The time course of the probability of transition into and out of REM sleep

Is paradoxical sleep setting up innate and acquired complex sensorimotor and adaptive behaviours?: A proposed function based on literature review

We summarize here the progress in identifying the neuronal network as well as the function of paradoxical sleep and the gaps of knowledge that should be filled in priority. The core system generating paradoxical sleep localized in the brainstem is now well identified, and the next step is to clarify the role of the forebrain in particular that of the hypothalamus including the melanin-concentrating hormone neurons and of the basolateral amygdala. We discuss these two options, and also the discovery that cortical activation during paradoxical sleep is restricted to a few limbic cortices activated by the lateral supramammillary nucleus and the claustrum. Such activation nicely supports the findings recently obtained showing that neuronal reactivation occurs during paradoxical sleep in these structures, and induces both memory consolidation of important memory and forgetting of less relevant ones. The question that still remains to be answered is whether paradoxical sleep is playing more crucial roles in processing emotional and procedural than other types of memories. One attractive hypothesis is that paradoxical sleep is responsible for erasing negative emotional memories, and that this function is not properly functioning in depressed patients. On the other hand, the presence of a muscle atonia during paradoxical sleep is in favour of a role in procedural memory as new types of motor behaviours can be tried without harm during the state. In a way, it also fits with the proposed role of paradoxical sleep in setting up the sensorimotor system during development.

A probabilistic model for the ultradian timing of REM sleep in mice

A salient feature of mammalian sleep is the alternation between rapid eye movement (REM) and non-REM (NREM) sleep. However, how these two sleep stages influence each other and thereby regulate the timing of REM sleep episodes is still largely unresolved. Here, we developed a statistical model that specifies the relationship between REM and subsequent NREM sleep to quantify how REM sleep affects the following NREM sleep duration and its electrophysiological features in mice. We show that a lognormal mixture model well describes how the preceding REM sleep duration influences the amount of NREM sleep till the next REM sleep episode. The model supports the existence of two different types of sleep cycles: Short cycles form closely interspaced sequences of REM sleep episodes, whereas during long cycles, REM sleep is first followed by an interval of NREM sleep during which transitions to REM sleep are extremely unlikely. This refractory period is characterized by low power in the theta and sigma range of the electroencephalogram (EEG), low spindle rate and frequent microarousals, and its duration proportionally increases with the preceding REM sleep duration. Using our model, we estimated the propensity for REM sleep at the transition from NREM to REM sleep and found that entering REM sleep with higher propensity resulted in longer REM sleep episodes with reduced EEG power. Compared with the light phase, the buildup of REM sleep propensity was slower during the dark phase. Our data-driven modeling approach uncovered basic principles underlying the timing and duration of REM sleep episodes in mice and provides a flexible framework to describe the ultradian regulation of REM sleep in health and disease.

Changes in physiological network connectivity of body system in narcolepsy during REM sleep

BACKGROUND

Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the body's systemic networks.

METHOD

Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine.

RESULT

In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ± 2.87, control connections: 21.34 ± 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively.

CONCLUSION

Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.

REM sleep-dependent short-term and long-term hourglass processes in the ultradian organization and recovery of REM sleep in the rat

STUDY OBJECTIVES

To evaluate the contribution of long-term and short-term REM sleep homeostatic processes to REM sleep recovery and the ultradian organization of the sleep wake cycle.

METHODS

Fifteen rats were sleep recorded under a 12:12 LD cycle. Animals were subjected during the rest phase to two protocols (2T2I or 2R2I) performed separately in non-consecutive experimental days. 2T2I consisted of 2 h of total sleep deprivation (TSD) followed immediately by 2 h of intermittent REM sleep deprivation (IRD). 2R2I consisted of 2 h of selective REM sleep deprivation (RSD) followed by 2 h of IRD. IRD was composed of four cycles of 20-min RSD intervals alternating with 10 min of sleep permission windows.

RESULTS

REM sleep debt that accumulated during deprivation (9.0 and 10.8 min for RSD and TSD, respectively) was fully compensated regardless of cumulated NREM sleep or wakefulness during deprivation. Protocol 2T2I exhibited a delayed REM sleep rebound with respect to 2R2I due to a reduction of REM sleep transitions related to enhanced NREM sleep delta-EEG activity, without affecting REM sleep consolidation. Within IRD permission windows there was a transient and duration-dependent diminution of REM sleep transitions.

CONCLUSIONS

REM sleep recovery in the rat seems to depend on a long-term hourglass process activated by REM sleep absence. Both REM sleep transition probability and REM sleep episode consolidation depend on the long-term REM sleep hourglass. REM sleep activates a short-term REM sleep refractory period that modulates the ultradian organization of sleep states.

Neural and Homeostatic Regulation of REM Sleep

Rapid eye movement (REM) sleep is a distinct, homeostatically controlled brain state characterized by an activated electroencephalogram (EEG) in combination with paralysis of skeletal muscles and is associated with vivid dreaming. Understanding how REM sleep is controlled requires identification of the neural circuits underlying its initiation and maintenance, and delineation of the homeostatic processes regulating its expression on multiple timescales. Soon after its discovery in humans in 1953, the pons was demonstrated to be necessary and sufficient for the generation of REM sleep. But, especially within the last decade, researchers have identified further neural populations in the hypothalamus, midbrain, and medulla that regulate REM sleep by either promoting or suppressing this brain state. The discovery of these populations was greatly facilitated by the availability of novel technologies for the dissection of neural circuits. Recent quantitative models integrate findings about the activity and connectivity of key neurons and knowledge about homeostatic mechanisms to explain the dynamics underlying the recurrence of REM sleep. For the future, combining quantitative with experimental approaches to directly test model predictions and to refine existing models will greatly advance our understanding of the neural and homeostatic processes governing the regulation of REM sleep.

Modeling the long term effects of thermoregulation on human sleep

The connection between human sleep and energy exertion has long been regarded as part of the reasoning for the need to sleep. A recent theory proposes that during REM sleep, energy utilized for thermoregulation is diverted to other relevant biological processes. We present a mathematical model of human sleep/wake regulation with thermoregulatory functions to gain quantitative insight into the effects of ambient temperature on sleep quality. Our model extends previous models by incorporating equations for the metabolic processes that control thermoregulation during sleep. We present numerical simulations that provide a quantitative answer for how humans adjust by changing the normal sleep stage progression when it is challenged with ambient temperatures away from thermoneutral. We explore the dynamics for a single night and several nights. Our results indicate that including the effects of temperature is a vital component of modeling sleep.

The temporal structure of behaviour and sleep homeostasis

The amount and architecture of vigilance states are governed by two distinct processes, which occur at different time scales. The first, a slow one, is related to a wake/sleep dependent homeostatic Process S, which occurs on a time scale of hours, and is reflected in the dynamics of NREM sleep EEG slow-wave activity. The second, a fast one, is manifested in a regular alternation of two sleep states – NREM and REM sleep, which occur, in rodents, on a time scale of ∼5–10 minutes. Neither the mechanisms underlying the time constants of these two processes – the slow one and the fast one, nor their functional significance are understood. Notably, both processes are primarily apparent during sleep, while their potential manifestation during wakefulness is obscured by ongoing behaviour. Here, we find, in mice provided with running wheels, that the two sleep processes become clearly apparent also during waking at the level of behavior and brain activity. Specifically, the slow process was manifested in the total duration of waking periods starting from dark onset, while the fast process was apparent in a regular occurrence of running bouts during the waking periods. The dynamics of both processes were stable within individual animals, but showed large interindividual variability. Importantly, the two processes were not independent: the periodic structure of waking behaviour (fast process) appeared to be a strong predictor of the capacity to sustain continuous wakefulness (slow process). The data indicate that the temporal organization of vigilance states on both the fast and the slow time scales may arise from a common neurophysiologic mechanism.

Reduced γ range activity at REM sleep onset and termination in fear-conditioned Wistar-Kyoto rats

Recent investigations of rapid eye movement sleep (REMS) continuity have emphasized the importance of transitions both into and out of REMS. We have previously reported that, compared to Wistar rats (WIS), Wistar-Kyoto rats (WKY) responded to fear conditioning (FC) with more fragmented REMS. Gamma oscillations in the electroencephalogram (EEG) are synchronized throughout the brain in periods of focused attention, and such synchronization of cell assemblies in the brain may represent a temporal binding mechanism. Therefore, we examined the effects of FC on EEG gamma range activity (30-50Hz) at REMS transitions in WKY compared to WIS. Relative power in the gamma range (measured as a percent of total power) at Baseline and upon re-exposure to the fear-inducing conditioning stimulus was measured 35s before REMS onset to 105s after REMS onset (ARO) and 85s before REMS termination (BRT) to 35s after REMS termination. After baseline recording, rats received 10 tones, each co-terminating with an electric foot shock. On Days 1 and 14 post-conditioning, rats were re-exposed to three tones. Fast-Fourier transforms created power spectral data in the gamma frequency domain. Relative power was extracted from an average of 4-5 REMS transitions. Relative gamma power was always higher in WIS. On Day 14, at 15s and 25s ARO, WKY had significant increases in relative gamma power from Baseline. WIS had a significant increase on Day 1 at 25s ARO. Despite the increases in relative gamma power, WKY never achieved levels attained by WIS. Moreover, at 5s BRT, only WKY had a significant decrease in relative gamma power from Baseline to Day 14. Gamma range activity may indicate neural activity underlying maintenance of REMS continuity. Low relative gamma power at REMS transitions may be associated with increased REMS fragmentation in WKY after FC.