Non-invasive, Integrated Monitoring of Rodent Physiology and Behaviors Using Guided Ultrasonic Waves

This talk will present the methodology of using the guided ultrasonic waves generated by the rodent to track its physiology, behavior, and psychological state. When behaving in an environment, muscle contractions (voluntary and involuntary) apply subtle forces to the surfaces the animal contacts. These forces generate elastic waves, which propagate through the material (i.e., waveguide) at ultrasonic frequencies and contain information about the mouse's physiology, behavior, and underlying mental states. To date, this stream of data has been ignored. If the information in this data were mined, it would lead to the development of a novel, non-invasive way to study rodent behavior.

In the experiments, an aluminum plate was used as the floor of an open field for the animal to stand and move on. As the rodent behaved, moved, or reacted in this modified open field, the subtle voluntary and involuntary forces applied to the plate's surface resulted in the generation of guided ultrasonic waves (GUWs) in the plate. Highly sensitive ultrasonic sensors were bonded to the bottom of the plate to detect and acquire the generated waves.

Two types of GUWs exist in the plate: the Lamb waves and shear horizontal (SH) waves. Lamb waves are characterized by in-plane and out-of-plane particle vibrations, while in the SH waves, particles vibrate in-plane perpendicular to the direction of propagation. Both these waves are dispersive (the wave’s velocity varies with frequency) and multimodal (wherein multiple modes can propagate simultaneously). These properties enable (1) differentiating GUWs resulting from physiological functions from the ones associated with ambulation and behavior and (2) conducting an intricate analysis of rodent behavior. Owing to the dispersive and multimodal nature, wherein a minuscule change in the applied energy (i.e., the frequency) leads to changes in the mode profiles (change in velocity, wavelength, and types of modes), the differences between involuntary forces (arising from heartbeat and breathing) and voluntary forces will cause a noticeable change in the generated GUWs, i.e., their wave characteristics. Further, as slight changes in the behavior and ambulatory movements will cause significant changes in the characteristics of the generated waves, variations between and within behaviors can also be identified with GUWs. Two types of experiments were conducted with male and female mice from two strains, C57BL/6J and 129sv/evTac mice. In these experiments, each mouse was treated as an acoustic source, and the generated GUWs were acquired as acoustic emissions (AE) based on the amplitude threshold-based data acquisition. This converts the continuous data stream into AE hits, such that each AE hit is a consequence of a behavior/movement of the animal. Each AE hit is analyzed in terms of the features of the AE hits, like Duration, Amplitude, Absolute Energy, Rise Time, etc. Effectively, this analysis converts the information in the physiological process/behavior into values of various AE hit features and thus enables a simpler mode of analysis. In the first set of studies, the mice were placed one at a time into the open field arena and allowed to explore freely for five minutes without any external stimulus. It was observed that every movement/behavior of the animal resulted in the generation of the Lamb wave packets in the frequency range of the R6a sensor (30-100 kHz). This demonstrated the capability of the R6a ultrasonic sensor to detect even the subtlest movements of the animal—which were imperceptible even in the visual feed. Further, plots also showed the technology's ability to identify variations in wave signatures in accordance with different behaviors and variations within the behaviors. In contrast to the R6a sensors, the sensors used to measure SH waves (in the range 75 to 230 kHz) respond to selective behaviors like sniffing and rearing. Simply put, SH modes in the 75 to 230 kHz range were generated when the animal reared (or sometimes sniffed), and this was a consistent observation across all the tested C57BL/6J mice. In addition to this, we were able to spot variations within the supported and unsupported rearing. Thus, with the AE data, we could note a variability within the behavior, which was not discernible visually. These results showed that GUWs contained valuable information on behaviors that can be used not only to classify them but also to identify variations within them. We believe that with the information in all the AE features across the entire frequency spectrum (20 to 600 kHz), all the known behaviors can be classified through a deep neural network model.

In the second type of experiment, while the animal was in the open field, the tweeters attached to the open field played an auditory startle stimulus (4 kHz, 110 dB, and 10 msec) to evoke a startle response from the animal. The free-moving animal’s response was quantified from the GUW data. Certain features of the AE hits were analyzed to interpret the animal’s startle response. Particularly, features like the absolute energy, duration, and amplitude revealed physical details of the animal’s reaction. The absolute energy of the AE hit quantified the “intensity” of the startle. Reaction time, calculated from the Time-of-hit, gives the time the animal takes to respond to the tone (from the start of the tone). Duration specifies the length of time the animal applied the “high-intensity” force, and Amplitude is an indirect indication of the maximum amplitude of the applied force. We hypothesize that the intensity of the startle (represented by the absolute energy of the AE hit) reflects the animal's psychological state, specifically regarding stress and anxiety. With just a video-based analysis, such detailed quantification of startle is imprecise. On the other hand, the evaluation from AE data offers a reliable and multi-dimensional description of the acoustic startle response. In our tests, owing to the animal being in an unconstrained environment, a wider variety of startle responses were observed, including retraction, jump, head/body jerk, and halt/freezing. Thus, we hypothesize the AE-based open-field startle analysis will yield richer, more informative insights about psychological state and the influence of behavioral, pharmacological, and neural manipulations. The results of these studies showcase sensors’ ability to detect and measure the subtlest motions and demonstrate the presence of valuable information in GUWs, which we believe can be used to undertake physiological measurements and perform enhanced behavioral analysis. Physiological measurements (like heart and breathing rates) require the detection of waves originating from involuntary forces—much like the acoustic startle response. The success of performing detailed acoustic startle response studies attests to the GUWs-based technology's capability to undertake other parameter (heart rate and respiratory) measurements. The techniques used in the startle response studies will serve as the starting point of the research aiming to undertake physiological measurements. Similarly, the results of the open-field behavioral studies have demonstrated the AE technology's potential to enhance rodent behavior analysis. They will serve as a platform to develop deep neural network models to assess freely moving rodents' behavioral and psychological parameters.

Learning Objectives: 

1. Summarize the methodology to analyze free-moving rodents' behavior from the generated guided-ultrasonic waves (GUWs).

2. Identify the wave signatures associated with common behaviors (freezing and sniffing) to establish the uniqueness of generated waves for behaviors.

3. Review acoustic startle response tests in the GUW-enabled open field to obtain multidimensional data of the response.