BrainCove: A Tool for Voxel-wise fMRI Brain Connectivity Visualization
Computer Graphics and Visualization, Dept. of INSY, Delft University of Technology, Delft
Eurographics Association, 2012
@inproceedings{VCBM12:99-106:2012,
booktitle={VCBM},
crossref={VCBM12-proc},
author={Andr'{e} F. van Dixhoorn and Julien R. Milles and Baldur van Lew and Charl P. Botha },
title={BrainCove: A Tool for Voxel-wise fMRI Brain Connectivity Visualization},
pages={99-106},
URL={http://diglib.eg.org/EG/DL/WS/VCBM/VCBM12/099-106.pdf},
DOI={10.2312/VCBM/VCBM12/099-106},
year={2012}
}
Functional brain connectivity from fMRI studies has become an important tool in studying functional interactions in the human brain as a complex network. Most recently, research has started focusing on whole brain functional networks at the voxel-level, where fMRI time-signals at each voxel are correlated with every other voxel in the brain to determine their functional connectivity. For a typical 4mm isotropic voxel resolution, this results in connectivity networks with more than twenty thousand nodes and over 400 million links. These cannot be effectively visualized or interactively explored using node-link representations, and due to their size are challenging to show as correlation matrix bitmaps. In this paper, we present a number of methods for the visualization and interactive visual analysis of this new high resolution brain network data, both in its matrix representation as well as in its anatomical context. We have implemented these methods in a GPU raycasting framework that enables real-time interaction, such as network probing and volume deformation, as well as real-time filtering. The techniques are integrated in a visual analysis application in which the different views are coupled, supporting linked interaction. Furthermore, we allow visual comparison of different brain networks with side-by-side and difference visualization. We have evaluated our approach via case studies with domain scientists at two different university medical centers.
October 26, 2012 by hgpu