@thesis{PB-VRVis-2022-054,
author = {Wi{\ss}mann, Monika},
title = {Anatomy-Driven Layouting for Brain Network Visualization},
year = {2022},
type = {Master´s Thesis},
institution = {Fakult{\"a}t f{\"u}r Informatik Technische Universit{\"a}t Wien},
subtitle = {Master's Thesis (TU Wien)},
url = {https://www.vrvis.at/publications/PB-VRVis-2022-054},
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, was
considered 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 neuroscien-
tists 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.},
}