Teaching Neuroimaging Data Analysis in the Cloud Using Neurodesk

This post is contributed by Vinod Kumar, a research scientist at the Max Planck Institute for Biological Cybernetics.

As a lecturer trying to teach complex neuroimaging concepts to students, I've always faced a significant hurdle: before students could even begin learning about brain imaging analysis, they first had to battle with software installation, compatibility issues, and computing resource limitations. This year, thanks to EGI's federated cloud infrastructure and the https://neurodesk.org platform, we were able to completely transform this experience.

The cloud-based nature of Neurodesk operated at CESNET-MCC, one of the resource providers of the EGI Federated infrastructure, using a lightweight Kubernetes distribution (K3s), eliminated the traditional barriers to entry. Students simply accessed the platform through their web browsers and immediately gained access to more than 100 neuroimaging tools and applications—all pre-installed, configured, and ready to use. This democratized access meant that every student had identical capabilities regardless of their personal computer specifications.

This equal footing proved crucial when we reached the computationally intensive portions of the course. Many neuroimaging processing pipelines require significant RAM and processing power that most student laptops don't possess. Previously, this meant limiting our course to simplified examples or waiting extremely long periods for processing to complete.

With Neurodesk, students could process full-resolution research-grade Magnetic Resonance Imaging (MRI) datasets using advanced techniques like cortical surface reconstruction and tractography—processes that would have been impossible on standard laptops. What would have taken hours on personal machines was completed in minutes on the cloud.

"The ability to work with real, complex datasets rather than simplified teaching examples gave students a much more authentic research experience. They could focus on interpreting results and understanding the neuroscience rather than worrying if their computer would crash".

Perhaps most importantly, this approach significantly improved knowledge retention. Comprehension improves dramatically when students do not have to divide their attention between troubleshooting technical issues and learning concepts. Our post-course assessments showed markedly higher scores compared to previous years.

This experience has convinced me that cloud-based platforms represent the future of technical education, particularly in data-intensive fields like neuroimaging. By removing technical barriers and providing enterprise-grade computing resources to students, we can focus on what really matters: understanding the brain and developing the next generation of neuroscientists.

As we plan future courses, we're excited to further leverage EGI partner’s federated infrastructure to expand our curriculum and reach more students. The combination of powerful cloud computing and domain-specific platforms like Neurodesk does not just make teaching easier—it fundamentally transforms what is possible in the classroom.

“EGI provisioned the computing resources that contributed to making the Statistical Magnetic Resonance Imaging in Neuroscience course organized at the Max Planck Institute for Biological Cybernetics a success. With Neurodesk on EGI's Federated infrastructure, students had seamless access to advanced neuroimaging tools, a Python coding environment, and significant computing power—regardless of their local hardware limitations. This support enhanced their learning through hands-on interaction with complex structural and functional imaging datasets and fostered innovation and collaboration. EGI's contribution greatly impacted our educational efforts, and we truly appreciate your commitment to advancing education”.