A four-state Markov model of sleep-wakefulness dynamics along light/dark cycle in mice

Abstract

Behavioral states alternate between wakefulness (wk), rapid eye movement (rem) and non-rem (nrem) sleep at time scale of hours i.e., light and dark cycle rhythms and from several tens of minutes to seconds (i.e., brief awakenings during sleep). Using statistical analysis of bout duration, Markov chains of sleep-wk dynamics and quantitative EEG analysis, we evaluated the influence of light/dark (ld) changes on brain function along the sleep-wk cycle. Bout duration (bd) histograms and Kaplan-Meier (km) survival curves of wk showed a bimodal statistical distribution, suggesting that two types of wk do exist: brief-wk (wkb) and long-wk (wkl). Light changes modulated specifically wkl bouts, increasing its duration during active/dark period. In contrast, wkb, nrem and rem bd histograms and km curves did not change significantly along ld cycle. Hippocampal eeg of both types of wk were different: in comparison wkb showed a lower spectral power in fast gamma and fast theta bands and less emg tone. After fitting a four-states Markov chain to mice hypnograms, moreover in states transition probabilities matrix was found that: in dark/active period, state-maintenance probability of wkl increased, and probability of wkl to nrem transition decreased; the opposite was found in light period, favoring the hypothesis of the participation of brief wk into nrem-rem intrinsic sleep cycle, and the role of wkl in SWS homeostasis. In conclusion, we propose an extended Markov model of sleep using four stages (wkl, nrem, rem, wkb) as a fully adequate model accounting for both modulation of sleep-wake dynamics based on the differential regulation of long-wk (high gamma/theta) epochs during dark and light phases.

Glutamatergic Neurons in the Preoptic Hypothalamus Promote Wakefulness, Destabilize NREM Sleep, Suppress REM Sleep, and Regulate Cortical Dynamics

“…Figure 5B depicts the probability of transitioning between/within states of wakefulness, NREM sleep and REM sleep. Consistent with a previous study (Perez-Atencio et al, 2018), the probability of remaining in one state was much higher injection. Figure 5D shows that the activation of glutamatergic neurons of the medial-lateral preoptic region significantly altered the probability distribution of bout duration by increasing the number of short bouts and decreasing the number of long bouts during both NREM sleep (p < 0.0001) and wakefulness (p < 0.0001).…”

Glutamatergic neurons in the preoptic hypothalamus promote wakefulness, destabilize NREM sleep, suppress REM sleep, and regulate cortical dynamics

“…Figure 7A depicts the probability of transitioning between/within states of wakefulness, NREM sleep and REM sleep. Consistent with a previous study (Perez-Atencio et al, 2018), the probability of remaining in one state was much higher than the probability of transitioning into a Second, to evaluate the stability of sleep-wake states in each 1-hour block, we calculated a fragmentation index (FI); FI = 1 indicates that the state is maximally unstable and fragmented. Because of the scarcity of REM sleep after CNO administration, we only performed this analysis for wakefulness and NREM sleep states.…”

State-Switching Probability Reveals Sleep-Related Biological Drives inDrosophila

“…In humans, sleep is regulated both by the intensity of the drive to fall asleep (higher in narcolepsy, lower in insomnia) and the tendency to wake up (higher in insomnia, lower in hypersomnolence and depression) (Barateau et al 2017; Kales and Kales 1974). To capture these drives, conditional probability models using neuronal firing or EEG data have been used to model the structure of healthy and disordered sleep in mammals (Bianchi et al 2012; Perez-Atencio et al 2018; Stephenson et al 2016; Yang and Hursch 1973), but this approach had not yet been applied to sleep in Drosophila .…”