title: Abnormal dynamic community structure of the salience network in depression.
abstract: To detect the consecutive variations of the internetwork interactions over time, which helps to discover the underlying dysfunction of depressive disorders. Abnormal interactions of resting-state functional networks have been reported in depression. However, little is known regarding the dynamics of how these crucial networks interact and the disease-related dysfunction.
  
  
  
  Functional magnetic resonance imaging data at 3.0T in the resting state were acquired from 20 depressed patients and 20 healthy controls. Twelve resting-state networks were extracted by group-independent component analysis, and their interactions were calculated through a sliding windowed Granger causality model analysis. The acquired effective connectivity matrices were used to construct multislice networks with modular structures that were detected via a multislice community detection method.
  
  
  
  No significant differences were observed in the modularity and total module numbers between the depressed patients and the healthy controls. The P values were 0.133 with a confidence interval (-0.0001 0.0093) and 0.136 with a confidence interval (-0.30 0.90), respectively. However, the depressed patients exhibited decreased flexibility of the salience network (SN) compared with the controls (P = 0.048, corrected, with a confidence interval 0.0068 0.066).
  
  
  
  SN was inclined to participate less in the multiple brain functional modules across the resting time in depression, and infrequently changed its modular allegiance. These findings support the potential importance of the SN in the neuropathological mechanism of depression.
  
  
  
  1 J. Magn. Reson. Imaging 2017;45:1135-1143.
authors.affiliation: Key Laboratory of Child Development and Learning Science, Research Center of Learning Science, Southeast University, Nanjing, China.
authors.forename: Maobin
authors.initials: M
authors.lastname: Wei
authors.affiliation: Key Laboratory of Child Development and Learning Science, Research Center of Learning Science, Southeast University, Nanjing, China.
authors.forename: Jiaolong
authors.initials: J
authors.lastname: Qin
authors.affiliation: Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Yan
authors.affiliation: Key Laboratory of Child Development and Learning Science, Research Center of Learning Science, Southeast University, Nanjing, China.
authors.forename: Kun
authors.initials: K
authors.lastname: Bi
authors.affiliation: Key Laboratory of Child Development and Learning Science, Research Center of Learning Science, Southeast University, Nanjing, China.
authors.forename: Chu
authors.initials: C
authors.lastname: Liu
authors.affiliation: Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.|Medical College of Nanjing University, Nanjing, China.
authors.forename: Zhijian
authors.initials: Z
authors.lastname: Yao
authors.affiliation: Key Laboratory of Child Development and Learning Science, Research Center of Learning Science, Southeast University, Nanjing, China.|Suzhou Research Institute of Southeast University, Suzhou, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Lu
mesh_terms: D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D002681:China; D003865:Major Depressive Disorder / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000000981:diagnostic imaging / Q000503:physiopathology; D012146:Rest; D055815:Young Adult
keywords: depression; dynamic; fMRI; multislice network; salience network