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      State-dependent brainstem ensemble dynamics and their interactions with hippocampus across sleep states

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          Abstract

          The brainstem plays a crucial role in sleep-wake regulation. However, the ensemble dynamics underlying sleep regulation remain poorly understood. Here, we show slow, state-predictive brainstem ensemble dynamics and state-dependent interactions between the brainstem and the hippocampus in mice. On a timescale of seconds to minutes, brainstem populations can predict pupil dilation and vigilance states and exhibit longer prediction power than hippocampal CA1 neurons. On a timescale of sub-seconds, pontine waves (P-waves) are accompanied by synchronous firing of brainstem neurons during both rapid eye movement (REM) and non-REM (NREM) sleep. Crucially, P-waves functionally interact with CA1 activity in a state-dependent manner: during NREM sleep, hippocampal sharp wave-ripples (SWRs) precede P-waves. On the other hand, P-waves during REM sleep are phase-locked with ongoing theta oscillations and are followed by burst firing of CA1 neurons. This state-dependent global coordination between the brainstem and hippocampus implicates distinct functional roles of sleep.

          eLife digest

          Though almost all animals sleep, its exact purpose remains an enigma. This is particularly true for the period of sleep where people dream most vividly, which is known as rapid eye movement sleep or REM sleep for short. In addition to the eye movements that give it its name, during this phase of sleep, the pupils of the eyes become smaller, muscles relax and neurons in part of the brain activate in a regular, repeating way known as pontine waves or P-waves.

          The brainstem is a key brain region that helps the body determine when it is time to sleep and when it is time to be awake. It is found at the back of the brain, and connects the brain to the spinal cord, serving as a conduit for nerve signals to and from the rest of the body. However, it was not clear how the brainstem’s activity during sleep interacts with other brain regions that are important in the sleep process, such as the hippocampus.

          REM sleep is not unique to humans; in fact, it occurs in all mammals. Tsunematsu et al. studied mice to better understand the role of the brainstem during sleep. In the experiments, the brain waves, muscle tone and pupil sizes of the mice were monitored, while a probe inserted into the brainstem of the mice measured the activity of the neurons. Analysis of the probe data could predict changes in pupil size ten seconds beforehand and transitions between wakefulness, REM sleep and non-REM sleep up to sixty seconds in advance. This long timescale suggests that there are a number of complex interactions following brainstem activity that lead to the changes in sleep state.

          Tsunematsu et al. were also able to detect P-waves for the first time in mice and found that they are timed with activity from the hippocampus depending on the sleep state. During REM sleep, the P-waves precede the hippocampal activity, while during non-REM sleep, they follow it. These results further imply that the two sleep states serve different purposes. The detection of P-waves in mice shows that they are similar to other mammals that have previously been studied. Further studies in mice could help to provide more insight into the mechanisms of sleep and the purpose of the different stages.

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          Most cited references64

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          DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

          Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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            Theta oscillations in the hippocampus.

            Theta oscillations represent the "on-line" state of the hippocampus. The extracellular currents underlying theta waves are generated mainly by the entorhinal input, CA3 (Schaffer) collaterals, and voltage-dependent Ca(2+) currents in pyramidal cell dendrites. The rhythm is believed to be critical for temporal coding/decoding of active neuronal ensembles and the modification of synaptic weights. Nevertheless, numerous critical issues regarding both the generation of theta oscillations and their functional significance remain challenges for future research.
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              Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration.

              Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity. Copyright © 2014 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                14 January 2020
                2020
                : 9
                : e52244
                Affiliations
                [1 ]deptStrathclyde Institute of Pharmacy and Biomedical Sciences University of Strathclyde GlasgowUnited Kingdom
                [2 ]deptSuper-Network Brain Physiology, Graduate School of Life Sciences Tohoku University SendaiJapan
                [3 ]deptFrontier Research Institute for Interdisciplinary Sciences Tohoku University SendaiJapan
                [4 ]deptPrecursory Research for Embryonic Science and Technology Japan Science and Technology Agency KawaguchiJapan
                [5 ]deptSchool of Informatics University of Edinburgh EdinburghUnited Kingdom
                University of Texas at Austin United States
                University of Texas at Austin United States
                University of Texas at Austin United States
                Author information
                http://orcid.org/0000-0001-7387-5535
                https://orcid.org/0000-0001-6796-411X
                Article
                52244
                10.7554/eLife.52244
                6996931
                31934862
                27d11ff3-605e-4368-a61c-aa31eabdca8a
                © 2020, Tsunematsu et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 26 September 2019
                : 14 January 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BB/M00905X/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000275, Leverhulme Trust;
                Award ID: RPG-2015-377
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002283, Alzheimer’s Research UK;
                Award ID: ARUK-PPG2017B-005
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000703, Action on Hearing Loss;
                Award ID: S45
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/S005692/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008732, Uehara Memorial Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002241, Japan Science and Technology Agency;
                Award ID: JPMJPR1887
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: 17H06520
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                Sub-second pontine waves functionally interact with hippocampal population activity in a state-dependent manner across sleep states, while brainstem ensemble dynamics exhibit slow, long-lasting state-predictive activity.

                Life sciences
                brainstem,brain state,sleep,neural ensemble dynamics,neural oscillations,p/pgo waves,mouse
                Life sciences
                brainstem, brain state, sleep, neural ensemble dynamics, neural oscillations, p/pgo waves, mouse

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