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Anat Sakov

Publications and source records attributed to Anat Sakov.

3 recordsLinked to original sources

Genotype-environment interactions in mouse behavior: a way out of the problem.

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 single-laboratory 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 x 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 x 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.

Analysis of Variance↗

New replicable anxiety-related measures of wall vs center behavior of mice in the open field.

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.

Algorithms↗

The dynamics of spatial behavior: how can robust smoothing techniques help?

A variety of setups and paradigms are used in the neurosciences for automatically tracking the location of an animal in an experiment and for extracting features of interest out of it. Many of these features, however, are critically sensitive to the unavoidable noise and artifacts of tracking. Here, we examine the relevant properties of several smoothing methods and suggest a combination of methods for retrieving locations and velocities and recognizing arrests from time series of coordinates of an animal's center of gravity. We accomplish these by using robust nonparametric methods, such as Running Median (RM) and locally weighted regression methods. The smoothed data may, subsequently, be segmented to obtain discrete behavioral units with proven ethological relevance. New parameters such as the length, duration, maximal speed, and acceleration of these units provide a wealth of measures for, e.g., mouse behavioral phenotyping, studies on spatial orientation in vertebrates and invertebrates, and studies on rodent hippocampal function. This methodology may have implications for many tests of spatial behavior.

Algorithms↗