Dataset Information

Novel analysis of sleep patterns in rats separates periods of vigilance cycling from long-duration wake events.

ABSTRACT: Rats are polyphasic sleepers. However, a formal definition of when one sleep episode ends and another begins has not been put forth. In the present study we examine the distribution of wake episode durations and based on this distribution conclude there are multiple components of wake. If the wake episode exceeds 300 s the wake episode is assigned to long-duration wake (LDW), if the episode is less than 300 s it is assigned to brief wake (BW). Further support for this separation was found in close analysis of the EEG power spectrum in BW versus LDW. We then used LDW episodes to separate one sleep episode from another. We term the sleep episodes vigilance cycling (VC) because the rat is cycling between the vigilance states of BW, slow-wave sleep (SWS), and rapid-eye movement sleep (REMS). We find that the characteristics of VC are different in the light period versus the dark period. We further find that as VC episodes progress, SWS pressure lessens, but the amount of time spent in REMS increases. These findings suggest that VC episodes are regulated and meaningful to the sleep behavior of rats. The use of the concepts of LDW and VC provides additional insights into the description of sleep patterns in rats that may be important in the development of a complete description of sleep behavior in this animal.

SUBMITTER: Simasko SM

PROVIDER: S-EPMC2617706 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Novel analysis of sleep patterns in rats separates periods of vigilance cycling from long-duration wake events.

Simasko Steven M SM Mukherjee Sanjib S

Behavioural brain research 20080916 2

Rats are polyphasic sleepers. However, a formal definition of when one sleep episode ends and another begins has not been put forth. In the present study we examine the distribution of wake episode durations and based on this distribution conclude there are multiple components of wake. If the wake episode exceeds 300 s the wake episode is assigned to long-duration wake (LDW), if the episode is less than 300 s it is assigned to brief wake (BW). Further support for this separation was found in clo ...[more]

PMID: 18835301

Similar Datasets

Project description:ObjectivesConventional scoring of sleep provides little information about the process of transitioning between vigilance states. We applied the state space technique (SST) using frequency band ratios to follow normal maturation of different sleep/wake states, velocities of movements, and transitions between states of juvenile (postnatal day 34, P34) and young adult rats (P71).Design24-h sleep recordings of eight P34 and nine P71 were analyzed using conventional scoring criteria and SST one week following implantation of telemetric transmitter. SST is a non-categorical approach that allows novel quantitative and unbiased examination of vigilance-states dynamics and state transitions. In this approach, behavioral changes are described in a 2-dimensional state space that is derived from spectral characteristics of the electroencephalography.Measurements and resultsWith maturation sleep intensity declines, the duration of deep slow wave sleep (DSWS) and light slow wave sleep (LSWS) decreases and increases, respectively. Vigilance state determination, as a function of frequency, is not constant; there is a substantial shift to higher ratio 1 in all vigilance states except DSWS. Deep slow wave sleep decreases in adult relative to juvenile animals at all frequencies. P71 animals have 400% more trajectories from Wake to LSWS (p = 0.005) and vice versa (p = 0.005), and 100% more micro-arousals (p = 0.021), while trajectories from LSWS to DSWS (p = 0.047) and vice versa (p = 0.033) were reduced by 60%. In both juvenile and adult animals, no significant changes were found in sleep velocity at all regions of the 2-dimensional state space plot; suggesting that maturation has a partial effect on sleep stability.ConclusionsHere, we present novel and original evidence that SST enables visualization of vigilance-state intensity, transitions, and velocities that were not evident by traditional scoring methods. These observations provide new perspectives in sleep state dynamics and highlight the usefulness of this technique in exploring the development of sleep-wake activity.

Project description:Importance:Single self-reported measures of sleep duration are associated with adverse health outcomes; however, long-term patterns of self-reported sleep duration and their association with cardiovascular events (CVEs) and all-cause mortality remain unknown. Objective:To determine whether trajectories of long-term vs single-measure sleep duration are associated with subsequent risk of CVEs and all-cause mortality. Design, Setting, and Participants:The Kailuan study is a prospective, population-based cohort study that began in 2006. The present cohort included 52 599 Chinese adults without atrial fibrillation, myocardial infarction, stroke, or cancer to 2010. Trajectories in sleep duration from January 1, 2006, to December 31, 2010, were identified to investigate the association with risk of CVEs and all-cause mortality from January 1, 2010, to December 31, 2017. Data analysis was conducted from July 1 to October 31, 2019. Exposures:Habitual self-reported nocturnal sleep durations were collected in 2006, 2008, and 2010. Trajectories in sleep duration for 4 years were identified by latent mixture modeling. Main Outcomes and Measures:All-cause mortality and first incident CVEs (atrial fibrillation, myocardial infarction, and stroke) from 2010 to 2017 were confirmed by medical records. Based on the baseline sleep duration and patterns over time, 4 trajectories were categorized (normal stable, normal decreasing, low increasing, and low stable). Results:Of the 52 599 adults included in the study (mean [SD] age at baseline, 52.5 [11.8] years), 40 087 (76.2%) were male and 12 512 (23.8%) were female. Four distinct 4-year sleep duration trajectory patterns were identified: normal stable (range, 7.4 to 7.5 hours [n = 40 262]), normal decreasing (mean decrease from 7.0 to 5.5 hours [n = 8074]), low increasing (mean increase from 4.9 to 6.9 hours [n = 3384]), and low stable (range, 4.2 to 4.9 hours [n = 879]). During a mean (SD) follow-up of 6.7 (1.1) years, 2361 individuals died and 2406 had a CVE. Compared with the normal-stable pattern and adjusting for potential confounders, a low-increasing pattern was associated with increased risk of first CVEs (hazard ratio [HR], 1.22; 95% CI, 1.04-1.43), a normal-decreasing pattern was associated with increased risk of all-cause mortality (HR, 1.34; 95% CI, 1.15-1.57), and the low-stable pattern was associated with the highest risk of CVEs (HR, 1.47; 95% CI, 1.05-2.05) and death (HR, 1.50; 95% CI, 1.07-2.10). Conclusions and Relevance:In this study, sleep duration trajectories with lower or unstable patterns were significantly associated with increased risk of subsequent first CVEs and all-cause mortality. Longitudinal sleep duration patterns may assist in more precise identification of different at-risk groups for possible intervention. People reporting consistently sleeping less than 5 hours per night should be regarded as a population at higher risk for CVE and mortality.

Project description:BackgroundActigraphs are wrist-worn devices that record tri-axial accelerometry data used clinically and in research studies. The expense of research-grade actigraphs, however, limit their widespread adoption, especially in clinical settings. Tri-axial accelerometer-based consumer wearable devices have gained worldwide popularity and hold potential for a cost-effective alternative. The lack of independent validation of minute-to-minute accelerometer data with polysomnographic data or even research-grade actigraphs, as well as access to raw data has hindered the utility and acceptance of consumer-grade actigraphs.MethodsSleep clinic patients wore a consumer-grade wearable (Huami Arc) on their non-dominant wrist while undergoing an overnight polysomnography (PSG) study. The sample was split into two, 20 in a training group and 21 in a testing group. In addition to the Arc, the testing group also wore a research-grade actigraph (Philips Actiwatch Spectrum). Sleep was scored for each 60-s epoch on both devices using the Cole-Kripke algorithm.ResultsBased on analysis of our training group, Arc and PSG data were aligned best when a threshold of 10 units was used to examine the Arc data. Using this threshold value in our testing group, the Arc has an accuracy of 90.3%±4.3%, sleep sensitivity (or wake specificity) of 95.5%±3.5%, and sleep specificity (wake sensitivity) of 55.6%±22.7%. Compared to PSG, Actiwatch has an accuracy of 88.7%±4.5%, sleep sensitivity of 92.6%±5.2%, and sleep specificity of 60.5%±20.2%, comparable to that observed in the Arc.ConclusionsAn optimized sleep/wake threshold value was identified for a consumer-grade wearable Arc trained by PSG data. By applying this sleep/wake threshold value for Arc generated accelerometer data, when compared to PSG, sleep and wake estimates were adequate and comparable to those generated by a clinical-grade actigraph. As with other actigraphs, sleep specificity plateaus due to limitations in distinguishing wake without movement from sleep. Further studies are needed to evaluate the Arc's ability to differentiate between sleep and wake using other sources of data available from the Arc, such as high resolution accelerometry and photoplethysmography.

Dataset Information

Novel analysis of sleep patterns in rats separates periods of vigilance cycling from long-duration wake events.

Publications

Novel analysis of sleep patterns in rats separates periods of vigilance cycling from long-duration wake events.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure