Divergent Topological Architecture of the Default Mode Network As a Pretreatment Predictor of Early Antidepressant Response in Major Depressive Disorder

Overview

Journal Sci Rep

Specialty Science

Date 2016 Dec 15

PMID 27966645

Citations 13

Authors

Zhenghua Hou

Zan Wang

Wenhao Jiang

Yingying Yin

Yingying Yue

Yuqun Zhang

Xiaopeng Song

Yonggui Yuan

Affiliations

Soon will be listed here.

Abstract

Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n = 42 treatment-responsive depression (RD) and n = 40 non-responding depression (NRD)] and 50 healthy controls (HC) for this study. Based on the thresholded partial correlation matrices of 58 specific brain regions, a graph theory approach was applied to analyse the topological properties. When compared to HC, both RD and NRD patients exhibited a lower nodal degree (D) in the left anterior cingulate gyrus; as for RD, the D of the left superior medial orbitofrontal gyrus was significantly reduced, but the right inferior orbitofrontal gyrus was increased (all P < 0.017, FDR corrected). Moreover, the nodal degree in the right dorsolateral superior frontal cortex (SFGdor) was significantly lower in RD than in NRD. Receiver operating characteristic curve analysis demonstrated that the λ and nodal degree in the right SFGdor exhibited a good ability to distinguish nonresponding patients from responsive patients, which could serve as a specific maker to predict an early response to antidepressants. The disrupted topological configurations in the present study extend the understanding of pretreatment neuroimaging predictors for antidepressant medication.

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