Mar 28, 2024

Gabriele Chelini and colleagues at The University of Trento, under the mentorship of Yuri Bozzi, introduce SEB3R, an automated method for tracking mouse body language and studying emotional behaviors. SEB3R quickly identifies different postures of mice and associates them with emotions, offering a detailed analysis of their movements frame by frame. This advanced software enables researchers to explore emotional reactions like fear and curiosity. It also tracks the progression of emotional expression over time, potentially revolutionizing researchers’ understanding of mice emotional fluctuations.

In the validation study, SEB3R analyzed videos of the responses of mice to whisker stimulation, predicting emotional responses based on specific “bodily hotspots.” The tool accurately identified subtle changes in behavior and reliably distinguished between different experimental conditions. These findings highlight SEB3R’s sensitivity to stimulus-driven changes in behavior and emotional states, making it a valuable tool for studying stimulus-evoked emotional behaviors in mice.

Overall, the development and validation of SEB3R offer new insights into mouse behavior and provide a reliable method for studying their emotional responses. This breakthrough has the potential to uncover the biological underpinnings of neuropsychiatric disorders, opening up new avenues for research in the field.


This research tool was created by your colleagues. Please acknowledge the Principal Investigator, cite the article in which the tool was described, and include an RRID in the Materials and Methods of your future publications. RRID: SCR_025161

Special thanks to Emma Pilz, a neuroscience undergraduate at American University, for providing this project summary.

Access the code from GitHub!

Check out the repository on GitHub.

Read more about it!

Check out more about the development and validation of this project from the eNeuro publication!

Have questions? Send us an email!