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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.
Hsu et al. (Thu,) studied this question.