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Although dysfunctions in resting\u2010state functional connectivity have been reported in IGD, mapping relationships from abnormal connectivity patterns to behavioral measures have not been fully investigated. Connectome\u2010based predictive modeling (CPM)\u2014a recently developed machine\u2010learning approach\u2014has been used to examine potential neural mechanisms in addictions and other psychiatric disorders. To identify the resting\u2010state connections associated with IGD, we modified the CPM approach by replacing its core learning algorithm with a support vector machine. Resting\u2010state functional magnetic resonance imaging (fMRI) data were acquired in 72 individuals with IGD and 41 healthy comparison participants. The modified CPM was conducted with respect to classification and regression. A comparison of whole\u2010brain and network\u2010based analyses showed that the default\u2010mode network (DMN) is the most informative network in predicting IGD both in classification (individual identification accuracy\u00a0=\u00a078.76%) and regression (correspondence between predicted and actual psychometric scale score: <jats:italic>r<\/jats:italic>\u00a0=\u00a00.44, <jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.001). To facilitate the characterization of the aberrant resting\u2010state activity in the DMN, the identified networks have been mapped into a three\u2010subsystem division of the DMN. Results suggest that individual differences in DMN function at rest could advance our understanding of IGD and variability in disorder etiology and intervention outcomes.<\/jats:p>","DOI":"10.1111\/adb.12969","type":"journal-article","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T05:43:58Z","timestamp":1602567838000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Resting\u2010state connectome\u2010based support\u2010vector\u2010machine predictive modeling of internet gaming disorder"],"prefix":"10.1111","volume":"26","author":[{"given":"Kun\u2010Ru","family":"Song","sequence":"first","affiliation":[{"name":"State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University  Beijing China"},{"name":"IDG\/McGovern Institute for Brain Research Beijing Normal University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc N.","family":"Potenza","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Yale University School of Medicine  New Haven Connecticut USA"},{"name":"Child Study Center Yale University School of Medicine  New Haven Connecticut USA"},{"name":"Department of Neuroscience Yale University School of Medicine, Connecticut Mental Health Center, New Haven, Connecticut Council on Problem Gambling  Wethersfield Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao\u2010Yi","family":"Fang","sequence":"additional","affiliation":[{"name":"Institute of Developmental Psychology Beijing Normal University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gao\u2010Lang","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University  Beijing China"},{"name":"IDG\/McGovern Institute for Brain Research Beijing Normal University  Beijing 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