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      Rapid fast-delta decay following prolonged wakefulness marks a phase of wake-inertia in NREM sleep

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          Abstract

          Sleep-wake driven changes in non-rapid-eye-movement sleep (NREM) sleep (NREMS) EEG delta (δ-)power are widely used as proxy for a sleep homeostatic process. Here, we noted frequency increases in δ-waves in sleep-deprived mice, prompting us to re-evaluate how slow-wave characteristics relate to prior sleep-wake history. We identified two classes of δ-waves; one responding to sleep deprivation with high initial power and fast, discontinuous decay during recovery sleep (δ2) and another unrelated to time-spent-awake with slow, linear decay (δ1). Reanalysis of previously published datasets demonstrates that δ-band heterogeneity after sleep deprivation is also present in human subjects. Similar to sleep deprivation, silencing of centromedial thalamus neurons boosted subsequent δ2-waves, specifically. δ2-dynamics paralleled that of temperature, muscle tone, heart rate, and neuronal ON-/OFF-state lengths, all reverting to characteristic NREMS levels within the first recovery hour. Thus, prolonged waking seems to necessitate a physiological recalibration before typical NREMS can be reinstated.

          Abstract

          Changes in EEG delta-activity are widely used as proxy of sleep propensity. Here the authors demonstrate in mice and humans the presence of two types of delta-waves, only one of which reports on prior sleep-wake history with dynamics denoting a wake-inertia process accompanying deepest non-rapid-eye-movement sleep (NREM) sleep.

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          Cortical firing and sleep homeostasis.

          The need to sleep grows with the duration of wakefulness and dissipates with time spent asleep, a process called sleep homeostasis. What are the consequences of staying awake on brain cells, and why is sleep needed? Surprisingly, we do not know whether the firing of cortical neurons is affected by how long an animal has been awake or asleep. Here, we found that after sustained wakefulness cortical neurons fire at higher frequencies in all behavioral states. During early NREM sleep after sustained wakefulness, periods of population activity (ON) are short, frequent, and associated with synchronous firing, while periods of neuronal silence are long and frequent. After sustained sleep, firing rates and synchrony decrease, while the duration of ON periods increases. Changes in firing patterns in NREM sleep correlate with changes in slow-wave activity, a marker of sleep homeostasis. Thus, the systematic increase of firing during wakefulness is counterbalanced by staying asleep.
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            Timing of human sleep: recovery process gated by a circadian pacemaker.

            A model for the timing of human sleep is presented. It is based on a sleep-regulating variable (S)--possibly, but not necessarily, associated with a neurochemical substance--which increases during wakefulness and decreases during sleep. Sleep onset is triggered when S approaches an upper threshold (H); awakening occurs when S reaches a lower threshold (L). The thresholds show a circadian rhythm controlled by a single circadian pacemaker. Time constants of the S process were derived from rates of change of electroencephalographic (EEG) power density during regular sleep and during recovery from sleep deprivation. The waveform of the circadian threshold fluctuations was derived from spontaneous wake-up times after partial sleep deprivation. The model allows computer simulations of the main phenomena of human sleep timing, such as 1) internal desynchronization in the absence of time cues, 2) sleep fragmentation during continuous bed rest, and 3) circadian phase dependence of sleep duration during isolation from time cues, recovery from sleep deprivation, and shift work. The model shows that the experimental data are consistent with the concept of a single circadian pacemaker in humans. It has implications for the understanding of sleep as a restorative process and its timing with respect to day and night.
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              The homeostatic regulation of sleep need is under genetic control.

              Delta power, a measure of EEG activity in the 1-4 Hz range, in slow-wave sleep (SWS) is in a quantitative and predictive relationship with prior wakefulness. Thus, sleep loss evokes a proportional increase in delta power, and excess sleep a decrease. Therefore, delta power is thought to reflect SWS need and its underlying homeostatically regulated recovery process. The neurophysiological substrate of this process is unknown and forward genetics might help elucidate the nature of what is depleted during wakefulness and recovered during SWS. We applied a mathematical method that quantifies the relationship between the sleep-wake distribution and delta power to sleep data of six inbred mouse strains. The results demonstrated that the rate at which SWS need accumulated varied greatly with genotype. This conclusion was confirmed in a "dose-response" study of sleep loss and changes in delta power; delta power strongly depended on both the duration of prior wakefulness and genotype. We followed the segregation of the rebound of delta power after sleep deprivation in 25 BXD recombinant inbred strains by quantitative trait loci (QTL) analysis. One "significant" QTL was identified on chromosome 13 that accounted for 49% of the genetic variance in this trait. Interestingly, the rate at which SWS need decreases did not vary with genotype in any of the 31 inbred strains studied. These results demonstrate, for the first time, that the increase of SWS need is under a strong genetic control, and they provide a basis for identifying genes underlying SWS homeostasis.
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                Author and article information

                Contributors
                jeffrey.hubbard@unil.ch
                paul.franken@unil.ch
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                19 June 2020
                19 June 2020
                2020
                : 11
                : 3130
                Affiliations
                [1 ]ISNI 0000 0001 2165 4204, GRID grid.9851.5, Center for Integrative Genomics, , University of Lausanne, ; Lausanne, Switzerland
                [2 ]ISNI 0000 0004 0479 0855, GRID grid.411656.1, Department of Neurology, , Inselspital University Hospital Bern, ; Bern, Switzerland
                [3 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, Department of Veterinary Anesthesia, , University of Zürich, ; Zürich, Switzerland
                [4 ]ISNI 0000 0001 2292 3357, GRID grid.14848.31, Department of Neuroscience, , Université de Montréal, ; Montreal, QC Canada
                [5 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, Institute of Pharmacology and Toxicology, , University of Zürich, ; Zurich, Switzerland
                [6 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, Sleep & Health Zürich, , University of Zürich, ; Zürich, Switzerland
                [7 ]ISNI 0000 0001 0726 5157, GRID grid.5734.5, Department of Biomedical Research, , University of Bern, ; Bern, Switzerland
                Author information
                http://orcid.org/0000-0002-0405-9398
                http://orcid.org/0000-0002-0478-5461
                http://orcid.org/0000-0002-6242-9166
                http://orcid.org/0000-0003-0887-9403
                http://orcid.org/0000-0002-2500-2921
                Article
                16915
                10.1038/s41467-020-16915-0
                7305232
                32561733
                5aa8eb4a-c13f-492e-92a7-d539022fb377
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 September 2019
                : 30 May 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: 146694
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                sleep,slow-wave sleep,neural circuits,neurophysiology
                Uncategorized
                sleep, slow-wave sleep, neural circuits, neurophysiology

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