Neural fingerprint of the dark triad: Resting state BOLD power (fALFF) alterations in executive and default mode networks.
Richard Bakiaj, Clara Isabel Pantoja Muñoz, Alessandro Grecucci
Cognitive, Affective, & Behavioral Neuroscience November 4, 2025 DOI: 10.3758/s13415-025-01352-7 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study using group Independent Component Analysis Peer reviewed |
|---|---|
| Sample size | 200 |
| Population | Adults, mean age 32.43 years, 105 females |
| Topics | Default mode network |
| Keywords | Nerve net Humans Magnetic resonance imaging Brain mapping Machiavellianism Narcissism Antisocial personality disorder Female Young adult Executive function Personality traits Dark triad Unsupervised machine learning Low-frequency bold power |
| Key findings | Of 20 resting-state macro-networks, two predicted Dark Triad traits: higher Dark Triad scores correlated with increased fALFF in the Central Executive Network and decreased fALFF in the posterior hub of the Default Mode Network. The authors propose the Central Executive Network finding may reflect enhanced manipulative and strategic abilities, supported by its association with the Machiavellianism subscale, and the Default Mode Network finding may correspond to diminished self-reflection and emotional capacity. |
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 blood-oxygen-level-dependent (BOLD) low-frequency spectral power (fractional Amplitude of Low-Frequency Fluctuations; fALFF) 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 fALFF within the CEN and decreased fALFF 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.
Comparable studies
Other observational and cohort studies on default mode network, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| A default mode of brain function Normal adult human brain | 2001 | Observational study | |
| Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network Healthy subjects | 2008 | Observational cohort | |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis Healthy subjects | 2008 | Observational study |