Identifing two distinct cortical progression subtypes of Parkinson's disease through multimodal neuroimaging

To investigate whether distinct patterns of cortical involvement exist in Parkinson's disease (PD) and to characterize their potential progression trajectories using multimodal neuroimaging and data-driven disease progression modeling.

In this cross-sectional multimodal imaging study, we enrolled 317 patients with clinically diagnosed PD and 61 healthy controls. All participants underwent simultaneous FDG-PET and MRI scanning. We applied the Subtype and Stage Inference (SuStaIn) model to cortical glucose metabolism and thickness data to identify latent disease progression patterns. Network-level characteristics were further examined within a whole-brain gradient framework. Robustness was assessed through age- and sex-balanced sensitivity analyses in the local cohort and external validation using harmonized T1-weighted MRI data from the Parkinson's Progression Markers Initiative (PPMI) dataset.

Two distinct cortical involvement subtypes were identified. One subtype showed predominant alterations in higher-order association networks, including the default mode and frontoparietal networks, whereas the other was characterized by greater involvement of lower-order sensorimotor and limbic systems. These subtype patterns remained stable across sensitivity analyses and external validation. Disease duration showed a significant correlation with the inferred disease stage (r = 0.15, p = 0.01). Imaging findings further revealed hypermetabolism in brainstem and trans-entorhinal regions accompanied by widespread cortical hypometabolism.

Our findings reveal two robust cortical progression patterns in PD, highlighting substantial heterogeneity in network-level metabolic and structural involvement. This framework provides new insights into PD phenotypic variability and may support future efforts toward disease stratification and personalized research.