Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia.

ABSTRACT: Brain-age prediction is a novel approach to assessing deviated brain aging trajectories in different diseases. However, most studies have used an average brain age gap (BAG) of individuals with schizophrenia of different illness durations for comparison with healthy participants. Therefore, this study investigated whether declined brain structures as reflected by BAGs may be present in schizophrenia in terms of brain volume, cortical thickness, and fractional anisotropy across different illness durations. We used brain volume, cortical thickness, and fractional anisotropy as features to train three models from the training dataset. Three models were applied to predict brain ages in the hold-out test and schizophrenia datasets and calculate BAGs. We divided the schizophrenia dataset into mu

SUBMITTER: Zhu JD 

PROVIDER: S-EPMC9810255 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature