Behavioral phenotyping: Getting more information from video tracking data

W.W. Kuurman

Department of Animals, Science and Society, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands

Mice are used worldwide as a research animal or model for a large variety of biological processes. The search for mice with interesting or exceptional phenotypes is important, so that this research can be done effectively. To facilitate this search PhenoLab^®, (Noldus Information Technology bv, The Netherlands) was developed. In this PhenoLab several mice are individually housed for a week in a home cage and tracked using and overhead camera and EthoVision^® (Noldus Information Technology bv, The Netherlands). Based on contrast differences, X- and Y-coordinates of the center of gravity for the mouse are recorded a number of times each second. During the week the mouse shows various spontaneous and induced behaviors. The behavioral phenotype of the mouse needs to be deduced from the X- and Y-coordinates (integration of location and movement).

Video tracking data is often summarized in time bins and then mean of each time bin is used for statistical analysis. For data which does not have a known unimodal distribution, however, mean and standard deviation ignore variation in the data. This variation can be utilized using all data available and mathematical modeling of each variable. Parameter estimates from these models can then be used as phenotypic information for each mouse tested. This approach yields high-resolution phenotypic information, so that detection of mice with interesting or exceptional phenotypes is more successful.

Some examples of this approach are velocity and distance to home cage wall data. For velocity a mathematical model was developed to distinguish low, intermediate, and high velocities. This model can be used to analyze locomotive activity in greater detail. For distance to home cage wall a mathematical model was developed to determine how often the mouse stays close to the wall, where the border is between being close and being away from the wall, and how these measures change over time. This model can be used to analyze anxiety and habituation in greater detail.

Additional models should be developed to utilize information collected by video tracking software. The future challenge is to integrate information obtained with this approach, so as to facilitate interpretation of the data.

Paper presented at Measuring Behavior 2005 , 5^th International Conference on Methods and Techniques in Behavioral Research, 30 August - 2 September 2005, Wageningen, The Netherlands.

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