Enhancing rodent behavioral phenotyping using guided ultrasonic waves

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Rodents are a cornerstone of neuroscience and physiology research, but the methods for monitoring behavior and physiology in these animals suffer from several key limitations. Monitoring physiological processes such as heart rate, breathing, and muscle activity requires invasive sensors that impede natural behavior of the animals. Similarly, behavioral assays can require highly constraining apparatus. This proposal is based on the hypothesis that we can greatly improve the sensitivity, accuracy, and reliability of rodent behavior quantification by taking advantage of a stream of information that, to date, has been completely ignored. When an animal is behaving in an environment, muscle contractions associated with breathing, heart rate, and voluntary and involuntary movements apply subtle forces to the surfaces the animal contacts. These forces generate elastic waves that propagate through the material (i.e., waveguide) at ultrasonic frequencies. The overarching hypothesis of this proposal is that these elastic waves − Guided Ultrasonic Waves (GUWs) − provide valuable information about mouse physiology, behavior, and underlying mental states. Under this proposal we will test the hypothesis that GUWs can be used to non-invasively track low-amplitude responses, such as breathing, heart rate, and startle, which currently can only be monitored using invasive or highly constraining apparatus. In addition, we predict that GUWs can be used to improve the sensitivity and accuracy with which other rodent behaviors can be identified and tracked. We will conduct experiments in which GUWs are recorded as mice explore an arena outfitted with piezoelectric sensors. We will simultaneously record information about physiology and behavior using traditional video tracking and implanted telemetry devices. By comparing these streams of information, we will identify GUW features that report mouse heart rate, startle, and a variety of other behaviors. In addition, we will use supervised and unsupervised machine learning approaches to develop GUW metrics that precisely and accurately classify behaviors that cannot be detected using current methods. Completion of these aims will yield hardware and associated analytics that will enhance the precision and objectivity of rodent behavior monitoring and allow researchers to simultaneously monitor behavior and physiology without the need for restrictive or invasive apparatus. Our goal is that the piezoelectric-based apparatus and associated analyses developed here will allow labs without specialized equipment or expertise to perform precise monitoring of mouse behavior and physiology without the need for invasive test equipment.