Functional connectivity dynamics: modeling the switching behavior of the resting state

Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean...

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Detalles Bibliográficos
Autores: Hansen, Enrique C.A., Battaglia, Demian, Spiegler, Andreas, Deco, Gustavo, Jirsa, Viktor K.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/36871
Acceso en línea:http://hdl.handle.net/10230/36871
http://dx.doi.org/10.1016/j.neuroimage.2014.11.001
Access Level:acceso abierto
Palabra clave:Functional connectivity
Functional connectivity dynamics
Structural connectivity
Resting state
Brain dynamics
Whole brain computational model
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spelling Functional connectivity dynamics: modeling the switching behavior of the resting stateHansen, Enrique C.A.Battaglia, DemianSpiegler, AndreasDeco, GustavoJirsa, Viktor K.Functional connectivityFunctional connectivity dynamicsStructural connectivityResting stateBrain dynamicsWhole brain computational modelFunctional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state.Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Herewe showthat this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit timeaveraged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of themost frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations.The research reported herein was supported by the Brain Network Recovery Group through the James S. McDonnell Foundation and funding from the European Union Seventh Framework Programme (FP7-ICT BrainScales and Human Brain Project (grant no. 60402)). DB was supported by the Marie Curie career development fellowship FP7-IEF 330792 (DynViB) and by the Federal Ministry of Education and Research (BMBF) Germany under grant number 01GQ1005B. We thank Patrick Hagmann and his group for providing the empirical data.Elsevier201920192015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/36871http://dx.doi.org/10.1016/j.neuroimage.2014.11.001reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésNeuroimage. 2015 Jan 15;105:525-35. DOI: 10.1016/j.neuroimage.2014.11.001info:eu-repo/grantAgreement/EC/FP7/60402info:eu-repo/grantAgreement/EC/FP7/330792© 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/)http://creativecommons.org/licenses/by-nc-sa/3.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/368712026-05-29T05:05:01Z
dc.title.none.fl_str_mv Functional connectivity dynamics: modeling the switching behavior of the resting state
title Functional connectivity dynamics: modeling the switching behavior of the resting state
spellingShingle Functional connectivity dynamics: modeling the switching behavior of the resting state
Hansen, Enrique C.A.
Functional connectivity
Functional connectivity dynamics
Structural connectivity
Resting state
Brain dynamics
Whole brain computational model
title_short Functional connectivity dynamics: modeling the switching behavior of the resting state
title_full Functional connectivity dynamics: modeling the switching behavior of the resting state
title_fullStr Functional connectivity dynamics: modeling the switching behavior of the resting state
title_full_unstemmed Functional connectivity dynamics: modeling the switching behavior of the resting state
title_sort Functional connectivity dynamics: modeling the switching behavior of the resting state
dc.creator.none.fl_str_mv Hansen, Enrique C.A.
Battaglia, Demian
Spiegler, Andreas
Deco, Gustavo
Jirsa, Viktor K.
author Hansen, Enrique C.A.
author_facet Hansen, Enrique C.A.
Battaglia, Demian
Spiegler, Andreas
Deco, Gustavo
Jirsa, Viktor K.
author_role author
author2 Battaglia, Demian
Spiegler, Andreas
Deco, Gustavo
Jirsa, Viktor K.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Functional connectivity
Functional connectivity dynamics
Structural connectivity
Resting state
Brain dynamics
Whole brain computational model
topic Functional connectivity
Functional connectivity dynamics
Structural connectivity
Resting state
Brain dynamics
Whole brain computational model
description Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state.Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Herewe showthat this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit timeaveraged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of themost frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations.
publishDate 2015
dc.date.none.fl_str_mv 2015
2019
2019
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/10230/36871
http://dx.doi.org/10.1016/j.neuroimage.2014.11.001
url http://hdl.handle.net/10230/36871
http://dx.doi.org/10.1016/j.neuroimage.2014.11.001
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Neuroimage. 2015 Jan 15;105:525-35. DOI: 10.1016/j.neuroimage.2014.11.001
info:eu-repo/grantAgreement/EC/FP7/60402
info:eu-repo/grantAgreement/EC/FP7/330792
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
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:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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