Abstract
Little attention has gone into linking to its neuronal substrates the dynamic structure of non-rapid-eye-movement (NREM) sleep, defined as the pattern of time-course power in all frequency bands across an entire episode. Using the spectral power time-courses in the sleep electroencephalogram (EEG), we showed in the typical first episode, several moves towards-and-away from deep sleep, each having an identical pattern linking the major frequency bands beta, sigma and delta. The neuronal transition probability model (NTP) – in fitting the data well – successfully explained the pattern as resulting from stochastic transitions of the firing-rates of the thalamically-projecting brainstem-activating neurons, alternating between two steady dynamic-states (towards-and-away from deep sleep) each initiated by a so-far unidentified flip-flop. The aims here are to identify this flip-flop and to demonstrate that the model fits well all NREM episodes, not just the first. Using published data on suprachiasmatic nucleus (SCN) activity we show that the SCN has the information required to provide a threshold-triggered flip-flop for timing the towards-and-away alternations, information provided by sleep-relevant feedback to the SCN. NTP then determines the pattern of spectral power within each dynamic-state. NTP was fitted to individual NREM episodes 1–4, using data from 30 healthy subjects aged 20–30 years, and the quality of fit for each NREM measured. We show that the model fits well all NREM episodes and the best-fit probability-set is found to be effectively the same in fitting all subject data. The significant model-data agreement, the constant probability parameter and the proposed role of the SCN add considerable strength to the model. With it we link for the first time findings at cellular level and detailed time-course data at EEG level, to give a coherent picture of NREM dynamics over the entire night and over hierarchic brain levels all the way from the SCN to the EEG.
Highlights
Sleep is a complex behaviour under the control of several interrelated processes: a circadian process which determines periods of sleep propensity over 24 h; an ultradian process which is responsible for the architecture of the sleep period with its regular cyclic alternation of the two major sleep states, non-rapid eye movement (NREM) and rapid eye movement (REM) sleep; and dynamic processes which determine the temporal structure within these states
There are several reasons to suppose that the suprachiasmatic nucleus (SCN) provides the flip-flop switching action postulated more than a decade ago by the neuronal transition probability (NTP) model: It is widely accepted that the SCN is the source of the circadian signal for the occurrence and consolidation of sleep and wakefulness; there exists anatomical evidence that sleep-promoting and wakepromoting neurons receive input both directly and indirectly from the SCN [16,32,33,34]; and more to the point, recent studies [35,36] provide evidence that suggests to us the existence at the SCN of the flip-flop switch we are looking for
– in line with the core ideas of our NTP model and with the consensus that SCN controls wake/sleep alternations and is a causal agent with regard to the vigilance states - we propose that it is the sleep centres that are being controlled by the SCN
Summary
Sleep is a complex behaviour under the control of several interrelated processes: a circadian process which determines periods of sleep propensity over 24 h; an ultradian process which is responsible for the architecture of the sleep period with its regular cyclic alternation of the two major sleep states, non-rapid eye movement (NREM) and rapid eye movement (REM) sleep; and dynamic processes which determine the temporal structure within these states. Individual subject data, often exhibit a more complex and revealing behaviour: Using the time-courses of spectral power in the sleep EEG, we showed [3] that there are in a typical first NREM episode, several moves towards and away from deep sleep (Figure 1), in each of which there is an identical distinct pattern linking the major frequency bands beta, sigma and delta (Figure 2) The essential of this pattern is that in the move towards deep sleep beta power drops exponentially, delta power rises in an S-curve and sigma power peaks while delta is still rising. This remarkable pattern repeatedly characterising the dynamic structure within NREM imperatively called for an explanation and this was given by our neuronal transition probability (NTP) model [2,3]
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