A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment.

ABSTRACT:

Background

Alzheimer's Disease (AD) poses a major challenge as a neurodegenerative disorder, and early detection is critical for effective intervention. Magnetic resonance imaging (MRI) is a critical tool in AD research due to its availability and cost-effectiveness in clinical settings.

Objective

This study aims to conduct a comprehensive analysis of machine learning (ML) methods for MRI-based biomarker selection and classification to investigate early cognitive decline in AD. The focus to discriminate between classifying healthy control (HC) participants who remained stable and those who developed mild cognitive impairment (MCI) within five years (unstable HC or uHC).

Methods

3-Tesla (3T) MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open

SUBMITTER: Ahmad AL 

PROVIDER: S-EPMC11648692 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature