Data Fusion Machine Learning Approach to Characterize the Neural Bases of the Dark Triad Personality

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Abstract

The Dark Triad (DT) traits—Narcissism, Machiavellianism, and Psychopathy—pose significant societal challenges. Previous efforts to map these traits to brain structures have yielded inconsistent results, partly due to the limitations of mass-univariate analyses. Our study advances this understanding by applying the multivariate, unsupervised machine learning algorithm transposed Independent Vector Analysis (tIVA) to brain scans of 201 individuals (mean age: 32.43, 105 females). tIVA uncovered fifteen joint gray matter (GM) and white matter (WM) networks. Brain areas within four macro-networks revealed predictive associations with DT traits: central-executive, default mode, reward, and visual networks. Notably, components overlapping with reward and default mode networks correlated respectively with Narcissism and Machiavellianism. The reward-associated component exhibited decreased GM and WM concentration, potentially reflecting deficits in emotion regulation linked to Narcissistic traits. Conversely, the default mode network component showed increased GM and WM concentration, suggesting heightened astuteness and manipulative tendencies in Machiavellianism. Additionally, sex differences emerged, with males displaying higher DT scores, in line with established trends. This study underscores the utility of tIVA in discerning brain networks associated with complex personality traits, offering valuable insights for future research and potential clinical applications.

Authors

• Richard Bakiaj, University of Trento

• Clara Isabel Pantoja Munoz, University of Trento

• Andrea Bizzego, University of Trento

• Alessandro Grecucci, University of Trento