Data-driven discovery of canonical large-scale brain dynamics

Autores
Piccinini, Juan Ignacio; Deco, Gustavo; Kringelbach, Morten; Laufs, Helmut; Sanz Perl Hernandez, Yonatan; Tagliazucchi, Enzo Rodolfo
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.
Fil: Piccinini, Juan Ignacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Deco, Gustavo. Universitat Pompeu Fabra; España
Fil: Kringelbach, Morten. University of Oxford; Reino Unido
Fil: Laufs, Helmut. University of Kiel; Alemania
Fil: Sanz Perl Hernandez, Yonatan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Tagliazucchi, Enzo Rodolfo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
BRAIN DYNAMICS
WHOLE-BRAIN MODELS
DATA-DRIVEN MODELS
SLEEP
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/206393
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/206393
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Data-driven discovery of canonical large-scale brain dynamicsPiccinini, Juan IgnacioDeco, GustavoKringelbach, MortenLaufs, HelmutSanz Perl Hernandez, YonatanTagliazucchi, Enzo RodolfoBRAIN DYNAMICSWHOLE-BRAIN MODELSDATA-DRIVEN MODELSSLEEPhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.Fil: Piccinini, Juan Ignacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Deco, Gustavo. Universitat Pompeu Fabra; EspañaFil: Kringelbach, Morten. University of Oxford; Reino UnidoFil: Laufs, Helmut. University of Kiel; AlemaniaFil: Sanz Perl Hernandez, Yonatan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tagliazucchi, Enzo Rodolfo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaOxford University Press2022-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/206393Piccinini, Juan Ignacio; Deco, Gustavo; Kringelbach, Morten; Laufs, Helmut; Sanz Perl Hernandez, Yonatan; et al.; Data-driven discovery of canonical large-scale brain dynamics; Oxford University Press; Cerebral Cortex Communications; 3; 4; 10-2022; 1-122632-7376CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/cercorcomms/article/doi/10.1093/texcom/tgac045/6794020info:eu-repo/semantics/altIdentifier/doi/10.1093/texcom/tgac045info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:33:43Zoai:ri.conicet.gov.ar:11336/206393instacron: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:34982025-10-15 15:33:43.814CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Data-driven discovery of canonical large-scale brain dynamics
title Data-driven discovery of canonical large-scale brain dynamics
spellingShingle Data-driven discovery of canonical large-scale brain dynamics
Piccinini, Juan Ignacio
BRAIN DYNAMICS
WHOLE-BRAIN MODELS
DATA-DRIVEN MODELS
SLEEP
title_short Data-driven discovery of canonical large-scale brain dynamics
title_full Data-driven discovery of canonical large-scale brain dynamics
title_fullStr Data-driven discovery of canonical large-scale brain dynamics
title_full_unstemmed Data-driven discovery of canonical large-scale brain dynamics
title_sort Data-driven discovery of canonical large-scale brain dynamics
dc.creator.none.fl_str_mv Piccinini, Juan Ignacio
Deco, Gustavo
Kringelbach, Morten
Laufs, Helmut
Sanz Perl Hernandez, Yonatan
Tagliazucchi, Enzo Rodolfo
author Piccinini, Juan Ignacio
author_facet Piccinini, Juan Ignacio
Deco, Gustavo
Kringelbach, Morten
Laufs, Helmut
Sanz Perl Hernandez, Yonatan
Tagliazucchi, Enzo Rodolfo
author_role author
author2 Deco, Gustavo
Kringelbach, Morten
Laufs, Helmut
Sanz Perl Hernandez, Yonatan
Tagliazucchi, Enzo Rodolfo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv BRAIN DYNAMICS
WHOLE-BRAIN MODELS
DATA-DRIVEN MODELS
SLEEP
topic BRAIN DYNAMICS
WHOLE-BRAIN MODELS
DATA-DRIVEN MODELS
SLEEP
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.
Fil: Piccinini, Juan Ignacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Deco, Gustavo. Universitat Pompeu Fabra; España
Fil: Kringelbach, Morten. University of Oxford; Reino Unido
Fil: Laufs, Helmut. University of Kiel; Alemania
Fil: Sanz Perl Hernandez, Yonatan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Tagliazucchi, Enzo Rodolfo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.
publishDate 2022
dc.date.none.fl_str_mv 2022-10
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/11336/206393
Piccinini, Juan Ignacio; Deco, Gustavo; Kringelbach, Morten; Laufs, Helmut; Sanz Perl Hernandez, Yonatan; et al.; Data-driven discovery of canonical large-scale brain dynamics; Oxford University Press; Cerebral Cortex Communications; 3; 4; 10-2022; 1-12
2632-7376
CONICET Digital
CONICET
url http://hdl.handle.net/11336/206393
identifier_str_mv Piccinini, Juan Ignacio; Deco, Gustavo; Kringelbach, Morten; Laufs, Helmut; Sanz Perl Hernandez, Yonatan; et al.; Data-driven discovery of canonical large-scale brain dynamics; Oxford University Press; Cerebral Cortex Communications; 3; 4; 10-2022; 1-12
2632-7376
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://academic.oup.com/cercorcomms/article/doi/10.1093/texcom/tgac045/6794020
info:eu-repo/semantics/altIdentifier/doi/10.1093/texcom/tgac045
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
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
_version_ 1846083466670637056
score 13.22299

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