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

Zhang-James, Yanli, Helminen, Emily C., Liu, Jinru, The ENIGMA-ADHD Working Group, Franke, Barbara, Hoofman, Martine, Faraone, Stephen V. and Cercignani, Mara ORCID: https://orcid.org/0000-0002-4550-2456 2021. Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis. Translational Psychiatry 11 , 82. 10.1038/s41398-021-01201-4

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Abstract

Attention-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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information:	Mara Cercignani is a member of the NIGMA-ADHD Working Group.
Publisher:	Springer Nature
ISSN:	2158-3188
Date of First Compliant Deposit:	10 March 2021
Date of Acceptance:	5 January 2021
Last Modified:	06 May 2023 09:47
URI:	https://orca.cardiff.ac.uk/id/eprint/139598

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