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 journalArticle

8 Citations (Scopus)
8 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)3-13
Number of pages11
JournalNeuroinformatics
Volume16
Issue number1
Early online date3 Oct 2017
DOIs
Publication statusPublished - Jan 2018

Fingerprint

Benchmarking
Biological systems
Anatomy
Topology

Keywords

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

Cite this

Kanari, L., Dlotko, P., Scolamiero, M., Levi, R., Shillcock, J. C., Hess, K., & Markram, H. (2018). A Topological Representation of Branching Neuronal Morphologies. Neuroinformatics, 16(1), 3-13. https://doi.org/10.1007/s12021-017-9341-1

A Topological Representation of Branching Neuronal Morphologies. / Kanari, Lida; Dlotko, Pawel; Scolamiero, Martina; Levi, Ran; Shillcock, Julian Charles; Hess, Kathryn; Markram, Henry.

In: Neuroinformatics, Vol. 16, No. 1, 01.2018, p. 3-13.

Research output: Contribution to journalArticle

Kanari, L, Dlotko, P, Scolamiero, M, Levi, R, Shillcock, JC, Hess, K & Markram, H 2018, 'A Topological Representation of Branching Neuronal Morphologies' Neuroinformatics, vol. 16, no. 1, pp. 3-13. https://doi.org/10.1007/s12021-017-9341-1
Kanari L, Dlotko P, Scolamiero M, Levi R, Shillcock JC, Hess K et al. A Topological Representation of Branching Neuronal Morphologies. Neuroinformatics. 2018 Jan;16(1):3-13. https://doi.org/10.1007/s12021-017-9341-1
Kanari, Lida ; Dlotko, Pawel ; Scolamiero, Martina ; Levi, Ran ; Shillcock, Julian Charles ; Hess, Kathryn ; Markram, Henry. / A Topological Representation of Branching Neuronal Morphologies. In: Neuroinformatics. 2018 ; Vol. 16, No. 1. pp. 3-13.
@article{896e69a4c0cc42708412e152a7c25e30,
title = "A Topological Representation of Branching Neuronal Morphologies",
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 = "topological data analysis, neuronal morphologies, branching morphology , clustering trees",
author = "Lida Kanari and Pawel Dlotko and Martina Scolamiero and Ran Levi and Shillcock, {Julian Charles} and Kathryn Hess and Henry Markram",
note = "The online version of this article (https://doi.org/10.1007/s12021-017-9341-1) contains supplementary material, which is available to authorized users. Among others, we thank Athanassia Chalimourda and Katherine Turner for helpful conversations in various stages of this research and Jay Coggan for a critical reading of the manuscript. We also thank Hanchuan Peng and Xiaoxiao Liu for providing and curating the BigNeuron datasets. This work was supported by funding for the Blue Brain Project (BBP) from the ETH Domain. P.D. and R.L. were supported part by the Blue Brain Project and by the start-up grant of KH. Partial support for P.D. has been provided by the Advanced Grant of the European Research Council GUDHI (Geometric Understanding in Higher Dimensions). MS was supported by the SNF NCCR “Synapsy”.",
year = "2018",
month = "1",
doi = "10.1007/s12021-017-9341-1",
language = "English",
volume = "16",
pages = "3--13",
journal = "Neuroinformatics",
issn = "1559-0089",
publisher = "Springer",
number = "1",

}

TY - JOUR

T1 - A Topological Representation of Branching Neuronal Morphologies

AU - Kanari, Lida

AU - Dlotko, Pawel

AU - Scolamiero, Martina

AU - Levi, Ran

AU - Shillcock, Julian Charles

AU - Hess, Kathryn

AU - Markram, Henry

N1 - The online version of this article (https://doi.org/10.1007/s12021-017-9341-1) contains supplementary material, which is available to authorized users. Among others, we thank Athanassia Chalimourda and Katherine Turner for helpful conversations in various stages of this research and Jay Coggan for a critical reading of the manuscript. We also thank Hanchuan Peng and Xiaoxiao Liu for providing and curating the BigNeuron datasets. This work was supported by funding for the Blue Brain Project (BBP) from the ETH Domain. P.D. and R.L. were supported part by the Blue Brain Project and by the start-up grant of KH. Partial support for P.D. has been provided by the Advanced Grant of the European Research Council GUDHI (Geometric Understanding in Higher Dimensions). MS was supported by the SNF NCCR “Synapsy”.

PY - 2018/1

Y1 - 2018/1

N2 - 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.

AB - 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.

KW - topological data analysis

KW - neuronal morphologies

KW - branching morphology

KW - clustering trees

U2 - 10.1007/s12021-017-9341-1

DO - 10.1007/s12021-017-9341-1

M3 - Article

VL - 16

SP - 3

EP - 13

JO - Neuroinformatics

JF - Neuroinformatics

SN - 1559-0089

IS - 1

ER -