An important approach in recent brain research is to model the brain as a complex network. For this purpose functional as well as structural networks are modelled. But these two networks do not necessarily match and the way to merge them is an open research question. We propose a method to fuse the networks by modeling a graph with additional intermediate nodes. Such a node is defined as a voxel through which at least two tracks run, and thus the case is modeled that the tracks are connected inside the node. The distinctive feature of the graph is to model both, the voxels and the tracks themselves as nodes. This way, a bipartite graph is created that comprises all potential paths in the brain that run via intermediate nodes. On this graph a search for shortest paths is conducted between voxels belonging to start and target regions. We demonstrate an example application for 12 subjects and assess the result by a transformation of representatives of the nodes for each subject to MNI space.
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- Fusion of Functional and Structural Brain Networks Using Graph Modeling with Intermediate Nodes