Identifying and validating subtypes of Parkinson's disease based on multimodal MRI data via hierarchical clustering analysis.

ABSTRACT:

Objective

We wished to explore Parkinson's disease (PD) subtypes by clustering analysis based on the multimodal magnetic resonance imaging (MRI) indices amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV). Then, we analyzed the differences between PD subtypes.

Methods

Eighty-six PD patients and 44 healthy controls (HCs) were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling (AAL) partition using Data Processing and Analysis for Brain Imaging (DPABI) software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF and GMV between groups.

Results

Two subtypes of PD were identified. The "diffuse malignant subtype" was characterized by reduced ALFF in

SUBMITTER: Cao K 

PROVIDER: S-EPMC9372337 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature