Detecting ataxia using automated analysis of motor coordination and balance of mice on the balance beam

BackgroundThe balance beam assay is a well-known paradigm to assess motor coordination in mouse models of neurodegenerative diseases. Classically, these experiments are quantified using manual annotation, which is time-consuming and prone to inter-experimenter variability. MethodsWe present an open-source analysis pipeline that allows for the automated quantification of motor function in mice crossing the balance beam. ResultsUsing an established Pcp2-Ppp3r1 ataxia mouse model, we validated the analysis pipeline by comparing the motor performance of Pcp2-Ppp3r1 animals to their wildtype littermates. Pcp2-Ppp3r1 animals showed a significant increase in the number of missteps and increased time to traverse the beam. Moreover, we compared the results of the automated classification of missteps and stops to that of 3 independent observers, which showed no significant differences between the classifier and the cumulative observer score for missteps. ConclusionWe show that our pipeline can reliably report crossing time and missteps, offering a high-throughput, automated option for the analysis of balance beam data. Method summaryOur method consists of an easy-to-follow and low-cost manual for building a balance beam setup and an analysis pipeline of mouse movement while crossing the beam. We present a MATLAB script combining FFMPEG and ImageJ (using the MIJ package) to pre-process the videos and extract the time for the mouse to cross the beam. Mice movements on the beam were then tracked using motr and stops and missteps were extracted using a trained JAABA classifier. The trained classifier, the code and all accompanying files were deposited in an open source repository