Meet the expert of eBRAIN-Health
Prof. Dr. Paul Tiesinga
Professor Paul Tiesinga and his PhD candidate, Vivek Sharma, are computational neuroscientists at Donders Institute for Brain, Cognition, and Behaviour, Radboud University, whose work focuses on understanding large-scale brain activity and the principles that govern brain function. Their expertise lies in combining multimodal brain data with simulation tools to study complex neural processes.
Both researchers contribute to — and partly supervise — the development of BrainX3, an interactive platform designed to make brain models and data more accessible. Through this work, they have gained extensive experience in neuroinformatics, data visualization, and the creation of tools that support both scientific research and public understanding of neuroscience.
Role within eBRAIN-Health
Prof. Tiesinga's team contributes through the development of BrainX3, which offers an intuitive way to explore brain organisation, connectivity, and activity in 3D.
Using BrainX3, researchers can examine brain scans, follow how different regions connect, and visualize activity patterns in an intuitive 3D environment. This makes it easier to work with complex multimodal data and to translate intricate neuroscientific concepts into accessible visualisations.
The tool is also used extensively in science communication and public outreach. Its interactive and visual design helps students, clinicians, and the general public better understand how the brain is organised and how different regions interact.
Through this work, their team aims to provide a clear, engaging, and scientifically grounded way to navigate brain data — supporting both advanced research and broader understanding of neuroscience across Europe.
Recent Results and Key Milestones
Over the last year, the SRU team has significantly advanced the development of BrainX3, culminating in the release of version 3.0. This marks an important milestone for the team, as the updated version features a cleaner and more intuitive interface, combined with a full Python version and support for several widely used brain atlases.
A particularly impactful feature is the new MRI-to-Atlas function. This feature allows users to select a point from an MRI scan and directly map it onto the 3D brain, showing the corresponding region in whichever atlas they prefer.
Once the area is identified, users can explore relevant scientific literature, read functional descriptions, and better understand the role of that area within the brain. Additionally, the team also introduced built-in tools for basic signal processing and a simple whole-brain simulation, enabling users to explore, clean, and test their data directly within the platform.
Together, these updates make BrainX3 easier to use and more practical for research and teaching.
Hurdles and How They Were Overcome
The team encountered several challenges throughout the development of BrainX3. Working with many different types of brain data often requires rethinking parts of the software, as each dataset introduces its own specific complications. Performance was another ongoing concern, particularly when handling detailed 3D visualisations or running longer simulations.
Most of these issues were addressed through extensive testing and by updating specific components to ensure smoother and more efficient operation. User feedback, coordinated by the team member Sajad Kahali, also played an important role. Observing how researchers and clinicians interacted with the tool in practice frequently highlighted aspects that had not been initially apparent. This iterative process helped refine BrainX3 into a more robust and user-friendly platform.
Contribution to the eBRAIN-Health Research Platform
The team’s work on BrainX3 directly contributes to the central goal of eBRAIN-Health: building a FAIR and interoperable research platform for digital neuroscience. By integrating multimodal visualisation, data exploration, and whole-brain modelling into a single interactive tool, BrainX3 enables researchers to transition more easily from empirical data to mechanistic understanding.
This reduces technical barriers and makes advanced modelling accessible to both clinical and scientific users across Europe. Ultimately, BrainX3 strengthens the platform’s capacity to support personalised predictions, virtual patient simulations, and transparent neuroscientific workflows — all key pillars for advancing healthcare and brain research.
