Disrupted rich-club network organization and individualized identification of patients with major depressive disorder

重性抑郁障碍 默认模式网络 心理学 磁共振弥散成像 静息状态功能磁共振成像 神经科学 功能磁共振成像 模块化(生物学) 神经心理学 俱乐部 连接体 磁共振成像 医学 功能连接 生物 认知 遗传学 解剖 放射科
作者
Xinyi Liu,Cancan He,Dandan Fan,Yao Zhu,Feifei Zang,Qing Wang,Haisan Zhang,Zhijun Zhang,Hongxing Zhang,Chunming Xie
出处
期刊:Progress in Neuro-psychopharmacology & Biological Psychiatry [Elsevier BV]
卷期号:108: 110074-110074 被引量:40
标识
DOI:10.1016/j.pnpbp.2020.110074
摘要

Altered structural and functional brain networks have been extensively studied in major depressive disorder (MDD) patients. However, whether the differential connectivity patterns in the rich-club organization, assessed from structural brain network analyses, and the associated connections of these regions are particularly susceptible to depression remain unclear. We acquired resting-state functional magnetic resonance imaging (R-fMRI) and diffusion tensor imaging (DTI) from 31 unmedicated MDD patients and 32 cognitively normal (CN) subjects and completed a series of neuropsychological tests. Rich-club organization, network properties, and coupling between structural and functional connectivity (SC-FC) were explored. Furthermore, whether these indices could potentially deliver effective clinical predictive value for MDD patients were examined. The MDD patients showed disrupted structural rich-club organization and modularity, as well as a distinct correlation pattern between global efficiency and rich-club organization. Importantly, reduced SC-FC coupling, reflecting a decreased agreement in the integrity of the networks, was significantly associated with the strength of structural rich-club connections in the MDD patients. Furthermore, the disrupted structural rich-club organization, which was primarily located in the default mode network (DMN) and executive control network (ECN), emerged as a valuable indicator to distinguish between MDD and CN. Findings of this study identified that the disrupted rich-club structural organization significantly influenced brain structural network modularity and integrity and could serve as a promising biological marker for the identification of MDD patients.
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