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David Kosson

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2 papers
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2

YNICL Journal 2019 Journal Article

Psychopathy is associated with shifts in the organization of neural networks in a large incarcerated male sample

  • Scott Tillem
  • Keith Harenski
  • Carla Harenski
  • Jean Decety
  • David Kosson
  • Kent A. Kiehl
  • Arielle Baskin-Sommers

Psychopathy is a personality disorder defined by antisocial behavior paired with callousness, low empathy, and low interpersonal emotions. Psychopathic individuals reliably display complex atypicalities in emotion and attention processing that are evident when examining task performance, activation within specific neural regions, and connections between regions. Recent advances in neuroimaging methods, namely graph analysis, attempt to unpack this type of processing complexity by evaluating the overall organization of neural networks. Graph analysis has been used to better understand neural functioning in several clinical disorders but has not yet been used in the study of psychopathy. The present study applies a minimum spanning tree graph analysis to resting-state fMRI data collected from male inmates assessed for psychopathy with the Hare Psychopathy Checklist-Revised (n = 847). Minimum spanning tree analysis provides several metrics of neural organization optimality (i. e. , the effectiveness, efficiency, and robustness of neural network organization). Results show that inmates higher in psychopathy exhibit a more efficiently organized dorsal attention network (β = =0. 101, pcorrected = =0. 018). Additionally, subcortical structures (e. g. , amygdala, caudate, and hippocampus) act as less of a central hub in the global flow of information in inmates higher in psychopathy (β = =−0. 104, pcorrected = =0. 048). There were no significant effects of psychopathy on neural network organization in the default or salience networks. Together, these shifts in neural organization suggest that the brains of inmates higher in psychopathy are organized in a fundamentally different way than other individuals.

YNICL Journal 2018 Journal Article

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

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.

v2026.09.13