The aim of this proposal is to deliver an innovative and easy-to-use experimental platform for measuring and quantifying naturalistic behaviors of mammalian animal models used for biomedical research, including rodents and monkeys, across a range of spatial and temporal scales. This will require developing a method for tracking movements freely behaving animals with far higher spatiotemporal resolution and more kinematic detail than currently possible. To overcome the limitations of current technologies, a new solution is proposed that synergistically combines two methods - marker based motion capture and a video- based machine learning approach. First, using marker-based motion capture, the gold standard for 3D tracking in humans, the position of experimental subjects' head, trunk, and limbs will be tracked in 3D with submillimeter precision. An innovative marker design, placement strategy, and post-processing pipeline will ensure an unprecedentedly detailed description of rodent behavior over a large range of timescales. To make the system more efficient, robust, affordable and better suited for high-throughput longitudinal studies, the unprecedentedly rich and large 3D datasets generated by the motion capture experiments will be leveraged to train a deep neural network to predict pose and appendage positions from a set of 1-6 normal video cameras. To best capitalize on the large training datasets, the latest advances in convolutional neural networks for image analysis will be incorporated. Together, these advances will promote generalization of the high-resolution 3D tracking system to a variety of animals and environments, thus establishing a cheap, flexible, and easy-to use kinematic tracking method that can easily be scaled up and adopted by other labs. The large ground-truth datasets will allow the system to be benchmarked and compared against state-of-the art technologies in quantitative and rigorous ways. Preliminary studies have been very positive and suggest large improvements over current methods both when it comes to the range of behaviors that can be tracked and the precision with which they can be measured. Importantly, all new technology will be readily shared with the scientific community, thereby leveraging from this single grant the potential for numerous investigators to dramatically improve the efficiency of their research programs requiring rigorous quantitative descriptions of animal behavior.

Public Health Relevance

We will develop and disseminate innovative new technology for measuring precise 3D kinematics in freely moving animals over long time-periods. Our proposed experimental platform will illuminate how natural behaviors are organized and help us understand how they are controlled by the nervous system, and how this control goes awry in disease. The technological leap made possible by this grant will catalyze a host of studies on the neural mechanisms underlying motor control, learning, and mental disorders, and thus help in the discovery of new diagnostic and therapeutic approaches for afflicted patients.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Bioengineering of Neuroscience, Vision and Low Vision Technologies Study Section (BNVT)
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Sammak, Paul J
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Harvard University
Schools of Arts and Sciences
United States
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