25 December 2023

Isolating Neural Features of Antisociality from those of Substance Use Disorders

By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, makes it very difficult for researchers to delineate the shared/unique features associated with each disorder. Making matters more difficult still, few labs collect systematic data on both disorders (which essentially removes any ability to compare/contrast/parse causes/consequences of the two disorders.


With these difficulty in mind, one of CANdiLab’s core priorities is to identify neural features that are most associated with psychopathy from those that are most associated with history/severity of substance use. To this end, we have undertaken equally detailed assessments of both psychopathy and SUD within a large forensic sample, and have an existing NSERC Discovery Grant to engage a variety of data-driven approaches to help differentiate between neural features that are most associated with an individual’s level of psychopathy, and neural features that are most associated with the severity/length of their substance use histories. Simard, Denomme and Shane (2019) for instance, used hierarchical regression analyses to demonstrate that instability in resting-stating network connectivity were more closely associated with participants substance use history than with their level of psychopathy. In contrast, Denomme, Simard & Shane (2018) demonstrated that differences in sensitivity to reward cues was more associated with participants level of psychopathy than with the severity of their substance use history. While still early in its development, these respective findings highlight how careful parcellation of the neural features associated with one or the other disorder can help better inform the underlying features of both disorders. Currently, CANdiLab researchers are employing sophisticated machine learning techniques to further bolster our approaches (see Denomme & Shane (2021) for a comprehensive overview of our thoughts on this issue, and on some of the techniques we believe can be employed).

CANdiLab is the Clinical Affective Neuroscience Laboratory for Discovery and Innovation at Ontario Tech University