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

• Type: Article in International Scientific Journal
• Date: 2013

Authors

Paulo Aguiar (Author) FCUP; Mafalda Sousa (Author) Other; Peter Szucs (Author) Other

Journal

Title: Neuroinformatics

Vol. 11

Pages: 393-403

ISSN: 1539-2791

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 16 Citations

Scopus - 18 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-006-6RK

DOI: 10.1007/s12021-013-9188-z

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11