International Conference on Brain Informatics and Health

BIH 2015: Brain Informatics and Health pp 67-76 | Cite as

BRAINtrinsic: A Virtual Reality-Compatible Tool for Exploring Intrinsic Topologies of the Human Brain Connectome

  • Giorgio Conte
  • Allen Q. Ye
  • Angus G. Forbes
  • Olusola Ajilore
  • Alex Leow
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9250)

Abstract

Thanks to advances in non-invasive technologies such as functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI), highly-detailed maps of brain structure and function can now be collected. In this context, brain connectomics have emerged as a fast growing field that aims at understanding these comprehensive maps of brain connectivity using sophisticated computational models. In this paper we present BRAINtrinsic, an innovative web-based 3D visual analytics tool that allows users to intuitively and iteratively interact with connectome data. Moreover, BRAINtrinsic implements a novel visualization platform that reconstructs connectomes’ intrinsic geometry, i.e., the topological space as informed by brain connectivity, via dimensionality reduction. BRAINtrinsic is implemented with virtual reality in mind and is fully compatible with the Oculus Rift technology. Last, we demonstrate its effectiveness through a series of case studies involving both structural and resting-state MR imaging data.

Keywords

Connectomics Connectome datasets Intrinsic geometry Neuroimaging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alper, B., Hollerer, T., Kuchera-Morin, J., Forbes, A.: Stereoscopic highlighting: 2d graph visualization on stereo displays. IEEE Transactions on Visualization and Computer Graphics 17(12), 2325–2333 (2011)CrossRefGoogle Scholar
  2. 2.
    Borg, I., Groenen, P.J.: Modern multidimensional scaling: Theory and applications. Springer Science & Business Media (2005)Google Scholar
  3. 3.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10(3), 186–198 (2009)CrossRefGoogle Scholar
  4. 4.
    Bullmore, E.T., Bassett, D.S.: Brain graphs: graphical models of the human brain connectome. Annual review of clinical psychology 7, 113–140 (2011)CrossRefGoogle Scholar
  5. 5.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische mathematik 1(1), 269–271 (1959)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Febretti, A., Nishimoto, A., Thigpen, T., Talandis, J., Long, L., Pirtle, J., Peterka, T., Verlo, A., Brown, M., Plepys, D., et al.: Cave2: a hybrid reality environment for immersive simulation and information analysis. In: IS&T/SPIE Electronic Imaging, pp. 864903–864903. International Society for Optics and Photonics (2013)Google Scholar
  7. 7.
    Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)CrossRefGoogle Scholar
  8. 8.
    Forbes, A., Villegas, J., Almryde, K.R., Plante, E.: A stereoscopic system for viewing the temporal evolution of brain activity clusters in response to linguistic stimuli. In: IS&T/SPIE Electronic Imaging, pp. 90110I–90110I. International Society for Optics and Photonics (2014)Google Scholar
  9. 9.
    GadElkarim, J.J., Schonfeld, D., Ajilore, O., Zhan, L., Zhang, A.F., Feusner, J.D., Thompson, P.M., Simon, T.J., Kumar, A., Leow, A.D.: A framework for quantifying node-level community structure group differences in brain connectivity networks. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 196–203. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  10. 10.
    Gerhard, S., Daducci, A., Lemkaddem, A., Meuli, R., Thiran, J.P., Hagmann, P.: The connectome viewer toolkit: an open source framework to manage, analyze and visualize connectomes. Frontiers in Neuroinformatics 5(3) (2011). http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2011.00003/abstract
  11. 11.
    Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C.J., Wedeen, V.J., Sporns, O.: Mapping the structural core of human cerebral cortex. PLoS biology 6(7), e159 (2008)CrossRefGoogle Scholar
  12. 12.
    van den Heuvel, M.P., Sporns, O.: Rich-club organization of the human connectome. The Journal of neuroscience 31(44), 15775–15786 (2011)CrossRefGoogle Scholar
  13. 13.
    Howells, S., Maxwell, R., Peet, A., Griffiths, J.: An investigation of tumor 1h nuclear magnetic resonance spectra by the application of chemometric techniques. Magnetic resonance in medicine 28(2), 214–236 (1992)CrossRefGoogle Scholar
  14. 14.
    Jolliffe, I.: Principal component analysis. Wiley Online Library (2002)Google Scholar
  15. 15.
    Jones, D.K.: Diffusion MRI: Theory, methods, and applications. Oxford University Press (2010)Google Scholar
  16. 16.
    LaPlante, R.A., Douw, L., Tang, W., Stufflebeam, S.M.: The connectome visualization utility: Software for visualization of human brain networks. PLoS ONE 9(12), e113838 (2014). http://dx.doi.org/10.1371%2Fjournal.pone.0113838
  17. 17.
    van der Maaten, L.J., Postma, E.O., van den Herik, H.J.: Dimensionality reduction: A comparative review. Journal of Machine Learning Research 10(1–41), 66–71 (2009)Google Scholar
  18. 18.
    Munzner, T.: Process and pitfalls in writing information visualization research papers. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 134–153. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  19. 19.
    Robertson, G.G., Mackinlay, J.D., Card, S.K.: Cone trees: animated 3d visualizations of hierarchical information. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 189–194. ACM (1991)Google Scholar
  20. 20.
    Sporns, O.: The human connectome: a complex network. Annals of the New York Academy of Sciences 1224(1), 109–125 (2011)CrossRefGoogle Scholar
  21. 21.
    Sporns, O., Tononi, G., Kötter, R.: The human connectome: a structural description of the human brain. PLoS computational biology 1(4), e42 (2005)CrossRefGoogle Scholar
  22. 22.
    Vujovic, S., Henderson, S., Presneau, N., Odell, E., Jacques, T., Tirabosco, R., Boshoff, C., Flanagan, A.: Brachyury, a crucial regulator of notochordal development, is a novel biomarker for chordomas. The Journal of pathology 209(2), 157–165 (2006)CrossRefGoogle Scholar
  23. 23.
    Ware, C., Mitchell, P.: Visualizing graphs in three dimensions. ACM Transactions on Applied Perception (TAP) 5(1), 2 (2008)Google Scholar
  24. 24.
    Xia, M., Wang, J., He, Y.: Brainnet viewer: a network visualization tool for human brain connectomics. PloS one 8(7), e68910 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Giorgio Conte
    • 1
  • Allen Q. Ye
    • 2
  • Angus G. Forbes
    • 1
  • Olusola Ajilore
    • 2
  • Alex Leow
    • 2
  1. 1.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA
  2. 2.Department of Psychiatry and BioengineeringUniversity of Illinois at ChicagoChicagoUSA

Personalised recommendations