AUTHOR=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. , Schachar R. , Georgiades S. , Nicolson R. , Anagnostou E. , Kushki A. 

TITLE=Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance

JOURNAL=Frontiers in Child and Adolescent Psychiatry

VOLUME=2

YEAR=2023

URL=https://www.frontiersin.org/journals/child-and-adolescent-psychiatry/articles/10.3389/frcha.2023.1171337

DOI=10.3389/frcha.2023.1171337

ISSN=2813-4540

ABSTRACT=<sec><title>Introduction</title><p>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.</p></sec><sec><title>Methods</title><p>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 [<italic>n</italic> = 262, median(IQR) age = 12.2(5.9), 22% female], and ADHD [<italic>n</italic> = 171, median(IQR) age = 11.1(4.0), 21% female] as well neurotypical children [<italic>n</italic> = 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.</p></sec><sec><title>Results</title><p>Our findings suggest two large clusters, which differed in measures of adaptive functioning (<italic>χ</italic><sup>2</sup> = 7.8, <italic>P</italic> = 0.004), inattention (<italic>χ</italic><sup>2</sup> = 11.169, <italic>P</italic> &lt; 0.001), hyperactivity (<italic>χ</italic><sup>2</sup> = 18.44, <italic>P</italic> &lt; 0.001), IQ (<italic>χ</italic><sup>2</sup> = 9.24, <italic>P</italic> = 0.002), age (<italic>χ</italic><sup>2</sup> = 70.87, <italic>P</italic> &lt; 0.001), and sex (<italic>χ</italic><sup>2</sup> = 105.6, <italic>P</italic> &lt; 0.001).</p></sec><sec><title>Discussion</title><p>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.</p></sec>