Socioemotional processing in female offenders - Resubmission 01



 DESCRIPTION (provided by applicant): Increasing public awareness has accompanied recent scientific progress understanding the relationship between mental illness and some forms of persistent antisocial behavior. This has incited calls for research into possible interventions and preventive measures. A critical barrier to research in this arena has been an outdated, descriptive taxonomy of psychiatric constructs with overlapping symptomatology and little integration of emerging knowledge from neuroscience research. An array of traits and symptoms characterized within the framework of internalizing and externalizing psychopathology are features of several psychiatric constructs common in forensic settings. It will be essential for continued progress to identify basic features of pathology that are closely aligned with specific neurobiological systems underlying domains of cognitive processing. Among these, systems governing social processing, including emotion-related cognition and perspective-taking are particularly relevant in antisocial outcomes due to psychopathology. Our research team has previously explored the domains of social-affective processing as they relate to psychopathic traits in a large, forensic male sample. Here we propose to extend this work in a female forensic sample. Further, we integrate a wider array of dimensional constructs of pathology in socio-affective processing by examining features of psychopathic traits as well borderline personality disorder. Our research strategy utilizes functional magnetic resonance imaging for the investigation of neural circuits involved in dynamic facial affective processing, inferring affective states from social situations, and emotional perspective-taking. These data will provide us with essential information about gender differences in these processes, and whether critical features of pathology are uniquely related to variation in these circuits. Furthermore, we will examine the utility of variation within these circuits to predict poor behavioral outcomes of interest including antisocial behavior, substance abuse, and suicide. Importantly, to determine key features predictive of poor outcomes, we plan to compare traditional hierarchical modeling procedures with more advanced data-driven approaches. Traditional approaches utilize regions of interest identified through prior neuroimaging work, combined with psychological traits of interest and other key demographic variables. Advanced data-driven approaches utilize Independent Component Analysis for determining key functional networks of brain activity, and utilize machine learning approaches for selecting features essential for building appropriate models. Comparing these approaches will inform our planned future efforts for developing remediation strategies and evaluating efficacy at both a neurological level as well as behavioral level. These are essential, incremental steps toward a larger translational goal to develop improved, targeted treatment strategies informed by emerging neuroscience.