Multi-Scale Permutation Entropy: A Potential Measure for the Impact of Sleep Medication on Brain Dynamics of Patients with Insomnia

Insomnia is a common sleep disorder that is closely associated with the occurrence and deterioration of cardiovascular disease, depression and other diseases. The evaluation of pharmacological treatments for insomnia brings significant clinical implications. In this study, a total of 20 patients with mild insomnia and 75 healthy subjects as controls (HC) were included to explore alterations of electroencephalogram (EEG) complexity associated with insomnia and its pharmacological treatment by using multi-scale permutation entropy (MPE). All participants were recorded for two nights of polysomnography (PSG). The patients with mild insomnia received a placebo on the first night (Placebo) and temazepam on the second night (Temazepam), while the HCs had no sleep-related medication intake for either night. EEG recordings from each night were extracted and analyzed using MPE. The results showed that MPE decreased significantly from pre-lights-off to the period during sleep transition and then to the period after sleep onset, and also during the deepening of sleep stage in the HC group. Furthermore, results from the insomnia subjects showed that MPE values were significantly lower for the Temazepam night compared to MPE values for the Placebo night. Moreover, MPE values for the Temazepam night showed no correlation with age or gender. Our results indicated that EEG complexity, measured by MPE, may be utilized as an alternative approach to measure the impact of sleep medication on brain dynamics.

Microsleep versus Sleep Onset Latency during Maintenance Wakefulness Tests: Which One Is the Best Marker of Sleepiness?

The interpretation of the Maintenance Wakefulness Test (MWT) relies on sleep onset detection. However, microsleeps (MSs), i.e., brief periods of sleep intrusion during wakefulness, may occur before sleep onset. We assessed the prevalence of MSs during the MWT and their contribution to the diagnosis of residual sleepiness in patients treated for obstructive sleep apnea (OSA) or hypersomnia. The MWT of 98 patients (89 OSA, 82.6% male) were analyzed for MS scoring. Polysomnography parameters and clinical data were collected. The diagnostic value for detecting sleepiness (Epworth Sleepiness Scale > 10) of sleep onset latency (SOL) and of the first MS latency (MSL) was assessed by the area under the receiver operating characteristic (ROC) curve (AUC, 95% CI). At least one MS was observed in 62.2% of patients. MSL was positively correlated with SOL (r = 0.72, p < 0.0001) but not with subjective scales, clinical variables, or polysomnography parameters. The use of SOL or MSL did not influence the diagnostic performance of the MWT for subjective sleepiness assessment (AUC = 0.66 95% CI (0.56, 0.77) versus 0.63 95% CI (0.51, 0.74)). MSs are frequent during MWTs performed in patients treated for sleep disorders, even in the absence of subjective sleepiness, and may represent physiological markers of the wake-to-sleep transition.

Night sleep in toddlers

A regular restful sleep for a recommended time affects the quality of life and psychic/physical health and prevents cognitive, emotional, and somatic disorders. Meanwhile, the rate of nocturnal awakenings among toddlers is 20–66%. This paper reviews current data on night sleep in toddlers. Techniques for assessing sleep duration in children are addressed. Research findings demonstrate that the most important causes of sleep disturbances are breastfeeding, mother’s anxiety and depression, and sleeping with parents. Sleeping with parents is not only a culturological feature but also indicates potential problems in the family being a means of compensating for the lack of partner intimacy and family relations. Factors affecting sleep transition and sleep quality (including the specifics of food intake) are discussed. It was demonstrated that amino acids are important for falling asleep and sleep regulation. A high-carbohydrate diet is also beneficial. KEYWORDS: night sleep, toddlers, nocturnal awakenings, sleeping with parents, nutrition. FOR CITATION: Dubrovskaya M.I., Ryazanova O.V., Netrebenko O.K. et al. Night sleep in toddlers. Russian Journal of Woman and Child Health. 2021;4(2):173–177. DOI: 10.32364/2618-8430-2021-4-2-173-177.

0746 Dynamics of Sleep Stage Transitions in Patients with Narcolepsy and Other Hypersomnias

Introduction Narcolepsy is a chronic sleep disorder characterized by excessive daytime sleepiness and abnormal REM sleep phenomena. Narcolepsy can be distinguished into type 1 (NT1; with cataplexy) and type 2 (NT2; without cataplexy). It has been reported that sleep stage sequences at sleep-onset as well as sleep-wake dynamics across the night may be useful in the differential diagnosis of hypersomnia. Here we studied dynamic features of sleep stage transitions during whole night sleep in patients with NT1, NT2, and other types of hypersomnia (o-HS). Methods Twenty patients with NT1, 14 patients with NT2, and 35 patients with o-HS underwent overnight PSG. Transition probabilities between sleep stages (wake, N1, N2, N3, and REM) and survival curves of continuous runs of each sleep stage were compared between groups. Transition-specific survival curves of continuous runs of each sleep stage, dependent on the subsequent stage of the transition, were also compared. Results The probability of transitions from N1-to-wake was significantly greater in NT1 than in NT2 and o-HS while that from N1-to-N2 was significantly smaller in NT1 than in NT2 and o-HS. The probability of transitions from N2-to-REM was significantly smaller in NT1 than in o-HS. Wake and N1 were significantly more continuous in NT1 than in NT2; specifically, N1 followed by N2 was significantly more continuous in NT1 than in NT2 and o-HS. N2 was significantly less continuous in NT1 and NT2 than in o-HS; this was specifically confirmed for N2 followed by N1/wake. REM sleep was significantly less continuous in NT1 than in NT2 and o-HS; specifically, REM sleep followed by wake was significantly less continuous in NT1 than in o-HS. Continuity of N3 did not differ significantly between groups. Conclusion Dynamics of sleep stage transitions differed between NT1, NT2, and o-HS. Dynamic features of sleep such as sleep instability, persistency of wake/N1, and REM fragmentation may differentiate NT1 from NT2, while N2 continuity may differentiate narcolepsy from o-HS. The results suggest that sleep transition analysis may be of clinical utility and provide insights into the underlying pathophysiology of hypersomnia and narcolepsy. Support JSPS KAKENHI (18K17891 to AK).

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.

Microsleep episodes in the borderland between wakefulness and sleep

Study objectives The wake-sleep transition zone represents a poorly defined borderland, containing, for example, microsleep episodes (MSEs), which are of potential relevance for diagnosis and may have consequences while driving. Yet, the scoring guidelines of the American Academy of Sleep Medicine (AASM) completely neglect it. We aimed to explore the borderland between wakefulness and sleep by developing the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring, focusing on MSEs visible in the electroencephalography (EEG), as opposed to purely behavior- or performance-defined MSEs. Methods Maintenance of Wakefulness Test (MWT) trials of 76 randomly selected patients were retrospectively scored according to both the AASM and the newly developed BERN scoring criteria. The visual scoring was compared with spectral analysis of the EEG. The quantitative EEG analysis enabled a reliable objectification of the visually scored MSEs. For less distinct episodes within the borderland, either ambiguous or no quantitative patterns were found. Results As expected, the latency to the first MSE was significantly shorter in comparison to the sleep latency, defined according to the AASM criteria. In certain cases, a large difference between the two latencies was observed and a substantial number of MSEs occurred between the first MSE and sleep. Series of MSEs were more frequent in patients with shorter sleep latencies, while isolated MSEs were more frequent in patients who did not reach sleep. Conclusion The BERN criteria extend the AASM criteria and represent a valuable tool for in-depth analysis of the wake-sleep transition zone, particularly important in the MWT.

A Robust Method with High Time Resolution for Estimating the Cortico-Thalamo-Cortical Loop Strength and the Delay when Using a Scalp Electroencephalography Applied to the Wake-Sleep Transition

Summary Objectives: This study aimed to describe a robust method with high time resolution for estimating the cortico-thalamo-cortical (CTC) loop strength and the delay when using a scalp electroencephalography (EEG) and to illustrate its applicability for analyzing the wake-sleep transition. Methods: The basic framework for the proposed method is the parallel use of a physiological model and a parametric phenomenological model: a neural field theory (NFT) of the corticothalamic system and an autoregressive (AR) model. The AR model is a “stochastic” model that shortens the time taken to extract spectral features and is also a “linear” model that is free from the local-minimum problem. From the relationship between the transfer function of the AR model and the transfer function of the NFT in the low frequency limit, we successfully derived a direct expression of CTC loop strength and the loop delay using AR coefficients. Results: Using this method to analyze sleep-EEG data, we were able to clearly track the wake-to-sleep transition, as the estimated CTC loop strength (c 2) decreased to almost zero. We also found that the c 2-distribution during nocturnal sleep is clearly bimodal in nature, which can be well approximated by the superposition of two Gaussian distributions that correspond to sleep and wake states, respectively. The estimated loop delay distributed ∼0.08 s, which agrees well with the previously reported value estimated by other methods, confirming the validity of our method. Conclusions: A robust method with high time resolution was developed for estimating the cortico-thalamo-cortical loop strength and the delay when using a scalp electroencephalography. This method can contribute not only to detecting the wake-sleep transition, but also to further understanding of the transition, where the cortico-thalamo-cortical loop is thought to play an important role.