Unbiased analysis of mouse social behaviour using unsupervised machine learning
Mouse models are broadly used to study the mechanisms of neuropsychiatric disorders and to test potential treatments. In these models, automation to monitor behavioural differences during social interactions is currently limited. We propose in the present study a new method to conduct automatic behavioural classification, using an original unsupervised machine learning. We applied the proposed method to mice mutated in Shank2, a gene associated with autism spectrum disorders. We validated our results by comparing automatically extracted results to rule-based classifier labelling. We discovered seven behavioural states matching from 80 to 95% previous rule-based classification, and two unsuspected behaviours. Interestingly, we also highlighted genotype-related differences in two behavioural categories, namely locomotion and facing the conspecific.