The rapid development of new tools and molecular interventions in rodents, as well as the growing number of available transgenic mouse lines, increase the need to accurately and efficiently detect and quantify rodent behavior.
In summary, our approach helps to improve the quality and accuracy of behavioral data, while outperforming commercial systems at a fraction of the cost.Īccurate analysis of rodent behavior is crucial when assessing treatment efficacy in preclinical research. Finally, we show that the resulting machine learning approach eliminates variation both within and between human annotators. This new combined approach allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, while outperforming commercial solutions.
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We then developed supervised machine learning classifiers that integrate the skeletal representation with the manual annotations. Using simple post-analyses, we were able to track animals based on their skeletal representation in a range of classic behavioral tests at similar or greater accuracy than commercial behavioral tracking systems.
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Using these data, we then deployed the pose estimation software DeepLabCut to extract skeletal mouse representations. We provide a set of videos-carefully annotated by several human raters-of three widely used behavioral tests (open field test, elevated plus maze, forced swim test). Here, we compare the performance of commercially available platforms (EthoVision XT14, Noldus TSE Multi-Conditioning System, TSE Systems) to cross-verified human annotation. Critical advances based on deep-learning and machine vision over the last couple of years now enable markerless tracking of individual body parts of freely moving rodents with high precision. Commercial platforms have enabled semi high-throughput behavioral analyses by automating animal tracking, yet they poorly recognize ethologically relevant behaviors and lack the flexibility to be employed in variable testing environments. To study brain function, preclinical research heavily relies on animal monitoring and the subsequent analyses of behavior.