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

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.

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.

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.

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.

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.