Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance - Sadat-Nejad, Younes; Vandewouw, Marlee M.; Cardy, R.; Lerch, J.; Taylor, M. J.; Iaboni, A.; Hammill, C.; Syed, B.; Brian, J. A.; Kelley, E.; Ayub, M.; Crosbie, J.; et al. (Frontiers in Child and Adolescent Psychiatry, 2023)
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. 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. Our findings suggest two large clusters, which differed in measures of adaptive functioning (2 =7.8 , P=0.004), inattention (2 =11.169 , P<0.001), hyperactivity (2 =18.44 , P<0.001), IQ (2 =9.24 , P= 0.002), age (2 =70.87, P<0.001), and sex (2 =105.6, P<0.001). These clusters did not align with existing diagnostic labels, suggesting that brain structure is more likely to be associated with differences in adaptive functioning, IQ, and ADHD features.