Real-Time Assessment of Rodent Engagement Using ArUco Markers: A Scalable and Accessible Approach for Scoring Behavior in a Nose-Poking Go/No-Go Task

Abstract

In the field of behavioral neuroscience, the classification and scoring of animal behavior play pivotal roles in the quantification and interpretation of complex behaviors displayed by animals. Traditional methods have relied on video examination by investigators, which is labor-intensive and susceptible to bias. To address these challenges, research efforts have focused on computational methods and image-processing algorithms for automated behavioral classification. Two primary approaches have emerged: marker- and markerless-based tracking systems. In this study, we showcase the utility of “Augmented Reality University of Cordoba” (ArUco) markers as a marker-based tracking approach for assessing rat engagement during a nose-poking go/no-go behavioral task. In addition, we introduce a two-state engagement model based on ArUco marker tracking data that can be analyzed with a rectangular kernel convolution to identify critical transition points between states of engagement and distraction. In this study, we hypothesized that ArUco markers could be utilized to accurately estimate animal engagement in a nose-poking go/no-go behavioral task, enabling the computation of optimal task durations for behavioral testing. Here, we present the performance of our ArUco tracking program, demonstrating a classification accuracy of 98% that was validated against the manual curation of video data. Furthermore, our convolution analysis revealed that, on average, our animals became disengaged with the behavioral task around 75-minutes, providing a quantitative basis for limiting experimental session durations. Overall, our approach offers a scalable, efficient, and accessible solution for automated scoring of rodent engagement during behavioral data collection.Significance StatementThis paper presents an accessible and effective solution for automating the scoring of rodent engagement during a go/no-go nose-poking behavioral task. Here, we showcase the effectiveness of implementing a marker-based tracking approach by mounting ArUco markers to the animal's head and using webcams with open-source tracking software to reveal transition points between states of engagement and distraction throughout a behavioral session. This approach offers a significant advancement for simplifying the scoring process with marker-based tracking while also showing promise in broad applicability towards quantifying optimal task durations for various behavioral paradigms. By making this software freely available, we aim to facilitate knowledge exchange, encourage the scientific community's engagement, and expand its application across diverse research endeavors.