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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/136124
Ver los metadatos del registro completo
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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 |
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1844614328000446464 |
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13.070432 |