Skip to main content
SpringerLink
Log in

Morphological Neuron Classification Based on Dendritic Tree Hierarchy

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells. The methodology consists in using different strategies for decomposing the dendritic tree along its hierarchies, allowing the identification of relevant parts (possibly related to specific neuronal functions) for classification tasks. A set of more than 5000 neurons corresponding to 10 classes were examined with supervised classification algorithms based on this strategy. It was found that classification accuracies similar to those obtained by using whole neurons can be achieved by considering only parts of the neurons. Branches close to the soma were found to be particularly relevant for classification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. http://cng.gmu.edu:8080/Lm/

  2. http://farsight-toolkit.org/wiki/L_Measure_functions

References

  • Armañanzas, R., & Ascoli, G.A. (2015). Towards the automatic classification of neurons. Trends in Neurosciences, 38(5), 307–318.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ascoli, G.A. (2002). Computational neuroanatomy: principles and methods. Springer Science & Business Media.

  • Ascoli, G.A., Donohue, D.E., Halavi, M. (2007). Neuromorpho.org: a central resource for neuronal morphologies. The Journal of Neuroscience, 27(35), 9247–9251.

    Article  CAS  PubMed  Google Scholar 

  • Barbosa, M.S., da Fontoura Costa, L., de Sousa Bernardes, E. (2003). Neuromorphometric characterization with shape functionals. Physical Review E, 67(6), 061910.

    Article  Google Scholar 

  • Bazán, N. G., & Lolley, R.N. (2013). Neurochemistry of the retina: proceedings of the international symposium on the neurochemistry of the retina held in Athens, Greece, August 28-September 1 1979. Elsevier.

  • Bernard, A., Sorensen, S.A., Lein, E.S. (2009). Shifting the paradigm: new approaches for characterizing and classifying neurons. Current Opinion in Neurobiology, 19(5), 530–536.

    Article  CAS  PubMed  Google Scholar 

  • Bota, M., & Swanson, L.W. (2007). The neuron classification problem. Brain Research Reviews, 56(1), 79–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cajal, S.R.y, & Azoulay, L. (1955). Histologie du systèeme nerveux de l’homme & des vertébrés. Instituto Ramon Y Cajal: Consejo superior de investigaciones cientificas.

    Google Scholar 

  • Cannon, R., Turner, D., Pyapali, G., Wheal, H. (1998). An on-line archive of reconstructed hippocampal neurons. Journal of Neuroscience Methods, 84(1), 49–54.

    Article  CAS  PubMed  Google Scholar 

  • Chang, C.-C., & Lin, C.-J. (2011). Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.

    Google Scholar 

  • Comin, C.H., & da Fontoura Costa, L. (2013). Shape, connectedness and dynamics in neuronal networks. Journal of Neuroscience Methods, 220(2), 100–115.

    Article  PubMed  Google Scholar 

  • Costa, L.D.F., Zawadzki, K., Miazaki, M., Viana, M.P., Taraskin, S. (2010). Unveiling the neuromorphological space. Frontiers in Computational Neuroscience, 4, 150.

    Article  PubMed Central  Google Scholar 

  • da Costa, F.L., & Velte, T.J. (1999). Automatic characterization and classification of ganglion cells from the salamander retina. Journal of Comparative Neurology, 404(1), 33–51.

    Article  PubMed  Google Scholar 

  • DeFelipe, J. (2001). Cortical interneurons: from cajal to 2001. Progress in Brain Research, 136, 215–238.

    Article  Google Scholar 

  • Ding, M., & Glanzman, D. (2011). The dynamic brain: an ex- ploration of neuronal variability and its functional significance. USA: Oxford University Press.

    Book  Google Scholar 

  • Dong, X., Shen, K., Bülow, H. E. (2015). Intrinsic and extrinsic mechanisms of dendritic morphogenesis. Annual Review of Physiology, 77, 271–300.

    Article  CAS  PubMed  Google Scholar 

  • Gillette, T., & Ascoli, G. (2015). Topological characterization of neuronal arbor morphology via sequence representation. i. Motif analysis.

  • Guyon, I., Weston, J., Barnhill, S., Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1), 389–422.

    Article  Google Scholar 

  • Halavi, M., Hamilton, K.A., Parekh, R., Ascoli, G.A. (2012). Digital reconstructions of neuronal morphology: three decades of research trends. Frontiers in Neuroscience, 6(49), 1–11.

    Google Scholar 

  • Hosp, J.A., Strüber, M., Yanagawa, Y., Obata, K., Vida, I., Jonas, P., Bartos, M. (2014). Morphophysiological criteria divide dentate gyrus interneurons into classes. Hippocampus, 24(2), 189–203.

    Article  CAS  PubMed  Google Scholar 

  • Kandel, E.R., Schwartz, J.H., Jessell, T.M., et al. (2000). Principles of neural science Vol. 4. New York: McGrawhill.

    Google Scholar 

  • Lefebvre, J.L., Sanes, J.R., Kay, J.N. (2015). Development of dendritic form and function. Annual Review of Cell and Developmental Biology, 31, 741–777.

    Article  CAS  PubMed  Google Scholar 

  • López-Cruz, P L., Larrañaga, P., DeFelipe, J., Bielza, C. (2014). Bayesian network modeling of the consensus between experts: an application to neuron classification. International Journal of Approximate Reasoning, 55(1), 3–22.

    Article  Google Scholar 

  • Lu, Y., Carin, L., Coifman, R., Shain, W., Roysam, B. (2015). Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on l-measure. Neuroinformatics, 13(1), 47–63.

    Article  PubMed  Google Scholar 

  • Magee, J.C. (2000). Dendritic integration of excitatory synaptic input. Nature Reviews Neuroscience, 1(3), 181.

    Article  CAS  PubMed  Google Scholar 

  • Mainen, Z.F., & Sejnowski, T.J. (1996). In uence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382(6589), 363.

    Article  CAS  PubMed  Google Scholar 

  • McGarry, L.M., Packer, A.M., Fino, E., Nikolenko, V., Sippy, T., Yuste, R. (2010). Quantitative classification of somatostatin-positive neocortical interneurons identifies three interneuron subtypes. Frontiers in Neural Circuits, 4, 12.

    PubMed  PubMed Central  Google Scholar 

  • Mihaljević, B., Benavides-Piccione, R., Bielza, C., DeFelipe, J., Larrañaga, P. (2015). Bayesian network classifiers for categorizing cortical gabaergic interneurons. Neuroinformatics, 13(2), 193–208.

    Article  PubMed  Google Scholar 

  • Mottini, A., Descombes, X., Besse, F. (2014). Axonal tree classification using an elastic shape analysis based distance. In 2014 IEEE 11th International symposium on biomedical imaging (ISBI) (pp. 850–853). IEEE.

  • Parekh, R., & Ascoli, G.A. (2013). Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron, 77(6), 1017–1038.

    Article  CAS  PubMed  PubMed Central  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.

    Article  CAS  PubMed  Google Scholar 

  • Ruz, I.D., & Schultz, S.R. (2014). Localising and classifying neurons from high density mea recordings. Journal of Neuroscience Methods, 233, 115–128.

    Article  Google Scholar 

  • Santana, R., McGarry, L., Bielza, C., Larrañaga, P., Yuste, R. (2013). Classification of neocortical interneurons using affinity propagation. Frontier in Neural Circuits, 7, 185.

    Google Scholar 

  • 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(5), 866–876.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sharpee, T.O. (2014). Toward functional classification of neuronal types. Neuron, 83(6), 1329–1334.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sümbül, U., Zlateski, A., Vishwanathan, A., Masland, R.H., Seung, H.S. (2014a). Automated computation of arbor densities: a step toward identifying neuronal cell types. Frontiers in Neuroanatomy, 8, 139.

  • Sümbül, U., Song, S., McCulloch, K., Becker, M., Lin, B., Sanes, J.R., Masland, R.H., Seung, H.S. (2014b). A genetic and computational approach to structurally classify neuronal types. Nature Communications, 5.

  • Teeter, C.M., & Stevens, C.F. (2011). A general principle of neural arbor branch density. Current Biology, 21(24), 2105–2108.

    Article  CAS  PubMed  Google Scholar 

  • Torben-Nielsen, B. (2014). An efficient and extendable python library to analyze neuronal morphologies. Neuroinformatics, 12(4), 619.

    Article  PubMed  Google Scholar 

  • Uji, Y., Yamamura, H., et al. (1995). Morphological classification of retinal ganglion cells in mice. Journal of Comparative Neurology, 356(3), 368–386.

    Article  PubMed  Google Scholar 

  • Uylings, H.B., & van Pelt, J. (2002). Measures for quantifying dendritic arborizations. Network: Computation in Neural Systems, 13(3), 397–414.

    Article  Google Scholar 

  • Uylings, H.B., Van Pelt, J., Verwer, R.W., McConnell, P. (1989). Statistical analysis of neuronal populations. In Computer techniques in neuroanatomy (pp. 241–264). Springer.

  • Zhao, T., & Plaza, S.M. (2014). Automatic neuron type identification by neurite localization in the drosophila medulla. arXiv:1409.1892.

Download references

Acknowledgements

C. H. Comin thanks FAPESP (Grant No. 15/18942-8) for financial support. L. da F. Costa thanks CNPq (Grant No. 307333/2013-2) for support. This work has also been supported by the FAPESP grant 2015/22308-2, Capes and CNPq.

Author information

Authors and Affiliations

  1. Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil

    Evelyn Perez Cervantes & Roberto Marcondes Cesar Junior

  2. Department of Computer Science, Federal University of São Carlos, São Carlos, Brazil

    Cesar Henrique Comin

  3. São Carlos Institute of Physics, University of São Paulo, PO Box 369, 13560-970, São Carlos, SP, Brazil

    Luciano da Fontoura Costa

Authors
  1. Evelyn Perez Cervantes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cesar Henrique Comin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roberto Marcondes Cesar Junior
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luciano da Fontoura Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Evelyn Perez Cervantes.

Ethics declarations

Conflict of interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cervantes, E.P., Comin, C.H., Junior, R.M.C. et al. Morphological Neuron Classification Based on Dendritic Tree Hierarchy. Neuroinform 17, 147–161 (2019). https://doi.org/10.1007/s12021-018-9388-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-018-9388-7

Keywords

Search

Navigation