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.