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-fie...
| Authors: | ; ; ; ; |
|---|---|
| Format: | Article |
| Status: | Published version |
| Publication date: | 2015 |
| Country: | España |
| Institution: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repository: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/36871 |
| Online Access: | http://hdl.handle.net/10230/36871 http://dx.doi.org/10.1016/j.neuroimage.2014.11.001 |
| Access Level: | Open access |
| Keyword: | Functional connectivity Functional connectivity dynamics Structural connectivity Resting state Brain dynamics Whole brain computational model |
| _version_ | 1878735191292248064 |
|---|---|
| author | Hansen, Enrique C.A. |
| author2 | Battaglia, Demian Spiegler, Andreas Deco, Gustavo Jirsa, Viktor K. |
| author2_role | author author author author |
| author_browse | Battaglia, Demian Deco, Gustavo Hansen, Enrique C.A. Jirsa, Viktor K. Spiegler, Andreas |
| author_facet | Hansen, Enrique C.A. Battaglia, Demian Spiegler, Andreas Deco, Gustavo Jirsa, Viktor K. |
| author_role | author |
| collection | Recercat. Dipósit de la Recerca de Catalunya |
| 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_77d5b6b563cf4e58d4d9be73c3ebe303 |
| institution | 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) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:recercat.cat:10230/36871 |
| publishDate | 2015 |
| publishDateSort | 2015 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| reponame_str | Recercat. Dipósit de la Recerca de Catalunya |
| spelling | Functional connectivity dynamics: modeling the switching behavior of the resting state Hansen, Enrique C.A. Battaglia, Demian Spiegler, Andreas Deco, Gustavo Jirsa, Viktor K. Functional connectivity Functional connectivity dynamics Structural connectivity Resting state Brain dynamics Whole brain computational model 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. 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. Elsevier http://hdl.handle.net/10230/36871 http://dx.doi.org/10.1016/j.neuroimage.2014.11.001 |
| 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 |
| status_str | publishedVersion |
| title | 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_short | Functional connectivity dynamics: modeling the switching behavior of the resting state |
| title_sort | Functional connectivity dynamics: modeling the switching behavior of the resting state |
| topic | Functional connectivity Functional connectivity dynamics Structural connectivity Resting state Brain dynamics Whole brain computational model |
| topic_facet | Functional connectivity Functional connectivity dynamics Structural connectivity Resting state Brain dynamics Whole brain computational model |
| url | http://hdl.handle.net/10230/36871 http://dx.doi.org/10.1016/j.neuroimage.2014.11.001 |