Eye2Brain

Thanks to its special connection to the brain and its accessibility to measurements, the eye provides a unique window on the brain, thereby offering non-invasive access to a large set of potential biomarkers that might help in the early diagnosis and clinical care of Neuro Degenerative Diseases (NDD) .

However, characterizing ocular biomarkers as surrogates of cerebral or systemic vascular status is far from trivial. Clinical measurements are influenced by many factors that vary among individuals and cannot be isolated in vivo, thereby posing serious challenges for the interpretation of such measurements. This difficult, yet extremely appealing, opportunity of using the eye as a window on the brain provides the main rationale of our contribution to the project, which, more specifically, stems from the basic ideas that:

an ocular measurement per se does not allow to draw any conclusion on what might be the fluid-dynamical and/or metabolic status of the brain in a given patient, unless some other factors specific to that patient are properly taken into account;

mathematical modeling can provide quantitative tools to help accounting for patient-specific factors when interpreting potential ocular biomarkers.

We are motivated by the need of mathematical and computational methods to study the Eye Brain system (we call it Eye2Brain) and aid the interpretation of ocular measurements as biomarkers for the brain status.

We currently develop a reliable and efficient computational framework of the Eye2Brain system allowing for computer-aided interpretations of the clinical data.

To this end, it requires mathematical models parametrized with patient-specific data that

quantitatively describe the fluid-dynamical and metabolic connections between the eye and the brain;

identify the main factors that influence these connections.

The complexity of this Eye2Brain system calls for a multiscale modeling approach. Network based models allow to capture the main dynamics of complex systems at relatively low computational costs, whereas detailed 3d models allow to interface with clinical data that are 3d in nature, e.g. MRI maps and FD-OCT images.

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