Literature DB >> 28705767

Common and distinct dysfunctional patterns contribute to triple network model in schizophrenia and depression: A preliminary study.

Yuchao Jiang1, Mingjun Duan2, Xi Chen1, Xin Chang1, Hui He1, YingJia Li1, Cheng Luo3, Dezhong Yao4.   

Abstract

BACKGROUND: Schizophrenia (SCH) and depression (DEP) are prevalent psychiatric disorders and share common and distinguished elements in their pathophysiology. A triple network model composed of the default mode network (DMN), salience network (SN) and central executive network (CEN) may represent a major abnormality across several psychiatric disorders including SCH and DEP. However, common and distinct dysfunctional patterns between SCH and DEP across three core networks remain unclear.
METHOD: Resting-state functional magnetic resonance imaging (fMRI) was obtained in 20 patients with SCH, 20 patients with DEP and 20 healthy controls (HC). Both functional connectivity (FC) and Granger causal connectivity across DMN, SN and CEN were evaluated to uncover common and distinct dysfunctional patterns between SCH and DEP.
RESULTS: Two patient groups showed identical abnormal causal connectivity between key nodes of DMN and SN, as well as opposing aberrant FC of DMN-CEN and SN-CEN. Compared with HC, the FC between CEN and DMN was increased in SCH while decreased in DEP. Conversely, DEP showed enhanced FC between CEN and SN, whereas SCH showed decreased FC. LIMITATIONS: The sample size was relatively small, and all participants were taking medication.
CONCLUSIONS: Our results identified common patterns including dysconnectivity between DMN and SN, which may contribute to shared cognitive and affective impairment in DEP and SCH. Moreover, opposing dysconnectivity patterns of DMN-CEN may be associated with different self-referential processing abnormalities. These opposing dysconnectivity patterns may indicate an unbalanced recruitment between SN and CEN. Therefore, this study provides dysconnectivity patterns to advance the understanding of the triple network model with regard to psychiatric disorders.
Copyright © 2017 Elsevier Inc. All rights reserved.

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Year:  2017        PMID: 28705767     DOI: 10.1016/j.pnpbp.2017.07.007

Source DB:  PubMed          Journal:  Prog Neuropsychopharmacol Biol Psychiatry        ISSN: 0278-5846            Impact factor:   5.067


  18 in total

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2.  Brain Networks Involved in Depression in Patients with Frontotemporal Dementia and Parkinson's Disease: An Exploratory Resting-State Functional Connectivity MRI Study.

Authors:  Vincenzo Alfano; Giovanni Federico; Giulia Mele; Federica Garramone; Marcello Esposito; Marco Aiello; Marco Salvatore; Carlo Cavaliere
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3.  Resting-state functional connectivity of salience network in schizophrenia and depression.

Authors:  Huan Huang; Cheng Chen; Bei Rong; Qirong Wan; Jingang Chen; Zhongchun Liu; Yuan Zhou; Gaohua Wang; Huiling Wang
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4.  Abnormalities of intrinsic regional brain activity in first-episode and chronic schizophrenia: a meta-analysis of resting-state functional MRI

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6.  Distinct pattern of cerebral blood flow alterations specific to schizophrenics experiencing auditory verbal hallucinations with and without insight: a pilot study.

Authors:  Rixing Jing; Jiangjie Huang; Deguo Jiang; Xiaodong Lin; Xiaolei Ma; Hongjun Tian; Jie Li; Chuanjun Zhuo
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9.  Temporal Dynamics in Degree Centrality of Brain Functional Connectome in First-Episode Schizophrenia with Different Short-Term Treatment Responses: A Longitudinal Study.

Authors:  Yingchan Wang; Yuchao Jiang; Wenjun Su; Lihua Xu; Yanyan Wei; Yingying Tang; Tianhong Zhang; Xiaochen Tang; Yegang Hu; Huiru Cui; Jinhong Wang; Dezhong Yao; Cheng Luo; Jijun Wang
Journal:  Neuropsychiatr Dis Treat       Date:  2021-05-18       Impact factor: 2.570

10.  Music Intervention Leads to Increased Insular Connectivity and Improved Clinical Symptoms in Schizophrenia.

Authors:  Hui He; Mi Yang; Mingjun Duan; Xi Chen; Yongxiu Lai; Yang Xia; Junming Shao; Bharat B Biswal; Cheng Luo; Dezhong Yao
Journal:  Front Neurosci       Date:  2018-01-23       Impact factor: 4.677

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