Abstract
BACKGROUND: Major depressive disorder (MDD) is a common mental illness associated with brain morphological abnormalities. Although extensive studies have examined gray matter volume (GMV) changes in MDD, inconsistencies persist in reported findings. In the current study, we employed source-based morphometry (SBM) and structural covariance network (SCN) analyses to a large multi-center sample from the REST-meta-MDD database, aiming to characterize robust results of structural abnormalities in MDD. METHODS: We analyzed 798 MDD patients and 974 healthy controls (HCs) from the REST-meta-MDD consortium. Voxel-based morphometry was applied to generate GMV maps. SBM was used to adaptively parcellate brain into different components, and SCN was constructed based on SBM components. Volume scores in each component and SCNs between the components were both compared between MDD and HC groups, as well as between first-episode drug-naive (FEDN) and recurrent MDD subgroups. RESULTS: SBM identified 20 stable components. Three components encompassing the middle temporal gyrus, middle orbitofrontal gyrus and superior frontal gyrus exhibited volumetric differences between the MDD and HC groups. Volume differences were observed in the cingulate cortex and medial frontal gyrus between the FEDN and recurrent groups. SCN analysis revealed 9 aberrant pairs in MDD vs. HCs, and 7 pairs in FEDN vs. recurrent groups. All aberrant component pairs in the SCN implicated the prefrontal cortex. CONCLUSIONS: These findings demonstrated brain structural deficits in MDD, and highlighted the prefrontal cortex as a central hub of SCN alterations. Our findings advance the understanding of MDD's neural mechanisms and suggest directions for diagnostic research.