Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry
Bibliographic record
Abstract
Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental conditions that share genetic etiology and frequently co-occur. Given this comorbidity and well-established clinical heterogeneity, identifying individuals with similar brain signatures may be valuable for predicting clinical outcomes and tailoring treatment strategies. Cortical myelination is a prominent developmental process, and its disruption is a candidate mechanism for both disorders. Yet, no studies have attempted to identify subtypes using T1w/T2w-ratio, a magnetic resonance imaging (MRI) based proxy for intracortical myelin. Moreover, cortical variability arises from numerous biological pathways, and multimodal approaches can integrate cortical metrics into a single network. We analyzed data from 310 individuals aged 2.6-23.6 years, obtained from the Province of Ontario Neurodevelopmental (POND) Network consisting of individuals diagnosed with ASD (n = 136), ADHD (n = 100), and typically developing (TD) individuals (n = 74). We first tested for differences in T1w/T2w-ratio between diagnostic categories and controls. We then performed unimodal (T1w/T2w-ratio) and multimodal (T1w/T2w-ratio, cortical thickness, and surface area) spectral clustering to identify diagnostic-blind subgroups. Linear models revealed no statistically significant case-control differences in T1w/T2w-ratio. Unimodal clustering mostly isolated single individual- or minority clusters, driven by image quality and intensity outliers. Multimodal clustering suggested three distinct subgroups, which transcended diagnostic boundaries, showing separate cortical patterns but similar clinical and cognitive profiles. T1w/T2w-ratio features were the most relevant for demarcation, followed by surface area. While our analysis revealed no significant case-control differences, multimodal clustering incorporating the T1w/T2w-ratio among cortical features holds promise for identifying biologically similar subsets of individuals with neurodevelopmental conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".