Individual Differences in Volitional Social Self-Administration and Motivation in Male and Female Mice Following Social Stress.

BACKGROUND: A key challenge in developing treatments for neuropsychiatric illness is the disconnect between preclinical models and the complexity of human social behavior. We integrate voluntary social self-administration into a rodent model of social stress as a platform for the identification of fundamental brain and behavior mechanisms underlying stress-induced individual differences in social motivation. METHODS: Here, we introduced an operant social stress procedure in male and female mice composed of 3 phases: 1) social self-administration training, 2) social stress exposure concurrent with reinforced self-administration testing, and 3) poststress operant testing under nonreinforced and reinforced conditions. We used social-defeat and witness-defeat stress in male and female mice. RESULTS: Social defeat attenuated social reward seeking in males but not females, whereas witness defeat had no effect in males but promoted seeking behavior in females. We resolved social stress-induced changes to social motivation by aggregating z-scored operant metrics into a cumulative social index score to describe the spectrum of individual differences exhibited during operant social stress. Clustering does not adequately describe the relative distributions of social motivation following stress and is better described as a nonbinary behavioral distribution defined by the social index score, capturing a dynamic range of stress-related alterations in social motivation inclusive of sex as a biological variable. CONCLUSIONS: We demonstrated that operant social stress can detect stable individual differences in stress-induced changes to social motivation. The inclusion of volitional behavior in social procedures may enhance the understanding of behavioral adaptations that promote stress resiliency and their mechanisms under more naturalistic conditions.

Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.