Novel Markers for Detecting Early Progression of Glaucoma

Changes in the superficial optic nerve head (ONH) surface and loss of retinal nerve fibre layer (RNFL) thickness detected with clinical imaging are predictive of future visual field loss. Imaging of the deep ONH, the likely origin of glaucomatous damage, represents the next logical next step, but has eluded clinicians because of the lack of capable technology. New advances in optical coherence tomography (OCT) imaging now offer an exciting opportunity to close the gap between the histomorphometric knowledge on deep ONH changes gained with research in experimental monkey glaucoma and imaging in clinical glaucoma. There is compelling evidence that gross ONH and retinal hemodynamic changes are functional indicators of glaucoma progression. Accurate tracking of blood flow in the ONH is a logical step, but has evaded researchers for several reasons including the highly reflective ONH tissue which variably inhibits signal penetration making the complex nature of retinal and posterior ciliary contributions to ONH flow difficult to segregate. Even though glaucoma damage originates in the ONH, retinal ganglion cell (RGC) axons may show the earliest functional alterations as they have high metabolic demand and vulnerability to damage. Therefore, tracking blood flow in the RNFL, which is highly segmental and resolvable, could be a better and more sensitive approach compared to that in the ONH. The macula contains almost 50% of the entire RGC population; likewise, monitoring blood flow in the macular inner vascular plexus corresponding to the ganglion cell layer (GCL) is likely to be highly informative for glaucoma progression. OCT based angiography (OCTA), which maps vessel density in different retinal vascular beds with unparalleled axial resolution, will finally allow us to quantify highly localized parameters related to blood flow and identify patients with higher progression risk. Current data analysis of progression detection based on inter-subject or population-based variability models are inefficient, leading to false-positive and false-negative results. Innovative data analysis techniques that build accurate models of intra-subject variability will add cumulative value to the novel imaging markers for progression.