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

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Brain age gap (BAG) predicts cognitive deficits in schizophrenia. Dynamic functional network connectivity (dFNC)-based models reveal BAG as a sensitive biomarker for attention and working memory impairments.

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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.