Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mobilizing the base of neuroscience data: the case of neuronal morphologies


Despite the explosive growth of bioinformatics, data sharing has not yet become routine in neuroscience, possibly because of several broad-spanning issues, from data heterogeneity to privacy regulations. We present the case of neuronal morphology as an ideal example of shareable data. Drawing from recent experience, we argue that the tremendous research potential of existing (and largely unused) digital reconstructions should diffuse any reticence to sharing this type of data.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: Digital reconstruction of neuronal morphology.
Figure 2: Examples of applications of digital reconstructions.
Figure 3: Numerical summaries of the neuronal morphologies inventoried so far under the Neuroscience Information Framework contracted by the National Institutes of Health and in partnership with the Society for Neuroscience.
Figure 4: Possible data-mining scenarios enabled by large-scale archiving of neuronal morphologies.


  1. Human Brain Project [Online], (2006).

  2. Koslow, S. H. & Hirsch, M. D. Celebrating a decade of neuroscience databases: looking to the future of high-throughput data analysis, data integration, and discovery neuroscience. Neuroinformatics 2, 267–270 (2004).

    Article  Google Scholar 

  3. NIH Blueprint for Neuroscience Research [Online], (2006).

  4. Insel, T. R., Volkow, N. D., Li, T. K., Battey, J. F. Jr & Landis, S. C. Neuroscience networks: data-sharing in an information age. PLoS Biol. 1, e17 (2003).

    Article  Google Scholar 

  5. Koslow, S. H. Should the neuroscience community make a paradigm shift to sharing primary data? Nature Neurosci. 3, 863–865 (2000).

    Article  CAS  Google Scholar 

  6. Eckersley, P. et al. Neuroscience data and tool sharing: a legal and policy framework for neuroinformatics. Neuroinformatics 1, 149–165 (2003).

    Article  Google Scholar 

  7. Ascoli, G. A., De Schutter, E. & Kennedy, D. N. An information science infrastructure for neuroscience. Neuroinformatics 1, 1–2 (2003).

    Article  Google Scholar 

  8. The neuroinformatics site [Online], (2006).

  9. Neuroscience Database Gateway [Online], (2006).

  10. Gardner, D. & Shepherd, G. M. A gateway to the future of neuroinformatics. Neuroinformatics 2, 271–274 (2004).

    Article  Google Scholar 

  11. Koslow, S. H. Sharing primary data: a threat or asset to discovery? Nature Rev. Neurosci. 3, 311–313 (2002).

    Article  CAS  Google Scholar 

  12. Editorial. A debate over fMRI data sharing. Nature Neurosci. 3, 845–846 (2000).

  13. Van Horn, J. D., Grafton, S. T., Rockmore, D. & Gazzaniga, M. S. Sharing neuroimaging studies of human cognition. Nature Neurosci. 7, 473–481 (2004).

    Article  CAS  Google Scholar 

  14. Martone, M. E. et al. The cell-centered database: a database for multiscale structural and protein localization data from light and electron microscopy. Neuroinformatics 1, 379–395 (2003).

    Article  Google Scholar 

  15. Becker, K. G. The sharing of cDNA microarray data. Nature Rev. Neurosci. 2, 438–440 (2001).

    Article  CAS  Google Scholar 

  16. Geschwind, D. H. Sharing gene expression data: an array of options. Nature Rev. Neurosci. 2, 435–438 (2001).

    Article  CAS  Google Scholar 

  17. Miles, M. F. Microarrays: lost in a storm of data? Nature Rev. Neurosci. 2, 441–443 (2001).

    Article  CAS  Google Scholar 

  18. Bowden, D. M. & Dubach, M. F. NeuroNames 2002. Neuroinformatics 1, 43–59 (2003).

    Article  Google Scholar 

  19. Kotter, R. Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Neuroinformatics 2, 127–144 (2004).

    Article  Google Scholar 

  20. Bota, M., Dong, H. W. & Swanson, L. W. Brain architecture management system. Neuroinformatics 3, 15–48 (2005).

    Article  Google Scholar 

  21. Mirsky, J. S., Nadkarni, P. M., Healy, M. D., Miller, P. L. & Shepherd, G. M. Database tools for integrating and searching membrane property data correlated with neuronal morphology. J. Neurosci. Methods 82, 105–121 (1998).

    Article  CAS  Google Scholar 

  22. Ascoli, G. A., Krichmar, J. L., Nasuto, S. J. & Senft, S. L. Generation, description and storage of dendritic morphology data. Phil. Trans. R. Soc. Lond. B 356, 1131–1145 (2001).

    Article  CAS  Google Scholar 

  23. Capowski, J. J. Computer Techniques in Neuroanatomy (Plenum, New York, 1985).

    Google Scholar 

  24. Glaser, J. R. & Glaser, E. M. Neuron imaging with Neurolucida — a PC-based system for image combining microscopy. Comput. Med. Imaging Graph. 14, 307–317 (1990).

    Article  CAS  Google Scholar 

  25. Brown, K. M., Donohue, D. E., D'Alessandro, G. & Ascoli, G. A. A cross-platform freeware tool for digital reconstruction of neuronal arborizations from image stacks. Neuroinformatics 3, 343–360 (2005).

    Article  Google Scholar 

  26. Cannon, R. C., Wheal, H. V. & Turner, D. A. Dendrites of classes of hippocampal neurons differ in structural complexity and branching patterns. J. Comp. Neurol. 413, 619–633 (1999).

    Article  CAS  Google Scholar 

  27. Li, Y., Brewer, D., Burke, R. E. & Ascoli, G. A. Developmental changes in spinal motoneuron dendrites in neonatal mice. J. Comp. Neurol. 483, 304–317 (2005).

    Article  Google Scholar 

  28. The NEURON Simulation Environment [Online], (2006).

  29. Senselab ModelDB [Online], (2006).

  30. Mainen, Z. F. & Sejnowski, T. J. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363–366 (1996).

    Article  CAS  Google Scholar 

  31. Krichmar, J. L., Nasuto, S. J., Scorcioni, R., Washington, S. D. & Ascoli, G. A. Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study. Brain Res. 941, 11–28 (2002).

    Article  CAS  Google Scholar 

  32. Ascoli, G. A. Neuroanatomical algorithms for dendritic modelling. Network 13, 247–260 (2002).

    Article  Google Scholar 

  33. Stepanyants, A. & Chklovskii, D. B. Neurogeometry and potential synaptic connectivity. Trends Neurosci. 28, 387–394 (2005).

    Article  CAS  Google Scholar 

  34. Markram, H. The blue brain project. Nature Rev. Neurosci. 7, 153–160 (2006).

    Article  CAS  Google Scholar 

  35. Gardner, D. et al. Towards effective and rewarding data sharing. Neuroinformatics 1, 289–295 (2003).

    Article  Google Scholar 

  36. He, W. et al. Automated three-dimensional tracing of neurons in confocal and brightfield images. Microsc. Microanal. 9, 296–310 (2003).

    Article  CAS  Google Scholar 

  37. Rodriguez, A. et al. Automated reconstruction of three-dimensional neuronal morphology from laser scanning microscopy images. Methods 30, 94–105 (2003).

    Article  CAS  Google Scholar 

  38. Schmitt, S., Evers, J. F., Duch, C., Scholz, M. & Obermayer, K. New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks. Neuroimage 23, 1283–1298 (2004).

    Article  Google Scholar 

  39. The inventory [Online], (2006).

  40. Turner, D. A., Cannon, R. C. & Ascoli, G. A. in Neuroscience Databases – A Practical Guide (ed. Kotter, R.) 81–98 (Elsevier, Amsterdam, 2002).

    Google Scholar 

  41. Cannon, R. C., Howell, F. W., Goddard, N. H. & De Schutter, E. Non-curated distributed databases for experimental data and models in neuroscience. Network 13, 415–428 (2002).

    Article  CAS  Google Scholar 

  42. Scorcioni, R., Lazarewicz, M. T. & Ascoli, G. A. Quantitative morphometry of hippocampal pyramidal cells: differences between anatomical classes and reconstructing laboratories. J. Comp. Neurol. 473, 177–193 (2004).

    Article  Google Scholar 

  43. Samsonovich, A. V. & Ascoli, G. A. Morphological homeostasis in cortical dendrites. Proc. Natl Acad. Sci. USA 103, 1569–1574 (2006).

    Article  CAS  Google Scholar 

  44. Kennedy, D. N. The impact of neuroinformatics. Neuroinformatics 3, 287–292 (2005).

    Article  Google Scholar 

  45. Hines, M. L., Morse, T., Migliore, M., Carnevale, N. T. & Shepherd, G. M. ModelDB: A database to support computational neuroscience. J. Comput. Neurosci. 17, 7–11 (2004).

    Article  Google Scholar 

  46. International Consortium for Brain Mapping [Online], (2006).

  47. Mouse Brain Library [Online], (2006).

  48. Allen Brain Atlas [Online], (2006).

  49. BrainInfo [Online], (2006).

  50. The LNI neurophysiology database [Online], (2006).

  51. Synapse Web [Online], (2006).

  52. Cell Centered Database [Online], (2006).

  53. Computational Neurobiology and Imaging Center [Online], (2006).

  54. Computational Neuroscience on the Web [Online], (2006).

  55. Neuron_Morpho Plugin for ImageJ [Online], (2006).

  56. Cvapp: Neuron morphology and conversion tool [Online], (2006).

  57. L-Measure: Morphometric analysis of neuronal reconstructions [Online], (2006).

  58. Scorcioni, R. & Ascoli, G. A. Algorithmic extraction of morphological statistics from electronic archives of neuroanatomy. Lect. Notes Comp. Sci. 2084, 30–37 (2001).

    Article  Google Scholar 

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

    Article  Google Scholar 

  60. The GENESIS Simulation Environment [Online], (2006).

  61. Migliore, M., Ferrante, M. & Ascoli, G. A. Signal propagation in oblique dendrites of CA1 pyramidal cells. J. Neurophysiol. 94, 4145–4155 (2005).

    Article  Google Scholar 

  62. Samsonovich, A. V. & Ascoli, G. A. Statistical determinants of dendritic morphology in hippocampal pyramidal neurons: a hidden Markov model. Hippocampus 15, 166–183 (2005).

    Article  Google Scholar 

Download references


I am grateful to all members of my laboratory. In particular, illustration material for this manuscript was contributed by K. Brown, D. Donohue, M. Ferrante, M. Halavi, D. Ropireddy and A. Samsonovich. This work was supported in part by a grant co-funded by the National Institute of Neural Disorders and Stroke, the National Institute of Mental Health and the National Science Foundation (NSF) under the Human Brain Project; by a grant from the National Institute of Aging as part of the NSF/NIH (National Institutes of Health) Collaborative Research in Computational Neuroscience program; and by a contract from the National Institute of Drug Abuse under the Neuroscience Blueprint initiative.

Author information

Authors and Affiliations


Ethics declarations

Competing interests

The author declares no competing financial interests.

Related links

Related links


Brain architecture management system



Cell Centered Database

Computational Neuroanatomy Group




Neuronal Morphology Inventory




Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ascoli, G. Mobilizing the base of neuroscience data: the case of neuronal morphologies. Nat Rev Neurosci 7, 318–324 (2006).

Download citation

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing