Automating Behavioral Analysis in Neuroscience: Development of an Open-Source Python Software for More Consistent and Reliable Results.

BACKGROUND:The application of automated analyses in neuroscience has become a practical approach. With automation, the algorithms and tools employed perform fast and accurate data analysis. It minimizes the inherent errors of manual analysis performed by a human experimenter. It also reduces the time required to analyze a large amount of data and the need for human and financial resources.METHODS:In this work, we describe a protocol for the automated analysis of the Morris Water Maze (MWM) and the Open Field (OF) test using the OpenCV library in Python. This simple protocol tracks mice navigation with high accuracy.RESULTS:In the MWM, both automated and manual analysis revealed similar results regarding the time the mice stayed in the target quadrant (p = 0.109). In the OF test, both automated and manual analysis revealed similar results regarding the time the mice stayed in the center (p = 0.520) and in the border (p = 0.503) of the field.CONCLUSIONS:The automated analysis protocol has several advantages over manual analysis. It saves time, reduces human errors, can be customized, and provides more consistent information about animal behavior during tests. We conclude that the automated protocol described here is reliable and provides consistent behavioral analysis in mice. This automated protocol could lead to deeper insight into behavioral neuroscience.