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Connexel visualization: A software implementation of glyphs and edge-bundling for dense connectivity data using brainGL

MPG-Autoren
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Böttger,  Joachim
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schurade,  Ralph
Methods and Development Unit - MEG and Cortical Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Jakobsen,  Estrid
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schäfer,  Alexander
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Margulies,  Daniel S.
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Zitation

Böttger, J., Schurade, R., Jakobsen, E., Schäfer, A., & Margulies, D. S. (2014). Connexel visualization: A software implementation of glyphs and edge-bundling for dense connectivity data using brainGL. Frontiers in Neuroscience, 8: 15. doi:10.3389/fnins.2014.00015.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0015-1136-0
Zusammenfassung
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.