{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T03:01:17Z","timestamp":1788836477496,"version":"build-2803163510"},"reference-count":61,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["031L0200A"],"award-info":[{"award-number":["031L0200A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCPP Advances"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Prediction of mental disorders based on neuroimaging is an emerging area of research with promising first results in adults. However, research on the unique demographic of children is underrepresented and it is doubtful whether findings obtained on adults can be transferred to children.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using data from 6916 children aged 9\u201310 in the multicenter Adolescent Brain Cognitive Development study, we extracted 136 regional volume and thickness measures from structural magnetic resonance images to rigorously evaluate the capabilities of machine learning to predict 10 different psychiatric disorders: major depressive disorder, bipolar disorder (BD), psychotic symptoms, attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, post\u2010traumatic stress disorder, obsessive\u2010compulsive disorder, generalized anxiety disorder, and social anxiety disorder. For each disorder, we performed cross\u2010validation and assessed whether models discovered a true pattern in the data via permutation testing.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Two of 10 disorders can be detected with statistical significance when using advanced models that (i) allow for non\u2010linear relationships between neuroanatomy and disorder, (ii) model interdependencies between disorders, and (iii) avoid confounding due to sociodemographic factors: ADHD (AUROC\u00a0=\u00a00.567, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.002) and BD (AUROC\u00a0=\u00a00.551, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.002). In contrast, traditional models perform consistently worse and predict only ADHD with statistical significance (AUROC\u00a0=\u00a00.529, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.002).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>While the modest absolute classification performance does not warrant application in the clinic, our results provide empirical evidence that embracing and explicitly accounting for the complexities of mental disorders via advanced machine learning models can discover patterns that would remain hidden with traditional models.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcv2.12184","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T06:23:34Z","timestamp":1687933414000},"source":"Crossref","is-referenced-by-count":14,"title":["Can we diagnose mental disorders in children? A large\u2010scale assessment of machine learning on structural neuroimaging of 6916 children in the adolescent brain cognitive development study"],"prefix":"10.1002","volume":"3","author":[{"given":"Richard","family":"Gaus","sequence":"first","affiliation":[{"name":"The Lab for Artificial Intelligence in Medical Imaging (AI\u2010Med) Department of Child and Adolescent Psychiatry Ludwig\u2010Maximilians\u2010Universit\u00e4t  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1607-7550","authenticated-orcid":false,"given":"Sebastian","family":"P\u00f6lsterl","sequence":"additional","affiliation":[{"name":"The Lab for Artificial Intelligence in Medical Imaging (AI\u2010Med) Department of Child and Adolescent Psychiatry Ludwig\u2010Maximilians\u2010Universit\u00e4t  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ellen","family":"Greimel","sequence":"additional","affiliation":[{"name":"Department 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