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NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies

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Abstract

We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.

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References

  • Aeschlimann, M. (2000). Biophysical models of axonal pathfinding. Ph.D. thesis, University of Lausanne.

  • Ascoli, G., & Krichmar, J. (2000). L-neuron: a modeling tool for the efficient generation and parsimonious description of dendritic morphology. Neurocomputing, 32–33, 1003–1011.

    Article  Google Scholar 

  • Ascoli, G., Krichmar, J., Nasuto, S., & Senft, S. (2001a). Generation, description and storage of dendritic morphology data. Philosophical Transactions of the Royal Society of London B, 356, 1131–1145.

    Article  CAS  Google Scholar 

  • Ascoli, G., Krichmar, J., Scorcioni, R., Nasuto, S., & Senft, S. (2001b). Computer generation and quantitative morphometric analysis of virtual neurons. Anatomy and Embryology, 204, 283–301.

    Article  CAS  Google Scholar 

  • Bamburg, J. (2003). Introduction to cytoskeletal dynamics and pathfinding of neuronal growth cones. Journal of Histochemistry & Cytochemistry, 51(4), 407–409.

    CAS  Google Scholar 

  • Belmonte, M., & Bourgeron, T. (2006). Fragile x syndrome and autism at the intersection of genetic and neural networks. Nature Neuroscience, 9, 1221–1225.

    Article  PubMed  CAS  Google Scholar 

  • Braitenberg, V., & Schütz, A. (1998). Cortex: statistics and geometry of neuronal connectivity. Berlin: Springer.

    Google Scholar 

  • Butz, M., Lehmann, K., Dammasch, I., & Teuchert-Noodt, G. (2006). A theoretical network model to analyse neurogenesis and synaptogenesis in the dentate gyrus. Neural Networks, 19, 1490–1505.

    Article  PubMed  Google Scholar 

  • da Costa, L. F., Manoel, E., Faucereau, J., Chelly, J., Van Pelt, J., & Ramakers, G. (2002). A shape analysis framework for neuromorphometry. Network, 13(3), 283–310.

    Article  Google Scholar 

  • Dityatev, A. E., Chmykhova, N. M., Studer, L., Karamian, O. A., Kozhanov, V. M., & Clamann, H. P. (1995). Comparison of the topology and growth rules of motoneuronal dendrites. J. Comp Neurol., 363, 505–516.

    Article  PubMed  CAS  Google Scholar 

  • Douglas, R., & Martin, K. (2004). Neuronal circuits in the neocortex. Annual Review of Neuroscience, 27, 419–451.

    Article  PubMed  CAS  Google Scholar 

  • Eberhard, J. P., Wanner, A., & Wittum, G. (2006). NeuGen: A tool for the generation of realistic morphology of cortical neurons and neuronal networks in 3D. Neurocomputing, 70(1–3), 327–342.

    Article  Google Scholar 

  • Fields, R., & Itoh, K. (1996). Neural cell adhesion molecules in activity-dependent development and synaptic plasticity. Trends in Neurosciences, 19, 473–480.

    Article  PubMed  CAS  Google Scholar 

  • Gleeson, P., Steuber, V., & Silver, R. (2007). neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron, 54, 219–235.

    Article  PubMed  CAS  Google Scholar 

  • Goldberg, D., & Burmeister, D. (1989). Looking into growth cones. Trends in Neurosciences, 12(12), 503–506.

    Article  PubMed  CAS  Google Scholar 

  • Goodhill, J. G. (1998). Mathematical guidance for axons. Trends in Neurosciences, 21, 226–231.

    Article  PubMed  CAS  Google Scholar 

  • Gordon-Weeks, P. R. (2000). Neuronal Growth Cones. Cambridge, United Kingdom: Cambridge University Press.

    Google Scholar 

  • Graham, B., & Van Ooyen, A. (2004). Transport limited effects in a model of dendritic branching. Journal of Theoretical Biology, 230, 421–432.

    Article  PubMed  Google Scholar 

  • Hellwig, B. (2000). A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex. Biol. Cybern., 82, 111–121.

    Article  PubMed  CAS  Google Scholar 

  • Hely, T., Graham, B., & Van Ooyen, A. (2001). A computational model of dendrite elongation and branching based on map2 phosphorylation. Journal of Theoretical Biology, 210, 375–384.

    Article  PubMed  CAS  Google Scholar 

  • Hentschel, H. G. E., & Van Ooyen, A. (1999). Models of axon guidance and bundling during development. Proceedings of the Royal Society of London Series B, 266, 2231–2238.

    Article  PubMed  CAS  Google Scholar 

  • Hillman, D. (1979). Neuronal shape parameters and substructures as a basis of neuronal form. In F. Schmitt (Ed.), The neurosciences, 4th study program (pp. 477–498). Cambridge: MIT.

    Google Scholar 

  • Hillman, D. E. (1988). Parameters of dendritic shape and substructure: intrinsic and extrinsic determination? In R. Lasek & M. Black (Eds.), Intrinsic determinants of neuronal form and function (pp. 83–113). New York: Alan R. Liss, Inc.

    Google Scholar 

  • Hines, M., & Carnevale, N. (1997). The NEURON simulation environment. Neural Computation, 9, 1179–1209.

    Article  PubMed  CAS  Google Scholar 

  • Hines, M., & Moore, J. (1993). A NEURON simulation program. In 23 rd Annual Meeting of the Society for Neuroscience.

  • Isbister, C., & O’Connor, T. (1999). Filopodial adhesion does not predict growth cone steering events in vivo. Journal of Neuroscience, 20, 2589–2600.

    Google Scholar 

  • Jan, Y.-N., & Jan, L.-Y. (2003). The control of dendrite development. Neuron, 40, 229–242.

    Article  PubMed  CAS  Google Scholar 

  • Kaiser, M., & Hilgetag, C. (2007). Development of multi-cluster cortical networks by time windows for spatial growth. Neurocomputing, 70, 1829–1832.

    Article  Google Scholar 

  • Kater, S. B. & Guthrie, P. B. (1990). Neuronal growth cone as an integrator of complex environmental information. In: Cold Spring Harbor Symposia on Quantitative Biology, vol. LV, Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 35.

  • Kiddie, G., McLean, D., van Ooyen, A., & Graham, B. (2005). Biologically plausible models of neurite outgrowth. In J. van Pelt, M. Kamermans, C. Levelt, A. van Ooyen, G. Ramakers, and P. Roelfsema, (Eds.), Development, dynamics and pathology of neuronal networks: from molecules to functional circuits, Progress in Brain Research, 147, 67–80. Elsevier.

  • Koene, R., Postma, F., de Ridder, S., Hoedemaker, S., van Pelt, J., & van Ooyen A. (2009) NETMORPH Manual. http://www.neurodynamics.nl/.

  • Konur, S., & Ghosh, A. (2005). Calcium signaling and the control of dendritic development. Neuron, 46, 401–405.

    Article  PubMed  CAS  Google Scholar 

  • Kowalski, R. J., & Williams, R. J. (1993). Microtubule-associated protein 2 alters the dynamic properties of microtubule assembly and disassembly. Journal Biological Chemistry, 268, 9847–9855.

    CAS  Google Scholar 

  • Lamoureux, P., Buxbaum, R. E., & Heidemann, S. R. (1998). Axonal outgrowth of cultured neurons is not limited by growth cone competition. Journal of Cell Science, 111, 3245–3252.

    PubMed  CAS  Google Scholar 

  • Larkman, A. U. (1991). Dendritic morphology of pyramidal neurons of the visual cortex of the rat I. Branching patterns. Journal of Comparative Neurology, 306, 307–319.

    Article  PubMed  CAS  Google Scholar 

  • Larkman, A. U., Major, G., Stratford, K. J., & Jack, J. J. B. (1992). Dendritic morphology of pyramidal neurones of the visual cortex of the rat. IV: Electrical geometry. Journal of Comparative Neurology, 323, 137–152.

    Article  PubMed  CAS  Google Scholar 

  • Le Bé, J.-V., Silberberg, G., Wang, Y., & Markram, H. (2007). Morphological, electrophysiological, and synaptic properties of corticocallosal pyramidal cells in the neonatal rat neocortex. Cerebral Cortex, 17, 2204–2213.

    Article  PubMed  Google Scholar 

  • Letourneau, P., Kater, S., & Macagno, E. (eds). (1991). The Nerve Growth Cone. New York: Raven.

    Google Scholar 

  • Luczak, A. (2006). Spatial embedding of neuronal trees modeled by diffusive growth. Journal of Neuroscience Methods, 157, 132–141.

    Article  PubMed  Google Scholar 

  • Maskery, S. M., Buettner, H. M., & Shinbrot, T. (2004). Growth Cone Pathfinding: a competition between deterministic and stochastic events. BMC Neuroscience, 5, 22.

    Article  PubMed  Google Scholar 

  • Nowakowski, R. S., Hayes, N. L., & Egger, M. D. (1992). Competitive interactions during dendritic growth: a simple stochastic growth algorithm. Brain Research, 576, 152.

    Article  PubMed  CAS  Google Scholar 

  • Peters, A. (1979). Thalamic input to the cerebral cortex. Trends in Neurosciences, 2, 1183–1185.

    Article  Google Scholar 

  • Polinsky, M., Balzovich, K., & Tosney, K. (2000). Identification of an invariant response: Stable contact with schwann cells induces veil extension in sensory growth cones. Journal of Neuroscience, 20, 1044–1055.

    PubMed  CAS  Google Scholar 

  • Rall, W. (1959). Branching dendritic trees and motoneuron membrane resistivity. Experimental Neurology, 1, 491–527.

    Article  PubMed  CAS  Google Scholar 

  • Ramakers, G. J. A., Winter, J., Hoogland, T., Lequin, M. B., Van Pelt, J., & Pool, C. W. (1998). Depolarization stimulates lamellipodia formation and axonal but not dendritic branching in cultured rat cerebral cortex neurons. Development Brain Research, 108, 205–216.

    Article  CAS  Google Scholar 

  • Samsonovich, A., & Ascoli, G. (2007). Computational models of dendritic morphology: From parsimonious description to biological insight. In M. Laubichler & G. Müller (Eds.), Modeling Biology, Structures, Behaviors, Evolution (pp. 91–113). Cambridge, Massachusetts: MIT.

    Google Scholar 

  • Sánchez, C. J., Díaz-Nido, J., & Avila, J. (2000). Phosphorylation of microtubule-associated protein 2 (MAP2) and its relevance for the regulation of the neuronal cytoskeleton function. Progress in Neurobiology, 61, 133–168.

    Article  PubMed  Google Scholar 

  • Scheff, S. W., Prince, D. A., Schmitt, F. A., DeKosky, S. T., & Mufson, E. F. (2007). Synaptic alterations in CA1 mild Alzheimer’s disease and mild cognitive impairment. Neurology, 68, 1501–1508.

    Article  PubMed  CAS  Google Scholar 

  • Schierwagen, A., & Grantyn, R. (1986). Quantitative morphological analysis of deep superior colliculus neurons stained intracellularly with HRP in the cat. J Hirnforsch, 27, 611–623.

    PubMed  CAS  Google Scholar 

  • Schubert, D., Kötter, R., Luhmann, H. J., & Staiger, J. F. (2006). Morphology, electrophysiology and functional input connectivity of pyramidal neurons characterizes a genuine layer Va in the primary somatosensory cortex. Cereb Cortex, 16(2), 223–36.

    Article  PubMed  CAS  Google Scholar 

  • Segev, R., & Ben-Jacob, E. (2000). Generic modeling of chemotactic based self-wiring of neural networks. Neural Networks, 13(2), 185–199.

    Article  PubMed  CAS  Google Scholar 

  • Senft, S., & Ascoli, G. (1999). Reconstruction of brain networks by algorithmic amplification of morphometry data. Lecture Notes in Computer Science, 1606, 25–33.

    Article  Google Scholar 

  • Shepherd, G. M., & Svoboda, K. (2005). Laminar and columnar organization of ascending excitatory projections to layer 2/3 pyramidal neurons in rat barrel cortex. Journal of Neuroscience, 25(24), 5670–5679.

    Article  PubMed  CAS  Google Scholar 

  • Sporns, O., Chialvo, D., Kaiser, M., & Hilgetag, C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8, 418–425.

    Article  PubMed  Google Scholar 

  • Stepanyants, A., & Chklovskii, D. (2005). Neurogeometry and potential synaptic connectivity. Trends in Neurosciences, 28(7), 387–394.

    Article  PubMed  CAS  Google Scholar 

  • Stepanyants, A., Tamas, G., & Chklovskii, D. B. (2004). Class-specific features of neuronal wiring. Neuron, 43, 251–259.

    Article  PubMed  CAS  Google Scholar 

  • Uylings, H., & Smit, G. (1975). Three dimensional branching structure of pyramidal cell dendrites. Brain Research, 87, 55–60.

    Article  PubMed  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Uylings, H. B. M., Kuypers, K. & Veltman, W. A. M. (1978). Environmental influences on the neocortex in later life. In M. A. Corner (Ed.) Maturation of the nervous system. Progress in Brain Research, Vol 48, pp. 261–273. Elsevier, Amsterdam.

  • Uylings, H. B. M., Van Pelt, J., Parnavelas, J. G., & Ruiz-Marcos, A. (1994). Geometrical and topological characteristics in the dendritic development of cortical pyramidal and nonpyramidal neurons. In J. van Pelt, M. A. Corner, H. B. M. Uylings & F. H. Lopes da Silva (Eds.), Progress in Brain Research, Vol. 102, the self-organizing brain: from growth cones to functional networks (pp. 109–123). Amsterdam: Elsevier.

    Chapter  Google Scholar 

  • Van Ooyen, A., & Van Pelt, J. (1994). Activity-dependent outgrowth of neurons and overshoot phenomena in developing neural networks. Journal of Theoretical Biology, 167, 27–43.

    Article  Google Scholar 

  • Van Ooyen, A., & Van Pelt, J. (1996). Complex periodic behavior in a neural network model with activity-dependent neurite outgrowth. Journal of Theoretical Biology, 179, 229–242.

    Article  PubMed  Google Scholar 

  • Van Ooyen, A., Van Pelt, J., & Corner, M. (1995). Implications of activity dependent neurite outgrowth for neuronal morphology and network development. Journal of Theoretical Biology, 172, 63–82.

    Article  PubMed  Google Scholar 

  • Van Ooyen, A., Pakdaman, K., Houweling, A., Van Pelt, J., & Vibert, J.-F. (1996). Networks connectivity changes through activity-dependent neurite outgrowth. Neural Processing Letters, 3, 123–130.

    Article  Google Scholar 

  • Van Ooyen, A., Graham, B., & Ramakers, G. (2001). Competition for tubulin between growing neurites during development. Neurocomputing, 38–40, 73–78.

    Article  Google Scholar 

  • Van Pelt, J., & Uylings, H. (1999). Modeling the natural variability in the shape of dendritic trees: Application to basal dendrites of small rat cortical layer 5 pyramidal neurons. Neurocomputing, 26–27, 305–311.

    Google Scholar 

  • Van Pelt, J., & Uylings, H. (2002). Branching rates and growth functions in the outgrowth of dendritic branching patterns. Network: Computational Neural Systems, 13, 261–281.

    Article  Google Scholar 

  • Van Pelt, J., & Uylings, H. (2003). Growth functions in dendritic outgrowth. Brain and Mind, 4:51–65. In A. van Ooyen, (Ed.), Modeling Neural Development, 75–94. Cambridge, MA: MIT Press.

    Google Scholar 

  • Van Pelt, J., & Uylings, H. B. M. (2005). Natural variability in the geometry of dendritic branching patterns. In: G. N. Reeke, R. R. Poznanski, K. A. Lindsay, J. R. Rosenberg, & O. Sporns (Eds.), Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics. CRC Press, 2005, pp. 89–115.

  • Van Pelt J. and Uylings H.B.M. (2007) Modeling neuronal growth and shape. In Modeling Biology—Structures, Behaviors, Evolution, Manfred D. Laubichler and Gerd B. Müller (Eds). The MIT Press, 2007, Cambridge, pp. 195–215.

  • Van Pelt, J., Uylings, H. B. M., Verwer, R. W. H., Pentney, R. J., & Woldenberg, M. J. (1992). Tree asymmetry—a sensitive and practical measure for binary topological trees. Bulletin of Mathematical Biology, 54, 759–784.

    Article  PubMed  Google Scholar 

  • Van Pelt, J., Dityatev, A. E., & Uylings, H. B. M. (1997). Natural variability in the number of dendritic segments: Model-based inferences about branching during neurite outgrowth. Journal of Comparative Neurology, 387, 325–340.

    Article  PubMed  Google Scholar 

  • Van Pelt, J., Van Ooyen, A., & Uylings, H. B. M. (2001a). Modeling dendritic geometry and the development of nerve connections. In: De Schutter E (Ed.), Cannon RC (CD-ROM) Computational Neuroscience: Realistic modeling for experimentalist, Chapter 7, CRC Press. pp 179–208.

  • Van Pelt, J., Van Ooyen, A., & Uylings, H. (2001b). The need for integrating neuronal morphology databases and computational environments in exploring neuronal structure and function. Anatomy and Embryology, 204, 255–265.

    Article  Google Scholar 

  • Van Pelt, J., Schierwagen, A., & Uylings, H. B. M. (2001c). Modeling dendritic morphological complexity of deep layer cat superior colliculus neurons. Neurocomputing, 38–40, 403–408.

    Article  Google Scholar 

  • Van Pelt, J., Graham, B., & Uylings, H. (2003). Formation of dendritic branching patterns. In A. van Ooyen (Ed.), Modeling neural development (pp. 75–94). Cambridge, Massachusets: The MIT.

    Google Scholar 

  • Van Veen, M. P., & Van Pelt, J. (1993). Terminal and intermediate segment lengths in neuronal trees with finite length. Bulletin of Mathematical Biology, 55, 277–294.

    Article  PubMed  Google Scholar 

  • Wang, Y., Gupta, A., Toledo-Rodriguez, M., Wu, C. Z., & Markram, H. (2002). Anatomical, physiological, molecular and circuit properties of nest basket cells in the developing somatosensory cortex. Cerebral Cortex, 12(4), 395–410.

    Article  PubMed  Google Scholar 

  • Zubler, F., & Douglas, R. (2008) CX3D: a java package for simulation of cortical development in 3D. Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics 2008. doi:10.3389/conf.neuro.11.2008.01.127.

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Acknowledgment

The development of the NETMORPH code by Dr. Randal Koene was supported by the Netherlands Organization for Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek) through the Program Computational Life Sciences grant CLS2003 (635.100.005) to Dr. Jaap van Pelt and Dr. Arjen van Ooyen, and by the EC Marie Curie Research and Training Network (RTN) NEURoVERS-it 019247. The validation of NETMORPH was additionally supported by the EU BIO-ICT Project SECO (grant 216593). The authors thank Nikos Green for contributing data sets of cultured neurons.

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Koene, R.A., Tijms, B., van Hees, P. et al. NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies. Neuroinform 7, 195–210 (2009). https://doi.org/10.1007/s12021-009-9052-3

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