Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals

Abstract

The personality dimensions of neuroticism and extraversion are strongly associated with emotional experience and affective disorders. Previous studies reported functional magnetic resonance imaging (fMRI) activity correlates of these traits, but no study has used brain-based measures to predict them. Here, using a fully cross-validated approach, we predict novel individuals' neuroticism and extraversion from functional connectivity (FC) data observed as they simply rested during fMRI scanning. We applied a data-driven technique, connectome-based predictive modeling (CPM), to resting-state FC data and neuroticism and extraversion scores (self-reported NEO Five Factor Inventory) from 114 participants of the Nathan Kline Institute Rockland sample. After dividing the whole brain into 268 nodes using a predefined functional atlas, we defined each individual's FC matrix as the set of correlations between the activity timecourses of every pair of nodes. CPM identified networks consisting of functional connections correlated with neuroticism and extraversion scores, and used strength in these networks to predict a left-out individual's scores. CPM predicted neuroticism and extraversion in novel individuals, demonstrating that patterns in resting-state FC reveal trait-level measures of personality. CPM also revealed predictive networks that exhibit some anatomical patterns consistent with past studies and potential new brain areas of interest in personality.

Fig. 1.

CPM predicts neuroticism and extraversion scores in novel individuals. Scatterplot of predicted scores vs observed scores for neuroticism and extraversion. Predicted scores were generated using edges positively correlated with prediction (positive network) and negatively correlated with prediction (negative network). A GLM was also constructed to combine positive and negative networks to generate predicted scores. P values were determined with permutation testing.

Fig. 2.

Canonical network pairs in positive and negative prediction. (a) Edges between macroscale regions. Each semicircle represents a hemisphere of the brain, and nodes are organized around the circle by anatomical location. Edges are represented by lines; red edges are stronger in individuals with higher scores, and blue edges are stronger in individuals with lower scores. (b) Proportion of total edges of each canonical functional network pair utilized in prediction, calculated by dividing the number of edges in each network pair included in prediction by the total number of possible edges between that pair of networks. The higher the proportion, the more ‘utilized’ the network pair is in prediction. Orange represents higher utilization in positive prediction and green represents higher utilization in negative prediction. Line saturation indicates the proportion of edges of the particular network pair involved in prediction: the darker the line, the higher proportion. Line width indicates the total number of edges possible in the pair: the thicker the line, the more possible edges. Curves show between-network connections and circles show within-network connections.