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

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_EEB83B552F2B
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis.
Journal
Translational psychiatry
Author(s)
Zhang-James Y., Helminen E.C., Liu J., Franke B., Hoogman M., Faraone S.V.
Working group(s)
ENIGMA-ADHD Working Group
Contributor(s)
Busatto G.F., Calvo A., Cercignani M., Chaim-Avancini T.M., Gabel M.C., Harrison N.A., Lazaro L., Lera-Miguel S., Louza M.R., Nicolau R., Rosa PGP, Schulte-Rutte M., Zanetti M.V., Ambrosino S., Asherson P., Banaschewski T., Baranov A., Baumeister S., Baur-Streubel R., Bellgrove M.A., Biederman J., Bralten J., Bramati I.E., Brandeis D., Brem S., Buitelaar J.K., Castellanos F.X., Chantiluke K.C., Christakou A., Coghill D., Conzelmann A., Cubillo A.I., Dale A.M., de Zeeuw P., Doyle A.E., Durston S., Earl E.A., Epstein J.N., Ethofer T., Fair D.A., Fallgatter A.J., Frodl T., Gogberashvili T., Haavik J., Hartman C.A., Heslenfeld D.J., Hoekstra P.J., Hohmann S., Høvik M.F., Jahanshad N., Jernigan T.L., Kardatzki B., Karkashadze G., Kelly C., Kohls G., Konrad K., Kuntsi J., Lesch K.P., Lundervold A.J., Malpas C.B., Mattos P., McCarthy H., Mehta M.A., Namazova-Baranova L., Nigg J.T., Novotny S.E., O'Gorman Tuura R.L., Weiss E.O., Oosterlaan J., Oranje B., Paloyelis Y., Pauli P., Plessen K.J., Ramos-Quiroga J.A., Reif A., Reneman L., Rubia K., Schrantee A., Schwarz L., Schweren LJS, Seitz J., Shaw P., Silk T.J., Skokauskas N., Vila JCS, Stevens M.C., Sudre G., Tamm L., Thompson P.M., Tovar-Moll F., van Erp TGM, Vance A., Vilarroya O., Vives-Gilabert Y., von Polier G.G., Walitza S., Yoncheva Y.N., Ziegler G.C.
ISSN
2158-3188 (Electronic)
ISSN-L
2158-3188
Publication state
Published
Issued date
01/02/2021
Peer-reviewed
Oui
Volume
11
Number
1
Pages
82
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: epublish
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.
Keywords
Adolescent, Adult, Attention Deficit Disorder with Hyperactivity/diagnostic imaging, Brain/diagnostic imaging, Child, Humans, Machine Learning, Magnetic Resonance Imaging, Young Adult
Pubmed
Web of science
Open Access
Yes
Create date
20/07/2021 8:30
Last modification date
12/01/2022 8:14
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