Management and Analysis of Mouse Behavioral Datasets

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DESCRIPTION (provided by applicant): A high-resolution quantitative approach to the monitoring of spontaneous home cage behavior in the mouse has a potential to reveal novel insights into the organization of mammalian behaviors relevant to common neurobehavioral disorders. In addition, it can provide a sensitive tool for detecting the impact of genetic factors on brain function. We have developed monitoring systems for the collection of high- resolution home cage behavioral data, and now plan to develop tools for managing the large volumes of information contained within these novel datasets. The proposal consists of three Specific Aims. In Aim 1, we will develop methods for the efficient evaluation of data quality. In particular, tools will be developed to detect errors and variability resulting from device malfunction, idiosyncratic interactions between mice and devices, environmental factors and experimenter error. In Aim 2, we will develop systems for behavioral data management with the aid of Dr. Victor Markowitz, head of the Biological Data Management and Technology Center at Lawrence Berkeley National Laboratory to. These systems will be designed for the efficient organization, storage, retrieval and dissemination of both raw and analyzed home cage behavioral data. In Aim 3, we will establish methods for the identification of basic behavioral elements. Data reduction methods will facilitate the development of analytical approaches to uncover mouse behavioral patterns and their sensitivity to genetic background. Procedures will also be developed for cluster analysis of behavioral patterns and for group comparisons. To assess the sensitivity of these analytical tools, they will be applied to datasets reflecting the behavioral patterns of genetically diverse strains of inbred mice. We anticipate that the tools and datasets to be developed in this proposal will be broadly applicable to the use of mice for biomedical research.