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

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Schizophrenia is marked by attention and working memory deficits.
• Brain age gap (BAG) is a potential biomarker for brain dysfunction.
• The link between BAG and dynamic brain function is not well understood.

Purpose of the Study:

• To investigate the association between brain age gap (BAG) and cognitive function in schizophrenia.
• To compare the efficacy of static (sFNC) and dynamic (dFNC) functional network connectivity in predicting cognitive deficits.
• To identify specific brain networks associated with BAG and cognitive impairment.

Main Methods:

• Developed brain age models using static (sFNC) and dynamic (dFNC) functional network connectivity from large resting-state fMRI datasets (UK Biobank, HCP).
• Validated models in an independent schizophrenia cohort (FBIRN).
• Assessed the association between BAG and attention/working memory performance.

Main Results:

• Higher BAGs were significantly linked to poorer attention and working memory (FDR p < 0.01).
• dFNC-based BAG models demonstrated stronger associations with cognitive deficits than sFNC models.
• Network-specific BAGs in cognitive control, default mode, and subcortical networks predicted cognitive impairment.

Conclusions:

• Dynamic functional network connectivity (dFNC)-based BAG is a sensitive biomarker for cognitive dysfunction in schizophrenia.
• Dynamic connectivity measures are valuable for advancing precision diagnostics and patient stratification in schizophrenia.
• This study enhances understanding of brain dysfunction in schizophrenia using advanced neuroimaging techniques.