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dc.contributor.authorChouzouris, Teresa-
dc.contributor.authorOmelchenko, Iryna-
dc.contributor.authorZakharova, Anna-
dc.contributor.authorHlinka, Jaroslav-
dc.contributor.authorJiruska, Premysl-
dc.contributor.authorSchöll, Eckehard-
dc.date.accessioned2020-02-27T11:20:36Z-
dc.date.available2020-02-27T11:20:36Z-
dc.date.issued2018-04-09-
dc.identifier.issn1054-1500-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10735-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9632-
dc.descriptionThis article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Chaos 28, 045112 (2018) and may be found at https://doi.org/10.1063/1.5009812.en
dc.description.abstractComplex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of both the brain function and disease. We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex networks motivated by its potential application to epileptology and epilepsy surgery. We compare two topologies: an empirical structural neural connectivity derived from diffusion-weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states and obtain regions of their stability in the parameter planes. Furthermore, we qualitatively simulate the dynamics of epileptic seizures and study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic surgery.en
dc.description.sponsorshipDFG, 163436311, SFB 910: Kontrolle selbstorganisierender nichtlinearer Systeme: Theoretische Methoden und Anwendungskonzepteen
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc530 Physikde
dc.subject.othercomplex networksen
dc.subject.otherfractalsen
dc.subject.othermagnetic resonanceen
dc.titleChimera states in brain networks: Empirical neural vs. modular fractal connectivityen
dc.typeArticleen
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn1089-7682-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1063/1.5009812en
dcterms.bibliographicCitation.journaltitleChaosen
dcterms.bibliographicCitation.originalpublisherplaceMelville, NYen
dcterms.bibliographicCitation.volume28en
dcterms.bibliographicCitation.originalpublishernameAmerican Institute of Physics (AIP)en
dcterms.bibliographicCitation.issue4en
dcterms.bibliographicCitation.articlenumber045112en
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