Structure and Topology Dynamics of Hyper-Frequency Networks during Rest and Auditory Oddball Performance.

Viktor Müller,Dionysios Perdikis,Ulman Lindenberger,Rita Sleimen-Malkoun,Timo Von Oertzen,Viktor Jirsa

doi:10.3389/fncom.2016.00108

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.

Highlights

As noted by Prigogine and Stengers (1984, p. 77), “Nature speaks with thousand voices, and we have only begun to listen.” So does the human brain
The brain maps of coupling calculated across the entire 10-s window are shown for WITHIN-FREQUENCY COUPLING (WFC) and CROSS-FREQUENCY COUPLING (CFC) at the 10 different frequencies
Synchronization within the frequencies is stronger than between the frequencies, but there are no recognizable differences between the conditions for both WFC and CFC

Summary

Introduction

As noted by Prigogine and Stengers (1984, p. 77), “Nature speaks with thousand voices, and we have only begun to listen.” So does the human brain. One of the candidate mechanisms underlying integration and communication between cell assemblies is crossfrequency coupling, allowing accurate timing between different oscillatory rhythms (Jensen and Colgin, 2007; Jirsa and Müller, 2013), selective and dynamic control of distributed functional cell assemblies (cf Canolty et al, 2010), and promotion of different dimensions of brain integration (Varela et al, 2001; Buzsáki and Draguhn, 2004; Buzsáki, 2006) Despite these general claims, surprisingly little is known about the mechanisms underlying complex interactions of spatially segregated cell assemblies. HFN is defined here as a network that represents all interactions among frequencies and electrode sites (see below)

Methods

Results

Conclusion