A Topological Representation of Branching Neuronal Morphologies

Lida Kanari, Pawel Dlotko, Martina Scolamiero, Ran Levi, Julian Charles Shillcock, Kathryn Hess, Henry Markram

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)
12 Downloads (Pure)


Many biological systems consist of branching structures that exhibit a wide variety of shapes. Our understanding of their systematic roles is hampered from the start by the lack of a fundamental means of standardizing the description of complex branching patterns, such as those of neuronal trees. To solve this problem, we have invented the Topological Morphology Descriptor (TMD), a method for encoding the spatial structure of any tree as a “barcode”, a unique topological signature. As opposed to traditional morphometrics, the TMD couples the topology of the branches with their spatial extents by tracking their topological evolution in 3-dimensional space. We prove that neuronal trees, as well as stochastically generated trees, can be accurately categorized based on their TMD profiles. The TMD retains sufficient global and local information to create an unbiased benchmark test for their categorization and is able to quantify and characterize the structural differences between distinct morphological groups. The use of this mathematically rigorous method will advance our understanding of the anatomy and diversity of branching morphologies.
Original languageEnglish
Pages (from-to)3-13
Number of pages11
Issue number1
Early online date3 Oct 2017
Publication statusPublished - Jan 2018


  • topological data analysis
  • neuronal morphologies
  • branching morphology
  • clustering trees


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