Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task

Autores
Josefsson, Alexandra; Ibañez, Agustin Mariano; Parra, Mario; Escudero, Javier
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The early diagnosis of Alzheimer’s disease (AD) is particularly challenging. Mild cognitive impairment (MCI) has been linked to AD and electroencephalogram (EEG) recordings are able to measure brain activity directly with high temporal resolution. In this context, with appropriate processing, the EEG recordings can be used to construct a graph representative of brain functional connectivity. This work studies a functional network created from a non-linear measure of coupling of beta-filtered EEG recordings during a short-term memory binding task. It shows that the values of the small-world characteristic and eccentricity are, respectively, lower and higher in MCI patients than in controls. The results show how MCI leads to EEG functional connectivity changes. They expect that the network differences between MCIs and control subjects could be used to gain insight into the early stages of AD.
Fil: Josefsson, Alexandra. University of Edinburgh; Reino Unido
Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Universidad Adolfo Ibañez; Chile. Universidad Autónoma del Caribe; Colombia
Fil: Parra, Mario. University of Strathclyde; Reino Unido. Universidad Autónoma del Caribe; Colombia
Fil: Escudero, Javier. University of Edinburgh; Reino Unido
Materia
BRAIN
NEUROPHYSIOLOGY
DISEASES
COGNITION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/136070
Acceder
id CONICETDig_5e69fc7d218b1d13ab474ce1fd525085
oai_identifier_str oai:ri.conicet.gov.ar:11336/136070
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding taskJosefsson, AlexandraIbañez, Agustin MarianoParra, MarioEscudero, JavierBRAINNEUROPHYSIOLOGYDISEASESCOGNITIONhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3The early diagnosis of Alzheimer’s disease (AD) is particularly challenging. Mild cognitive impairment (MCI) has been linked to AD and electroencephalogram (EEG) recordings are able to measure brain activity directly with high temporal resolution. In this context, with appropriate processing, the EEG recordings can be used to construct a graph representative of brain functional connectivity. This work studies a functional network created from a non-linear measure of coupling of beta-filtered EEG recordings during a short-term memory binding task. It shows that the values of the small-world characteristic and eccentricity are, respectively, lower and higher in MCI patients than in controls. The results show how MCI leads to EEG functional connectivity changes. They expect that the network differences between MCIs and control subjects could be used to gain insight into the early stages of AD.Fil: Josefsson, Alexandra. University of Edinburgh; Reino UnidoFil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Universidad Adolfo Ibañez; Chile. Universidad Autónoma del Caribe; ColombiaFil: Parra, Mario. University of Strathclyde; Reino Unido. Universidad Autónoma del Caribe; ColombiaFil: Escudero, Javier. University of Edinburgh; Reino UnidoInstitution of Engineering and Technology2019-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/136070Josefsson, Alexandra; Ibañez, Agustin Mariano; Parra, Mario; Escudero, Javier; Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task; Institution of Engineering and Technology; Healthcare Technology Letters; 6; 2; 4-2019; 27-312053-37132053-3713CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1049/htl.2018.5060info:eu-repo/semantics/altIdentifier/url/https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/htl.2018.5060info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:30:11Zoai:ri.conicet.gov.ar:11336/136070instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:30:11.687CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task
title Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task
spellingShingle Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task
Josefsson, Alexandra
BRAIN
NEUROPHYSIOLOGY
DISEASES
COGNITION
title_short Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task
title_full Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task
title_fullStr Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task
title_full_unstemmed Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task
title_sort Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task
dc.creator.none.fl_str_mv Josefsson, Alexandra
Ibañez, Agustin Mariano
Parra, Mario
Escudero, Javier
author Josefsson, Alexandra
author_facet Josefsson, Alexandra
Ibañez, Agustin Mariano
Parra, Mario
Escudero, Javier
author_role author
author2 Ibañez, Agustin Mariano
Parra, Mario
Escudero, Javier
author2_role author
author
author
dc.subject.none.fl_str_mv BRAIN
NEUROPHYSIOLOGY
DISEASES
COGNITION
topic BRAIN
NEUROPHYSIOLOGY
DISEASES
COGNITION
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv The early diagnosis of Alzheimer’s disease (AD) is particularly challenging. Mild cognitive impairment (MCI) has been linked to AD and electroencephalogram (EEG) recordings are able to measure brain activity directly with high temporal resolution. In this context, with appropriate processing, the EEG recordings can be used to construct a graph representative of brain functional connectivity. This work studies a functional network created from a non-linear measure of coupling of beta-filtered EEG recordings during a short-term memory binding task. It shows that the values of the small-world characteristic and eccentricity are, respectively, lower and higher in MCI patients than in controls. The results show how MCI leads to EEG functional connectivity changes. They expect that the network differences between MCIs and control subjects could be used to gain insight into the early stages of AD.
Fil: Josefsson, Alexandra. University of Edinburgh; Reino Unido
Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Universidad Adolfo Ibañez; Chile. Universidad Autónoma del Caribe; Colombia
Fil: Parra, Mario. University of Strathclyde; Reino Unido. Universidad Autónoma del Caribe; Colombia
Fil: Escudero, Javier. University of Edinburgh; Reino Unido
description The early diagnosis of Alzheimer’s disease (AD) is particularly challenging. Mild cognitive impairment (MCI) has been linked to AD and electroencephalogram (EEG) recordings are able to measure brain activity directly with high temporal resolution. In this context, with appropriate processing, the EEG recordings can be used to construct a graph representative of brain functional connectivity. This work studies a functional network created from a non-linear measure of coupling of beta-filtered EEG recordings during a short-term memory binding task. It shows that the values of the small-world characteristic and eccentricity are, respectively, lower and higher in MCI patients than in controls. The results show how MCI leads to EEG functional connectivity changes. They expect that the network differences between MCIs and control subjects could be used to gain insight into the early stages of AD.
publishDate 2019
dc.date.none.fl_str_mv 2019-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/136070
Josefsson, Alexandra; Ibañez, Agustin Mariano; Parra, Mario; Escudero, Javier; Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task; Institution of Engineering and Technology; Healthcare Technology Letters; 6; 2; 4-2019; 27-31
2053-3713
2053-3713
CONICET Digital
CONICET
url http://hdl.handle.net/11336/136070
identifier_str_mv Josefsson, Alexandra; Ibañez, Agustin Mariano; Parra, Mario; Escudero, Javier; Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task; Institution of Engineering and Technology; Healthcare Technology Letters; 6; 2; 4-2019; 27-31
2053-3713
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1049/htl.2018.5060
info:eu-repo/semantics/altIdentifier/url/https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/htl.2018.5060
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institution of Engineering and Technology
publisher.none.fl_str_mv Institution of Engineering and Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082777316851712
score 13.22299

Publicaciones similares