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

Determining the generalizability of biological mechanisms supporting psychological con-structs 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 satis-faction. 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 pre-vious 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 differ-ences in scanning acquisition, analytic methods, and participant demographics. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study aimed to generalize the relationship between self-esteem and frontostriatal connectivity by using an out-of-sample predictive modeling approach on a larger independent sample. The results showed a significant predictive power of frontostriatal integrity on self-esteem in the independent dataset, indicating the robustness of these findings across different scanning acquisition, analytic methods, and participant demographics.