Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study
Abstract
Background Schizophrenia (SZ) and bipolar disorders (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, BD and healthy controls.
Methods We obtained DTI data from patients with SZ (n=648), BD (n=185) and healthy controls (n=990) across 10 clinical cohorts. We trained six cross-validated models using different combinations of DTI data from 927 controls, and applied the models to estimate individual brain ages in the test sets. We assessed group differences using linear models, accounting for age, sex and scanner.
Results 10-fold cross-validation revealed high accuracy for all models. Compared to controls, the model including all feature sets significantly over-estimated the age of patients with SZ (d=.29) and BD (d=.15), with similar effects for the other models. Meta-analysis converged on the same findings. Fractional anisotropy (FA) based models were more sensitive than models based on other metrics. Using a reduced set of global features instead of regional features revealed converging results.
Conclusions Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity and health. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
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