Age of Gray Matters: Neuroprediction of Recidivism
Kiehl, K.A., Anderson, N.E., Aharoni, E., Maurer, J.M., Harenski, K., Rao, V., Claus, E.D., Harenski, C.L., Koenigs, M., Decety, J., Kosson, D., Wager, T.D., Calhoun, V.D., & Steele, V.R.
In one sentence. Built a brain-age model from structural MRI in 1332 incarcerated males and found that ICA-derived brain-age components in frontal and temporal regions outperformed chronological age in predicting recidivism in an independent sample of 93 offenders.
Abstract
Age is one of the best predictors of antisocial behavior. Risk models of recidivism often combine chronological age with demographic, social and psychological features to aid in judicial decision-making. Here we use independent component analyses (ICA) and machine learning techniques to demonstrate the utility of using brain-based measures of cerebral aging to predict recidivism. First, we developed a brain-age model that predicts chronological age based on structural MRI data from incarcerated males (
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Citation: Kiehl, K.A., Anderson, N.E., Aharoni, E., Maurer, J.M., Harenski, K., Rao, V., Claus, E.D., Harenski, C.L., Koenigs, M., Decety, J., Kosson, D., Wager, T.D., Calhoun, V.D., & Steele, V.R. (2018). Age of Gray Matters: Neuroprediction of Recidivism. NeuroImage: Clinical, 19, 813–823. https://doi.org/10.1016/j.nicl.2018.05.036