Evidence map›Paper›PMID 38911229›Full record

ArticleFrontiers in human neuroscience2024

Atypical low-frequency cortical encoding of speech identifies children with developmental dyslexia.

João Araújo, Benjamin D Simons, Varghese Peter, Kanad Mandke, Marina Kalashnikova, Annabel Macfarlane, Fiona Gabrielczyk, Angela Wilson, Giovanni M Di Liberto, Denis Burnham and 1 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

João AraújoCentre for Neuroscience in Education, Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Benjamin D SimonsDepartment of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Cambridge, United Kingdom.
Varghese PeterSchool of Health, University of the Sunshine Coast, Maroochydore, QLD, Australia.
Kanad MandkeCentre for Neuroscience in Education, Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Marina KalashnikovaBasque Center on Cognition, Brain, and Language, San Sebastian, Spain.
Annabel MacfarlaneCentre for Neuroscience in Education, Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Fiona GabrielczykCentre for Neuroscience in Education, Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Angela WilsonCentre for Neuroscience in Education, Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Giovanni M Di LibertoADAPT Centre, School of Computer Science and Statistics, Trinity College, The University of Dublin, Dublin, Ireland.
Denis BurnhamMARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Sydney, NSW, Australia.
Usha GoswamiCentre for Neuroscience in Education, Department of Psychology, University of Cambridge, Cambridge, United Kingdom.

Funding

Medical Research Council MC_PC_17230
6 · The paper itself

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.

Indexed as

classificationcommon spatial patternsdevelopmental dyslexiaoscillationsspeechsupervised learningunsupervised learning

Identifiers

PMID38911229
PMCPMC11190370

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.