The Development of Lateralized Brain Oscillations in Infants: Lessons From Autism
Bibliographic record
Abstract
The lateralization of brain activity is important for language processing and attention, and atypical patterns of lateralization have been linked to many neurodevelopmental disorders, including autism spectrum disorder (ASD). However, the developmental timing of these patterns and their relationship to emerging ASD characteristics are unclear. In this study, we used data from EEG-IP (International Infant EEG Data Integration Platform), a longitudinal cohort bringing together infants at elevated likelihood for ASD and age-equivalent controls across two sites. We examined brain lateralization in electroencephalography (EEG) power during the first year of life. Overall, we identified differences in gamma band lateralization in infants later diagnosed with ASD at 12 months but not at 6 months. Additionally, we observed a shift from high left gamma band asymmetry at 6 months toward more symmetry by 12 months in our control group, highlighting between-group differences in developmental trajectories in brain oscillatory activity. We found key differences in the lateralization across groups in brain regions within the auditory network, which is thought to be important for language learning. Overall, examining the developmental trajectories of lateralization is a crucial step toward creating more accurate models of brain development and better understanding the underlying mechanisms of neurodevelopmental disorders.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".