Understanding the complex interplay of persistent and antipersistent regimes in animal movement trajectories as a prominent characteristic of their behavioral pattern profiles: Towards an automated and robust model based quantification of anxiety test data

Abstract

Rapid advancement in computer vision technologies provides increasing opportunities for the quantitative characterization of animal behavior, although reduction of their analysis to several scalar metrics appears a common limitation for the representation of complex behavioral patterns. Here we suggest an alternative approach to the quantitative assessment of animal behavioral patterns by parameterization of a generalized scalable model based on fractional Brownian motion using detrended fluctuation analysis of the observational movement trajectories and validate it using novel tank test data. In a zebrafish model representative movement patterns are characterized by two asymptotic regimes, with persistent increments at small scales and antipersistent increments at large scales. A single crossover between these asymptotic regimes that appears a single free parameter of the animal movement model acts as a complementary behavioral indicator leading to a more explicit characterization of both stimulative and sedative effects. We show explicitly that the model can be also used for a robust estimation of interpretable scalar metrics commonly used in behavioral analysis leading to the emphasized differences between experimental groups. We believe that this approach, due to its universality, robustness and clear physical interpretation, is a perspective tool for the analysis of animal behavior complexity under various experimental and natural conditions.