Morris Water Maze Test: Optimization for Mouse Strain and Testing Environment.
Daniel S Weitzner, Elizabeth B Engler-Chiurazzi, Linda A Kotilinek, Karen Hsiao Ashe, Miranda Nicole Reed
Author Information
Daniel S Weitzner: Department of Psychology, Behavioral Neuroscience, West Virginia University.
Elizabeth B Engler-Chiurazzi: Department of Physiology and Pharmacology, West Virginia University.
Linda A Kotilinek: Department of Neurology, N. Bud Grossman Center for Memory Research and Care, University of Minnesota.
Karen Hsiao Ashe: Department of Neurology, N. Bud Grossman Center for Memory Research and Care, University of Minnesota; Department of Neuroscience, N. Bud Grossman Center for Memory Research and Care, University of Minnesota; GRECC, VA Medical Center.
Miranda Nicole Reed: Department of Psychology, Behavioral Neuroscience, West Virginia University; Center for Neuroscience, Center for Basic and Translational Stroke Research, West Virginia University; Miranda.Reed@mail.wvu.edu.
The Morris water maze (MWM) is a commonly used task to assess hippocampal-dependent spatial learning and memory in transgenic mouse models of disease, including neurocognitive disorders such as Alzheimer's disease. However, the background strain of the mouse model used can have a substantial effect on the observed behavioral phenotype, with some strains exhibiting superior learning ability relative to others. To ensure differences between transgene negative and transgene positive mice can be detected, identification of a training procedure sensitive to the background strain is essential. Failure to tailor the MWM protocol to the background strain of the mouse model may lead to under- or over- training, thereby masking group differences in probe trials. Here, a MWM protocol tailored for use with the F1 FVB/N x 129S6 background is described. This is a frequently used background strain to study the age-dependent effects of mutant P301L tau (rTg(TauP301L)4510 mice) on the memory deficits associated with Alzheimer's disease. Also described is a strategy to re-optimize, as dictated by the particular testing environment utilized.
