ArticleFrontiers in human neuroscience2024
Atypical low-frequency cortical encoding of speech identifies children with developmental dyslexia.
Article in Frontiers in human neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Rhythm training improves word-reading in children with dyslexia.Scientific reports · 2025Trial
- Towards a Brain-Computer Interface (BCI) for Improving Phonological Processing in Developmental Dyslexia: An Exploratory Study.The European journal of neuroscience · 2026Article
- Passive OPM-MEG paradigm reveals altered brain network organization in children with DLD.iScience · 2026Article
- Functional Networks in Developmental Dyslexia: Auditory Discrimination of Words and Pseudowords.NeuroSci · 2026Article
- Rhythm Processing Across Development: Origins, Links to Language Processing, and Perspectives for Intervention.Annals of the New York Academy of Sciences · 2026Review
- Children's Improvement After Language and Rhythm Training With the Digital Medical Device Poppins for Dyslexia: Single-Arm Intervention Study.JMIR serious games · 2025Article
- Infant low-frequency EEG cortical power, cortical tracking and phase-amplitude coupling predicts language a year later.PloS one · 2024Article
- Neural processing of rhythmic speech by children with developmental language disorder (DLD): An EEG study.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Neural responses to natural and enhanced speech edges in children with and without dyslexia.Frontiers in human neuroscience · 2023Article
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Authors and funding
11 authors.
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Abstract
Slow cortical oscillations play a crucial role in processing the speech amplitude envelope, which is perceived atypically by children with developmental dyslexia. Here we use electroencephalography (EEG) recorded during natural speech listening to identify neural processing patterns involving slow oscillations that may characterize children with dyslexia. In a story listening paradigm, we find that atypical power dynamics and phase-amplitude coupling between delta and theta oscillations characterize dyslexic versus other child control groups (typically-developing controls, other language disorder controls). We further isolate EEG common spatial patterns (CSP) during speech listening across delta and theta oscillations that identify dyslexic children. A linear classifier using four delta-band CSP variables predicted dyslexia status (0.77 AUC). Crucially, these spatial patterns also identified children with dyslexia when applied to EEG measured during a rhythmic syllable processing task. This transfer effect (i.e., the ability to use neural features derived from a story listening task as input features to a classifier based on a rhythmic syllable task) is consistent with a core developmental deficit in neural processing of speech rhythm. The findings are suggestive of distinct atypical neurocognitive speech encoding mechanisms underlying dyslexia, which could be targeted by novel interventions.
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