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. 2020 Apr 2;15(4):e0230900.
doi: 10.1371/journal.pone.0230900. eCollection 2020.

Repeatability Analysis Improves the Reliability of Behavioral Data

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Free PMC article

Repeatability Analysis Improves the Reliability of Behavioral Data

Juliane Rudeck et al. PLoS One. .
Free PMC article

Abstract

Reliability of data has become a major concern in the course of the reproducibility crisis. Especially when studying animal behavior, confounding factors such as novelty of the test apparatus can lead to a wide variability of data which may mask treatment effects and consequently lead to misinterpretation. Habituation to the test situation is a common practice to circumvent novelty induced increases in variance and to improve the reliability of the respective measurements. However, there is a lack of published empirical knowledge regarding reasonable habituation procedures and a method validation seems to be overdue. This study aimed at setting up a simple strategy to increase reliability of behavioral data measured in a familiar test apparatus. Therefore, exemplary data from mice tested in an Open Field (OF) arena were used to elucidate the potential of habituation and how reliability of measures can be confirmed by means of a repeatability analysis using the software R. On seven consecutive days, male C57BL/6J, BALB/cJ and 129S1/SvImJ mice were tested in an OF arena once daily and individual mouse behavior was recorded. A repeatability analysis was conducted with regard to repeated trials of habituation. Our data analysis revealed that monitoring animal behavior during habituation is important to determine when individual differences of the measurements are stable. Repeatability values from distance travelled and average activity increased over the habituation period, revealing that around 60% of the variance of the data can be explained by individual differences between mice. The first day of habituation was significantly different from the following 6 days. A three-day habituation period appeared to be sufficient in this study. Overall, these results emphasize the importance of habituation and in depth analysis of habituation data to define the correct starting point of the experiment for improving the reliability and reproducibility of experimental data.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Frequency of observed behavior decreases during habituation.
(A) Distance travelled [cm] and (B) average activity [%] are depicted. Values are presented as exact value per each animal (n = 38 C57BL/6J, n = 15 BALB/cJ and n = 15 129S1/SvImJ male mice). The Friedman test with Dunn’s multiple comparisons test was used for statistical evaluation (n.s. = not significant (A) p-value = 0.7119, (B) p-value > 0.9999, **** p-value < 0.0001 (A, B).
Fig 2
Fig 2. Animal ID and strain repeatability increase during habituation.
(A, B) Calculated animal ID repeatability value and (C, D) strain repeatability value for the factors (A, C) distance travelled and (B, D) average activity were presented. Each repeatability value (R, black points) was calculated over three adjacent days resulting in five groupings (n = 38 C57BL/6J, n = 15 BALB/cJ and n = 15 129S1/SvImJ male mice). Estimation of repeatability was conducted with a linear mixed-effect model based on Gaussian distribution. The [2.5%, 97.5%] confidence intervals (CI) were displayed in green, resulting from 500 bootstrapping runs and 100 permutations.
Fig 3
Fig 3. Animal ID repeatability adjusted for strain increase during habituation.
Calculated animal ID repeatability values adjusted for the fixed factor strain were presented for (A) distance travelled and (B) average activity. Each repeatability value (R, black points) was calculated over three adjacent days resulting in five groupings (n = 38 C57BL/6J, n = 15 BALB/cJ and n = 15 129S1/SvImJ male mice). Estimation of repeatability was conducted with a linear mixed-effect model for locomotion as well as activity and with a generalized linear mixed-effect model based on Gaussian distribution for rearing and sniffing behavior. The [2.5%, 97.5%] confidence intervals (CI) were displayed in green, resulting from 500 bootstrapping runs and 100 permutations.
Fig 4
Fig 4. Strategy for the implementation of behavioral experiments.
This basic strategy can be applied to all experiments with laboratory animals to determine the optimal starting point of your experiment which needs a familiar environment.

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Grant support

This work was supported by the Federal Ministry of Education and Research (BMBF) ‘[grant number 031A262D]’ and by the Deutsche Forschungsgemeinschaft (DFG) [FOR 2591; LE 2356/5-1]. Prof. Schoenfelder wrote the BMBF proposal and raised the BMBF funding. Prof. Lewejohann wrote the DFG proposal and raised the DFG funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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