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 (f...
| Publicado en: | Addiction Vol. 118; no. 2; pp. 327 - 340 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161103575&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161103575 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09652140 AIO jtl: Addiction issn: 09652140 maglogo: Y pubinfo: dt: Feb2023 vid: 118 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 161103575 159255177 161103575 161103575 10.1111/add.16047 161103575 ppf: 327 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Subgroups of internet gaming disorder based on addiction‐related resting‐state functional connectivity. aug: au: Wang, Zi‐Liang Potenza, Marc N. Song, Kun‐Ru Dong, Guang‐Heng Fang, Xiao‐Yi Zhang, Jin‐Tao affil: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China sug: subj: Internet Addiction Psychosocial Factors Functional Connectivity Methods Magnetic Resonance Imaging Methods Video Games Psychosocial Factors Human Male Female Support Vector Machine Sensitivity and Specificity Secondary Analysis Craving Emotional Regulation Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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