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We employed a whole\u2010brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I\/II diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Five support vector machine (SVM) models were used to detect differences in GMVs between inpatient adolescents aged 13\u201317 with BD\u2010I\/II (\n                      <jats:italic>n<\/jats:italic>\n                      = 34), other specified BD (OSB) (\n                      <jats:italic>n<\/jats:italic>\n                      = 106), other non\u2010bipolar psychopathology (OP) (\n                      <jats:italic>n<\/jats:italic>\n                      = 52), and healthy controls (HC) (\n                      <jats:italic>n<\/jats:italic>\n                      = 27). We examined the most discriminative GMVs and tested their associations with clinical symptoms.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Whole\u2010brain classifiers in the model BD\u2010I\/II versus OSB achieved total accuracy of 79%, (AUC = 0.70,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.002); BD versus OP 66%, (AUC = 0.61,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.014); BD versus HC 66%, (AUC = 0.67,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.011); OSB versus HC 77%, (AUC = 0.61,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.01); OP versus HC 68%, (AUC = 0.70,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.001). The most discriminative GMVs that contributed to the classification were in areas associated with movement, sensory processing, and cognitive control. Correlations between these GMVs and self\u2010reported mania, negative affect, or anxiety were observed in all inpatient groups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well\u2010characterized BD\u2010I\/II from other forms of psychopathology, including other specified BD, in a pediatric population. These results may contribute to enhancing diagnostic accuracy and guiding earlier, more targeted interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.70589","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T11:08:17Z","timestamp":1749726497000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9544-8198","authenticated-orcid":false,"given":"Renata","family":"Rozovsky","sequence":"first","affiliation":[{"name":"Department of Psychiatry University of Pittsburgh  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Wolfe","sequence":"additional","affiliation":[{"name":"Western 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