Exploring Brain Connectivity with Two-Dimensional Maps

  • Çağatay Demiralp
  • Radu Jianu
  • David H. Laidlaw
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

We present and compare two low-dimensional visual representations, 2D point and 2D path, for studying tractography datasets. The goal is to facilitate the exploration of dense tractograms by reducing visual complexity both in static representations and during interaction. The proposed planar maps have several desirable properties, including visual clarity, easy tract-of-interest selection, and multiscale hierarchy. The 2D path representations convey the anatomical familiarity of 3D brain models and cross-sectional views. We demonstrate the utility of both types of representation in two interactive systems where the views and interactions of the standard 3D streamtube representation are linked to those of the planar representations. We also demonstrate a web interface that integrates precomputed neural-path representations into a geographical digital-maps framework with associated labels, metrics, statistics, and linkouts. We compare the two representations both anecdotally and quantitatively via expert input. Results indicate that the planar path representation is more intuitive and easier to use and learn. Similarly, users are faster and more accurate in selecting bundles using the path representation than the 2D point representation. Finally, expert feedback on the web interface suggests that it can be useful for collaboration as well as quick exploration of data.

Keywords

Fiber Tract Visual Complexity Zoom Level Path Representation Hierarchical Cluster Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Song Zhang for generously providing us the 2D point representation tool used for comparison in our quantitative evaluation. This work was supported by NIH grant 1R01EB00415501A1.

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Çağatay Demiralp
    • 1
  • Radu Jianu
    • 1
  • David H. Laidlaw
    • 1
  1. 1.Brown UniversityProvidenceUSA

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