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      Altered functional network activities for behavioral adjustments and Bayesian learning in young men with Internet gaming disorder

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          Abstract

          Background and aims

          Deficits in cognitive control represent a core feature of addiction. Internet Gaming Disorder (IGD) offers an ideal model to study the mechanisms underlying cognitive control deficits in addiction, eliminating the confounding effects of substance use. Studies have reported behavioral and neural deficits in reactive control in IGD, but it remains unclear whether individuals with IGD are compromised in proactive control or behavioral adjustment by learning from the changing contexts.

          Methods

          Here, fMRI data of 21 male young adults with IGD and 21 matched healthy controls (HC) were collected during a stop-signal task. We employed group independent component analysis to investigate group differences in temporally coherent, large-scale functional network activities during post-error slowing, the typical type of behavioral adjustments. We also employed a Bayesian belief model to quantify the trial-by-trial learning of the likelihood of stop signal – P(Stop) – a broader process underlying behavioral adjustment, and identified the alterations in functional network responses to P(Stop).

          Results

          The results showed diminished engagement of the fronto-parietal network during post-error slowing, and weaker activity in the ventral attention and anterior default mode network in response to P(Stop) in IGD relative to HC.

          Discussion and conclusions

          These results add to the literatures by suggesting deficits in updating and anticipating conflicts as well as in behavioral adjustment according to contextual information in individuals with IGD.

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          Most cited references70

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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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            An inventory for measuring depression.

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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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                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
                : 112-122
                Affiliations
                [1 ]Institute of Developmental Psychology, Beijing Normal University , Beijing, China
                [2 ]Department of Psychiatry, Yale University School of Medicine , New Haven, CT, USA
                [3 ]Department of Neuroscience, Yale University School of Medicine , New Haven, CT, USA
                [4 ]Faculty of Education, Beijing Normal University , Beijing, China
                [5 ]State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University , Beijing, China
                [6 ]IDG/McGovern Institute for Brain Research, Beijing Normal University , Beijing, China
                [7 ]Beijing Key Lab of Applied Experimental Psychology, School of Psychology, Beijing Normal University , Beijing, China
                [8 ]German Institute of Human Nutrition Potsdam-Rehbruecke , 14558 Nuthetal, Germany
                [9 ]Einstein Center for Neurosciences Berlin , Charitéplatz 1, 10117 Berlin, Germany
                Author notes
                [* ]Corresponding authors. E-mail: zhangjintao@ 123456bnu.edu.cn (J.-T. Zhang) fangxy@ 123456bnu.edu.cn (X.-Y. Fang)
                Article
                10.1556/2006.2021.00010
                8969861
                33704083
                12ab141a-7dea-4a8f-815d-75c57fdfd3d6
                © 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: 4, Tables: 1, Equations: 0, References: 69, Pages: 11
                Product
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 31871122
                Award ID: 31170990
                Award ID: 31700966
                Funded by: State Key Laboratory of Cognitive Neuroscience and Learning
                Categories
                Article

                bayesian learning,post-error adjustment,functional brain networks,independent component analysis,internet gaming disorder,stop-signal task

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