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.
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References
Armañanzas, R., & Ascoli, G.A. (2015). Towards the automatic classification of neurons. Trends in Neurosciences, 38(5), 307–318.
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.
Barbosa, M.S., da Fontoura Costa, L., de Sousa Bernardes, E. (2003). Neuromorphometric characterization with shape functionals. Physical Review E, 67(6), 061910.
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.
Bota, M., & Swanson, L.W. (2007). The neuron classification problem. Brain Research Reviews, 56(1), 79–88.
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.
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.
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.
Comin, C.H., & da Fontoura Costa, L. (2013). Shape, connectedness and dynamics in neuronal networks. Journal of Neuroscience Methods, 220(2), 100–115.
Costa, L.D.F., Zawadzki, K., Miazaki, M., Viana, M.P., Taraskin, S. (2010). Unveiling the neuromorphological space. Frontiers in Computational Neuroscience, 4, 150.
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.
DeFelipe, J. (2001). Cortical interneurons: from cajal to 2001. Progress in Brain Research, 136, 215–238.
Ding, M., & Glanzman, D. (2011). The dynamic brain: an ex- ploration of neuronal variability and its functional significance. USA: Oxford University Press.
Dong, X., Shen, K., Bülow, H. E. (2015). Intrinsic and extrinsic mechanisms of dendritic morphogenesis. Annual Review of Physiology, 77, 271–300.
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.
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.
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.
Kandel, E.R., Schwartz, J.H., Jessell, T.M., et al. (2000). Principles of neural science Vol. 4. New York: McGrawhill.
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.
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.
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.
Magee, J.C. (2000). Dendritic integration of excitatory synaptic input. Nature Reviews Neuroscience, 1(3), 181.
Mainen, Z.F., & Sejnowski, T.J. (1996). In uence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382(6589), 363.
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.
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.
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.
Rall, W. (1967). Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. Journal of Neurophysiology, 30(5), 1138–1168.
Ruz, I.D., & Schultz, S.R. (2014). Localising and classifying neurons from high density mea recordings. Journal of Neuroscience Methods, 233, 115–128.
Santana, R., McGarry, L., Bielza, C., Larrañaga, P., Yuste, R. (2013). Classification of neocortical interneurons using affinity propagation. Frontier in Neural Circuits, 7, 185.
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.
Sharpee, T.O. (2014). Toward functional classification of neuronal types. Neuron, 83(6), 1329–1334.
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.
Torben-Nielsen, B. (2014). An efficient and extendable python library to analyze neuronal morphologies. Neuroinformatics, 12(4), 619.
Uji, Y., Yamamura, H., et al. (1995). Morphological classification of retinal ganglion cells in mice. Journal of Comparative Neurology, 356(3), 368–386.
Uylings, H.B., & van Pelt, J. (2002). Measures for quantifying dendritic arborizations. Network: Computation in Neural Systems, 13(3), 397–414.
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.
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.
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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
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DOI: https://doi.org/10.1007/s12021-018-9388-7