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This study aimed to identify neural networks predictive of problematic-gaming severity in youth using connectome-based predictive modelling (CPM), a machine-learning approach that employs whole-brain functional connectivity data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      From the Adolescent Brain Cognitive Development study at the two-year follow-up, 1,036 participants (\n                      <jats:italic>M<\/jats:italic>\n                      <jats:sub>age<\/jats:sub>\n                      = 12.0, 60.7% male) were studied. CPM with 10-fold cross-validation was applied to problematic-gaming scores and functional magnetic resonance imaging (fMRI) data collected during the performance of a reward-processing task. To determine generalizability, additional CPM analyses were performed using other task-based (e.g., those relevant to response inhibition, emotion regulation, and working memory) and resting-state fMRI data.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      CPM successfully predicted problematic-gaming scores (\n                      <jats:italic>r<\/jats:italic>\n                      = 0.12,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.002). Predictive networks involved several connections within and between canonical networks implicated in visual processing (visual area 2 and visual association networks), cognitive control and executive functioning (frontoparietal and medial frontal networks), and relevance and motor response (salience and sensorimotor networks). CPM predicted problematic-gaming scores across all analyzed brain states and found shared predictive canonical networks, indicating generalizability. Applying the final reward-processing model to other\u00a0task-based and resting-state fMRI data also successfully predicted problematic-gaming severity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The identified large-scale networks predictive of problematic-gaming severity in adolescents may serve as promising targets for personalized and novel interventions. Before using these results to guide clinical advances, future research should use external samples to evaluate replicability of the identified network.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1556\/2006.2025.00103","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T18:33:51Z","timestamp":1768934031000},"page":"305-319","update-policy":"https:\/\/doi.org\/10.1556\/policypage.crossmark.crossref.2","source":"Crossref","is-referenced-by-count":3,"title":["Connectome-based predictive modelling of problematic gaming in youth from the ABCD study"],"prefix":"10.1556","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7988-4704","authenticated-orcid":true,"given":"Jennifer J.","family":"Park","sequence":"first","affiliation":[{"name":"Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3130-4156","authenticated-orcid":true,"given":"Cheryl M.","family":"Lacadie","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6301-1167","authenticated-orcid":true,"given":"Dustin","family":"Scheinost","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8862-6715","authenticated-orcid":true,"given":"Li Yan","family":"McCurdy","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA"},{"name":"Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6323-1354","authenticated-orcid":true,"given":"Marc N.","family":"Potenza","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA"},{"name":"Child Study Center, Yale University School of Medicine, New Haven, CT, USA"},{"name":"Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA"},{"name":"Connecticut Council on Problem Gambling, Wethersfield, CT, USA"},{"name":"Connecticut Mental Health Center, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8730-0476","authenticated-orcid":true,"given":"Yihong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Columbia University School of Nursing, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"425","reference":[{"key":"B1","series-title":"Diagnostic and statistical manual of mental disorders","doi-asserted-by":"publisher","DOI":"10.1176\/appi.books.9780890425596","author":"American Psychiatric Association","year":"2013","unstructured":"American Psychiatric Association. 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