Graph-based visualization of neuronal connectivity using matrix block partitioning and edge bundling

Abstract

Neuronal connectivity matrices contain information vital to the understanding of brain structure and function. In this work we present graph-based visualization techniques for macroscale connectivity matrices that retain anatomical context while reducing the clutter and occlusion problems that plague 2D and 3D node-link diagrams. By partitioning the connectivity matrix into blocks corresponding to brain hemispheres and bundling graph edges we are able to generate intuitive visualizations that permit investigation at multiple scales (hemisphere, lobe, anatomical region). We demonstrate our approach on connectivity matrices computed using tractography of high angular resolution diffusion images acquired as part of a Parkinson’s disease study.