Improving behavioral test data collection and analysis in animal models with an image processing program

Abstract

Behavioral studies are commonly used as a standard procedure to evaluate anxiety and depression in animal models. Recently, different methods have been developed to improve data collection and analysis of the behavioral tests. Currently available methods, including manual analysis and commercially available products, are either time-consuming or costly. The objective of this study was to improve the collection and analysis of behavioral test data in animal models by developing an image processing program. Eleven behavioral parameters were evaluated by three different methods, including (i) manual detection, (ii) commercially available TopScan software (CleverSys Inc, USA), and (iii) In-housed-developed Advanced Move Tracker (AMT) software. Results obtained from different methods were compared to validate the accuracy and efficiency of AMT. Results showed that AMT software provides highly accurate and reliable data analysis compared to other methods. Less than 5% tolerance was reported between results obtained from AMT compared to TopScan. In addition, the analysis processing time was remarkably reduced (68.3%) by using AMT compared to manual detection. Overall, the findings confirmed that AMT is an efficient program for automated data analysis, significantly enhancing research outcomes through accurate analysis of behavioral test data in animal models.