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 |
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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 |
format | Article |
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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.</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 & 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.
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><subject>Activation</subject><subject>Addictions</subject><subject>Addictive behaviors</subject><subject>Behavior modification</subject><subject>Behavior, Addictive - psychology</subject><subject>Brain</subject><subject>Brain - diagnostic imaging</subject><subject>Brain mapping</subject><subject>Brain Mapping - methods</subject><subject>Clustering</subject><subject>Cognitive behavioral therapy</subject><subject>Cognitive style</subject><subject>Computer & video games</subject><subject>Craving</subject><subject>Craving - physiology</subject><subject>Emotional regulation</subject><subject>Functional connectivity</subject><subject>Functional magnetic resonance imaging</subject><subject>Humans</subject><subject>Immunoglobulin D</subject><subject>Inhibition</subject><subject>Internet</subject><subject>Internet Addiction Disorder - diagnostic imaging</subject><subject>internet gaming disorder</subject><subject>Machine learning</subject><subject>magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Neural networks</subject><subject>Neuroimaging</subject><subject>Reactivity</subject><subject>Research subjects</subject><subject>Responsiveness</subject><subject>Resting</subject><subject>support vector machine learning</subject><subject>video games</subject><subject>Video Games - psychology</subject><issn>0965-2140</issn><issn>1360-0443</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10b1OwzAQAGALgWj5GXgBZIkFhrTnONjJWPEvVWIA5siJncpVahc7AXXjEXhGnoQrBQYkPNjy-dP5dEfIEYMRwzVWWo-YgExukSHjAhLIMr5NhlCI8yRlGQzIXoxzAJB5ke2SAZq8yNNsSOYPfTULvl9G6htqXWeCMx2dqYV1M6pt9EGbQCsVjabeUfzK1p317uPtPZhWdRgOJnaoMRI7DNCmd19EtbT2zhm8vNhudUB2GtVGc_h97pOn66vHi9tken9zdzGZJjU_5zLhBdeqqpjhWgNwJUWV16mSDGShIeW5qGXesEI2WudQc1GwFDeNmuE78H1yusm7DP65x9rKhY21aVvljO9jmUrGOYhMcKQnf-jc9wELXyuB_SpEnqE626g6-BiDacplsAsVViWDcj2AErtSfg0A7fF3xr5aGP0rfzqOYLwBr7Y1q_8zlZPLy03KTwitkbo</recordid><startdate>202302</startdate><enddate>202302</enddate><creator>Wang, Zi‐Liang</creator><creator>Potenza, Marc N.</creator><creator>Song, Kun‐Ru</creator><creator>Dong, Guang‐Heng</creator><creator>Fang, Xiao‐Yi</creator><creator>Zhang, Jin‐Tao</creator><general>Blackwell Publishing Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7TK</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4807-1196</orcidid></search><sort><creationdate>202302</creationdate><title>Subgroups of internet gaming disorder based on addiction‐related resting‐state functional connectivity</title><author>Wang, Zi‐Liang ; 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 & 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 & Medical Complete (Alumni)</collection><collection>Nursing & 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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source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
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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