Spatial-Data-Driven Layouting for Brain Network Visualization

Abstract

Recent advances in neuro-imaging enable scientists to create brain network data that can lead to novel insights into neurocircuitry, and a better understanding of the brain’s organization. These networks inherently involve a spatial component, depicting which brain regions are structurally, functionally or genetically related. Their visualization in 3D suffers from occlusion and clutter, especially with increasing number of nodes and connections, while 2D representations such as connectograms, connectivity matrices, and node-link diagrams neglect the spatio-anatomical context. Approaches to arrange 2D-graphs manually are tedious, species-dependent, and require the knowledge of domain experts. In this paper, we present a spatial-data-driven approach for layouting 3D brain networks in 2D node-link diagrams, while maintaining their spatial organization. The produced graphs do not need manual positioning of nodes, are consistent (even for sub-graphs), and provide a perspective-dependent arrangement for orientation. Furthermore, we provide a visual design for highlighting anatomical context, including the shape of the brain, and the size of brain regions. We present in several case-studies the applicability of our approach for different neuroscience-relevant species, including the mouse, human, and Drosophila larvae. In a user study conducted with several domain experts, we demonstrate its relevance and validity, as well as its potential for neuroscientific publications, presentations, and education.