Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness,...

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 (χ ² = 7.8, P = 0.004), inattention (χ ² = 11.169, P < 0.001), hyperactivity (χ ² = 18.44, P < 0.001), IQ (χ ² = 9.24, P = 0.002), age (χ ² = 70.87, P < 0.001), and sex (χ 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.