Altered EEG microstate transition patterns and visual hallucinations in Parkinson's Disease

Visual hallucinations (VH) in Parkinson's disease (PD) have been linked to alterations in functional brain networks. EEG microstate analysis enables the examination of large-scale network dynamics at the millisecond scale. This study aimed to investigate whether specific EEG microstate transition patterns differ between PD patients with and without VH, and to determine their predictive value.

A total of 38 PD patients (18 VH+, 20 VH-) underwent 5-min, eyes-closed resting-state EEG recordings. Microstate segmentation was performed using a modified k-means clustering algorithm. Transition probabilities between microstates were calculated, and binomial logistic regression was applied to identify transitions that predicted VH presence.

A regression analysis was conducted to examine the predictive value of three microstate transitions (D → B, E → F, and F → C) for the presence of VH in PD patients. The model was statistically significant and demonstrated high classification performance, with an overall accuracy of 89.5%, sensitivity of 88.9%, and specificity of 90.0%. The transition probability from D → B was negatively associated with VH, indicating that a reduction in this transition increased the likelihood of experiencing VH. In contrast, the transitions from E → F and from F → C were positively associated with VH.

Our findings indicate that VH in PD is associated not with static alterations in microstate temporal parameters but with changes in the temporal dynamics of large-scale brain networks. Increased DMN-related transitions and reduced dorsal attention-visual network transitions may reflect a network imbalance that predisposes to internally generated percepts. EEG microstate transition analysis could serve as a sensitive tool to detect subtle connectivity changes underlying VH, complementing existing neuroimaging approaches.