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dc.contributor.authorJedynak, M
dc.contributor.authorPons, AJ
dc.contributor.authorGarcia-Ojalvo, J
dc.contributor.authorGoodfellow, M
dc.date.accessioned2016-11-28T09:23:55Z
dc.date.issued2016-11-16
dc.description.abstractMacroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in non-modelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/f(b) spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain. To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates "healthy" or "epileptiform" dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an Ornstein-Uhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the δ and θ bands. Crucially, these are rhythms that are found to be enhanced prior to seizures in humans and animal models of epilepsy. In order to understand why these rhythms can generate epileptiform dynamics, we analyse the response of the model to sinusoidal driving and explain how the bifurcation structure of the model gives rise to these findings. Our results provide insight into how ongoing fluctuations in brain dynamics can facilitate the onset and propagation of epileptiform rhythms in brain networks. Furthermore, we highlight the need to combine large-scale models with noise of a variety of different types in order to understand brain (dys-)function.en_GB
dc.description.sponsorshipThis work was supported by the European Commission through the FP7 Marie Curie Initial Training Network 289146 (NETT: Neural Engineering Transformative Technologies), by the Spanish Ministry of Economy and Competitiveness and FEDER (project FIS2012-37655-C02-01). J.G.O. also acknowledges support from the ICREA Academia programme, the Generalitat de Catalunya (project 2014SGR0947), and the “María de Maeztu” Programme for Units of Excellence in R&D (Spanish Ministry of Economy and Competitiveness, MDM-2014-0370) M.G. gratefully acknowledges the financial support of the EPSRC via grant EP/N014391/1. The contribution of M.G. was generously supported by a Wellcome Trust Institutional Strategic Support Award (WT105618MA).en_GB
dc.identifier.citationDOI: 10.1016/j.neuroimage.2016.11.034en_GB
dc.identifier.doi10.1016/j.neuroimage.2016.11.034
dc.identifier.otherS1053-8119(16)30652-8
dc.identifier.urihttp://hdl.handle.net/10871/24595
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pubmed/27865920en_GB
dc.rights© 2016 The Authors. Published by Elsevier Inc.Open Access funded by Engineering and Physical Sciences Research Council Under a Creative Commons Attribution license. https://creativecommons.org/licenses/by/4.0/en_GB
dc.subjectEpilepsyen_GB
dc.subjectIctogenesisen_GB
dc.subjectJansen-Rit modelen_GB
dc.subjectNeural mass modelsen_GB
dc.subjectNonlinear dynamicsen_GB
dc.subjectOrnstein-Uhlenbeck noiseen_GB
dc.subjectStochastic effectsen_GB
dc.titleTemporally correlated fluctuations drive epileptiform dynamicsen_GB
dc.typeArticleen_GB
dc.date.available2016-11-28T09:23:55Z
dc.identifier.issn1053-8119
exeter.place-of-publicationUnited Statesen_GB
dc.descriptionPublished onlineen_GB
dc.descriptionJournal Articleen_GB
dc.identifier.journalNeuroImageen_GB
dc.identifier.pmid27865920


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