Subgroups of internet gaming disorder based on addiction‐related resting‐state functional connectivity

Aims To identify subgroups of people with internet gaming disorder (IGD) based on addiction‐related resting‐state functional connectivity and how these subgroups show different clinical correlates and responses to treatment. Design Secondary analysis of two functional magnetic resonance imaging (fMR...

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Veröffentlicht in:Addiction (Abingdon, England) England), 2023-02, Vol.118 (2), p.327-339
Hauptverfasser: Wang, Zi‐Liang, Potenza, Marc N., Song, Kun‐Ru, Dong, Guang‐Heng, Fang, Xiao‐Yi, Zhang, Jin‐Tao
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container_issue 2
container_start_page 327
container_title Addiction (Abingdon, England)
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creator Wang, Zi‐Liang
Potenza, Marc N.
Song, Kun‐Ru
Dong, Guang‐Heng
Fang, Xiao‐Yi
Zhang, Jin‐Tao
description Aims To identify subgroups of people with internet gaming disorder (IGD) based on addiction‐related resting‐state functional connectivity and how these subgroups show different clinical correlates and responses to treatment. Design Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets. Setting Zhejiang province and Beijing, China. Participants One hundred and sixty‐nine IGD and 147 control subjects. Measurements k‐Means algorithmic and support‐vector machine‐learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue–reactivity and guessing‐related measures. Findings Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 (‘craving‐related subgroup’) and subgroup 2 (‘mixed psychological subgroup’). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10‐fold validation (n = 218) and out‐of‐sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue–reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non‐adaptive emotion‐regulation strategies and guessing‐task fMRI measures. Subgroups 1 and 2 showed largely opposite functional–connectivity patterns in overlapping networks. Conclusions There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.
doi_str_mv 10.1111/add.16047
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Design Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets. Setting Zhejiang province and Beijing, China. Participants One hundred and sixty‐nine IGD and 147 control subjects. Measurements k‐Means algorithmic and support‐vector machine‐learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue–reactivity and guessing‐related measures. Findings Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 (‘craving‐related subgroup’) and subgroup 2 (‘mixed psychological subgroup’). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10‐fold validation (n = 218) and out‐of‐sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue–reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non‐adaptive emotion‐regulation strategies and guessing‐task fMRI measures. Subgroups 1 and 2 showed largely opposite functional–connectivity patterns in overlapping networks. Conclusions There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.</description><identifier>ISSN: 0965-2140</identifier><identifier>EISSN: 1360-0443</identifier><identifier>DOI: 10.1111/add.16047</identifier><identifier>PMID: 36089824</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>Activation ; Addictions ; Addictive behaviors ; Behavior modification ; Behavior, Addictive - psychology ; Brain ; Brain - diagnostic imaging ; Brain mapping ; Brain Mapping - methods ; Clustering ; Cognitive behavioral therapy ; Cognitive style ; Computer &amp; video games ; Craving ; Craving - physiology ; Emotional regulation ; Functional connectivity ; Functional magnetic resonance imaging ; Humans ; Immunoglobulin D ; Inhibition ; Internet ; Internet Addiction Disorder - diagnostic imaging ; internet gaming disorder ; Machine learning ; magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Neural networks ; Neuroimaging ; Reactivity ; Research subjects ; Responsiveness ; Resting ; support vector machine learning ; video games ; Video Games - psychology</subject><ispartof>Addiction (Abingdon, England), 2023-02, Vol.118 (2), p.327-339</ispartof><rights>2022 Society for the Study of Addiction.</rights><rights>2023 Society for the Study of Addiction</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3537-393dabb1e3dd003a76b8c2a71079d02386c78f197fdd80c36912369ddd01d0203</citedby><cites>FETCH-LOGICAL-c3537-393dabb1e3dd003a76b8c2a71079d02386c78f197fdd80c36912369ddd01d0203</cites><orcidid>0000-0002-4807-1196</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fadd.16047$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fadd.16047$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36089824$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Zi‐Liang</creatorcontrib><creatorcontrib>Potenza, Marc N.</creatorcontrib><creatorcontrib>Song, Kun‐Ru</creatorcontrib><creatorcontrib>Dong, Guang‐Heng</creatorcontrib><creatorcontrib>Fang, Xiao‐Yi</creatorcontrib><creatorcontrib>Zhang, Jin‐Tao</creatorcontrib><title>Subgroups of internet gaming disorder based on addiction‐related resting‐state functional connectivity</title><title>Addiction (Abingdon, England)</title><addtitle>Addiction</addtitle><description>Aims To identify subgroups of people with internet gaming disorder (IGD) based on addiction‐related resting‐state functional connectivity and how these subgroups show different clinical correlates and responses to treatment. Design Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets. Setting Zhejiang province and Beijing, China. Participants One hundred and sixty‐nine IGD and 147 control subjects. Measurements k‐Means algorithmic and support‐vector machine‐learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue–reactivity and guessing‐related measures. Findings Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 (‘craving‐related subgroup’) and subgroup 2 (‘mixed psychological subgroup’). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10‐fold validation (n = 218) and out‐of‐sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue–reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non‐adaptive emotion‐regulation strategies and guessing‐task fMRI measures. Subgroups 1 and 2 showed largely opposite functional–connectivity patterns in overlapping networks. 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Potenza, Marc N. ; Song, Kun‐Ru ; Dong, Guang‐Heng ; Fang, Xiao‐Yi ; Zhang, Jin‐Tao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3537-393dabb1e3dd003a76b8c2a71079d02386c78f197fdd80c36912369ddd01d0203</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Activation</topic><topic>Addictions</topic><topic>Addictive behaviors</topic><topic>Behavior modification</topic><topic>Behavior, Addictive - psychology</topic><topic>Brain</topic><topic>Brain - diagnostic imaging</topic><topic>Brain mapping</topic><topic>Brain Mapping - methods</topic><topic>Clustering</topic><topic>Cognitive behavioral therapy</topic><topic>Cognitive style</topic><topic>Computer &amp; video games</topic><topic>Craving</topic><topic>Craving - physiology</topic><topic>Emotional regulation</topic><topic>Functional connectivity</topic><topic>Functional magnetic resonance imaging</topic><topic>Humans</topic><topic>Immunoglobulin D</topic><topic>Inhibition</topic><topic>Internet</topic><topic>Internet Addiction Disorder - diagnostic imaging</topic><topic>internet gaming disorder</topic><topic>Machine learning</topic><topic>magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Neural networks</topic><topic>Neuroimaging</topic><topic>Reactivity</topic><topic>Research subjects</topic><topic>Responsiveness</topic><topic>Resting</topic><topic>support vector machine learning</topic><topic>video games</topic><topic>Video Games - psychology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Zi‐Liang</creatorcontrib><creatorcontrib>Potenza, Marc N.</creatorcontrib><creatorcontrib>Song, Kun‐Ru</creatorcontrib><creatorcontrib>Dong, Guang‐Heng</creatorcontrib><creatorcontrib>Fang, Xiao‐Yi</creatorcontrib><creatorcontrib>Zhang, Jin‐Tao</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Addiction (Abingdon, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Zi‐Liang</au><au>Potenza, Marc N.</au><au>Song, Kun‐Ru</au><au>Dong, Guang‐Heng</au><au>Fang, Xiao‐Yi</au><au>Zhang, Jin‐Tao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Subgroups of internet gaming disorder based on addiction‐related resting‐state functional connectivity</atitle><jtitle>Addiction (Abingdon, England)</jtitle><addtitle>Addiction</addtitle><date>2023-02</date><risdate>2023</risdate><volume>118</volume><issue>2</issue><spage>327</spage><epage>339</epage><pages>327-339</pages><issn>0965-2140</issn><eissn>1360-0443</eissn><abstract>Aims To identify subgroups of people with internet gaming disorder (IGD) based on addiction‐related resting‐state functional connectivity and how these subgroups show different clinical correlates and responses to treatment. Design Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets. Setting Zhejiang province and Beijing, China. Participants One hundred and sixty‐nine IGD and 147 control subjects. Measurements k‐Means algorithmic and support‐vector machine‐learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue–reactivity and guessing‐related measures. Findings Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 (‘craving‐related subgroup’) and subgroup 2 (‘mixed psychological subgroup’). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10‐fold validation (n = 218) and out‐of‐sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue–reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non‐adaptive emotion‐regulation strategies and guessing‐task fMRI measures. Subgroups 1 and 2 showed largely opposite functional–connectivity patterns in overlapping networks. Conclusions There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>36089824</pmid><doi>10.1111/add.16047</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-4807-1196</orcidid></addata></record>
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subjects Activation
Addictions
Addictive behaviors
Behavior modification
Behavior, Addictive - psychology
Brain
Brain - diagnostic imaging
Brain mapping
Brain Mapping - methods
Clustering
Cognitive behavioral therapy
Cognitive style
Computer & video games
Craving
Craving - physiology
Emotional regulation
Functional connectivity
Functional magnetic resonance imaging
Humans
Immunoglobulin D
Inhibition
Internet
Internet Addiction Disorder - diagnostic imaging
internet gaming disorder
Machine learning
magnetic resonance imaging
Magnetic Resonance Imaging - methods
Neural networks
Neuroimaging
Reactivity
Research subjects
Responsiveness
Resting
support vector machine learning
video games
Video Games - psychology
title Subgroups of internet gaming disorder based on addiction‐related resting‐state functional connectivity
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