Abstract
A systematic classification of neuron types is a critical topic of debate in neuroscience. In this study, we propose a semi-supervised projected clustering algorithm based on finite mixture models and the expectation-maximization (EM) algorithm, that is useful for classifying neuron types. Specifically, we analyzed cortical GABAergic interneurons from different animals and cortical layers. The new algorithm, called SeSProC, is a probabilistic approach for classifying known classes and for discovering possible new groups of interneurons. Basic morphological features containing information about axonal and dendritic arborization sizes and orientations are used to characterize the interneurons. SeSProC also identifies the relevance of each feature and group separately. This article aims to present the methodological approach, reporting results for known classes and possible new groups of interneurons.
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References
Petilla Interneuron Nomenclature Group: Petilla terminology: Nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008)
Marin, E.C., Jefferys, G., Komiyama, T., Zhu, H., Luo, L.: Representation of the glomerular olfactory map in the drosophila brain. Cell 149, 243–255 (2002)
Guerra, L., McGarry, L.M., Robles, V., Bielza, C., Larrañaga, P., Yuste, R.: Comparison between supervised and unsupervised classifications of neuronal cell types: A case study. Dev. Neurobiol. 71(1), 71–82 (2011)
Tsiola, A., Hamzei-Sichani, F., Peterlin, Z., Yuste, R.: Quantitative morphological classification of layer 5 neurons from mouse primary visual cortex. J. Compar. Neurol. 461, 415–428 (2003)
Benavides-Piccione, R., Sichani, F.H., Yaez, I.B., DeFelipe, J., Yuste, R.: Dendritic size of pyramidal neurons differs among mouse cortical regions. Cereb. Cortex. 16, 990–1001 (2005)
Cauli, B., Porter, J.T., Tsuzuki, K., Lambolez, B., Rossier, J., Quenet, B., Audinat, E.: Classification of fusiform neocortical interneurons based on unsupervised clustering. Proc. Natl. Acad. Sci. 97(11), 6144–6149 (2000)
Karagiannis, A., Gallopin, T., Csaba, D., Battaglia, D., Geoffroy, H., Rossier, J., Hillman, E., et al.: Classification of NPY-expressing neocortical interneurons. J. Neurosci. 29, 3642–3659 (2009)
McGarry, L.M., Packer, A., Fino, E., Nikolenko, V., Sippy, T., Yuste, R.: Quantitative classification of somatostatin-positive neocortical interneurons identifies three interneuron subtypes. Front. Neural Circuits. 4(12), 1–19 (2010)
DeFelipe, J.: Cortical interneurons: From Cajal to 2001. Prog. Brain Res. 136, 215–238 (2002)
DeFelipe, J., et al.: New insights in the classification and nomenclature of cortical GABAergic interneurons. Nat. Rev. Neurosci. 14(3), 202–216 (2013)
McLachlan, G., Basford, K.: Mixture Models: Inference and Applications to Clustering. Marcel Dekker (1988)
Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press (2006)
Zhu, X., Goldberg, A.: Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers (2009)
Parsons, L., Haque, E., Liu, H.: Subspace clustering for high dimensional data: A review. ACM SIGKDD Explorations Newsletter - Special Issue on Learning From Imbalanced Datasets 6(1), 90–105 (2004)
Kriegel, H., Kröger, P., Zimek, A.: Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering and correlation clustering. ACM Trans. Knowl. Discov. Data. 3(1), 1–58 (2009)
Law, M.H.C., Figueiredo, M.A.T., Jain, A.K.: Simultaneous feature selection and clustering using mixture models. IEEE T. Pattern Anal. 26(9), 1154–1166 (2004)
Vellido, A., Lisboa, P.J.G., Vicente, D.: Robust analysis of MRS brain tumour data using t-GTM. Neurocomputing 69(79), 754–768 (2006)
Ascoli, G.A., Donohue, D.E., Halavi, M.: NeuroMorpho. Org: A central resource for neuronal morphologies. J. Neurosci. 27(35), 9247–9251 (2007)
Guerra, L., Bielza, C., Robles, V., Larrañaga, P.: Semi-supervised projected model-based clustering. Data Min. Knowl. Disc. (2012) (submitted)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39(1), 1–38 (1977)
Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)
Minsky, M.: Steps toward artificial intelligence. In: Computers and Thought, pp. 406–450. McGraw-Hill (1961)
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Guerra, L., Benavides-Piccione, R., Bielza, C., Robles, V., DeFelipe, J., Larrañaga, P. (2013). Semi-supervised Projected Clustering for Classifying GABAergic Interneurons. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_24
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DOI: https://doi.org/10.1007/978-3-642-38326-7_24
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