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Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis

Learning disorders (LDs) are diagnosed in children whose academic skills of reading, writing or mathematics are impaired and lagging according to their age, schooling and intelligence. Children with LDs experience substantial working memory (WM) deficits, even more pronounced if more than one of the...

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Autores principales: Martínez-Briones, Benito J., Fernández-Harmony, Thalía, Garófalo Gómez, Nicolás, Biscay-Lirio, Rolando J., Bosch-Bayard, Jorge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694181/
https://www.ncbi.nlm.nih.gov/pubmed/33158135
http://dx.doi.org/10.3390/brainsci10110817
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author Martínez-Briones, Benito J.
Fernández-Harmony, Thalía
Garófalo Gómez, Nicolás
Biscay-Lirio, Rolando J.
Bosch-Bayard, Jorge
author_facet Martínez-Briones, Benito J.
Fernández-Harmony, Thalía
Garófalo Gómez, Nicolás
Biscay-Lirio, Rolando J.
Bosch-Bayard, Jorge
author_sort Martínez-Briones, Benito J.
collection PubMed
description Learning disorders (LDs) are diagnosed in children whose academic skills of reading, writing or mathematics are impaired and lagging according to their age, schooling and intelligence. Children with LDs experience substantial working memory (WM) deficits, even more pronounced if more than one of the academic skills is affected. We compared the task-related electroencephalogram (EEG) power spectral density of children with LDs (n = 23) with a control group of children with good academic achievement (n = 22), during the performance of a WM task. sLoreta was used to estimate the current distribution at the sources, and 18 brain regions of interest (ROIs) were chosen with an extended version of the eigenvector centrality mapping technique. In this way, we lessened some drawbacks of the traditional EEG at the sensor space by an analysis at the brain-sources level over data-driven selected ROIs. Results: The LD group showed fewer correct responses in the WM task, an overall slower EEG with more delta and theta activity, and less high-frequency gamma activity in posterior areas. We explain these EEG patterns in LD children as indices of an inefficient neural resource management related with a delay in neural maturation.
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spelling pubmed-76941812020-11-28 Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis Martínez-Briones, Benito J. Fernández-Harmony, Thalía Garófalo Gómez, Nicolás Biscay-Lirio, Rolando J. Bosch-Bayard, Jorge Brain Sci Article Learning disorders (LDs) are diagnosed in children whose academic skills of reading, writing or mathematics are impaired and lagging according to their age, schooling and intelligence. Children with LDs experience substantial working memory (WM) deficits, even more pronounced if more than one of the academic skills is affected. We compared the task-related electroencephalogram (EEG) power spectral density of children with LDs (n = 23) with a control group of children with good academic achievement (n = 22), during the performance of a WM task. sLoreta was used to estimate the current distribution at the sources, and 18 brain regions of interest (ROIs) were chosen with an extended version of the eigenvector centrality mapping technique. In this way, we lessened some drawbacks of the traditional EEG at the sensor space by an analysis at the brain-sources level over data-driven selected ROIs. Results: The LD group showed fewer correct responses in the WM task, an overall slower EEG with more delta and theta activity, and less high-frequency gamma activity in posterior areas. We explain these EEG patterns in LD children as indices of an inefficient neural resource management related with a delay in neural maturation. MDPI 2020-11-04 /pmc/articles/PMC7694181/ /pubmed/33158135 http://dx.doi.org/10.3390/brainsci10110817 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Martínez-Briones, Benito J.
Fernández-Harmony, Thalía
Garófalo Gómez, Nicolás
Biscay-Lirio, Rolando J.
Bosch-Bayard, Jorge
Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis
title Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis
title_full Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis
title_fullStr Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis
title_full_unstemmed Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis
title_short Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis
title_sort working memory in children with learning disorders: an eeg power spectrum analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694181/
https://www.ncbi.nlm.nih.gov/pubmed/33158135
http://dx.doi.org/10.3390/brainsci10110817
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