Article thumbnail

Spatial Point Pattern Analysis of Neurons Using Ripley's K-Function in 3D

By Mehrdad Jafari-Mamaghani, Mikael Andersson and Patrik Krieger

Abstract

The aim of this paper is to apply a non-parametric statistical tool, Ripley's K-function, to analyze the 3-dimensional distribution of pyramidal neurons. Ripley's K-function is a widely used tool in spatial point pattern analysis. There are several approaches in 2D domains in which this function is executed and analyzed. Drawing consistent inferences on the underlying 3D point pattern distributions in various applications is of great importance as the acquisition of 3D biological data now poses lesser of a challenge due to technological progress. As of now, most of the applications of Ripley's K-function in 3D domains do not focus on the phenomenon of edge correction, which is discussed thoroughly in this paper. The main goal is to extend the theoretical and practical utilization of Ripley's K-function and corresponding tests based on bootstrap resampling from 2D to 3D domains

Topics: Neuroscience
Publisher: Frontiers Research Foundation
OAI identifier: oai:pubmedcentral.nih.gov:2889688
Provided by: PubMed Central

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles

Citations

  1. (2003). A gene expression atlas of the central nervous system based on bacterial artifi cial chromosomes.
  2. (1993). An Introduction to the Bootstrap.
  3. (1993). Analysis of a three-dimensional point pattern with replication.
  4. (2008). Analysis of spatial relationships in three dimensions: tools for the study of nerve cell patterning.
  5. (2010). Cell-type specifi c properties of pyramidal neurons in neocortex underlying a layout that is modifi able depending on the cortical area.
  6. (1993). Cortical modules in the posteromedial barrel subfi eld (Sml) of the mouse.
  7. (2008). Diagnosis of solitary lung nodules using the local form of Ripley’s K function applied to three-dimensional CT data.
  8. (1986). Displaced amacrine cells in the retina of a rabbit: analysis of a bivariate spatial point pattern.
  9. (1988). Edge-corrected estimators for the reduced second moment measure of point processes.
  10. (1994). Evaluation of neuronal numerical density by Dirichlet tessellation.
  11. (1994). Fractals, Random Shapes and Point Fields.
  12. (2000). Improving ratio estimators of second order point process characteristics.
  13. (1999). New derivation reduces bias and increases power of Ripley’s L index.
  14. (2009). paper pending published: 05
  15. (2000). Quantitative analysis of cell columns in the cerebral cortex.
  16. (2008). Quasi-plus sampling edge correction for spatial point patterns.
  17. (2004). Somatodendritic minicolumns of output neurons in the rat visual cortex.
  18. (2010). Spatial point pattern analysis of neurons using Ripley’s K-function in 3D.
  19. (1991). Spatial segregation between populations of ponto-cerebellar neurons. Statistical analysis of multivariate spatial interactions.
  20. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. (Wiley-Interscience). tion of neurons and dendritic bundles in primary somatosensory cortex of the rat.
  21. (2003). Statistical Analysis of Spatial Point Patterns.
  22. (2005). Statistical analysis of the threedimensional structure of centromeric heterochromatin in interphase nuclei.
  23. (2010). Structure-function analysis of genetically defi ned neuronal populations,”
  24. (2007). Three-dimensional atlas system for mouse and rat brain imaging data.
  25. (2006). Towards neural circuit reconstruction with volume electron microscopy techniques.
  26. (2007). Voronoi analysis uncovers relationship between mosaics of normally placed and displaced amacrine cells in the thraira retina.
  27. (2000). Voronoi tessellation to study the numerical density and the spatial distribution of neurons.