Automated measuring method based on Machine learning for optomotor response in mice

Optomotor response (OMR) describes an innate orienting behavior for numerous kinds of model animals when the surrounding visual scene is moving, and is often used for evaluating animals’ visual function and nervous system. OMR measurements are generally performed and analyzed by skillful operators, but this manual measurement suffers from low accuracy and efficiency, which further yields unreliable and even misleading results. In this paper, we present a quantitative method to automatically measure the OMR in mice. The presented measurement method consists of a set of algorithms, e.g., convolutional neural network (CNN) to track the orientation of the mouse’s head, and a feature extraction method to measure the OMRs. Compared with existing techniques, the proposed method can measure the exact starting and ending time instants of each OMR in a video segment of a mouse motion record, based on the visual detection and analysis algorithms for mice motion, which is more precise and consistent compared to only judging the presence or absence of OMR for a whole segment of a motion record in almost all the existing OMR measuring methods. In the experiments, 21 mice (7 wt mice, 7 OPTNE50K mice, and 7 OPTNE50K + BMCs mice) with three levels of optic nerve injury treatments are performed the OMR tests. The observed statistical difference in results of the three groups of mice with different vision verifies the validity of the method. Compared with the calibration results from ophthalmological experts, the system can achieve the recognition rate of 94.89%. The proposed method also provides a quantitative and analytical alternative of the OMR behavior of mice in neuroscience and ophthalmology studies.