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Machine Learning Classification of Attention-Deficit/Hyperactivity Disorder Using Structural MRI Data

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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Abstract

Background Clinical symptoms-based ADHD diagnosis is considered “subjective”. Machine learning (ML) classifiers have been explored to develop objective diagnosis of ADHD using magnetic resonance imaging (MRI) biomarkers.

Methods We reviewed previous literature and developed ensemble classifiers using the ENIGMA-ADHD dataset, with the implementation of data balancing to control for age, sex, diagnostic groups, and sample sites and a held-out test set for independent evaluation.

Results Our review showed that classification accuracies reported previously using cross-validation (CV) samples were inflated and did not generalize well to independent test samples. Our results showed a significant discrimination between ADHD and control samples for both adult and children, but the accuracies were modest (the area under the receiver operating characteristic curve (AUC): 66% and 67% respectively). We found that child samples were informative for predicting adult ADHD, and vice versa. The most important brain MRI structures for prediction were intracranial volume (ICV), followed by surface area and some subcortical volumes. The cortical thickness measurements were the least useful.

Conclusions Although previous ML classification studies reported overly optimistic accuracies and suffered methodological limitations, our results suggest that clinically useful classification of ADHD may be possible with larger samples. In contrast to prior reports of ENIGMA-ADHD studies, our work finds ADHD-related sMRI differences in adults and shows that the brain differences between cases and controls seen in youth can be useful in discriminating adults with and without ADHD. This provides additional evidence for the continuity of ADHD’s pathophysiology from childhood to adulthood.

Footnotes

  • Potential conflicts of Interest: Yanli Zhang-James, Emily C Helminen, Jinru Liu and Martine Hoogman declare no conflict of interest. Barbara Franke has received educational speaking fees from Shire and Medice. Dr. Stephen V Faraone received income, travel expenses and/or research support from and/or has been on an Advisory Board for Pfizer, Ironshore, Shire, Akili Interactive Labs, CogCubed, Alcobra, VAYA Pharma, Neurovance, Impax, NeuroLifeSciences and research support from the National Institutes of Health (NIH). With his institution, he has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of ADHD. In previous years, he received consulting fees or was on Advisory Boards or participated in continuing medical education programs sponsored by: Shire, Alcobra, Otsuka, McNeil, Janssen, Novartis, Pfizer and Eli Lilly. Dr. Faraone receives royalties from books published by Guilford Press: Straight Talk about Your Child’s Mental Health, Oxford University Press: Schizophrenia: The Facts and Elsevier, ADHD: Non-Pharmacologic Treatments.

Copyright 
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 February 21, 2019.
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Machine Learning Classification of Attention-Deficit/Hyperactivity Disorder Using Structural MRI Data
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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Machine Learning Classification of Attention-Deficit/Hyperactivity Disorder Using Structural MRI Data
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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