Many hundred-thousand rodents, mostly mice, are used in behavioural neuroscience research every year. This is necessary, because behaviour is the ultimate readout for brain function in health and disease, and developing treatments for brain disorders requires proof that behavioural disturbances can be corrected. Unfortunately, behaviour tests are often poorly reproducible and assess behaviour only superficially, thus ignoring the majority of relevant biological information. To improve this inefficient practice, we pursue three major goals:
1) Develop advanced machine-learning tools based on a popular motion tracking software (DeepLabCut) to analyse complex, naturalistic animal behaviours reproducibly and in great detail, while keeping the amount of data manageable.
2) Prove that our approach can identify biologically relevant differences in behaviour using homecage video recordings in a mouse model of obsessive-compulsive disorder, while using fewer mice and fewer behaviour tests compared to standard analyses.
3) Facilitate widespread dissemination of our algorithms, by packaging them into easy-to-use software modules, and by developing additional free online analyses tools to effortlessly (re)analyse, visualize and share the collected data.
We anticipate that widespread dissemination of our approach will reduce wasteful practices in preclinical behaviour testing by removing variance within and between labs through automated in-depth behaviour scoring, and by enabling data sharing and re-analysis of existing videos. Our approach will refine experimental procedures by extracting more detailed behaviour data, thus requiring fewer and less stressful behaviour tests and improving welfare for thousands of experimental mice each year.
Prof. Johannes Bohacek
Department of Health Sciences and Technology, ETH Zurich