Long-term history and immediate preceding state affect EEG slow wave characteristics at NREM sleep onset in C57BL/6 mice

Cui, Nanyi

doi:10.12871/0002982920142310

Cited by 6 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, the probability of failed transitions was highly non-random and depended upon the type of transition, with REM-to-awake transitions showing relatively low failure rates, but REM-to-NREM transitions showing high failures. This differential failure rate may explain the preponderance of brief awake periods between REM and NREM sleep, as it may be easier for the underlying neuronal networks to transition from REM to awake, and then to NREM, rather than to transition directly from REM to NREM [ 51 – 54 ]. In addition, we revealed that the animal’s recent sleep-wake history affects the probability of observing different types of failed transitions.…”

Section: Discussionmentioning

confidence: 99%

“…However, vigilance states can be short-lived, which poses a difficult problem for such an approach. For example, mice often transition from REM sleep to NREM sleep via a brief period in which their EEG and EMG activity reflect the awake state [51][52][53][54]. A probabilistic classifier that can systematically integrate contextual information without smoothing is the hidden Markov model (HMM).…”

Section: Establishing a Probabilistic Vigilance State Classifiermentioning

confidence: 99%

“…The same pattern of failed transitions remained when a more conservative threshold criterion was adopted such as a state probability below 0.95. These observations may explain why animals often appear to enter a brief awake state between REM sleep and NREM sleep [51][52][53][54], as transitions from REM to awake, and from awake to NREM, fail much more rarely than transitions from REM to NREM.…”

Section: Plos Computational Biologymentioning

confidence: 99%

See 2 more Smart Citations

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

Brodersen,

Alfonsa,

Krone

et al. 2024

PLoS Comput Biol

View full text Add to dashboard Cite

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"—a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Establishing a Probabilistic Vigilance State Classifiermentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

See 1 more Smart Citation

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

Brodersen,

Alfonsa,

Krone

et al. 2024

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

Different Effects of Sleep Deprivation and Torpor on EEG Slow-Wave Characteristics in Djungarian Hamsters

Vyazovskiy

Palchykova²,

Achermann

et al. 2017

Cerebral Cortex

View full text Add to dashboard Cite

It has been shown previously in Djungarian hamsters that the initial electroencephalography (EEG) slow-wave activity (power in the 0.5–4.0 Hz band; SWA) in non-rapid eye movement (NREM) sleep following an episode of daily torpor is consistently enhanced, similar to the SWA increase after sleep deprivation (SD). However, it is unknown whether the network mechanisms underlying the SWA increase after torpor and SD are similar. EEG slow waves recorded in the neocortex during sleep reflect synchronized transitions between periods of activity and silence among large neuronal populations. We therefore set out to investigate characteristics of individual cortical EEG slow waves recorded during NREM sleep after 4 h SD and during sleep after emergence from an episode of daily torpor in adult male Djungarian hamsters. We found that during the first hour after both SD and torpor, the SWA increase was associated with an increase in slow-wave incidence and amplitude. However, the slopes of single slow waves during NREM sleep were steeper in the first hour after SD but not after torpor, and, in contrast to sleep after SD, the magnitude of change in slopes after torpor was unrelated to the changes in SWA. Furthermore, slow-wave slopes decreased progressively within the first 2 h after SD, while a progressive increase in slow-wave slopes was apparent during the first 2 h after torpor. The data suggest that prolonged waking and torpor have different effects on cortical network activity underlying slow-wave characteristics, while resulting in a similar homeostatic sleep response of SWA. We suggest that sleep plays an important role in network homeostasis after both waking and torpor, consistent with a recovery function for both states.

show abstract

Sleep homeostasis during daytime food entrainment in mice

Northeast

Huang

McKillop

et al. 2019

Sleep

View full text Add to dashboard Cite

Twenty-four hour rhythms of physiology and behavior are driven by the environment and an internal endogenous timing system. Daily restricted feeding (RF) in nocturnal rodents during their inactive phase initiates food anticipatory activity (FAA) and a reorganization of the typical 24-hour sleep–wake structure. Here, we investigate the effects of daytime feeding, where food access was restricted to 4 hours during the light period ZT4-8 (Zeitgeber time; ZT0 is lights on), on sleep–wake architecture and sleep homeostasis in mice. Following 10 days of RF, mice were returned to ad libitum feeding. To mimic the spontaneous wakefulness associated with FAA and daytime feeding, mice were then sleep deprived between ZT3-6. Although the amount of wake increased during FAA and subsequent feeding, total wake time over 24 hours remained stable as the loss of sleep in the light phase was compensated for by an increase in sleep in the dark phase. Interestingly, sleep that followed spontaneous wake episodes during the dark period and the extended period of wake associated with FAA, exhibited lower levels of slow-wave activity (SWA) when compared to baseline or after sleep deprivation, despite a similar duration of waking. This suggests an evolutionary mechanism of reducing sleep drive during negative energy balance to enable greater arousal for food-seeking behaviors. However, the total amount of sleep and SWA accumulated during the 24 hours was similar between baseline and RF. In summary, our study suggests that despite substantial changes in the daily distribution and quality of wake induced by RF, sleep homeostasis is maintained.

show abstract

Long-term history and immediate preceding state affect EEG slow wave characteristics at NREM sleep onset in C57BL/6 mice

Cited by 6 publications

References 0 publications

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

Different Effects of Sleep Deprivation and Torpor on EEG Slow-Wave Characteristics in Djungarian Hamsters

Sleep homeostasis during daytime food entrainment in mice

Contact Info

Product

Resources

About