Videos of animal behavior are used to quantify researcher-defined behaviors-of-interest to study neural function, gene mutations, and pharmacological therapies. Behaviors-of-interest are often scored manually, which is time-consuming, limited to few behaviors, and variable across researchers. We created DeepEthogram: software that uses supervised machine learning to convert raw video pixels into an ethogram, the behaviors-of-interest present in each video frame. DeepEthogram is designed to be general-purpose and applicable across species, behaviors, and video-recording hardware. It uses convolutional neural networks to compute motion, extract features from motion and images, and classify features into behaviors. Behaviors are classified with above 90% accuracy on single frames in videos of mice and flies, matching expert-level human performance. DeepEthogram accurately predicts rare behaviors, requires little training data, and generalizes across subjects. A graphical interface allows beginning-to-end analysis without end-user programming. DeepEthogram's rapid, automatic, and reproducible labeling of researcher-defined behaviors-of-interest may accelerate and enhance supervised behavior analysis.

Researchers commonly acquire videos of animal behavior and quantify the prevalence of behaviors of interest to study nervous system function, the effects of gene mutations, and the efficacy of pharmacological therapies. This analysis is typically performed manually and is therefore immensely time consuming, often limited to a small number of behaviors, and variable across researchers. Here, we created DeepEthogram: software that takes raw pixel values of videos as input and uses machine learning to output an ethogram, the set of user-defined behaviors of interest present in each frame of a video. We used convolutional neural network models that compute motion in a video, extract features from motion and single frames, and classify these features into behaviors. These models classified behaviors with greater than 90% accuracy on single frames in videos of flies and mice, matching expert-level human performance. The models accurately predicted even extremely rare behaviors, required little training data, and generalized to new videos and subjects. DeepEthogram runs rapidly on common scientific computer hardware and has a graphical user interface that does not require programming by the end-user. We anticipate DeepEthogram will enable the rapid, automated, and reproducible assignment of behavior labels to every frame of a video, thus accelerating all those studies that quantify behaviors of interest.Code is available at: https://github.com/jbohnslav/deepethogram

The lack of sensitive and robust behavioral assessments of pain in preclinical models has been a major limitation for both pain research and the development of novel analgesics. Here, we demonstrate a novel data acquisition and analysis platform that provides automated, quantitative, and objective measures of naturalistic rodent behavior in an observer-independent and unbiased fashion. The technology records freely behaving mice, in the dark, over extended periods for continuous acquisition of 2 parallel video data streams: (1) near-infrared frustrated total internal reflection for detecting the degree, force, and timing of surface contact and (2) simultaneous ongoing video graphing of whole-body pose. Using machine vision and machine learning, we automatically extract and quantify behavioral features from these data to reveal moment-by-moment changes that capture the internal pain state of rodents in multiple pain models. We show that these voluntary pain-related behaviors are reversible by analgesics and that analgesia can be automatically and objectively differentiated from sedation. Finally, we used this approach to generate a paw luminance ratio measure that is sensitive in capturing dynamic mechanical hypersensitivity over a period and scalable for highthroughput preclinical analgesic efficacy assessment.

Highlights d Common scratching parameters such as bout number and duration can vary independently d Itch severity should be probed beyond measures of scratching incidence d High-speed video of scratching reveals potential metrics of scratching intensity

Small-fiber neuropathy (SFN), characterized by distal unmyelinated/thinlymyelinated fiber loss, produces a combination of sensory dysfunction and neuropathic pain. Gain-of-function variants in the sodium channel Na v 1.7 that produce DRG neuron hyperexcitability are present in 5-10% of patients with idiopathic painful SFN.We created two independent knock-in mouse-lines carrying the Na v 1.7-I228M gainof-function variant, found in idiopathic SFN. Whole-cell patch-clamp and multielectrode-array recordings show that Na v 1.

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We carried out a gene expression-based in silico screen in order to identify small molecules with gene-expression profiles that are anticorrelated with a gene-expression profile for Parkinson's disease (PD). We identified the cyclin-dependent kinase 2/5 (CDK2/5) inhibitor GW8510 as our most significant hit and characterized its effects in rodent MN9D cells and in human neuronal cells derived from induced pluripotent stem cells. GW8510 demonstrated neuroprotective ability in MN9D cells in the presence of 1-methyl-4-phenylpyridium (MPP(+)), a widely used neurotoxin model for Parkinson's disease. In order to delineate the nature and extent of GW8510's neuroprotective properties, we studied GW8510 in human neuronal cells in the context of various mechanisms of cellular stress. We found that GW8510 was protective against small-molecule mitochondrial and endoplasmic reticulum stressors. Our findings illustrate an approach to using small-molecule gene expression libraries to identify compounds with therapeutic potential in human diseases.

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