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      Impulsivity-related right superior frontal gyrus as a biomarker of internet gaming disorder

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          Abstract

          Background

          Internet gaming disorder (IGD) is a mental health issue that affects individuals worldwide. However, the lack of knowledge about the biomarkers associated with the development of IGD has restricted the diagnosis and treatment of this disorder.

          Aims

          We aimed to reveal the biomarkers associated with the development of IGD through resting-state brain network analysis and provide clues for the diagnosis and treatment of IGD.

          Methods

          Twenty-six patients with IGD, 23 excessive internet game users (EIUs) who recurrently played internet games but were not diagnosed with IGD and 29 healthy controls (HCs) performed delay discounting task (DDT) and Iowa gambling task (IGT). Resting-state functional magnetic resonance imaging (fMRI) data were also collected.

          Results

          Patients with IGD exhibited significantly lower hubness in the right medial orbital part of the superior frontal gyrus (ORBsupmed) than both the EIU and the HC groups. Additionally, the hubness of the right ORBsupmed was found to be positively correlated with the highest excessive internet gaming degree during the past year in the EIU group but not the IGD group; this might be the protective mechanism that prevents EIUs from becoming addicted to internet games. Moreover, the hubness of the right ORBsupmed was found to be related to the treatment outcome of patients with IGD, with higher hubness of this region indicating better recovery when undergoing forced abstinence. Further modelling analysis of the DDT and IGT showed that patients with IGD displayed higher impulsivity during the decision-making process, and impulsivity-related parameters were negatively correlated with the hubness of right ORBsupmed.

          Conclusions

          Our findings revealed that the impulsivity-related right ORBsupmed hubness could serve as a potential biomarker of IGD and provide clues for the diagnosis and treatment of IGD.

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          Most cited references62

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          Diagnostic and Statistical Manual of Mental Disorders

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            Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

            An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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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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                Author and article information

                Journal
                Gen Psychiatr
                Gen Psychiatr
                gpsych
                gpsych
                General Psychiatry
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2517-729X
                2023
                10 August 2023
                : 36
                : 4
                : e100985
                Affiliations
                [1 ] departmentHefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Sciences and Medicine , Ringgold_12652University of Science and Technology of China , Hefei, Anhui, China
                [2 ] departmentKey Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management , Shanghai International Studies University , Shanghai, China
                [3 ] departmentDepartment of Psychology, School of Humanities and Social Sciences , University of Science and Technology of China , Hefei, Anhui, China
                [4 ] departmentDrug Rehabilitation Administration , Ringgold_154533Ministry of Justice of the People's Republic of China , Beijing, China
                [5 ] departmentApplication Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology , University of Science and Technology of China , Hefei, Anhui, China
                [6 ] departmentSchool of Psychology, Beijing Key Laboratory of Learning and Cognition , Ringgold_12379Capital Normal University , Beijing, China
                [7 ] departmentDepartment of Psychological Medicine , Chinese People's Liberation Army General Hospital , Beijing, China
                [8 ] departmentInstitute of Health and Medicine , Hefei Comprehensive Science Center , Hefei, Anhui, China
                Author notes
                [Correspondence to ] Dr Xiaochu Zhang; zxcustc@ 123456ustc.edu.cn ; Dr Zhengde Wei; zdwei@ 123456ustc.edu.cn

                PZ and YP are joint first authors.

                Author information
                http://orcid.org/0000-0002-7541-0130
                Article
                gpsych-2022-100985
                10.1136/gpsych-2022-100985
                10423834
                37583792
                cf60b331-ef70-4fcb-8972-0276c44f62d2
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 19 November 2022
                : 12 July 2023
                Funding
                Funded by: CAS-VPST Silk Road Science Fund 2021;
                Award ID: GLHZ202128
                Funded by: The Chinese National Programs for Brain Science and Brain-like Intelligence Technology;
                Award ID: 2021ZD0202101
                Funded by: The National Natural Science Foundation of China;
                Award ID: 31900766
                Award ID: 32161143022
                Award ID: 32171080
                Award ID: 71874170
                Award ID: 71942003
                Funded by: Major Project of Philosophy and Social Science Research, Ministry of Education of China;
                Award ID: 19JZD010
                Funded by: Collaborative Innovation Program of Hefei Science Center, CAS;
                Award ID: 2020HSC-CIP001
                Funded by: Anhui Provincial Key Research and Development Project;
                Award ID: 202004b11020013
                Categories
                Original Research
                1506
                2618
                Custom metadata
                unlocked

                behaviour, addictive,brain,cognitive neuroscience
                behaviour, addictive, brain, cognitive neuroscience

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