An ANOVA approach for statistical comparisons of brain networks

Autores
Fraiman Borrazás, Daniel Edmundo; Fraiman, Jacob Ricardo
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.
Fil: Fraiman Borrazás, Daniel Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina
Fil: Fraiman, Jacob Ricardo. Universidad de la República; Uruguay. Instituto Pasteur de Montevideo; Uruguay
Materia
Neuroimage
Networks
Inference
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:11336/136124

id CONICETDig_c6d45070df94315e4e0820d0f7674417
oai_identifier_str oai:ri.conicet.gov.ar:11336/136124
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An ANOVA approach for statistical comparisons of brain networksFraiman Borrazás, Daniel EdmundoFraiman, Jacob RicardoNeuroimageNetworksInferenceFMRIhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.Fil: Fraiman Borrazás, Daniel Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; ArgentinaFil: Fraiman, Jacob Ricardo. Universidad de la República; Uruguay. Instituto Pasteur de Montevideo; UruguayNature Publishing Group2018-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/136124Fraiman Borrazás, Daniel Edmundo; Fraiman, Jacob Ricardo; An ANOVA approach for statistical comparisons of brain networks; Nature Publishing Group; Scientific Reports; 8; 1; 12-2018; 1-142045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41598-018-23152-5info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-018-23152-5info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:31:39Zoai:ri.conicet.gov.ar:11336/136124instacron: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 10:31:39.673CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An ANOVA approach for statistical comparisons of brain networks
title An ANOVA approach for statistical comparisons of brain networks
spellingShingle An ANOVA approach for statistical comparisons of brain networks
Fraiman Borrazás, Daniel Edmundo
Neuroimage
Networks
Inference
FMRI
title_short An ANOVA approach for statistical comparisons of brain networks
title_full An ANOVA approach for statistical comparisons of brain networks
title_fullStr An ANOVA approach for statistical comparisons of brain networks
title_full_unstemmed An ANOVA approach for statistical comparisons of brain networks
title_sort An ANOVA approach for statistical comparisons of brain networks
dc.creator.none.fl_str_mv Fraiman Borrazás, Daniel Edmundo
Fraiman, Jacob Ricardo
author Fraiman Borrazás, Daniel Edmundo
author_facet Fraiman Borrazás, Daniel Edmundo
Fraiman, Jacob Ricardo
author_role author
author2 Fraiman, Jacob Ricardo
author2_role author
dc.subject.none.fl_str_mv Neuroimage
Networks
Inference
FMRI
topic Neuroimage
Networks
Inference
FMRI
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.
Fil: Fraiman Borrazás, Daniel Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina
Fil: Fraiman, Jacob Ricardo. Universidad de la República; Uruguay. Instituto Pasteur de Montevideo; Uruguay
description The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.
publishDate 2018
dc.date.none.fl_str_mv 2018-12
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/11336/136124
Fraiman Borrazás, Daniel Edmundo; Fraiman, Jacob Ricardo; An ANOVA approach for statistical comparisons of brain networks; Nature Publishing Group; Scientific Reports; 8; 1; 12-2018; 1-14
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/136124
identifier_str_mv Fraiman Borrazás, Daniel Edmundo; Fraiman, Jacob Ricardo; An ANOVA approach for statistical comparisons of brain networks; Nature Publishing Group; Scientific Reports; 8; 1; 12-2018; 1-14
2045-2322
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41598-018-23152-5
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-018-23152-5
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 Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
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
_version_ 1844614328000446464
score 13.070432