The subcortical correlates of autistic traits in school-age children: a population-based neuroimaging study
Bibliographic record
Abstract
BACKGROUND: There is emerging evidence that the neuroanatomy of autism forms a spectrum which extends into the general population. However, whilst several studies have identified cortical morphology correlates of autistic traits, it is not established whether morphological differences are present in the subcortical structures of the brain. Additionally, it is not clear to what extent previously reported structural associations may be confounded by co-occurring psychopathology. To address these questions, we utilised neuroimaging data from the Adolescent Brain Cognitive Development Study to assess whether a measure of autistic traits was associated with differences in child subcortical morphology, and if any observed differences persisted after adjustment for child internalising and externalising symptoms. METHODS: Our analyses included data from 7005 children aged 9-10 years (female: 47.19%) participating in the Adolescent Brain Cognitive Development Study. Autistic traits were assessed using scores from the Social Responsiveness Scale (SRS). Volumes of subcortical regions of interest were derived from structural magnetic resonance imaging data. RESULTS: Overall, we did not find strong evidence for an association of autistic traits with differences in subcortical morphology in this sample of school-aged children. Whilst lower absolute volumes of the nucleus accumbens and putamen were associated with higher scores of autistic traits, these differences did not persist once a global measure of brain size was accounted for. LIMITATIONS: It is important to note that autistic traits were assessed using the SRS, of which higher scores are associated with general behavioural problems, and therefore may not be wholly indicative of autism-specific symptoms. In addition, individuals with a moderate or severe autism diagnosis were excluded from the ABCD study, and thus, the average level of autistic traits will be lower than in the general population which may bias findings towards the null. CONCLUSIONS: These findings from our well-powered study suggest that other metrics of brain morphology, such as cortical morphology or shape-based phenotypes, may be stronger candidates to prioritise when attempting to identify robust neuromarkers of autistic traits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".