A novel automated approach for improving standardization of the marble burying test enables quantification of burying bouts and activity characteristics

Abstract

The marble burying test is a commonly used paradigm to screen phenotypes in mouse models of neurodevelopmental and psychiatric disorders. The current methodological approach relies solely on reporting the number of buried marbles at the end of the test. By measuring the proxy of the behavior (buried marbles), rather than the behavior itself (burying bouts), many important characteristics regarding the temporal aspect of this assay are lost. Here we introduce a novel, automated method to quantify mouse behavior throughout the duration of the marble burying test with the focus on the burying bouts. 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 classifiers accuracy and uncover the behavioral meaning of the marble burying test, we performed marble burying test in three mouse models: Ube3am-/p+ (Angelman Syndrome model), Shank2-/- (autism model), and Sapap3-/- (obsessive-compulsive disorder model) mice. The classifier scored burying behavior accurately and consistent with the literature in the Ube3am-/p+ mice, which showed decreased levels of burying compared to controls. Shank2-/- mice showed a similar pattern of decreased burying behavior, which was not found in Sapap3-/- mice. Tracking mouse behavior throughout the test enabled us to quantify activity characteristics, revealing hypoactivity in Ube3am-/p+ and hyperactivity in the Shank2-/- mice, indicating that mouse activity is unrelated to burying behavior. Together, we demonstrate that our classifier is an accurate method for the analysis of the marble burying test, providing more information than the currently used methods. Significance StatementThe marble burying test is widely used in phenotyping neurodevelopmental and neuropsychiatric disorder mouse models. Currently, its analysis consists of manually scoring the number of buried marbles upon the completion of the assay. This approach is not standardized across laboratories, and leaves out important variables such as movement characteristics and information about the burying bouts. This leads to divergent interpretations of the marble burying test, ranging from anxiety to cognitive impairment. We introduce a method that reliably tracks mouse behavior throughout the experiment, classifies the duration and number of the burying bouts, and is generalizable across laboratories. Using machine learning for measuring the actual burying behavior standardizes this method, and provides rich information about the burying characteristics.