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      Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder

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          Abstract

          The clinical misdiagnosis ratio of bipolar disorder (BD) patients to major depressive disorder (MDD) patients is high. Recent findings hypothesize that the ability to flexibly recruit functional neural networks is differently altered in BD and MDD patients. This study aimed to explore distinct aberrance of network flexibility during dynamic networks configuration in BD and MDD patients. Resting state functional magnetic resonance imaging of 40 BD patients, 61 MDD patients, and 61 matched healthy controls were recruited. Dynamic functional connectivity matrices for each subject were constructed with a sliding window method. Then, network switching rate of each node was calculated and compared among the three groups. BD and MDD patients shared decreased network switching rate of regions including left precuneus, bilateral parahippocampal gyrus, and bilateral dorsal medial prefrontal cortex. Apart from these regions, MDD patients presented specially decreased network switching rate in the bilateral anterior insula, left amygdala, and left striatum. Taken together, BD and MDD patients shared decreased network switching rate of key hubs in default mode network and MDD patients presented specially decreased switching rate in salience network and striatum. We found shared and distinct aberrance of network flexibility which revealed altered adaptive functions during dynamic networks configuration of BD and MDD.

          Abstract

          This study explored distinct aberrance of network flexibility during dynamic networks configuration in BD and MDD patients. We found shared and distinct aberrance of network flexibility which revealed altered adaptive functions during dynamic networks configuration of BD and MDD.

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          Most cited references42

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          Interoception: the sense of the physiological condition of the body.

          Converging evidence indicates that primates have a distinct cortical image of homeostatic afferent activity that reflects all aspects of the physiological condition of all tissues of the body. This interoceptive system, associated with autonomic motor control, is distinct from the exteroceptive system (cutaneous mechanoreception and proprioception) that guides somatic motor activity. The primary interoceptive representation in the dorsal posterior insula engenders distinct highly resolved feelings from the body that include pain, temperature, itch, sensual touch, muscular and visceral sensations, vasomotor activity, hunger, thirst, and 'air hunger'. In humans, a meta-representation of the primary interoceptive activity is engendered in the right anterior insula, which seems to provide the basis for the subjective image of the material self as a feeling (sentient) entity, that is, emotional awareness.
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            Dynamic reconfiguration of human brain networks during learning.

            Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes--flexibility and selection--must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.
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              The cognitive control network: Integrated cortical regions with dissociable functions.

              Consensus across hundreds of published studies indicates that the same cortical regions are involved in many forms of cognitive control. Using functional magnetic resonance imaging (fMRI), we found that these coactive regions form a functionally connected cognitive control network (CCN). Network status was identified by convergent methods, including: high inter-regional correlations during rest and task performance, consistently higher correlations within the CCN than the rest of cortex, co-activation in a visual search task, and mutual sensitivity to decision difficulty. Regions within the CCN include anterior cingulate cortex/pre-supplementary motor area (ACC/pSMA), dorsolateral prefrontal cortex (DLPFC), inferior frontal junction (IFJ), anterior insular cortex (AIC), dorsal pre-motor cortex (dPMC), and posterior parietal cortex (PPC). We used a novel visual line search task which included periods when the probe stimuli were occluded but subjects had to maintain and update working memory in preparation for the sudden appearance of a probe stimulus. The six CCN regions operated as a tightly coupled network during the 'non-occluded' portions of this task, with all regions responding to probe events. In contrast, the network was differentiated during occluded search. DLPFC, not ACC/pSMA, was involved in target memory maintenance when probes were absent, while both regions became active in preparation for difficult probes at the end of each occluded period. This approach illustrates one way in which a neuronal network can be identified, its high functional connectivity established, and its components dissociated in order to better understand the interactive and specialized internal mechanisms of that network.
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                Author and article information

                Contributors
                qiancui26@gmail.com
                chenhf@uestc.edu.cn
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                13 May 2020
                15 August 2020
                : 41
                : 12 ( doiID: 10.1002/hbm.v41.12 )
                : 3295-3304
                Affiliations
                [ 1 ] The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu China
                [ 2 ] MOE Key Lab for Neuroinformation High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China Chengdu China
                [ 3 ] School of Public Affairs and Administration, University of Electronic Science and Technology of China Chengdu China
                Author notes
                [*] [* ] Correspondence

                Huafu Chen, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.

                Email: chenhf@ 123456uestc.edu.cn

                Qian Cui, School of Public Administration, University of Electronic Science and Technology of China, Chengdu, China.

                Email: qiancui26@ 123456gmail.com

                Author information
                https://orcid.org/0000-0003-1400-4478
                https://orcid.org/0000-0002-4062-4753
                Article
                HBM25017
                10.1002/hbm.25017
                7375077
                32400932
                b9e76cec-7bcf-47fa-b6fb-1c4bf2d606c2
                © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 October 2019
                : 20 March 2020
                : 11 April 2020
                Page count
                Figures: 4, Tables: 1, Pages: 10, Words: 8601
                Funding
                Funded by: China Postdoctoral Science Foundation Grant
                Award ID: 2019M653383
                Funded by: Youth Innovation Project of Sichuan Provincial Medical Association
                Award ID: Q14014
                Funded by: Scientific Research Project of Sichuan Medical Association
                Award ID: S15012
                Funded by: Key Project of Research and Development of Ministry of Science and Technology
                Award ID: 2018AAA0100705
                Funded by: Natural Science Foundation of China , open-funder-registry 10.13039/501100001809;
                Award ID: 81771919
                Award ID: U1808204
                Award ID: 61533006
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                August 15, 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.5 mode:remove_FC converted:22.07.2020

                Neurology
                bipolar disorder,dynamic networks configuration,fmri,major depressive disorder,multilayer network method,network switching rate

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