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Neurobehavioural analysis of mouse phenotypes requires the monitoring of mouse behaviour over long periods of time. In this study, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviours. We provide software and an extensive manually annotated video database used for training and testing the system. Our system performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home-cage behaviours of two standard inbred and two non-standard mouse strains. From these data, we were able to predict in a blind test the strain identity of individual animals with high accuracy. Our video-based software will complement existing sensor-based automated approaches and enable an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of mouse behaviour. Studying the behaviour of captive mice requires considerable time and effort. Here, video-based software is designed and implemented to automatically quantitate mouse behaviour; the system performs well in comparison with manual human observations.

Nature Communications 1: Article number: 68 (2010); Published: 7 September 2010; Updated: 31 January 2012. Jim Mutch, who conducted experiments described in this article, was inadvertently omitted from the author list. The correct author list and contact details for Jim Mutch are as follows: