Task functional networks predict individual differences in the speed of emotional facial discrimination

Every individual experiences negative emotions, such as fear and anger, significantly influencing how external information is perceived and processed. With the gradual rise in brain-behavior relationship studies, analyses investigating individual differences in negative emotion processing and a more objective measure such as the response time (RT) remain unexplored. This study aims to address this gap by establishing that the individual differences in the speed of negative facial emotion discrimination can be predicted from whole-brain functional connectivity when participants were performing a face discrimination task. Employing the connectome predictive modeling (CPM) framework, we demonstrated this in the young healthy adult group from the Human Connectome Project-Young Adults (HCP-YA) dataset and the healthy group of the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset. We identified distinct network contributions in the adult and adolescent predictive models. The highest represented brain networks involved in the adult model predictions included representations from the motor, visual association, salience, and medial frontal networks. Conversely, the adolescent predictive models showed substantial contributions from the cerebellum-frontoparietal network interactions. Finally, we observed that despite the successful within-dataset prediction in healthy adults and adolescents, the predictive models failed in the cross-dataset generalization. In conclusion, our study shows that individual differences in the speed of emotional facial discrimination can be predicted in healthy adults and adolescent samples using their functional connectivity during negative facial emotion processing. Future research is needed in the derivation of more generalizable models.