Uncovering abnormal gray and white matter connectivity patterns in Alzheimer's disease spectrum: a dynamic graph theory analysis for early detection.

ABSTRACT:

Background

Alzheimer's disease (AD) requires early intervention at preclinical stages like subjective memory complaints (SMC). Traditional static brain network analyses lack sensitivity to detect early functional disruptions in SMC. This study aimed to improve preclinical AD stratification by integrating dynamic gray-white matter functional connectivity (DFC) and machine learning.

Methods

Using multi-cohort ADNI data [N = 1,415 participants across cognitive normal[CN], SMC, and cognitive impairment [CI]groups],dynamic functional networks were constructed via sliding-window analysis (20-50 TR windows, 98% overlap) of 200 gray matter (Schaefer atlas) and 128 data-driven white matter nodes. DFC metrics (standard deviation of Fisher z-transformed correlations) were used t

SUBMITTER: Jiang J 

PROVIDER: S-EPMC12321872 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature