Applications of Epsilon Radial Networks in Neuroimage Analyses

  • Nagesh Adluru
  • Moo K. Chung
  • Nicholas T. Lange
  • Janet E. Lainhart
  • Andrew L. Alexander
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)


Is the brain ’wiring’ different between groups of populations?” is an increasingly important question with advances in diffusion MRI and abundance of network analytic tools. Recently, automatic, data-driven and computationally efficient framework for extracting brain networks using tractography and epsilon neighborhoods were proposed in the diffusion tensor imaging (DTI) literature [1]. In this paper we propose new extensions to that framework and show potential applications of such epsilon radial networks (ERN) in performing various types of neuroimage analyses. These extensions allow us to use ERNs not only to mine for topo-physical properties of the structural brain networks but also to perform classical region-of-interest (ROI) analyses in a very efficient way. Thus we demonstrate the use of ERNs as a novel image processing lens for statistical and machine learning based analyses. We demonstrate its application in an autism study for identifying topological and quantitative group differences, as well as performing classification. Finally, these views are not restricted to ERNs but can be effective for population studies using any computationally efficient network-extraction procedures.


DTI brain connectivity tractography brain networks network measures classification toplogical group differences autism 


  1. 1.
    Chung, M., Adluru, N., Dalton, K., Alexander, A., Davidson, R.: Scalable brain network construction on white matter fibers. In: SPIE Medical Imaging (2011)Google Scholar
  2. 2.
    Greicius, M., et al.: Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. 100, 253–258 (2003)CrossRefGoogle Scholar
  3. 3.
    Jones, D., et al.: Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magn. Reson. Med. 42, 37–41 (1999)CrossRefGoogle Scholar
  4. 4.
    Julien, D.J., Peled, S., Berezovskii, V., Delzescaux, T., et al.: Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain. NeuroImage 37(2), 530–538 (2007)CrossRefGoogle Scholar
  5. 5.
    Catani, M., Thiebaut de Schotten, M.: A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 44(8), 1105–1132 (2008)CrossRefGoogle Scholar
  6. 6.
    Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C., et al.: Mapping the structural core of human cerebral cortex. PLoS Biol. 6(7), e159Google Scholar
  7. 7.
    Adluru, N., et al.: Characterizing brain connectivity using ε-radial nodes: application for classifying autism. In: MICCAI Workshop on CDMRI (2010)Google Scholar
  8. 8.
    Danielle, S., et al.: Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLoS Comp. Biol., 1–14 (2010)Google Scholar
  9. 9.
    Zhang, L., et al.: Quantifying degeneration of white matter in normal aging using fractal dimension. Neurobiol. of Aging 28, 1543–1555 (2007)CrossRefGoogle Scholar
  10. 10.
    Chen, B., Hall, D., Chklovskii, D.: Wiring optimization can relate neuronal structure and function. Proc. Natl. Acad. Sci. 103, 4723–4728Google Scholar
  11. 11.
    Watts, D., Strogatz, S.: Collective dynamics of ’small-world’ networks. Nature 393, 440–442 (1998)CrossRefzbMATHGoogle Scholar
  12. 12.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52, 1059–1069 (2010)CrossRefGoogle Scholar
  13. 13.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001) Software,
  14. 14.
    Jou, R., et al.: Reduced central white matter volume in autism: Implications for long-range connectivity. Psychiatry and Clinical Neurosci. 65, 98–101 (2011)CrossRefGoogle Scholar
  15. 15.
    Anderson, J., et al.: Decreased interhemispheric functional connectivity in autism. Cerebral Cortex 21(5), 1134–1146 (2011)CrossRefGoogle Scholar
  16. 16.
    Massey, F.J.: The kolmogorov-smirnov test for goodness of fit. J. Am. Stat. Assoc. 46, 68–78 (1951)CrossRefzbMATHGoogle Scholar
  17. 17.
    Adluru, N., et al.: Classification in DTI using shapes of white matter tracts. In: IEEE EMBS, pp. 2719–2722 (2009)Google Scholar
  18. 18.
    Ingalhalikar, M., Parker, D., Bloy, L., Roberts, T., Verma, R.: Diffusion based abnormality markers of pathology: Toward learned diagnostic prediction of ASDGoogle Scholar
  19. 19.
    Lange, N., et al.: Atypical diffusion tensor hemispheric asymmetry in autism. In: Autism Research, pp. 350–358Google Scholar
  20. 20.
    Herbert, M., et al.: Localization of white matter volume increase in autism and developmental language disorder. Ann. Neurol. 55, 530–540 (2004)CrossRefGoogle Scholar
  21. 21.
    Hardan, A., Muddasani, S., Vemulapalli, M., et al.: An MRI study of increased cortical thickness in autism. Am. J. Psychiatry 163, 1290–1292 (2006)CrossRefGoogle Scholar
  22. 22.
    Hausmann, J.: On the Vietoris-Rips complexes and a cohomology theory for metric spaces. Annals of Mathematics Studies 138, 175–188 (1995)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nagesh Adluru
    • 1
  • Moo K. Chung
    • 1
    • 2
  • Nicholas T. Lange
    • 3
  • Janet E. Lainhart
    • 4
  • Andrew L. Alexander
    • 1
  1. 1.Waisman CenterUniversity of Wisconsin-MadisonUSA
  2. 2.Dept. of Brain and Cog. Sci.Seoul National UniversityKorea
  3. 3.Dept. of Psychiatry and BiostatisticsHarvard UniversityUSA
  4. 4.Dept. of Psychiatry and PediatricsUniversity of UtahUSA

Personalised recommendations