diagnosis and assessment, BCIs provide objective and quantitative measures for evaluating visual disorders and the integrity of the visual pathway through neural signals, using modalities such as steady-state visual evoked potentials, functional near-infrared spectroscopy, and functional magnetic resonance imaging. In treatment, implantable visual prostheses, particularly retinal and cortical prostheses, have shown substantial progress in partial visual reconstruction, while noninvasive BCI-related approaches are being increasingly explored for rehabilitation. However, major barriers remain, including difficulties in signal acquisition, immature encoding and decoding algorithms, limited electrode array performance, inefficient wireless transmission, implantation-related complications, high device costs, and insufficient evidence for some noninvasive interventions. Legal, regulatory, and ethical concerns also constrain large-scale clinical implementation.

CONCLUSIONS: BCI technology holds considerable promise in ophthalmology, but significant technical and translational challenges remain. Future advances in artificial intelligence, flexible electronics, virtual reality, wireless systems, and closed-loop strategies are expected to improve precision, safety, adaptability, and accessibility, thereby enabling more effective diagnostic and visual rehabilitation solutions for patients with severe visual impairment.},
}

@article {pmid42573251,
year = {2026},
author = {Zhu, L and Yue, Q and Huang, A and Zhang, J and Yuan, P},
title = {Contrastive Learning Network based on Multi-Scale Transformer (CLMT-net): EEG decoding for fine-grained motor imagery of movements within the same limb.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-17},
doi = {10.1080/10255842.2026.2714841},
pmid = {42573251},
issn = {1476-8259},
abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) decode EEG signals into control commands. However, fine-grained MI decoding within the same limb remains challenging due to highly similar neural patterns. This paper proposes a Contrastive Learning Network based on a Multi-Scale Transformer (CLMT-Net) for fine-grained MI decoding. CLMT-Net integrates multi-scale temporal convolution, FFT-based frequency fusion, spatial convolution, and dual-path Transformer to learn complementary EEG representations. Supervised contrastive learning further improves feature discrimination. On the MI-2 dataset, CLMT-Net achieves an accuracy of 76.13 ± 6.77% with a 95% confidence interval of [73.33, 78.92], demonstrating competitive performance for same-limb MI decoding.},
}

@article {pmid42574409,
year = {2026},
author = {Jing, Y and Wang, J and Que, X and Li, C and Liu, S and Xiang, K and Lin, T and Shi, W and Lv, Z and Zhao, Y and Hou, ZG and Wang, W},
title = {Game Theory-Based Adaptive Human-Machine Joint Learning for Online MI-BCI Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3722389},
pmid = {42574409},
issn = {2168-2208},
abstract = {Motor imagery based brain-computer interface (MI-BCI) has been extensively researched for neurorehabilitation and motor assistance, while the performance of previous MI-BCI systems for online decoding motor intentions is still not satisfactory. Therefore, a game theory-based adaptive human-machine joint (AHMJ) learning method integrating subject learning and decoder updating was proposed for MI-BCI decoding, by which multi-class MI for unilateral upper limb can be successfully decoded online. On the one hand, a novel MI training method was proposed to facilitate subjects' learning to generate separable electroencephalogram (EEG) data, where the MI training process was modeled as a two-player zero-sum minimax game and the task difficulty was adaptively regulated according to each subject's performance by solving the minimax problem. On the other hand, a new online adaptive algorithm was designed to ensure stable updating of the decoding model, integrating knowledge distillation and prototype-guided domain adaptation for different MI training intervals. Online MI-BCI experiment on a total of fourteen healthy subjects and online simulation experiment on a public dataset from twenty-five healthy subjects were conducted. Compared with the traditional method that relies solely on subject learning and the previous human-machine joint learning method, the average decoding accuracy was significantly improved by 9.7% and 5.6% (paired t-test, both $p < 0.01$), respectively. The online adaptive algorithm also outperformed previous updating approaches in both accuracy and stability. The proposed AHMJ learning method can be applied to improve the online MI-BCI decoding accuracy for neurorehabilitation and motor assistance.},
}

@article {pmid42574930,
year = {2026},
author = {Ni, J and Qiao, MX and Lin, YG and Yu, H and Li, T},
title = {Non-invasive neurostimulation for enhancing peripheral brain-derived neurotrophic factor levels in major depressive disorder: A meta-analysis of randomized controlled trials.},
journal = {Journal of psychiatric research},
volume = {202},
number = {},
pages = {11-23},
doi = {10.1016/j.jpsychires.2026.08.001},
pmid = {42574930},
issn = {1879-1379},
abstract = {BACKGROUND: Brain-derived neurotrophic factor (BDNF), a critical modulator of neuroplasticity, has been strongly implicated in the pathophysiology of major depressive disorder (MDD). Non-invasive brain stimulation (NIBS), including repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS), shows therapeutic promise for MDD. However, their impact on peripheral BDNF levels remains inconclusive.

OBJECTIVE: This meta-analysis aimed to quantify the effects of NIBS on peripheral BDNF levels in MDD patients and to evaluate BDNF as a potential biomarker of treatment response.

METHODS: Following PRISMA guidelines, randomized controlled trials (RCTs) comparing NIBS with sham stimulation in MDD were identified through a systematic search of five databases. Sixteen RCTs (n = 1271) were included. Random-effects models were used to estimate pooled standardized mean differences (SMDs) for peripheral BDNF levels and depressive symptom severity. Subgroup and moderator analyses examined the effects of BDNF elevation, stimulation modality, and treatment duration.

RESULTS: NIBS significantly increased peripheral BDNF levels (SMD = 1.05, 95% CI: 0.57 to 1.52, p < 0.001) and reduced depressive symptoms (SMD = -0.90, 95% CI: -1.13 to -0.67, p < 0.001). Moderator analyses demonstrated that BDNF elevation was associated with greater antidepressant effects. Subgroup analyses revealed that rTMS significantly increased BDNF levels, whereas tDCS did not. Extended rTMS protocols (≥4 weeks) were associated with greater BDNF increases than shorter durations.

CONCLUSION: NIBS is associated with increased peripheral BDNF levels and improved depressive symptoms in MDD patients, suggesting the potential utility of BDNF as a biomarker for neuromodulation efficacy.

PROSPERO REGISTRATION: CRD42025630845.},
}

@article {pmid42576031,
year = {2026},
author = {Liu, Z and Yin, H and Zhu, K and Shao, L and Wang, Y and Yuan, Y and Zhang, J and Lin, S and Wang, Q and Jin, L and Chen, J and Liu, Q and He, W and Ying, M and Zhang, L},
title = {LECT2-RPS27A interaction involving Lys48 attenuates neuroinflammation in diabetic retinopathy.},
journal = {Diabetologia},
volume = {},
number = {},
pages = {},
pmid = {42576031},
issn = {1432-0428},
support = {No. LY23H280005//the Natural Science Foundation of Zhejiang Province/ ; No. GZY-ZJ-KJ-24065//the Science and Technology Department of the State Administration of Traditional Chinese Medicine and the Zhejiang Provincial Administration of Traditional Chinese Medicine jointly build technology plan projects/ ; No. 2024JKZKTS25//the Research Project of Zhejiang Chinese Medical University/ ; No. 82374110//National Natural Science Foundation of China/ ; No. 82173959//National Natural Science Foundation of China/ ; No. 82204713//National Natural Science Foundation of China/ ; No.2023ZR080//Zhejiang Province Traditional Chinese Medicine Science and Technology project/ ; },
abstract = {AIMS/HYPOTHESIS: Leukocyte cell-derived chemotaxin 2 (LECT2), a newly discovered hepatokine involved in immunomodulation and inflammatory processes, has recently been implicated in diabetic retinopathy pathogenesis. However, the specific role and mechanism of LECT2 in diabetic retinopathy remain largely unclear.

METHODS: A publicly available liquid biopsy proteomics dataset was reanalysed and validated using retinal samples from individuals with diabetic retinopathy. A LECT2-knockdown (Lect2[+/-]) mouse model of diabetic retinopathy was established to evaluate the role of LECT2 in vivo. Retinal pathology was assessed by Periodic Acid-Schiff staining and H&E staining, and LECT2 expression was assessed by western blotting. In parallel, a high glucose (HG)-induced human Müller cell model was used to investigate the subcellular localisation and functional effects of LECT2. LECT2 expression and inflammatory signalling were analysed following LECT2 knockdown or overexpression, and its nucleus-cytoplasm distribution was examined by fractionation assays. The interaction between LECT2 and ribosomal protein S27a (RPS27A) was characterised by immunoprecipitation-MS, co-immunoprecipitation, molecular docking and split-GFP assays, followed by RPS27A silencing to assess its effects on inflammatory protein expression.

RESULTS: Proteomic reanalysis of proliferative diabetic retinopathy liquid biopsy data identified 1479 upregulated proteins, with chemotaxis among the most significantly enriched Gene Ontology terms (false discovery rate <0.001). Among 17 chemotaxis-related candidate proteins, LECT2 emerged as a potential regulator of diabetes-associated inflammation, a finding further supported by its elevated expression in the retinas of individuals with diabetic retinopathy. In diabetic Lect2[+/-] mice, LECT2 deficiency aggravated retinal microvascular injury and inflammatory responses. LECT2 was predominantly expressed in retinal Müller cells, implicating it in glia-associated retinal inflammation. In HG-treated human retinal Müller cells, RPS27A was identified as a downstream target of LECT2 and was upregulated under hyperglycaemic conditions. Mechanistically, HG stimulation increased the expression and nuclear accumulation of both LECT2 and RPS27A. LECT2 interacted with RPS27A at the Lys48 site, promoting its nuclear retention and limiting its cytoplasmic translocation. This, in turn, inhibited IκBα ubiquitination and degradation, thereby suppressing NF-κB-driven inflammatory signalling.

CONCLUSIONS/INTERPRETATION: Taken together, our studies indicated the protective role of LECT2 in diabetes-induced dysfunction of retinal Müller cells, supporting the feasibility of targeting LECT2 in the management of diabetic inflammation and microvasculopathy.},
}

@article {pmid42577157,
year = {2026},
author = {Wang, H and Zhang, G and Xie, X and Qin, Y and Zheng, T and Zhou, C and Huang, J and Tian, F and Wei, S},
title = {Decoding Chinese speech across multiple neural conditions via EEG: dataset construction and interpretability driven spatial optimization.},
journal = {Frontiers in psychology},
volume = {17},
number = {},
pages = {1833448},
pmid = {42577157},
issn = {1664-1078},
abstract = {The integration of artificial intelligence (AI) and brain-computer interfaces (BCIs) technologies shows great potential in assisting patients with speech impairments and improving cognitive-linguistic decline. Electroencephalogram (EEG) based BCIs, characterized by non-invasiveness, low cost, and high temporal resolution, hold significant application value in speech decoding and cognitive rehabilitation. Currently, most mainstream public EEG datasets rely on Western languages. As a tonal language, Chinese Mandarin differs significantly from Western languages in speech production mechanisms, making existing data insufficient to support future BCI research for Mandarin-speaking patients. To address this gap, we establish a systematic Mandarin EEG dataset and conduct effective speech decoding and related analyses. We design four distinct experimental conditions, namely overt, overt-noisy, intend, and imagine, to simulate different types of speech disorders in clinical scenarios. Using typical Mandarin tonal-vowels and common vocabularies as stimuli, we construct an EEG dataset collected from a healthy adult. We evaluate the speech decoding performance using short-time Fourier transform combined with support vector machine (STFT-SVM) and EEG-Conformer models. Furthermore, we design a multi-task architecture based on the EEG-Conformer to perform a unified decoding task for the two stimulus types and a classification task across the four dataset conditions. To interpret the model, we combine Shapley value computation and decision trees to calculate the importance of different electrodes during classification. Experimental results show that the models achieve effective decoding on our dataset. The EEG-Conformer model performs significantly above chance level across all data, reaching an accuracy of 69.83% in normal speaking conditions and up to 61.46% in conditions simulating speech disorders. In the multi-task setting, the classification accuracy across different conditions exceeds 97%. By utilizing the important electrodes identified through interpretability methods as new feature inputs, the classification performance further improves even with a reduction of over 50% in the channels. These results demonstrate the potential of neural signal decoding technologies in communication assistance, reveal the decodability of Chinese Mandarin EEG datasets, and provide feasible recommendations for the future design of Chinese BCI applications.},
}

@article {pmid42578734,
year = {2026},
author = {Huang, S and Pollak, SD and Xie, W},
title = {Conceptual priors drive development of predictive attention to emotion.},
journal = {Child development},
volume = {},
number = {},
pages = {},
doi = {10.1093/chidev/aacag149},
pmid = {42578734},
issn = {1467-8624},
support = {32200876//National Natural Science Foundation of China/ ; 32571245//National Natural Science Foundation of China/ ; 325B2042//National Natural Science Foundation of China/ ; R01MH61285/MH/NIMH NIH HHS/United States ; },
abstract = {Emotion inference is not purely reactive but is supported by conceptual priors that contribute to active sampling of meaningful cues as social events unfold, yet how children learn to anticipate when and where such cues will emerge remains unclear. This study examined whether conceptual emotion knowledge serves as a prior supporting developmental increase in anticipatory attention, and whether such attention facilitates more adult-like emotion inferences. Children aged 5-11 years (N = 180, Mage = 8.02 years, 84 female; data collected 2024-2025) from China viewed videos while eye movements were simultaneously recorded and continuously rated the target character's emotion on a valence-arousal grid. Anticipatory attention was indexed by the extent to which children's saccades were directed toward regions about to contain meaningful information. Conceptual emotion knowledge was assessed with a composite that combined emotion granularity, diversity, and performance on a standardized emotion understanding test. Multilevel, moderated mediation models revealed that conceptual emotion knowledge mediated age-related increases in anticipatory saccades toward meaningful regions, which in turn predicted more adult-like emotion judgments. Together, these findings suggest that conceptual knowledge acts as a developmental mechanism bridging children's prior experience and growing attentional strategies, enabling more mature emotion understanding in social environments.},
}

@article {pmid42578886,
year = {2026},
author = {Patil, S and Sultane, D and Shah, PK},
title = {Enhance motor imagery EEG classification using DWT and chirplet transform.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2026.2704678},
pmid = {42578886},
issn = {1476-8259},
abstract = {Brain Computer Interfaces promote seamless interaction between individuals with movement limitations and their surrounding environment by transforming electroencephalography signals derived using Motor Imagery. The procedure relies on accurately classifying various MI activities, which requires dependable approaches for EEG signal classification to be continuously improved. In this paper, discrete wavelet transform, and chriplet transform are proposed to enhance the performance of test system with demonstrating critical importance of time-related data and is implemented in visual studio code python. The proposed method holds 91% efficiency, accuracy 94.8% for CBCIC and 93.72% for BCI Competition IV Dataset with response time of 1.03 sec.},
}

@article {pmid42566247,
year = {2026},
author = {Zhao, X and Li, Y and Zhang, L and Wang, J and Jialin, A and An, Q and Fu, Y and Yao, C and Deng, W},
title = {Sighing Dynamics as a Candidate Digital Biomarker for Anxiety in Daily Life Using Wearable Respiratory Monitoring: Intensive Longitudinal Study.},
journal = {JMIR formative research},
volume = {10},
number = {},
pages = {e89485},
pmid = {42566247},
issn = {2561-326X},
mesh = {Humans ; Male ; Female ; Longitudinal Studies ; Adult ; Biomarkers/analysis ; *Anxiety/diagnosis/physiopathology/psychology ; *Wearable Electronic Devices/standards ; Middle Aged ; *Anxiety Disorders/diagnosis/physiopathology ; Monitoring, Physiologic/methods/instrumentation ; Ecological Momentary Assessment ; Digital Health ; },
abstract = {BACKGROUND: Sighing has been proposed as a primary respiratory reset mechanism that is often linked to emotional regulation. However, evidence linking sighing to anxiety relies heavily on laboratory studies, which lack ecological validity. It remains unclear whether ambulatory sighing dynamics in free-living settings reflect momentary (state) symptom fluctuations or enduring (trait) pathology. Evidence from daily-life monitoring is needed to evaluate sighing dynamics as a candidate digital biomarker for anxiety disorders (ADs).

OBJECTIVE: This study aimed to evaluate daily-life sighing dynamics as a candidate digital biomarker of anxiety by disentangling state and trait anxiety-sigh associations and comparing these dynamics between individuals with ADs and healthy controls (HCs). A secondary objective was to assess the feasibility, signal quality, and joint data coverage of a synchronized ecological momentary assessment (EMA) and respiratory inductance plethysmography (RIP) protocol.

METHODS: We conducted an intensive longitudinal study integrating smartphone-based EMA with continuous RIP using a Hexoskin smart shirt. Thirty-eight adults were enrolled (n=15 with ADs; n=23 HCs) and completed four 36-hour intensive monitoring blocks distributed over 1 to 2 weeks. Participants wore the Hexoskin RIP smart shirt during each 36-hour block and completed 6 randomly timed EMA prompts per day during the daytime hours (9 AM to 9 PM). Sighs were operationally defined as breaths with tidal volume ≥2 × each participant's median tidal volume. Each EMA entry was linked to the preceding 5-minute respiratory window, and the primary outcome was sigh proportion (sigh breaths/total breaths) per window. Multilevel generalized linear mixed models were used to analyze anxiety-sigh coupling, decomposing anxiety into within-person (state) and between-person (trait) components.

RESULTS: The analysis included 1279 synchronized psychophysiological windows from 33 participants. Five participants were excluded due to insufficient valid synchronized windows. In the primary beta-binomial model of sigh proportion, higher between-person (trait) anxiety was significantly associated with a lower overall sigh proportion (odds ratio [OR] 0.80, 95% CI 0.74-0.87; P<.001), while HCs showed a lower baseline sigh probability than the anxiety disorder group (OR 0.78, 95% CI 0.65-0.93; P=.005). The within-person anxiety-by-group interaction was significant (OR 1.14, 95% CI 1.03-1.26; P=.01), indicating that sighing tended to increase with higher momentary anxiety in HCs but was attenuated in participants with ADs. Feasibility was high (EMA completion: 1626/2046, 79.5%; high-quality respiratory samples: 10.85/12.90 million, 84.1%; EMA-RIP linkage: 1319/1626, 81.1%).

CONCLUSIONS: Daily-life sighing dynamics showed a state-trait dissociation, with reduced state-dependent coupling in ADs versus HCs, supporting sighing as a candidate digital biomarker of anxiety. The synchronized EMA-RIP protocol was feasible and yielded high-integrity real-world data.},
}

Humans
Male
Female
Longitudinal Studies
Adult
Biomarkers/analysis
*Anxiety/diagnosis/physiopathology/psychology
*Wearable Electronic Devices/standards
Middle Aged
*Anxiety Disorders/diagnosis/physiopathology
Monitoring, Physiologic/methods/instrumentation
Ecological Momentary Assessment
Digital Health

@article {pmid42566374,
year = {2026},
author = {Zhang, H and Zhu, J and Chen, Y and Li, H},
title = {HCFT: A Hierarchical Convolutional Fusion Transformer for Cross-Task EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3721596},
pmid = {42566374},
issn = {2168-2208},
abstract = {Electroencephalography (EEG) decoding remains challenging due to the non-stationary nature of neural signals and the limited generalization of existing models across tasks and subjects. To address this challenge, we propose a lightweight and generalizable decoding framework named Hierarchical Convolutional Fusion Transformer (HCFT), which combines dual-branch convolutional encoders and hierarchical Transformer blocks for multi-scale EEG representation learning. Specifically, the model first captures local temporal and spatiotemporal dynamics through time-domain and time space convolutional branches, and then aligns these features via a cross-attention mechanism that enables interaction between branches at each stage. Subsequently, a hierarchical Transformer fusion structure is employed to encode global dependencies across all feature stages. A task-adaptive stabilization strategy of Dynamic Tanh normalization is introduced to enhance transient feature detection and training stability. Extensive experiments are conducted on two representative cross-task benchmark datasets, BCI Competition IV-2b and CHB-MIT, covering both event-related classification and continuous seizure prediction tasks. Results show that HCFT achieves 80.83% average accuracy and a Cohen's kappa of 0.6165 on BCI IV 2b, as well as 99.10% sensitivity, 0.0236 false positives per hour, and 98.82% specificity on CHB-MIT, consistently outperforming over ten state-of-the-art baseline methods. Ablation studies confirm the effect of each core component of the proposed framework. The model also exhibits strong cross-subject generalization and structural interpretability, offering a scalable and versatile framework for advancing general-purpose neural decoding systems.},
}

@article {pmid42567572,
year = {2026},
author = {Ge, H and Shen, X and Huang, C},
title = {Molecularly imprinted polymer-based sensors for forensic science: A review of the way forward for presumptive testing.},
journal = {Analytica chimica acta},
volume = {1418},
number = {},
pages = {345794},
doi = {10.1016/j.aca.2026.345794},
pmid = {42567572},
issn = {1873-4324},
mesh = {*Molecularly Imprinted Polymers/chemistry ; *Forensic Sciences/methods ; Humans ; *Molecular Imprinting ; *Polymers/chemistry ; },
abstract = {Molecularly imprinted polymers (MIPs) offer stable, reusable, and specific synthetic recognition sites for chemical sensing. MIP-based drug sensors are promising tools for forensic biological-matrix drug screening before confirmatory analysis, but their translation into forensic practice is constrained less by peak analytical sensitivity than by explicit decision rules at the program screening cutoff, embedded quality control (QC), and traceable records compatible with chain-of-custody review. To evaluate this translation gap, we reviewed 114 studies published from 2015 to early 2026 and organized a standards-aligned framework with three boundary conditions (BCs). BC-I (matrix robustness) requires matrix-robust, interpretable signals in realistic specimens. BC-II (cutoff-centered decision rules) requires explicit decision rules at the program screening cutoff, optionally with a predefined Inconclusive Zone and prespecified follow-up actions. BC-III (lifecycle comparability) requires consistent decisions across lots, devices, operators and over time via embedded QC, lot-bridging, and version-controlled configuration. Using representative MIP platforms and drug-sensing case studies, we identify recurring gaps that hinder MIP deployment in forensic presumptive testing, including narrow matrix panels, incomplete raw-trace provenance, absent borderline handling, and limited lifecycle-comparability documentation. We believe that practical progress will often depend on adopting mature quality system practices, routine near-cutoff testing, and complete documentation to support traceable screening decisions.},
}

*Molecularly Imprinted Polymers/chemistry
*Forensic Sciences/methods
Humans
*Molecular Imprinting
*Polymers/chemistry

@article {pmid42569243,
year = {2025},
author = {Kovacs, J and Krusienski, D and Maninder, M and Wriggers, W},
title = {Revealing spatiotemporal neural activation patterns in electrocorticography recordings of human speech production by mutual information.},
journal = {Neuroscience informatics},
volume = {5},
number = {4},
pages = {},
pmid = {42569243},
issn = {2772-5286},
support = {R35 GM153431/GM/NIGMS NIH HHS/United States ; },
abstract = {BACKGROUND: Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

NEW METHOD: We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, we also implemented a novel "masked analysis", which excludes periods of silence, and compared it with the standard (unmasked) analysis.

RESULTS: Our findings show that previous results, obtained through more complex statistical methods, can be reproduced using CC with an appropriate threshold cutoff. Moreover, both standard MI and CC are influenced by broad transitions between silence and speech, but masking allows the detection of intrinsic correspondences between the two signals, revealing more localized activity.

Compared to the standard CC, masked MI highlights early prefrontal and premotor activations emerging ∼440 ms before speech onset. It also identifies sharper, anatomically coherent activations in key speech-related areas, demonstrating improved sensitivity to the fine-grained spatiotemporal dynamics of continuous speech production.

CONCLUSION: These findings deepen our understanding of the neural pathways underlying speech and underscore the potential of masked MI for advancing neural decoding in future speech-based braincomputer interface applications.},
}

@article {pmid42569597,
year = {2023},
author = {Kim, Y and Druschel, LN and Mueller, N and Sarno, D and Gisser, K and Hess-Dunning, A and Capadona, JR},
title = {In Vivo Validation of a Mechanically Adaptive Microfluidic Intracortical Device as a Platform for Sustained Local Drug Delivery.},
journal = {Frontiers in biomaterials science},
volume = {2},
number = {},
pages = {},
pmid = {42569597},
issn = {2813-3749},
support = {I01 RX003083/RX/RRD VA/United States ; },
abstract = {Intracortical microelectrodes (IME) are vital to properly functioning brain-computer interfacing (BCI). However, the recording electrodes have shown a steady decline in performance after implantation, mainly due to chronic inflammation. Compliant materials have been explored to decrease differential strain resulting in lower neural inflammation. We have previously developed a fabrication method for creating mechanically adaptive microfluidic probes made of a cellulose nanocrystal (CNC) polymer nanocomposite material that can become compliant after implantation. Here, we hypothesized that our device would have a similar tissue response to the industry standard, allowing drug delivery therapeutics to improve neural inflammation in the future. RNA expression analysis was performed to determine the extent of neural inflammation and oxidative stress in response to the device compared to controls and to naïve shame tissue. Results presented for both four- and eight-weeks post-implantations suggest that our device offers a promising platform technology that can be used to deliver therapeutic strategies to improve IME performance.},
}

@article {pmid42563025,
year = {2026},
author = {Offenberg, EC and Keller, D and Mehrkanoon, S and Berezutskaya, J and Vansteensel, MJ and Branco, MP},
title = {Optimal Size of Electrocorticography Grids for Classification of Hand Movements.},
journal = {Neuroinformatics},
volume = {24},
number = {3},
pages = {},
pmid = {42563025},
issn = {1559-0089},
support = {024.005.022//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; 17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; 19072//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; 101070939//HORIZON EUROPE Framework Programme/ ; },
mesh = {Humans ; *Electrocorticography/methods/instrumentation ; *Hand/physiology ; *Brain-Computer Interfaces ; Movement/physiology ; Electrodes, Implanted ; Female ; Male ; Adult ; Epilepsy/physiopathology ; Young Adult ; },
abstract = {Implanted Brain-Computer Interfaces (BCIs) hold significant promise as a replacement for conventional assistive technologies for people with extensive motor impairments. In recent years, significant progress has been made in enhancing BCI performance, bringing clinically viable systems within reach. Some of these developments rely on increasingly large numbers of high spatial-density electrocorticography (ECoG) electrodes, which enable the extraction of spatially detailed information from extensive brain areas, but may also elevate surgical burden, posing a challenge for the clinical adoption of implanted BCIs. To mitigate this risk, we conducted an exhaustive investigation of hand movement classification performance involving 4, 5, and 8 classes in nine individuals with epilepsy, exploring all possible rectangular ECoG subgrids within the 32-, 64-, and 128-channel grids that were implanted in these individuals. Our findings reveal that the surface area of ECoG grids can be substantially reduced by 75-94% compared to the original grids, without a meaningful decline in classification performance, if electrodes are placed over informative areas. Classification performance across datasets was stable for progressively smaller subgrids until a critical threshold of approximately 60mm[2] was reached, below which performance declined substantially. We show that smallest subgrids with an area above the threshold achieved equally high classification F1 scores (range 81.64-99.71%) as the full grids with an area > 230mm[2] (range 82.85-96.75%). We conclude that ECoG-based BCI can be used to accurately decode up to seven different hand movements from well-located grids with a small number of electrodes, paving the way for smaller and safer BCI implants.},
}

Humans
*Electrocorticography/methods/instrumentation
*Hand/physiology
*Brain-Computer Interfaces
Movement/physiology
Electrodes, Implanted
Female
Male
Adult
Epilepsy/physiopathology
Young Adult

@article {pmid42563155,
year = {2026},
author = {Zuo, C and Yin, Y and Zhang, C and Ma, X and Zheng, Z and Wang, S and Ye, C},
title = {The effect of MI-BCI combined with tDCS in early rehabilitation after anterior cruciate ligament reconstruction monitored by resting-state fMRI: the first case report.},
journal = {BMC neurology},
volume = {26},
number = {1},
pages = {},
pmid = {42563155},
issn = {1471-2377},
support = {2024M764330//China Postdoctoral Science Foundation/ ; },
mesh = {Humans ; Male ; Adult ; Magnetic Resonance Imaging ; *Anterior Cruciate Ligament Reconstruction/rehabilitation ; *Transcranial Direct Current Stimulation/methods ; *Imagery, Psychotherapy/methods ; Motor Cortex/diagnostic imaging/physiopathology ; *Brain/diagnostic imaging/physiopathology ; },
abstract = {BACKGROUND: To recover lower-limb motor function is a primary goal for rehabilitation after anterior cruciate ligament (ACL) reconstruction. Although quantitative testing and questionnaire evaluation provide a lot of valuable information, functional magnetic resonance imaging (fMRI) provides a powerful method to preliminarily explore the neural recovery process of novel rehabilitation interventions. In this case, we aimed to investigate changes in functional connectivity (FC) of motor imagery-based brain-computer interface (MI-BCI) combined with tDCS intervention in early post-ACL reconstruction rehabilitation, monitored via resting-state fMRI.

CASE PRESENTATION: A 38-year-old man patient who underwent left ACL reconstruction received MI-BCI combined with tDCS intervention 5sessions/peek for 4 weeks. before and after the intervention, the resting-state fMRI was assessed. Seed-based analysis was performed using the primary motor cortex (M1) and supplementary motor area (SMA) as the seeds, representing the sensorimotor network (SMN). The results showed increased connectivity between the SMN and specific motor-related (e.g., precentral gyrus, postcentral gyrus, pallidum and paracentral lobule) and sensory information processing areas (e.g., calcarine, lingual gyrus, calcarine, thalamus, posterior cingulum). In addition, decreased FC between both M1 and the bilateral precentral gyrus was observed, and the FC between the left M1, SMA, and the right frontal midline regions was also reduced.

CONCLUSIONS: Our preliminary results showed that MI-BCI combined with tDCS could be an effective method to recovery the lower-limb motor function and promote neural remodeling of an ACL reconstruction patient in early stage. Alterations of the SMN functional connectivity after intervention seems to partially explain the neural remodeling process, if applied on a large sample sizes, could provide more information about specific motor area.},
}

Humans
Male
Adult
Magnetic Resonance Imaging
*Anterior Cruciate Ligament Reconstruction/rehabilitation
*Transcranial Direct Current Stimulation/methods
*Imagery, Psychotherapy/methods
Motor Cortex/diagnostic imaging/physiopathology
*Brain/diagnostic imaging/physiopathology

@article {pmid42563536,
year = {2026},
author = {Burke, KM and Johnson, S and Chomko, K and Escalante, A and Hall, KC and Lydston, M and Uek, C and Casagrande, G and Kim, HJ and Arnold, J and Pont-Esteban, D and Young, H and Walsh, C and Paganoni, S},
title = {Enabling Functional Independence: A Scoping Review of Upper Extremity Assistive Devices for Adults With Progressive Neuromuscular Diseases.},
journal = {Muscle & nerve},
volume = {},
number = {},
pages = {},
doi = {10.1002/mus.70357},
pmid = {42563536},
issn = {1097-4598},
abstract = {BACKGROUND: Assistive technology offers an important means of compensating for lost upper extremity function in adults with progressive neuromuscular diseases (NMD), enabling participation in daily activities, supporting independence, and promoting quality of life. However, the range of available technologies and the evidence supporting their use have not been comprehensively summarized.

AIM: The aim of this scoping review was to identify and characterize upper extremity assistive technologies tested in adults with NMD and to summarize the current evidence regarding their functional applications and clinical outcomes.

MATERIALS AND METHODS: Electronic searches for published and unpublished literature were conducted using MEDLINE, Embase.com, Web of Science, Cochrane Central, and IEEE Xplore. The search strategy incorporated controlled vocabulary and free-text synonyms for the concepts of upper extremity, rehabilitation, selected progressive neurodegenerative diseases, and assistive equipment. Following title/abstract and full-text screening, studies evaluating assistive devices tested on adults with NMD during functional task performance were included.

RESULTS: After screening 2289 articles, 27 studies met the inclusion criteria. The studies collectively demonstrate the potential benefits and diverse range of assistive devices available to support upper extremity function. These devices ranged from low-tech solutions, such as static mobile arm supports and fabricated splints, to high-tech devices, including dynamic mobile arm supports, robotic systems, exoskeletons, and brain-computer interface systems. However, most studies were feasibility or case studies that primarily demonstrated proof of concept, with limited evidence regarding long-term effectiveness, functional outcomes, or quality of life.

DISCUSSION: The findings illustrate the rapidly evolving landscape of upper extremity assistive devices for adults with NMD and their potential to improve functional performance, while highlighting the need for prospective studies that assess meaningful improvements in function, participation, and quality of life.

CONCLUSIONS: As advances in disease-modifying therapies extend survival and preserve function for individuals with NMD, interdisciplinary collaboration among engineers, clinicians, therapists, individuals with NMD, caregivers, and regulators will be essential to develop, evaluate, and implement assistive technologies that meet users' evolving needs.},
}

@article {pmid42556397,
year = {2026},
author = {Tao, S and Feng, L and Jia, S and Xu, B and Yu, S},
title = {BiGSTF-Net: inter-modal mutual guidance and intra-modal spatio-temporal fusion for EEG-fNIRS cognitive classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9595},
pmid = {42556397},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide complementary temporal and spatial information for brain-computer interfaces (BCIs). However, effectively exploiting this complementarity remains challenging due to the heterogeneous characteristics of electrophysiological and hemodynamic signals.

APPROACH: In this study, we propose BiGSTF-Net, a multimodal architecture designed to improve cognitive state decoding by leveraging the complementary properties of EEG and fNIRS signals. The proposed framework first employs heterogeneous spatio-temporal feature extractors to capture temporal-oriented and spatial-oriented representations from each modality. To facilitate cross-modal interaction, a Modal Residual Interaction Unit (MRIU) is introduced to enable bidirectional inter-modal guidance while preserving modalityspecific characteristics. Subsequently, a Spatio-Temporal Gated Unit (STGU) performs intra-modal feature integration to produce compact and discriminative representations.

MAIN RESULTS: Experiments conducted under cross-session evaluation on multiple BCI datasets demonstrate that BiGSTF-Net consistently outperforms representative multimodal fusion baselines. Ablation studies further verify the effectiveness of the proposed architectural components, while visualization analyses reveal activation patterns that align with known neurophysiological characteristics of EEG and fNIRS signals.

SIGNIFICANCE: These results indicate that the proposed framework provides an effective approach for multimodal neural signal decoding.},
}

@article {pmid42556966,
year = {2026},
author = {Liao, W},
title = {Mapping the Therapeutic Network of Emotion Recovery Across Psychiatric Disorders.},
journal = {Biological psychiatry},
volume = {100},
number = {5},
pages = {468-469},
doi = {10.1016/j.biopsych.2026.06.013},
pmid = {42556966},
issn = {1873-2402},
}

@article {pmid42557261,
year = {2026},
author = {Zeng, L and Sun, K and Yuan, Y and Gao, Y and Wang, X and Wen, Z and Wu, D},
title = {An fNIRS Dataset for Cognitive Decoding during a Multi-day Block-design Stroop Task.},
journal = {Scientific data},
volume = {13},
number = {1},
pages = {},
pmid = {42557261},
issn = {2052-4463},
support = {2023KXKT056//Quick Response Project of Fourth Military Medical University/ ; },
mesh = {Humans ; *Stroop Test ; *Cognition ; Spectroscopy, Near-Infrared ; Young Adult ; Adult ; },
abstract = {Cognitive state decoding is key to brain-computer interface technology, serves as a promising tool for psychiatric diagnosis and rehabilitation, and offers a novel perspective for neuroscientific study. Datasets for decoding cognitive states using optical neuroimaging modalities remain scarce. To address this, we provide an fNIRS dataset acquired during a Stroop task, consisting of frontal hemoglobin responses from 55 young adults. Each participant completed three sessions of color-word Stroop task within approximately 2 weeks, to collect more than 30 trials per condition while avoiding mental fatigue caused by a single session. This dataset supports a range of applications, from studying conflict inhibition to building decoders for neurofeedback training and large-scale cross-subject fNIRS models, while also facilitating the development of signal processing algorithms for hemodynamic signals.},
}

Humans
*Stroop Test
*Cognition
Spectroscopy, Near-Infrared
Young Adult
Adult

@article {pmid42560905,
year = {2026},
author = {Yang, X and An, J and Liu, D and Xu, X and Wu, D},
title = {ReCIL: Rehearsal-Based Class Incremental Learning for Cross-Subject Motor Imagery Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3721154},
pmid = {42560905},
issn = {1558-2531},
abstract = {OBJECTIVE: Electroencephalography (EEG) based motor imagery (MI) is a classic brain-computer interface (BCI) paradigm, which can be used in neuro-rehabilitation and device control. Existing MI classification approaches only consider the scenario that the number of MI classes is pre-determined and fixed; when new MI classes arrive, data for all classes must be recollected and the model retrained, which is inefficient, and sometimes impossible, as the training data for previous classes may be lost or inaccessible due to privacy concern.

METHODS: This paper proposes rehearsal-based class incremental learning (ReCIL) for cross-subject MI classification, where the model learns new MI classes sequentially, and the test subjects are different from the training subjects. ReCIL uses Euclidean Alignment to reduce the inter-subject EEG data distribution shift, global-local replay to preserve the knowledge of old tasks, and dimensionality reduction to facilitate reliable similarity computation among EEG samples.

RESULTS: Experiments on three public MI datasets showed that ReCIL achieved a good balance between plasticity and stability.

SIGNIFICANCE: To our knowledge, this is the first study on cross-subject class incremental learning for MI classification, offering a practical solution for incremental class expansion without retraining from scratch.},
}

@article {pmid42562010,
year = {2026},
author = {Calvo Merino, E and Sun, Q and Dauwe, I and Carrette, E and Meurs, A and Van Roost, D and Boon, PAJM and Van Hulle, MM},
title = {Medial wall contributions to finger motor decoding from electrocorticography.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9645},
pmid = {42562010},
issn = {1741-2552},
abstract = {OBJECTIVE: Future motor brain-computer interfaces (BCIs) are expected to benefit from integrating neural signals from multiple motor-related brain regions. While decoding studies have largely focused on lateral sensorimotor cortex, the medial wall of the cerebral hemisphere remains relatively underexplored. Here, we investigate the contribution of medial wall regions to finger movement decoding using human electrocorticography (ECoG) recordings.

APPROACH: We analyzed ECoG data from four subjects performing finger movements. Single- and multi-channel decoding analyses were applied to medial wall electrodes, examining the contribution of time-domain and frequency-domain features, including local motor potentials (LMP) and oscillatory power in the alpha (8-12 Hz) and beta (12-34 Hz) bands. Decoding performance was assessed for movement detection and finger discrimination.

MAIN RESULTS: Significantly above-chance finger movement detection was observed across multiple medial wall subregions. LMP and alpha-beta band power contributed most strongly to decoding performance. Feature dynamics shared key properties with primary motor cortex, including pre-movement alpha-beta desynchronization, while also exhibiting region-specific patterns such as anatomically dependent positive or negative LMP modulations. Although movement detection was the dominant outcome, medial wall channels in two subjects enabled significant differentiation between individual fingers. In one subject, both contralateral and ipsilateral finger movements could be decoded with some generalization across hands; however, this observation is based on a single case and should be interpreted as preliminary.

SIGNIFICANCE: These findings identify the medial wall as a viable source of motor-related signals for finger movement decoding, with potential for future invasive motor BCI applications, while underscoring the need for further studies to confirm generalizability across individuals.},
}

@article {pmid42562060,
year = {2026},
author = {Liu, X and Huang, P and Wu, L and Cai, L},
title = {Effects of Brain-Computer Interface-Based Training on Post-Stroke Lower Limb Rehabilitation: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.},
journal = {Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association},
volume = {},
number = {},
pages = {108711},
doi = {10.1016/j.jstrokecerebrovasdis.2026.108711},
pmid = {42562060},
issn = {1532-8511},
abstract = {BACKGROUND: Lower-limb motor dysfunction after stroke severely compromises mobility and quality of life. Brain-computer interface (BCI) technology, which employs a "central-peripheral-central" closed-loop to promote neuroplasticity, offers a promising rehabilitation approach. However, its optimal dosing parameters remain unclear.

OBJECTIVE: This study aimed to evaluate the efficacy of BCI-based training on lower-limb motor function, balance, walking capacity, and activities of daily living (ADL) after stroke, and to explore the impact of total training dose, session duration, and stroke phase.

METHODS: We systematically searched major databases for randomized controlled trials (RCTs) published up to April 2026. All included studies were RCTs. Methodological quality was assessed using the PEDro scale. A meta-analysis was conducted using RevMan 5.4 to calculate mean differences (MD) and 95% confidence intervals (CI).

RESULTS: Ten RCTs involving 366 participants were included. BCI training significantly improved lower-limb motor function (Fugl-Meyer Assessment for Lower Extremity: MD = 2.38, 95% CI 1.72 to 3.04, P < 0.00001). Although this mean difference is below the anchor-based Minimal Clinically Important Difference (MCID) of 6 points reported for chronic stroke populations, it represents a statistically robust and consistent improvement across RCTs, suggesting potential clinical relevance, particularly in subacute patients or specific intervention subgroups.

CONCLUSIONS: BCI-based training effectively improves lower-limb motor recovery after stroke. Subgroup analyses suggested that a moderate total dose (401-800 minutes) combined with 20-40-minute sessions may represent a potentially optimal regimen, although these findings are based on limited RCTs and require confirmation in larger studies.},
}

@article {pmid42480585,
year = {2026},
author = {Zhou, Y and Feng, R and Song, Y and Cheng, S and Xu, M and He, F and Fan, H and Ming, D},
title = {Transcranial acoustoelectric brain imaging reveals current field of deep brain stimulation with millivolt-level amplitude resolution.},
journal = {Journal of neural engineering},
volume = {23},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ae8e01},
pmid = {42480585},
issn = {1741-2552},
mesh = {Animals ; *Deep Brain Stimulation/methods ; Rats ; *Brain/physiology/diagnostic imaging ; Male ; Rats, Sprague-Dawley ; *Brain Mapping/methods ; },
abstract = {Objective.Deep brain stimulation (DBS) is a technology employed to stimulate the central nervous system, with amplitude being the main parameter for regulating DBS. Given the effectiveness of DBS therapy depends significantly on lead placement and stimulus intensity, it is crucial to accurately map the lead field and monitor dynamic changes of stimulus amplitude. Transcranial acoustoelectric brain imaging (tABI) has been initially proved as a non-invasive method for mapping DBS currents. This study utilizes tABI to map amplitude-varying DBS currents to explore its potential for DBS monitoring.Approach.tABI was applied to six living rats' brain while DBS was delivered with 50 mV amplitude increments. The tABI images of amplitude-varying DBS currents are analyzed for spatiotemporal resolution, while the method's capability to decode DBS current is evaluated in terms of amplitude, frequency, and time domains.Main results.The results show that tABI can map the lead field of DBS with millivolt-level amplitude resolution and reveal dynamic changes with ∼2 mm spatial resolution within a single stimulus period of 7.69 ms, achieving a mean SNR of 20.4 dB. With sensitivity of 167.74μV V[-1]MPa[-1], the acoustoelectric intensity and stimulus amplitude exhibit a strong positive correlation, with a coefficient of determination ofR[2]= 0.9982 for the linear fit. Additionally, the decoded acoustoelectric signal exhibits a correlation coefficient above 0.819 with the DBS current in the time domain.Significance.This study first demonstrates that tABI can reveal the spatial distribution and dynamic changes ofin vivoDBS lead currents with millivolt-level amplitude resolution. Further validation in disease-relevant animal models is warranted to assess clinical translatability.},
}

Animals
*Deep Brain Stimulation/methods
Rats
*Brain/physiology/diagnostic imaging
Male
Rats, Sprague-Dawley
*Brain Mapping/methods

@article {pmid42553203,
year = {2026},
author = {Avaltroni, P and Cappellini, G and Sylos-Labini, F and La Scaleia, B and Lacquaniti, F and Ivanenko, Y},
title = {Real-time approach to evaluate spinal cord topography of motor pool activation during gait.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {14},
number = {},
pages = {1753344},
pmid = {42553203},
issn = {2296-4185},
abstract = {Real-time assessment of spinal motor output ("spinal maps") can provide insight into how neural control strategies during walking differ between healthy individuals and patients with neurological disorders. Abnormal spatiotemporal activation of spinal motor pools may underlie gait impairments, making spinal maps a potential physiological marker for guiding personalized rehabilitation. We present a stride-by-stride online visualization method for estimating spinal locomotor output from multi-muscle EMG signals mapped onto the approximate rostrocaudal location of motor pools, identifying activation patterns across lumbar and sacral motor pools. The primary objective of this framework is to serve as a near real-time neurophysiological monitoring and assessment tool during clinical gait analysis. Beyond this core diagnostic capability, the system is designed as an extensible platform that may support real-time feedback to therapists, exploratory biofeedback to patients, or future integration into closed-loop spinal neuromodulation protocols. The system enables online visualization of spinal maps and related parameters (such as timing, intensity, and coactivation of lumbar and sacral motor pools) through a flexible, user-friendly software interface. Preliminary tests in eight children with cerebral palsy and comparing with typically developing peers show that, despite stride-to-stride variability, the method may effectively differentiate in real-time impaired from typical spinal activation patterns, supporting its promise for clinical gait rehabilitation.},
}

@article {pmid42554274,
year = {2026},
author = {Jiang, L and Ye, J and Zhu, Y and Ma, S and Yao, D and Yuan, Z and Ma, X and Xu, P and Dong, D and Li, F},
title = {Molecular Architecture of Temporal Variability Network Dysfunction in Bipolar Disorder.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2750004},
doi = {10.1142/S0129065727500043},
pmid = {42554274},
issn = {1793-6462},
abstract = {Characterized by recurrent fluctuations in mood states, bipolar disorder (BD) is widely conceptualized as a disconnection syndrome associated with dysregulated brain dynamics. Nevertheless, the molecular mechanisms underlying this aberrant connectivity dynamics in BD remain elusive. Using resting-state electroencephalography (EEG) data from BD patients and healthy controls, this study first delineated the characteristic alterations in temporal variability of functional connectivity in BD and further elucidated their underlying molecular mechanisms and clinical relevance. Current findings revealed significantly reduced temporal variability within large-scale brain subnetworks, most notably in the dorsal attention, somatomotor, and visual networks. Importantly, these neurodynamic signatures effectively predicted the symptom severity in individuals with BD. Moreover, the spatial patterns of these dynamic alterations are associated with the expression of BD risk genes enriched in synaptic function and metabolic pathways, as well as with the spatial organizations of various neurotransmitter receptors, including CB1, mGluR5, H3, and MOR. Collectively, these results provide evidence for a multiscale pathophysiological framework that links genetic susceptibility and chemoarchitectural alterations to dynamic brain network instability, ultimately underpinning the core clinical manifestations in BD.},
}

@article {pmid42555002,
year = {2026},
author = {Lan, Y and Hou, X and Zeng, H and Chen, L},
title = {Subliminal cues accompanied by action generate temporal expectancy and the role of supraliminal experience.},
journal = {Perception},
volume = {},
number = {},
pages = {3010066261474477},
doi = {10.1177/03010066261474477},
pmid = {42555002},
issn = {1468-4233},
abstract = {Sensory events associated with motor actions are critical for perceptual calibration and motor control. Due to the stable temporal relationships between these events, individuals can anticipate the timing of one event based on another. While temporal expectancy is well-established for supraliminal stimuli (conscious cues) through temporal orienting, less is known about the role of subliminal cues generated by actions. Although subliminal stimuli are known to influence perception and behavior-specifically in forming spatial expectancies-their role in temporal expectancy remains unclear. Furthermore, evidence suggests that prior supraliminal experience may enhance the utilization of subliminal information. Integrating the conceptual link between space and time, the present study investigated whether individuals can form temporal expectancies based on action-generated subliminal stimuli and explored how supraliminal experience modulates this process. Our results indicate that participants do not form temporal expectancies through subliminal cues alone. Instead, the formation of such expectancy and transferred learning benefits upon target discrimination (i.e., orientation discrimination) depends on prior supraliminal experience with the association between the cue and the temporal onset of the subsequent target.},
}

@article {pmid42555310,
year = {2026},
author = {Kosnoff, J and Zhang, J and Zhang, Y and Ding, Y and Johnson, Z and He, B},
title = {Transcranial focused ultrasound enhances the discriminative power of targeted voluntary motor signals in humans.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3720609},
pmid = {42555310},
issn = {1558-0210},
abstract = {Transcranial focused ultrasound (tFUS) is an emerging neuromodulation technique with high spatial specificity and deep brain penetration, but its effects on upper-limb voluntary motor signals relevant to brain-computer interfaces (BCIs) remain unknown. In this study, we investigated whether tFUS targeted at the motor cortex can enhance movement-related cortical potentials (MRCPs) and improve the discriminability of hand grasp signals. Eighteen healthy participants performed visually-cued left and right hand grasps while EEG and EMG were recorded during three condition types: left motor cortex-targeted tFUS, right prefrontal cortex-targeted tFUS, and a non-modulated baseline. Ultrasound pulse trains were delivered time-locked to movement cues using pulse repetition frequencies (PRFs) of 30 and 3000 Hz and duty cycles of 30% and 60%. Left motor cortex-targeted tFUS significantly amplified both MRCP amplitude and signal discriminability for BCI classification relative to both spatial sham and non-modulated conditions. Neither MRCP amplification nor BCI classification accuracy were found to change with the investigated PRFs or duty cycles. These findings demonstrate that tFUS can selectively enhance voluntary motor cortical signals and improve motor EEG-based BCI performance.},
}

@article {pmid42555311,
year = {2026},
author = {Liang, J and Bezsudnova, Y and Kowalczyk, A and Sun, Y and Jensen, O},
title = {Enhanced Neural Decoding with Optically Pumped Magnetometer MEG Using Multivariate Pattern Analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3719472},
pmid = {42555311},
issn = {1558-0210},
abstract = {Multivariate pattern analysis (MVPA) provides a sensitive means to decode distributed neural activity from magnetoencephalography (MEG) by jointly exploiting temporal and spatial information. Optically pumped magnetometer (OPM)-based MEG enables close-to-scalp measurements and is therefore expected to capture neuromagnetic fields with higher spatial resolution compared to conventional systems based on superconducting quantum interference devices (SQUIDs). In this study, we conducted a within-subject comparison of OPM- and SQUID-MEG using time-resolved MVPA to decode neural responses to visual objects presented as images and corresponding words, recorded from the same participants under an identical experimental paradigm and matched preprocessing. To isolate the contribution of spatial sampling, decoding performance was evaluated under controlled sensor counts and spatial-frequency content, quantified using a spherical harmonic expansion of the sensor topographies. A complementary full-array SQUID analysis was also included as a reference. The results indicated that OPM-MEG achieved higher decoding accuracy than SQUID-MEG in the sensor-matched comparison, with the advantage being particularly evident for word decoding, where OPM also exceeded the full-array SQUID across a broad range of sensor counts. Further analysis of spatial-frequency content revealed that OPM-MEG benefited from the inclusion of higher-order spatial components. These findings demonstrate that OPM-MEG enhances the recoverability of fine-grained neural representations for multivariate decoding, with implications for cognitive neuroscience research and neural engineering applications such as brain-computer interfaces.},
}

@article {pmid42507556,
year = {2026},
author = {Wu, S and Principe, JC and Wang, Y},
title = {Transregional Neural Prostheses: Applications and Computational Tools.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {3539-3555},
doi = {10.1109/TNSRE.2026.3717163},
pmid = {42507556},
issn = {1558-0210},
mesh = {Humans ; *Neural Prostheses/trends ; Prosthesis Design ; Computer Simulation ; Brain-Computer Interfaces ; Animals ; Algorithms ; Soft Computing ; *Neurological Rehabilitation/methods ; Models, Neurological ; },
abstract = {Transregional neural prostheses represent an emerging frontier in neural rehabilitation, integrating advances in clinical translation, neuroscience, and engineering. Damage to communication pathways within the interconnected nervous system often results in diverse disabilities. Recent progress in high-density neural interfaces and computational algorithms now makes it possible to bypass damaged circuits and restore lost behavioral functions through transregional prostheses. Moving beyond replacing sensory or motor functions with external devices, transregional neural prostheses offer the potential to re-establish endogenous causal communication of neural circuits, which can support limb control, improve higher-order cognitive functions, and promote long-term rehabilitation through dynamic, active intervention. This review surveys current applications of transregional prostheses under a unified computational framework to provide a systematic view. We then summarize advances in computational predictive models that approximate transregional communication and stimulation models for stimulation parameter optimization. Finally, we outline future directions for transregional neural prostheses from perspectives of neural engineering innovations, neuroscience research, and clinical considerations.},
}

Humans
*Neural Prostheses/trends
Prosthesis Design
Computer Simulation
Brain-Computer Interfaces
Animals
Algorithms
Soft Computing
*Neurological Rehabilitation/methods
Models, Neurological

@article {pmid42548755,
year = {2026},
author = {Pinto Neto, O},
title = {Architecture-data matching for EEG-EMG decoding: compact deep models match classical spectral decoders on the WAY-EEG-GAL grasp-and-lift dataset.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1874302},
pmid = {42548755},
issn = {1662-4548},
abstract = {Modern deep learning has broadened the tools available for non-invasive neural decoding, but its advantage over well-engineered classical pipelines remains unclear at clinical neural-engineering sample sizes. We compared four classical decoders, three multilayer perceptron (MLP) variants, and three deep-learning architectures (an EEGNet-style compact convolutional network, a four-layer Transformer encoder trained from scratch, and a graph attention network) on the public WAY-EEG-GAL grasp-and-lift dataset (12 participants, 3,528 trials). Models were evaluated using leave-one-subject-out (LOSO) cross-validation to decode object weight (165, 330, and 660 g) and grasp-surface friction (sandpaper, suede, and silk). After Benjamini-Hochberg false discovery rate (BH-FDR) correction within the primary/robustness family, none of the deep-versus-best-classical comparisons reached significance. The graph attention network led nominally on weight (0.643), and the compact convolutional network (CNN) led nominally on surface (0.565), but neither exceeded the best classical baselines (HGBM = 0.617 for weight; logistic regression = 0.562 for surface). Two-direction bandwidth controls showed that the nominal graph neural network (GNN) advantage on weight reflected access to higher-frequency electromyography (EMG) content rather than a robust architectural gain. The GNN weight signal was concentrated in the first 500 ms of sustained hold (0.639 early vs. 0.540 late, q = 0.007), whereas the compact CNN was comparatively robust to bandwidth and phase. A four-layer Transformer trained from scratch underperformed (q = 0.003 for both tasks), consistent with a parameter-data mismatch at n = 12. Modality ablation showed that both tasks were dominated by peripheral EMG features: electroencephalography (EEG)-only decoding was near chance for weight (0.34-0.37) and surface (0.36-0.38; all q < 0.0015 vs. EMG-only). EMG-only HGBM achieved the highest accuracy in the study (0.712 for weight and 0.584 for surface), and adding EEG channels reduced HGBM weight decoding (q = 0.007) and CNN surface decoding (q = 0.012). In contrast, the graph attention network was robust to EEG-induced fusion dilution (q > 0.5), consistent with attention-based down-weighting of low-information channels. Together, bandwidth, modality, phase, and conditioning controls indicate that, at clinical sample sizes, the critical design choice is not architectural complexity but modality-relevant channel selection and the ability to suppress low-information channels.},
}

@article {pmid42550073,
year = {2026},
author = {Shao, M and Sun, L and Qu, W and Wang, L and Ma, N and Liu, J and Tang, Y and Tian, Y and Ji, W and Xu, X and Han, C and Wang, H and Lin, X and Fu, T and Zheng, S and Jiang, M and Zeng, P and Tao, TH and Meng, F and Zhang, W},
title = {Multifunctional Electrode/Neural Integration Interface Enabling Chronic High-Fidelity Neural Recording and an Order-of-Magnitude Neuromodulation Quality.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e74519},
doi = {10.1002/adma.74519},
pmid = {42550073},
issn = {1521-4095},
support = {2023M743594//China Postdoctoral Science Foundation/ ; 82471265//National Natural Science Foundation of China/ ; GZB20230780//National Grants for Postdoctoral Researchers Program/ ; 2023YFB3809903//National Key Research and Development Program of China/ ; 2021ZD0201600//National Key Research and Development Program of China/ ; 2022ZD0209300//National Key Research and Development Program of China/ ; 2022YFF0706500//National Key Research and Development Program of China/ ; 2022YFC2405100//National Key Research and Development Program of China/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; G202512038//Beijing High-level Innovative and Entrepreneurial Talent Support Program/ ; //CAS Pioneer Hundred Talents Program/ ; },
abstract = {Neural electrodes face persistent challenges, including inflammation, biofouling, and impedance, which compromise long-term recording and stimulation quality. Here, we introduced a multifunctional polyamino acid interface that enhances neural interfacing by combining biocompatibility, antimicrobial activity, and antifouling properties. Applied to flexible electrodes, this coating reduces foreign body reaction and preserves neuronal proximity, ensuring stable integration with brain tissue. In chronic rodent models, functionalized electrodes achieve high-fidelity single-unit recordings for over 300 days with significantly higher spike amplitudes and yield than bare controls. Notably, the interface enables an order-of-magnitude improvement in neuromodulation efficiency, evoking robust motor responses at only 2 µA compared to 100 µA for uncoated probes. Multi-omics analysis reveals a molecularly profound alteration in the host tissue response, transitioning from a pro-inflammatory injury signature to an attenuated inflammatory state with improved tissue homeostasis. Furthermore, the interface's resistance to biological "cementing" facilitates damage-free electrode removal and recovery, preserving the neural architecture and enabling the possibility of chronic replacement. This universal platform offers a promising and practical strategy for the next generation of stable, clinically viable neural-computer interfaces.},
}

@article {pmid42551095,
year = {2026},
author = {Lou, C and Chen, L},
title = {The process of object individuation shared across visual and tactile modalities.},
journal = {Cognitive psychology},
volume = {166},
number = {},
pages = {101830},
doi = {10.1016/j.cogpsych.2026.101830},
pmid = {42551095},
issn = {1095-5623},
abstract = {Distinguishing objects from the background, a process known as Object Individuation (OI), is fundamental for us to interact with the environment and relies critically on location information across sensory modalities. Nonetheless, it remains unclear and contested in the literature whether the enumeration of tactile and visual events relies on the OI process (especially given spatial constraints), or if the representation of numerosity is governed by a modality-independent mechanism common to both visual and tactile systems. In this study, we used a cross-modal enumeration and a working memory dual-task paradigm to investigate whether OI processes in tactile and visual modalities draw upon a shared cognitive resource. We implemented two experiments. In Experiment 1, we combined a tactile working memory (WM) task with visual enumeration, and in Experiment 2, we used a visual WM task with tactile enumeration. Both experiments revealed that the task-irrelevant WM load significantly modulated subitizing performance (enumeration of small quantities) in the target modality. Under high WM load, participants showed increased error rates and reduced subitizing capacity compared to low load. This modulation is selective to the subitizing range and cannot be attributed to general dual-task costs, ruling out general dual-tasking effects. The data shows that visual and tactile working memory and enumeration ("subitizing") share a common OI process that operates on location, independent of the sensory modality. This finding is consistent with existent neuroimaging evidence that highlights the modality-shared role of frontoparietal brain regions (e.g., IPS, LPFC) in enumeration and working memory.},
}

@article {pmid42551560,
year = {2026},
author = {Zhao, R and Li, S and He, X and Wang, X and Cichocki, A and Lu, Y and Jin, J},
title = {Hands-free motor imagery EEG classification via LLM multi-agents.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110871},
doi = {10.1016/j.jneumeth.2026.110871},
pmid = {42551560},
issn = {1872-678X},
abstract = {BACKGROUND: Motor imagery (MI) brain-computer interfaces (BCI) rely on precise electroencephalogram (EEG) classification. However, issues such as the reliance on extensive manual experience for MI-EEG model design, parameter tuning, and optimization directions, along with the poor task flexibility of foundation models and state degradation during long-term multi-agent iterations, severely restrict the state-of-the-art (SOTA) efficiency of MI-EEG.

NEW METHOD: To address these challenges, we propose AutoMI, a novel framework that uses multi-agent automated rapid iterations to construct SOTA MI-EEG models. AutoMI introduces a hybrid decision mechanism that tightly couples Q-learning strategies with deterministic rules. By integrating planning, execution, and output agents with predefined tools, AutoMI ensures broad general applicability across various hyperparameter optimizations and structural improvements. Furthermore, AutoMI integrates experience tracking and rollback mechanisms to prevent ambiguous optimization.

RESULTS: In evaluations on the IV2a, OpenBMI, and ECUST-MI datasets, the SOTA models finally constructed through AutoMI iterations achieve accuracies of 77.62%, 78.08%, and 83.02%, with maximum improvement reaching 24.69%, 23.35%, and 23.28% respectively. Furthermore, the average time per iteration for a single subject on the OpenBMI dataset is approximately 500 s.

Compared with automated optimization algorithms, the accuracies increase by 18.42%, 9.27%, and 19.25% respectively, demonstrating the effectiveness of the proposed AutoMI framework and proving that its optimization capability reaches SOTA.

CONCLUSION: Experimental results indicate that AutoMI provides a novel perspective and framework design reference for future BCI model optimization.},
}

@article {pmid42551709,
year = {2026},
author = {Kumari, A and Edla, DR and Ramesh, D and Shirodkar, VR},
title = {Synergistic EEG signal processing for brain-computer interfaces using hybrid MothCray optimization and deep learning.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2026.07.060},
pmid = {42551709},
issn = {1873-7544},
abstract = {Electroencephalography (EEG) based brain-computer interface (BCI) systems require optimal channel selection to achieve high signal quality, reduced setup complexity, and robust usability. This study introduces a comprehensive signal-processing framework to improve the efficiency and accuracy of EEG-driven BCIs. Signal pre-processing incorporates standard EEG denoising techniques, including notch filtering, independent component analysis (ICA), and segmentation into temporal windows to mitigate artifacts and enhance data quality. The channel selection phase employs the novel mothcray optimization (MCO) algorithm, a hybrid approach that integrates moth flame optimization and crayfish optimization to identify the most informative channels. Feature extraction encompasses time-domain statistics, frequency-domain attributes, and connectivity measures, enabling a rich representation of underlying neural dynamics. Classification is performed using an advanced deep neural network architecture tailored for the spatial-temporal characteristics of EEG data. Validated on the BCI competition IV dataset IIa, the proposed model achieves an accuracy rate of 93.92%, outperforming established methods and underscoring its effectiveness. The proposed MCO-based channel selection, multi-domain feature fusion, and enhanced EEGNet classifier demonstrate a significant step forward for practical BCI deployment that requires fast setup, robust operation, and efficient use of resources. It is especially advantageous in applications that rely on a reduced number of EEG channels to enable more comfortable and portable headsets, must sustain stable performance under varying recording conditions in home- or clinic-based neurorehabilitation and assistive communication settings.},
}

@article {pmid42537670,
year = {2026},
author = {K M, D and Parashiva, PK and Vinod, AP},
title = {Graph convolutional network-based harmonization of EEG for cross-dataset transfer in motor imagery in BCI.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9344},
pmid = {42537670},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalogram (EEG) electrode configurations vary across Motor Imagery Brain-Computer Interface (MI-BCI) datasets, limiting transfer learning and system performance due to small dataset sizes. This work proposes a spatial harmonization framework that maps heterogeneous EEG recordings to a common physical electrode montage while preserving task-relevant motor imagery information.

APPROACH: Each EEG trial is modeled as a graph, with electrodes as nodes and electrode samples as node embeddings. A two-layer Graph Convolutional Network (GCN) is introduced to capture spatio-temporal relationships between electrodes and EEG samples. The harmonized EEG is evaluated using time-domain, frequency-domain, and spatial-domain analyses, as well as downstream MI classification with EEGNet, FBCNet, and ADFCNN. Performance is assessed under three protocols-within-dataset classification, source-only cross-dataset transfer, and target-domain fine-tuning-across three public MI EEG datasets.

MAIN RESULTS: The proposed GCN yields lower harmonization error than spherical spline interpolation while preserving the principal temporal, spectral, and spatial characteristics of motor imagery EEG. In within-dataset classification, combining real and harmonized EEG improved decoding performance, increasing accuracy from 56.57% to 66.20% for EEGNet and from 61.96% to 72.54% for FBCNet on Dataset A. In cross-dataset experiments, the harmonized representation supported both source-only transfer and fine-tuning, with the combined condition generally yielding the highest performance.

SIGNIFICANCE: The proposed method addresses electrode-layout incompatibility at the EEG signal level without restricting datasets to a small subset of shared electrodes. By enabling heterogeneous MI EEG datasets to be represented in a common physical montage, the framework provides a practical basis for signal-level harmonization, dataset augmentation, and cross-dataset MI decoding across diverse recording setups.},
}

@article {pmid42538382,
year = {2026},
author = {Yao, Y and Luo, J and Hui, Y and Lyu, J and Ke, Y and Shen, W and Xu, Y and Yu, Y and Chen, H and Chen, J and Chen, G and Sawan, M and Tao, L and Zhou, N},
title = {3D-printed implantable bioelectronics enabled by anti-swelling and biphasic conductive hydrogels.},
journal = {Nature materials},
volume = {},
number = {},
pages = {},
pmid = {42538382},
issn = {1476-4660},
support = {12473094//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82402676//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32430002//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ23C010001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; LRG25C010001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Hydrogel bioelectronics are promising candidates to bridge biological and electronic systems. However, maintaining stable communication between hydrogel devices and biological materials in wet physiological environments is challenging owing to the swelling-induced mechanical degradation of hydrogel encapsulation and electrical failure of conductive networks. To address this, we report a micellar self-assembly method to fabricate soft, stretchable and anti-swelling hydrogels as building blocks for implantable hydrogel bioelectronics. Compared with conventional swelling hydrogels and silicones, these anti-swelling hydrogels show reduced foreign-body reactions during long-term implantation. Using a microgel strategy, we engineer the anti-swelling hydrogel into a supporting matrix and a biphasic conductive hydrogel ink, enabling embedded 3D printing of hydrogel bioelectronics. Through regulating the monomer diffusion during the manufacturing process, we tailor the conductive phase of the conductive hydrogel, achieving conductivities of up to 4,000 S cm[-1], and a strain at electrical failure exceeding 1,300% when equilibrated in an aqueous environment. Different types of hydrogel bioelectronic implant are printed, including brain-computer interfaces, wirelessly powered optoelectronics and sciatic-nerve stimulators. These devices show long-term stability and reliable operation following implantation in rats.},
}

@article {pmid42542090,
year = {2026},
author = {Zhang, J and Wang, Y and Hua, J and Zhang, Y and Yuan, J and Han, J and Tan, Z and Ding, N and Northoff, G},
title = {Impaired low-frequency temporal prediction links to psychomotor dysfunction in depression.},
journal = {Journal of psychiatric research},
volume = {201},
number = {},
pages = {706-713},
doi = {10.1016/j.jpsychires.2026.07.024},
pmid = {42542090},
issn = {1879-1379},
abstract = {Major depressive disorder (MDD) exhibits psychomotor retardation which concerns abnormal slowness of both thoughts and movements. While traditionally viewed as a motor deficit, the underlying cognitive mechanisms remain unclear. Here, we investigated whether psychomotor disturbance in MDD reflects a disruption of temporal prediction, a cognitive capacity whereby the motor system provides predictive timing signals to sensory regions. We tested MDD subjects' (N = 38) ability to synchronize finger taps to rhythmic tone sequences across multiple temporal frequencies, compared with healthy controls (N = 65). Critically, we found frequency-specific desynchronization: timing between finger taps and tones was altered in MDD mainly at low frequencies below 1 Hz-the range corresponding to the motor system's natural delta rhythm-while basic motor functions (tapping speed, stability, interval) remained intact. This low-frequency auditory-motor desynchronization correlated with depression severity and specifically with psychomotor anhedonia and vegetative symptoms. Our findings reveal that psychomotor retardation in MDD involves impaired temporal prediction at slow timescales, suggesting dysfunction in motor-to-sensory predictive signaling rather than motor execution per se. This supports a cognitive and therefore psychomotor, rather than purely motor, mechanism underlying psychomotor symptoms in depression, with implications for the role of motor-based temporal prediction networks in mood disorders.},
}

@article {pmid42546762,
year = {2026},
author = {Wang, X and Luo, X and Wang, Y and Deng, X and Zheng, C},
title = {Integrated decoding of local and prospective spatial representations for future decision prediction.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae93f5},
pmid = {42546762},
issn = {1741-2552},
abstract = {Decoding future spatial decisions from the brain's cognitive map represents a critical step toward cognitive brain-computer interfaces (BCIs) and closed-loop neuromodulation for neurological disorders. However, accurately predicting future paths from hippocampal neural dynamics remains challenging, partly because previous studies have largely overlooked the temporal evolution of early decision-making phases. Approach. We recorded hippocampal CA1 ensemble activity from rats performing a continuous spatial decision task in a modified T-maze. By segmenting the decision process into starting, running, and approaching phases, we examined how local and prospective spatial representations dynamically evolved over time. Main results. Central arm place cells exhibited increasing trajectory dependence as animals approached the choice point, with local theta sequences consistently overrepresenting the actual choice. Concurrently, prospective representations driven by choice arm place cells showed a dynamic transition from preferentially predicting the actual choice during the running phase to representing potential paths more equally near the choice point. Integrating local and prospective features improved decoding performance, reaching 74.4% accuracy for future choice prediction in Test trials and 78.2% accuracy for upcoming trajectory decoding in Sample trials. Significance. These findings demonstrate that incorporating spatiotemporal hippocampal features improves behavioral decoding and provides a framework for developing hippocampus-based BCIs to predict future decisions.},
}

@article {pmid42547559,
year = {2026},
author = {Fan, Y and Ma, Y and Zolotavin, P and Topalli, G and Wang, W and Karlsson, M and Karlsson, M and Luan, L and Xie, C and Chi, T},
title = {High-channel-count neural recording and stimulation platform with 5376 simultaneous recording channels.},
journal = {npj biomedical innovations},
volume = {3},
number = {1},
pages = {},
pmid = {42547559},
issn = {3005-1444},
support = {2238833//NSF/ ; U01 U01NS131086//NIH/ ; U01 U01NS131086//NIH/ ; },
abstract = {Advancing neural interfaces requires large-scale, high-density recording technologies capable of capturing full-spectrum neural activity across cortical and subcortical regions. Here, we present a scalable approach to integrate neural electrodes with advanced application-specific integrated circuits (ASICs). Specifically, we custom-designed an ASIC with 5376 simultaneous channels, each sampling at 20 kS/s and enabling >1.3 Gb/s total data streaming throughput. The ASIC incorporates in-pixel amplification, time-division multiplexed ADCs, and on-chip stimulation capabilities, ensuring precise signal acquisition with minimal power consumption while maintaining a low noise level of 5.5 µVrms. We further developed an interconnect strategy using gold bump bonding, which allows for high-density integration of the flexible probe and rigid chip. We demonstrate the capacity of this platform through the integration with a flexible μECoG array. The resulting device allows for the high-resolution mapping of in vivo field potentials on the cortical surfaces of rat brains, supported by the precise localization of evoked sensory activities. These results prove an effective approach towards highly integrated neural interfaces with applications in brain-computer interfaces, neuroprosthetics, and large-scale functional brain mapping.},
}

@article {pmid42548576,
year = {2026},
author = {Suffian, M and Mammone, N and Ieracitano, C and Tripodi, GG and Pascarella, A and Ferlazzo, E and Morabito, FC},
title = {EEGDecoder-x: an explainable deep learning framework for cross-subject EEG-based detection of Alzheimer's and Creutzfeldt-Jakob disease.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1851752},
pmid = {42548576},
issn = {1664-2295},
abstract = {Early detection of neurodegenerative diseases is critical. Distinguishing early-stage Creutzfeldt-Jakob disease (CJD) from "mimics" like Alzheimer's disease (AD) remains a major challenge; while EEG is valuable in advanced CJD, early-stage abnormalities are often non-specific and overlap with other rapidly progressive dementias. Deep learning offers promising EEG-based diagnostic solutions, but clinical adoption requires transparent decision-making, the interpretability of the features learned by deep learning models is equally important. In this context, careful model design and explainability are essential. In this paper, we propose a novel interpretable framework, EEGDecoder-x, for decoding EEG signals from subjects with Alzheimer's disease, Creutzfeldt-Jakob disease, and healthy controls, while providing insight into the model's learned characteristics. The EEGDecoder-x framework comprises two main components: a hybrid attention network for disease decoding (EEGDecoder-Net) and an explainability module (EEGDecoder-XAI). EEGDecoder-Net combines a convolutional neural network with a dual attention mechanism, followed by a classification layer, enabling efficient spatio-temporal feature extraction. EEGDecoder-XAI provides a comprehensive local and global explanations of the network's learning process for spatio-temporal dimensions. We validate the proposed framework using a Leave-One-Subject-Out evaluation paradigm, achieving 97.22% classification accuracy on a dataset of 36 subjects (12 with AD, 12 with CJD, and 12 healthy controls), and outperforming the baseline models, demonstrating both the effectiveness and interpretability of EEGDecoder-x.},
}

@article {pmid42537358,
year = {2026},
author = {Li, S and Luo, G and Zhou, L and Zhang, Y and Ma, L and Wang, C},
title = {Adaptive graph convolutional neural network incorporating ECG for individualized motor imagery EEG classification.},
journal = {Computer methods and programs in biomedicine},
volume = {286},
number = {},
pages = {109564},
doi = {10.1016/j.cmpb.2026.109564},
pmid = {42537358},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: The recognition of motor imagery electroencephalogram (EEG) signals, which non-invasively capture the macroscopic electrical activity of the brain, is critical for medical rehabilitation and intelligent control. In these applications, reliable prediction is essential due to the safety risks associated with misclassification. However, existing methods often suffer from limited generalization in cross-subject scenarios caused by substantial inter-subject variability. To address this challenge, this work develops adaptive modeling strategies to improve robust cross-subject recognition performance.

METHODS: We propose a Hybrid Adaptive Domain Graph Convolutional Network (HAD-GCN) to enhance decoding performance through multi-level adaptability. At the spatial level, an adaptive generator synthesizes electrocardiogram (ECG) signals, which record the electrical activity of the heart, from EEG signals and concatenates them within a connected graph structure, thereby mitigating the limitations of non-invasive data acquisition. At the temporal level, an adaptive splitter selects the most suitable time-frequency domain processing method for each subject's signal and routes the data into the corresponding branches for feature extraction.

RESULTS: Accuracy and the Kappa coefficient, which are widely adopted in motor imagery research, are used as evaluation metrics. Cross-subject experiments conducted on the Mixed dataset and the BCI Competition IV-2a dataset achieve accuracies of 83.10% ± 6.54% and 74.81% ± 8.97%, respectively, with corresponding Kappa values of 0.778 ± 0.06 and 0.655 ± 0.09.

CONCLUSIONS: Experimental results demonstrate that HAD-GCN significantly improves cross-subject classification performance and prediction reliability while maintaining strong generalization capabilities. The proposed multi-level adaptive approach consistently enhances classification accuracy for individual subjects, highlighting its potential for practical applications in EEG-based technologies. Our code is available at https://github.com/chuanlaiair/HAD-GCN.},
}

@article {pmid42537638,
year = {2026},
author = {Li, KY and Zhu, Z and Miao, L and Wang, Q and Deng, GC and Pan, L and Wu, J and Lou, H and Zeng, LH and Li, S and Feng, X and Liu, Y and Yang, H and Li, XM and Duan, S and Sun, L and Yu, YQ},
title = {HCN1 channels in GABAergic amygdalar neurons underpin male-biased aggressive behaviors.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2026.07.013},
pmid = {42537638},
issn = {1097-4199},
abstract = {Aggressive behaviors typically vary between sexes, but the molecular mechanisms driving these disparities in neural coding remain unclear. We found that aggression selectively activates GABAergic neurons in the posterior substantia innominata (pSI), an extended amygdala region critical for aggression in both sexes, with male mice exhibiting elevated neuronal activity during attacks. Utilizing single-nucleus RNA sequencing, we characterized the diverse molecular landscape of pSI neurons, revealing significant differences in ion channels and hormone regulator genes that may underpin sex-specific aggression. Male pSI[Vgat] neurons exhibited remarkable hyperexcitability driven by an elevated Ih. Strikingly, modulating HCN1 expression not only regulated this hyperexcitability but also influenced sexual dimorphism in aggression: silencing HCN1 in the pSI[Vgat] neurons reduced male aggression, while its overexpression significantly heightened aggression in female mice. Furthermore, testosterone intensifies aggression by upregulating HCN1 expression and remodeling pSI circuits. These findings provide novel insights into sex-specific molecular mechanisms underlying social behaviors.},
}

@article {pmid42536381,
year = {2026},
author = {Zhao, Y and Wang, Y and Zhang, J and Zhang, X},
title = {Temporal dynamics of adapting to novel contexts during the generalization of learned spatial suppression.},
journal = {Journal of vision},
volume = {26},
number = {7},
pages = {18},
doi = {10.1167/jov.26.7.18},
pmid = {42536381},
issn = {1534-7362},
mesh = {Humans ; *Attention/physiology ; *Generalization, Psychological/physiology ; Photic Stimulation/methods ; *Space Perception/physiology ; Female ; Male ; Young Adult ; Adult ; *Adaptation, Physiological/physiology ; Color Perception/physiology ; },
abstract = {The spatial suppression of a high-probability distractor location (HPDL) acquired through statistical learning critically reduces its attentional priority. The present study conducted four experiments to systematically investigate the mechanisms underlying how this learned suppression generalizes to novel tasks. Specifically, we focused on how generalization is influenced by the priority-enhancing attentional capture of a salient target and the competing demands of a newly introduced suppression process. Following training, Experiments 1 and 2 employed a salient color singleton target to test attentional capture. Experiment 1 reused training stimuli, whereas Experiment 2 introduced novel shapes to determine if the capture effect of novel stimuli would completely override the learned suppression. Experiment 3 introduced a new suppression process to evaluate the impact of competing suppression demands. Finally, Experiment 4 utilized a feature search paradigm as a test task devoid of color singletons to eliminate these interfering factors. The results revealed that the intense capture effect of novel stimuli in Experiment 2 completely masked the generalization. Furthermore, sliding window analyses in Experiments 1 and 3 uncovered a dynamic process where generalization only manifested during the middle of the test phase, indicating a competition between these newly introduced factors and the previously acquired suppression. Conversely, eliminating all interference in Experiment 4 yielded a highly stable and persistent generalization of the HPDL suppression. These findings demonstrate that novel interfering factors drive a dynamic adjustment of the HPDL weight within the priority map, revealing the highly flexible and adaptive nature of human attentional control.},
}

Humans
*Attention/physiology
*Generalization, Psychological/physiology
Photic Stimulation/methods
*Space Perception/physiology
Female
Male
Young Adult
Adult
*Adaptation, Physiological/physiology
Color Perception/physiology

@article {pmid42530021,
year = {2026},
author = {Lum, AM and de Wit, M and Hernandez, JA and Jacob, SI and DiRocco, S and Rotstein, D and Barbeau, B and Bonde, RK and Stacy, NI},
title = {Thyroid hormones and serum iodine in clinically normal versus chronically malnourished Florida manatees Trichechus manatus latirostris.},
journal = {Diseases of aquatic organisms},
volume = {167},
number = {},
pages = {1-12},
doi = {10.3354/dao03928},
pmid = {42530021},
issn = {0177-5103},
mesh = {Animals ; *Trichechus manatus/blood ; *Iodine/blood ; *Thyroid Hormones/blood ; *Malnutrition/veterinary/blood ; Male ; Florida ; Female ; },
abstract = {An unusual mortality event (UME) was declared from December 2020 to April 2022 after increased deaths of emaciated Florida (FL) manatees Trichechus manatus latirostris occurred in the Indian River Lagoon (FL, USA) following seagrass reduction. While thyroid hormones are known to fluctuate with fasting, stress, and disease in pinnipeds and cetaceans, investigations in sirenians are limited. Serum from clinically normal free-ranging FL manatees (n = 35) and individuals affected by the UME (n = 20) was analyzed via radioimmunoassay and chemiluminescent immunoassays for total triiodothyronine (TT3), total thyroxine (TT4), and free thyroxine (fT4) quantification after assay validation. Total iodine (TI) and inorganic iodine (INORG-I) were measured using inductively coupled plasma mass spectrometry; organic iodine (ORG-I) was calculated from these values. Reference intervals for TT3 and TT4 were generated for clinically normal manatees. There were no differences in TT3, TT4, TI, or INORG-I between groups. Body condition index weight difference expressed as a percentage of the population mean (BCI%) and fT4 were significantly lower in UME manatees, most consistent with chronic starvation. However, BCI% and fT4 were not significantly different between UME manatees who survived rehabilitation versus those who died, suggesting these are unreliable prognostic markers. Serum ORG-I was significantly higher in UME manatees. High thyroid tissue iodine in necropsied UME manatees (n = 6), associated with follicular cysts in a subset (n = 3), suggests a potential impact on thyroid gland metabolism. These data expand the current understanding of pathophysiological aspects of the UME due to chronic malnutrition and may help guide supportive care for FL manatees undergoing rehabilitation.},
}

Animals
*Trichechus manatus/blood
*Iodine/blood
*Thyroid Hormones/blood
*Malnutrition/veterinary/blood
Male
Florida
Female

@article {pmid42530047,
year = {2026},
author = {Zhang, X and Zhu, S and Lan, J and Duan, S and Han, Y and Shi, P},
title = {Brain-Wide Connectivity of GLP1R Neurons in the Thalamic Reticular Nucleus.},
journal = {Journal of integrative neuroscience},
volume = {25},
number = {7},
pages = {52117},
doi = {10.31083/JIN52117},
pmid = {42530047},
issn = {0219-6352},
support = {24ZR1463100//Natural Science Foundation of Shanghai/ ; },
mesh = {Animals ; *Neurons/metabolism/cytology ; *Glucagon-Like Peptide-1 Receptor/metabolism ; Neural Pathways/metabolism/cytology ; Male ; Mice, Transgenic ; *Thalamic Nuclei/metabolism/cytology ; Mice ; Neuroanatomical Tract-Tracing Techniques ; },
abstract = {BACKGROUND: Glucagon-like peptide-1 receptor (GLP1R) is a G-protein-coupled receptor recognized for its essential role in metabolic homeostasis and insulin secretion. Emerging evidence suggests that central GLP-1 signaling also modulates sensory information processing and cognitive functions. The thalamic reticular nucleus (TRN) serves as a critical inhibitory hub that filters and prioritizes sensory transmission between the thalamus and the cerebral cortex. We have identified a distinct population of GLP1R-expressing neurons distributed within the TRN; however, their long-range structural connectivity remains largely uncharacterized.

METHODS: In this study we used Glp1r-Cre mice combined with viral-genetic tracing strategies to map the whole-brain inputs and outputs of GLP1R-linked neurons. To identify direct monosynaptic inputs, a Cre-dependent retrograde rabies virus system was employed. To delineate the efferent axonal projections, a Cre-dependent synaptophysin-based tracing strategy was employed.

RESULTS: GLP1R neurons exhibited a distinct rostrocaudal distribution, with most located in the middle region of the TRN. The starter neurons, identified by the colocalization of adeno-associated virus (AAV)-double-floxed inverted orientation (DIO)-enhanced green fluorescent protein (EGFP)-tumor virus A receptor (TVA), AAV-DIO-rabies virus glycoprotein from the CVS-N2c strain (N2cG), and rabies virus (RV)-envelope protein A (EnvA)-glycoprotein-deleted (ΔG)-mCherry, were primarily located in the TRN. Retrograde-labeled neurons were identified across numerous brain regions. Dense clusters of input neurons were observed in the primary and secondary motor cortices (M1 and M2, respectively) and the primary somatosensory cortex (S1). Substantial inputs were observed, including from the ventrolateral (VL), central lateral (CL), and posterior (Po) thalamic nuclei. Additionally, notable presynaptic labeling was detected in subcortical regions such as the zona incerta (ZI) and lateral hypothalamic area (LH), as well as midbrain structures including the substantia nigra pars reticulata (SNR) and the deep mesencephalic nucleus (DpMe). Anterograde synaptophysin-based mapping revealed that TRN[GLP1R] neurons selectively project to the ventral medial nucleus (VM), paracentral thalamic nucleus (PC), mediodorsal thalamus, lateral part (MDL), and lateral habenula (LHb) nuclei.

CONCLUSIONS: The results confirm the existence of complex long-range afferent and efferent circuits associated with TRN[GLP1R] neurons, providing a morphological basis for studying their role in integrating metabolic states with sensory gating.},
}

Animals
*Neurons/metabolism/cytology
*Glucagon-Like Peptide-1 Receptor/metabolism
Neural Pathways/metabolism/cytology
Male
Mice, Transgenic
*Thalamic Nuclei/metabolism/cytology
Mice
Neuroanatomical Tract-Tracing Techniques

@article {pmid42532166,
year = {2026},
author = {Zhao, X and Shi, Y and Du, J and Xu, R and Meng, L and Ming, D},
title = {Cerebellar iTBS enhances gait adaptation by modulating cortical sensorimotor network dynamics: a randomized controlled trial.},
journal = {NeuroImage},
volume = {339},
number = {},
pages = {122153},
doi = {10.1016/j.neuroimage.2026.122153},
pmid = {42532166},
issn = {1095-9572},
abstract = {Gait adaptation enables individuals to maintain locomotor stability under persistent perturbations. Although the cerebellum is critical for sensory prediction error-based (SPE) adaptation, how cerebellar neuromodulation reshapes cortical sensorimotor networks to enhance gait adaptation remains unclear. This study investigated the behavioral effects and underlying cortical neurodynamic mechanisms of cerebellar intermittent theta-burst stimulation (iTBS) on gait adaptation. Thirty-two healthy adults received either active or sham cerebellar iTBS. Participants performed a split-belt treadmill adaptation task before and after intervention. Cortical responsiveness was evaluated using TMS-evoked EEG over primary motor cortex (M1), while resting-state EEG was analyzed to assess spectral power and directional functional connectivity. Compared to sham, cerebellar iTBS significantly enhanced gait adaptation, evidenced by a faster adaptation rate (p = 0.035) and enhanced Early Adaptation SLS (p = 0.011), without altering initial perturbation responses or post-adaptation outcomes. The iTBS increased TMS-evoked α (p = 0.031) and γ (p = 0.022) power in M1, while the α power was correlated with faster adaptation (r = 0.526, p = 0.002). Furthermore, iTBS strengthened PPC-to-M1 directed connectivity in the β (p = 0.025) and γ (p = 0.013) bands. Enhanced parieto-motor directionality were positively associated with adaptation rate (β: r = 0.515, p = 0.003; γ: r = 0.463, p = 0.009). These findings suggest that cerebellar iTBS facilitates gait adaptation by modulating cortical responsiveness and directional sensorimotor network connectivity, providing multi-level neurodynamic evidence for the cerebello-cortical modulation during gait adaptation and offering a strong physiological rationale for targeted neuromodulation in gait rehabilitation strategies.},
}

@article {pmid42533005,
year = {2026},
author = {Wang, Z and Liu, Y and Huang, S and Wu, W and Huang, H and Gui, Z and Li, Z and Guo, J and Zhang, H and Xu, M and Ming, D},
title = {A multi-paradigm and longitudinal EEG dataset including the "sixth-finger" and "affected-hand" motor imagery of stroke patients.},
journal = {Scientific data},
volume = {13},
number = {1},
pages = {},
pmid = {42533005},
issn = {2052-4463},
support = {2023YFF1204300//National Key Research and Development Program of China/ ; 2023YFF1204300//National Key Research and Development Program of China/ ; 2023YFF1204300//National Key Research and Development Program of China/ ; 2023YFF1204300//National Key Research and Development Program of China/ ; 2023YFF1204300//National Key Research and Development Program of China/ ; 2023YFF1204300//National Key Research and Development Program of China/ ; 2023YFF1204300//National Key Research and Development Program of China/ ; 2023YFF1204300//National Key Research and Development Program of China/ ; 2023YFF1204300//National Key Research and Development Program of China/ ; 2023YFF1204300//National Key Research and Development Program of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; 25HHNJSS00006//the Haihe Laboratory Self-Initiated Projects/ ; TJWJ2025MS029//Sponsored by Tianjin Health Research Project/ ; },
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; *Stroke/physiopathology ; Machine Learning ; Fingers ; },
abstract = {Motor imagery-based brain-computer interface (MI-BCI) applications in stroke rehabilitation aim to match brain activity with real-time feedback, thereby establishing closed-loop neural pathways and providing a basis for evaluating patients' neuroplasticity changes. Thus, constructing EEG datasets under MI paradigms is crucial for optimizing MI-BCI systems and understanding the neural rehabilitation process. However, the current limitations of single MI paradigms and the lack of relevant EEG datasets may restrict the accurate interpretation and effective application in stroke rehabilitation. This study collected EEG data from 24 stroke patients during MI tasks, including a novel "sixth finger" MI and an affected-hand MI paradigm. The dataset comprehensively covers the complete longitudinal stages of stroke rehabilitation: pre-training, post-training, and follow-up periods. The data materials include: (1) raw EEG data, (2) preprocessed data, and (3) patient clinical information. Preliminary analysis using classical machine learning algorithms (CSP + SVM and CSP + LDA) demonstrated an average classification accuracy between the two MI paradigms maintained at approximately 85%~86%. We anticipate that this dataset will facilitate research on MI-BCI paradigms and neuroplasticity for stroke, and contribute to the development of high-efficiency MI-BCI systems in the field of stroke rehabilitation.},
}

Humans
*Electroencephalography
*Brain-Computer Interfaces
*Stroke Rehabilitation
*Stroke/physiopathology
Machine Learning
Fingers

@article {pmid42533095,
year = {2026},
author = {Li, Q and Yin, X and Li, C and Guo, L and Wang, Y and Cheng, Y and Liu, N and Yin, Q and Ding, Y and Yan, M and Liu, Z and Chen, J and Zhang, J and Huang, S and Gu, W and Guo, A and Deng, H and Li, Y and Chen, D and Chambers, I and Zheng, H and Hu, R and Zhang, M and Li, J},
title = {TRIM37 interacts with TRIM28 to maintain primordial germ cell identity during migration.},
journal = {Cell research},
volume = {},
number = {},
pages = {},
pmid = {42533095},
issn = {1748-7838},
support = {32488101//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32030029//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32293231//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32370909//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {During primordial germ cell (PGC) specification, repression of somatic programs is essential for the establishment of germline identity. However, mechanisms that safeguard PGC fate thereafter remain unknown. Here, we identify the E3 ubiquitin ligase TRIM37 as a critical safeguard of PGC fate during migration. Trim37 deficiency causes severe PGC defects beginning at embryonic day 9.5 (E9.5) with complete PGC depletion by E12.5, and leads to an aberrant transition toward somatic cell states. Mechanistically, TRIM37 binds TRIM28 through its MATH domain and ubiquitinates TRIM28 via its RING domain, enhancing the TRIM37-TRIM28 interaction and promoting the nuclear retention of TRIM37. Forced nuclear export of TRIM37 results in PGC loss. Moreover, disruption of TRIM37 ligase activity or mutation of TRIM28 ubiquitination sites compromises PGC maintenance. We further show that the TRIM37-TRIM28 complex, likely acting in cooperation with AP2γ, restricts chromatin accessibility and H3K27ac levels at somatic gene loci, thereby repressing somatic transcriptional programs in PGCs. Together, our findings uncover a TRIM37-TRIM28-AP2γ regulatory complex that safeguards germ cell fate by preventing the activation of somatic transcriptional programs during PGC migration.},
}

@article {pmid42534420,
year = {2026},
author = {Ashouri, D and Weh, L and Borrmann, V and Coenen, C and Kraft, EM and Loufs, V and Mehnert, W and Muhr, P and Müller, O and Şahinol, M and Schmidt, M and Seyfried, G and Soedibjo, K and Stewart, IE and Wolbring, G and Youssef, S and Stieglitz, T},
title = {Neurotechnology through the lens of users and as a novel field for society.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1842746},
pmid = {42534420},
issn = {1664-2295},
abstract = {Successful and responsible innovation in neurotechnology requires clear ethical priorities and a deep understanding of individual and societal needs as well as public concerns. Recent cases of consumer exploitation, misleading claims, and inadequate patient aftercare reveal critical gaps in current practices and underscore the urgent need for more ethical, transparent, and user-centered engagement in this rapidly developing field. This study focuses on four complementary domains: (1) neuroethics and embodiment; (2) the cultural embedding of neurotechnologies; (3) art and culture in relation to neurotechnology; and (4) human enhancement, technovisions, and sociotechnical imaginaries. Across these domains, the manuscript explores user and societal perceptions, highlighting often overlooked asymmetries in communication between scientists and entrepreneurs and those who ultimately receive research outcomes in the form of products. Drawing on the authors' multidisciplinary expertise and a synthesis of the relevant literature, the manuscript outlines a possible foundation for developing more balanced, inclusive and symmetric communication formats that empower stakeholders regardless of status or expertise. Integrating insights from neurotechnology with applied ethics, the humanities, social sciences, technology assessment and the arts, this work seeks to contribute to a broader understanding of the societal and individual impacts of emerging neurotechnologies and to support the protection and empowerment of users by prioritizing their needs.},
}

@article {pmid41401516,
year = {2026},
author = {Xu, M and Gao, X and Li, F and Zhang, B and Chen, Y},
title = {BFRCNet: addressing the class imbalance problem in the rapid serial visual presentation paradigm for decoding.},
journal = {Journal of neural engineering},
volume = {23},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ae2d9a},
pmid = {41401516},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/classification ; *Photic Stimulation/methods ; *Neural Networks, Computer ; Classification Algorithms ; Algorithms ; },
abstract = {Objective.Imbalanced sample sizes in rapid serial visual presentation (RSVP) can substantially compromise the classification accuracy of electroencephalogram (EEG) analyses based on RSVP system.Approaches.We propose a balanced strategy-based network for feature representation, recombination, and classification for RSVP paradigm (BFRCNet), a specialized neural network architecture designed to enhance classification under imbalanced EEG data conditions. This architecture comprises three stages, in feature representation stage, a pyramid structure integrates multiscale spatiotemporal patterns while mimicking visual physiological mechanisms to enhance EEG feature extraction. The recombination stage incorporates anchor samples as auxiliary categories, transforming the imbalanced distribution into a balanced representation. The last classification stage leverages a novel focal loss function that integrates class and sample weights, thereby enhancing the reward for minority samples.Main results.BFRCNet demonstrated significant performance improvements in addressing class imbalance for RSVP tasks, achieving balanced accuracy scores of 89.53% on THU and 90.15% on CAS datasets. This represents a 3.04% improvement on THU and 3.27% on CAS, substantially outperforming current state-of-the-art methods.Significance.This method to handle imbalanced EEG data effectively improves classification performance in class-imbalanced BCI-paradigms.},
}

Humans
*Electroencephalography/methods/classification
*Photic Stimulation/methods
*Neural Networks, Computer
Classification Algorithms
Algorithms

@article {pmid42507740,
year = {2026},
author = {Wang, Y and Ju, Q and Jia, R and Yuan, M and Peng, Y},
title = {Revealing dichotomous prior biases in social anxiety through a social prism model.},
journal = {PLoS computational biology},
volume = {22},
number = {7},
pages = {e1014509},
pmid = {42507740},
issn = {1553-7358},
mesh = {Humans ; Bayes Theorem ; *Anxiety/psychology ; *Models, Psychological ; Female ; Male ; Social Behavior ; *Social Cognition ; Judgment ; Adult ; Young Adult ; Computational Biology ; },
abstract = {Social situations can be overwhelming for some people, triggering avoidance and social anxiety (SA). However, it remains unknown why identical situations lead to different interpretations. Here, we developed a Social Prism Model to systematically address mechanisms underlying atypical social cognition in SA within a Bayesian cognitive framework. Through eight social valence judgment experiments (N = 541), we demonstrated that social anxiety may be shaped not by how sensory evidence is processed, but by robust dichotomous prior biases depending on social cues. The dichotomous prior biases indicated two parallel mental shortcuts that predispose individuals toward social fear, where negative prior biases were associated with an intolerance to uncertainty and a fear of social evaluations, and over-positive prior biases were associated with negative social learning. Our Bayesian simulations further demonstrated how variations in prior expectations parameters can give rise to biased social judgments, providing mechanistic support for the proposed framework. Collectively, these findings showed that the negative interpretation of the social situations can be understood within the Bayesian framework, highlighting a key role of prior expectation in shaping human social cognition.},
}

Humans
Bayes Theorem
*Anxiety/psychology
*Models, Psychological
Female
Male
Social Behavior
*Social Cognition
Judgment
Adult
Young Adult
Computational Biology

@article {pmid42525944,
year = {2026},
author = {Choi, DH and Cheon, SI and Ha, S and Je, M},
title = {A Bias-Electrode-Free Multichannel ExG Readout IC with Time-Multiplexed PAM-4 Body Channel Communication.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2026.3717910},
pmid = {42525944},
issn = {1940-9990},
abstract = {We propose a multi-channel ExG recording system integrated with body channel communication (BCC), targeting wearable multimodal human-machine interfaces, such as extended reality (XR) and brain-computer interfaces (BCIs). To suppress noise from the BCC transmitter that appears as common-mode interference (CMI) at the ExG analog front end (AFE), we use time-multiplexed acquisition scheme that allocates separate time slots for ExG recording and BCC transmission. This temporal separation preserves ultra-low-noise ExG acquisition, enabling reliable recording of EEG and EOG in addition to EMG and ECG. Because the BCC transmitter must transmit multi-channel ExG data within a limited slot, it requires a high data rate. Using PAM-4 signaling, the proposed system achieves a 10-Mbps data rate. This modulation scheme is enabled by a bias-electrode-free ExG AFE, which increases the BCC signal amplitude by approximately 2.13×, thereby providing sufficient amplitude margin for reliable PAM-4 level discrimination. In addition, a charge-pump-based CMI cancellation loop incorporating a multi-channel least-mean-square (LMS) filter, together with a DC servo loop, mitigates inter-channel mismatch and suppresses differential-mode artifacts. Fabricated in 180-nm CMOS, the chips achieve a total common-mode rejection ratio (CMRR) of 101.1 dB and suppress electrode DC offsets up to 500 mV, while maintaining stable operation under 18-Vpp common-mode interference and 15% electrode mismatch.},
}

@article {pmid42526080,
year = {2026},
author = {Huang, J and Huang, S and Kong, Y and Zhou, M and Yang, H and Hong, D and Zhou, Y and Yin, W and Guo, J and Dang, CP and Song, Y},
title = {Age-related alterations in early and late auditory processing in children with attention-deficit/hyperactivity disorder: Evidence from event-related potentials.},
journal = {NeuroImage. Clinical},
volume = {51},
number = {},
pages = {104043},
doi = {10.1016/j.nicl.2026.104043},
pmid = {42526080},
issn = {2213-1582},
abstract = {This study investigated age-related differences in auditory detection and attentional processing in school-aged children with attention-deficit/hyperactivity disorder (ADHD) compared to typically developing (TD) peers, focusing on mismatch negativity (MMN), P3a, and P3b components. Participants were children aged 7-12 years with ADHD (N = 80; 12.0% female) and age-matched TD children (N = 80; 26.3% female). All children completed a three-stimulus auditory oddball task with unilateral presentation of standard, nontarget deviant, and target deviant tones while electroencephalography (EEG) was recorded. Compared with the steady increase in MMN amplitudes observed in TD children, children with ADHD showed reduced MMN amplitudes at ages 9-12 years and a U-shaped MMN age-related pattern. In TD children, more negative MMN amplitudes were associated with faster responses on hit trials. In contrast, children with ADHD showed reduced P3a amplitude at ages 7-8 years, and their P3b latency was significantly associated with behavioral performance. Our findings suggest that children with ADHD may rely more on late-stage cognitive processing to complete auditory attention tasks, whereas TD children benefit from more efficient early perceptual and involuntary attention mechanisms. Altered age-related differences across MMN, P3a, and P3b highlight disruptions in the development of auditory attention systems and may represent candidate neural indices that warrant further evaluation in longitudinal and intervention studies.},
}

@article {pmid42527227,
year = {2026},
author = {Klei, DS and Verstegen, SBH and Ahmetagic, D and van Osch, D and van Wessem, KJP},
title = {Low incidence of severe cardiac complications in traumatic sternal fractures: Rethinking routine rhythm monitoring.},
journal = {Injury},
volume = {},
number = {},
pages = {113541},
doi = {10.1016/j.injury.2026.113541},
pmid = {42527227},
issn = {1879-0267},
abstract = {BACKGROUND: Blunt cardiac injury (BCI) is a common finding after traumatic sternal fracture. For suspected BCI, cardiac rhythm monitoring is generally advised. However, the risk of severe complications of BCI remains unclear. This study analysed the occurrence of severe cardiac complications among hospitalised sternal fracture patients.

METHODS: A single-centre retrospective cohort study (January 2011 to December 2022) was conducted including adult blunt trauma patients with traumatic sternal fractures. Patients were excluded in case of non-blunt trauma, military status, transfer from another hospital > 24 h after trauma, transfer to another hospital during admission, or cardiopulmonary resuscitation prior to imaging. Primary outcome was the occurrence of severe cardiac complications, defined as sustained ventricular tachycardia, high-degree atrioventricular block, need for cardiac surgical intervention, primary cardiac arrest, or death from a primary cardiac cause. Secondary outcome was the occurrence of clinically relevant cardiac findings requiring treatment or outpatient follow-up.

RESULTS: Suspected BCI, defined as elevated troponin levels and/or ECG abnormalities within 6 h of presentation, occurred in 113 patients (32.3%). Two cases (2/113, 1.8%) of severe cardiac complications occurred in this group (both transient high-degree AV-block). Four patients (1.1%) developed cardiac arrest during admission due to a secondary non-cardiac cause. In total, 40 patients (11.4%) had clinically significant cardiac findings: 31 patients had new abnormalities, 7 patients showed changes in pre-existing cardiac conditions, and 2 patients had potential cardiac syncope as trauma cause. No major structural cardiac injury was found on echocardiography; 6 patients (1.7%) showed minor abnormalities on echocardiography.

CONCLUSIONS: Severe cardiac complications are rare after traumatic sternal fracture. Cardiac arrest occurred secondary to trauma-related, non-cardiac causes. These findings suggest that suspected BCI alone is not a sufficient indication for routine cardiac rhythm monitoring, and that admission to a monitored unit should be based on the patient's overall clinical condition. Non-urgent cardiac abnormalities were common, underlining the importance of cardiology consultation in these patients.},
}

@article {pmid42527303,
year = {2026},
author = {De Toledo, OF and Gutierrez-Aguirre, SF and Nogueira, BV and Sauvageau, E and Aghaebrahim, A and Hanel, RA},
title = {A Multimodal Imaging Workflow for Intraprocedural Targeting of Endovascular Stentrode Deployment: Technical Feasibility in a Phantom Model.},
journal = {AJNR. American journal of neuroradiology},
volume = {},
number = {},
pages = {},
doi = {10.3174/ajnr.A9551},
pmid = {42527303},
issn = {1936-959X},
abstract = {The Stentrode (Synchron) is a self-expanding nitinol stent brain-computer interface device deployed within the superior sagittal sinus adjacent to the precentral gyrus. Millimetric precision is required, underscoring the need for a reproducible imaging workflow translating preoperative planning into intraprocedural guidance. This non-clinical, preprocedural technical feasibility assessment used a human head model to simulate imaging steps. The workflow included thin-section CT acquisition; DICOM transfer to an external core lab for target identification and marking; re-importation of marked CT; 3D rotational angiography; and co-registration with marker reconstruction and intraprocedural display on live fluoroscopy. The CT provided a suitable dataset for anatomical analysis, and the core lab successfully marked the intended deployment region. Markers were preserved during transfer and re-importation. The annotated CT was successfully fused with 3DRA, without qualitatively apparent misalignment or artifact. This workflow was technically feasible for generating intraprocedural targeting guidance for Stentrode deployment.},
}

@article {pmid42527436,
year = {2026},
author = {Yan, W and Luo, Q and Du, C and Li, H and Chen, S and Yuan, Z and Hou, X and Wang, H},
title = {Cross-region neural signal reconstruction to lift electrode placement constraints in SSVEP brain-computer interfaces.},
journal = {npj biomedical innovations},
volume = {3},
number = {1},
pages = {},
pmid = {42527436},
issn = {3005-1444},
support = {52475311//National Natural Science Foundation of China/ ; 2024GX-YBXM-119//Key Research and Development Projects of Shaanxi Province/ ; 2023-021//State Key Laboratory of Robotics/ ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) rely on occipital EEG recordings, which are infeasible in many clinical scenarios (e.g., supine positioning, traumatic brain injury), restricting SSVEP-BCI access for patients with urgent communication needs. We propose dynamic sliding point-wise reconstruction with a triple-band cross-fusion network (DSTF-Net), a cross-brain-region framework enabling accurate SSVEP decoding exclusively from frontal EEG signals. DSTF-Net integrates four core innovations: ①a dynamic sliding point-wise reconstruction strategy that abandons static n-to-m mapping (directly mapping n frontal EEG samples to m occipital samples) to capture fine-grained temporal dependencies; ②a triple-band cross-fusion sub-network processing frontal EEG frequency bands through parallel convolutional streams and cyclic cross-attention; ③a stage-wise hierarchical training mechanism mitigating gradient interference by sequentially training single-band streams before fusion optimization; and ④a neurophysiologically constrained loss function enforcing validated unidirectional occipital-to-frontal SSVEP propagation. We trained DSTF-Net using paired frontal-occipital EEG from a healthy participant, then transferred it to 20 new users (including 8 brain-injured patients maintaining a supine position) via cross-subject transfer, reconstructing occipital activity solely from frontal EEG. Our framework achieves a maximum 33.47% decoding accuracy improvement over baselines. By eliminating occipital electrode requirements, our work expands SSVEP-BCI accessibility for clinically constrained populations and establishes a generalizable cross-brain-region neural mapping framework.},
}

@article {pmid42528394,
year = {2026},
author = {He, S and Yang, C and Ye, M and Tang, H and Gao, F and Wang, C and Zhao, Q and Song, J},
title = {Mechanically Programmable Electromagnetic Metamaterials for Generalized Phase Tailoring With Zero Static Power Consumption.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e74397},
doi = {10.1002/adma.74397},
pmid = {42528394},
issn = {1521-4095},
support = {12225209//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; 12572190//National Natural Science Foundation of China/ ; 2023M743011//China Postdoctoral Science Foundation/ ; 2022YFA1404902//Key Research and Development Program of the Ministry of Science and Technology/ ; R25F010016//Zhejiang Provincial Natural Science Foundation/ ; },
abstract = {Mechanically modulated reconfigurable electromagnetic metamaterials represent a promising avenue for flexible wavefront manipulation. However, most mechanically tunable designs rely on collective deformations and continuous external loading, leading to limited programmability and high static power consumption. Here, we present a mechanically programmable electromagnetic metamaterial enabled by 3D-printed shape memory polymer (SMP) compression-torsion coupling structures integrated with the three-fold symmetric three-armed meta-atoms (C3 meta-atoms) for generalized phase tailoring with zero static power consumption. The compression-torsion coupling structures enable deterministic and independent in-plane rotation of each unit cell under vertical compression, while the C3 meta-atoms provide sixfold cross-circularly polarized phase amplification, achieving full 0°-360° phase coverage with a narrow rotational angular range of 0°-60°. Leveraging the intrinsic shape-locking and shape-recovery properties of SMP, arbitrary phase distribution patterns are attainable via mechanical coding without sustained power consumption, and can be repeatedly erased and rewritten via thermal recovery. Numerical simulations and experimental characterizations reveal the design principle and operation mechanism of the metamaterial, verifying its programmable functionalities through demonstrations of anomalous refraction, reconfigurable metalens, and orbital-angular-momentum (OAM) generators. These findings provide a conceptual framework for low-energy, programmable, and reconfigurable wavefront modulation, laying a foundation for advancing next-generation mechanically programmable electromagnetic metamaterials.},
}

@article {pmid42529036,
year = {2026},
author = {Zhang, P and Karlsson, P and Chiu, D and Zou, R and Zhang, C and McEwan, A and Sun, B and Zhao, H},
title = {XR-integrated brain-computer interfaces for augmentative and alternative communication: a systematic review.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1842938},
pmid = {42529036},
issn = {1662-5161},
abstract = {INTRODUCTION: Communication is a fundamental human right and should not be constrained by disability; advances in modern technology are reshaping how communication is conceived and delivered, creating new possibilities for people who rely on augmentative and alternative communication (AAC). This systematic review examined whether immersive virtual, augmented, and mixed reality (VR/AR/MR; hereafter referred to as XR) has been integrated with brain-computer interfaces (BCIs) to support AAC for people who rely on it, and what evidence exists regarding usefulness and usability.

METHODS: Searches were conducted in five databases, with the initial search completed on 30 June 2021 and the updated search completed on 18 November 2025.

RESULTS: Two eligible studies were included. The evidence base was limited and methodologically heterogeneous, spanning distinct integration pathways: (i) an immersive-display implementation of a classic P300 speller suggesting communication performance comparable to conventional displays; and (ii) a wearable mixed-reality AAC concept contrasting non-BCI access (eye-gaze) with BCI-based access, with non-BCI access appearing more practically usable while BCI access remained challenging. Overall, the included studies indicate that immersive XR platforms can technically accommodate BCI-mediated interaction for communication-related purposes.

DISCUSSION: Current evidence remains sparse and heterogeneous, with usability and usefulness varying by application target and evaluation endpoint. This review therefore synthesises the available feasibility and usability/usefulness evidence and offers an open discussion of design and evaluation priorities for advancing BCI-enabled immersive AAC toward practical use in people who rely on it.

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021273338, identifier: CRD42021273338.},
}

@article {pmid42529248,
year = {2026},
author = {Strony, JT and Sinkler, MA and Piper, M and Harlow, E and Smith, K and Megerian, M and Apostolakos, JM and Chen, RE and Gillespie, RJ},
title = {A comparison of outcomes after arthroscopic rotator cuff repair with a bioinductive collagen implant, acellular dermal allograft, or no patch augmentation.},
journal = {JSES international},
volume = {10},
number = {5},
pages = {101754},
pmid = {42529248},
issn = {2666-6383},
abstract = {BACKGROUND: Onlay augmentation may enhance healing after arthroscopic rotator cuff repair (aRCR), but the optimal graft remains unclear. This study compared radiographic healing after aRCR performed without augmentation versus with acellular dermal allograft (ADA) or bioinductive collagen implant (BCI) augmentation.

METHODS: This was a retrospective cohort of prospectively collected data. Patients with full-thickness rotator cuff tears who underwent aRCR between May 2021 and April 2024 were included. Tear characteristics and fatty infiltration were assessed radiographically and arthroscopically. Post-operative repair integrity was evaluated on magnetic resonance imaging obtained ≥6 months after surgery. Functional outcomes were recorded pre-operatively and at 3, 6, and 12 months.

RESULTS: One hundred twelve patients were included (40 no augmentation, 40 BCI, 32 ADA; mean age 61 ± 9.2 years). BCI showed the highest overall healing rate (78%) versus ADA (53%) and controls (55%) (P = .049). In small-to-medium tears (no augmentation, n = 27; ADA, n = 18; BCI, n = 20), BCI demonstrated a 95% healing rate compared with ADA (72%) and controls (70%) (P = .06). Among patients with Goutallier grade ≤1, BCI was associated with significantly greater healing (85%) than ADA (64%) and controls (57%) (P = .03). On multivariable Poisson regression, BCI augmentation was independently associated with improved healing compared to no augmentation (adjusted relative risk 1.58, 95% confidence interval 1.16-2.16; P < .01). Functional outcomes did not differ among groups at any time point.

CONCLUSION: BCI augmentation was associated with higher healing rates, with the greatest predicted benefit observed in patients with both small-to-medium tears and minimal fatty infiltration. Healing rates were similar between ADA augmentation and no augmentation. Functional outcomes were comparable across groups.},
}

@article {pmid42529862,
year = {2026},
author = {Wang, H and Saeed, S and Yan, N and Wang, D},
title = {Beyond the role in motor function, entopeduncular nucleus, a critical neural hub in psychiatric disorders and sleep regulation.},
journal = {Annals of medicine},
volume = {58},
number = {1},
pages = {2682576},
doi = {10.1080/07853890.2026.2682576},
pmid = {42529862},
issn = {1365-2060},
mesh = {Animals ; *Entopeduncular Nucleus/physiopathology/physiology ; Humans ; *Mental Disorders/physiopathology ; *Sleep/physiology ; Mice ; Rats ; *Sleep Wake Disorders/physiopathology ; Deep Brain Stimulation ; },
abstract = {BACKGROUND: The entopeduncular nucleus (EP) is a major output nucleus of the basal ganglia and plays a critical role in integrating motor, emotional, and behavioral processes. Although traditionally linked to motor control, accumulating evidence indicates that the EP is deeply involved in psychiatric disorders, sleep regulation, addiction, and mood-related behaviors. As the rodent homolog of the human internal Globus pallidus (GP) the EP represents a key translational structure bridging preclinical and clinical neuroscience.

METHODS: This narrative review was conducted following established narrative review methodology. Relevant literature was identified through a structured search of major biomedical databases. Peer-reviewed experimental and clinical studies investigating EP anatomy, connectivity, neurotransmitter systems, and functional roles were included. The review synthesizes findings from mouse and rat models employing optogenetic and chemogenetic manipulation, electrophysiological recording, neuroanatomical tracing, molecular approaches, and behavioral assays, alongside human neuroimaging, lesion, and deep brain stimulation studies.

RESULTS: Evidence from rodent models demonstrates that the EP functions as a convergence hub integrating GABAergic, glutamatergic, dopaminergic, serotonergic, and endocannabinoid signaling. EP projections to the lateral habenula regulate aversive processing, addiction-related behaviors, and mood states. Cell-type-specific mouse studies identify entopeduncular circuits implicated in anxiety regulation. Dysregulation of EP circuits is implicated in depression, anxiety, and Parkinsonian motor dysfunction. Clinical and preclinical neuromodulation studies further support the therapeutic relevance of targeting EP circuits.

CONCLUSION: The EP is a multifunctional neural hub extending beyond motor control. Integrating evidence from rodent and human studies highlights its importance in psychiatric and sleep disorders and supports its potential as a translational therapeutic target.},
}

Animals
*Entopeduncular Nucleus/physiopathology/physiology
Humans
*Mental Disorders/physiopathology
*Sleep/physiology
Mice
Rats
*Sleep Wake Disorders/physiopathology
Deep Brain Stimulation

@article {pmid42519170,
year = {2026},
author = {Grevet, E and Forge, K and Tadiello, S and Izac, M and Amadieu, F and Brunel, L and Pillette, L and Py, J and Gasq, D and Jeunet-Kelway, C},
title = {Correction: Modeling the acceptability of BCIs for motor rehabilitation after stroke: a large scale study on the general public.},
journal = {Frontiers in neuroergonomics},
volume = {7},
number = {},
pages = {1854498},
doi = {10.3389/fnrgo.2026.1854498},
pmid = {42519170},
issn = {2673-6195},
abstract = {[This corrects the article DOI: 10.3389/fnrgo.2022.1082901.].},
}

@article {pmid42519235,
year = {2026},
author = {Wang, J and Xu, G and Du, C and Li, Z and Li, H and Chen, S and Han, C and Zhang, S},
title = {Correction: Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1903945},
doi = {10.3389/fnins.2026.1903945},
pmid = {42519235},
issn = {1662-4548},
abstract = {[This corrects the article DOI: 10.3389/fnins.2026.1851006.].},
}

@article {pmid42520165,
year = {2026},
author = {Hu, Y and Ding, H and Zou, L and Wen, Z and Wu, H and Ni, T and He, L and Gao, Q and Cao, H and Zhou, J and Chen, J and Jiang, B and Qiu, S and Yu, L and Yan, M},
title = {A glutamatergic S1-VPL-S1 corticothalamocortical loop amplifies mechanical hypersensitivity in neuropathic pain.},
journal = {Pain},
volume = {},
number = {},
pages = {},
pmid = {42520165},
issn = {1872-6623},
support = {82371217//the National Natural Science Foundation of China/ ; 82300566//National Natural Science Foundation of China/ ; 82071227//National Natural Science Foundation of China/ ; LZ23H090003//Natural Science Foundation of Zhejiang Province/ ; No.18 (2020)//the Leading Health Talents of Zhejiang Province, Zhejiang Health Office/ ; 2021-595 LCZDZK-01//the National Clinical Key Specialty Construction Project of China/ ; 2023M733095//the China Postdoctoral Science Foundation/ ; 2025T180562//the China Postdoctoral Science Foundation/ ; 82471230//National Natural Science Foundation of China/ ; },
abstract = {Although cortical-thalamic projections contribute to pain modulation, their downstream targets and functional roles are not well defined. Here, we show that glutamatergic neurons in the hindlimb region of primary somatosensory cortex (S1HLGlu) project to a subset of glutamatergic neurons in the ventral posterolateral thalamic nucleus (VPLGlu) that are anatomically separated from VPL neurons receiving peripheral afferent inputs. These VPLGlu neurons preferentially innervate S1HLGlu neurons, forming a recurrent glutamatergic corticothalamocortical pathway. Optogenetic and chemogenetic activation of the S1HLGlu-VPLGlu-S1HLGlu pathway reduced mechanical withdrawal thresholds under baseline conditions, whereas circuit inhibition alleviated mechanical allodynia and spontaneous pain in the spared nerve injury model without altering baseline nociception of mice. In vivo fiber photometry further demonstrated that this pathway enhanced S1HLGlu responses to normally subthreshold mechanical stimuli, suggesting a role in sensory amplification. Together, these findings indicate that the S1HLGlu-VPLGlu-S1HLGlu pathway becomes pathologically engaged during neuropathic pain and contributes to mechanical hypersensitivity. This work provides mechanistic insight into corticothalamic involvement in cortical pain processing and highlights a selective excitatory pathway as a potential target for neuropathic pain intervention.},
}

@article {pmid42520418,
year = {2026},
author = {Tabatabai, TS and Tabatabai, TS and Vaez, A and Ehterami, A and Atashi, A and Semyari, H and Salehi, M},
title = {Combined effects of an ointment comprising Eisenia fetida oil, Valeriana officinalis, and zinc oxide nanoparticles on healing in a rat model of second-degree burns.},
journal = {Burns : journal of the International Society for Burn Injuries},
volume = {52},
number = {8},
pages = {108140},
doi = {10.1016/j.burns.2026.108140},
pmid = {42520418},
issn = {1879-1409},
abstract = {Burns are one of the most devastating traumatic injuries, and in cases of advanced burns, patients require urgent and specialized care to minimize mortality. Studies have shown that a variety of herbal medicines and nanoparticles can be used to treat burn wounds. Among them mixture of Eisenia fetida oil, valerian, and zinc oxide (ZnO) nanoparticles can be a good choice due to their healing properties. This study aimed to synthesize an ointment based on Eisenia fetida oil, Valeriana officinalis, and zinc oxide to investigate its effect on skin wound healing. The hemocompatibility and biocompatibility of the designed ointment were evaluated using blood clotting index (BCI), hemolysis test, and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) test. The antibacterial effects of the designed ointment were evaluated using minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and time-kill tests. Finally, the healing ability of the designed ointment was evaluated in a rat model by creating a 3 × 3 cm burn wound. Wound healing was evaluated after 14 days by hematoxylin-eosin (H&E) and Verhoeff-Van Gieson (VVG) staining. The results showed that the addition of Valeriana officinalis and zinc oxide to Eisenia fetida oil improved hemocompatibility, biocompatibility, improved cell migration, and improved antibacterial properties. Additionally, the results of H&E and VVG staining demonstrated the good efficacy of the designed ointment in increasing epidermal thickness, promoting angiogenesis, and enhancing collagen and elastin fibers. These results indicate the potential of the designed ointment as a promising tool for skin wound healing and clinical trials.},
}

@article {pmid42521627,
year = {2026},
author = {Blumenthal, GH and Dekleva, BM and Gontier, C and Gonzalez, IC and Gonzalez-Martinez, JA and Yu, BM and Batista, AP and Sobinov, AR and Miller, LE and Gaunt, RA and Boninger, ML and Chase, SM and Collinger, JL},
title = {Distinct neural modes carry information about attempted grasp timing and force in the sensorimotor cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0205-26.2026},
pmid = {42521627},
issn = {1529-2401},
abstract = {Humans perform a variety of complex hand movements to manipulate objects, requiring precise control of changing forces. Understanding the role of sensorimotor cortex and the cortical dynamics underlying these actions is crucial for developing interventions that restore dexterous hand function after injury or disease. In this study, two male individuals with tetraplegia resulting from cervical spinal cord injury attempted a series of isometric grasps. Neural activity was recorded from the motor and somatosensory cortices using intracortical microelectrode arrays while participants attempted to exert a static or ramping force up and down. Despite their inability to execute movement and limited afferent input, the spiking activity in motor and somatosensory cortex was modulated with the task. Within the neural response we identified independent neural modes - distinct patterns of population-level neural activity that were informative about both the timing and magnitude of the attempted force. Moreover, distinct neural modes were observed during static and dynamic grasping conditions, suggesting independent control schemes for maintaining and changing forces. These modes were related to phases of the task, including the onset, offset, holding periods, as well as increasing and decreasing attempted forces. These results will inform the design of intracortical brain-computer interface (iBCI) systems that can leverage the patterns of grasp and force control evident in sensorimotor cortex during attempted movement to restore dexterous hand function.Significance Statement Restoring dexterous hand function after injury remains a major challenge, partly due to an incomplete understanding of the cortical dynamics underlying grasping and force control. In this study, we investigated neural activity within the motor and somatosensory cortices of individuals with tetraplegia attempting to perform grasps to different target forces with varying temporal profiles. We identified distinct neural modes modulated during specific phases of grasp that encode attempted force information throughout the task. These findings suggest that brain-computer interfaces could leverage these neural modes to restore grasping and force modulation.},
}

@article {pmid42522230,
year = {2026},
author = {He, J and Zhao, H and Lei, X and Qiu, J and Chen, L and Feng, T and Chen, H and Turel, O and He, Q},
title = {From behavioral profiles to neural scaffolds: cortico-striatal integrity predicts longitudinal stability and transition in psychological adaptability during early adulthood.},
journal = {Psychological medicine},
volume = {56},
number = {},
pages = {e243},
doi = {10.1017/S0033291726105352},
pmid = {42522230},
issn = {1469-8978},
support = {31972906//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Magnetic Resonance Imaging ; Longitudinal Studies ; Male ; *Adaptation, Psychological/physiology ; Female ; Adult ; *Corpus Striatum/physiology/diagnostic imaging ; Young Adult ; Impulsive Behavior/physiology ; *Prefrontal Cortex/physiology/diagnostic imaging ; Connectome ; Reward ; },
abstract = {BACKGROUND: Psychological adaptability hinges on the dynamic balance between cognitive regulation (self-control) and emotional reactivity (impulsivity, reward/punishment sensitivity). However, traditional variable-centered approaches often fail to capture how these traits holistically co-occur, and the neural architectures predicting their longitudinal transitions in early adulthood remain under-explored.

METHODS: This longitudinal study (N = 1,229 baseline; N = 432 2-year follow-up) integrated a person-centered behavioral approach with resting-state fMRI. We employed Latent Profile Analysis (LPA) to identify trait configurations based on self-control, impulsivity, and sensitivity to reward/punishment. Network-Based Statistic (NBS) analysis with family-wise error (FWE) correction was utilized to evaluate functional connectivity differences among groups. Finally, hierarchical regression and formal interaction models were conducted to test the prospective predictive validity of the identified neural circuits.

RESULTS: LPA delineated three distinct baseline profiles: Adaptive, Moderate, and Maladaptive. NBS analysis revealed that the 'Adaptive' profile is underpinned by robust cortico-striatal functional connectivity, integrating the medial prefrontal cortex and striatum. Longitudinally, initial regression models demonstrated that the baseline integrity of this circuit prospectively predicted behavioral adaptation at the 2-year follow-up. Furthermore, hierarchical regression and formal interaction analyses confirmed that this cortico-striatal circuit provided significant incremental predictive validity - above and beyond massive baseline behavioral stability - specifically in males.

CONCLUSIONS: These findings highlight cortico-striatal integration as a significant, gender-specific prospective predictor of longitudinal adaptation. By bridging person-centered profiling with network neuroscience, this study elucidates the neural correlates supporting future adaptive functioning and psychological resilience during the early adult transition.},
}

Humans
Magnetic Resonance Imaging
Longitudinal Studies
Male
*Adaptation, Psychological/physiology
Female
Adult
*Corpus Striatum/physiology/diagnostic imaging
Young Adult
Impulsive Behavior/physiology
*Prefrontal Cortex/physiology/diagnostic imaging
Connectome
Reward

@article {pmid42523358,
year = {2026},
author = {Wang, J and Kathios, N and Kubit, B and Lopez, K and Kim, JC and Large, E and Noble, S and Loui, P},
title = {When Music Loses Its Pleasure: Hippocampal Cingulum White Matter as a Structural Mediator of Musical Reward Decline in Aging.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.07.16.738989},
pmid = {42523358},
issn = {2692-8205},
abstract = {UNLABELLED: Individuals' ability to obtain pleasure from music, referred to as musical reward sensitivity, declines with age, yet the neural mechanisms underlying this decline remain unclear. In this study, we investigated musical reward sensitivity measured in 58 older adults and 131 young adults. Consistent with prior findings, young adults reported higher musical reward sensitivity than older adults (p < 0.05). To identify neuroanatomical predictors of musical reward sensitivity, we employed the elastic-net model to predict musical reward sensitivity using white matter microstructural properties and gray matter morphometric properties from the whole brain. In older adults, fractional anisotropy (FA) in the bilateral hippocampal cingulum (CGH) and external capsule (EC) reliably predicted individual differences in musical reward sensitivity. Moreover, FA in the right CGH significantly mediated the relationship between age and musical reward sensitivity in older adults. Notably, these associations were specific to musical reward sensitivity in older adults-that is, they did not replicate in young adults, and did not extend to general reward sensitivity. Together, these findings highlight the critical role of white matter integrity, particularly within the hippocampal-limbic pathways, in age-related changes in musical reward processing, and suggest a potential neurobiological target for interventions aimed at enhancing well-being in older adulthood.

KEY POINTS: Whole-brain machine learning identified the FA in the hippocampal cingulum and external capsule as robust predictors of musical reward sensitivity in older adults.FA in the right hippocampal cingulum mediated the association between age and musical reward sensitivity.These findings extend previous association-based studies by identifying hippocampal-limbic white matter as a key neural substrate supporting musical reward sensitivity decline during aging.},
}

@article {pmid42523363,
year = {2026},
author = {Smith, C and Inchyna, S and Barrentine, B and Nelson, M},
title = {Decoding and Characterizing the Intracranial Representation of Semantic Information.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.07.13.738249},
pmid = {42523363},
issn = {2692-8205},
abstract = {Brain-computer interfaces (BCIs) have achieved impressive performance by decoding motor and articulatory signals associated with speech production. However, considerably less is known about whether higher-level semantic representations can be decoded from human cortical activity. Demonstrating semantic decoding would advance both our understanding of language organization and the development of BCIs that rely on conceptual rather than purely articulatory information. We recorded intracranial neural activity from patients undergoing stereotactic electroencephalography (sEEG) for clinical epilepsy monitoring while they performed language tasks requiring semantic processing. High-gamma power was extracted from local field potentials and used to generate trial-level features for supervised machine-learning classification. Classification performance was evaluated using cross-validation. Semantic category information was decoded significantly above chance, with mean classification accuracy reaching 29.8% across 15 semantic categories (chance = 6.7%). These findings demonstrate that high-gamma activity contains information about conceptual category membership that can be extracted on individual trials. These results provide evidence that semantic information is accessible from intracranial population recordings and support the feasibility of semantic decoding as a complementary direction for future language BCIs. Beyond neuroprosthetic applications, this work contributes to understanding how conceptual knowledge is represented in the distributed human language network.},
}

@article {pmid42523883,
year = {2026},
author = {Jiang, Y and Wang, Q and Zhang, J and Shan, M and Guo, Q},
title = {Dynamic frailty and depressive symptoms in relation to incident stroke: findings from five harmonized longitudinal cohorts.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1880619},
pmid = {42523883},
issn = {1664-2295},
abstract = {BACKGROUND: Frailty and depressive symptoms are common in later life and may be related to cerebrovascular risk. Evidence remains limited on whether frailty burden and frailty change are associated with incident stroke across diverse aging cohorts.

METHODS: We analyzed harmonized longitudinal data from five population-based aging cohorts: the Health and Retirement Study (HRS), China Health and Retirement Longitudinal Study (CHARLS), Survey of Health, Ageing and Retirement in Europe (SHARE), English Longitudinal Study of Ageing (ELSA), and Mexican Health and Aging Study (MHAS). Frailty was measured using a harmonized 24-item deficit-accumulation frailty index (FI). The primary analysis used cohort-specific Cox proportional hazards models to estimate associations between baseline FI and first observed incident stroke during follow-up. Secondary exploratory analyses evaluated nonlinearity, FI change, competing mortality, depressive symptoms as a pathway marker, and two-wave cross-lagged associations.

RESULTS: The analytic sample included 81482 participants and 5,089 incident stroke events. In fully adjusted cohort-specific Cox models, each 0.1-unit increase in FI was associated with higher stroke risk in HRS, CHARLS, SHARE, and MHAS, but not in ELSA. Substantial between-cohort heterogeneity was observed; therefore, cohort-specific estimates were interpreted as the primary results and the random-effects pooled estimate was treated as descriptive. Fine-Gray sensitivity analyses treating death as a competing event supported positive frailty-stroke associations across all five cohorts. Restricted cubic spline (RCS) analyses suggested nonlinear associations for baseline FI and FI change. Exploratory pathway analyses indicated that depressive symptoms statistically accounted for part of selected frailty-stroke associations, although patterns varied by cohort and exposure definition. Two-wave cross-lagged panel models (CLPMs) suggested small, cohort-specific prospective associations between elevated frailty vulnerability and later depressive symptoms or stroke; these findings were interpreted as exploratory temporal associations rather than causal within-person effects.

CONCLUSION: Higher frailty burden was associated with incident stroke in most, but not all, harmonized aging cohorts, with substantial heterogeneity across populations. The findings support repeated frailty assessment and integrated mood evaluation in older adults while emphasizing the need for cohort-specific interpretation and confirmatory studies with adjudicated stroke outcomes.},
}

@article {pmid42524812,
year = {2026},
author = {Su, TF and Qin, P and So, A and Devecioğlu, İ and Vickery, RM and Birznieks, I and Shivdasani, MN and Moalem-Taylor, G and Guo, T and Aplin, FP},
title = {Single-unit Characterization of Electrically Evoked Peripheral Nerve Entrainment Failure.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neurom.2026.06.469},
pmid = {42524812},
issn = {1525-1403},
abstract = {OBJECTIVES: Evidence suggests that peripheral somatosensory nerves do not reliably entrain to electric stimulation even at low frequencies (<100 Hz). This is a concern for peripheral neuromodulation devices because it may cause discrepancies between expected and actual stimulation outcomes. To study this, we investigated the relationship between single-unit peripheral nerve responses (spikes) and the duration, frequency, and amplitude of electric stimulation.

MATERIALS AND METHODS: Single-unit teased-fiber recordings of mechanosensitive units were obtained from Sprague Dawley rat sciatic nerves. To characterize spike entrainment failure, electric stimulation was applied to the hindpaw at various durations, frequencies, and amplitudes. In addition, interleaved trains of electric and mechanic stimulus pulses were delivered to examine their interaction in generating responses. Spike response probability, spike latency, and spike amplitude were compared across stimulation conditions using linear mixed-effects regression models. To assess the ability of a standard nerve model to explain our findings, electric stimulation was simulated in a COMSOL/NEURON McIntyre-Richardson-Grill model of myelinated fibers.

RESULTS: Sustained electric stimulation resulted in spike entrainment failure at frequencies as low as 50 Hz. Units were not uniformly affected; those with faster initial conduction velocity were more strongly affected by long-duration (five minutes) electric stimulation at 50 Hz, while slower units displayed more entrainment failure at high frequencies over shorter durations (three seconds). Increased stimulation amplitude restored entrainment. Furthermore, electric stimulation generated changes in both spike latency and amplitude and interfered with mechanically evoked activity. The standard nerve model also showed entrainment failure, but the time course was dissimilar to our in vivo observations.

CONCLUSIONS: Our data support accounts of spike entrainment failure due to electric stimulation in the peripheral somatosensory system. We hypothesize from our findings that initial failure of spike entrainment is related to previously reported slow axonal K[+] channel activity, but progressive failure is better explained by the sodium-potassium pump conductance. This work provides important insight into mechanisms limiting the efficacy of clinical neuromodulation devices.},
}

@article {pmid42505470,
year = {2026},
author = {Ghosh, S and Sindhujaa, P and Senthil Kumar, P and Mohan, A and Mahalakshmi, P and Gulyás, B and Máthé, D and Padmanabhan, P},
title = {Hybrid Edge-Cloud Asymmetric Analytics for Portable Multimodal BCI Biosensors.},
journal = {Biosensors},
volume = {16},
number = {7},
pages = {},
pmid = {42505470},
issn = {2079-6374},
mesh = {*Biosensing Techniques ; Humans ; Electroencephalography ; *Brain-Computer Interfaces ; Machine Learning ; Signal Processing, Computer-Assisted ; Algorithms ; Electromyography ; },
abstract = {Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge-cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k = 5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation.},
}

*Biosensing Techniques
Humans
Electroencephalography
*Brain-Computer Interfaces
Machine Learning
Signal Processing, Computer-Assisted
Algorithms
Electromyography

@article {pmid42505547,
year = {2026},
author = {Drăgoi, MV and Nisipeanu, I and Marin, I and Cristoiu, CA and Alexe, CG},
title = {Bio-Inspired Gaze and Neural Command Fusion for Assistive Smartphone Interaction.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {11},
number = {7},
pages = {},
pmid = {42505547},
issn = {2313-7673},
support = {//National University of Science and Technology POLITEHNICA Bucharest/ ; },
abstract = {This paper presents an assistive smartphone interaction system that combines mobile gaze tracking with EEG-based BCI commands. The Android application estimates the user's gaze with the front camera, MediaPipe facial landmarks, a TinyTrackerS TFLite model, temporal smoothing, and a calibrated mapping from model output to screen coordinates. The gaze point is used to locate the intended screen area, while the BCI layer uses Emotiv Cortex commands for click, scroll, and back actions. A FastAPI and MongoDB backend manages profiles, calibration data, validation reports, runtime data, and WebSocket control events. Android Accessibility is used to execute the selected actions, with raw tap fallback when needed. The system was tested with 36 student volunteers during a short Patient Assist task. In the evaluation, 33 out of 36 gaze mappings were promoted to the active profile. The average static mean error was 390.02 px, and the average static p95 error was 789.09 px. BCI command success was 89.58% for click, 72.22% for scroll, and 77.78% for back. The Android layer acknowledged 228 out of 234 accepted control events. The average usability score was 4.21 out of 5.},
}

@article {pmid42505829,
year = {2026},
author = {Hu, CX and Yang, T and Wu, HY and Lin, YJ and Gao, YX and Gao, YJ and Yu, DN and Zhang, JY},
title = {Comparative Mitogenomics Reveals Gene Rearrangement and Phylogenetic Relationships in Siphlonuroidea (Insecta: Ephemeroptera).},
journal = {Insects},
volume = {17},
number = {7},
pages = {},
pmid = {42505829},
issn = {2075-4450},
support = {32470475//National Natural Science Foundation of China/ ; 202501A21//the Natural Science Foundation of Xinjiang Uygur Autonomous Region/ ; },
abstract = {Siphlonuroidea is a superfamily within Ephemeroptera, yet the phylogenetic relationships among its constituent families and their placement relative to other mayfly lineages remain unresolved. To address these questions, we generated and analyzed 16 newly assembled mitochondrial genomes from 14 species across Ephemeroptera, including three species of Ameletidae, four of Siphlonuridae, and nine mitochondrial genomes from seven species of Isonychiidae. Comparative mitogenomic analysis revealed two distinct tRNA gene rearrangement patterns within Siphlonuridae, including trnI-trnQ-trnM-trnQ-trnM-trnQ-trnM-trnQ-trnM and trnI-trnM-trnQ-trnM. In contrast, all Ameletidae mitogenomes share an identical rearrangement of trnI-trnQ-trnM-trnM, which constitutes a potential synapomorphy supporting the monophyly of this family. Compositional analysis further showed that Siphlonuridae and Ameletidae exhibit significantly higher and highly similar A+T contents, clustering together in hierarchical analyses. This characteristic contrasts sharply with Isonychiidae, which displays markedly lower A+T content. Phylogenomic inference based on the PCG12 dataset supports a sister-group relationship between Siphlonuridae and Ameletidae, with this clade itself forming the sister group to a well-supported clade of Isonychiidae and Heptageniidae. Divergence time estimation places the origin of the Ameletidae and Siphlonuridae lineage in the Late Jurassic (174.71 Mya), while Isonychiidae diverged in the Early Cretaceous (136.81 Mya). In conclusion, Siphlonuridae and Ameletidae show a closer affinity and belong to Siphlonuroidea. Isonychiidae shares a closer relationship with Heptageniidae and remains outside Siphlonuroidea. Siphluriscidae is recovered as the sister lineage to all other extant Ephemeroptera, confirming its status as the earliest-diverging extant mayfly lineage.},
}

@article {pmid42507616,
year = {2026},
author = {Cascella, M and De Simone, M and Vittori, A and Fasolino, S and Iaconetta, G},
title = {An overview of current and emerging strategies for phantom limb pain.},
journal = {Expert review of neurotherapeutics},
volume = {},
number = {},
pages = {1-11},
doi = {10.1080/14737175.2026.2709803},
pmid = {42507616},
issn = {1744-8360},
abstract = {INTRODUCTION: Phantom limb pain (PLP) is a disabling neuropathic pain syndrome affecting many individuals following limb amputation. Despite decades of research, PLP management remains challenging because of its complex and incompletely understood pathophysiology.

AREAS COVERED: According to the SANRA recommendations, this updated narrative review provides a critical overview of current pathophysiological concepts and clinically relevant established and emerging therapeutic strategies for PLP. Relevant literature was identified through searches of PubMed/MEDLINE, Scopus, and Web of Science databases using predefined combinations of terms related to PLP. The review integrates evidence from pharmacological studies, rehabilitation trials, neuromodulation research, and emerging digital-health applications.

EXPERT OPINION: PLP should be considered a biologically heterogeneous pain condition associated with multiple potential mechanisms, including peripheral, spinal, central, and psychosocial factors. Current evidence suggests that the relative contribution of these mechanisms varies across individuals, and no single mechanism adequately explains all cases of PLP. Conventional pharmacological strategies provide limited and inconsistent benefit, whereas multimodal interventions combining sensorimotor rehabilitation, neuromodulation, psychological support, and targeted surgical approaches are promising. Future progress will likely depend on mechanism-based phenotyping, integration of artificial intelligence, brain-computer interfaces, and closed-loop neuromodulation systems to enable personalized management strategies. However, high-quality studies with standardized outcome measures and long-term assessments are urgently needed.},
}

@article {pmid42509357,
year = {2026},
author = {Sandbrink, JD and Young, MJ},
title = {Advancing data protections for implantable brain-computer interfaces.},
journal = {Communications medicine},
volume = {6},
number = {1},
pages = {},
pmid = {42509357},
issn = {2730-664X},
abstract = {Implantable brain-computer interfaces (iBCIs) are rapidly transitioning from proof-of-concept devices to early clinical application. The high-resolution neural signals they capture may yield insights beyond those derived from conventional health data. In this Review, we examine how clinical iBCI data remain insufficiently protected, despite existing privacy laws like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). Five core gaps are identified: overreliance on conventional de-identification, limited individual control and rights, conflated consent practices, limited guardrails against misuse, and underspecified ownership. We examine strategies to address these gaps, including protections for de-identified data, stronger iBCI data rights and control, separate data consent, limits on harmful secondary uses, and monetization guardrails. As iBCIs transition from research tools to real-world clinical practice, clinicians, researchers, developers, and regulators, in dialogue with prospective and current iBCI users, will play central roles in advancing patient autonomy and privacy.},
}

@article {pmid42510401,
year = {2026},
author = {Kulkarni, AR and Kuber, PM},
title = {Detecting and Improving Human Cognitive State in Real-Time Using Data-Driven Adaptive Systems: A Systematic Review.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {7},
pages = {},
doi = {10.3390/bioengineering13070734},
pmid = {42510401},
issn = {2306-5354},
abstract = {Changes in human attention, workload, or alertness over time can affect task performance and may even increase the risk of injury. Detecting these changes in real time can be beneficial in improving system performance and safety. We reviewed 27 studies that developed models to sense physiological signals, classify one's cognitive state, and deliver automated intervention. Interventions included providing real-time feedback, adjusting the task's difficulty, or modifying automation levels across driving, education, rehabilitation, and human-robot collaboration applications. The findings showed that electroencephalography (EEG) sensors were used in 70% of studies, with attention (56%) and mental workload (26%) considered as the most targeted cognitive states. Within-subject classification reached 81.85-95.81% for multi-class tasks in laboratory settings. The most common interventions included neurofeedback display (30%) and task difficulty adjustment (19%), while automation adjustment was less frequent (11%). Only 33% of studies mentioned a latency of 15 milliseconds to 2.5 s, and all systems operated reactively by detecting cognitive states after their onset rather than anticipating them. The provided recommendations focus on the detection of multiple interacting cognitive states and predictive cognitive state trajectories. This review presents key directions for future research and provides a foundation for designing more effective cognitive state adaptive systems.},
}

@article {pmid42512497,
year = {2026},
author = {Zhang, C and Ma, Y and Li, M and Gao, X and Wu, X},
title = {Align and Fuse: A Transformer-Based Framework for EEG-Augmented Visual Recognition.},
journal = {Brain sciences},
volume = {16},
number = {7},
pages = {},
doi = {10.3390/brainsci16070723},
pmid = {42512497},
issn = {2076-3425},
support = {2024AH040115//Scientific Research Project of Colleges and Universities in Anhui Province/ ; 202423k09020041//Anhui Province Science and Technology Innovation and Tackling Key Problems Project/ ; },
abstract = {Background: Integrating human neural signals with computational vision systems offers a promising route toward more robust visual recognition, yet supporting mixed-granularity recognition, where both coarse- and fine-grained categories must be distinguished within a unified system, remains challenging due to the heterogeneous feature spaces of electroencephalography (EEG) and visual data. Methods: We propose "Align and Fuse," a two-stage Transformer-based framework. Stage 1 constructs a shared semantic space using a hardness-aware multimodal supervised contrastive loss with Hard Negative Weighting to explicitly target confusable class pairs. Stage 2 employs a multimodal Transformer with co-attention to fuse the aligned features for classification. Results: On the 80-class EEG-ImageNet benchmark, our framework achieved 91.12% Top-1 accuracy under a temporally separated control protocol, improving over the corresponding vision-only (89.08%) and Standard Transformer (89.95%) baselines. Under the original stratified random split, it achieved 92.56% Top-1 accuracy; on the 40-class EEGCVPR dataset, accuracy reaches 95.82%. Cross-subject experiments yield 90.92% average Top-1 accuracy on four unseen subjects, and Grad-CAM analysis suggests that aligned EEG signals shift the model's attention toward semantically relevant regions. Conclusions: Coupling hardness-aware alignment with decoupled multimodal fusion supports EEG-augmented recognition by leveraging complementary stimulus-related information under the evaluated protocols. Because EEG features are required at inference time, the framework is positioned as a human-in-the-loop EEG-augmented recognition system rather than a standalone vision model.},
}

@article {pmid42512505,
year = {2026},
author = {Calabrò, RS and Calderone, A and Gregorio, TD and Onesta, MP and Quartarone, A},
title = {Robotic Rehabilitation in Spinal Cord Injury: Neurophysiological Basis and Severity-Based Clinical Framework.},
journal = {Brain sciences},
volume = {16},
number = {7},
pages = {},
doi = {10.3390/brainsci16070732},
pmid = {42512505},
issn = {2076-3425},
support = {RRC-2026-23688274//Ministry of Health/ ; },
abstract = {Background/Objectives: Spinal cord injury (SCI) causes heterogeneous motor, sensory, autonomic, and participation limitations; recovery priorities vary by injury level, completeness, time since injury and residual function. Robotic rehabilitation has expanded from assistive technology to restorative, compensatory and health-promoting interventions, but patient-tailored prescription frameworks remain underdeveloped. Methods: PubMed/MEDLINE was searched from database inception to May 2026 using predefined domain-specific strategies, and findings were synthesized narratively to integrate mechanistic, clinical, safety and implementation evidence. Results: Robotic systems can increase task-specific repetition, sensorimotor feedback, active engagement and quantitative monitoring. Upper-limb robotics are feasible in cervical SCI and may support reach, grasp and activities of daily living, although SCI-specific controlled evidence remains limited. Lower-limb exoskeletons and locomotor robots can support gait practice, upright mobility, exercise exposure and selected secondary health outcomes, but walking speed, energy expenditure, cost, supervision needs and community translation remain important barriers. Sensory and non-motor effects, including proprioceptive input, spasticity, pain, bowel routine, cardiometabolic conditioning, participation and psychological well-being, are clinically relevant but should be interpreted according to evidence strength. Robotics combined with functional electrical stimulation, virtual reality, brain-computer interfaces, non-invasive brain stimulation and artificial intelligence-driven adaptation is promising but not yet routine. Conclusions: Robotic rehabilitation in SCI should be prescribed through a severity-based process that considers lesion level, American Spinal Injury Association Impairment Scale grade, residual voluntary and sensory function, safety, patient priorities and measurable goals. The proposed framework supports transparent selection and prospective validation of individualized robotic rehabilitation and shifts decisions beyond device availability toward clinically meaningful and equitable implementation.},
}

@article {pmid42512975,
year = {2026},
author = {Yan, H and Xu, X},
title = {Recent Progress in In-Ear EEG Technology and Its Emerging Real-World Applications: A Review.},
journal = {Micromachines},
volume = {17},
number = {7},
pages = {},
doi = {10.3390/mi17070764},
pmid = {42512975},
issn = {2072-666X},
abstract = {Electroencephalography (EEG) is a core technique for brain activity monitoring. However, conventional EEG systems suffer from complicated setup and poor portability, which drives the development of ear EEG technology. Ear EEG is divided into in-ear and around-ear types, both with unique application strengths. This review mainly discusses in-ear EEG, as it features a compact structure and fits well with daily wearable use cases. Current research on in-ear EEG is limited to feasibility verification and small-sample experiments. Researchers have not yet combined personalized design with signal processing algorithms systematically, and multi-center clinical trials are still absent. These issues have become the major bottleneck hindering its clinical transformation. This paper reviews the latest advances in ear-EEG systems, focusing on structural innovation and material development to summarize key achievements in hardware design. It also summarizes its typical applications in brain-computer interfaces (BCI), covering steady-state responses, event-related potentials and motor imagery. Meanwhile, it analyzes the application of in-ear EEG in brain state monitoring, including sleep tracking, epilepsy detection, drowsiness evaluation and emotion recognition. Finally, future directions for in-ear EEG are outlined, including personalized design and intelligent signal processing. This review provides a technical framework for beginners and identifies key directions for future research.},
}

@article {pmid42513041,
year = {2026},
author = {Ding, Y and Zhang, R and Fan, X and Chen, W and Yang, Z},
title = {An Ultra-Compact ARCL-Based MEMS Radar Filter for Mobile Robotic Platforms.},
journal = {Micromachines},
volume = {17},
number = {7},
pages = {},
doi = {10.3390/mi17070830},
pmid = {42513041},
issn = {2072-666X},
abstract = {To address the stringent requirements for miniaturization and high reliability in the perception systems of mobile robotic platforms, this article presents an ultra-compact bandpass filter based on air core recta-coax lines using micro-electro-mechanical systems technology. The proposed filter features an air-filled cavity structure with internal coupled lines and a fully enclosed metal shield, which effectively minimizes dielectric and radiation losses while achieving a highly compact footprint. This compactness is particularly critical for robotic radar front-ends, where limited payload capacity demands high integration density. By leveraging classical filter synthesis theory, the design achieves a high-order response within a minimized volume. Furthermore, the inherent high-Q characteristic of the air cavity significantly improves out-of-band rejection, thereby effectively suppressing interference in complex electromagnetic environments and enhancing the signal-to-noise ratio for robotic detection. A prototype operating at 75 GHz was fabricated and measured. The experimental results demonstrate a low insertion loss of 1.5 dB and a compact size of 0.875 mm[3], showing reasonable agreement with simulations. The proposed design offers a promising solution for next-generation, high-performance sensing units in autonomous robotics.},
}

@article {pmid42513312,
year = {2026},
author = {Kordas, B},
title = {Multimodal Assessment of Consciousness with Brain-Computer Interfaces and Artificial Intelligence: From Acquired Brain Injury to Neurodegenerative Disease.},
journal = {Journal of clinical medicine},
volume = {15},
number = {14},
pages = {},
pmid = {42513312},
issn = {2077-0383},
support = {2023/49/N/NZ4/03958//National Science Centre/ ; },
abstract = {The assessment of consciousness has been shaped largely by research on acquired disorders of consciousness after acute or chronic brain injury, but similar problems of unreliable behavioral expression increasingly arise in neurodegenerative disease. This translational overlap is especially relevant when preserved cognition, awareness, or intentionality cannot be reliably expressed because of severe motor impairment, fluctuating arousal, cognitive decline, aphasia, apraxia, or impaired cooperation. In neurodegenerative disease, degeneration of arousal systems, large-scale brain networks, cognition, and motor pathways may similarly make observable behavior an unreliable measure of awareness. The challenge is not only to determine if a patient responds, but also to ask if residual awareness, intentionality, or covert cognition can still be detected through physiological signals. This review discusses how contemporary modalities reshape this assessment. Electroencephalography has moved from a descriptive measure of background activity to a bedside tool capable of probing event-related responses, network organization, and cortical complexity. Magnetic resonance methods reveal altered connectivity within thalamocortical and default mode network systems, while functional near-infrared spectroscopy adds a portable hemodynamic approach that may be repeated at the bedside and integrated with active paradigms. Brain-computer interfaces provide a translational step by converting neural responses into evidence of command following or, in selected patients, into communication, and artificial intelligence strengthens these approaches by extracting clinically meaningful patterns from complex neural and hemodynamic data. Additionally, autonomic measures, including heart rate variability and baroreflex indices, are considered as auxiliary physiological context for arousal and engagement, and not as direct markers of awareness. Because the most mature evidence for covert awareness and cognitive-motor dissociation comes from acquired disorders of consciousness, this review treats brain injury literature as a methodological foundation instead of as directly interchangeable evidence for neurodegenerative disease. It then examines how these approaches may be adapted to neurodegenerative contexts, especially ALS, severe dementia, Lewy body disease with fluctuating cognition, and conditions in which communication or motor output becomes unreliable.},
}

@article {pmid42513606,
year = {2026},
author = {Shankar, R and Lo, YT and Fong, CL and Keong, N and Chua, KSG},
title = {Motor Imagery Brain-Computer Interface (MI-BCI)-Assisted Upper Limb Neurorehabilitation for Acute Stroke During Inpatient Rehabilitation: A Prospective Feasibility Study with Economic Evaluation Protocol.},
journal = {Journal of clinical medicine},
volume = {15},
number = {14},
pages = {},
pmid = {42513606},
issn = {2077-0383},
abstract = {Background: Stroke is a leading cause of neurological disability worldwide, with upper limb impairment affecting approximately 70% of survivors and only 5-20% achieving complete dexterity recovery at six months. Brain-computer interface (BCI) neurorehabilitation decodes motor intentions from electroencephalographic (EEG) signals to deliver synchronized functional electrical stimulation (FES) and virtual reality feedback, creating a closed-loop neurofeedback system that reinforces motor learning. While existing evidence supports BCI efficacy and safety in chronic stroke, its feasibility, safety, and cost-effectiveness during the acute and subacute phase (2 to 12 weeks post-stroke), when neuroplasticity is heightened, remain underexplored. Furthermore, there is a paucity of data regarding preliminary health economic analyses for BCI rehabilitation in acute stroke rehabilitation settings. Methods: This prospective, open-label, single-arm pragmatic feasibility pilot trial will recruit 12 patients with hemorrhagic or ischemic stroke (2-12 weeks post-stroke) undergoing inpatient rehabilitation from a public healthcare institution. Up to 15 sessions of BCI-rehabilitation of 30 min each using the recoveriX system will be supervised by a trained therapist or clinical research assistant (4-5 sessions/week over 3-4 weeks), followed by standard occupational therapy within 30-60 min of BCI-rehabilitation. Primary outcomes assessing feasibility and adherence include eligibility and recruitment rate (%/screened); tolerability using self-rated System Usability Scale (SUS) score; within-session adherence > 80%/240 trials, summated for completed trials per patient; programme completion number > 80% of scheduled (>12/15) sessions; and training-related adverse events per patient ≤ 17% (≤2/12 sessions). Secondary outcome measures include clinical efficacy by arm impairment scale using hemiplegic Upper Limb Fugl-Meyer Motor Assessment (FMA-UE), hand function using Action Research Arm Test (ARAT), admission and discharge functional status (Functional Independence Measure-FIM (18-126), Modified Barthel Index-MBI (0-100), stroke impact scale (SIS_3.0), arm, participation domains), and economic analysis. All outcomes will be measured by trained therapists/researchers at baseline week 0, week 3-4 (post-BCI-rehabilitation), and week 12 and 24 (follow-up). BCI-rehabilitation EEG-derived electrophysiological correlates of recovery will be extracted to better understand participant progress over time. An incremental cost-utility analysis will compare the BCI-rehabilitation participants against propensity-matched historical controls from the TTSH stroke rehabilitation registry (2017 to 2025), stratified by baseline motor severity. Discussion: This study will provide preliminary evidence on feasibility, tolerability, safety, clinical efficacy, and cost-effectiveness of early BCI-rehabilitation in acute/subacute stroke to better inform clinicians on its implementation.},
}

@article {pmid42515340,
year = {2026},
author = {Kutteri, SG and Vinod, AP},
title = {Exploring Kinematics Information Decoding from EEG Slow Cortical Potentials During Movement Imagination and Observation.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {14},
pages = {},
doi = {10.3390/s26144456},
pmid = {42515340},
issn = {1424-8220},
support = {MOE-T2EP20123-0001//Ministry of Education/ ; },
abstract = {Motor Imagery-based brain computer interface (MI-BCI) systems capable of decoding imagined movements and their kinematics are a rapidly advancing area of BCI research. Such BCIs can enhance human-computer interaction and have potential neurorehabilitation and assistive technology applications. This study explores the feasibility of decoding kinematic information, including movement direction and speed of imagined hand movements, from EEG slow cortical potentials (SCPs). EEG data from fourteen healthy subjects, associated with bidirectional center-out right-hand movement imaginations at two different speeds, is analyzed in this study. Peak negativity of movement-related cortical potential derived from fifteen primary motor cortex EEG channels is used to decode the direction and speed of imagined and observed hand movements. A Pearson correlation coefficient-based channel selection is further applied to identify a subject-specific set of channels from the pool of fifteen channels for decoding the kinematic information. Pairwise classification of direction-speed combinations achieved an average accuracy of 63.44 ± 9%. In contrast, slow-versus-fast speed classification achieved a lower accuracy of 53.87 ± 6.4% for motor imagery, which was not significantly different from the empirical chance distribution. The same analysis applied to movement observation resulted in an average direction-speed pair classification accuracy of 57.74 ± 8.6%, while speed classification achieved 50.74 ± 8.1%. These findings demonstrate that SCP features contain reliable information related to movement direction, whereas speed-related information appears weaker and less consistent across subjects. The results highlight the potential of SCP-based decoding for directional control and motivate further investigation of speed-related neural signatures. The findings from direction decoding during movement observation open avenues for future investigations into shared neural representations underlying passive movement observation.},
}

@article {pmid42515387,
year = {2026},
author = {Megalingam, RK and Kuttankulangara Manoharan, S and Cheriyan Manjooran, D and Kamble, D},
title = {Thrivaad: A Multilingual, Predictive Eye-Sign-Based AAC System Powered by Optimized Deep Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {14},
pages = {},
doi = {10.3390/s26144503},
pmid = {42515387},
issn = {1424-8220},
abstract = {Around 1.5% of the global population is suffering from speech impairments; the major causes for this are cerebral palsy and ALS, and the only way for these individuals to communicate is through Augmentative and Alternative Communication (AAC). These systems are either electronic or non-electronic. Based on new study developments, electronic methods, such as Brain-Computer Interaction (BCI) and eye-gaze-based communication, are assessed as the best choices, but they have their own limitations, incorporating limited adaptability to changing conditions, such as setup variations and user fatigue, which reduces the system's robustness. Our previous study, Netravad, shows potential for addressing these gaps, but it lacks multilingual support and will not yield the same results under changing lighting conditions. This study, Thrivaad, provides multilingual support and text prediction and integrates optimized deep learning to accurately capture eye movements even in varying environmental lighting conditions. Thrivaad uses eye movements as input from a webcam, and the Optuna-optimized YOLOv5 model is used to detect the eye direction accurately. Then communication is established in English, Malayalam, and Hindi. The text-prediction feature of this system improves communication by reducing the number of eye gestures required to form a message. This study included a total of 60 participants across three age groups with 35,263 eye-sign images collected. With this data, the YOLOv5 model is trained and then optimized by Optuna. The proposal system provides accurate eye direction, text prediction, multilingual support, and improved adaptability to changing conditions for eye-based AAC.},
}

@article {pmid42516272,
year = {2026},
author = {Chen, T and He, M and Basang, S and Ren, H and Fu, Y and Li, Y and Shi, X and Chen, L},
title = {Global trends in epilepsy and the Chinese experience: Epidemiological transitions, underlying mechanisms, and integrated strategies (1990-2021).},
journal = {Neuroprotection (Chichester, England)},
volume = {},
number = {},
pages = {},
pmid = {42516272},
issn = {2770-730X},
abstract = {Epilepsy represents a global public health challenge, with its burden distribution profoundly illuminating patterns of health inequality. Based on the Global Burden of Disease Study 2021 (GBD 2021) data, this review constructs a three-dimensional analytical framework encompassing biological determinants, health system performance, and social determinants of health. Over the past three decades, although the absolute number of individuals with epilepsy worldwide has continued to increase, age-standardized mortality rates have declined substantially. However, 87.9% of the associated disability-adjusted life years (DALYs) are concentrated in low- and lower-middle-income countries (LMICs). China, with approximately 9-10 million people living with epilepsy, confronts a triple disparity encompassing regional disparities, urban-rural gaps, and health system hierarchy barriers: mortality rates in western provinces are approximately nine times higher than those in eastern regions; the treatment gap in rural areas reaches 60%-90%; and primary care capacity deficits within the tiered diagnosis and treatment system create bottlenecks in case identification and referral. Concurrently, the disease spectrum is undergoing a profound transition, with an increasing burden of post-stroke epilepsy in older adults, while special populations, including children and women, continue to face persistent challenges. Despite China's notable achievements in reducing mortality and establishing a tertiary epilepsy center network, multiple interacting factors perpetuate a vicious cycle of poverty, disease, and stigma among affected individuals. Future efforts require alignment with the World Health Organization Intersectoral Global Action Plan on Epilepsy and Other Neurological Disorders 2022-2031 (IGAP) to develop precision prevention and control strategies, thereby contributing the Chinese experience to global epilepsy governance.},
}

@article {pmid42516593,
year = {2026},
author = {Haridharan, H and Dhanasekar, G and Nageswaran, S},
title = {Cognitive load gating system in motor imagery BCIs: a dual-task EEG study with differential entropy-based reliability estimation.},
journal = {Frontiers in artificial intelligence},
volume = {9},
number = {},
pages = {1859963},
pmid = {42516593},
issn = {2624-8212},
abstract = {Brain-computer interface (BCI) systems based on motor imagery hold significant clinical value for individuals who have lost voluntary movement, but most studies test BCI assistive devices like powered wheelchairs under ideal conditions where motor imagery signals are not interfered by simultaneous cognitive load. This work records electroencephalography (EEG) from 13 participants across four tasks: baseline rest, mental arithmetic (Easy, Medium, Hard), pure left/right motor imagery and both tasks simultaneously, to build a two-layer classification system. The first layer decodes motor intent using a model chosen from nine classifiers, from Filter Bank Common Spatial Pattern (FBCSP) and Riemannian geometry families. The second layer is a subject-specific safety gate that combines the classifier's decision-margin confidence score with 39-dimensional Differential Entropy (DE) features extracted from the theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands across all 13 electrodes, feeding a logistic regression boundary to predict trial-level MI prediction reliability. FBCSP + SVM-Linear model emerged as the best-performing model on pure motor imagery cross-validation. Under 10-fold cross-validated pure motor imagery, the mean balanced accuracy across all subjects was 0.594 and degraded to 0.517 on dual-task trials, a statistically significant reduction (Wilcoxon W = 14.0, p = 0.026). The DE-based safety gate, operating at a mean rejection rate of 25.0%, produced statistically significant reductions in false positive commands (from 8.00 to 5.62 per subject, p < 0.001) and false negative commands (from 13.00 to 9.00 per subject, p < 0.001). Post-gate balanced accuracy improved significantly (p = 0.013). An ablation study showed that DE features drive gate performance. These results demonstrate that a learned cognitive load gate has the potential to improve the safety profile of a motor imagery BCI in a preliminary proof-of-concept offline evaluation with healthy participants.},
}

@article {pmid42517464,
year = {2026},
author = {Zhang, J and Zhou, J and Zhang, X},
title = {Spatial and Temporal Characteristics of Distractor-Induced Repulsion Effect in Visual Working Memory: A Behavioral and ERP Study.},
journal = {Psychophysiology},
volume = {63},
number = {7},
pages = {e70366},
pmid = {42517464},
issn = {1469-8986},
support = {2021ZD0200500//STI 2030-the Major Projects/ ; 2021ZD0204300//STI 2030-the Major Projects/ ; 61632014//The Key Program of National Natural Science Foundation of China/ ; 01900-110190101//Fundamental Research Funds for the Faculty of Psychology, Beijing Normal University/ ; },
abstract = {In visual working memory, relation-based distractors can induce a memory repulsion effect, yet its spatial boundary and cognitive stage remain unclear. Across two experiments, we examined the spatial modulation and temporal dynamics of this effect. Experiment 1 presented a central target with peripheral distractors arranged as a regular pentagon and manipulated target-distractor spatial distance. Results showed that the repulsion effect decreased monotonically with increasing distance, supporting an absolute-distance account rather than a strict central attentional window account. Experiment 2 employed EEG and orthogonally manipulated distractor color (identical to vs. different from the target) and spatial distance (small vs. large). Behaviorally, the distance gradient was replicated, and repulsion occurred only for different-color distractors. Critically, the ERP difference between different-color and identical-color conditions emerged as a late positive component (LPC) between 382 and 604 ms post-stimulus, and its amplitude positively correlated with the individual behavioral repulsion effect. Early N1 activity (approximately 96-202 ms) was sensitive to distance but showed no direct correlation with behavioral outcomes. These findings indicate that memory repulsion arises during post-perceptual working memory stages and is modulated by absolute spatial proximity. This work clarifies the spatiotemporal architecture of relational distractor filtering in visual working memory.},
}

@article {pmid42518224,
year = {2026},
author = {Del Mauro, G and Li, Y and Yu, J and Wickwire, E and Kochunov, P and Chen, S and Rao, H and Wang, Z},
title = {From Sensorimotor to Transmodal Cortex: Sleep Quality Aligns Brain Entropy with the Cortical Functional Gradient.},
journal = {Sleep},
volume = {},
number = {},
pages = {},
doi = {10.1093/sleep/zsag205},
pmid = {42518224},
issn = {1550-9109},
abstract = {STUDY OBJECTIVES: Sleep is fundamental to brain health, yet the mechanisms by which habitual sleep quality shapes large-scale neural dynamics during wakefulness remain unclear. This work aims at determining whether habitual sleep quality is associated with systematic alterations in regional and cross-regional temporal complexity of spontaneous neural activity.

METHODS: Regional brain entropy (BEN) and cross-regional brain entropy (CRBEN) were estimated from resting-state fMRI data of the UK Biobank, with replication in the Human Connectome Project (HCP) and in a randomized total sleep deprivation experiment. Temporal complexity of spontaneous neural activity was correlated to self-reported habitual sleep quality and sleep amount in observational cohorts and experimental total sleep deprivation in the laboratory study.

RESULTS: Better sleep quality was associated with increased BEN in sensory and sensorimotor cortices and decreased BEN in frontoparietal control regions. High-quality sleep enhanced differentiation of temporal complexity among sensory networks while strengthening coordination within higher-order control systems. In the independent HCP cohort, sleep amount predicted increased BEN in visual and somatomotor regions. Moreover, the strength of the association between sleep measures and BEN was strongly and negatively correlated with the major cortical functional gradient. Exploratory results suggest convergent effects following total sleep deprivation. Habitual sleep quality is associated with systematic reconfiguration of the brain's temporal complexity architecture at both regional and network levels.

CONCLUSION: These findings position sleep as a fundamental determinant of the brain's dynamic operating regime and identify temporal complexity as a mechanistically informative neural signature linking sleep health to cognitive function and neuropsychiatric vulnerability.},
}

@article {pmid42518885,
year = {2026},
author = {Chen, K and Li, X and Chen, H and He, Y and Huang, Y and Wang, W and Ran, S and Huang, Z and Qiu, K and Shu, X and Jiang, X and Long, X and Li, W and Liao, W and Zhou, X and Li, J},
title = {White matter functional connectome topology and its clinical correlations in adolescent major depressive disorder.},
journal = {Psychoradiology},
volume = {6},
number = {},
pages = {kkag025},
doi = {10.1093/psyrad/kkag025},
pmid = {42518885},
issn = {2634-4416},
abstract = {BACKGROUND: Adolescence is a critical period for brain network remodeling and the onset of major depressive disorder (MDD); however, white matter (WM) functional topology in adolescent MDD remains underexplored. Given that WM functional signals reflect meaningful neural activity and are disrupted in psychiatric disorders, this study aimed to characterize WM functional connectome alterations in adolescents with MDD and examine their clinical associations.

METHODS: Resting-state fMRI data were obtained from a cohort of adolescents with MDD (n = 320) and healthy controls (HCs, n = 144), as well as from an independent replication cohort. Following the construction of thresholded WM functional networks, graph-theoretical analyses were used to calculate global topological properties. Canonical correlation analysis (CCA) was used to examine associations between topology and clinical symptoms, while exploratory classification assessed their discriminative information and generalizability. Furthermore, subgroup analyses were conducted to evaluate the effects of a history of suicide attempt, non-suicidal self-injury, childhood trauma, and sex.

RESULTS: Compared with HCs, adolescent MDD exhibited significant reductions in the clustering coefficient, characteristic path length, and local efficiency. CCA identified distinct covariation patterns: reduced global integration was linked to severe suicidal ideation and depressed mood, while impaired local segregation was associated with vegetative symptoms such as weight loss and insomnia. Subgroup analyses revealed significant sexual dimorphism, with male patients demonstrating more severe topological impairments than females. A similar pattern was observed in the independent replication cohort. The classification analysis achieved above-chance accuracy (69.6 and 60% in the two cohorts).

CONCLUSIONS: Our results reveal a topologically shifted WM functional connectome structure in adolescent MDD, providing new clues to aid in understanding the pathophysiology of its pathophysiology.},
}

@article {pmid42498714,
year = {2026},
author = {Xie, L and Fang, M and Liu, Z and Zhu, Y},
title = {EEG-based automated evaluation of automotive sound quality using ensemble deep learning.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {42498714},
issn = {2045-2322},
support = {52175111//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Deep Learning ; *Auditory Perception/physiology ; *Automobiles ; *Sound ; Male ; Female ; },
abstract = {The evaluation of automotive sound quality is of considerable significance for improving driving comfort. However, existing methodologies suffer from notable limitations, including inconsistencies in subjective evaluations and weak correlations between objective metrics and auditory perception. In response to these challenges, an automated evaluation method incorporating electroencephalogram (EEG) signals and ensemble deep learning is proposed herein. Initially, EEG data is acquired from 30 subjects during exposure to 16 automobile sounds with sporty quality. Subsequently, the LSTMS-B model is incorporating Swish activation into LSTM to mitigate gradient vanishing and enhancing Bagging through optimized majority voting, achieving 90.8% accuracy with superior performance over conventional LSTM variants; Furthermore, an innovative ResNet-based regression model is developed to establish the automobile sound-EEG feature mapping, enabling the LSTMS-B model to achieve 89.75% average F1 score in sound quality classification using brain auditory representations while reducing reliance on conventional EEG paradigms. This study develops a novel sound quality evaluation paradigm through deep-ensemble learning integration, where the proposed cross-modal feature mapping method provides a transferable AI framework for interpreting human auditory perception mechanisms.},
}

Humans
*Electroencephalography/methods
*Deep Learning
*Auditory Perception/physiology
*Automobiles
*Sound
Male
Female

@article {pmid42498742,
year = {2026},
author = {Jia, H and Qian, B and Qu, Y and Wang, J and Chen, J and Li, T and Zheng, C and Han, J and Zhang, G and Jin, Y and Lee, S and Chen, X and Chen, H and Xu, J and Xu, K and Rong, Y and Jiang, Y and Yang, Y and Li, W and Guo, Q and Li, Y and Li, Y and Wu, J and Zhang, Q and Lu, F and Zhuang, W and Lei, B and Byeon, SH and Lee, J and Smedowski, A and Swierczynska, M and Zhang, D and Rim, TH and Grzybowski, A and Sheng, B and Wong, TY and Sun, X},
title = {AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
pmid = {42498742},
issn = {1546-170X},
support = {82471130//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62502206//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2525004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82388101//National Natural Science Foundation of China (National Science Foundation of China)/ ; 20250412//SJTU | School of Medicine, Shanghai Jiao Tong University (School of Medicine, Shanghai Jiao Tong University)/ ; },
abstract = {The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated Retina4IRD using multimodal data with color fundus photographs and optical coherence tomography scans from 1,843 genetically confirmed patients (3,376 eyes) across China, South Korea and Poland. The top-5 prediction accuracy was 0.904 (95% confidence interval (CI): 0.896-0.912) and 0.856 (95% CI: 0.850-0.863) for internal and external validation, respectively. We conducted a randomized controlled trial with 300 participants with suspected IRD randomized 1:1 to either Retina4IRD-assisted specialist arm or specialist-only arm. Of these, 295 participants (median age 33 years, 114 (38.6%) females) with available next-generation sequencing reports were included in the final analysis. The primary outcome was met: top-5 genetic accuracy was significantly higher in the Retina4IRD-assisted specialist arm versus the specialist-only arm (88.5% versus 67.3%, P < 0.001). For secondary endpoints, top-1 to top-4 accuracies all favored the Retina4IRD-assisted specialist arm, with top-1 accuracy of 37.8% versus 22.4% and top-4 accuracy of 81.8% versus 53.1%, respectively. Post hoc analyses demonstrated that, with Retina4IRD assistance, clinicians made better management decisions, and the composite downstream management score indicated significantly higher scores relative to the control group (37.7 versus 28.5, P < 0.001). Our study shows that Retina4IRD is a CDSS tool prior to genetic testing and aligns with clinical workflow for patients with suspected IRDs. ClinicalTrials.gov identifier: NCT06839170 .},
}

@article {pmid42499509,
year = {2026},
author = {Pan, Y and Huang, Y and Bao, M and Sun, X},
title = {Evolution of brain-computer interface technologies for stroke rehabilitation: a bibliometric integration of neural decoding and functional recovery (2016-2025).},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1871816},
pmid = {42499509},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-computer interface (BCI) technology represents a critical frontier in neurorehabilitation. This study aims to systematically analyze the global research landscape, hotspot distribution, and evolving trends of BCI interventions for upper limb rehabilitation in stroke survivors between 2016 and 2025.

METHODS: Bibliometric analysis and systematic mapping were conducted using data from the Web of Science Core Collection and PubMed. Literature was retrieved using terms related to "stroke," "brain-computer interface," and "upper limb rehabilitation." Screening followed the PRISMA guidelines. Visualization and quantitative mapping were performed using CiteSpace (v.6.4.R2) and VOSviewer (v.1.6.20) to evaluate publication volume, international collaboration, and keyword co-occurrence clusters.

RESULTS: Annual publications increased steadily from 37 in 2016 to 104 in 2025, with 65.6% published since 2020. The United States (n = 144), China (n = 83), and Italy were the most productive countries. Keyword analysis revealed a paradigm shift from functional electrical stimulation toward robotics-assisted therapy, motor imagery, and AI-driven decoding. Significant burst strengths were observed for "closed-loop systems," "generative AI," and "multi-modal feedback," indicating these as the current primary frontiers.

DISCUSSION: BCI research for post-stroke recovery is transitioning from experimental signal processing to intelligent, multi-modal, and personalized clinical systems. Bibliometric evidence confirms that integrating BCI with robotic-assisted rehabilitation or functional electrical stimulation (FES) has become the mainstream clinical trend. Future efforts must focus on improving EEG signal stability and developing user-friendly hardware to facilitate the transition of BCI from research settings to daily clinical practice. China has emerged as the second most productive country, though international cooperation with European institutions remains an area for further growth.},
}