Predicting Empathy from Resting Brain Connectivity: A Multivariate Approach

Abstract

Recent studies suggest that individual differences in empathic concern may be mediated by continuous interactions between self-other resonance and cognitive control networks. To test this hypothesis, we used machine learning to examine whether resting fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of task demands) of resonance and control networks could predict trait empathy (n=58). Indeed, resonance and control networks interconnectivity predicted empathic concern. Empathic concern was also predicted by connectivity within the somatomotor network. In light of numerous reported sex differences in empathy, we controlled for biological sex and also studied separately what aspect of these features could predict participants sex. Sex was best predicted by the interconnectivity of the visual system with the resonance, somatomotor, and cingulo-opercular network, as well as the somatomotor-control network connectivity. These findings confirm that variation in empathic responses to others reflects characteristic network properties detectable regardless of task demands. Furthermore, network properties of the visual system may be a locus of sex differences previously unaccounted for in empathy research. Finally, these findings suggest that it may be possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments.