White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.

ABSTRACT:

Background

Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health.

Methods

This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC11007501 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature