Analysis of Two Neuroanatomical Subtypes of Parkinson's Disease and Their Motor Progression Based on Semi-Supervised Machine Learning.

ABSTRACT:

Background

The high heterogeneity of Parkinson's disease (PD) hinders personalized interventions. Brain structure reflects damage and neuroplasticity and is one of the biological bases of symptomatology. Subtyping PD in the framework of brain structure helps in the prediction of disease trajectories and optimizes treatment strategies.

Methods

The study included a total of 283 de novo PD and 141 healthy controls (HC). Structural heterogeneity between PD and HC was compared, and patients were classified using Heterogeneity through Discriminative Analysis. Gray matter volume (GMV), clinical symptoms, and substantia nigra free water (SNFW) among all subtypes were compared. These subtypes were followed for an average of 2.5 years to monitor motor impairment.

Results

Early

SUBMITTER: Zhou H 

PROVIDER: S-EPMC11829112 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature