Behavioral training and testing with rat models are important in countless research areas, from the exploration of cognitive processes to disease states and more1. Typically, this training and testing is conducted with single animals in one-on-one sessions, with a researcher manually removing the animal from their home caging and temporarily placing them in some kind of apparatus. Unfortunately, there are several difficulties and limitations with this approach. First, behavioral testing can take a great deal of time for researchers, and when training is necessary, that time requirement becomes even greater. Second, this approach automatically affects-or even potentially confounds-the acquired data, as has been established elsewhere2. These confounds are especially salient when considering enrichment-related variables. Specifically, laboratory rats are traditionally housed in small cages that are just big enough for one or two rats3, and if running wheels are not provided, they may go a lifetime without meaningful opportunities to exercise. Additionally, isolated housing can be a major source of stress in a social species such as the rat4. Some of these welfare-related drawbacks likely impact rat physiology5,6, which may preempt the development of species-typical behavioral expression4 and impact the quality of rodent models as applied to human contexts.
Researchers have pursued several types of solutions to these problems in recent years. The simplest type of solution has been to automate behavioral testing and training7,8,9,10, thus removing the requirement for a single researcher to attend to a single animal. An additional solution has been to automate animal transfer to experimental chambers11,12, further removing the need for human involvement. Last, several setups have been explored which allow animals to be housed in colony caging with other animals and with more room for exploration and enrichment13. Despite these advantages, such colony setups can limit or complicate the efforts to gather individually differentiated behavioral data (though see efforts to use computer vision)14,15. If individual behavioral data is required, it can be more difficult or complex to identify and retrieve animals from colony caging for behavioral sessions as well. At present, few systems exist for collecting individual behavioral data from (enriched) colony housing16,17,18.
These drawbacks may specifically impact research on the behavioral effects of acquired brain injury. First, it is clear that the presence and/or sex of humans as well as handling practices affect rodent behavior2,19, and these variables may differentially impact the behavior of rats before vs. after stroke. Second, human behavioral outcomes after stroke can be worsened by voluntarily decreased engagement with the recommended dosage of rehabilitation exercises20. Currently, rodent experiments tend to not model this sort of context, because rats are not free to choose to engage or abstain from behavioral sessions.
This article introduces a protocol designed to facilitate individual behavioral testing within the framework of enriched colony caging. This approach not only addresses the constraints of current practices but also opens avenues for the exploration of innovative measures. A one-rat turnstile (ORT) has been developed and can be affixed to a colony cage, enabling animals to enter behavioral chambers independently and initiate their own training and testing sessions. The system is affordable; each ORT can be assembled at low cost (given access to a 3D printer). In the past, validation of this system was carried out using a basic operant chamber, showing that animals could be consistently trained to perform a simple operant lever press without the presence of an experimenter16. Nevertheless, the question of whether this configuration is applicable to other scenarios remains unresolved. The aim is to validate the effectiveness of the ORT-colony caging setup, which was previously established, for training and quantifying skilled reach behavior relevant to motor impairment following a stroke. The configuration was utilized to generate novel variables that are typically not explored in stroke research. These variables include performance metrics for the skilled reach task and measurements of self-initiation, which could be pertinent to motivation and decision-making. Furthermore, stroke-induced changes in the circadian patterns of daily self-initiation across the entire 24 h period were effectively detected.