Retinal Ganglion Cell Content Underlying Standard Automated Perimetry Size I to V Visual Sensitivities in the Non-Human Primate Experimental Glaucoma Model

Abstract

Purpose: To determine the relationship between visual sensitivities from white-on-white Goldmann size I to V stimuli and the underlying retinal ganglion cell (RGC) content in the non-human primate (NHP) experimental glaucoma model.

Methods: Normative data were collected from 13 NHPs. Unilateral experimental glaucoma was induced in seven animals with the least variable fields who were monitored using optical coherence tomography and 30-2 full-threshold standard automated perimetry (SAP). At varying endpoints, animals were euthanized followed by perfusion fixation, and 1-mm retinal punches were obtained from 34 corresponding SAP locations. RGCs were immunolabeled with an antibody against an RNA-binding protein (RBPMS) marker and imaged using confocal microscopy. RGC counts from each location were then related to visual sensitivities for each stimulus size, after accounting for ocular magnification.

Results: At the endpoint, the circumpapillary retinal nerve fiber layer thickness for experimental glaucoma eyes ranged from 47 to 113 µm. RGC density in control eyes was greatest for the 4.24° sample (18,024 ± 6869 cells/mm2) and decreased with eccentricity. Visual sensitivity at each tested location followed that predicted by spatial summation, with the critical area increasing with eccentricity (slope = 0.0036, R2 = 0.44). The relationship between RGC counts and visual sensitivity was described using a two-line fit, where the intercept of the first segment and hinge points were dependent on eccentricity.

Conclusions: In NHPs, SAP visual thresholds are related to the underlying RGCs. The resulting spatial summation based structure-function model can be used to estimate RGC content from any standard white-on-white stimulus size.

Figure 1.

(A, B) Endpoint widefield SLO image from an eye with experimental glaucoma (A), with corresponding visual field locations marked from a size III 30-2 field (B). The color locations indicate the regions punched for histological evaluation of RGC density (the dashed circle indicates the nominal size of the punched region).

Figure 2.

(A, B) Mean sensitivity (±95% CI) along the horizontal (A) and vertical (B) meridians of the 30-2 field for size I through V stimuli. (C) Spatial summation plot for the closest eccentricity evaluated. (D) Spatial summations plots for the 10 30-2 eccentricities, illustrating the change in y-intercept and critical area with eccentricity.

Figure 3.

(A–C) Single mid-ganglion cell layer images from confocal z-stacks of a control eye at 4.24° (A), 12.72° (B), and 27.2° (C) eccentricities, each acquired at 20× magnification. RGCs are labeled with RBPMS (red), displaced amacrine cells with ChAT (green), and cell nuclei with DAPI (blue). (D) Average ± SD cell densities of the seven control eyes for the six eccentricities sampled are shown.

Figure 4.

(A) Retinal ganglion cell densities estimated using the nonlinear model were correlated with those measured. (B) Bland–Altman analysis with 95% limits of agreement showed a small proportional bias.

Figure 5.

The relationship between logRGC counts and logDLS in control (open symbols) and experimental glaucoma (closed symbols) samples. The red and blue lines illustrate the segmental fits for the experimental glaucoma and control samples.

Figure 6.

(A–C) Relationship between the intercept of the first segment (A), hinge point (B), and slope of the second segment (C) with eccentricity for a two-line segmental fit to the data illustrated in Figure 5. (D) Structure–function relationship for each histologically sampled eccentricity, using the coefficients estimated from the regressions A to C.