Development and testing of a new system for assessing wheel-running behaviour in rodents
© Chomiak et al. 2016
Received: 9 January 2016
Accepted: 21 April 2016
Published: 5 May 2016
Wheel running is one of the most widely studied behaviours in laboratory rodents. As a result, improved approaches for the objective monitoring and gathering of more detailed information is increasingly becoming important for evaluating rodent wheel-running behaviour. Here our aim was to develop a new quantitative wheel-running system that can be used for most typical wheel-running experimental protocols.
Here we devise a system that can provide a continuous waveform amenable to real-time integration with a high-speed video ideal for wheel-running experimental protocols. While quantification of wheel running behaviour has typically focused on the number of revolutions per unit time as an end point measure, the approach described here allows for more detailed information like wheel rotation fluidity, directionality, instantaneous velocity, and acceleration, in addition to total number of rotations, and the temporal pattern of wheel-running behaviour to be derived from a single trace. We further tested this system with a running-wheel behavioural paradigm that can be used for investigating the neuronal mechanisms of procedural learning and postural stability, and discuss other potentially useful applications.
This system and its ability to evaluate multiple wheel-running parameters may become a useful tool for screening new potentially important therapeutic compounds related to many neurological conditions.
Wheel running is one of the most widely studied behaviours in laboratory rodents. For example, it is used to evaluate motor deficits in rodent models of Multiple Sclerosis and Parkinson’s Disease, and hyperactivity in a genetic model of attention-deficit-hyperactivity-disorder [1–5]. It has also been used to study exercise in rehabilitation after spinal cord injury and for autistic-like behavioural phenotyping [6, 7]. In addition, the use of voluntary wheel running is important for studying motivation and modelling instrumental goal-directed behaviour in rodents [8, 9]. Here, instrumental behaviour (running) is required to turn the wheel which then provides positive sensorimotor-feedback to reinforce goal-directed behaviour that is highly motivational [8, 9]. A novel running-wheel task has also been developed to model and investigate neural mechanisms of motor-skill procedural learning and memory , an area of research that can offer important contributions in the understanding of both motor function and learning and memory [10, 11].
Quantification of wheel running behaviour has typically focused on the number of revolutions per unit time as an end point measure [10, 12–17]. For this, the simplest approach is to use a mechanical counter to count the absolute number to revolutions [10, 13]. Of course, one problem with this is that if it is not automated, the logging of information can often be impractical when more time points are required for higher temporal resolution and/or when longer monitoring periods are needed. Furthermore, most currently utilized experimental set-ups are unable to establish and record wheel-running direction. Knowing when and what direction the rodent is running is, for example, extremely important when investigating mechanisms of direction-dependent neural activity . Previous studies have utilized automated systems to report more detailed information, like maximum velocity (revolutions per min), using a rotation sensor with 16/turn or a photocell counter [1, 16, 17]. However, although the temporal resolution is improved, these automated approaches still lack directionality, and thus the ability to evaluate relative differences in continuous wheel rotation fluidity. Hence, as a better approach to wheel running quantification and more objective monitoring is increasingly becoming important, we have devised a system that can provide a continuous waveform amenable to real-time integration with high-speed video if desired that is ideal for most typical wheel-running experimental protocols. We further test this system with a running-wheel behavioural paradigm that can be used for investigating the neuronal mechanisms of procedural learning and discuss other potentially useful applications.
System design and utility
Second, previous studies of mice selectively bred for high voluntary wheel running have suggested that the hyperactivity is associated with dysfunction in the dopaminergic neuromodulatory system and that high-running mice may represent a useful genetic model for attention-deficit-hyperactivity-disorder . However, unlike that of Rhodes and Garland  that calculated an average running speed based on total revolutions divided by the number of 1-min intervals with any revolutions , our approach does not require averaging over intervals. In fact, speeds can be determined for an individual rotation regardless of how quick or slow the animal rotates it (Fig. 1b). More importantly, however, this approach prevents averaging in time points with which the animal is either not moving or is no longer in the wheel (e.g. 1 rotation in 1-min).
Finally, wheel-skill learning is based on previous findings that noted that during the beginning of the running-wheel training (i.e. when naive rats are first exposed to the running wheel), rats are unable to run with an appropriate speed to remain at the bottom of the wheel. As a result, the rat often moves too fast or too slow relative to the speed of the wheel, thus generating a “wobble” . While running-wheel training is a pre-requisite for wheel-skill learning , this has been difficult to illustrate quantitatively. However, by combining real-time video with a real-time continuous positional waveform (Fig. 3; Additional file 2: Video S1), running-wheel learning can also be objectively monitored and quantitatively illustrated over time.
In short, we have developed a system for assessing wheel-running behaviour in rodents amenable to real-time integration with a high-speed video suitable for most typical wheel-running experiments. In addition, our system allows for detailed information like wheel rotation fluidity, directionality, instantaneous velocity, average and maximum velocity, acceleration, total number of rotations, and the temporal pattern of wheel-running behaviour to be derived from a single trace. While it is ideal for evaluating mechanisms of procedural motor learning using wheel-skill learning, it can be applied to many other experimental paradigms given that detailed information required for most wheel-running experiments can be derived from a single continuous trace.
Male Sprague–Dawley rats (6 weeks old) were housed under standard laboratory conditions in the University of Calgary Animal Resource Center in accordance with our approved Animal Care Committee protocol (AC12-0239). Animals had free access to food (standard rodent chow) and water. Wheel-skill learning is based on wheel-running . Training and testing were performed in the same room, and before the day of wheel-skill learning, all animals (n = 8; randomly divided into two groups) were first habituated to the test/training room and wheel by placing them in the wheel under the locked condition for about 1 h. In addition, during the day of testing, the rat was again first placed inside the locked wheel for an approximately 2-min habituation period. The wheel with the rat was then gently rotated as occurs naturally during wheel running such that the rat (with head up ) was at a 90° position (“rotation phase”). Upon release (“release phase”), the wheel swings back and forth until the rat stops the swinging by counterbalancing. Therefore, wheel-skill performance was measured by quantifying the total or cumulative rotational distance of the wheel during the release phase until the wheel typically failed to rotate greater than 3° for 1500 ms. Performance was evaluated before (pre) and after (post) the training period in which the running-wheel group had free access to wheel running, while the wheel was locked in the locked-wheel group (~40 min). The running wheels were approved by the University of Calgary Animal Care Committee (AC12-0239) and consisted of a rotating metal chamber with a wire mesh floor (Additional file 1: Figure S1; diameter, 36 cm; width, 14 cm) attached to a stationary metal wall with an access opening that could be closed. All data is presented as mean ± SEM and two-tailed t test p value determination was accomplished using the statistical software GraphPad Prism. Data for traces were acquired at 20 or 100 Hz. All attempts were made to minimise the handling of animals and the number of animals used.
Availability of supporting data
The design of the system using LabVIEW system design software (National Instruments) supporting the results of this article are included within the article and its additional file.
TC and EB participated in design of the study, carried out experiments, performed statistical analysis. TC drafted the manuscript. TC, EB, AB, GCT, and BH participated in design of the study and revision of the manuscript. All authors read and approved the final manuscript.
This study was supported by grants from Canadian Institutes of Health Research, CIHR Regenerative Medicine Initiative, and NSERC. TC was also supported by a Sinneave Family Foundation Fellowship.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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