Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis

Resting state functional magnetic resonance imaging (rfMRI) can be used to measure functional connectivity and then identify brain networks and related brain disorders and diseases. To explore these complex networks, however, huge amounts of data are necessary. Recent advances in neuroimaging techno...

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Detalles Bibliográficos
Autores: Phinyomark, Angkoon, Ibáñez-Marcelo, Esther, Petri, Giovanni
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/151696
Acceso en línea:http://hdl.handle.net/10609/151696
https://doi.org/10.1109/TBDATA.2017.2734883
Access Level:acceso abierto
Palabra clave:big data
brain network
functional connectivit
graph theor
preprocessing pipeline
resting-state fMRI
topological data analysis
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spelling Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data AnalysisPhinyomark, AngkoonIbáñez-Marcelo, EstherPetri, Giovannibig databrain networkfunctional connectivitgraph theorpreprocessing pipelineresting-state fMRItopological data analysisResting state functional magnetic resonance imaging (rfMRI) can be used to measure functional connectivity and then identify brain networks and related brain disorders and diseases. To explore these complex networks, however, huge amounts of data are necessary. Recent advances in neuroimaging technologies, and the unique methodological approach of rfMRI, have enabled us to an era of Biomedical Big Data. The recent progress of big data sharing projects with their challenges are discussed. This increasing amount of neuroimaging data has greatly increased the importance of developing preprocessing pipelines and advanced analytic techniques, which are better at handling large-scale datasets. Before applying any analysis method on rfMRI data, several preprocessing steps need to be applied to reduce all unwanted effects. Three alternative ways to get access to big preprocessed rfMRI data are presented involving the minimal preprocessing pipelines. There are several commonly used methods to examine functional connectivity. However, they become limited in the analysis of big data, and a new tool to explore such data is necessary. We propose a number of novel methods rooted in algebraic topology and collectively referred to as Topological Data Analysis to rfMRI functional connectivity. Their properties for big data analysis are also discussed.IEEE Explore202420242017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/151696https://doi.org/10.1109/TBDATA.2017.2734883reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésIEEE Transactions on Big DataIEEE Transactions on Big Data, 2017, 3, (4)https://doi.org/10.1109/TBDATA.2017.2734883© 2017 IEEEinfo:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1516962026-05-28T12:42:01Z
dc.title.none.fl_str_mv Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis
title Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis
spellingShingle Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis
Phinyomark, Angkoon
big data
brain network
functional connectivit
graph theor
preprocessing pipeline
resting-state fMRI
topological data analysis
title_short Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis
title_full Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis
title_fullStr Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis
title_full_unstemmed Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis
title_sort Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis
dc.creator.none.fl_str_mv Phinyomark, Angkoon
Ibáñez-Marcelo, Esther
Petri, Giovanni
author Phinyomark, Angkoon
author_facet Phinyomark, Angkoon
Ibáñez-Marcelo, Esther
Petri, Giovanni
author_role author
author2 Ibáñez-Marcelo, Esther
Petri, Giovanni
author2_role author
author
dc.subject.none.fl_str_mv big data
brain network
functional connectivit
graph theor
preprocessing pipeline
resting-state fMRI
topological data analysis
topic big data
brain network
functional connectivit
graph theor
preprocessing pipeline
resting-state fMRI
topological data analysis
description Resting state functional magnetic resonance imaging (rfMRI) can be used to measure functional connectivity and then identify brain networks and related brain disorders and diseases. To explore these complex networks, however, huge amounts of data are necessary. Recent advances in neuroimaging technologies, and the unique methodological approach of rfMRI, have enabled us to an era of Biomedical Big Data. The recent progress of big data sharing projects with their challenges are discussed. This increasing amount of neuroimaging data has greatly increased the importance of developing preprocessing pipelines and advanced analytic techniques, which are better at handling large-scale datasets. Before applying any analysis method on rfMRI data, several preprocessing steps need to be applied to reduce all unwanted effects. Three alternative ways to get access to big preprocessed rfMRI data are presented involving the minimal preprocessing pipelines. There are several commonly used methods to examine functional connectivity. However, they become limited in the analysis of big data, and a new tool to explore such data is necessary. We propose a number of novel methods rooted in algebraic topology and collectively referred to as Topological Data Analysis to rfMRI functional connectivity. Their properties for big data analysis are also discussed.
publishDate 2017
dc.date.none.fl_str_mv 2017
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10609/151696
https://doi.org/10.1109/TBDATA.2017.2734883
url http://hdl.handle.net/10609/151696
https://doi.org/10.1109/TBDATA.2017.2734883
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Big Data
IEEE Transactions on Big Data, 2017, 3, (4)
https://doi.org/10.1109/TBDATA.2017.2734883
dc.rights.none.fl_str_mv © 2017 IEEE
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2017 IEEE
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE Explore
publisher.none.fl_str_mv IEEE Explore
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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