PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment

Abstract

PyMouseTracks (PMT) is a scalable and customizable computer vision and RFID-based system for multiple rodent tracking and behavior assessment that can be set up within minutes in any user-defined arena at minimal cost. PMT is composed of the online Raspberry Pi-based video and RFID acquisition with subsequent offline analysis tools. The system is capable of tracking up to 6 mice in experiments ranging from minutes to days. PMT maintained a minimum of 88% detections tracked with an overall accuracy >85% when compared to manual validation of videos containing 1-4 mice in a modified home-cage. As expected, chronic recording in home-cage revealed diurnal activity patterns. In open-field, it was observed that novel non-cagemate mouse pairs exhibit more similarity in travel trajectory patterns than cagemate pairs over a 10-minute period. Therefore, shared features within travel trajectories between animals may be a measure of sociability that has not been previously reported. Moreover, PMT can interface with open-source packages such as Deeplabcut and Traja for pose estimation and travel trajectory analysis, respectively. In combination with Traja, PMT resolved motor deficits exhibited in stroke animals. Overall, we present an affordable, open-sourced, and customizable/scalable mouse behavior recording and analysis system.Statement of SignificanceAn affordable, customizable, and easy-to-use open-source rodent tracking system is described. Most current tools, commercial or otherwise, can only be fully automated to track multiple animals in a single defined environment and are not easily setup within custom arenas or cages. Moreover, many tools require users to have extensive hardware and software knowledge. In contrast, PMT is easy to install and can be adapted to track rodents of any coat color in any user-defined environment with few restrictions allowing quantification of behavior in multiple animals simultaneously.

Deep behavioural phenotyping of the Q175 Huntington disease mouse model: effects of age, sex, and weight

“…For this calculation, time to platform was divided by a factor calculated for each animal (individual weight/average zQ175dn weight). In addition, we used PyMouseTracks tracking software to analyze the mouse's time spent in different regions of the T-maze [15].…”

“…We used the software PyMouseTracks [15] to track mouse position in the maze and determine the amount of time spent at the decision point (Fig. 4A).…”

“…To examine the T-maze results more closely, we investigated four possible explanations for deficits in time to platform. Since male manifest zQ175dn mice did not show increased number of arm entries or decreased swimming speed, we explored time at decision point and stopping/starting (accelerations) using PyMouseTracks [15]. For both WT and zQ175dn mice, our analysis provided evidence that these behavioural features contribute to the amount of time mice take to reach the platform, particularly on the final day of the acquisition phase.…”

“…Recent work from our group has revealed interesting behavioural deficits in the YAC128 mouse model, such as altered paw kinematics on the accelerating rotarod, even in mice that were performing normally on the task according to traditional measures [13]. In this study, we have combined machine learning-based analysis approaches [14,15] to characterize behaviour on the rotarod, open field, water T-maze, and a home cage-based lever pulling assay in the zQ175dn mouse model. We compare and contrast behaviour at premanifest and manifest disease stages, between males and females, as well as to open field behaviour of the Q175FDN model, which differs in background strain.…”

IntelliCage: the development and perspectives of a mouse- and user-friendly automated behavioral test system

“…Nonetheless, once achieved an effective motion tracking system, there will be newly developed software packages dealing with the immense data sets recorded, chiefly based on artificial intelligence (AI) including machine learning, such as DeepLabCut which enables body point estimation and tracking of individual animals housed singly or in a group ( Mathis et al, 2018 ; Nath et al, 2019 ). There is an increasing number of AI-based technologies available to track and further analyse fine body movements according to trained classifiers for mouse behavior which are objective and independent of human definitions ( Nilsson et al, 2020 ; Dunn et al, 2021 ; Fong et al, 2023 ; Sakamoto et al, 2023 ). Thus, one may expect further support by video techniques for the fine-grained analysis of mouse behavior in an IntelliCage, but the amount of data analysis associated with it (the so-called data footprint) will require careful consideration of specific experimental questions to be asked.…”