@article {pmid42465470,
year = {2026},
author = {Lin, LJ and Callier, T and Heiles, B and Pejsa, K and Liu, CY and Shapiro, MG and Andersen, RA},
title = {Functional ultrasound imaging through a human cranial window for mesoscopic mapping of motor effector encoding within the sensorimotor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.07.03.735688},
pmid = {42465470},
issn = {2692-8205},
abstract = {Understanding movement encoding within human cortical circuits has been essential for advancing brain computer interfaces (BCIs). However, there are limited minimally invasive, high resolution neurorecording methods sensitive enough to detect single-trial movement-correlated neural activity. Functional ultrasound imaging (fUSI) provides submillimeter spatial resolution of deep cortical tissue with high sensitivity and, when paired with acoustically transparent skull implants, enables transcutaneous recording of human neurovascular changes. Prior studies have used fUSI in participants with acoustically transparent skull implants for on-off task mapping and decoding. Here, we demonstrate fUSI's ability to reliably resolve multi-body-part and single digit movement encoding within the primary sensorimotor cortex in a participant with an acoustically transparent skull implant. We obtained fine-grained mappings of individual effector representation that were consistent with classic somatotopy for both multi-body-part and single digit movement. We were able to resolve single-trial event-related activity, enabling single-trial decoding of both conditions. Analysis of voxels important for decoding suggested differential encoding of single digit movement information across the different Brodmann areas. Finally, we show that these patterns can be approximated across different sessions, allowing for cross session decoding. These results establish that fUSI can reliably delineate somatotopically organized motor representations at submillimeter resolution, bridging a critical gap between invasive electrophysiology and noninvasive hemodynamic imaging in a human subject.},
}

@article {pmid42461518,
year = {2026},
author = {Qu, Y and Lou, X and Meng, H and Li, Z and Wang, J and Mao, K and Li, X},
title = {EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography.},
journal = {Brain informatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40708-026-00321-1},
pmid = {42461518},
issn = {2198-4018},
support = {62576066//National Natural Science Foundation of China/ ; KJZD-K202400602//Key Project of Science and Technology Research Program of Chongqing Municipal Education Commission/ ; },
abstract = {PURPOSE: Motor imagery electroencephalography (MI-EEG) decoding remains challenging due to low signal-to-noise ratio and complex temporal-spectral characteristics. This study aims to develop a robust deep learning framework for effective EEG representation learning.

METHODS: We propose EEG-DBNet, a dual-branch neural network that jointly models temporal dynamics and spectral representations of EEG signals. The model integrates local and global convolutional modules to enable multi-scale feature extraction, complementing the dual-branch design for multi-dimensional temporal-spectral representation learning. To validate robustness, experiments are conducted on two public datasets as well as a self-collected MI-EEG dataset acquired under controlled laboratory conditions.

RESULTS: Experimental results show that EEG-DBNet achieves the best average performance on the two public benchmark datasets, BCI Competition IV-2a and IV-2b. On the self-collected CQUPT dataset, EEG-DBNet obtains competitive performance compared with representative baseline methods, suggesting its potential applicability to laboratory-acquired MI-EEG decoding. These results indicate that the proposed temporal-spectral dual-branch design is effective, while further validation on larger self-collected datasets is still needed.

CONCLUSION: The proposed EEG-DBNet provides an effective solution for MI-EEG decoding with improved robustness. The inclusion of multiple datasets, particularly laboratory-acquired self-collected data, highlights its potential for practical brain-computer interface applications.},
}

@article {pmid42462767,
year = {2026},
author = {Cetera, A and Ghafoori, S and Rabiee, A and Farhadi, MH and Shahriari, Y and Abiri, R},
title = {Macroscopic EEG reveals discriminative low-frequency oscillations in plan-to-grasp visuomotor tasks.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae8bd9},
pmid = {42462767},
issn = {1741-2552},
abstract = {OBJECTIVE: The vision-based grasping brain network integrates visual perception with cognitive and motor processes for visuomotor tasks. While invasive recordings have successfully decoded localized neural activity related to grasp type planning and execution, macroscopic neural activation patterns captured by noninvasive electroencephalography (EEG) remain far less understood.

METHODS: We introduce a vision-based grasping platform to investigate grasp-type-specific (precision, power, no-grasp) neural activity across large-scale brain networks using EEG neuroimaging. The platform isolates grasp-specific planning from its associated execution phases in naturalistic visuomotor tasks, where the Filter-Bank Common Spatial Pattern (FBCSP) technique was designed to extract discriminative frequency-specific features within each phase. Support vector machine (SVM) classification discriminated binary (precision vs. power, grasp vs. no-grasp) and multiclass (precision vs. power vs. no-grasp) scenarios for each phase, and were compared against traditional Movement-Related Cortical Potential (MRCP) methods.

RESULTS: Low-frequency oscillations (0.5-8 Hz) carry grasp-related information established during planning and maintained throughout execution, with consistent classification performance across both phases (75.3-77.8%) for precision vs. power discrimination, compared to 61.1% using MRCP. Higher-frequency activity (12-40 Hz) showed phase-dependent results with 93.3% accuracy for grasp vs. no-grasp classification but 61.2% for precision vs. power discrimination. Feature importance using SVM coefficients identified discriminative features within frontoparietal networks during planning and motor networks during execution.

CONCLUSION: This work demonstrated the role of low-frequency oscillations in decoding grasp type during planning using noninvasive EEG.

SIGNIFICANCE: These findings provide a foundation toward scalable, intention-driven Brain-Machine-Interface (BMI) control strategies.},
}

@article {pmid42463660,
year = {2026},
author = {Spalding, Z and Duraivel, S and Rahimpour, S and Wang, C and Barth, K and Schmitz, C and Lad, SP and Friedman, AH and Southwell, DG and Viventi, J and Cogan, GB},
title = {Shared latent representations of speech production for cross-patient speech decoding.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-75455-1},
pmid = {42463660},
issn = {2041-1723},
support = {R01DC019498//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; R01DC019498//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; R01DC019498//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; R01DC019498//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; R01DC019498//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; W81XWH-21-0538//United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs (CDMRP)/ ; W81XWH-21-0538//United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs (CDMRP)/ ; W81XWH-21-0538//United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs (CDMRP)/ ; R01NS129703//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; UG3NS120172//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS129703//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; UG3NS120172//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS129703//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS129703//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS129703//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS129703//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; UG3NS120172//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
abstract = {Speech brain-computer interfaces (BCIs) can restore communication in individuals with neuromotor disorders who are unable to speak. However, current speech BCIs limit patient usability and successful deployment by requiring large volumes of patient-specific data collected over long periods of time. A promising solution to facilitate usability and accelerate their successful deployment is to combine data from multiple patients. This has proven difficult, however, due to differences in user neuroanatomy, varied placement of electrode arrays, and sparse sampling of targeted anatomy. Here, by aligning patient-specific neural data to a shared latent space, we show that speech BCIs can be trained on data combined across patients. Using canonical correlation analysis and high-density micro-electrocorticography (μECoG), we uncovered shared neural latent dynamics with preserved micro-scale speech information. This approach enabled cross-patient decoding models to achieve improved performance relative to patient-specific models facilitated by the high resolution and broad coverage of μECoG. Our findings support future speech BCIs that are more accurate and rapidly deployable, ultimately improving the quality of life for people with impaired communication from neuromotor disorders.},
}

@article {pmid42463883,
year = {2026},
author = {Chandrasekaran, S and Wandelt, SK and Jangam, A and Elias, Z and Ibroci, E and Maffei, C and Rosenthal, IA and Ramdeo, R and Kim, JW and Xu, J and Glasser, MF and Neuwirth, A and Goldstein, TA and Crone, NE and Fifer, MS and Tostaeva, G and Bickel, S and Griffin, D and Funaro, M and Carras, NG and Pruitt, R and Ben-Shalom, N and Stein, AB and Mehta, AD and Bouton, CE},
title = {A neuroprosthesis for restoring hand movement and sensation in a person with complete tetraplegia.},
journal = {Nature medicine},
volume = {32},
number = {7},
pages = {2591-2601},
pmid = {42463883},
issn = {1546-170X},
support = {C37718GG//New York State Department of Health (NYS Department of Health)/ ; },
mesh = {Humans ; *Quadriplegia/physiopathology/therapy/rehabilitation ; *Hand/physiopathology ; Movement/physiology ; *Spinal Cord Injuries/physiopathology/therapy ; Brain-Computer Interfaces ; Recovery of Function/physiology ; *Neural Prostheses ; *Sensation/physiology ; Male ; Adult ; },
abstract = {Millions of people worldwide are living with movement and sensory impairments owing to spinal cord injury, stroke and other neurological conditions. Here we report a double neural bypass (DNB), a hybrid neuroprosthetic system designed to restore both immediate and lasting gains in movement and sensation after a severe, complete spinal cord injury. The DNB links an intracortical brain-computer interface with targeted and patterned neuromodulation of the spinal cord and cortex. This allows brain signals associated with movement intention to directly control the movement of the user's own hand in real time while also promoting long-term sensorimotor recovery-even after the system is turned off. The DNB system uses recurrent artificial neural networks and reinforcement learning for fine grasp control, together with patterned spinal cord stimulation and activity-informed intracortical microstimulation ('cortical mirroring') to promote neuroplasticity and durable recovery of function. In a participant with chronic C4 sensory/C5 motor complete tetraplegia, this hybrid approach enabled recovery of functional abilities including self-feeding and manipulation of delicate objects, while also producing significant and persistent improvements in elbow flexion and wrist tactile sensation. These findings demonstrate the potential of combining a sensorimotor neuroprosthesis with targeted brain and spinal neuromodulation to restore clinically relevant function in severe paralysis.},
}

Humans
*Quadriplegia/physiopathology/therapy/rehabilitation
*Hand/physiopathology
Movement/physiology
*Spinal Cord Injuries/physiopathology/therapy
Brain-Computer Interfaces
Recovery of Function/physiology
*Neural Prostheses
*Sensation/physiology
Male
Adult

@article {pmid42463967,
year = {2026},
author = {Xu, X and Liu, D and Sun, X and Yang, K and Lv, P and Lu, J and Ruan, W and Xia, X and Chen, J and Chen, Y and Wang, H and Wu, X and Ge, D and Yang, B and Wan, M and Liang, H and Lan, X and Gai, Y and Zhang, B},
title = {In vivo multimodal PET/MRI imaging and plasma biomarkers implicate glymphatic dysfunction linking neuroinflammation to tau pathology in the early Alzheimer's disease continuum.},
journal = {European journal of nuclear medicine and molecular imaging},
volume = {},
number = {},
pages = {},
pmid = {42463967},
issn = {1619-7089},
support = {2022ZD0211800//Instituto Nacional de Ciência e Tecnologia Centro de Estudos das Adaptações da Biota Aquática da Amazônia/ ; },
abstract = {PURPOSE: Neuroinflammation is a key factor contributing to cognitive decline in Alzheimer's disease (AD). This study aims to investigate the mechanistic associations among neuroinflammation, glymphatic dysfunction, tau pathology, and cognitive decline in AD spectrum.

METHODS: The study included 355 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and a supportive cohort of 59 individuals from Wuhan Union Hospital (WHUH). Tau pathology was quantified using [18]F-AV1451 positron emission tomography (PET). Glymphatic function was estimated through diffusion tensor image analysis along the perivascular space (DTI-ALPS). Neuroinflammation was assessed via plasma glial fibrillary acidic protein (GFAP) in two cohorts and translocator protein (TSPO) PET imaging with [18]F-DPA-714 in supportive cohort. Correlation analyses and mediation models were employed to evaluate the directional relationships among tau deposition, inflammation, glymphatic function, and cognition.

RESULTS: Higher levels of inflammation were significantly associated with lower DTI-ALPS index (β = -0.171, P = 0.046), which in turn was associated with higher tau burden (β = 0.162, P = 0.010). Path analysis revealed significant indirect associations linking neuroinflammation to cognitive performance through glymphatic dysfunction and tau pathology, with total indirect effects of - 0.165 (95% CI, - 0.266 to - 0.105) in ADNI and - 0.143 (95% CI, - 0.386 to - 0.013) in WHUH. These findings support a hypothesized inflammation-glymphatic-tau pathway rather than a definitive causal cascade.

CONCLUSION: Our findings are consistent with a hypothesized inflammation-glymphatic-tau association in which greater neuroinflammation is linked to reduced glymphatic function and higher regional tau burden, particularly in preclinical and prodromal stages.

This study obtained ethical approval from the Institutional Review Committee of Nanjing Drum Tower Hospital (ChiCTR-BRC-17011316, date:20170506; ChiCTR1900022526, date:20190415).},
}

@article {pmid42455675,
year = {2026},
author = {Gao, Y and Fan, J and Xu, N and Zhao, L and Deng, L and Yang, R and Chen, L and Jing, Z and Huang, T and Wang, X and Wang, J and Teng, P and Cai, P and Luan, G and Du, Y and Wang, Q and Fang, F},
title = {Alpha rhythms in the left auditory cortex set the speed limit for speech comprehension.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {29},
pages = {e2601610123},
doi = {10.1073/pnas.2601610123},
pmid = {42455675},
issn = {1091-6490},
support = {2022ZD0204800//Brain Science and Brain-like Intelligence Technology -- National Science and Technology Major Project/ ; 2025ZD0219400//Brain Science and Brain-like Intelligence Technology -- National Science and Technology Major Project/ ; 2021ZD0201500//Brain Science and Brain-like Intelligence Technology --National Science and Technology Major Project/ ; T2421004//MOST | National Natural Science Foundation of China (NSFC)/ ; 32200862//MOST | National Natural Science Foundation of China (NSFC)/ ; 32441106//MOST | National Natural Science Foundation of China (NSFC)/ ; //High-performance Computing Platform of Peking University/ ; //MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; GJ202005//Institute of Psychology, CAS/ ; },
mesh = {Humans ; *Auditory Cortex/physiology ; *Alpha Rhythm/physiology ; Female ; *Speech Perception/physiology ; Male ; Adult ; Acoustic Stimulation ; *Comprehension/physiology ; Transcranial Direct Current Stimulation ; Electroencephalography ; Young Adult ; *Speech/physiology ; },
abstract = {Human cognition operates within distinct temporal windows spanning from very short to extremely long. Each cognitive function has its own processing speed range, beyond which performance deteriorates or even becomes impossible. Speech comprehension, as one of the most important cognitive functions in civilized societies, exemplifies this constraint: Performance declines markedly when speech rates exceed about 10 syllables per second. However, the neural mechanisms underlying this speed limit remain unclear. Here, by combining psychophysics, scalp-electroencephalography, transcranial alternating current stimulation (tACS), repetitive sensory stimulation, and stereo-electroencephalography (sEEG), we showed that the intrinsic alpha band activity determined the maximum rate of speech comprehension. In healthy participants, individual alpha frequency (IAF) precisely predicted the speech recognition rate threshold. Crucially, causally accelerating endogenous alpha rhythms via high-definition tACS and rhythmic auditory stimulation shifted this threshold, enhancing comprehension of ultrafast speech. Furthermore, sEEG recordings from the human auditory cortex revealed that this speed-limiting mechanism was locally determined: Faster alpha band activity in the left auditory cortex facilitated stronger neural entrainment to the speech envelope. These findings identify alpha band activity as a fundamental, malleable temporal bottleneck for linguistic processing, suggesting that the brain's intrinsic sampling rate sets the physiological limit on human communication.},
}

Humans
*Auditory Cortex/physiology
*Alpha Rhythm/physiology
Female
*Speech Perception/physiology
Male
Adult
Acoustic Stimulation
*Comprehension/physiology
Transcranial Direct Current Stimulation
Electroencephalography
Young Adult
*Speech/physiology

@article {pmid42455705,
year = {2026},
author = {Li, M and Nan, Y and Tang, W and Wang, W and Liu, C},
title = {Frequency modulation detection in Mandarin Chinese-speaking amusics across modulation rate, carrier frequency, and stimulus duration.},
journal = {JASA express letters},
volume = {6},
number = {7},
pages = {},
doi = {10.1121/10.0044337},
pmid = {42455705},
issn = {2691-1191},
mesh = {Humans ; *Auditory Perceptual Disorders/physiopathology ; Female ; Language ; Acoustic Stimulation ; Male ; Auditory Threshold ; *Pitch Perception ; Adult ; China ; East Asian People ; },
abstract = {Frequency modulation (FM) detection was examined in Mandarin Chinese-speaking listeners with congenital amusia, including pure amusics and tone agnosics (without and with lexical tone deficits, respectively). Thresholds were measured across modulation rates, carrier frequencies, and stimulus durations. Amusics exhibited elevated FM detection thresholds relative to controls, with no difference between pure amusics and tone agnosics. No significant interactions were observed between listener group and acoustic parameters, suggesting the group difference did not vary reliably across modulation rates, carrier frequencies, or stimulus durations. These findings indicate an impairment in dynamic pitch encoding in congenital amusia, independent of lexical tone deficits.},
}

Humans
*Auditory Perceptual Disorders/physiopathology
Female
Language
Acoustic Stimulation
Male
Auditory Threshold
*Pitch Perception
Adult
China
East Asian People

@article {pmid42455722,
year = {2026},
author = {Yu, T and Xin, J and Gao, W and Yu, Z and Li, Y and Gu, Z and Xiao, J},
title = {Supervised Contrastive Learning Enables High Performance P300 Spelling with Minimal Calibration.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3712320},
pmid = {42455722},
issn = {2168-2208},
abstract = {The P300 speller is a widely adopted brain computer interface (BCI) paradigm that enables hands free character selection based on event-related potentials elicited through an oddball stimulus paradigm. Despite its utility, the system's performance is often constrained by the low signal-to-noise ratio and complex spatiotemporal characteristics of EEG signals, especially when only a limited number of repetitions or labeled samples are available. Moreover, substantial within-session calibration is typically required to achieve reliable decoding before online spelling, posing a major practical barrier. To tackle these challenges, we propose SCL-EEGMixer, a lightweight, end to-end neural architecture that combines a convolutional mixer network with supervised contrastive learning. The model extracts discriminative spatiotemporal representations via the convolutional mixer and enhances learning with a hybrid loss that fuses cross-entropy and supervised contrastive objectives. This design promotes intra-class compactness and inter-class separability, enabling robust learning from scarce labeled data. Extensive evaluations on both a public benchmark and a self-collected dataset demonstrate that SCL-EEGMixer consistently outperforms representative baselines in both binary P300 classification and character recognition tasks under a within-session protocol. Notably, it maintains high accuracy and information transfer rate even when trained with as few as one or two calibration characters, highlighting its potential for reducing within-session calibration burden in P300 spelling.},
}

@article {pmid42455723,
year = {2026},
author = {Lin, X and Wang, Y and Ding, Y and Zhang, S and Chen, Z and Wu, M and Jiang, M and Thomas, K and Robinson, N and Ng, HW and Khendry, N and Wai, AAP and Xiao, W and Kuah, CWK and Wee, SK and Chua, KSG and Guan, C},
title = {Neuroscience-Inspired Hierarchical GNN for Grasping Attempt Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3714001},
pmid = {42455723},
issn = {2168-2208},
abstract = {Brain-Computer Interfaces (BCI) have shown promise in facilitating upper limb rehabilitation following stroke. However, restoring fine hand functions, such as grasping, remains a significant challenge. To address this, we focus on decoding hand grasp attempts from electroencephalography (EEG) to enable BCI-driven hand rehabilitation. In this work, we propose several novel methods. First, inspired by the Small-World Brain Network Theory, we introduce a Small-world Hierarchical Interconnected Graph Neural Network (SHINE). SHINE captures transient power dynamics using multiscale convolution, overlapping windows, and learnable variance. It also simulates the characteristic architecture of the brain, where strong local connections coexist with weaker long-range links. This design advances existing Graph Neural Network (GNN) approaches, which typically model functional connectivity using a single, distance-agnostic metric, treating all brain regions uniformly. Second, we propose a Progressive Decay Graph (PDG) mechanism that progressively weakens long-range connections according to distance and training epoch, allowing the model's connectivity structure to evolve alongside the learning process. We evaluated SHINE on two EEG datasets comprising 50 healthy subjects and 19 post-stroke patients performing attempted hand opening and closing, which are the two complementary phases of a grasp. SHINE achieved superior performance over state-of-the-art methods, with improvements of 2.32% (healthy open vs. rest), 1.98% (healthy close vs. rest), 3.97% (stroke open vs. rest), and 2.66% (stroke close vs. rest) ($p< 0.01$ for all tasks), respectively.},
}

@article {pmid42457692,
year = {2026},
author = {Wang, H and Zhang, Y and Karrenbach, M and Ding, Y and He, B},
title = {Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {42457692},
issn = {2041-1723},
support = {NS124564, NS131069, NS127849, NS096761//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; EB029365//U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Machine Learning ; Electroencephalography/methods ; Male ; Female ; Adult ; Adaptive Algorithms ; Learning ; Algorithms ; Young Adult ; Reinforcement Machine Learning ; Neuronal Plasticity ; *Imagination/physiology ; },
abstract = {Brain-computer interfaces (BCIs) offer the potential to restore function and augment human capabilities. However, non-invasive electroencephalography (EEG)-based BCIs still face challenges in learning efficiency and control precision, particularly for naïve users performing complex tasks. Here, we present a sensory-guided joint learning framework that integrates human motor learning with adaptive machine learning to improve BCI training and performance. In 31 BCI-naïve participants, the framework enabled rapid skill acquisition, achieving average online discrete accuracies of 86.0% for one-dimensional (1D) and 77.5% for two-dimensional (2D) motor imagery tasks, along with continuous control accuracies of 77.5% (1D) and 66.9% (2D). Mechanistically, tactile guidance reduced user exploration and accelerated neural adaptation, while sample reweighting aligned decoder updates with human learning trajectories. By coupling reinforcement-driven neural plasticity with adaptive algorithmic optimization, this framework advances BCI training from passive calibration to active human-machine joint learning, enabling practical and scalable neural interfaces for communication and rehabilitation.},
}

Humans
*Brain-Computer Interfaces
*Machine Learning
Electroencephalography/methods
Male
Female
Adult
Adaptive Algorithms
Learning
Algorithms
Young Adult
Reinforcement Machine Learning
Neuronal Plasticity
*Imagination/physiology

@article {pmid42457715,
year = {2026},
author = {Zeng, B and Liu, B},
title = {DST-GNN: A dynamic spatio-temporal graph neural network for motor imagery classification.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-58803-5},
pmid = {42457715},
issn = {2045-2322},
support = {Grant No. 62596987//National Natural Science Foundation of China/ ; },
abstract = {Electroencephalography (EEG)-based motor imagery classification plays an important role in brain-computer interface (BCI) systems. However, existing methods often struggle to effectively capture the complex spatial and temporal dependencies among EEG channels and usually rely on manually designed prior knowledge. To address these limitations, this paper proposes a Dynamic Spatial-Temporal Graph Neural Network (DST-GNN) for motor imagery classification. The proposed framework models EEG signals as dynamic graphs and jointly learns spatial interactions and temporal patterns from multi-channel EEG data. In addition, a graph readout mechanism is employed to generate hierarchical spatial-temporal representations, enabling more comprehensive feature aggregation for classification. Extensive experiments conducted on a public motor imagery dataset demonstrate that DST-GNN consistently outperforms representative baseline methods and achieves competitive classification performance.},
}

@article {pmid42458338,
year = {2026},
author = {Fang, H and Yuan, Y and Hu, Z and Cai, J and Chen, T and Jin, W and Wang, S and Yu, L},
title = {Geographical disparities and spatial non-stationarity in stroke prevalence across China: a Bayesian analysis.},
journal = {BMC neurology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12883-026-05173-0},
pmid = {42458338},
issn = {1471-2377},
support = {LY24H270007//Natural Science Foundation of Zhejiang Province/ ; LZYQ25H270001//Natural Science Foundation of Zhejiang Province/ ; 82505384//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: As stroke remains a major public health challenge in China, numerous studies have characterized the epidemiological features and distribution of stroke prevalence across provinces. However, conventional non-spatial analytical approaches may lack the capability to capture spatial dependency and regional variation in the impact of risk factors. This study aims to estimate province-level stroke prevalence in China and quantify how its association with individual-level risk factors varies across provinces, accounting for spatial dependency.

METHODS: In this study, 19,713 adults were included from the fourth China Health and Retirement Longitudinal Study (CHARLS 2018), spanning 28 provinces, autonomous regions, and municipalities. Within each province, prevalence estimates were standardized to the 7th National Census (2020) distribution of age, sex, and residence type. Stroke prevalence and 95% Bayesian credible intervals (BCIs) were estimated by a Bayesian spatially varying coefficient model. Global and local Moran's I statistics were used to assess spatial autocorrelation and identify clustering patterns. Eight metabolic, lifestyle, and socioeconomic risk factors were considered: lower educational attainment, hypertension, diabetes, heart disease, dyslipidemia, smoking, alcohol consumption, and physical inactivity. The model estimated how the association of each with stroke varied across provinces.

RESULTS: Stroke prevalence at province level in China showed marked geographic disparities, ranging from 1.89% (95% BCI: 0.92%-3.61%) to 8.64% (95% BCI: 6.98%-10.63%). A distinct "North-high, South-low" spatial gradient was observed, with significant positive spatial autocorrelation (I = 0.428, p < 0.001). Local cluster analysis identified high-high clusters in Northeast and North China and low-low clusters in South and East China. Except for low educational attainment, the association between stroke prevalence and smoking, drinking, physical inactivity, hypertension, diabetes, dyslipidemia, and heart disease exhibited significant provincial variation. Hypertension showed the strongest association with stroke, with odds ratios ranging from 2.39 to 3.38 across provinces.

CONCLUSIONS: Stroke burden in China is spatially clustered, and the associations between stroke and its risk factors vary markedly across provinces rather than being uniform nationwide. By integrating spatial non-stationarity with census-based demographic standardization, this study provides spatially refined evidence to support region-specific stroke-prevention strategies and optimize the allocation of healthcare resources.},
}

@article {pmid42458811,
year = {2026},
author = {E, S and J, S and E, A},
title = {Successful Single-Session Neural Self-Regulation Through Neurofeedback Varies Between Features.},
journal = {Human brain mapping},
volume = {47},
number = {11},
pages = {e70611},
doi = {10.1002/hbm.70611},
pmid = {42458811},
issn = {1097-0193},
support = {20190099//Knowledge Foundation/ ; },
mesh = {Humans ; *Neurofeedback/physiology/methods ; Male ; Female ; Electroencephalography ; Adult ; Young Adult ; *Self-Control ; *Learning/physiology ; Brain-Computer Interfaces ; *Brain Waves/physiology ; *Brain/physiology ; },
abstract = {Neurofeedback (NFB) and Brain-Computer Interface (BCI) research seldom present within-session individual learning dynamics. This is even though a large proportion of NFB and BCI users cannot learn the neural self-regulation required to control the feedback. Understanding the time course and learning dynamics between subjects will enable us to design more effective NFB and BCI protocols that promote the learning of neural self-regulation. In this study, we aimed to analyze individual learning trajectories of self-regulation of four different cortical rhythms, in terms of both frequency and spatial selectivity. Twenty healthy subjects performed four sessions of NFB training, each session with feedback reflecting a different cortical rhythm as measured with an electroencephalogram. We specifically tested frontal midline (fm) Theta, occipital Alpha, unilateral centrotemporal sensorimotor rhythms (SMR), and central Beta. We show that all subjects were able to self-regulate at least two of these features; however, with varied specificity in the spatial and frequency domains. Unexpectedly, we show that none of the subjects succeeded in regulating fm Theta. Using a clustering approach, we identified two different learning dynamics among the learners across features: a linear increase/decrease and a non-linear plateau-like trajectory. This is the first NFB study employing an intra-subject cross-over experimental design, enabling the direct comparison of neural self-regulation between multiple features. Our results provide important insights into the "non-learner" problem, showing that it is not a feature-universal personal trait. We further show feature-specific spatial and frequency selectivity of neural self-regulation, providing important considerations for future NFB protocols.},
}

Humans
*Neurofeedback/physiology/methods
Male
Female
Electroencephalography
Adult
Young Adult
*Self-Control
*Learning/physiology
Brain-Computer Interfaces
*Brain Waves/physiology
*Brain/physiology

@article {pmid42459855,
year = {2026},
author = {Zheng, Z and Zhang, C and Lv, M and Qian, C and Gao, G and Wang, S and Zhu, R and Yang, J and Zhang, C and Xu, H},
title = {The effect of rehabilitation training based on brain-computer interface on limb function in stroke patients: a systematic review and meta-analyses.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1750875},
pmid = {42459855},
issn = {1664-2295},
abstract = {BACKGROUND: Characterized by high incidence rate, high disability rate, high mortality rate and high recurrence rate, stroke has become the second leading cause of death globally and the primary cause of adult disability. Though traditional rehabilitation methods have played a significant role in post-stroke functional recovery, their therapeutic efficacy is limited. In recent years, brain-computer interfaces (BCI) have advanced rapidly and are being used more frequently in rehabilitation training for stroke patients.

OBJECTIVE: This systematic review and meta-analyses aimed to systematically assess the effect of brain-computer interface-based rehabilitation training on limb function in patients after stroke, and further investigate the efficacy differences among various types of brain-computer interfaces and treatment protocols.

METHODS: The search strategy was conducted in 5 databases (PubMed, Scopus, Web of Science, Embase, and Cochrane Library databases) from inception to August 29, 2025. The studies that explored the impact of BCI combined with rehabilitation on limb function in stroke patients was mainly focused. A meta-analyses was performed using a random effects model, with the weighted mean difference (WMD) and 95% confidence intervals (CIs) as the effect sizes.

RESULTS: Twenty-seven RCTs, including 23 that reported changes in upper limb function and four that reported changes in lower limb function, were included. The results showed that the training based on BCI significantly improved FMA-UE (Fugl-Meyer Assessment upper - extremity) [WMD = 3.50, 95% CI: (2.09, 4.90), p < 0.001] and FMA-LE (Fugl-Meyer Assessment lower -extremity) [WMD = 2.59, 95% CI: (1.94, 3.23), p < 0.001], compared with the control group.

CONCLUSION: The combined therapy was effective in improving the limb function of patients. BCI-based training might be a reliable rehabilitation program to improve limb function.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251038208.},
}

@article {pmid42459893,
year = {2026},
author = {Ogino, M},
title = {Single-subject auditory ERP-BCI performance enhancement in ALS via an AI coding assistant prompt.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1869918},
pmid = {42459893},
issn = {1662-5161},
abstract = {INTRODUCTION: Auditory event-related potential (ERP) brain-computer interfaces (BCIs) offer communication support for individuals with amyotrophic lateral sclerosis (ALS) who eventually progress to completely locked-in states. However, individual-specific BCI pipeline optimization is technically demanding and time-consuming, leaving substantial room for performance improvement in practice. A central challenge is increasing selection speed while maintaining reliable classification accuracy, since slower selections reduce the sense of agency and undermine the motivational and feedback dynamics essential for sustained BCI use.

METHODS: We investigated whether an AI coding assistant could address this challenge for individual patients. A three-class auditory ERP-BCI was optimized for a single ALS patient using Claude Code (Anthropic, Inc.), which iteratively generated and evaluated 23 optimization scripts over approximately 24 hours with minimal human-in-the-loop oversight. The resulting AI-Designed ERP classifier (AIDE) was evaluated on 189 EEG trials spanning 3.5 years using five cross-validation strategies.

RESULTS: For the baseline models, halving the stimulus repetitions to shorten selection time degraded classification accuracy; AIDE prevented this degradation, achieving 85.03% mean cross-validation accuracy (selection time 17 s; ITR 2.92 bits/min). This doubled the information transfer rate from 1.43 to 2.92 bits/min. Accuracy exceeded 84% across four of five cross-validation strategies. Feature space visualization revealed that the AI autonomously selected and combined EEG features established in prior studies into an effective discriminative architecture, without domain-specific algorithmic guidance from the human researcher. In addition, online test confirmed 66.7% accuracy for AIDE versus 50.0% for the baseline model.

DISCUSSION: These findings provide proof of concept that single-subject BCI performance can be improved via a single prompt, offering an efficient pathway to individualized optimization in clinical and research settings.},
}

@article {pmid42460654,
year = {2026},
author = {Lu, S and Yang, T and Geng, Y and Wu, H and Huang, Y and Zheng, T and Chen, H and Huang, S and Cao, Y and Yang, J and Yan, W and Zhang, Y and Wu, W},
title = {A Whole-Head Finite Element Model for Electrical Neuromodulation via Visual Brain-Machine Interfaces.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e11252},
doi = {10.1002/advs.202511252},
pmid = {42460654},
issn = {2198-3844},
support = {2022YFA1105501//National Key R&D Program of China/ ; 82471080//National Natural Science Foundation of China/ ; 82171048//National Natural Science Foundation of China/ ; 81800842//National Natural Science Foundation of China/ ; 2021C03065//Key R&D Program of Zhejiang Province/ ; YNZD1201902//Key R&D Program of Wenzhou Eye Hospital/ ; ZY2022021//Key R&D Program of Wenzhou/ ; H20220008//Key R&D Program of Wenzhou/ ; },
abstract = {Brain-machine interfaces (BMIs) for vision restoration require models that accurately simulate the anatomy and electrical properties of visual pathways. However, current models focus only on isolated structures, such as the retina or brain, and overlook surrounding tissues. Here, we present a comprehensive computational model of the human head that incorporates the entire visual pathway-including the eye, optic nerve, and brain-along with critical neighboring tissues such as the orbit and paranasal sinuses, thereby enabling precise simulations. Validation using human and large-animal data shows a strong correlation between the simulated and measured electric potentials. Component-elimination analysis reveals that the optimized comprehensive model outperforms simplified versions. The model demonstrates its utility in multiple applications: (1) comparative analysis of electrical neuromodulation technologies for optic neuropathy, revealing the electric field intensity limitations of noninvasive approaches and the safety concerns of invasive intraorbital approaches; (2) identification of the optimal stimulation site, showing that transnasal stimulation at the optic chiasm outperforms traditional approaches; and (3) in silico design of electrode arrays for optic nerve prostheses, demonstrating theoretical advantages in invasiveness and visual field coverage compared to existing retinal and cortical prosthetics. This validated and versatile computational resource supports the development of neuromodulation strategies and visual BMI technologies.},
}

@article {pmid42461296,
year = {2026},
author = {Zhang, S and Gong, Y and Zhang, B and Cui, Y and Deng, Y and Li, Q and Li, W and Lo, V and Qian, J and Liang, F and Guo, T and Sun, J},
title = {Efficacy and EEG-ECG mechanisms of taVNS combined with TEAS for negative symptoms of schizophrenia: a protocol for a 2 × 2 factorial randomized controlled trial.},
journal = {European archives of psychiatry and clinical neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42461296},
issn = {1433-8491},
support = {202301AZ070001-179//Yunnan Province Traditional Chinese Medicine Joint Special General Project/ ; 202305AF150072//'Liang Fanrong Expert Workstation' of Yunnan Province-Yunnan Science and Technology Programme/ ; TZJH2024014//Xidian University Specially Funded Project for Interdisciplinary Exploration/ ; },
abstract = {Negative symptoms of schizophrenia (NSS) represent a core feature of the disorder associated with substantial disease burden and poor functional prognosis, yet current therapeutic options remain limited. Given this unmet clinical need, we will explore the therapeutic potential of a combined non-pharmacological intervention using transcutaneous auricular vagus nerve stimulation (taVNS) and transcutaneous electrical acupoint stimulation (TEAS). In this single-blind, prospective, randomized controlled trial utilizing a 2 × 2 factorial design, 120 NSS participants will be randomly assigned in a 1:1:1:1 ratio to four groups: taVNS plus TEAS, taVNS plus sham TEAS, sham taVNS plus TEAS, and sham taVNS plus sham TEAS. Participants will receive 30-minute treatment sessions every other day for 4 weeks, with a subsequent 4-week follow-up period. The primary outcome is the reduction in the Positive and Negative Syndrome Scale-Factor Score for Negative Symptoms (PANSS-FSNS) at the end of the intervention at week 4 relative to baseline. This study aims to evaluate the individual and combined efficacy and safety of taVNS and TEAS in the treatment of NSS. Additionally, dual-modal synchronous electroencephalography (EEG) and electrocardiography (ECG) recordings will be used to investigate the neurophysiological mechanisms underlying the interventions and potential electrophysiological changes associated with the therapeutic effects. Trial registration: International Traditional Medicine Clinical Trial Registry (ITMCTR2025000634); registered on April 2, 2025.},
}

@article {pmid42443158,
year = {2026},
author = {Yang, M and Li, H and Li, M and Wang, Q and Deng, L and Zhao, L and Yu, H and Li, X and Deng, W and Guo, W and Wei, W and Li, T},
title = {Widespread white matter microstructural abnormalities in treatment‑naïve patients with first‑episode schizophrenia revealed by fixel-based analysis.},
journal = {Translational psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41398-026-04277-y},
pmid = {42443158},
issn = {2158-3188},
support = {82230046//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Fixel-based analysis (FBA) is an advanced diffusion imaging method that enables the direct estimation of white matter microstructural properties beyond the limitations of traditional diffusion tensor imaging (DTI). Despite its potential, FBA has been rarely applied in schizophrenia research, and its value in providing complementary information to conventional tensor-based approaches remains to be fully established. In this study, we investigated white matter abnormalities of treatment-naïve, first-episode schizophrenia (FES) patients using both FBA and tensor-based method, and examined the concordance between the two approaches to better characterize the nature of white matter pathology in early SZ. MRI data were acquired from 94 treatment-naïve FES patients and 114 healthy controls (HCs). Fractional anisotropy (FA) and mean diffusivity (MD) were calculated using a conventional tensor-based method. In parallel, fibre density (FD), fibre-bundle cross-section (FC), and their combined metric (FDC) were estimate with FBA. White matter was segmented into 72 anatomically defined tracts based on fibre tracking. Between-group comparisons were conducted using a multivariate general linear model (GLM) to assess differences across diffusion metrics. Using the tensor-based method, six white matter tracts exhibited significantly altered FA, while 34 tracts showed significantly increased MD in FES patients compared to HCs (all t-values > 2.34 or t-values < -2.36, all FDR-p < 0.05). In contrast, FBA revealed more widespread abnormalities: 46 tracts showed significantly reduced FD, 29 tracts showed significantly reduced FC, and 52 tracts showed significantly reduced FDC (all t-values < -2.29, all FDR-p < 0.05). Notably, all tracts with significantly reduced FC metrics also demonstrated corresponding FDC reductions. No significant correlation was observed between any diffusion metrics and clinical characteristics (all FDR-p ˃ 0.05). This study highlights the remarkable advantages of the FBA in detecting WM microstructural abnormalities in individuals with FES.},
}

@article {pmid42443230,
year = {2026},
author = {Wu, J and Xu, Z and Yang, R and Zhu, B and Zhan, Y},
title = {Open dataset and deep learning model for intelligent diagnosis of neonatal respiratory distress syndrome and aspiration syndrome in newborns.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {42443230},
issn = {2045-2322},
support = {62276089//the National Natural Science Foundation of China/ ; 24JCJQJC00200 and 24JCQNJC01230//the Natural Science Foundation of Tianjin, China/ ; F2024202064//the Natural Science Foundation of Hebei Province, China/ ; BJ2025004//the Science Research Project of Hebei Education Department, China/ ; RSTH-2023-135-1//the Ministry of Human Resources and Social Security, China/ ; 24464401D//the S&T Program of Hebei/ ; },
mesh = {Humans ; *Respiratory Distress Syndrome, Newborn/diagnosis/diagnostic imaging ; Infant, Newborn ; *Deep Learning ; *Diagnosis, Computer-Assisted/methods ; },
abstract = {Neonatal pulmonary diseases such as aspiration syndrome (AS) and respiratory distress syndrome (NRDS) require timely diagnosis, yet manual interpretation of neonatal X-rays is labor-intensive and subjective. To support AI-assisted diagnosis, we constructed the first Chinese neonatal pulmonary ailment dataset (NPA), covering both normal and diseased cases of varying severity. However, the NPA dataset exhibits severe class imbalance, and traditional augmentations randomly mix lesions, often corrupting pathological semantics. To address this, we propose a Polluted CutMix framework that selectively blends normal and diseased images, ensuring meaningful lesion synthesis, and an uncertainty-aware module that filters unreliable pseudo-labels during training. The novelty of this work lies in (1) introducing the first neonatal pulmonary dataset and (2) unifying targeted augmentation with uncertainty modeling for robust learning under data imbalance. On the NPA dataset, our approach surpasses existing baselines by 4-7% in classification accuracy, demonstrating improved generalization and diagnostic reliability. We hope that our novel framework and dataset will inspire further research in this field.},
}

Humans
*Respiratory Distress Syndrome, Newborn/diagnosis/diagnostic imaging
Infant, Newborn
*Deep Learning
*Diagnosis, Computer-Assisted/methods

@article {pmid42445514,
year = {2026},
author = {Li, W and Li, X and Yang, H and Tang, C and Wang, Z and Wang, Q and Nie, Y and Liu, Z and Yang, Y and Wang, H and Zhang, S and Zhang, XY and Wang, S and Peng, H and Sun, X},
title = {MRI-compatible soft fiber bioelectronics for multimodal assessment of electrical neural stimulation on whole-brain activation.},
journal = {National science review},
volume = {13},
number = {13},
pages = {nwag325},
pmid = {42445514},
issn = {2053-714X},
abstract = {Deciphering mechanisms of electrical neural stimulation using multimodal approaches combining electrophysiology and magnetic resonance imaging (MRI) is pivotal for advancing neuromodulation therapies. However, this paradigm has been hindered by the lack of high-performance neural electrodes that are compatible with ultra-high-field MRI while possessing exceptional electrochemical properties. Here, we report an MRI-compatible fiber neural electrode (MFE) fabricated from structurally optimized conductive polymer fiber emulating brain tissue characteristics. The MFE induces little-to-no MRI artifacts at 11.7 T and combines low modulus, low impedance and high charge-injection limit, enabling precise neural stimulation and recording. Utilizing these MFEs, we investigated frequency-dependent whole-brain responses to electrical stimulation of the medial prefrontal cortex in wild-type and autism-model rats, revealing responses potentially relevant to autism intervention. This was achieved through electrical stimulation synchronized with electrophysiological recording and multimodal MRI, including functional MRI, diffusion-weighted imaging (tissue structural assessment) and magnetic resonance spectroscopy (metabolite profiling). Our MFE enables previously unattained simultaneous acquisition of multimodal information, providing a powerful tool for in-depth mechanistic studies of neuromodulation.},
}

@article {pmid42447014,
year = {2026},
author = {Li, X and Song, X and Ma, Q and Li, P and Li, F and Zhang, X and Lv, Z},
title = {RAP[2]G: Relation-Aware Progressive Pseudo-label Generation for Cross-subject MI-EEG Recognition.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3713522},
pmid = {42447014},
issn = {1558-2531},
abstract = {OBJECTIVE: Motor imagery electroencephalography (MI-EEG) classification is essential for brain-computer interfaces (BCIs), but achieving high accuracy across different individuals remains challenging due to significant inter-subject variability. Recently, unsupervised domain adaptation (UDA) methods have addressed this problem by adapting models without target labels, often using pseudo-labeling. However, existing pseudo label techniques evaluate each sample in isolation and employ a simple threshold-based strategy, overlooking the relationship among samples and often excluding useful data points. We aim to overcome these limitations for cross-subject MI-EEG classification.

METHODS: We propose the relation-aware progressive pseudo-label generation (RAP2G) method, a novel UDA framework combining Optimal Transport (OT) with structure aware regularization and dynamic pseudo-label selection. RAP2G leverages the inherent structure within the target subject's data by incorporating feature similarity into the OT-based pseudo label generation process. It also adaptively selects pseudo-labeled samples using a progressive schedule based on OT confidence. We evaluated RAP2G on three public benchmarks, BCI Competition IV dataset 2a, BCI Competition IV dataset 2b, and the High Gamma Dataset, using leave-one-subject-out cross-validation.

RESULTS: RAP2G consistently outperforms existing state-of-the-art UDA techniques and baseline models. Ablation studies confirm the contribution of the structure-aware component. Visualizations show enhanced feature separability after adaptation, and the learned attention maps are qualitatively consistent with known motor-cortex organization.

CONCLUSION: RAP2G provides an effective approach for robust cross-subject MI-EEG classification.

SIGNIFICANCE: By improving label-free adaptation across subjects, this work supports more reliable and practical BCI systems for biomedical applications.},
}

@article {pmid42447184,
year = {2026},
author = {Gökçe Aslan, S and Yılmaz, B},
title = {Subject-independent EEG classification of imagined swallowing: Impact of saliva vs. water paradigms.},
journal = {PloS one},
volume = {21},
number = {7},
pages = {e0353570},
pmid = {42447184},
issn = {1932-6203},
mesh = {Humans ; *Deglutition/physiology ; *Electroencephalography/methods ; *Saliva ; Female ; Brain-Computer Interfaces ; Water ; Male ; Adult ; *Imagination/physiology ; Young Adult ; },
abstract = {Dysphagia poses a significant burden on global health, necessitating innovative neurorehabilitation tools. Brain-Computer Interfaces (BCIs) based on motor imagery offer a promising avenue, yet the neural differentiation between distinct swallowing paradigms remains under-explored. This study investigates the electrophysiological characteristics of imagined swallowing to establish a robust, subject-independent framework for neural decoding. We recorded EEG signals from 30 participants across two experimental paradigms: imagined saliva and imagined water swallowing. A rigorous analytical pipeline was implemented, featuring artifact removal, multidimensional feature extraction, and fold-wise statistical feature selection utilizing False Discovery Rate (FDR) correction and effect size criteria. To ensure the clinical translatability of the findings, a Leave-One-Subject-Out (LOSO) cross-validation scheme and permutation testing were employed for classification and statistical validation. Our findings demonstrate that EEG-based features can distinguish rest from imagined swallowing with near-ceiling performance (~99% accuracy), regardless of the paradigm. While the discrimination between imagined saliva and water yielded moderate accuracy (~63%), the results reveal critical insights into the inherent neural similarities of these motor imagery tasks. This study provides a statistically validated, subject-independent benchmark for decoding swallowing intentions. The high classification performance underlines the feasibility of EEG-based BCIs for dysphagia rehabilitation. While established as a proof-of-concept in healthy individuals, this framework paves the way for future neurofeedback applications in clinical populations.},
}

Humans
*Deglutition/physiology
*Electroencephalography/methods
*Saliva
Female
Brain-Computer Interfaces
Water
Male
Adult
*Imagination/physiology
Young Adult

@article {pmid42447807,
year = {2026},
author = {Yu, X and Wei, L and Zheng, Y and Qi, X and Mu, X and Wang, Q and Hua, Y and Chen, Q and Zhang, Y and Jiang, H and Wang, Z and Ni, P and Guo, W and Deng, W and Li, T},
title = {Honokiol attenuates neuroinflammation and enhances remyelination in mouse models of multiple sclerosis through PPARγ-mediated ERK/AKT signalling.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {23},
number = {4},
pages = {e00965},
doi = {10.1016/j.neurot.2026.e00965},
pmid = {42447807},
issn = {1878-7479},
abstract = {Multiple sclerosis is characterized by inflammatory demyelination and insufficient myelin repair, which together drive progressive neurological impairment. While existing immunomodulatory treatments have demonstrated efficacy in reducing acute relapse frequency, they fail to address the critical deficits in oligodendrocyte precursor cell (OPC) differentiation and subsequent remyelination, leaving patients with irreversible neurological disability. This highlights the pressing necessity for pharmacological interventions that can simultaneously mitigate neuroinflammation and stimulate endogenous myelin regeneration. In the present work, we evaluated the therapeutic potential of honokiol, a naturally occurring bioactive polyphenol derived from Magnolia officinalis, using both experimental autoimmune encephalomyelitis (EAE) and cuprizone-induced demyelination paradigm. Our results demonstrate that honokiol improved myelin restoration and exerted potent anti-inflammatory effects by suppressing glial activation, modulating inflammatory cytokine profiles, and restoring lipid homeostasis. Transcriptomic analysis indicated a global downregulation of immune-related pathways, and significant upregulation of signalling cascades critically involved in myelination and oligodendrocyte maturation. Molecular dynamics simulations revealed that honokiol stably bound to cannabinoid receptors and peroxisome proliferator-activated receptor gamma (PPARγ) receptor via energetically favourable interactions. In primary OPC cultures, honokiol directly facilitated the differentiation of OPC into mature oligodendrocytes. Pharmacological blockade of PPARγ eliminated both honokiol-induced OPC maturation and subsequent activation of the extracellular signal-regulated kinase/protein kinase B (ERK/AKT) signalling axis. Taken together, these findings indicate that honokiol attenuates neuroinflammation and may facilitate myelin repair through both immunomodulatory actions and direct effects on oligodendrocyte lineage cells, supporting its potential as a disease-modifying strategy for demyelinating disorders.},
}

@article {pmid42448255,
year = {2026},
author = {Yang, R and Ru, X and Song, J and Zhao, Y and Fan, B and Na, S and Gao, JH},
title = {Magnetically Compatible and Fiberless fNIRS Enables Simultaneous Multimodal Imaging with Optically Pumped Magnetometer MEG.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122133},
doi = {10.1016/j.neuroimage.2026.122133},
pmid = {42448255},
issn = {1095-9572},
abstract = {Simultaneous acquisition of functional near-infrared spectroscopy (fNIRS) and magnetoencephalography (MEG) provides complementary hemodynamic and electrophysiological information for studies of neurovascular coupling and has previously been demonstrated using fiber-based fNIRS implementations. Compared with fiber-based systems, fiberless fNIRS is lightweight and eliminates fiber-induced mechanical constraints; however, its integration with MEG remains challenging due to stringent magnetic compatibility requirements. Here, we present a magnetically compatible and fiberless fNIRS system enabling flexible and non-invasive multimodal imaging with optically pumped magnetometer (OPM) MEG. We developed magnetically compatible source/detector optodes and implemented a multipole moment flexible printed circuit design that suppresses driving-current-induced magnetic fields by more than 1000-fold. The optodes and cables generated less than 1 nT of magnetic field at ∼1 cm from the OPM sensor, with no measurable impact on OPM sensitivity. Simultaneous fiberless fNIRS and OPM-MEG acquisition was demonstrated in a somatosensory paradigm, capturing concurrent hemodynamic and evoked magnetic responses, thereby demonstrating the feasibility and robustness of our integrated multimodal system. By addressing a key magnetic-compatibility barrier between fiberless fNIRS and OPM-MEG, this work paves the way for neurovascular coupling studies using flexible multimodal platforms, and supports future developments in wearable multimodal neuroimaging and multimodal brain-computer interface systems.},
}

@article {pmid42450621,
year = {2026},
author = {He, T and Wang, H and Ni, H and Sun, Y and Gao, X and Zhou, F and Zhang, J and Xu, K},
title = {The Distinct Electrophysiological Mechanisms in the Cortico-Striatal Circuit of LID Rats.},
journal = {Biology},
volume = {15},
number = {13},
pages = {},
doi = {10.3390/biology15131074},
pmid = {42450621},
issn = {2079-7737},
support = {2021ZD0200405//STI 2030-Major Projects/ ; 2023C03078//the Public Projects of Zhejiang province/ ; LR23H180002//the Outstanding Youth Program of Zhejiang Natural Science Foundation/ ; },
abstract = {Levodopa-induced dyskinesia (LID) is a severe motor complication associated with long-term levodopa (L-DOPA) treatment for Parkinson's disease (PD). Its underlying mechanisms remain unclear, and candidate biomarkers lack consistency. To investigate cortico-striatal network alterations associated with LID, we simultaneously recorded single-neuron spikes and local field potentials (LFPs) from the dorsolateral striatum (DLS) and the primary motor cortex (M1) in LID rats. Our results showed that in the DLS, the LID group had a greater number of putative fast-spiking interneurons (FSIs) with lower firing rates, and fewer putative medium spiny neurons (MSNs) with higher firing rates. In M1, pyramidal neurons were fewer but fired faster, while interneurons were more numerous with no change in firing rate. Although gamma power increased and delta power decreased in both regions in LID rats, delta-gamma phase-amplitude coupling (PAC) was present in the DLS but absent in M1. Furthermore, cross-regional PAC analysis revealed significantly stronger coupling between the low-frequency phase of M1 and the high-frequency amplitude of the DLS than in the opposite direction, indicating an asymmetric pattern of cortico-striatal coupling in LID. These findings demonstrate region-specific alterations in neuronal activity and oscillatory coupling associated with LID and suggest that asymmetric cortico-striatal PAC may serve as a promising electrophysiological marker for characterizing abnormal network dynamics underlying dyskinesia.},
}

@article {pmid42451245,
year = {2026},
author = {Aldayel, M and Al-Nafjan, A},
title = {Automated Anxiety Detection System Integrating a Brain-Computer Interface for Neurofeedback Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {13},
pages = {},
doi = {10.3390/s26134004},
pmid = {42451245},
issn = {1424-8220},
support = {IMSIU-DDRSP2604//Imam Mohammad ibn Saud Islamic University/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Neurofeedback/methods ; Convolutional Neural Networks ; Adult ; *Anxiety/diagnosis/physiopathology ; Middle Aged ; Female ; Deep Learning ; Male ; Adolescent ; Young Adult ; },
abstract = {Anxiety disorders pose an increasing challenge to the mental health of individuals, particularly in regions with limited healthcare access. This study investigated the potential of integrating a brain-computer interface for processing electroencephalography (EEG) data with deep learning models to accurately classify anxious and non-anxious states. In the first phase, a convolutional neural network (CNN) was developed and validated on the public GAMEEMO dataset, achieving a classification accuracy of 95.72%. In the second phase, we conducted a separate experimental validation with seven participants (aged 18-60 years) using a within-subjects design. The protocol comprised a custom Stroop test to elicit acute cognitive stress and anxiety-related arousal, followed by a guided 4-7-8 breathing exercise to induce relaxation. EEG data from this experiment were used to classify anxious versus non-anxious states with the same CNN architecture after domain adaptation. On this self-collected dataset, the CNN achieved an accuracy of 86.58%. These results demonstrate proof-of-concept transferability while highlighting the performance gap between controlled benchmark data and real-world, small-sample recordings. The deep learning model can subsequently be coupled with neurofeedback techniques to manage anxiety levels. Overall, the findings support the potential of the developed automated system for detecting stress-induced anxious states, with possible future integration into neurofeedback-based management systems.},
}

Humans
*Brain-Computer Interfaces
Electroencephalography/methods
*Neurofeedback/methods
Convolutional Neural Networks
Adult
*Anxiety/diagnosis/physiopathology
Middle Aged
Female
Deep Learning
Male
Adolescent
Young Adult

@article {pmid42451327,
year = {2026},
author = {Memon, PQ and Anderson, C and Memon, ZQ and Memon, S and Qadir, A},
title = {Comparative Analysis of Tri-Polar Concentric Ring and Conventional Electrodes for Overt and Covert Speech.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {13},
pages = {},
doi = {10.3390/s26134084},
pmid = {42451327},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; Electrodes ; Female ; Brain-Computer Interfaces ; *Speech/physiology ; Male ; Convolutional Neural Networks ; Algorithms ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Brain/physiology ; Adult ; },
abstract = {The Brain-Computer Interface (BCI) is a system that enables communication between the brain and external devices by translating brain activity into commands. Electroencephalography (EEG) is a commonly used modality for measuring brain activity. However, its low signal-to-noise ratio (SNR) and electrode reference problems lead to poor spatial resolution. As a result, EEG signals are often contaminated with physiological artifacts such as muscle movements. Therefore, this study used novel tripolar concentric ring electrodes (TCREs) to record brain signals related to overt and covert speech. Brain signals associated with overt and covert speech were recorded using TCRE and disc electrodes. Classification algorithms, including K-Nearest Neighbors (KNN), Fully Connected Neural Networks (FCNN), and Convolutional Neural Networks (CNN), were used to classify the TCRE and conventional EEG signals. The data were collected from 16 healthy participants, consisting of 10 males and 6 females. The experimental results demonstrate that TCREs provide superior performance compared to conventional disc electrodes. In addition, the 0.5-1.2s interval, corresponding to the peak stimulus window, exhibits a maximum power of 250μV. The average accuracy achieved during this peak epoch was 86.25%, whereas the remaining epoch shows an accuracy of 83.5% using TCREs.},
}

Humans
*Electroencephalography/methods
Electrodes
Female
Brain-Computer Interfaces
*Speech/physiology
Male
Convolutional Neural Networks
Algorithms
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Brain/physiology
Adult

@article {pmid42451342,
year = {2026},
author = {Wang, J and Yang, H},
title = {A Dual-Branch Spatiotemporal Framework with Dynamic Weighted Permutation Entropy for Short-Window Motor Imagery EEG Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {13},
pages = {},
doi = {10.3390/s26134101},
pmid = {42451342},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; Entropy ; Signal Processing, Computer-Assisted ; Convolutional Neural Networks ; Long Short Term Memory ; Algorithms ; },
abstract = {Decoding short-window electroencephalography (EEG) signals is critical for low-latency brain-computer interfaces (BCIs), yet current models struggle to extract robust features under high cross-subject variability and low signal-to-noise ratios. To address this, we propose a spatiotemporal decoding framework integrating dynamic weighted permutation entropy (DWPE) with a hybrid neural network. We introduce DWPE to quantify nonlinear dynamic complexity while retaining amplitude information. These features are subsequently processed by a cascaded convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architecture with spatial attention, enabling the simultaneous extraction of topological patterns and temporal dependencies. The framework was evaluated on three public motor imagery datasets (hBCI, BCI Competition IV-2a, and IV-2b) using a fixed 3 s window. Empirical results demonstrate that our approach achieves an average accuracy of 84.35% and an AUC of 0.8821 on the hBCI dataset, significantly outperforming current representative recent baselines (p < 0.01). Ablation studies confirm that integrating DWPE yields a 3.89% accuracy improvement over the spatial-temporal backbone alone. With a single-sample inference time of 20.94 ms and an estimated total decision latency of approximately 3.02 s under the 3 s window setting, the proposed method provides a favorable balance between decoding accuracy and computational efficiency for short-window and near-online BCI applications.},
}

*Electroencephalography/methods
Humans
Brain-Computer Interfaces
Entropy
Signal Processing, Computer-Assisted
Convolutional Neural Networks
Long Short Term Memory
Algorithms

@article {pmid42452187,
year = {2026},
author = {Abu-Ellail, FFB and Zhao, L and Tang, S and Liu, J and Yao, L and Zhao, P and Zan, F},
title = {Prediction-Based Family Selection in Early Stage Sugarcane Breeding: Comparing BLUP, BLUE, Phenotypic Indices, and Machine Learning.},
journal = {Plants (Basel, Switzerland)},
volume = {15},
number = {13},
pages = {},
doi = {10.3390/plants15131980},
pmid = {42452187},
issn = {2223-7747},
support = {202502AE090035//China International Science and Technology Cooperation/ ; },
abstract = {Selecting superior families at the seedling stage is crucial for accelerating genetic gain in sugarcane, yet systematic comparisons of selection methods remain limited. This study evaluated seven selection strategies: phenotypic check-based selection (Pheno), a three-trait combined index (CI3), Best Linear Unbiased Prediction (BLUP), Best Linear Unbiased Estimation (BLUE), tiered family selection (Tiered), logistic regression (LASSO), and the Multi-Trait Family Ideotype Distance Index (MFIDI). The experiment followed an augmented block design with four blocks, two check varieties, and included 125 test families comprising 10,955 seedlings. Using a combined index of standardized cane and sugar yields, families were classified as elite (top 20%), moderate (60%), and weak (bottom 20%). BLUP and BLUE rankings were consistent (Spearman's ρ > 0.95, TCI = 88%, Jaccard = 0.79). Elite families showed median index values of 0.90 (BLUP) and 0.88 (BLUE) with wide interquartile ranges, whereas weak families had medians of -0.70 with narrow ranges. LASSO achieved excellent predictive performance: AUC = 0.95, accuracy = 0.92, sensitivity = 0.90, specificity = 0.94, identifying cane yield, sugar yield, and millable cane as key drivers. Agreement for inferior families was lower across methods (BCI ≤ 68%). BLUP with a multi-trait index proved most effective for discriminating elite families. Families F31 and F71 consistently ranked top. Combining selection approaches with agreement indices improves early-stage decisions for family selection in sugarcane breeding.},
}

@article {pmid42452959,
year = {2026},
author = {Liu, DH and Iwane, F and Zhang, M and Cohen, LG and Millán, JDR},
title = {Brain-Computer Interface Training Fosters Perceptual Skills to Detect Errors.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e76153},
doi = {10.1002/advs.76153},
pmid = {42452959},
issn = {2198-3844},
support = {//Intramural Research Program of the National Institutes of Health (NIH)/ ; },
abstract = {Accurate perception of subtle visuo-motor errors is essential for perceptual and sensorimotor learning, and supports timely corrective actions in precision-based task. However, conventional perceptual training, typically based on response-accuracy feedback, is limited in improving sensitivity to small, subtle errors. While prior approaches have focused on modulating sensory regions to enhance perceptual learning, we propose an alternative approach that targets a cognitive neural marker: the error positivity (Pe), a component of the error-related potential (ErrP) originating in the anterior cingulate cortex, a key decision-making region. We hypothesize that the Pe, which reflects conscious awareness of errors, serves as a modifiable neural correlate of error perception. In a five-day longitudinal study, we show that providing real-time feedback on the presence or absence of ErrPs during perceptual training accelerates perceptual learning at 3 ∘ $3^\circ$ errors and enhances perceptual performance at 6 ∘ $6^\circ$ errors without accelerating the learning rate, relative to behavioral training alone. These behavioral gains were accompanied by increase in Pe amplitude. Together, these findings offer new neurophysiological insights into the mechanisms of error perception, and establish ErrP-based brain-computer interface interventions as a promising approach for fostering perceptual learning in domains where detecting subtle errors is critical.},
}

@article {pmid42453642,
year = {2026},
author = {Ciferri, M and Ferrante, M and Toschi, N},
title = {A modular semantic-structural pipeline for visual decoding from primate spiking data via selective temporal integration.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {4},
number = {},
pages = {},
pmid = {42453642},
issn = {2837-6056},
abstract = {Characterizing the information content of intracortical signals during visual processing is a central challenge in systems neuroscience. We address the problem of decoding visual information from high-density intracortical recordings in primates, using the THINGS Ventral Stream Spiking Dataset. We systematically evaluate the effects of model architecture, training objectives, and data scaling on decoding performance. Results show that decoding accuracy is jointly driven by non-linearity and selective temporal aggregation, rather than heavier sequence modelling in this data regime. A simple model combining temporal attention with a shallow MLP achieves up to 70% top-1 image retrieval accuracy, outperforming linear baselines as well as recurrent and convolutional approaches. Scaling analyses reveal predictable diminishing returns with increasing input dimensionality and dataset size. Building on these findings, we design a modular generative decoding pipeline that combines low-resolution latent reconstruction with semantically conditioned diffusion, generating plausible images from 200 ms of brain activity. This framework provides principles for brain-computer interfaces and semantic neural decoding.},
}

@article {pmid42454243,
year = {2026},
author = {Liu, H and Li, Z and Li, W and Yang, R and Chen, X},
title = {Optimization of stimulus color for peripheral SSVEP-based brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1832475},
pmid = {42454243},
issn = {1662-5161},
abstract = {BACKGROUND: Most existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) struggle to balance user experience with system performance. Although recent studies have shown that peripheral vision stimulation can evoke SSVEPs with high user comfort, the impact of stimulus color on peripheral SSVEP performance remains underexplored. Therefore, this study attempted to investigate the effect of stimulus color on peripheral SSVEPs.

METHODS: Four conventional stimulus colors (i.e., blue, green, red, and white) were evaluated using ultra-low frequency SSVEP stimuli, with the stimulation frequencies ranging from 2 Hz to 3.32 Hz. Based on the results, the optimized stimulus color was used to build a 12-target peripheral SSVEP-based BCI. Task-discriminant component analysis (TDCA) algorithm was adopted to detect SSVEPs. The feasibility of the proposed system was verified through offline experiments with 13 participants and online experiments with 11 participants.

RESULTS: The offline experiments with 13 participants showed no significant differences in classification accuracy and information transfer rates (ITRs) among the four-color paradigms. However, green stimulation received the highest subjective comfort ratings. Consequently, green stimulation was selected for building the 12-target peripheral SSVEP-based BCI. The online results achieved a mean classification accuracy of 89.93 ± 6.10% and an ITR of 47.96 ± 6.98 bits/min.

CONCLUSION: The present findings support a comfort-driven color selection strategy for peripheral ultra-low-frequency SSVEP stimulation while maintaining comparable performance among the tested colors. These findings may provide practical guidance for more visually tolerable SSVEP-based BCI systems based on peripheral visual stimulation.},
}

@article {pmid42455255,
year = {2026},
author = {Liu, Y and Wang, M and Chen, D and Tan, Y and Zhang, X and Wang, X},
title = {Optimizing Extended Adjuvant Endocrine Therapy in Early HR+/HER2- Breast Cancer: The Emerging Role of Genomic Assays and ctDNA MRD.},
journal = {Advances in therapy},
volume = {},
number = {},
pages = {},
pmid = {42455255},
issn = {1865-8652},
abstract = {Hormone receptor-positive (HR+) and HER2-negative (HER2-) breast cancer represents the most prevalent subtype of early-stage breast cancer. Adjuvant endocrine therapy (ET) substantially reduces recurrence and breast cancer mortality; however, late relapse remains a major challenge, with a considerable proportion of recurrences occurring beyond 5 years after diagnosis. Although extended ET can modestly reduce late recurrence, it is associated with cumulative toxicities and impaired quality of life, highlighting the urgent need for biomarkers to identify patients who truly benefit from treatment escalation or extension, while sparing low-risk individuals from overtreatment. Tissue-based multigene expression assays, including the Breast Cancer Index (BCI), EndoPredict, Prosigna, Oncotype DX, and MammaPrint, have improved risk stratification and informed treatment decisions, with BCI demonstrating the strongest evidence for predicting benefit from extended endocrine therapy. Among currently available assays, BCI has the strongest level of evidence supporting its use in guiding extended endocrine therapy decisions. In parallel, liquid biopsy approaches, particularly circulating tumor DNA (ctDNA)/minimal residual disease (MRD) detection, have emerged as promising tools for dynamic monitoring and early detection of molecular relapse. This review summarizes current evidence supporting biomarker-guided decision-making in early-stage HR+/HER2- breast cancer, focusing on three clinically relevant questions: who requires treatment escalation, who benefits from extended endocrine therapy, and who may safely de-escalate. We further discuss challenges and future directions toward integrated models combining genomic assays with longitudinal ctDNA monitoring to refine personalized adjuvant endocrine strategies. However, current evidence remains limited, and prospective validation is required.},
}

@article {pmid42438888,
year = {2026},
author = {Wang, J and Li, E and An, X and Ming, D},
title = {Heartbeat-evoked potentials reveal interoceptive dysfunction in clinical disorders: Experimental frameworks and promising applications.},
journal = {Psychological medicine},
volume = {56},
number = {},
pages = {e216},
doi = {10.1017/S003329172610453X},
pmid = {42438888},
issn = {1469-8978},
support = {No.2022ZD0208500//National Major Science and Technology Projects of China/ ; No.62276181, No.82020108013//Innovative Research Group Project of the National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Interoception/physiology ; *Evoked Potentials/physiology ; *Heart Rate/physiology ; *Mental Disorders/physiopathology ; *Brain/physiopathology/physiology ; },
abstract = {Interoception refers to the ability to perceive and integrate physiological signals originating from within the body, such as heartbeat and respiration. This process involves both bottom-up and top-down. As a key neurophysiological marker of interoception, the heartbeat-evoked potential (HEP) reflects the cortical processing of cardiac signals in the brain. In this review, we first outline the neural mechanisms underlying interoception and HEP, followed by a comprehensive overview of the methodologies commonly employed in HEP research. Based on the directionality of interoceptive information flow, we categorize HEP-related experimental designs into three types: bottom-up bodily sensory input, top-down predictive perception, and top-down regulation. Additionally, we explore the clinical relevance of HEP in areas such as psychiatric disorders and cardiac-related conditions. Finally, we recommend expanding research on top-down predictive perception and top-down regulation in clinical contexts.},
}

Humans
*Interoception/physiology
*Evoked Potentials/physiology
*Heart Rate/physiology
*Mental Disorders/physiopathology
*Brain/physiopathology/physiology

@article {pmid42440064,
year = {2026},
author = {Lyu, J and Cheng, W and Li, CY and Wang, J and Zhang, X and Lou, H and Hu, Y and Gao, Z and Duan, S and Cao, K},
title = {PLX3397 Reshapes Hepatic Lipid Metabolism Independent of Microglial Depletion.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {42440064},
issn = {1995-8218},
abstract = {Colony-stimulating factor 1 receptor (CSF1R) inhibitors, such as PLX5622 and PLX3397 (pexidartinib), are widely used for in vivo microglial depletion and for investigating microglial functions and therapeutic potential. Although CSF1R inhibitor-based studies have uncovered important roles for microglia in processes, such as anesthesia, addiction, and obesity, whether the resulting phenotypes reflect microglial depletion alone remains increasingly debated. Our previous work has shown that PLX5622 activates hepatic constitutive androstane receptor (CAR)-dependent xenobiotic metabolism, altering the metabolism of anesthetics and addictive drugs, and amplifying apparent microglial phenotypes. Whether other CSF1R inhibitors, particularly the FDA-approved PLX3397, exert systemic metabolic effects that may influence the interpretation of brain phenotypes remains unknown. Here, we demonstrate that PLX3397 exerts hepatic metabolic effects that are mechanistically distinct from those induced by PLX5622. Although PLX3397 only weakly affects xenobiotic metabolism, it markedly enhances endogenous hepatic lipid metabolism, inducing a fasting-like state characterized by increased lipid utilization and ketogenesis despite the absence of nutrient deprivation. By uncovering previously unrecognized peripheral effects of PLX3397, our findings identify brain-periphery interactions as a potential source of confounding in studies of microglial function. These results suggest that systemic metabolic effects should be carefully considered when interpreting neural or behavioral phenotypes in pharmacological microglia depletion paradigms.},
}

@article {pmid42441380,
year = {2026},
author = {Bobier, C and Hurst, DJ},
title = {Implanted Pediatric Brain-Computer Interface Research: Recommendations for the Ethical Design of Clinical Trials.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/21507740.2026.2699704},
pmid = {42441380},
issn = {2150-7759},
abstract = {For children with severe physical and cognitive disabilities (e.g. iatrogenic neurologic injury, congenital myelopathy, or quadriplegic cerebral palsy), there are limited therapeutic options. Scientific and clinical developments in implantable brain-computer interface (BCI) technology are in clinical trials in adults. The ethical issues around implantable BCI research in adults have recently been examined, but to date, only limited literature is available on the ethical issues that are attendant with implantable pediatric BCI research. Here, we summarize the ethical issues, focusing on (1) whether invasive BCI research should proceed in children, (2) regulatory considerations, (3) study design considerations, (4) pediatric recipient selection for invasive BCI trials, (5) special problems regarding informed consent in this context, and (6) related psychosocial and public perception considerations. We conclude with specific recommendations regarding ethically informed design of invasive pediatric BCI trials.},
}

@article {pmid42443111,
year = {2026},
author = {Suzuki, S and Nagashima, S and Sugiura, K},
title = {Cortical-SSM: A deep state space model for motor imagery decoding from EEG signals.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae89e8},
pmid = {42443111},
issn = {1741-2552},
abstract = {OBJECTIVE: Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with motor impairments. These signals remain inherently susceptible to physiological artifacts (e.g., eye blinking, swallowing), which pose persistent challenges. Although Transformer-based approaches for classifying EEG signals have been widely adopted, they often struggle to capture fine-grained dependencies within them.

APPROACH: To overcome these limitations, we propose Cortical-SSM, a novel architecture that extends deep state space models to capture integrated dependencies of EEG signals across temporal, spatial, and frequency domains. We validated our method across two large-scale public MI EEG datasets containing more than 50 subjects.

MAIN RESULTS: Our method outperformed baseline methods on the two benchmarks. Furthermore, visual explanations derived from our model indicate that it effectively captures neurophysiologically relevant regions of EEG signals. Significance These results indicate that Cortical-SSM provides a robust and interpretable alternative to attention-based architectures for MI EEG decoding. By enabling physiologically grounded feature learning, our method advances the reliability of subject-independent EEG classification and supports the development of practical and clinically deployable brain-computer interface systems.},
}

@article {pmid42443114,
year = {2026},
author = {Wu, L and Liu, A and Wang, J and Ward, RK and Chen, X},
title = {Deep-learning based electroencephalogram denoising: A literature review.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae89e6},
pmid = {42443114},
issn = {1741-2552},
abstract = {Electroencephalography (EEG) is a pivotal tool for exploring brain functions. However, the low amplitude of EEG signals renders them inherently susceptible to contamination from diverse physiological and environmental artifacts, including electromyogram artifacts, electrocardiogram interference, and electrical noise from power lines. These contaminants significantly hinder the analysis and interpretation of EEG data, posing substantial challenges for signal processing. Recently, deep learning paradigms have catalyzed significant progress in EEG denoising, with many studies reporting competitive reconstruction fidelity and artifact suppression in benchmarked settings. Despite this progress, there remains a notable gap in the literature regarding comprehensive reviews of deep learning-based EEG denoising strategies. To bridge this gap, we use the end-to-end denoising pipeline as an analytical framework, examining how data/target construction, input representation, modular architecture, objective design, and evaluation strategies influence model assumptions, the interpretation of model performance, and practical utility. We further discuss selective and multi-task denoising strategies, downstream validation, and model deployment as key issues for translating reconstruction performance into usable EEG applications. Finally, we identify future research directions aimed at developing more reliable, interpretable, and practically useful EEG denoising systems, thereby enhancing the utility of EEG technologies in broader applications.},
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

RESULTS: Recruitment is ongoing.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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