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      Gender-related differences in frontal-parietal modular segregation and altered effective connectivity in internet gaming disorder

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          Abstract

          Background

          Although previous studies have revealed gender-related differences in executive function in internet gaming disorder (IGD), neural mechanisms underlying these processes remain unclear, especially in terms of brain networks.

          Methods

          Resting-state fMRI data were collected from 78 subjects with IGD (39 males, 20.8 ± 2.16 years old) and 72 with recreational game use (RGU) (39 males, 21.5 ± 2.56 years old). By utilizing graph theory, we calculated participation coefficients among brain network modules for all participants and analyzed the diagnostic-group-by-gender interactions. We further explored possible causal relationships between networks through spectral dynamic causal modeling (spDCM) to assess differences in between-network connections.

          Results

          Compared to males with RGU, males with IGD demonstrated reduced modular segregation of the frontal-parietal network (FPN). Male IGD subjects also showed increased connections between the FPN and cingulo-opercular network (CON); however, these differences were not found in female subjects. Further spDCM analysis indicated that the causal influence from CON to FPN in male IGD subjects was enhanced relative to that of RGU males, while this influence was relatively reduced in females with IGD.

          Conclusions

          These results suggest poor modular segmentation of the FPN and abnormal FPN/CON connections in males with IGD, suggesting a mechanism for male vulnerability to IGD. An increased “bottom-up” effect from the CON to FPN in male IGD subjects could reflect dysfunction between the brain networks. Different mechanisms may underlie in IGD, suggesting that different interventions may be optimal in males and females with IGD.

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          Most cited references60

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          G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

          G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
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            Complex network measures of brain connectivity: uses and interpretations.

            Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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              A dual-networks architecture of top-down control.

              Complex systems ensure resilience through multiple controllers acting at rapid and slower timescales. The need for efficient information flow through complex systems encourages small-world network structures. On the basis of these principles, a group of regions associated with top-down control was examined. Functional magnetic resonance imaging showed that each region had a specific combination of control signals; resting-state functional connectivity grouped the regions into distinct 'fronto-parietal' and 'cingulo-opercular' components. The fronto-parietal component seems to initiate and adjust control; the cingulo-opercular component provides stable 'set-maintenance' over entire task epochs. Graph analysis showed dense local connections within components and weaker 'long-range' connections between components, suggesting a small-world architecture. The control systems of the brain seem to embody the principles of complex systems, encouraging resilient performance.
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                Author and article information

                Contributors
                Journal
                J Behav Addict
                J Behav Addict
                JBA
                Journal of Behavioral Addictions
                Akadémiai Kiadó (Budapest )
                2062-5871
                2063-5303
                10 March 2021
                April 2021
                April 2021
                : 10
                : 1
                : 123-134
                Affiliations
                [1 ]Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University , Hangzhou, PR China
                [2 ]Shenzhen Key Laboratory of Affective and Social Neuroscience, Center for Brain Disorders and Cognitive Science, Shenzhen University , Shenzhen, PR China
                [3 ]Institute of Psychological Sciences, Hangzhou Normal University , Hangzhou, PR China
                [4 ]Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine , Shanghai, PR China
                [5 ]Department of Neuroscience, Yale University , New Haven, CT, USA
                [6 ]Connecticut Council on Problem Gambling , Wethersfield, CT, USA
                [7 ]Connecticut Mental Health Center , New Haven, CT, USA
                [8 ]Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou, Zhejiang Province, PR China
                Author notes
                [* ]Corresponding author. E-mail: dongguangheng@ 123456hznu.edu.cn
                [†]

                These authors contribute equal to this work.

                Article
                10.1556/2006.2021.00015
                8969857
                33704084
                c0c1ef71-765e-436c-83d1-fd5fe137601f
                © 2021 The Author(s)

                Open Access. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated.

                Page count
                Figures: 3, Tables: 3, Equations: 1, References: 61, Pages: 12
                Product
                Funding
                Funded by: Zhejiang Provincial Natural Science Foundation
                Award ID: LY20C090005
                Categories
                Article

                internet gaming disorder,sex difference,frontal-parietal network,participation coefficient,dynamic causal modeling

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