A distribution-dependent analysis of open-field test movies

TL;DR

This paper introduces a distribution-based analysis of open-field test videos for elderly mice, revealing that traditional indicators like travel distance are unreliable, and proposing a more nuanced understanding of behavioral parameters.

Contribution

It presents a novel distribution-dependent approach for analyzing open-field test data, incorporating behavioral stability parameters and challenging conventional indicators.

Findings

Locomotor activity clusters into dash and search with normal acceleration distributions.

Speed and cluster duration follow exponential distributions, with an instability parameter.

Travel distance is not a robust indicator of animal condition.

Abstract

Although the open-field test has been widely used, its reliability and compatibility are frequently questioned. Although many indicating parameters were introduced for this test, they did not take data distributions into consideration. This oversight may have caused the problems mentioned above. Here, an exploratory approach for the analysis of video records of tests of elderly mice was taken that described the distributions using the least number of parameters. First, the locomotor activity of the animals was separated into two clusters: dash and search. The accelerations found in each of the clusters were distributed normally. The speed and the duration of the clusters exhibited an exponential distribution. Although the exponential model includes a single parameter, an additional parameter that indicated instability of the behaviour was required in many cases for fitting to the data. As this instability parameter exhibited an inverse correlation with speed, the function of the brain that maintained stability would be required for a better performance. According to the distributions, the travel distance, which has been regarded as an important indicator, was not a robust estimator of the animals' condition.