Resting state functional connectivity explains individual scores of multiple clinical measures for major depression

Abstract

Recent studies have revealed that resting state functional connectivity is associated with major depressive disorder (MDD). However, the relationship between functional connectivity and clinical measures for the detailed assessment of depression remains unclear. The objective of our study is thus to associate functional connectivity of depressed patients and healthy controls with their individual clinical measures, using a statistical method called partial least squares analysis (PLS). We demonstrated that this method could predict certain clinical measures based on a limited number of functional connections and provided benefits to the prediction performance through incorporation of the subject's age and the estimation of multiple measures simultaneously. Generalizability of the prediction model was assured through leave one out cross validation. The results showed that for BDI-II and SHAPS the most contributing connections concerned cuneus, precuneus and middle frontal cortex and areas of the cerebellum. While the relationship was similar for PANAS(n), it showed its strongest relation with functional connection between calcarine and insula.