Long-range correlations of different EEG derivations in rats: sleep stage-dependent generators may play a key role

Abstract

For the electroencephalogram (EEG), topographic differences in the long-range temporal correlations would imply that these signals might be affected by specific mechanisms related to the generation of a given neuronal process. So the properties of the generators of various EEG oscillations might be investigated by their spatial differences of the long-range temporal correlations. In the present study, these correlations were characterized with respect to their topography during different vigilance states by detrended fluctuation analysis (DFA). The results indicated that (1) most of the scaling exponents acquired from different EEG derivations for various oscillations were significantly different in each vigilance state; these differences might be resulted from the different quantities and different locations of sleep stage-dependent generators of various neuronal processes; (2) there might be multiple generators of delta and theta over the brain and many of them were sleep stage-dependent; (3) the best site of the frontal electrode in a fronto-parietal bipolar electrode for sleep staging might be above the anterior midline cortex. We suggest that DFA analysis can be used to explore the properties of the generators of a given neuronal oscillation, and the localizations of these generators if more electrodes are involved.

Electroencephalogram bands modulated by vigilance states in an anuran species: a factor analytic approach

“…2). This is consistent with the idea that EEG properties differ along the antero-posterior and left-right axes of the brain in anurans as well as humans and other mammals (Saastamoinen et al 2007;Young and McNaughton 2009;Fang et al 2010). Because of these differences, the averaged EEG power spectra between both sides of the telencephalon and between both sides of the mesencephalon might be more suited for factor analysis.…”

“…Since there are significant differences in EEG power spectra among various brain regions, especially between anterior and posterior areas (Saastamoinen et al 2007;Young and McNaughton 2009;Fang et al 2010), factor analysis was performed for the telencephalon and the mesencephalon, respectively, in this study. For each vigilance state and each frog, the log-transformed power spectra of 60 epochs were averaged between both sides of each brain region, and then averaged in blocks of 5.…”

Scale-Free Brain-Wave Music from Simultaneously EEG and fMRI Recordings

“…Parameter a denotes the scale characteristics, and it is determined by the following detrended fluctuation analysis (DFA) [19], [20].…”

EEG Bands of Wakeful Rest, Slow-Wave and Rapid-Eye-Movement Sleep at Different Brain Areas in Rats

“…Actually, previous studies show that the frequency bands acquired from principal component analysis (PCA) are more consistent with the underlying physiological mechanisms in rats (Corsi-Cabrera et al, 2001 ). Note that electrophysiological oscillations are sensitive to recording sites (Siapas and Wilson, 1998 ; Klimesch, 1999 ; Sirota et al, 2003 ), indicating the possible involvement of different brain oscillatory generators (Mitchell et al, 2008 ; Young and McNaughton, 2009 ; Fang et al, 2010 ; Timofeev and Chauvette, 2013 ). Therefore, considering the spatial factor in the division of frequency bands would be more fulfilling (Klimesch, 1999 ).…”