Infants at high and low likelihood for autism show different EEG developmental trajectories in speech tracking and statistical learning.

Overview

1. Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
2. Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
3. Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, France
4. Département d’étude Cognitives, École Normale Supérieure, Paris, France
5. Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France

Abstract

Delayed onset of canonical babbling and first words is often reported in infants later diagnosed with autism spectrum disorder. Identifying the neural mechanisms underlying language acquisition in autism is therefore critical to inform early diagnosis, prognosis, and intervention strategies. In this study, we investigated two speech processing mechanisms previously identified as atypical in children and adults with autism: the neural ability to track syllables, and statistical learning, the capacity to detect speech regularities beneath surface variability. We recorded 83 longitudinal high-density electroencephalograms from 44 infants (2.5–22.6 months) at high (HL) and low (LL) likelihood for autism and assessed their verbal outcomes at 20 months. Neural entrainment was measured at syllable and word frequencies during exposure to a multi-speaker stream of concatenated tri-syllabic words, followed by a word recognition test using evoked response potential (ERP) recording. Our findings revealed reduced tracking abilities at the syllabic level in HL infants, a measure that correlated with verbal outcomes. While HL infants did not exhibit deficits in statistical learning itself, they displayed reduced novelty orientation during the word recognition test, indicated by a reduced late ERP. By contrast, multi-talker variability temporarily disrupted word segmentation around 12 months in LL infants, but not in HL infants, potentially reflecting decreased sensitivity to human voices variability in the HL group. These results emphasize the importance of longitudinal protocols employing online, implicit measures to track the hierarchical stages of speech processing in both HL and LL infants.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• zenodo:22102052, at Zenodo; found in “Data availability”

Data availability

All data used to generate the figures of the present manuscript that can be shared without compromising participant confidentiality are available at https://doi.org/10.5281/zenodo.22102052 together with the documentation describing the dataset structure. Individual-level raw EEG data are not publicly available because they constitute sensitive health-related data, and the consent obtained from participants' caregivers did not include unrestricted public dissemination of these data. Access to the raw EEG data may be granted for research purposes under controlled-access conditions. Requests should be submitted to Prof. Marie Schaer and must include a description of the proposed research and evidence of approval by the relevant ethics authority, or a formal determination that such approval is not required. Access is subject to compliance with the conditions of the original participant consent, applicable data-protection requirements, and execution of an appropriate data-use agreement. Data may not be used for commercial purposes. The analyses reported in this study were conducted using publicly available tools. Partial least squares correlation analyses and associated plots were performed using myPLS (https://github.com/danizoeller/myPLS, danizoeller, 2022); linear mixed-effects models and associated visualizations using myMixedModelsTrajectories (https://github.com/danizoeller/myMixedModelsTrajectories, Zoeller, 2020); and automated EEG preprocessing using the NeuroKidsLab EEG preprocessing pipeline (https://github.com/neurokidslab/eeg_preprocessing, Flo and Leroy, 2025), which is based on the EEGLAB toolbox 2020.0 (https://sccn.ucsd.edu/eeglab/). Scripts were run in MATLAB R2018b (https://ch.mathworks.com/fr/products/matlab.html).

The following dataset was generated:

Godel M. 2026. Infant_EEG_Reveals_Divergent_Developmental_Trajectories_eLife/Dataset. Zenodo.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 1 keyword, 11 MeSH terms, 1 funder, 95 references.

Cite

This paper

Godel, M., Fló, A., Benjamin, L., Dehaene-Lambertz, G., & Schaer, M. (2026). Infants at high and low likelihood for autism show different EEG developmental trajectories in speech tracking and statistical learning. eLife, 14, RP109901. https://doi.org/10.7554/elife.109901

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