Dataset Information

Distinct brain structure and behavior related to ADHD and conduct disorder traits.

ABSTRACT: Attention-Deficit/Hyperactivity Disorder (ADHD) and conduct disorder (CD) exemplify top-down dysregulation conditions that show a large comorbidity and shared genetics. At the same time, they entail two different types of symptomology involving mainly non-emotional or emotional dysregulation. Few studies have tried to separate the specific biology underlying these two dimensions. It has also been suggested that both types of conditions consist of extreme cases in the general population where the symptoms are widely distributed. Here we test whether brain structure is specifically associated to ADHD or CD symptoms in a general population of adolescents (n?=?1093) being part of the IMAGEN project. Both ADHD symptoms and CD symptoms were related to similar and overlapping MRI findings of a smaller structure in prefrontal and anterior cingulate cortex. However, our regions of interest (ROI) approach indicated that gray matter volume (GMV) and surface area (SA) in dorsolateral/dorsomedial prefrontal cortex and caudal anterior cingulate cortex were negatively associated to ADHD symptoms when controlling for CD symptoms while rostral anterior cingulate cortex GMV was negatively associated to CD symptoms when controlling for ADHD symptoms. The structural findings were mirrored in performance of neuropsychological tests dependent on prefrontal and anterior cingulate regions, showing that while performance on the Stop Signal test was specifically related to the ADHD trait, delayed discounting and working memory were related to both ADHD and CD traits. These results point towards a partially domain specific and dimensional capacity in different top-down regulatory systems associated with ADHD and CD symptoms.

SUBMITTER: Bayard F

PROVIDER: S-EPMC7577834 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Distinct brain structure and behavior related to ADHD and conduct disorder traits.

Bayard Frida F Nymberg Thunell Charlotte C Abé Christoph C Almeida Rita R Banaschewski Tobias T Barker Gareth G Bokde Arun L W ALW Bromberg Uli U Büchel Christian C Quinlan Erin Burke EB Desrivières Sylvane S Flor Herta H Frouin Vincent V Garavan Hugh H Gowland Penny P Heinz Andreas A Ittermann Bernd B Martinot Jean-Luc JL Martinot Marie-Laure Paillère MP Nees Frauke F Orfanos Dimitri Papadopoulos DP Paus Tomáš T Poustka Luise L Conrod Patricia P Stringaris Argyris A Struve Maren M Penttilä Jani J Kappel Viola V Grimmer Yvonne Y Fadai Tahmine T van Noort Betteke B Smolka Michael N MN Vetter Nora C NC Walter Henrik H Whelan Robert R Schumann Gunter G Petrovic Predrag P

Molecular psychiatry 20180814 11

Attention-Deficit/Hyperactivity Disorder (ADHD) and conduct disorder (CD) exemplify top-down dysregulation conditions that show a large comorbidity and shared genetics. At the same time, they entail two different types of symptomology involving mainly non-emotional or emotional dysregulation. Few studies have tried to separate the specific biology underlying these two dimensions. It has also been suggested that both types of conditions consist of extreme cases in the general population where the ...[more]

PMID: 30108313

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Project 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.

Dataset Information

Distinct brain structure and behavior related to ADHD and conduct disorder traits.

Publications

Distinct brain structure and behavior related to ADHD and conduct disorder traits.

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