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Abstract
Understanding the dynamics of brain activity manifested in the EEG, local field potentials (LFP), and neuronal spiking is essential for explaining their underlying mechanisms and physiological significance. Much has been learned about sleep regulation using conventional EEG power spectrum, coherence, and period-amplitude analyses, which focus primarily on frequency and amplitude characteristics of the signals and on their spatio-temporal synchronicity. However, little is known about the effects of ongoing brain state or preceding sleep-wake history on the nonlinear dynamics of brain activity. Recent advances in developing novel mathematical approaches for investigating temporal structure of brain activity based on such measures, as Lempel-Ziv complexity (LZC) can provide insights that go beyond those obtained with conventional techniques of signal analysis. Here, we used extensive data sets obtained in spontaneously awake and sleeping adult male laboratory rats, as well as during and after sleep deprivation, to perform a detailed analysis of cortical LFP and neuronal activity with LZC approach. We found that activated brain states—waking and rapid eye movement (REM) sleep are characterized by higher LZC compared with non-rapid eye movement (NREM) sleep. Notably, LZC values derived from the LFP were especially low during early NREM sleep after sleep deprivation and toward the middle of individual NREM sleep episodes. We conclude that LZC is an important and yet largely unexplored measure with a high potential for investigating neurophysiological mechanisms of brain activity in health and disease.
Highlights
BRAIN STATES CHANGE CONTINUOUSLY on a fast time scale of seconds and minutes, as dictated by inputs from the environment and ongoing behavior
We found that activated brain states—waking and rapid eye movement (REM) sleep are characterized by higher Lempel-Ziv complexity (LZC) compared with non-rapid eye movement (NREM) sleep
The signals recorded from the neocortex in different vigilance states—waking, non-rapid-eye movement (NREM) and rapid eye movement (REM) sleep are markedly different in terms of the total signal amplitude and frequency content (Vyazovskiy et al 2009b)
Summary
The final position of the wires was identified to reside in deep layers, as judged from the positivity of LFP slow waves during NREM sleep, corresponding to the neuronal population silent periods At this stage, special care was taken to avoid displacing the array in the horizontal dimension. The initial coarse-graining for the symbolization of the sequence was not needed, and the Lempel-Ziv algorithm from 1976 was directly applied to the spike trains sequence to estimate its complexity. As for LFP SWA, some of the analyses have been performed on relative values of LZC, which were calculated by normalizing them within an individual for each time interval, as the percentage of the mean value over the entire recording period
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