Dynamic primitives of brain network interaction

What dynamic processes underly functional brain networks? Functional connectivity (FC) and functional connectivity dynamics (FCD) are used to represent the patterns and dynamics of functional brain networks. FC(D) is related to the synchrony of brain activity: when brain areas oscillate in a coordin...

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Autores: Schirner, Michael, Kong, Xiaolu, Yeo, B.T. Thomas, Deco, Gustavo, Ritter, Petra
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/56020
Acceso en línea:http://hdl.handle.net/10230/56020
http://dx.doi.org/10.1016/j.neuroimage.2022.118928
Access Level:acceso abierto
Palabra clave:Xarxes neuronals (Neurobiologia)
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spelling Dynamic primitives of brain network interactionSchirner, MichaelKong, XiaoluYeo, B.T. ThomasDeco, GustavoRitter, PetraXarxes neuronals (Neurobiologia)What dynamic processes underly functional brain networks? Functional connectivity (FC) and functional connectivity dynamics (FCD) are used to represent the patterns and dynamics of functional brain networks. FC(D) is related to the synchrony of brain activity: when brain areas oscillate in a coordinated manner this yields a high correlation between their signal time series. To explain the processes underlying FC(D) we review how synchronized oscillations emerge from coupled neural populations in brain network models (BNMs). From detailed spiking networks to more abstract population models, there is strong support for the idea that the brain operates near critical instabilities that give rise to multistable or metastable dynamics that in turn lead to the intermittently synchronized slow oscillations underlying FC(D). We explore further consequences from these fundamental mechanisms and how they fit with reality. We conclude by highlighting the need for integrative brain models that connect separate mechanisms across levels of description and spatiotemporal scales and link them with cognitive function.We acknowledge support by H2020 Research and Innovation Action grants Human Brain Project SGA2 785907, SGA3 945539, VirtualBrainCloud 826421 and ERC 683049; Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative. Several computations have also been performed on the HPC for Research cluster of the Berlin Institute of Health. We acknowledge the use of Fenix Infrastructure resources, which are partially funded from the European Union's Horizon 2020 research and innovation programme through the ICEI project under the grant agreement No. 800858. German Research Foundation SFB 1436 (project ID 425899996); SFB 1315 (project ID 327654276); SFB 936 (project ID 178316478); SFB-TRR 295 (project ID 424778381); SPP Computational Connectomics RI 2073/6-1, RI 2073/10-2, RI 2073/9-1. BTTY is supported by the Singapore National Research Foundation (NRF) Fellowship (Class of 2017), the NUS Yong Loo Lin School of Medicine (NUHSRO/2020/124/TMR/LOA), the Singapore National Medical Research Council (NMRC) LCG (OFLCG19May-0035) and the United States National Institutes of Health (R01MH120080).Elsevier202320232022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/56020http://dx.doi.org/10.1016/j.neuroimage.2022.118928reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésNeuroImage. 2022;250:118928.https://github.com/BrainModes/Review_DynamicPrimitivesinfo:eu-repo/grantAgreement/EC/H2020/785907info:eu-repo/grantAgreement/EC/H2020/945539info:eu-repo/grantAgreement/EC/H2020/826421info:eu-repo/grantAgreement/EC/H2020/683049info:eu-repo/grantAgreement/EC/H2020/800858© 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/560202026-06-12T07:21:37Z
dc.title.none.fl_str_mv Dynamic primitives of brain network interaction
title Dynamic primitives of brain network interaction
spellingShingle Dynamic primitives of brain network interaction
Schirner, Michael
Xarxes neuronals (Neurobiologia)
title_short Dynamic primitives of brain network interaction
title_full Dynamic primitives of brain network interaction
title_fullStr Dynamic primitives of brain network interaction
title_full_unstemmed Dynamic primitives of brain network interaction
title_sort Dynamic primitives of brain network interaction
dc.creator.none.fl_str_mv Schirner, Michael
Kong, Xiaolu
Yeo, B.T. Thomas
Deco, Gustavo
Ritter, Petra
author Schirner, Michael
author_facet Schirner, Michael
Kong, Xiaolu
Yeo, B.T. Thomas
Deco, Gustavo
Ritter, Petra
author_role author
author2 Kong, Xiaolu
Yeo, B.T. Thomas
Deco, Gustavo
Ritter, Petra
author2_role author
author
author
author
dc.subject.none.fl_str_mv Xarxes neuronals (Neurobiologia)
topic Xarxes neuronals (Neurobiologia)
description What dynamic processes underly functional brain networks? Functional connectivity (FC) and functional connectivity dynamics (FCD) are used to represent the patterns and dynamics of functional brain networks. FC(D) is related to the synchrony of brain activity: when brain areas oscillate in a coordinated manner this yields a high correlation between their signal time series. To explain the processes underlying FC(D) we review how synchronized oscillations emerge from coupled neural populations in brain network models (BNMs). From detailed spiking networks to more abstract population models, there is strong support for the idea that the brain operates near critical instabilities that give rise to multistable or metastable dynamics that in turn lead to the intermittently synchronized slow oscillations underlying FC(D). We explore further consequences from these fundamental mechanisms and how they fit with reality. We conclude by highlighting the need for integrative brain models that connect separate mechanisms across levels of description and spatiotemporal scales and link them with cognitive function.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
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/10230/56020
http://dx.doi.org/10.1016/j.neuroimage.2022.118928
url http://hdl.handle.net/10230/56020
http://dx.doi.org/10.1016/j.neuroimage.2022.118928
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv NeuroImage. 2022;250:118928.
https://github.com/BrainModes/Review_DynamicPrimitives
info:eu-repo/grantAgreement/EC/H2020/785907
info:eu-repo/grantAgreement/EC/H2020/945539
info:eu-repo/grantAgreement/EC/H2020/826421
info:eu-repo/grantAgreement/EC/H2020/683049
info:eu-repo/grantAgreement/EC/H2020/800858
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
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:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
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