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

BackgroundSchizophrenia 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. MethodsLeveraging 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 clinical sample (FBIRN; N=153) including individuals with schizophrenia and healthy controls. Associations between BAGs and cognitive measures (attention vigilance, working memory) were evaluated using general linear models, controlling for key demographic and clinical covariates. ResultsBoth sFNC and dFNC models demonstrated robust prediction accuracy in healthy individuals (sFNC: r=0.8755; dFNC: r=0.8675). Wide-brain BAGs (wBAGs) showed strong negative associations with attention vigilance (sFNC: r=-0.2923, FDR p=0.0013; dFNC: r=- 0.2715, FDR p=0.0016) and working memory (sFNC: r=-0.2237, FDR p=0.0088; dFNC: r=- 0.2508, FDR p=0.0064). Sub-network BAGs (subBAGs) within subcortical, sensorimotor, cognitive control, and default mode networks robustly predicted cognitive deficits, with dFNC-derived subBAGs showing the strongest effects (FDR p<0.01). ConclusionsOur findings establish dFNC-based BAGs as a sensitive and clinically relevant biomarker of cognitive impairment in schizophrenia, outperforming sFNC and highlighting the translational potential of dynamic connectivity for precision diagnosis and treatment.