神经科学
疾病
签名(拓扑)
医学
生物
神经影像学
功能连接
脑病
病理
神经网络
大脑定位
作者
Ru-Kai Chen,Jie Lin,Chan Zhang,Wuxiang Shi,Yurong Li,Zhuo-Peng Hu,Chen Wan-jin,Nai-qing Cai
标识
DOI:10.1016/j.clinph.2026.2111517
摘要
• EEG microstate analysis revealed abnormal temporal dynamics in microstates B, C, and D in Wilson’s disease, linked to clinical severity. • Aberrant microstate transitions in Wilson’s disease patients suggest impaired network coordination and cognition. • EEG microstates provide a tool for assessing spatiotemporal patterns of brain activity interactions in Wilson’s disease patients. Resting-state electroencephalogram (EEG) microstates serve as dynamic markers of intrinsic brain activity, reflecting the transient coordination of large-scale neural networks. However, their role in Wilson’s disease (WD) remains largely unexplored. This study aimed to investigate the spatio-temporal dynamics of EEG microstates in WD and explore their relationship with clinical manifestations. Resting-state EEG data were collected from 17 patients with WD and 17 healthy controls (HCs). Microstate parameters were compared between the two groups. Four commonly recognized typical microstate classes (A-D) were calculated. Microstate analysis results revealed significant differences in the temporal parameters of microstates B, C, and D ( p < 0.05) between the HCs and WD patients. Additionally, WD patients exhibited distinct microstate transition patterns ( p < 0.05). In the WD cohort, the occurrence and time coverage of microstate B and the transition probability from microstate C to microstate A significantly correlated with the Unified Wilson Disease Rating Scale (UWDRS) scores. The ROC analysis further indicated good discriminative power of these microstate features for differentiating WD patients from HCs. Abnormal microstate dynamics represent a key aspect of neurological impairments in WD, potentially reflecting underlying functional deficits, thereby enhancing our understanding of WD pathophysiology. This study offers a thorough understanding of the spatiotemporal patterns of brain activity interactions in WD patients.
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