Lévy statistics define anxiety and depression in mice subjected to chronic stress.

Abstract

Introduction: Current rodent models for anxiety and depression assessment face methodological challenges compromising both scientific rigor and animal welfare.

Methods: This study introduced a novel approach using the Lévy flight (LF) statistical method to analyze spontaneous movement in open spaces. We employed three models: Chronic unpredictable mild stress (CUMS), Electric shock stress (ES), and Chronic Restraint Stress (CRS)-utilizing a total of 540 mice for LF fitting. A support vector machine algorithm was applied to distinguish each model group based on the two-dimensional distribution of the variables γ and μ in the LF. Statistical analysis was performed using a two-dimensional Kolmogorov-Smirnov test before and after drug administration.

Results: We found that the ES model primarily exhibited anxiety-like behaviors, the CRS model predominantly exhibited depression-like behaviors, and the CUMS model displayed both depression-like and anxiety-like behaviors. All three stress models were suitable for LF fitting, with the distribution of CUMS in the γ-μ plane lying between the ES and CRS groups. To assess the therapeutic effect of fluoxetine (FXT) on CUMS, we excluded the CUMS-resistant mice and performed LF analysis. Following FXT treatment, the mice gradually shifted toward the normal area in the γ-μ plane, with a more pronounced shift toward the depression area observed as the modeling time increased.

Discussion: This study identifies a previously unrecognized statistical locomotor pattern in mice with anxiety and depression. By integrating scientific rigor with ethical considerations, this approach also presents a humane paradigm shift in preclinical assessment, accelerating translational breakthroughs in neuroscience research.