Trait Aggression is Reflected by a Lower Temporal Stability of EEG Resting Networks

Abstract

Trait aggression can lead to catastrophic consequences for individuals and society. However, it remains unclear how aggressive people differ from others regarding basic, task-independent brain characteristics. We used EEG microstate analysis to investigate how the temporal organization of neural resting networks might help explain inter-individual differences in aggression. Microstates represent whole-brain networks, which are stable for short timeframes (40–120 ms) before quickly transitioning into other microstate types. Recent research demonstrates that the general temporal stability of microstates across types predicts higher levels of self-control and inhibitory control, and lower levels of risk-taking preferences. Given that these outcomes are inversely related to aggression, we investigated whether microstate stability at rest would predict lower levels of trait aggression. As males show higher levels of aggression than females, and males and females express aggression differently, we also tested for possible gender-differences. As hypothesized, people with higher levels of trait aggression showed lower microstate stability. This effect was moderated by gender, with men showing stronger associations compared to women. These findings support the notion that temporal dynamics of sub-second resting networks predict complex human traits. Furthermore, they provide initial indications of gender-differences in the functional significance of EEG microstates.

Electroencephalography microstates highlight specific mindfulness traits

“…Accordingly, microstate temporal dynamics are assumed to provide a window into the higher‐order integration processes at the brain scale level (Michel & Koenig, 2018). Moreover, recent studies have shown that microstate dynamics showed good long‐term retest‐reliability over time (Kleinert et al, 2023) and might have some heritability (da Cruz et al, 2020), supporting the notion that they also represent stable mental traits. In this line, they were investigated in a wide range of studies from state to trait brain processes, such as states of alertness (Brodbeck et al, 2012; Comsa et al, 2019; Zanesco, Denkova, & Jha, 2021), spontaneous phenomenal experiences (Lehmann et al, 2010; Pipinis et al, 2017), self‐generated cognition (Bréchet et al, 2019; Milz et al, 2016; Seitzman et al, 2017), personality traits (Kleinert et al, 2022; Schiller et al, 2020; Zanesco et al, 2020) or psychiatric disorders (Damborská et al, 2019; Grieder et al, 2016; Rieger et al, 2016).…”

EEG Microstates in Social and Affective Neuroscience

“…Microstate analysis of continuous EEG has been used to illuminate the sources of individual differences in socio-affective traits, in domains such as aggression (Kleinert and Nash 2022 ), anxiety (Schiller et al 2019b ; Du et al 2022 ; Nash et al 2023 ), approach vs. withdrawal tendency (Takehara et al 2020 ; Kaur et al 2020 ), disgust sensitivity (Li et al 2021 ), empathy (Zhang et al 2021 ), personality (Zanesco et al 2020 ; Guo et al 2020 ; Tomescu et al 2022 ), prosociality (Schiller et al 2020b ), religious belief (Schlegel et al 2012 ; Nash et al 2022 ), or somatic awareness (Pipinis et al 2017 ; Tarailis et al 2021 ; Zanesco et al 2021a ). The majority of these studies have relied on regression or correlation analysis to uncover associations between features of the prototypical microstate classes (e.g., duration, occurrence, coverage, transition probabilities; see Box 4 ) and socio-affective traits (see Fig.…”

“…These correlations suggest that individuals show a general tendency for more (i.e., fewer but longer-lasting network activations) or less (i.e., more but shorter-lasting network activations) network stability, potentially indicating the stability of one’s mental processing at rest. Following up on research (Kleinert et al 2022 ) demonstrating a positive association between network stability and trait self-control, Kleinert and Nash ( 2022 ) found that individuals with higher levels of trait aggression (which is inversely related to self-control) showed less stable neural networks (indexed by shorter durations and more occurrences of microstates across microstate classes). In a related line of research, Tomescu et al ( 2022 ) demonstrated more stable neural networks in individuals who are less neurotic, more conscientious, more extraverted, and report to have more coherent thoughts.…”

Social functioning predicts individual changes in EEG microstates following intranasal oxytocin administration: A double‐blind, cross‐over randomized clinical trial

“…However, an important limitation of these studies, namely the use of only four microstates, hinders the interpretation of the findings. Indeed, objective approaches to identifying EEG microstates indicate that explaining 80%–90% of the variance requires more than four microstates (Custo et al., 2017; Férat et al., 2022; Kleinert et al., 2023; Tomescu et al., 2022; Zanesco et al., 2020, 2021). By reducing the number of microstates to four based on a priori subjective decisions, temporal dynamics of salience vs. default mode networks related microstates (C, F) might overlap and intermingle (Michel & Koenig, 2018).…”