Stroke is a leading cause of global mortality and disability, often resulting in severe motor dysfunction. Acupuncture has shown promise in stroke rehabilitation, but its neural mechanisms remain unclear. This study investigated the immediate effects of acupuncture at GV26, PC6, and SP6 on cortical and corticomuscular functional connectivity in stroke patients with motor dysfunction. Fifteen stroke patients were recruited and their resting-state electroencephalography (EEG), as well as EEG and electromyography (EMG) during unilateral static ankle dorsiflexion were recorded under five conditions: no acupuncture, acupuncture at GV26, PC6, SP6, and all above three acupoints. Then, we analyzed resting-state brain networks and corticomuscular coherence (CMC) during ankle dorsiflexion tasks. Acupuncture at PC6 and all three acupoints significantly enhanced topological parameters of brain networks in the theta band, indicating improved cortical functional integration. Additionally, acupuncture at PC6, SP6, and all three acupoints increased CMC in beta and gamma bands, suggesting strengthened corticomuscular coupling. These results demonstrate that acupuncture acutely modulates both central and peripheral neural pathways, with acupoint-specific immediate effects. The study provides preliminary neurophysiological evidence supporting the acupoint specificity effects in stroke patients and highlights the potential for developing personalized acupuncture prescriptions pending further long-term validation.

Spatial cognition is a key ability of human cognition and intelligence. In this study, we validated the feasibility and effectiveness of origami training in enhancing spatial cognition and elucidated the underlying neural mechanisms. We assigned participants to either an origami group or a control group, with the origami group completing a training program. We collected electroencephalography (EEG) signals and eye movement data during the spatial tasks pre-, during-, and post-training. A cognitive questionnaire was also collected. We then compared event-related synchronization and event-related desynchronization in different bands and constructed weighted Phase Lag Index brain network maps. We also analyzed eye-tracking metrics. Origami training enhanced cognitive performance, improving accuracy and reducing response time. The origami training increased the frontal midline θ power and decreased the parietal α power. The origami training modulated brain connectivity differently across tasks. Eye-tracking data revealed a reduction in cognitive load, increased focus, and more efficient cognitive processing following the training. The frontal, parieto-occipital, and frontal-occipital regions actively contribute to spatial cognition. Origami training enhances spatial cognition by re-shaping the brain networks and functional connectivity. These findings support the development of a portable and cost-effective digital therapy for neurodegenerative disorders.

Brain development in preterm infants shows marked heterogeneity, often obscured by group-level analyses. Between the group-level and the individual difference, subgroup can model the heterogeneity of early developmental trajectories. To characterize this, we analyzed longitudinal functional connectome data from 90 preterm infants (scanned at birth and term-equivalent age) and 521 full-term controls from the developing Human Connectome Project. A machine learning model predicted individual brain-age gap (BAG), quantifying maturational deviation. Clustering of longitudinal BAG trajectories revealed two distinct preterm subgroups with divergent developmental pathways. These subgroups exhibited significantly different functional network architectures and, at 19-month follow-up, distinct behavioral outcomes in cognitive, language, and motor domains. Our findings establish that early preterm brain maturation follows identifiable, heterogeneous trajectories, providing a data-driven framework for early risk stratification.

As a prevalent non-invasive screening technique, Wireless Capsule Endoscopy is often hindered by poor image quality, including under-/overexposure and low light condition. While illumination correction based on diffusion modeling or frequency-domain decomposition has shown effectiveness, existing methods often (1) underexploit structural information, and (2) lack adaptive strategies for varying illumination degradations, leading to suboptimal restoration and unnecessary computation. To this end, we propose Brownian Bridge Diffusion Transformer-Mixture-of-Experts (BiT-MoFE), a unified adaptive framework that integrates the merits of the two paradigms for endoscopic illumination correction. We adopt a Brownian Bridge Diffusion framework, in which an efficient Transformer serves as the backbone network, and design a frequency-decomposed MoFEs module to explicitly handle illumination and image structure simultaneously. By dynamically selecting the most suitable experts conditioned on exposure cues and diffusion timesteps, our framework achieves a strong balance between restoration fidelity and computational efficiency. Extensive experiments on multiple public datasets demonstrate that BiT-MoFE achieves state-of-the-art performance on both exposure correction and low-light enhancement tasks.

Postural instability and gait disorder (PIGD) subtype of Parkinson's disease (PD) is marked by heterogeneous motor and cognitive impairments, making rehabilitation response difficult to predict. Identifying robust multimodal predictors is essential for precision rehabilitation. This study aimed to identify key multimodal features associated with response to motor-cognitive interactive rehabilitation and to develop a generalizable prediction framework. Twenty-one PD patients with PIGD completed a motor-cognitive interactive rehabilitation program. Multimodal data, including demographics, clinical scales, gait parameters, magnetic resonance imaging (MRI), and EEG, were collected across 14 feature modalities. A multimodal sequential forward selection framework based on mutual information (MSFSF-MI) was proposed where predictive stability of selected feature sets was assessed across five machine learning models (support vector machine, RBF, random forest, stochastic gradient boosting, and XGB). Multimodal feature subsets derived by the proposed framework consistently outperformed unimodal models across classifiers. Cross-model analyses highlighted functional connectivity, cortical thickness, low-frequency power spectral density, and phase–amplitude coupling as reproducible predictors, forming a key feature set mainly from MRI and EEG domains. This study identified predictive and potentially robust multimodal neural features of PD rehabilitation response. The introduced nested prediction framework demonstrates strong potential for future generalization, providing a methodological foundation for personalized neurorehabilitation strategies.

Visually induced motion sickness (VIMS) is characterized by symptoms such as nausea, disorientation, and oculomotor discomfort, arising from a visually induced illusionary sense of self-motion. Although previous studies have produced inconsistent spectral findings, particularly in the alpha band, electroencephalogram (EEG) offers high temporal resolution for objectively assessing VIMS. This scoping review synthesizes 28 studies to investigate the sources of this heterogeneity, focusing on the impact of experimental settings (inducing scenarios and presentation equipment) on EEG outcomes. By categorizing studies into abstract motion cues, virtual scene videos, vehicle driving, and gaming scenes, we reveal distinct neurophysiological patterns. Results indicate that while an increase in delta and theta band activities serves as a consistent correlate of sensory conflict, alpha band activity exhibits context-dependent divergence: predominantly increasing in passive viewing scenarios but decreasing in active tasks. Furthermore, head-mounted displays were more frequently associated with alpha enhancement compared to monitors, likely due to higher immersion and spatial orientation demands. This review clarifies these method-dependent variations and offers practical recommendations for selecting experimental scenarios and parameters to improve the reliability of VIMS assessment.

Evidence regarding the prognosis of thyroid carcinoma is heterogeneous, ranging from age effects and nodal burden metrics, such as lymph node ratio (LNR) and log odds of positive nodes (LODDS), to preoperative imaging models comprising ultrasound, CEUS, and radiomics. We conducted a systematic review in line with PRISMA 2020 and SWiM, tabulated under four domains: age relative to the American Joint Committee on Cancer (AJCC-8) staging system, LNR and LODDS, and preoperative prediction models. Terminology and units were standardized through dual data extraction and Python-based harmonization. Prognostic studies were evaluated by Quality in Prognosis Studies, and prediction models were assessed using PROBAST. The AJCC-8 55-year threshold remains pragmatically useful, yet continuous nonlinear modeling of age offers better support for individualized risk estimates. Supplementing anatomic N staging with LNR significantly enhances prognostication, with compartment-specific ratios refining the N1 subgroup. LODDS should be coreported with LNR because it is less sensitive to lymph-node yield, preserves information at extreme values, and often equals or outperforms LNR. Preoperative radiomics and nomograms are promising but often lack external validation and adequate calibration, limiting clinical readiness. Common limitations include endpoint heterogeneity, variable follow-up, node-yield dependency, and sparse reporting of calibration or decision-curve analysis. Residual confounding in retrospective cohorts and reporting bias remain significant challenges.