COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status.

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Abstract

Background Disruption of rhythms in activity and rest occur in many diseases, and provide an important indicator of healthy physiology and behaviour. However, outside the field of sleep and circadian rhythm research, these rhythmic processes are rarely measured due to the requirement for specialised resources and expertise. Until recently, the primary approach to measuring activity in laboratory rodents has been based on voluntary running wheel activity. By contrast, measuring sleep requires the use of electroencephalography (EEG), which involves invasive surgical procedures and time-consuming data analysis. Methods Here we describe a simple, non-invasive system to measure home cage activity in mice based upon passive infrared (PIR) motion sensors. Careful calibration of this system will allow users to simultaneously assess sleep status in mice. The use of open-source tools and simple sensors keeps the cost and the size of data-files down, in order to increase ease of use and uptake. Results In addition to providing accurate data on circadian activity parameters, here we show that extended immobility of >40 seconds provides a reliable indicator of sleep, correlating well with EEG-defined sleep (Pearson's r >0.95, 4 mice). Conclusions Whilst any detailed analysis of sleep patterns in mice will require EEG, behaviourally-defined sleep provides a valuable non-invasive means of simultaneously phenotyping both circadian rhythms and sleep. Whilst previous approaches have relied upon analysis of video data, here we show that simple motion sensors provide a cheap and effective alternative, enabling real-time analysis and longitudinal studies extending over weeks or even months. The data files produced are small, enabling easy deposition and sharing. We have named this system COMPASS - Continuous Open Mouse Phenotyping of Activity and Sleep Status. This simple approach is of particular value in phenotyping screens as well as providing an ideal tool to assess activity and rest cycles for non-specialists.

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Most cited references 19

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The use of a running wheel to measure activity in rodents: relationship to energy balance, general activity, and reward.

Colleen Novak, Paul Burghardt, James Levine (2012)

Running wheels are commonly employed to measure rodent physical activity in a variety of contexts, including studies of energy balance and obesity. There is no consensus on the nature of wheel-running activity or its underlying causes, however. Here, we will begin by systematically reviewing how running wheel availability affects physical activity and other aspects of energy balance in laboratory rodents. While wheel running and physical activity in the absence of a wheel commonly correlate in a general sense, in many specific aspects the two do not correspond. In fact, the presence of running wheels alters several aspects of energy balance, including body weight and composition, food intake, and energy expenditure of activity. We contend that wheel-running activity should be considered a behavior in and of itself, reflecting several underlying behavioral processes in addition to a rodent's general, spontaneous activity. These behavioral processes include defensive behavior, predatory aggression, and depression- and anxiety-like behaviors. As it relates to energy balance, wheel running engages several brain systems-including those related to the stress response, mood, and reward, and those responsive to growth factors-that influence energy balance indirectly. We contend that wheel-running behavior represents factors in addition to rodents' tendency to be physically active, engaging additional neural and physiological mechanisms which can then independently alter energy balance and behavior. Given the impact of wheel-running behavior on numerous overlapping systems that influence behavior and physiology, this review outlines the need for careful design and interpretation of studies that utilize running wheels as a means for exercise or as a measurement of general physical activity. Copyright Â© 2012 Elsevier Ltd. All rights reserved.

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Novel method for high-throughput phenotyping of sleep in mice.

T Kay, Karen Svenson, Jie Lian … (2007)

Assessment of sleep in mice currently requires initial implantation of chronic electrodes for assessment of electroencephalogram (EEG) and electromyogram (EMG) followed by time to recover from surgery. Hence, it is not ideal for high-throughput screening. To address this deficiency, a method of assessment of sleep and wakefulness in mice has been developed based on assessment of activity/inactivity either by digital video analysis or by breaking infrared beams in the mouse cage. It is based on the algorithm that any episode of continuous inactivity of > or =40 s is predicted to be sleep. The method gives excellent agreement in C57BL/6J male mice with simultaneous assessment of sleep by EEG/EMG recording. The average agreement over 8,640 10-s epochs in 24 h is 92% (n = 7 mice) with agreement in individual mice being 88-94%. Average EEG/EMG determined sleep per 2-h interval across the day was 59.4 min. The estimated mean difference (bias) per 2-h interval between inactivity-defined sleep and EEG/EMG-defined sleep was only 1.0 min (95% confidence interval for mean bias -0.06 to +2.6 min). The standard deviation of differences (precision) was 7.5 min per 2-h interval with 95% limits of agreement ranging from -13.7 to +15.7 min. Although bias significantly varied by time of day (P = 0.0007), the magnitude of time-of-day differences was not large (average bias during lights on and lights off was +5.0 and -3.0 min per 2-h interval, respectively). This method has applications in chemical mutagenesis and for studies of molecular changes in brain with sleep/wakefulness.

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Rapid assessment of sleep-wake behavior in mice.

Stuart N. Peirson, Mark W. Hankins, Carina A. Pothecary … (2012)

Sleep is a fundamental biological rhythm involving the interaction of numerous brain structures and diverse neurotransmitter systems. The primary measures used to define sleep are the electroencephalogram (EEG) and electromyogram (EMG). However, EEG-based methods are often unsuitable for use in high-throughput screens as they are time-intensive and involve invasive surgery. As such, the dissection of sleep mechanisms and the discovery of novel drugs that modulate sleep would benefit greatly from further development of rapid behavioral assays to assess sleep in animal models. Here is described an automated noninvasive approach to evaluate sleep duration, latency, and fragmentation using video tracking of mice in their home cage. This approach provides a high correlation with EEG/EMG measures under both baseline conditions and following administration of pharmacological agents. Moreover, the dose-dependent effects of sedatives, stimulants, and light can be readily detected. This approach is robust yet relatively inexpensive to implement and can be easily incorporated into ongoing screening programs to provide a powerful first-pass screen for assessing sleep and allied behaviors.