Characterizing Dynamic Functional Connectivity Across Sleep Stages from EEG
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.
KeywordsSleep EEG Small-world network Synchronization likelihood Nonlinear interdependence Graph theoretic clustering Variational information
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