From Random to Regular: Variation in the Patterning of Retinal Mosaics

TL;DR

This paper critically evaluates statistical methods for analyzing retinal neuron mosaics, highlighting their limitations, and demonstrates variability in patterning across different retinal neuron types with implications for development and function.

Contribution

It provides a comprehensive assessment of spatial statistical analyses for retinal mosaics and applies multiple methods to reveal natural variation in neuronal patterning.

Findings

Retinal mosaics show a range from random to regular patterning.

Common statistics can misrepresent the true variability in neuronal spacing.

Patterning variability reflects developmental and functional factors.

Abstract

The various types of retinal neurons are each positioned at their respective depths within the retina where they are believed to be assembled as orderly mosaics, in which like-type neurons minimize proximity to one another. Two common statistical analyses for assessing the spatial properties of retinal mosaics include the nearest neighbor analysis, from which an index of their "regularity" is commonly calculated, and the density recovery profile derived from auto-correlation analysis, revealing the presence of an exclusion zone indicative of anti-clustering. While each of the spatial statistics derived from these analyses, the regularity index and the effective radius, can be useful in characterizing such properties of orderly retinal mosaics, they are rarely sufficient for conveying the natural variation in the self-spacing behavior of different types of retinal neurons and the extent to which that behavior generates uniform intercellular spacing across the mosaic. We consider the strengths and limitations of different spatial statistical analyses for assessing the patterning in retinal mosaics, highlighting a number of misconceptions and their frequent misuse. Rather than being diagnostic criteria for determining simply whether a population is "regular", they should be treated as descriptive statistics that convey variation in the factors that influence neuronal positioning. We subsequently apply multiple spatial statistics to the analysis of eight different mosaics in the mouse retina, demonstrating conspicuous variability in the degree of patterning present, from essentially random to notably regular. This variability in patterning has both a developmental as well as a functional significance, reflecting the rules governing the positioning of different types of neurons as the architecture of the retina is assembled (abstract truncated).