Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis

• DOI: 10.1038/s41398-021-01201-4
• Publication Date: 2021-02-01T08:04:32Z
• Authors: Yanli Zhang-James; Emily C. Helminen; Jinru Liu; ; Geraldo F. Busatto; Anna Calvo; Mara Cercignani; Tiffany M. Chaim-...; Matt C. Gabel; Neil A. Harrison; Luisa Lazaro; Sara Lera-Miguel; Mario R. Louza; Rosa Nicolau; Pedro G. P. Rosa; Martin Schulte-Rutte; Marcus V. Zanetti; Sara Ambrosino; Philip Asherson; Tobias Banaschewski; Alexandr Baranov; Sarah Baumeister; Ramona Baur-Streubel; Mark A. Bellgrove; Joseph Biederman; Janita Bralten; Ivanei E. Bramati; Daniel Brandeis; Silvia Brem; Jan K. Buitelaar; Francisco. X. Cas...; Kaylita C. Chanti...; Anastasia Christakou; David Coghill; Annette Conzelmann; Ana I. Cubillo; Anders M. Dale; Patrick de Zeeuw; Alysa E. Doyle; Sarah Durston; Eric A. Earl; Jeffrey N. Epstein; Thomas Ethofer; Damien A. Fair; Andreas J. Fallga...; Thomas Frodl; Tinatin Gogberash...; Jan Haavik; Catharina A. Hartman; Dirk J. Heslenfeld; Pieter J. Hoekstra; Sarah Hohmann; Marie F. Høvik; Neda Jahanshad; Terry L. Jernigan; Bernd Kardatzki; Georgii Karkashadze; Clare Kelly; Gregor Kohls; Kerstin Konrad; Jonna Kuntsi; Klaus-Peter Lesch; Astri J. Lundervold; Charles B. Malpas; Paulo Mattos; Hazel McCarthy; Mitul A. Mehta; Leyla Namazova-Ba...; Joel T. Nigg; Stephanie E. Novotny; Ruth L. O’Gorman ...; Eileen Oberwellan...; Jaap Oosterlaan; Bob Oranje; Yannis Paloyelis; Paul Pauli; Kerstin J. Plessen; J. Antoni Ramos-Q...; Andreas Reif; Liesbeth Reneman; Katya Rubia; Anouk Schrantee; Lena Schwarz; Lizanne J. S. Sch...; Jochen Seitz; Philip Shaw; Tim J. Silk; Norbert Skokauskas; Juan Carlos Soliv...; Michael C. Stevens; Gustavo Sudre; Leanne Tamm; Paul M. Thompson; Fernanda Tovar-Moll; Theo G. M. van Erp; Alasdair Vance; Oscar Vilarroya; Yolanda Vives-Gil...; Georg G. von Polier; Susanne Walitza; Yuliya N. Yoncheva; Georg C. Ziegler; Barbara Franke; Martine Hoogman; Stephen V. Faraone
• Keywords: Biological Psychology; 150; /dk/atira/pure/subjectarea/asjc/2800/2804; Clinical sciences; Machine Learning; /dk/atira/pure/subjectarea/asjc/2800/2803; Psychology; 2.1 Biological and endogenous factors; Aetiology; Child; info:eu-repo/classification/ddc/610; Pediatric; Brain; /dk/atira/pure/subjectarea/asjc/2700/2738; Psychiatry - Radboud University Medical Center; Magnetic Resonance Imaging; 3. Good health; Psychiatry and Mental health; Mental Health; Public Health and Health Services; Mental health; Adolescent; Adult; Attention Deficit Disorder with Hyperactivity/diagnostic imaging; Brain/diagnostic imaging; Child; Humans; Machine Learning; Magnetic Resonance Imaging; Young Adult; social and economic factors; RC321-571; Adult; Adolescent; Clinical Sciences; Radboud University Medical Center; Neurosciences. Biological psychiatry. Neuropsychiatry; Predictive markers; Article; Cellular and Molecular Neuroscience; Young Adult; name=Psychiatry and Mental health; 2.3 Psychological; Behavioral and Social Science; Machine learning; Journal Article; Humans; name=Biological Psychiatry; Biological Psychiatry; Radboudumc 7: Neurodevelopmental disorders DCMN: Donders Center for Medical Neuroscience; Neurosciences; ENIGMA-ADHD Working Group; Attention Deficit Hyperactivity Disorder (ADHD); Brain Disorders; Attention deficit disorder with hyperactivity; Attention Deficit Disorder with Hyperactivity; Biological psychology; Human Genetics - Radboud University Medical Center; Psychiatric disorders; name=Cellular and Molecular Neuroscience

AbstractAttention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.