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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/224803
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:14:23Zoai: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:34982025-09-29 10:14:23.902CONICET 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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1844614070748053504 |
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13.070432 |