The development of lateralized brain oscillations in infancy: what we can learn from autism
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impaired social and communication skills.Recent studies suggest that these impairments could be caused by atypical lateralization in the brain, where one hemisphere is more active than the other during cognitive processing.A growing body of research indicates that hemisphere specificity is reduced in adults diagnosed with ASD, but more research is needed to understand whether these patterns emerge early in infancy.This study used data from the International Infant EEG Data Integration Platform (EEG-IP); a multi-site cohort study of infants at risk for ASD and age-equivalent controls (London: 7, 14 months; Seattle: 6,12,18 months) to explore developmental trajectories of lateralization.We extracted brain lateralization indices from cortical sources reconstructed from EEG recordings collected while participants watched a video.The study included 92 infants at familial risk for autism (at least one full sibling, with an existing ASD diagnosis) and 91 controls.We found that at twelve months, infants at risk for autism had stronger left-hemisphere lateralization patterns in gamma activity compared to controls.These differences were further accentuated in the superior temporal gyrus (p's < 0.05).The superior temporal gyrus is important for phoneme discrimination and auditory attention and, in some cases, can be considered an important precursor for language learning.Lateralization is a key part of development, and our study can shed light on the developmental differences that can impact various cognitive processes in 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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".