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March 15, 2024
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This research aims to address typical limitations in behavior-based experiments by enabling animals to live in enriched colony housing. The goal is to facilitate self-initiated behavioral testing and training, and to demonstrate how that approach can generate valid individualized data, including post-stroke dependent variables, both typical and novel. There are several challenges for researchers conducting behavioral experiments, including spending time on behavioral training and minimizing any potential confounds associated with either human handling of rodents and deprivation of food, exercise, and social interaction.
Hopefully, the findings from this protocol show the utility of pursuing automated, human free and or ecologically enriched approaches for collecting behavioral data. We hope that this will encourage other researchers to utilize such high throughput methods to explore novel variables and an array of experimental paradigms. To begin, obtain a preassembled one rat turnstile or ORT.
Attach a radio frequency identification, or RFID reader, to the ORT then affix the RFID antenna to the ORT tube. Attach the ORTs between the behavioral apparatus and colony caging. Install the RFID system to read animals as they pass through the ORT.
Introduce a same sized cohort of rats into the colony caging. Train the rat to regularly enter the behavioral apparatus via the ORT. Next, introduce the poll handle into the skilled reach apparatus.
In the Arduino program, to set the pull handle to the highest sensitivity, go to the RAT. H file and define the force threshold required to initiate a pull. Retract the lever daily by 0.25 to 0.5 millimeters until reaching a final position of one to 1.25 centimeters outside the chamber.
Initiate a percentile training program to progressively increase the required pull forces to activate the handle. Once the rat reliably reaches the final criterion range of 120 gram pulls remove the percentile training program and fix the criterion for handle activation to 120 grams. Recover the stroke induced rat in a traditional caging for three to seven days.
After recovery, return the rat to the colony caging with the ORT attached to the skilled reach apparatus. Complete the behavioral testing at pull requirements of 120 grams until sufficient data is collected to evaluate post-stroke deficits. In skilled reach assessments, significant variations in success rate, mean force per pull and pulls per bout of rats were observed after stroke inducement.
The stroke did not affect the session initiations. Females consistently initiated more sessions than males with no change in rate after stroke. In contrast, most rats exhibited increased chamber duration, possibly due to reduced bout success rates.
The stroke affected the circadian patterning of session distributions throughout the day in both males and females. Before stroke animals engaged with the task primarily early in the light cycle and in females just before the dark cycle onset. After the stroke, both males and females, shifted toward gradually increasing engagement throughout the light cycle.
Translational stroke research currently utilizes various crucial behavior assays, but the majority require labor intensive and one-on-one testing and occasional training with animals. Also, self-initiation variables are seldom captured as the assays predominantly feature experiment or initiated sessions and tasks. Compared to typical behavioral training and testing approaches, this protocol offers several advantages.
It addresses confounds associated with human handling, reduces the daily labor of researchers, enables several sources of enrichment, and even provides novel measures of circadian and initiation variables.
A system for acquiring data from self-initiated individual behavior sessions within a social colony cage setting is presented. The efficacy of this system is demonstrated using an automated skilled reach assessment, enabling the characterization of post-stroke motor impairments, potential behavioral alterations related to motivation, circadian variations, and other innovative dependent variables.
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Cite this Article
Armshaw, J., Butcher, G., Becker, A. Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits. J. Vis. Exp. (205), e64967, doi:10.3791/64967 (2024).
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