CEREBRA: a 3-D visualization and processing tool for brain network extracted from fMRI data

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2017
Nasır, Barış
In this thesis, we introduce a new tool, CEREBRA, for visualizing 3D network of human brain, extracted from the functional magnetic resonance imaging (fMRI) data. The tool aims to visualize the selected voxels as the nodes of the network and the edge weights are estimated by modeling the relationships among the voxel time series as a set of linear regression equations. This way, researchers can analyze the active brain regions/voxels and observe the interactions among them by analyzing the edge weights and node degree distributions of the brain network, for the underlying brain state(s). CEREBRA provides an easy to use interactive interface with basic display options for users to examine the details of the brain network. CEREBRA simplifies the network by built-in processors of graph reduction algorithms to display various properties of the network. The reduction algorithms vary from basic filtering methods to more complex graph sparsifier metrics. The toolbox is, also, capable of space-time representation of the dynamically changing voxel intensity and edge strength values, by animating the 3D voxel time series.