A Real-Time Approach for Assessing Rodent Engagement in a Nose-Poking Go/No-Go Behavioral Task Using ArUco Markers

Abstract

Behavioral neuroscience requires precise and unbiased methods for animal behavior assessment to elucidate complex brain–behavior interactions. Traditional manual scoring methods are often labor-intensive and can be prone to error, necessitating advances in automated techniques. Recent innovations in computer vision have led to both marker- and markerless-based tracking systems. In this protocol, we outline the procedures required for utilizing Augmented Reality University of Cordoba (ArUco) markers, a marker-based tracking approach, to automate the assessment and scoring of rodent engagement during an established intracortical microstimulation-based nose-poking go/no-go task. In short, this protocol involves detailed instructions for building a suitable behavioral chamber, installing and configuring all required software packages, constructing and attaching an ArUco marker pattern to a rat, running the behavioral software to track marker positions, and analyzing the engagement data for determining optimal task durations. These methods provide a robust framework for real-time behavioral analysis without the need for extensive training data or high-end computational resources. The main advantages of this protocol include its computational efficiency, ease of implementation, and adaptability to various experimental setups, making it an accessible tool for laboratories with diverse resources. Overall, this approach streamlines the process of behavioral scoring, enhancing both the scalability and reproducibility of behavioral neuroscience research. All resources, including software, 3D models, and example data, are freely available at https://github.com/tomcatsmith19/ArucoDetection.