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Anxiety is a widely studied psychiatric disorder and is thought to be a complex and multidimensional phenomenon. Sensitive behavioral discrimination of animal models of anxiety is crucial for the elucidation of the behavioral components of anxiety and the physiological processes that mediate them. Commonly used behavior paradigms of anxiety usually include only a few automatically collected measures; these do not exhaust the behavioral richness exhibited by animals, thus perhaps missing important differences between preparations. The aim of the present study was to expand the repertoire of automatically collected measures in a classical test of anxiety: behavior in relation to the wall in the open field. We present an algorithm, based on the Software for the Exploration of Exploration strategy, which automatically partitions the mouse path into intrinsically defined patterns of movement near the wall and in the center. These patterns are used to design new end points, which provide an articulated description of various aspects of behavior near the wall and in the center. Sixteen new end points were designed with data from C57BL/6J and DBA/2J mice tested in three laboratories. The strain differences in all end points were evaluated on another data set to assess their validity and were found to remain stable. Ten of the sixteen end points were found to discriminate between the two strains in a replicable manner. The entire set of end points can be used on various genetic and pharmacological models of anxiety with good prospects of providing fine discrimination in a replicable manner.

In behavior genetics, behavioral patterns of mouse genotypes, such as inbred strains, crosses, and knockouts, are characterized and compared to associate them with particular gene loci. Such genotype differences, however, are usually established in singlelaboratory experiments, and questions have been raised regarding the replicability of the results in other laboratories. A recent multilaboratory experiment found significant laboratory effects and genotype ؋ laboratory interactions even after rigorous standardization, raising the concern that results are idiosyncratic to a particular laboratory. This finding may be regarded by some critics as a serious shortcoming in behavior genetics. A different strategy is offered here: (i) recognize that even after investing much effort in identifying and eliminating causes for laboratory differences, genotype ؋ laboratory interaction is an unavoidable fact of life. (ii) Incorporate this understanding into the statistical analysis of multilaboratory experiments using the mixed model. Such a statistical approach sets a higher benchmark for finding significant genotype differences. (iii) Develop behavioral assays and endpoints that are able to discriminate genetic differences even over the background of the interaction. (iv) Use the publicly available multilaboratory results in single-laboratory experiments. We use software-based strategy for exploring exploration (SEE) to analyze the open-field behavior in eight genotypes across three laboratories. Our results demonstrate that replicable behavioral measures can be practically established. Even though we address the replicability problem in behavioral genetics, our strategy is also applicable in other areas where concern about replicability has been raised.across-laboratory replicability ͉ mixed-model ANOVA ͉ open-field behavior I n behavior genetics, behavior patterns of standardized mouse genotypes, such as inbred strains or knockouts, are characterized to associate them with particular gene loci. The need for such characterization, referred to as behavioral phenotyping, has prompted the design of behavioral test batteries for mice (1-3). A practical problem well known to most experimenters in the field, however, is that it can be difficult to replicate behavioral phenotyping results in a different laboratory. This replicability problem was largely ignored until brought to light in 1999 by Crabbe, Wahlsten, and Dudek (3). In this pioneering study they conducted an experiment concurrently in three laboratories, comparing eight genotypes by using seven standard behavioral characteristics (endpoints) in a well coordinated study closely following identical protocols. Their main positive finding was that large genotype differences were demonstrated in all studied endpoints. On the negative side they found significant differences between laboratories across all genotypes in many endpoints. Although the difficulties raised by such significant laboratory effects can be overcome by running a common genotype as a local control, they ...

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Conventional tests of behavioral phenotyping frequently have difficulties differentiating certain genotypes and replicating these differences across laboratories and protocol conditions. This study explores the hypothesis that automated tests can be designed to quantify ethologically relevant behavior patterns that more readily characterize heritable and replicable phenotypes. It used SEE (Strategy for the Exploration of Exploration) to phenotype the locomotor behavior of the C57BL/6 and DBA/2 mouse inbred strains across 3 laboratories. The 2 genotypes differed in 15 different measures of behavior, none of which had a significant genotype-laboratory interaction. Within the same laboratory, most of these differences were replicated in additional experiments despite the test photoperiod phase being changed and saline being injected. Results suggest that well-designed tests may considerably enhance replicability across laboratories.