Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory

Autores
Roffet, Facundo Alejandro; Delrieux, Claudio Augusto; Patow, Gustavo
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.
Fil: Roffet, Facundo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Patow, Gustavo. Universidad de Girona; España
Materia
HARMONIZATION
INFORMATION THEORY
MULTI-SITE ACQUISITION
NEUROSCIENCE
RS-FMRI
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:11336/205101

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network_name_str CONICET Digital (CONICET)
spelling Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information TheoryRoffet, Facundo AlejandroDelrieux, Claudio AugustoPatow, GustavoHARMONIZATIONINFORMATION THEORYMULTI-SITE ACQUISITIONNEUROSCIENCERS-FMRIhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.Fil: Roffet, Facundo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Patow, Gustavo. Universidad de Girona; EspañaMDPI2022-09-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/205101Roffet, Facundo Alejandro; Delrieux, Claudio Augusto; Patow, Gustavo; Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory; MDPI; Brain Sciences; 12; 9; 9-9-2022; 1-162076-3425CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/brainsci12091219info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:49:27Zoai:ri.conicet.gov.ar:11336/205101instacron: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:34982025-09-03 09:49:28.004CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory
title Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory
spellingShingle Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory
Roffet, Facundo Alejandro
HARMONIZATION
INFORMATION THEORY
MULTI-SITE ACQUISITION
NEUROSCIENCE
RS-FMRI
title_short Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory
title_full Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory
title_fullStr Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory
title_full_unstemmed Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory
title_sort Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory
dc.creator.none.fl_str_mv Roffet, Facundo Alejandro
Delrieux, Claudio Augusto
Patow, Gustavo
author Roffet, Facundo Alejandro
author_facet Roffet, Facundo Alejandro
Delrieux, Claudio Augusto
Patow, Gustavo
author_role author
author2 Delrieux, Claudio Augusto
Patow, Gustavo
author2_role author
author
dc.subject.none.fl_str_mv HARMONIZATION
INFORMATION THEORY
MULTI-SITE ACQUISITION
NEUROSCIENCE
RS-FMRI
topic HARMONIZATION
INFORMATION THEORY
MULTI-SITE ACQUISITION
NEUROSCIENCE
RS-FMRI
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.
Fil: Roffet, Facundo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Patow, Gustavo. Universidad de Girona; España
description Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.
publishDate 2022
dc.date.none.fl_str_mv 2022-09-09
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/11336/205101
Roffet, Facundo Alejandro; Delrieux, Claudio Augusto; Patow, Gustavo; Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory; MDPI; Brain Sciences; 12; 9; 9-9-2022; 1-16
2076-3425
CONICET Digital
CONICET
url http://hdl.handle.net/11336/205101
identifier_str_mv Roffet, Facundo Alejandro; Delrieux, Claudio Augusto; Patow, Gustavo; Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory; MDPI; Brain Sciences; 12; 9; 9-9-2022; 1-16
2076-3425
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/brainsci12091219
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 MDPI
publisher.none.fl_str_mv MDPI
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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