Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis

Gallego-Molina, Nicolás J.; Ortíz, Andrés; Martínez-Murcia, Francisco Jesús; Formoso, Marco A.; Giménez, Almúdena

doi:10.1016/j.knosys.2021.108098

Cited by 18 publications

(10 citation statements)

References 54 publications

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“…SVMs have been frequently used as classifiers in the EEG literature (see Ref. [38] for a recent example) and have shown performances ranging from 0.6-0.95 AUC in EEG classification problems using longer inputs. Each classifier was trained with a different number of CSP filters to detect the optimal number of features.…”

Section: Resultsmentioning

confidence: 99%

“…Large non-linear EEG-based classifiers have previously been engineered for dyslexia using long time windows of AM noise, with degrees of success reaching an AUC ~ 0.8 [37, 38]. By contrast, we test classification performances for both dyslexic and typically-developing children using a linear classifier and short epochs of naturalistic speech listening data.…”

Section: Discussionmentioning

confidence: 99%

“…Electrophysiology studies with dyslexic children may also provide useful biomarkers for accurate diagnosis, similarly to studies conducted with Autism Spectrum Disorder [35] or Parkinson’s disease [36]. In fact, recent work has shown that it is possible to build dyslexia classifiers from the EEG of dyslexic children listening to rhythmic auditory stimuli (white noise) presented at different rates [37, 38]. These studies use several different types of features (time, frequency, fractal or CFC graph networks), with large and complex non-linear models, achieving good performance metrics.…”

Section: Introductionmentioning

confidence: 99%

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Atypical cortical encoding of speech identifies children with Dyslexia versus Developmental Language Disorder

Araújo

Simons

Peter

et al. 2022

Preprint

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Slow cortical oscillations play a crucial role in processing the speech envelope, which is perceived atypically by children with Developmental Language Disorder (DLD) and developmental dyslexia. Here we use electroencephalography (EEG) and natural speech listening paradigms to identify neural processing patterns that characterize dyslexic versus DLD children. Using a story listening paradigm, we show that atypical power dynamics and phase-amplitude coupling between delta and theta oscillations characterize dyslexic and DLD children groups, respectively. We further identify EEG common spatial patterns (CSP) during speech listening across delta, theta and beta oscillations describing dyslexic versus DLD 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 in a rhythmic syllable task EEG, suggesting a core developmental deficit in neural processing of speech rhythm. These findings suggest that there are distinct atypical neurocognitive mechanisms underlying dyslexia and DLD.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Atypical cortical encoding of speech identifies children with Dyslexia versus Developmental Language Disorder

Araújo

Simons

Peter

et al. 2022

Preprint

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“…The ability to use high temporal (EEG) and spatial (fMRI) resolution during experimental paradigms make these techniques excellent tools for the identification of physiological correlates of reading processes (Carter et al, 2019;Duffy et al, 1980). It has been demonstrated that these tools can be useful in the evaluation of reading impairments (Gallego-Molina et al, 2022;Ortiz et al, 2020). Importantly, these studies have demonstrated that assessment of reading processes using neuro-imaging tools can lower the age of diagnosis for children with dyslexia (Chyl et al, 2021;Ortiz et al, 2020) and identify abnormal neural activation patterns prior to formal literacy instruction (Schiavone et al, 2014;Wilkinson et al, 2020).…”

Section: Neuroscience Of Reading Researchmentioning

confidence: 99%

The Use of Neuronal Response Signals as Early Biomarkers of Dyslexia

Carrasco

2022

Adv Neurodev Disord

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Objectives Brain imaging techniques have broadened our understanding of structural and functional properties of neuronal networks in children with developmental disabilities. The present work examines current models of neuronal response properties implicated in dyslexia and reading difficulties. Methods This review analyzes the use of functional techniques (fMRI and EEG) employed in the assessment of neuronal markers associated with reading ability. Results Neuro-imaging studies have provided evidence of neuronal networks involved in the emergence of reading fluency. Using this information, it is now possible to employ physiological assessments in the screening of reading ability before behavioral evaluations can be conducted. Conclusions Analyses of neuro-imaging studies show that abnormal neuronal activation in specific brain areas can be used to help identify reading impairments in children. These neuronal assessments permit earlier identification of dyslexia than those requiring behavioral assessments.

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“…ASSR (Auditory steady-state response) EEGs measure the response that is evoked by a periodically repeated auditory stimulus (Farahani et al 2021;Hwang et al 2020). This kind of neurophysiological response has been used successfully to study patients with schizofrenia (Koshiyama et al 2021), bipolar disorder, depression and autism (Jefsen et al 2022) and, more recently, developmental dyslexia (Gallego-Molina et al 2022). Event-Related Potentials (ERPs) are not available in those cases so that clustering of ICs must be tackled using other features such as spectra-time frequency results or source-localization (e.g.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

et al. 2023

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Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.

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Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis

Cited by 18 publications

References 54 publications

Atypical cortical encoding of speech identifies children with Dyslexia versus Developmental Language Disorder

Atypical cortical encoding of speech identifies children with Dyslexia versus Developmental Language Disorder

The Use of Neuronal Response Signals as Early Biomarkers of Dyslexia

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

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