Reconstruction of brain networks by algorithmic amplification of morphometry data

  • Stephen L. Senft
  • Giorgio A. Ascoli
Neural Modeling (Biophysical and Structural Models)
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1606)


The peculiar shapes of neurons have been fascinating since their discovery in the late 1800’s, when the Golgi impregnation technique established circuits of neurons and glia to be the hallmark of brain organization. Although only a small fraction of all brain cells have been carefully measured, it is a natural complement to reductionism to try to logically re-construct brain circuitry using its basic units. This paper describes a computational strategy to produce virtual models of biological neural networks detailed at the micron level. Our algorithm uses cylindrical primitives in a virtual reality environment, and assembles them according to basic growth rules. Morphometric parameters (e.g., the axonal stem’s diameter) are measured from libraries of experimentally traced neurons and stored as statistical distributions. When the algorithm uses a parameter to generate a neuron (e.g. when an axon stems from the soma and its initial diameter needs to be determined), a value is stochastically sampled from the statistical distribution. This procedure can produce a large number of non-identical virtual neurons, whose morphological characteristics are statistically equivalent to those of the original experimental neuron. Thus, an amplification of morphometry data is achieved. This stochastic and statistical approach is highly efficient, allowing the creation of large-scale, anatomically accurate neural networks.


Pyramidal Cell Morphometric Parameter Apical Dendrite Axonal Initial Segment Schaffer Collateral 
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  1. 1.
    Golgi, C.: Sulla fina anatomia del cervelletto umano. Istologia Normale 1 (1874) 99–111Google Scholar
  2. 2.
    Ramon y Cajal, S.: Histology of the nervous system. Oxford Press, NY (1904) transl. 1995Google Scholar
  3. 3.
    Lorente del Nò, R.: Studies on the structure of the cerebral cortex. II. Continuation of the study of the ammonic system. J. Psychol. Neurol. Leipzig 46 (1934) 113–177Google Scholar
  4. 4.
    Strahler, A.: Quantitative analysis of geomorphology. Trans. Am. Geophys. Un. 38 (1957) 913–920.CrossRefGoogle Scholar
  5. 5.
    Sholl, D.A.: Dendritic organization of the neurons of the visual and motor cortices of the cat. J. Anat. 87 (1953) 387–406Google Scholar
  6. 6.
    Uylings, H.B.M., Ruiz-Marcos, A., van Pelt, J.: The metric analysis of three-dimensional dendritic tree patterns: a methodological review. J. Neurosci. Meth. 18 (1986) 127–151.CrossRefGoogle Scholar
  7. 7.
    Percheron, G.: Quantitative analysis of dendritic branching. I&II. Neurosci. Lett. 14 (1979) 287–293CrossRefGoogle Scholar
  8. 8.
    Hockfield, S., McKay, R.D.G.: Identification of major cell classes in the developing mammalian nervous system. J. Neurosci. 5 (1985) 3310–3328Google Scholar
  9. 9.
    Prochiantz, A.: Neuronal Growth and Shape. Dev. Neurosci. 7 (1985) 189–198CrossRefGoogle Scholar
  10. 10.
    Hillman, D.E.: Neuronal shape parameters and substructures as a basis of neuronal form. In: Schmitt, F., (ed.): The Neurosciences, Fourth study program. Cambridge, MA: MIT Press (1979) 477–498Google Scholar
  11. 11.
    Capowski, J.J.: The reconstruction, display, and analysis of neuronal structure using a computer. In Mize, R.R. (ed.) The microcomputer in cell and neurobiology research. Elsevier 4 (1985) 85–109Google Scholar
  12. 12.
    Wann, D.F., Woolsey, T.A., Dierker, M.L., Cowan, M.: An on-line digital-computer system for the semi-automatic analysis of Golgi-impregnated neurons. IEEE Trans. BME-20 N.4 (1973) 233–247Google Scholar
  13. 13.
    Glaser, E.M., Van der Loos, H.: A semi-automatic computer-microscope. Biophys. Soc. Proc. 8th annual meeting (1964)Google Scholar
  14. 14.
    Macagno, E.R., Levinthal, C., Sobel, I.: Three dimensional computer reconstruction of neurons and neuronal assemblies. Ann. Rev. Biophys. Bioeng. 8 (1979) 323–351CrossRefGoogle Scholar
  15. 15.
    Senft, S.L.: A statistical framework to present developmental neuroanatomy. In Donahoe, J. (Ed.), Biobehavioral Foundations. Elsevier Press (1997)Google Scholar
  16. 16.
    Senft, S.L.: Derivation of neuron geometry from confocal scans. Neurosci. Abs. 21 (1995) 1078Google Scholar
  17. 17.
    Merkle, R.C.: Large scale analysis of neural structures. Xerox PARC technical report, CSL-89-10 November (1989) 89–173. Scholar
  18. 18.
    Burke, R.E., Marks, W.B., Ulfhake, B.: A parsimonious description of motoneurons dendritic morphology using computer simulation. J. Neurosci. 12(6) (1992) 2403–2416Google Scholar
  19. 19.
    Tamori, Y.: Theory of dendritic morphology. Phys. Rev. E 48(4) (1993) 3124–3129CrossRefGoogle Scholar
  20. 20.
    Solomon, F.: Endogenous specification of cell morphology. J. Cell Biol. 90 (1981) 547–553CrossRefGoogle Scholar
  21. 21.
    Larkman, A.U.: Dendritic morphology of pyramidal neurones of the visual cortex of the rat: I. Branching patterns. J. Comp. Neurol. 306 (1991) 307–319CrossRefGoogle Scholar
  22. 22.
    Amaral, D.G., Ishizuka, N., Claiborne, B.: Neurons, numbers and the hippocampal network. Progr. Brain Res. 83 (1990) 1–11CrossRefGoogle Scholar
  23. 23.
    Bernard, C., Wheal, H.V.: Model of Local connectivity patterns in CA3 and CA1 areas of the hippocampus. Hippocampus 4 (1994) 497–529CrossRefGoogle Scholar
  24. 24.
    Levy, W.B., Colbert, C.M., Desmond, N.L.: Another network model bites the dust: entorhinal inputs are no more than weakly excitatory in the hippocampal CAI region. Hippocampus 5 (1995) 137–40CrossRefGoogle Scholar
  25. 25.
    Tamamaki, N., Nojyo, Y.: Preservation of topography in the connections between the subiculum, field CA1, and the entorhinal cortex in rats. J. Comp. Neur. 353 (1995) 379–390CrossRefGoogle Scholar
  26. 26.
    Freund, T.F., Buzsaki, G.: Interneurons of the hippocampus. Hippocampus 6 (1996) 347–470CrossRefGoogle Scholar
  27. 27.
    Ishizuka, N., Cowan, W.M., Amaral, D.G.: A quantitative analysis of the dendritic organization of pyramidal cells in the rat hippocampus. J. Comp. Neurol. 362 (1995) 17–45CrossRefGoogle Scholar
  28. 28.
    Pyapali, G.K., Sik, A., Penttonen, M., Buzsaki, G., Turner, D.A.: Dendritic properties of hippocampal CA1 pyramidal neurons in the rat: intracellular staining in vivo and in vitro. J. Comp. Neurol. 391 (1998) 335–352CrossRefGoogle Scholar
  29. 29.
    Shepherd, G.M.G., Harris, K.M.: Three-dimensional structure and composition of CA3→CA1 axons in rat hippocampal slices. J. Neurosci. 18(20) (1998) 8300–8310Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Stephen L. Senft
    • 1
  • Giorgio A. Ascoli
    • 1
  1. 1.The Krasnow Institute for Advanced Study at George Mason UniversityFairfaxUSA

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