<p>Brain
connectome network visualization could help the neurologists inspect the brain
structure easily and quickly. In the thesis, the model of the brain connectome network is visualized in both three
dimensions (3D) environment and two dimensions (2D) environment. One is named “Brain
Explorer for Connectomic Analysis” (BECA) developed by the previous research
already. It could present the 3D model of brain structure with region of
interests (ROIs) in different colors [5]. The other is mainly for the
information visualization of brain connectome in 2D. It adopts the force-directed
layout to visualize the network. However, the brain network visualization could
not bring the user intuitively ideas about brain structure. Sometimes, with the
increasing scales of ROIs (nodes), the visualization would bring more visual
clutter for readers [3]. So, brain connectome network properties visualization
becomes a useful complement to brain network visualization. For a better
understanding of the effect of Alzheimer’s disease on the brain nerves, the
thesis introduces several methods about the brain graph properties
visualization. There are the five selected graph properties discussed in the
thesis. The degree and closeness are node properties. The shortest path,
maximum flow, and clique are edge
properties. Except for clique, the other properties are visualized in both 3D
and 2D. The clique is visualized only in 2D. For the clique, a new hypergraph
visualization method is proposed with three different algorithms. Instead of
using an extra node to present a clique, the thesis uses a “belt” to connect
all nodes within the same clique. The
methods of node connections are based on the traveling salesman problem (TSP)
and Law of cosines. In addition, the thesis also applies the result of the clique to adjust the force-directed layout of
brain graph in 2D to dramatically eliminate the visual clutter. Therefore, with the support of the graph properties
visualization, the brain connectome network visualization tools become more
flexible.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7424285 |
| Date | 17 January 2019 |
| Creators | Chenfeng Zhang (5931164) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/BRAIN_CONNECTOME_NETWORK_PROPERTIES_VISUALIZATION/7424285 |
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