Using computational models to relate structural and functional brain connectivity
Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured...
| Main Authors: | , |
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| Format: | Article |
| Published: |
Wiley
2012
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| Online Access: | https://eprints.nottingham.ac.uk/2512/ |
| _version_ | 1848790803911016448 |
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| author | Hlinka, Jaroslav Coombes, Stephen |
| author_facet | Hlinka, Jaroslav Coombes, Stephen |
| author_sort | Hlinka, Jaroslav |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to
the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in
functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we use
a modeling approach to explore the way in which this can arise and to highlight the important role that local population
dynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population is
taken to be of the Wilson–Cowan type, whilst the structural connectivity patterns used, describing long-range anatomical
connections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailed
theoretical study. We have calculated graph–theoretic measures of functional network topology from numerical simulations of
model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the
correlation between structural and functional connectivity. We document a profound and systematic dependence of the
simulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weakly
coupled oscillator theory explaining these correlations and their variation across parameter space can be developed. This
theoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity in
diseases through changes in local dynamics. |
| first_indexed | 2025-11-14T18:18:26Z |
| format | Article |
| id | nottingham-2512 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:18:26Z |
| publishDate | 2012 |
| publisher | Wiley |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-25122020-05-04T20:21:33Z https://eprints.nottingham.ac.uk/2512/ Using computational models to relate structural and functional brain connectivity Hlinka, Jaroslav Coombes, Stephen Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which this can arise and to highlight the important role that local population dynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population is taken to be of the Wilson–Cowan type, whilst the structural connectivity patterns used, describing long-range anatomical connections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailed theoretical study. We have calculated graph–theoretic measures of functional network topology from numerical simulations of model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the correlation between structural and functional connectivity. We document a profound and systematic dependence of the simulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weakly coupled oscillator theory explaining these correlations and their variation across parameter space can be developed. This theoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity in diseases through changes in local dynamics. Wiley 2012-07 Article PeerReviewed Hlinka, Jaroslav and Coombes, Stephen (2012) Using computational models to relate structural and functional brain connectivity. European Journal of Neuroscience, 36 (2). pp. 2137-2145. ISSN 0953-816X http://onlinelibrary.wiley.com/doi/10.1111/j.1460-9568.2012.08081.x/pdf doi:10.1111/j.1460-9568.2012.08081.x doi:10.1111/j.1460-9568.2012.08081.x |
| spellingShingle | Hlinka, Jaroslav Coombes, Stephen Using computational models to relate structural and functional brain connectivity |
| title | Using computational models to relate structural and
functional brain connectivity |
| title_full | Using computational models to relate structural and
functional brain connectivity |
| title_fullStr | Using computational models to relate structural and
functional brain connectivity |
| title_full_unstemmed | Using computational models to relate structural and
functional brain connectivity |
| title_short | Using computational models to relate structural and
functional brain connectivity |
| title_sort | using computational models to relate structural and
functional brain connectivity |
| url | https://eprints.nottingham.ac.uk/2512/ https://eprints.nottingham.ac.uk/2512/ https://eprints.nottingham.ac.uk/2512/ |