Dataset Information

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.

SUBMITTER: Cohn BA

PROVIDER: S-EPMC4527213 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

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

Cohn Brian A BA Collin Shaun P SP Wainwright Peter C PC Schmitz Lars L

Journal of vision 20150101 9

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 betwe ...[more]

PMID: 26230981

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Project description:Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps have been created from raw count data that have been subjected to only limited analysis (linear interpolation) and, in many cases, have been presented as iso-density contour maps with contour lines that have been smoothed 'by eye'. With the use of stereological approach to count neuronal distribution, a more rigorous approach to analysing the count data is warranted and potentially provides a more accurate representation of the neuron distribution pattern. Moreover, a formal spatial analysis of retinal topography permits a more robust comparison of topographic maps within and between species. In this paper, we present a new R-script for analysing the topography of retinal neurons and compare methods of interpolating and smoothing count data for the construction of topographic maps. We compare four methods for spatial analysis of cell count data: Akima interpolation, thin plate spline interpolation, thin plate spline smoothing and Gaussian kernel smoothing. The use of interpolation 'respects' the observed data and simply calculates the intermediate values required to create iso-density contour maps. Interpolation preserves more of the data but, consequently includes outliers, sampling errors and/or other experimental artefacts. In contrast, smoothing the data reduces the 'noise' caused by artefacts and permits a clearer representation of the dominant, 'real' distribution. This is particularly useful where cell density gradients are shallow and small variations in local density may dramatically influence the perceived spatial pattern of neuronal topography. The thin plate spline and the Gaussian kernel methods both produce similar retinal topography maps but the smoothing parameters used may affect the outcome.

Dataset Information

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

Publications

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

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