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Abstract:
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Innovations in automated home-cage systems allow the study of spontaneous behavior in mice, and yield unbiased long-term continuous observations of behavior. Conventional statistical techniques typically used on these data, however, discard the most interesting and novel aspect of the data: information on the dynamics of behavior. Here, we develop and implement a statistical tool based on Hidden Markov Models (HMM) in a Bayesian context to describe the temporal organization of behavior of groups of mice. As it is biologically implausible that the amount of time spent in a behavioral state is a function of a memoryless process, the model is extended to an explicit duration HMM. In addition, to allow for statistical comparisons of behavioral patterns between groups of mice, the model is extended to a hierarchical model. We illustrate our proposed model using a real data example, comparing the behavioral pattern of young adult and aged C57BL/6J mice, clearly showing the advantage of the model over conventional analyses. Our proposed framework is one of the first that models the behavior of multiple animals simultaneously, allowing for formal group comparisons on all model parameters.
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