The impact of normalization and segmentation on resting state brain networks
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/33578 |
Resumo: | Graph theory has recently received a lot of attention from the neuroscience community as a method to represent and characterize brain networks. Still, there is a lack of a gold standard for the methods that should be employed for the preprocessing of the data and the construction of the networks, as well as a lack of knowledge on how different methodologies can affect the metrics reported. We used graph theory analysis applied to resting-state functional Magnetic Resonance Imaging (rs-fMRI) to investigate the influence of different node-defining strategies and the effect of normalizing the functional acquisition on several commonly reported metrics used to characterize brain networks. The nodes of the network were defined using either the individual FreeSurfer segmentation of each subject or the FreeSurfer segmented MNI (Montreal National Institute) 152 template, using the Destrieux and sub-cortical atlas. The functional acquisition was either kept on the functional native space or normalized into MNI standard space. The comparisons were done at three levels: on the connections, on the edge properties and on the network properties levels. Our results reveal that different registration and brain parcellation strategies have a strong impact on all the levels of analysis, possibly favoring the use of individual segmentation strategies and conservative registration approaches. In conclusion, several technical aspects must be considered so that graph theoretical analysis of connectivity MRI data can provide a framework to understand brain pathologies. |
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The impact of normalization and segmentation on resting state brain networksNeuroimagingFMRIGraph-TheoryPre-ProcessingBrain parcellationNormalizationScience & TechnologyGraph theory has recently received a lot of attention from the neuroscience community as a method to represent and characterize brain networks. Still, there is a lack of a gold standard for the methods that should be employed for the preprocessing of the data and the construction of the networks, as well as a lack of knowledge on how different methodologies can affect the metrics reported. We used graph theory analysis applied to resting-state functional Magnetic Resonance Imaging (rs-fMRI) to investigate the influence of different node-defining strategies and the effect of normalizing the functional acquisition on several commonly reported metrics used to characterize brain networks. The nodes of the network were defined using either the individual FreeSurfer segmentation of each subject or the FreeSurfer segmented MNI (Montreal National Institute) 152 template, using the Destrieux and sub-cortical atlas. The functional acquisition was either kept on the functional native space or normalized into MNI standard space. The comparisons were done at three levels: on the connections, on the edge properties and on the network properties levels. Our results reveal that different registration and brain parcellation strategies have a strong impact on all the levels of analysis, possibly favoring the use of individual segmentation strategies and conservative registration approaches. In conclusion, several technical aspects must be considered so that graph theoretical analysis of connectivity MRI data can provide a framework to understand brain pathologies.(undefined)Mary Ann LiebertUniversidade do MinhoMagalhães, Ricardo José SilvaMarques, Paulo César GonçalvesSoares, José MiguelAlves, VitorSousa, Nuno20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/33578eng2158-001410.1089/brain.2014.029225420048http://www.liebertpub.cominfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:19:18Zoai:repositorium.sdum.uminho.pt:1822/33578Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:12:12.922381Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
The impact of normalization and segmentation on resting state brain networks |
title |
The impact of normalization and segmentation on resting state brain networks |
spellingShingle |
The impact of normalization and segmentation on resting state brain networks Magalhães, Ricardo José Silva Neuroimaging FMRI Graph-Theory Pre-Processing Brain parcellation Normalization Science & Technology |
title_short |
The impact of normalization and segmentation on resting state brain networks |
title_full |
The impact of normalization and segmentation on resting state brain networks |
title_fullStr |
The impact of normalization and segmentation on resting state brain networks |
title_full_unstemmed |
The impact of normalization and segmentation on resting state brain networks |
title_sort |
The impact of normalization and segmentation on resting state brain networks |
author |
Magalhães, Ricardo José Silva |
author_facet |
Magalhães, Ricardo José Silva Marques, Paulo César Gonçalves Soares, José Miguel Alves, Vitor Sousa, Nuno |
author_role |
author |
author2 |
Marques, Paulo César Gonçalves Soares, José Miguel Alves, Vitor Sousa, Nuno |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Magalhães, Ricardo José Silva Marques, Paulo César Gonçalves Soares, José Miguel Alves, Vitor Sousa, Nuno |
dc.subject.por.fl_str_mv |
Neuroimaging FMRI Graph-Theory Pre-Processing Brain parcellation Normalization Science & Technology |
topic |
Neuroimaging FMRI Graph-Theory Pre-Processing Brain parcellation Normalization Science & Technology |
description |
Graph theory has recently received a lot of attention from the neuroscience community as a method to represent and characterize brain networks. Still, there is a lack of a gold standard for the methods that should be employed for the preprocessing of the data and the construction of the networks, as well as a lack of knowledge on how different methodologies can affect the metrics reported. We used graph theory analysis applied to resting-state functional Magnetic Resonance Imaging (rs-fMRI) to investigate the influence of different node-defining strategies and the effect of normalizing the functional acquisition on several commonly reported metrics used to characterize brain networks. The nodes of the network were defined using either the individual FreeSurfer segmentation of each subject or the FreeSurfer segmented MNI (Montreal National Institute) 152 template, using the Destrieux and sub-cortical atlas. The functional acquisition was either kept on the functional native space or normalized into MNI standard space. The comparisons were done at three levels: on the connections, on the edge properties and on the network properties levels. Our results reveal that different registration and brain parcellation strategies have a strong impact on all the levels of analysis, possibly favoring the use of individual segmentation strategies and conservative registration approaches. In conclusion, several technical aspects must be considered so that graph theoretical analysis of connectivity MRI data can provide a framework to understand brain pathologies. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/33578 |
url |
http://hdl.handle.net/1822/33578 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2158-0014 10.1089/brain.2014.0292 25420048 http://www.liebertpub.com |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Mary Ann Liebert |
publisher.none.fl_str_mv |
Mary Ann Liebert |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132556459769856 |