Brain Morphometry in Infants Later Diagnosed With Autism is Related to Later Language Skills
Bibliographic record
Abstract
Autism spectrum disorder (ASD) presents early in life with distinct social and language differences. This study explores the association between infant brain morphometry and language abilities using an infant-sibling design. Participants included infants who had an older sibling with autism (high likelihood, HL) who were later diagnosed with autism (HL-ASD; n = 31) and two non-autistic control groups: HL-Neg (HL infants not diagnosed with autism; n = 126) and LL-Neg (typically developing infants who did not have an older sibling with autism; n = 77). Using a whole-brain approach, we measured cortical thickness and surface area at 6 and 12 months and expressive and receptive language abilities at 24 months. Partial least squares correlation analyses were computed separately for each of the three groups. Results from the HL-ASD group indicated negative associations between surface area in the left inferior frontal gyrus and 24-month language abilities. Notably, regions outside the standard adult language network were also associated with language in the HL-ASD group. Results in the HL-ASD group highlight the distinct processing guiding development of surface area and cortical thickness; associations were mostly negative for surface area at 6 months but mostly positive for cortical thickness at the same time point. Results from this data-driven study align with the theory of interactive specialization-a theory highlighting the dynamic nature of the infant brain-and advocate for a whole-brain approach in investigating early brain-behavior neurodevelopment in ASD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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 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".