Age of gray matters: Neuroprediction of recidivism

Kent A Kiehl; Nathaniel E Anderson; Eyal Aharoni; J Michael Maurer; Keith A Harenski; Vikram Rao; Eric D Claus; Carla Harenski; Mike Koenigs; Jean Decety; David Kosson; Tor D Wager; Vince D Calhoun; Vaughn R Steele

doi:10.1016/j.nicl.2018.05.036

Age of gray matters: Neuroprediction of recidivism

Neuroimage Clin. 2018 Jun 3:19:813-823. doi: 10.1016/j.nicl.2018.05.036. eCollection 2018.

Authors

Affiliations

¹ The nonprofit Mind Research Network (MRN) & Lovelace Biomedical, Albuquerque, NM, USA; Department of Psychology, University of New Mexico, Albuquerque, NM, USA; Department of Neurosciences, University of New Mexico, Albuquerque, NM, USA; University of New Mexico School of Law, Albuquerque, NM, USA. Electronic address: kkiehl@mrn.org.
² The nonprofit Mind Research Network (MRN) & Lovelace Biomedical, Albuquerque, NM, USA.
³ Department of Psychology, Georgia State University, Atlanta, GA, USA.
⁴ The nonprofit Mind Research Network (MRN) & Lovelace Biomedical, Albuquerque, NM, USA; Department of Psychology, University of New Mexico, Albuquerque, NM, USA.
⁵ Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA.
⁶ Department of Psychology, Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA.
⁷ Department of Psychology, Rosalind Franklin University, Chicago, IL, USA.
⁸ Department of Psychology, University of Colorado-Boulder, Boulder, CO, USA.
⁹ The nonprofit Mind Research Network (MRN) & Lovelace Biomedical, Albuquerque, NM, USA; Department of Neurosciences, University of New Mexico, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA.

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 (n = 1332). We then test the model's ability to predict recidivism in a new sample of offenders with longitudinal outcome data (n = 93). Consistent with hypotheses, inclusion of brain-age measures of the inferior frontal cortex and anterior-medial temporal lobes (i.e., amygdala) improved prediction models when compared with models using chronological age; and models that combined psychological, behavioral, and neuroimaging measures provided the most robust prediction of recidivism. These results verify the utility of brain measures in predicting future behavior, and suggest that brain-based data may more precisely account for important variation when compared with traditional proxy measures such as chronological age. This work also identifies new brain systems that contribute to recidivism which has clinical implications for treatment development.

Keywords: Age; Antisocial; MRI; Neuroprediction; Recidivism.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Age Factors
Aged
Antisocial Personality Disorder / diagnostic imaging*
Antisocial Personality Disorder / psychology
Brain / diagnostic imaging*
Child
Criminals / psychology*
Gray Matter / diagnostic imaging*
Humans
Magnetic Resonance Imaging
Male
Middle Aged
Prisoners / psychology
Recidivism*
Risk Factors
Young Adult

Abstract

Publication types

MeSH terms

Grants and funding