Regional Callosal Morphology in Autism and Macrocephaly
Bibliographic record
Abstract
Previous investigations have reported decreased size of the corpus callosum (CC) in autism. However, little is known of the regional distribution of these callosal abnormalities. Additional uncertainty exists regarding the role of head size with respect to variations in callosal size in individuals with autism. This study investigated the size of the CC in 5 groups of high functioning individuals: (1) normocephalic autistic individuals; (2) autism with macrocephaly; (3) non-autistic normocephalic controls; (4) non-autistic participants with benign macrocephaly; and (5) a reading disordered (RD) group, comprised of non-autistic individuals with a deficit in reading. The CC was traced from midsaggital MRIs and the outlines partitioned into 99 equidistant width measures. Factor analysis of the 99 widths revealed 10 contiguous callosal regions. Individuals with macrocephaly (autistic and non-autistic) had larger total CC size. Regional analysis revealed a significantly larger CC midbody in macrocephaly, regardless of presence or absence of autism. Within normocephalic individuals, those with autism had a smaller CC genu and midbody than either non-autistic controls or RD individuals. These results underscore the importance of considering head size in studies of CC morphology in autism. These findings add to the literature implicating problems of interhemispheric connectivity being present in individuals with autism.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".