A deep learning-based approach for unbiased kinematic analysis in CNS injury

Abstract

Traumatic spinal cord injury (SCI) is a devastating condition that impacts over 300,000 individuals in the US alone. Depending on the severity of the injury, SCI can lead to varying degrees of sensorimotor deficits and paralysis. Despite advances in our understanding of the underlying pathological mechanisms of SCI and the identification of promising molecular targets for repair and functional restoration, few therapies have made it into clinical use. To improve the success rate of clinical translation, more robust, sensitive, and reproducible means of functional assessment are required. The gold standards for the evaluation of locomotion in rodents with SCI are the Basso Beattie Bresnahan (BBB) scale and Basso Mouse Scale (BMS).

To overcome the shortcomings of current methods, we developed two separate markerless kinematic analysis paradigms in mice, MotorBox and MotoRater, based on deep-learning algorithms generated with the DeepLabCut open-source toolbox. The MotorBox system uses an originally designed, custom-made chamber, and the MotoRater system was implemented on a commercially available MotoRater device. We validated the MotorBox and MotoRater systems by comparing them with the traditional BMS test and extracted metrics of movement and gait that can provide an accurate and sensitive representation of mouse locomotor function post-injury, while eliminating investigator bias and variability. The integration of MotorBox and/or MotoRater assessments with BMS scoring will provide a much wider range of information on specific aspects of locomotion, ensuring the accuracy, rigor, and reproducibility of behavioral outcomes after SCI.