Exploring the neural mechanisms modulating complex social behavior requires a holistic understanding of both central and peripheral body states. In freely interacting mice, social behaviors are often registered by changes in autonomic nervous function, including altered blood pressure, heart and breathing rates, and core body temperature. Unfortunately, these physiological metrics are difficult to obtain during complex social behavior due to substantial hardware requirements, like collars and tethers, restricting full movement and interaction. In collaboration with an industry partner, we are developing a fully implantable, battery-free device for wireless data acquisition of physiological data, including heart and respiratory rate, temperature, and other behavioral information such as locomotion and orientation of mice using biomechano-acoustic (MA) methods. Here, we validate the use of MA devices in both anesthetized and freely moving mice. First, we tested MA devices during emergence from anesthesia and compared anesthetized recordings using MA devices to a widely used and commercially available rodent pulse oximetry device. Second, we obtained MA recordings in freely interacting mice during complex social behaviors. This technology represents a crucial advanced tool for experimental behavioral research that enables non-invasive operations in cages with simple or complex environments in an individual or groups of animals.

Rapid and smooth emergence from the anesthetized state to the awake state is important for patient safety and perioperative efficiency, yet is currently a passive process and the underlying mechanism is not well understood. In mice, emergence from anesthesia is modeled by the return of righting reflex (RORR) signaled by righting from the supine to prone position as the mouse emerges to an awake state. Using this model, it is possible to investigate the neuropharmacological mechanisms of emergence. While commonly studied in concert with neuronal recordings and optogenetic manipulation, these approaches can be combined with high-throughput automated behavior analysis using deep and machine learning approaches. Here, my goal is to create an automated behavioral classification pipeline for annotating the RORR in combination with experimental manipulations and recordings. I aim to characterize the transition between unconscious and awake states to define a binary output. This is accomplished by using DeepLabCut pose-estimation software to track subject mouse body parts, followed by the generation of supervised behavioral classifiers for RORR-related behaviors using the SimBA (Simple Behavioral Analysis) machine learning pipeline. My ongoing directions focus on performing unsupervised classification with this model to cluster additional behaviors. This use of advanced behavioral analysis will enable a better understanding of behaviorally-relevant neural activity in emergence and help bridge the gap between preclinical animal models and clinical intervention.

Misinformation is a major problem in today’s online information ecosystem. One way that platforms address this problem is by providing viewers with signals so they can assess the credibility of the content that they encounter. However, there are few existing signals on online video-sharing platforms, which is concerning as video platforms like YouTube are a common way for people to get information. We seek to understand how citations can be applied as credibility signals to videos on the video-sharing platform YouTube. First, in an interview study, we investigated how users interacted with existing credibility signals on YouTube, and found that video intent and context shaped how users would apply both existing signals as well as future citations. We used these findings to inform our design and implementation of a browser plug-in that allows users to create and view citations on all videos they see on YouTube. We now want to explore how users might use this system to apply citations on YouTube and how viewing citations on a video might impact user behavior. In this second study, we plan to observe how the system affects the user's perception of video credibility and the effectiveness of our system as a credibility signal. 

Neuroligins (NLGNs) are a family of postsynaptic cell adhesion proteins that are essential to the formation and proper functioning of synapses and play a critical role in maintaining neural excitation/ inhibition balance. Neuroligin mutations are linked to several neuropsychiatric disorders like autism, although their role in maladaptive social behavior remains unclear. Inappropriate aggression and agitation are often comorbid with neuropsychiatric disease and understanding the neural pathways underlying aggressive behavior may help to identify potential therapeutic targets. Neuroligin-2 (NLGN-2) specifically supports inhibitory synapse function and plays a key role in regulating social stress behaviors. Here, we examine the role of NLGN-2 in mediating adaptive and maladaptive aggressive behavior in adult male outbred CD-1 mice. In Experiment 1, we use immunohistochemistry to localize and quantify NLGN-2 in Fos-positive cells in nucleus accumbens of mice following resident-intruder reactive aggression. In Experiment 2, we train mice in an operant aggression self-administration procedure and examine changes in NLGN-2 in nucleus accumbens Fos-positive neurons following appetitive, or rewarding, aggression. In Experiment 3, we selectively knockdown NLGN-2 in nucleus accumbens in a neural circuit-specific manner to determine the functional effects of NLGN-2 manipulation on adaptive and maladaptive aggressive behavior. Together, these data demonstrate an important role for nucleus accumbens NLGN-2 in mediating the spectrum of aggressive behavior.