Neurodegenerative Disorders of the Eye and of the Brain: A Perspective on Their Fluid-Dynamical Connections and the Potential of Mechanism-Driven Modeling

Abstract

Neurodegenerative disorders (NDD) such as Alzheimer's and Parkinson's diseases are significant causes of morbidity and mortality worldwide. The pathophysiology of NDD is still debated, and there is an urgent need to understand the mechanisms behind the onset and progression of these heterogenous diseases. The eye represents a unique window to the brain that can be easily assessed via non-invasive ocular imaging. As such, ocular measurements have been recently considered as potential sources of biomarkers for the early detection and management of NDD. However, the current use of ocular biomarkers in the clinical management of NDD patients is particularly challenging. Specifically, many ocular biomarkers are influenced by local and systemic factors that exhibit significant variation among individuals. In addition, there is a lack of methodology available for interpreting the outcomes of ocular examinations in NDD. Recently, mathematical modeling has emerged as an important tool capable of shedding light on the pathophysiology of multifactorial diseases and enhancing analysis and interpretation of clinical results. In this article, we review and discuss the clinical evidence of the relationship between NDD in the brain and in the eye and explore the potential use of mathematical modeling to facilitate NDD diagnosis and management based upon ocular biomarkers.

Keywords: cerebrospinal fluid pressure; fluid-dynamics; glaucoma; intraocular pressure; mathematical modeling; neurodegenerative disorders.

Figure 1

Techniques for non-invasive ocular measurements (Guidoboni et al., 2013). (A) Fundus camera; (B) Color Doppler Imaging; (C) Fourier-Domain Optical Coherence Tomography (FD-OCT); (D) Retinal Oximetry. Images reproduced from Guidoboni et al. (2013), with permission.

Figure 2

Schematic overview of the multiscale modeling framework implemented in the Ocular Mathematical Virtual Simulator (OMVS). Image reproduced from Sala et al. (2018a), with permission. OA, ophthalmic artery; OV, ophthalmic vein; CRA, central retinal artery; CRV, central retinal vein; NPCA, nasal posterior ciliary artery; TPCA, temporal posterior ciliary artery; IOP, intraocular pressure; CSF, cerebrospinal fluid.

Figure 3

Illustrative schematic of the outputs that can be predicted by means of the Ocular Mathematical Virtual Simulator (OMVS) (Sala, 2019). The outputs include: quantitative three-dimensional colormaps of pressure, perfusion, and displacement fields in the lamina cribrosa (A,E), with the possibility of plotting profiles along specific sections or lines (B,F,H); quantitative three-dimensional colormaps of distributions of displacement in the ocular tissues (C,D); time-dependent velocity waveforms in the central artery and vein (G). LC, lamina cribrosa; CRA, central retinal artery; CRV, central retinal vein.

Figure 4

Cerebral venous hemodynamics. (Left) Pressure field. (Right) Streamlines. Images reproduced from Bertoluzza et al. (2017), with permission.