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.

Keywords: Functional networks; Gender differences; Hormonal influence; Machine learning; NEO-FFI; Resting-state functional connectivity.

Fig. 1

Samples selection overview: first Sample 1 (or “main” sample) was created aiming for the largest number of participants. Once 430 subjects were selected for this sample, the same procedure was applied on the remaing subjects of the HCP to generate Sample 2 (or “replication” sample). The two samples result in this was related to each other (as siblings of the subjects in Sample 1 are present in Sample 2), but, within each sample, there are no subjects related to each other

Fig. 2

Emp: empathy; AM: Autobiographic memory; WM: working memory; Emo: emotional processing; Face: face processing; Rew: reward; SM: semantic memory; VA: vigilant attention; Pain: pain processing. Summary of the networks for which FC patterns significantly predicted the five personality traits. For each network-trait combination in either Men or Women, and here, it is reported the conjunction between the correlation coefficients (i.e., minimum r value). Only predictions with r > 0.1 are displayed. While the nine meta-analytic networks are represented as slices (triangles) of the five personality circles, the connectome is represented as well as a circle. Triangles and circles are scaled based on the r values of the predicting networks (r values reported in the axis). Meta-analytic networks are underlined if a significant prediction is detected in either Men or Women. Asterisks mark significant gender differences in Sample 1

Fig. 3

Scatter plots of the predictions of personality scores significant at p < 0.05 in both samples. Continuous regression lines, dashed lines, representing the standard deviation, and mean absolute errors (MAE) are displayed

Fig. 4

Summary of the most used nodes (i.e., above 80% of the models) between regions from a the reward (Rew), vigilant attention (VA), and pain processing (Pain) networks in the prediction of Openness; b the Rew and face processing (Face) networks in the prediction of Extraversion. Summary of the most used connections between regions from c the autobiographic memory (AM) network in the prediction of Agreeableness, d the Pain and emotional processing (Emo) networks in the prediction of Neuroticism