Cortical Anatomy in Autism Spectrum Disorder: An In Vivo MRI Study on the Effect of Age
Bibliographic record
Abstract
There is increasing evidence that children with autism spectrum disorder (ASD) have age-related differences from controls in cortical volume (CV). It is less clear, however, if these persist in adulthood and whether these reflect alterations in cortical thickness (CT) or cortical surface area (SA). Hence, we used magnetic resonance imaging to investigate the relationship between age and CV, CT, and SA in 127 males aged 10 through 60 years (76 with ASD and 51 healthy controls). "Regional" analyses (using cortical parcellation) identified significant age-by-group interactions in both CV and CT (but not SA) in the temporal lobes and within these the fusiform and middle temporal gyri. Spatially nonbiased "vertex-based" analysis replicated these results and identified additional "age-by-group" interactions for CT within superior temporal, inferior and medial frontal, and inferior parietal cortices. Here, CV and CT were 1) significantly negatively correlated with age in controls, but not in ASD, and 2) smaller in ASD than controls in childhood but vice versa in adulthood. Our findings suggest that CV dysmaturation in ASD extends beyond childhood, affects brain regions crucial to social cognition and language, and is driven by CT dysmaturation. This may reflect primary abnormalities in cortical plasticity and/or be secondary to disturbed interactions between individuals with ASD and their environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".