We thank all lab members for their contribution to the writing and careful reading of this manuscript. We are grateful to Paul Franken for stimulating discussions, Dr. Gis&#232;le Ferrand for helpful comments on the surgical protocol, and Dr. Jean-Yves Chatton for providing the original Labview executable files for the noise exposure. Funding was provided by the Swiss National Science Foundation (Grants 31003A_146244 and 31003A_166318) and the Etat de Vaud.

<ol>
	<li>Lecci, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coordinated+infra-slow+neural+and+cardiac+oscillations+mark+fragility+and+offline+periods+in+mammalian+sleep.">Coordinated infra-slow neural and cardiac oscillations mark fragility and offline periods in mammalian sleep.</a> Sci Adv. 3 (2), 1602026 (2017).</li><li>Rechtschaffen, A., Kales, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+manual+of+standardized+terminology,+techniques+and+scoring+system+for+sleep+of+human+subjects.">A manual of standardized terminology, techniques and scoring system for sleep of human subjects.</a> U.S. Department of Health, Education, and Welfare. , (1968).</li><li>Blake, H., Gerard, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+potentials+during+sleep.">Brain potentials during sleep.</a> Am J Physiol. 119, 692-703 (1937).</li><li>Rechtschaffen, A., Hauri, P., Zeitlin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Auditory+awakening+threshold+in+REM+and+NREM+sleep+stages.">Auditory awakening threshold in REM and NREM sleep stages.</a> Percept Mot Skills. 22 (3), 927-942 (1966).</li><li>Achermann, P., Borb&#233;ly, A. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Low-frequency+(&lt;+1+Hz)+oscillations+in+the+human+sleep+electroencephalogram.">Low-frequency (&lt; 1 Hz) oscillations in the human sleep electroencephalogram.</a> Neuroscience. 81 (1), 213-222 (1997).</li><li>Aeschbach, D., Borb&#233;ly, A. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=All-night+dynamics+of+the+human+sleep+EEG.">All-night dynamics of the human sleep EEG.</a> J. Sleep Res. 2 (2), 70-81 (1993).</li><li>Astori, S., Wimmer, R. D., L&#252;thi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Manipulating+sleep+spindles--expanding+views+on+sleep,+memory,+and+disease.">Manipulating sleep spindles--expanding views on sleep, memory, and disease.</a> Trends Neurosci. 36 (12), 738-748 (2013).</li><li>Brown, R. E., Basheer, R., McKenna, J. T., Strecker, R. E., McCarley, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Control+of+sleep+and+wakefulness.">Control of sleep and wakefulness.</a> Physiol Rev. 92 (3), 1087-1187 (2012).</li><li>Buzs&#225;ki, G., Wang, X. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanisms+of+gamma+oscillations.">Mechanisms of gamma oscillations.</a> Annu Rev Neurosci. 35, 203-225 (2012).</li><li>Rasch, B., Born, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=About+sleep's+role+in+memory.">About sleep's role in memory.</a> Physiol Rev. 93 (2), 681-766 (2013).</li><li>Hal&#225;sz, P., B&#242;dizs, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Dynamic structure of NREM sleep. , (2013).</li><li>Terzano, M. G., Parrino, L., Spaggiari, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cyclic+alternating+pattern+sequences+in+the+dynamic+organization+of+sleep.">The cyclic alternating pattern sequences in the dynamic organization of sleep.</a> Electroencephalogr Clin Neurophysiol. 69 (5), 437-447 (1988).</li><li>Gottesmann, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+seven+sleep-waking+stages+in+the+rat.">Detection of seven sleep-waking stages in the rat.</a> Neurosci Biobehav Rev. 16 (1), 31-38 (1992).</li><li>Benington, J. H., Kodali, S. K., Heller, H. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scoring+transitions+to+REM+sleep+in+rats+based+on+the+EEG+phenomena+of+pre-REM+sleep:+an+improved+analysis+of+sleep+structure.">Scoring transitions to REM sleep in rats based on the EEG phenomena of pre-REM sleep: an improved analysis of sleep structure.</a> Sleep. 17 (1), 28-36 (1994).</li><li>Sullivan, D., Mizuseki, K., Sorgi, A., Buzs&#225;ki, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+sleep+spindles+and+theta+oscillations+in+the+hippocampus.">Comparison of sleep spindles and theta oscillations in the hippocampus.</a> J Neurosci. 34 (2), 662-674 (2014).</li><li>Vas, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+adaptation+of+REM+sleep+latency,+intermediate+stage+and+theta+power+effects+of+escitalopram+after+chronic+treatment.">Differential adaptation of REM sleep latency, intermediate stage and theta power effects of escitalopram after chronic treatment.</a> J Neural Transm (Vienna). 120 (1), 169-176 (2013).</li><li>Schiffelholz, T., Aldenhoff, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Novel+object+presentation+affects+sleep-wake+behavior+in+rats.">Novel object presentation affects sleep-wake behavior in rats.</a> Neurosci Lett. 328 (1), 41-44 (2002).</li><li>Wimmer, R. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sustaining+sleep+spindles+through+enhanced+SK2-channel+activity+consolidates+sleep+and+elevates+arousal+threshold.">Sustaining sleep spindles through enhanced SK2-channel activity consolidates sleep and elevates arousal threshold.</a> J Neurosci. 32 (40), 13917-13928 (2012).</li><li>Fernandez, L. M. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+dynamic+spatiotemporal+organization+of+low-frequency+activities+during+behavioral+states+in+the+mouse+cerebral+cortex.">Highly dynamic spatiotemporal organization of low-frequency activities during behavioral states in the mouse cerebral cortex.</a> Cereb Cortex. , (2016).</li><li>Franken, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Long-term+vs.+short-term+processes+regulating+REM+sleep.">Long-term vs. short-term processes regulating REM sleep.</a> J Sleep Res. 11 (1), 17-28 (2002).</li><li>Feige, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+microstructure+of+sleep+in+primary+insomnia:+an+overview+and+extension.">The microstructure of sleep in primary insomnia: an overview and extension.</a> Int J Psychophysiol. 89 (2), 171-180 (2013).</li><li>Parrino, L., Halasz, P., Tassinari, C. A., Terzano, M. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CAP,+epilepsy+and+motor+events+during+sleep:+the+unifying+role+of+arousal.">CAP, epilepsy and motor events during sleep: the unifying role of arousal.</a> Sleep Med Rev. 10 (4), 267-285 (2006).</li><li>Akinseye, O. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sleep+as+a+mediator+in+the+pathway+linking+environmental+factors+to+hypertension:+a+review+of+the+literature.">Sleep as a mediator in the pathway linking environmental factors to hypertension: a review of the literature.</a> Int J Hypertens. 2015, 926414 (2015).</li><li>Campbell, K., Muller-Gass, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+extent+of+processing+of+near-hearing+threshold+stimuli+during+natural+sleep.">The extent of processing of near-hearing threshold stimuli during natural sleep.</a> Sleep. 34 (9), 1243-1249 (2011).</li><li>Nir, Y., Vyazovskiy, V. V., Cirelli, C., Banks, M. I., Tononi, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Auditory+responses+and+stimulus-specific+adaptation+in+rat+auditory+cortex+are+preserved+across+NREM+and+REM+sleep.">Auditory responses and stimulus-specific adaptation in rat auditory cortex are preserved across NREM and REM sleep.</a> Cereb Cortex. 25 (5), 1362-1378 (2015).</li><li>Dang-Vu, T. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interplay+between+spontaneous+and+induced+brain+activity+during+human+non-rapid+eye+movement+sleep.">Interplay between spontaneous and induced brain activity during human non-rapid eye movement sleep.</a> Proc Natl Acad Sci USA. 108 (37), 15438-15443 (2011).</li><li>Elton, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Event-related+potentials+to+tones+in+the+absence+and+presence+of+sleep+spindles.">Event-related potentials to tones in the absence and presence of sleep spindles.</a> J Sleep Res. 6 (2), 78-83 (1997).</li><li>McKinney, S. M., Dang-Vu, T. T., Buxton, O. M., Solet, J. M., Ellenbogen, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Covert+waking+brain+activity+reveals+instantaneous+sleep+depth.">Covert waking brain activity reveals instantaneous sleep depth.</a> PLoS One. 6 (3), 17351 (2011).</li><li>Mang, G. M., Franken, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sleep+and+EEG+phenotyping+in+mice.">Sleep and EEG phenotyping in mice.</a> Curr Protoc Mouse Biol. 2 (1), 55-74 (2012).</li><li>Borb&#233;ly, A. A., Tobler, I., Hanagasioglu, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+sleep+deprivation+on+sleep+and+EEG+power+spectra+in+the+rat.">Effect of sleep deprivation on sleep and EEG power spectra in the rat.</a> Behav Brain Res. 14 (3), 171-182 (1984).</li><li>Jurysta, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+impact+of+chronic+primary+insomnia+on+the+heart+rate--EEG+variability+link.">The impact of chronic primary insomnia on the heart rate--EEG variability link.</a> Clin Neurophysiol. 120 (6), 1054-1060 (2009).</li><li>Silvani, A., Calandra-Buonaura, G., Benarroch, E. E., Dampney, R. A. L., Cortelli, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bidirectional+interactions+between+the+baroreceptor+reflex+and+arousal:+an+update.">Bidirectional interactions between the baroreceptor reflex and arousal: an update.</a> Sleep Med. , (2015).</li></ol>

The authors declare that they have no competing financial interest.

Here, we show how to establish a continuous temporal profile of non-REM sleep that integrates EEG, EMG, and ECG variables. This is a first step towards developing an integrative description of mouse sleep, which can help to identify the previously unrecognized timescale over which high and low resilience to noise are organized during non-REM sleep1. A similar temporal structure was also described in human non-REM sleep through an analogous analysis1.
<p class="jove_cont...

Mammalian sleep is a behavioral state of rest and of resilience to environmental stimuli. In spite of this apparent uniformity, polysomnographic and autonomic parameters indicate that sleep moves between qualitatively and quantitatively different neural and somatic states on various temporal and spatial scales1. Over minutes to tens of minutes, the switch between non-REM and REM sleep occurs. Non-REM sleep is accompanied by large-amplitude, low-frequency activity in the EEG, with a spectral peak around ~0.5 - 4 Hz, whereas REM sleep shows regular EEG activity in the theta band (6 - 10 Hz), together with muscle atonia2. Within non-REM sleep, humans cycle through light (S2) and deep slow-wave sleep (SWS). As their naming indicates, these two stages show lower and higher arousal thresholds3,4, respectively, and they differ primarily in the density of low-frequency cortical EEG power, referred to as slow-wave activity (SWA; 0.75 - 4 Hz). Non-uniformity persists throughout individual bouts of S2 and SWS on the minute- to sub-second-timescale, as extensively documented by the variable presence of SWA over the course of a bout5,6, but also by EEG and field potential rhythms at higher frequencies, including spindle waves in the sigma band (10-15 Hz) and gamma rhythms (80 - 120 Hz) (for a review, see7,8,9,10).
Rather than being subtle, these variations do shift the sleeping cortical state in humans to the extremes of the spectrum. For non-REM sleep, these range from a predominance of SWA to states that approximate wake-like activity because they contain a substantial proportion of high-frequency components11,12. In rodents and cats, although non-REM sleep is not subdivided into stages, a brief period called intermediate sleep (IS) emerges prior to REM sleep onset13. During IS, REM sleep features such as hippocampal theta activity and ponto-geniculo-occipital waves initiate, while non-REM sleep signatures, such as spindle waves and SWA, are still present, indicating a mix between the two sleep states14,15. Nevertheless, IS could be functionally distinct because it is modulated by antidepressants16 and through novel object presentation during prior waking17, and it contributes to setting the arousal threshold18. Furthermore, state space plots of EEG and EMG parameters of freely moving rats show a cluster of points14 that is continuous between non-REM sleep, REM sleep, and wakefulness. There are also sporadic declines in SWA, without entering wakefulness or REM sleep, which leads to substantial fluctuations in the relative presence of the low- and high-frequency components during a consolidated non-REM sleep bout14,19,20. Finally, variable ratios of SWA and higher frequency rhythms during non-REM sleep occur not only in time, but also show regional differences in amplitude and synchronization between cortical areas19.
Mammalian non-REM sleep is far from uniform. However, whether such non-uniformity leads to states that differ in function and behavioral attributes is not clear. In several types of sleep disorders, continuous sleep is disrupted by spontaneous awakenings and inappropriate motor behavior. Moreover, spectral analyses show alterations in the relative presence of higher frequencies in the EEG21 and in autonomic parameters, such as rates of breathing and cardiac beating22. The orderly sequence of stable sleep states is thus perturbed, and elements of cortical and/or autonomic arousal intrude in an uncontrolled manner. Therefore, understanding the continuum of sleep states is of possible relevance for disease. Additionally, the perturbance of sleep by environmental noise in urban environments is associated with general health risks, rendering it crucial to identify moments of heightened vulnerability during sleep23.
Behavioral arousal experiments in sleeping humans indicate that it is hardest to wake up from SWA-dominated non-REM sleep (stage S3), whereas light non-REM sleep (stage S2) and REM sleep show comparable and lower arousal thresholds4. The cortical processing of short sound stimuli varies substantially between REM sleep, S2, and S324,25, indicating that state-specific cortical activity patterns modulate the first stages of sensory processing. For non-REM sleep in humans, the propensity to wake up in response to noise varies with the presence of spindle waves and alpha rhythms in the EEG26,27,28. Thalamocortical rhythmicity during spindles is accompanied by enhanced synaptic inhibition at both the thalamic and cortical levels, which is thought to contribute to the attenuation of sensory processing7.
How are noise-resistant and vulnerable periods of sleep organized in time, and what are their determinants? In both mice and human, we recently identified an infra-slow, 0.02-Hz oscillation in neural rhythms. Depending upon the phase of this 0.02-Hz oscillation, mice showed variable reactivity to external stimuli, either waking up or sleeping through the noise. Interestingly, this oscillation was correlated with the rate of the heartbeat, indicating that the autonomic nervous system participates in the modulation of sleep&#39;s vulnerability to external stimuli1. Memory-related hippocampal rhythms were also organized within this rhythm, and, most strikingly, its strength correlated with the quality of memory consolidation in humans. The 0.02-Hz oscillation thus appears to be an organizing principle of rodent and human non-REM sleep that modulates both sensitivity to the environment and internal memory processing. This again highlights the need for multiparametric and continuous assessments of sleep states to recognize their functionality and to identify sites of potential vulnerability.
Here, we present a procedure to extract the waveform of these dynamics, including the surgical implantation of mice for combined EEG/ECoG and EMG-ECG measurements, exposure to sensory stimuli, and analysis routines. This procedure provides a basis for viewing sleep as a continuously varying yet highly organized vigilance state during which different fundamental sleep functions are sequentially executed. More generally, the procedure is applicable to approaches aiming to extract the spectral and autonomic features that precede a behavioral outcome during sleep in both health and disease states.

<table><tbody><tr><td>2-components epoxy glue</td><td>Henkel</td><td>Loctite EA 3450</td><td></td></tr><tr><td>Absorbable Suturing Fiber (Prolene)</td><td>Ethicon</td><td>5-0 FS-3</td><td></td></tr><tr><td>Adson Forceps</td><td>FST</td><td>11006-12</td><td></td></tr><tr><td>Antiseptic swab</td><td>VWR</td><td>149-0332</td><td></td></tr><tr><td>Attane Isoflurane</td><td>Piramal</td><td>Isoflurane 250mL</td><td></td></tr><tr><td>Connectors 3 x 2-channels</td><td>ENA AG</td><td>2.316</td><td>Raster 2.54 x 2.54 mm; size 5 x 8 x 9 mm; pin size 5 mm; http://www.ena.ch/</td></tr><tr><td>Dragonfly commutator</td><td>Dragonfly</td><td>Model #SL-10</td><td></td></tr><tr><td>EMBLA amplifier</td><td>EMBLA</td><td>A10 amplifier</td><td></td></tr><tr><td>Fine scissors</td><td>FST</td><td>14108-09</td><td></td></tr><tr><td>Flat Head Gold-plated steel screw</td><td>J.I. Morris</td><td>FF00CE125</td><td>https://jimorrisco.com/</td></tr><tr><td>Gold wire</td><td>CMSA</td><td>T.69 5gr</td><td>http://www.cmsa.ch/en/</td></tr><tr><td>Hemostatic sponge</td><td>Pfizer</td><td>Gelfoam</td><td></td></tr><tr><td>iodine-based disinfectant (Betadine)</td><td>Mundipharma</td><td>standart solution 60mL</td><td></td></tr><tr><td>Komet drill steel 1/005PM104</td><td>UNOR AG</td><td>22310</td><td></td></tr><tr><td>Matlab Analysis Software</td><td>MathWorks</td><td>R2016b</td><td>https://ch.mathworks.com/products/matlab.html</td></tr><tr><td>Microdrill</td><td>Fine Science Tools</td><td>96758</td><td></td></tr><tr><td>Mouse Gas Anesthesia Head Holder</td><td>Kopf Instruments</td><td>Model 923-B</td><td>http://kopfinstruments.com/product/model-923-b-mouse-gas-anesthesia-head-holder/</td></tr><tr><td>Ophtalmic ointment</td><td>Pharmamedica</td><td>VITA-POS</td><td></td></tr><tr><td>Paladur (liquid)</td><td>UNOR AG</td><td>2260215</td><td>for dental cement</td></tr><tr><td>Palavit (powder)</td><td>UNOR AG</td><td>5410929</td><td>for dental cement</td></tr><tr><td>Small Animal Stereotaxic Frame</td><td>Kopf Instruments</td><td>Model 930</td><td>http://kopfinstruments.com/product/model-930-small-animal-stereotaxic-frame-assembly/</td></tr><tr><td>Soldering wire</td><td>Stannol</td><td>593072</td><td></td></tr><tr><td>Temperature controller - Mini rectal probe</td><td>Phymep</td><td>4090502</td><td>http://www.phymep.com/produit/dc-temperature-controller/</td></tr><tr><td>Temperature controller- heating pad</td><td>Phymep</td><td>4090205</td><td>http://www.phymep.com/produit/dc-temperature-controller/</td></tr></tbody></table>

quantifying infra-slow dynamics of spectral power and heart rate in sleeping mice

Three vigilance states dominate mammalian life: wakefulness, non-rapid eye movement (non-REM) sleep, and REM sleep. As more neural correlates of behavior are identified in freely moving animals, this three-fold subdivision becomes too simplistic. During wakefulness, ensembles of global and local cortical activities, together with peripheral parameters such as pupillary diameter and sympathovagal balance, define various degrees of arousal. It remains unclear the extent to which sleep also forms a continuum of brain states-within which the degree of resilience to sensory stimuli and arousability, and perhaps other sleep functions, vary gradually-and how peripheral physiological states co-vary. Research advancing the methods to monitor multiple parameters during sleep, as well as attributing to constellations of these functional attributes, is central to refining our understanding of sleep as a multifunctional process during which many beneficial effects must be executed. Identifying novel parameters characterizing sleep states will open opportunities for novel diagnostic avenues in sleep disorders.
We present a procedure to describe dynamic variations of mouse non-REM sleep states via the combined monitoring and analysis of electroencephalogram (EEG)/electrocorticogram (ECoG), electromyogram (EMG), and electrocardiogram (ECG) signals using standard polysomnographic recording techniques. Using this approach, we found that mouse non-REM sleep is organized into cycles of coordinated neural and cardiac oscillations that generate successive 25-s intervals of high and low fragility to external stimuli. Therefore, central and autonomic nervous systems are coordinated to form behaviorally distinct sleep states during consolidated non-REM sleep. We present surgical manipulations for polysomnographic (i.e., EEG/EMG combined with ECG) monitoring to track these cycles in the freely sleeping mouse, the analysis to quantify their dynamics, and the acoustic stimulation protocols to assess their role in the likelihood of waking up. Our approach has already been extended to human sleep and promises to unravel common organizing principles of non-REM sleep states in mammals.

Figure 2A (top panel) shows 100-min stretches of spontaneous sleep-wake behavior, recorded through polysomnographic electrodes implanted as described (see Figure 1). The increases and decreases of the EEG and EMG amplitude at non-REM sleep onset are clearly visible. Intermittent REM sleep is marked by a decrease in EEG amplitude and a further decrease in EMG tone that is not visible on this compressed time scale....

Watch this Scientific Journal Video about Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice at JoVE.com

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Here, we present experimental and analytical procedures to describe the temporal dynamics of the neural and cardiac variables of non-REM sleep in mice, which modulate sleep responsiveness to acoustic stimuli.

Three vigilance states dominate mammalian life: wakefulness, non-rapid eye movement (non-REM) sleep, and REM sleep. As more neural correlates of behavior ...

quantifying-infra-slow-dynamics-spectral-power-heart-rate-sleeping

Research

JoVE Journal

Neuroscience

9.9K Views.  University of Lausanne.  Hier presenteren we experimentele en analytische procedures om de temporale dynamiek van de neurale en hartvariabelen van niet-REM slaap in muizen te beschrijven, die de respons op slaapresponsen voor akoestische stimuli moduleren. 

Video: Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

In epilepsy, seizures can evoke cardiac rhythm disturbances such as heart rate changes, conduction blocks, asystoles, and arrhythmias, which can potentially increase risk of sudden unexpected death in epilepsy (SUDEP). Electroencephalography (EEG) and electrocardiography (ECG) are widely used clinical diagnostic tools to monitor for abnormal brain and cardiac rhythms in patients. Here, a technique to simultaneously record video, EEG, and ECG in mice to measure behavior, brain, and cardiac activities, respectively, is described. The technique described herein utilizes a tethered (i.e., wired) recording configuration in which the implanted electrode on the head of the mouse is hard-wired to the recording equipment. Compared to wireless telemetry recording systems, the tethered arrangement possesses several technical advantages such as a greater possible number of channels for recording EEG or other biopotentials; lower electrode costs; and greater frequency bandwidth (i.e., sampling rate) of recordings. The basics of this technique can also be easily modified to accommodate recording other biosignals, such as electromyography (EMG) or plethysmography for assessment of muscle and respiratory activity, respectively. In addition to describing how to perform the EEG-ECG recordings, we also detail methods to quantify the resulting data for seizures, EEG spectral power, cardiac function, and heart rate variability, which we demonstrate in an example experiment using a mouse with epilepsy due to Kcna1 gene deletion. Video-EEG-ECG monitoring in mouse models of epilepsy or other neurological disease provides a powerful tool to identify dysfunction at the level of the brain, heart, or brain-heart interactions.

Here, we present a protocol to record brain and heart bio signals in mice using simultaneous video, electroencephalography (EEG), and electrocardiography (ECG). We also describe methods to analyze the resulting EEG-ECG recordings for seizures, EEG spectral power, cardiac function, and heart rate variability.

In epilepsy, seizures can evoke cardiac rhythm disturbances such as heart rate changes, conduction blocks, asystoles, and arrhythmias, which can ...

Genetics

Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice

The laboratory mouse is the animal species of choice for most biomedical research, in both the academic sphere and the pharmaceutical industry. Mice are a manageable size and relatively easy to house. These factors, together with the availability of a wealth of spontaneous and experimentally induced mutants, make laboratory mice ideally suited to a wide variety of research areas.
In cardiovascular, pharmacological and toxicological research, accurate measurement of parameters relating to the circulatory system of laboratory animals is often required. Determination of heart rate, heart rate variability, and duration of PQ and QT intervals are based on electrocardiogram (ECG) recordings. However, obtaining reliable ECG curves as well as physiological data such as core body temperature in mice can be difficult using conventional measurement techniques, which require connecting sensors and lead wires to a restrained, tethered, or even anaesthetized animal. Data obtained in this fashion must be interpreted with caution, as it is well known that restraining and anesthesia can have a major artifactual influence on physiological parameters1, 2.
Radiotelemetry enables data to be collected from conscious and untethered animals. Measurements can be conducted even in freely moving animals, and without requiring the investigator to be in the proximity of the animal. Thus, known sources of artifacts are avoided, and accurate and reliable measurements are assured. This methodology also reduces interanimal variability, thus reducing the number of animals used, rendering this technology the most humane method of monitoring physiological parameters in laboratory animals3, 4. Constant advancements in data acquisition technology and implant miniaturization mean that it is now possible to record physiological parameters and locomotor activity continuously and in realtime over longer periods such as hours, days or even weeks3, 5.
Here, we describe a surgical technique for implantation of a commercially available telemetry transmitter used for continuous measurements of core body temperature, locomotor activity and biopotential (i.e. onelead ECG), from which heart rate, heart rate variability, and PQ and QT intervals can be established in freeroaming, untethered mice. We also present pre-operative procedures and protocols for post-operative intensive care and pain treatment that improve recovery, well-being and survival rates in implanted mice5, 6.

A surgical technique for implantation of commercially available telemetry transmitters used for continuous measurement of biopotential (one-lead ECG), heart rate, core body temperature and locomotor activity in freely moving mice is shown. Recommendations and protocols for post-operative care and pain relief, improving recovery, well being and survival rate are also presented.

The laboratory mouse is the animal species of choice for most biomedical research, in both the academic sphere and the pharmaceutical industry. Mice are a ...

Medicine

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Traditionally, sleep is monitored by an electroencephalogram (EEG). EEG studies in rodents require surgical implantation of the electrodes followed by a long recovery period. To perform an EEG recording, the animal is connected to a receiver, creating an unnatural tether to the head-mount. EEG monitoring is time consuming, carries risk to the animal, and is not a completely natural setting for the measurement of sleep. Alternative methods to detect sleep, particularly in a high-throughput fashion, would greatly advance the field of sleep research. Here, we describe a validated method for detecting sleep via activity-based home-cage monitoring. Previous studies have shown that sleep assessed via this method has a high degree of agreement with sleep defined by traditional EEG-based measures. Whereas this method is validated for total sleep time, it is important to note that sleep bout duration should be assessed by an EEG which has better temporal resolution. The EEG can also differentiate rapid eye movement (REM) and non-REM sleep, giving more detail about the exact nature of sleep. Nevertheless, activity-based sleep determination can be used to analyze multiple days of undisturbed sleep and to assess sleep as a response to an acute event (like stress). Here, we show the power of this system to detect the response of mice to daily intraperitoneal injections.

We describe a high-throughput method of measuring sleep by means of activity-based home-cage monitoring. This method offers advantages over traditional EEG-based methods. It is well validated for the determination of total sleep duration and can be a powerful tool to monitor sleep in rodent models of human disease.

Traditionally, sleep is monitored by an electroencephalogram (EEG). EEG studies in rodents require surgical implantation of the electrodes followed by a ...

Behavior

Polygraphic Recording Procedure for Measuring Sleep in Mice

Recording of the epidural electroencephalogram (EEG) and electromyogram (EMG) in small animals, like mice and rats, has been pivotal to study the homeodynamics and circuitry of sleep-wake regulation. In many laboratories, a cable-based sleep recording system is used to monitor the EEG and EMG in freely behaving mice in combination with computer software for automatic scoring of the vigilance states on the basis of power spectrum analysis of EEG data. A description of this system is detailed herein. Steel screws are implanted over the frontal cortical area and the parietal area of 1 hemisphere for monitoring EEG signals. In addition, EMG activity is monitored by the bilateral placement of wires in both neck muscles. Non-rapid eye movement (Non-REM; NREM) sleep is characterized by large, slow brain waves with delta activity below 4 Hz in the EEG, whereas a shift from low-frequency delta activity to a rapid low-voltage EEG in the theta range between 6 and 10 Hz can be observed at the transition from NREM to REM sleep. By contrast, wakefulness is identified by low- to moderate-voltage brain waves in the EEG trace and significant EMG activity.

The recording of electroencephalogram (EEG) and electromyogram (EMG) in freely behaving mice is a critical step to correlate behavior and physiology with sleep and wakefulness. The experimental protocol described herein provides a cable-based system for acquiring EEG and EMG recordings in mice.

Recording of the epidural electroencephalogram (EEG) and electromyogram (EMG) in small animals, like mice and rats, has been pivotal to study the ...

Novel Approach for Simultaneous Recording of Renal Sympathetic Nerve Activity and Blood Pressure with Intravenous Infusion in Conscious, Unrestrained Mice.

Renal sympathetic nerves contribute significantly to both physiological and pathophysiological phenomena. Evaluating renal sympathetic nerve activity (RSNA) is of great interest in many areas of research such as chronic kidney disease, hypertension, heart failure, diabetes and obesity. Unequivocal assessment of the role of the sympathetic nervous system is thus imperative for proper interpretation of experimental results and understanding of disease processes. RSNA has been traditionally measured in anesthetized rodents, including mice. However, mice usually exhibit very low systemic blood pressure and hemodynamic instability for several hours during anesthesia and surgery. Meaningful interpretation of RSNA is confounded by this non-physiological state, given the intimate relationship between sympathetic nervous tone and cardiovascular status. To address this limitation of traditional approaches, we developed a new method for measuring RSNA in conscious, freely-moving mice. Mice were chronically instrumented with radio-telemeters for continuous monitoring of blood pressure as well as a jugular venous infusion catheter and custom-designed bipolar electrode for direct recording of RSNA. Following a 48-72 hour recovery period, survival rate was 100% and all mice behaved normally. At this time-point, RSNA was successfully recorded in 80% of mice, with viable signals acquired up to 4 and 5 days post-surgery in 70% and 50% of mice, respectively. Physiological blood pressures were recorded in all mice (116&plusmn;2 mmHg; n=10). Recorded RSNA increased with eating and grooming, as well-established in the literature. Furthermore, RSNA was validated by ganglionic blockade and modulation of blood pressure with pharmacological agents. Herein, an effective and manageable method for clear recording of RSNA in conscious, freely-moving mice is described.

Anesthetized mice exhibit non-physiological systemic blood pressure, which precludes meaningful assessment of autonomic tone given the intimate relationship between blood pressure and the autonomic nervous system. Thus, a novel method to simultaneously record renal sympathetic nerve activity and blood pressure with intravenous infusion in conscious mice is outlined.

Renal sympathetic nerves contribute significantly to both physiological and pathophysiological phenomena. Evaluating renal sympathetic nerve activity ...

Echocardiographic Measurement of Right Ventricular Diastolic Parameters in Mouse

Diastolic dysfunction is a prominent feature of right ventricular (RV) remodeling associated with conditions of pressure overload. However, the RV diastolic function is rarely quantified in experimental studies. This might be due to technical difficulties in the visualization of the RV in the apical four-chamber view in rodents. Here we describe two positions facilitating the visualization of the apical four-chamber view in mice to assess the RV diastolic function.
The apical four-chamber view is enabled by tilting the mouse fixation platform to the left and caudally (LeCa) or to the right and cranially (RiCr). Both positions provide images of comparable quality. The results of the RV diastolic function obtained from two positions are not significantly different. Both positions are comparably easy to perform. This protocol can be incorporated into published protocols and enables detailed investigations of the RV function.

Here we describe and compare two positions for obtaining the apical four-chamber view in mice. These positions enable the quantification of the right ventricular function, provide comparable results, and can be used interchangeably.

Diastolic dysfunction is a prominent feature of right ventricular (RV) remodeling associated with conditions of pressure overload. However, the RV ...

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Current web resources provide limited, user friendly tools to compute spectrograms for visualizing and quantifying electroencephalographic (EEG) data. This paper describes a Windows-based, open source code for creating EEG multitaper spectrograms. The compiled program is accessible to Windows users without software licensing. For Macintosh users, the program is limited to those with a MATLAB software license. The program is illustrated via EEG spectrograms that vary as a function of states of sleep and wakefulness, and opiate-induced alterations in those states. The EEGs of C57BL/6J mice were wirelessly recorded for 4 h after intraperitoneal injection of saline (vehicle control) and antinociceptive doses of morphine, buprenorphine, and fentanyl. Spectrograms showed that buprenorphine and morphine caused similar changes in EEG power at 1&#8722;3 Hz and 8&#8722;9 Hz. Spectrograms after administration of fentanyl revealed maximal average power bands at 3 Hz and 7 Hz. The spectrograms unmasked differential opiate effects on EEG frequency and power. These computer-based methods are generalizable across drug classes and can be readily modified to quantify and display a wide range of rhythmic biological signals.

This protocol provides an open source, compiled MATLAB program that generates multitaper spectrograms for electroencephalographic data.

Current web resources provide limited, user friendly tools to compute spectrograms for visualizing and quantifying electroencephalographic (EEG) data. ...

Implantation of Combined Telemetric ECG and Blood Pressure Transmitters to Determine Spontaneous Baroreflex Sensitivity in Conscious Mice

Blood pressure (BP) and heart rate (HR) are both controlled by the autonomic nervous system (ANS) and are closely intertwined due to reflex mechanisms. The baroreflex is a key homeostatic mechanism to counteract acute, short-term changes in arterial BP and to maintain BP in a relatively narrow physiological range. BP is sensed by baroreceptors located in the aortic arch and carotid sinus. When BP changes, signals are transmitted to the central nervous system and are then communicated to the parasympathetic and sympathetic branches of the autonomic nervous system to adjust HR. A rise in BP causes a reflex decrease in HR, a drop in BP causes a reflex increase in HR.
Baroreflex sensitivity (BRS) is the quantitative relationship between changes in arterial BP and corresponding changes in HR. Cardiovascular diseases are often associated with impaired baroreflex function. In various studies reduced BRS has been reported in e.g., heart failure, myocardial infarction, or coronary artery disease. 
Determination of BRS requires information from both BP and HR, which can be recorded simultaneously using telemetric devices. The surgical procedure is described beginning with the insertion of the pressure sensor into the left carotid artery and positioning of its tip in the aortic arch to monitor arterial pressure followed by the subcutaneous placement of the transmitter and ECG electrodes. We also describe postoperative intensive care and analgesic management. After a two-week period of post-surgery recovery long-term ECG and BP recordings are performed in conscious and unrestrained mice. Finally, we include examples of high-quality recordings and the analysis of spontaneous baroreceptor sensitivity using the sequence method.

The baroreflex is a heart-rate regulation mechanism by the autonomic nervous system in response to blood-pressure changes. We describe a surgical technique to implant telemetry transmitters for continuous and simultaneous measurement of electrocardiogram and blood pressure in mice. This can determine spontaneous baroreflex sensitivity, an important prognostic marker for cardiovascular disease.

Blood pressure (BP) and heart rate (HR) are both controlled by the autonomic nervous system (ANS) and are closely intertwined due to reflex mechanisms. ...

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Arrhythmias are common, affecting millions of patients worldwide. Current treatment strategies are associated with significant side effects and remain ineffective in many patients. To improve patient care, novel and innovative therapeutic concepts causally targeting arrhythmia mechanisms are needed. To study the complex pathophysiology of arrhythmias, suitable animal models are necessary, and mice have been proven to be ideal model species to evaluate the genetic impact on arrhythmias, to investigate fundamental molecular and cellular mechanisms, and to identify potential therapeutic targets.
Implantable telemetry devices are among the most powerful tools available to study electrophysiology in mice, allowing continuous ECG recording over a period of several months in freely moving, awake mice. However, due to the huge number of data points (&gt;1 million QRS complexes per day), analysis of telemetry data remains challenging. This article describes a step-by-step approach to analyze ECGs and to detect arrhythmias in long-term telemetry recordings using the software, Ponemah, with its analysis modules, ECG Pro and Data Insights, developed by Data Sciences International (DSI). To analyze basic ECG parameters, such as heart rate, P wave duration, PR interval, QRS interval, or QT duration, an automated attribute analysis was performed using Ponemah to identify P, Q, and T waves within individually adjusted windows around detected R waves. 
Results were then manually reviewed, allowing adjustment of individual annotations. The output from the attribute-based analysis and the pattern recognition analysis was then used by the Data Insights module to detect arrhythmias. This module allows an automatic screening for individually defined arrhythmias within the recording, followed by a manual review of suspected arrhythmia episodes. The article briefly discusses challenges in recording and detecting ECG signals, suggests strategies to improve data quality, and provides representative recordings of arrhythmias detected in mice using the approach described above.

Here we present a step-by-step protocol for a semiautomated approach to analyze murine long-term electrocardiography (ECG) data for basic ECG parameters and common arrhythmias. Data are obtained by implantable telemetry transmitters in living and awake mice and analyzed using Ponemah and its analysis modules.

Arrhythmias are common, affecting millions of patients worldwide. Current treatment strategies are associated with significant side effects and remain ...

Automated Sleep Stage Scoring Tool for Rodents Using LightGBM

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring

Sleep stage scoring in rodents is the process of identifying the three stages: nonrapid eye movement sleep (NREM), rapid eye movement sleep (REM), and wake. Sleep stage scoring is crucial for studying sleep stage-specific measures and effects. 
Sleep patterns in rodents differ from those in humans, characterized by shorter episodes of NREM and REM interspaced by waking, and traditional manual sleep stage scoring by human experts is time-consuming. To address this issue, previous studies have used machine learning-based approaches to develop algorithms to automatically categorize sleep stages, but high-performing models with great generalizability are often not publicly available/cost-free nor user-friendly for non-trained sleep researchers. 
Therefore, we developed a machine learning-based LightGBM algorithm trained with a large dataset. To make the model available to sleep researchers without coding experience, a software tool named IntelliSleepScorer (v1.2- newest version) was developed based on the model, which features an easy-to-use graphic user interface. In this manuscript, we present step-by-step instructions for using the software to demonstrate a convenient and effective automatic sleep stage scoring tool in mice for sleep researchers.

Author Spotlight: IntelliSleepScorer &#8212; A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

We present a software package with a graphic user interface for researchers without coding experience to score sleep stages in mice with a simple download and operation.

Sleep stage scoring in rodents is the process of identifying the three stages: nonrapid eye movement sleep (NREM), rapid eye movement sleep (REM), and ...

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video