Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer. (15th November 2021)

• Record Type: Journal Article
• Authors: Shane, Matthew S.; Denomme, William J.
• Is Part Of: Personality neuroscience. Volume 4(2021)
• Publication Date: 2021-11-15
• Subjects: Externalizing -- Antisocial -- Substance abuse -- Neuroimaging -- Machine learning; Neurosciences -- Periodicals; Personality -- Periodicals; 612.823
• DOI: 10.1017/pen.2021.2
• Languages: English
• ISSNs: 2513-9886

Abstract

Abstract: 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, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.