Bachelor of Science, University of California Davis (2004)
Master of Science, University of California Davis (2006)
Doctor of Philosophy, University of California Davis (2011)
Pre-clinical investigation of human CNS disorders relies heavily on mouse models. However these show low predictive validity for translational success to humans, partly due to the extensive use of rapid, high-throughput behavioral assays. Improved assays to monitor rodent behavior over longer time scales in a variety of contexts while still maintaining the efficiency of data collection associated with high-throughput assays are needed. We developed an apparatus that uses radio frequency identification device (RFID) technology to facilitate long-term automated monitoring of the behavior of mice in socially or structurally complex cage environments. Mice that were individually marked and implanted with transponders were placed in pairs in the apparatus, and their locations continuously tracked for 24 h. Video observation was used to validate the RFID readings. The apparatus and its associated software accurately tracked the locations of all mice, yielding information about each mouse's location over time, its diel activity patterns, and the amount of time it was in the same location as the other mouse in the pair. The information that can be efficiently collected in this apparatus has a variety of applications for pre-clinical research on human CNS disorders, for example major depressive disorder and autism spectrum disorder, in that it can be used to quantify validated endophenotypes or biomarkers of these disorders using rodent models. While the specific configuration of the apparatus described here was designed to answer particular experimental questions, it can be modified in various ways to accommodate different experimental designs.
View details for DOI 10.1016/j.jneumeth.2012.06.001
View details for Web of Science ID 000307132700009
View details for PubMedID 22698663