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Neural Fingerprint of the Dark Triad: Resting State Functional Connectivity Alterations in Executive and Default Mode Networks

Richard Bakiaj, Clara Isabel Pantoja Muñoz, Alessandro Grecucci

DOI: 10.22541/au.173018005.50876434/v1 (opens in new tab)

Summary

AI-generated from the abstract

Higher scores on the Dark Triad personality traits—narcissism, Machiavellianism, and psychopathy—are linked to distinct patterns of brain network activity. Analyzing resting-state brain scans from 200 participants (mean age 32.43 years; 105 females) with group Independent Component Analysis, two macro-networks predicted DT traits: the Central Executive Network (CEN) showed increased temporal variability, while the posterior hub of the Default Mode Network (DMN) showed decreased variability. Increased CEN activity may relate to enhanced strategic planning and manipulative abilities, particularly for Machiavellianism. Reduced DMN activity may correspond to diminished self-reflection and emotional capacities. The findings suggest specific neural correlates underlying these socially impactful traits.

Study at a glance

Characteristics Cross-sectional, observational study using resting-state fMRI and group Independent Component Analysis
Sample size 200
Population Participants with a mean age of 32.43 years, 105 females
Key finding Higher Dark Triad scores correlated with increased BOLD temporal variability in the Central Executive Network and decreased variability in the Default Mode Network.

Abstract

The neural foundations of the Dark Triad (DT) personality traits—narcissism, Machiavellianism, and psychopathy—remain largely unexplored despite their profound impact on social behavior. Previous research has indicated potential structural and functional alterations in the default mode network (DMN) and reward-related regions in individuals exhibiting high levels of DT traits. However, these studies have often been limited by univariate analytical methods and small, unbalanced sample sizes. To address these gaps, we analyzed brain scans from a robust sample of 200 participants (mean age: 32.43 years; 105 females) using group Independent Component Analysis (gICA), an unsupervised machine learning approach. This method allowed us to extract temporal variability associated with resting-state macro-networks without relying on predefined regions of interest. Our analysis identified 20 macro-networks, of which two were significantly predictive of DT traits: the Central Executive Network (CEN) and the posterior hub of the Default Mode Network (DMN). Notably, higher DT scores correlated with increased blood-oxygen-level-dependent (BOLD) temporal variability within the CEN and decreased variability within the DMN. The heightened activity in the CEN may reflect enhanced manipulative abilities and strategic planning commonly observed in individuals with high DT traits, which is supported by the association between the CEN and the Machiavellianism subscale. Conversely, reduced functionality in the DMN may correspond to diminished self-reflective and emotional capacities in these individuals. These findings contribute to a deeper understanding of the neurobiological basis of DT traits and hold implications for future research and interventions.

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