Structural covariance networks in children with autism or ADHD

Abstract:

Abstract While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, they share some phenotypic features and have high comorbidity. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics. Graph theory was used to analyse properties of cortical thickness structural covariance networks across both conditions and relative to a neurotypical (NT; n=87) group using data from the ABIDE (autism; n=62) and ADHD-200 datasets (ADHD; n=69). This was analysed in a theoretical framework examining potential differences in long and short range connectivity. We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance compared to a neurotypical population. The two conditions also show divergence: less modular overlap between the two conditions than there is between each condition and the neurotypical group. Lastly, the ADHD group also showed reduced wiring costs compared to the autism groups. Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Both groups show a distance-covariance relation that more strongly favours short-range over long-range. Thus, on some network features the groups seem to converge, yet on others there is divergence.