Simplification and visualization of brain network extracted from fMRI data using CEREBRA

Abstract

In this paper, we introduce graph simplification capabilities of a new tool, CEREBRA, which is used to visualize the 3D network of human brain, extracted from the fMRI data. The nodes of the network are defined as the voxels with the attributes corresponding to the intensity values changing by time and the coordinates in three dimensional Euclidean space. The arc weights are estimated by modeling the relationships among the voxel activation records. We aim to help researchers to reveal the underlying brain state by examining the active regions of the brain and observe the interactions among them. Although the tool provides many features for displaying the fMRI data as a dynamical network, in this study, we have mainly focused on two main features. The first one is the unique graph simplification module that allows users to eliminate redundant edges according to some weighted similarity criterion. The second one is visualizing the output of the external algorithms for voxel selection, clustering or network representation of fMRI data. Thus, users are able to display, analyze and further process the output of their own algorithms.