Abnormal subthalamic nucleus functional connectivity and machine learning classification in Parkinson's disease: a multisite functional magnetic resonance imaging study.

ABSTRACT:

Introduction

Parkinson's disease (PD) is a progressive neurodegenerative disorder imposing a significant global burden, characterized by motor dysfunction linked to aberrant basal ganglia activity. This multisite study analyzed pooled resting-state functional magnetic resonance imaging (rs-fMRI) data to characterize subthalamic nucleus (STN) functional connectivity (FC) abnormalities and to evaluate their utility in machine learning classification of PD.

Methods

We analyzed rs-fMRI data from 232 participants (158 PD patients and 74 healthy controls [HCs]) across four repositories: Parkinson's Progression Markers Initiative (PPMI), OpenfMRI, and FCP/INDI (NEUROCON dataset and Tao Wu dataset). Seed-based FC analysis focused on bilateral STNs. Group comparisons (PD vs. HCs) wer

SUBMITTER: Qin B 

PROVIDER: S-EPMC12708609 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature