Deep Learning-Based Behavioral Quantification of Upper Limb Rehabilitation Dose in a Rat Model of Ischemic Stroke

Abstract

Seventy percent of stroke survivors experience loss of upper limb function after stroke and rehabilitative therapy is the only option to reduce impairments. However, uncertainty remains as to the optimal dose of therapy that should be prescribed. It has been suggested to report multiple parameters of dose, to increase standardization within the field, and to gain a better understanding of the dose-response relationship. This study investigated the automatic quantification of multiple dose parameters in a rat model of ischemic stroke, with rehabilitation paradigms whereby rats repeatedly grasp for food pellets to train their forelimb function. Starting 7 days post-stroke, groups of rats received 4, 8, or 12 rehabilitative training sessions for 10 days, practicing either high-quality (precision practice) or less skilled (mass practice) reaching movements. Pellet consumption was measured after each session and various metrics were analyzed using deep learning-based software (DeepLabCut, DLC) to represent parameters of dose intensity (number of reaches, paw path length) and session density (time on task). Functional outcome was assessed with the Montoya staircase task. Computer algorithms were validated against human analysis, demonstrating reach detection accuracy and reliability >80%. Interestingly, the number of training sessions did not alter the accumulated movement practice across rehabilitation, in either task. However, the number of sessions inversely affected training intensity, resulting in more forelimb use per session in rats with 4 sessions compared to 12 sessions. We found strong positive correlations between the number of reaches, time on task, paw path length, and pellets consumed in the precision practice, but only between reaches and pellets consumed in mass practice. This work demonstrates the quantification of multiple dose parameters using deep learning software and shows subtle differences between the two commonly used forelimb training tasks. Moreover, our data suggest that rehabilitative training at a frequency that is too high may negatively impact performance per session.