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Anatomical connectivity and the resting state activity of large cortical networks
This paper uses mathematical modelling and simulations to explore the dynamics that emerge in large scale cortical networks, with a particular focus on the topological properties of the structural connectivity and its relationship to functional connectivity. We exploit realistic anatomical connectiv...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3520011/ https://www.ncbi.nlm.nih.gov/pubmed/23085498 http://dx.doi.org/10.1016/j.neuroimage.2012.10.016 |
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author | Pinotsis, D.A. Hansen, E. Friston, K.J. Jirsa, V.K. |
author_facet | Pinotsis, D.A. Hansen, E. Friston, K.J. Jirsa, V.K. |
author_sort | Pinotsis, D.A. |
collection | PubMed |
description | This paper uses mathematical modelling and simulations to explore the dynamics that emerge in large scale cortical networks, with a particular focus on the topological properties of the structural connectivity and its relationship to functional connectivity. We exploit realistic anatomical connectivity matrices (from diffusion spectrum imaging) and investigate their capacity to generate various types of resting state activity. In particular, we study emergent patterns of activity for realistic connectivity configurations together with approximations formulated in terms of neural mass or field models. We find that homogenous connectivity matrices, of the sort of assumed in certain neural field models give rise to damped spatially periodic modes, while more localised modes reflect heterogeneous coupling topologies. When simulating resting state fluctuations under realistic connectivity, we find no evidence for a spectrum of spatially periodic patterns, even when grouping together cortical nodes into communities, using graph theory. We conclude that neural field models with translationally invariant connectivity may be best applied at the mesoscopic scale and that more general models of cortical networks that embed local neural fields, may provide appropriate models of macroscopic cortical dynamics over the whole brain. |
format | Online Article Text |
id | pubmed-3520011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Academic Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-35200112013-01-15 Anatomical connectivity and the resting state activity of large cortical networks Pinotsis, D.A. Hansen, E. Friston, K.J. Jirsa, V.K. Neuroimage Technical Note This paper uses mathematical modelling and simulations to explore the dynamics that emerge in large scale cortical networks, with a particular focus on the topological properties of the structural connectivity and its relationship to functional connectivity. We exploit realistic anatomical connectivity matrices (from diffusion spectrum imaging) and investigate their capacity to generate various types of resting state activity. In particular, we study emergent patterns of activity for realistic connectivity configurations together with approximations formulated in terms of neural mass or field models. We find that homogenous connectivity matrices, of the sort of assumed in certain neural field models give rise to damped spatially periodic modes, while more localised modes reflect heterogeneous coupling topologies. When simulating resting state fluctuations under realistic connectivity, we find no evidence for a spectrum of spatially periodic patterns, even when grouping together cortical nodes into communities, using graph theory. We conclude that neural field models with translationally invariant connectivity may be best applied at the mesoscopic scale and that more general models of cortical networks that embed local neural fields, may provide appropriate models of macroscopic cortical dynamics over the whole brain. Academic Press 2013-01-15 /pmc/articles/PMC3520011/ /pubmed/23085498 http://dx.doi.org/10.1016/j.neuroimage.2012.10.016 Text en © 2013 Elsevier Inc. https://creativecommons.org/licenses/by/3.0/ Open Access under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/) license |
spellingShingle | Technical Note Pinotsis, D.A. Hansen, E. Friston, K.J. Jirsa, V.K. Anatomical connectivity and the resting state activity of large cortical networks |
title | Anatomical connectivity and the resting state activity of large cortical networks |
title_full | Anatomical connectivity and the resting state activity of large cortical networks |
title_fullStr | Anatomical connectivity and the resting state activity of large cortical networks |
title_full_unstemmed | Anatomical connectivity and the resting state activity of large cortical networks |
title_short | Anatomical connectivity and the resting state activity of large cortical networks |
title_sort | anatomical connectivity and the resting state activity of large cortical networks |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3520011/ https://www.ncbi.nlm.nih.gov/pubmed/23085498 http://dx.doi.org/10.1016/j.neuroimage.2012.10.016 |
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