Characterizing Dynamic Functional Connectivity Across Sleep Stages from EEG

Abstract

Following a nonlinear dynamics approach, we investigated the emergence of functional clusters which are related with spontaneous brain activity during sleep. Based on multichannel EEG traces from 10 healthy subjects, we compared the functional connectivity across different sleep stages. Our exploration commences with the conjecture of a small-world patterning, present in the scalp topography of the measured electrical activity. The existence of such a communication pattern is first confirmed for our data and then precisely determined by means of two distinct measures of non-linear interdependence between time-series. A graph encapsulating the small-world network structure along with the relative interdependence strength is formed for each sleep stage and subsequently fed to a suitable clustering procedure. Finally the delineated graph components are comparatively presented for all stages revealing novel attributes of sleep architecture. Our results suggest a pivotal role for the functional coupling during the different stages and indicate interesting dynamic characteristics like its variable hemispheric asymmetry and the isolation between anterior and posterior cortical areas during REM.

Development of a Large-Scale Functional Brain Network during Human Non-Rapid Eye Movement Sleep

“…Achard et al (2006) observed that small-world properties of a large-scale functional brain network with 90 nodes were most robust for the 0.03-0.06 Hz frequency band. We analyzed this frequency band of interest throughout sleep and observed a main effect of sleep on local clustering but not characteristic path length, which was also noted in studies on small-world properties in sleep that analyzed EEG synchronization (Ferri et al, 2007(Ferri et al, , 2008Dimitriadis et al, 2009). In line with Ferri et al (2008), we found that the clustering coefficient was increased in light sleep; however, in our data the clustering coefficients in light sleep were at the same time closest to clustering coefficients of randomly rewired graphs.…”

“…Here, we apply graph theoretical analysis to study functional connectivity in sleep, as the loss of consciousness in sleep is paradoxically accompanied by similar or even increased functional connectivity (Ferri et al, 2007(Ferri et al, , 2008Horovitz et al, 2008;Dimitriadis et al, 2009;Larson-Prior et al, 2009). Graph theory analysis of scalp electroencephalography (EEG) during sleep revealed not only increased neocortical connectivity but also increased small-world properties on specific frequency bands (Ferri et al, 2007(Ferri et al, , 2008Dimitriadis et al, 2009).…”

“…Graph theory analysis of scalp electroencephalography (EEG) during sleep revealed not only increased neocortical connectivity but also increased small-world properties on specific frequency bands (Ferri et al, 2007(Ferri et al, , 2008Dimitriadis et al, 2009). fMRI studies on slow (Ͻ0.1 Hz) spontaneous blood oxygen level-dependent (BOLD) signal fluctuations during sleep have focused on so-called resting networks, and reported no or minimal changes in the default mode network during light sleep (Horovitz et al, 2008) or a trend for increased connectivity in other resting networks (Larson-Prior et al, 2009).…”

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes

“…Connectivity at rest and during cognitive tasks has been the subject of various researches, based on different methodological approaches (Sporns et al 2000;Ioannides et al 2012;Stam et al 2007a, b;Micheloyannis 2012;Dimitriadis et al 2009Dimitriadis et al , 2010aDimitriadis et al , b, 2012aTurk-Browne 2013). Graph theoretic principles and algorithms are in the heart of all these approaches that are aimed at anatomical (He et al 2007;Hagmann et al 2008) and functional networks defined empirically from EEG, MEG and fMRI recordings (Basset and Bullmore 2006;Stam et al 2007a, b).…”

Effects of selective REM sleep deprivation on prefrontal gamma activity and executive functions

“…Frontal regions are not only less activated during REM sleep, but are also decoupled from posterior associative regions. It has been demonstrated that the temporal coupling of Gamma activity between frontal and posterior association regions of the same hemisphere is lower than during waking and NREM sleep in humans (Pérez-Garci et al, 2001;Dimitriadis et al, 2009) and cats (Castro et al, 2013).…”