Open Access
Structure and topology dynamics of hyper-frequency networks during rest and auditory oddball performance
Loading...
Files
Structure_and_Topology.pdf (14.26 MB)
Full-text in Open Access
License
Creative Commons CC BY-4.0
Access Rights
Cadmus Permanent Link
Full-text via DOI
ISBN
ISSN
1662-5188
Issue Date
Type of Publication
Keyword(s)
Functional connectivity; Directional coupling; Hyper-frequency network; Network topology dynamics; Graph-theoretic approach; Resting state; Auditory oddball performance; Life-Span differences; Functional connectivity; Neuronal oscillations; Phase synchronization; Brain connectivity; Cortical networks; Memory processes; Small-world; State; Cortex
LC Subject Heading
Other Topic(s)
EUI Research Cluster(s)
Initial version
Published version
Succeeding version
Preceding version
Published version part
Earlier different version
Initial format
Citation
Frontiers in computational neuroscience, 2016, Vol. 10 (Art. 108)
Cite
MUELLER, Viktor, PERDIKIS, Dionysios, VON OERTZEN, Timo, SLEIMEN-MALKOUN, Rita, JIRSA, Viktor, LINDENBERGER, Ulman, Structure and topology dynamics of hyper-frequency networks during rest and auditory oddball performance, Frontiers in computational neuroscience, 2016, Vol. 10 (Art. 108) - https://hdl.handle.net/1814/61429
Abstract
Resting-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.
Table of Contents
Additional Information
Published online: 17 October 2016
External Links
Publisher
Geographical Coverage
Temporal Coverage
Version
Source
Source Link
Research Projects
Sponsorship and Funder Information
Max Planck Society
Brain Network Recovery Group through the James S. McDonnell Foundation
European Union Seventh Framework Program (FP7-ICT) [60402]
Brain Network Recovery Group through the James S. McDonnell Foundation
European Union Seventh Framework Program (FP7-ICT) [60402]