Deconstructing EEG Microstate Sequence Complexity in Parkinson’s Disease: The Role of Temporal Organization

Developing objective and interpretable neurophysiological measures is important for characterizing Parkinson’s disease (PD). EEG microstates capture sub-second large-scale brain-state dynamics, while sequence-level measures provide complementary information on their temporal organization. We compared microstate duration, occurrence, and coverage between patients with PD and healthy controls (HCs), and assessed microstate sequence complexity using multiscale entropy (MSE). To address the arbitrary numerical coding of microstate labels, all 120 possible label recodings were tested, together with symbolic and surrogate analyses. Under the primary group-specific template representation, PD showed reduced duration and coverage of microstates A and B, shorter duration but increased occurrence of microstate C, increased occurrence of microstate D, and increased duration, occurrence, and coverage of microstate E. MSE was higher in PD across all 20 scales, with the direction preserved across all label recodings despite variation in effect magnitude. Full-sequence Lempel–Ziv complexity and conditional entropy were also higher in PD. These effects were markedly attenuated after run compression, and no significant residual group differences were detected under surrogate models preserving occupancy, run-length structure, or first-order transitions. In a common-template sensitivity analysis using the same diagnosis-independent spatial reference for all participants, the class-specific temporal effects and symbolic sequence effects were substantially attenuated, whereas MSE remained higher in PD across all 20 scales, with significant overall group and group × scale effects. These findings suggest that MSE provides a comparatively robust integrative description of PD-related microstate sequence organization, while the observed increase in sequence complexity appears to be closely related to lower-order temporal reorganization rather than representing an independent higher-order complexity abnormality.