Multivariate Statistical Analysis of Whole Brain Structural Networks Obtained Using Probabilistic Tractography

  • Emma C. Robinson
  • Michel Valstar
  • Alexander Hammers
  • Anders Ericsson
  • A. David Edwards
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)


This paper presents a new framework for the analysis of anatomical connectivity derived from diffusion tensor MRI. The framework has been applied to estimate whole brain structural networks using diffusion data from 174 adult subjects. In the proposed approach, each brain is first segmented into 83 anatomical regions via label propagation of multiple atlases and subsequent decision fusion. For each pair of anatomical regions the probability of connection and its strength is then estimated using a modified version of probabilistic tractography. The resulting brain networks have been classified according to age and gender using non-linear support vector machines with GentleBoost feature extraction. Classification performance was tested using a leave-one-out approach and the mean accuracy obtained was 85.4%.


Support Vector Machine Fractional Anisotropy Multivariate Statistical Analysis Brain Network Orientation Distribution Function 
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  1. 1.
    Behrens, T., Woolrich, M., Jenkinson, M., Johansen-Burg, H., Nunes, R., Clare, S., Matthews, P., Brady, J., Smith, S.: Characterization and propagation of uncertainty in diffusion-weighted mr imaging. Magn. Res. Med. Anal. 50, 1077–1088 (2003)CrossRefGoogle Scholar
  2. 2.
    Behrens, T., Johansen-Burg, H.: Relating connectional architecture to grey matter function using diffusion imaging. Phil. Trans. R. Soc. B 360, 903–911 (2005)CrossRefGoogle Scholar
  3. 3.
    Counsell, S., Dyet, L., Larkman, D., Nunes, R., Boardman, J., Allsop, J., Fitzpatrick, J., Srinivasan, L., Cowan, F., Hajnal, J., Rutherford, M.: Edwards: Thalamo-cortical connectivity in children born preterm mapped using probabilistic magnetic resonance tractography. Neuroimage 34, 896–904 (2006)CrossRefGoogle Scholar
  4. 4.
    Honey, C., Kötter, R., Breakspear, M., Sporns, O.: Network structure of cerebral cortex shapes functional connectivity on multiple time scales. PNAS 24, 10240–10245 (2007)CrossRefGoogle Scholar
  5. 5.
    Achard, S., Salvador, R., Whitcher, B., Suckling, J., Bullmore, E.: A resilient low frequency small-world human brain functional network with highly connected association cortical hubs. J. Neuroscience 26, 63–72 (2006)CrossRefGoogle Scholar
  6. 6.
    Iturria-Medina, Y., Canales-Rodrígues, E., Melie-García, L., Valdés-Hernández, P., Martínez-Montes, E., Alemán-Gómez, Y., Sánchez-Bornot, J.: Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. Neuroimage 36, 645–660 (2007)CrossRefGoogle Scholar
  7. 7.
    Burges, C.: A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  8. 8.
    Valstar, M.F., Pantic, M.: Fully automatic facial action unit detection and temporal analysis. In: CVPR, pp. 149–126 (2006)Google Scholar
  9. 9.
    Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V., Meuli, R., Thiran, J.: Mapping human whole-brain structural networks with diffusion MRI. PLoSONE 7, 597 (2007)Google Scholar
  10. 10.
    Aljabar, P., Heckemann, Hammers, A., Hajnal, J.V., Rueckert, D.: Classifier selection strategies for label fusion using large scale atlas databases. In: Medical Image Computing and Computer-Assisted Intervention, pp. 523–531 (2006)Google Scholar
  11. 11.
    Ashburner, J., Friston, K.: Unified segmentation. Neuroimage 26, 839–851 (2005)CrossRefGoogle Scholar
  12. 12.
    Madden, D., Whiting, W., Huettel, S., White, L., MacFall, J., Provenzale, J.: Diffusion tensor imaging of adult age differences in cerebral white matter:relation to response time. NeuroImage 21, 1174–1181 (2004)CrossRefGoogle Scholar
  13. 13.
    Oh, J., Song, I., Lee, J., Kang, H., Park, K., Kang, E., Loo, D.: Tractography guided statistics in diffusion tensor imaging for the detection of gender difference in fiber integrity of the corpora callosa. NeuroImage 36, 606–616 (2007)CrossRefGoogle Scholar
  14. 14.
    Davatzikos, C., Resnick, S.: Sex differences in anatomic measures of interhemispheric connectivity; correlations with cognition in women but not men. Cerebral Cortex 8, 635–640 (1998)CrossRefGoogle Scholar
  15. 15.
    Heckemann, R., Hajnal, J., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33, 115–126 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Emma C. Robinson
    • 1
  • Michel Valstar
    • 1
  • Alexander Hammers
    • 2
  • Anders Ericsson
    • 1
  • A. David Edwards
    • 3
  • Daniel Rueckert
    • 1
  1. 1.Department of Computing, Imperial College LondonUK
  2. 2.MRC Clinical Sciences Centre and Division of Neuroscience, Faculty of Medicine, Imperial College LondonUK
  3. 3.Department of Paediatrics, Imperial College LondonUK

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