Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder.
- Author:
Jian-Huai CHEN
;
Zhi-Jian YAO
1
;
Jiao-Long QIN
;
Rui YAN
;
Ling-Ling HUA
;
Qing LU
Author Information
- Publication Type:Journal Article
- MeSH: Adult; Anisotropy; Brain; pathology; Depressive Disorder, Major; pathology; Diffusion Tensor Imaging; methods; Female; Humans; Male
- From: Chinese Medical Journal 2016;129(6):679-689
- CountryChina
- Language:English
-
Abstract:
BACKGROUNDMost previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients.
METHODSThe diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory.
RESULTSCompared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions.
CONCLUSIONSAll these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.