CITATION.cff

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cff-version: 1.2.0
title: >-
  A Topological Representation of Branching Neuronal
  Morphologies.
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - family-names: Kanari
    given-names: Lida
    orcid: 'https://orcid.org/0000-0002-9539-5070'
  - family-names: Dłotko
    given-names: Paweł
  - family-names: Scolamiero
    given-names: Martina
  - family-names: Levi
    given-names: Ran
  - family-names: Shillcock
    given-names: Julian
    orcid: 'https://orcid.org/0000-0002-7885-735X'
  - given-names: Kathryn
    family-names: Hess
    orcid: 'https://orcid.org/0000-0003-2788-9754'
  - family-names: Markram
    given-names: Henry
    orcid: 'https://orcid.org/0000-0001-6164-2590'
  - family-names: Arnaudon
    given-names: Alexis
    orcid: 'https://orcid.org/0000-0001-9486-1458'
identifiers:
  - type: doi
    value: 10.1007/s12021-017-9341-1
    description: The DOI of the related article.
repository-code: 'https://github.com/BlueBrain/TMD'
abstract: >-
  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.
keywords:
  - Branching morphology
  - Clustering trees
  - Neuronal morphologies
  - Topological data analysis
license: LGPL-3.0