This work was supported by German Research Foundation (DFG; Clinician Scientist Program In Vascular Medicine (PRIME), MA 2186/14-1 to P. Tomsits and D. Sch&#252;ttler), German Centre for Cardiovascular Research (DZHK; 81X2600255 to S. Clauss), the Corona Foundation (S199/10079/2019 to S. Clauss), the ERA-NET on Cardiovascular Diseases (ERA-CVD; 01KL1910 to S. Clauss), the Heinrich-and-Lotte-M&#252;hlfenzl Stiftung (to S. Clauss) and the China Scholarship Council (CSC, to R. Xia). The funders had no role in manuscript preparation.

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Cardiology. 16 (8), 457-475 (2019).</li><li>Sch&#252;ttler, 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=Animal+models+of+atrial+fibrillation.">Animal models of atrial fibrillation.</a> Circulation Research. 127 (1), 91-110 (2020).</li><li>Dobrev, 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=Mouse+models+of+cardiac+arrhythmias.">Mouse models of cardiac arrhythmias.</a> Circulation Research. 123 (3), 332-334 (2018).</li><li>Rosero, S. Z., 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=Ambulatory+ECG+monitoring+in+atrial+fibrillation+management.">Ambulatory ECG monitoring in atrial fibrillation management.</a> Progress in cardiovascular diseases. 56 (2), 143-152 (2013).</li><li>Russell, D. 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=A+high+bandwidth+fully+implantable+mouse+telemetry+system+for+chronic+ECG+measurement.">A high bandwidth fully implantable mouse telemetry system for chronic ECG measurement.</a> Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology. 2011, 7666-7669 (2011).</li><li>McCauley, M. 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=Ambulatory+ECG+recording+in+mice.">Ambulatory ECG recording in mice.</a> Journal of Visualized Experiments : JoVE. (39), e1739 (2010).</li><li>Mehendale, A. 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N., 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=Electrocardiogram+of+the+normal+mouse,+Mus+musculus:+general+considerations+and+genetic+aspects.">Electrocardiogram of the normal mouse, Mus musculus: general considerations and genetic aspects.</a> Cardiovascular Research. 2 (1), 93-99 (1968).</li><li>Kaese, 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=The+ECG+in+cardiovascular-relevant+animal+models+of+electrophysiology.">The ECG in cardiovascular-relevant animal models of electrophysiology.</a> Herzschrittmachertherapie und Elektrophysiologie. 24 (2), 84-91 (2013).</li><li>Speerschneider, 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=Physiology+and+analysis+of+the+electrocardiographic+T+wave+in+mice.">Physiology and analysis of the electrocardiographic T wave in mice.</a> Acta Physiologica. 209 (4), 262-271 (2013).</li><li>Toib, 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=Remodeling+of+repolarization+and+arrhythmia+susceptibility+in+a+myosin-binding+protein+C+knockout+mouse+model.">Remodeling of repolarization and arrhythmia susceptibility in a myosin-binding protein C knockout mouse model.</a> American Journal of Physiology. Heart and Circulatory Physiology. 313 (3), 620-630 (2017).</li><li>Thireau, 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=Heart+rate+variability+in+mice:+a+theoretical+and+practical+guide.">Heart rate variability in mice: a theoretical and practical guide.</a> Experimental Physiology. 93 (1), 83-94 (2008).</li><li>Hilgard, 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=Significance+of+ventricular+pauses+of+three+seconds+or+more+detected+on+twenty-four-hour+Holter+recordings.">Significance of ventricular pauses of three seconds or more detected on twenty-four-hour Holter recordings.</a> American Journal of Cardiology. 55 (8), 1005-1008 (1985).</li></ol>

The surface ECG is the primary diagnostic tool for patients suffering from heart rhythm disorders, providing insights into many electrophysiological phenomena. Nevertheless, sufficient analysis of cardiac surface ECG pathologies requires knowledge and definition of normal physiologic parameters. Many years of epidemiological research have led to broad consent on what is physiologic in humans and thus enabled physicians worldwide to clearly distinguish the pathologic. However, the analysis of surface ECG data is a major challenge in murine models; distinguishing between physiological and pathological ECG results can be difficult due to incomplete understanding and definition of basic ECG parameters15,16. In 1968, Goldbarg et al. were the first to describe ECG in healthy mice17. Besides showing heart rates and basic ECG patterns, such as PR interval and QRS duration, they described major differences between anesthetized and awake animals and differences between various anesthetics and different murine breeds, which was later confirmed by other groups16,17. 
These early data emphasize why interpretation of murine ECG data is delicate and complicated. With growing interest in murine models for arrhythmia research in the past decades, more research has been focused on mouse electrophysiology and has generated evidence on the patterns of activation and repolarization in the mouse heart. The interested reader may be referred to a recent article by Boukens et al. for a detailed review of the murine ECG and its underlying currents15. Kaese et al. provided an overview on murine ECG standard values and major differences between human and murine ECG traces18. The first major difference is heart rate: healthy awake mice have a heart rate of 550-725 beats per minute, PR intervals of 30-56 ms, a QRS duration of 9-30 ms, and a repolarization phase that is very distinct from that observed in humans14. Further, the murine ECG regularly shows the occurrence of J-waves and a small and less distinctive T-wave, making analysis of the ST-segment and QT interval difficult18,19. Overall, murine models have become the most widely used model organism for cardiovascular research, including arrhythmias8. 
Taking into consideration the above described interspecies differences that very likely also influence arrhythmogenesis, these models can provide valuable insights. The analysis of basic ECG parameters, such as heart rate and duration of different intervals, can be reliably done using software such as Ponemah, LabChart, or ECGAuto among many others with their respective analysis algorithms. Examples for data display are shown in Figure 5. Arrhythmia detection, however, is far more delicate, and there are no widely established approaches for murine long-term ECG analysis for arrhythmias. Different approaches have been used to overcome the technical and methodological difficulties associated with arrhythmia detection of long-term ECG recordings in mice. These approaches range from only using short recordings for the manual analysis for arrhythmias20 to simple considerations accepting inaccuracy as described by Thireau et al.21. These researchers performed heart rate variability analysis by simply excluding all sections of their recording with R-R intervals not contained in the range of the mean R-R interval &plusmn; 2 standard deviations to exclude all arrhythmias, ectopic beats, and artefacts without any manual review. This is the reason for this semimanual approach using Ponemah and its consecutive analysis modules, ECG Pro and Data Insights. This software solution can be used to analyze a vast range of physiologic signals, ranging from ECG in large mammals to blood pressure or temperature data in very small species. 
The software comes with many resources on how to analyze different types of data. Nevertheless, although working quite well with ECG signals from larger animals, the low signal amplitude and therefore, high noise of signals derived from species, such as living and awake mice, can lead to a number of difficulties using a common approach to analysis. Noise will often mask P or T waves and thus disable the use of most of the predefined search rules within Data Insights. Care must be taken to define optimal values of the QRS detection threshold and to keep the attribute values used to identify QRS complexes and distinguish between clear cycles and noise events. A high percentage of the QRS detection threshold may result in undersensing (i.e., some R waves may be not detected), whereas a low percentage may result in oversensing (i.e., other peaks, such as T waves, may be misinterpreted as R waves). Further, specific questions in arrhythmia research in mice are understandably not the main topic of the materials provided by DSI, and finding specific information can be difficult. Within this protocol, a simple and pragmatic approach is used to define different arrhythmias extrapolating established human definitions. 
For example, in human long-term ECG data, a pause longer than 3 s is considered significant22. This results in a human heart rate of 20/min., representing a third of the minimum physiologic heart rate of 60/min. As described by Kaese et al.18, the murine minimum physiologic heart rate equals 550/min., making 200/min. approximately a third of that rate. According to the human definition, pauses of more than 0.3 s can be assumed to be significant in mice. Further, it is a simple and pragmatic approach to describe differences in baseline parameters as relative changes to the respective control. This takes into consideration the differences between individual mouse lines and is an elegant way to identify the probable pathologic without relying on (often lacking) established normal values. This simple approach, summarized in Figure 7, is suitable for all groups studying cardiac arrhythmias in murine models using implantable telemetry devices. It leads to the evaluation of general ECG parameters as well as data on heart rate over time and the detection of a wide variety of arrhythmias. Therefore, this article attempts to provide a step-by-step approach for ECG and arrhythmia analysis and adds significantly to the guidance and manuals that have already been published.

Cardiac arrhythmias are common, affecting millions of patients worldwide1. Ageing populations show a growing incidence and thus a major public health burden resulting from cardiac arrhythmias and their morbidity and mortality2. Current treatment strategies are limited and often associated with significant side effects and remain ineffective in many patients3,4,5,6. Novel and innovative therapeutic strategies that causally target arrhythmia mechanisms are urgently needed. To study the complex pathophysiology of arrhythmias, suitable animal models are necessary; mice have been proven to be an ideal model species to evaluate the genetic impact on arrhythmias, to investigate fundamental molecular and cellular mechanisms, and to identify potential therapeutic targets7,8,9. Continuous ECG recording is a well-established concept in the clinical routine of arrhythmia detection10.
Implantable telemetry devices are among the most powerful tools available to study electrophysiology in mice as they allow continuous recording of the ECG (a common approach is to implant the leads in a lead-II position) over a period of several months in freely moving, awake mice11,12. However, due to the huge number of data points (up to more than 1 million QRS complexes per day) and limited knowledge of murine standard values, the analysis of telemetry data remains challenging. Commonly available telemetry transmitters for mice last up to 3 months, leading to the recording of up to 100 million QRS complexes. This means that pragmatic analysis protocols are much needed to reduce the time spent with each individual dataset and will allow researchers to handle and interpret this huge amount of data. To obtain a clean ECG signal upon recording, transmitter implantation needs to be optimal-the lead positions should be as far apart as possible to allow higher signal amplitudes.
The interested reader may be referred to a protocol by McCauley et al.12 for more information. Further, to minimize noise, cages and transmitters must be placed in a silent environment not prone to any disturbance, such as a ventilated cabinet with controlled environmental factors (temperature, light, and humidity). During the experimental period, lead positioning must be checked regularly to avoid loss of signal due to lead perforation or wound healing issues. Physiologically, there is a circadian alteration in ECG parameters in rodents as in humans, generating the need for a standardized approach to obtaining baseline ECG parameters from a continuous recording. Rather than calculating mean values of ECG parameters over a long period, analysis of a resting ECG similar to that in humans should be performed to obtain basic parameters such as resting heart rate, P wave duration, PR interval, QRS duration, or QT/QTc interval. In humans, a resting ECG is recorded over 10 s, at a normal heart rate of 50-100/min. This ECG includes 8 to 17 QRS complexes. An analysis of 20 consecutive QRS complexes is recommended in the mouse as &#34;resting ECG equivalent&#34;. Because of the above-mentioned circadian alteration, a simple approach is to analyze two resting ECGs per day, one at daytime and one at night time. Depending on the light on/off cycle in the animal facility, suitable times are selected (e.g., 12 AM/PM), and basic parameters are obtained.
Next, a heart rate plot over time is used to detect relevant tachy- and bradycardia, with consecutive manual exploration of these episodes to get a first impression. This heart rate plot then leads to the important parameters of maximum and minimum heart rate over the recorded period as well as heart rate variability over time. After that, the dataset is analyzed for arrhythmias. This article describes a step-by-step approach to obtain these baseline ECG data from long-term telemetry recordings of awake mice over a recording period of up to three months. Further, it describes how to detect arrhythmias using the software, Ponemah version 6.42, with its analysis modules, ECG Pro and Data Insights, developed by Data Sciences International (DSI). This version is compatible with both Windows 7 (SP1, 64 bit) and Windows 10 (64 bit).

<table><tbody><tr><td>Ponemah Software</td><td>Data Science international</td><td></td><td>ECG Analysis Software</td></tr></tbody></table>

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.

Recording long-term ECGs results in huge data sets. The options for further analyses are manifold and depend on the individual research project. This protocol provides a description of some very basic readouts that can be used by most researchers, especially for screening experiments, e.g., when characterizing a transgenic mouse line or when investigating the effects of a specific treatment in a disease model. A previous project involved the study of a novel drug candidate to determine whether it possessed cardiotoxic effects by analyzing ECG parameters over time. Telemetry transmitters were implanted 20 days before treatment, and ECG recordings were started 10 days before treatment to allow sufficient wound healing and acclimation of the mouse. Before treatment, the ECG was studied every three days; within the first week after treatment, the ECG was studied every day, after which the ECG was analyzed every seven days until the end of recording three weeks after treatment.
This approach allowed the detection of periods of reduced heart rate, increased atrioventricular (PR interval) and ventricular (QRS duration) conduction, as well as altered repolarization (QTc interval) in mice treated with the new drug as shown in Figure 5. This first step served as a &#34;screening&#34; that allowed the identification of time periods within the recording that potentially contained arrhythmias. A more detailed examination of the ECG revealed sinus pauses causing reduced heart rate two days after treatment and various degrees of atrioventricular (AV) blocks causing reduced heart rate six days after treatment. The latter finding was further supported by the prolonged PR intervals at this time point. To obtain these ECG parameters, 20 QRS complexes should be analyzed per time point and may therefore not be able to detect paroxysmal arrhythmia episodes at other time points.
To address this issue, it is advisable to specifically search for bradycardia and tachycardia episodes as well as for pauses using the ECG Pro module followed by manual review of detected episodes. This approach allows the detection of all relevant arrhythmias and the determination of the specific type of arrhythmia within the whole recording. For example, a tachycardia episode was detected in this study, which was identified as an atrial fibrillation.
As previously demonstrated, this approach further allows the determination of the time course of arrhythmia occurrence, e.g., the time to first AV block after macrophage depletion14. Representative traces, as shown in Figure 6, are obtained as described above (Figure 6A: normal sinus rhythm; Figure 6B: sinus pause; Figure 6C: AV-block I&#176;, Figure 6D: AV-block II&#176; type Mobitz 1; Figure 6E: AV block II&#176; type Mobitz 2; Figure 6F: AV block III&#176;; Figure 6G: atrial fibrillation).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62386/62386fig01.jpg" /> 
Figure 1: Loading and reviewing data in Ponemah. (A) Load Review Dat dialog providing an overview of all the mice and signals recorded within the loaded experiment. (B) Graph Setup Dialog to set graphical windows providing both raw data (e.g., ECG signals) and derived parameters. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/62386/62386fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62386/62386fig02.jpg" /> 
Figure 2: Template setup in Ponemah. (A) Template Setup window to configure and select a new or browse already configured Template Library. (B)&#160;Graph setup page for Template settings. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/62386/62386fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62386/62386fig03.jpg" /> 
Figure 3: ECG tracings. (A) Screenshot of the windows containing the ECG trace; (B) heart rate plot; and (C) Template window. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/62386/62386fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62386/62386fig04.jpg" /> 
Figure 4: Analysis of attributes of an ECG tracing. (A)&#160;An&#160;ECG Analysis Attributes dialog. At the top of this dialog, several tabs (QRS, PT, Advanced, Noise, Marks, Notes, Precision) allow the adjustment of various settings. The settings are presented in the middle part of the dialog. At the bottom of the dialog, the ECG tracing is shown in the waveform window. At the top of the waveform window, the ECG tracing is shown; at the bottom, the derivative of the ECG tracing, including a visualization of the setting thresholds above, is shown. In the example presented here, a QRS Detection Threshold of 40% is defined, which is indicated by the pink background at the bottom. (B) Template Analysis Dialog: Select the desired Template Match Region to which all other ECG cycles will be compared. In this example, the T Wave is selected as the Match Region for analysis with a Minimum Match of 85%. This means that if the T Region does not match with at least 85% confidence, the cycle will not be marked as a match. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/62386/62386fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62386/62386fig05.jpg" /> 
Figure 5: Basic ECG Parameters over time in a drug intervention cohort. Blue panel: night time, yellow panel: daytime. From left to right: Heart Rate, PR interval, QRS duration, QTc interval. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/62386/62386fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/62386/62386fig06.jpg" /> 
Figure 6: Representative ECG traces. (A) Normal sinus rhythm, (B) sinus pause, (C) AV-block I&#176;, (D) AV-block II&#176; type Mobitz 1, (E) AV block II&#176; type Mobitz 2, (F) AV block III&#176;, (G) atrial fibrillation. Scale bars = 100 ms. Abbreviation: AV = atrioventricular. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/62386/62386fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/62386/62386fig07.jpg" /> 
Figure 7: Analysis flowchart. Abbreviation: HR = heart rate. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/62386/62386fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice at JoVE.com

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in 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.

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

analyzing-long-term-electrocardiography-recordings-to-detect

Universidad San Sebastian

Research

JoVE Journal

Medicine

3.6K Views.  University Hospital Munich, Campus Großhadern, Ludwig-Maximilians University Munich (LMU). Analyse van grote ECG-datasets, vooral wanneer u screent op aritmie, is zeer uitdagend en arbeidsintensief. Onze semi-geautomatiseerde aanpak zorgt voor een veel snellere en gemakkelijkere screening op aritmieën bij muizen. Deze semi-geautomatiseerde aanpak leidt tot nauwkeurigere resultaten.De procedure zal worden gedemonstreerd door Aparna Chivukula, een promovendus in mijn lab. Begin met het analyseren van basis-ECG-parameters. Klik op Onderwerpinstellingen of Kanaaldetails om een ...

Video: Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Programmed Electrical Stimulation in Mice

Genetically-modified mice have emerged as a preferable animal model to study the molecular mechanisms underlying conduction abnormalities, atrial and ventricular arrhythmias, and sudden cardiac death.1 Intracardiac pacing studies can be performed in mice using a 1.1F octapolar catheter inserted into the jugular vein, and advanced into the right atrium and ventricle. Here, we illustrate the steps involved in performing programmed electrical stimulation in mice. Surface ECG and intracardiac electrograms are recorded simultaneously in the atria, atrioventricular junction, and ventricular myocardium, whereas intracardiac pacing of the atrium is performed using an external stimulator. Thus, programmed electrical stimulation in mice provides unique opportunities to explore molecular mechanisms underlying conduction defects and cardiac arrhythmias.

Programmed electrical stimulation provides the ability to determine conduction properties of the heart, and the possibility to induce and terminate cardiac arrhythmias using various pacing protocols. Using a transvenous catheter, intracardiac electrogram recordings can be obtained in mice following programmed electrical stimulation protocols to identify arrhythmogenic substrates.

Genetically-modified mice have emerged as a preferable animal model to study the molecular mechanisms underlying conduction abnormalities, atrial and ...

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

Multi-electrode Array Recordings of Human Epileptic Postoperative Cortical Tissue

Epilepsy, affecting about 1% of the population, comprises a group of neurological disorders characterized by the periodic occurrence of seizures, which disrupt normal brain function. Despite treatment with currently available antiepileptic drugs targeting neuronal functions, one third of patients with epilepsy are pharmacoresistant. In this condition, surgical resection of the brain area generating seizures remains the only alternative treatment. Studying human epileptic tissues has contributed to understand new epileptogenic mechanisms during the last 10 years. Indeed, these tissues generate spontaneous interictal epileptic discharges as well as pharmacologically-induced ictal events which can be recorded with classical electrophysiology techniques. Remarkably, multi-electrode arrays (MEAs), which are microfabricated devices embedding an array of spatially arranged microelectrodes, provide the unique opportunity to simultaneously stimulate and record field potentials, as well as action potentials of multiple neurons from different areas of the tissue. Thus MEAs recordings offer an excellent approach to study the spatio-temporal patterns of spontaneous interictal and evoked seizure-like events and the mechanisms underlying seizure onset and propagation. Here we describe how to prepare human cortical slices from surgically resected tissue and to record with MEAs interictal and ictal-like events ex vivo.

We here describe how to perform multi-electrode array recordings of human epileptic cortical tissue. Epileptic tissue resection, slice preparation and multi-electrode array recordings of interictal and ictal events are demonstrated in detail.

Epilepsy, affecting about 1% of the population, comprises a group of neurological disorders characterized by the periodic occurrence of seizures, which ...

Ambulatory ECG Recording in Mice

Telemetric ECG recording in mice is essential to understanding the mechanisms behind arrhythmias, conduction disorders, and sudden cardiac death. Although the surface ECG is utilized for short-term measurements of waveform intervals, it is not practical for long-term studies of heart rate variability or the capture of rare episodes of arrhythmias. Implantable ECG telemeters offer the advantages of simple surgical implantation, long-term recording of electrograms in ambulatory mice, and scalability with simultaneous recordings of multiple animals. Here, we present a step-by-step guide to the implantation of telemeters for ambulatory ECG recording in mice. Careful adherence to aseptic technique is required for favorable survival results with the possibility of implantation and recording from weeks to months. Thus, implantable ECG telemetry is a valuable tool for detection of critical information on cardiac electrophysiology in ambulatory animal models such as the mouse. 

Telemetric ECG has emerged as an essential tool in evaluating animal models for cardiac arrhythmias and sudden cardiac death. Here, we present a stepwise guide to telemetric ECG recordings for application in long-term ambulatory ECG monitoring in mice.

Telemetric ECG recording in mice is essential to understanding the mechanisms behind arrhythmias, conduction disorders, and sudden cardiac death. Although ...

Biology

Long-Term Catheterization of the Intestinal Lymph Trunk and Collection of Lymph in Neonatal Pigs

Catheterization of the intestinal lymph trunk in neonatal pigs is a technique allowing for the long-term collection of large quantities of intestinal (central) efferent lymph. Importantly, the collection of central lymph from the intestine enables researchers to study both the mechanisms and lipid constitutes associated with lipid metabolism, intestinal inflammation and cancer metastasis, as well as cells involved in immune function and immunosurveillance. A ventral mid-line surgical approach permits excellent surgical exposure to the cranial abdomen and relatively easy access to the intestinal lymph trunk vessel that lies near the pancreas and the right ventral segment of the portal vein underneath the visceral aspect of the right liver lobe. The vessel is meticulously dissected and released from the surrounding fascia and then dilated with sutures allowing for insertion and subsequent securing of the catheter into the vessel. The catheter is exteriorized and approximately 1 L/24 hr of lymph is collected over a 7 day period. While this technique enables the collection of large quantities of central lymph over an extended period of time, the success depends on careful surgical dissection, tissue handling and close attention to proper surgical technique. This is particularly important with surgeries in young animals as the lymph vessels can easily tear, potentially leading to surgical and experimental failure. The video demonstrates an excellent surgical technique for the collection of intestinal lymph.

We present a surgical procedure to catheterize the intestinal lymph trunk in neonatal pigs to collect large quantities of lipid metabolism components from efferent lymph.

Catheterization of the intestinal lymph trunk in neonatal pigs is a technique allowing for the long-term collection of large quantities of intestinal ...

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.

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 ...

Neuroscience

Noninvasive Electrocardiography in the Perinatal Mouse

Electrocardiography (ECG) has long been relied upon as an effective and reliable method of assessing cardiovascular (and cardiopulmonary) function in both human and animal models of disease. Individual heart rate, rhythm, and regularity, combined with quantitative parameters collected from ECG, serve to assess the integrity of the cardiac conduction system as well as the integrated physiology of the cardiac cycle. This article provides a comprehensive description of the methods and techniques used to perform a noninvasive ECG on perinatal and neonatal mouse pups as early as the first postnatal day, without requiring the use of anesthetics. This protocol was designed to directly address a need for a standardized and repeatable method for obtaining ECG in newborn mice. From a translational perspective, this protocol proves to be entirely effective for characterization of congenital cardiopulmonary defects generated using transgenic mouse lines, and particularly for analysis of defects causing lethality at or during the first postnatal days. This protocol also aims to directly address a gap in the scientific literature to characterize and provide normative data associated with maturation of the early postnatal cardiac conduction system. This method is not limited to a specific postnatal timepoint, but rather allows for ECG data collection in neonatal mouse pups from birth to postnatal day 10 (P10), a window that is of critical importance for modeling human diseases in vivo, with particular emphasis on congenital heart disease (CHD).

Here, we present a noninvasive electrocardiography (ECG) protocol, optimized for early postnatal mice, that does not require the use of anesthetics.

Electrocardiography (ECG) has long been relied upon as an effective and reliable method of assessing cardiovascular (and cardiopulmonary) function in both ...

Electrocardiogram Recordings in Anesthetized Mice using Lead II

The electrocardiogram is a valuable tool for evaluating the cardiac conduction system. Animal research has helped generate novel genetic and pharmacological information regarding the electrocardiogram. However, making electrocardiogram measurements in small animals in vivo, such as mice, has been challenging. To this end, we used an electrocardiogram recording method in anesthetized mice with many advantages: it is a technically simple procedure, is inexpensive, has short measuring time, and is affordable, even in young mice. Despite the limitations with using anesthesia, comparisons between control and experimental groups can be performed with enhanced sensitivity. We treated mice with agonists and antagonists of the autonomic nervous system to determine the validity of this protocol and compared our results with previous reports. Our ECG protocol detected increased heart rates and QTc intervals on treatment with atropine, decreased heart rates and QTc intervals after carbachol treatment, and higher heart rates and QTc intervals with isoprenaline but did not note any change in ECG parameters on administration of propranolol. These results are supported by previous reports, confirming the reliability of this ECG protocol. Thus, this method can be used as a screening approach to making ECG measurements that otherwise would not be attempted due to high cost and technical difficulties.

We present an ECG protocol that is technically easy, inexpensive, fast, and affordable in small mice, and can be performed with enhanced sensitivity. We suggest this method as a screening approach for studying pharmacological agents, genetic modifications, and disease models in mice.

The electrocardiogram is a valuable tool for evaluating the cardiac conduction system. Animal research has helped generate novel genetic and ...

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

The sinoatrial node (SAN), located in the right atrium, contains the pacemaker cells of the heart, and dysfunction of this region can cause tachycardia or bradycardia. Reliable identification of cardiac pacemaking defects requires the measurement of intrinsic heart rates by largely preventing the influence of the autonomic nervous system, which can mask rate deficits. Traditional methods to analyze intrinsic cardiac pacemaker function include drug-induced autonomic blockade to measure in vivo heart rates, isolated heart recordings to measure intrinsic heart rates, and sinoatrial strip or single-cell patch-clamp recordings of sinoatrial pacemaker cells to measure spontaneous action potential firing rates. However, these more traditional techniques can be technically challenging and difficult to perform. Here, we present a new methodology to measure intrinsic cardiac firing rate by performing microelectrode array (MEA) recordings of whole-mount sinoatrial node preparations from mice. MEAs are composed of multiple microelectrodes arranged in a grid-like pattern for recording in vitro extracellular field potentials. The method described herein has the combined advantage of being relatively faster, simpler, and more precise than previous approaches for recording intrinsic heart rates, while also allowing easy pharmacological interrogation.

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.

The sinoatrial node (SAN), located in the right atrium, contains the pacemaker cells of the heart, and dysfunction of this region can cause tachycardia or ...

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Regular physical exercise is a major contributor to cardiovascular health, influencing various metabolic as well as electrophysiological processes. However, in certain cardiac diseases such as inherited arrhythmia syndromes, e.g., arrhythmogenic cardiomyopathy (ACM) or myocarditis, physical exercise may have negative effects on the heart leading to a proarrhythmogenic substrate production. Currently, the underlying molecular mechanisms of exercise-related proarrhythmogenic remodeling are largely unknown, thus it remains unclear which frequency, duration, and intensity of exercise can be considered safe in the context of disease(s).
The proposed method allows to study proarrhythmic/antiarrhythmic effects of physical exercise by combining treadmill training with real-time monitoring of the ECG. Implantable telemetry devices are used to continuously record the ECG of freely moving mice over a period of up to 3 months both at rest and during treadmill training. Data acquisition software with its analysis modules is used to analyze basic ECG parameters such as heart rate, P wave duration, PR interval, QRS interval, or QT duration at rest, during and after training. Furthermore, heart rate variability (HRV) parameters and occurrence of arrhythmias are evaluated. In brief, this manuscript describes a step-by-step approach to experimentally explore exercise induced effects on cardiac electrophysiology, including potential proarrhythmogenic remodeling in mouse models.

Electrocardiogram (ECG) is the key variable to understanding cardiac electrophysiology. Physical exercise has beneficial effects but may also be harmful in the context of cardiovascular diseases. This manuscript provides a method of recording real-time ECG during exercise, which can serve to investigate its effects on cardiac electrophysiology in mice.

Regular physical exercise is a major contributor to cardiovascular health, influencing various metabolic as well as electrophysiological processes. ...

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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