Spatiotemporal abnormalities in brain networks as a signature of neurological damage in Wilson's disease.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Abnormal microstate dynamics represent a key aspect of neurological impairments in WD, potentially reflecting underlying functional deficits, thereby enhancing our understanding of WD pathophysiology. SIGNIFICANCE: This study offers a thorough understanding of the spatiotemporal patterns of brain activity interactions in WD patients.