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

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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.

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References

  • Billeci, L., Magliaro, C., et al. (2013). NEuronMOrphological analysis tool: open-source software for quantitative morphometrics. Front Neuroinform, 7, 2.

    Article  PubMed  Google 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.

    Article  PubMed  Google Scholar 

  • Cuntz, H., Forstner, F., et al. (2011). The TREES toolbox–probing the basis of axonal and dendritic branching. Neuroinformatics, 9(1), 91–96.

    Article  PubMed  Google Scholar 

  • Gulledge, A. T., Kampa, B. M., et al. (2005). Synaptic integration in dendritic trees. Journal of Neurobiology, 64(1), 75–90.

    Article  PubMed  CAS  Google Scholar 

  • Hines, M. L., & Carnevale, N. T. (1997). The NEURON simulation environment. Neural Computation, 9(6), 1179–1209.

    Article  PubMed  CAS  Google 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.

    PubMed  CAS  Google Scholar 

  • Jaffe, D. B., & Carnevale, N. T. (1999). Passive normalization of synaptic integration influenced by dendritic architecture. Journal of Neurophysiology, 82(6), 3268–3285.

    PubMed  CAS  Google 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.

    Article  PubMed  CAS  Google Scholar 

  • Kalisman, N., Silberberg, G., et al. (2003). Deriving physical connectivity from neuronal morphology. Biological Cybernetics, 88(3), 210–218.

    Article  PubMed  Google 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.

    Article  PubMed  CAS  Google Scholar 

  • Manor, Y., Koch, C., et al. (1991b). Effect of geometrical irregularities on propagation delay in axonal trees. Biophysical Journal, 60(6), 1424–1437.

    Article  PubMed  CAS  Google Scholar 

  • Rall, W. (1967). Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. Journal of Neurophysiology, 30(5), 1138–1168.

    PubMed  CAS  Google 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.

    Article  PubMed  CAS  Google 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.

    Article  PubMed  Google 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.

    Article  PubMed  CAS  Google Scholar 

  • Segev, I., & London, M. (2000). Untangling dendrites with quantitative models. Science, 290(5492), 744–750.

    Article  PubMed  CAS  Google 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.

    Article  PubMed  CAS  Google 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.

    PubMed  Google 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.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

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.

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Correspondence to Paulo Aguiar.

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Aguiar, P., Sousa, M. & Szucs, P. Versatile Morphometric Analysis and Visualization of the Three-Dimensional Structure of Neurons. Neuroinform 11, 393–403 (2013). https://doi.org/10.1007/s12021-013-9188-z

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