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

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

IntroductionAttention-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 a...

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Main Authors: Younes Sadat-Nejad, Marlee M. Vandewouw, R. Cardy, J. Lerch, M. J. Taylor, A. Iaboni, C. Hammill, B. Syed, J. A. Brian, E. Kelley, M. Ayub, J. Crosbie, R. Schachar, S. Georgiades, R. Nicolson, E. Anagnostou, A. Kushki
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-09-01
Series:Frontiers in Child and Adolescent Psychiatry
Subjects:
neurodevelopmental conditions
autism
ADHD
clustering
brain structure
data driven
Online Access:https://www.frontiersin.org/articles/10.3389/frcha.2023.1171337/full
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Summary:IntroductionAttention-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.MethodsIn 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.ResultsOur findings suggest two large clusters, which differed in measures of adaptive functioning (χ2 = 7.8, P = 0.004), inattention (χ2 = 11.169, P < 0.001), hyperactivity (χ2 = 18.44, P < 0.001), IQ (χ2 = 9.24, P = 0.002), age (χ2 = 70.87, P < 0.001), and sex (χ2 = 105.6, P < 0.001).DiscussionThese 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.
ISSN:2813-4540

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