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      Decreased effective connection from the parahippocampal gyrus to the prefrontal cortex in Internet gaming disorder: A MVPA and spDCM study

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          Abstract

          Objectives

          Understanding the neural mechanisms underlying Internet gaming disorder (IGD) is essential for the condition's diagnosis and treatment. Nevertheless, the pathological mechanisms of IGD remain elusive at present. Hence, we employed multi-voxel pattern analysis (MVPA) and spectral dynamic causal modeling (spDCM) to explore this issue.

          Methods

          Resting-state fMRI data were collected from 103 IGD subjects (male = 57) and 99 well-matched recreational game users (RGUs, male = 51). Regional homogeneity was calculated as the feature for MVPA based on the support vector machine (SVM) with leave-one- out cross-validation. Mean time series data extracted from the brain regions in accordance with the MVPA results were used for further spDCM analysis.

          Results

          Results display a high accuracy of 82.67% (sensitivity of 83.50% and specificity of 81.82%) in the classification of the two groups. The most discriminative brain regions that contributed to the classification were the bilateral parahippocampal gyrus (PG), right anterior cingulate cortex (ACC), and middle frontal gyrus (MFG). Significant correlations were found between addiction severity (IAT and DSM scores) and the ReHo values of the brain regions that contributed to the classification. Moreover, the results of spDCM showed that compared with RGU, IGD showed decreased effective connectivity from the left PG to the right MFG and from the right PG to the ACC and decreased self-connection in the right PG.

          Conclusions

          These results show that the weakening of the PG and its connection with the prefrontal cortex, including the ACC and MFG, may be an underlying mechanism of IGD.

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          Most cited references69

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          DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

          Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.
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            What is a support vector machine?

            Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?
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              The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire

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                Author and article information

                Journal
                2006
                Journal of Behavioral Addictions
                J Behav Addict
                Akadémiai Kiadó (Budapest )
                2062-5871
                2063-5303
                07 April 2020
                : 9
                : 1
                : 105-115
                Affiliations
                [1 ] State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research , univBeijing Normal University , Beijing, PR China
                [2 ] Center for Cognition and Brain Disorders , univThe Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province, PR China
                [3 ] deptDepartment of Psychology , univZhejiang Normal University , Jinhua, PR China
                [4 ] deptDepartment of Physics , Shanghai Key Laboratory of Magnetic Resonance , univEast China Normal University , Shanghai, PR China
                [5 ] Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou, Zhejiang Province, PR China
                Author notes
                []Corresponding author. Tel./fax: +86 10 58800728. E-mail: zhangjintao@ 123456bnu.edu.cn
                [∗∗ ]Corresponding author. Center for Cognition and Brain Disorders , univHangzhou Normal University , Hangzhou, Zhejiang Province, P.R. China. Tel.: +86 15 867949909. E-mail: dongguangheng@ 123456hznu.edu.cn
                Author information
                https://orcid.org/0000-0002-4807-1196
                https://orcid.org/0000-0001-8813-8730
                Article
                10.1556/2006.2020.00012
                e3708fce-96dc-4827-9dec-5bfe62378d8f
                © 2020 The Author(s)

                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.

                History
                : 07 September 2019
                : 10 December 2019
                : 04 February 2020
                : 07 February 2020
                Page count
                Figures: 03, Tables: 05, Equations: 00, References: 70, Pages: 11
                Funding
                Funded by: National Natural Science Foundation
                Award ID: 31371023 and 31871122
                Funded by: Zhejiang Natural Science Foundation
                Award ID: LY20C090005
                Categories
                Full-Length Report

                Evolutionary Biology,Medicine,Psychology,Educational research & Statistics,Social & Behavioral Sciences
                Internet gaming disorder,spectral dynamic causal modeling,parahippocampal gyrus,multi-voxel pattern analysis

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