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 s...

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Autores principales:	Sung-Ho Park, Justin Baik, Jiso Hong, Hanna Antila, Benjamin Kurland, Shinjae Chung, Franz Weber
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/45cb5c2d0f17413cbfea3996ca92ec78
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spelling	oai:doaj.org-article:45cb5c2d0f17413cbfea3996ca92ec782021-12-02T19:58:01ZA probabilistic model for the ultradian timing of REM sleep in mice.1553-734X1553-735810.1371/journal.pcbi.1009316https://doaj.org/article/45cb5c2d0f17413cbfea3996ca92ec782021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009316https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A 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.Sung-Ho ParkJustin BaikJiso HongHanna AntilaBenjamin KurlandShinjae ChungFranz WeberPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009316 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sung-Ho Park Justin Baik Jiso Hong Hanna Antila Benjamin Kurland Shinjae Chung Franz Weber A probabilistic model for the ultradian timing of REM sleep in mice.
description	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.
format	article
author	Sung-Ho Park Justin Baik Jiso Hong Hanna Antila Benjamin Kurland Shinjae Chung Franz Weber
author_facet	Sung-Ho Park Justin Baik Jiso Hong Hanna Antila Benjamin Kurland Shinjae Chung Franz Weber
author_sort	Sung-Ho Park
title	A probabilistic model for the ultradian timing of REM sleep in mice.
title_short	A probabilistic model for the ultradian timing of REM sleep in mice.
title_full	A probabilistic model for the ultradian timing of REM sleep in mice.
title_fullStr	A probabilistic model for the ultradian timing of REM sleep in mice.
title_full_unstemmed	A probabilistic model for the ultradian timing of REM sleep in mice.
title_sort	probabilistic model for the ultradian timing of rem sleep in mice.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/45cb5c2d0f17413cbfea3996ca92ec78
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A probabilistic model for the ultradian timing of REM sleep in mice.

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