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dc.contributor.authorMUELLER, Viktor
dc.contributor.authorPERDIKIS, Dionysios
dc.contributor.authorVON OERTZEN, Timo
dc.contributor.authorSLEIMEN-MALKOUN, Rita
dc.contributor.authorJIRSA, Viktor
dc.contributor.authorLINDENBERGER, Ulman 
dc.date.accessioned2019-03-01T14:53:07Z
dc.date.available2019-03-01T14:53:07Z
dc.date.issued2016
dc.identifier.citationFrontiers in computational neuroscience, 2016, Vol. 10 (Art. 108)
dc.identifier.issn1662-5188
dc.identifier.urihttp://hdl.handle.net/1814/61429
dc.descriptionPublished online: 17 October 2016
dc.description.abstractResting-state and task-related recordings are characterized by oscillatory brain activity and widely distributed networks of synchronized oscillatory circuits. Electroencephalographic recordings (EEG) were used to assess network structure and network dynamics during resting state with eyes open and closed, and auditory oddball performance through phase synchronization between EEG channels. For this assessment, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that CFC generally differentiates between task conditions better than WFC. CFC was the highest during resting state with eyes open. Using a graph-theoretical approach (GTA), we found that HFNs possess small-world network (SWN) topology with a slight tendency to random network characteristics. Moreover, analysis of the temporal fluctuations of HFNs revealed specific network topology dynamics (NTD), i.e., temporal changes of different graph theoretical measures such as strength, clustering coefficient, characteristic path length (CPL), local, and global efficiency determined for HFNs at different time windows. The different topology metrics showed significant differences between conditions in the mean and standard deviation of these metrics both across time and nodes. In addition, using an artificial neural network approach, we found stimulus-related dynamics that varied across the different network topology metrics. We conclude that functional connectivity dynamics (FCD), or NTD, which was found using the HFN approach during rest and stimulus processing, reflects temporal and topological changes in the functional organization and reorganization of neuronal cell assemblies.
dc.description.sponsorshipMax Planck Society
dc.description.sponsorshipBrain Network Recovery Group through the James S. McDonnell Foundation
dc.description.sponsorshipEuropean Union Seventh Framework Program (FP7-ICT) [60402]
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation60402
dc.relation.ispartofFrontiers in computational neuroscience
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectFunctional connectivity
dc.subjectDirectional coupling
dc.subjectHyper-frequency network
dc.subjectNetwork topology dynamics
dc.subjectGraph-theoretical approach
dc.subjectResting state
dc.subjectAuditory oddball performance
dc.subjectLife-Span Differencesen
dc.subjectFunctional Connectivityen
dc.subjectNeuronal Oscillationsen
dc.subjectPhase Synchronizationen
dc.subjectBrain Connectivityen
dc.subjectCortical Networksen
dc.subjectMemory Processesen
dc.subjectSmall-Worlden
dc.subjectStateen
dc.subjectCortexen
dc.titleStructure and topology dynamics of hyper-frequency networks during rest and auditory oddball performance
dc.typeArticle
dc.identifier.doi10.3389/fncom.2016.00108
dc.identifier.volume10
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dc.rights.licenseCreative Commons CC BY-4.0


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