Connectome-based model predicts individual psychopathic traits in college students

Abstract

Background: Psychopathic traits have been suggested to increase the risk of violations of socio-moral norms. Previous studies revealed that abnormal neural signatures are associated with elevated psychopathic traits; however, whether the intrinsic network architecture can predict psychopathic traits at the individual level remains unclear. Methods: The present study utilized connectome-based predictive modeling (CPM) to investigate whether whole-brain resting-state functional connectivity (RSFC) can predict psychopathic traits in the general population. RS functional magnetic resonance imaging data were collected from 84 college students with varying psychopathic traits measured by the Levenson Self-Report Psychopathy Scale (LSRP). Results: We found that RSFC of the negative networks predicted individual differences in total LSRP and secondary psychopathy scores but not primary psychopathy score. Particularly, nodes with the most connections in the predictive connectome anchored in the prefrontal cortex (e.g., anterior prefrontal cortex and orbitofrontal cortex) and limbic system (e.g., anterior cingulate cortex and insula). In addition, the connections between the occipital network (OCCN) and cingulo-opercular network (CON) served as a significant predictive connectome for total LSRP and secondary psychopathy score. Conclusion: CPM constituted by whole-brain RSFC significantly predicted psychopathic traits individually in the general population. The prefrontal cortex and limbic system at the anatomic level and the CON and OCCN at the functional network level plays a special role in the predictive model-reflecting atypical executive control and affective processing for individuals with elevated psychopathic traits. These findings may provide some implications for early detection and potential intervention of psychopathic tendency.