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)


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.


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.



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.


  1. 1.
    Akers, D.: Wizard of Oz for participatory design: inventing an interface for 3D selection of neural pathway estimates. In: Proceedings of CHI 2006 Extended Abstracts, Montréal, pp. 454–459. ACM Press, New York (2006)Google Scholar
  2. 2.
    Akers, D., Sherbondy, A., Mackenzie, R., Dougherty, R., Wandell, B.: Exploration of the brain’s white matter pathways with dynamic queries. In: Proceedings of Visualization, Austin, TX, pp. 377–384 (2004)Google Scholar
  3. 3.
    Arakawa, K., Tamaki, S., Kono, N., Kido, N., Ikegami, K., Ogawa, R., Tomita, M.: Genome Projector: zoomable genome map with multiple views. BMC Bioinformatics 10(1), 31 (2009)Google Scholar
  4. 4.
    Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. B 103(3), 247–254 (1994)Google Scholar
  5. 5.
    Bostock, M., Heer, J.: Protovis: A graphical toolkit for visualization. IEEE Trans. Vis. Comput. Graph. 15(6), 1121–1128 (2009)Google Scholar
  6. 6.
    Chalmers, M.: A linear iteration time layout algorithm for visualising high-dimensional data. In: Proceedings of the 7th Conference on Visualization’96. IEEE Computer Society Press, Los Alamitos (1996)Google Scholar
  7. 7.
    Chen, W., Ding, Z., Zhang, S., MacKay-Brandt, A., Correia, S., Qu, H., Crow, J.A., Tate, D.F., Yan, Z., Peng, Q.: A novel interface for interactive exploration of DTI fibers. IEEE Trans. Vis. Comput. Graph. (Proc. of Visualization) 15(16), 1433–1440 (2009)Google Scholar
  8. 8.
    Danis, C., Viegas, F., Wattenberg, M.: Your place or mine? Visualization as a community component. In: Proceedings of CHI, Florence. ACM, New York (2008)Google Scholar
  9. 9.
    de Leeuw, J.: Applications of convex analysis to multidimensional scaling. In: Barra, J., Brodeau, F., Romier, G., Cutsem, B.V. (eds.) Recent Developments in Statistics, pp. 133–146. North Holland Publishing Company, Amsterdam/New York (1977)Google Scholar
  10. 10.
    Demiralp, C., Laidlaw, D.H.: Similarity coloring of DTI fiber tracts. In: Proceedings of DMFC Workshop at MICCAI, London, UK (2009)Google Scholar
  11. 11.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2000)Google Scholar
  12. 12.
    Eades, P.: A heuristic for graph drawing. Congr. Numer. 42(149160), 194–202 (1984)Google Scholar
  13. 13.
    Fairchild, M.D.: Color Appearance Models. Wiley-IS&T, Chichester, UK (2005)Google Scholar
  14. 14.
  15. 15.
    GoogleMapsAPI. (2012)
  16. 16.
    Holten, D.: Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans. Vis. Comput. Graph. 12, 741–748 (2006). doi: Google Scholar
  17. 17.
  18. 18.
    Jeong, W.K., Beyer, J., Hadwiger, M., Vazquez, A., Pfister, H., Whitaker, R.T.: Scalable and interactive segmentation and visualization of neural processes in EM datasets. IEEE Trans. Vis. Comput. Graph. 15, 1505–1514 (2009)Google Scholar
  19. 19.
    Jianu, R., Demiralp, C., Laidlaw, D.: Exploring 3D DTI fiber tracts with linked 2D representations. IEEE Trans. Vis. Comput. Graph. (Proc. of Visualization) 15(6), 1449–1456 (2009)Google Scholar
  20. 20.
    Jianu, R., Demiralp, C., Laidlaw, D.H.: Exploring brain connectivity with two-dimensional neural maps. In: IEEE Visualization 2010 Poster Compendium, Salt Lake City, UT (2010)Google Scholar
  21. 21.
    Johnson, D., Jankun-Kelly, T.: A scalability study of web-native information visualization. In: Proceedings of Graphics Interface, Windsor, pp. 163–168 (2008)Google Scholar
  22. 22.
    Kleinberg, J.M.: An impossibility theorem for clustering. In: Advances in Neural Information Processing Systems (NIPS), Vancouver, BC, pp. 446–453. MIT Press, Cambridge (2002)Google Scholar
  23. 23.
    Mori, S., Van Zijl, P.: Fiber tracking: principles and strategies – a technical review. NMR Biomed. 15(7–8), 468–480 (2002)Google Scholar
  24. 24.
  25. 25.
    Tanner, J.E.: Transient diffusion in system partitioned by permeable barriers. Application to NMR measurements with a pulsed field gradient. J. Chem. Phys. 69(4), 1748–1754 (1978)Google Scholar
  26. 26.
    Viégas, F., Wattenberg, M., Van Ham, F., Kriss, J., McKeon, M.: ManyEyes: a site for visualization at internet scale. IEEE Trans. Vis. Comput. Graph. 13(6), 1121 (2007)Google Scholar
  27. 27.
    Viégas, F., Wattenberg, M., McKeon, M., Van Ham, F., Kriss, J.: Harry Potter and the meat-filled freezer: a case study of spontaneous usage of visualization tools. In: Proceedings of HICSS, Waikoloa. IEEE Computer Society Press, Los Alamitos (2008)Google Scholar
  28. 28.
    Yates, T., Okoniewski, M., Miller, C.: X: map: annotation and visualization of genome structure for Affymetrix exon array analysis. Nucleic Acids Res. 36 (Database issue), D780 (2008)Google Scholar
  29. 29.
    Zhang, S., Demiralp, C., Laidlaw, D.: Visualizing diffusion tensor MR images using streamtubes and streamsurfaces. IEEE Trans. Vis. Comput. Graph. 9(4), 454–462 (2003)Google Scholar

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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