Enhancing rodent behavioral phenotyping using guided ultrasonic waves

啮齿动物是神经科学和生理学研究的基石，但监测方法 这些动物的行为和生理受到几个关键的限制。监测生理 心率、呼吸和肌肉活动等过程需要侵入式传感器， 动物的行为。类似地，行为测定可能需要高度约束的装置。这项建议 是基于这样的假设，即我们可以大大提高啮齿动物的敏感性，准确性和可靠性 行为量化，利用信息流，到目前为止，已经完全 忽视当一个动物在一个环境中活动时，与呼吸、心脏和呼吸有关的肌肉收缩， 速度，以及自愿和不自愿的运动施加微妙的力量，动物接触的表面。这些 力产生通过材料传播的弹性波（即，波导）。 该提案的首要假设是，这些弹性波-超声导波（GUW）- 提供有关小鼠生理学、行为和潜在精神状态的有价值的信息。根据本 我们将测试GUW可以用于非侵入性地跟踪低振幅响应的假设， 例如呼吸、心率和惊吓，目前只能使用侵入性或高度侵入性的方法来监测。 约束装置此外，我们预测，GUW可以用来提高灵敏度和准确性 通过它可以识别和跟踪其他啮齿动物的行为。我们将进行实验， 当老鼠探索一个配备有压电传感器的竞技场时，我们将同时记录 使用传统的视频跟踪和植入的遥测设备来获得关于生理和行为的信息。通过 比较这些信息流，我们将识别报告小鼠心率、惊吓 以及其他各种行为。此外，我们将使用监督和无监督机器学习 开发GUW指标的方法，这些指标可以精确地对无法检测到的行为进行分类 使用当前的方法。这些目标的完成将产生硬件和相关的分析， 啮齿动物行为监测的精确性和客观性，并允许研究人员同时监测 行为和生理学，而不需要限制性或侵入性设备。我们的目标是 基于压电的设备和相关的分析，在这里开发将允许实验室没有专门的 设备或专业知识来执行小鼠行为和生理学的精确监测，而不需要 侵入性测试设备

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.