Association between spectral EEG power and autism risk and diagnosis: Utilizing large scale data-platforms to advance our understanding of the early development of ASD
Bibliographic record
Abstract
Background: Autism spectrum disorder (ASD) has its origins in the atypical development of brain networks.Infants who are at high familial risk for ASD and are later diagnosed with the condition have early brain overgrowth, altered development of white matter pathways, and atypical connectivity.Electroencephalography (EEG) oscillatory power is a measure of cortical activity and has also been associated with familial risk, and recently reported to be associated with ASD outcomes.However, infant-sibling studies are often constrained by relatively small sample sizes, and the field would benefit from a large data-platform of existing infant-sibling datasets, similar to other data-platforms that have been established in the broader autism research field.Methods: We established the EEG-Integrated Platform (EEG-IP), a large multi-site dataset with 432 participants, including 222 at high-risk (HR) for ASD and 193 at low-risk (LR) for ASD, from whom repeated measurements of resting EEG were collected between the ages of 3-36 months, along with comprehensive diagnostic assessments in toddlerhood.A latent growth curve model was applied to test whether familial risk status predicts developmental trajectories of spectral power development across the first 3 years of life, and then whether these trajectories predict ASD outcome.Results: Independent of ASD risk and outcome, change in spectral EEG power in all frequency
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".