# Dynamic Brain Age Modeling Identifies Network-Specific Cognitive Deficits in Schizophrenia

## TL;DR

This study shows that dynamic brain connectivity can reveal cognitive issues in schizophrenia, particularly in attention and memory.

## Contribution

The study introduces dynamic brain age modeling as a novel method to identify cognitive deficits in schizophrenia.

## Key findings

- Dynamic brain age gaps (BAGs) are strongly linked to attention and working memory deficits in schizophrenia.
- Network-specific BAGs in cognitive control and default mode networks are robust predictors of cognitive impairment.
- Dynamic connectivity models outperform static models in predicting cognitive dysfunction.

## Abstract

Schizophrenia is characterized by deficits in attention and working memory. The brain age gap (BAG), the difference between brain-predicted and chronological age, has emerged as a biomarker of brain dysfunction, but its association with dynamic brain function remains unclear. We developed brain age models using static (sFNC) and dynamic (dFNC) functional network connectivity from a large resting-state fMRI dataset (N = 22,569; UK Biobank, HCP-Young Adult, HCP-Aging) and validated them in an independent schizophrenia cohort (FBIRN; N = 153). Higher BAGs were significantly associated with lower attention and working memory performance (FDR p < 0.01), with dFNC-based models showing more potent effects than sFNC. Network-specific BAGs, particularly within cognitive control, default mode, and subcortical networks, were robust predictors of cognitive impairment. These findings establish dFNC-based BAG as a sensitive biomarker of cognitive dysfunction in schizophrenia and highlight the value of dynamic connectivity measures for advancing precision diagnostics and stratification.