Understanding the nature of face processing in early autism: A prospective study
Bibliographic record
Abstract
Abstract Dimensional approaches to psychopathology interrogate the core neurocognitive domains interacting at the individual level to shape diagnostic symptoms. Embedding this approach in prospective longitudinal studies could transform our understanding of the mechanisms underlying neurodevelopmental disorders. Such designs require us to move beyond traditional group comparisons and determine which domain-specific atypicalities apply at the level of the individual, and whether they vary across distinct phenotypic subgroups. As a proof of principle, this study examines how the domain of face processing contributes to a clinical diagnosis of Autism Spectrum Disorder (ASD). We used an event-related potentials (ERPs) task in a cohort of 8-month-old infants with (n=148) and without (n=68) an older sibling with ASD, and combined traditional case-control comparisons with machine-learning techniques like supervised classification for prediction of clinical outcome at 36 months and Bayesian hierarchical clustering for stratification into subgroups. Our findings converge to indicate that a broad profile of alterations in the time-course of neural processing of faces is an early predictor of later ASD diagnosis. Furthermore, we identified two brain response-defined subgroups in ASD that showed distinct alterations in different aspects of face processing compared to siblings without ASD diagnosis, suggesting that individual differences between infants contribute to the diffuse pattern of alterations predictive of ASD in the first year of life. This study shows that moving from group-level comparisons to pattern recognition and stratification can help to understand and reduce heterogeneity in clinical cohorts, and improve our understanding of the mechanisms that lead to later neurodevelopmental outcomes. General Scientific Summary This study suggests that neural processing of faces is diffusely atypical in Autism Spectrum Disorder, and that it represents a strong candidate predictor of outcome at an individual level in the first year of life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".