Using simple behavioral analysis (SimBA) and Deep Lab Cut (DLC), we can create predictive behavior classifiers using pose estimation (PE) data obtained through DLC. PE is a computerized technique to track and predict the location of mice by training the video dataset with labeled frames using specific regions of interest (ROIs). With this, we can create machine-learning (ML) predictive classifiers of complex social behavior in SimBA. Social behaviors and interactions are difficult to manually track due to their rapid successions. To overcome this, I plan to use ML classification using our SimBA pipeline for behavioral classification allowing us to exceed human performance and increase throughput and consistency. I plan to create accurate classifiers for social behaviors that I will use to analyze the behavioral motifs of mice undergoing an operant social stress procedure. First, we train male and female C57BL/6J mice to self-administer (SA) their same sex cage mate. Experimental mice are then subjected to either physical stress for males or witness stress for females. Following social stress, non-reinforced SA is used to assess social reward seeking. Next, social interaction (SI) tests are performed to document time spent approaching the familiar same-sex conspecific cage mates and the aggressive CD-1 mice. All behavior was recorded, and transferred to DLC, followed by frame extraction. Using these frames, we trained the operant behavioral dataset to track the orientation of the mice. Next, we evaluate the dataset for a low error margin as observed by a continuous plateau of iteration loss. Although not complete, I expect to create behavioral classifiers for mice during social decision making in a social reward context following social stress inclusive of sex differences. Providing descriptive statistics of both movement and probability of successive behaviors as they occur in real-time.

Neuropsychiatric disorders pose a difficult challenge for healthcare providers. Treatments for such disorders vary in efficacy and come with detrimental costs for patients and their communities. Historically, preclinical animal models have failed to incorporate the nuances of volitional human social behavior. This project used chronic social defeat stress to induce depression-like behaviors in male and female mice, this was followed by self-administered social interactions within an operant chamber in which lever presses were reinforced by social contact. The goal is to develop preclinical animal models that can be assessed to identify mechanisms responsible for stress-induced social motivation. The mice will be injected with a nuclear localized tag (oNLS) and viral retrograde tracer rAAV2-retro-GFP. Male and female mice will train to self-administer social interaction with a sex and age-matched housing partner over the course of ten 12-trial sessions. Next, experimental male and female mice will be subjected to physical and witness defeats followed by operant social self-administration. Before and after the 10-day operant social stress sessions, we will test social reward seeking via non-reinforced self-administration of social reward followed by a progressive ratio test. Brain tissue will be collected and prepared for immunohistochemistry and iDISCO+ whole-brain clearing for cfos labelling. We predict results will show differential cfos activity in sexually dimorphic brain regions such as the hippocampus, prefrontal cortex, amygdala and the bed nucleus of stria terminalis. We determine that operant social stress can be used to discern differences in social motivation in male and female mice as a result of stress-induced factors. There is great potential in using whole-brain activity mapping to identify brain structures activated during social reward following social stress, as this can also serve as a technical resource for the field by identifying relevant non-canonical brain regions and circuits that govern such behaviors.