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:
  1. 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; and that
  2. math ematical modeling can provide quantitative tools to help accounting for patient-specific factors when interpreting potential ocular biomarkers.
Motivated by the need of mathematical and computational methods to study the Eye-Brain system (which we refer to as Eye2Brain) and aid the interpretation of ocular measurements as biomarkers for the brain status, we are currently developing a multi-component platform combining detailed descriptions of the eye and the brain with a systemic view of the Eye2Brain connections.
The complexity of this Eye2Brain system calls for a multiscale modelling 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. images obtained with magnetic resonance or optical coherence tomography.
The development of an articulated computational platform that can simultaneously process and integrate medical images and measurements obtained with various instruments on the same patient bears a tremendous importance for the clinical viewpoint. Despite the significant advances in medical imaging, it is still extremely challenging for the attending physicians to have a clear full picture of the clinical status of the patient. This is due mainly to two reasons:
  1. each instrument targets different parts and functions of the patient’s tissue; however, the relative significance of various measurements might depend on the patient’s status;
  2. many inst ruments do come with specific softwares that attempt to classify the measured data as normal, suspicious or abnormal; however, these softwares are based on different cohorts for healthy and disease, making it difficult to combine the outcomes.
Thus, the development of an articulated platform capable of providing physicians with an integrated view of the patient’s status will significantly improve our current ability to monitor health and to prevent, detect, treat and manage disease in a personalized manner. Within this project, we propose to develop such a platform for application in ophthalmology, with the specific goal of developing, testing and delivering a software that can be used in ophthalmology clinics to improve diagnosis and care of ocular disease s (e.g. glaucoma, diabetic retinopathy, age-related macular degeneration) and other pathologies that also manifest in the eye (e.g. diabetes, hypertension, NDD). This application clearly leverages the resources and expertise that we have gathered within
the Eye2Brain project, and it extends them to build a tool that can directly impact the clinical practice.
Fundus camera images are processed to extract geometrical information on the retinal vasculature; computational techniques developed within the AngioTk platform (see above) which is adapted to generate computational domains for the blood vessels where we simulate blood flow using Feel++/CFD using the clinically measured values of blood pressure as patient-specific inputs (specifically, we will adapt the mathematical model described in Dziubek et al (2015)). Data from the Heidelberg Retinal Flowmeter will be used to properly tailor the microvascular levels of the model. Retinal Oximetry data will be used to incorporate oxygen dynamics into the vascular model, following a similar procedure as in Causin et al (2015). Color Doppler Imaging data is used to tailor the lumped model describing the blood flow in the central retinal artery and vein to the patient-specific
measurements, following a similar procedure to that described in Guidoboni et al (2014). Images obtained via Optical Coherence Tomography is processed to extract geometrical information regarding the structure of the optic nerve head and is integrated within the rest of the ocular platform as a component to be visualized and explored in detail.
The resulting software platform will be tested within the clinics at the Eugene and Marilyn Glick Eye Institute in Indianapolis (IN, USA) and the Eye Clinic of Lithuanian University of Health Sciences under the supervision of Prof. Harris.
This application represents a challenge for an integration into MSO4SC due to both the rich and possibly interconnected model components and data flow coming from different sources which need to be exploited by the different model components.