The Hopf whole-brain model and its linear approximation
Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local...
| Autores: | , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/401583 |
| Acesso em linha: | https://hdl.handle.net/2117/401583 https://dx.doi.org/10.1038/s41598-024-53105-0 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Neurology Brain -- Research Computational neuroscience Neural circuits Whole-brain models Neurologia Cervell -- Investigació Classificació AMS::92 Biology and other natural sciences::92C Physiological, cellular and medical topics Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Neurologia Àrees temàtiques de la UPC::Enginyeria biomèdica |
| Resumo: | Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in diferent brain states. Here, we studied the linear fuctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation—that holds in the case of heterogeneous parameters and time-delays—allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics. |
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