Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson's disease.

ABSTRACT:

Background

Early detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease.

Objectives

To investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods.

Methods

We included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), su

SUBMITTER: Huang X 

PROVIDER: S-EPMC10755095 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature