, Volume 10, Issue 4, pp 379–389 | Cite as

Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells

  • Krissia Zawadzki
  • Christoph Feenders
  • Matheus P. Viana
  • Marcus KaiserEmail author
  • Luciano da F. CostaEmail author
Original Article


We report a morphology-based approach for the automatic identification of outlier neurons, as well as its application to the database, with more than 5,000 neurons. Each neuron in a given analysis is represented by a feature vector composed of 20 measurements, which are then projected into a two-dimensional space by applying principal component analysis. Bivariate kernel density estimation is then used to obtain the probability distribution for the group of cells, so that the cells with highest probabilities are understood as archetypes while those with the smallest probabilities are classified as outliers. The potential of the methodology is illustrated in several cases involving uniform cell types as well as cell types for specific animal species. The results provide insights regarding the distribution of cells, yielding single and multi-variate clusters, and they suggest that outlier cells tend to be more planar and tortuous. The proposed methodology can be used in several situations involving one or more categories of cells, as well as for detection of new categories and possible artifacts.


neuromorphometry Archetypes Outliers Neuroscience 



Luciano da F. Costa is grateful to FAPESP (05/00587-5) and CNPq (301303/06-1 and 573583/2008-0) for sponsorship. Krissia Zawadzki is grateful to FAPESP sponsorship (2010/01994-1). Matheus P. Viana is grateful to FAPESP sponsorship (2010/16310-0). Marcus Kaiser and Christoph Feenders acknowledge support by EPSRC (EP/G03950X/1) and the CARMEN e-science Neuroinformatics project ( ) funded by EPSRC (EP/E002331/1). Marcus Kaiser is also funded through the WCU program of the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (R32-10142).

Conflict of interests

The authors declare that the research was conducted commercial or financial relationships that could be construed as a potential conflict of interest.


  1. Ascoli, G. A. (Ed.) (2002). Computational neuroanatomy: Principles and methods. Totawa, NJ: Humana Press.Google Scholar
  2. Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007). A central resource for neuronal morphologies. Journal of Neuroscience, 27(35), 9247.PubMedCrossRefGoogle Scholar
  3. Binzegger, T., Douglas, R. J., & Martin, K. A. (2005). Axons in cat visual cortex are topologically self-similar. Cereb Cortex, 15(2), 152–165.CrossRefGoogle Scholar
  4. Botev, Z. I., Grotowski, J. F., & Krose, D. P. (2010). Kernel density estimation via diffusion. The Annals of Statistics, 38(5), 2916.CrossRefGoogle Scholar
  5. Cajal, S. R. (1989). Recollections of my life. Massachussetts: MIT Press.Google Scholar
  6. Cook, J. E. (1998). Getting to grips with neuronal diversity: What is a neuronal type? In L. Chalupa & B. Finlay (Eds.), Development and organization of the retina (pp. 91). New York: Plenum.CrossRefGoogle Scholar
  7. Costa, L. da F., Manoel, E. T. M., Faucereau, F., Chelly, J., van Pelt, J., & Ramakers, G. (2002). A shape analysis framework for neuromorphometry. Network: Computation in Neural Systems, 13(3), 283.CrossRefGoogle Scholar
  8. Costa, L. da F., Rodrigues, F. A., Hilgetag, C. C., & Kaiser, M. (2009). Beyond the average: Detecting global singular nodes from local features in complex networks. Europhysics Letters, 87, 18008.CrossRefGoogle Scholar
  9. Costa, L. da F. & Velte, T. J. (1999). Automatic characterization and classification of ganglion cells from salamander retina. Journal of Comparative Neurology, 404, 33.CrossRefGoogle Scholar
  10. Costa, L. da F., Zawadzki, K., Miazaki, M., Viana, M. P., & Taraskin, S. N. (2010). Unveiling the neuromorphological space. Frontiers in Neuroscience, 4, 1.Google Scholar
  11. Donohue, D. E., & Ascoli, G. A. (2010). Automated reconstruction of neuronal morphology: An overview. Brain Research Review, 67,(1–2), 165.Google Scholar
  12. Eadie, W. T., Drijard, D., James, F. E., Roos, M., & Sadoulet, B. (1971). Statistical methods in experimental physics. North-Holland, Amsterdam.Google Scholar
  13. Echtermeyer, C., Costa, L. da F., Rodrigues, F. A., & Kaiser, M. (2011). Automatic network fingerprinting through single-node motifs. PLoS ONE, 6(1), 9.CrossRefGoogle Scholar
  14. Echtermeyer, C., Han, C. E., Rotarska-Jagiela, A., Mohr, H., Uhlhass, P., & Kaiser, M. (2011). Integrating temporal and spatial scales: Human structural network motifs across age and region of interest size. Frontiers in Neuroinformatics, 5, 14.CrossRefGoogle Scholar
  15. Gleeson, P., Steuber, V., & Silver, R. A. (2007). Neuroconstruct: A tool for modeling networks of neurons in 3D space. Neuron, 54(2), 219.PubMedCrossRefGoogle Scholar
  16. Halavi, M., Polavaram, S., Donohue, D. E., Hamilton, G., Hoyt, J., Smith, K. P., et al. (2008). implementation of digital neuroscience: Dense coverage and integration with the NIF. Journal of Neuroinformatics, 6(3), 241–252.CrossRefGoogle Scholar
  17. Härdle, W. K., & Simar, L. (2007). Applied multivariate statistical analysis (2nd ed.). Springer.Google Scholar
  18. Kaiser, M., & Hilgetag, C. C. (2006). Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Computational Biology, 7(95), 806.Google Scholar
  19. Kaiser, M., Hilgetag, C. C., & van Ooyen, A. (2009). A simple rule for axon outgrowth and synaptic competition generates realistic connection lengths and filling fractions. Cerebral Cortex, 19(12), 3001.PubMedCrossRefGoogle Scholar
  20. Loewi, O. (1921). Über humorale Übertragbarkeit der Herznervenwirkung. Pflügers Archiv, 189, 239.CrossRefGoogle Scholar
  21. Loewi, O. (1955). Salute to Henry Hallet Dale. The British Medical Journal, 1(4926), 1356.CrossRefGoogle Scholar
  22. Lu, J., Fiala, F. C., & Lichtman, J. W. (2009). Semi-automated reconstruction of neural processes from large numbers of fluorescence images. PLoS ONE, 4(5), e5655.CrossRefGoogle Scholar
  23. McGhee, G. R. (2006). The geometry of evolution: Adaptive landscapes and theoretical morphospaces. Cambridge University Press.Google Scholar
  24. Montague, P. R., & Friedlander, M. J. (1991). Morphogenesis and territorial coverage by isolated mammalian retinal ganglion cells. Journal of Neuroscience, 11, 1440.PubMedGoogle Scholar
  25. Poznanski, R. R. (1992). Modelling the electronic structure of starburst amacrine cells in the rabbit retina: Functional interpretation of dendritic morphology. Bulletin of Mathematical Biology, 54, 905.PubMedGoogle Scholar
  26. Schierwagen, A. (2008). Neuronal morphology: Shape characteristics and model. Neurophysiology, 40(4), 310.CrossRefGoogle Scholar
  27. Scorcioni, R., Polavaram, S., & Ascoli, G. A. (2008). L-measure: A web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nature Protocols, 3, 866.PubMedCrossRefGoogle Scholar
  28. Sholl, D. A. (1953). Dendritic organization in the neurons of the visual and motor cortices of the cat. Journal of Anatomy, 87, 387.PubMedGoogle Scholar
  29. Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8(9), 418.PubMedCrossRefGoogle Scholar
  30. Srinivasan, R., Zhou, X., Miller, E., Lu, J., Litchman, J., & Wong, S. T. C. (2007). Automated axon tracking of 3D confocal laser scanning microscopy images using guided probabilistic region merging. Neuroinformatics, 5(3), 189.PubMedCrossRefGoogle Scholar
  31. Stepanyants, A., & Chklovskii, D. B. (2005). Neurogeometry and potential synaptic connectivity. Trends in Neuroscience, 28(7), 387.CrossRefGoogle Scholar
  32. Stepanyants, A., Hof, P. R., & Chklovskii, D. B. (2002). Geometry and structural plasticity of synaptic connectivity. Neuron, 34(2), 275.PubMedCrossRefGoogle Scholar
  33. Toris, C. B., Eiesland, J. L., & Miller, R. F. (1995). Morphology of ganglion cells in the neotenous tiger salamander retina. Journal of Comparative Neurology, 352(4), 535.PubMedCrossRefGoogle Scholar
  34. Wen, Q., & Chklovskii, D. B. (2008). A cost-benefit analysis of neuronal morphology. Journal of Neurophysiology, 99, 497.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Krissia Zawadzki
    • 1
  • Christoph Feenders
    • 2
    • 3
  • Matheus P. Viana
    • 1
  • Marcus Kaiser
    • 2
    • 4
    • 5
    Email author
  • Luciano da F. Costa
    • 1
    Email author
  1. 1.Institute of Physics at São CarlosUniversity of São PauloSão CarlosBrazil
  2. 2.School of Computing ScienceNewcastle UniversityNewcastle-upon-TyneUK
  3. 3.Institute for Chemistry and Biology of the Marine EnvironmentCarl von Ossietzky UniversityOldenburgGermany
  4. 4.Institute of NeuroscienceNewcastle UniversityNewcastle-upon-TyneUK
  5. 5.Department of Brain and Cognitive SciencesSeoul National UniversitySeoulRepublic of Korea

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