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We develop and apply advanced data-analytical approaches to study how eye diseases and brain injuries impact our ability to see. By analyzing big sets of brain imaging data, we aim to predict how well someone can function in their daily life with impaired vision. Our focus is on developing smart methods that use statistics and machine learning to understand how the brain adapts and changes when our vision is affected.

But we are not just stopping at understanding vision problems: we are also creating new ways to predict, measure and track the success of eye treatments, such as gene therapy and retinal prosthetics, as well as visual rehabilitation programs. In this context, our goals are to deliver objective outcome measures for clinical trials, and to equip medical professionals with enhanced tools that facilitate the prediction of treatment outcomes and the ongoing monitoring of patient progress.

Besides our focus on vision problems, we also study how vision works in general. We are exploring the basics of how we see things and using that knowledge to improve both human and computer vision. It is a two-way street: understanding how biology sees helps us teach computers to see better, and vice versa. 

Current research projects include:

• Bridging the gap between visual function and functional vision (NWO Vidi).
• Neuroimaging biomarkers for predicting vision training success after stroke (NWO KIC).
• Data-driven patient profiling using measures of visual cortex integrity (NWO Gravitation).

Partners

• Donders Institute, Centre for Cognitive Neuroimaging
• VR4eVR Consortium 
• Lifelong Vision Consortium