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.
Neuromorphology Mesh calculation Morphometric analysis Neuronal reconstruction data 3D visualization
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Research partly funded by the European Regional Development Fund through the program COMPETE and by the Portuguese Government through the FCT—Fundação para a Ciência e a Tecnologia under the project PEst-C/MAT/UI0144/2011. PA and PSz thanks FCT for financial support through the Ciência-2007 and POPH-QREN programs. MS was supported by FCT grant SFRH/BD/60690/2009.
Billeci, L., Magliaro, C., et al. (2013). NEuronMOrphological analysis tool: open-source software for quantitative morphometrics. Front Neuroinform, 7, 2.PubMedCrossRefGoogle Scholar
Bower, J. M., & Beeman, D. (1998). The book of GENESIS : exploring realistic neural models with the GEneral NEural SImulation System. Santa Clara, Calif: TELOS.Google Scholar
Budd, J. M., Kovacs, K., et al. (2010). Neocortical axon arbors trade-off material and conduction delay conservation. PLoS Computational Biology, 6(3), e1000711.PubMedCrossRefGoogle Scholar
Cuntz, H., Forstner, F., et al. (2011). The TREES toolbox–probing the basis of axonal and dendritic branching. Neuroinformatics, 9(1), 91–96.PubMedCrossRefGoogle Scholar
Gulledge, A. T., Kampa, B. M., et al. (2005). Synaptic integration in dendritic trees. Journal of Neurobiology, 64(1), 75–90.PubMedCrossRefGoogle Scholar
Hines, M. L., & Carnevale, N. T. (1997). The NEURON simulation environment. Neural Computation, 9(6), 1179–1209.PubMedCrossRefGoogle Scholar
Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500–544.PubMedGoogle Scholar
Jaffe, D. B., & Carnevale, N. T. (1999). Passive normalization of synaptic integration influenced by dendritic architecture. Journal of Neurophysiology, 82(6), 3268–3285.PubMedGoogle Scholar
Joris, P. X., Smith, P. H., et al. (1998). Coincidence detection in the auditory system: 50 years after Jeffress. Neuron, 21(6), 1235–1238.PubMedCrossRefGoogle Scholar
Kalisman, N., Silberberg, G., et al. (2003). Deriving physical connectivity from neuronal morphology. Biological Cybernetics, 88(3), 210–218.PubMedCrossRefGoogle Scholar
Manor, Y., Gonczarowski, J., et al. (1991a). Propagation of action potentials along complex axonal trees. Model and implementation. Biophysical Journal, 60(6), 1411–1423.PubMedCrossRefGoogle Scholar
Manor, Y., Koch, C., et al. (1991b). Effect of geometrical irregularities on propagation delay in axonal trees. Biophysical Journal, 60(6), 1424–1437.PubMedCrossRefGoogle Scholar
Rall, W. (1967). Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. Journal of Neurophysiology, 30(5), 1138–1168.PubMedGoogle Scholar
Rinzel, J., & Rall, W. (1974). Transient response in a dendritic neuron model for current injected at one branch. Biophysical Journal, 14(10), 759–790.PubMedCrossRefGoogle Scholar
Ropireddy, D., & Ascoli, G. A. (2011). Potential synaptic connectivity of different neurons onto pyramidal cells in a 3D reconstruction of the rat hippocampus. Front Neuroinform, 5, 5.PubMedCrossRefGoogle Scholar
Scorcioni, R., Polavaram, S., et al. (2008). L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nature Protocols, 3(5), 866–876.PubMedCrossRefGoogle Scholar
Shepherd, G. M., Raastad, M., et al. (2002). General and variable features of varicosity spacing along unmyelinated axons in the hippocampus and cerebellum. Proceedings of the National Academy of Sciences of the United States of America, 99(9), 6340–6345.PubMedCrossRefGoogle Scholar
Szucs, P., Luz, L. L., et al. (2013). Axon diversity of lamina I local-circuit neurons in the lumbar spinal cord. Journal of Comparative Neurology. doi: 10.1002/cne.23311.
van Pelt, J., Carnell, A., et al. (2010). An algorithm for finding candidate synaptic sites in computer generated networks of neurons with realistic morphologies. Frontiers in Computational Neuroscience, 4, 148.PubMedGoogle Scholar
Wearne, S. L., Rodriguez, A., et al. (2005). New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales. Neuroscience, 136(3), 661–680.PubMedCrossRefGoogle Scholar