Data-Driven Anatomical Layouting of Brain Network Graphs

Gwendolyn Rippberger
Data-Driven Anatomical Layouting of Brain Network Graphs
[Bachelor Thesis] [image]

Information

Abstract

The visualization of brain networks today offers a variety of different tools and approaches. Representations in 2D such as connectograms, connectivity matrices, and node-link diagrams are common but an abstract visualization of the network without any anatomical context. Visualizations tools show anatomical context in 2D but adjust it especially for a certain species as for example the fruit fly’s brain. This project presents a tool for data-driven brain network visualization using the open-source graph library Cytoscape.js to avoid hard coded spatial constraints. The goal of the project was to find a layout algorithm that resembles the anatomical structure of the brain visualized without any hard coded constraints. After testing the layouts, they have been evaluated on different properties like symmetry, node overlapping, and anatomical resemblence. Additionally, we conducted an open discussion with collaborators of the Research Institute of Molecular Pathology (IMP) in Vienna and present the results.

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BibTeX

@bachelorsthesis{Rippberger_2019,
  title =      "Data-Driven Anatomical Layouting of Brain Network Graphs",
  author =     "Gwendolyn Rippberger",
  year =       "2019",
  abstract =   "The visualization of brain networks today offers a variety
               of different tools and approaches. Representations in 2D
               such as connectograms, connectivity matrices, and node-link
               diagrams are common but an abstract visualization of the
               network without any anatomical context. Visualizations tools
               show anatomical context in 2D but adjust it especially for a
               certain species as for example the fruit fly’s brain. This
               project presents a tool for data-driven brain network
               visualization using the open-source graph library
               Cytoscape.js to avoid hard coded spatial constraints. The
               goal of the project was to find a layout algorithm that
               resembles the anatomical structure of the brain visualized
               without any hard coded constraints. After testing the
               layouts, they have been evaluated on different properties
               like symmetry, node overlapping, and anatomical resemblence.
               Additionally, we conducted an open discussion with
               collaborators of the Research Institute of Molecular
               Pathology (IMP) in Vienna and present the results.",
  month =      apr,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2019/Rippberger_2019/",
}