An automated behavioral box to assess forelimb function in rats

• Published in: Journal of neuroscience methods Vol. 246; pp. 30 - 37
• Main Authors: Wong, Chelsea C., Ramanathan, Dhakshin S., Gulati, Tanuj, Won, Seok Joon, Ganguly, Karunesh
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.05.2015
• Subjects: Analysis of Variance; Animals; Behavior, Animal - physiology; Conditioning, Operant; Electrophysiology; Feeding Behavior - physiology; Forelimb - physiology; Male; Motor Cortex - physiology; Motor learning; Pattern Recognition, Automated; Psychomotor Performance - physiology; Rats; Rats, Long-Evans; Reach; Electrophysiology; Reach; Motor learning
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2015.03.008

•We develop a low-cost automated behavioral box to measure forelimb function in rats.•We illustrate camera-based automated detection of behavioral outcomes.•We demonstrate the ability to easily vary task structure and practice schedules.•Our automated setup is able to monitor deficits after unilateral ischemic stroke.•We show compatibility with modern chronic electrophysiological approaches. Rodent forelimb reaching behaviors are commonly assessed using a single-pellet reach-to-grasp task. While the task is widely recognized as a very sensitive measure of distal limb function, it is also known to be very labor-intensive, both for initial training and the daily assessment of function. Using components developed by open-source electronics platforms, we have designed and tested a low-cost automated behavioral box to measure forelimb function in rats. Our apparatus, made primarily of acrylic, was equipped with multiple sensors to control the duration and difficulty of the task, detect reach outcomes, and dispense pellets. Our control software, developed in MATLAB, was also used to control a camera in order to capture and process video during reaches. Importantly, such processing could monitor task performance in near real-time. We further demonstrate that the automated apparatus can be used to expedite skill acquisition, thereby increasing throughput as well as facilitating studies of early versus late motor learning. The setup is also readily compatible with chronic electrophysiological monitoring. Compared to a previous version of this task, our setup provides a more efficient method to train and test rodents for studies of motor learning and recovery of function after stroke. The unbiased delivery of behavioral cues and outcomes also facilitates electrophysiological studies. In summary, our automated behavioral box will allow high-throughput and efficient monitoring of rat forelimb function in both healthy and injured animals.