On the pros and cons of using temporal derivatives to assess brain functional connectivity

Autores
Ochab, Jeremi K.; Tarnowski, Wojciech; Nowak, Maciej A.; Chialvo, Dante Renato
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The study of correlations between brain regions is an important chapter of the analysis of large-scale brain spatiotemporal dynamics. In particular, novel methods suited to extract dynamic changes in mutual correlations are needed. Here we scrutinize a recently reported metric dubbed “Multiplication of Temporal Derivatives” (MTD) which is based on the temporal derivative of each time series. The formal comparison of the MTD formula with the Pearson correlation of the derivatives reveals only minor differences, which we find negligible in practice. A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups, including a realistic stationary network detection, reveals lower sensitivity of derivatives to low frequency drifts and to autocorrelations but also lower signal-to-noise ratio. It does not indicate any evident mathematical advantages of the proposed metric over commonly used correlation methods.
Fil: Ochab, Jeremi K.. Universidad Jagellónica; Polonia
Fil: Tarnowski, Wojciech. Universidad Jagellónica; Polonia
Fil: Nowak, Maciej A.. Universidad Jagellónica; Polonia
Fil: Chialvo, Dante Renato. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
FMRI
FUNCTIONAL CONNECTIVITY
RESTING STATE NETWORKS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/117218
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling On the pros and cons of using temporal derivatives to assess brain functional connectivityOchab, Jeremi K.Tarnowski, WojciechNowak, Maciej A.Chialvo, Dante RenatoFMRIFUNCTIONAL CONNECTIVITYRESTING STATE NETWORKShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The study of correlations between brain regions is an important chapter of the analysis of large-scale brain spatiotemporal dynamics. In particular, novel methods suited to extract dynamic changes in mutual correlations are needed. Here we scrutinize a recently reported metric dubbed “Multiplication of Temporal Derivatives” (MTD) which is based on the temporal derivative of each time series. The formal comparison of the MTD formula with the Pearson correlation of the derivatives reveals only minor differences, which we find negligible in practice. A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups, including a realistic stationary network detection, reveals lower sensitivity of derivatives to low frequency drifts and to autocorrelations but also lower signal-to-noise ratio. It does not indicate any evident mathematical advantages of the proposed metric over commonly used correlation methods.Fil: Ochab, Jeremi K.. Universidad Jagellónica; PoloniaFil: Tarnowski, Wojciech. Universidad Jagellónica; PoloniaFil: Nowak, Maciej A.. Universidad Jagellónica; PoloniaFil: Chialvo, Dante Renato. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2019-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/117218Ochab, Jeremi K.; Tarnowski, Wojciech; Nowak, Maciej A.; Chialvo, Dante Renato; On the pros and cons of using temporal derivatives to assess brain functional connectivity; Elsevier; Journal Neuroimag; 184; 1-2019; 577-5851053-8119CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S1053811918318676info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2018.09.063info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:21:29Zoai:ri.conicet.gov.ar:11336/117218instacron: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:34982025-09-10 13:21:29.41CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On the pros and cons of using temporal derivatives to assess brain functional connectivity
title On the pros and cons of using temporal derivatives to assess brain functional connectivity
spellingShingle On the pros and cons of using temporal derivatives to assess brain functional connectivity
Ochab, Jeremi K.
FMRI
FUNCTIONAL CONNECTIVITY
RESTING STATE NETWORKS
title_short On the pros and cons of using temporal derivatives to assess brain functional connectivity
title_full On the pros and cons of using temporal derivatives to assess brain functional connectivity
title_fullStr On the pros and cons of using temporal derivatives to assess brain functional connectivity
title_full_unstemmed On the pros and cons of using temporal derivatives to assess brain functional connectivity
title_sort On the pros and cons of using temporal derivatives to assess brain functional connectivity
dc.creator.none.fl_str_mv Ochab, Jeremi K.
Tarnowski, Wojciech
Nowak, Maciej A.
Chialvo, Dante Renato
author Ochab, Jeremi K.
author_facet Ochab, Jeremi K.
Tarnowski, Wojciech
Nowak, Maciej A.
Chialvo, Dante Renato
author_role author
author2 Tarnowski, Wojciech
Nowak, Maciej A.
Chialvo, Dante Renato
author2_role author
author
author
dc.subject.none.fl_str_mv FMRI
FUNCTIONAL CONNECTIVITY
RESTING STATE NETWORKS
topic FMRI
FUNCTIONAL CONNECTIVITY
RESTING STATE NETWORKS
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 correlations between brain regions is an important chapter of the analysis of large-scale brain spatiotemporal dynamics. In particular, novel methods suited to extract dynamic changes in mutual correlations are needed. Here we scrutinize a recently reported metric dubbed “Multiplication of Temporal Derivatives” (MTD) which is based on the temporal derivative of each time series. The formal comparison of the MTD formula with the Pearson correlation of the derivatives reveals only minor differences, which we find negligible in practice. A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups, including a realistic stationary network detection, reveals lower sensitivity of derivatives to low frequency drifts and to autocorrelations but also lower signal-to-noise ratio. It does not indicate any evident mathematical advantages of the proposed metric over commonly used correlation methods.
Fil: Ochab, Jeremi K.. Universidad Jagellónica; Polonia
Fil: Tarnowski, Wojciech. Universidad Jagellónica; Polonia
Fil: Nowak, Maciej A.. Universidad Jagellónica; Polonia
Fil: Chialvo, Dante Renato. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description The study of correlations between brain regions is an important chapter of the analysis of large-scale brain spatiotemporal dynamics. In particular, novel methods suited to extract dynamic changes in mutual correlations are needed. Here we scrutinize a recently reported metric dubbed “Multiplication of Temporal Derivatives” (MTD) which is based on the temporal derivative of each time series. The formal comparison of the MTD formula with the Pearson correlation of the derivatives reveals only minor differences, which we find negligible in practice. A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups, including a realistic stationary network detection, reveals lower sensitivity of derivatives to low frequency drifts and to autocorrelations but also lower signal-to-noise ratio. It does not indicate any evident mathematical advantages of the proposed metric over commonly used correlation methods.
publishDate 2019
dc.date.none.fl_str_mv 2019-01
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/11336/117218
Ochab, Jeremi K.; Tarnowski, Wojciech; Nowak, Maciej A.; Chialvo, Dante Renato; On the pros and cons of using temporal derivatives to assess brain functional connectivity; Elsevier; Journal Neuroimag; 184; 1-2019; 577-585
1053-8119
CONICET Digital
CONICET
url http://hdl.handle.net/11336/117218
identifier_str_mv Ochab, Jeremi K.; Tarnowski, Wojciech; Nowak, Maciej A.; Chialvo, Dante Renato; On the pros and cons of using temporal derivatives to assess brain functional connectivity; Elsevier; Journal Neuroimag; 184; 1-2019; 577-585
1053-8119
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S1053811918318676
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2018.09.063
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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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score 12.48226

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