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Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning Analysis
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Translational Psychiatry
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2019-02-11
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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.