A RAT MODEL FOR THE QUANTIFICATION OF UPPER-EXTREMITY VOLITIONAL MUSCLE PERFORMANCE

Abstract

An in vivo animal model for the study of musculoskeletal disorders associated with repetitive strain is described. Using intact rats, operant conditioning with food rewards was used to train voluntary isometric exertions of the forelimb. The procedures allowed the acquisition of highly repetitive and uniform responding that was maintained in daily sessions conducted up to several weeks duration. Rats were operantly conditioned with food rewards to respond on a force lever which recorded response force in real time. The force criterion was set to 0.25 N, and 0.50 N in some cases, and food rewards were delivered according to variable-interval schedules of reinforcement. This schedule produced rapid and steady rates of responding. Sessions lasted 1–2 hours each were conducted five days per week for up to 8 weeks and functional measures such as force, rate, and duration of responses were recorded throughout. Highly uniform response topographies were obtained with all rats. Representative response patterns obtained under various work conditions will be shown along with force-time response functions of individual animals to illustrate the response topographies that resulted under various biomechanical requirements and reinforcement contingencies. There are several advantages of this model compared to invasive in vitro or in situ preparations of isolated muscle fibers or other less invasive in vivo models such as rodent dynamometry and treadmill running. Biomechanical parameters such as the force, duration, and rate of responding can be precisely controlled by manipulations of the reinforcement contingencies, while leaving muscle-tendon complex and normal neuromuscular-control processes intact. When combined with biochemical and histological analyses, this model can provide a comprehensive and externally valid model for studying muscle pathomechanics and varying work-rest cycles that will broaden the scope of musculoskeletal research.