Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks

Brain networks offers a new insight about connections between function and anatomical regions of human brain. We present results from brain networks built from functional magnetic resonance images during finger tapping paradigm. Pearson voxel-voxel correlation in time and frequency domains were perf...

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Detalles Bibliográficos
Autores: Jiménez, Salvador, Rotger, Laura, Aguirre Maeso, Carlos, Muñoz, Alberto, Granados, Sergio, Tornero, Jesús
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/695786
Acceso en línea:http://hdl.handle.net/10486/695786
https://dx.doi.org/10.1371/journal.pone.0238994
Access Level:acceso abierto
Palabra clave:Brain networks
Functional magnetic resonance images
fMRI paradigm
Standard voxel-voxel correlation
Informática
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spelling Prefiltering based on experimental paradigm for analysis of fMRI complex brain networksJiménez, SalvadorRotger, LauraAguirre Maeso, CarlosMuñoz, AlbertoGranados, SergioTornero, JesúsBrain networksFunctional magnetic resonance imagesfMRI paradigmStandard voxel-voxel correlationInformáticaBrain networks offers a new insight about connections between function and anatomical regions of human brain. We present results from brain networks built from functional magnetic resonance images during finger tapping paradigm. Pearson voxel-voxel correlation in time and frequency domains were performed for all subjects. Besides this standard framework we have implemented a new approach consisting in filtering the data with respect to the fMRI paradigm (finger tapping) in order to obtain a better understanding of the network involved in the execution of the task. The main topological graph measures have been compared in both cases: Voxel-voxel correlation and voxel-paradigm filtering plus voxel-voxel correlation. With the standard voxel-voxel correlation a clearly free-scale network was obtained. On the other hand, when we prefiltered the paradigm we obtained two different kind of networks: 1) free-scale; 2) random-like. To our best knowledge, this behaviour is reported here for first time for brain networks. We suggest that paradigm signal prefiltering can provide more infomation about the brain networks.CA is supported by MINECO/FEDER, under grant PGC2018-095895-B-I00. Ministerio de Asuntos Económicos y Transformación Digital. https:\\www.mineco.gob.es. SJ is partially supported by MINECO under grant MTM2015- 67396-P. Ministerio de Asuntos Económicos y Transformación Digital. https:\\www.mineco.gob.Public Library of ScienceDepartamento de Ingeniería InformáticaEscuela Politécnica Superior20202020-10-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/695786https://dx.doi.org/10.1371/journal.pone.0238994reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/6957862026-06-23T12:46:27Z
dc.title.none.fl_str_mv Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks
title Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks
spellingShingle Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks
Jiménez, Salvador
Brain networks
Functional magnetic resonance images
fMRI paradigm
Standard voxel-voxel correlation
Informática
title_short Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks
title_full Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks
title_fullStr Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks
title_full_unstemmed Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks
title_sort Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks
dc.creator.none.fl_str_mv Jiménez, Salvador
Rotger, Laura
Aguirre Maeso, Carlos
Muñoz, Alberto
Granados, Sergio
Tornero, Jesús
author Jiménez, Salvador
author_facet Jiménez, Salvador
Rotger, Laura
Aguirre Maeso, Carlos
Muñoz, Alberto
Granados, Sergio
Tornero, Jesús
author_role author
author2 Rotger, Laura
Aguirre Maeso, Carlos
Muñoz, Alberto
Granados, Sergio
Tornero, Jesús
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
dc.subject.none.fl_str_mv Brain networks
Functional magnetic resonance images
fMRI paradigm
Standard voxel-voxel correlation
Informática
topic Brain networks
Functional magnetic resonance images
fMRI paradigm
Standard voxel-voxel correlation
Informática
description Brain networks offers a new insight about connections between function and anatomical regions of human brain. We present results from brain networks built from functional magnetic resonance images during finger tapping paradigm. Pearson voxel-voxel correlation in time and frequency domains were performed for all subjects. Besides this standard framework we have implemented a new approach consisting in filtering the data with respect to the fMRI paradigm (finger tapping) in order to obtain a better understanding of the network involved in the execution of the task. The main topological graph measures have been compared in both cases: Voxel-voxel correlation and voxel-paradigm filtering plus voxel-voxel correlation. With the standard voxel-voxel correlation a clearly free-scale network was obtained. On the other hand, when we prefiltered the paradigm we obtained two different kind of networks: 1) free-scale; 2) random-like. To our best knowledge, this behaviour is reported here for first time for brain networks. We suggest that paradigm signal prefiltering can provide more infomation about the brain networks.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-10-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/695786
https://dx.doi.org/10.1371/journal.pone.0238994
url http://hdl.handle.net/10486/695786
https://dx.doi.org/10.1371/journal.pone.0238994
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
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