Synchronization in functional networks of the human brain

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dc.contributor.author Hövel, Philipp
dc.contributor.author Viol, Aline
dc.contributor.author Loske, Philipp
dc.contributor.author Merfort, Leon
dc.contributor.author Vuksanović, Vesna
dc.date.accessioned 2018-11-23T11:48:49Z
dc.date.available 2018-11-23T11:48:49Z
dc.date.issued 2018-10-25
dc.identifier.citation Hövel, P., Viol, A., Loske, P., Merfort, L. and Vuksanović, V. (2018) 'Synchronization in Functional Networks of the Human Brain', Journal of Nonlinear Science, doi: 10.1007/s00332-018-9505-7 en
dc.identifier.startpage 1 en
dc.identifier.endpage 24 en
dc.identifier.issn 1432-1467
dc.identifier.uri http://hdl.handle.net/10468/7138
dc.identifier.doi 10.1007/s00332-018-9505-7
dc.description.abstract Understanding the relationship between structural and functional organization represents one of the most important challenges in neuroscience. An increasing amount of studies show that this organization can be better understood by considering the brain as an interactive complex network. This approach has inspired a large number of computational models that combine experimental data with numerical simulations of brain interactions. In this paper, we present a summary of a data-driven computational model of synchronization between distant cortical areas that share a large number of overlapping neighboring (anatomical) connections. Such connections are derived from in vivo measures of brain connectivity using diffusion-weighted magnetic resonance imaging and are additionally informed by the presence of significant resting-state functionally correlated links between the areas involved. The dynamical processes of brain regions are simulated by a combination of coupled oscillator systems and a hemodynamic response model. The coupled oscillatory systems are represented by the Kuramoto phase oscillators, thus modeling phase synchrony between regional activities. The focus of this modeling approach is to characterize topological properties of functional brain correlation related to synchronization of the regional neural activity. The proposed model is able to reproduce remote synchronization between brain regions reaching reasonable agreement with the experimental functional connectivities. We show that the best agreement between model and experimental data is reached for dynamical states that exhibit a balance of synchrony and variations in synchrony providing the integration of activity between distant brain regions. en
dc.description.sponsorship Deutsche Forschungsgemeinschaft (under Grant No. HO4695/3-1 and within the framework of Collaborative Research Center 910) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer en
dc.relation.uri https://link.springer.com/article/10.1007%2Fs00332-018-9505-7
dc.rights © Springer Science+Business Media, LLC, part of Springer Nature 2018. This is a post-peer-review, pre-copyedit version of an article published in Journal of Nonlinear Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00332-018-9505-7 en
dc.subject Nonlinear dynamics en
dc.subject Synchronization en
dc.subject Brain connectivity en
dc.subject Kuramoto phase oscillator en
dc.subject Neural activity en
dc.title Synchronization in functional networks of the human brain en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Philipp Hoevel, School Of Mathematical Sciences, University College Cork, Cork, Ireland. +353-21-490-3000 Email: philipp.hoevel@ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 12 months after publication by request of the publisher en
dc.check.date 2019-10-25
dc.date.updated 2018-11-23T11:40:40Z
dc.description.version Accepted Version en
dc.internal.rssid 460208413
dc.contributor.funder Deutsche Forschungsgemeinschaft en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Journal of Nonlinear Science en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress philipp.hoevel@ucc.ie en


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