Abstract
BackgroundMultiple technologies have been brought to bear on understanding the three-dimensional morphology of individual neurons and glia within the brain, but little progress has been made on understanding the rules controlling cellular patterning. We describe new matlab-based software tools, now available to the scientific community, permitting the calculation of spatial statistics associated with 3D point patterns. The analyses are largely derived from the Delaunay tessellation of the field, including the nearest neighbor and Voronoi domain analyses, and from the spatial autocorrelogram.ResultsOur tools enable the analysis of the spatial relationship between neurons within the central nervous system in 3D, and permit the modeling of these fields based on lattice-like simulations, and on simulations of minimal-distance spacing rules. Here we demonstrate the utility of our analysis methods to discriminate between two different simulated neuronal populations.ConclusionTogether, these tools can be used to reveal the presence of nerve cell patterning and to model its foundation, in turn informing on the potential developmental mechanisms that govern its establishment. Furthermore, in conjunction with analyses of dendritic morphology, they can be used to determine the degree of dendritic coverage within a volume of tissue exhibited by mature nerve cells.
Highlights
Multiple technologies have been brought to bear on understanding the threedimensional morphology of individual neurons and glia within the brain, but little progress has been made on understanding the rules controlling cellular patterning
What has been lacking is an understanding of the determinants of the positioning of neurons in 3D space: what controls the position a neuron will come to occupy within a brain structure relative to other cells in the local environment? In some structures, cells are packed side-by-side and there is no mystery to their spacing; in others, the distance between members of a cell type is often conspicuous, but we know nothing of the constraints imposing this spacing
Delaunay tessellation analysis By inputting x, y, z positional information for a population of cells, a variety of spatial statistics can be generated based upon the tessellation of the field by Delaunay tetrahedrons, including the derivation of Voronoi domains
Summary
Our tools enable the analysis of the spatial relationship between neurons within the central nervous system in 3D, and permit the modeling of these fields based on lattice-like simulations, and on simulations of minimal-distance spacing rules. We demonstrate the utility of our analysis methods to discriminate between two different simulated neuronal populations
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