A Novel Automated Approach for Improving Standardization of the Marble Burying Test Enables Quantification of Burying Bouts and Activity Characteristics

The marble burying test is a commonly used paradigm to describe phenotypes in mouse models of neurodevelop-mental and psychiatric disorders. The current methodological approach relies predominantly on reporting the number of buried marbles at the end of the test. By measuring the proxy of the behavior (buried marbles), many important characteristics regarding the temporal aspect of this assay are lost. Here, we introduce a novel, automated method to quantify mouse behavior during the marble burying test with the focus on the burying bouts and movement dy-namics. Using open-source software packages, we trained a supervised machine learning algorithm (the “classifier”) to distinguish burying behavior in freely moving mice. In order to confirm the classifier’s accuracy and characterize burying events in high detail, we performed the marble burying test in three mouse models: Ube3am-/p+ [Angelman syndrome (AS) model], Shank2/ (autism model), and Sapap3/ [obsessive-compulsive disorder (OCD) model] mice. The classifier scored burying behavior accurately and consistent with the previously reported phenotype of the Ube3am-/p+ mice, which showed decreased levels of burying compared with controls. Shank2/ mice showed a similar pattern of decreased burying behavior, which was not found in Sapap3/ mice. Tracking mouse behavior throughout the test revealed hypoactivity in Ube3am-/p+ and hyperactivity in the Shank2/ mice, indicating that mouse activity is unrelated to burying behavior. Reducing activity with midazolam in Shank2/ mice did not alter the burying behavior. Together, we demonstrate that our classifier is an accurate method for the analysis of the marble burying test, providing more information than currently used methods.