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Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning Analysis

View ORCID ProfileYanli Zhang-James, Emily C Helminen, Jinru Liu, The ENIGMA-ADHD Working Group, Barbara Franke, Martine Hoogman, Stephen V Faraone
doi: https://doi.org/10.1101/546671
Yanli Zhang-James
1Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, New York
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  • ORCID record for Yanli Zhang-James
Emily C Helminen
2Department of Psychology, Syracuse University, Syracuse, New York
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Jinru Liu
3University of Illinois at Urbana-Champaign
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Barbara Franke
4Department of Psychiatry, Radboud university medical center, Nijmegen, The Netherlands
5Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands
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Martine Hoogman
5Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands
6Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
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Stephen V Faraone
1Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, New York
7Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, New York
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  • For correspondence: sfaraone@childpsychresearch.org
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Abstract

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 in the pathophysiology of the disorder, it is not clear if adults with ADHD have similar neuroanatomical impairments as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural abnormalities 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 common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s pathophysiology from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.

Footnotes

  • ↵* The ENIGMA-ADHD working group contributing members: Sara Ambrosino, Anatoly Anikin, Philip Asherson, Tobias Banaschewski, Alexandr Baranov, Sarah Baumeister, Ramona Baur-Streubel, Mark A. Bellgrove, Joseph Biederman, Janita Bralten, Ivanei E. Bramati, Daniel Brandeis, Daniel Brandeis, Silvia Brem, Jan K Buitelaar, Francisco .X. Castellanos, Kaylita C Chantiluke, 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. Fallgatter, Thomas Frodl, Tinatin Gogberashvili, Jan Haavik, Catharina A. Hartman, Dirk J. Heslenfeld, Pieter J. Hoekstra, Sarah Hohmann, Marie F. Høvik, Neda Jahanshad, Terry L Jernigan, Dmitry Kapilushniy, Bernd Kardatzki, Georgii Karkashadze, Clare Kelly, Sabin Khadka, Gregor Kohls, Kerstin Konrad, Jonna Kuntsi, Klaus-Peter Lesch, Astri J Lundervold, Charles B Malpas, Paulo Mattos, Hazel McCarthy, Mitul A Mehta, Jeanette Mostert, Leyla Namazova-Baranova, Joel T Nigg, Stephanie E. Novotny, Eileen Oberwelland Weiss, Ruth L O’Gorman Tuura, Jaap Oosterlaan, Bob Oranje, Yannis Paloyelis, Paul Pauli, Kerstin J. Plessen, J. Antoni Ramos-Quiroga, Andreas Reif, Liesbeth Reneman, Katya Rubia, Anouk Schrantee, Lena Schwarz, Lizanne J.S. Schweren, Jochen Seitz, Philip Shaw, Tim J Silk, Tim J Silk, Norbert Skokauskas, Juan Carlos Soliva Vila, Anastasia Solovieva, Michael C. Stevens, Gustavo Sudre, Leanne Tamm, Paul M. Thompson, Fernanda Tovar-Moll, Theo GM van Erp, Alasdair Vance, Oscar Vilarroya, Yolanda Vives-Gilabert, Georg G von Polier, Susanne Walitza, Thomas Wolfers, Yuliya N Yoncheva, Mariam Zentis, Georg C. Ziegler, Kathrin C. Zierhut.

  • Revised analysis and results

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 11, 2020.
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Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning Analysis
Yanli Zhang-James, Emily C Helminen, Jinru Liu, The ENIGMA-ADHD Working Group, Barbara Franke, Martine Hoogman, Stephen V Faraone
bioRxiv 546671; doi: https://doi.org/10.1101/546671
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Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning Analysis
Yanli Zhang-James, Emily C Helminen, Jinru Liu, The ENIGMA-ADHD Working Group, Barbara Franke, Martine Hoogman, Stephen V Faraone
bioRxiv 546671; doi: https://doi.org/10.1101/546671

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