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dc.contributor.author
Zhang-James, Yanli
dc.contributor.author
Helminen, Emily C.
dc.contributor.author
Liu, Jinru
dc.contributor.author
Franke, Barbara
dc.contributor.author
Hoogman, Martine
dc.contributor.author
Faraone, Stephen V.
dc.contributor.author
ENIGMA-ADHD Working Group
dc.contributor.author
Brandeis, Daniel
dc.contributor.author
Brem, Silvia
dc.contributor.author
et al.
dc.date.accessioned
2021-02-15T13:27:30Z
dc.date.available
2021-02-08T16:45:16Z
dc.date.available
2021-02-15T13:27:30Z
dc.date.issued
2021
dc.identifier.issn
2158-3188
dc.identifier.other
10.1038/s41398-021-01201-4
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/468405
dc.identifier.doi
10.3929/ethz-b-000468405
dc.description.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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Publishing Group
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-02-01
ethz.journal.title
Translational Psychiatry
ethz.journal.volume
11
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
Transl Psychiatr
ethz.pages.start
82
en_US
ethz.size
9 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-02-08T16:45:32Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-02-15T13:27:45Z
ethz.rosetta.lastUpdated
2022-03-29T05:13:39Z
ethz.rosetta.versionExported
true
ethz.COinS
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