We chose to use the Curcio and Allen model
8 for RGC density and the Drasdo et al.
6 model for RGC receptive field density because these models have been used by others
10,13 to assist clinical comparison of macular OCT data to visual fields. However, other models are in the literature; for example, the model of Watson,
9 where different formulas were used for the calculation of the density of RGC and RGC receptive fields. A key difference is that the models of Watson were fitted mainly to peripheral cell counts (outside 11°), not counts in the macula (within 10°), which is the focus of this study.
Our model estimates contain some asymmetry relative to the center of the fovea. This is fairly small in the nasal/temporal meridian, but greater in the superior/inferior meridian. The results seem reasonable in that the average of our customized displacement population is similar to the average reported by Drasdo et al,
6,9 where the average displacements peaked at approximately 2.2° at the eccentricity of 1.9° in the temporal retina meridian and peaked at approximately 1.9° at the eccentricity of 2° in the nasal retina meridian. In our study, the largest displacement occurred in the 10-2 tests at the eccentricities between 1° and 3° near the four meridians. The largest average displacements in the 10-2 tests near the temporal and nasal retina regions are 2.44° and 2.15°, respectively, which are very close to the results in these studies.
6,9 Moreover, the largest displacements near the inferior and superior retina regions in our study are 2.27° and 2.07°, which are greater than the reported total displacement of 0.37 ± 0.03 mm along the vertical meridian (within 1.8°–2.9°) in the study of Sjöstrand et al.,
7 probably because of the limited number of human retinas (5 retinas) examined.
As mentioned in the Methods, receptive field estimates were obtained from previously reported functional/psychophysical data,
6 and not collected individually for our participants. They were held constant across all individuals, which is a potential weakness of this study. Given that our study population included healthy, young adults only, it did not seem that the extensive additional functional testing required to test the validity of this assumption was justified. In a population of wider age range, the assumption of consistent receptive field density is less likely to hold. Neuronal density decreases in older retinae; however, the decrease is relatively less in the macula than in other regions, so the effects of age may not be pronounced.
14 Collecting estimates of receptive field density psychophysically is difficult, but obtaining individual estimates from imaging may be less laborious. One possible imaging approach could be to use the outer nuclear layer (ONL) in the OCT data to adjust receptive field counts in the same way that RGC counts are altered. However, Henle fibers comprise up to 70% of the ONL,
15 and these are the fiber lengths we are trying to model, so it seems confounded to include them in the model. Furthermore, the segmentation of the ONL layer is not as easy as the segmentation of the RGC+ layers, because of lack of contrast in this region of the OCT images, as shown in
Figure 2. We did attempt some initial analyses using the ONL layer, but the results were wildly variable, and seemed unlikely to be plausible.
There are several points of clinical relevance to consider in interpreting the results of our study. Firstly, we used RGC+ thickness to individually estimate the number of RGCs at a given eccentricity. Our approach is limited to areas of healthy macula, becoming invalid once the RGC+ is thinned due to disease. Consequently, customized mapping of macular structure–function in a clinical setting would be useful only in the initial stages of the disease, or at baseline, to provide a reference point for future changes. Alternately, other individual features of macular anatomy might assist once disease is onset. One possible feature is the size of the foveal avascular zone, which has been shown to relate to macular shape in normal observers.
16 Foveal pit depth may seem like a possible candidate; however, in our data there was no significant correlation between foveal pit depth (as measured by the OCT) and displacement for 63 of the 68 visual field locations (Spearman correlations,
P > 0.05, data not shown), so this factor is unlikely to be particularly helpful.
A second point of clinical relevance is that the largest displacements were in the inferior retina. The inferior macular area is susceptible to damage in glaucoma;
1 hence, the area most likely to be damaged also is the region that is most likely to benefit from customized mapping. In this spatial area of high clinical relevance, there is the greatest predicted interindividual difference in the mapping, of up to 2° between individuals. Given that the 10-2 visual field grid separates visual field locations by 2°, this level of individual shift in mapping is sufficient to map an area on the OCT image to different visual field test locations in different individuals. One application for such mapping may be for the exploration of the relationship between structural and function data collected in patients with glaucoma, particularly to assist in the detection and monitoring of early progression. We did not investigate structure and function in our current cohort with the two mapping schema as there is limited relationship between structural and functional data in healthy eyes.
17–19 The benefits of such mapping are unlikely to be revealed by looking at structure–function correlations averaged across a population, as most people within a population have relatively typical anatomy. Benefits of individualized mapping are most likely in those people with atypical anatomy, where individual tracking of progression may be assisted by moving away from population norms. Furthermore, customized mapping may pave the way for future visual field test grids that are individually adjusted depending on anatomical features.