Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance
Bibliographic record
Abstract
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 < 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). Discussion 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".