Anatomy-Driven Layouting for Brain Network Visualization

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s): not specified
• Date: 2022
• Open Access: yes
• First Supervisor: Eduard Gröller
• Pages: 125
• Keywords: brain connectivity visualization, Node-Link Diagram

Abstract

Technological advances have dramatically expanded our ability to collect data of neural connectivity in the brain and apply this data in the field of connectomics. The focus of research is thus increasingly shifting towards the analysis of this complex data. Many applications visualize neurological data in three-dimensional space. However, these require interactivity to view hidden data and are not always applicable. To support neuroscientific research we present Spatial-Data-Driven Layouts, a novel web-tool to visualize neuronal networks of multiple species in two-dimensional space. Our method is data-driven and is therefore independent of species or perspective. We generate node-link diagrams where nodes represent brain regions, while the edges correspond the connectivity. To realize this data-driven approach we apply Parcellation-derived Connectivity, generated from brain atlases in combination with a standard force-directed graph layout algorithm. We provide further guidance by visually encoding anatomical context of the underlying brain hierarchy. Colored parcellations in the background encapsulate and cluster nodes that belong to the same super-regions. Additionally the background provides an overall shape, similar to the brain and is independent of the graph’s completeness, facilitating the comparison of sub-networks with each other as well as with the entire network. The background is customizable in terms of anatomical details to reflect either the anatomical size or the number of connections per region.We conduct case studies for two species, mouse and human, to validate our visualizations and show that the spatial distribution of nodes reflects the anatomy of the brain. Nodes are adjacent to each other if they also represent neighboring regions in the reference space.The results provided by Spatial-Data-Driven Layouts are evaluated in a web-based user study involving domain experts in neuroscience, computer science, computational science, bioinformatics, and computational biology. Evaluating the studies for two different species, mouse and human, shows that our methodology can be applied data-driven and species-independent. The feedback obtained from the experts indicates clear potential.Spatial-Data-Driven Layouts quickly and easily recreate illustrations in literature that usually are created with a great deal of effort. Added context in sub-networks to preserve the overall shape of the brain and to make those networks comparable to each other, wasconsidered very useful. Spatial-Data-Driven Layouts is a novelty in the visualization of neuronal circuits of the Drosophila melanogaster larval brain and considered a first good step in this direction.In the future, we plan to extend the application with interactivity to provide neuroscientists with an intuitive representation of their data. The customization of brain regions, connectivity, as well as details of the layout via parameters, can be adapted to their interests. In addition, we aim to improve neuron-level visualization and visual encoding of the Drosophila larval network graphs to provide a more detailed representation of circuits.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.34726/hss.2022.78887

BibTeX

@mastersthesis{wissmann-2022-alb,
  title =      "Anatomy-Driven Layouting for Brain Network Visualization",
  author =     "Monika Wi{\ss}mann",
  year =       "2022",
  abstract =   "Technological advances have dramatically expanded our
               ability to collect data of neural connectivity in the brain
               and apply this data in the field of connectomics. The focus
               of research is thus increasingly shifting towards the
               analysis of this complex data. Many applications visualize
               neurological data in three-dimensional space. However, these
               require interactivity to view hidden data and are not always
               applicable. To support neuroscientific research we present
               Spatial-Data-Driven Layouts, a novel web-tool to visualize
               neuronal networks of multiple species in two-dimensional
               space. Our method is data-driven and is therefore
               independent of species or perspective. We generate node-link
               diagrams where nodes represent brain regions, while the
               edges correspond the connectivity. To realize this
               data-driven approach we apply Parcellation-derived
               Connectivity, generated from brain atlases in combination
               with a standard force-directed graph layout algorithm. We
               provide further guidance by visually encoding anatomical
               context of the underlying brain hierarchy. Colored
               parcellations in the background encapsulate and cluster
               nodes that belong to the same super-regions. Additionally
               the background provides an overall shape, similar to the
               brain and is independent of the graph’s completeness,
               facilitating the comparison of sub-networks with each other
               as well as with the entire network. The background is
               customizable in terms of anatomical details to reflect
               either the anatomical size or the number of connections per
               region.We conduct case studies for two species, mouse and
               human, to validate our visualizations and show that the
               spatial distribution of nodes reflects the anatomy of the
               brain. Nodes are adjacent to each other if they also
               represent neighboring regions in the reference space.The
               results provided by Spatial-Data-Driven Layouts are
               evaluated in a web-based user study involving domain experts
               in neuroscience, computer science, computational science,
               bioinformatics, and computational biology. Evaluating the
               studies for two different species, mouse and human, shows
               that our methodology can be applied data-driven and
               species-independent. The feedback obtained from the experts
               indicates clear potential.Spatial-Data-Driven Layouts
               quickly and easily recreate illustrations in literature that
               usually are created with a great deal of effort. Added
               context in sub-networks to preserve the overall shape of the
               brain and to make those networks comparable to each other,
               wasconsidered very useful. Spatial-Data-Driven Layouts is a
               novelty in the visualization of neuronal circuits of the
               Drosophila melanogaster larval brain and considered a first
               good step in this direction.In the future, we plan to extend
               the application with interactivity to provide
               neuroscientists with an intuitive representation of their
               data. The customization of brain regions, connectivity, as
               well as details of the layout via parameters, can be adapted
               to their interests. In addition, we aim to improve
               neuron-level visualization and visual encoding of the
               Drosophila larval network graphs to provide a more detailed
               representation of circuits.",
  pages =      "125",
  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",
  keywords =   "brain connectivity visualization, Node-Link Diagram",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2022/wissmann-2022-alb/",
}