Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy
IntroductionThe externalizing disorders of attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) are common in adolescence and are strong predictors of adult psychopathology. While treatable, substantial diagnostic overlap complicates interve...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1280326/full |
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| author | Nina de Lacy Nina de Lacy Michael J. Ramshaw Michael J. Ramshaw |
| author_facet | Nina de Lacy Nina de Lacy Michael J. Ramshaw Michael J. Ramshaw |
| author_sort | Nina de Lacy |
| collection | DOAJ |
| description | IntroductionThe externalizing disorders of attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) are common in adolescence and are strong predictors of adult psychopathology. While treatable, substantial diagnostic overlap complicates intervention planning. Understanding which factors predict the onset of each disorder and disambiguating their different predictors is of substantial translational interest.Materials and methodsWe analyzed 5,777 multimodal candidate predictors from children aged 9–10 years and their parents in the ABCD cohort to predict the future onset of ADHD, ODD, and CD at 2-year follow-up. We used deep learning optimized with an innovative AI algorithm to jointly optimize model training, perform automated feature selection, and construct individual-level predictions of illness onset and all prevailing cases at 11–12 years and examined relative predictive performance when candidate predictors were restricted to only neural metrics.ResultsMultimodal models achieved ~86–97% accuracy, 0.919–0.996 AUROC, and ~82–97% precision and recall in testing in held-out, unseen data. In neural-only models, predictive performance dropped substantially but nonetheless achieved accuracy and AUROC of ~80%. Parent aggressive and externalizing traits uniquely differentiated the onset of ODD, while structural MRI metrics in the limbic system were specific to CD. Psychosocial measures of sleep disorders, parent mental health and behavioral traits, and school performance proved valuable across all disorders. In neural-only models, structural and functional MRI metrics in subcortical regions and cortical-subcortical connectivity were emphasized. Overall, we identified a strong correlation between accuracy and final predictor importance.ConclusionDeep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. While externalizing disorders are frequently co-morbid in adolescents, certain predictors were specific to the onset of ODD or CD vs. ADHD. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders with the same design and participant cohort to enable direct comparisons, analyze >200 multimodal features, and include many types of neuroimaging metrics. Future study to test our observations in external validation data will help further test the generalizability of these findings. |
| first_indexed | 2024-03-09T01:57:31Z |
| format | Article |
| id | doaj.art-305b413c8e59480c86e839ebba3279a4 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-03-09T01:57:31Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-305b413c8e59480c86e839ebba3279a42023-12-08T12:30:44ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402023-12-011410.3389/fpsyt.2023.12803261280326Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracyNina de Lacy0Nina de Lacy1Michael J. Ramshaw2Michael J. Ramshaw3Huntsman Mental Health Institute, Salt Lake City, UT, United StatesDepartment of Psychiatry, University of Utah, Salt Lake City, UT, United StatesHuntsman Mental Health Institute, Salt Lake City, UT, United StatesDepartment of Psychiatry, University of Utah, Salt Lake City, UT, United StatesIntroductionThe externalizing disorders of attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) are common in adolescence and are strong predictors of adult psychopathology. While treatable, substantial diagnostic overlap complicates intervention planning. Understanding which factors predict the onset of each disorder and disambiguating their different predictors is of substantial translational interest.Materials and methodsWe analyzed 5,777 multimodal candidate predictors from children aged 9–10 years and their parents in the ABCD cohort to predict the future onset of ADHD, ODD, and CD at 2-year follow-up. We used deep learning optimized with an innovative AI algorithm to jointly optimize model training, perform automated feature selection, and construct individual-level predictions of illness onset and all prevailing cases at 11–12 years and examined relative predictive performance when candidate predictors were restricted to only neural metrics.ResultsMultimodal models achieved ~86–97% accuracy, 0.919–0.996 AUROC, and ~82–97% precision and recall in testing in held-out, unseen data. In neural-only models, predictive performance dropped substantially but nonetheless achieved accuracy and AUROC of ~80%. Parent aggressive and externalizing traits uniquely differentiated the onset of ODD, while structural MRI metrics in the limbic system were specific to CD. Psychosocial measures of sleep disorders, parent mental health and behavioral traits, and school performance proved valuable across all disorders. In neural-only models, structural and functional MRI metrics in subcortical regions and cortical-subcortical connectivity were emphasized. Overall, we identified a strong correlation between accuracy and final predictor importance.ConclusionDeep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. While externalizing disorders are frequently co-morbid in adolescents, certain predictors were specific to the onset of ODD or CD vs. ADHD. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders with the same design and participant cohort to enable direct comparisons, analyze >200 multimodal features, and include many types of neuroimaging metrics. Future study to test our observations in external validation data will help further test the generalizability of these findings.https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1280326/fullexternalizing disordersadolescencepredictonsetdeep learningartificial intelligence |
| spellingShingle | Nina de Lacy Nina de Lacy Michael J. Ramshaw Michael J. Ramshaw Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy Frontiers in Psychiatry externalizing disorders adolescence predict onset deep learning artificial intelligence |
| title | Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy |
| title_full | Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy |
| title_fullStr | Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy |
| title_full_unstemmed | Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy |
| title_short | Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy |
| title_sort | selectively predicting the onset of adhd oppositional defiant disorder and conduct disorder in early adolescence with high accuracy |
| topic | externalizing disorders adolescence predict onset deep learning artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1280326/full |
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