Connectome-based predictive modeling of trait forgiveness

Abstract

AbstractForgiveness is a positive, prosocial manner of reacting to transgressions and is strongly associated with mental health and well-being. Despite recent studies exploring the neural mechanisms underlying forgiveness, a model capable of predicting trait forgiveness at the individual level has not been developed. Herein, we applied a machine-learning approach, connectome-based predictive modeling (CPM), with whole-brain resting-state functional connectivity (rsFC) to predict individual differences in trait forgiveness in a training set (dataset 1, N = 100, 35 men, 17–24 years). As a result, CPM successfully predicted individual trait forgiveness based on whole-brain rsFC, especially via the functional connectivity of the limbic, prefrontal and temporal areas, which are key contributors to the prediction model comprising regions previously implicated in forgiveness. These regions include the retrosplenial cortex, temporal pole, dorsolateral prefrontal cortex (PFC), dorsal anterior cingulate cortex, precuneus and dorsal posterior cingulate cortex. Importantly, this predictive model could be successfully generalized to an independent sample (dataset 2, N = 71, 17 men, 16–25 years). These findings highlight the important roles of the limbic system, PFC and temporal region in trait forgiveness prediction and represent the initial steps toward establishing an individualized prediction model of forgiveness.