Characterizing replicability in the clustering structure of brain morphology in autism, attention-deficit/hyperactivity disorder, and obsessive compulsive disorder
Bibliographic record
Abstract
In neurodevelopmental research, within-diagnosis heterogeneity and across-diagnosis overlap necessitate a shift from case-control designs to data-driven clustering approaches. However, our understanding of the replicability of these clustering structures across independent datasets remains limited. Our objective was to examine the replicability of clustering structure in measures of brain morphology in neurodiverse children across two independent datasets, namely the Province of Ontario Neurodevelopmental Disorder (POND) Network and the Healthy Brain Network (HBN). POND and HBN data were collected across various institutions in Ontario, Canada, and New York, United States, respectively. Participants were 5-19 years old and had diagnoses of autism, attention deficit/hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), or were neurotypical. We used measures of cortical volume, surface area, cortical thickness, and subgroup volume from structural MRI data. Principal component analysis (PCA) and clustering were used to examine the replicability of clustering structures across the datasets. Correlations among principle components, measures of clusterability, and alignment between the four brain measures as well as male/female subsets were examined. Brain-behaviour associations were examined using univariate and multivariate approaches. The POND dataset included 747 participants with (autism n = 312, ADHD n = 220, OCD n = 70, neurotypical n = 145). The HBN dataset included 582 participants (autism n = 60, ADHD n = 445, OCD n = 19, neurotypical n = 58). Our results showed significant between-dataset correlations in 82.1% of the principal components derived from brain measures. A two-cluster structure was replicated across datasets, brain measures, and the female/male subsets, however the participant composition of clusters were only aligned between cortical volume and surface area, and cortical thickness and subcortical volume. Regional effect sizes for between-cluster differences were highly correlated across datasets (beta = 0.92+/-0.01, p < 0.0001; adjusted R-squared=0.93). Data-driven clusters did not align with diagnostic labels across datasets. Brain-behaviour associations were only replicated for male subsets and subcortical volume using multivariate analysis. We found evidence of replicability of the clustering structure across two independent datasets; however, caution must be exercised in integrating multiple measures in clustering and interpretation of brain-behaviour associations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".