Journal
TRANSLATIONAL PSYCHIATRY
Volume 11, Issue 1, Pages -Publisher
SPRINGERNATURE
DOI: 10.1038/s41398-021-01201-4
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Funding
- European Union's Seventh Framework Programme for research, technological development and demonstration [602805]
- European Union [667302, 728018]
- NIMH [5R01MH101519, U01 MH109536-01]
- Netherlands Organization for Scientific Research (NWO) [016-130-669, 91619115]
- NIH Big Data to Knowledge (BD2K) award [U54 EB020403]
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Using deep learning neural network classification models, this study found neuroanatomical differences in children and adult ADHD patients, with better prediction performance and effect sizes in the child sample. The results support the continuity of ADHD's brain differences from childhood to adulthood.
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.
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