Brain Connectivity and Machine Learning Approaches to assess the underlying neurobiology and prediction accuracy of anorexia nervosa

Abstract

Resting-state fMRI has been used to study aberrant functional connectivity properties in patients with anorexia nervosa (AN) at several stages of the illness. One popular way to extract these metrics is to use graph theory to showcase aberrant brain connectivity between patients with AN versus controls. However, most studies use classic analyses to investigate these differences, which could limit the number and choices of features used in one model. Instead, machine learning models have proven to be a promising tool in studying the functional connectivity of various disorders. In this study, we employ a combination of local graph metrics and a support vector machine to distinguish between first-onset AN (N = 56) cases and controls (N = 64). We replicate and extend prior work evaluating the predictive value of an existing machine learning approaches in detecting functional connectivity differences in patients with AN. Our method achieves an average classification accuracy of 65% with cross-validation evaluation. We further demonstrate that the results are driven mainly by the participation index of the nodes that are implicated in distinguishing the two groups. Our findings contribute to the growing body of evidence supporting the predictive value of resting-state fMRI in the study of anorexia nervosa.