Abstract
The visualization of brain connectivity becomes progressively more challenging as analytic and computational advances begin to facilitate connexel-wise analyses, which include all connections between pairs of voxels. Drawing full connectivity graphs can result in depictions that, rather than illustrating connectivity patterns in more detail, obfuscate patterns owing to the data density. In an effort to expand the possibilities for visualization, we describe two approaches for presenting connexels: edge-bundling, which clarifies structure by grouping geometrically similar connections; and, connectivity glyphs, which depict a condensed connectivity map at each point on the cortical surface. These approaches can be applied in the native brain space, facilitating interpretation of the relation of connexels to brain anatomy. The tools have been implemented as part of brainGL, an extensive open-source software for the interactive exploration of structural and functional brain data.
Highlights
The term connexel was first introduced to describe the basic unit in brain connectomics—the relationship between two threedimensional (3D) positions (Worsley et al, 1998)
This section describes the results of the implementation of connectivity glyphs and edge-bundling in brainGL: After an introduction of the user interface, we present example applications for the visualization of functional connectivity and experiences regarding the runtimes and interactiveness of the resulting visualizations
USER INTERFACE The graphical user interface of brainGL is divided into several views, which can be freely arranged using the intuitive layout mechanisms of the GUI library
Summary
The term connexel was first introduced to describe the basic unit in brain connectomics—the relationship between two threedimensional (3D) positions (Worsley et al, 1998). Connexels are modality-independent, as they can describe the relationship between pairs of voxels as assessed using any data type. They are well suited for “pathless” methodologies that solely describe the weight of a connection between two points. The resulting data are still highly complex, since connectivity can be calculated between every pair of gray matter locations in the brain. This complexity makes their visualization and exploration challenging
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