Versatile Morphometric Analysis and Visualization of the Three-Dimensional Structure of Neurons
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SOFTWARE ORIGINAL ARTICLE
Versatile Morphometric Analysis and Visualization of the Three-Dimensional Structure of Neurons Paulo Aguiar & Mafalda Sousa & Peter Szucs
# Springer Science+Business Media New York 2013
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 P. Aguiar (*) Centro de Matemática da Universidade do Porto, Rua do Campo Alegre s/n, 4169-007 Porto, Portugal e-mail: [email protected] P. Aguiar : M. Sousa : P. Szucs Instituto de Biologia Molecular e Celular, Rua do Campo Alegre 823, 4150-180 Porto, Portugal M. Sousa : P. Szucs Departamento de Biologia Experimental, Faculdade de Medicina da Universidade do Porto, Porto, Portugal
of freedom to formulate and test new hypotheses. The application was developed in Python and uses Blender (opensource software) to produce detailed 3D data representations. Keywords Neuromorphology . Mesh calculation . Morphometric analysis . Neuronal reconstruction data . 3D visualization
Introduction One of the biggest successes in neuroscience was the identification and quantification, by means of a mathematical model, of the mechanisms that give rise to the electrophysiological properties of neurons (Hodgkin and Huxley 1952). It is known that the functional characteristics of a neuron (in particular its response function) are also strongly related to its morphology, including both dendritic and axonal structures. But unlike electrophysiology, scientific advances in analysis of morphological features (i.e. morphometry) have been more modest. The reason lies primarily with the difficult
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