Abstract
Several theories link consciousness to complex cortical dynamics, as suggested by comparison of brain signal diversity between conscious states and states where consciousness is lost or reduced. In particular, Lempel-Ziv complexity, amplitude coalition entropy and synchrony coalition entropy distinguish wakefulness and REM sleep from deep sleep and anesthesia, and are elevated in psychedelic states, reported to increase the range and vividness of conscious contents. Some studies have even found correlations between complexity measures and facets of self-reported experience. As suggested by integrated information theory and the entropic brain hypothesis, measures of differentiation and signal diversity may therefore be measurable correlates of consciousness and phenomenological richness. Inspired by these ideas, we tested three hypotheses about EEG signal diversity related to sleep and dreaming. First, diversity should decrease with successively deeper stages of non-REM sleep. Second, signal diversity within the same sleep stage should be higher for periods of dreaming vs. non-dreaming. Third, specific aspects of dream contents should correlate with signal diversity in corresponding cortical regions. We employed a repeated awakening paradigm in sleep deprived healthy volunteers, with immediate dream report and rating of dream content along a thought-perceptual axis, from exclusively thought-like to exclusively perceptual. Generalized linear mixed models were used to assess how signal diversity varied with sleep stage, dreaming and thought-perceptual rating. Signal diversity decreased with sleep depth, but was not significantly different between dreaming and non-dreaming, even though there was a significant positive correlation between Lempel-Ziv complexity of EEG recorded over the posterior cortex and thought-perceptual ratings of dream contents.
Highlights
While there is still no universally accepted theory of consciousness, or full consensus on what the neural correlates of consciousness may be, there seems to be some points of approximate convergence
Sleep stage was a significant factor in the model for both Lempel-Ziv complexity (LZC) [F(4,139) = 32.5, p < 0.0001], amplitude coalition entropy (ACE) [F(4,169) = 67.7, p < 0.0001] and synchrony coalition entropy (SCE) [F(4,235) = 37.4.0, p < 0.0001]
The contrast between wakefulness and NREM1 sleep was not significant. This data set only included a single trial of REM sleep, because we were mostly interested in variation in signal diversity with depth of non-REM sleep, and within-state analysis of dreaming in NREM2 sleep
Summary
While there is still no universally accepted theory of consciousness, or full consensus on what the neural correlates of consciousness may be, there seems to be some points of approximate convergence. Schartner et al applied measures of complexity and entropy to spontaneous multichannel brain signals, suggesting that Lempel-Ziv complexity (LZC), amplitude coalition entropy (ACE) and synchrony coalition entropy (SCE) may serve as indicators of consciousness (Schartner et al, 2015; Schartner, 2017) While these diversity measures do not (directly) capture cortical integration, they have shown promise in distinguishing states associated with conscious experience, such as wakefulness and REM sleep, from states were conscious experience is degraded or possibly lost, such as slow wave sleep and propofol anesthesia (Schartner et al, 2015, 2017b). Single channel LZC has been shown to decrease with sleep depth in humans (Andrillon et al, 2016), and to be higher in wakefulness and REM sleep than non-REM sleep in rats (Abásolo et al, 2015), in line with evidence from studies using alternative measures of (temporal) complexity and entropy (Ma et al, 2018)
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