Altered power spectra in antisocial males during rest as a function of cocaine dependence: A network analysis

The neurofunctional basis of human aggression varies by levels of femininity

Aggression can be categorized into reactive aggression (RA) and proactive aggression (PA) based on their underlying motivations. However, previous research has rarely identified the relationship between femininity and RA/PA, and there is a lack of understanding regarding the femininity-related neurofunctional basis of these aggressive behaviors. Thus, this study first examined the relationships between femininity and aggression, then explored the aggression-by-femininity interactions on the fractional amplitude of low-frequency fluctuations using resting-state fMRI among 705 university participants (mean age = 19.14 ± 0.99). The behavioral data indicated that femininity was more negatively associated with RA and PA when masculinity was controlled for. Additionally, the neural data revealed that femininity-specific relationships of RA in the left middle occipital gyrus (i.e. individuals with low femininity had positive relationships between RA and the left middle occipital gyrus, whereas those with high femininity had negative relationships) as well as of PA in the left middle frontal gyrus (i.e. individuals with high femininity showed significant negative relationships, whereas those with low femininity did not exhibit significant relationships). These findings reflect that individuals with varying levels of femininity exhibit distinct neural bases when expressing different subtypes of aggression, which are associated with societal expectations of gender.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

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.