Generalizing effects of frontostriatal structural connectivity on self-esteem using predictive modeling

Abstract

Determining the generalizability of biological mechanisms supporting psychological constructs is a central goal of cognitive neuroscience. Self-esteem is a popular psychological construct that is associated with a variety of measures of mental health and life satisfaction. Recently, there has been interest in identifying biological mechanisms that support individual differences in self-esteem. Understanding the biological basis of self-esteem requires identifying predictive biomarkers of self-esteem that generalize across groups of individuals. Previous research using diffusion magnetic resonance imaging has shown that self-esteem is related to the integrity of structural connections linking frontostriatal brain systems involved in self-referential processing and reward. However, these findings were based on a small, relatively homogeneous group of participants. In the current study, we used an out-of-sample predictive modeling approach to generalize the results of the previous study to an independent sample of participants more than twice the size of the original study. We found that both linear univariate and multivariate machine learning models trained on frontostriatal integrity from the original data significantly predicted self-esteem in the independent dataset. These findings underscore the relationship between self-esteem and frontostriatal connectivity and suggest these results are robust to differences in scanning acquisition, analytic methods, and participant demographics.