Automated Assessment of Endpoint and Kinematic Features of Skilled Reaching in Rats

Background: Neural injury to the motor cortex may result in long-term impairments. As a model for human impairments, rodents are often used to study deficits related to reaching and grasping, using the single-pellet reach-to-grasp task. Current assessments of this test capture mostly endpoint outcome. While qualitative features have been proposed, they usually involve manual scoring. Objective: To detect three phases of movement during the single-pellet reach-to-grasp test and assess completion of each phase. To automatically monitor rat forelimb trajectory so as to extract kinematics and classify phase outcome. Methods: A top-view camera is used to monitor three rats during training, healthy and impaired testing, over 33 days. By monitoring the coordinates of the forelimb tip along with the position of the pellet, the algorithm divides a trial into reaching, grasping and retraction. Unfulfilling any of the phases results in one of three possible errors: miss, slip or drop. If all phases are complete, the outcome label is success. Along with endpoints, movement kinematics are assessed: variability, convex hull, mean and maximum reaching speed, length of trajectory and peak forelimb extension. Results: The set of behavior endpoints was extended to include miss, slip, drop and success rate. The labeling algorithm was tested on pre- and post-lesion datasets, with overall accuracy rates of 86% and 92%, respectively. These endpoint features capture a drop in skill after motor cortical lesion as the success rate of 59.6 ± 11.8% pre-lesion decreases to 13.9 ± 8.2% post-lesion, along with a significant increase in miss rate from 7.2 ± 6.7% pre-lesion to 50.2 ± 18.7% post-lesion. Kinematics reveals individual-specific strategies of improvement during training, with a common trend of trajectory variability decreasing with success. Correlations between kinematics and endpoints reveal a more complex pattern of relationships during rehabilitation (18 significant pairs of features) than during training (nine correlated pairs). Conclusion: Extended endpoint outcomes and kinematics of reaching and grasping are captured automatically with a robust computer program. Both endpoints and kinematics capture intra-animal drop in skill after a motor cortical lesion. Correlations between kinematics and endpoints change from training to rehabilitation, suggesting different mechanisms that underlie motor improvement.