title: The Altered Triple Networks Interaction in Depression under Resting State Based on Graph Theory.
abstract: The triple network model (Menon, 2011) has been proposed, which helps with finding a common framework for understanding the dysfunction in core neurocognitive network across multiple disorders. The alteration of the triple networks in the major depression disorder (MDD) is not clear. In our study, the altered interaction of the triple networks, which include default model network (DMN), central executive network (CEN), and salience network (SN), was examined in the MDD by graph theory method. The results showed that the connectivity degree of right anterior insula (rAI) significantly increased in MDD compared with healthy control (HC), and the connectivity degree between DMN and CEN significantly decreased in MDD. These results not only supported the proposal of the triple network model, but also prompted us to understand the dysfunction of neural mechanism in MDD.
authors.affiliation: College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, Beijing 100875, China.
authors.forename: Hongna
authors.initials: H
authors.lastname: Zheng
authors.affiliation: College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, Beijing 100875, China.
authors.forename: Lele
authors.initials: L
authors.lastname: Xu
authors.affiliation: College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, Beijing 100875, China.
authors.forename: Fufang
authors.initials: F
authors.lastname: Xie
authors.affiliation: College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, Beijing 100875, China.
authors.forename: Xiaojuan
authors.initials: X
authors.lastname: Guo
authors.affiliation: College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, Beijing 100875, China.
authors.forename: Jiacai
authors.initials: J
authors.lastname: Zhang
authors.affiliation: College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, Beijing 100875, China ; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Li
authors.initials: L
authors.lastname: Yao
authors.affiliation: College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, Beijing 100875, China ; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China ; State Key Laboratories of Transducer Technology, Chinese Academy of Sciences, Shanghai 200050, China.
authors.forename: Xia
authors.initials: X
authors.lastname: Wu
mesh_terms: D000328:Adult; D003863:Depression / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D012146:Rest