Retinal topography maps in R: New tools for the analysis and visualization of spatial retinal data

Abstract

Retinal topography maps are a widely used tool in vision science, neuroscience, and visual ecology, providing an informative visualization of the spatial distribution of cell densities across the retinal hemisphere. Here, we introduce Retina, an R package for computational mapping, inspection of topographic model fits, and generation of average maps. Functions in Retina take cell count data obtained from retinal wholemounts using stereology software. Accurate visualizations and comparisons between different eyes have been difficult in the past, because of deformation and incisions of retinal wholemounts. We account for these issues by incorporation of the R package Retistruct, which results in a retrodeformation of the wholemount into a hemispherical shape, similar to the original eyecup. The maps are generated by thin plate splines, after the data were transformed into a two-dimensional space with an azimuthal equidistant plot projection. Retina users can compute retinal topography maps independent of stereology software choice and assess model fits with a variety of diagnostic plots. Functionality of Retina also includes species average maps, an essential feature for interspecific analyses. The Retina package will facilitate rigorous comparative studies in visual ecology by providing a robust quantitative approach to generate retinal topography maps.

Eye Histology and Ganglion Cell Topography of Northern Elephant Seals (Mirounga angustirostris)

“… (a) Retinal ganglion cell layer (RGCL) topography of the northern elephant seal ( Mirounga angustirostris) , visualized with the R package retina (Cohn et al, ). The color gradient represents the interpolated cell density retrieved from a thin plate spline smoothing function.…”

“…The detailed description of gross morphology and histology of NES eyes provides a wealth of information for a better understanding of the potential evolutionary (Cohn et al, 2015). The color gradient represents the interpolated cell density retrieved from a thin plate spline smoothing function.…”

“…The distribution of ganglion cells across the entire retina was visualized with the open‐source tool “retina” (Cohn et al, ) developed for the statistical platform “R” (R Development Core Team, ). “Retina” formats the count data, retinal wholemount outline, and the optic disc outline and passes it on to “retistruct” (Sterratt et al, ) for a reconstruction of the hemispherical nature of the retinal cup, digitally stitching the incisions made for flattening the retinal tissue onto the microscope slide.…”

“…The coloration of each pixel in the projection represents the predicted retinal ganglion cell density from the spline function, and the x‐marks in the reconstructed map show retinal locations where density was recorded. The thin plate spline function represents an objective representation of the count data and does not rely on manual interpolation (Cohn et al, ). The choice of lambda parameter will influence how closely the surface follows the actual counts.…”

Using Vascular Landmarks to Orient 3D Optical Coherence Tomography Images of the Mouse Eye

“…We are unaware of methods to orient noninvasive images of the eye acquired from other commonly used non-primate research animals. However, comparative studies of retinal topography in fish species used images that were oriented with the assumption that the distribution of topographic features is similar among individuals (Cohn, Collin, Wainwright, & Schmitz, 2015). In human clinical studies, a related approach has been described to align and scale fundus images based on general features of the retinal vessel arcades (Abramoff, Garvin, & Sonka, 2010), which may find application as “atlas landmarks” that enable large comparative studies of fundus image data and automated detection of abnormal pathology.…”