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Archetypes and Outliers in the Neuromorphological Space

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The Computing Dendrite

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI,volume 11))

Abstract

Neuromorphology has a long history of meticulous analysis and fundamental studies about the intricacies of neuronal shape. These studies converged to a plethora of information describing in detail many neuronal characteristics, as well as comprehensive data about cell localization, animal type, age, among others. Much of this information has notably been compiled through efforts of the Computational Neuroanatomy Group at the Krasnow Institute for Advanced Study, George Mason University, thus originating the NeuroMorpho.org repository, a resource that incorporates a large set of data and related tools. In the current work we present a methodology that can be used to search for novel relationships in cell morphology contained in databases such as the NeuroMorpho.org. More specifically, we try to understand which morphological characteristics can be considered universal for a given cell type, or to what extent we can represent an entire cell class through an archetypal shape. This analysis is done by taking a large number of characteristics from cells into account, and then applying multivariate techniques to analyze the data. The neurons are then classified as archetypes or outliers according to how close they are to the typical shape of the class. We find that granule and medium spiny neurons can be associated with a typical shape, and that different animals and brain regions show distinct distributions of shapes.

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Notes

  1. 1.

    Here we use the lab names that appear on NeuroMorpho.org.

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Acknowledgements

 CHC is grateful to FAPESP (2011/22639-8 and 2011/50761-2) for sponsorship. JT is grateful to CNPq (560353/2010-3) and FAPESP (2012/17057-2) for a postdoctoral scholarship. MPV thanks to FAPESP for financial support (2010/16310-0). ACR is grateful to CNPq (306040/2010-7) for financial support. LdFC is grateful to FAPESP (05/00587- 5 and 2011/50761-2) and CNPq (301303/06-1 and 573583/2008-0) for the financial support.

Author information

Authors and Affiliations

  1. Institute of Physics at Sao Carlos, Sao Carlos, Brazil

    Cesar H. Comin, Matheus P. Viana & Luciano da F. Costa

  2. Department of Physics, School of Philosophy, Science and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil

    Julian Tejada & Antonio C. Roque

Authors
  1. Cesar H. Comin
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  2. Julian Tejada
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  3. Matheus P. Viana
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  4. Antonio C. Roque
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  5. Luciano da F. Costa
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Corresponding author

Correspondence to Luciano da F. Costa .

Editor information

Editors and Affiliations

  1. Institute of Clinical Neuroanatomy, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society & Goethe-University, Frankfurt, Germany

    Hermann Cuntz

  2. Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany

    Michiel W.H. Remme

  3. Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel

    Benjamin Torben-Nielsen

Appendix:

Appendix:

1.1 Tables with the Names of the Archetypes and Outliers Found

Table 3.4 Names of the archetypes identified in each cell class
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Table 3.5 Names of the outliers identified in each cell class
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Comin, C.H., Tejada, J., Viana, M.P., Roque, A.C., Costa, L.d.F. (2014). Archetypes and Outliers in the Neuromorphological Space. In: Cuntz, H., Remme, M., Torben-Nielsen, B. (eds) The Computing Dendrite. Springer Series in Computational Neuroscience, vol 11. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8094-5_3

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