Skip to main content

On Comparing Neuronal Morphologies with the Constrained Tree-edit-distance


The constrained tree-edit-distance provides a computationally practical method for comparing morphologies directly without first extracting distributions of other metrics. The application of the constrained tree-edit-distance to hippocampal dendrites by Heumann and Wittum is reviewed and considered in the context of other applications and potential future uses. The method has been used on neuromuscular projection axons for comparisons of topology as well as on trees for comparing plant architectures with particular parameter sets that may inform future efforts in comparing dendritic morphologies. While clearly practical on a small scale, testing and extrapolation of run-times raise questions as to the practicality of the constrained tree-edit-distance for large-scale data mining projects. However, other more efficient algorithms may make use of it as a gold standard for direct morphological comparison.

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

Fig. 1
Fig. 2


  1. Cannon, R. C., Wheal, H. V., & Turner, D. A. (1999). Dendrites of classes of hippocampal neurons differ in structural complexity and branching patterns. Journal of Comparative Neurology, 413, 619–633.

    PubMed  Article  CAS  Google Scholar 

  2. Ferraro, P., & Godin, C. (2000). A distance measure between plant architectures. Annals of Forest Science, 57, 445–461.

    Article  Google Scholar 

  3. Halavi, M., Polavaram, S., Donohue, D., Hamilton, G., Hoyt, J., Smith, K., et al. (2008). implementation of digital neuroscience: dense coverage and integration with the NIF. Neuroinformatics, 6, 241–252.

    PubMed  Article  Google Scholar 

  4. Heumann, H., & Wittum, G. (2009) The tree-edit-distance, a measure for quantifying neuronal morphology. Neuroinformatics.

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

    PubMed  Article  CAS  Google Scholar 

  6. Lu, J., Tapia, J., White, O., & Lichtman, J. (2009). The interscutularis muscle connectome. PLoS Biology, 7, 265–277.

    Google Scholar 

  7. Segura, V., Ouangraoua, A., Ferraro, P., & Costes, E. (2008). Comparison of tree architecture using tree edit distances: application to 2-year-old apple hybrids. Euphytica, 161, 155–164.

    Article  Google Scholar 

  8. Sholl, D. A. (1953). Dendritic organization in the neurons of the visual and motor cortices of the cat. Journal of Anatomy, 87, 387–4061.

    PubMed  CAS  Google Scholar 

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

    PubMed  Article  Google Scholar 

  10. Vetter, P., Roth, A., & Hausser, M. (2001). Propagation of action potentials in dendrites depends on dendritic morphology. Journal of Neurophysiology, 85, 926–937.

    PubMed  CAS  Google Scholar 

  11. Zhang, K. (1996). A constrained edit distance between unordered labeled trees. Algorithmica, 15, 205–222.

    Article  CAS  Google Scholar 

Download references


We would like to thank Giorgio Ascoli for his valuable discussions as well as Maryam Halavi and Deepak Ropireddy for their useful feedback.

Editorial Note

The authors of the target article (Heumann and Wittum 2009) acknowledge that the commentary is correct and thank its authors. They also add that complexity issues have not been considered in the present paper, since there was no problem to run the code on the specified data. Because the algorithms can be parallelized easily, they do not see serious complexity problems in using the methods on larger datasets. Complexity issues will be the topic of a forthcoming paper.

Information Sharing Statement

The source code for the algorithm, compilation and usage instructions, and sample reconstruction files (.hoc) were provided by the authors through the Neuroinformatics editors. NeuroMorpho.Org data was retrieved through the v.3.2 database with permission and access provided by the database curator.

Author information



Corresponding author

Correspondence to Todd A. Gillette.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Gillette, T.A., Grefenstette, J.J. On Comparing Neuronal Morphologies with the Constrained Tree-edit-distance. Neuroinform 7, 191–194 (2009).

Download citation


  • Tree edit distance
  • Morphology
  • Morphometry
  • Computational efficiency