A Review of Freely Available, Open-Source Software for the Automated Analysis of the Behavior of Adult Zebrafish

Abstract

The analysis of behavior in animal models is an important objective in many research fields, including neuroscience, psychology, toxicology, and neuropsychopharmacology. Animal models have been used for many years, and several behavioral paradigms, such as locomotor activity, social interactions, and cognitive behavior, have been studied in animal models to correlate the behaviors with pharmacological or environmental interventions and with molecular, biochemical, and physiological findings. We reviewed the literature looking for open-source, freely available software to analyze animal behavior and found 12 freely available programs: ToxTrack, EthoWatcher, Mouse Behavior Tracker, Mouse Move, JAABA, wrMTrck, AnimalTracker, id-Tracker, Ctrax, Mousetracker, VideoHacking, and Cowlog, which were developed with different programs, work on different platforms, and have particular types of inputs and outputs and analysis capabilities. We reviewed some examples of their use, tested some of them, and provided several recommendations for the future development of programs for the automated analysis of behavior in animal models. In conclusion, we show freely available software for the automated analysis of behavior in animal models such as adult zebrafish and provide information for researchers and students looking for quick, easy-to-implement, and inexpensive behavior analysis alternatives.

Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology

“…In the 21st century, many disciplines of bioscience have been utilizing low-cost digital video recording to conduct behavioural studies (Bownik, 2017; Huang, Campana & Wlodkowic, 2017; Huang et al, 2016). Chemobehavioural phenotypic analysis has been rejuvenated with an increase in user-friendly automated acquisition systems and animal tracking suites to measure behavioural endpoints (Franco-Restrepo, Forero & Vargas, 2019; Green et al, 2012; Noldus, Spink & Tegelenbosch, 2001; Rodriguez et al, 2018).…”

“…Digital video-based tracking of animal behavior involves deconvolution of pattern analysis in a grid of pixels on individual frames of video file. Software algorithms analyze each frame of the video file to distinguish the tracked animals from the background (Franco-Restrepo, Forero & Vargas, 2019; Noldus, Spink & Tegelenbosch, 2001; Rodriguez et al, 2018). This is performed on the basis of semi-automated adjustment of a pixel intensity threshold and color saturation values.…”

“…This is performed on the basis of semi-automated adjustment of a pixel intensity threshold and color saturation values. Each detected object is then assigned a mathematical center of gravity (centroid) that is derived from the average surface area (Franco-Restrepo, Forero & Vargas, 2019; Noldus, Spink & Tegelenbosch, 2001; Rodriguez et al, 2018). Automatic frame-by-frame tracking leads to time-stamped x-y coordinate pairs assigned to centroids of each detected objects and provides the foundation to reconstruct animal trajectories and estimate occupancy heatmaps (Noldus, Spink & Tegelenbosch, 2001).…”

“…The time-stamped x-y coordinates assigned to centroids of each detected object are used for calculations of quantitative behavioural data sets such as; average speed of locomotion, average distance travelled of an animal or the distance between several individually identified animals (Franco-Restrepo, Forero & Vargas, 2019; Noldus, Spink & Tegelenbosch, 2001; Rodriguez et al, 2018).…”

Peer Review #1 of "Comparison between two- and three-dimensional scoring of zebrafish response to psychoactive drugs: identifying when three-dimensional analysis is needed (v0.1)"

“…Traditional behavioral phenotyping leveraged the use of a single camera positioned on top or in front of the experimental tank and the subsequent use of behavioral scoring software, in which the phenotype of interest had to be input by a trained observer (Cianca et al 2013;Spinello et al 2013). Albeit extremely productive, this approach was prone to observer bias and has been recently complemented by tracking algorithms capable of automatically coding and scoring zebrafish behavior with limited human supervision (Delcourt et al 2018;Franco-Restrepo et al 2019;Nema et al 2016;Perez-Escudero et al 2014).…”