BrainCove: A Tool for Voxel-wise fMRI Brain Connectivity Visualization

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.

[1]  O. Sporns,et al.  Network centrality in the human functional connectome. , 2012, Cerebral cortex.

[2]  Michael Friendly,et al.  Effect ordering for data displays , 2003, Comput. Stat. Data Anal..

[3]  Werner M. Jainek,et al.  Illustrative Hybrid Visualization and Exploration of Anatomical and Functional Brain Data , 2008, Comput. Graph. Forum.

[4]  Daniel S. Margulies,et al.  A software tool for interactive exploration of intrinsic functional connectivity opens new perspectives for brain surgery , 2011, Acta Neurochirurgica.

[5]  Philippe Castagliola,et al.  On the Readability of Graphs Using Node-Link and Matrix-Based Representations: A Controlled Experiment and Statistical Analysis , 2005, Inf. Vis..

[6]  Julien Milles,et al.  Non-parametric model selection for subject-specific topological organization of resting-state functional connectivity , 2011, NeuroImage.

[7]  Hans Knutsson,et al.  A GPU accelerated interactive interface for exploratory functional connectivity analysis of FMRI data , 2011, 2011 18th IEEE International Conference on Image Processing.

[8]  Ravi S. Menon,et al.  Resting-state connectivity identifies distinct functional networks in macaque cingulate cortex. , 2012, Cerebral cortex.

[9]  Charl P. Botha,et al.  Visual Analysis of Integrated Resting State Functional Brain Connectivity and Anatomy , 2010, VCBM.

[10]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[11]  Ravin Balakrishnan,et al.  Using deformations for browsing volumetric data , 2003, IEEE Visualization, 2003. VIS 2003..

[12]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[13]  R. Yin Case Study Research: Design and Methods , 1984 .

[14]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[15]  Robert W. Cox,et al.  AFNI: What a long strange trip it's been , 2012, NeuroImage.

[16]  Cornelis J. Stam,et al.  Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain , 2008, NeuroImage.