Cross-network interaction for diagnosis of major depressive disorder based on resting state functional connectivity

Xueling Zhu,Fulai Yuan,Dongcui Wang,Jilin Nie,Lingyu Kong,Gaofeng Zhou,Ping Hu,Lirong Ouyang,Weihua Liao

doi:10.1007/s11682-020-00326-2

Abstract

Previous studies have suggested that resting-state functional connectivity plays a central role in the physiopathology of major depressive disorder (MDD). However, the individualized diagnosis of MDD based on resting-state functional connectivity is still unclear, especially in first episode drug-naive patients with MDD. Resting state functional magnetic resonance imaging was enrolled from 30 first episode drug-naive patients with MDD and age- and gender-matched 31 healthy controls. Whole brain functional connectivity was computed and viewed as classification features. Multivariate pattern analysis (MVPA) was performed to discriminate patients with MDD from controls. The experimental results exhibited a correct classification rate of 82.25% (p < 0.001) with sensitivity of 83.87% and specificity of 80.64%. Almost all of the consensus connections (125/128) were cross-network interaction among default mode network (DMN), salience network (SN), central executive network (CEN), visual cortex network (VN), Cerebellum and Other. Moreover, the supramarginal gyrus exhibited high discriminative power in classification. Our findings suggested cross-network interaction can be used as an effective biomarker for MDD clinical diagnosis, which may reveal the potential pathological mechanism for major depression. The current study further confirmed reliable application of MVPA in discriminating MDD patients from healthy controls.

Highlights

As one of the most common psychiatric disorders worldwide, major depressive disorder (MDD) is characterized by persistent, pervasive feelings of sadness, guilt, and worthlessness, which leads to serious economic impact to the families and bring great burden to the society (World Health Organization 2017)
In order to reduce the influence of addictive substance, all subjects were required to be abstinent from caffeine, nicotine, alcohol and other addictive substance at least one week prior to the fMRI scanning
Four main results were revealed: (1) a correct classification rate was 82.25% and the area under the ROC curve (AUC) value was 0.892, indicating the important value of whole brain resting state functional connectivity to identify MDD patients from healthy controls; (2) almost all of the consensus connections (125/128) used to distinguish MDD belonged to cross-network connection among default mode network (DMN), salience network (SN), central executive network (CEN), visual cortex network (VN), Cerebellum and Other; (3) The consensus connections with greater weight were mainly located across DMN, SN, CEN and VN. (4) The supramarginal gyrus exhibited the highest discriminative power

Summary

Introduction

As one of the most common psychiatric disorders worldwide, major depressive disorder (MDD) is characterized by persistent, pervasive feelings of sadness, guilt, and worthlessness, which leads to serious economic impact to the families and bring great burden to the society (World Health Organization 2017). Findings are somewhat inconsistent, previous studies have revealed that the pathophysiology of MDD involves a large-scale dysfunction in brain functional networks such as DMN, SN and CEN (Greicius et al 2007; Sexton et al 2012; Zhu et al 2012; Hamilton et al 2013; Guo et al 2014b; Manoliu et al 2014a). Most of these studies traditionally adopt the univariate analysis, which has neglect the highly interconnected nature of the brain (Davatzikos, 2004). Whether altered resting-state functional connectivity could be used in the individualized diagnosis of MDD is still unknown

Objectives

Methods

Findings

Discussion

Conclusion