The rod photoreceptors are implicated in a number of devastating retinal diseases. However, routine imaging of these cells has remained elusive, even with the advent of adaptive optics imaging. Here, we present the first in vivo images of the contiguous rod photoreceptor mosaic in nine healthy human subjects. The images were collected with three different confocal adaptive optics scanning ophthalmoscopes at two different institutions, using 680 and 775 nm superluminescent diodes for illumination. Estimates of photoreceptor density and rod:cone ratios in the 5°–15° retinal eccentricity range are consistent with histological findings, confirming our ability to resolve the rod mosaic by averaging multiple registered images, without the need for additional image processing. In one subject, we were able to identify the emergence of the first rods at approximately 190 μm from the foveal center, in agreement with previous histological studies. The rod and cone photoreceptor mosaics appear in focus at different retinal depths, with the rod mosaic best focus (i.e., brightest and sharpest) being at least 10 μm shallower than the cones at retinal eccentricities larger than 8°. This study represents an important step in bringing high-resolution imaging to bear on the study of rod disorders.

Previous studies have reported race- and sex-associated differences in macular thickness, and the inference has been that these differences represent similar anatomic features. However, the data on pit morphology collected in the present study reveal an important and significant variation. Between the sexes, the differences are due to global variability in retinal thickness, whereas the variation in thickness observed between the races appears to be driven by differences in foveal pit morphology. These differences have important implications for the use of SD-OCT in detecting and diagnosing retinal disease.

Presence of a fovea centralis is directly linked to molecular specification of an avascular area in central retina, before the fovea (or `pit') begins to form. Modeling suggests that mechanical forces, generated within the eye, initiate formation of a pit within the avascular area, and its later remodeling in the postnatal period. Within the avascular area the retina is dominated by `midget' circuitry, in which signals are transferred from a single cone to a single bipolar cell, then a single ganglion cell. Thus in inner, central retina there are relatively few lateral connections between neurons. This renders the region adaptable to tangential forces, that translocate of ganglion cells laterally / centrifugally, to form the fovea. Optical coherence tomography enables live imaging of the retina, and shows that there is greater variation in the morphology of foveae in humans than previously thought. This variation is associated with differences in size of the avascular area and appears to be genetically based, but can be modified by environmental factors, including prematurity. Even when the fovea is absent (foveal hypoplasia), cones in central retina adopt an elongated and narrow morphology, enabling them to pack more densely to increase the sampling rate, and to act as more effective waveguides. Given these findings, what then is the adaptive advantage of a fovea? We suggest that the advantages of having a pit in central retina are relatively few, and minor, but together work to enhance acuity.

Purpose To assess the repeatability and measurement error associated with cone density and nearest neighbor distance (NND) estimates in images of the parafoveal cone mosaic obtained with an adaptive optics scanning light ophthalmoscope (AOSLO). Methods Twenty-one participants with no known ocular pathology were recruited. Four retinal locations, approximately 0.65° eccentricity from the center of fixation were imaged 10 times in randomized order with an AOSLO. Cone coordinates in each image were identified using an automated algorithm (with or without manual correction), from which cone density and NND were calculated. Owing to naturally occurring fixational instability, the 10 images recorded from a given location did not overlap entirely. We thus analyzed each image set both before and after alignment. Results Automated estimates of cone density on the unaligned image sets showed a coefficient of repeatability of 11,769 cones/mm2 (17.1%). The primary reason for this variability appears to be fixational instability, as aligning the 10 images to include the exact same retinal area, results in an improved repeatability of 4,358 cones/mm2 (6.4%) using completely automated cone identification software. Repeatability improved further by manually identifying cones missed by the automated algorithm, with a coefficient of repeatability of 1,967 cones/mm2 (2.7%). NND showed improved repeatability, and was generally insensitive to the undersampling by the automated algorithm. Conclusions As our data were collected in a young, healthy population, this likely represents a best-case estimate for corresponding measurements in patients with retinal disease. Similar studies need to be carried out on other imaging systems (including those using different imaging modalities, wavefront correction technology, and/or cone identification software), as repeatability would be expected to be highly sensitive to initial image quality and the performance of cone identification algorithms. Separate studies addressing inter-session repeatability and inter-observer reliability are also needed.