In the lab, we study brain plasticity. One of our goals is to identify the neural circuits and mechanisms that are engaged and understand what’s wrong in disease, so we can find suitable targets for interventions that are lacking. One of our critical needs is to have robust training protocols to evaluate, to induce plasticity, and evaluate the impact of genetic manipulations in healthy mice and in disease models.
Current research needs sensitive, versatile, and automatic techniques to assess mice behavior. We are mainly interested in motor behavioral learning, and traditional tests requires sequential implementation, which consumes significant time and resources. Also, traditional tests don’t always have enough accuracy.
However, Erasmus Ladder allows motor learning future explanation and analysis in a single automated setup. While existing paradigms often focus on specific aspects of motor behavior, our approach aims to discriminate between fine motor learning, challenge motor learning, and associative motor learning in an automated and non invasive way, filling a gap in current methodologies. Testing is easy to conduct, automated, reproducible, and allow researchers to study different aspects of motor behavior separately using a single mouse cohort.
The automatic software and adjustable parameters announce the precision of data collection and analysis, and also the versatility and customization of the protocol according to the scientific question. Our lab will focus on further refining the Erasmus Ladder protocols, combining cellular and molecular techniques to investigate motor adaptation and the underlying neuronal mechanisms. In particular, one of our projects focuses on myelin plasticity, a phenomenon triggered during complex motor skill learning that could help finding cures for patients with dysmyelinating disease.
Staffa, A., Chatterjee, M., Diaz-Tahoces, A., Leroy, F., Perez-Otaño, I. Monitoring Fine and Associative Motor Learning in Mice Using the Erasmus Ladder. J. Vis. Exp. (202), e65958, doi:10.3791/65958 (2023).