Anatomical Location of the Raphe and Extended Raphe in the Human Retina: Implications for Assessment of the Optic Nerve with OCT

A Model of Progression to Help Identify Macular Damage Due to Glaucoma

The central macula contains a thick donut shaped region of the ganglion cell layer (GCL) that surrounds the fovea. This region, which is about 12 degrees (3.5 mm) in diameter, is essential for everyday functions such as driving, reading, and face recognition. Here, we describe a model of progression of glaucomatous damage to this GCL donut. This model is based upon assumptions supported by the literature, and it predicts the patterns of glaucomatous damage to the GCL donut, as seen with optical coherence tomography (OCT). After describing the assumptions and predictions of this model, we test the model against data from our laboratory, as well as from the literature. Finally, three uses of the model are illustrated. One, it provides an aid to help clinicians focus on the essential central macula and to alert them to look for other, non-glaucomatous causes, when the GCL damage does not fit the pattern predicted by the model. Second, the patterns of progression predicted by the model suggest alternative end points for clinical trials. Finally, the model provides a heuristic for future research concerning the anatomic basis of glaucomatous damage.

Factors affecting two-point discrimination in Argus II patients

Two of the main obstacles to the development of epiretinal prosthesis technology are electrodes that require current amplitudes above safety limits to reliably elicit percepts, and a failure to consistently elicit pattern vision. Here, we explored the causes of high current amplitude thresholds and poor spatial resolution within the Argus II epiretinal implant. We measured current amplitude thresholds and two-point discrimination (the ability to determine whether one or two electrodes had been stimulated) in 3 blind participants implanted with Argus II devices. Our data and simulations show that axonal stimulation, lift and retinal damage all play a role in reducing performance in the Argus 2, by either limiting sensitivity and/or reducing spatial resolution. Understanding the relative role of these various factors will be critical for developing and surgically implanting devices that can successfully subserve pattern vision.

Test of a Retinal Nerve Fiber Bundle Trajectory Model Using Eyes With Glaucomatous Optic Neuropathy

Purpose

To test a model of retinal nerve fiber bundle trajectories that predicts the arcuate-shaped patterns seen on optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability/deviation maps (p-maps) in glaucomatous eyes.

Methods

Thirty-one glaucomatous eyes from a database of 250 eyes had clear arcuate-shaped patterns on RNFL p-maps derived from an OCT cube scan. The borders of the arcuate patterns were extracted from the RNFL p-maps. Next, the trajectories from an arcuate model were compared against these borders via a normalized root-mean-square difference analysis. The model's parameter, β, was varied, and the best-fitting, initial clock-hour position of the trajectory to the border was found for each β. Finally, the regions, as determined by the arcuate border's best-fit, initial clock-hour positions, were compared against the abnormal regions on the circumpapillary retinal nerve fiber layer (cpRNFL) profile.

Results

The arcuate model's mean β_Sup and β_Inf parameters minimized large differences between the trajectories and the arcuate borders on the RNFL p-maps. Furthermore, on average, 68% of the cpRNFL regions defined by the arcuate border's best-fit, initial clock-hour positions were abnormal (i.e., below the ≤5% threshold).

Conclusions

The arcuate model performed well in predicting the borders of arcuate patterns seen on RNFL p-maps. It also predicted the associated abnormal regions of the cpRNFL thickness plots.

Translational Relevance

This model should prove useful in helping clinicians understand topographical comparisons among different OCT representations and should improve structure-structure, as well as structure-function agreement analyses.