Validation of a system for automatic quantitative analysis of laboratory mice behavior based on locomotor pose

Abstract

Automatic recognition and accurate quantitative analysis of rodent behavior play an important role in brain neuroscience, pharmacological and toxicological. Currently, most behavior recognition systems used in experiments mainly focus on the indirect measurements of animal movement trajectories, while neglecting the changes of animal body pose that can indicate more psychological factors. Thus, this paper developed and validated an hourglass network-based behavioral quantification system (HNBQ), which uses a combination of body pose and movement parameters to quantify the activity of mice in an enclosed experimental chamber. In addition, The HNBQ was employed to record behavioral abnormalities of head scanning in the presence of food gradients in open field test (OFT). The results proved that the HNBQ in the new object recognition (NOR) experiment was highly correlated with the scores of manual observers during the latent exploration period and the cumulative exploration time. Moreover, in the OFT, HNBQ was able to capture the subtle differences in head scanning behavior of mice in the gradient experimental groups. Satisfactory results support that the combination of body pose and motor parameters can regard as a new alternative approach for quantification of animal behavior in laboratory.