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Versatile Morphometric Analysis and Visualization of the Three-Dimensional Structure of Neurons

Versatile Morphometric Analysis and Visualization of the Three-Dimensional Structure of Neurons The computational properties of a neuron are intimately related to its morphology. However, unlike electrophysiological properties, it is not straightforward to collapse the complexity of the three-dimensional (3D) structure into a small set of measurements accurately describing the structural properties. This strong limitation leads to the fact that many studies involving morphology related questions often rely solely on empirical analysis and qualitative description. It is possible however to acquire hierarchical lists of positions and diameters of points describing the spatial structure of the neuron. While there is a number of both commercially and freely available solutions to import and analyze this data, few are extendable in the sense of providing the possibility to define novel morphometric measurements in an easy to use programming environment. Fewer are capable of performing morphometric analysis where the output is defined over the topology of the neuron, which naturally requires powerful visualization tools. The computer application presented here, Py3DN, is an open-source solution providing novel tools to analyze and visualize 3D data collected with the widely used Neurolucida (MBF) system. It allows the construction of mathematical representations of neuronal topology, detailed visualization and the possibility to define non-standard morphometric analysis on the neuronal structures. Above all, it provides a flexible and extendable environment where new types of analyses can be easily set up allowing a high degree of freedom to formulate and test new hypotheses. The application was developed in Python and uses Blender (open-source software) to produce detailed 3D data representations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

Versatile Morphometric Analysis and Visualization of the Three-Dimensional Structure of Neurons

Aguiar, Paulo; Sousa, Mafalda; Szucs, Peter
Neuroinformatics , Volume 11 (4) – Jun 14, 2013
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References (20)

Publisher
Springer Journals
Copyright
Copyright © 2013 by Springer Science+Business Media New York
Subject
Biomedicine; Neurosciences; Bioinformatics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Neurology
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1007/s12021-013-9188-z
pmid
23765606
Publisher site
See Article on Publisher Site

Abstract

The computational properties of a neuron are intimately related to its morphology. However, unlike electrophysiological properties, it is not straightforward to collapse the complexity of the three-dimensional (3D) structure into a small set of measurements accurately describing the structural properties. This strong limitation leads to the fact that many studies involving morphology related questions often rely solely on empirical analysis and qualitative description. It is possible however to acquire hierarchical lists of positions and diameters of points describing the spatial structure of the neuron. While there is a number of both commercially and freely available solutions to import and analyze this data, few are extendable in the sense of providing the possibility to define novel morphometric measurements in an easy to use programming environment. Fewer are capable of performing morphometric analysis where the output is defined over the topology of the neuron, which naturally requires powerful visualization tools. The computer application presented here, Py3DN, is an open-source solution providing novel tools to analyze and visualize 3D data collected with the widely used Neurolucida (MBF) system. It allows the construction of mathematical representations of neuronal topology, detailed visualization and the possibility to define non-standard morphometric analysis on the neuronal structures. Above all, it provides a flexible and extendable environment where new types of analyses can be easily set up allowing a high degree of freedom to formulate and test new hypotheses. The application was developed in Python and uses Blender (open-source software) to produce detailed 3D data representations.

Journal

Neuroinformatics – Springer Journals

Published: Jun 14, 2013

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