Markerless tracking enables distinction between strategic compensation and functional recovery after spinal cord...

Injuries to the cervical spinal cord represent around 60% of all spinal cord injuries (SCIs). A major priority for patients with cervical SCIs is the recovery of any hand or arm function. The similarities between human and rodent "reach-to-eat movements" indicate that analyzing mouse forelimb reaching behavior may be a method of identifying clinically relevant treatments for people with cervical SCIs. One popular behavioral measure of forelimb functional recovery comprises the Single Pellet Retrieval Task (SPRT). The most common outcome measure for this task, however (percentage of pellets successfully retrieved), cannot readily distinguish between recovery of pre-injury motor patterns and strategic compensation. Our objective was to establish outcome measures for the SPRT that are readily adopted by different investigators and capable of measuring recovery of limb function after SCI. We used a simple semi-automated approach to high-speed tracking of mouse forepaw movements during pellet retrieval. DeepLabCut™, a machine learning based computer vision software package, was used to track individual features of the mouse forepaw, allowing a more detailed assessment of reaching behavior after SCI. Interestingly, kinematic analysis of movements pre- and post-injury illuminated persistent deficits in specific features of the reaching motor patterns, namely pronation and paw trajectory, that were poorly correlated with recovery of the ability to successfully retrieve pellets. Thus, we have developed an inexpensive method for detailed analysis of mouse reach-to-eat behavior following SCI. Further, our results suggest that binary success/fail outcome measures primarily assess an animal's ability to compensate rather than a restoration of normal function in the injured pathways and networks.