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 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 classifier's 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.

Mouse Behavior in Virtual Reality System under Optogenetic Manipulation via UCNPs

It describes the motor test of mouse vias virtual reality system.

Machine Learning in Modeling of Mouse Behavior

Mouse behavior is a primary outcome in evaluations of therapeutic efficacy. Exhaustive, continuous, multiparametric behavioral phenotyping is a valuable tool for understanding the pathophysiological status of mouse brain diseases. Automated home cage behavior analysis produces highly granulated data both in terms of number of features and sampling frequency. Previously, we demonstrated several ways to reduce feature dimensionality. In this study, we propose novel approaches for analyzing 33-Hz data generated by CleverSys software. We hypothesized that behavioral patterns within short time windows are reflective of physiological state, and that computer modeling of mouse behavioral routines can serve as a predictive tool in classification tasks. To remove bias due to researcher decisions, our data flow is indifferent to the quality, value, and importance of any given feature in isolation. To classify day and night behavior, as an example application, we developed a data preprocessing flow and utilized logistic regression (LG), support vector machines (SVM), random forest (RF), and one-dimensional convolutional neural networks paired with long short-term memory deep neural networks (1DConvBiLSTM). We determined that a 5-min video clip is sufficient to classify mouse behavior with high accuracy. LG, SVM, and RF performed similarly, predicting mouse behavior with 85% accuracy, and combining the three algorithms in an ensemble procedure increased accuracy to 90%. The best performance was achieved by combining the 1DConv and BiLSTM algorithms yielding 96% accuracy. Our findings demonstrate that computer modeling of the home-cage ethome can clearly define mouse physiological state. Furthermore, we showed that continuous behavioral data can be analyzed using approaches similar to natural language processing. These data provide proof of concept for future research in diagnostics of complex pathophysiological changes that are accompanied by changes in behavioral profile.

Author Correction: Study of mouse behavior in different gravity environments

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Standardized and reproducible measurement of decision-making in mice

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.

Efficient and stochastic mouse action switching during probabilistic decision making

To gain insight into the process by which animals choose between actions, we trained mice in a two-armed bandit task with time-varying reward probabilities. Whereas past work has modeled the selection of the higher rewarding port in such tasks, we sought to also model the trial-to-trial changes in port selection − i.e. the action switching behavior. We find that mouse behavior deviates from the theoretically optimal agent performing Bayesian inference in a hidden Markov model (HMM). Instead the strategy of mice can be well-described by a set of models that we demonstrate are mathematically equivalent: a logistic regression, drift diffusion model, and ′sticky′ Bayesian model. Here we show that switching behavior of mice is characterized by several components that are conserved across models, namely a stochastic action policy, a representation of action value, and a tendency to repeat actions despite incoming evidence. When fit to mouse behavior, the expected reward under these models lies near a plateau of the value landscape even in changing reward probability contexts. These results indicate that mouse behavior reaches near-maximal performance with reduced action switching and can be described by models with a small number of relatively fixed-parameters.