Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF

Autores
Herzog, Rubén; Mediano, Pedro A. M.; Rosas, Fernando E.; Luppi, Andrea I.; Sanz Perl Hernandez, Yonatan; Tagliazucchi, Enzo Rodolfo; Kringelbach, Morten L.; Cofré, Rodrigo; Deco, Gustavo
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Different whole-brain computational models have been recently developed to investigate hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is particularly attractive, combining a biophysically realistic model that is scaled up via a mean-field approach and multimodal imaging data. However, an important barrier to the widespread usage of the DMF model is that current implementations are computationally expensive, supporting only simulations on brain parcellations that consider less than 100 brain regions. Here, we introduce an efficient and accessible implementation of the DMF model: the FastDMF. By leveraging analytical and numerical advances—including a novel estimation of the feedback inhibition control parameter and a Bayesian optimization algorithm—the FastDMF circumvents various computational bottlenecks of previous implementations, improving interpretability, performance, and memory use. Furthermore, these advances allow the FastDMF to increase the number of simulated regions by one order of magnitude, as confirmed by the good fit to fMRI data parcellated at 90 and 1,000 regions. These advances open the way to the widespread use of biophysically grounded whole-brain models for investigating the interplay between anatomy, function, and brain dynamics and to identify mechanistic explanations of recent results obtained from fine-grained neuroimaging recordings.
Fil: Herzog, Rubén. Paris Brain Institute; Francia
Fil: Mediano, Pedro A. M.. University of Cambridge; Reino Unido
Fil: Rosas, Fernando E.. University of Oxford; Reino Unido
Fil: Luppi, Andrea I.. University of Cambridge; Reino Unido
Fil: Sanz Perl Hernandez, Yonatan. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; 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. Universidad Adolfo Ibañez; Chile
Fil: Kringelbach, Morten L.. University of Oxford; Reino Unido
Fil: Cofré, Rodrigo. Universite Paris-saclay (universite Paris-saclay);
Fil: Deco, Gustavo. Universitat Pompeu Fabra; España
Materia
Whole-brain model
Mean-field model
Neuroimaging
Local Inhibition
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:11336/251968
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/251968
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMFHerzog, RubénMediano, Pedro A. M.Rosas, Fernando E.Luppi, Andrea I.Sanz Perl Hernandez, YonatanTagliazucchi, Enzo RodolfoKringelbach, Morten L.Cofré, RodrigoDeco, GustavoWhole-brain modelMean-field modelNeuroimagingLocal Inhibitionhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Different whole-brain computational models have been recently developed to investigate hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is particularly attractive, combining a biophysically realistic model that is scaled up via a mean-field approach and multimodal imaging data. However, an important barrier to the widespread usage of the DMF model is that current implementations are computationally expensive, supporting only simulations on brain parcellations that consider less than 100 brain regions. Here, we introduce an efficient and accessible implementation of the DMF model: the FastDMF. By leveraging analytical and numerical advances—including a novel estimation of the feedback inhibition control parameter and a Bayesian optimization algorithm—the FastDMF circumvents various computational bottlenecks of previous implementations, improving interpretability, performance, and memory use. Furthermore, these advances allow the FastDMF to increase the number of simulated regions by one order of magnitude, as confirmed by the good fit to fMRI data parcellated at 90 and 1,000 regions. These advances open the way to the widespread use of biophysically grounded whole-brain models for investigating the interplay between anatomy, function, and brain dynamics and to identify mechanistic explanations of recent results obtained from fine-grained neuroimaging recordings.Fil: Herzog, Rubén. Paris Brain Institute; FranciaFil: Mediano, Pedro A. M.. University of Cambridge; Reino UnidoFil: Rosas, Fernando E.. University of Oxford; Reino UnidoFil: Luppi, Andrea I.. University of Cambridge; Reino UnidoFil: Sanz Perl Hernandez, Yonatan. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; 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; Argentina. Universidad Adolfo Ibañez; ChileFil: Kringelbach, Morten L.. University of Oxford; Reino UnidoFil: Cofré, Rodrigo. Universite Paris-saclay (universite Paris-saclay);Fil: Deco, Gustavo. Universitat Pompeu Fabra; EspañaMIT Press2024-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/251968Herzog, Rubén; Mediano, Pedro A. M.; Rosas, Fernando E.; Luppi, Andrea I.; Sanz Perl Hernandez, Yonatan; et al.; Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF; MIT Press; Network Neuroscience; 8; 4; 12-2024; 1590-16122472-1751CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://direct.mit.edu/netn/article/8/4/1590/123888/Neural-mass-modeling-for-the-masses-Democratizinginfo:eu-repo/semantics/altIdentifier/doi/10.1162/netn_a_00410info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:45:58Zoai:ri.conicet.gov.ar:11336/251968instacron: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:34982025-09-29 09:45:59.235CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF
title Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF
spellingShingle Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF
Herzog, Rubén
Whole-brain model
Mean-field model
Neuroimaging
Local Inhibition
title_short Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF
title_full Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF
title_fullStr Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF
title_full_unstemmed Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF
title_sort Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF
dc.creator.none.fl_str_mv Herzog, Rubén
Mediano, Pedro A. M.
Rosas, Fernando E.
Luppi, Andrea I.
Sanz Perl Hernandez, Yonatan
Tagliazucchi, Enzo Rodolfo
Kringelbach, Morten L.
Cofré, Rodrigo
Deco, Gustavo
author Herzog, Rubén
author_facet Herzog, Rubén
Mediano, Pedro A. M.
Rosas, Fernando E.
Luppi, Andrea I.
Sanz Perl Hernandez, Yonatan
Tagliazucchi, Enzo Rodolfo
Kringelbach, Morten L.
Cofré, Rodrigo
Deco, Gustavo
author_role author
author2 Mediano, Pedro A. M.
Rosas, Fernando E.
Luppi, Andrea I.
Sanz Perl Hernandez, Yonatan
Tagliazucchi, Enzo Rodolfo
Kringelbach, Morten L.
Cofré, Rodrigo
Deco, Gustavo
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Whole-brain model
Mean-field model
Neuroimaging
Local Inhibition
topic Whole-brain model
Mean-field model
Neuroimaging
Local Inhibition
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Different whole-brain computational models have been recently developed to investigate hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is particularly attractive, combining a biophysically realistic model that is scaled up via a mean-field approach and multimodal imaging data. However, an important barrier to the widespread usage of the DMF model is that current implementations are computationally expensive, supporting only simulations on brain parcellations that consider less than 100 brain regions. Here, we introduce an efficient and accessible implementation of the DMF model: the FastDMF. By leveraging analytical and numerical advances—including a novel estimation of the feedback inhibition control parameter and a Bayesian optimization algorithm—the FastDMF circumvents various computational bottlenecks of previous implementations, improving interpretability, performance, and memory use. Furthermore, these advances allow the FastDMF to increase the number of simulated regions by one order of magnitude, as confirmed by the good fit to fMRI data parcellated at 90 and 1,000 regions. These advances open the way to the widespread use of biophysically grounded whole-brain models for investigating the interplay between anatomy, function, and brain dynamics and to identify mechanistic explanations of recent results obtained from fine-grained neuroimaging recordings.
Fil: Herzog, Rubén. Paris Brain Institute; Francia
Fil: Mediano, Pedro A. M.. University of Cambridge; Reino Unido
Fil: Rosas, Fernando E.. University of Oxford; Reino Unido
Fil: Luppi, Andrea I.. University of Cambridge; Reino Unido
Fil: Sanz Perl Hernandez, Yonatan. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; 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. Universidad Adolfo Ibañez; Chile
Fil: Kringelbach, Morten L.. University of Oxford; Reino Unido
Fil: Cofré, Rodrigo. Universite Paris-saclay (universite Paris-saclay);
Fil: Deco, Gustavo. Universitat Pompeu Fabra; España
description Different whole-brain computational models have been recently developed to investigate hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is particularly attractive, combining a biophysically realistic model that is scaled up via a mean-field approach and multimodal imaging data. However, an important barrier to the widespread usage of the DMF model is that current implementations are computationally expensive, supporting only simulations on brain parcellations that consider less than 100 brain regions. Here, we introduce an efficient and accessible implementation of the DMF model: the FastDMF. By leveraging analytical and numerical advances—including a novel estimation of the feedback inhibition control parameter and a Bayesian optimization algorithm—the FastDMF circumvents various computational bottlenecks of previous implementations, improving interpretability, performance, and memory use. Furthermore, these advances allow the FastDMF to increase the number of simulated regions by one order of magnitude, as confirmed by the good fit to fMRI data parcellated at 90 and 1,000 regions. These advances open the way to the widespread use of biophysically grounded whole-brain models for investigating the interplay between anatomy, function, and brain dynamics and to identify mechanistic explanations of recent results obtained from fine-grained neuroimaging recordings.
publishDate 2024
dc.date.none.fl_str_mv 2024-12
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/11336/251968
Herzog, Rubén; Mediano, Pedro A. M.; Rosas, Fernando E.; Luppi, Andrea I.; Sanz Perl Hernandez, Yonatan; et al.; Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF; MIT Press; Network Neuroscience; 8; 4; 12-2024; 1590-1612
2472-1751
CONICET Digital
CONICET
url http://hdl.handle.net/11336/251968
identifier_str_mv Herzog, Rubén; Mediano, Pedro A. M.; Rosas, Fernando E.; Luppi, Andrea I.; Sanz Perl Hernandez, Yonatan; et al.; Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF; MIT Press; Network Neuroscience; 8; 4; 12-2024; 1590-1612
2472-1751
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://direct.mit.edu/netn/article/8/4/1590/123888/Neural-mass-modeling-for-the-masses-Democratizing
info:eu-repo/semantics/altIdentifier/doi/10.1162/netn_a_00410
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 MIT Press
publisher.none.fl_str_mv MIT 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
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score 13.070432

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