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Neuroinformatics
Erschienen in: Neuroinformatics 4/2008

01.12.2008

Non-parametric Algorithmic Generation of Neuronal Morphologies

verfasst von: Benjamin Torben-Nielsen, Stijn Vanderlooy, Eric O. Postma

Erschienen in: Neuroinformatics | Ausgabe 4/2008

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Abstract

Generation algorithms allow for the generation of Virtual Neurons (VNs) from a small set of morphological properties. The set describes the morphological properties of real neurons in terms of statistical descriptors such as the number of branches and segment lengths (among others). The majority of reconstruction algorithms use the observed properties to estimate the parameters of a priori fixed probability distributions in order to construct statistical descriptors that fit well with the observed data. In this article, we present a non-parametric generation algorithm based on kernel density estimators (KDEs). The new algorithm is called KDE-Neuron and has three advantages over parametric reconstruction algorithms: (1) no a priori specifications about the distributions underlying the real data, (2) peculiarities in the biological data will be reflected in the VNs, and (3) ability to reconstruct different cell types. We experimentally generated motor neurons and granule cells, and statistically validated the obtained results. Moreover, we assessed the quality of the prototype data set and observed that our generated neurons are as good as the prototype data in terms of the used statistical descriptors. The opportunities and limitations of data-driven algorithmic reconstruction of neurons are discussed.
Vorheriger Artikel Reviewing Multi-Disciplinary Papers: a Challenge in Neuroscience?
Nächster Artikel Learning Vector Quantization Neural Networks Improve Accuracy of Transcranial Color-coded Duplex Sonography in Detection of Middle Cerebral Artery Spasm—Preliminary Report

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Fußnoten
1
For the sake of readability, ‘generation’ and ‘reconstruction’ are used as synonyms in the remainder of the text.
 
2
Mathematical details related to this section can be found in the Appendix.
 
3
More advanced methods (in efficiency and effectivity) are possible but this is not the focus in the current work. We refer the interested reader to Ch. 11 of Bishop (2006)
 
4
To avoid a pre-processing step, we directly work with compartments that are specified in the SWC format, rather than with segments.
 
5
Outnumbering occurs because every bifurcation compartment is surrounded by prolongating compartments, and terminating compartments are always preluded by prolongating compartments.
 
6
We selected the options in a simple manner by trying out a few different settings in preliminary experiments. So, even though we will show good results with these options, there is still space for more fine-tuning. A grid search over the options can for example be used for an extensive parameter search.
 
7
Electrophysiological simulations are more precise when an odd number of compartments is used (Carnevale and Hines 2006).
 
8
The low number of filtered neurons can be attributed to the fixed distribution of branching angles because, in order to pass the filter, a particular sequence of branch angles and segments lengths is required. For instance, the filter tests the Euclidean distance between soma and terminal tips, which is entirely defined by the angles and lengths of a branch.
 
Literatur
Alpaydin, E. (2004). Introduction to machine learning. Cambridge, MA: MIT Press.
Ambros-Ingerson, J., & Holmes, W. R. (2005). Analysis and comparison of morphological reconstructions of hippocampal field CA1 pyramidal cells. Hippocampus, 15, 302–315.PubMedCrossRef
Ascoli, G. A. (1999). Progress and perspectives in computational neuroanatomy. Anatomical Record, 257, 195–207.PubMedCrossRef
Ascoli, G. A. (2006). Mobilizing the base of neuroscience data: The case of neuronal morphologies. Nature Neuroscience Reviews, 318(7), 318–324.CrossRef
Ascoli, G. A. (2007). Success and rewards in sharing digital reconstructions of neuronal morphology. Neuroinformatics, 5, 154–160.PubMedCrossRef
Ascoli, G. A., & Krichmar, J. L., (2000). L-Neuron: A modeling tool for the efficient generation and parsimonious description of dendritic morphology. Neurocomputing, 32–33, 1003–1011.CrossRef
Ascoli, G. A., Krichmar, J. L., Scorcioni, R., Nasuto, S. J., & Senft, S. L. (2001). Computer generation and quantitative morphometric analysis of virtual neurons. Anatomy and Embryology, 204, 283–301.PubMedCrossRef
Bishop, C. (2006). Pattern recognition and machine learning. New York, NY: Springer-Verlag.
Burke, R., Marks, W., & Ulfhake, B. (1992). A parsimonious description of motorneuron dendritic morphology using computer simulation. Journal of Neuroscience, 12(6), 2403–2416.PubMed
Burns, G. (2001). Knowledge management of the neuroscientific literature: The data model of the neuroscholar system. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 356, 1187–1208.PubMedCrossRef
Cameron, W., He, F., Kalipatnapu, P., Jodkowski, J., & Guthrie, R. (1991). Morphometric analysis of phrenic motoneurons in the cat during postnatal development. Journal of Comparative Neurology, 314(4), 763–776.PubMedCrossRef
Cannon, R., Turner, D., Pyapali, G., & Wheal, H. (1998). An on-line archive of reconstructed hippocampal neurons. Journal of Neuroscience Methods, 84(1–2), 49–54.PubMedCrossRef
Carnevale, N., & Hines, M. (2006). The NEURON Book. Cambridge, UK: Cambridge University Press.
da F. Costa, L., Barbosa, M., & Coupez, V. (2005). On the potential of the excluded volume and autocorrelation as neuromorphometric descriptors. Physica A: Statistical Mechanics and its Applications, 348, 317–326.CrossRef
De Schutter, E., & Bower, J. M. (1994). An active membrane model of the cerebellar purkinje cell I. simulation of current clamps in slice. Journal of Neurophysiology, 71(1), 375–400.PubMed
Devroye, L. (1986). Non-Uniform Random Variate Generation. New York, NY: Springer-Verlag.
Donohue, D. E., & Ascoli, G. A. (2005). Local diameter fully constraints dendritic size in basal but not apical trees of ca1 pyramidal neurons. Journal of Computational Neuroscience, 19, 223–238.PubMedCrossRef
Eberhard, J., Wanner, A., & Wittum, G. (2007). NeuGen: A tool for the generation of realistic morphology of cortical neurons and neural networks in 3D. Neurocomputing, 70(1–3), 327–342.
Feng, N., Ning, G., & Zheng, X. (2005). A framework for simulating axon guidance. Neurocomputing, 68, 70–84.CrossRef
Fernández, E., & Jelinek, H. F. (2001). Use of fractal theory in neuroscience: Methods, advantages and potential problems. Methods, 24, 309–321.PubMedCrossRef
Glaser, J., & Glaser, E. (1990). Neuron imaging with Neurolucida - a PC-based system for image combining microscopy. Computerized Medical Imaging and Graphics, 14, 307–317.PubMedCrossRef
Hillman, D. (1979). Neuronal shape parameters and substructures as a basis of neuronal form, volume The neurosciences, Fourth Study Program, chapter 27. Cambridge, MA: The MIT Press.
Horch, H. W., & Katz, L. C. (2002). Bdnf release from single cells elicits local dendritic growth in nearby neurons. Nat Neurosci, 5(11), 1177–1184.PubMedCrossRef
Jones, C., Marron, J., & Sheather, S. (1996). A brief survey of bandwidth selection for density estimation. Journal of American Statistical Association, 91(433), 401–407.CrossRef
Kaspirzhny, A. V., Gogan, P., Horcholle-Bossavit, G., & Tyc-Dumont, S. (2002). Neuronal morphology data bases: Morphological noise and assessment of data quality. Network: Computation in Neural Systems, 13, 357–380.CrossRef
Lehmann, E., & Romano, J. (2006). Testing statistical hypotheses (3rd ed.). Springer-Verlag, Berlin Heidelberg.
Lien, J.-M., Morales, M., & Amato, N. M. (2003). Neuron PRM: A framework for constructing cortical networks. Neurocomputing, 52–54, 191–197.CrossRef
Lindsay, K. A., Maxwell, D. J., Rosenberg, J. R., & Tucker, G. (2007). A new approach to reconstruction models of dendritic branching patterns. Mathematical Biosciences, 205(2), 271–296.PubMedCrossRef
Loader, C. (1999). Bandwidth selection: Classical or plug-in? The Annals of Statistics, 27(2), 451–438.CrossRef
Luczak, A. (2006). Spatial embedding of neuron trees modeled by diffusive growth. Journal of Neuroscience Methods, 157, 132–141.PubMedCrossRef
Myatt, D., Hadlington, T., Ascoli, G., & Nasuto, S. (2007). Inter-user variability of semi-manually reconstructed dendritic trees with the freeware tool neuromantic. Journal of Microscopy, (submitted).
Neapolitan, R. E. (2003). Learning bayesian networks. Prentice Hall.
Nowakowski, R. S., Hayes, N. S., & Egger, M. D. (1992). Competitive interactions during dendritic growth: A simple stochastic growth algorithm. Brain Research, 576, 152–156.PubMedCrossRef
Parzen, E. (1962). On estimation of probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065–1076.CrossRef
Raykar, V., & Duraiswami, R. (2006). Very fast optimal bandwidth selection for univariate kernel density estimation. Technical Report CS-TR-4774, University of Maryland, College Park.
Rihn, L., & Claiborne, B. (1990). Dendritic growth and regression in rat dentate granule cells during late postnatal development. Brain Research. Developmental Brain Research, 54(1), 115–124.PubMedCrossRef
Robert, C. (2007). The Bayesian Choice: From decision-theoretic foundations to computational Implementation. New York, NY: Springer-Verlag.
Samsonovich, A. V., & Ascoli, G. A. (2003). Statistical morphological analysis of hippocampal principal neurons indicates cell-specific repulsion of dendrites from their own cell. Journal of Neuroscience Research, 71, 173–187.PubMedCrossRef
Samsonovich, A. V., & Ascoli, G. A. (2005a). Algortihmic description of hippocampal granule cell dendritic morphology. Neurocomputing, 65–66, 253–260.CrossRef
Samsonovich, A. V., & Ascoli, G. A. (2005b). Statistical determinants of dendritic morphology in hippocampal pyramidal neurons: A hidden markov model. Hippocampus, 15, 166–183.PubMedCrossRef
Scorcioni, R., Lazarewicz, M. T., & Ascoli, G. A. (2004). Quantitative morphometry of hippocampal pyramidal cells: Differences between anatomical classes and reconstructing laboratories. Journal Comparative Neurology, 473, 177–193.CrossRef
Scott, D. (1992). Multivariate density estimation: theory, practice, and visualization. New York, NY: John Wiley and Sons Inc.
Scott, E., & Luo, L. (2001). How do dendrites take their shape? Nature (neuroscience), 4(4), 359–365.CrossRef
Silverman, B. (1986). Density estimation for statistics and data analysis. London, UK: Chapman and Hall.
Steuber, V., De Schutter, E., & Jaeger, D. (2004). Passive models of neurons in the deep cerebellar nuclei: The effect of reconstruction errors. Neurocomputing, 58–60, 563–568.CrossRef
Szilágyi, T., & De Schutter, E. (2004). Effects of variability in anatomical reconstruction techniques on models of synaptic integration by dendrites: a comparison of three internet archives. European Journal of Neuroscience, 19, 1257–1266.PubMedCrossRef
Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge, Mass: Oxford University Press.
Torben-Nielsen, B., Tuyls, K., & Postma, E. O. (2008). Evol-neuron: Virtual neuron generation. Neurocomputing, 71(4–6), 963–972.CrossRef
van Pelt, J., & Schierwagen, A. (2004). Morphological analysis and modeling of neuronal dendrites. Mathematical Biosciences, 188, 147–155.PubMedCrossRef
Wand, M., & Jones, C. (1995). Kernel smoothing. London, UK: Chapman and Hall.
Witten, I. H., & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques (2nd ed.). San Francisco, CA: Morgan Kaufmann.
Metadaten
Titel
Non-parametric Algorithmic Generation of Neuronal Morphologies
verfasst von
Benjamin Torben-Nielsen
Stijn Vanderlooy
Eric O. Postma
Publikationsdatum
01.12.2008
Verlag
Humana Press Inc
Erschienen in
Neuroinformatics / Ausgabe 4/2008
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI
https://doi.org/10.1007/s12021-008-9026-x

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