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Neuroinformatics

, Volume 13, Issue 4, pp 487–499 | Cite as

BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies

  • Yinan Wan
  • Fuhui Long
  • Lei Qu
  • Hang Xiao
  • Michael Hawrylycz
  • Eugene W. Myers
  • Hanchuan PengEmail author
Original Article

Abstract

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

Keywords

Neuron comparison Neuron morphology Tree matching Neuron reconstruction 

Notes

Acknowledgments

This work was supported primarily by the Janelia Research Campus of HHMI and the Allen Institute for Brain Science. Lei Qu was also partially supported by Chinese Natural Science Foundation Project (61201396, 61301296, 61377006, U1201255); Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry; Technology Foundation for Selected Overseas Chinese Scholar, Ministry of Personnel of China. We thank Zhi Zhou for providing some neuron reconstructions for testing in Fig. 8.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Yinan Wan
    • 1
  • Fuhui Long
    • 1
    • 2
  • Lei Qu
    • 1
    • 4
  • Hang Xiao
    • 1
  • Michael Hawrylycz
    • 2
  • Eugene W. Myers
    • 1
    • 3
  • Hanchuan Peng
    • 1
    • 2
    Email author
  1. 1.Janelia Research CampusHoward Hughes Medical InstituteAshburnUSA
  2. 2.Allen Institute for Brain ScienceSeattleUSA
  3. 3.Max Planck Institute of Molecular Cell Biology and GeneticsDresdenGermany
  4. 4.Key Laboratory of Intelligent Computation and Signal Processing, Ministry of EducationAnhui UniversityHefeiChina

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