Functional Brain Age Acceleration from Dynamic and Static Connectivity Predicts Working Memory and Attention Deficits in Schizophrenia.

ABSTRACT:

Background

Schizophrenia is characterized by deficits in attention and working memory. In recent years, the brain age gap (BAG), defined as the difference between neuroimaging-predicted and chronological age, has emerged as a biomarker of brain dysfunction. Prior studies primarily use structural MRI or static functional network connectivity (sFNC), while the potential of dynamic functional network connectivity (dFNC) to quantify BAG in relationship with cognition remains underexplored.

Methods

Leveraging one of the largest resting-state fMRI datasets to date (N=22,569; UK Biobank, HCP, HCP-Aging), we developed robust brain age prediction models incorporating both wide-brain and sub-network variables derived from sFNC and dFNC. These models were validated in an independent cl

SUBMITTER: Edwards-Swart SJ 

PROVIDER: S-EPMC12330426 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature