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，但监测的方法 这些动物的行为和生理受到几个关键限制。监测生理学 心率、呼吸和肌肉活动等过程需要侵入性传感器来阻碍自然 动物的行为。同样，行为分析可能需要高度约束的仪器。这项建议 是基于这样一个假设，即我们可以极大地提高啮齿动物的敏感度、准确性和可靠性 通过利用信息流来量化行为，到目前为止，信息流完全是 已被忽略。当动物在环境中行为时，肌肉收缩与呼吸、心脏 自愿性和非自愿性运动对动物接触的表面施加微妙的力量。这些 力产生弹性波，以超声波频率通过材料(即波导)传播。 这一提议的首要假设是这些弹性波−引导的超声波(GUW)− 提供有关老鼠生理、行为和潜在精神状态的有价值的信息。在这下面 我们将测试GUW可用于非侵入性跟踪低幅度响应的假设， 例如呼吸、心率和惊吓，目前只能使用有创或高度 约束装置。此外，我们预测GUW可以用来提高灵敏度和准确度 可以用来识别和跟踪其他啮齿动物的行为。我们将进行实验，在其中GUW 当老鼠探索装有压电传感器的竞技场时，这段视频被记录了下来。我们将同时录制 使用传统的视频跟踪和植入的遥测设备提供有关生理和行为的信息。通过 比较这些信息流，我们将确定GUW功能，报告老鼠心率、惊吓、 以及各种其他行为。此外，我们还将使用监督和非监督机器学习 开发精确和准确地对无法检测的行为进行分类的GUW度量的方法 使用当前的方法。完成这些目标将产生硬件和相关分析，从而增强 啮齿动物行为监测的准确性和客观性，并允许研究人员同时监测 不需要限制性或侵入性器械的行为和生理。我们的目标是 这里开发的基于压电的仪器和相关分析将允许实验室在没有专门的 对老鼠的行为和生理进行精确监控的设备或专业知识，无需 侵入性检测设备。

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.