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

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1101
Author(s):  
Yanping Guo ◽  
Yingying Chen ◽  
Qianru Yang ◽  
Fengzhen Hou ◽  
Xinyu Liu ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Sleep Onset ◽  
Permutation Entropy ◽  
Brain Dynamics ◽  
Multi Scale ◽  
Sleep Medication ◽  
Alternative Approach ◽  
Eeg Recordings ◽  
Sleep Transition ◽  
The Impact

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.

scholarly journals Changes in EEG permutation entropy in the evening and in the transition from wake to sleep

SLEEP ◽  
2020 ◽  
Author(s):  
Fengzhen Hou ◽  
Lulu Zhang ◽  
Baokun Qin ◽  
Giulia Gaggioni ◽  
Xinyu Liu ◽  
...  
Keyword(s):  
Sleep Onset ◽  
Sleep Time ◽  
Large Datasets ◽  
Permutation Entropy ◽  
Brain State ◽  
Eeg Signal ◽  
Spectral Measures ◽  
Progressive Change ◽  
Eeg Recordings ◽  
The Brain

Abstract Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power—Ptheta: 4–8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25–101 years. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. An MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

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

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A283-A284
Author(s):  
A Kishi ◽  
T Kitajima ◽  
R Kawai ◽  
M Hirose ◽  
N Iwata ◽  
...  
Keyword(s):  
Rem Sleep ◽  
Transition Probabilities ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Stages ◽  
Subsequent Stage ◽  
Dynamic Features ◽  
Survival Curves ◽  
Transition Analysis ◽  
Sleep Transition

Abstract 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).

scholarly journals Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks

2021 ◽  
Vol 1 ◽  
Author(s):  
Klaus Lehnertz ◽  
Thorsten Rings ◽  
Timo Bröhl
Keyword(s):  
Spectral Characteristics ◽  
Brain Networks ◽  
Biological Rhythms ◽  
Brain Dynamics ◽  
Eeg Signals ◽  
Functional Brain ◽  
Non Invasive ◽  
Functional Brain Networks ◽  
Eeg Recordings ◽  
The Impact

Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.

Prospects for the use of the “model budget” in the distribution of equalization grants in Russia

2019 ◽  
pp. 21-38
Author(s):  
Alexander N. Deryugin ◽  
Ilya A. Sokolov
Keyword(s):  
Intergovernmental Relations ◽  
Unfunded Mandates ◽  
Interregional Inequality ◽  
Alternative Approach ◽  
The Russian Federation ◽  
Regional Authorities ◽  
High Degree ◽  
Equalization Grants ◽  
The Impact ◽  
Budget Expenditure

The paper analyzes the impact of the “model budget” on the problems of intergovernmental relations in the Russian Federation: a high proportion of expenditure obligations of regional and local budgets and a high degree of interregional inequality in fiscal capacity and socio-economic development. It was concluded that the planned broader use of the “model budget” will not solve the problem of unfunded mandates and will lead first to a significant reduction in incentives for regional authorities to develop the territorial revenue base, and then to economic slowdown in the country. As an alternative approach to improving intergovernmental relations, options are being considered for adjusting the parameters of the equalization transfers distribution formula, the procedure for determining their total volume and calculating the budget expenditure index. In solving the problem of unfunded mandates, an equally important role is given to the procedure for preparing a financial and economic rationale for draft laws.

scholarly journals 229 Sleep-wake behavior, meal timings and digital media duration of Indians during COVID-19 Lockdown

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A91-A92
Author(s):  
Babita Pande ◽  
Meenakshi Sinha ◽  
Ramanjan Sinha
Keyword(s):  
Digital Media ◽  
Sleep Duration ◽  
Sleep Onset ◽  
Therapeutic Management ◽  
Daily Routine ◽  
Age Group ◽  
Younger Age ◽  
Gender And Age ◽  
Delayed Sleep ◽  
The Impact

Abstract Introduction Lockdown and stay home order has been imposed on people in many countries including India to prevent the community transmission of COVID-19 pandemic. However this social restriction led to disturbed daily routine and lifestyle behaviour that is needed to be attended for proper therapeutic management of overall health during such crisis. The impact of lockdown on the most apparent behavioral changes viz. sleep-wake behaviour, major meal timings, and digital screen duration of Indians were investigated. In addition the effects of gender and age were explored. Methods After seeking permission from Ethical Institution, an online questionnaire based survey was circulated within India in the first week of May, 2020 for which total 1511 male and female (age ≥18 years) subjects participated. The sleep-wake behavior observed were sleep-wake timings, sleep duration, mid sleep time (MST) as function of lockdown, and social (lockdown) jetlag (SJL = MST before lockdown-MST during lockdown). Results The sleep onset-wakeup and meal times were significantly delayed during lockdown, which was more pronounced in younger age group. The sleep duration increased, specifically in young individuals during lockdown. Females showed more delayed sleep onset-waking times and first meal timing with longer sleep duration during lockdown. Increased digital media duration was observed in all age groups, primarily in males. The younger age group and specifically female reported higher SJL and delayed MST. A positive association was obtained between sleep duration & first meal time, and SJL & major meal timings/screen duration, and a significant negative relationship of sleep duration and SJL with age. Conclusion The study shows delayed sleep-wake schedule, meal timings and increased digital media duration among Indians during COVID-19 lockdown compared to before lockdown. Also, gender and age emerged as important mediating factors for this alteration. The pandemic has given opportunity to sleep more and compensate for the sleep. In spite of that, the higher social jetlag in young age group and female showed the compromised sleep and maladaption with societal timing. These findings have applied implications in sleep health during longer social isolation conditions and for proper therapeutic management. Support (if any) No

024 Quiescent Wakefulness: Characterising the Impact of Oxytocin on Sleep-Wake Behaviour in Male and Female Rats

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A11-A11
Author(s):  
Joel Raymond ◽  
Nicholas Everett ◽  
Anand Gururajan ◽  
Michael Bowen
Keyword(s):  
Rem Sleep ◽  
Sleep Onset ◽  
Conscious State ◽  
Positive Control ◽  
Female Rats ◽  
Male And Female ◽  
Routes Of Administration ◽  
Male And Female Rats ◽  
Competing Hypotheses ◽  
The Impact

Abstract Introduction Oxytocin is a versatile hypothalamic neuropeptide involved in diverse neurobehavioural processes. Since oxytocin can elicit anxiolytic and serenic effects, one could hypothesise that oxytocin should prime the brain for sleep and promote hypnogenesis. However, based on the social salience hypothesis—that oxytocin promotes prosocial behaviour and directs attention toward social stimuli—one could also posit that oxytocin should promote wakefulness. At present, little research has comprehensively characterised the effect of oxytocin on sleep-wake behaviour and no explanation to reconcile these two seemingly competing hypotheses has been proposed. Methods This study investigated the effects of oxytocin on sleep-wake outcomes using radiotelemetry-based polysomnography in adult male and female Wistar rats. Oxytocin was administered via the intraperitoneal (IP; 0.1, 0.3 and 1 mg/kg) and intranasal (IN; 0.06, 1, 3 mg/kg) routes. Caffeine (IP and IN; 10 mg/kg) was also administered as a wake-promoting positive control. Additionally, pre-treatment with the oxytocin receptor (OTR) antagonist L-368,899 (IP; 5 mg/kg) and vasopressin 1a receptor (V1aR) antagonist SR49059 (IP; 1 mg/kg) followed by oxytocin (IP; 1 mg/kg) was conducted to determine which receptor(s) mediated sleep-wake effects of oxytocin. Results In both male and female rats, IP oxytocin produced dose-dependent effects on sleep-wake behaviour. Specifically, oxytocin initially promoted quiescent wakefulness (a restful but conscious state) at the cost of reducing both active wakefulness and sleep. Throughout the 1.5-hour period post-administration, oxytocin delayed REM sleep onset and reduced the proportion of both NREM and REM sleep. Conversely, IN oxytocin did not significantly alter any sleep-wake parameters at any dose tested. Caffeine demonstrated wake-promoting effects under both the IP and IN routes of administration. The involvement of OTR and V1aR binding in oxytocin-induced effects on sleep-wake outcomes will be discussed. Conclusion These findings appear to reconcile the two competing hypotheses: in rats, IP oxytocin appears to promote a state of quiescent wakefulness—one of calm and rest, but also of conscious responsivity to environmental stimuli. IN oxytocin demonstrated little to no effect on sleep-wake behaviour, which is a crucial finding given the escalating use of IN oxytocin as a therapeutic for conditions with comorbid disordered sleep. Support (if any) None.

scholarly journals 1142 Comprehensive Phenotyping Of Ambulatory Sleep Patterns In Multiple Sclerosis

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A435-A435
Author(s):  
T J Braley ◽  
A L Kratz ◽  
D Whibley ◽  
C Goldstein
Keyword(s):  
Multiple Sclerosis ◽  
Sleep Quality ◽  
Extreme Values ◽  
Sleep Onset ◽  
External Support ◽  
Poor Sleep ◽  
Sleep Patterns ◽  
Poor Sleep Quality ◽  
Historical Cohort ◽  
The Impact

Abstract Introduction The majority of sleep research in persons with multiple sclerosis (PwMS) has been siloed, restricted to evaluation of one or a few sleep measures in isolation. To fully characterize the impact of sleep disturbances in MS, multifaceted phenotyping of sleep is required. The objective of this study was to more comprehensively quantify sleep in PwMS, using a recently developed multi-domain framework of duration, continuity, regularity, sleepiness/alertness, and quality. Methods Data were derived from a parent study that examined associations between actigraphy and polysomnography-based measures of sleep and cognitive function in MS. Actigraphy was recorded in n=55 PwMS for 7-12 days (Actiwatch2®, Philips Respironics). Sleep metrics included: duration=mean total sleep time (TST, minutes); continuity=mean wake time after sleep onset (minutes), and regularity=stddev wake-up time (hours). ‘Extreme’ values for continuity/regularity were defined as the most extreme third of the distributions. ‘Extreme’ TST values were defined as the lowest or highest sixth of the distributions. Sleepiness (Epworth Sleepiness Scale score) and sleep quality [Pittsburgh Sleep Quality Index (PSQI) sleep quality item] were dichotomized by accepted cutoffs (>10 and >1, respectively). Results Sleep was recorded for a mean of 8.2 days (stddev=0.95). Median (1st, 3rd quartile) values were as follows: duration 459.79 (430.75, 490.60), continuity 37.00 (23.44, 52.57), regularity 1.02 (0.75, 1.32), sleepiness/alertness 8 (4, 12), and sleep quality 1.00 (1.00, 2.00). Extreme values based on data distributions were: short sleep <=426.25 minutes (18%), long sleep >515.5 minutes (16%), poor sleep continuity ≥45 minutes (33%), and poor sleep regularity ≥1.17 hours (33%). Sleepiness and poor sleep quality were present in 36% and 40% respectively. For comparison, in a historical cohort of non-MS patients, the extreme third of sleep regularity was a stddev of 0.75 hours, 13% had ESS of >10, and 16% had poor sleep quality. Conclusion In this study of ambulatory sleep patterns in PwMS, we found greater irregularity of sleep-wake timing, and higher prevalence of sleepiness and poor sleep quality than published normative data. Efforts should be made to include these measures in the assessment of sleep-related contributions to MS outcomes. Support The authors received no external support for this work.

scholarly journals Targeted memory reactivation in REM but not SWS selectively reduces arousal responses

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Isabel C. Hutchison ◽  
Stefania Pezzoli ◽  
Maria-Efstratia Tsimpanouli ◽  
Mahmoud E. A. Abdellahi ◽  
Penelope A. Lewis
Keyword(s):  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Emotional Memory ◽  
Rapid Eye Movement Sleep ◽  
Emotional Experiences ◽  
Memory Reactivation ◽  
Emotional Memories ◽  
The Impact ◽  
Subjective Arousal ◽  
Spontaneous Reactivation

AbstractA growing body of evidence suggests that sleep can help to decouple the memory of emotional experiences from their associated affective charge. This process is thought to rely on the spontaneous reactivation of emotional memories during sleep, though it is still unclear which sleep stage is optimal for such reactivation. We examined this question by explicitly manipulating memory reactivation in both rapid-eye movement sleep (REM) and slow-wave sleep (SWS) using targeted memory reactivation (TMR) and testing the impact of this manipulation on habituation of subjective arousal responses across a night. Our results show that TMR during REM, but not SWS significantly decreased subjective arousal, and this effect is driven by the more negative stimuli. These results support one aspect of the sleep to forget, sleep to remember (SFSR) hypothesis which proposes that emotional memory reactivation during REM sleep underlies sleep-dependent habituation.

Sleep-stage sequencing of sleep-onset REM periods in MSLT predicts treatment response in patients with narcolepsy

2015 ◽  
Vol 25 (2) ◽  
pp. 203-210 ◽  
Author(s):  
Panagis Drakatos ◽  
Kishankumar Patel ◽  
Chiraag Thakrar ◽  
Adrian J. Williams ◽  
Brian D. Kent ◽  
...  
Keyword(s):  
Treatment Response ◽  
Sleep Stage ◽  
Sleep Onset

Sleep Stage Assessment Using Power Spectral Indices of Heart Rate Variability With a Simple Algorithm

2012 ◽  
Vol 15 (3) ◽  
pp. 264-272 ◽  
Author(s):  
Keiko Tanida ◽  
Masashi Shibata ◽  
Margaret M. Heitkemper
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Low Frequency ◽  
Simple Algorithm ◽  
Sleep Stages ◽  
Spectral Indices ◽  
Current Form ◽  
Power Spectral

Clinical researchers do not typically assess sleep with polysomnography (PSG) but rather with observation. However, methods relying on observation have limited reliability and are not suitable for assessing sleep depth and cycles. The purpose of this methodological study was to compare a sleep analysis method based on power spectral indices of heart rate variability (HRV) data to PSG. PSG and electrocardiography data were collected synchronously from 10 healthy women (ages 20–61 years) over 23 nights in a laboratory setting. HRV was analyzed for each 60-s epoch and calculated at 3 frequency band powers (very low frequency [VLF]-hi: 0.016–0.04 Hz; low frequency [LF]: 0.04–0.15 Hz; and high frequency [HF]: 0.15–0.4 Hz). Using HF/(VLF-hi + LF + HF) value, VLF-hi, and heart rate (HR) as indices, an algorithm to categorize sleep into 3 states (shallow sleep corresponding to Stages 1 & 2, deep sleep corresponding to Stages 3 & 4, and rapid eye movement [REM] sleep) was created. Movement epochs and time of sleep onset and wake-up were determined using VLF-hi and HR. The minute-by-minute agreement rate with the sleep stages as identified by PSG and HRV data ranged from 32 to 72% with an average of 56%. Longer wake after sleep onset (WASO) resulted in lower agreement rates. The mean differences between the 2 methods were 2 min for the time of sleep onset and 6 min for the time of wake-up. These results indicate that distinguishing WASO from shallow sleep segments is difficult using this HRV method. The algorithm's usefulness is thus limited in its current form, and it requires additional modification.

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