| Issue |
E3S Web Conf.
Volume 203, 2020
Ecological and Biological Well-Being of Flora and Fauna (EBWFF-2020)
|
|
|---|---|---|
| Article Number | 01029 | |
| Number of page(s) | 10 | |
| Section | Veterinary Well-Being of Fauna | |
| DOI | https://doi.org/10.1051/e3sconf/202020301029 | |
| Published online | 05 November 2020 | |
Software for analyzing the behavioural test “Morris Water Maze”
1 Peter The Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation
2 UT Southwestern Medical Center at Dallas, Dallas, TX, USA
* Corresponding author: marina.bolsunovskaia@spbpu.com
The Morris Water Maze Behavioural Test is a universal method for testing cognitive functions in experimental rodents, and it is especially effective in detecting deviations in memory functions and learning, which makes it indispensable in the study of neurodegenerative diseases, effect of therapeutic drugs, rodent stroke and aging models etc. However, despite the wide range of possible applications, data analysis makes the use of this test difficult. Currently, automated tracking and analysis programs of rodent moving are becoming to be popular. Thus, our goal was to develop and create an available quality product, which will allow the scientist to carry out research as efficiently as possible doing various options of the “Morris water maze” using latest modern parameters. In this article, we analyze different types of the Morris water maze methodology and the current scientific parameters of this test to understand the necessary and optimal capabilities of the future program, then to overcome the limitations of the systems currently available we have combined detection and tracking techniques into one standalone tool. The result of the work is a software product that allows to quickly and accurately detect the trajectory of animal moving in the water, and also provides parameters for evaluating the cognitive functions of memory and learning.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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