@article {pmid41996427,
year = {2026},
author = {Campbell, E and Eddy, E and Scheme, E},
title = {Transformer-Based Context-Informed Incremental Learning With sDTW Alignment Unlocks Fast and Precise Regression-Based Myoelectric Control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {2118-2129},
doi = {10.1109/TNSRE.2026.3685074},
pmid = {41996427},
issn = {1558-0210},
mesh = {Humans ; *Electromyography/methods ; Male ; Adult ; Female ; Algorithms ; Young Adult ; Neural Networks, Computer ; Regression Analysis ; User-Computer Interface ; *Machine Learning ; Calibration ; Brain-Computer Interfaces ; },
abstract = {Regression-based myoelectric interfaces hold the promise of enabling intuitive proportional and simultaneous control but remain limited by calibration sensitivity, unpredictable dynamics, and inconsistent user behaviours. Temporal neural architectures have the potential to substantially improve these controllers by capturing the temporal structure of user behaviours, provided they are trained using dynamics that are sufficiently representative of closed-loop use. Context-informed incremental learning (CIIL) offers a mechanism for acquiring such data online; however, its reliance on environment-derived pseudo-labels makes it vulnerable to temporal deviations between assumed and true user intent. This study introduces T-sDTW-CIIL, a transformer-based incremental learning framework that integrates temporal modelling, closed-loop learning, and soft dynamic time warping (sDTW) to enable tolerant label alignment. Twelve participants completed an adaptive regression-based cursor-control task using four pipelines: static and CIIL variants of both MLP and transformer models. T-sDTW-CIIL achieved significantly higher success rates, throughputs, efficiencies, and simultaneity gains when evaluated in a high precision ISO-Fitts' environment. T-sDTW-CIIL achieved throughputs of $2.0\times $ , $2.4\times $ , and $3.7\times $ those of an MLP trained using conventional screen-guided training when acquiring large, medium, and small targets, respectively. Perhaps more importantly, it maintained success rates of 98.4% for small targets, whereas the static MLP degraded to only 23.4% success. T-sDTW-CIIL-based adaptation also reduced overall contraction intensities by ~10%. These results demonstrate the powerful combination of temporal learning with context-informed co-adaptation. T-sDTW-CIIL overcomes key limitations of existing regression-based myoelectric controllers, enabling robust, low-intensity human-computer interaction.},
}

Humans
*Electromyography/methods
Male
Adult
Female
Algorithms
Young Adult
Neural Networks, Computer
Regression Analysis
User-Computer Interface
*Machine Learning
Calibration
Brain-Computer Interfaces

@article {pmid42046874,
year = {2026},
author = {Kong, L and Yang, Y and Zhou, W and Hu, S},
title = {Sporadic Alzheimer's disease with bipolar-like features: a case report and a brief review of the current research status.},
journal = {Journal of Zhejiang University. Science. B},
volume = {27},
number = {4},
pages = {416-425},
doi = {10.1631/jzus.B2500307},
pmid = {42046874},
issn = {1862-1783},
mesh = {*Alzheimer Disease/pathology/diagnosis ; Humans ; Amyloid beta-Peptides/metabolism ; Female ; tau Proteins/metabolism ; Aged ; Male ; Oxidative Stress ; Brain/pathology ; },
abstract = {Alzheimer's disease (AD) is among the main causes of cognitive impairment, memory loss, and dementia, particularly in old adults. It has been listed as one of the most expensive, lethal, and burdening diseases of the 21st century and develops with the process of aging worldwide (Scheltens et al., 2021). Currently, it is widely acknowledged that the typical pathogenesis of AD involves the deposition of amyloid-β (Aβ) and Tau proteins in the cerebral parenchyma and vasculature, intraneuronal neurofibrillary tangles, and the gradual degeneration of synapses (Scheltens et al., 2016; Rostagno, 2022). According to several hypotheses, abnormalities and dysfunctions in vascular structure, mitochondrial metabolism, oxidative stress, glucose utilization, and neuroinflammation are considered fundamental for AD pathology (Scheltens et al., 2016).},
}

*Alzheimer Disease/pathology/diagnosis
Humans
Amyloid beta-Peptides/metabolism
Female
tau Proteins/metabolism
Aged
Male
Oxidative Stress
Brain/pathology

@article {pmid42048458,
year = {2026},
author = {Zhu, R and Hu, Z and Lou, Z and Xie, F and Zhao, S and Jiao, X and Wang, J and Fukuda, K and Chen, X and Hu, W and Cheng, HM and Li, X and Someya, T and Xu, X},
title = {An exceptionally conductive hydrogel for all-organic, ultraflexible, and chronic neural interfaces.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {18},
pages = {e2532840123},
doi = {10.1073/pnas.2532840123},
pmid = {42048458},
issn = {1091-6490},
support = {2023YFE0101400//Ministry of Science and Technology of the People's Republic of China (MOST)/ ; JPMJCR21P2//JST, CREST/ ; 52273249//National Natural Science Foundation of China/ ; 2021ZT09L197//GDSTC | Guangdong Provincial Introduction of Innovative Research and Development Team (Guangdong Innovative Research Team Program)/ ; KQTD20210811090112002//| Natural Science Foundation of Shenzhen Municipality (Shenzhen Natural Science Foundation)/ ; 2308085MA19//Anhui Provincial Natural Science Foundation/ ; },
mesh = {*Hydrogels/chemistry ; Electric Conductivity ; *Brain-Computer Interfaces ; Animals ; *Electrocorticography/methods/instrumentation ; Electrodes, Implanted ; },
abstract = {Chronic neural interfaces are essential for advancing brain-computer interfaces, neuroprosthetics, and neuromodulation technologies. However, a long-standing trade-off between performance and longevity persists due to the scarcity of materials that simultaneously achieve superior electrical performance, mechanical compliance, and biocompatibility. Here, we overcome this limitation with an all-organic, ultraflexible electrocorticography (ECoG) design that features a thickness of only 9 µm, achieving low electrode-tissue impedance and durability in vivo. Central to this design is a conductive hydrogel featuring an interfacial percolation (CHIP) microstructure, with tunable hydration levels and softness, achieving a highest in-plane electrical conductivity of 2,512 S cm[-1]. We further developed an in-plane swelling control with a dry, soft-protective etching strategy that preserves the structural integrity during hydrogel processing. The resulting all-organic ECoG array conforms to the cortical surface, minimizing foreign body response and providing exceptional signal quality, with the longest record up to 550 d.},
}

*Hydrogels/chemistry
Electric Conductivity
*Brain-Computer Interfaces
Animals
*Electrocorticography/methods/instrumentation
Electrodes, Implanted

@article {pmid42049850,
year = {2026},
author = {Xu, G and Yu, C and Shao, G and Pan, G and Wang, Y and Hao, Y},
title = {Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-10144-9},
pmid = {42049850},
issn = {2399-3642},
abstract = {Intracortical brain-computer interfaces (iBCIs) demand computationally efficient feature extraction methods to process high-bandwidth neural signals in resource-constrained implantable systems. We present the mean absolute of n-th difference (MAND), a feature extraction technique that utilizes optimized differencing operations to isolate spiking-band activity with minimal computational overhead. Through theoretical and empirical validation across multiple datasets encompassing human handwriting, primate reaching/grasping, and rodent cognitive tasks, MAND demonstrated superior performance compared to state-of-the-art features, significantly reducing velocity reconstruction error and improving classification accuracy. An extended MAND variant, incorporating a weighted sum of dual-differencing, achieved additional performance gains through enhanced spectral alignment with neural spiking activity. Hardware implementation on FPGA/MCU platforms confirmed MAND's exceptional efficiency - processing 10-second neural recordings in just 6 ms while consuming only 3 mW of power - representing orders-of-magnitude improvements in both speed and energy efficiency compared to conventional methods. These results establish MAND as a breakthrough solution that enables superior decoding performance with exceptional computational efficiency, paving the way for next-generation, fully implantable iBCI systems.},
}

@article {pmid42049861,
year = {2026},
author = {Tao, X and Pu, Y and Kong, XZ},
title = {Rebalancing psychology in China.},
journal = {Communications psychology},
volume = {4},
number = {1},
pages = {},
pmid = {42049861},
issn = {2731-9121},
}

@article {pmid42050355,
year = {2026},
author = {Zhao, X and Lin, Z and Zhang, H and Zhang, W and Liu, Z and Chen, C and Ji, H and Hu, S and Xu, X},
title = {Longitudinal associations of cardiovascular-kidney-metabolic syndrome with midlife or late-life mental disorders and dementia, and the mediating role of metabolomic signature.},
journal = {Communications medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/s43856-026-01608-4},
pmid = {42050355},
issn = {2730-664X},
abstract = {BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome assesses the interconnections among metabolic, kidney, and cardiovascular diseases, rendering significant prognostic value for age-related chronic diseases and mortality. We aimed to investigate the effects of CKM syndrome on transitions between healthy status, mental disorders, and dementia and evaluate the potential mediating role of a CKM-related metabolomic signature in these associations.

METHODS: This prospective longitudinal study used UK Biobank data from 375,203 midlife and older adults at baseline and 188,018 with metabolomic information. CKM was staged from 0 to 4. Mental disorders and dementia were identified via ICD-10. Multi-state models analyzed the impact of CKM on transitions from healthy status to mental disorders and dementia. Competing risk (death) models assessed the associations of CKM with specific mental disorders and dementia. Mediation role of CKM-related metabolomic signature was evaluated.

RESULTS: We show that per-stage CKM increase elevates hazards of transitioning from healthy to mental disorders (HR = 1.24[1.22-1.26]) and subsequently to dementia (HR = 1.38[1.21-1.58]), or directly to dementia (HR = 1.27[1.21-1.33]). Worsening CKM stages are associated with bipolar, depressive, and anxiety disorders; whilst only advanced stages (3/4) associated with all dementia types. The CKM metabolomic signature mediates 34.9% and 8.1% of associations of CKM with pre-dementia mental disorders and dementia, respectively.

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

@article {pmid42037083,
year = {2026},
author = {Eilts, H and Ivucic, G and Koenen, N and Wright, MN and Schultz, T and Putze, F},
title = {Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates.},
journal = {Human brain mapping},
volume = {47},
number = {6},
pages = {e70528},
doi = {10.1002/hbm.70528},
pmid = {42037083},
issn = {1097-0193},
support = {459360854//Deutsche Forschungsgemeinschaft/ ; 447089431//Deutsche Forschungsgemeinschaft/ ; },
mesh = {Humans ; *Electroencephalography/methods/classification ; *Deep Learning ; *Neural Networks, Computer ; Adult ; *Attention/physiology ; *Imagination/physiology ; *Cerebral Cortex/physiology ; Male ; Young Adult ; Female ; },
abstract = {While deep learning has drastically improved the performance of electroencephalography (EEG) analysis, it remains unclear what these models, such as EEGNet, learn from the data and how their learned features relate to neuroscientific concepts. In this work, we introduce a comprehensive interpretability framework for deep learning models of neural data based on Concept Relevance Propagation (CRP), an extension of layer-wise relevance propagation that enables the analysis of abstract concepts encoded by individual neurons and filters. We apply CRP to individual filters of convolutional neural networks (EEGNet) trained using leave-one-out cross-validation. To identify common classification strategies across models, we guide the selection of representative data for individual filters using relevance maximization, reduce dimensionality via UMAP, and identify clusters of filters encoding similar concepts through density-based clustering. To gain insight into the neural correlates of these tasks, we analyze the learned features across multiple data domains without requiring model retraining. We integrate a virtual inspection layer to project explanations into the frequency domain, enabling the simultaneous analysis of spatial, temporal, and spectral aspects using topographic maps, functional grouping, and independent component analysis (ICA). Using three EEG classification tasks-auditory attention, internal/external attention, and motor imagery-we demonstrate that our approach reveals interpretable, task-relevant neural patterns that generalize across participants. Overall, this framework provides a step toward understanding the models itself and gaining insights into the tasks in terms of neuroscience.},
}

Humans
*Electroencephalography/methods/classification
*Deep Learning
*Neural Networks, Computer
Adult
*Attention/physiology
*Imagination/physiology
*Cerebral Cortex/physiology
Male
Young Adult
Female

@article {pmid42037346,
year = {2026},
author = {Xue, Y and Li, Z and Wang, F and Zhao, L and Li, T and Gong, A and Nan, W and Fu, Y},
title = {[Ethical risks and regulatory considerations in neurofeedback technology].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {2},
pages = {414-420},
doi = {10.7507/1001-5515.202507052},
pmid = {42037346},
issn = {1001-5515},
mesh = {Humans ; *Neurofeedback/ethics ; Informed Consent/ethics ; Brain/physiology ; Personal Autonomy ; },
abstract = {Neurofeedback transforms real-time brain activity features into multimodal feedback to guide self-regulation of brain function, showing potential applications in neuropsychiatric treatment and cognitive enhancement. However, its use entails ethical risks including cognitive autonomy, personal identity integrity, safety and efficacy, privacy protection, and the safeguarding of vulnerable populations, with informed consent challenges being particularly pronounced in implicit neurofeedback. Based on these risks, this paper proposes establishing an ethical evaluation framework for neurofeedback, promoting ethics-embedded design, and strengthening international cooperation and public education, emphasizing responsible innovation to align technological development with ethical safeguards.},
}

Humans
*Neurofeedback/ethics
Informed Consent/ethics
Brain/physiology
Personal Autonomy

@article {pmid42037366,
year = {2026},
author = {Perdigão, B and Chang, B and Anand, GAE and Tao, L and Kim, K and Eriksen, AZ and Schönhoff, MA and Ferreira, G and Liu, X and Genelioglu, S and Lyngholm-Kjærby, J and Zahoor, N and Lind, JU and Fanta, ABDS and Hansen, TW and Cai, C and Han, A},
title = {Solid Ethanol as a Renewable, Low-Toxicity, Electron-Beam Direct Write, and Biomedical Material.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e75341},
doi = {10.1002/advs.75341},
pmid = {42037366},
issn = {2198-3844},
support = {R345-2020-1440//Lundbeck Foundation/ ; NNF24OC0092323//Novo Nordisk Foundation/ ; NNF20OC0064289//Novo Nordisk Foundation/ ; NNF24OC0095407//Novo Nordisk Foundation/ ; 1133-00016B//Danish Medical Research Council/ ; },
abstract = {3D ice lithography (3DIL) is an emerging direct-write technique that fabricates intricate 3D structures using frozen precursors. Here, we report the use of ethanol as a renewable and low-toxicity precursor for 3DIL, intended for the first time for the fabrication of intricate porous microstructures for in vitro and in vivo biomedical applications. The first nanoindentation analysis of 3DIL materials reveals mechanical properties (Young's modulus 2-4 GPa) comparable to biocompatible polymers. TEM shows that the material is an amorphous carbon that undergoes controlled graphitization under annealing at very high temperatures (1300°C). Due to its chemical composition, mechanical properties, and stability in water, cross-linked ethanol scaffolds support in vitro endothelial cell adhesion and proliferation with high confluency. Patterned neurostimulation electrodes implanted in mouse brains elicit no significant increase in astrocytic or microglial activation, indicating excellent in vivo biocompatibility. Additionally, we present for the first time the use of optically transparent substrates and the first patterning of neurostimulation electrodes using 3DIL. This study positions 3DIL using ethanol as a versatile, direct-write technique using renewable precursors to produce novel microdevices in biomedical engineering.},
}

@article {pmid42037370,
year = {2026},
author = {Xing, Y and Yang, Y and Hong, Z and Tian, C and Chu, H and Prokop, SC and Cai, H and Gu, M and Tchieu, J and Mackie, K and Guo, F},
title = {Organoid Brain-Machine-Interface Devices for Central Nervous System Repair.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e75444},
doi = {10.1002/advs.75444},
pmid = {42037370},
issn = {2198-3844},
support = {U54AG090792//National Institute of Health Awards/ ; R01GM160423//National Institute of Health Awards/ ; U01DA056242//National Institute of Health Awards/ ; EFRI2422149//National Science Foundation Award/ ; },
abstract = {Central nervous system (CNS) repair and regeneration suffer from tremendous clinical challenges due to current limitations in replacing lost neural tissues and restoring long-term neural circuits. Neural organoids, 3D lab-cultured neural tissues derived from stem cells, can recapitulate key cellular, structural, and physiological features of the human CNS, showing promising potential for neural regeneration. Here, we envision organoid brain-machine-interface (Organoid-BMI) devices as a new kind of neuroelectrical interface for CNS repair. The Organoid-BMI devices employ neural organoids and bioelectrodes as biohybrid bidirectional communication pathways to connect the human CNS and the external world. Acting as a biologically compatible intermediate, this approach may facilitate structural incorporation and functional alignment with host neural circuits for addressing persistent challenges of CNS repair including graft-host mismatch and long-term circuit stability. Through implementing adaptive and closed-loop strategies, this approach can modulate interaction and functional communication with the host for promoting CNS circuit remodeling and functional recovery. Together, this innovative technology may open new avenues for personalized regenerative medicine.},
}

@article {pmid42037657,
year = {2026},
author = {Aabedi, A and Mashiach, D and Fraix, MP and Agrawal, DK},
title = {Most Effective Interventions for Improving Upper Extremity Function in Patients with Hemiparesis.},
journal = {Cardiology and cardiovascular medicine},
volume = {9},
number = {6},
pages = {504-511},
pmid = {42037657},
issn = {2572-9292},
abstract = {Hemiparesis, commonly resulting from stroke, leads to significant impairments in upper extremity function, limiting daily activities and reducing quality of life. Effective rehabilitation strategies are essential to enhance motor recovery and restore functional independence. This review evaluates the most effective interventions for improving upper extremity function in patients with hemiparesis. A comprehensive literature review was conducted, analyzing systematic reviews, randomized controlled trials, and clinical guidelines. The efficacy of various interventions, including task-specific training, constraint-induced movement therapy (CIMT), neuromuscular electrical stimulation (NMES), mirror therapy, virtual reality, bilateral arm training, pharmacological approaches, and robotic-assisted rehabilitation, was assessed based on their impact on motor function and daily activities. The review highlights the role of neuroplasticity in motor recovery, emphasizing interventions that promote cortical reorganization. Task-specific training, CIMT, and NMES demonstrate strong evidence in enhancing motor function. Emerging technologies, such as brain-computer interfaces and robotics, show promise in optimizing rehabilitation outcomes. Factors influencing recovery, including stroke severity, time since onset, and patient motivation, are discussed. Studies consistently support the effectiveness of CIMT and task-specific training in improving upper extremity function. NMES and mirror therapy are beneficial adjunct therapies, particularly for patients with moderate impairment. Virtual reality and robotics enhance engagement and motor learning, while pharmacological and stem cell therapies are emerging areas with potential but require further research. A multimodal rehabilitation approach combining task-oriented therapies, neuromodulation, and emerging technologies yields the best outcomes for upper extremity recovery in hemiparesis patients. Future research should focus on optimizing individualized treatment plans and integrating novel therapeutic modalities to maximize functional gains.},
}

@article {pmid42039372,
year = {2026},
author = {Zhao, P and Liang, T and Jia, H and Dayan, A and Dinarès-Ferran, J and Solé-Casals, J},
title = {MCFANet: a multi-class fusion attention network for motor imagery EEG classification.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1811759},
pmid = {42039372},
issn = {1662-5161},
abstract = {INTRODUCTION: This paper proposes a Multi-Class Fusion Attention Network (MCFANet) that combines the multi-class spatial filtering outputs of FBCSP with the spatiotemporal feature extraction capability of convolutional neural networks for multi-class motor imagery EEG classification. In multi-class motor imagery decoding, traditional spatial filtering methods extract effective discriminative spatial features but decompose the task into independent binary subproblems, and typically retain only energy statistics while discarding temporal dynamics. Deep learning methods can learn spatiotemporal features but must learn spatial patterns from the beginning, making it difficult to fully capture established neurophysiological priors under limited training samples.

METHODS: MCFANet concatenates the spatial filtering outputs from all classes and sub-bands along the channel dimension to construct a virtual channel representation containing the discriminative responses of all classes. The full time series is preserved and fed into a convolutional module for spatiotemporal feature extraction, and a channel attention module adaptively reweights the feature maps to focus on the most discriminative representations. Four-class classification experiments were conducted on two public datasets.

RESULTS: On Dataset 2a, MCFANet achieved an accuracy of 67.94% ±13.70, outperforming FBEEGNet (63.98%) and EEGNet (58.79%). On the High Gamma Dataset, MCFANet achieved 87.10% ±10.09, improving over FBEEGNet by approximately 2.5 percentage points. Paired t-tests and effect size analysis confirm that the improvements over the main baseline methods are statistically significant.

DISCUSSION: The results suggest that reorganizing multi-class spatial discriminative responses into a unified representation that preserves temporal dynamics provides an effective path for bridging traditional spatial filtering and deep learning.},
}

@article {pmid42039954,
year = {2026},
author = {Yıldırım, Y and Ertaş, E},
title = {Surgical outcomes and the role of probe exit site in nasal endoscopy-guided interventions for congenital nasolacrimal duct obstruction: a cross-sectional study.},
journal = {International journal of ophthalmology},
volume = {19},
number = {5},
pages = {901-908},
pmid = {42039954},
issn = {2222-3959},
abstract = {AIM: To evaluate the clinical presentation, nasal endoscopic findings, and surgical outcomes of probing surgery (PS) or bicanalicular silicone tube intubation (BCI) performed under nasal endoscopic guidance (NEG) in pediatric patients with congenital nasolacrimal duct obstruction (CNLDO), regardless of previous surgical history.

METHODS: This retrospective cross-sectional study included CNLDO patients with data on demographics, fluorescein dye disappearance test (FDDT) results, dacryoscintigraphy findings, prior surgeries, and outcomes of NEG-PS or NEG-BCI. NEG-BCI using Crawford stents was performed intraoperatively in complex cases. Intraoperative and postoperative complications were recorded. Surgical success was evaluated clinically and with FDDT at postoperative months 1 and 6. Stents were retained for a minimum of 12wk, with follow-up for at least 6mo after removal.

RESULTS: Of the 67 pediatric patients (67 eyes, mean age 37.4±17.5mo), 44 (65.7%) were female. Preoperative FDDT was graded 3+ in 85.1% of cases, and dacryoscintigraphy confirmed obstruction in 92.5%. Nine patients (13.4%) had a history of PS. At 6mo, surgical success was achieved in 96.6% (28/29) of the NEG-PS group and 71.1% (27/38) of the NEG-BCI group (P=0.009). All cases with probe exit through the inferior meatus (IM) were successful, whereas exits through the inferior concha (IC) or submucosal IM (SM) were significantly associated with failure (P<0.001).

CONCLUSION: NEG allows intraoperative classification of CNLDO and selection of surgical method based on real-time anatomical findings. Probe exit through the IM predicts a high likelihood of success, whereas IC or SM exits are risk factors for failure. Incorporating NEG into routine practice may improve surgical precision and reduce the need for repeated procedures.},
}

@article {pmid42040188,
year = {2026},
author = {Wang, N and Chai, X and He, Y and Song, J and Cao, T and He, Q and Zhu, S and Jia, Y and Si, J and Yang, Y and Zhao, J},
title = {Graph-Theoretical Signature from Neural and Vascular Signals Reveals Spinal Cord Stimulation Frequency-Specific Brain Network in Disorders of Consciousness Patients.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0539},
pmid = {42040188},
issn = {2692-7632},
abstract = {Introdution: Spinal cord stimulation (SCS) has emerged as a promising neuromodulatory intervention for patients with disorders of consciousness (DoC). However, the identification of optimal stimulation frequencies remains a subject of ongoing debate. Although previous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) studies have suggested the therapeutic efficacy of 5- and 70-Hz, respectively, the integrative neurovascular mechanisms and frequency-specific network dynamics underlying these effects remain to be elucidated. Objective and Impact Statement: This study aims to characterize frequency-dependent network reconfiguration in DoC using simultaneous EEG-fNIRS recordings and graph theoretical analysis. By delineating distinct neurophysiological and hemodynamic signatures, our findings establish a mechanistic framework for the optimization of SCS parameters, thereby advancing personalized neuromodulation strategies for the promotion of consciousness recovery. Methods: This prospective trial used simultaneous EEG-fNIRS and graph theory in 16 patients with DoC undergoing multifrequency SCS at 5, 20, 70, and 100 Hz to decode frequency-specific network dynamics. Our integrated EEG-fNIRS analysis revealed 3 principal advances. First, multimodal cortical mapping via a unified anatomical atlas quantified frequency-dependent network reconfiguration, generating graph-theoretical metrics (global and nodal efficiency, characteristic path length, and clustering coefficients) from source-localized EEG (delta-gamma bands) and fNIRS (oxyhemoglobin and deoxygenated) data. Second, we identified frequency-dependent neurophysiological profiles. Results: Five-hertz stimulation produced acute enhancement of theta-band global network efficiency coupled with elevated gamma-band nodal efficiency in the right cingulate motor area, indicating immediate frontolimbic engagement. Conversely, 70-Hz stimulation selectively evoked delayed hemodynamic responses in the visual cortices and increased occipital hemoglobin oxygenation without concomitant EEG alterations, suggesting preferential retinotopic pathway recruitment. Conclusion: Multimodal EEG-fNIRS analysis elucidates frequency-specific SCS mechanisms, where 5-Hz stimulation optimizes local information integration through theta and gamma modulation, while 70-Hz enhances long-range connectivity, exposing frequency-specific neural plasticity mechanisms.},
}

@article {pmid42041774,
year = {2026},
author = {Wang, Z and Ma, Y and Du, Y and She, Q},
title = {A Band-Aware Riemannian Network with Domain Adaptation for Motor Imagery EEG Signal Decoding.},
journal = {Brain sciences},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/brainsci16040363},
pmid = {42041774},
issn = {2076-3425},
support = {LZ26F010007//Zhejiang Provincial Natural Science Foundation/ ; 62371172//National Natural Science Foundation of China/ ; 2025ZY01045//Central Government-Guided Local Science and Technology Development Fund/ ; 2025E10015//Zhejiang Provincial Key Laboratory of Brain Computer Collaborative Intelligence Technology and Applications/ ; },
abstract = {BACKGROUND: The decoding of motor imagery electroencephalography (MI-EEG) is constrained by core issues including low signal-to-noise ratio (SNR) and cross-session as well as cross-subject domain shift, which seriously impedes the practical deployment of brain-computer interfaces (BCIs).

METHODS: To address these challenges, this paper proposes a novel end-to-end MI-EEG decoding method named BARN-DA. Two innovative modules, Band-Aware Channel Attention (BACA) and Multi-Scale Kernel Perception (MSKP), are designed: one enhances discriminative channel features by modeling channel information fused with frequency band feature representation, and the other captures complex data correlations via multi-scale parallel convolutions to improve the discriminability of the network's feature extraction. Subsequently, the features are mapped onto the Riemannian manifold. For the source and target domain features residing on this manifold, a Riemannian Maximum Mean Discrepancy (R-MMD) loss is designed based on the log-Euclidean metric. This approach enables the effective embedding of Symmetric Positive Definite (SPD) matrices into the Reproducing Kernel Hilbert Space (RKHS), thereby reducing cross-domain discrepancies.

RESULTS: Experimental results on four public datasets demonstrate that the BARN-DA method achieves average cross-session classification accuracies of 84.65% ± 8.97% (BCIC IV 2a), 89.19% ± 7.69% (BCIC IV 2b), and 61.76% ± 12.68% (SHU), as well as average cross-subject classification accuracies of 65.49% ± 11.64% (BCIC IV 2a), 78.78% ± 8.44% (BCIC IV 2b), and 78.14% ± 14.41% (BCIC III 4a). Compared with state-of-the-art methods, BARN-DA obtains higher classification accuracy and stronger cross-session and cross-subject generalization ability.

CONCLUSIONS: These results confirm that BARN-DA effectively alleviates low SNR and domain shift problems in MI-EEG decoding, providing an efficient technical solution for practical BCI systems.},
}

@article {pmid42041797,
year = {2026},
author = {Zhang, H and Tao, C},
title = {Deep Learning Decoding of Steady-State Visual Evoked Potential (SSVEP) for Real-Time Mobile Brain-Computer Interfaces: A Narrative Review from Laboratory Settings to Lightweight Engineering Applications.},
journal = {Brain sciences},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/brainsci16040387},
pmid = {42041797},
issn = {2076-3425},
support = {QY25232//National Natural Science Foundation of China/ ; },
abstract = {Background/Objectives: SSVEP-BCI has broad application potential in mobile human-computer interaction due to its high information transfer rate and stable signal characteristics. The introduction of deep learning technology has significantly advanced SSVEP decoding performance, offering novel approaches for processing short-duration signals and tackling complex classification tasks. The establishment of the Tsinghua Benchmark dataset provides a standardized benchmark for evaluating algorithm performance, accelerating the development of deep learning-based SSVEP decoding. However, a summary of SSVEP deep learning decoding technologies for real-time mobile applications is lacking. Methods: We conducted a comprehensive literature review of SSVEP deep learning decoding studies published since 2023, using the Tsinghua Benchmark dataset. This review focuses on technical developments targeting real-time performance, low computational complexity, and high robustness. Results: We summarize the key technologies developed for real-time mobile SSVEP decoding. Our analysis thoroughly examines how these techniques address core challenges in the engineering implementation of mobile brain-computer interfaces, including real-time processing requirements, resource constraints, and environmental robustness. Conclusions: This review provides a comprehensive overview of SSVEP deep learning decoding technologies for mobile applications, establishing a technical foundation to advance mobile brain-computer interfaces from laboratory settings to practical deployment.},
}

@article {pmid42041805,
year = {2026},
author = {Christodoulides, P and Peschos, D and Zakopoulou, V},
title = {The Use of EEG in the Study of Emotional States and Visual Word Recognition with or Without Musical Stimulus in University Students with Dyslexia.},
journal = {Brain sciences},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/brainsci16040396},
pmid = {42041805},
issn = {2076-3425},
abstract = {This study investigated neural oscillatory dynamics underlying visual word recognition in university students with dyslexia using a portable brain-computer interface (BCI) EEG system. The sample included university students with dyslexia (N = 12) and matched controls (N = 14) who completed auditory discrimination and visual word recognition tasks, with and without musical accompaniment. Through these experimental conditions, the researchers assessed (a) the cortical activation across frequency bands, (b) the modulatory effect of background music, and (c) the relationship between emotional states and brain activity. Results revealed significant group differences in oscillatory patterns, with reduced β- and γ-band activity in the left occipito-temporal cortex among participants with dyslexia, confirming disrupted temporal coordination in posterior reading networks. Compensatory right-hemisphere activation was observed, particularly under musical conditions, accompanied by increased α-band power and reduced δ activity, indicating enhanced attentional engagement and reduced cognitive fatigue. Emotional assessment using the DASS-21 revealed higher stress and anxiety scores in the dyslexic group, suggesting that affective factors may modulate oscillatory dynamics. The presence of background music appeared to attenuate these effects, supporting improved emotional regulation and cognitive focus. These findings demonstrate that dyslexia reflects a distributed disruption in neural synchrony and cross-frequency coupling, influenced by both cognitive and affective mechanisms. The integration of portable EEG technology with rhythmic auditory stimulation offers new insights into the neurophysiological and emotional aspects of dyslexia, highlighting the potential of rhythm- and music-based approaches for both diagnostic and therapeutic applications.},
}

@article {pmid42041832,
year = {2026},
author = {Padilla, GL and Farfán, FD},
title = {Stimulus Size Modulates Periodic and Aperiodic EEG Components in SSVEP-Based BCIs.},
journal = {Brain sciences},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/brainsci16040424},
pmid = {42041832},
issn = {2076-3425},
abstract = {Background/Objectives: Steady-State Visual Evoked Potential-based Brain-Computer Interfaces face a critical trade-off between system accuracy and user visual fatigue. To address this challenge, the objective of this study was to determine how the spatial manipulation of stimulus size modulates the full spectral dynamics of the Electroencephalogram, encompassing both the periodic oscillatory response and the aperiodic (1/f) background noise. Methods: Twenty-two healthy subjects completed a sustained visual attention task using a competitive stimulus paradigm (20 Hz and 30 Hz) presented in three spatial dimensions (Small, Medium, and Big). Parieto-occipital brain signals were decomposed using the spectral parameterization algorithm (SpecParam) to extract frequency-specific visually evoked response power and the aperiodic slope, while visual fixation was continuously monitored via eyetracking. Results: Increasing stimulus size induced a statistically significant gain in the power of the attended signal (Target) without increasing the response of the peripheral distractor. Simultaneously, larger stimuli produced a significant increase in the aperiodic slope during 20 Hz attention and visual rest, suggesting increased cortical inhibition and a reduction in broadband neural activity. This aperiodic modulation was not observed at 30 Hz. Conclusions: The improvement in Signal-to-Noise Ratio with increasing stimulus size arises from a dual neurophysiological mechanism: enhancement of the periodic evoked response together with a reduction in background neural noise.},
}

@article {pmid42042585,
year = {2026},
author = {Urfy, M and Mir, MT},
title = {A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.},
journal = {Journal of personalized medicine},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/jpm16040218},
pmid = {42042585},
issn = {2075-4426},
abstract = {Background/Objectives: Stroke generates 157 million disability-adjusted life-years (DALYs) annually, making it the leading neurological cause of global disease burden. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies across the stroke care continuum. This narrative review maps the trajectory of AI in stroke medicine over the decade from 2015 to 2025. Methods: We conducted a narrative review with a structured, pre-specified search strategy across eight pre-specified thematic clusters using PubMed/MEDLINE (January 2015-December 2025), identifying 8549 records and including 1335 studies after screening. Inclusion criteria encompassed primary research articles, systematic reviews, meta-analyses, and RCTs reporting quantitative performance metrics or clinical outcome data for AI/ML in stroke. Results: Stroke imaging AI is the most commercially mature domain, with over 30 FDA-cleared tools. Automated ASPECTS scoring reduced radiologist reading time by 74.8% (AUC 84.97%; 95% CI: 83.1-86.8%). The only triage AI RCT demonstrated an 11.2 min reduction in door-to-groin time without significant improvement in 90-day functional independence (OR 1.3, 95% CI 0.42-4.0). Brain-computer interface rehabilitation showed significant upper limb recovery in a 17-center RCT (FMA-UE mean difference +3.35 points, 95% CI 1.05-5.65; p = 0.0045). AF detection AI is FDA-cleared and RCT-validated. LLMs and federated learning are pre-regulatory but growing exponentially. Conclusions: AI in stroke has achieved diagnostic maturity but therapeutic immaturity. Bridging algorithmic performance to patient outcomes, addressing equity gaps, and building the economic evidence base for scalable deployment are the defining challenges of the next decade.},
}

@article {pmid42044749,
year = {2026},
author = {Pan, X and Zhang, R and Xia, X and Cui, H and Liu, L and Chen, X},
title = {Feasibility of a Hybrid SSVEP-Motor Imagery BCI with Robotic Feedback for Upper Limb Motor Rehabilitation in Stroke Patients.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110780},
doi = {10.1016/j.jneumeth.2026.110780},
pmid = {42044749},
issn = {1872-678X},
abstract = {BACKGROUND: Stroke remains a leading cause of long-term disability, necessitating innovative neurorehabilitation strategies to address persistent motor deficits. Traditional therapies often exhibit limited efficacy due to therapeutic plateau, highlighting the critical need for alternative rehabilitation paradigms.

NEW METHOD: This study assesses the feasibility of a novel hybrid brain-computer interface (BCI) that integrates motor imagery (MI) and steady-state visual evoked potentials (SSVEP), with robotic glove-assisted feedback used to optimize overall system performance. Thirty-two stroke patients were divided into a control group (conventional therapy) and an experimental group (conventional therapy plus BCI intervention with 10- or 20-day cycles).

RESULTS: Outcomes assessed via Fugl-Meyer Assessment (FMA) scores, electroencephalography (EEG) classification accuracy, laterality coefficients (LC), and weighted brain connectivity analysis indicated promising trends. The experimental group showed considerable improvements in FMA scores compared with the control group. The proposed BCI system successfully achieved satisfactory EEG classification accuracy (maximum value of 98.08%) and robust system operation. Furthermore, increases in EEG accuracy, normalization of laterality coefficients (LC), and reinforcement of task-specific weighted brain connectivity were observed, particularly after prolonged training.

The proposed hybrid BCI system demonstrates a potential to overcome the limitations of conventional therapies and traditional single-modality BCIs, offering a more engaging and adaptive rehabilitation approach.

CONCLUSIONS: These findings demonstrate the feasibility of the proposed hybrid BCI system and the observed improvements in motor function, neurophysiological markers, and brain connectivity underscore its promise as a novel paradigm to enhance neuroplasticity and recovery outcomes.},
}

@article {pmid42045070,
year = {2026},
author = {Canfield, RA and Ouchi, T and Fang, H and Macagno, B and Smith, LI and Scholl, LR and Orsborn, AL},
title = {The spatiotemporal structure of neural activity in motor cortex during reaching.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1965-25.2026},
pmid = {42045070},
issn = {1529-2401},
abstract = {Intracortical brain-computer interfaces (BCI) leverage knowledge about neural representations to translate movement-related neural activity into actions. BCI implants have targeted broad cortical regions known to have relevant motor representations, but emerging technologies allow flexible targeting to specific neural populations. The structure of motor representations in neural populations across frontal motor cortices, which span centimeters, has not been well characterized. We investigate how motor representations and population dynamics (temporal coordination) vary across a large expanse of frontal motor cortices. We used high-density, laminar, microelectrode arrays to record many neurons, sampling neural populations across frontal motor cortex while two male monkeys performed a reaching task. Our experiments allowed us to map neuronal activity across three spatial dimensions and relate them to movement. Target decoding analysis revealed that target direction information (one key aspect of task information) was heterogeneously distributed across the cortical surface and in depth. Similarly, we found that the temporal dynamics of different neural populations was highly variable, but that the amount of task information predicted which neural populations had similar dynamics. The neural populations with the most similar dynamics were composed of neurons with high task information regardless of spatial location. Our results highlight the spatiotemporal complexity of motor representations across frontal motor cortex at the level of neurons and neural populations, where well-learned movements consistently recruit a spatially distributed subset of neurons. Further insights into the spatiotemporal structure of neural activity patterns across frontal motor cortex will be critical to guide future implants for improved BCI performance.Significance Statement Motor brain-computer interfaces (BCI) translate neural activity into movement, but how to target implants within motor cortices to maximize performance remains unclear. We used high-density recordings of neural activity spanning a large cortical area and related them to movement to map the spatial distribution of task information and the evolution of neural population activity over time. Our measurements revealed that neurons with the most task information were highly distributed across cortex yet also evolved coherently in time, suggesting that spatially distributed neurons coordinate to control movements. Our results provide new links between neuron- and population-level maps of motor representations, and highlight the complex spatiotemporal structure of activity that may need to be considered when designing next-generation BCIs.},
}

@article {pmid42045188,
year = {2026},
author = {Wang, H and Zhang, Y and Chai, Q and He, Q and Hu, J and Bai, Y and Liu, G and Li, Z and Chai, J and He, X and Zhao, M and Xue, G and Liu, K and Fu, Y and Tang, H and Xu, Y and Yu, B},
title = {Artificial plateau neurons with in-situ spike-malleability for rhythmic quadrupedal locomotion.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-72428-2},
pmid = {42045188},
issn = {2041-1723},
support = {DT23F0401//National Natural Science Foundation of China (National Science Foundation of China)/ ; LDT23F04011F04//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Whole-body intelligent locomotion systems face persistent challenges of redundant actuation and poor energy efficiency, limiting real-world deployment. Bio-inspired central pattern generators offer a promising framework for rhythmic control, yet hardware implementations struggle to match the efficiency and adaptability of biological systems. Here, we introduce an in-situ spike-malleable artificial plateau neuron integrating a bistable plateau gate with a transient threshold-switch. The neuron generates amplitude-programmable rhythmic spike bursts, achieving energy-efficient, antagonistic activation of extensors and flexors via a scalable circuit comprising two paired units (plateau gate and threshold-switch). The design leverages distributed encoding for coordinated muscle control, operating at ultra-low energy dissipation (141.37 pJ/spike). An expanded four-unit circuit enhances dynamic spike malleability, enabling parallel processing for multi-joint coordination. On a quadruped robot (Unitree Go2), these distributed circuits directly drive joint-level proportional derivative controllers using the Gaussian-filtered rhythmic spikes, enabling energy-efficient trotting without centralized computation. Critically, the system achieves stable on-ground locomotion and demonstrates adaptive gait transitions in real-world environments. Our approach merges ultra-compact hardware with bio-inspired architecture, advancing neuromorphic systems for energy-efficient autonomous robotics.},
}

@article {pmid42034279,
year = {2026},
author = {Long, Y and Yao, P and Zou, G and Guo, Y and Shao, Y and Li, Y and Liu, J and Zhou, S and Xu, J and Sun, H and Zou, Q and Gao, JH},
title = {EEG microstates during wake and NREM sleep in insomnia disorder.},
journal = {Progress in neuro-psychopharmacology & biological psychiatry},
volume = {},
number = {},
pages = {111718},
doi = {10.1016/j.pnpbp.2026.111718},
pmid = {42034279},
issn = {1878-4216},
abstract = {Insomnia disorder (ID) is prevalent and debilitating, yet its neurophysiological basis remains unclear. Abnormalities in temporal parameters of electroencephalography (EEG) microstates have been linked to diverse neuropsychiatric disorders, but their dynamics across wakefulness and non-rapid eye movement (NREM) sleep in chronic insomnia remain unexplored. In this study, EEG microstate dynamics across wakefulness and NREM sleep were examined in adults with ID (n = 33) and healthy controls (HC; n = 29). Simultaneous EEG and functional magnetic resonance imaging (fMRI) were acquired during nocturnal sleep. Microstate parameters were tested for group differences, diagnostic classification, and associations with insomnia symptom course. Six stable microstates across stages were identified in ID. Linear mixed-effects models revealed significant interactions for Group × Map and main effects of Group on duration and occurrence of the microstates. Compared to HC, ID exhibited significantly shorter mean duration of microstates 1-3, and higher overall occurrence rates of microstates 4-6. The best-performing classifier achieved 90.0% accuracy in distinguishing ID from HC. The most influential predictor was the mean duration of Microstate 2, which was negatively associated with insomnia course. Together, these findings suggest stage- and map-dependent alterations in sleep microstate dynamics in ID, especially during N2 sleep.},
}

@article {pmid42034616,
year = {2026},
author = {Zhai, X and Guo, J and Shen, Q and Chen, LN and Wang, G and Shen, DD and Zhang, C and Xu, X and Mao, C and Zhang, Y and Liu, Z},
title = {Core conformation of arrestin coupling to parathyroid hormone type 1 receptor.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-72448-y},
pmid = {42034616},
issn = {2041-1723},
abstract = {The recruitment of β-arrestin (βarr) by G-protein-coupled receptor (GPCR) holds imperative importance in physiological processes, while the mechanisms underlying arrestin engagement with receptors remain obscure. The parathyroid hormone type 1 receptor (PTH1R), as a prototypical class B1 receptor, incorporates arrestin for signaling and regulates G-protein signaling by distinct mechanisms. Here, we report three cryo-electron microscopy structures of β-arrestin1 (βarr1) engaged with the activated wild-type and chimeric PTH1R in core conformation, revealing a distinctive binding mode of βarr1 coupling to PTH1R compared to other GPCRs. In addition to the pronounced kinking of transmembrane (TM) 6, βarr1 establishes extensive interactions with the core cavity of PTH1R by promoting the outward movement of TM5 and intracellular loop (ICL) 2, stabilizing the core conformation of the complex. Further, our work shows that the core coupling mode of βarr with PTH1R mediates receptor internalization and trafficking. Collectively, our work offers a paradigm for the arrestin coupling to class B1 GPCR and regulating the signaling transduction.},
}

@article {pmid42035423,
year = {2026},
author = {Ji, YL and Yin, B and Zhu, G and Wang, X and Zhang, Y and Zhao, Q},
title = {Discontinuous 3D Printing of Amorphous Photonic Crystal Hydrogels for Multifunctional Applications.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e73576},
doi = {10.1002/smll.73576},
pmid = {42035423},
issn = {1613-6829},
support = {62575143//National Natural Science Foundation of China/ ; 62174085//National Natural Science Foundation of China/ ; 62288102//National Natural Science Foundation of China/ ; BK20240034//Natural Science Foundation of Jiangsu Province/ ; },
abstract = {Amorphous photonic crystals (APCs) offer angle-independent structural color for reliable sensing, yet their precise 3D fabrication remains challenging due to the tendency of particles to self-assemble into ordered structures. We develop a discontinuous digital light processing 3D printing strategy combining discrete ink reflow and rapid curing to construct disordered APCs hydrogels. The printed hydrogels integrate single-sized polymer nanospheres and MXene nanosheets to achieve structural color, mechanical robustness, and interactive optical/electrical responsiveness. The structural color responds to moisture-induced swelling yet remains unchanged under mechanical deformation because of strain-accommodating microcracks. These features ensure reliable visual feedback without strain interference. Meanwhile, mechanical deformation modulates the conductive network and thus provides a complementary electrical response. In diabetic wound models, the hydrogel enables precise electrical stimulation and provides visual alerts of micro-swelling to prevent secondary damage from unnoticed volumetric changes. This strategy provides a generalizable pathway for precise intervention and real-time monitoring in wound management.},
}

@article {pmid42035944,
year = {2026},
author = {Poslu Karademir, F and Özçelik, SS and Efe, AÇ and Ulaş, MG and Diri, İ and Kaynak, P and Taşkapılı, M},
title = {Outcomes of probing with or without bicanalicular intubation in children aged three years and older: a decade of experience at a tertiary eye hospital.},
journal = {Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus},
volume = {},
number = {},
pages = {104841},
doi = {10.1016/j.jaapos.2026.104841},
pmid = {42035944},
issn = {1528-3933},
abstract = {PURPOSE: To evaluate the clinical efficacy of probing with or without bicanalicular intubation (BCI) for congenital nasolacrimal duct obstruction (CNLDO) in children at least 3 years of age and to identify factors influencing surgical success.

METHODS: The medical records of children treated between 2014 and 2024 at Health Sciences University Beyoğlu Eye Training and Research Hospital were reviewed retrospectively. All patients underwent probing with or without bicanalicular silicone intubation (BCI) using the square knot technique. Surgical success was defined as resolution of symptoms and a normal fluorescein dye disappearance test.

RESULTS: A total of 95 children (116 eyes) were included. Mean patient age was 4.57 ± 1.98 years (range, 3-14). Mean follow-up was 15.5 ± 15.4 months. BCI was performed initially in 102 eyes. Mean tube retention was 66.8 ± 43.0 days. Overall success was 87%, increasing to 95% after reprobing and BCI in failed cases. Age, sex, obstruction type, canalicular stenosis, Rosenmüller's valve hypertrophy, and inferior turbinate infracture were not significantly associated with success (P > 0.05). Tube retention for 45-90 days was significantly associated with higher success compared with retention <45 days (P = 0.013; OR = 12.75; 95% CI, 1.72-94.48).

CONCLUSIONS: In our study cohort of children undergoing surgery for CNLDO at 3 years of age and older, probing and BCI achieved high success, especially if the tube was successfully retained for at least 45 days. Reintubation in failed cases can improve outcomes.},
}

@article {pmid42036262,
year = {2026},
author = {Wang, H and Liu, S and Bai, L},
title = {A dimmer switch for reward: the vagus sets the gain.},
journal = {Trends in neurosciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tins.2026.04.002},
pmid = {42036262},
issn = {1878-108X},
abstract = {The vagus nerve, among its various functions, carries gut nutrient signals to brain reward circuits. In a recent study, Onimus and colleagues have shown it is more than a simple relay: vagal integrity is required for maintaining dopamine release and the circuit structure of the mesolimbic system to mount reward responses. These findings reveal the vagus as a tonic gatekeeper of motivation and reward.},
}

@article {pmid42036584,
year = {2026},
author = {Tsai, HJ and Tsai, SJ},
title = {Neurofeedback in Major Depression.},
journal = {Advances in experimental medicine and biology},
volume = {1502},
number = {},
pages = {461-475},
pmid = {42036584},
issn = {0065-2598},
mesh = {Humans ; *Neurofeedback/methods ; *Major Depressive Disorder/therapy/physiopathology/psychology ; *Brain/physiopathology ; Electroencephalography ; Brain-Computer Interfaces ; Magnetic Resonance Imaging ; Neuronal Plasticity/physiology ; Emotions/physiology ; },
abstract = {Emerging evidence highlights the significant interplay between mental health and brain health, underscoring the potential of non-pharmacological interventions for major depressive disorder. Brain-computer interfaces offer a promising avenue in psychiatry, advancing self-regulation techniques to elucidate relationships among human behavior, emotional processes, and brain functionality. By visualizing brain function and enabling active modulation of cortical activity through real-time feedback, neurofeedback utilizes signals derived from electroencephalography and/or real-time functional magnetic resonance imaging, providing patients with interactive indicators for self-brain training. This closed-loop system targets specific brain activity or regions known to be associated with depression for upregulation or downregulation, thereby enhancing emotion regulation and executive functioning via mechanisms underlying neuroplasticity. Clinical evidence demonstrates promising outcomes, including strengthened neural connectivity, symptom improvement, and increased remission rates in depression. By coupling the advantages of psychotherapy and neuromodulation, neurofeedback aligns with the field's shift toward personalized, technology-driven psychiatry. This chapter also addresses the practical challenges, including protocol standardization, precision targeting, long-term assessment, and scalable delivery, that are essential for translating promising pilot data into routine clinical practice and for empowering patients to actively engage in their brain health toward mental health improvement.},
}

Humans
*Neurofeedback/methods
*Major Depressive Disorder/therapy/physiopathology/psychology
*Brain/physiopathology
Electroencephalography
Brain-Computer Interfaces
Magnetic Resonance Imaging
Neuronal Plasticity/physiology
Emotions/physiology

@article {pmid42026803,
year = {2026},
author = {Zhang, F and Hu, K and Sun, C and Chen, R and Ni, G and Liu, X and Wei, L and Su, R},
title = {Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.},
journal = {Gut microbes},
volume = {18},
number = {1},
pages = {2662690},
doi = {10.1080/19490976.2026.2662690},
pmid = {42026803},
issn = {1949-0984},
mesh = {Humans ; *Immune Checkpoint Inhibitors/therapeutic use ; *Gastrointestinal Microbiome/genetics/drug effects ; *Neoplasms/drug therapy/microbiology ; Deep Learning ; Biomarkers, Tumor/genetics ; *Bacteria/classification/genetics/isolation & purification ; Female ; Male ; Metagenomics ; },
abstract = {Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has improved survival across multiple cancer types, but the variability in patient response highlights the need for better predictive biomarkers. Existing studies rely on taxonomic abundance derived from reference genome databases, limiting the discovery and functional interpretation of uncharacterized microbes. Here, we integrated metagenomic data from multiple ICI-treated cohorts spanning diverse cancer types and geographic regions and developed a deep learning model, named BioP-VAE, that incorporates biological prior knowledge via protein sequence embeddings and uses gene-level microbial abundance features as input. Gene-level microbial abundance outperformed taxonomy abundance in predicting both ICI response and 12-month progression-free survival (PFS). In patients receiving combination immune checkpoint blockade (CICB), BioP-VAE achieved a mean AUC of 0.89 in intracohort and 0.88 in cross-cohort evaluation. Notably, in the monotherapy-treated intracohorts, BioP-VAE achieved a mean AUC of 0.97. Feature attribution analysis revealed key microbial genes. Additionally, we identified distinct predictive microbial signatures via age-stratified analysis, suggesting that host age may modulate microbiome‒immune interactions. Importantly, this is the first large-scale study to evaluate gene-level microbial abundance features for ICI response prediction across multiple cancer types by deep learning. Our findings demonstrate that incorporating biological prior knowledge into deep learning models can improve the discovery of microbial biomarkers that can be generalized across cancer types and treatment settings, offering a novel strategy for patient stratification in immunotherapy.},
}

Humans
*Immune Checkpoint Inhibitors/therapeutic use
*Gastrointestinal Microbiome/genetics/drug effects
*Neoplasms/drug therapy/microbiology
Deep Learning
Biomarkers, Tumor/genetics
*Bacteria/classification/genetics/isolation & purification
Female
Male
Metagenomics

@article {pmid42028018,
year = {2026},
author = {Zhang, J and Zhang, Y and Zhang, X and Xu, B and Zhao, H and Sun, T and Wang, J and Lu, S and Shen, X},
title = {Erratum: A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking.},
journal = {iScience},
volume = {29},
number = {5},
pages = {115705},
doi = {10.1016/j.isci.2026.115705},
pmid = {42028018},
issn = {2589-0042},
abstract = {[This corrects the article DOI: 10.1016/j.isci.2024.110164.].},
}

@article {pmid42028123,
year = {2026},
author = {Mediana, E and Hamid, ARAH and Rahman, F and Mochtar, CA and Umbas, R and Abol-Enein, H},
title = {Quality of Life After Radical Cystectomy: Meta-analysis of Neobladder and Ileal Conduit Outcomes Across Multiple Assessment Tools.},
journal = {European urology open science},
volume = {87},
number = {},
pages = {115-124},
pmid = {42028123},
issn = {2666-1683},
abstract = {BACKGROUND AND OBJECTIVE: Radical cystectomy (RC) requires urinary diversion, commonly orthotopic neobladder (ONB) or ileal conduit (IC). While ONB preserves natural voiding, IC is technically simpler. This study aimed to compare long-term (>12 mo) quality of life (QoL) outcomes between ONB and IC to aid preoperative shared decision-making.

METHODS: Following PRISMA guidelines, we searched PubMed, Cochrane Library, and Google Scholar up to September 15, 2025. We included studies comparing ONB and IC in adults with follow-up >12 mo. Heterogeneity was explored using meta-regression. The Newcastle-Ottawa Scale assessed bias, and Review Manager v5.4 was used for analysis.

KEY FINDINGS AND LIMITATIONS: Nineteen studies involving 2379 patients were analyzed. For all assessment tools used (EORTC QLQ-C30, FACT-BL, SF-36, and Bladder Cancer Index [BCI]), higher scores indicate better QoL or function. Pooled analysis showed that ONB was associated with higher global health status (EORTC QLQ-C30: mean difference [MD] = -9.42, p = 0.009; negative value indicates higher score in ONB) and functional well-being (FACT-BL -2.60, p = 0.010). Conversely, the IC group demonstrated higher scores in urinary outcomes (BCI Urinary: MD = 22.81, p = 0.02; positive value indicates higher score in IC). Heterogeneity among studies was moderate to high. Meta-regression indicated geographic location and tumor characteristics influenced heterogeneity. Limitations include observational design and potential selection bias.

ONB reconstruction is associated with higher overall QoL scores, while IC is associated with higher urinary scores. These findings represent clinical trade-offs rather than superiority. Surgical selection should be individualized, balancing patient preference for body image against the risk of functional management challenge.},
}

@article {pmid42031565,
year = {2026},
author = {Haller, D and Beermann, F and Sîmpetru, RC and Hofbeck, L and Enoka, RM and Del Vecchio, A},
title = {Voluntary Dissociation of Motor Unit Activity in the Vastii Muscles.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1982-25.2026},
pmid = {42031565},
issn = {1529-2401},
abstract = {The CNS controls movement with consistent activation patterns across muscles and motor units (MU), suggesting the presence of a relatively fixed and high-dimensional number of neural constraints on voluntary actions. In the human quadriceps, the vastus medialis (VM) and vastus lateralis (VL) contribute to the knee extensor torque and are considered a synergistic pair largely activated by shared neural inputs. However, some evidence suggests that these muscles, or even subregions within them, can be controlled independently. We investigated whether humans can dissociate neural input to VM and VL during isometric contractions. Ten participants (6 males, 4 females) received real-time feedback from multiple intramuscular electromyography (EMG) electrodes inserted into different regions of the VM and VL while attempting to activate each muscle or region selectively. Nine out of ten participants were able to separate VM and VL activity based on the intramuscular EMG feedback. However, MU decomposition from the intramuscular EMGs revealed that selective recruitment of a unique set of MUs was possible only within the proximal region of VM. In contrast, we found highly correlated activity between MUs in VL and distal VM. Correlation analyses confirmed that the proximal VM exhibited distinct activation profiles compared with both distal VM and VL, supporting the existence of compartmentalized control within VM. These findings demonstrate that it is possible to dissociate the activation of MUs within this synergistic muscle group during low-force isometric contractions.Significance Statement Humans are typically thought to lack voluntary control over individual quadriceps muscles due to a shared neural input and a common distal tendon. With real-time EMG feedback from multiple muscle implants we found that participants were able to activate distinct MU populations within vastus medialis, partially dissociating its activity from the vastus lateralis. These results reveal a relatively flexible, region-specific neural control within a pair of synergistic muscles that offers new perspectives for motor learning and targeted rehabilitation.},
}

@article {pmid42031765,
year = {2026},
author = {Kim, C and Yeh, JY},
title = {Molecular evolution and antigenic stability of ibaraki virus: evidence for regional circulation from a South Korean whole-genome analysis.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-47936-2},
pmid = {42031765},
issn = {2045-2322},
support = {RS-2025-02304897//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry/ ; 2020-0325//Incheon National University/ ; },
}

@article {pmid42032248,
year = {2026},
author = {Tang, Y and Wang, Y and Zhang, W and Liu, X and Li, Y and Hu, W and Ding, L and Feng, F and Chen, X and Feng, J and Xu, S and Chen, S and Wang, J},
title = {Magnetic NeuroRing: a portable adaptive brain-computer interface for real-time transcranial magnetic stimulation in post-stroke motor rehabilitation.},
journal = {npj biomedical innovations},
volume = {3},
number = {1},
pages = {},
pmid = {42032248},
issn = {3005-1444},
support = {82202798, 5230130320, 22205254//National Natural Science Foundation of China/ ; 82202798, 5230130320, 22205254//National Natural Science Foundation of China/ ; 82202798, 5230130320, 22205254//National Natural Science Foundation of China/ ; 24YL1900202//the Project of Shanghai Science and Technology Commission/ ; 22YF1404200//Shanghai Sailing Program/ ; },
abstract = {Stroke often causes persistent upper limb and hand motor dysfunction due to disrupted neural reorganization. To address this, we developed the Magnetic NeuroRing: a portable brain-computer interface integrating real-time electroencephalogram (EEG) with closed-loop continuous theta burst stimulation (cTBS) for adaptive transcranial magnetic stimulation (TMS). A multi-channel EEG array over motor cortical regions (FC3, FC4, CP3, CP4, FT7, FT8, TP7, TP8) detects event-related desynchronization (ERD), indicating motor intent. When ERD/ERS falls below a threshold (ERD/ERS < 0 over five consecutive activations), the system delivers inhibitory cTBS to hyperactive regions, aiming to rebalance stroke-impaired interhemispheric dynamics. The lightweight, patient-specific headgear uses magnetic levitation for precise targeting and EEG-TMS synchronization. In healthy subjects, adaptive cTBS significantly modulated resting-state and task-related neural metrics, aligning with prior large-device findings and demonstrating feasibility for inducing neuroplastic changes. By bridging real-time diagnostics with targeted neuromodulation, the Magnetic NeuroRing enables dynamic, data-driven rehabilitation across clinical and home settings.},
}

@article {pmid42032320,
year = {2025},
author = {Li, J and Chen, G and Li, G and Xiao, L and Jia, R and Zhang, K},
title = {Flexible brain electronic sensors advance wearable brain-computer interface.},
journal = {npj biomedical innovations},
volume = {2},
number = {1},
pages = {},
pmid = {42032320},
issn = {3005-1444},
support = {2024NSFJQ0048//Sichuan Provincial Science and Technology Support Program/ ; 82022033//National Natural Science Foundation of China/ ; },
abstract = {The emerging field of wearable brain-computer interface (BCI) strives to achieve both high spatial and temporal resolution. The performance of flexible brain electronic sensor (FBES) has been validated across a variety of experimental settings, demonstrating their potential for real-world applications. As a result, FBES are increasingly shaping the landscape of health monitoring and disease treatment by enabling non-invasive, precise neural data acquisition. This review summarizes recent studies recent progress in wearable brain computer interface technology and FBES development, while provides insights into future clinical application of FBES within BCI systems. Additionally, we propose strategic directions to bridge the gap between laboratory research and practical healthcare implementations.},
}

@article {pmid42032610,
year = {2026},
author = {Bourgeois, A and Maiani, M and Minhas, A and Shaw, P and Nikitovic, D and Irvine, B and Robu, I and Brand, N and Jadavji, Z and Wilding, G and Ambrogiano, M and Schrag, E and Hilderley, A and Márquez, DC and Carlson, HL and Romanow, N and Kirton, A and Kinney-Lang, E},
title = {Creating an engaging brain computer interface, electrical stimulation therapy for children with hemiparesis: a pilot study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01990-z},
pmid = {42032610},
issn = {1743-0003},
}

@article {pmid42021568,
year = {2026},
author = {Li, Z and Ge, R and Zhao, Z and Xiao, H and Du, C and Lai, Y and Wang, L},
title = {From Bio-Interface Materials to Neural Integration: The Next-Generation Brain-Machine Interfaces Powered by Hydrogels.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e23422},
doi = {10.1002/adma.202523422},
pmid = {42021568},
issn = {1521-4095},
support = {22322803//National Natural Science Foundation of China/ ; 22375047//National Natural Science Foundation of China/ ; 22361162607//International Cooperation and Exchanges NSFC/ ; 20240305028YY//Key Research Development Program of Jilin Province/ ; 2022YFB3804905//National Key Research and Development Program of China/ ; 2022YFB3804900//National Key Research and Development Program of China/ ; //Graduate Innovation Fund of Jilin University/ ; FZ2025038//State Key Laboratory of New Textile Materials and Advanced Pro- cessing/ ; },
abstract = {Brain-machine interfaces (BMIs), which serve as revolutionary tools for neural recording, modulation, and rehabilitation, are highly dependent on the biocompatibility and mechanical suitability of their electrode materials. Although traditional metal electrodes possess excellent conductivity, their inherent rigidity causes a substantial mechanical mismatch with soft neural tissue, leading to chronic inflammatory responses and poor long-term stability. The emergence of hydrogel electrodes has provided a breakthrough solution to this fundamental limitation. Hydrogels, characterized by their softness, high ionic conductivity, and tissue-like compliance, offer a viable solution to mitigate these issues. This review systematically explores the material properties of hydrogel-integrated BMIs, providing an in-depth investigation of key hydrogel characteristics, including toughness, adhesion, conductivity, and biocompatibility. Additionally, hydrogel-based BMIs are categorized into non-invasive and invasive systems, each defined by its characteristic operational principles and signal-acquisition mechanisms. The study further reviews critical issues, including surgical implantation strategies, multimodal data fusion, integration of artificial intelligence, as well as system integration and clinical translation. From a therapeutic perspective, this work highlights the application of BMIs in treating neurological disorders such as Alzheimer's disease, Parkinson's disease, epilepsy, stroke, neuropathic pain, and depression. Furthermore, this review critically examines the persistent challenges faced by hydrogel-based BMIs and proposes innovative strategies for future development. Ultimately, it outlines a developmental roadmap for next-generation hydrogel-based biotherapeutic technologies aimed at achieving high-fidelity, stable and clinically translatable BMI systems.},
}

@article {pmid42022239,
year = {2026},
author = {Roualdes, V and Moussaoui, S and Normand, JM and Kuhn, E and Nizard, J and Van Langhenhove, A},
title = {EEG-based brain-computer interface with immersive virtual reality for phantom limb pain: a single-center pilot neurofeedback trial.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1697837},
pmid = {42022239},
issn = {1662-5161},
abstract = {BACKGROUND: Phantom limb pain (PLP) is a challenging neuropathic pain condition following limb amputation or brachial plexus injury. Non-pharmacological interventions such as motor imagery training, phantom motor execution and mirror therapy have shown potential to alleviate PLP by engaging sensorimotor circuits, but their effects are debated. We developed GHOST, a portable EEG-based brain-computer interface (BCI) coupled with immersive virtual reality (VR), allowing patients to control a virtual limb via motor imagery in real time, as a neurofeedback-based rehabilitation tool.

METHODS: We conducted a single-center exploratory pilot trial to assess the feasibility and preliminary efficacy of this device. Seven patients with chronic upper-limb PLP (amputees or brachial plexus avulsion, pain ≥3/10) underwent 10 training sessions over 2 weeks. Daily pain diaries (distinguishing continuous pain vs. paroxysmal pain episodes) were recorded for 1 month before and 1 month after the intervention, with follow-up to 6 months. Motor imagery ability, anxiety-depression (HADS), and quality of life (SF-36) were also evaluated.

RESULTS: Six patients completed ≥8 sessions. All participants achieved BCI control of the virtual hand, with high success rates (>70%) even as task difficulty increased, demonstrating system feasibility. No adverse events occurred. Compared to baseline, patients experienced a significant short-term reduction in paroxysmal pain (frequency and intensity of pain "flare-ups"), with up to >80% median decrease in weekly cumulated pain episode intensity (p < 0.001). Three of five patients also reported around 30% improvement in average daily pain during the first post-training month. HADS anxiety/depression scores showed a non-significant improving trend. By 3-6 months post-training, pain levels had largely returned to pre-intervention values.

CONCLUSION: This pilot study supports the safety and feasibility of EEG-BCI with VR for PLP and suggests that it can yield short-term analgesic effects, particularly on paroxysmal pain. These findings support the hypothesis that sensorimotor re-engagement could effectively target maladaptive neural processes underlying PLP, while warranting confirmation in controlled trials. Future work will optimize the training protocol and investigate neuroplastic changes associated with this BCI-VR intervention.},
}

@article {pmid42023246,
year = {2026},
author = {Kumaresan, V and Pahari, S and Hung, CY and Hermann, BP and Schlesinger, LS and Seshu, J},
title = {Role of dual specificity phosphatase 1 in influencing inflammatory pathways in macrophages modulated by Borrelia burgdorferi lipoproteins.},
journal = {Frontiers in immunology},
volume = {17},
number = {},
pages = {1766756},
pmid = {42023246},
issn = {1664-3224},
mesh = {Animals ; *Borrelia burgdorferi/immunology ; *Macrophages/immunology/metabolism ; *Lipoproteins/immunology ; Mice ; Signal Transduction/immunology ; *Lyme Disease/immunology/microbiology ; *Dual Specificity Phosphatase 1/metabolism/genetics/immunology ; *Inflammation/immunology ; Humans ; Proteomics ; Host-Pathogen Interactions/immunology ; Mice, Inbred C57BL ; },
abstract = {Borrelia burgdorferi (Bb), the spirochetal agent of Lyme disease, has a large array of lipoproteins that play a significant role in mediating host-pathogen interactions within ticks and vertebrates. While prior work has established that borrelial lipoproteins (BbLP) modulate immune signaling pathways, the broader transcriptional and proteomic programs induced by these molecules in macrophages are unclear. Here, we used integrated multi-omics approaches to characterize host signaling pathways activated specifically by purified borrelial lipoproteins in murine bone marrow derived macrophages (BMDMs). Single-cell RNA-Seq (scRNA-Seq) performed on BMDMs treated with various concentrations of borrelial lipoproteins revealed macrophage subsets within the BMDMs. Differential expression analysis showed that genes encoding various receptors, type I IFN-stimulated genes, signaling chemokines are upregulated while mitochondrial and ribosomal genes are downregulated in BMDMs in response to lipoproteins. Unbiased proteomics analysis of lysates of BMDMs treated with lipoproteins corroborated several of these findings. Notably, dual specificity phosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to BbLP. Pharmacological inhibition with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to BbLP, demonstrated that DUSP1 negatively regulates NLRP3-mediated pro-inflammatory signaling and positively regulates the expression of interferon-stimulated genes and those encoding Ccl5, Il1b, and Cd274. Using human monocytic reporter cell lines, we showed MyD88- and IKK-dependent pathways contribute to mitochondrial alterations upon stimulation with lipoproteins. Extracellular flux analysis using the Seahorse assay revealed decreased oxygen consumption rate (OCR) and increased extracellular acidification rate (ECAR), indicating time-dependent metabolic reprogramming and a shift toward a glycolytic, pro-inflammatory metabolic state in BMDMs following BbLP stimulation. Collectively, these findings define signaling networks, regulatory nodes and metabolic alterations induced by borrelial lipoproteins in macrophages and highlight DUSP1 as a key modulator of lipoprotein-driven innate immune responses. This work provides a mechanistic framework for understanding how borrelial lipoproteins shape macrophage signaling, independent of the broader complexity of infection with intact pathogen.},
}

Animals
*Borrelia burgdorferi/immunology
*Macrophages/immunology/metabolism
*Lipoproteins/immunology
Mice
Signal Transduction/immunology
*Lyme Disease/immunology/microbiology
*Dual Specificity Phosphatase 1/metabolism/genetics/immunology
*Inflammation/immunology
Humans
Proteomics
Host-Pathogen Interactions/immunology
Mice, Inbred C57BL

@article {pmid42024948,
year = {2026},
author = {Ma, W and Zhang, H and Li, Y and Wei, M},
title = {NeuroDecoder: A new framework for image decoding and reconstruction of EEG signals.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3686624},
pmid = {42024948},
issn = {2168-2208},
abstract = {Brain-Computer Interface (BCI) technology holds great promise for enhancing human health and quality of life, with visual stimulus reconstruction from EEG signals being a key application. However, the complexity and noise of EEG data challenge existing reconstruction methods. To address these issues, we propose NeuroDecoder, an end-to-end multimodal guidance generation framework that produces high-quality images from EEG signals. The key innovation is the collaborative mitigation of EEG noise and cross-modal representation discrepancies through a noise-robust encoder, mask-based triple-contrastive alignment, and a fixed generative model. Specifically, NeuroDecoder consists of three integrated learning stages: 1) EEG Decoding, 2) Modality Alignment, and 3) Image Reconstruction. In the decoding stage, a novel visual decoding model extracts visually relevant features with superior classification accuracy. In the alignment stage, a mask-based triple contrastive learning strategy achieves efficient cross-modal alignment of EEG, text, image, and edge map embeddings into a unified space. In the generation stage, a new reconstruction pipeline feeds the aligned EEG embeddings into a pre-trained stable diffusion model, enabling high-quality visual stimulus reconstruction with enhanced semantic and structural fidelity, without fine-tuning the generative model. On three EEG datasets, NeuroDecoder achieved subject-dependent classification accuracies of 99.76%, 94.41%, and 56.67%, respectively; in the subject-independent setting, it performed near random on EEGCVPR40 but reached 91.61% and 37.63% on the other two. For image reconstruction, it obtained Fréchet Inception Distance of 62.84 and 63.12 on the first two datasets. Extensive experiments demonstrate that NeuroDecoder outperforms prior methods in both EEG classification accuracy and image reconstruction quality.},
}

@article {pmid42016063,
year = {2026},
author = {Fankhauser, CD and Röthlin, K and Baumeister, P and Mattei, A and Piatti, P and Chew, YC and Kaufmann, E},
title = {Validation of the diagnostic accuracy of a urine-based DNA methylation marker test in patients with upper urinary tract lesions.},
journal = {BJUI compass},
volume = {7},
number = {3},
pages = {e70195},
pmid = {42016063},
issn = {2688-4526},
abstract = {OBJECTIVES: This study aims to validate the diagnostic accuracy of a novel urine-based DNA methylation test in patients with suspected upper tract urothelial carcinoma (UTUC) on CT urography and to assess its potential to eliminate the need for diagnostic ureterorenoscopy (URS) in selected patients, expedite treatment and identify high-grade tumours suitable for neoadjuvant chemotherapy.

PATIENTS AND METHODS: We prospectively collected urine samples from 46 consecutive patients with suspected UTUC in computed tomography and analysed them using the Bladder CARE™ methylation test. Test performance was evaluated against final pathology from URS biopsies and/or surgical specimens. We performed Youden Index analysis to optimise diagnostic cut-off values and assessed correlations between Bladder CARE Index (BCI) levels and tumour characteristics, particularly grade differentiation.

RESULTS: Using the manufacturer's cut-off (BCI > 2.5), the test demonstrated 95% sensitivity, 69% specificity, 70% positive predictive value and 95% negative predictive value (NPV), significantly outperforming cytology (11% sensitivity). An optimised, study-derived cut-off (4.35) further improved specificity to 92% with sensitivity and NPV remaining ≥95%. Importantly, a higher threshold (BCI > 10) yielded 100% specificity and 100% PPV, although at the expense of sensitivity (65%). Median BCI values differed between high-grade (38.6) and low-grade tumours (9.45), suggesting utility for non-invasive grade assessment. BCI also correlated with tumour size (β = 12 mm per log10 increase, p = 0.08).

CONCLUSION: This novel urine-based DNA methylation test offers high diagnostic accuracy for UTUC detection. However, clinical interpretation should be threshold dependent. While BCI values >2.5 show high sensitivity, the PPV of 70% indicates a relevant proportion of false-positive results, and diagnostic URS remains warranted in this range. In contrast, high positive values (BCI > 10) demonstrated 100% specificity and PPV and could enable direct progression to definitive surgery without diagnostic URS, avoiding procedure-related complications and expediting treatment. The correlation with tumour grade addresses a critical need for identifying candidates for neoadjuvant chemotherapy without invasive tissue diagnosis.},
}

@article {pmid42018586,
year = {2026},
author = {Zhao, Y and He, D and Ren, F and Xia, Q and Xu, L and Xie, G and Zhang, X and Yang, R and Zou, S and Jiang, B},
title = {RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.},
journal = {PloS one},
volume = {21},
number = {4},
pages = {e0347671},
doi = {10.1371/journal.pone.0347671},
pmid = {42018586},
issn = {1932-6203},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; *Imagination/physiology ; },
abstract = {Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). However, deep learning models often struggle with inter-subject variability, which limits their ability to generalize across subjects. This study proposes RMETNet, a novel framework that integrates TSLANet, a spatio-temporal convolution module, and a multi-scale Riemannian geometry feature module. TSLANet suppresses noise and captures complex temporal patterns for preliminary signal decoding, while the spatio-temporal convolution module extracts higher-order representations. The Riemannian branch learns geometry-based distribution features across subjects, and the fused features are used for classification. To address inter-subject distribution shifts, RMETNet incorporates Maximum Mean Discrepancy (MMD) loss for domain adaptation, aligning feature distributions between source and target domains. Experiments show that on the four-class BCI Competition IV 2a (BCICIV2a) dataset, RMETNet achieved accuracies of 71.39% in the cross-subject setting and 80.71% in the subject-dependent setting; on the two-class BCI Competition IV 2b (BCICIV2b) dataset, it achieved 80.93% and 86.76%, respectively. The model consistently outperformed baseline algorithms. Ablation and visualization analyses further validated its effectiveness in reducing inter-subject feature distribution disparities and enhancing MI-EEG decoding. The code is available at: https://github.com/rokanfeermecer486/RMETNet.},
}

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

@article {pmid42019053,
year = {2026},
author = {Zhang, S and Zhang, H and Wei, M and Yang, C},
title = {An Optimized Encoding BCI Framework: Implementing Massive Command with Minimal Calibration.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3686710},
pmid = {42019053},
issn = {1558-0210},
abstract = {As a critical metric for brain-computer interfaces (BCIs), the number of commands directly defines the control capacity for practical applications. However, existing BCIs often suffer from limited command sets and prohibitive calibration costs. To address these problems, this study presents a functional optimizationbased encoding framework to generate massive com8 mands with high discriminability while minimizing calibration burden. Specifically, a functional optimization theory enhances command distinguishability by optimizing the encoding function, while a few-shot training strategy leverages symbol reusability to reduce calibration data. Additionally, a symbol-joint decoding approach improves recognition accuracy. Using this framework, we developed an online BCI system with an unprecedented 1,008 commands-establishing a dual state-of-the-art (SOTA) in both command scale and calibration efficiency for large-scale BCIs (>100 commands). Comparative analysis shows that the functional optimization strategy improved accuracy by 13.94% and the information transfer rate (ITR) by 26.12% over the widely adopted baseline. Remarkably, with only 72 seconds of calibration data, the system achieved an average accuracy of 86.60 ± 13.35% and an average ITR of 122.74 ± 24.64 bits/min across 15 subjects, peaking at 100%. The framework features high flexibility in command encoding and robust cross-paradigm compatibility, significantly enhancing BCI performance and practicality.},
}

@article {pmid42019267,
year = {2026},
author = {Cui, Y and Yun, R and Zhang, S and Gong, Y and Li, Z and Chen, Y and Su, M and Wu, D and Wu, J and Wang, Q and Wang, J and Tian, Q and Yuan, Y and Mei, S and Wu, L and Li, X and Zhang, B and Guo, T and Sun, J},
title = {EEG predict response to transcutaneous auricular vagus nerve stimulation in treatment-resistant schizophrenia.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {187},
number = {},
pages = {2111892},
doi = {10.1016/j.clinph.2026.2111892},
pmid = {42019267},
issn = {1872-8952},
}

@article {pmid42019559,
year = {2026},
author = {Xu, P and Chen, Y and Wei, X and Qi, J and Chen, Y and Li, L},
title = {Resting-State EEG Networks Predict Individual Differences in Cognitive Flexibility.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111895},
doi = {10.1016/j.brainresbull.2026.111895},
pmid = {42019559},
issn = {1873-2747},
abstract = {Cognitive flexibility, the ability to adapt behavior and switch between tasks in response to changing goals, is a core component of executive function. However, the multiscale resting-state mechanisms underlying individual differences remain poorly understood. Here, resting-state electroencephalography (EEG) from 128 healthy participants (66 male; age 18-35 years) was analyzed to characterize frequency-specific connectivity and network topology. Results show that, delta-band fronto-temporal connectivity and associated graph metrics associated with repeat task performance, whereas beta-band fronto-parietal, fronto-occipital, and prefronto-frontal connections associated with shift task performance. Individuals with low switching costs exhibited stronger intra- and inter-hemispheric alpha-, beta-, and gamma-band connectivity, which were associated with more efficient cognitive flexibility. Multivariate models using connectivity features reliably predicted repeat RT and shift RT. Together, these findings indicate that hierarchical, frequency-specific resting-state networks constitute core neural mechanisms of cognitive flexibility and highlight the potential for resting-state EEG networks to account for individual differences in executive function.},
}

@article {pmid42019892,
year = {2026},
author = {Wang, M and Xu, S and Ball, LJ},
title = {Frequency-specific prefrontal inter-brain synchrony and reinforcement learning signatures differentiate cooperative and competitive risky decision-making: an fNIRS hyperscanning study.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121942},
doi = {10.1016/j.neuroimage.2026.121942},
pmid = {42019892},
issn = {1095-9572},
abstract = {The neural and computational mechanisms that distinguish cooperative from competitive strategies in risky decision-making remain incompletely understood. In this study, we combine frequency-specific prefrontal inter-brain synchrony (IBS) measured via functional near-infrared spectroscopy (fNIRS) hyperscanning with reinforcement learning modeling to examine how social context shapes dyadic choice. Sixty female dyads performed cooperative or competitive variants of a modified Iowa Gambling Task (IGT). Behaviorally, competitive pairs achieved significantly higher cumulative earnings than cooperative pairs. Reinforcement learning analyses indicated that the Outcome Representation Learning (ORL) model provided the best account of behavior. Cooperative dyads showed increased sensitivity to win frequency (βfre), suggesting a tendency to favor frequent but suboptimal gains. In contrast, competitive dyads adopted more flexible strategies that were less dependent on reward frequency. Neuroimaging results revealed dissociable frequency related patterns. Ultra-low frequency coupling in the dorsolateral prefrontal cortex (DLPFC) within the range of 0.015 to 0.017 Hz was associated with goal directed control and higher earnings. Higher frequency coupling in the frontopolar cortex (FPC) within the range of 0.340 to 0.381 Hz was associated with opponent monitoring and sustained competitive engagement, and was reduced during cooperation, consistent with reduced individual responsibility. These findings support a dual pathway account in which competition engages both control and monitoring processes to facilitate performance, whereas cooperation may incur performance costs through socially shaped learning biases. The results provide mechanistic insight into social decision making and identify candidate neural markers for adaptive behavior in interactive contexts.},
}

@article {pmid42020458,
year = {2026},
author = {Perez-Blanco, JG and Huegel, JC and Hernández-Rojas, LG and Valdez-Calderón, A and Lizárraga-Torreblanca, H and Cruz-Ortiz, D and Ballesteros, M and Gomez-Correa, M and Antelis, JM},
title = {An EEG-EMG-kinematics dataset from wrist pointing tasks for biomarker research in neurorehabilitation.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07287-z},
pmid = {42020458},
issn = {2052-4463},
support = {1278020//SECIHTI/ ; SECTEI/081/2024//SECTEI/ ; },
abstract = {This work presents a multimodal dataset containing synchronized electroencephalography (EEG), electromyography (EMG), and kinematic recordings acquired during wrist motor tasks performed with a three degree of freedom robotic exoskeleton (BiomechWrist) coupled to a virtual interface. Designed as a normative baseline and benchmark resource for studying electrophysiological biomarkers and motor performance in healthy individuals, the dataset includes recordings from 45 healthy participants, each completing 320 trials of standardized wrist movements. The exoskeleton operated in transparent mode (actuators de-energized) to capture voluntary movements through high resolution encoders. Data are formatted according to the Brain Imaging Data Structure (BIDS) standard and follow FAIR principles, comprising raw biosignals, encoder trajectories, event markers, and derived performance metrics. To assess data quality, we provide subject level validation analyses, including power spectral density (PSD) and event related desynchronization/synchronization (ERDS) for EEG, as well as an EMG-Kinematic coupling analysis through Electromechanical Delay (EMD), and kinematic trajectory evaluation with performance metrics (accuracy, execution time, trajectory efficiency). This dataset supports research on wrist rehabilitation technologies and biomarker driven neuromodulation therapies, while also enabling studies in biosignal processing, artifact removal, machine learning for motor intention decoding, and the development of brain computer interfaces (BCI) and assistive devices targeting wrist mobility.},
}

@article {pmid42020535,
year = {2026},
author = {Margaret, MJ and Banu, NMM and Madhumithaa, S and Pathan, AK},
title = {On the prediction models for brain signal-based emotion recognition.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-47622-3},
pmid = {42020535},
issn = {2045-2322},
}

@article {pmid41880655,
year = {2026},
author = {Van Ransbeeck, W and Yuan, Z and Maes, PJ and Leman, M and Verhulst, S and Botteldooren, D},
title = {An EEG-based framework for exploring adaptive rhythmic human-machine interaction.},
journal = {Journal of neural engineering},
volume = {23},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ae573d},
pmid = {41880655},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; *Brain-Computer Interfaces ; *Periodicity ; Photic Stimulation/methods ; *Man-Machine Systems ; },
abstract = {Objective.Understanding rhythmic human-human interaction and its underlying mechanisms can enhance experiential value and enjoyment by providing a tailored experience and supporting applications in medical human-machine contexts. Existing experimental paradigms often lack a unified and holistic analysis, characterised by limited ecological validity in partner realism, active engagement, and visual interaction. These can produce hidebound insights due to variable partner behaviour, inflexible design, or insufficient user experience analysis. The study presents and validates a multimodal paradigm that addresses these limitations and enables controlled evaluation of human-human rhythm interaction and its extension to virtual AI agents.Approach.Participants completed a tapping paradigm with an audio-visual drum animation driven by either a human or AI-based partner under simple and complex (polyrhythmic) conditions. Portable electroencephalography (EEG) recordings and post-trial questionnaires assessed neural and subjective responses.Main results.The framework improves ecological validity relative to existing approaches and effectively masks partner identity (human vs AI) without reducing experienced flow, arousal, or enjoyment, which remained positive overall. Notably, the AI-based partner considered a first attempt to create a virtual AI-driven interacting drummer, suitable for future consideration of alternative algorithms. Additionally, the design supports unobtrusive, portable EEG measurement of neural modulation and temporal alignment with both performed and presented stimuli.Significance.This paradigm offers a flexible foundation for studying rhythmic interaction in human-machine systems, balancing ecological realism with experimental partner control while supporting future adaptive or biofeedback-driven systems that optimise rhythm interaction in real-time.},
}

Humans
*Electroencephalography/methods
Male
Female
Adult
Young Adult
*Brain-Computer Interfaces
*Periodicity
Photic Stimulation/methods
*Man-Machine Systems

@article {pmid42009354,
year = {2026},
author = {Kim, M and Heo, D and Kim, J and Kim, SP},
title = {Enhancing the Performance of Event-Related Potential-Based Brain-Computer Interfaces under Cognitive Distraction: A Multiwindow Adaptive Approach.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3685282},
pmid = {42009354},
issn = {1558-0210},
abstract = {Event-related potential (ERP)-based brain- computer interfaces (BCIs) require focused attention to presented stimuli. However, their applications in real life frequently involve environments that demand multitasking and impose cognitive distraction. Such distractions degrade ERP amplitudes and consequently reduce BCI performance. This study proposes a multiwindow adaptive model to mitigate the adverse effects of cognitive distraction on visual ERP-based BCIs. The proposed approach divides poststimulus intervals into multiple overlapping windows, each with dedicated spatial filters and classifiers that continuously update through adaptive semi-supervised learning. Offline experiments on a BCI control dataset collected during concurrent speaking demonstrate that the proposed method significantly outperforms single-window or fixed (i.e., nonadaptive) models, yielding an accuracy of 91.08%. Further validation in an online experiment confirms that the multiwindow adaptive approach effectively restores BCI performance, achieving an accuracy of 93.20% despite cognitive distraction. These findings highlight the practical benefits of temporally tailored feature extraction and continuous adaptation for real-world ERP-based BCIs, enabling robust performance even under cognitive distraction.},
}

@article {pmid42010188,
year = {2026},
author = {Mesgarani, N},
title = {From Selective Listening to Brain-Controlled Hearing: A Perspective on the Future of Auditory Technology.},
journal = {Journal of the Association for Research in Otolaryngology : JARO},
volume = {},
number = {},
pages = {},
pmid = {42010188},
issn = {1438-7573},
abstract = {Understanding speech in noisy environments is a major challenge for millions, a problem that conventional hearing aids often exacerbate by amplifying all sounds indiscriminately. Auditory Attention Decoding (AAD) offers a revolutionary alternative: a brain-computer interface that decodes a listener's attentional focus from their neural signals to selectively enhance the desired sound source. For over a decade, research has demonstrated the scientific feasibility of attention decoding, yet the field has faced a critical barrier in translating this promise into a real-time system that provides a demonstrable perceptual benefit in real-world listening conditions. This perspective charts the journey of AAD, from its foundational neuroscientific discoveries to the current engineering hurdles that must be overcome for real-world deployment. We outline the key remaining challenges, including the need to define user-centric metrics for success, develop practical and power-efficient wearable sensors, design low-latency and computationally efficient decoding algorithms, and ensure robust performance in complex, naturalistic scenes. By addressing these questions, the field can move beyond passive amplification and create the next generation of assistive technology: one that listens with the brain to restore or augment the hearing experience, making it fully aligned with the user's intent.},
}

@article {pmid42012068,
year = {2026},
author = {Ye, Y and Wu, J and Zhang, Y and Ma, H and Deng, Q and Jian, J and Tang, R and Sun, B and Zeng, Y and Song, Y and Wang, J and Lin, H and Zhao, S and Li, L},
title = {Reconfigurable in-Sensor Image Enhancement Based on Tunable Band Alignment of In2Se3/PdSe2 Heterojunction.},
journal = {Nano letters},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.nanolett.5c06055},
pmid = {42012068},
issn = {1530-6992},
abstract = {In-sensor computing has emerged as a promising paradigm to overcome power consumption and latency bottlenecks in vision systems. Here, we demonstrate a reconfigurable in-sensor image enhancement strategy based on an In2Se3/PdSe2 ferroelectric heterojunction. The photodetector exhibits a broadband spectral response (400-1550 nm) and a high external quantum efficiency exceeding 10[4]%. By synergistically leveraging electrostatic and ferroelectric fields to tune the band alignment, we achieve programmable carrier collection efficiency, leading to a gate-tunable nonlinear photocurrent response. This hardware-level nonlinearity enables dual imaging modes for adaptive imaging: a low-light signal amplification mode to boost brightness and an overexposure recovery mode to compress contrast. By implementing a programmable photoresponse into a single photodetector, our approach bypasses redundant data transmission, providing a compact and energy-efficient solution for intelligent vision systems.},
}

@article {pmid42013255,
year = {2026},
author = {Wu, X and Daly, I and Lau, AT and Chen, W and Wang, C and Cichocki, A and Jin, J},
title = {Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks with Pyramid Squeeze Attention.},
journal = {IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TIP.2026.3684399},
pmid = {42013255},
issn = {1941-0042},
abstract = {Steady state visual evoked potential (SSVEP)-based brain-computer interfaces have been widely studied for their fast response speeds and high information transfer rates. However, how to fully utilize the potential information of existing subjects to realize the mining of common information among different subjects and then realize the information migration in a small amount of data scenarios is a difficult problem faced by current research. In order to solve the above problems, this study proposes a deep neural network based on the pyramid squeeze attention (PSA-DNN) mechanism to enhance the performance of SSVEP-BCI through common information migration. Specifically, the band-pass filtered EEG signals were first Fourier transformed to obtain the frequency domain information; subsequently, the frequency domain information is input into a deep neural network, followed by a spatial convolution step to extract spatial domain information. In order to further enhance the quality of information extraction, a pyramid attention module is introduced into the network to realize the enhancement of frequency domain and spatial domain information. Time domain information from the EEG signals is then mined using temporal convolution. Finally, the full connectivity layer is used to output the recognition results. The model is trained in a three-stage stepped approach for SSVEP target recognition. The first stage uses data from all participants in the training set for common information learning and transfers the model parameters trained in the first stage to the network model in the second stage. In the second stage, some of the information from participants in the test set is used for fine-tuning and to mine personalized information from these new participants. The third stage uses the remaining data from participants in the test set to produce classification results. The proposed method is systematically evaluated using the Benchmark and BETA datasets, where it demonstrates favorable performance compared to established baselines. These findings contribute theoretical insights and methodological references for the application of SSVEP-based brain-computer interfaces in real-world scenarios.},
}

@article {pmid42013272,
year = {2026},
author = {Zhang, J and Liu, J and Wang, L and Peng, Y and Kong, W and Cichocki, A},
title = {BR-SFDA: A Source-Target Bidirectional Refined SFDA for Privacy Preserving EEG-based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3686008},
pmid = {42013272},
issn = {2168-2208},
abstract = {Due to significant inter-subject variability in feature distributions caused by the diversity of neural activity patterns, Electroencephalography (EEG)-based brain-computer interface (BCI) systems face considerable challenges in cross-subject EEG decoding. Though transfer learning has been widely introduced for knowledge transfer from source subject(s) to target subject and exhibited great success, a non-negligible issue is that source subjects' EEG data usually contains privacy information and should be protected. To address both issues, we propose a source-target bidirectional refined source-free domain adaptation (BR-SFDA) framework in this paper for privacy preserving cross-subject EEG classification. BR-SFDA makes improvements from two aspects under the popular 'pretraining and fine-tuning' paradigm. On one hand, it locally performs data augmentation and builds a multi-criteria fused metric to select representative EEG sample for model pre-training. On the other hand, a structured graph learning strategy is employed to better guide the model finetuning in a self-supervised manner. Both improvements collaborate respectively from the front-end and back-end, leading to a bidirectional refined SFDA framework. Extensive experiments are conducted on two tasks of cross-subject motor imagery decoding and emotion recognition, and the results on four datasets demonstrate that BR-SFDA achieves superior performance to some competitive models. Besides, the effectiveness of data augmentation and filtering, structured graph learning and domain adaptation is well evaluated.},
}

@article {pmid42013704,
year = {2026},
author = {Han, L and Smagghe, G and Yang, J and Yu, J and Zheng, H and Chen, X and Wu, X},
title = {Synergistic removal of morpholine fungicides and cadmium from agricultural water by a biochar-immobilized bacterial-duckweed system: Quantifying roles of biodegradation, adsorption, and phyto-uptake.},
journal = {Journal of hazardous materials},
volume = {510},
number = {},
pages = {142125},
doi = {10.1016/j.jhazmat.2026.142125},
pmid = {42013704},
issn = {1873-3336},
abstract = {The co-contamination of agricultural water by morpholine fungicides (e.g., flumorph and dimethomorph) and cadmium (Cd) poses significant ecological threats, challenging conventional treatment approaches. This study developed an innovative bioremediation system integrating biochar-immobilized microbial consortia with phytoremediation, and quantified the individual contributions of biodegradation, biosorption, phyto-uptake, and biochar-adsorption to the synergistic removal of pesticide and Cd co-contaminants. A novel Cd-tolerant and flumorph-degrading bacterium, Alcaligenes faecalis X4, was combined with a dimethomorph-degrading strain (Bacillus cereus WL08) to form a stable consortium. This consortium was capable of simultaneously metabolizing both fungicides into less toxic products and adsorbing cadmium. The consortium was immobilized on bamboo charcoal to produce a biocomposite (BCI-X4 + WL08), which achieved high removal efficiencies under optimized conditions: 97.65% for flumorph (50 mg/L), 94.23% for dimethomorph (50 mg/L), and 82.68% for cadmium (10 mg/L). Subsequent introduction of duckweed (Lemna minor) contributed an additional 15.40-28.00% removal via phyto-accumulation. Partitioning analysis confirmed true synergistic interactions-rather than merely additive effects-enhancing overall removal by up to 3.27-fold while alleviating oxidative stress in the plants. A compound ecological filter bed incorporating both BCI-X4 + WL08 and duckweed demonstrated practical applicability under outdoor conditions, achieving average simultaneous removal rates of 94.96% (flumorph), 91.43% (dimethomorph), and 85.42% (Cd) across three consecutive seasons, along with improved water quality parameters. This work presents a scalable, eco-friendly strategy for the in situ remediation of surface waters co-contaminated with pesticides and heavy metals, and provides a quantitative assessment of the distinct microbial, plant, and biochar contributions to the synergistic remediation process.},
}

@article {pmid42014579,
year = {2026},
author = {Miao, Y and Fu, Z and Zhang, J and Tao, Y and Pang, K and Wang, C and Jiang, Q and Shen, L and Xia, T and Lu, P and Xu, Z and Xia, L and Zuo, L and Dong, R and Liu, Y and Wang, Z and Zhang, N and Song, J and Gao, C and Jiang, R and Deng, D and Zhu, Y},
title = {Theoretical quantitative model and clinical outcome predictions of conductive cardiac patches for electrophysiological treatments.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {42014579},
issn = {2157-846X},
support = {2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2019YFE0117400//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&T for Social Development)/ ; 2019YFE0117400//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&T for Social Development)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Myocardial infarction (MI) impairs cardiac electrical signal transmission, which could be partially remedied by implantable electroactive biomaterials. Here we characterize electroactive cardiac patches (eCarPs) with conductivities spanning five orders of magnitude both in vitro and in rat models. In contrast to common belief, we reveal that highly conductive eCarPs are more effective in lowering the risk of post-MI arrhythmia and preserving cardiac function with respect to eCarPs with conductivity similar to normal myocardium. We show that highly conductive eCarPs restore electrical signal conduction velocity across infarcted myocardium to healthy levels, while less conductive eCarPs fail to do this. We quantitatively demonstrate that three-dimensional cardiac simulation based on the monodomain model accurately replicates the effect of high-conductivity patches in eliminating conduction blocks in porcine myocardium and the locations of reentrant circuits in patients with MI. Our results suggest that eCarP conductivity higher than healthy human myocardium is preferred for lowering the risk of arrhythmia in patients by reducing the number of reentrants and stabilizing the reentrant routes.},
}

@article {pmid42014794,
year = {2026},
author = {Zou, J and Poeppel, D and Ding, N},
title = {Constituent-constrained word prediction during language comprehension.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42014794},
issn = {1546-1726},
abstract = {Next-word prediction has been hypothesized as the central computational objective of the human language system, akin to that of current large language models. Here we put this conjecture to the test, investigating whether the brain predicts each upcoming word as precisely as possible when listening to connected speech. In three magnetoencephalography experiments with Mandarin Chinese speakers, we demonstrate that the response related to word unpredictability, that is, word surprisal calculated using large language models, is significantly stronger for words within an ongoing constituent than words across a major constituent boundary, and this effect is further modulated by the certainty of a constituent boundary. This constituent-boundary effect is also observed behaviorally unless speech is very slowly presented, and it is confirmed by analyzing a dataset of electrocorticography responses to natural English narratives. The constituent-boundary effect demonstrates that the language system does not solely optimize word-prediction precision; rather, it balances word-prediction contributions by constituent-constrained management of linguistic contextual representations.},
}

@article {pmid42014805,
year = {2026},
author = {Nasiraee, H and Nazari, F and Samsami-Khodadad, F and Liu, X},
title = {Neural-LWE: a biometric-anchored authenticated key agreement for post-quantum brain-computer interfaces.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-48527-x},
pmid = {42014805},
issn = {2045-2322},
}

@article {pmid42002556,
year = {2026},
author = {Selvam, AK and Loganathan, A},
title = {An intelligent EEG-based ensemble framework for communication assistance in Locked-In Syndrome patients.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-47041-4},
pmid = {42002556},
issn = {2045-2322},
}

@article {pmid42003430,
year = {2026},
author = {Maya, I and Noiret, B and Denost, Q},
title = {How to avoid APR after failure of organ preservation in ultra-low rectal cancer? A video vignette.},
journal = {Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland},
volume = {28},
number = {4},
pages = {e70458},
doi = {10.1111/codi.70458},
pmid = {42003430},
issn = {1463-1318},
}

@article {pmid42004294,
year = {2024},
author = {Collyer, J},
title = {Fasciola hepatica: can the coproantigen ELISA replace the faecal egg sedimentation test?.},
journal = {Veterinary evidence},
volume = {9},
number = {4},
pages = {},
pmid = {42004294},
issn = {2396-9776},
abstract = {PICO QUESTION: In adult cattle, is the sensitivity of the coproantigen ELISA test equal or superior to the sensitivity of the faecal egg sedimentation test for the diagnosis of Fasciola hepatica?

CATEGORY OF RESEARCH: Diagnosis.

Three studies were appraised. This included two cross-sectional diagnostic accuracy studies and one case control diagnostic accuracy study.

STRENGTH OF EVIDENCE: Moderate.

OUTCOMES REPORTED: The first study reported the findings from 619 tested cattle over 3 sample periods comparing the sensitivity and specificity of the different tests. The sensitivity of the faecal egg sedimentation test varied greatly between the sample periods from 0.81 (95% beta coefficient (BCI) 0.72-0.90) to 0.58 (95% BCI 0.43-0.72) with the coproantigen ELISAs sensitivity remaining consistent at 0.77 (95% BCI 0.64-0.88) throughout.The second study reported the findings of 200 tested cattle over 2 sampling periods comparing the sensitivity and specificity of the different tests. The mean sensitivity of the coproantigen ELISA was significantly higher than the 4 g/10 g preparations of the faecal egg sedimentation tests at 94% (95% CI 87%-98%) (P < 0.001). The third study reported the findings of Coproantigen ELISA testing on 250 bovine faecal samples with 94 confirmed positive for liver fluke via faecal sedimentation testing. The sensitivity of the coproantigen ELISA was 80% and the specificity was 100% compared with 70% and 80% respectively for the faecal egg sedimentation test.

CONCLUSION: All three studies demonstrated either an increased or equivalent sensitivity of the coproantigen ELISA to the faecal sedimentation test, but only one study reported a statistically significant increase in test sensitivity. Whilst all three studies were diagnostic accuracy validity studies, the systematic sampling strategy of one study was superior to the convenience sampling method of one of the other studies and to the case control method of the other.Several sources of bias also exist within the included studies. Sampling and selection bias is present in the two of studies due to the animals selected only being sampled over one year. The results of these studies are susceptible to changes in the fluke lifecycle of that year, and the sampled animals are more likely to be fit and well-conditioned as they are presenting for slaughter, and as such are less likely to carry significant/chronic fluke burdens. All three studies are susceptible to validity issues due to an absence of clinical information regarding flukicide treatment and concurrent parasitic diseases which, whilst not impacting the efficacy of diagnostic testing, may cause issues if the studies are to be repeated.The coproantigen ELISA can be utilised as a suitable adjunctive test to aid in the diagnosis of Fasciola hepatica in adult cattle and has the scope to be used as an early diagnostic test, but whilst the results of the reported studies indicate that the coproantigen ELISA is an accurate and reliable test, it does not provide definitive evidence to warrant the discontinuation of the simple and affordable faecal egg sedimentation test. In order to come to a conclusion regarding the more sensitive test more literature is required that directly compares the coproantigen ELISA to the faecal egg sedimentation test in different clinical scenarios and exploring different diagnostic techniques.},
}

@article {pmid42004547,
year = {2026},
author = {Andong, FA and Mayowa, ES and Nwanozie, PO and Ejere, VC and Afyare, AAA},
title = {Double burden: microfilariae infection amplifies metabolic costs of moult in breeding male village weavers (Ploceus cucullatus).},
journal = {Biochemistry and biophysics reports},
volume = {46},
number = {},
pages = {102576},
pmid = {42004547},
issn = {2405-5808},
abstract = {Breeding male birds face high energetic demands due to simultaneous investment in reproduction and feather moult, yet the metabolic consequences of parasitic infection during this period are poorly understood. To address this gap, we focused on non-moulting and actively moulting breeding adult male village weavers (Ploceus cucullatus) to investigate how microfilariae infection affects host biochemical energy status and overall condition. Using plasma glucose, triglycerides, β-hydroxybutyrate, and body mass adjusted for structural size as integrative markers, we examined how infection influences energy allocation and imposes physiological costs during this critical life-history stage. Specifically, we aimed to: (i) determine whether microfilariae infection and active moult influence short-term energy availability by examining plasma glucose concentrations, and whether absolute body mass modulates the effect of infection; and (ii) evaluate the combined and independent effects of infection and moult on lipid and ketone metabolism, while incorporating absolute body mass and size-corrected body condition index (BCI) to assess overall energetic reserves and physiological trade-offs. A total of 128 breeding males were trapped and screened for microfilariae and moult status. Our results indicate infected birds that are actively moulting experienced higher β-hydroxybutyrate, lower glucose and reduced BCI, when compared with the non-infected birds that were non-moulting. On the other hand, non-infected male birds that were also non-moulting maintained higher triglyceride levels. Our regression analyses indicate both infection and moult independently increased ketone concentrations and decreased triglycerides (P < 0.05), with no significant interaction for most markers. However, for β-hydroxybutyrate, the interaction may approach significance (P = 0.08), which suggest a marginal tendency toward non-additive effects. These results highlight a 'double burden,' where concurrent parasitism and moult constrain energy allocation, shifting metabolism from carbohydrates toward lipid catabolism. This study may provide mechanistic insight into how microfilariae infection amplifies energetic costs during high-demand life-history stages in breeding male village weavers.},
}

@article {pmid42006915,
year = {2026},
author = {Taquet, L and Conway, BJ and Boerger, TF and Goetschel, K and Young, SC and Botros, NE and Raghavan, M and Schmit, BD and Krucoff, MO},
title = {The frequency-dependent effects of primary hand motor cortex stimulation on volitional finger movement.},
journal = {Clinical neurophysiology practice},
volume = {11},
number = {},
pages = {252-261},
pmid = {42006915},
issn = {2467-981X},
abstract = {OBJECTIVE: We conducted a prospective study in human patients undergoing awake craniotomies to examine whether the effects of cortical stimulation in hand primary motor cortex (M1) can be (1) frequency dependent and (2) inhibitory.

METHODS: In 11 participants undergoing clinically indicated awake craniotomies, we delivered bursts of 1-333 Hz stimulation during a finger-flexion task. Synchronized electrocorticography (ECoG), finger joint kinematics, electromyography (EMG), and video were recorded.

RESULTS: Inability to flex the index finger during subthreshold stimulation was noted in 3 participants at frequencies >250 Hz when the electrodes were in locations that induced extension of the forefinger at higher amplitudes. Other than these trials, all stimulation events either induced muscle contractions or had no measurable effect.

CONCLUSION: Data presented here represent the first evidence of (1) movement inhibition of the human hand caused by electrical stimulation of M1, as well as (2) the frequency-dependence of net downstream effects of hand M1 stimulation during task. Our findings support the hypothesis that the mechanism of movement inhibition may be activation of indirect, net-inhibitory mechanisms, as opposed to direct inhibition of the stimulated motor neurons.

SIGNIFICANCE: There is growing interest in using continuous electrical stimulation of the brain to remap anatomical-functional relationships away from invasive lesions. Achieving this type of neuroplasticity requires a better understanding of the direct and indirect effects of cortical stimulation. Here we demonstrate the frequency-dependent effects of cortical M1 stimulation on volitional finger movement.},
}

@article {pmid41253390,
year = {2026},
author = {Ji, X and Deng, S},
title = {Cognitive Change as an Early Warning for Late-Life Depression: Implications for Population Health Screening Strategies.},
journal = {Population health management},
volume = {29},
number = {1},
pages = {27-37},
doi = {10.1177/19427891251395738},
pmid = {41253390},
issn = {1942-7905},
mesh = {Humans ; Aged ; Male ; Female ; *Depression/diagnosis/epidemiology ; Longitudinal Studies ; Aged, 80 and over ; *Population Health ; *Cognitive Dysfunction/diagnosis ; *Mass Screening/methods ; *Cognition ; },
abstract = {Cognitive decline and late-life depression are intertwined public health challenges for aging populations globally. To inform effective prevention, the current study investigated the dynamic temporal associations between multidimensional cognitive functions and depressive symptoms. Using four waves of longitudinal data (2013-2020) from a large panel study of older adults, the current study employed an integrated framework combining optimized dynamic time warping, cross-lagged panel models, and network analysis to model complex, lagged relationships. Results provided strong empirical support for the "cognition-first" hypothesis, with declines in several cognitive domains-notably temporal orientation, calculation, and immediate recall-acting as significant upstream predictors of subsequent depressive symptoms. A modest but significant protective feedback effect from positive affect to cognitive maintenance was also identified, while negative affect showed no significant predictive role sample of older adults who were cognitively and emotionally healthy at baseline. These findings offer preliminary empirical support for a strategic shift in population health management from reactive treatment toward proactive prevention. Based on these results, the current study discusses a conceptual framework for integrating cognitive screening into primary care to identify at-risk older adults, an approach that warrants further investigation and validation. This proactive approach could enable timely, low-cost interventions aimed at promoting positive affect and cognitive resilience, offering a potentially cost-effective strategy to mitigate the long-term burden of mental illness and advance the goals of healthy aging.},
}

Humans
Aged
Male
Female
*Depression/diagnosis/epidemiology
Longitudinal Studies
Aged, 80 and over
*Population Health
*Cognitive Dysfunction/diagnosis
*Mass Screening/methods
*Cognition

@article {pmid41253750,
year = {2025},
author = {Sun, X and Dias, L and Peng, C and Zhang, Z and Ge, H and Wang, Z and Jin, J and Jia, M and Xu, T and Guo, W and Zheng, W and He, Y and Wu, Y and Cai, X and Agostinho, P and Qu, J and Cunha, RA and Zhou, X and Bai, R and Chen, JF},
title = {Author Correction: 40 Hz light flickering facilitates the glymphatic flow via adenosine signaling in mice.},
journal = {Cell discovery},
volume = {11},
number = {1},
pages = {92},
doi = {10.1038/s41421-025-00845-6},
pmid = {41253750},
issn = {2056-5968},
}

@article {pmid41253791,
year = {2025},
author = {Shi, J and Chen, D and Zhao, X and Zhao, Z and Li, S and Xu, Y and Ding, T and Zhu, Z and Zhang, P and Ye, Q and Tang, Y and Zhang, P and Tao, B and Tang, Z},
title = {HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1816},
pmid = {41253791},
issn = {2052-4463},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Cerebral Hemorrhage/rehabilitation/physiopathology ; Spectroscopy, Near-Infrared ; },
abstract = {This study introduces the first hybrid brain-computer interface dataset specifically designed for research on intracerebral hemorrhage (ICH) rehabilitation. It offers a novel data source through the synchronized acquisition of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The dataset innovatively incorporated neural recordings from 17 normal subjects and 20 patients with ICH under standardized left-right hand motor imagery (MI) paradigms, featuring systematically collected and preprocessed dual-modality neural data. Beyond raw neural signals, the resource provides feature-engineered data optimized for classification algorithms and multidimensional signal decoding. The public availability of this dataset can facilitate the validation and optimization of MI decoding algorithms and advance the development of precision rehabilitation systems based on multimodal neural feedback.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography
*Cerebral Hemorrhage/rehabilitation/physiopathology
Spectroscopy, Near-Infrared

@article {pmid41255549,
year = {2025},
author = {Hyung, W and Kim, M and Kim, Y and Im, CH},
title = {DeepAttNet: deep neural network incorporating cross-attention mechanism for subject-independent mental stress detection in passive brain-computer interfaces using bilateral ear-EEG.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1685087},
pmid = {41255549},
issn = {1662-5161},
abstract = {INTRODUCTION: Electroencephalography (EEG)-based mental stress detection has the potential to be applied in diverse real-world scenarios, including workplace safety, mental health monitoring, and human-computer interaction. However, most previous passive brain-computer interface (BCI) studies have employed EEG recorded during the performance of specific tasks, making the classification results susceptible to task engagement effects rather than reflecting stress alone. To address this limitation, we introduce a rest-versus-rest paradigm that compares resting EEG recorded immediately after exposure to a stressor with that recorded after meditation, thereby isolating mental stress from the task-related confounds. EEG recording setups were designed under the assumption of bilateral ear-EEG, a compact and discreet form factor suitable for real-world applications. Furthermore, we developed a novel subject-independent deep learning classifier tailored to model interhemispheric neural dynamics for enhanced mental stress detection performance.

METHODS: Thirty-two adults participated in the experiment. To classify mental stress status in a subject-independent manner, we proposed DeepAttNet, a deep learning model based on cross-attention and pointwise temporal compression, specifically designed to effectively capture left and right hemispherical interactions. Classification performance was assessed using eight-fold subject-level cross-validation against conventional deep learning models, including EEGNet, ShallowConvNet, DeepConvNet, and TSception. Ablation studies evaluated the impact of the cross-attention and/or pointwise compression modules.

RESULTS: DeepAttNet achieved the highest average accuracy and macro-F1 values, with performance declining when either the cross-attention or pointwise compression module was removed in the ablation studies. Explainability analyses indicated lower cross-attention entropy with stronger directional ear-to-ear asymmetry under stress, and temporal occlusion identified mid-late windows supporting stress decisions. Moreover, six of seven canonical scalp-EEG markers were FDR-significant for post-stressor vs. post-relaxation rest.

CONCLUSION: The proposed rest-versus-rest paradigm and DeepAttNet enabled robust, subject-independent mental stress detection with a fairly high accuracy using only two-channel EEG recordings. This approach is expected to offer a practical solution for continuous stress monitoring, potentially advancing passive BCI applications outside laboratory settings.},
}

@article {pmid41255819,
year = {2025},
author = {Vooijs, M and Bassil, K and van den Brink, A and van Stuijvenberg, OC and Ramsey, NF and Jongsma, KR},
title = {Ethical, legal, and sociocultural considerations in neural device explantation: a systematic review.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1568800},
pmid = {41255819},
issn = {1662-4548},
abstract = {INTRODUCTION: Implantable neural devices, including brain-computer interfaces and spinal cord stimulators, hold significant therapeutic promise for conditions such as paralysis and chronic pain. However, the novelty of these technologies introduces unique ethical challenges. While much of the existing literature emphasizes development-related concerns such as device safety, the ethical issues surrounding explantation remain relatively underexplored.

METHODS: We conducted a systematic review to identify ethical, legal, and sociocultural considerations relevant to the explantation of neural devices. The review applied the IEEE BRAIN Neuroethics framework as a guiding structure for the categorization of the themes. A subsequent thematic analysis was performed to categorize and synthesize findings across studies.

RESULTS: Thematic analysis revealed that medical motives were the predominant factor in discussions of explantation, with 83% of studies citing medical complications as a central concern. Additional themes identified included changes in cognition and behavior, emotional well-being, lack of therapeutic benefit, identity, financial issues, autonomy, post-trial considerations, and neurorights.

DISCUSSION: Our findings underscore the multifaceted nature of neural device explantation, extending beyond purely medical considerations to include psychological, financial, legal, and sociocultural dimensions. These results highlight the necessity of interdisciplinary approaches to adequately address the broad spectrum of challenges associated with explantation.},
}

@article {pmid41256173,
year = {2025},
author = {Candrea, DN and Angrick, M and Luo, S and Ganji, R and Coogan, C and Milsap, GW and Rosenblatt, KR and Uchil, A and Clawson, L and Maragakis, NJ and Vansteensel, MJ and Tenore, FV and Ramsey, NF and Fifer, MS and Crone, NE},
title = {Longitudinal study of gesture decoding in a clinical trial participant with ALS.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {41256173},
support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) have the potential to preserve or restore communication and device control in people with paralysis from a variety of causes. For people living with amyotrophic lateral sclerosis (ALS), however, the progressive loss of cortical motor neurons could theoretically pose a challenge to the stability of BCI performance. Here we tested the stability of gesture decoding with a chronic electrocorticographic (ECoG) BCI in a man living with ALS and participating in a clinical trial (ClinicalTrials.gov, NCT03567213). We evaluated offline decoding performance of attempted gestures over two periods: a 5-week period beginning roughly 2 years post-implant and a 6-week period ending roughly 5 months later. Decoder sensitivity was high in both periods (90 - 98%), while classification accuracy was 37 - 68% in the first period and worsened to 23 - 39% in the second. We investigated multiple frequency bands that were used as model features in both periods, and we observed reductions in high gamma band power (70 - 110 Hz) and between-class separation during the second period compared to the first. Over the 5-month period motor function did not appreciably decline. These results, albeit preliminary, suggest that declines in the neural population responses that drive ECoG BCI performance can occur without overt signs of disease progression in people living with ALS, and could serve as a biomarker for disease progression in the future.},
}

@article {pmid41256527,
year = {2025},
author = {Shah, NP and Krasa, BA and Kunz, E and Hahn, N and Kamdar, F and Avansino, D and Hochberg, LR and Henderson, JM and Sussillo, D},
title = {Improved interpretability in LFADS models using a learned, context-dependent per-trial bias.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41256527},
issn = {2692-8205},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
abstract = {The computation-through-dynamics perspective argues that biological neural circuits process information via the continuous evolution of their internal states. Inspired by this perspective, Latent Factor Activity using Dynamical systems (LFADS, [1]) identifies a generative model consistent with the neural activity recordings. LFADS models neural dynamics with a recurrent neural network (RNN) generator, which results in excellent fit to the data. However, it has been difficult to understand the dynamics of the LFADS generator. In this work, we show that this poor interpretability arises in part because the generator implements complex, multi-stable dynamics. We introduce a simple modification to LFADS that ameliorates issues with interpretability by providing an inferred per-trial bias (modeled as a constant input) to the RNN generator, enabling it to contextually adapt a simpler dynamical system to individual trials. In both simulated neural recordings from pendulum oscillations and real recordings during arm movements in nonhuman primates, we observed that the standard LFADS learned complex, multi-stable dynamics, whereas the modified LFADS learned easier-to-understand contextual dynamics. This enabled direct analysis of the generator, which reproduced at a single-trial level previous results shown only through more complex analyses at the trial average. Finally, we applied the per-trial inferred bias LFADS model to human intracortical brain computer interface recordings during attempted finger movements and speech. We show that modifying neural dynamics using linear operations of the per-trial bias addresses non-stationarity and identifies the extent of behavioral variability, problems known to plague BCI. We call our modification to LFADS as "contextual LFADS".},
}

@article {pmid41257158,
year = {2025},
author = {Ali, U and Khan, JA and Ahsan, MT and Altaf, B and Azreen, S and Alamu, OS and Rana, MS},
title = {Brain-Computer Interfaces in the Rehabilitation of Stroke and Spinal Cord Injury: A Systematic Review and Meta-Analysis of Clinical Efficacy.},
journal = {Cureus},
volume = {17},
number = {10},
pages = {e94833},
pmid = {41257158},
issn = {2168-8184},
abstract = {Brain-computer interfaces (BCIs) have emerged as innovative tools for neurorehabilitation, enabling patients with stroke and spinal cord injury (SCI) to engage in task-specific training through direct neural control of external devices. Despite growing evidence, the overall clinical efficacy of BCIs in functional recovery remains debated. This systematic review and meta-analysis evaluated the effectiveness of BCI-based rehabilitation on motor recovery in stroke and SCI, with a focus on upper and lower limb function. We systematically searched PubMed, EMBASE, Web of Science, and Cochrane CENTRAL for clinical trials published between January 2008 and October 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible studies included randomized controlled trials and controlled interventional trials employing BCI interventions for motor rehabilitation. Risk of bias was assessed with RoB-2 and ROBINS-I. Meta-analysis was performed using a random-effects model. Seventeen studies met the inclusion criteria, comprising both stroke (acute, subacute, and chronic phases) and SCI populations. The pooled analysis demonstrated a significant mean difference of 3.26 points on the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) in favour of BCI interventions (95% CI: 2.73-3.78, p < 0.001). Heterogeneity was negligible (I[2] = 0%). Subgroup analyses suggested that combining BCI with functional electrical stimulation or robotics yielded larger gains. BCI-based rehabilitation significantly improves motor function in stroke and SCI populations, with effect sizes exceeding the minimal clinically important difference for FMA-UE. These findings highlight the translational potential of BCIs as adjunctive therapies in neurorehabilitation. Larger, multicenter trials with standardised protocols are warranted to establish long-term efficacy and guide clinical integration.},
}

@article {pmid41257892,
year = {2025},
author = {Xiong, W and Ma, L and Li, H},
title = {Adaptive EEG preprocessing to mitigate electrode shift variability for robust motor imagery classification.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {40808},
pmid = {41257892},
issn = {2045-2322},
mesh = {*Electroencephalography/instrumentation/methods ; Electrodes ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Humans ; Brain-Computer Interfaces ; Neurological Rehabilitation/methods ; Imagination/physiology ; Motor Cortex/physiology ; Datasets as Topic ; },
abstract = {Electrode placement variability poses a critical challenge in EEG-based motor imagery tasks, often resulting in reduced classification robustness. We present the Adaptive Channel Mixing Layer (ACML), a plug-and-play preprocessing module that dynamically adjusts input signal weights through a learnable transformation matrix based on inter-channel correlations. By leveraging the inherent spatial structure of EEG caps, ACML effectively compensates for electrode misalignments and noise, enhancing resilience to signal distortion. Experimental validation on two motor imagery datasets with varying channel counts demonstrated consistent improvements in accuracy (up to 1.4%), kappa scores (up to 0.018), and robust performance across subjects, using five neural network architectures including a state-of-the-art model (ATCNet). Notably, ACML requires minimal computational overhead and no task-specific hyperparameter tuning, ensuring compatibility with diverse applications. This method offers a robust and efficient solution for advancing EEG-based motor imagery classification, with potential applications in real-time brain-computer interface systems and neurorehabilitation.},
}

*Electroencephalography/instrumentation/methods
Electrodes
*Signal Processing, Computer-Assisted
*Neural Networks, Computer
Humans
Brain-Computer Interfaces
Neurological Rehabilitation/methods
Imagination/physiology
Motor Cortex/physiology
Datasets as Topic

@article {pmid41259181,
year = {2025},
author = {Qin, C and Yang, R and Zhu, L and Chen, Z and Huang, M and Alsaadi, FE and Wang, Z},
title = {EEG-Infinity: A Mathematical Modeling-Inspired Architecture for Addressing Cross-Device Challenges in Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {4669-4686},
doi = {10.1109/TNSRE.2025.3635018},
pmid = {41259181},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; Algorithms ; Adult ; Neural Networks, Computer ; Male ; Female ; *Models, Theoretical ; Databases, Factual ; },
abstract = {The distribution of electroencephalogram (EEG) data generally varies across datasets due to the huge difference between the physical structure of brain-computer interface devices, known as cross-device variability. Such variability poses great challenges in EEG decoding and hinders the standardized utilization of EEG datasets. In this study, we explore a new issue concerning the cross-device variability problem, pointing to the gap in the existing studies facing cross-device variability. To tackle this challenge, our paper is the first to model the cross-device variability problem through a "sequentially comprehensive formula" and a "spatial comprehensive formula". Inspired by this modeling, a novel deep domain adaptation network named EEG-Infinity is proposed, incorporating replaceable EEG feature extraction backbones with a novel structure named "alignment head". To show the effectiveness of the proposed EEG-Infinity, systematic experiments are conducted across four different EEG-based motor imagery datasets under 48 cases. The experimental results highlight the superior performance of the proposed EEG-Infinity over commonly used approaches with an average classification accuracy improvement of 1.51% across 34 cases, laying a foundation for research in large-scale EEG models. The code can be assessed at https://github.com/Baizhige/cd-infinity.},
}

Humans
*Electroencephalography/methods
*Imagination/physiology
*Brain-Computer Interfaces
Algorithms
Adult
Neural Networks, Computer
Male
Female
*Models, Theoretical
Databases, Factual

@article {pmid41260504,
year = {2026},
author = {Wang, J and Gan, X and Han, M and Dong, W and He, J and Fu, K and Bore, MC and Xu, T and Klugah-Brown, B and Ferraro, S and Becker, B},
title = {Effects of exogenous oxytocin on human brain function are regulated by oxytocin gene expression: A meta-analysis of 20 years of oxytocin neuroimaging and transcriptomic analyses.},
journal = {Neuroscience and biobehavioral reviews},
volume = {180},
number = {},
pages = {106478},
doi = {10.1016/j.neubiorev.2025.106478},
pmid = {41260504},
issn = {1873-7528},
mesh = {Humans ; *Oxytocin/pharmacology/metabolism ; *Brain/drug effects/diagnostic imaging/metabolism/physiology ; Receptors, Oxytocin/metabolism ; *Transcriptome ; Neuroimaging ; Magnetic Resonance Imaging ; *Gene Expression ; },
abstract = {Over the past two decades, numerous pharmaco-imaging studies have examined the role of exogenous oxytocin (OT) in human cognition and behavior, yet results remain highly heterogeneous and the link between large-scale functional effects and molecular architecture remains unclear. To address this, we conducted a comprehensive analysis of OT-administration fMRI studies combining neuroimaging meta-analysis, meta-analytic connectivity modeling, and transcriptomics. Across 75 task-fMRI experiments (n = 2247), consistent, domain-general effects of OT administration emerged in the left thalamus, pallidum, caudate, and insula, but not in the amygdala. Connectivity modeling showed these regions form an integrated thalamus-striatum-insula circuit directly modulated by exogenous OT. Transcriptomic analyses revealed that the expression of three OT pathway genes (CD38, OXT, and OXTR) is enriched in these subcortical regions and associated with the observed neural effects. Exogenous OT's neural effects were also strongly linked with acetylcholinergic, dopaminergic, and opioidergic gene distributions, potentially reflecting functional interactions with these systems. Findings provide convergent evidence that exogenous OT exerts robust effects on human brain function via a biologically-plausible core circuit and can inform effective pharmacotherapeutic targets.},
}

Humans
*Oxytocin/pharmacology/metabolism
*Brain/drug effects/diagnostic imaging/metabolism/physiology
Receptors, Oxytocin/metabolism
*Transcriptome
Neuroimaging
Magnetic Resonance Imaging
*Gene Expression

@article {pmid41261122,
year = {2025},
author = {Gobert, F and Merida, I and Maby, E and Seguin, P and Jung, J and Morlet, D and André-Obadia, N and Dailler, F and Berthomier, C and Otman, A and Le Bars, D and Scheiber, C and Hammers, A and Bernard, E and Costes, N and Bouet, R and Mattout, J},
title = {Disorder of consciousness rather than complete Locked-In Syndrome for end stage Amyotrophic Lateral Sclerosis: a case series.},
journal = {Communications medicine},
volume = {5},
number = {1},
pages = {482},
pmid = {41261122},
issn = {2730-664X},
abstract = {BACKGROUND: The end-stage of amyotrophic lateral sclerosis (ALS) is commonly regarded as a complete Locked-In Syndrome (cLIS). Shifting the perspective from cLIS (assumed consciousness) to Cognitive Motor Dissociation (potentially demonstrable consciousness), we aimed to assess the preservation of covert awareness (internally preserved but externally inaccessible) using a multimodal battery.

METHODS: We evaluate two end-stage ALS patients using neurophysiological testing, passive and active auditory oddball paradigms, an auditory Brain-Computer Interface (BCI), functional activation-task imaging, long-term EEG, brain morphology, and resting-state metabolism to characterize underlying brain function.

RESULTS: Patient 1 initially follows simple commands but fails twice at BCI control. At follow-up, command following is no longer observed and his oddball cognitive responses disappear. Patient 2, at a single evaluation, is unable to follow commands or control the BCI. Both patients exhibit altered wakefulness, brain atrophy, and a global cortico-subcortical hypometabolism pattern consistent with a disorder of consciousness, regarded as an extreme manifestation of ALS-associated fronto-temporal dementia.

CONCLUSIONS: Although it is not possible to firmly prove the absence of awareness, each independent measure concurred with suggesting that a "degenerative disorder of consciousness" rather than a cLIS may constitute the final stage of ALS. This condition appears pathophysiologically distinct from typical tetraplegia and anarthria, in which behavioural communication and BCI use persist to enhance quality of life. Identifying the neuroimaging signatures of this condition represents a substantial milestone in understanding end-stage ALS. Large-scale longitudinal investigations are warranted to determine the prevalence of this profile among patients whose communication appears impossible.},
}

@article {pmid41262557,
year = {2025},
author = {Zhang, X and Wang, S and Gao, Y and Wang, Y and Qiu, S and He, H},
title = {Enhancing visual brain-computer interface through V1-targeted RTMS by modulating visual attention.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {3},
number = {},
pages = {},
pmid = {41262557},
issn = {2837-6056},
abstract = {Brain-computer interfaces (BCIs) enable users to control devices directly through brain activity. Despite recent advancements in machine-learning algorithms, the signal-to-noise ratio (SNR) of the brain's responses still limits decoding performance, highlighting the necessity for targeted neuromodulation techniques to overcome this limitation. To evaluate whether 5 Hz repetitive transcranial magnetic stimulation (rTMS) targeting the primary visual cortex (V1) can enhance SSVEP-based BCI performance by improving neural signal SNR and modulating visual network dynamics. Twenty-four healthy subjects underwent both real and sham rTMS in a randomized order. The rTMS was precisely implemented through magnetic resonance imaging (MRI)-guided navigation to stimulate V1 in participants. Electroencephalograms (EEGs) were recorded during SSVEP tasks and resting-state before, immediately after, and 20 min after rTMS. SSVEP tasks were conducted across four frequency bands: low frequency (LF: 8-12 Hz), middle frequency (MF: 18-22 Hz), high frequency (HF: 28-32 Hz), and super high frequency (SHF: 38-42 Hz). The discriminability of BCI commands in the MF (+7.53%) and HF (+11.4%) bands significantly improved (p < 0.001), driven by enhanced prominence of both fundamental and harmonic components (p < 0.01). Quantitative analysis indicated that the improved SNR was due to the suppression of the background activity (p < 0.05). This effect was linked to rTMS-induced enhancements in visual attention, evidenced by increased occurrence and contribution of microstate B during the SSVEP task (p < 0.01). This study highlights the potential of 5 Hz rTMS as an effective neuromodulatory tool for optimizing BCI performance, particularly through facilitating visual attention.},
}

@article {pmid41264938,
year = {2025},
author = {Fang, S and Zhao, X and Wang, Z and Si, Y and Liu, H and Hu, H and Xu, T and Zhou, T},
title = {Enhancing SSVEP-BCI performance through multi-stimulus discriminant fusion analysis.},
journal = {Journal of neural engineering},
volume = {22},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ae220d},
pmid = {41264938},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; *Photic Stimulation/methods ; Female ; Adult ; Discriminant Analysis ; Young Adult ; Algorithms ; },
abstract = {Objective.To enhance frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), particularly under short data acquisition and complex environmental conditions.Approach.We propose multi-stimulus discriminant fusion analysis (MSDFA), a novel method that integrates multi-stimulus strategies with discriminant modeling. MSDFA was evaluated on two public datasets (Benchmark and BETA) and compared with conventional approaches including eCCA, eTRCA, and their variants.Main results.MSDFA consistently outperformed existing methods across different data lengths and training block quantities. It achieved maximum information transfer rates of 247.17 ± 10.15 bpm on the Benchmark dataset and 192.72 ± 9.44 bpm on the BETA dataset, demonstrating superior robustness and efficiency.Significance.By combining complementary algorithmic strengths, MSDFA improves adaptability to individual variability and complex environments, advancing the practical utility and reliability of SSVEP-BCI systems.},
}

Humans
*Brain-Computer Interfaces
*Evoked Potentials, Visual/physiology
*Electroencephalography/methods
Male
*Photic Stimulation/methods
Female
Adult
Discriminant Analysis
Young Adult
Algorithms

@article {pmid41265571,
year = {2026},
author = {Luo, R and Meng, J and Wei, Y and Mai, X and Li, G},
title = {Outcome processing response coupled to feedback-related EEG dynamics during discrete and continuous performance monitoring.},
journal = {Journal of neuroscience methods},
volume = {426},
number = {},
pages = {110629},
doi = {10.1016/j.jneumeth.2025.110629},
pmid = {41265571},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Young Adult ; Adult ; *Psychomotor Performance/physiology ; *Feedback, Psychological/physiology ; *Evoked Potentials/physiology ; *Brain/physiology ; Reaction Time/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Error-related potential (ErrP) reflects the inconsistency between internal expectation and external feedback outcome. Despite the exploration of numerous experimental paradigms, ErrP components exhibit distinct latency and amplitude across different paradigms. However, previous studies have not quantitatively correlated potential influencing factors with this ErrP variability. Additionally, these qualitatively analyzed factors offer limited predictions for ErrP in new paradigms.

NEW METHOD: We proposed that a neutral condition removing goal-directed outcome expectations reflects cross-paradigm variability in correct and erroneous outcome responses. This neutral condition was designed as a control condition for each paradigm. Three different paradigms were designed to provide discrete and continuous varied feedback outcomes. Correlations were assessed between neutral condition responses and correct and erroneous outcome responses in latency and amplitude. The predictive effectiveness of neutral condition responses for new paradigms was further evaluated through single-trial cross-paradigm classification.

RESULTS: Correct and erroneous outcome responses were observed to have significant latency and amplitude coupling with these neutral condition responses in the middle frontal and bilateral parietal regions. Results from source reconstruction, pupillometry data, and workload score confirm that the neutral condition serves as the baseline response for outcome processing responses. This baseline relationship explains the cross-paradigm ErrP variability.

The single-trial decoding results show that utilizing neutral condition responses can significantly increase the accuracy of cross-paradigm classification by up to 7 % and 17 % with covariance-based and amplitude-based approaches.

CONCLUSION: These findings provide a quantitative physiological explanation for cross-paradigm ErrP variability and promote transfer learning applications in ErrP-based BCIs.},
}

Humans
*Electroencephalography/methods
Male
Female
Young Adult
Adult
*Psychomotor Performance/physiology
*Feedback, Psychological/physiology
*Evoked Potentials/physiology
*Brain/physiology
Reaction Time/physiology
Signal Processing, Computer-Assisted

@article {pmid41266142,
year = {2025},
author = {Chu, JP and Coulter, ME and Denovellis, EL and Nguyen, TTK and Liu, DF and Deng, X and Eden, UT and Kemere, CT and Frank, LM},
title = {RealtimeDecoder: A Fast Software Module for Online Clusterless Decoding.},
journal = {eNeuro},
volume = {12},
number = {12},
pages = {},
pmid = {41266142},
issn = {2373-2822},
support = {F32 MH123003/MH/NIMH NIH HHS/United States ; R01 NS115233/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Software ; *Hippocampus/physiology ; Algorithms ; Rats ; Action Potentials/physiology ; *Neurons/physiology ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing neural activity. That, in turn, allows experimenters to test hypotheses about the role of that content in driving downstream activity patterns and behaviors. While multiple real-time systems have been developed, they are typically implemented with a compiled programming language, making them more difficult for users to quickly adapt for new experiments. Here we present a software system written in the widely used Python programming language to facilitate rapid experimentation. Our solution implements the state space based clusterless decoding algorithm for an online, real-time environment. The parallelized application processes neural data with temporal resolution of 6 ms and median computational latency <50 ms for medium- to large-scale (32+ tetrodes) rodent hippocampus recordings without the need for spike sorting. It also executes auxiliary functions such as detecting sharp wave ripples from local field potential data. Even with an interpreted language, the performance is similar to state-of-the-art solutions that use compiled programming languages. We demonstrate this real-time decoder in a rat behavior experiment in which the decoder allowed closed-loop neurofeedback based on decoded hippocampal spatial representations. Overall this system provides a powerful and easy-to-modify tool for real-time feedback experiments.},
}

Animals
*Software
*Hippocampus/physiology
Algorithms
Rats
Action Potentials/physiology
*Neurons/physiology
Male
*Signal Processing, Computer-Assisted

@article {pmid41266624,
year = {2025},
author = {Sen, O and Soni, R and Virmani, D and Parekh, A and Lehman, P and Jena, S and Katikhaneni, A and Khalifa, A and Chatterjee, B},
title = {A low-latency neural inference framework for real-time handwriting recognition from EEG signals on an edge device.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {41040},
pmid = {41266624},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Handwriting ; Male ; Female ; Adult ; Signal-To-Noise Ratio ; Machine Learning ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) hold significant promise for restoring communication in individuals with severe motor or speech impairments. Imagined handwriting, as a form of motor imagery, offers an intuitive paradigm for character-level neural decoding. While invasive techniques such as electrocorticography (ECoG) offer high decoding accuracy, their surgical requirements pose clinical risks and hinder scalability. Non-invasive alternatives like electroencephalography (EEG) are safer and more accessible but suffer from low signal-to-noise ratio (SNR) and spatial resolution, limiting their effectiveness in high-resolution decoding. Here, we investigate how advanced machine learning, combined with informative feature extraction, can overcome these limitations, enabling EEG-based decoding performance that approaches invasive methods, while supporting real-time inference on edge devices. We present the first real-time, low-latency, high-accuracy system for decoding imagined handwriting from non-invasive EEG signals on a portable edge device. EEG data were collected from 15 participants using a 32-channel headcap and preprocessed with bandpass filtering and artifact subspace reconstruction (ASR). We extracted 85 time domain, frequency domain, and graphical features, then applied Pearson correlation coefficient-based feature selection to reduce latency while preserving accuracy. We developed a hybrid architecture, EEdGeNet, which integrates a Temporal Convolutional Network (TCN) with a multilayer perceptron (MLP), trained on the extracted features and deployed on the NVIDIA Jetson TX2 for real-time inference. The system achieved [Formula: see text] accuracy with 914.18 ms per-character inference latency. By selecting only ten key features, the model incurred a minimal accuracy loss of [Formula: see text], while achieving a [Formula: see text] reduction in inference latency (202.62 ms) compared to the full 85-feature set. These findings show that non-invasive EEG, combined with efficient feature and model design, can enable accurate, real-time neural decoding on low-power edge devices, paving the way for practical, portable BCIs.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
*Handwriting
Male
Female
Adult
Signal-To-Noise Ratio
Machine Learning
Young Adult
Signal Processing, Computer-Assisted

@article {pmid41268354,
year = {2025},
author = {Haro, S and Beauchene, C and Quatieri, TF and Smalt, CJ},
title = {A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.},
journal = {IEEE access : practical innovations, open solutions},
volume = {13},
number = {},
pages = {189903-189914},
pmid = {41268354},
issn = {2169-3536},
support = {T32 DC000038/DC/NIDCD NIH HHS/United States ; },
abstract = {There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement. This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy were used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding. In this study, we found evidence of suppression of (i.e., reduction in) net neural tracking and decoding of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.02, Cohen's d = -1.29, 95% CI [-0.02, -0.01] and p = 0.01, Cohen's d = -1.56, 95% CI [-7.25, -3.44], respectively). We did not find a statistically significant increase in the neural tracking or decoding of the attended talker. These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.},
}

@article {pmid41272829,
year = {2025},
author = {Shi, Y and Ma, J and Zhao, X and Zhao, H and Wang, D and Zhang, X and Zhu, X and Meng, L and Ming, D},
title = {Bilateral intermittent theta-burst stimulation as a priming strategy to enhance action observation and imitation training in early parkinson's disease: a proof-of-concept study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {247},
pmid = {41272829},
issn = {1743-0003},
support = {82372083//National Natural Science Foundation of China/ ; 2022YFF1202500//National Key Research and Development Program of China/ ; },
mesh = {Humans ; *Parkinson Disease/rehabilitation/physiopathology ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Double-Blind Method ; Middle Aged ; Aged ; Cross-Over Studies ; Proof of Concept Study ; Evoked Potentials, Motor/physiology ; *Gait Disorders, Neurologic/rehabilitation/physiopathology/etiology ; Motor Cortex/physiopathology ; *Imitative Behavior/physiology ; Postural Balance/physiology ; Theta Rhythm/physiology ; },
abstract = {BACKGROUND: Action observation and imitation training (AOIT) is an evidence-based cognitive-motor rehabilitation strategy for Parkinson's disease (PD), particularly for the postural instability and gait disorder (PIGD) subtype. However, its effectiveness may decline with disease-related impairments in neuroplasticity. Intermittent theta burst stimulation (iTBS), a patterned repetitive transcranial magnetic stimulation protocol, can induce LTP-like plasticity and may enhance responsiveness to rehabilitation. This study investigated whether iTBS priming augments AOIT effects on gait and cognition in early-stage PIGD and explored underlying neurophysiological mechanisms.

METHODS: Fifteen patients with early-stage PIGD participated in a randomized, double-blind, sham-controlled crossover trial. Each phase included five consecutive days of AOIT preceded by either real or sham iTBS applied over the bilateral leg region of the primary motor cortex, separated by a washout period of more than four weeks. Pre- and post-intervention assessments included dual-task gait analysis, cognitive tests, clinical scales, neurophysiological measures (motor evoked potentials, cortical silent period), and resting-state EEG power spectral density.

RESULTS: Both conditions improved balance and gait measures. However, real iTBS significantly enhanced dual-task gait automaticity (F = 5.558, P = 0.026) and global cognition (F = 5.294, P = 0.026) compared to sham. Real iTBS also increased cortical silent period (F = 4.655, P = 0.040) and MEP-based cortical plasticity response (F = 6.131, P = 0.020). Improvements in cortical plasticity were significantly correlated with better gait performance (r = - 0.429, P = 0.020) and motor scores (r = - 0.463, P = 0.011). No adverse events were reported.

CONCLUSIONS: Bilateral iTBS targeting the leg representation of the primary motor cortex can potentiate AOIT effects in early-stage PIGD by enhancing cortical plasticity and motor learning. These findings support the integration of iTBS as a priming strategy within cognitive-motor rehabilitation protocols for PD. Trial registration Chinese Clinical Trial Registry, ChiCTR2300067657. Registered January 17, 2023.},
}

Humans
*Parkinson Disease/rehabilitation/physiopathology
*Transcranial Magnetic Stimulation/methods
Male
Female
Double-Blind Method
Middle Aged
Aged
Cross-Over Studies
Proof of Concept Study
Evoked Potentials, Motor/physiology
*Gait Disorders, Neurologic/rehabilitation/physiopathology/etiology
Motor Cortex/physiopathology
*Imitative Behavior/physiology
Postural Balance/physiology
Theta Rhythm/physiology

@article {pmid41273580,
year = {2026},
author = {Miloulis, ST and Kakkos, I and Zorzos, I and Karampasi, A and Anastasiou, A and Asvestas, P and Ventouras, EC and Kalatzis, I and Matsopoulos, GK},
title = {Deep Learning Discrimination for BCI Implementation Using 3D Convolutional Neural Network and EEG Topographic Maps.},
journal = {Advances in experimental medicine and biology},
volume = {1487},
number = {},
pages = {405-413},
pmid = {41273580},
issn = {0065-2598},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Deep Learning ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Adult ; Male ; Convolutional Neural Networks ; },
abstract = {The growing interest in improved rehabilitation systems and assistive technologies for individuals with motor impairments necessitates the need for new applications of Deep Learning approaches for Brain-Computer Interface (BCI) implementation. This study investigates the application of Deep Learning techniques, specifically the Hierarchical 3D Convolutional Neural Network (H3DCNN) model, for enhancing classification systems utilizing electroencephalography (EEG) data. As such, topographic maps were extracted from EEG signals in a real motion task experiment integrating 4 different motions. The H3DCNN model was then employed in a step-wise fashion to classify and decode the EEG signals, demonstrating its effectiveness in distinguishing between different movement intentions. Moreover, three different optimizers were implemented, including RMSprop, Adam, and Stochastic Gradient Descent (SGD), to further assess and enhance the model performance. The findings indicate that the integration of advanced deep learning techniques can significantly enhance the accuracy and reliability of BCI systems, with RMSprop and SGD showing superior results in terms of accuracy. Moreover, our results illustrate the possibility of decoding neural mechanisms via deep learning paradigms, paving the way for future developments in BCI applications, thus aiming to improve the quality of life for individuals with motor impairments.},
}

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

@article {pmid41274087,
year = {2026},
author = {Xu, H and Lin, N},
title = {Neurovista: A bidirectional masked cross-Modal fusion network for robust EEG-to-Image decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {195},
number = {},
pages = {108297},
doi = {10.1016/j.neunet.2025.108297},
pmid = {41274087},
issn = {1879-2782},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Brain/physiology ; Attention/physiology ; },
abstract = {Electroencephalography (EEG)-based visual decoding has significant potential in brain-computer interfaces but faces substantial challenges due to noise, inter-subject variability, and limited fine-grained alignment between neural signals and visual representations. Existing approaches predominantly utilize global EEG embeddings and static fusion methods, restricting their capability to capture nuanced cross-modal interactions. To address these limitations, We propose NeuroVista, a novel framework that integrates localized EEG masking with dynamic bidirectional cross-modal attention, achieving state-of-the-art EEG-to-image decoding performance. Specifically, NeuroVista employs a channel-level EEG masking strategy during training, encouraging the model to learn robust, context-sensitive neural features, thus significantly improving generalization and noise resistance. Simultaneously, our bidirectional cross-modal attention module dynamically aligns EEG embeddings with corresponding visual features, enhancing semantic coherence across modalities. Extensive experiments on standard EEG-to-image benchmarks demonstrate that NeuroVista consistently outperforms state-of-the-art methods, achieving up to +16.0 % top-1 accuracy improvement in both subject-dependent and subject-independent settings. Our results validate the effectiveness of combining localized masking and interactive cross-modal attention, establishing NeuroVista as a robust, interpretable, and highly generalizable approach for EEG-based visual decoding tasks.},
}

*Electroencephalography/methods
Humans
*Neural Networks, Computer
*Brain-Computer Interfaces
*Brain/physiology
Attention/physiology

@article {pmid41274911,
year = {2025},
author = {Hardstone, R and Ostrowski, LM and Dusang, AN and López-Larraz, E and Jesser, J and Cash, SS and Cramer, SC and Hochberg, LR and Ramos-Murguialday, A and Lin, DJ},
title = {Extension of voxel-based lesion mapping to multidimensional neurophysiological data.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {41488},
pmid = {41274911},
issn = {2045-2322},
support = {IK2 RX004237/RX/RRD VA/United States ; FMD clinical research fellowship//MGH ECOR/ ; Clinical research Training Scholar//American Academy of Neurology/ ; 1IK2RX004237//U.S. Department of Veterans Affairs/ ; },
mesh = {Humans ; Electroencephalography/methods ; Male ; *Brain Mapping/methods ; Female ; *Stroke/physiopathology/pathology/diagnostic imaging ; Middle Aged ; Aged ; *Brain/physiopathology/pathology ; Adult ; },
abstract = {Focal brain lesions cause neurophysiological changes in local and distributed neural systems. While electroencephalography (EEG) has a long history in post-stroke neurophysiological assessment, the observed changes have rarely been linked to specific lesion locations, leaving neuroanatomical-neurophysiological relationships after stroke unclear. Current data-driven methods, such as voxel-based lesion symptom mapping (VLSM), relate lesion locations to single-feature "symptoms" but currently cannot associate anatomical injury with multidimensional data such as EEG, with its rich spatiotemporal information. To overcome this limitation, we introduce MD-VLM, an extension of VLSM to multidimensional "symptoms" that identifies relationships between lesion locations and neurophysiology. MD-VLM is data-agnostic, compatible with various lesion (e.g., lesion maps, lesion network maps) and neurophysiological (e.g., channel-level or source-localized EEG) inputs, and uses robust statistics to test for the existence of significant neuroanatomical-neurophysiological relationships. We demonstrate MD-VLM's feasibility by applying it to EEG from chronic stroke patients performing a cued-movement task. MD-VLM revealed significant associations between frontal white-matter lesions and reduced ipsilesional parietal cue-evoked responses, consistent with damage to known fronto-parietal networks. MD-VLM is a novel data-driven extension to VLSM for multidimensional "symptoms". Applying MD-VLM to link lesions to neurophysiological data can improve mechanistic understanding of post-stroke neurological impairments and guide future biomarker development.},
}

Humans
Electroencephalography/methods
Male
*Brain Mapping/methods
Female
*Stroke/physiopathology/pathology/diagnostic imaging
Middle Aged
Aged
*Brain/physiopathology/pathology
Adult

@article {pmid41275665,
year = {2026},
author = {Li, T and An, X and Di, Y and Wang, H and Yan, Y and Liu, S and Dong, Y and Ming, D},
title = {Fuzzy symbolic convergent cross mapping: A causal coupling measure for EEG signals in disorders of consciousness patients.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {195},
number = {},
pages = {108318},
doi = {10.1016/j.neunet.2025.108318},
pmid = {41275665},
issn = {1879-2782},
mesh = {Humans ; *Electroencephalography/methods ; *Consciousness Disorders/physiopathology/diagnosis ; *Fuzzy Logic ; Male ; Adult ; *Brain/physiopathology ; Female ; Middle Aged ; },
abstract = {Accurate and timely diagnosis in disorders of consciousness (DOC) patients remains a core clinical challenge. Electroencephalography (EEG) shows strong potential for detecting physiological biomarkers of consciousness, and brain network analysis serves as an effective technique. Therefore, a robust approach to brain network construction is of great significance. The convergent cross mapping (CCM) is a powerful tool for capturing the coupling relationship between two signals. However, a major drawback of CCM is its sensitivity to noise. To address this problem, we proposed a symbolic method that combines fuzzy membership functions called fuzzy symbolic convergent cross mapping (FuzzSCCM). Through the simulation results, we verified its robustness to noise, sensitivity to coupling, and data length. Building on this coupling measure, we constructed EEG brain networks and validated the approach on real DOC EEG datasets. In patients with DOC, FuzzSCCM identified distinct network features between vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). Specifically, compared with the MCS group, the VS group showed greater asymmetry between the left hemisphere and the right hemisphere in the α band, and was relatively less active in the anterior in the θ band. Moreover, our results demonstrate spontaneous transitions between distinct brain network states, suggesting these dynamic reconfigurations may constitute a fundamental mechanism underlying consciousness modulation. These findings provide novel insights into the dynamic neural signatures of DOC, while establishing a potential diagnostic tool.},
}

Humans
*Electroencephalography/methods
*Consciousness Disorders/physiopathology/diagnosis
*Fuzzy Logic
Male
Adult
*Brain/physiopathology
Female
Middle Aged

@article {pmid41278188,
year = {2025},
author = {Li, Z and Kambara, H and Koike, Y},
title = {Neural signatures of engagement in driving: comparing active control and passive observation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1698625},
pmid = {41278188},
issn = {1662-4548},
abstract = {Understanding how the human brain differentiates between active engagement and passive observation is a fundamental question in cognitive neuroscience. Using a matched-stimulus driving paradigm to isolate engagement from sensory input, we recorded whole-brain EEG while participants performed a manual control task and passively viewed a replay of their own performance. Manual control elicited distinct spectral signatures, including stronger frontal midline theta power and, paradoxically, greater occipital alpha power, consistent with heightened cognitive control and active attentional filtering. While a classifier could distinguish these states with high within-subject accuracy, performance declined in cross-subject validation, highlighting inter-individual variability. These findings delineate the distinct neural signatures of active versus passive engagement under controlled conditions. This work establishes a foundational neurophysiological baseline that can inform research on cognitive state monitoring and the design of neuroadaptive systems in complex human-machine interaction.},
}

@article {pmid41279978,
year = {2025},
author = {Canfield, RA and Ouchi, T and Fang, H and Macagno, B and Smith, LI and Scholl, LR and Orsborn, AL},
title = {The spatiotemporal structure of neural activity in motor cortex during reaching.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41279978},
issn = {2692-8205},
support = {P51 OD010425/OD/NIH HHS/United States ; R01 NS134634/NS/NINDS NIH HHS/United States ; TL1 TR002318/TR/NCATS NIH HHS/United States ; UF1 NS126485/NS/NINDS NIH HHS/United States ; },
abstract = {Intracortical brain-computer interfaces (BCI) leverage knowledge about neural representations to translate movement-related neural activity into actions. BCI implants have targeted broad cortical regions known to have relevant motor representations, but emerging technologies will allow flexible targeting to specific neural populations. The structure of motor representations at this scale, however, has not been well characterized across frontal motor cortices. Here, we investigate how motor representations and population dynamics (temporal coordination) vary across a large expanse of frontal motor cortices. We used high-density, laminar, microelectrode arrays to simultaneously record many neurons and then sampled neural populations across frontal motor cortex in two monkeys while they performed a reaching task. Our experiments allowed us to map neuronal activity across three spatial dimensions and relate them to movement. Target decoding analysis revealed that task information was heterogeneously distributed across the cortical surface and in depth. Similarly, we found that the temporal dynamics of different neural populations were heterogeneous, but that the amount of task information predicted which neural populations had similar dynamics. The neural populations with the most similar dynamics were composed of neurons with high task information regardless of spatial location. Our results highlight the spatiotemporal complexity of motor representations across frontal motor cortex at the level of neurons and neural populations, where well-learned movements consistently recruit a spatially distributed subset of neurons. Further insights into the spatiotemporal structure of neural activity patterns across frontal motor cortex will be critical to guide future implants for improved BCI performance.},
}

@article {pmid41280332,
year = {2025},
author = {Wu, Z and Yu, S and Tian, D and Cheng, L and Jing, J},
title = {Microglial TREM2 and cognitive impairment: insights from Alzheimer's disease with implications for spinal cord injury and AI-assisted therapeutics.},
journal = {Frontiers in cellular neuroscience},
volume = {19},
number = {},
pages = {1705069},
pmid = {41280332},
issn = {1662-5102},
abstract = {Cognitive impairment is a frequent but underrecognized complication of neurodegenerative and traumatic central nervous system disorders. Although research on Alzheimer's disease (AD) revealed that microglial triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in inhibiting neuroinflammation and improving cognition, its contribution to cognitive impairment following spinal cord injury (SCI) is unclear. Evidence from AD shows that TREM2 drives microglial activation, promotes pathological protein clearance, and disease-associated microglia (DAM) formation. SCI patients also experience declines in attention, memory, and other functions, yet the specific mechanism of these processes remains unclear. In SCI, microglia and TREM2 are involved in inflammation and repair, but their relationship with higher cognitive functions has not been systematically examined. We infer that TREM2 might connect injury-induced neuroinflammation in the SCI with cognitive deficits, providing a new treatment target. Artificial intelligence (AI) offers an opportunity to accelerate this endeavor by incorporating single-cell transcriptomics, neuroimaging, and clinical data for the identification of TREM2-related disorders, prediction of cognitive trajectories, and applications to precision medicine. Novel approaches or modalities of AI-driven drug discovery and personalized rehabilitation (e.g., VR, brain-computer interface) can more precisely steer these interventions. The interface between lessons learned from AD and SCI for generating new hypotheses and opportunities for translation.},
}

@article {pmid41281231,
year = {2025},
author = {Patel, D and Tanveer, MS and Gonzalez-Ferrer, J and Loeffler, A and Kagan, BJ and Mostajo-Radji, MA and Wang, G},
title = {A Computational Perspective on NeuroAI and Synthetic Biological Intelligence.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {41281231},
issn = {2331-8422},
support = {RM1 HG011543/HG/NHGRI NIH HHS/United States ; U24 MH132628/MH/NIMH NIH HHS/United States ; U24 NS146314/NS/NINDS NIH HHS/United States ; },
abstract = {NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied intelligence. In this review, we organize the NeuroAI landscape into three interacting domains: hardware, software, and wetware. We outline computational frameworks that integrate biological and non-biological systems and highlight recent advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning. These developments collectively point toward a new class of systems that compute through interactions between living neural tissue and digital algorithms.},
}

@article {pmid41281344,
year = {2025},
author = {Xu, M and He, Z and Zhou, J and Zhao, J and Tian, X and Cheng, Q and Lin, Y and Xin, H and Mou, C and Xue, Q and Luo, B},
title = {Altered oral microbiomes in patients with prolonged disorders of consciousness.},
journal = {Journal of oral microbiology},
volume = {17},
number = {1},
pages = {2577220},
pmid = {41281344},
issn = {2000-2297},
abstract = {BACKGROUND: The host microbiome is increasingly recognized as a key modulator of brain function and disease progression, yet the role of the oral microbiome in patients with prolonged disorders of consciousness remains underexplored.

METHODS: This study characterized oral microbiota differences among pDoC patients (n = 89) in the vegetative state (VS), the minimally conscious state (MCS), and emerging from the MCS (EMCS), with a particular focus on the impact of antibiotic use. We used 16S ribosomal RNA sequencing to profile oral microbiota in patients with different levels of consciousness.

RESULTS: β-diversity was significantly reduced in the VS group compared to the EMCS group. Differential abundance analysis identified five taxa (i.e., species Streptococcus danieliae, species Corynebacterium durum, family Lachnospiraceae, species Phocaeicola abscessus, and species Campylobacter showae) that exhibited significant differences between VS and EMCS, suggesting they were potentially involved in regulating oral microbial dysbiosis and brain-microbiome interactions. Antibiotic treatment induced pronounced microbial shifts in the VS group, while no such effect was observed in the MCS or EMCS groups. Prognostic models built using differential and dominant microbiota panels demonstrated strong predictive performance, achieving areas under the curve of 0.820 and 0.920, respectively.

CONCLUSIONS: These findings highlight oral microbiome alterations in pDoC and their potential relevance to disease progression, emphasizing the importance of microbiome-informed clinical strategies.},
}

@article {pmid41281720,
year = {2025},
author = {Gan, L and Yuan, S and Guo, M and Wang, Q and Deng, Z and Jia, B},
title = {Triboelectric nanogenerators for neural data interpretation: bridging multi-sensing interfaces with neuromorphic and deep learning paradigms.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1691017},
pmid = {41281720},
issn = {1662-5188},
abstract = {The rapid growth of computational neuroscience and brain-computer interface (BCI) technologies require efficient, scalable, and biologically compatible approaches for neural data acquisition and interpretation. Traditional sensors and signal processing pipelines often struggle with the high dimensionality, temporal variability, and noise inherent in neural signals, particularly in elderly populations where continuous monitoring is essential. Triboelectric nanogenerators (TENGs), as self-powered and flexible multi-sensing devices, offer a promising avenue for capturing neural-related biophysical signals such as electroencephalography (EEG), electromyography (EMG), and cardiorespiratory dynamics. Their low-power and wearable characteristics make them suitable for long-term health and neurocognitive monitoring. When combined with deep learning models-including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and spiking neural networks (SNNs)-TENG-generated signals can be efficiently decoded, enabling insights into neural states, cognitive functions, and disease progression. Furthermore, neuromorphic computing paradigms provide an energy-efficient and biologically inspired framework that naturally aligns with the event-driven characteristics of TENG outputs. This mini review highlights the convergence of TENG-based sensing, deep learning algorithms, and neuromorphic systems for neural data interpretation. We discuss recent progress, challenges, and future perspectives, with an emphasis on applications in computational neuroscience, neurorehabilitation, and elderly health care.},
}

@article {pmid41282818,
year = {2025},
author = {Johnson, TR and Foli, C and Conlan, EC and Koenig, KA and Lowe, MJ and Memberg, WD and Kirsch, RF and Herring, EZ and Bazarek, SF and Graczyk, EL and Taylor, DM and Ajiboye, AB and Sweet, J},
title = {Targeting Optimal Grasp-Related Cortical Areas for Intracortical Brain-Machine Interfaces after Spinal Cord Injury.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.10.10.25337598},
pmid = {41282818},
abstract = {OBJECTIVE: This study aimed to optimize intracortical microelectrode array implantation sites for grasp-related motor decoding by integrating anatomical, functional, and vascular imaging with preoperative 3D modeling.

METHODS: A participant with C5 tetraplegia underwent anatomical magnetic resonance imaging (MRI), diffusion-weighted imaging, and task-based functional MRI (fMRI) to identify grasp-related cortical regions while avoiding vasculature and speech-critical areas. Quicktome software was used to refine target selection by integrating structural connectivity and functional activation data. A 3D-printed skull and cortical model enabled preoperative planning, including craniotomy and electrode positioning simulations. Electrode placement was validated post-operatively using neural data collected from the implanted arrays during attempted movements of the arm and hand.

RESULTS: Functional imaging identified distinct grasp-related activation in anterior intraparietal area (AIP), ventral premotor cortex (PMv), and inferior frontal gyrus (IFG). AIP was selected based on its strong connectivity with motor cortex and distinct functional activation. Subregions 6v and 6r of PMv, which exhibited robust grasp-related activity and were surgically accessible, were chosen over the posterior IFG region, which extended into a sulcus making implantation difficult. Post-surgically, the arrays enabled high-fidelity decoding of arm/hand movements, achieving a combined accuracy of 96%.

CONCLUSION: This study presents a multi-modal approach for optimizing intracortical electrode placement by combining MRI-based anatomical mapping, fMRI-guided functional localization, connectivity information, and 3D surgical modeling. These findings demonstrate an effective method for identifying surgically feasible grasp network implant locations in a paralyzed individual. This is an essential step for brain-machine interface (BMI) systems that use grasp-related brain activity to command devices, such as neuromuscular stimulation systems for restoring upper limb function in individuals with spinal cord injury (SCI).},
}

@article {pmid41282873,
year = {2025},
author = {Chen, H and Wang, J and Lai, S and Peng, G and Zong, G and Yuan, C and Luo, B},
title = {Smoking Cessation, Weight Change, and Risk of Dementia: A Prospective Cohort Study.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {41282873},
abstract = {OBJECTIVES: To assess the associations of smoking cessation and post-cessation weight gain with the risk of dementia and cognitive trajectories.

DESIGN: Prospective cohort study.

SETTING: The U.S. Health and Retirement Study (1995-2020).

PARTICIPANTS: A total of 32,802 dementia-free participants were included, with a mean age of 60.5 years (SD 10.7) and 57.1% female.

EXPOSURE: Smoking status and body weight were collected biennially via structural interviews.

MAIN OUTCOME MEASURES: Dementia was identified using the Langa-Weir algorithm. Cognitive function was assessed using a 27-unit scale. Cox proportional hazard models estimated hazard ratio (HR) of dementia by smoking cessation status, subsequent weight change, and duration of cessation. Among participants who quit during follow-up, linear mixed models assessed cognitive trajectories before and after cessation.

RESULTS: Over 25 years of follow-up, 5,868 dementia cases were documented. Compared with current smokers, individuals who quit during follow-up had a lower dementia risk after quitting (HR: 0.82, 95% confidence interval: 0.72-0.93), similar to those who had quit before baseline (0.76, 0.69-0.83) and to never smokers (0.72, 0.66-0.79). The benefits of cessation were largely limited to participants with no or modest weight gain (≤5 kg). By contrast, quitting accompanied by >10 kg weight gain was marginally associated with higher dementia risk (1.31, 0.95-1.80). Dementia risk declined steadily with increasing cessation duration, reaching the level of never smokers after approximately 5-7 years. Cognitive trajectory analyses showed that quitting was associated with long-term slower cognitive decline but no transient change, especially among those with no or modest weight gain.

CONCLUSIONS: Smoking cessation was associated with a sustained lower dementia risk and slower cognitive decline, comparable to benefits observed in never smokers and without evidence of a short-term risk increase. However, substantial post-cessation weight gain may attenuate these advantages. Smoking cessation programs should incorporate weight-management strategies to optimize long-term brain health.},
}

@article {pmid41284444,
year = {2025},
author = {Liu, J and Li, M and Li, Z and Yang, Y and Qi, Q},
title = {DA-META: A Dual Attention Meta-Learning Framework for Unsupervised Motor Imagery Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3636462},
pmid = {41284444},
issn = {2168-2208},
abstract = {Motor imagery electroencephalography (MI-EEG) decoding demonstrates significant potential for paralysis rehabilitation, and its generalization capability is often compromised by intersubject variability and scarcity of labeled target domain data. Meta-learning has emerged as a promising approach for unsupervised domain adaptation problem. However, existing implementations suffer from two critical limitations: insufficient feature extraction and overlooking the guiding role of unlabeled target data. To overcome these challenges, we propose a dual-attention meta-learning framework (DA-META) with model-agnostic architecture in this paper. The framework comprises three stages: meta-task construction, guided meta-training, and fine-tuning-free meta-testing. In the guided meta-training stage, DA-META incorporates two key attention mechanisms: an enhanced temporal attention module for effective feature extraction, and a cosine similarity-based attention module to leverage the guidance of target domain. Using EEGNet as the backbone network, DA-META achieves mean classification accuracies of 68.04% and 76.61% on self-collected datasets from patients and healthy subjects, and 73.29% and 80.93% on the public BCI Competition IV 2a and 2b datasets, outperforming state-of-the-art methods. When employing EEGNet, DeepConvNet, and EEG Conformer as backbone networks respectively, the framework achieves accuracy improvements of 5.17%, 2.56%, and 0.85% on the 2a dataset, compared to the baseline. These results demonstrate the framework's superior ability to handle inter-subject variability and its significant potential to improve practical applicability.},
}

@article {pmid41284455,
year = {2026},
author = {Baradaran, Y and Rojas, RF and Goecke, R and Ghahramani, M},
title = {Exploring Prefrontal Cortex Involvement in Postural Control Across Degraded Sensory Conditions Using fNIRS and Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {30},
number = {2},
pages = {1418-1428},
doi = {10.1109/JBHI.2025.3636169},
pmid = {41284455},
issn = {2168-2208},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Prefrontal Cortex/physiology/diagnostic imaging ; *Postural Balance/physiology ; Male ; Female ; Young Adult ; Adult ; Machine Learning ; Hemodynamics/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {The prefrontal cortex (PFC) of the brain is involved in processing visual, vestibular, and somatosensory inputs to stabilise postural balance. However, the PFC's activation map for a standing person and different sensory inputs remains unclear. This study aimed to explore the PFC activity map and distinct haemodynamic responses during postural control when sensory inputs change. To this end, functional near-infrared spectroscopy (fNIRS) was employed to capture the haemodynamic responses throughout the PFC from a group of young adults standing in four sensory conditions. The results revealed distinct PFC activation patterns supporting sensory processing, motor planning, and cognitive control to maintain balance under different degraded sensory conditions. Furthermore, by applying machine learning classifiers and multivariate feature selection, the PFC locations and haemodynamic responses indicative of different sensory conditions were identified. The findings of this study offer valuable insights for optimising rehabilitation approaches, enhancing the design of fNIRS studies, and advancing brain-computer interface technologies for balance assessment and training.},
}

Humans
Spectroscopy, Near-Infrared/methods
*Prefrontal Cortex/physiology/diagnostic imaging
*Postural Balance/physiology
Male
Female
Young Adult
Adult
Machine Learning
Hemodynamics/physiology
Signal Processing, Computer-Assisted

@article {pmid41285049,
year = {2025},
author = {Lampert, F and Baker, MR and Jensen, MA and Ayyoubi, AH and Bentler, C and Bowersock, JL and Esteller, R and Herron, JA and Johnson, GW and Kipke, DR and Kovach, CK and Kremen, V and Mivalt, F and Neimat, JS and Netoff, TI and Opri, E and Rockhill, A and Rosenow, JM and Sellers, KK and Staff, NP and Swamy, CP and Viswanathan, A and Schalk, G and Denison, T and Hermes, D and Ince, NF and Brunner, P and Worrell, GA and Miller, KJ},
title = {Adaptive neuromodulation dialogues: navigating current challenges and emerging innovations in neuromodulation system development.},
journal = {Journal of neural engineering},
volume = {22},
number = {6},
pages = {},
pmid = {41285049},
issn = {1741-2552},
support = {R01 MH122258/MH/NIMH NIH HHS/United States ; U24 NS113637/NS/NINDS NIH HHS/United States ; U01 NS128612/NS/NINDS NIH HHS/United States ; UH3 NS117944/NS/NINDS NIH HHS/United States ; R01 NS124650/NS/NINDS NIH HHS/United States ; R01 NS092882/NS/NINDS NIH HHS/United States ; UH2 NS095495/NS/NINDS NIH HHS/United States ; R01 NS112497/NS/NINDS NIH HHS/United States ; UH3 NS095495/NS/NINDS NIH HHS/United States ; UH3 NS121565/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces/trends ; Animals ; Equipment Design/trends ; *Implantable Neurostimulators/trends ; },
abstract = {Adaptive neuromodulation systems and implantable brain-computer interfaces have made notable strides in recent years, translating experimental prototypes into clinical applications and garnering substantial attention from the public. This surge in interest is accompanied by increased scrutiny related to the safety, efficacy, and ethical implications of these systems, all of which must be directly addressed as we introduce new neurotechnologies. In response, we have synthesized the insights resulting from discussions between groups of experts in the field and summarized them into five key domains essential to therapeutic device development: (1) analyzing current landscape of neuromodulation devices and translational platforms (2) identifying clinical need, (3) understanding neural mechanisms, (4) designing viable technologies, and (5) addressing ethical concerns. The role of translational research platforms that allow rapid, iterative testing of hypotheses in both preclinical and clinical settings is emphasized. These platforms must balance experimental flexibility with patient safety and clear clinical benefit. Furthermore, requirements for interoperability, modularity, and wireless communication protocols are explored to support long-term usability and scalability. The current regulatory processes and funding models are examined alongside the ethical responsibilities of researchers and device manufacturers. Special attention is given to the role of patients as active contributors to research and to the long-term obligations we have to them as the primary burden-bearers of the implanted neurotechnologies. This article represents a synthesis of scientific, engineering, and clinical viewpoints to inform key stakeholders in the neuromodulation and brain-computer interface spaces.},
}

Humans
*Brain-Computer Interfaces/trends
Animals
Equipment Design/trends
*Implantable Neurostimulators/trends

@article {pmid41285817,
year = {2025},
author = {Li, S and Chen, J and Zhang, C and Tang, S and Xie, Y and Wang, L},
title = {Flexible Use of Limited Resources for Sequence Working Memory in Macaque Prefrontal Cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {10386},
pmid = {41285817},
issn = {2041-1723},
mesh = {Animals ; *Prefrontal Cortex/physiology ; *Memory, Short-Term/physiology ; Neurons/physiology ; Macaca mulatta/physiology ; Male ; Behavior, Animal/physiology ; },
abstract = {Our brain is remarkably limited in how many items it can hold simultaneously, but it can also represent unbounded novel items through generalization. How the brain rationally uses limited resources in working memory (WM) remains unexplored. We investigated mechanisms of WM resource allocation using calcium imaging and electrophysiological recording in the prefrontal cortex of monkeys performing sequence WM (SWM) tasks. We found that changes in the neural representation of SWM, including geometry, generalizable and separate rank subspaces, reflected WM load. SWM resources, represented by neurons' signal strength and spatial tuning projected onto each rank subspace, were shared flexibly between ranks. Crucially, the prefrontal cortex dynamically utilized shared tuning neurons to ensure generalization, while engaging disjoint and spatially shifted neurons to minimize interference, thus achieving a trade-off between behavioral and neural costs within capacity. The allocated resources can predict monkeys' behavior. Thus, the geometry of compositionality underlies the flexible use of limited resources in SWM.},
}

Animals
*Prefrontal Cortex/physiology
*Memory, Short-Term/physiology
Neurons/physiology
Macaca mulatta/physiology
Male
Behavior, Animal/physiology

@article {pmid41286920,
year = {2025},
author = {Sun, Z and Hu, S and Zhu, J and Ye, Z and Ma, M and Ma, G},
title = {The impact of non-invasive brain-computer interface technology on the therapeutic effect of patients with spinal cord injury: a summary of evidence based on meta-analysis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {250},
pmid = {41286920},
issn = {1743-0003},
support = {No. YC2024022//Graduate Student Research and Innovation Fund of Jilin Sport University/ ; No. YC2024022//Graduate Student Research and Innovation Fund of Jilin Sport University/ ; No. 2019B122//Social Science Foundation of Jilin Province/ ; },
mesh = {Humans ; *Spinal Cord Injuries/rehabilitation ; *Brain-Computer Interfaces ; Activities of Daily Living ; },
abstract = {BACKGROUND: The objective of this study is to systematically evaluate the effects of non-invasive brain-computer interface technology on motor and sensory functions and daily living abilities of patients with spinal cord injuries. In addition, the study will investigate the related modifying factors. Ultimately, the study will provide evidence-based recommendations for clinical practice.

METHODS: A systematic search was conducted on PubMed, Web of Science, Scopus, Wiley Online Library, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data Resource System, and VIP Database for relevant literature from database inception to February 2025. The quality of the studies was assessed using Review Manager 5.4, with the risk of bias visually represented. The presence of publication bias was assessed through the utilization of the "metafor" package (version 4.6-0) in R (version 4.4.1). The certainty of the evidence was evaluated using the GRADE framework.

RESULTS: A total of 9 papers were included, including 4 randomized controlled trials and 5 self-controlled trials with 109 spinal cord injury patients. Compared with the control group, the non-invasive brain-computer interface intervention had a significant impact on patients' motor function (SMD = 0.72, 95% CI: [0.35,1.09], P < 0.01, I[2] = 0%, medium level of evidence), sensory function (SMD = 0.95, 95% CI: [0.43,1.48], P < 0.01, I[2] = 0%, medium level of evidence), activities of daily living (SMD = 0.85, 95% CI: [0.46,1.24], P < 0.01, I[2] = 0%, low level of evidence) reached statistical significance. Subgroup analyses showed that for the current summary of evidence, noninvasive brain-computer interface interventions in patients with subacute stage spinal cord injuries showed statistically stronger effects on motor function, sensory function, and ability to perform activities of daily living than in patients with slow chronic stage spinal cord injuries.

CONCLUSION: As far as the existing literature is concerned, non-invasive brain-computer interface technology shows the potential to improve motor and sensory functioning as well as the ability to perform activities of daily living in patients with spinal cord injury. However, the conclusions are preliminary and hypothetical, and as the current evidence for non-invasive BCI interventions for people with spinal cord injury remains limited, this paper does not recommend the application of the conclusions to clinical practice until future large-sample RCTs.},
}

Humans
*Spinal Cord Injuries/rehabilitation
*Brain-Computer Interfaces
Activities of Daily Living

@article {pmid41287227,
year = {2021},
author = {Glannon, W},
title = {Ethical and social aspects of neural prosthetics.},
journal = {Progress in biomedical engineering (Bristol, England)},
volume = {4},
number = {1},
pages = {},
doi = {10.1088/2516-1091/ac23e6},
pmid = {41287227},
issn = {2516-1091},
abstract = {Neural prosthetics are devices or systems that bypass, modulate, supplement, or replace regions of the brain and its connections to the body that are damaged and dysfunctional from congenital abnormalities, brain and spinal cord injuries, limb loss, and neuropsychiatric disorders. Some prosthetics are implanted in the brain. Others consist of implants and systems outside the brain to which they are connected. Still others are completely external to the brain. But they all send inputs to and receive outputs from neural networks to modulate or improve connections between the brain and body. As artificial systems, neural prosthetics can improve but not completely restore natural sensory, motor and cognitive functions. This review examines the main ethical and social issues generated by experimental and therapeutic uses of seven types of neural prosthetics: auditory and visual prosthetics for deafness and blindness; deep brain stimulation for prolonged disorders of consciousness; brain-computer and brain-to-brain interfaces to restore movement and communication; memory prosthetics to encode and retrieve information; and optogenetics to modulate or restore neural function. The review analyzes and discusses how recipients of neural prosthetics can benefit from them in restoring autonomous agency, how they can be harmed by trying and failing to use or adapt to them, how these systems affect their identities, how to protect people with prosthetics from external interference, and how to ensure fair access to them. The review concludes by emphasizing the control these systems provide for people and a brief exploration of the future of neural prosthetics.},
}