Machine-learning-based video analysis of grasping behavior during recovery from cervical spinal cord injury.

Comprehensive characterizations of hand grasping behaviors after cervical spinal cord injuries are fundamental for developing rehabilitation strategies to promote recovery in spinal-cord-injured primates. We used the machine-learning-based video analysis software, DeepLabCut, to sensitively quantify kinematic aspects of grasping behavioral deficits in squirrel monkeys with C5-level spinal cord injuries. Three squirrel monkeys were trained to grasp sugar pellets from wells of varying depths before and after a left unilateral lesion of the cervical dorsal column. Using DeepLabCut, we identified post-lesion deficits in kinematic grasping behavior that included changes in digit orientation, increased variance in vertical and horizontal digit movement, and longer time to complete the task. While video-based analyses of grasping behavior demonstrated deficits in fine-scale digit function that persisted through at least 14 weeks post-injury, traditional end-point behavioral analyses showed a recovery of global hand function as evidenced by recovery of the proportion of successful retrievals by approximately 14 weeks post-injury. The combination of traditional end-point and video-based kinematic analyses provides a more comprehensive characterization of grasping behavior and highlights that global grasping performance may recover despite persistent fine-scale kinematic deficits in digit function. Machine-learning-based video analysis of kinematic digit function, in conjunction with traditional end-point behavioral analyses of grasping behavior, provide sensitive and specific indices for monitoring recovery of fine-grained hand sensorimotor behavior after spinal cord injury that can aid future studies that seek to develop targeted therapeutic interventions for improving behavioral outcomes.