Large-Scale Network Dysfunction inMajor Depressive Disorder AMeta-analysis of Resting-State Functional Connectivity

IMPORTANCE Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. OBJECTIVE To investigate network dysfunction in MDD through ameta-analysis of rsFC studies. DATA SOURCES Seed-based voxelwise rsFC studies comparing individuals withMDDwith healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data. STUDY SELECTION Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in themeta-analysis. DATA EXTRACTION AND SYNTHESIS Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in whichMDDwas associated wit