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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/136070
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:40:28Zoai: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:34982025-09-29 09:40:29.128CONICET 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 |
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1844613280604094464 |
score |
13.070432 |