Reliable and generalizable brain-based predictions of cognitive functioning across common psychiatric illness
Chopra, S.; Dhamala, E.; Lawhead, C.; Ricard, J. A.; Orchard, E. R.; An, L.; Chen, P.; Wulan, N.; Kumar, P.; Rubenstein, A.; Moses, J.; Chen, L.; Levi, P.; Aquino, K.; Fornito, A.; Harpaz-Rotem, I.; Germine, L. T.; Baker, J. T.; Yeo, B. T.; Holmes, A. J.
Show abstract
A primary aim of precision psychiatry is the establishment of predictive models linking individual differences in brain functioning with clinical symptoms. In particular, cognitive impairments are transdiagnostic, treatment resistant, and contribute to poor clinical outcomes. Recent work suggests thousands of participants may be necessary for the accurate and reliable prediction of cognition, calling into question the utility of most patient collection efforts. Here, using a transfer-learning framework, we train a model on functional imaging data from the UK Biobank (n=36,848) to predict cognitive functioning in three transdiagnostic patient samples (n=101-224). The model generalizes across datasets, and brain features driving predictions are consistent between populations, with decreased functional connectivity within transmodal cortex and increased connectivity between unimodal and transmodal regions reflecting a transdiagnostic predictor of cognition. This work establishes that predictive models derived in large population-level datasets can be exploited to boost the prediction of cognitive function across clinical collection efforts.
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