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

Front Hum Neurosci. 2024 Jun 7:18:1403677. doi: 10.3389/fnhum.2024.1403677. eCollection 2024.

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.

Keywords: classification; common spatial patterns; developmental dyslexia; oscillations; speech; supervised learning; unsupervised learning.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The research was funded by a donation from the Yidan Prize Foundation to U.G. Collection of the EEG datasets was funded by a grant awarded to U.G. by the Fondation Botnar (project number 6064) and an Australian Research Council Discovery Project grant (DP110105123) awarded to U.G. and D.B. B.D.S is funded by a Royal Society E.P. Abraham Research Professorship (RP/R1/180165). M.K. receives support from the Basque Government through the BERC 2018–2021 program, the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation CEX2020-001010-S, and the Spanish Ministry of Science and Innovation through the Ramon y Cajal Research Fellowship, RYC2018-024284-I. G.D.L. work was conducted with the financial support of Science Foundation Ireland under Grant Agreement No. 13/RC/2106_P2 at the ADAPT SFI Research Centre at Trinity College, The University of Dublin. ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology, is funded by Science Foundation Ireland through the SFI Research Centres Programme. The sponsors played no role in the study design, data interpretation or writing of the report.