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An individual based, multidimensional approach to identify emotional reactivity profiles in inbred mice
Marloes H van der Goot1, Hetty Boleij1, Jan van den Broek2
1Department Population Health Sciences, Unit Animals in Science and Society, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands.
Background:
Despite extensive environmental standardization and the use of genetically and microbiologically defined mice of similar age and sex, individuals of the same mouse inbred strain commonly differ in quantitative traits. This is a major issue as it affects the quality of experimental results. Standard analysis practices summarize numerical data by means and associated measures of dispersion, while individual values are ignored. Perhaps taking individual values into account in statistical analysis may improve the quality of results.
New Method:
The present study re-inspected existing data on emotional reactivity profiles in 125 BALB/cJ and 129 mice, which displayed contrasting patterns of habituation and sensitization when repeatedly exposed to a novel environment (modified Hole Board). Behaviors were re-analyzed on an individual level, using a multivariate approach, in order to explore whether this yielded new information regarding subtypes of response, and their expression between and within strains.
Results:
Clustering individual mice across multiple behavioral dimensions identified two response profiles: a habituation and a sensitization cluster.
Comparison With Existing Method(S):
These retrospect analyses identified habituation and sensitization profiles that were similar to those observed in the original data but also yielded new information such as a more pronounced sensitization response. Also, it allowed for the identification of individuals that deviated from the predominant response profile within a strain.
Conclusions:
The present approach allows for the behavioral characterization of experimental animals on an individual level and as such provides a valuable contribution to existing approaches that take individual variation into account in statistical analysis.
