Levy statistics define anxiety and depression in mice subjected to chronic stress

Abstract

IntroductionAnxiety and depression are recognized as adaptive responses to external stressors in organisms. Current methods for evaluating anxiety and depression in rodents are both burdensome and stressful. The objective of this investigation is to explore a simplified methodology for identifying stress-induced and stress-free states, as well as anxiety and depression levels, by analyzing the movement patterns of rodents. MethodsTo address this issue, we utilized Levy statistics to examine the movement patterns of stressed rodents and compared them to non-stressed controls. We employed the two-dimensional Kolmogorov-Smirnov test to identify significant differences in the {gamma} and parameters derived from Levy flight (LF) between anxiety, depression, and control mice. Additionally, we employed the support vector machine algorithm to optimize the classification of each group. ResultsOur analysis revealed that stressed mice displayed heavy-tailed distributions of movement velocity in open fields, resembling the movement patterns observed in animal predators searching for scarce food sources in nature. In contrast, non-stressed mice exhibited a normal distribution of speed. Notably, the effectiveness of this methodology in the field of drug discovery was confirmed by the response of stressed mice to fluoxetine, a well-established selective serotonin reuptake inhibitor (SSRI). ConclusionThis study unveils a previously unidentified statistical walking pattern in mice experiencing anxiety and depression. These findings offer a novel and accessible approach for distinguishing between anxiety, depression, and healthy mice. This method provides a one-step gentle approach (free walk in an open field) instead of the traditional multi-step stressful tests.