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作者:

Li, Huaizhou (Li, Huaizhou.) | Zhou, Haiyan (Zhou, Haiyan.) | Yang, Yang (Yang, Yang.) | Wang, Haiyuan (Wang, Haiyuan.) | Zhong, Ning (Zhong, Ning.)

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摘要:

Previous studies have reported the enhanced randomization of functional brain networks in patients with major depressive disorder (MDD). However, little is known about the changes of key nodal attributes for randomization, the resilience of network, and the clinical significance of the alterations. In this study, we collected the resting-state functional MRI data from 19 MDD patients and 19 healthy control (HC) individuals. Graph theory analysis showed that decreases were found in the small-worldness, clustering coefficient, local efficiency, and characteristic path length (i.e., increase of global efficiency) in the network of MDD group compared with HC group, which was consistent with previous findings and suggested the development toward randomization in the brain network in MDD. In addition, the greater resilience under the targeted attacks was also found in the network of patients with MDD. Furthermore, the abnormal nodal properties were found, including clustering coefficients and nodal efficiencies in the left orbital superior frontal gyrus, bilateral insula, left amygdala, right supramarginal gyrus, left putamen, left posterior cingulate cortex, left angular gyrus. Meanwhile, the correlation analysis showed that most of these abnormal areas were associated with the clinical status. The observed increased randomization and resilience in MDD might be related to the abnormal hub nodes in the brain networks, which were attacked by the disease pathology. Our findings provide new evidence to indicate that the weakening of specialized regions and the enhancement of whole brain integrity could be the potential endophenotype of the depressive pathology. (C) 2017 Elsevier Ltd. All rights reserved.

关键词:

Randomization Complex network Major depressive disorder Resilience Small-worldness Resting state functional magnetic resonance imaging

作者机构:

  • [ 1 ] [Li, Huaizhou]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
  • [ 2 ] [Yang, Yang]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
  • [ 3 ] [Wang, Haiyuan]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
  • [ 4 ] [Zhong, Ning]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
  • [ 5 ] [Li, Huaizhou]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 6 ] [Zhou, Haiyan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 7 ] [Wang, Haiyuan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 8 ] [Zhong, Ning]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 9 ] [Li, Huaizhou]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
  • [ 10 ] [Zhou, Haiyan]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
  • [ 11 ] [Yang, Yang]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
  • [ 12 ] [Wang, Haiyuan]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
  • [ 13 ] [Zhong, Ning]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
  • [ 14 ] [Li, Huaizhou]Beijing Key Lab MRI & Brain Informat, Beijing, Peoples R China
  • [ 15 ] [Zhou, Haiyan]Beijing Key Lab MRI & Brain Informat, Beijing, Peoples R China
  • [ 16 ] [Yang, Yang]Beijing Key Lab MRI & Brain Informat, Beijing, Peoples R China
  • [ 17 ] [Wang, Haiyuan]Beijing Key Lab MRI & Brain Informat, Beijing, Peoples R China
  • [ 18 ] [Zhong, Ning]Beijing Key Lab MRI & Brain Informat, Beijing, Peoples R China
  • [ 19 ] [Yang, Yang]Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gunma, Japan
  • [ 20 ] [Zhong, Ning]Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gunma, Japan

通讯作者信息:

  • 钟宁

    [Zhong, Ning]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

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来源 :

JOURNAL OF CLINICAL NEUROSCIENCE

ISSN: 0967-5868

年份: 2017

卷: 44

页码: 274-278

2 . 0 0 0

JCR@2022

ESI学科: NEUROSCIENCE & BEHAVIOR;

ESI高被引阀值:218

中科院分区:4

被引次数:

WoS核心集被引频次: 33

SCOPUS被引频次: 33

ESI高被引论文在榜: 0 展开所有

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