High-throughput gait acquisition system for freely moving mice.

Abstract

Normal and pathological locomotion can be discriminated by analyzing an animal's gait on a linear walkway. This step is labor intensive and introduces experimental bias due to the handling involved while placing and removing the animal between trials. We designed a system consisting of a runway embedded within a larger arena, which can be traversed ad libitum by unsupervised, freely moving mice, triggering the recording of short clips of locomotor activity. Multiple body parts were tracked using DeepLabCut{trade mark, serif} and fed to an analysis pipeline (GaitGrapher) to extract gait metrics. We compared the results from unsupervised against the standard experimenter-supervised approach and found that gait parameters analyzed via the new approach were similar to a previously validated approach (Visual Gait Lab or VGL suite). These data show the utility of incorporating an unsupervised, automated, approach for collecting kinematic data for gait analysis.