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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.

We report a functional magnetic resonance imaging (fMRI) study of 102 healthy participants who completed a demanding Go/NoGo task. The primary purpose of this study was to delineate the neural systems underlying responses to errors in a large sample. We identified a number of regions engaged during error processing including the anterior cingulate, left lateral prefrontal areas and bilateral inferior frontal gyrus, and the subthalamic nucleus. The power afforded by the large cohort enabled identification of regions not consistently measured during Go/NoGo tasks thus helping to incrementally refine our understanding of the neural correlates of error processing. With the present fMRI results, in combination with our previous exploration of response inhibition (Steele et al.), we outline a comprehensive set of regions associated with both response inhibition and error processing., (Copyright © 2014 Elsevier B.V. All rights reserved.)

A recently published study by the present authors reported evidence that functional changes in the anterior cingulate cortex within a sample of 96 criminal offenders who were engaged in a Go/No-Go impulse control task significantly predicted their rearrest following release from prison. In an extended analysis, we use discrimination and calibration techniques to test the accuracy of these predictions relative to more traditional models and their ability to generalize to new observations in both full and reduced models. Modest to strong discrimination and calibration accuracy were found, providing additional support for the utility of neurobiological measures in predicting rearrest.

We report a functional magnetic resonance imaging (fMRI) study of healthy adult participants who completed a demanding Go/NoGo task. The primary purpose of this study was to delineate the neural systems underlying successful and unsuccessful response inhibition using a large sample (N=102). We identified a number of regions uniquely engaged during successful response inhibition, including a fronto-parietal network involving the anterior cingulate, supplementary motor areas, lateral and inferior prefrontal regions, and the inferior parietal lobule. Unique hemodynamic activity was also noted in the amygdala and in frontostriatal regions including the inferior frontal gyrus and portions of the basal ganglia. Also, contrasts were defined to explore three variants of hemodynamic response allowing for more specificity in identifying the underlying cognitive mechanisms of response inhibition. Addressing issues raised by prior small sample studies, we identified a stable set of regions involved in successful response inhibition. The present results help to incrementally refine the specificity of the neural correlates of response inhibition., (Copyright © 2013. Published by Elsevier B.V.)

Identification of factors that predict recurrent antisocial behavior is integral to the social sciences, criminal justice procedures, and the effective treatment of high-risk individuals. Here we show that error-related brain activity elicited during performance of an inhibitory task prospectively predicted subsequent rearrest among adult offenders within 4 y of release (N = 96). The odds that an offender with relatively low anterior cingulate activity would be rearrested were approximately double that of an offender with high activity in this region, holding constant other observed risk factors. These results suggest a potential neurocognitive biomarker for persistent antisocial behavior.