Predicting personality from network-based resting-state functional connectivity

Abstract

Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC–personality relations should not be considered independently of gender.

Brain flexibility associated with need for cognition contributes to creative achievement

“…Emerging evidence has suggested that multiple functional networks together constitute a domain‐general neural system subserving a wide range of psychological functions (Chanes & Barrett, ; Cole et al, ; Deco et al, ; Kleckner et al, ; Shine & Poldrack, ). Especially in the field of personality network neuroscience, researchers have proposed that the neurobiological basis of personality can be more comprehensively understood by capturing the brain‐wide interactions between distinct regions or large‐scale networks instead of a few local regions or single smaller‐scale circuits (Liu, Kohn, & Fernandez, ; Markett, Montag, & Reuter, ; Nostro et al, ). Similar to previous personality studies (Liu et al, ; Nostro et al, ), the level of NFC does not depend on the functional specificity of a single network but the result of the functional integration of multiple networks, which supports the emerging view above.…”

Within and Between-person Correlates of the Temporal Dynamics of Resting EEG Microstates

“…Recent large-sample neuroimaging studies have begun to explore these questions in some detail (Barch et al, 2013). These studies have investigated the contribution of demographic characteristics (Kharabian Masouleh et al, 2019;Smith et al, 2015), personality (Dubois et al, 2018;Nostro et al, 2018), intelligence (Cox et al, 2019), and cognitive function (Lerman-Sinkoff et al, 2017) to patterns of structural and functional connectivity. Much of this work has examined average connectivity patterns of activity derived from whole neuroimaging sessionsso-called "static" functional brain connectivity.…”

A machine learning tool for interpreting differences in cognition using brain features

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