Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.

ABSTRACT: The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure-function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A

SUBMITTER: Wu H 

PROVIDER: S-EPMC11974459 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature