Unveiling the Distinctive Brain Functional Dynamics Between Parkinson's Disease and Progressive Supranuclear Palsy.

BACKGROUND: Dynamic analysis of resting-state fMRI (rs-fMRI) offers a novel approach to differentiate Parkinson's disease (PD) from progressive supranuclear palsy (PSP) by capturing temporal features of brain network activity, which may shed light on the mechanisms underlying non-motor symptoms. OBJECTIVES: To characterize differences in brain dynamics between PD and PSP using dynamic brain metrics, evaluate their exploratory discriminative performance, and investigate associations with non-motor symptoms. METHODS: Sixty-nine healthy controls, 82 PD patients, and 29 PSP patients underwent standardized clinical assessment and rs-fMRI. Hidden Markov models extracted temporal features including fraction occurrence (FO), dwell time, and transition probability. These metrics were used for group comparisons, correlation analyses, and machine learning classification. RESULTS: PD and PSP showed opposite trends in fraction occurrence of unimodal network-dominant states. Compared to PD and controls, PSP exhibited prolonged duration in the dorsal attention and limbic network (DAN&LIM) state and increased occurrence in the dorsal attention and frontoparietal control network (DAN&FPCN) state. Reduced unimodal state occupancy correlated with cognitive decline in both groups, while increased DAN&LIM and unimodal persistence linked to worse mood and sleep disturbances in PSP. Machine learning with these metrics achieved moderate accuracy in differentiating PD from PSP. CONCLUSIONS: Temporal features of brain network dynamics may provide candidate imaging markers for distinguishing PD and PSP while offering mechanistic insights into non-motor symptomatology. However, their diagnostic applicability requires validation in independent external multicenter cohorts.