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Neuroinformatics

, Volume 7, Issue 3, pp 191–194 | Cite as

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

  • Todd A. Gillette
  • John J. Grefenstette
Article

Abstract

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.

Keywords

Tree edit distance Morphology Morphometry Computational efficiency 

Notes

Acknowledgements

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.

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Copyright information

© Humana Press Inc. 2009

Authors and Affiliations

  1. 1.Center for Neural Informatics, Structure, & Plasticity and Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA
  2. 2.Bioinformatics and Computational BiologyGeorge Mason UniversityManassasUSA

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