Abstract

Preclinical mental health research relies upon animal models, and whilst many encouraging advances are being made, reproducibility and translational relevance may be limited by sub-optimal testing or model choices. Animal behaviors are complex and test batteries should be designed to include their multifaceted nature. However, multiple behavioral testing is often avoided due to cost, availability or statistical rigor. Additionally, despite the disparity in the incidence of mental health problems between the sexes, a move toward reducing animal numbers could be a deterrent to including both male and female animals. The current study introduces a unified scoring system for specific behavioral traits with the aim of maximizing the use of all data generated whilst reducing the incidence of statistical errors. Female and male mice from two common background strains were tested on behavior batteries designed to probe multiple aspects of anxiety-related and social behavioral traits. Results for every outcome measure were normalized to generate scores for each test and combined to give each mouse a single unified score for each behavioral trait. The unified behavioral scores revealed clear differences in the anxiety and stress-related, and sociability traits of mice. Principle component analysis of data demonstrated significant clustering of animals into their experimental groups. In contrast, individual tests returned an ambiguous mixture of non-significant trends and significant effects for various outcome measures. Utilizing a range of behavioral measures and combining all outcome measure data to produce unified scores provides a useful tool for detecting subtle behavioral traits in preclinical models.

Highlights

  • IntroductionMental health disorders, such as anxiety and depression, constitute one of the main causes of disease burden worldwide (Vos et al, 2015), and their prevalence in the United Kingdom is growing (Martín-Merino et al, 2009; Fineberg et al, 2013).Behavioral disruption related to environmental or genetic changes are commonly evaluated through the use of animal models (Steimer, 2011; Rossignol and Frye, 2012), often the methods and tests used are suboptimal, leading to mixed results and findings that may not translate well (Perel et al, 2007; Open Science Collaboration., 2015)

  • The current study introduces a novel data-inclusive analysis of a comprehensive anxiety-related and social behavioral battery designed to produce a robust method of measuring subtle behavioral traits of two widely used background strains in behavioral research, the C57BL6/J and 129S2/SvHsd mouse strains

  • For the direct social interaction test, PC1 did not reflect the directional influence of sociability-related factors, indicating that this represents an extraneous factor to sociability, Figure 2D, with an environmental exploration component providing a more feasible explanation

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Summary

Introduction

Mental health disorders, such as anxiety and depression, constitute one of the main causes of disease burden worldwide (Vos et al, 2015), and their prevalence in the United Kingdom is growing (Martín-Merino et al, 2009; Fineberg et al, 2013).Behavioral disruption related to environmental or genetic changes are commonly evaluated through the use of animal models (Steimer, 2011; Rossignol and Frye, 2012), often the methods and tests used are suboptimal, leading to mixed results and findings that may not translate well (Perel et al, 2007; Open Science Collaboration., 2015). Studies may use a single behavioral probe to represent complex behavioral traits, whereas behavioral outcomes are a culmination of a Unified Behavioral Scores multifaceted system, are frequently subtle, and have aspects which can present in different ways (Ferreri et al, 2011). This could lead to subtle behavioral changes being missed, or anomalous data being given undue prominence. A targeted battery of behavioral tests can give insight to a greater number of behavioral traits and give a more accurate representation of the specific behaviors being studied. A consequence of multiple testing is an increase in the probability of generating type I statistical errors and for this reason many studies restrict the number of tests performed (Shaffer, 1995)

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