Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance.
Sadat-Nejad Y., Vandewouw MM., Cardy R., Lerch J., Taylor MJ., Iaboni A., Hammill C., Syed B., Brian JA., Kelley E., Ayub M., Crosbie J., Schachar R., Georgiades S., Nicolson R., Anagnostou E., Kushki A.
INTRODUCTION: Attention-deficit/hyperactivity disorder (ADHD) and autism are multi-faceted neurodevelopmental conditions with limited biological markers. The clinical diagnoses of autism and ADHD are based on behavioural assessments and may not predict long-term outcomes or response to interventions and supports. To address this gap, data-driven methods can be used to discover groups of individuals with shared biological patterns. METHODS: In this study, we investigated measures derived from cortical/subcortical volume, surface area, cortical thickness, and structural covariance investigated of 565 participants with diagnoses of autism [n = 262, median(IQR) age = 12.2(5.9), 22% female], and ADHD [n = 171, median(IQR) age = 11.1(4.0), 21% female] as well neurotypical children [n = 132, median(IQR) age = 12.1(6.7), 43% female]. We integrated cortical thickness, surface area, and cortical/subcortical volume, with a measure of single-participant structural covariance using a graph neural network approach. RESULTS: Our findings suggest two large clusters, which differed in measures of adaptive functioning (χ 2 = 7.8, P = 0.004), inattention (χ 2 = 11.169, P