To study brain function, preclinical research heavily relies on animal monitoring and the subsequent analyses of behavior. 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. 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. Here, we compare the performance of commercially available platforms (EthoVision XT14, Noldus; TSE Multi-Conditioning System, TSE Systems) to cross-verified human annotation. 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). Using these data, we then deployed the pose estimation software DeepLabCut to extract skeletal mouse representations. 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. We then developed supervised machine learning classifiers that integrate the skeletal representation with the manual annotations. This new combined approach allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, while outperforming commercial solutions. Finally, we show that the resulting machine learning approach eliminates variation both within and between human annotators. In summary, our approach helps to improve the quality and accuracy of behavioral data, while outperforming commercial systems at a fraction of the cost.

Studying the stress response is a major pillar of neuroscience research not only because stress is a daily reality but also because the exquisitely fine-tuned bodily changes triggered by stress are a neuroendocrinological marvel. While the genome-wide changes induced by chronic stress have been extensively studied, we know surprisingly little about the complex molecular cascades triggered by acute stressors, the building blocks of chronic stress. The acute stress (or fight-or-flight) response mobilizes organismal energy resources to meet situational demands. However, successful stress coping also requires the efficient termination of the stress response. Maladaptive coping-particularly in response to severe or repeated stressors-can lead to allostatic (over)load, causing wear and tear on tissues, exhaustion, and disease. We propose that deep molecular profiling of the changes triggered by acute stressors could provide molecular correlates for allostatic load and predict healthy or maladaptive stress responses. We present a theoretical framework to interpret multiomic data in light of energy homeostasis and activity-dependent gene regulation, and we review the signaling cascades and molecular changes rapidly induced by acute stress in different cell types in the brain. In addition, we review and reanalyze recent data from multiomic screens conducted mainly in the rodent hippocampus and amygdala after acute psychophysical stressors. We identify challenges surrounding experimental design and data analysis, and we highlight promising new research directions to better understand the stress response on a multiomic level.

To study brain function, preclinical research relies heavily on animal monitoring and the subsequent analyses of behavior. Commercial platforms have enabled semi high-throughput behavioral analyses by providing accurate tracking of animals, yet they often struggle with the analysis of ethologically relevant behaviors and lack the flexibility to adapt to variable testing environments. In the last couple of years, substantial advances in deep learning and machine vision have given researchers the ability to take behavioral analysis entirely into their own hands. Here, we directly compare the performance of commercially available platforms (Ethovision XT14, Noldus; TSE Multi Conditioning System, TSE Systems) to cross-verified human annotation. To this end, we provide a set of videos -carefully annotated by several human raters -of three widely used behavioral tests (open field, elevated plus maze, forced swim test). Using these data, we show that by combining deep learning-based motion tracking (DeepLabCut) with simple post-analysis, we can track animals in a range of classic behavioral tests at similar or even greater accuracy than commercial behavioral solutions. In addition, we integrate the tracking data from DeepLabCut with post analysis supervised machine learning approaches. This combination allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, thus outperforming commercial solutions. Moreover, the resulting machine learning approach eliminates variation both within and between human annotators. In summary, our approach helps to improve the quality and accuracy of behavioral data, outperforming commercial systems at a fraction of the cost.