Spatial-Data-Driven Layouting for Brain Network Visualization

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: June 2022
• DOI: 10.1016/j.cag.2022.04.014
• ISSN: 1873-7684
• Journal: Computers & Graphics
• Open Access: yes
• Pages: 13
• Volume: 105
• Publisher: Elsevier
• Pages: 12 – 24
• Keywords: networks, neuroscience, graph layouting, brain parcellation, anatomical layouts

Abstract

Recent advances in neuro-imaging enable scientists to create brain network data that can lead to novel insights into neurocircuitry, and a better understanding of the brain’s organization. These networks inherently involve a spatial component, depicting which brain regions are structurally, functionally or genetically related. Their visualization in 3D suffers from occlusion and clutter, especially with increasing number of nodes and connections, while 2D representations such as connectograms, connectivity matrices, and node-link diagrams neglect the spatio-anatomical context. Approaches to arrange 2D-graphs manually are tedious, species-dependent, and require the knowledge of domain experts. In this paper, we present a spatial-data-driven approach for layouting 3D brain networks in 2D node-link diagrams, while maintaining their spatial organization. The produced graphs do not need manual positioning of nodes, are consistent (even for sub-graphs), and provide a perspective-dependent arrangement for orientation. Furthermore, we provide a visual design for highlighting anatomical context, including the shape of the brain, and the size of brain regions. We present in several case-studies the applicability of our approach for different neuroscience-relevant species, including the mouse, human, and Drosophila larvae. In a user study conducted with several domain experts, we demonstrate its relevance and validity, as well as its potential for neuroscientific publications, presentations, and education.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1016/j.cag.2022.04.014

BibTeX

@article{Ganglberger-2022-cg,
  title =      "Spatial-Data-Driven Layouting for Brain Network
               Visualization",
  author =     "Florian Ganglberger and Monika Wi{\ss}mann and Hsiang-Yun Wu
               and Nicolas Swoboda and Andreas Thum and Wulf  Haubensak and
               Katja B\"{u}hler",
  year =       "2022",
  abstract =   "Recent advances in neuro-imaging enable scientists to create
               brain network data that can lead to novel insights into
               neurocircuitry, and a better understanding of the brain’s
               organization. These networks inherently involve a spatial
               component, depicting which brain regions are structurally,
               functionally or genetically related. Their visualization in
               3D suffers from occlusion and clutter, especially with
               increasing number of nodes and connections, while 2D
               representations such as connectograms, connectivity
               matrices, and node-link diagrams neglect the
               spatio-anatomical context. Approaches to arrange 2D-graphs
               manually are tedious, species-dependent, and require the
               knowledge of domain experts. In this paper, we present a
               spatial-data-driven approach for layouting 3D brain networks
               in 2D node-link diagrams, while maintaining their spatial
               organization. The produced graphs do not need manual
               positioning of nodes, are consistent (even for sub-graphs),
               and provide a perspective-dependent arrangement for
               orientation. Furthermore, we provide a visual design for
               highlighting anatomical context, including the shape of the
               brain, and the size of brain regions. We present in several
               case-studies the applicability of our approach for different
               neuroscience-relevant species, including the mouse, human,
               and Drosophila larvae. In a user study conducted with
               several domain experts, we demonstrate its relevance and
               validity, as well as its potential for neuroscientific
               publications, presentations, and education.",
  month =      jun,
  doi =        "10.1016/j.cag.2022.04.014",
  issn =       "1873-7684",
  journal =    "Computers & Graphics",
  pages =      "13",
  volume =     "105",
  publisher =  "Elsevier",
  pages =      "12--24",
  keywords =   "networks, neuroscience, graph layouting, brain parcellation,
               anatomical layouts",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2022/Ganglberger-2022-cg/",
}