Reduced Dynamics in Multivariate Regression-based Dynamic Connectivity of Depressive Disorder

Major depressive disorder (MDD) is accompanied by abnormal changes in functional connectivities (FC) among brain regions. However, most studies estimated the pairwise connectivity without the thorough consideration of the influence of other regions and assumed that the brain functional connectivity was static, which may be insufficient for the accurate identification of the pathological mechanisms underlying MDD. The purpose of this study was to explore the pathological mechanisms of MDD based on dynamic FC taking the influence of other regions into account. We performed time-varying connectivity analysis on resting-state functional magnetic resonance imaging (rs-fMRI) of 58 MDD patients and 63 matched healthy controls. The dynamic functional connectivity matrices were constructed using a novel Multivariate Vector Regression-based Connectivity (MVRC) method, which could regress time series of all regions while estimating the pairwise association between two regions. Then we analyzed two commonly used dynamic characteristics in brain network analysis, including dynamic FC (dFC) variability and node flexibility. Both dFC variability and node flexibility of MDD patients showed significant decreases in frontal, temporal, occipital, and parietal gyrus. Notably, we found the reduced dynamics of regions in frontal, temporal, and parietal gyrus was strongly negatively associated with depression severity. Our results demonstrated that the decreased dFC variability and node flexibility based on dynamic MVRC (dMVRC) were pathological manifestations of MDD.