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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/205101
Ver los metadatos del registro completo
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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/ |
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MDPI |
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MDPI |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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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 |
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