@article {pmid41444014,
year = {2025},
author = {Knopman, J and Davies, JM and Mokin, M and Hassan, AE and Harbaugh, RE and Khalessi, A and Fiehler, J and Levy, EI and Gross, BA and Grandhi, R and Tarpley, J and Sivakumar, W and Bain, M and Crowley, RW and Link, TW and Fraser, JF and Levitt, MR and Chen, PR and Hanel, RA and Bernard, JD and Jumaa, M and Youssef, PP and Cress, MC and Chaudry, MI and Shakir, HJ and Lesley, WS and Billingsley, J and Jones, J and Koch, MJ and Paul, AR and Mack, WJ and Osbun, JW and Dlouhy, KM and Grossberg, JA and Kellner, CP and Sahlein, DH and Santarelli, J and Schirmer, CM and Mazaris, P and Liu, JJ and Majjhoo, AQ and Wolfe, T and Patel, NV and Roark, CD and Siddiqui, AH and , },
title = {EMBOLISE randomized surgical trial for subdural hematoma: clinical benefits beyond reoperation with middle meningeal artery embolization.},
journal = {Journal of neurointerventional surgery},
volume = {},
number = {},
pages = {},
doi = {10.1136/jnis-2025-024587},
pmid = {41444014},
issn = {1759-8486},
abstract = {BACKGROUND: Randomized clinical trials have demonstrated that middle meningeal artery embolization (MMAe) reduces reoperation rates in surgically treated patients with subacute/chronic subdural hematoma (SDH). The effect of embolization on outcomes beyond reoperation remains to be determined. We analyzed the impact of reoperation and healthcare encounters among patients enrolled in the EMBOLISE trial.

METHODS: Symptomatic subacute/chronic SDH patients were randomized to surgical evacuation alone (control) or surgical evacuation plus Onyx MMAe (treatment). Changes in modified Rankin Scale (mRS) scores, frequency of unscheduled follow-up visits, and radiographic evolution of hematomas in patients with versus without reoperation were analyzed.

RESULTS: A total of 197 patients were randomly assigned to the treatment group and 203 to the control group. Patients who required reoperation compared with those who did not exhibited a ~threefold higher incidence of mRS >2 (37.0% vs 12.9%, P=0.0025) and an ~2.5 fold increase in mRS worsening (22.2% vs 9.5%, P=0.0503) at 180 days. In patients who did not receive MMAe, there was a ~threefold fold increase in rate of SDH recurrence/progression even among those who did not require reoperation (14.3% vs 5.3%, P=0.0045) and a ~twofold increase in unscheduled physician follow-up visits (27.1% vs 14.7%, P=0.0031).

CONCLUSION: Among patients with symptomatic subacute/chronic SDH, reoperation was associated with increased rates of mRS worsening and higher mRS scores at follow-up. Adjunctive Onyx MMAe resulted in lower rates of hematoma recurrence/progression and fewer unscheduled physician follow-up visits. Thus, in addition to reducing surgical reoperation rates, adjunctive MMAe led to improved clinical outcomes and reduced healthcare encounters.},
}

@article {pmid41443766,
year = {2025},
author = {Zhang, H and Liao, Y and Wen, H and Pang, T and Zhao, X and Zhang, W and Lou, X and Chen, C and Liu, Z and Hu, S and Xu, X},
title = {Clinical Manifestations.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {21 Suppl 3},
number = {},
pages = {e097364},
doi = {10.1002/alz70857_097364},
pmid = {41443766},
issn = {1552-5279},
mesh = {Humans ; Male ; Female ; *Dementia/epidemiology ; Middle Aged ; United Kingdom/epidemiology ; Aged ; *Bipolar Disorder/epidemiology ; Incidence ; Comorbidity ; Adult ; *Mood Disorders/epidemiology ; Depression/epidemiology ; },
abstract = {BACKGROUND: Mood disorders including depression and bipolar disorders have been linked to dementia. However, early manifestation of bipolar disorder, especially manic symptom, were easily overlooked. The present study aimed to investigate the association of midlife and late-life mood symptoms, especially their comorbidity, with long-term dementia incidence among multi-regional and ethnic adults.

METHOD: The study used UK Biobank as a discovery dataset and three Asian studies as validation datasets. Participants aged > 35 were included in the analysis. Individuals with diagnosed mood disorders and dementia were excluded at baseline. Baseline mood symptoms were classified as: normal, manic symptoms, depressive symptoms, and comorbidity of depressive and manic symptoms. Long-term (12 years) incident mood disorders (depression, mania and bipolar) and dementia were diagnosed and recorded. Primary outcome was dementia incidence. Secondary outcomes were domain-specific cognitive function and metabolomics. Fine-Gray sub-distribution hazard models and linear regression were used to estimate the associations of mood symptoms with dementia risk, cognitive function and selected metabolites.

RESULT: The study included 142,670 UK and 1,610 Asian participants (mean [SD] age, 57.2 [8.2] and 70.5 [7.3] years, respectively). Mood symptoms were prevalent (11.4% and 31.2%) among 1462 (1.0%) and 74 (19.4%) who developed dementia during a mean follow-up of 11.0 and 4.4 years in community and clinical settings, respectively. The average durations from mood symptoms and disorders to dementia onset were 7.5 and 1.7 years, respectively. Comorbidity of depressive and manic symptoms was associated with an earlier onset and a higher risk of developing dementia (sub-distribution hazard ratios [sHR]=9.46, 95% confidence interval [CI]=4.07-21.97; and sHR=4.32, 95%CI=2.10-8.88; respectively), as compared to single symptom or none (on average 0.9 and 1.6 year earlier). Comorbidity of symptoms were associated with worse cognition (B=-0.32; 95% CI=-0.38--0.25), especially in reasoning and numeric memory, and an exacerbation of metabolic dysfunction, especially in fatty acids, lipoproteins and triglycerides.

CONCLUSION: Mood symptoms were prevalent among incident dementia patients. Comorbidity of mood symptoms in midlife and late-life could lead to a higher cumulative risk of dementia. Future studies warrant in-depth investigation of distinct pathophysiological mechanisms.},
}

Humans
Male
Female
*Dementia/epidemiology
Middle Aged
United Kingdom/epidemiology
Aged
*Bipolar Disorder/epidemiology
Incidence
Comorbidity
Adult
*Mood Disorders/epidemiology
Depression/epidemiology

@article {pmid41443376,
year = {2025},
author = {Xia, XY and Huang, ZQ and Lin, HH and Liu, ZY and Zhang, L and Li, MC and Tu, YQ and Chen, NP and Ni, J and Chen, QL and Hu, JP and Gan, SR and Chen, XY},
title = {Diffusion along perivascular spaces as a marker for Glymphatic system impairment in spinocerebellar Ataxia type 3.},
journal = {Neurobiology of disease},
volume = {},
number = {},
pages = {107232},
doi = {10.1016/j.nbd.2025.107232},
pmid = {41443376},
issn = {1095-953X},
abstract = {Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder characterized by the accumulation of polyglutamylated ATXN3 protein within neurons, which can potentially compromise the integrity of the brain's glymphatic system. Our objective is to investigate whether glymphatic function is impaired in patients with SCA3 and its clinical relevance. This study recruited 129 SCA3 subjects, including 98 symptomatic (ataxic SCA3) and 31 presymptomatic (preataxic SCA3) individuals, along with 67 healthy controls (HCs). We calculated the index for diffusion tensor image analysis along the perivascular space (DTI-ALPS) across groups and examined its correlation with SCA3 clinical features. Except for the left cerebral hemisphere DTI-ALPS index showing no statistically significant difference between HC and preataxic SCA3, statistically significant differences in ALPS index were observed among the remaining three groups. The DTI-ALPS index decreased in the order HC group > preataxic SCA3 group > ataxic SCA3 group. The Ataxic SCA3 group exhibited a significantly lower DTI-ALPS index than the HC group. The mean DTI-ALPS index showed negative correlations with the Scale for the Assessment and Rating of Ataxia (SARA) scores and International Cooperative Ataxia Rating Scale (ICARS) scores. In this study, we demonstrate that glymphatic waste clearance is impaired in SCA3 and that the magnitude of ALPS-detected dysfunction parallels clinical burden. DTI-ALPS may serve as a potential indicator for evaluating glymphatic system alterations and disease.},
}

@article {pmid41442763,
year = {2025},
author = {Okoye, C and Cuffaro, L and Pozzi, FE and Ferrara, MC and Noale, M and Calciolari, S and Chicco, D and Cincotti, F and Daini, R and Finazzi, A and Francioso, L and Gasparini, F and Pagan, E and Ribino, P and Romeo, Z and Sala, G and Solfrizzi, V and Zambon, A and Maggi, S and Bellelli, G and Ferrarese, C and , },
title = {Multicomponent interventions and technologies to reduce the burden of frailty, functional, and cognitive decline: insights from the Age-It Research Program.},
journal = {The journals of gerontology. Series B, Psychological sciences and social sciences},
volume = {80},
number = {Supplement_2},
pages = {S180-S188},
doi = {10.1093/geronb/gbaf186},
pmid = {41442763},
issn = {1758-5368},
support = {DM 1557//Next Generation EU/ ; //National Recovery and Resilience Plan/ ; //Ageing Well in an Ageing Society/ ; },
mesh = {Humans ; *Frailty/prevention & control ; *Cognitive Dysfunction/prevention & control ; Aged ; Aged, 80 and over ; *Frail Elderly ; Aging ; Cost-Benefit Analysis ; Male ; },
abstract = {OBJECTIVES: Preventing age-related complications is a critical priority for health systems. Within the Age-It program, Spoke 8 aims to evaluate scalable, multicomponent, technology-assisted interventions to prevent frailty and mitigate functional and cognitive decline in older adults across different care settings.

METHODS: Spoke 8 includes three clinical studies conducted in community, hospital, and long-term care settings, supported by cross-cutting work packages on digital infrastructure, technology development, and economic evaluation. The intervention model integrates physical, cognitive, nutritional, and psychosocial components, supported by digital tools, biomarkers of aging, and a centralized data platform.

RESULTS: The project is expected to generate evidence on the effectiveness, feasibility, and cost-effectiveness of multidomain interventions implemented across diverse real-world settings, including community, hospital, and long-term care. Technology-assisted strategies-such as wearable sensors and digital cognitive tools-may enhance adherence and enable remote monitoring, while also supporting more personalized care delivery. The integration of artificial intelligence will facilitate the interpretation of complex clinical and biological data, improving risk stratification and the early identification of individuals most likely to benefit from targeted interventions. Together, these approaches may help reduce hospitalizations, delay functional decline, and promote aging in place.

DISCUSSION: This initiative supports the transition toward more integrated and equitable care models for older adults. Through the implementation of scalable, person-centered interventions within routine services, the project offers policy-relevant strategies to address frailty and functional decline-contributing to the redesign of aging care in Italy and providing insights applicable across diverse health systems facing the challenges of population aging countries.},
}

Humans
*Frailty/prevention & control
*Cognitive Dysfunction/prevention & control
Aged
Aged, 80 and over
*Frail Elderly
Aging
Cost-Benefit Analysis
Male

@article {pmid41442279,
year = {2025},
author = {Zhao, Y and Yang, Z and Shi, S and Hao, H and Li, X and Ma, D and Su, N and Zhao, W and Shao, J and An, Y and Wang, K and Liu, Y and Zou, L and Qi, J and Zhang, H and Guo, J and Du, X},
title = {Structure basis for the activation of KCNQ2 by endogenous and exogenous ligands.},
journal = {Cell reports},
volume = {45},
number = {1},
pages = {116771},
doi = {10.1016/j.celrep.2025.116771},
pmid = {41442279},
issn = {2211-1247},
abstract = {The voltage-gated potassium channel KCNQ2 is crucial for stabilizing neuronal membrane potential, and its mutations can cause various epilepsies. KCNQ2 is activated by endogenous ligand phosphatidylinositol-4,5-bisphosphate (PIP2) and exogenous ligands, yet the structural mechanisms underlying these activations remain unclear. Here, we report the cryo-electron microscopy structures of human KCNQ2 in complex with exogenous ligands QO-58 and QO-83 in the absence or presence of PIP2 in either closed or open conformation. While QO-83 binds in the classical fenestration pocket of the pore domain, QO-58 mainly binds at the flank of S4 in the voltage-sensing domain. These structures, along with electrophysiological assays and computational studies, provide mechanistic insights into the ligand activation of KCNQ2 and may guide the development of anti-epileptic drugs targeting KCNQ2.},
}

@article {pmid41441860,
year = {2025},
author = {He, C and Ding, Y and Rabczuk, T and Ding, C},
title = {Reliable AI Platform for Monitoring BCI Caused Brain Injury and Providing Real-Time Protection.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e06747},
doi = {10.1002/advs.202506747},
pmid = {41441860},
issn = {2198-3844},
support = {TZ2025006//Science Challenge Project/ ; 2023YFA1008902//National Key R and D Program of China/ ; 12472191//National Natural Science Foundation of China/ ; //Fundamental Research Funds for the Central Universities, Peking University/ ; },
abstract = {Invasive brain-computer interface (BCI) holds great promise for restoring motor, sensory, and cognitive functions in patients with disabilities, yet chronic implantation induces neuroinflammation and degeneration at the electrode-tissue interface, impairing neural connectivity and device long-term stability. Current brain injury assessment approaches cannot simultaneously meet the requirements of efficiency and interpretability in healthcare with high-risk diagnoses and treatment. Meanwhile, limited and expensive biomechanics data pose significant challenges in AI training. Herein, feature-based Gaussian process emulators are proposed to enable interpretable data-driven modeling with limited biomechanics data under noise. Furthermore, a reliable AI platform, BrainGuard is developed, for efficiently providing a reliable and quantitative patient-specific basis and real-time monitoring of BCI caused brain injury. These results demonstrate exceptional performance of BrainGuard in rapidly and accurately predicting and monitoring the full-field von Mises strain revealing the brain injury even under challenging noise conditions. By constructing interpretable digital brain twins to offer reliable digital healthcare solutions, the platform enhances real-time patient protection and improves the security and durability of long-term BCI-based measurement and treatment strategies.},
}

@article {pmid41440053,
year = {2025},
author = {Zhang, N and Jian, H and Li, X and Jiang, G and Tang, X},
title = {LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.},
journal = {Brain sciences},
volume = {15},
number = {12},
pages = {},
doi = {10.3390/brainsci15121257},
pmid = {41440053},
issn = {2076-3425},
abstract = {Objectives: Existing motor imagery electroencephalography (MI-EEG) decoding approaches are constrained by their reliance on sole representations of brain connectivity graphs, insufficient utilization of multi-scale information, and lack of adaptability. Methods: To address these constraints, we propose a novel Local-Partition-Global Graph learning Network (LPGGNet). The Local Learning module first constructs functional adjacency matrices using partial directed coherence (PDC), effectively capturing causal dynamic interactions among electrodes. It then employs two layers of temporal convolutions to capture high-level temporal features, followed by Graph Convolutional Networks (GCNs) to capture local topological features. In the Partition Learning module, EEG electrodes are divided into four partitions through a task-driven strategy. For each partition, a novel Gaussian median distance is used to construct adjacency matrices, and Gaussian graph filtering is applied to enhance feature consistency within each partition. After merging the local and partitioned features, the model proceeds to the Global Learning module. In this module, a global adjacency matrix is dynamically computed based on cosine similarity, and residual graph convolutions are then applied to extract highly task-relevant global representations. Finally, two fully connected layers perform the classification. Results: Experiments were conducted on both the BCI Competition IV-2a dataset and a laboratory-recorded dataset, achieving classification accuracies of 82.9% and 87.5%, respectively, which surpass several state-of-the-art models. The contribution of each module was further validated through ablation studies. Conclusions: This study demonstrates the superiority of integrating multi-view brain connectivities with dynamically constructed graph structures for MI-EEG decoding. Moreover, the proposed model offers a novel and efficient solution for EEG signal decoding.},
}

@article {pmid41439901,
year = {2025},
author = {Lee, DG and Lee, SB},
title = {Robust Motor Imagery-Brain-Computer Interface Classification in Signal Degradation: A Multi-Window Ensemble Approach.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {12},
pages = {},
doi = {10.3390/biomimetics10120832},
pmid = {41439901},
issn = {2313-7673},
support = {Bisa Research Grant: project number 20240421//Keimyung University/ ; },
abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) mimics the brain's intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. However, this biomimetic interaction is highly vulnerable to signal degradation, particularly in mobile or low-resource environments where low sampling frequencies obscure these MI-related oscillations. To address this limitation, we propose a robust MI classification framework that integrates spatial, spectral, and temporal dynamics through a filter bank common spatial pattern with time segmentation (FBCSP-TS). This framework classifies motor imagery tasks into four classes (left hand, right hand, foot, and tongue), segments EEG signals into overlapping time domains, and extracts frequency-specific spatial features across multiple subbands. Segment-level predictions are combined via soft voting, reflecting the brain's distributed integration of information and enhancing resilience to transient noise and localized artifacts. Experiments performed on BCI Competition IV datasets 2a (250 Hz) and 1 (100 Hz) demonstrate that FBCSP-TS outperforms CSP and FBCSP. A paired t-test confirms that accuracy at 110 Hz is not significantly different from that at 250 Hz (p < 0.05), supporting the robustness of the proposed framework. Optimal temporal parameters (window length = 3.5 s, moving length = 0.5 s) further stabilize transient-signal capture and improve SNR. External validation yielded a mean accuracy of 0.809 ± 0.092 and Cohen's kappa of 0.619 ± 0.184, confirming strong generalizability. By preserving MI-relevant neural patterns under degraded conditions, this framework advances practical, biomimetic BCI suitable for wearable and real-world deployment.},
}

@article {pmid41439390,
year = {2025},
author = {Gupta, D and Brangaccio, JA and Hill, NJ},
title = {Methodological optimization for eliciting robust median nerve somatosensory evoked potentials for realtime single trial applications.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae30ac},
pmid = {41439390},
issn = {1741-2552},
abstract = {OBJECTIVE: Single-trial measurement of median nerve Somatosensory Evoked Potentials (SEPs) with noninvasive electroencephalography (EEG) is challenging due to low signal-to-noise ratio (SNR), limiting its use in real-time neurorehabilitation applications. We describe and evaluate methodological optimizations for eliciting reliable median nerve SEPs measurable in real time, with reduced reliance on post-processing.

METHODS: In twelve healthy participants, two sessions each, SEPs were assessed at three pulse widths (0.1, 0.5, 1 msec), at a low-frequency stimulation (0.5 Hz ± 10%), and at an intensity sufficient to evoke consistent and robust sensory nerve action potentials (SNAPs) and compound muscle action potentials (CMAPs). The Evoked Potential Operant Conditioning System platform was used to monitor responses in real time. Feasibility was also evaluated in a participant with incomplete spinal cord injury (iSCI).

RESULTS: SEP P50 and N70 were reliably elicited in healthy participants, and in individual with iSCI, across all tested pulse widths with minimal discomfort. N70 amplitude increased significantly with pulse width (χ[2]= 17.64, p= 0.0001, w= 0.80), while P50 amplitude remained unchanged. SNR showed a significant pulse width-dependent increase (χ[2]= 7.82, p= 0.02, w= 0.35) with improvements of 40% and 52% at 0.5 and 1 msec, respectively. N70 single-trial separability significantly improved at 1 msec (AUC of 0.83, χ[2]= 8.17, p= 0.017), including the iSCI participant (0.84-less impaired hand, 0.79-more impaired hand). Test-retest reliability (ICC= 0.70-0.84, p< 0.05) was highest at 0.5 msec, indicating more consistent N70 and P50 measurements across sessions at a longer pulse width.

SIGNIFICANCE: Robust median nerve SEPs can be measured at single trials with methodological optimizations such as a longer pulse width (0.5-1ms), low frequency (0.5 Hz), a consistent afferent excitation guided by nerve and muscle responses, and a robust EEG acquisition system. This setup can be useful for real time SEP-based brain computer interface applications for rehabilitation.},
}

@article {pmid41438920,
year = {2025},
author = {Kundu, B and Pleitez, J},
title = {Brain Implants in the Age of Artificial Intelligence.},
journal = {Missouri medicine},
volume = {122},
number = {6},
pages = {517-524},
pmid = {41438920},
issn = {0026-6620},
mesh = {Humans ; *Artificial Intelligence/trends ; *Brain/physiology ; *Brain-Computer Interfaces ; },
abstract = {Brain implants are routinely used to treat movement disorders and other network disorders such as obsessive-compulsive disorder. Closed-loop intracranial brain stimulation systems can now detect neural biomarkers of disease in real-time and therapeutically stimulate the brain based on these signals. Research devices can measure neural data on the order of single neurons and transform these data, via machine learning algorithms, into cursor movements and keyboard clicks, so that a quadriplegic patient can control a robotic arm. It is still a challenge to find the important brain signals of interest, that encode a patient's intentions or needs. Furthermore, the ethics of developing devices that allow for human cognitive and physical enhancement should be a part of societal discussion. The hope is that artificial intelligence (AI) will continue to advance neurotechnology's role in human health.},
}

Humans
*Artificial Intelligence/trends
*Brain/physiology
*Brain-Computer Interfaces

@article {pmid41438236,
year = {2025},
author = {Li, T and Gao, Y and Zhou, J and Chen, Y and Zhang, S and Gong, X and Liu, Y},
title = {Advancements in the application of brain-computer interfaces based on different paradigms in amyotrophic lateral sclerosis.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1658315},
pmid = {41438236},
issn = {1662-4548},
abstract = {Amyotrophic lateral sclerosis (ALS) is a progressive neurological condition that leads to the gradual loss of movement and communicative abilities, significantly diminishing the quality of life for affected individuals. Recent advancements in neuroscience and engineering have propelled the swift evolution of brain-computer interfaces (BCIs), which are now extensively utilised in medical rehabilitation, military applications, assistive technologies, and various other domains. As a communication medium facilitating direct interaction between the brain and the external world independent of the peripheral nervous system, BCI provides ALS patients with an innovative method for communication and control, offering unparalleled prospects for improving their quality of life. Recent collaborative endeavours among several specialists have markedly enhanced the precision and velocity of diverse BCI paradigms, signifying a breakthrough in BCI applications for ALS. Nonetheless, obstacles and constraints remain. This study methodically extracted pertinent literature from the Web of Science and PubMed databases in accordance with PRISMA guidelines. Following stringent inclusion and exclusion criteria, 23 studies were identified. This data allows us to summarise the application results and current limitations of several BCI paradigms in motor control and communication, while delineating prospects in multimodal fusion and adaptive calibration. This review presents evidence-based references for the effective translation and application of BCI technology in ALS rehabilitation.},
}

@article {pmid41437397,
year = {2025},
author = {Qian, MB and Wang, L and Huang, JL and Zhou, CH and Zhu, TJ and Zhou, XN and Lai, YS and Li, SZ},
title = {Disability-adjusted life years of clonorchiasis in China: a high-resolution spatial analysis.},
journal = {Infectious diseases of poverty},
volume = {14},
number = {1},
pages = {126},
pmid = {41437397},
issn = {2049-9957},
support = {82373645//National Natural Science Foundation of China/ ; 82073665//National Natural Science Foundation of China/ ; 2021YFC2300800//National Key Research and Development Program of China/ ; },
mesh = {Humans ; China/epidemiology ; *Clonorchiasis/epidemiology/parasitology ; Male ; Female ; *Disability-Adjusted Life Years ; Clonorchis sinensis/isolation & purification/physiology ; Spatial Analysis ; Middle Aged ; Adult ; Animals ; Child ; Aged ; Young Adult ; Bayes Theorem ; Adolescent ; Prevalence ; Child, Preschool ; Incidence ; Quality-Adjusted Life Years ; },
abstract = {BACKGROUND: Clonorchiasis is caused by the ingestion of raw freshwater fish containing infective metacercariae of Clonorchis sinensis. This study aimed to fully evaluate disease burden in terms of disability-adjusted life years (DALYs) for clonorchiasis in China.

METHODS: Following our previous study which established the fine-scale prevalence distribution of C. sinensis infection in China, we further adopted Bayesian geostatistical models to estimate the infection intensity in terms of eggs per gram of feces (EPG) in infected individuals based on the national surveillance data of clonorchiasis between 2016 and 2021. Disability weight was then captured through its quantitative association with EPG, and used to estimate years of life living with a disability (YLDs). Incidence of cholangiocarcinoma attributed to C. sinensis infection was employed to calculate years of life lost (YLLs). DALYs was then estimated at 5 × 5 km[2] resolution, and aggregated by areas and populations.

RESULTS: In 2020, 431,009 [95% Bayesian credible interval (BCI): 370,427 to 500,553] DALYs were exerted due to clonorchiasis in China, of which 372,918 (95% BCI: 318,775-435,727) was due to YLDs and 57,998 (95% BCI: 50,816-66,069) due to YLLs. The DALYs, YLDs and YLLs per 1000 were 0.31 (95% BCI: 0.26-0.35), 0.26 (95% BCI: 0.23-0.31), and 0.04 (95% BCI: 0.04-0.05), respectively. The DALYs predominantly distributed in southern areas including Guangxi (201,029, 95% BCI: 157,589-248,287) and Guangdong (161,958, 95% BCI: 128,326-211,358). The DALYs was over doubled in male (302,678, 95% BCI: 262,028-348,300) than in female (127,970, 95% BCI: 106,834-151,699), and high in middle aged population.

CONCLUSIONS: Clonorchiasis causes significant disease burden in China especially in southern areas including Guangxi and Guangdong. Urgent control is needed for clonorchiasis in the endemic areas with high burden, and adult males need to be prioritized.},
}

Humans
China/epidemiology
*Clonorchiasis/epidemiology/parasitology
Male
Female
*Disability-Adjusted Life Years
Clonorchis sinensis/isolation & purification/physiology
Spatial Analysis
Middle Aged
Adult
Animals
Child
Aged
Young Adult
Bayes Theorem
Adolescent
Prevalence
Child, Preschool
Incidence
Quality-Adjusted Life Years

@article {pmid41437118,
year = {2025},
author = {Chen, H and Dai, H and Zhang, L and Deng, Y and Zhang, K and Yu, J and Peng, G and Guo, Z and Zhang, J and Yuan, C and Xie, F and Luo, B},
title = {The biomarker and clinical changes across the Alzheimer's continuum study (BCAS): rationale, design, and baseline characteristics of the first 1,013 participants.},
journal = {Alzheimer's research & therapy},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13195-025-01937-x},
pmid = {41437118},
issn = {1758-9193},
support = {2022C03064//Key R&D Program of Zhejiang/ ; },
abstract = {INTRODUCTION: Alzheimer's disease (AD) is the leading cause of dementia in China, but deeply phenotyped clinical cohorts remain limited. The Biomarker and Clinical changes across the Alzheimer's continuum Study (BCAS) was established at the First Affiliated Hospital, Zhejiang University School of Medicine to capture biological and clinical changes across the AD spectrum.

METHODS: BCAS is an ongoing, longitudinal memory clinic-based cohort initiated in 2016 in Zhejiang, one of China's most economically vigorous and rapidly aging regions. Individuals aged ≥ 40 years with cognitive concerns are recruited and undergo standardized clinical evaluation, comprehensive neuropsychological testing, biospecimen collection, and multimodal neuroimaging including MRI and amyloid and tau PET in subsets. Participants are followed every 1-2 years with repeat assessments. This paper reports baseline characteristics and preliminary findings from the first 1,013 participants enrolled up to January 2025.

RESULTS: Participants had a mean age of 66.5 years (SD 9.6), with 49.8% women and an average of 9.7 years of education. Hypertension (41.4%), diabetes (14.6%), and hypercholesterolemia (12.0%) were the most prevalent comorbidities. The mean MoCA score was 19.2 (SD 6.1). Mean cognitive scores showed gradient decline across diagnostic groups from cognitively unimpaired, mild cognitive impairment to dementia, consistent with expected disease severity. Tau PET positivity showed a numerically larger cognitive z-score difference (-0.973 for T + vs. T-) compared with amyloid PET positivity (-0.530 for A + vs. A-). Among risk factors, higher age and diabetes were linked to lower scores, whereas higher education, tea consumption, and higher BMI were associated with better cognitive performance.

CONCLUSIONS: The BCAS served as a biomarker-rich and multimodal resource to study the clinical and biological progression of AD in China. Preliminary analyses demonstrate expected associations and support the data quality. BCAS will act as a platform for biomarker validation and precision approaches to AD diagnosis and intervention.},
}

@article {pmid41434442,
year = {2025},
author = {Zhao, X and Lin, Z and Zhang, H and Chen, C and Ji, H and Liu, Z and Hu, S and Xu, X},
title = {Public Health.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {21 Suppl 6},
number = {},
pages = {e097185},
doi = {10.1002/alz70860_097185},
pmid = {41434442},
issn = {1552-5279},
mesh = {Humans ; Male ; Female ; Aged ; Middle Aged ; *Dementia/epidemiology ; *Mental Disorders/epidemiology ; *Public Health ; United Kingdom/epidemiology ; *Cardiovascular Diseases/epidemiology ; Risk Factors ; *Renal Insufficiency, Chronic/epidemiology ; },
abstract = {BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome describes pathological interactions among metabolic risk factors, chronic kidney disease, and cardiovascular dysfunction. These conditions are shared risk factors for psychiatric disorders and dementia. This study examined the associations of CKM syndrome with psychiatric disorders and dementia in middle-aged and older adults.

METHOD: Using data from the UK Biobank, we included participants free of psychiatric disorders and dementia at baseline. CKM syndrome was categorized into five stages (0 to 4) based on AHA definitions. Psychiatric disorders (psychotic, bipolar, depressive, and anxiety disorders) and dementia (Alzheimer's and vascular dementia) were identified using ICD-10 codes. Multi-state models analyzed the impact of CKM on transitions from healthy status to psychiatric disorders and dementia. Competing risk (death) models assessed the associations of CKM with specific psychiatric disorders and dementia. Additionally, Cox regression models and XGBoost classifiers were employed to identify key metabolomics associated with CKM stage-related outcomes.

RESULT: Among 389,314 participants, CKM stages were distributed as follows: stage 0 (10.0%), stage 1 (8.0%), stage 2 (57.6%), stage 3 (17.9%), and stage 4 (6.5%). Multi-state model results indicated that each one-stage increment in CKM stage was associated with higher hazards of psychiatric disorders (Healthy → Psychiatric Disorder: HR=1.26, 95% CI: [1.24, 1.29]) and subsequent transition to dementia (Psychiatric Disorder → Dementia: HR=1.30, 95% CI: [1.41, 1.49]). However, each CKM stage increment increased the hazards of directly developing dementia (Healthy → Dementia: HR=1.26, 95% CI: [1.31, 1.49]) but was not linked to subsequent psychiatric disorders. Competing risk analyses revealed that worsening CKM stages were associated with greater hazards of developing pre-dementia psychiatric disorders, including bipolar disorder, depressive disorder, and anxiety disorder whilst only advanced CKM stages (CKM stage 3/4) were associated with all-cause, Alzheimer's and vascular dementia. We identified several key predictors of pre-dementia psychiatric disorders at different CKM stages (e.g., citrate at CKM stages 1 and 2; degree of unsaturation at CKM stages 3 and 4).

CONCLUSION: CKM syndrome is associated with pre-dementia psychiatric disorders and dementia, emphasizing the need for regular monitoring and early intervention to manage CKM progression and reduce geriatric neuropsychiatric disturbances.},
}

Humans
Male
Female
Aged
Middle Aged
*Dementia/epidemiology
*Mental Disorders/epidemiology
*Public Health
United Kingdom/epidemiology
*Cardiovascular Diseases/epidemiology
Risk Factors
*Renal Insufficiency, Chronic/epidemiology

@article {pmid41433160,
year = {2025},
author = {Hamdan, E and Luo, Y and Forelli, R and Liufu, M and Zhou, N and Shridhar, S and Quattrocchi, E and Leveroni, Z and Ogrenci, S and Tran, N and Cetin, AE and Yu, JY},
title = {Real-time Instantaneous Phase Estimation Using a Deep Dual-Branch Complex Neural Network.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3647598},
pmid = {41433160},
issn = {1558-2531},
abstract = {Estimating the instantaneous phase of neural oscillations is crucial for technology that interfaces with the brain, such as brain-computer interfaces (BCIs) and neuromodulation systems. In these systems, phase information from the oscillating neural signal can be used to guide subsequent decisions to apply experimental perturbation. Traditional methods for phase estimation rely on the Hilbert transform computed using the Discrete Fourier Transform (DFT), which introduces a phase lag due to dependency on past and present signal values. This paper proposes a deep learning algorithm utilizing a dual-branch structure similar to the complex wavelet transform to generate a pseudo-complex valued signal for instantaneous phase estimation. The network has Discrete Cosine Transform (DCT) layers, which help to extract latent space representations for the real and imaginary signal components, respectively. An additional design goal was to make this Deep Learning (DL)-based algorithm suitable for deployment on portable edge devices with limited computing resources such as field-programmable gate arrays (FPGAs). This work demonstrates a proof-of-principle for real-time instantaneous phase estimation in neuromodulation applications. Our generalized model achieves an improvement of 40.3% in phase estimation accuracy over the endpoint-corrected Hilbert Transform (ecHT) method and an improvement of 9.2% over conventional deep learning model architectures.},
}

@article {pmid41432054,
year = {2025},
author = {Chen, Q and Wu, H and Xie, S and Zhu, F and Xu, F and Xu, Q and Sun, L and Yang, Y and Xie, L and Xie, J and Li, H and Dai, A and Zhang, W and Wang, L and Jiao, C and Zhang, H and Zhou, X and Xu, ZZ and Chen, X},
title = {GPR30 in spinal cholecystokinin-positive neurons modulates neuropathic pain.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
doi = {10.7554/eLife.102874},
pmid = {41432054},
issn = {2050-084X},
support = {82371220//National Natural Science Foundation of China/ ; 82171206//National Natural Science Foundation of China/ ; ZDFY2022-4XA102//4+X Clinical Research Project of Women's Hospital, School of Medicine, Zhejiang University/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 226-2022-00227//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Animals ; *Receptors, G-Protein-Coupled/metabolism/genetics ; *Neuralgia/physiopathology/metabolism ; *Cholecystokinin/metabolism ; Mice ; *Neurons/metabolism ; *Receptors, Estrogen/metabolism/genetics ; Male ; Mice, Inbred C57BL ; Disease Models, Animal ; *Spinal Cord ; },
abstract = {Neuropathic pain, a major health problem affecting 7-10% of the global population, lacks effective treatment due to its elusive mechanisms. Cholecystokinin-positive (CCK[+]) neurons in the spinal dorsal horn (SDH) are critical for neuropathic pain, yet the underlying molecular mechanisms remain unclear. Here, we show that the membrane estrogen receptor G-protein coupled estrogen receptor (GPER/GPR30) in spinal neurons was significantly upregulated in chronic constriction injury (CCI) mice and that inhibition of GPR30 in CCK[+] neurons reversed CCI-induced neuropathic pain. Furthermore, GPR30 in spinal CCK[+] neurons was essential for the enhancement of AMPA-mediated excitatory synaptic transmission in CCI mice. Moreover, GPR30 was expressed in spinal CCK[+] neurons that received direct projection from the primary sensory cortex (S1-SDH). Chemogenetic inhibition of S1-SDH post-synaptic neurons alleviated CCI-induced neuropathic pain. Conversely, chemogenetic activation of these neurons mimicked neuropathic pain symptoms, which were attenuated by spinal inhibition of GPR30. Finally, we confirmed that GPR30 in S1-SDH post-synaptic neurons was required for CCI-induced neuropathic pain. Taken together, our findings suggest that GPR30 in spinal CCK[+] neurons and S1-SDH post-synaptic neurons is pivotal for neuropathic pain, thereby representing a promising therapeutic target for neuropathic pain.},
}

Animals
*Receptors, G-Protein-Coupled/metabolism/genetics
*Neuralgia/physiopathology/metabolism
*Cholecystokinin/metabolism
Mice
*Neurons/metabolism
*Receptors, Estrogen/metabolism/genetics
Male
Mice, Inbred C57BL
Disease Models, Animal
*Spinal Cord

@article {pmid41431688,
year = {2024},
author = {Lee, J and Letner, JG and Lim, J and Atzeni, G and Liao, J and Kamboj, A and Mani, B and Jeong, S and Kim, Y and Sun, Y and Koo, B and Richie, J and Valle, ED and Patel, PR and Sylvester, D and Kim, HS and Jang, T and Phillips, JD and Chestek, CA and Weiland, J and Blaauw, D},
title = {A Sub-mm[3] Wireless Neural Stimulator IC for Visual Cortical Prosthesis With Optical Power Harvesting and 7.5-kb/s Data Telemetry.},
journal = {IEEE journal of solid-state circuits},
volume = {59},
number = {4},
pages = {1110-1122},
pmid = {41431688},
issn = {0018-9200},
abstract = {This article proposes StiMote, an untethered, free-floating and individually addressable stimulator mote designed for visual cortex stimulation, aimed at vision restoration. The system is optically powered by a custom photovoltaic (PV) layer. In addition, the photodiode (PD) layer captures the light modulation and forwards it to the optical receiver (ORX) including a tranimpedance amplifier. Translated instructions can assign a unique slot, up to 1024 available, to each mote within the time-division multiple access (TDMA) framework. In this work, we propose an automatic charge balance (CB) technique that monitors the injected charge to balance in bi-phasic switched-capacitor stimulation (SCS). The chip was confirmed fully functional when operated completely wirelessly using harvested light. Measurement results revealed a power consumption of 4.48 μ W with a 7.5-kb/s optical downlink data rate, corresponding to continuous updates at 2.5 Hz of 1024 motes to their individual 3-b stimulation intensity levels. The dc-dc converter, responsible for providing high voltage for stimulation, demonstrated 4.3-V output voltage when unloaded, with a maximum efficiency of 67.4%. The proposed CB circuit exhibited linear controllability of stimulation charge up to 16 nC, with a charge imbalance of less than 0.2 nC. Furthermore, in vitro testing confirmed the absence of chemical reactions at electrodes, and in vivo experiments conducted on live rats verified the effectiveness of the stimulation through StiMote.},
}

@article {pmid41429310,
year = {2025},
author = {Rizzuto, DS and Herrema, HG and Hu, Z and Utin, D and Kahn, J and Ho, C and Smiles, A and Gross, RE and Lega, BC and Das, SR and Kahana, MJ},
title = {A wireless, 60-channel, AI-enabled neurostimulation platform.},
journal = {Brain stimulation},
volume = {},
number = {},
pages = {103013},
doi = {10.1016/j.brs.2025.103013},
pmid = {41429310},
issn = {1876-4754},
abstract = {OBJECTIVE: Closed-loop neuromodulatory therapies require devices that can decode ongoing brain states and deliver multi-site stimulation.

METHODS: We describe the Smart Neurostimulation System (SNS), a cranially mounted implant with 60 configurable recording/stimulation channels, inductive power, and onboard spectral-feature classification. In three freely-moving sheep, we streamed local-field potentials and conducted two parameter-sweep experiments.

RESULTS: Cross-validated movement classifiers achieved an average AUC exceeding 0.95. Increasing stimulation amplitude and frequency produced post-stimulation elevations in α-band (8-12 Hz) and γ-band (78-82 Hz) power at most target locations.

CONCLUSION: The SNS unifies high-density sensing, real-time brain state decoding, and programmable closed-loop stimulation in a single device, demonstrating behavioral-state prediction and parameter-dependent neuromodulation in vivo.

SIGNIFICANCE: These findings establish a preclinical foundation for biomarker-guided stimulation targeting distributed cortical networks underlying memory and cognition.},
}

@article {pmid41429054,
year = {2025},
author = {Radman, M and Podmore, JJ and Poli, R and Paulmann, S and Daly, I},
title = {Decoding semantic categories: Insights from an fMRI ALE meta analysis.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae302b},
pmid = {41429054},
issn = {1741-2552},
abstract = {The human brain organizes conceptual knowledge into semantic categories; however, the extent to which these categories share common or distinct neural representations remains unclear. This study aims to clarify this organizational structure by identifying consistent, modality-controlled activation patterns across several widely used and frequently investigated semantic domains in fMRI research. By quantifying the distinctiveness and overlap among these patterns, we provide a more precise foundation for understanding the brain's semantic architecture, as well as for applications such as semantic brain-computer interfaces (BCI). Approach: Following PRISMA guidelines, we conducted a systematic review and meta-analysis of 75 fMRI studies covering six semantic categories: animals, tools, food, music, body parts, and pain. Using Activation Likelihood Estimation (ALE), we identified convergent activation patterns for each category while controlling for stimulus modality (visual, auditory, tactile, and written). Subsequently, Jaccard-based overlap analyses were performed to quantify the degree of neural commonality and separability across concept-modality pairs, thereby revealing the underlying structure of representational similarity. Main Results: Distinct yet partially overlapping activation networks were identified for each semantic category. Tools and animals showed shared activity in the lateral occipital and ventral temporal regions, reflecting common object-based visual processing. In contrast, food-related stimuli primarily recruited limbic and subcortical structures associated with affective and motivational processing. Music and animal sounds overlapped within the superior temporal and insular cortices, whereas body parts and pain engaged occipito-parietal and cingulo-insular networks, respectively. Together, these findings reveal a hierarchically organized and modality-dependent semantic architecture in the human brain. Significance: This meta-analysis offers a quantitative and integrative characterization of how semantic knowledge is distributed and differentiated across cortical systems. By demonstrating how conceptual content and sensory modality jointly shape neural organization, the study refines theoretical models of semantic cognition and provides a methodological basis for evaluating conceptual separability. These insights have direct implications for semantic neural decoding and for the development of BCI systems grounded in meaning-based neural representations. .},
}

@article {pmid41428932,
year = {2025},
author = {Lu, R and Deng, W and Gao, T and Huang, S and Zhang, Z and Liu, Y and Zhong, SH},
title = {Mutual Generation for Cross-domain Challenge in Stroke Patients' Motor Imagery Classification and Functional Recovery Prediction.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3646871},
pmid = {41428932},
issn = {2168-2208},
abstract = {The accumulating body of research indicates that Motor Imagery (MI)-BCIs have the potential to enhance the quality of life for individuals with disabilities and to advance our understanding of brain function and rehabilitation strategies. Among these diseases, stroke is the leading cause of long-term motor disability across the globe, thereby underscoring the need for innovative rehabilitation strategies, such as MI-BCI technologies. In contrast with these expectations, the majority of existing research is built upon data obtained from healthy subjects. The construction of effective classification models for Motor Imagery tasks in patients with brain diseases, particularly stroke, remains a significant challenge. The lateralization of the left and right hemispheres is more pronounced in patients who have suffered a stroke than in healthy individuals. Moreover, the specific locations of lesions and the regions of influence result in significant variations in the electroencephalogram (EEG) data of patients with different hemiplegic sides. This paper explores the potential of generative models in addressing the issue of domain differences arising from different hemiplegic sides EEG data. Furthermore, this paper circumvents the potential adverse effects of rigorous optimization of low-quality samples on model performance through the utilization of label softening algorithm. Two MI-EEG datasets of stroke patients performing Motor Imagery tasks are used to validate our method. In comparison to both classical machine learning methods and those state-of-the-art models for MI classification, the classification model in this paper achieves a noticeable performance improvement in different data partitioning strategies, including subject-dependent and subject-independent scenarios. Each sub-module, and each designed loss function, contributes to the final performance growth. In addition, this paper also investigates the potential of the proposed framework for predicting a patient's level of functional recovery. Our findings indicate that the addition of a prediction layer to the proposed model enables the accurate prediction of functional recovery level in stroke patients. The source code is available at https://github.com/arrogant-R/MutualGeneration.},
}

@article {pmid41428911,
year = {2025},
author = {Fu, X and Liu, R and Wai, AAP and Pulferer, H and Robinson, N and Muller-Putz, GR and Guan, C},
title = {EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding.},
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.2025.3647101},
pmid = {41428911},
issn = {1558-0210},
abstract = {Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for accurate temporal and spectral feature extraction, and the scarcity of high-quality gait EEG datasets. To address these issues, we propose EEG2GAIT, a novel hierarchical graph-based model that captures multi-level spatial embeddings of EEG channels using a Hierarchical Graph Convolutional Network (GCN) Pyramid. To further improve decoding performance, we introduce a Hybrid Temporal-Spectral Reward (HTSR) loss function, which integrates time-domain, frequency-domain, and reward-based loss components. In addition, we contribute a new Gait-EEG Dataset (GED), consisting of synchronized EEG and lower-limb joint angle data collected from 50 participants across two laboratory visits. Extensive experiments demonstrate that EEG2GAIT with HTSR achieves superior performance on the GED dataset, reaching a Pearson correlation coefficient (r) of 0.959, a coefficient of determination (R[2]) of 0.914, and a Mean Absolute Error (MAE) of 0.193. On the MoBI dataset, EEG2GAIT likewise consistently outperforms existing methods, achieving an r of 0.779, an R[2] of 0.597, and an MAE of 4.384. Statistical analyses confirm that these improvements are significant compared to all prior models. Ablation studies further validate the contributions of the hierarchical GCN modules and the proposed HTSR loss, while saliency analysis highlights the involvement of motor-related brain regions in decoding tasks. Collectively, these findings underscore EEG2GAIT's potential for advancing brain-computer interface applications, particularly in lower-limb rehabilitation and assistive technologies.},
}

@article {pmid41426299,
year = {2025},
author = {Huang, S and Chen, C and Mo, Y and Zhao, Y and Zhu, Y and Dong, K and Xu, T},
title = {Exploring the n-back task: insights, applications, and future directions.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1721330},
pmid = {41426299},
issn = {1662-5161},
abstract = {The n-back task has become a central paradigm for investigating the mechanisms of working memory (WM) and related executive functions. This review provides an integrative analysis of the n-back experiment, covering its cognitive operations, task variants, neuroimaging findings, and practical applications across multiple domains. We first delineate three core cognitive components-updating, maintenance, and attentional control-and summarize converging evidence that these functions rely on overlapping fronto-striatal and fronto-parietal networks. We then examine major task variants and review applications in: (1) cognitive training and transfer effects, particularly the proposed association between WM and fluid intelligence; (2) clinical contexts including attention deficit hyperactivity disorder (ADHD), depression, and neurological rehabilitation; (3) developmental and educational settings; and (4) emerging research on social cognition, stress, and emotional regulation. Critically, this review evaluates ongoing inconsistencies in how the n-back task is interpreted as a measure of WM and highlights methodological factors, such as task heterogeneity, multi-process interference, and mental fatigue, that complicate both behavioral and neural inferences. To address these issues, we outline methodological recommendations including adaptive task design, multimodal physiological monitoring, and standardized experimental protocols. We further discuss future directions involving virtual reality (VR), mobile platforms, and brain-computer interface (BCI) integration to improve ecological validity and translational relevance. By synthesizing behavioral and neural evidence, this review underscores the n-back task's versatility while emphasizing the need for improved construct clarity and methodological rigor.},
}

@article {pmid41426186,
year = {2025},
author = {Khan, H and Nazeer, H and Minhas, HS and Naseer, N and Mirtaheri, P},
title = {Open-access fNIRS dataset for motor imagery of lower-limb knee and ankle joint tasks.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1695169},
pmid = {41426186},
issn = {2296-9144},
}

@article {pmid41424861,
year = {2026},
author = {Li, X and Zheng, C and Tian, Y and Ming, D},
title = {Channel-specific differential effects of bacterial mechanosensitive channels for ultrasound neuromodulation in precision sonogenetics.},
journal = {Theranostics},
volume = {16},
number = {5},
pages = {2447-2465},
pmid = {41424861},
issn = {1838-7640},
mesh = {Animals ; Rats ; *Ion Channels/genetics/metabolism ; *Hippocampus/metabolism/physiology/radiation effects ; Male ; Rats, Sprague-Dawley ; Ultrasonic Waves ; *Escherichia coli Proteins/genetics/metabolism ; Mechanotransduction, Cellular ; Dependovirus/genetics ; },
abstract = {Rationale: Ultrasound neuromodulation offers promising therapeutic potential, but its effectiveness is limited by imprecise targeting of neural circuits. Engineering mechanosensitive ion channels can enhance ultrasound sensitivity, providing a more precise approach for targeted neuromodulation. This study aimed to compare three bacterial mechanosensitive channels (MscL-G22S, MscL-G22N, and MscS) for mediating ultrasound-responsive hippocampal activity to identify optimal candidates for precision sonogenetics applications. Methods: We expressed MscL-G22S, MscL-G22N, and MscS in the rat hippocampus using AAV vectors and applied focused ultrasound stimulation at various intensities while recording local field potentials. Neural oscillatory patterns, ultrasound-evoked potentials, behavioral outcomes, immunohistology, and transcriptomic analyses were conducted to assess response consistency, efficacy, and biosafety. Results: Each channel conferred distinct neuromodulatory signatures: MscL-G22S exhibited remarkable ultrasound sensitivity with non-monotonic intensity-response amplification of evoked potentials (2.3-fold increase at maximum intensity), and accelerated response timing (latency reduction). Notably, MscL-G22N showed weaker ultrasound responses despite having a lower mechanical threshold than G22S, suggesting ultrasound sensitivity depends on factors beyond mechanical gating thresholds. Conversely, MscS displayed diminished responses at higher intensities. No statistically significant differences were detected in behavior assessments and histology evaluations. All channels maintained normal anxiety indices, spatial memory, and neuronal morphology, though MscS selectively increased depressive-like behaviors. Transcriptomic analysis revealed that MscS demonstrated exceptional genomic compatibility with minimal off-target gene alterations (9 vs. >400 in MscL variants). Conclusion: This characterization provides insights for potential precision sonogenetics applications: MscS offers a biosafety-optimized option with minimal genomic footprint, whereas MscL-G22S enables modulation of neural oscillations. These findings contribute to the development of customized neuromodulation approaches for targeting pathological circuits in neurological disorders.},
}

Animals
Rats
*Ion Channels/genetics/metabolism
*Hippocampus/metabolism/physiology/radiation effects
Male
Rats, Sprague-Dawley
Ultrasonic Waves
*Escherichia coli Proteins/genetics/metabolism
Mechanotransduction, Cellular
Dependovirus/genetics

@article {pmid41357970,
year = {2025},
author = {Wei, Y and Wang, Y and Wei, T and Lu, X and Li, D and Sherwood, CC and Zhang, Y and Cheng, C and Jiang, T and Fan, L and Cheng, L},
title = {Cross-Species Cortical Geometry Reveals Conserved Gradients Across Primates and Human-Specific Expansion.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41357970},
issn = {2692-8205},
support = {R24 NS092988/NS/NINDS NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; R01 AG067419/AG/NIA NIH HHS/United States ; R01 AG087945/AG/NIA NIH HHS/United States ; R01 HG011641/HG/NHGRI NIH HHS/United States ; },
abstract = {The primate cerebral cortex, characterized by its complex structural geometry, underlies advanced cognitive functions and represents a defining feature distinguishing primates from other mammals. However, cross-species patterns of cortical geometry and the links between human cortical geometry and transcriptional architecture remain poorly understood. We developed a geometry-based cross-species cortical alignment framework to systematically investigate the similarities and differences in structural connectivity and cortical expansion characteristics among macaques, chimpanzees, and humans, and additionally explored the transcriptional underpinnings of human cortical geometry. Our analysis revealed conserved spatial patterns of cortical geometric features across species, providing the foundation for constructing a cross-species structural common space to support the alignment framework. We found that primary sensory, somatomotor, and face-selective regions exhibited high structural connectivity similarity across species, whereas prefrontal and parietal association cortices displayed significant divergence. We also identified disproportionate cortical expansion in the default mode network, with a consistent expansion trend across different evolutionary lineages in primates. Furthermore, neuroimage-transcription analysis indicated that cortical geometric features were correlated with transcriptional profiles enriched in neurodevelopmental and connectivity-related pathways. These results highlight a conserved yet hierarchically differentiated organization of the cerebral cortex in primates, providing new insights into the biological basis of human brain evolution.},
}

@article {pmid41329575,
year = {2026},
author = {Zhang, W and Lai, J and Xu, B and Zeng, H and Wu, T and Hu, H and Song, A},
title = {The Role of Vibrotactile Stimulation in Soft Rehabilitation Glove-Assisted Hand Rehabilitation Training: A Pilot Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {149-162},
doi = {10.1109/TNSRE.2025.3639490},
pmid = {41329575},
issn = {1558-0210},
mesh = {Humans ; Male ; Pilot Projects ; Female ; *Vibration ; *Stroke Rehabilitation/methods/instrumentation ; *Hand/physiopathology ; Electroencephalography ; Adult ; Middle Aged ; Robotics/instrumentation ; Spectroscopy, Near-Infrared ; Young Adult ; Motor Cortex/physiology/physiopathology ; Aged ; Somatosensory Cortex/physiology ; Hand Strength ; Brain-Computer Interfaces ; Stroke/physiopathology ; },
abstract = {Brain-controlled robotic hand rehabilitation systems based on motor intention recognition have been used to promote recovery of hand function in stroke patients. However, the low decoding accuracy of motor imagery (MI) and unclear neural response mechanisms limit its widespread application. This study introduces a novel vibrotactile-assisted brain-controlled soft robotic hand rehabilitation system to validate its effectiveness in activating the motor sensory areas of the brain and to explore the neural response mechanisms of vibration stimulation in hand rehabilitation training. A total of 23 healthy subjects and 5 stroke patients were recruited to perform EEG and fNIRS-based experiments. Healthy subjects performed an EEG-based active rehabilitation task and an fNIRS-based passive rehabilitation task driven by the soft glove. Stroke patients only completed an EEG-based passive rehabilitation task. All experiments were conducted under two conditions: with and without vibrotactile stimulation. EEG results revealed that vibration stimulation significantly enhanced motor-sensory cortex activation during MI, and improved the online decoding performance of subjects with poor training outcomes. Grasping and stretching movements driven by the soft glove effectively activated the subjects' motorsensory cortex. Vibration stimulation boosted the event-related desynchronization (ERD) phenomenon in the contralateral somatosensory cortex of the healthy subjects, but was not significant in the motor cortex. Meanwhile, it strengthened bilateral sensorimotor activation in stroke patients. Moreover, fNIRS results indicated that vibration stimulation increased the concentration of HbO in the motor-sensory areas during passive movement and enhanced the bidirectional functional connectivity between the left and right hemispheres. These findings suggest that the proposed tactile-assisted hand rehabilitation system can effectively enhance neural activation in the motor-sensory cortex, potentially leading to improved hand function recovery in stroke patients.},
}

Humans
Male
Pilot Projects
Female
*Vibration
*Stroke Rehabilitation/methods/instrumentation
*Hand/physiopathology
Electroencephalography
Adult
Middle Aged
Robotics/instrumentation
Spectroscopy, Near-Infrared
Young Adult
Motor Cortex/physiology/physiopathology
Aged
Somatosensory Cortex/physiology
Hand Strength
Brain-Computer Interfaces
Stroke/physiopathology

@article {pmid41325123,
year = {2026},
author = {Wei, R and Hua, C and Chen, J and Mu, D and Zhao, J},
title = {Improving Generalization in Federated Learning for Steady-State Visual Evoked Potential Classification and Its Application in Soft Gripper.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {126-136},
doi = {10.1109/TNSRE.2025.3639091},
pmid = {41325123},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Male ; *Hand Strength/physiology ; Adult ; Algorithms ; Female ; Brain-Computer Interfaces ; *Machine Learning ; Young Adult ; Signal Processing, Computer-Assisted ; Databases, Factual ; Federated Learning ; },
abstract = {Conventional cross-subject electroencephalogram (EEG) signal identification frameworks require centralized aggregation of all subjects' data for feature extraction, which inherently poses substantial risks of data privacy breaches. In response to this critical issue, the present study delves into the classification of steady-state visual evoked potential (SSVEP) signals with an emphasis on data privacy preservation. First, we design a federated learning framework (FedGF) consisting of a central server and multiple clients, where the server generates global features and coordinates distributed training across clients, while retaining subject-specific raw data locally to ensure privacy protection. Then, to enhance model generalizability, FedGF employs data-free knowledge distillation (DFKD) to achieve knowledge transfer across clients through global feature learning. Extensive experiments on two public datasets (Dataset 1 'session01' and 2 'session02') and one private dataset (Dataset 3) demonstrate the superiority of the proposed method over baseline approaches, achieving performance improvements of 0.52%, 0.65%, and 0.53%, respectively. Finally, we develop a novel smart soft gripper with thermochromic capabilities and seamlessly integrate it with the trained network, demonstrating robust performance in daily grasping tasks. The source code is available at https://github.com/raow923/FedGF.},
}

Humans
*Electroencephalography/methods
*Evoked Potentials, Visual/physiology
Male
*Hand Strength/physiology
Adult
Algorithms
Female
Brain-Computer Interfaces
*Machine Learning
Young Adult
Signal Processing, Computer-Assisted
Databases, Factual
Federated Learning

@article {pmid41423674,
year = {2025},
author = {Van Den Kerchove, A and Meunier, J and de Moura, M and Willemssens, A and Geeurickx, D and Schiettecatte, E and Van Damme, P and Si-Mohammed, H and Cabestaing, F and Allart, E and Van Hulle, MM},
title = {Visual ERP-based brain-computer interface use with severe physical, speech and eye movement impairments: case studies.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01836-0},
pmid = {41423674},
issn = {1743-0003},
support = {G0A4321N//Fonds Wetenschappelijk Onderzoek/ ; G0A4118N//Fonds Wetenschappelijk Onderzoek/ ; GPUDL/20/031//KU Leuven Special Research Fund/ ; C24/18/098//KU Leuven Special Research Fund/ ; RITMEA//European Regional Development Fund/ ; RITMEA//European Regional Development Fund/ ; 101118964//HORIZON EUROPE Marie Sklodowska-Curie Actions/ ; 857375//Horizon 2020/ ; AKUL 043//Herculesstichting/ ; },
abstract = {BACKGROUND: Individuals who experience severe speech and physical impairment face significant challenges in communication and daily interaction. Visual brain-computer interfaces (BCIs) offer a potential assistive solution, but their usability is limited when facing restrictions in eye motor control, gaze redirection and fixation. This study investigates the feasibility of a gaze-independent visual oddball BCI for use as an augmentative and alternative communication (AAC) device in the presence of limited eye motor control.

METHODS: Seven participants with varying degrees of eye motor control were recruited and their conditions were thoroughly assessed. Visual oddball BCI decoding accuracy was evaluated with multiple decoders in three visuospatial attention (VSA) conditions: overt VSA, with fixation cued on the target, covert VSA, with fixation cued on the center of the screen, and free VSA without gaze cue.

RESULTS: covert VSA with central fixation leads to decreased accuracy, whereas free VSA is comparable to overt VSA for some participants. Furthermore, cross-condition decoder training and evaluation suggests that training with overt VSA may improve performance in BCI users experiencing gaze control difficulties.

CONCLUSIONS: These findings highlight the need for adaptive decoding strategies and further validation in applied settings in the presence of gaze impairment.},
}

@article {pmid41421050,
year = {2025},
author = {Ming, W and Zheng, Y and Lian, Q and Shen, C and Zhang, Y and Wang, Z and Wang, S and Li, F and Zheng, Z and Qi, Y and Zhu, J and Wu, H},
title = {Brain connectivity predict surgical outcomes of low-grade epilepsy-associated neuroepithelial tumors.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {183},
number = {},
pages = {2111478},
doi = {10.1016/j.clinph.2025.2111478},
pmid = {41421050},
issn = {1872-8952},
abstract = {OBJECTIVE: Low-grade epilepsy-associated neuroepithelial tumors (LEATs) often cause drug-resistant epilepsy. Despite complete resection of these lesions, approximately 20% of patients continue to experience suboptimal seizure control. This study aims to investigate the predictive value of quantitative features in determining the surgical outcomes for LEAT patients.

METHODS: We retrospectively analyzed 44 temporal LEAT patients who underwent gross-total lesionectomy. EEG features, including power spectral density (PSD) and weighted phase lag index (wPLI), were compared between patients with good (Engel I) and poor (Engel II-IV) outcomes. Significant EEG features were identified through these analyses. Domain Adversarial Neural Network (DANN) was employed to assess the predictive value of these features for surgical outcomes.

RESULTS: No significant PSD differences were found, but patients with good outcomes had higher alpha-band wPLI (p = 0.008). LEATnet, predicted outcomes with an AUC of 0.81and correctly classified 8 of 11 patients in the independent validation cohort.

CONCLUSIONS: Alpha-band functional connectivity is a key predictor of surgical outcomes in LEAT patients.

SIGNIFICANCE: EEG-based connectivity analysis may improve prognostic accuracy and aid clinical decision-making in LEAT epilepsy.},
}

@article {pmid41418936,
year = {2025},
author = {Zhou, Y and Jiang, R and Zhang, J},
title = {A multi-scale deep CNN based on attention mechanism for EEG emotion recognition.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110662},
doi = {10.1016/j.jneumeth.2025.110662},
pmid = {41418936},
issn = {1872-678X},
abstract = {BACKGROUND: Recognizing emotion is a crucial project within the domain of brain-computer interface technology. Recently, researchers have found that deep learning have been proven to be superior to machine learning, but how to obtain more discriminative features still faces great challenges.

NEW METHOD: We propose a multi-scale convolutional neural network (MSCNN) based on channel attention and spatial attention (CSA-MSCNN) for EEG emotion recognition. The channel attention enhances the feature extraction ability of critical channels by generating channel weights, while suppressing noise or interference from redundant channels. The spatial attention helps the model to more precisely locate key areas related to emotion by generating a spatial weight matrix. To extract more comprehensive features, CSA-MSCNN uses MSCNN for feature extraction, with smaller convolutional kernels capturing the local details of the signals, and larger convolutional kernels with a broader receptive field to obtain deeper signal information.

RESULTS: CSA-MSCNN achieves average accuracies of 95.75% and 95.39% for three-class classification of valence and arousal on DEAP, respectively, while achieving an average three-class classification accuracy of 90.48% on SEED.

The classification accuracy of CSA-MSCNN is not only significantly better than traditional machine learning models, but also shows strong competitiveness compared with mainstream deep learning models such as graph convolutional neural network (GCNN).

CONCLUSIONS: CSA-MSCNN addresses the issues of multiple EEG signal channels and complex regional information.},
}

@article {pmid41418897,
year = {2025},
author = {Zhang, T and Zhang, Q and Xiong, R and Zhang, J and Jin, Z and Li, L},
title = {Grey Matter Volume Predicts Decision Speed and Reveals Stage-Specific Contributions of Large-Scale Brain Networks in Gambling Tasks.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121659},
doi = {10.1016/j.neuroimage.2025.121659},
pmid = {41418897},
issn = {1095-9572},
abstract = {Large-scale brain networks are well-established in resting-state research and are increasingly being used in task-based functional magnetic resonance imaging (fMRI) studies. However, the mechanisms by which brain networks dynamically reorganize across the various stages of decision-making remain unclear. Here, we investigated the neural basis of decision-making by integrating voxel-based morphometry and fMRI within a modified "Wheel of Fortune" gambling task. Stage-specific brain activation was characterized using the Yeo-7 network atlas to delineate large-scale network dynamics across task stages. We found that: (1) Reaction time (RTs) were significantly longer during choose conditions compared to follow conditions; (2) Gray matter volume correlated with individual variability in RT and predicted RT during choose conditions using multivariate pattern analysis with a Kernel Ridge Regression model, effects absent during follow conditions; (3) A negative correlation was observed between RT and activation in the right superior temporal gyrus and left mid-cingulate cortex; (4) Choice stage involved more extensive network engagement than the result and rating stages, with the rating stage showing the lowest overall activation. Network-specific fractional contributions revealed dominant engagement of the ventral attention network, default mode network, and somato-motor network during the choice stage; the frontoparietal network (FPN), dorsal attention network (DAN), and visual network during the result stage; and the DAN and FPN during the rating stage. These findings provide structural and functional explanations for individual differences in decision speed within a gambling paradigm, revealing the distinct and dynamic roles of brain networks across decision stages and offering mechanistic insights into the neural architecture of this process.},
}

@article {pmid41417240,
year = {2025},
author = {Yakovlev, L and Miroshnikov, A and Syrov, N and Berkmush-Antipova, A and Kaplan, A},
title = {Sensorimotor event-related desynchronization and hemodynamic responses during motor and tactile imagery.},
journal = {Brain structure & function},
volume = {231},
number = {1},
pages = {4},
pmid = {41417240},
issn = {1863-2661},
support = {24-75-00163//Russian Science Foundation/ ; },
mesh = {Humans ; Male ; *Imagination/physiology ; Female ; Adult ; Electroencephalography ; Young Adult ; *Hemodynamics/physiology ; Spectroscopy, Near-Infrared ; *Touch Perception/physiology ; *Touch/physiology ; Brain Mapping ; *Cortical Synchronization/physiology ; *Sensorimotor Cortex/physiology ; },
abstract = {Mental imagery is widely used in cognitive neuroscience and rehabilitation studies, yet their neural mechanisms remain not fully understood. In this study, we investigated neural correlates of motor and tactile imagery using simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recordings. A total of 16 healthy participants performed motor and tactile imagery tasks while brain activity was assessed. We analyzed event-related desynchronization (ERD) of the mu-rhythm and hemodynamic responses in sensory-motor regions. Similar spatio-temporal EEG patterns were observed for both motor and tactile imagery conditions (e.g., prominent contralateral ERD at C3). Hemodynamic responses differed: motor imagery elicited activation in both precentral and postcentral regions (p = 0.433), whereas tactile imagery predominantly engaged postcentral regions. The latter effect reached significance only in the functional channels of interest (fCOI) analysis (p = 0.003) and showed a non-significant trend across the full anatomical channel groups (p = 0.101). Correlation analysis revealed a strong across-subject correlation (r = 0.84; p < 0.001) between ERD values in motor and tactile imagery, but no correlation between ERD and hemodynamic responses. Linear mixed model analysis revealed significant (p < 0.001) associations between precentral and postcentral HRs for both MI and TI. These findings suggest that although motor and tactile imagery share common sensorimotor engagement at the electrophysiological level, their hemodynamic signatures are distinct. The absence of linear associations between modalities highlights the complexity of brain dynamics and the importance of multimodal assessments. The findings have implications for the design of brain-computer interfaces and rehabilitation protocols using mental imagery.},
}

Humans
Male
*Imagination/physiology
Female
Adult
Electroencephalography
Young Adult
*Hemodynamics/physiology
Spectroscopy, Near-Infrared
*Touch Perception/physiology
*Touch/physiology
Brain Mapping
*Cortical Synchronization/physiology
*Sensorimotor Cortex/physiology

@article {pmid41416623,
year = {2025},
author = {Adhikary, S and Dutta, S and Bose, A and Ranjan, R},
title = {Brain computer interface to recognize hand movements by magnification of subtle electroencephalogram patterns.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2025.2602830},
pmid = {41416623},
issn = {1476-8259},
abstract = {Brain-computer interfacing facilitates usage of medical devices such as Electroencephalograms to study brain activities using signal processing techniques. Hand movements are motor activities which cause signature electrical signals in the electroencephalogram recordings. Signal processing and machine learning can be used to remove artefact contamination, amplify subtle features associated with hand movement and classify them. This paper experiments to utilize mathematical models to extract features and classify hand movement from electroencephalogram data up to 98% accuracy based on tests performed on an open-sourced dataset. The study, after further tests, can be used to build prosthetic limbs and mind-controlled robotic arms.},
}

@article {pmid41414721,
year = {2025},
author = {Zhang, Y and Huang, HF and Xie, JJ and Ni, W and Yu, H and Wu, ZY},
title = {Genetic and Clinical Characteristics of Chinese Adult Patients With Krabbe Disease.},
journal = {CNS neuroscience & therapeutics},
volume = {31},
number = {12},
pages = {e70708},
doi = {10.1002/cns.70708},
pmid = {41414721},
issn = {1755-5949},
support = {82230062//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholar of Zhejiang University/ ; },
mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; China ; Exome Sequencing ; *Galactosylceramidase/genetics ; *Leukodystrophy, Globoid Cell/genetics/diagnosis ; East Asian People/genetics ; },
abstract = {AIM: This study aims to expand the clinical and genetic spectrum of Krabbe disease (KD) in Chinese adult patients and to improve diagnosis and understanding of its phenotypic diversity.

METHODS: Patients clinically suspected of leukodystrophy were recruited between 2015 and 2025. Clinical features were collected, and whole-exome sequencing (WES) was performed to identify potential variants. The pathogenicity of detected variants was classified according to the American College of Medical Genetics and Genomics (ACMG) standards and guidelines. Functional assays assessing protein expression, processing, secretion, subcellular localization, and enzymatic activity were conducted to further validate variant pathogenicity.

RESULTS: Fourteen unrelated patients were genetically diagnosed with KD, and their genetic and clinical features were summarized. Eleven variants in GALC were identified, including a novel missense variant c.1019C>T (p.P340L) which is not reported in the Human Gene Mutation Database (HGMD). Unlike most adult patients who typically present with spastic paraplegia, the patient carrying this variant exhibited initial symptoms of peripheral neuropathy. Functional experiments demonstrated that the variant led to impaired protein processing and localization, as well as reduced GALC enzymatic activity. Other variants including p.D56H, p.L377X, p.L441X, and p.L634S also affected GALC functions to varying degrees.

CONCLUSION: This study enhances the genotypic and phenotypic characterization of KD in China, aiding in differential diagnosis and genetic counseling. Functional data reinforce the pathogenicity of identified variants.},
}

Adult
Female
Humans
Male
Middle Aged
Young Adult
China
Exome Sequencing
*Galactosylceramidase/genetics
*Leukodystrophy, Globoid Cell/genetics/diagnosis
East Asian People/genetics

@article {pmid41413923,
year = {2025},
author = {Xu, Y and Wei, Y and Xu, M and Zhou, H and Zheng, J and Chen, H and Chen, S and Chen, W},
title = {The relationship between heart rate variability and baseline state anxiety during stress and recovery.},
journal = {BMC psychology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40359-025-03823-5},
pmid = {41413923},
issn = {2050-7283},
support = {No. QD2025017//Scientific Research Foundation of Hang Zhou City University/ ; },
}

@article {pmid41413226,
year = {2025},
author = {Sun, Y and Si, X and He, R and Hu, X and Smielewski, P and Wang, W and Tong, X and Yue, W and Pang, M and Zhang, K and Song, X and Ming, D and Liu, X},
title = {An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework.},
journal = {NPJ digital medicine},
volume = {8},
number = {1},
pages = {768},
pmid = {41413226},
issn = {2398-6352},
support = {ZYGXQNJSKYCXNLZCXM-H15//Scientific Research Innovation Capability Support Project for Young Faculty/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; 82472098, 32300704//National Natural Science Foundation of China/ ; 24JCJQJC00250//Tianjin Natural Science Foundation-Outstanding Youth Project/ ; 24ZXZSSS00510//Major Science and Technology Special Projects and Engineering-Major Project of National Key Laboratories/ ; 2021YFF1200602//Key Technologies Research and Development Program/ ; 2024-JKCS-16//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; },
abstract = {Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited in application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial intelligence models. In this study, we describe an automated classifier, VIPEEGNet, which leverages the advantage of transfer learning from ImageNet-pretrained models to distinguish six types of brain activities. For the development cohort, the recall of VIPEEGNet ranges from 36.8% to 88.2%, and the precision ranges from 55.6% to 80.4%, with performance comparable to that of human experts. Notably, the external testing showed Kullback-Leibler divergence (KLD) values of 0.223 (public) and 0.273 (private), ranking second among the existing 2767 competing algorithms, while using only 0.7% of the parameters of the top-ranked algorithm. Its minimal parameter requirements and modular design offer a deployable solution for real-time brain monitoring, potentially expanding access to expert-level EEG interpretation in resource-limited settings.},
}

@article {pmid41412372,
year = {2025},
author = {Zhang, L and Li, B and Cao, M and Peng, C and Wang, H},
title = {Classification of EEG-fNIRS bimodal brain signals for motor imagery tasks based on wavelet transform and spatio-temporal domain processing.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2025.12.036},
pmid = {41412372},
issn = {1873-7544},
abstract = {The fusion of Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provides richer neural information for brain-computer interface decoding. However, due to their distinct physiological mechanisms and heterogeneous temporal and statistical properties, EEG and fNIRS are difficult to temporally align and to project into a shared latent representation. To address this challenge, we propose BiCAT, a lightweight bimodal decoding framework that integrates wavelet-based preprocessing, artifact-aware time-domain refinement, and early feature-level fusion with a compact Transformer encoder. Wavelet transform is first applied to separate signal and noise components across frequency bands, after which spatio-temporal domain processing suppresses motion and physiological artifacts while preserving task-relevant patterns. The cleaned EEG and fNIRS features are concatenated and fed into a single-encoder Transformer, where joint self-attention captures salient temporal cues within each segment.BiCAT is evaluated on two publicly available EEG-fNIRS datasets covering motor imagery (MI), mental arithmetic (MA), and word generation (WG) tasks. The model achieves 93.41 % accuracy on MI, outperforming the strongest unimodal baseline (fNIRS) by 4.39 percentage points. On MA and WG, BiCAT attains 96.47 % and 96.41 % accuracy, corresponding to gains of 10.39 and 3.86 points over the best unimodal fNIRS and HbR baselines, respectively. Despite having only 111 k parameters, BiCAT performs competitively with representative multimodal fusion methods on the same benchmarks. These results demonstrate that BiCAT provides effective bimodal feature integration and robust performance across multiple EEG-fNIRS tasks while maintaining low computational complexity.},
}

@article {pmid41410819,
year = {2025},
author = {Hickman, J and Tsai, A and Fullard, M and Korsmo, M and Forbes, E and Aslam, S and Baumgartner, AJ and Feuerstein, JS and Bayram, E},
title = {Early-Onset Parkinson's Disease: Unique Features and Management Approaches.},
journal = {Current neurology and neuroscience reports},
volume = {26},
number = {1},
pages = {3},
pmid = {41410819},
issn = {1534-6293},
support = {R01NS120850/NS/NINDS NIH HHS/United States ; R00AG073453/AG/NIA NIH HHS/United States ; },
mesh = {Humans ; *Parkinson Disease/therapy/diagnosis/epidemiology ; Age of Onset ; *Disease Management ; Risk Factors ; Disease Progression ; },
abstract = {PURPOSE OF REVIEW: To highlight the unique clinical features, risk factors, and management strategies associated with early-onset Parkinson's disease (EOPD), and contrast these with late-onset Parkinson's disease (LOPD). We outline how these differences influence diagnostic and therapeutic approaches and identify key knowledge gaps critical to improving clinical care.

RECENT FINDINGS: Compared to LOPD, EOPD (onset age 21-50) has a higher prevalence of monogenic risk factors, focal dystonia, depression, anxiety; slower motor progression; lower rates of cognitive decline; higher risk for delayed diagnosis. Treatment is complicated by earlier and more frequent dyskinesias, motor fluctuations, and unique considerations such as pregnancy and career impact. Risk factors, clinical presentation, progression, and management needs of EOPD can differ from LOPD. Despite advances in characterizing and diagnosing EOPD, most research remains focused on LOPD. There is a critical need to tailor research and clinical trials to address the distinct needs of people with EOPD.},
}

Humans
*Parkinson Disease/therapy/diagnosis/epidemiology
Age of Onset
*Disease Management
Risk Factors
Disease Progression

@article {pmid41408409,
year = {2025},
author = {Zhen, X and Yu, Z and Shi, Y and Zhao, Y},
title = {Fusing LandTrendr BCI and machine learning for spoil dump mapping.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-32957-0},
pmid = {41408409},
issn = {2045-2322},
}

@article {pmid41408286,
year = {2025},
author = {Zhang, Y and Li, M and Guo, M and Xu, G and Wang, A},
title = {Decoding preparatory movement state-based motor imagery with multi layer energy decoder.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01837-z},
pmid = {41408286},
issn = {1743-0003},
support = {F2024202019//Natural Science Foundation of Hebei Province/ ; 52320105008//International Cooperation and Exchange of the National Natural Science Foundation of China/ ; 2022YFC2402203//National Key Research and Development Program of China/ ; },
abstract = {BACKGROUND: Motor imagery (MI) is a widely used paradigm in brain-computer interface (BCI) research due to its potential applications in areas such as motor rehabilitation. As a purely cognitive process, MI produces low-amplitude, non-stationary EEG. Despite improving accuracies, cross-subject variability and limited generalization continue to motivate approaches that strengthen MI representations and enhance system robustness.

METHODS: We designed a task-guided preparatory movement state-based motor imagery (PMS-MI) paradigm that elicits a brief motor preparatory state before MI and captures EEG features from both the preparation and imagery phases. To decode the features effectively, we introduced a multilayer energy decoder (MLED) that integrates graph signal processing (GSP): EEG is modeled as intra- and cross-frequency multilayer brain networks, and a graph Fourier transform (GFT) projects the signals into network energy features before classification. We benchmarked the PMS-MI paradigm and the MLED method across multiple time window lengths using a panel of classical and deep-learning classifiers.

RESULTS: The PMS-MI paradigm elicited significant energy variations during the movement preparation phase and induced earlier event-related desynchronization (ERD) with broader frequency band activation during MI, compared to traditional MI paradigms. Classification performance using CSP in the PMS-MI paradigm surpassed that of the traditional paradigm at all time windows. Further accuracy improvements were achieved with the MLED method. Brain network analysis revealed distinct neural representations between the preparation and MI phases, and MLED effectively captured these differences. Feature fusion of preparation and MI stages resulted in classification accuracies exceeding 85% for both 1 s and 4 s windows. The results demonstrate that both algorithmic design and paradigm choice play important roles in MI EEG decoding, with their relative contributions varying across temporal windows and experimental conditions.

CONCLUSIONS: Integration of preparatory movement states into the movement imagery process can generate distinguishable features at different stages and improve the classification performance of BCI systems. The proposed PMS-MI paradigm, combined with the MLED decoding method, provides a promising direction for developing more accurate and robust BCIs, particularly in the context of neurorehabilitation.},
}

@article {pmid41407306,
year = {2025},
author = {Wang, J and Liu, H and Wu, W and Hu, X and Wu, Z and Zhu, S and Ma, G and Wan, H and Feng, C and Wang, H},
title = {Structure-Property Modulation in Pyrolytic Photoresist Films Enabled Size-Dependent Electrochemical Performance of Neural Interfaces.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c20142},
pmid = {41407306},
issn = {1944-8252},
abstract = {Neural probes are critical devices used to monitor and record brain activity, usually connected to neurons to measure neural activity. However, traditional metal electrodes face numerous challenges, including high Young's modulus, susceptibility to electromagnetic interference, insufficient biocompatibility, and the risk of corrosion and delamination. In this study, we explore a highly biocompatible carbon material, a pyrolytic photoresist film (PPF), developed through a photoresist pyrolysis process. The effects of the pyrolysis temperature and hold time on material properties were systematically studied. The optimal pyrolysis condition was identified as 1000 °C for 2 h. Furthermore, a quantitative model was established to link the electrode's geometric area with electrochemical performance and optimize the performance of PPF neural probes. Ultimately, we successfully fabricated a multichannel flexible neural probe with superior electrochemical performance.},
}

@article {pmid41406614,
year = {2025},
author = {Sultana, M and Matran-Fernandez, A and Halder, S and Nawaz, R and Jain, O and Scherer, R and Chavarriaga, R and Millan, JDR and Perdikis, S},
title = {An out-of-the-lab evaluation of dry EEG technology on a large-scale motor imagery brain-computer interface dataset.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2e8a},
pmid = {41406614},
issn = {1741-2552},
abstract = {OBJECTIVE: This study assesses the signal quality of state-of-the-art dry electroencephalography (EEG) under highly challenging, uncontrolled, real-world conditions and compares it to conventional wet EEG.

APPROACH: EEG data from 530 participants recorded during a public exhibition were benchmarked against several established signal quality metrics, including spiking activity, kurtosis, Auto-Mutual Information (AMI), spectral entropy, gamma-band power, and parameters extracted using the Fitting Oscillations and One-Over F (FOOF) model. Additionally, ICLabel decomposition was applied to quantify artifact influences across EEG channels. Dry electrode results were compared with their equivalents extracted on two control datasets comprising 71 and 80 participants, respectively, recorded with wet EEG systems in laboratory, home, or clinical surroundings. Main Results The analysis revealed condition-specific susceptibility to artifacts for both EEG modalities. The dry EEG system exhibited substantial robustness in moderate-noise scenarios, with artifact profiles comparable to controlled wet EEG recordings. However, recordings obtained in highly dynamic conditions showed increased muscle artifacts and broadband activity, notably in frontal and temporal regions. Wet EEG systems, under controlled conditions, were overall less inflicted by artifacts, yet, fronto-central ocular and muscular artifacts were consistently present. ICLabel analysis further confirmed these findings, indicating similar proportions of brain-related activity across systems (approximately 31-49.5%), but highlighted increased vulnerability to muscular and environmental artifacts in dry EEG during dynamic tasks.

SIGNIFICANCE: In agreement with recent similar investigations, our findings demonstrate that dry EEG caps have significantly matured, achieving signal quality comparable to wet EEG systems even in challenging real-world conditions, provided appropriate artifact mitigation strategies are employed. These results affirm the practical readiness and broad feasibility of dry EEG technologies for diverse Brain-Computer Interface (BCI) applications in naturalistic environments.},
}

@article {pmid41406276,
year = {2025},
author = {Wang, F and Cao, F and Gao, J and An, N and Yang, J and Wang, Y and Yu, D and Ma, X and Xiang, M and Ning, X},
title = {Exploring the Potential of SSVER-BCI Based on Contactless Measurement Using Optically Pumped Magnetometers.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3644887},
pmid = {41406276},
issn = {2168-2208},
abstract = {Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been widely applied in health monitoring and neurorehabilitation. However, EEG signals are often attenuated and distorted by tissues like the scalp and skull, limiting EEG-based BCI performance. In contrast, magnetoencephalography (MEG) with contactless measurement offers higher spatial resolution and immunity to volume conduction effects. Traditional MEG systems, based on superconducting quantum interference devices (SQUIDs), are hindered by their size and cost, while optically pumped magnetometers (OPMs) have made OPM-MEG-based BCIs more practical and accessible. Nevertheless, the performance potential of OPM-MEG in BCI applications remains underexplored. To address this, we developed an OPM-MEG BCI system based on steady-state visual evoked response (SSVER) and conducted a systematic evaluation of its performance, highlighting the practical advantages of OPM-MEG in this context. Furthermore, we proposed a fusion framework for OPM-MEG and EEG to further enhance system performance. Offline experiments conducted with 13 participants showed that the developed EEG-BCI achieved an average accuracy of 94.30% and an information transfer rate (ITR) of 122.76 bits/min, the developed OPM-MEG BCI achieved an average accuracy of 98.68% and an ITR of 138.20 bits/min, while the hybrid BCI achieved an average accuracy of 99.72% and an ITR of 159.4 bits/min. The findings highlight the advantages of OPM-MEG for BCI applications and validate the proposed fusion framework as a viable means to enhance decoding performance, thereby extending the potential use cases of OPM-MEG-based systems.},
}

@article {pmid41406275,
year = {2025},
author = {Carlino, MF and Gielen, G},
title = {An artifact-free 290$μ$m[2]/ch 610nW/ch neural readout frontend with hybrid EDO compensation for high-channel-count closed-loop neuromodulation.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3644137},
pmid = {41406275},
issn = {1940-9990},
abstract = {Next-generation neurorehabilitation implants demand high-channel-count closed-loop systems with ultra-low area and ultra-low-power readout and classification. This is essential in applications such as multi-type epileptic seizure detection, brain machine interfaces or brain-to-text conversion. Although recent designs achieve compactness and low power, they often cannot record neural signals during stimulation due to large, saturating artifacts. Conversely, artifact-tolerant solutions typically incur excessive area and power overhead to avoid saturation. We introduce a paradigm shift: enabling an ultra-compact, artifact-tolerant readout frontend by permitting brief saturation during stimulation pulses and applying backend interpolation to reconstruct the signals. High-fidelity neural features can thus be extracted with minimal error. To minimize the readout area footprint and to facilitate the routing from many electrodes, we reuse the whole frontend to read-out 64 inputs in a time-multiplexed fashion. Implemented in a 40nm CMOS process, our chip leverages the first published secondorder fully time-based incremental analog-to-digital converter, achieving a state-of-the-art 290-$μ$m[2]/ch area occupation and only 610-nW/ch of power consumption. The proposed hybrid electrode offset compensation further minimizes the area overhead without significantly compromising the noise or common-mode/power rejection across the full cancellation range. Artifact tolerance is validated in saline using an external stimulator chip. We demonstrate that the error on a broad set of features extracted from interpolated local-field-potential data remains below ±10%, even under harsh stimulation conditions.},
}

@article {pmid41404976,
year = {2025},
author = {Garg, M and Kaur, J and Prakash, NR},
title = {Ocular artifact from electroencephalogram - a comparative analysis of feature extraction, selection and classification.},
journal = {Journal of medical engineering & technology},
volume = {},
number = {},
pages = {1-8},
doi = {10.1080/03091902.2025.2600336},
pmid = {41404976},
issn = {1464-522X},
abstract = {An electroencephalogram (EEG) is a record of signals that represent surface potentials varying whenever the brain performs any task and can be recorded by placing an arrangement of electrodes at the scalp of the brain. These recordings are often contaminated by unwanted movement near these electrodes, resulting in non-cerebral signals called artefacts. The presence of artefacts makes the study of EEG signals difficult. This work focuses on a comparative analysis of classification of ocular artefacts from EEG signal that mainly comprise of eye blinks. Various feature extraction, feature selection and classification techniques are used to compare the prediction performance of the system. Three different methods were used to extract features from the EEG recording done on eight subjects, performing two different tasks. Then the diagnostic performance of three feature selection and 30 classification methods were evaluated using 5-fold cross-validation. Performance of the system on various combinations has been calculated in terms of accuracy and results have been discussed. The maximum accuracy of 93.8% was yielded by classifiers: Kernel Naïve Bayes, Linear Support Vector Machine (SVM) and Ensemble Bagged Trees using wavelet-based features, principal component analysis as feature selection algorithm. By methodically assessing 360 feature-classifier combinations, this study is innovative and provides one of the most thorough benchmarks for ocular artefact identification with exceptional accuracy. It also has great potential for real-time EEG preprocessing in clinical and BCI applications.},
}

@article {pmid41402805,
year = {2025},
author = {Wu, Y and Zhao, X and Jiang, Y and Chen, C and Liu, L and Hou, X and Xian, Q and Guo, J and Sun, L},
title = {Microbubble-enhanced ultrasound stimulation of β-cells improves insulin release and glycemic control in mice.},
journal = {Journal of nanobiotechnology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12951-025-03926-6},
pmid = {41402805},
issn = {1477-3155},
support = {C5053-22 GF//Hong Kong Research Grants Council Collaborative Research Fund/ ; 15126524//General Research Fund/ ; 2023YFC2410900//National Key Research and Development Program of Ministry of Science and Technology of China/ ; G-SACD//Hong Kong Polytechnic University/ ; 1-CE0M//Research Center for Non-invasive Brain Computer Interface/ ; 1-CDJM//Research Institute of Smart Ageing/ ; },
abstract = {Diabetes poses a significant global health burden, with complications such as cardiovascular disease, stroke, and kidney failure. While insulin therapy is central to type 2 diabetes (T2D) management, its limitations-including rapid degradation and the need for frequent injections-highlight the demand for non-invasive alternatives. Here, we present an ultrasound (US)-mediated approach to enhance insulin release by selectively stimulating pancreatic β-cells via targeted microbubbles (MBs). In vitro experiments using RINm5F β-cells demonstrated that US-MB stimulation induces significant calcium influx and subsequent insulin release. In addition, this method effectively decreased blood glucose levels in mice by promoting insulin release. Mechanistic studies revealed that mechanosensitive ion channels play a pivotal role, as their inhibition (via GdCl3) abolished the ultrasonic effect. Importantly, the approach exhibited high biosafety, with no detectable cell death or tissue damage. Our findings establish ultrasound-stimulated β-cell targeting as a promising non-invasive strategy for diabetes treatment, offering a potential alternative to conventional insulin therapy.},
}

@article {pmid41399831,
year = {2025},
author = {Sorokin N, I and Nesterova O, Y and Khokhlov M, A and Kamalov D, M and Dzitiev V, K and Strigunov A, A and Tereshina A, D and Veriaskina A, E and Kamalov A, A and Pshikhachev A, M and Mikhalchenko A, V},
title = {[Urodynamic risk factors for transient urinary incontinence after endoscopic enucleation of prostate hyperplasia].},
journal = {Urologiia (Moscow, Russia : 1999)},
volume = {},
number = {5},
pages = {104-112},
pmid = {41399831},
issn = {1728-2985},
mesh = {Humans ; Male ; *Prostatic Hyperplasia/surgery/physiopathology ; *Urinary Incontinence/etiology/physiopathology ; Aged ; *Urodynamics ; Risk Factors ; Middle Aged ; Prospective Studies ; *Postoperative Complications/etiology/physiopathology ; *Endoscopy/adverse effects ; *Prostatectomy/adverse effects/methods ; },
abstract = {INTRODUCTION: Urinary incontinence in men after endoscopic enucleation of benign prostate hyperplasia (BPH) can reach 55% and significantly impairing the quality of life and social rehabilitation of patients. A large number of individual patient parameters and features of surgical treatment are considered as potential risk factors. At the same time, the influence of urodynamic factors, including the external urethral sphincter function at the preoperative stage, fades into the background, and research on this issue is extremely limited.

OBJECTIVE: comprehensive assessment of urodynamic risk factors for urinary incontinence after endoscopic enucleation of BHP.

MATERIALS AND METHODS: This prospective study included 69 patients who underwent endoscopic enucleation of BPH (thulium fiber enucleation - 62 patients, bipolar enucleation - 7 patients) performed by single surgeon between October 2023 and August 2024. All patients underwent an invasive urodynamic study 1 day before the planned surgical treatment, including uroflowmetry, cystometry, flow/pressure study and profilometry performed by single urologist. In the postoperative period, the presence and duration of urinary incontinence were recorded in accordance with the definition of the International Continence Society. Statistical data processing was carried out using RStudio software in the R programming language.

RESULTS: Transient urinary incontinence after endoscopic enucleation was detected in 36.2% patients. In 100% cases, the duration of incontinence did not exceed a 3-month period. The independent urodynamic predictors of urinary incontinence were the bladder outlet obstruction index (BOOI), the bladder contractility index (BCI) and maximum intraurethral pressure (Pura max). Thus, with an increase in BOOI for 1 unit, the chance of urinary incontinence increased by 1,027 times or 2.7% (OR=1,027; 95%CI=1,003-1,052; p=0,027). With an increase in BCI for every 1, the chance of urinary incontinence increased by 1,020 times or 2.0% (OR=1,020; 95%CI=1,001-1,039; p=0,043). Large values of Pura max, on the contrary, led to a decrease in the chance of urinary incontinence, thereby acting as a protective factor. With an increase in Pura max for every 1 cm of H2O, the chance of urinary incontinence decreased by 1,087 times or by 8% (OR=0,920; 95%CI=0,876-0,966). The overall accuracy of the proposed model was 88,1% with sensitivity and specificity of 90,5 and 86,8% (ROC-AUC=0,897). The only independent intraoperative factor associated with urinary incontinence was the operation time: with an increase in the operation time for every 1 minute, the chance of urinary incontinence increased by 1,022 times or by 2,2%, regardless of the type of energy used and the early sphincter release (OR=1,022; 95%CI=1,005-1.040; p=0,011; ROC-AUC=0,721).

CONCLUSION: The chance of urinary incontinence at longer endoscopic enucleation, higher BOOI and BCI and low Pura max increases, which, thereby, can be used in predicting the functional results of endoscopic enucleation, taking into account individual urodynamic risk factors.},
}

Humans
Male
*Prostatic Hyperplasia/surgery/physiopathology
*Urinary Incontinence/etiology/physiopathology
Aged
*Urodynamics
Risk Factors
Middle Aged
Prospective Studies
*Postoperative Complications/etiology/physiopathology
*Endoscopy/adverse effects
*Prostatectomy/adverse effects/methods

@article {pmid41399276,
year = {2025},
author = {Wang, Z and Du, Y and Guo, D and Jiang, H and Li, Z and Wu, J and Yang, J and Li, H and Li, L and Fei, J and Li, Z},
title = {Brain-computer interface and functional electrical stimulation: a novel approach to motor rehabilitation in CNS injury patients.},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000004392},
pmid = {41399276},
issn = {1743-9159},
abstract = {Central nervous system (CNS) injuries, such as stroke and spinal cord injury, often result in persistent motor impairments that conventional rehabilitation can only partially alleviate. Recent developments in brain-computer interfaces (BCIs) combined with functional electrical stimulation (FES) have introduced a novel approach to motor rehabilitation by directly linking cortical signals with specific muscle activation. This closed-loop system compensates for disrupted neural transmission and simultaneously promotes activity-dependent plasticity, thereby supporting functional reorganisation within the CNS. Findings from pilot trials and preclinical studies indicate that BCI-FES enhances motor recovery in both upper and lower limbs, increases patient engagement, and facilitates long-term cortical reorganisation. However, significant limitations persist, such as inconsistent neural decoding, stimulation-related fatigue, and the lack of standardised treatment protocols. Moreover, ethical challenges such as informed consent, neural data privacy, and equitable access must be resolved before broad clinical adoption can be achieved. Future research should focus on rigorous multicentre trials, tailored intervention strategies, and integration with emerging digital health technologies. This review synthesises current evidence on BCI-FES paradigms, stimulation parameters, underlying mechanisms, and ethical considerations, and outlines future directions to accelerate its clinical translation in CNS rehabilitation.},
}

@article {pmid41399243,
year = {2025},
author = {Lawrence, D and Avraham, G and Yao, J and Li, L and Shi, C and Starr, PA and Little, SJ},
title = {Cortico-basal oscillations index naturalistic movements during deep brain stimulation.},
journal = {Brain : a journal of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1093/brain/awaf466},
pmid = {41399243},
issn = {1460-2156},
abstract = {The basal ganglia and sensorimotor cortex are essential nodes of a network that supports motor control. In Parkinson's disease, disruptions in this network lead to rigidity and slowness during movement execution. Deep brain stimulation (DBS) of the basal ganglia has proven effective in alleviating Parkinson's disease-related hypokinetic symptoms, and sensing-enabled neurostimulators now afford the opportunity to detect cortico-basal oscillations during motion. However, the specific contributions of these motor network nodes to chronic, naturalistic movement and the effects of DBS on circuit dynamics are not well understood. To address these gaps, we recorded over 530 hours of cortical and subcortical signals from 15 Parkinson's disease patients (27 hemispheres) during unsupervised, unconstrained daily activities and subthalamic or pallidal DBS. Synchronized wrist-worn accelerometers tracked forearm speeds, supporting the evaluation of neural biomarkers related to motion. Our study validated and extended the known relationship between cortical and subcortical beta power (13-30 Hz) and movement. We show that cortical low (13-20 Hz) and high (21-30 Hz) beta movement-related desynchronization (MRD) effectively distinguished between mobile and stationary states. In the subthalamic nucleus (STN) and globus pallidus interna (GPi), high beta MRD and gamma (40-80 Hz) movement-related synchronization (MRS) exhibited significant group-level correlations with movement kinematics. When stimulated at 130 Hz, cortical stimulation-entrained gamma oscillations at the half-harmonic (∼65 Hz) were observed. Further, cortical entrained gamma MRS was a stronger predictor of motion than broadband gamma MRS. We developed machine learning (ML) models to predict naturalistic movement over extended periods using spectral features from brief neural recordings (0.5-8 s epochs). Cortical models outperformed subcortical models, although combining cortico-basal signals yielded the highest model performance (AUC > 0.85 for binary movement state classifiers; Pearson r statistic > 0.68 for continuous forearm speed regressors). Higher DBS current amplitudes were associated with reduced beta MRD and low gamma (40-60 Hz) MRS in the STN/GPi. This negatively impacted the accuracy of the subcortical models, whereas cortical and cortico-basal model performance remained stable across stimulation amplitudes. Our study demonstrates that cortico-basal nodes of the motor network encode complementary kinematic information, which can be integrated to enhance the accuracy and stability of chronic, naturalistic movement decoding during deep brain stimulation. These insights support the development and integration of therapeutic brain-computer interfaces (BCIs) with closed-loop, adaptive DBS (aDBS) to leverage rapid and precise movement-predictive models for the treatment of motor network disorders.},
}

@article {pmid41398930,
year = {2025},
author = {Chen, Y and Ge, H and Deng, C},
title = {A novel method for EEG-based motor imagery classification using feature fusion.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2025.2568700},
pmid = {41398930},
issn = {1476-8259},
abstract = {This paper introduces a multi-scale feature fusion framework for EEG-based motor imagery (MI) classification, designed to leverage the spectral-temporal-spatial structure of EEG data, its nonlinear intrinsic characteristics, and convolutional features. Several proposed feature fusion models surpass current state-of-the-art classification systems for MI tasks. A support vector machine (SVM) model achieves an accuracy of 86.92% on the BCIC-IV-2a dataset. To mitigate redundancy, the proposed models incorporate dimensionality reduction via factor analysis (FA) and channel selection using common spatial pattern (CSP). Selecting 12 channels yields superior classification performance compared to using all 22or only 8 selected channels, achieving an accuracy of 88.17%.},
}

@article {pmid41398422,
year = {2025},
author = {Qu, B and Tan, X and Tang, Z and Wang, H and Lan, L and Schriver, KE and Pan, G and Friedman, RM and Lai, HY},
title = {Unveiling interactions of spatial-temporal information in tactile motion perception.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {43838},
pmid = {41398422},
issn = {2045-2322},
support = {2021ZD0200401//STI 2030-Major/ ; 2021ZD0200401//STI 2030-Major Projects/ ; 2021ZD0200401//STI 2030-Major Projects/ ; 2021YFF0702200//National Key R&D Program of China/ ; 82101323//National Natural Science Foundation of China/ ; 2021C03001//Key R&D Program of Zhejiang Province/ ; 2019XZZX003-20//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Tactile perception is inherently dynamic, relying on active manual exploration to extract information about motion and surface properties. Spatiotemporal inputs facilitate tactile motion perception by conveying information both direction and speed perception. Although previous studies have examined these features separately, the interactions between spatial and temporal features in shaping perceptual outcomes remain poorly understood. To address this gap, we conducted two psychophysical experiments in which tactile motion stimuli, varying in direction, speed and spatial frequency (wavelength), were delivered to the distal fingerpad of healthy participants, and then requested the participants to report their feedback directly. In Experiment I, we found that the anisotropic distortion of directional perceptual bias is quadrant-dependent, while variations in speed did not alter this general pattern. Experiment II revealed a dissociation between spatial and temporal contributions to perception. Spatial frequency primarily determined the overall pattern of perceptual bias, reflecting the structural properties of the stimulus. In contrast, speed modulates its dynamic expression by influencing the amplitude and phase of deviations. Additional psychometric function analyses indicated that tactile speed perception arises from a combination of linear and nonlinear processes. Collectively, these findings elucidate how the brain integrates spatiotemporal cues to construct a coherent tactile motion representation, thereby accounting for the systematic directional distortions and nonlinear speed estimation.},
}

@article {pmid41397373,
year = {2025},
author = {Jia, Y and Lian, Q and Wang, L and Wang, Y and Qi, Y},
title = {Learning discrete neural latent spaces for high-performance speech decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2ccd},
pmid = {41397373},
issn = {1741-2552},
abstract = {OBJECTIVE: Speech brain-computer interfaces (BCIs), which directly transform neural signals into intelligible voices, offer a promising avenue for people with aphasia. To decode speech information from brain signals, neural representation learning plays an important role.

APPROACH: Existing studies mainly explored continuous neural latent spaces for speech decoding and ignored the intrinsic discrete property in speech production. Here, we propose to learn a discrete neural latent spaces by constructing a quantized representation learning network for speech decoding.

MAIN RESULTS: Experiments with intracranial stereotactic EEG (sEEG) signals from 11 subjects demonstrated that our approach significantly improved the precision and robustness of speech decoding.

SIGNIFICANCE: These results underscore the potential of our method to improve the functionality and usability of speech BCIs for people with aphasia.},
}

@article {pmid41397350,
year = {2025},
author = {Xia, Y and Wei, Y and Li, S and Mai, X and Luo, R and Zhu, X and Meng, J},
title = {A potential field shared control approach for wheelchair navigation via brain‑computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2ccc},
pmid = {41397350},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalography (EEG) -based brain-computer interfaces (BCIs) can help patients with disabilities control external devices directly without peripheral pathways. Due to the limitations in EEG signal quality, the performance of EEG-based BCIs may not be satisfactory. Shared control has become an important research direction in the field of brain-controlled wheelchairs (BCWs). However, most existing studies do not achieve the flexible movement of BCW in environments with narrow spaces. This study proposes a shared controller based on the potential field method to integrate environmental information and user commands intelligently.

APPROACH: Considering the flexibility of wheelchair movement, we incorporated EEG decoding results obtained through the motor imagery paradigm and fused them with environmental information to create a fusion field. We then used these components separately to construct the BCI and obstacle fields. Twelve subjects participated in the virtual wheelchair navigation experiment, while five subjects took part in the real-world wheelchair navigation experiment, aiming to evaluate the control performance in different scenarios under three control modes (keyboard, BCI-only, and shared control).

MAIN RESULTS: The experimental results show that the proposed shared controller: 1) significantly enhances navigation performance in both general and narrow environments compared with BCI-only control; 2) improves the total success rate from 8.33% to 83.33% in virtual complex environments and from 23.33% to 66.67% in real-world two-way navigation; 3) achieves success rates that are statistically comparable to keyboard control (p > 0.05). Moreover, the shared control reduced the average navigation time by nearly 100 seconds compared with BCI-only control in real-world experiments.

SIGNIFICANCE: This new shared control method improves the ability of BCWs to move flexibly in challenging, narrow environments.},
}

@article {pmid41397305,
year = {2025},
author = {Zhang, L and Li, B and Shi, X and Peng, C},
title = {Hybrid BCI-Based Instruction Set for Dual Robotic Arm Control Using EEG and Eye Movement Signals.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae2c8f},
pmid = {41397305},
issn = {2057-1976},
abstract = {A brain-computer interface (BCI) establishes a pathway for information transmission between a human (or animal) and an external device. It can be used to control devices such as prosthetic limbs and robotic arms, which in turn assist, rehabilitate, and enhance human limb function. At present, although most studies focus on brain signal acquisition, feature extraction and recognition, and further explore the use of brain signals to control external devices, the features obtained via noninvasive approaches are fewer and less robust, which makes it difficult to directly control devices with more degrees of freedom such as robotic arms. To address these issues, we propose an extended instruction set based on motor imagery that fuses eyemovement signals and electroencephalogram (EEG) signals for motion control of a dual collaborative robotic arm. The method incorporates spatio-temporal convolution and attention mechanisms for brain-signal classification. Starting from a small base of control commands, the hybrid BCI combining eye-movement signals and EEG expands the command set, enabling motion control of the dual cooperative manipulator. On the Webots simulation platform, we carried out kinematic control and three-dimensional motion simulation of a dual 6-degree-of-freedom collaborative robotic arm (UR3e). The experimental results demonstrate the feasibility of the proposed method. Our algorithm achieves an average accuracy of 83.8% with only 8.8k parameters, and the simulation results are within the expected range. The results demonstrate that the proposed extended instruction set based on motor imagery is effective not only for controlling dual collaborative robotic arms to perform grasping tasks in complex scenarios, but also for operating other multi-degree-of-freedom peripheral devices.},
}

@article {pmid41397035,
year = {2025},
author = {Zhang, L and Shi, W and Zhao, Z and Wang, Z and Chu, C and Zhao, B and Zhang, J and Liu, Q and Lan, Y and Jiang, T},
title = {Lysergic acid diethylamide-derived excitatory/inhibitory ratio change enhances global synchrony in functional brain dynamics.},
journal = {PLoS computational biology},
volume = {21},
number = {12},
pages = {e1013822},
doi = {10.1371/journal.pcbi.1013822},
pmid = {41397035},
issn = {1553-7358},
abstract = {Lysergic acid diethylamide (LSD) has shown remarkable potential in modulating brain functional organization and dynamics. However, the exact mechanisms underlying its effects remain unclear. In this study, we employed a data-driven approach to analyze recurrent functional connectivity patterns in resting-state fMRI data and developed a parameterized feedback inhibition model to characterize excitatory/inhibitory (E/I) balance. The findings demonstrate that LSD enhances global brain synchrony and dynamic complexity. This enhanced synchrony likely stems from LSD's preferential stabilization of a globally synchronized yet functionally non-modular brain state - a pattern showing higher occurrence probability and acts as an "attractor" that recruits transitions from cognitive control networks. Crucially, these phenomena appear underpinned by LSD-induced convergence of excitatory/inhibitory balance across cortical hierarchies, particularly through Sensorimotor (SOM) suppression coupled with transmodal potentiation, where the Sensorimotor cortices emerge as potential regulatory hubs driving this neurochemical rebalancing. These convergent effects are consistent with the emergence of a brain state characterized by weakened sensory anchoring and enhanced cognitive flexibility, where the typical separation between concrete perception and abstract cognition becomes blurred. This neurophysiological remodeling therefore suggests a potential mechanism that could contribute to LSD's hallucinatory effects and its therapeutic potential in mental disorders characterized by rigid thought patterns.},
}

@article {pmid41397031,
year = {2025},
author = {Xu, K and Li, W and Yin, Y and Li, F and Wang, H and Sui, H and Zou, J and Mu, J and Wang, S},
title = {Hemi-obturator Nerve Innervated Latissimus Dorsi Muscle for Restoring Voluntary Voiding: Anatomic Study and Clinical Application.},
journal = {Plastic and reconstructive surgery},
volume = {},
number = {},
pages = {},
doi = {10.1097/PRS.0000000000012721},
pmid = {41397031},
issn = {1529-4242},
abstract = {This study presents a modified latissimus dorsi detrusor myoplasty (LDDM) technique using the hemi-obturator nerve for neurogenic underactive bladder (NUAB) reconstruction. Anatomical studies (n=22 hemipelves) revealed that the diameters of the anterior (mean: 0.209 cm) and posterior branches (mean: 0.199 cm) matched the thoracodorsal nerve's diameter (one-way ANOVA, p = 0.557), confirming their ideal donor potential. LDDM by using posterior branch of intrapelvic obturator nerve as the donor nerve was performed in five patients with NUAB. 4/5 (80%) patients restored voluntary voiding postoperatively, with post-void residual volume (PVR) decreasing significantly from 308.5(187.5) mL to 62.0 (58.8) mL (P=0.042) and bladder contractility index (BCI) improving significantly from 12.8(5.7) to 151.9(46.5) (P=0.007). These results demonstrate that LDDM using the hemi-obturator nerve is an effective surgical approach for functional detrusor reconstruction in NUAB patients.},
}

@article {pmid41396750,
year = {2025},
author = {Chen, W and Mei, J and Xiao, X and Li, A and Tao, L and Wang, K and Xu, M and Ming, D},
title = {An Online Adaptation Framework for Enhancing Calibration-Free SSVEP-Based BCI Performance.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3644250},
pmid = {41396750},
issn = {2168-2208},
abstract = {Accomplishing a plug-and-play steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a critical challenge, due to the unsatisfying performance of calibration-free decoding algorithms. A current method called online adaptive canonical correlation analysis (OACCA) has proved efficient in enhancing calibration-free performance by self-adaptation merely with online data. However, OACCA only concerns the adaptation of spatial filters and excludes other useful adaptive procedures like individual template estimation, hindering fully exploitable model decoding and adaptation. This study proposes a new online adaptation framework termed online adaptive extended correlation analysis (OAECA) to augment the calibration-free online adaptation loop. OAECA first recalls and cleans the online trials for reliable data learning, then tunes individual templates and spatial filters for complete model updating, and finally adopts extended feature matching to improve target recognition. The simulation results on two public SSVEP datasets revealed that OAECA significantly outperformed OACCA for almost all 105 subjects, and both offline and online experiments further confirmed the effectiveness of OAECA. Particularly, OAECA achieved the highest average information transfer rate (ITR) of 202.17 bits/min in the online experiment, significantly exceeding the state-of-the-art OACCA of 177.02 bits/min. This study enhanced the calibration-free performance through comprehensive online adaptation, hopefully advancing SSVEP-based BCIs toward practical plug-and-play real-world applications.},
}

@article {pmid41394963,
year = {2025},
author = {Wang, N and Si, J and He, Y and Song, J and Chai, X and Liu, D and Li, J and Zhang, T and Cao, T and He, Q and Zhu, S and Jia, Y and Ma, W and Yang, Y and Zhao, J},
title = {Cerebral Neurovascular Networks May Serve as Potential Targets for Identifying Disorders of Consciousness: A Synchronous Electroencephalography and Functional Near-Infrared Spectroscopy Study.},
journal = {MedComm},
volume = {6},
number = {12},
pages = {e70530},
pmid = {41394963},
issn = {2688-2663},
abstract = {The diagnosis and management of disorders of consciousness (DoC) remain a critical challenge in clinical medicine and neuroscience. The key bottleneck is the lack of reliable biomarkers and an incomplete understanding of the pathophysiological mechanisms that underlie DoC. In view of this, a bedside-compatible, multimodal technique based on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was utilized to simultaneously capture neuronal oscillations and accompanying hemodynamics, so as to explore neurovascular biomarkers that can effectively discriminate different states of DoC. Resting-state EEG-fNIRS data from 13 regions of interest (ROIs) were acquired and compared across healthy controls (HC), minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS) groups. Hemodynamics-based functional connectivity and the spectral power of neuronal activity were quantified and subsequently employed to interrogate neurovascular coupling. The results demonstrated significantly stronger neurovascular coupling and beta-band power in premotor and Broca's areas of the MCS group. A multimodal classifier achieved an accuracy of 87.9% in distinguishing between MCS and UWS. The noninvasive, bedside-suitable nature of this tool underscores its potential for routine monitoring and prognostic assessment in DoC, addressing a critical need for accessible and reliable biomarkers in both neurology and intensive-care practice.},
}

@article {pmid41394940,
year = {2025},
author = {Liu, P and Ge, Q and Dong, L and Jiao, L and Han, S and Kang, X and Wang, H and He, J and Zhang, H},
title = {Motor imagery-based brain-computer interface for differential diagnosis in prolonged disorders of consciousness.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1695730},
pmid = {41394940},
issn = {1662-5161},
abstract = {INTRODUCTION: Patients with prolonged disorders of consciousness (pDoC) present significant challenges to the assessment of consciousness. This study investigated the clinical utility of motor imagery-based brain-computer interface (MI-BCI) for discriminating consciousness levels in patients with pDoC.

METHODS: Thirty-one pDoC patients [12 with unresponsive wakefulness syndrome (UWS) and 19 in a minimally conscious state (MCS)] underwent EEG recordings during resting state and MI-BCI training. The analysis focused on relative power spectral density across five frequency bands (delta, theta, alpha, beta, gamma) in motor imagery-related regions (frontal and parietal cortices), along with BCI performance metrics (classification accuracy and attention indices).

RESULTS: We found that MCS patients exhibited multiband neural oscillation modulation during MI-BCI tasks, including slow-wave enhancement [(delta in frontal lobes (p = 0.003); theta in frontal (p = 0.026) and parietal lobes (p < 0.001)) and fast-wave suppression (alpha in frontal (p < 0.001) and parietal lobes (p = 0.049); beta in frontal (p = 0.014) and parietal lobes (p = 0.001); gamma in parietal lobes (p = 0.023)]. In contrast, UWS patients only showed localized parietal gamma enhancement (p = 0.042). Notably, the MCS group achieved significantly higher classification accuracy (55% vs. 38%, p = 0.02), and attention indices correlated moderately with CRS-R scores across all patients (Spearman's ρ = 0.43, p = 0.02).

CONCLUSION: The findings suggest that MI-BCI classification accuracy and attention indices may serve as auxiliary discriminators between UWS and MCS patients, with MCS patients demonstrating superior responsiveness to MI-BCI training.},
}

@article {pmid41293018,
year = {2025},
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 = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41293018},
issn = {2692-8205},
support = {UG1 EY032039/EY/NEI NIH HHS/United States ; },
abstract = {Action understanding requires internal models that link vision to motor goals. In monkeys, mirror neurons demonstrate motor resonance during observation, but single-unit evidence in humans is limited, leaving open whether such representations rely solely on motor resonance. We recorded neural activity from motor cortex (MC) and 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 only weak, feature-specific overlap during observation, evident primarily at the population level. SPL, in contrast, supported shared models across intended movement and observation formats at both single-unit and population levels. In variants with incongruent instructed and observed actions, SPL encoded observed actions only when 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 input with internal goals to support action understanding.},
}

@article {pmid41392156,
year = {2025},
author = {Xia, J and Zhang, L and Wang, S and Yu, Y and Ding, L and Zhang, F and Zhang, S and Luo, J and Huang, YYS and Occhipinti, L and Pan, G and Cao, Z and Ding, G and Dong, S},
title = {Implantable neural probes with monolithically integrated CNTFET arrays for multimodal monitoring.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-025-67535-5},
pmid = {41392156},
issn = {2041-1723},
abstract = {The implantable neural probe for simultaneous recording of various brain signals is one of the key technologies for neurological science and clinics that is yet to be broken through. Here, we introduce an implantable neural probe with integrated carbon nanotube field-effect transistors which is able to perform multimodal recording of electrical and chemical signals of the brain under magnetic resonance imaging (MRI). We demonstrate here a simultaneous measurement of an electrophysiological signal with high signal-to-noise ratio up to 40.34 dB and calcium concentration with a detection limit down to 0.47 nM. We use our neural probes to detect neural activity in rats and results reveal that changes in Ca[2+] concentration occur concurrently with the epileptiform local field potential events, providing an alternative method for accurate detection of epilepsy. Our work may provide a powerful means for the future studies of brain and holds great potential for practical diagnostic applications.},
}

@article {pmid41391167,
year = {2025},
author = {Pang, J and Sun, Y and Cheng, T and Wang, J and He, X and Xiang, Y and Zhu, W and Cao, Y and Wu, M and Pei, W and Pei, R and Cao, Y},
title = {Multifunctional Composite Coating-Enhanced Flexible Microelectrodes for Chronic, High-Fidelity Neural Signal Recording.},
journal = {Analytical chemistry},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.analchem.5c04799},
pmid = {41391167},
issn = {1520-6882},
abstract = {Implantable flexible neuroelectrodes are critical for brain-computer interface (BCI) applications. However, conventional flexible electrodes often face challenges such as increased electrochemical impedance upon miniaturization, mechanical mismatch with brain tissue, and implantation-induced damage, all of which compromise long-term signal stability and recording quality. Here, we present a multifunctional surface modification strategy to address these limitations. By integrating polycaprolactone/silk fibroin-methacrylate (PCL-SFMA) nanofibers loaded with anti-inflammatory minocycline hydrochloride (MH), nanostructured poly(3,4-ethylenedioxythiophene) (PEDOT) for impedance reduction, and a bioactive SFMA hydrogel layer for seamless neural integration, we developed a composite-coated flexible microelectrode (Au-PCLSFMA-PEDOT-GEL). Comprehensive in vitro and in vivo evaluations demonstrated that the modified electrode exhibited low impedance, enhanced biocompatibility, improved biointegration, and effective mitigation of both acute and chronic inflammation. Long-term electrophysiological recordings in freely moving mice revealed stable, high-fidelity neural signal acquisition for up to 8 months, maintaining a signal-to-noise ratio of approximately 20. This work establishes a durable and functionally stable neural interface, offering a promising platform for long-term neuroscience research and the development of next-generation BCIs.},
}

@article {pmid41389568,
year = {2025},
author = {Pfeffer, MA and Wong, JKW and Ling, SH},
title = {Transformer-based hybrid systems to combat BCI illiteracy.},
journal = {Computers in biology and medicine},
volume = {200},
number = {},
pages = {111378},
doi = {10.1016/j.compbiomed.2025.111378},
pmid = {41389568},
issn = {1879-0534},
abstract = {This study addresses the challenge of enhancing Brain-Computer Interfaces (BCIs), focusing on low Signal-to-Noise Ratios and "BCI illiteracy" often affecting up to 20% of users. Transformer-based models show promise but remain underexplored. Three experiments were conducted. Experiment A assessed the performance of architectures combining Convolutional and Transformer Blocks for binary Motor Imagery (MI) classification. Experiment B introduced a hybrid system, refining both block types and adding a Noise Focus Block to infuse Stochastic Noise, enhancing multi-class classification robustness. Experiment C evaluated the emerging architectures on 106 subjects, focusing on robustness across weak and strong learners. In Experiment A, the best networks achieved a validation accuracy of 0.914 and a loss of 0.146 (p=0.000967, F=12.675). In Experiment B, the proposed architecture improved multi-class MI classification to 84.5% on Dataset II, significantly improving performance for BCI-illiterate users. Experiment C showed a Kappa >83%, reduced standard deviation, and a highest validation accuracy of 88.69% across all individuals. The hybrid integration of Transformers, CNNs, and Noise-Resonance-based layers significantly enhances classification performance, particularly for weak BCI learners. Further research is recommended to optimize hybrid system architectures and hyperparameter settings to overcome current limitations in BCI performance.},
}

@article {pmid41389307,
year = {2025},
author = {Li, T and Zhao, ZH and Tang, HB and Chen, Z and Lu, ZW and Yang, XL and Zhao, LL and Li, Y and Dang, MJ and Chen, ZY and Zhang, GL and Liu, L and Fan, H},
title = {Advances in Bionic Therapies for Targeting Neural Circuit Reconstruction and Integration for Spinal Cord Injury.},
journal = {Cellular and molecular neurobiology},
volume = {},
number = {},
pages = {},
doi = {10.1007/s10571-025-01647-w},
pmid = {41389307},
issn = {1573-6830},
support = {82101551//National Natural Science Foundation of China/ ; 82471333//National Natural Science Foundation of China/ ; 82171361//National Natural Science Foundation of China/ ; 82171471//National Natural Science Foundation of China/ ; },
abstract = {Spinal cord injury (SCI) is one of the most common critical illnesses, which can cause neurological deficits and disabilities of motor, sensory and autonomic nervous system in mild cases, and lead to paralysis or even death following severe trauma. Although there are currently no effective and satisfactory clinical treatments, the efforts for repair SCI never stop. Besides the traditional strategies such as drugs, surgical interventions and rehabilitative care, the bionic therapies have attracted significant attention due to its considerable promise. The bionic therapies for SCI mainly included engineered biomaterials-based approaches aiming for reconstruction of internal neural circuit and brain machine interfaces (BMI)-based technologies to integrate extrinsic control and intrinsic circuit. This review provides an extensive overview of SCI research and bionic therapies, with focus on reconstruction and integration of neural circuit, which might provide promising insights on clinical treatment.},
}

@article {pmid41389026,
year = {2025},
author = {Zhou, T and Shang, K and Liu, C and Cui, Z and Liang, D},
title = {Deep equilibrium-adversarial robust unfolding network for MRI reconstruction.},
journal = {Medical physics},
volume = {52},
number = {12},
pages = {e70185},
doi = {10.1002/mp.70185},
pmid = {41389026},
issn = {2473-4209},
support = {JCYJ20240813155840052//Shenzhen Science and Technology Program/ ; 2022YFA1004203//National Key R&D Program of China/ ; 2021YFF0501503//National Key R&D Program of China/ ; 62125111//National Natural Science Foundation of China/ ; 62331028//National Natural Science Foundation of China/ ; 62476268//National Natural Science Foundation of China/ ; 62206273//National Natural Science Foundation of China/ ; },
mesh = {*Magnetic Resonance Imaging ; *Image Processing, Computer-Assisted/methods ; Artifacts ; Brain/diagnostic imaging ; Humans ; *Deep Learning ; Signal-To-Noise Ratio ; *Neural Networks, Computer ; Knee/diagnostic imaging ; },
abstract = {BACKGROUND: Deep unfolding neural networks have shown significant promise in magnetic resonance imaging (MRI) reconstruction by replacing traditional iterative prior modeling with more efficient and flexible network architectures. However, the iterative optimization process makes these methods susceptible to signal perturbations caused by noticeable artifacts in the reconstructed images.

PURPOSE: To develop a general framework that enhances the robustness of the reconstruction process against prominent artifacts and noise in k-space, while also improving the stability of the reconstruction.

METHODS: This paper proposes a deep equilibrium-adversarial robust unfolding network (DEAR-net), a novel framework that integrates adversarial learning with deep equilibrium architectures. In this design, adversarial learning enhances the capability of network to suppress perturbations during the reconstruction process, effectively addressing the issue of noise and artifacts amplification in deep equilibrium architectures. However, the modification of the learned mapping from clean k-space to MR images by adversarial learning may compromise the stability of the reconstruction. Fortunately, this problem can be mitigated through the application of deep equilibrium architectures.

RESULTS: Experimental results demonstrate that DEAR-net achieves superior reconstruction performance, delivering higher image quality and greater robustness to varying levels of noise and artifacts in k-space, as evidenced by tests on the fastMRI knee dataset and our private brain dataset.

CONCLUSIONS: DEAR-net enhances the robustness of the reconstruction process in the presence of mild noise and artifacts in under-sampled k-space. Furthermore, we provide a mathematical analysis of the reconstruction error.},
}

*Magnetic Resonance Imaging
*Image Processing, Computer-Assisted/methods
Artifacts
Brain/diagnostic imaging
Humans
*Deep Learning
Signal-To-Noise Ratio
*Neural Networks, Computer
Knee/diagnostic imaging

@article {pmid41388263,
year = {2025},
author = {Zhang, Y and Wang, Y and Guo, J and Fang, T and Wang, R and Liu, W and Zhao, X and Fan, Q and Chen, Y and Peng, Y},
title = {Age-dependent recovery of white matter integrity after surgical correction in children with infantile esotropia.},
journal = {BMC neurology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12883-025-04578-7},
pmid = {41388263},
issn = {1471-2377},
support = {82071994//National Natural Science Foundation of China/ ; 82202249//National Natural Science Foundation of China/ ; 12171330//National Natural Science Foundation of China/ ; DFL20221002//the Beijing Hospitals Authority's Ascent Plan/ ; 2021ZD0200508//the STI 2030-Major Projects/ ; },
abstract = {BACKGROUND: Infantile esotropia may interfere with white matter maturation during early childhood, a critical period of brain development. Surgical correction not only restores ocular alignment but may also influence neurodevelopmental trajectories. However, the role of age in modulating white matter recovery after surgery remains unclear. This study aimed to investigate the effects of age on white matter rehabilitation following surgical intervention in children with infantile esotropia, with the goal of identifying the optimal therapeutic window to maximize both neurodevelopmental and clinical outcomes.

METHODS: We included 29 typically developing children (F/M = 14/15) and 30 children with IE (F/M = 13/17), 17 of whom provided longitudinal data following surgical intervention. All participants underwent MRI scanning and clinical assessments. Diffusion tensor imaging (DTI) was performed to quantify white matter integrity using fractional anisotropy (FA) and mean diffusivity (MD). Automated fiber quantification was applied to analyze microstructural properties across 20 major white matter tracts. Cross-sectional and longitudinal analyses were conducted to evaluate developmental trajectories in patients versus controls.

RESULTS: Preoperatively, IE patients exhibited significantly elevated MD across multiple tracts, including the thalamic radiation and forceps minor. Following surgery, MD values decreased significantly in most tracts. FA alterations were less pronounced, with preoperative reductions and postoperative improvement limited to only a few tracts. In controls, age was negatively correlated with MD and FA changes. Longitudinal analysis revealed that surgical intervention was associated with accelerated growth in white matter microstructure compared to typical development, particularly in younger children.

CONCLUSIONS: Surgical correction of IE facilitates white matter restoration through mechanisms that operate independently of, and synergistically with, typical neurodevelopment. Earlier intervention is associated with faster rates of microstructural recovery, suggesting a higher sensitive period during which surgery can maximize white matter repair and optimize functional outcomes.},
}

@article {pmid41387461,
year = {2025},
author = {Zhou, L and Liu, P and Liu, J and Yuan, W and Wu, Z and Xu, D and Hu, B and Shao, Y and Lu, Y and Huang, N and Li, J and Li, Z and Liang, F and Wu, X and Ma, L and Wang, M and Di, Z and Li, R and Bi, Y and Xu, F and Mei, Y and Song, E},
title = {Wireless battery-free ultrathin lithium-niobate resonator as wearable and implantable electronics for continuous monitoring of mechanical vital signs.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-025-67413-0},
pmid = {41387461},
issn = {2041-1723},
support = {62204057//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Continuous monitoring of physiological parameters associated with dynamic biomechanics, such as intracranial pressure (ICP) and vital signs, is important for clinical diagnosis of brain diseases and timely medical intervention. Current skin-interfaced and implant technologies face challenges in terms of bulky tethers and/or percutaneous wires with high infection risks. Here, we report the wireless, battery-free, and lightweight devices for both wearable and fully implantable applications. The devices incorporate an ultrathin piezoelectric resonator with suspended lithium niobate thin film (LNTF, 3 μm thick), enabling the wireless tracking of mechanophysiological signals by detecting variations in resonance frequency. We experimentally and computationally establish the operational principles of the resonator sensor and assess the device performance as wearables for dynamically monitoring artery pulse and apnea events during respiration. Implantable wireless pressure sensors adapted from this scheme allow for untethered, minimally invasive ICP sensing with a low detection limit of 0.15 mmHg over a wide range up to 240 mmHg. In vivo experiments performed on rat models validate the device capabilities of accurately capturing clinically relevant ICP variations and elevated levels of ICP under pathophysiological conditions of hydrocephalus, with excellent biocompatibility after long-term implantation periods. These findings create the clinical significance of such battery-less and wireless devices for precise characterization of dynamic biomechanics of living tissues.},
}

@article {pmid41385954,
year = {2025},
author = {Kripalal, A and Sekar, C},
title = {Intelligent electroencephalogram feature engineering for rapid mental health diagnosis.},
journal = {Psychiatry research. Neuroimaging},
volume = {356},
number = {},
pages = {112103},
doi = {10.1016/j.pscychresns.2025.112103},
pmid = {41385954},
issn = {1872-7506},
abstract = {Schizophrenia is one of the serious disorders and, if left untreated, can result in a range of problems with cognition, behavior, and emotions that affect every area of life. Diagnosis based on behavioral and clinical investigations remains difficult with schizophrenia symptoms which are complex and heterogenic. Early detection of schizophrenia is essential for the timely treatment leading to betterment of the life of patients. In this study based on machine learning algorithms, we have identified the relevant set of features from the electroencephalogram (EEG) signal to improve the classification accuracy of patients with schizophrenia and healthy controls. Combinations of these identified relevant features have been used to diagnose schizophrenia.Furthermore, we validated this same feature set as the high performing feature subset on an independent dataset, confirming its robustness and generalizability. The results show that the selected features from the EEG signal achieve the highest accuracy of 94.7% and 96.4% for Logistic Regression (LR) and Support Vector Machines (SVM) respectively with reduced data. Reduction in training data with this feature selection enhances the performance of edge devices that are optimized for applications such as brain computer interfaces, neurological disorder detection, cognitive state monitoring, and neurofeedback training.},
}

@article {pmid41387563,
year = {2025},
author = {Peplow, M},
title = {Brain-computer interfaces race to the clinic.},
journal = {Nature nanotechnology},
volume = {},
number = {},
pages = {},
pmid = {41387563},
issn = {1748-3395},
}

@article {pmid41386385,
year = {2025},
author = {Schippers, A and Freudenburg, ZV and Vansteensel, MJ and Raemaekers, M and Ramsey, NF},
title = {High-density electrocorticography reveals sensorimotor cortex engagement in two distinct sites with different roles during audiovisual, audio, and visual speech perception.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121645},
doi = {10.1016/j.neuroimage.2025.121645},
pmid = {41386385},
issn = {1095-9572},
abstract = {Recent neuroimaging studies have shown the involvement of the speech motor system in the sensorimotor cortex (SMC) in speech perception, but knowledge on the relative contributions of visual and auditory speech information on SMC engagement and the cortical representation thereof remains scarce. To further elucidate the representation of different components of perceived speech on the SMC, we recorded high-density ECoG during a passive speech perception task. We found that audiovisual, visual-only, and auditory-only speech perception increased high frequency band activity in the SMC. We discovered two distinct regions of the SMC that are differentially engaged depending on the perceptual input modality, being a dorsally located cluster of activity associated with both unimodal and bimodal perception of auditory and visual information and a ventral cluster that is involved specifically in auditory speech perception. Together, these results shine a new light on the engagement of the sensorimotor cortex during speech perception and suggest that auditory and visual information play different roles.},
}

@article {pmid41385812,
year = {2025},
author = {Tian, X and Zhang, X and Zhou, C and Jiang, Y and Ren, X and Li, T and Ni, P},
title = {Generation and characterization of a human-derived iPSC line (HZSMHCi003-A) from a male child with fragile X syndrome.},
journal = {Stem cell research},
volume = {90},
number = {},
pages = {103880},
doi = {10.1016/j.scr.2025.103880},
pmid = {41385812},
issn = {1876-7753},
abstract = {This study reports the successful establishment of induced pluripotent stem cells (iPSCs) derived from a pediatric patient with Fragile X Syndrome (FXS), representing a valuable cellular model for studying the most prevalent hereditary form of intellectual disability. Blood samples were collected from an 8-year-old Han Chinese male presenting with intellectual disability and carrying a full FMR1 gene mutation (>200 CGG repeat expansion). A stable iPSC line designated HZSMHCi003-A was generated using episomal vector-mediated reprogramming with seven transcription factors (OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT). Comprehensive characterization confirmed normal chromosomal integrity, robust expression of pluripotency-associated markers, and tri-lineage differentiation potential as evidenced by teratoma formation assays. This FXS patient-derived iPSC line provides a unique platform for investigating neurodevelopmental pathophysiology and screening potential therapeutic interventions for intellectual disability associated with FMR1 dysfunction.},
}

@article {pmid41385417,
year = {2025},
author = {Meng, M and Yu, P and She, Q and Xi, X and Kong, W},
title = {ASA-STGCN: Adaptive Sparse Awareness-Spatiotemporal Graph Convolutional Network for Multi-Class Motor Imagery EEG Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3643173},
pmid = {41385417},
issn = {2168-2208},
abstract = {Graph Convolutional Networks (GCNs) have shown promise in motor imagery electroencephalogram (EEG) signals classification by modeling spatial dynamics and brain connectivity. However, over-smoothing remains a challenge, leading to homogenized node features and reduced discrimination. To address this, we propose an Adaptive Sparse Awareness-Spatiotemporal Graph Convolutional Network (ASA-STGCN) that combines adaptive sparse graph convolution with attention mechanisms. Notably, a Graph Sparse Convolutional Network (GSCN) in the Adaptive Sparse Awareness Spatial Module (ASAM) enhances brain region feature selection, while the Graph Node Neighborhood Awareness Layer (GNNAL) applies self-attention to reinforce critical topological relationships. The Multi-scale Temporal Convolution Module (MTCM) captures both transient and sustained temporal dependencies. Experimental results achieve accuracies of 97.2%±3.4% (binary) and 83.6%±4.9% (four-class) on BCIC-IV-2a, 96.6%±3.1% (binary) on BCIC-III IVa, and 83.41%±4.3 (binary) on OpenBMI. Discussion confirms the model's effectiveness and its potential to support EEG-based neurorehabilitation and clinical brain computer interface applications.},
}

@article {pmid41385296,
year = {2025},
author = {Sha, L and Li, H and Peng, A and Yang, H and Liu, X and Zhao, H and Ma, W and Hong, Q and Tang, Y and Zhang, M and Chen, L},
title = {Diagnostic value of saccades in mild cognitive impairment (MCI): a community-based study.},
journal = {The journals of gerontology. Series A, Biological sciences and medical sciences},
volume = {},
number = {},
pages = {},
doi = {10.1093/gerona/glaf264},
pmid = {41385296},
issn = {1758-535X},
abstract = {BACKGROUND: Accurate diagnosis and assessment of mild cognitive impairment (MCI) are essential. The efficacy of saccades in the detection of MCI lacks validation through large-scale clinical trials.

METHODS: All eligible participants underwent saccadic assessment in four tasks and cognitive assessment. MCI diagnoses were made on the basis of clinical indicators and MRI by experienced physicians. The physicians were blinded to the saccade experiments and the operators of saccade experiments were blind to the diagnosis of physicians. The classification models based on machine learning was constructed for assessing the diagnostic accuracy of MCI based on saccadic parameters.

RESULTS: Of the 559 residents who consented to participate, 383 (153 with MCI and 230 controls) were completely assessed. The classification model trained by saccadic parameters achieved high accuracy in dissociating MCI and control with AUROC of 0.945 (95% CI, 0.924-0.964), sensitivity of 0.824 (95% CI, 0.769-0.886) and specificity of 0.904 (95% CI, 0.867-0.935). The parameters of the memory-guided and antisaccade tasks demonstrated better diagnostic efficacy. The saccade model also exhibited a good diagnostic value in patients with borderline cognition being defined by the score of MoCA. When the borderline cognition was defined as 23-27 of MoCA score, the diagnosing accuracy of MCI based on saccadic parameters resulted with AUROC of 0.911 (95% CI: 0.836-0.972), sensitivity of 0.929 (95% CI, 0.762-1.000) and specificity of 0.796 (95% CI, 0.718-0.863).

CONCLUSIONS: Saccades can distinguish MCI from controls with great accuracy, offering a sensitive and objective diagnostic aid of MCI, especially in participants with borderline cognition.},
}

@article {pmid41385039,
year = {2025},
author = {Wu, YH and Chen, SF and Kuo, HC},
title = {Therapeutic outcomes and predictive factors of intradetrusor onabotulinumtoxinA for neurogenic detrusor overactivity (NDO) associated with spinal cord lesion.},
journal = {International urology and nephrology},
volume = {},
number = {},
pages = {},
pmid = {41385039},
issn = {1573-2584},
support = {TCMF-MP-114-02-01//Buddhist Tzu Chi Medical Foundation/ ; },
abstract = {PURPOSE: Intradetrusor onabotulinumtoxinA (Botox) is an established treatment for neurogenic detrusor overactivity (NDO), although predictors of success remain unclear. This study evaluated the therapeutic efficacy of Botox and identified predictors of response in patients with spinal cord lesion (SCL)-related NDO.

METHODS: We retrospectively reviewed 167 patients with SCL-related NDO who received intradetrusor 200 U Botox at a single center between January 1, 2002, and December 31, 2024. Treatment response was classified using the Global Response Assessment (GRA) as excellent (GRA = 3), moderately improved (GRA = 2), mildly improved (GRA = 1), or no change (GRA = 0). Success was defined as GRA = 3 or 2. Baseline demographics, neurological level, and videourodynamic (VUDS) parameters, including detrusor pressure at maximum flow (Pdet), maximum flow rate, voided volume, post-void residual, voiding efficiency (VE), bladder outlet obstruction index (BOOI), and bladder contractility index (BCI), were analyzed as predictors.

RESULTS: VUDS confirmed detrusor overactivity in 92.8%. Overall, 51.5% (86/167) achieved an excellent response, 43.1% (72/167) improved, and 5.4% (9/167) showed no change. Outcomes did not differ by neurological level (P = 0.665). Patients with successful outcomes had higher baseline Pdet (41.9 ± 20.3 vs 22.9 ± 13.4 cmH₂O, P < 0.001), BOOI (33.2 ± 21.9 vs 12.7 ± 15.2, P < 0.001), and BCI (63.6 ± 33.0 vs 48.4 ± 27.9, P = 0.010), but lower VE (0.28 ± 0.31 vs 0.40 ± 0.35, P = 0.045). Logistic regression analysis showed that higher Pdet, BOOI, and BCI predicted treatment success, while higher VE predicted nonresponse. Sex distribution differed across GRA groups (P = 0.004), with more men in the failure group. Acute adverse events were similar among groups.

CONCLUSIONS: Intradetrusor Botox produced good efficacy in SCL-related NDO. Higher baseline detrusor pressure, bladder contractility, and bladder outlet resistance predicted better outcomes, whereas greater VE was associated with nonresponse. Neurological level did not influence treatment success.},
}

@article {pmid41382998,
year = {2025},
author = {Ju, Y and Liu, J and Li, Z and Liu, Y and He, H and Liu, J and Liu, B and Wang, M and Zhang, Y},
title = {[Prospects and technical challenges of non-invasive brain-computer interfaces in manned space missions].},
journal = {Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences},
volume = {50},
number = {8},
pages = {1363-1370},
doi = {10.11817/j.issn.1672-7347.2025.250389},
pmid = {41382998},
issn = {1672-7347},
mesh = {*Brain-Computer Interfaces ; Humans ; *Space Flight ; *Astronauts/psychology ; Neurofeedback ; Cognition ; Electroencephalography ; Man-Machine Systems ; },
abstract = {During long-duration manned space missions, the complex and extreme space environment exerts significant impacts on astronauts' physiological, psychological, and cognitive functions, thereby posing direct risks to mission safety and operational efficiency. As a key bridge between the brain and external devices, brain-computer interface (BCI) technology enables precise acquisition and interpretation of neural signals, offering a novel paradigm for human-machine collaboration in manned spaceflight. Non-invasive BCI technology shows broad application prospects across astronaut selection, mission training, in-orbit task execution, and post-mission rehabilitation. During mission preparation, multimodal signal assessment and neurofeedback training based on BCI can effectively enhance cognitive performance and psychological resilience. During mission execution, BCI can provide real-time monitoring of physiological and psychological states and enable intention-based device control, thereby improving operational efficiency and safety. In the post-mission rehabilitation phase, non-invasive BCI combined with neuromodulation may improve emotional and cognitive functions, support motor and cognitive recovery, and contribute to long-term health management. However, the application of BCI in space still faces challenges, including insufficient signal robustness, limited system adaptability, and suboptimal data processing efficiency. Looking forward, integrating multimodal physiological sensors with deep learning algorithms to achieve accurate monitoring and individualized intervention, and combining BCI with virtual reality and robotics to develop intelligent human-machine collaboration models, will provide more efficient support for space missions.},
}

*Brain-Computer Interfaces
Humans
*Space Flight
*Astronauts/psychology
Neurofeedback
Cognition
Electroencephalography
Man-Machine Systems

@article {pmid41382997,
year = {2025},
author = {Li, Z and Liu, J and Liu, B and Wang, M and Ju, Y and Zhang, Y},
title = {[Potential biological mechanisms underlying spaceflight-induced depression symptoms in astronauts].},
journal = {Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences},
volume = {50},
number = {8},
pages = {1355-1362},
doi = {10.11817/j.issn.1672-7347.2025.250380},
pmid = {41382997},
issn = {1672-7347},
mesh = {*Space Flight ; Humans ; *Astronauts/psychology ; *Depression/etiology/physiopathology ; Gastrointestinal Microbiome ; *Weightlessness/adverse effects ; Oxidative Stress ; Brain/physiopathology ; Hypothalamo-Hypophyseal System ; Neuronal Plasticity ; Pituitary-Adrenal System ; },
abstract = {Long-term spaceflight exposes astronauts to multiple extreme environmental factors, such as cosmic radiation, microgravity, social isolation, and circadian rhythm disruption, that markedly increase the risk of depressive symptoms, posing a direct threat to mental health and mission safety. However, the underlying biological mechanisms remain complex and incompletely understood. The potential mechanisms of spaceflight-induced depressive symptoms involve multiple domains, including alterations in brain structure and function, dysregulation of neurotransmitters and neurotrophic factors, oxidative stress, neuroinflammation, neuroendocrine system imbalance, and gut microbiota disturbances. Collectively, these changes may constitute the biological foundation of depressive in astronauts during spaceflight. Space-related stressors may increase the risk of depressive symptoms through several pathways: impairing hippocampal neuroplasticity, suppressing dopaminergic and serotonergic system function, reducing neurotrophic factor expression, triggering oxidative stress and inflammatory responses, activating the hypothalamic-pituitary-adrenal axis, and disrupting gut microbiota homeostasis. Future research should integrate advanced technologies such as brain-computer interfaces to develop individualized monitoring and intervention strategies, enabling real-time detection and effective prevention of depressive symptoms to safeguard astronauts' psychological well-being and mission safety.},
}

*Space Flight
Humans
*Astronauts/psychology
*Depression/etiology/physiopathology
Gastrointestinal Microbiome
*Weightlessness/adverse effects
Oxidative Stress
Brain/physiopathology
Hypothalamo-Hypophyseal System
Neuronal Plasticity
Pituitary-Adrenal System

@article {pmid41382980,
year = {2025},
author = {Kasper-Jędrzejewska, M and Ptaszkowski, K and Rutkowski, T and Halski, T},
title = {Surface Electromyography Characteristics of Pelvic Floor Muscles in Healthy Women, Pelvic Floor Dyssynergia, and Urinary Incontinence: A Retrospective Comparative Study.},
journal = {Medical science monitor : international medical journal of experimental and clinical research},
volume = {31},
number = {},
pages = {e950086},
doi = {10.12659/MSM.950086},
pmid = {41382980},
issn = {1643-3750},
mesh = {Humans ; Female ; *Electromyography/methods ; *Pelvic Floor/physiopathology/physiology ; Retrospective Studies ; *Urinary Incontinence/physiopathology ; Adult ; Middle Aged ; Muscle Contraction/physiology ; *Pelvic Floor Disorders/physiopathology ; *Ataxia/physiopathology ; },
abstract = {BACKGROUND Surface electromyography (sEMG) of pelvic floor muscles (PFM) offers insights into neuromuscular control but lacks standardized normative values. This study aimed to evaluate baseline and contractile sEMG signal characteristics - including root mean square (RMS) amplitude in microvolts and normalized to maximum voluntary contraction (%MVC) - in a healthy control (H) group, pelvic floor dyssynergia (DS) group, and urinary incontinence (UI) group. MATERIAL AND METHODS A retrospective analysis included 68 women (H=28, UI=22, DS=18). UI was confirmed by the International Consultation on Incontinence Questionnaire-Short Form, and DS diagnosed via anorectal manometry. sEMG was recorded with a intravaginal probe using the Glazer protocol. RMS and %MVC were analyzed using Bayesian multivariate regression adjusted for age and BMI. RESULTS No significant differences were found at baseline rest or rapid contractions (P>0.05). The DS group showed higher RMS during tonic contractions vs H group (Δ=4.20, 95% BCI [0.99, 7.29], P<0.05) and UI (Δ=3.44, 95% BCI [0.48, 6.20], P<0.05), and impaired post-tonic relaxation vs H group (Δ=1.13, 95% BCI [0.10, 2.15], P<0.05). Normalized to %MVC, DS group showed lower rapid contraction activity than H group (Δ=-10.49, 95% BCI [-19.46, -1.86], P<0.05). H group outperformed UI group in tonic contraction (P<0.05). CONCLUSIONS DS showed higher RMS amplitudes during tonic contractions, impaired relaxation, and reduced %MVC efficiency, indicating paradoxical activity. UI patterns were heterogeneous, highlighting its multifactorial nature. Reliance on raw RMS alone may misclassify dysfunctions; multiparametric assessment and validation in larger cohorts are needed.},
}

Humans
Female
*Electromyography/methods
*Pelvic Floor/physiopathology/physiology
Retrospective Studies
*Urinary Incontinence/physiopathology
Adult
Middle Aged
Muscle Contraction/physiology
*Pelvic Floor Disorders/physiopathology
*Ataxia/physiopathology

@article {pmid41381932,
year = {2025},
author = {Qin, X and Li, H and Zhao, H and Wang, X},
title = {Photobiomodulation and Addiction: Exploring Mechanisms, Therapeutic Potential, and Future Directions in Substance Use Disorders.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41381932},
issn = {1995-8218},
}

@article {pmid41381864,
year = {2025},
author = {Tang, A and Chen, Y and Si, K and Lai, J and Gong, W and Hu, S},
title = {Gut microbiota modulates synaptic plasticity, connectivity, and dopamine transmission in the VTA-mPFC pathway in bipolar depression.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41381864},
issn = {1476-5578},
support = {LR20F050002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; LR22F050007//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82201676//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82471542//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Adequate evidence has shown that gut microbial dysbiosis is an emerging disease phenotype of bipolar disorder (BD), and is closely related to clinical symptoms of this intractable disease. However, how gut microbiota affects the nervous system in BD remains largely unclear. In this study, we constructed a BD depression-like mouse model via fecal microbiota transplantation, and explored the changes of synaptic plasticity and connectivity in the medial prefrontal cortex (mPFC) of BD mice. We found that bipolar depression-like mice presented with a decrease in the density of dendritic spines in medial prefrontal neurons, and "Translation at postsynapse" as a key contributor to the changes in synaptic plasticity. In addition, analysis of synaptic connectivity in the mPFC revealed that compared to control mice, less connections were observed between ventral tegmental area and mPFC glutamate neurons and dopamine response was decreased in BD mice. These findings suggest that gut microbiota from BD depression patients induces the development of bipolar depression possibly by modulating aberrant synaptic connectivity and dopamine transmission in the VTA-mPFC pathway, which sheds light on the microbiota-gut-brain mechanisms underlying BD.},
}

@article {pmid41381863,
year = {2025},
author = {Liang, R and Meng, L and Liu, X and Zhu, J and Wang, L and Ren, J and Zhao, M and Liu, J and Zheng, C and Yang, J and Ming, D},
title = {Syn III participated in rTMS-modulated emotional rescue in the prefrontal cortex under simulated space composite environment.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41381863},
issn = {1476-5578},
abstract = {Emotional state is a critical indicator of astronaut performance during long-duration space missions, significantly impacting both mission efficiency and post-mission adaptation to life on Earth. In this context, transcranial magnetic stimulation (TMS) may serve as a valuable tool for studying the psychological changes induced by the space environment. By combining whole-brain imaging, finite element model, cerebral blood flow imaging, genomics, and molecular validation, we tried to identify potential regulatory targets and their cofactors involved in rTMS-mediated improvement of emotional abnormalities under simulated spaceflight conditions. We identified the activation patterns of brain-wide neurons in simulated space composite environment (SSCE), particularly the reduced neuronal activity in the prefrontal cortex (PFC). The rTMS could activate PFC neurons and, on a macro scale, alleviate abnormal cortical hemodynamics. Importantly, synapsin III (Syn III) is a key candidate for rTMS-mediated improvement of emotional abnormalities under SSCE, working together with proteins such as MAPK, PSD95, and NR2B. Our work not only advances the understanding of spaceflight-associated neuropsychiatric risks but also establishes a molecular framework for developing targeted neuromodulation strategies in stress-related psychiatric disorders.},
}

@article {pmid41381618,
year = {2025},
author = {Yang, H and Fukuma, R and Namima, T and Okuda, K and Nishi, A and Iwata, T and Reza, A and Sasaki, KS and Kaiju, T and Gill, G and Kishima, H and Nishimoto, S and Yanagisawa, T},
title = {Longitudinal multitask wireless electrocorticography data from two fully implanted nonhuman primates.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-025-06359-w},
pmid = {41381618},
issn = {2052-4463},
support = {JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012 (TY)//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJCR24U2 (TY)//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJCR18A5 (TY)//MEXT | JST | Core Research for Evolutional Science and Technology (CREST)/ ; },
abstract = {We present a unique dataset of chronic wireless electrocorticography (ECoG) recordings obtained from two fully implanted nonhuman primates (adult Japanese macaques, Macaca fuscata) spanning hundreds of days post implantation. Each animal was equipped with bilateral subdural ECoG arrays targeting the sensorimotor cortices and a fully implantable wireless transmission unit. The dataset involves multiple tasks, including resting-state measurements, auditory listening paradigms, voluntary button presses, reaching movements, and somatosensory evoked potentials, providing a broad range of behavioural and stimulus conditions. All raw signals, event annotations, and metadata are organized according to the Brain Imaging Data Structure (BIDS) extension for intracranial electrophysiology, ensuring ease of reuse and interoperability with common neurophysiological software. We verified the data quality and stability through impedance monitoring, power spectral analyses, and task-specific event-related measures across the recording period, confirming the reliability and consistency of the ECoG signals. By offering open access to these longitudinal wireless ECoG data, we aim to facilitate the acquisition of new insights into long-term cortical dynamics and advance brain-computer interface (BCI) research.},
}

@article {pmid41381487,
year = {2025},
author = {Mao, L and Liu, P and Li, J and Wang, X and Su, H and Zhang, X and Sun, J and Li, T},
title = {Tactile-evoked EEG Dataset for Natural Perception Using an Integrated Stimulation-Recording Framework.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-025-06250-8},
pmid = {41381487},
issn = {2052-4463},
abstract = {The increasing demand for assistive living and medical technologies in aging societies has driven advancements in tactile-evoked Brian Computer Interface (BCI) systems, offering an alternative to traditional visual and auditory-based BCI systems. However, the development of such systems is constrained by challenges in quantifying tactile sensations and a lack of diverse datasets. This study presents an integrated system enabling natural tactile perception during dynamic touch experience while simultaneously recording electroencephalographic (EEG) responses. EEG signals were collected from 10 healthy participants (64 channels, 1000 Hz) in natural tactile perception tasks involving contact with three distinct materials. Preliminary analysis revealed significant differences in the P300 peak latency and amplitude between tactile conditions, highlighting the unique characteristics of tactile-evoked EEG signals. A three-class classification using Common Spatial Pattern (CSP) and Support Vector Machine (SVM) models demonstrated above-chance accuracy. This tactile-evoked EEG dataset provides a valuable resource for seeking tactile-related neural mechanisms and driving the practical application of BCI systems, offering a pathway to improved user experiences and functionality in real-world scenarios.},
}

@article {pmid41380689,
year = {2025},
author = {Wei, Y and Ma, Z and Zhang, B and Fu, L and Sun, X and Li, K and Wang, Z and Wang, Y and Yu, Q and Yang, H and Tan, C and Duan, S and Ni, JD},
title = {Sympathetic functional units encoded by genetically defined postganglionic neurons.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.10.028},
pmid = {41380689},
issn = {1097-4199},
abstract = {The sympathetic system connects the brain with internal organs through distinct functional pathways; however, our understanding of their organization is limited. Here, we employed genetic labeling and single-cell transcriptomic analysis and identified two molecularly defined subpopulations of celiac-superior mesenteric ganglia (CG-SMG) neurons that implement different sympathetic functional pathways. Calb2-positive CG-SMG neurons project exclusively to the muscular layer of the gastrointestinal tract, forming endings associated with myenteric ganglia. Conversely, Nxph4-positive neurons innervate blood vessels within multiple organs, creating perivascular endings. Functional manipulations demonstrated that Calb2-labeled sympathetic neurons regulate gut motility without affecting blood flow, whereas Nxph4-positive neurons act as visceral vasoconstrictors, regulating blood flow independently of gut motility. The selectively induced autonomic responses by these two transcriptionally distinct subsets of postganglionic neurons suggest that the sympathetic nervous system uses a labeled line logic to control organ physiology.},
}

@article {pmid41379999,
year = {2025},
author = {Lyu, B and Qin, L and Wang, X and Ou, J and Nour, MM and Ding, N and Gao, JH and Liu, Y},
title = {Building hierarchically nested structure by rapid neural sequences.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {50},
pages = {e2507417122},
doi = {10.1073/pnas.2507417122},
pmid = {41379999},
issn = {1091-6490},
support = {82327806 W2431053//MOST | National Natural Science Foundation of China (NSFC)/ ; 32271093//MOST | National Natural Science Foundation of China (NSFC)/ ; 2021ZD0200500 2021ZD0200506//National science and technology innovation 2030 major program/ ; 2022ZD0205500//National science and technology innovation 2030 major program/ ; n/a//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; 7100604651//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; },
mesh = {Humans ; Magnetoencephalography ; *Cognition/physiology ; Male ; Female ; *Brain/physiology ; Adult ; Young Adult ; },
abstract = {Hierarchically nested structures are fundamental to human cognition, enabling complex behaviors across domains including language, planning, and mathematics. However, the neural mechanisms that enable the flexible construction of these hierarchical structures are poorly understood. Here, we designed a task where participants mentally built sequences with nested, multidepth structures by recursively applying a fixed set of rules. Using magnetoencephalography, we find that the brain constructs nested hierarchies through rapid neural sequences that perform two recurring generative operations. The first operation identifies the hierarchy depth of a symbol and is associated with increased ripple-band power; while the second arranges the symbol into its correct order at that level, a process that scales with the number of depths, also positively correlated with planning time. These results reveal a fundamental neural computation for transforming sensory information into structured representations, which is essential for higher-order cognition.},
}

Humans
Magnetoencephalography
*Cognition/physiology
Male
Female
*Brain/physiology
Adult
Young Adult

@article {pmid41379898,
year = {2025},
author = {Chen, X and Li, Z and Shen, Y and Mahmud, M and Pham, H and Ng, MK and Pun, CM and Wang, S},
title = {High-Fidelity Functional Ultrasound Reconstruction via a Visual Auto-Regressive Framework.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3623196},
pmid = {41379898},
issn = {2168-2208},
abstract = {Functional ultrasound (fUS) imaging provides exceptional spatiotemporal resolution for neurovascular mapping, yet its practical application is significantly hampered by critical challenges. Foremost among these is data scarcity, arising from ethical considerations and signal degradation through the cranium, which collectively limit dataset diversity and compromise the fairness of downstream machine learning models. To address these limitations, we introduce UltraVAR (Ultrasound Visual Auto-Regressive model), the first data augmentation framework designed for fUS imaging that leverages a pre-trained visual auto-regressive generative model. UltraVAR is designed not only to mitigate data scarcity but also to enhance model fairness through the reconstruction of diverse and physiologically plausible fUS samples. The generated samples preserve essential neurovascular coupling features-specifically, the dynamic interplay between neural activity and microvascular hemodynamics. This capability distinguishes UltraVAR from conventional augmentation techniques, which often disrupt these vital physiological correlations and consequently fail to improve, or even degrade, downstream task performance. The proposed UltraVAR employs a scale-by-scale reconstruction mechanism that meticulously preserves the spatial topological relationships within vascular networks. The framework's fidelity is further enhanced by two integrated modules: the Smooth Scaling Layer, which ensures the preservation of critical image information during multi-scale feature propagation, and the Perception Enhancement Module, which actively suppresses artifact generation via a dynamic residual compensation mechanism. Comprehensive experimental validation demonstrates that datasets augmented with UltraVAR yield statistically significant improvements in downstream classification accuracy. This work establishes a robust foundation for advancing ultrasound-based neuromodulation techniques and brain-computer interface technologies by enabling the reconstruction of high-fidelity, diverse fUS data.},
}

@article {pmid41379894,
year = {2025},
author = {Ke, Y and Fu, Z and Yang, J and Shang, H and Basu, A},
title = {A 1024-Channel 0.8V 23.9-nW/Channel Event-based Compute In-memory Neural Spike Detector.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3642865},
pmid = {41379894},
issn = {1940-9990},
abstract = {The increasing data rate has become a major issue confronting next-generation intracortical brain-machine interfaces (iBMIs). The scaling number of recording sites requires complex analog wiring and lead to huge digitization power consumption. Compressive event-based neural frontends have been used in high-density neural implants to support the simultaneous recording of more channels. Event-based frontends (EBF) convert recorded signals into asynchronous digital events via delta modulation and can inherently achieve considerable compression. But EBFs are prone to false events that do not correspond to neural and may affect the output firing rate, which is the key feature for neural decoding. Spike detection (SPD) is a key process in the iBMI pipeline to detect neural spikes and further reduce the data rate. However, conventional digital SPD suffers from the increasing buffer size and frequent memory access power, and conventional spike emphasizers are not compatible with EBFs. In this work we introduced an event-based spike detection (Ev-SPD) algorithm for scalable compressive EBFs. To implement the algorithm effectively, we proposed a novel low-power 10-T eDRAM-SRAM hybrid random-access memory (HRAM) in-memory computing (IMC) bitcell for event processing. We fabricated the proposed 1024-channel IMC SPD macro in a 65nm process and tested the macro with both synthetic dataset and Neuropixel recordings. The proposed macro achieved a high spike detection accuracy of 96.06% on a synthetic dataset and 95.08% similarity and 0.05 firing pattern MAE on Neuropixel recordings. Our event-based IMC SPD macro achieved a high per channel spike detection energy efficiency of 23.9 nW per channel and an area efficiency of 375 μm[2] per channel. Our work presented a SPD scheme compatible with compressive EBFs for high-density iBMIs, achieving ultra-low power consumption with an IMC architecture while maintaining considerable accuracy.},
}

@article {pmid41377313,
year = {2025},
author = {Ali, MR and Talpur, Y and Irshad, NUN and Imran, SB},
title = {Brain-computer interfaces: a new horizon in communication for locked-in syndrome.},
journal = {Annals of medicine and surgery (2012)},
volume = {87},
number = {12},
pages = {9159-9160},
pmid = {41377313},
issn = {2049-0801},
}

@article {pmid41374720,
year = {2025},
author = {Jochumsen, M and Sulkjær, CS and Dalgaard, KS},
title = {Comparison of Classifier Calibration Schemes for Movement Intention Detection in Individuals with Cerebral Palsy for Inducing Plasticity with Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {23},
pages = {},
doi = {10.3390/s25237347},
pmid = {41374720},
issn = {1424-8220},
support = {22-B01-1432//Elsass Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Cerebral Palsy/physiopathology ; Electroencephalography/methods ; Movement/physiology ; Male ; Female ; Adult ; Calibration ; Intention ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) have successfully been used for stroke rehabilitation by pairing movement intentions with, e.g., functional electrical stimulation. It has also been proposed that BCI training is beneficial for people with cerebral palsy (CP). To develop BCI training for CP patients, movement intentions must be detected from single-trial EEG. The study aim was to detect movement intentions in CP patients and able-bodied participants using different classification scenarios to show the technical feasibility of BCI training in CP patients. Five CP patients and fifteen able-bodied participants performed wrist extensions and ankle dorsiflexions while EEG was recorded. All but one participant repeated the experiment on 1-2 additional days. The EEG was divided into movement intention and idle epochs that were classified with a random forest classifier using temporal, spectral, and template matching features to estimate movement intention detection performance. When calibrating the classifier on data from the same day and participant, 75% and 85% classification accuracies were obtained for CP- and able-bodied participants, respectively. The performance dropped by 5-15 percentage points when training the classifier on data from other days and other participants. In conclusion, movement intentions can be detected from single-trial EEG, indicating the technical feasibility of using BCIs for motor training in people with CP.},
}

Humans
*Brain-Computer Interfaces
*Cerebral Palsy/physiopathology
Electroencephalography/methods
Movement/physiology
Male
Female
Adult
Calibration
Intention
Young Adult

@article {pmid41374637,
year = {2025},
author = {Paredes Ocaranza, CR and Yun, B and Paredes Ocaranza, ED},
title = {Traditional Machine Learning Outperforms EEGNet for Consumer-Grade EEG Emotion Recognition: A Comprehensive Evaluation with Cross-Dataset Validation.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {23},
pages = {},
doi = {10.3390/s25237262},
pmid = {41374637},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Machine Learning ; Brain-Computer Interfaces ; Deep Learning ; Signal Processing, Computer-Assisted ; Adult ; },
abstract = {OBJECTIVE: Consumer-grade EEG devices have the potential for widespread brain-computer interface deployment but pose significant challenges for emotion recognition due to reduced spatial coverage and the variable signal quality encountered in uncontrolled deployment environments. While deep learning approaches have employed increasingly complex architectures, their efficacy in noisy consumer-grade signals and cross-system generalizability remains unexplored. We present a comprehensive systematic comparison of EEGNet architecture, which has become a benchmark model for consumer-grade EEG analysis versus traditional machine learning, examining when and why domain-specific feature engineering outperforms end-to-end learning in resource constrained scenarios.

APPROACH: We conducted comprehensive within-dataset evaluation using the DREAMER dataset (23 subjects, Emotiv EPOC 14-channel) and challenging cross-dataset validation (DREAMER→SEED-VII transfer). Traditional ML employed domain-specific feature engineering (statistical, frequency-domain, and connectivity features) with random forest classification. Deep learning employed both optimized and enhanced EEGNet architectures, specifically designed for low channel consumer EEG systems. For cross-dataset validation, we implemented progressive domain adaptation combining anatomical channel mapping, CORAL adaptation, and TCA subspace learning. Statistical validation included 345 comprehensive evaluations with fivefold cross-validation × 3 seeds × 23 subjects, Wilcoxon signed-rank tests, and Cohen's d effect size calculations.

MAIN RESULTS: Traditional ML achieved superior within-dataset performance (F1 = 0.945 ± 0.034 versus 0.567 for EEGNet architectures, p < 0.000001, Cohen's d = 3.863, 67% improvement) across 345 evaluations. Cross-dataset validation demonstrated good performance (F1 = 0.619 versus 0.007) through systematic domain adaptation. Progressive improvements included anatomical channel mapping (5.8× improvement), CORAL domain adaptation (2.7× improvement), and TCA subspace learning (4.5× improvement). Feature analysis revealed inter-channel connectivity patterns contributed 61% of the discriminative power. Traditional ML demonstrated superior computational efficiency (95% faster training, 10× faster inference) and excellent stability (CV = 0.036). Fairness validation experiments supported the advantage of traditional ML in its ability to persist even with minimal feature engineering (F1 = 0.842 vs. 0.646 for enhanced EEGNet), and robustness analysis revealed that deep learning degrades more under consumer-grade noise conditions (17% vs. <1% degradation).

SIGNIFICANCE: These findings challenge the assumption that architectural complexity universally improves biosignal processing performance in consumer-grade applications. Through the comparison of traditional ML against the EEGNet consumer-grade architecture, we highlight the potential that domain-specific feature engineering and lightweight adaptation techniques can provide superior accuracy, stability, and practical deployment capabilities for consumer-grade EEG emotion recognition. While our empirical comparison focused on EEGNet, the underlying principles regarding data efficiency, noise robustness, and the value of domain expertise could extend to comparisons with other complex architectures facing similar constraints in further research. This comprehensive domain adaptation framework enables robust cross-system deployment, addressing critical gaps in real-world BCI applications.},
}

Humans
*Electroencephalography/methods
*Emotions/physiology
*Machine Learning
Brain-Computer Interfaces
Deep Learning
Signal Processing, Computer-Assisted
Adult

@article {pmid41370926,
year = {2025},
author = {Qiu, X and Wang, ZY and Jiang, XH and Zhao, H and Yan, ZP and Li, KH and Zhang, L and Chen, L and Meng, L and Ni, J},
title = {Neural correlation between swallowing motor imagery and execution: An EEG analysis.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2b37},
pmid = {41370926},
issn = {1741-2552},
abstract = {OBJECTIVE: The relationship between swallowing motor imagery and actual swallowing remains unclear, leading to a lack of physiological basis for the application of swallowing imagery-based brain-computer interface (BCI) paradigms in rehabilitation. This research explored the link between swallowing execution and imagery, aiming to optimize brain-computer interface applications for swallowing rehabilitation in patients with dysphagia.

APPROACH: Thirty healthy participants performed swallowing motor imagery and saliva swallowing tasks under video cues, and Electroencephalography (EEG) signals from 64 channels and electromyographic (EMG) signals from the suprahyoid muscles were recorded. This study investigates swallowing onset detection using EMG, and explores neural dynamics during swallowing imagery and execution through EEG-based time-frequency analysis, functional connectivity analysis, and nonlinear dynamic analysis (Sample Entropy).

MAIN RESULTS: The results revealed event-related desynchronization (ERD) in the central region (CPz, CP1-CP4) and parietal region (Pz, P1-P4) for both swallowing motor imagery and actual swallowing. Pearson's correlation analysis indicated a weak but significant correlation (P = 0.0102). The ERD phenomenon during swallowing imagery was more similar to that during the pharyngeal stage, with a weak but significant correlation (P = 0.0139). Functional connectivity analysis revealed greater activation of the central region during swallowing imagery than during actual swallowing. In terms of sample entropy, swallowing motor execution exhibited higher signal complexity and dynamic characteristics compared to imagery.

SIGNIFICANCE: This study highlights the similarity in neural activation between swallowing imagery and execution, particularly in the central and parietal regions, supporting the application of the swallowing imagery paradigm in these regions for rehabilitation. Further research is required to enhance BCI applications in swallowing disorders.},
}

@article {pmid41370855,
year = {2025},
author = {Karaiskou, AI and Varon, C and Musluoglu, CA and Alaerts, K and De Vos, M},
title = {EEG-Based meditation decoding: Tackling subject variability with spatial and temporal alignment.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2b0f},
pmid = {41370855},
issn = {1741-2552},
abstract = {Objective. Meditation and mindfulness are increasingly recognized as important in improving mental well-being. However, Electroencephalography (EEG)-based neurofeedback systems supporting these practices typically fail to generalize to unseen subjects. This study investigates the application of both spatial and spectral alignment to EEG to improve the classification of meditation and rest states for new subjects without any model retraining.Approach. Two unsupervised domain adaptation techniques are employed to reduce differences between subjects in their EEG recordings. The first, Riemannian Space Data Alignment (RSDA), adjusts and brings together patterns of brain activity across electrodes (spatial domain). The second, Convolutional Monge Mapping Normalization (CMMN), aligns the distribution of brain rhythms across frequencies (spectral domain). Each method is evaluated separately, in combination, and in interaction with z-score normalization. Classification between meditation and rest is performed on the aligned time series using EEGNet, a compact convolutional neural network architecture, with leave-one-subject-out (LOSO) cross-validation to assess generalization across subjects. All experiments are based on a publicly available dataset of meditation EEG recordings from 53 subjects, including both novice and expert meditators.Main Results. The combined RSDA+CMMN approach significantly improved LOSO classification accuracy (66.6%) compared to non-aligned (55.7%) and z-score normalized (59.6%) baselines, even though it did not improve overall harmonization. Spectral analysis identified consistent classification contributions from the Theta (4-8 Hz), Alpha (8-14 Hz), and Beta (14-30 Hz) bands, while spatial analysis highlighted Frontopolar and Temporal regions as critical for distinguishing the mental states of meditation and rest.Significance. This work is the first to explore both spatial and spectral alignment in subject-independent meditation decoding for improved cross-subject generalization. Aligning EEG time series without retraining provides a practical solution for real-time neurofeedback, thereby reducing subject variability and paving the way toward calibration-free neurotechnology that supports mental well-being. .},
}

@article {pmid41369883,
year = {2025},
author = {Tan, Y and Li, B and Sun, Z and Duan, F and Solé-Casals, J},
title = {Multi-source self-guided domain adaptation framework for EEG-based emotion recognition.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {41369883},
issn = {1741-0444},
support = {No. 2025YFE0107700//National Key R&D Program of China/ ; No. 24ZXYXSY00140//Tianjin Science and Technology Plan Project/ ; },
}

@article {pmid41369868,
year = {2025},
author = {Lin, C and Lai, Q and Fang, E and Luo, B and Chen, Z and Huang, J and Hu, F and Yao, E},
title = {Serum urate, cardiovascular mediators, and atrial fibrillation: genetic evidence for URAT1-targeted therapy.},
journal = {Clinical rheumatology},
volume = {},
number = {},
pages = {},
pmid = {41369868},
issn = {1434-9949},
support = {2018-CX-20//Medical Innovation Project of Fujian Province/ ; },
abstract = {BACKGROUND: Current evidence indicates that high serum urate levels are associated with an increased occurrence of atrial fibrillation (AF), and urate-lowering drugs could potentially reduce this risk. Nonetheless, the processes driving this relationship remain unclear.

OBJECTIVE: To identify key mediators linking urate to AF and assess the direct effects of potential drug targets on AF risk.

METHODS: Genetic variants associated with serum urate levels, potential mediators, and urate-lowering drug targets were identified from genome-wide association studies (GWAS). Univariable Mendelian randomization, multivariable Mendelian randomization, and two-step-cis-MR were conducted. The Bayesian horseshoe prior MR approach was used as the primary method, and Genomic SEM was employed to support the mediation model.

RESULTS: The study identified a genetic and causal relationship between serum urate levels and AF onset. Key mediators included systolic blood pressure (proportion mediated 56.23%), diastolic blood pressure (25.27%), hypertension (49.46%), hypercholesterolemia (4.83%), coronary atherosclerosis (12.24%), myocardial infarction (30.32%), coronary artery disease (29.74%), and heart failure (47.66%). Drug target MR analysis found strong evidence for URAT1 inhibition reducing AF risk (odds ratio [OR] = 0.91, 95% Bayesian credible interval [BCI] 0.85 to 0.97; Bayesian posterior probability [BPP] = 0.997), which persisted after mediator adjustment. Under stricter flanking regions, evidence weakened after adjustment for heart failure (OR = 0.93, 95% BCI 0.84 to 1.04; BPP = 0.907) but remained robust for other mediators.

CONCLUSION: This study highlights several cardiovascular conditions (hypertension, hypercholesterolemia, heart failure, coronary artery diseases) as key mediators between serum urate and AF and supports URAT1 inhibition as a potential therapeutic strategy. Key points •Elevated serum urate increases the risk of atrial fibrillation, potentially through cardiovascular mediators such as hypertension, heart failure, and coronary artery diseases. •Genetic evidence from drug-target Mendelian randomization supports URAT1 inhibition as a potential therapeutic strategy for reducing atrial fibrillation risk. •The protective effect of URAT1 inhibition against atrial fibrillation persists after adjusting for key cardiovascular mediators, suggesting additional therapeutic pathways beyond those identified.},
}

@article {pmid41368697,
year = {2025},
author = {Huang, T and Yin, X and Jiang, E},
title = {EEG motor imagery classification through a two-dimensional CNN-LSTM deep architecture and fuzzy decision-making.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2025.2554256},
pmid = {41368697},
issn = {1476-8259},
abstract = {This study presents a robust deep learning framework for automatic motor imagery detection from raw EEG signals. Six band-power features were extracted using STFT, and dedicated 2D CNN-LSTM models were trained for each band. Their outputs were fused using a Choquet fuzzy integral to enhance decision reliability under noisy EEG conditions. Alpha- and sigma-band models achieved 88% and 87.1% accuracy, respectively. The fused architecture reached 90.4% on BCI IV-2a and 92.21% on BCI IV-1, outperforming existing methods in motor imagery classification.},
}

@article {pmid41368519,
year = {2025},
author = {Wang, P and Xie, T and Zhou, Y and Gong, P and Chan, RHM},
title = {TCPL: task-conditioned prompt learning for few-shot cross-subject motor imagery EEG decoding.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1689286},
pmid = {41368519},
issn = {1662-4548},
abstract = {Motor imagery (MI) electroencephalogram (EEG) decoding plays a critical role in brain-computer interfaces but remains challenging due to large inter-subject variability and limited training data. Existing approaches often struggle with few-shot cross-subject adaptation, as they require either extensive fine-tuning or fail to capture individualized neural dynamics. To address this issue, we propose a Task-Conditioned Prompt Learning (TCPL), which integrates a Task-Conditioned Prompt (TCP) module with a hybrid Temporal Convolutional Network (TCN) and Transformer backbone under a meta-learning framework. Specifically, TCP encodes subject-specific variability as prompt tokens, TCN extracts local temporal patterns, Transformer captures global dependencies, and meta-learning enables rapid adaptation with minimal samples. The proposed TCPL model is validated on three widely used public datasets, GigaScience, Physionet, and BCI Competition IV 2a, demonstrating strong generalization and efficient adaptation across unseen subjects. These results highlight the feasibility of TCPL for practical few-shot EEG decoding and its potential to advance the development of personalized brain-computer interface systems.},
}

@article {pmid41367898,
year = {2025},
author = {Paveliev, M and Melnikova, A and Samigullin, DV and Egorchev, AA and Titova, AA and Kiyasov, AP and Popova, IY and Parpura, V and Aganov, AV},
title = {Second harmonic generation for brain imaging: pathology-related studies.},
journal = {Biophysical reviews},
volume = {},
number = {},
pages = {},
pmid = {41367898},
issn = {1867-2450},
abstract = {Microscopy of the brain has been facing problems of contrast and thick tissue imaging. Second harmonic generation (SHG) is a non-linear effect of the light interaction with the imaged material, resulting in photon emission at half the wavelength of the absorbed light. SHG microscopy provides an unprecedented opportunity for imaging collagen and other noncentrosymmetric protein fibrils in unstained thick tissue samples and in the live brain via a regular multiphoton setup. This opens a remarkable methodological window for imaging pathological processes of high importance, including brain trauma, fibrosis, tumorigenesis, and neuroimplant-induced foreign body response. Moreover, SHG is a valuable tool for imaging astrocytes and nerve fiber microtubules. Third harmonic generation enhanced by three-photon resonance with the Soret band of hemoglobin is combined with SHG to resolve the microstructure of blood vessel walls and astrocyte-process endfeet on gliovascular interfaces. Here, we review current state-of-the-art methods in the field of brain imaging applications of SHG, including research on brain and spinal cord injury, glioma, ischemia, Alzheimer's disease, neuroimplantation, and brain meninges. We then address the method development perspective in the broader context of other tissue pathologies. Finally, we account for recent progress in artificial intelligence applications for SHG microscopy data analysis.},
}

@article {pmid41365192,
year = {2025},
author = {Li, Y and Ye, M and He, Q and Yang, B and Luo, P and Yang, X},
title = {Novel dual AMPK/NRF2 activation by leucocyanidin from Hawthorn (Crataegus) for mitochondria repair-Targeted therapy of hepatic steatosis.},
journal = {Phytomedicine : international journal of phytotherapy and phytopharmacology},
volume = {150},
number = {},
pages = {157614},
doi = {10.1016/j.phymed.2025.157614},
pmid = {41365192},
issn = {1618-095X},
abstract = {BACKGROUND AND PURPOSE: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a global health challenge with limited therapeutic options. This study identified leucocyanidin (Leuc), a bioactive flavonoid from the traditional herb Crataegus pinnatifida (hawthorn), as a novel dual-target therapeutic agent against MASLD.

METHODS AND RESULTS: We evaluated the effects of Leuc on a mouse model induced by a 60% high-fat diet and a cell model induced by free fatty acids (FFA). Compared to the model group, Leuc treatment dose-dependently significantly reduced liver weight, serum levels of TG and TC, hepatic inflammation markers (IL-6 and TNF-α), as well as cellular TG content. Histological and fluorescence analyses revealed a significant reduction in lipid droplet accumulation. Mechanistically, Leuc exerted its protective effects through two major pathways: (1) By activating the NRF2 antioxidant axis, Leuc attenuated oxidative stress-induced mitochondrial dysfunction and restored fatty acid β-oxidation capacity; (2) Through direct allosteric binding to AMPK, Leuc suppressed fatty acid uptake, inhibited lipogenesis, and enhanced mitochondrial fatty acid transport.

CONCLUSION: These coordinated mechanisms reestablished hepatic lipid homeostasis, positioning Leuc as a promising dual-target natural compound for MASLD intervention through simultaneous AMPK/NRF2 activation.},
}

@article {pmid41364937,
year = {2025},
author = {Patrick-Krueger, KM and Pavlidis, I and Contreras-Vidal, JL},
title = {The state of science convergence in implantable brain-computer interface clinical trials.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2a6f},
pmid = {41364937},
issn = {1741-2552},
abstract = {Advances in implantable brain-computer interfaces (iBCI) have rapidly accelerated in the last decade that promises to improve the quality of life of patients with communications, sensory, and motor control disabilities (CSM). In this Perspective, we quantify the extent and nature of scientific convergence across 21 research groups conducting iBCI clinical trials worldwide. Using Medical Subject Headers (MeSH) and Classification of Instructional Programs (CIP) taxonomies, we analyze topical and disciplinary integration within 161 publications from 1998-2023 to assess how deeply team composition aligns with research themes and translational impact. Our findings indicate uneven patterns of convergence, with many teams combining engineering and clinical expertise yet omitting ethical, legal, and social dimensions. This represents what we term short-cut convergence. We propose an operational definition of this phenomenon and identify practical steps for researchers and funders to strengthen full convergence to accelerate iBCI translation and implementation.},
}

@article {pmid41363017,
year = {2025},
author = {Rayson, H and Moreau, Q and Gailhard, S and Szul, MJ and Bonaiuto, JJ},
title = {Beta Burst Waveform Diversity: A Window onto Cortical Computation.},
journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry},
volume = {},
number = {},
pages = {10738584251390779},
doi = {10.1177/10738584251390779},
pmid = {41363017},
issn = {1089-4098},
abstract = {Neural activity in the beta band is increasingly recognized to occur not as sustained oscillations but as transient burst-like events. These beta bursts are diverse in shape, timing, and spatial distribution, but their precise functional significance remains unclear. Here, we review emerging evidence on beta burst properties, functional roles, and developmental trajectories and propose a new framework in which beta bursts are not homogeneous events but reflect distinct patterns of synaptic input from different brain regions targeting different cortical layers. We argue that burst waveform shape carries mechanistic and computational significance, offering a window into the dynamic integration of specific combinations of cortical and subcortical signals. This perspective repositions beta bursts as transient computational primitives, rather than generic inhibitory signals or averaged rhythms. We conclude by outlining key open questions and research priorities, including the need for improved detection methods, investigation into developmental and clinical biomarkers, and translational applications in neuromodulation and brain-computer interfaces.},
}

@article {pmid41362972,
year = {2025},
author = {Labor, VV and Mokienko, OA and Cherkasova, AN and Ikonnikova, ES and Lyukmanov, RK and Suponeva, NA},
title = {[Movement image training and brain-computer interface in cognitive rehabilitation].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {125},
number = {11},
pages = {27-35},
doi = {10.17116/jnevro202512511127},
pmid = {41362972},
issn = {1997-7298},
mesh = {Humans ; *Brain-Computer Interfaces ; Parkinson Disease/rehabilitation ; Cognition ; Movement ; Stroke Rehabilitation ; Multiple Sclerosis/rehabilitation ; *Neurological Rehabilitation/methods ; Cognitive Training ; },
abstract = {The paper provides an overview of studies on the use of movement image training and brain-computer interfaces (BCIs) for cognitive rehabilitation in patients with neurological diseases. Based on the analysis of studies published from 2004 to 2025, the effectiveness of these methods in recovering cognitive functions in patients with stroke (13 studies), Parkinson's disease (4 studies), and multiple sclerosis (2 studies) was evaluated. Most studies demonstrated a positive effect of movement image training on the cognitive functions of patients with neurological diseases and moderate cognitive deficits. The effectiveness of this approach is comparable to that of specialized cognitive training. In studies using BCI to control movement image training, an improvement in cognitive functions was also reported. Some studies showed a positive correlation between changes in cognitive indicators and the degree of motor recovery. In groups of patients with normal or near-normal baseline MoCA scores, no significant improvement in cognitive function was reported after a training course. The heterogeneity of the analyzed studies makes direct comparison between them difficult. The results of the analysis of published studies indicate the prospect of using the movement image training with BCI control in the cognitive rehabilitation of neurological patients. However, well-designed randomized controlled trials are necessary to study the mechanisms of the ideomotor training effects on cognitive functions and to develop standardized protocols for assessing their effectiveness.},
}

Humans
*Brain-Computer Interfaces
Parkinson Disease/rehabilitation
Cognition
Movement
Stroke Rehabilitation
Multiple Sclerosis/rehabilitation
*Neurological Rehabilitation/methods
Cognitive Training