EEG neurosubtyping of infants predicts language trajectories
Bibliographic record
Abstract
Autism spectrum disorder (ASD), like many other neurodevelopmental conditions, arises from complex interactions between genetics and environmental factors. In the last 50 years, significant efforts have been made to identify biomarkers and risk factors that can improve our understanding of autism etiology. However, high heterogeneity in causes, symptomology, and developmental trajectories has made this task challenging. One strategy to combat heterogeneity is to characterize individuals based on their brain phenotypes, commonly referred to as "neuro-subtyping". In this study, we analyzed electroencephalography (EEG) recordings from 144 infants aged 6-7 months and employed subtyping methods (latent profile analysis and hierarchical clustering) to identify subgroups. Our analyses revealed three distinct subgroups based on various language-associated EEG measures. We found that group membership was predictive of expressive and receptive language trajectories based on the Mullen Scales of Early Learning, with infants displaying high connectivity in language regions and lefthemisphere lateralization achieving the highest scores, while infants with overactivation of connectivity in the auditory network achieved lower scores. Notably, EEGderived subgroups did not predict a later ASD diagnosis, suggesting a lack of evidence for an ASD-specific phenotype during the first year of life. Results from this project contribute to a large body of research that supports using stratification approaches to decode heterogeneity in autism and its role in predicting behavioural outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".