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.
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