Source space connectomics of neurodegeneration: One-metric approach does not fit all

Autores
Prado, Pavel; Moguilner, Sebastian Gabriel; Mejía, Jhony A.; Sainz Ballesteros, Agustín; Otero, Mónica; Birba, Agustina; Santamaria Garcia, Hernando; Legaz, Agustina; Fittipaldi, María Sol; Cruzat, Josephine; Tagliazucchi, Enzo Rodolfo; Parra, Mario; Herzog, Rubén; Ibañez, Agustin Mariano
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.
Fil: Prado, Pavel. Universidad San Sebastián; Chile. Universidad Adolfo Ibañez; Chile
Fil: Moguilner, Sebastian Gabriel. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; Argentina
Fil: Mejía, Jhony A.. Universidad Adolfo Ibañez; Chile
Fil: Sainz Ballesteros, Agustín. Universidad Adolfo Ibañez; Chile
Fil: Otero, Mónica. Universidad San Sebastián; Chile
Fil: Birba, Agustina. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Santamaria Garcia, Hernando. Pontificia Universidad Javeriana; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Legaz, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina
Fil: Fittipaldi, María Sol. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cruzat, Josephine. Universidad Adolfo Ibañez; Chile
Fil: Tagliazucchi, Enzo Rodolfo. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Parra, Mario. University of Strathclyde; Reino Unido
Fil: Herzog, Rubén. Universidad Adolfo Ibañez; Chile
Fil: Ibañez, Agustin Mariano. Universidad Adolfo Ibañez; Chile. Pontificia Universidad Javeriana; Colombia. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
COMPOSITE CONNECTIVITY METRIC
CONNECTOMICS
DEMENTIA BIOMARKER
EEG SOURCE-SPACE
MULTI-FEATURE MACHINE LEARNING CLASSIFICATION
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:11336/224803
Acceder
id CONICETDig_8feca39c7f809d7393af2359e815ef9f
oai_identifier_str oai:ri.conicet.gov.ar:11336/224803
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Source space connectomics of neurodegeneration: One-metric approach does not fit allPrado, PavelMoguilner, Sebastian GabrielMejía, Jhony A.Sainz Ballesteros, AgustínOtero, MónicaBirba, AgustinaSantamaria Garcia, HernandoLegaz, AgustinaFittipaldi, María SolCruzat, JosephineTagliazucchi, Enzo RodolfoParra, MarioHerzog, RubénIbañez, Agustin MarianoCOMPOSITE CONNECTIVITY METRICCONNECTOMICSDEMENTIA BIOMARKEREEG SOURCE-SPACEMULTI-FEATURE MACHINE LEARNING CLASSIFICATIONhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.Fil: Prado, Pavel. Universidad San Sebastián; Chile. Universidad Adolfo Ibañez; ChileFil: Moguilner, Sebastian Gabriel. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; ArgentinaFil: Mejía, Jhony A.. Universidad Adolfo Ibañez; ChileFil: Sainz Ballesteros, Agustín. Universidad Adolfo Ibañez; ChileFil: Otero, Mónica. Universidad San Sebastián; ChileFil: Birba, Agustina. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Santamaria Garcia, Hernando. Pontificia Universidad Javeriana; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Legaz, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; ArgentinaFil: Fittipaldi, María Sol. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cruzat, Josephine. Universidad Adolfo Ibañez; ChileFil: Tagliazucchi, Enzo Rodolfo. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Parra, Mario. University of Strathclyde; Reino UnidoFil: Herzog, Rubén. Universidad Adolfo Ibañez; ChileFil: Ibañez, Agustin Mariano. Universidad Adolfo Ibañez; Chile. Pontificia Universidad Javeriana; Colombia. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaAcademic Press Inc Elsevier Science2023-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/224803Prado, Pavel; Moguilner, Sebastian Gabriel; Mejía, Jhony A.; Sainz Ballesteros, Agustín; Otero, Mónica; et al.; Source space connectomics of neurodegeneration: One-metric approach does not fit all; Academic Press Inc Elsevier Science; Neurobiology of Disease; 179; 4-2023; 1-160969-9961CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.nbd.2023.106047info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:44:27Zoai:ri.conicet.gov.ar:11336/224803instacron: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:34982025-10-22 11:44:28.375CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Source space connectomics of neurodegeneration: One-metric approach does not fit all
title Source space connectomics of neurodegeneration: One-metric approach does not fit all
spellingShingle Source space connectomics of neurodegeneration: One-metric approach does not fit all
Prado, Pavel
COMPOSITE CONNECTIVITY METRIC
CONNECTOMICS
DEMENTIA BIOMARKER
EEG SOURCE-SPACE
MULTI-FEATURE MACHINE LEARNING CLASSIFICATION
title_short Source space connectomics of neurodegeneration: One-metric approach does not fit all
title_full Source space connectomics of neurodegeneration: One-metric approach does not fit all
title_fullStr Source space connectomics of neurodegeneration: One-metric approach does not fit all
title_full_unstemmed Source space connectomics of neurodegeneration: One-metric approach does not fit all
title_sort Source space connectomics of neurodegeneration: One-metric approach does not fit all
dc.creator.none.fl_str_mv Prado, Pavel
Moguilner, Sebastian Gabriel
Mejía, Jhony A.
Sainz Ballesteros, Agustín
Otero, Mónica
Birba, Agustina
Santamaria Garcia, Hernando
Legaz, Agustina
Fittipaldi, María Sol
Cruzat, Josephine
Tagliazucchi, Enzo Rodolfo
Parra, Mario
Herzog, Rubén
Ibañez, Agustin Mariano
author Prado, Pavel
author_facet Prado, Pavel
Moguilner, Sebastian Gabriel
Mejía, Jhony A.
Sainz Ballesteros, Agustín
Otero, Mónica
Birba, Agustina
Santamaria Garcia, Hernando
Legaz, Agustina
Fittipaldi, María Sol
Cruzat, Josephine
Tagliazucchi, Enzo Rodolfo
Parra, Mario
Herzog, Rubén
Ibañez, Agustin Mariano
author_role author
author2 Moguilner, Sebastian Gabriel
Mejía, Jhony A.
Sainz Ballesteros, Agustín
Otero, Mónica
Birba, Agustina
Santamaria Garcia, Hernando
Legaz, Agustina
Fittipaldi, María Sol
Cruzat, Josephine
Tagliazucchi, Enzo Rodolfo
Parra, Mario
Herzog, Rubén
Ibañez, Agustin Mariano
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv COMPOSITE CONNECTIVITY METRIC
CONNECTOMICS
DEMENTIA BIOMARKER
EEG SOURCE-SPACE
MULTI-FEATURE MACHINE LEARNING CLASSIFICATION
topic COMPOSITE CONNECTIVITY METRIC
CONNECTOMICS
DEMENTIA BIOMARKER
EEG SOURCE-SPACE
MULTI-FEATURE MACHINE LEARNING CLASSIFICATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.
Fil: Prado, Pavel. Universidad San Sebastián; Chile. Universidad Adolfo Ibañez; Chile
Fil: Moguilner, Sebastian Gabriel. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; Argentina
Fil: Mejía, Jhony A.. Universidad Adolfo Ibañez; Chile
Fil: Sainz Ballesteros, Agustín. Universidad Adolfo Ibañez; Chile
Fil: Otero, Mónica. Universidad San Sebastián; Chile
Fil: Birba, Agustina. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Santamaria Garcia, Hernando. Pontificia Universidad Javeriana; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Legaz, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina
Fil: Fittipaldi, María Sol. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cruzat, Josephine. Universidad Adolfo Ibañez; Chile
Fil: Tagliazucchi, Enzo Rodolfo. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Parra, Mario. University of Strathclyde; Reino Unido
Fil: Herzog, Rubén. Universidad Adolfo Ibañez; Chile
Fil: Ibañez, Agustin Mariano. Universidad Adolfo Ibañez; Chile. Pontificia Universidad Javeriana; Colombia. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.
publishDate 2023
dc.date.none.fl_str_mv 2023-04
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/11336/224803
Prado, Pavel; Moguilner, Sebastian Gabriel; Mejía, Jhony A.; Sainz Ballesteros, Agustín; Otero, Mónica; et al.; Source space connectomics of neurodegeneration: One-metric approach does not fit all; Academic Press Inc Elsevier Science; Neurobiology of Disease; 179; 4-2023; 1-16
0969-9961
CONICET Digital
CONICET
url http://hdl.handle.net/11336/224803
identifier_str_mv Prado, Pavel; Moguilner, Sebastian Gabriel; Mejía, Jhony A.; Sainz Ballesteros, Agustín; Otero, Mónica; et al.; Source space connectomics of neurodegeneration: One-metric approach does not fit all; Academic Press Inc Elsevier Science; Neurobiology of Disease; 179; 4-2023; 1-16
0969-9961
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.nbd.2023.106047
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
application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc Elsevier Science
publisher.none.fl_str_mv Academic Press Inc Elsevier Science
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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score 12.982451

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