Name: Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice (Video) | JoVE Journal
Uploaded: 2017-08-02T14:00:00.000+00:00
Duration: 10 min 56 s
Description: Learn to quantify infra-slow dynamics of spectral power and heart rate in sleeping mice using polysomnography. This method reveals how neural and cardiac activities coordinate during non-REM sleep.

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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.

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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.

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

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....

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 ...

The overall goal of this procedure, is to describe how neural and cardiac activities are coordinated during mouse non REM sleep. Through monitoring and analyzing electroencephalographic, electromyographic, and electrocardiographic signals. This method can help answer key questions in the sleep field.Such as how sleep reconciles two essential and evolutionarily preserved needs. Which are to be both continuous and fragile. The main advantage of this technique is that it uses standard polysomnography to score non REM sleep in a novel way that is based on the variable behavioral responses to external stimuli.We first had the idea for this method when we observed that sleeping mice showed variable responses to noise such as waking up and sleeping through. Generally individuals new to this method will struggle because it combines surgical skill, bare minimal experimentation, and advancing analysis. Visual demonstration of this method is critical as several essential steps are new to the field.In particular concerning the behavioral design and data analysis. The implications of this technique extend towards diagnosis of sleep disorders in humans. Because it identifies the moments of sleep that are highly fragile.To begin the procedure, prepare to EEG ecogeolectrodes using 0.5 centimeter long pieces of gold wire, each soldered on top of a gold plated steel screw. Then clean them in 70%ethanol. Next prepare two EMG-ECG electrodes with three to four centimeter long gold wires.Bend the wires at a 90 degree angle one centimeter from one end and prepare a coil at the other end. Between the two ends bend the wire to create a small curvature that corresponds to the surface profile of the bone between the cerebellum and lambda. After that prepare a six channel female to male head connector by covering the connector of both female and male pins at the base with tape.Then add to the pins at the corners some tin to facilitate the soldering process. In this step, place an anesthetized animal on the stereotaxic apparatus by positioning the mouth. This will fix the mouse head.Then fix the head by positioning the ear bars at the skull with their blunt reverse ends. Subsequently lift the skin at the center of the skull and gently cut the lifted portion of the skin along the mid line from the top of the neck to the level of the eyes. Then remove the scalp.Ensure that the window is large enough to clearly see the bregma and the lambda fissures of the skull. Fix the skin on both sides with bulldog serafines to ensure access to the bone. Next carefully remove the conjunctive tissue with a scalpel.Clean the area with iodine based disinfectant and dry the skull with an antiseptic swab. Then using a sharp scalpel blade, scratch the skull to obtain a cleaned and mattified bone surface. Using only the scalpel tip, scratch a grid like meshwork of groves with a distance of one to two millimeters between the grooves.Afterward, perform four craniotomies in the skull at specific locations with a microdrill. While drilling, blow away the bone dust using a pasteur pipette and clean any bleeding with antiseptic swabs. If bleeding occurs, ensure that it is stopped completely before resuming the process.Use a hemostatic sponge to accelerate hemostasis. Fix the screw in a hemostatic clamp. On the left hemisphere, screw two gold plated screws through the craniotomies.Hold the clamp vertically above the craniotomy and rotate the screw while not deviating from the vertical position. Gently open the hemostatic clamp to release the screw. On the right hemisphere screw the previously prepared electrodes through the craniotomies.Add a drop of glue on the middle part of the skull to ensure tight adhesion of the left EMG-ECG. Carefully lift the border of the skin from the neck muscles with forceps and insert the EMG-ECG wires with the coiled ends in the muscles. To detect the heart ECG signals during sleep, ensure that the EMG-ECG wire is inserted into the muscle to a depth of about 0.8 to one centimeter.Position the left EMG-ECG next to the posterior left anchoring screw. Repeat the procedure to insert the right EMG-ECG. Position the right EMG-ECG next to the anterior left anchoring screw.Ensure that the loop ends are placed as far as possible from each other. It is important that the EMG-ECG wires are inserted deeply enough into the muscle in order for the heart rate to be detected reliably. Using a spatula apply the two component epoxy glue to the skull between and around the screws.Let it dry in the light but protect the eyes of the animal from excessive lighting. Try to place the connector pins as closely as possible to minimize the height of the implant using a small crocodile lamp. Solder the four pins at the corner of the connector to the four wires.Minimize the time in contact with the soldering tip as this rapidly heats the screws. Next, fill the space between the glue and the connector with dental cement and cover the soldered parts. Create smooth surface and avoid sharp edges that could hurt the animal.In addition, avoid touching the skin as this leads to itching. Now remove the bulldog serafines. Disinfect the wound with iodine based disinfectant.If necessary, close the wound with suture points in front and behind the connector. Afterwards, monitor the animal every day for a week and look for weight loss, reduced or abnormal activity, and signs of infection. Five to six days after surgery, connect the recording cable to the head connector on the animal, leaving it in its home cage.Wait an additional four to five days before the start of recording so that the animal is habituated to the condition and sleeps naturally. With a commercial polysomnographic software, record EEG and EMG-ECG data over 48 hours using typical settings such as 2000 x gain, a 2000 hertz sampling rate at acquisition, down sampled to 200 hertz after acquisition. Apply a 0.7 hertz high pass filter for all the EEG traces and a 10 hertz high pass filter for all the EMG-ECG traces.In this demonstration, the mouse has been sleeping for more than 40 seconds. Here are the characteristic low frequency, high amplitude signals in the EEG and the low muscle tone in the EMG traces. It is important that the mouse sleep stably for at least 40 seconds before the noise is played to ensure a long base line for the analysis of sleep, prior to wake up or sleep through.Now an acoustic stimulus, here a white noise, will be delivered. In this case, the mouse wakes up within 10 seconds. Note that the EEG desynchronizes when the mouse wakes up.In another case, the mouse does not wake up, even though the noise has been delivered for more than 20 seconds. Note that the EEG does not desynchronize. Shown here are the raw traces of the EEG in black and the corresponding EMG-ECG signals in gray, for 40 seconds before the noise onset and during the 20 seconds of noise.This is a sleep through event. And this is an example of the wake up event. The corresponding analyzed data for sigma power dynamics in the 40 seconds window before noise onset are shown here for sleep through and wake up.The key result of this analysis is that the sigma power is at its trough before noise onset for a sleep through event and at its peak for a wake up event. In contrast to the wake up and sleep through events presented before, we discarded the events with an early transition into REM sleep. And we also discarded the events with an early wake up before noise onset.While attempting this procedure, it's important to ensure that EMG signal detects the heart rate throughout non REM sleep. Moreover remember that the analysis procedure needs to be understood and implemented in a software script to extract the infra-slow oscillation. Following this procedure, other methods, such as local electrophysiological recordings, or imaging approaches can be performed to follow activity patterns in defined brain areas.After redevelopment, this technique paves the way for researchers in the field of human sleep to explore how neural and cardiac activities are organized in the different sleep stages in healthy sleepers and in sleep disorder patients. After watching this video, you should have a good understanding of how to score and analyze non REM sleep retrospectively based on the behavioral reactivity of the sleeping mouse to an acoustic stimulus.

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Research

JoVE Journal

Neuroscience

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.

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

This video demonstrates a protocol for implanting an electroencephalogram (EEG) and electromyogram (EMG) electrode assembly in an anesthetized mouse. The process involves securing the mouse on a stereotaxic frame, making a midline incision in the skull, drilling holes for the electrodes, and fixing the electrode unit for signal recording. After recovery, the mouse's sleep-wake behavior is analyzed using the recorded signals.

Analyzing Sleep Behavior in Mice through Electroencephalogram and Electromyogram Recordings

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.

Polygraphic Recording Procedure for Measuring Sleep in Mice

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.

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

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.

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

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.

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

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.

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

This protocol aims to describe a new methodology to measure intrinsic cardiac firing rate using microelectrode array recording of the whole sinoatrial node tissue to identify pacemaking defects in mice. Pharmacological agents can also be introduced in this method to study their effects on intrinsic pacemaking.

Microelectrode Array Recording of Sinoatrial Node Firing Rate to Identify Intrinsic Cardiac Pacemaking Defects in Mice

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.

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

A robust way to study neuronal avalanches, i.e. scale-invariant spatio-temporal activity bursts, indicative of critical state dynamics in cortex.  Avalanches emerge spontaneously in developing superficial layers of cultured cortex which allows for long-term measurements of the activity with planar integrated multi-electrode arrays (MEA) under precisely controlled conditions.

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Learn to quantify infra-slow dynamics of spectral power and heart rate in sleeping mice using polysomnography. This method reveals how neural and cardiac activities coordinate during non-REM sleep.

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

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