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Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

  • Dmitry PetrovEmail author
  • Alexander Ivanov
  • Joshua Faskowitz
  • Boris Gutman
  • Daniel Moyer
  • Julio Villalon
  • Neda Jahanshad
  • Paul Thompson
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC) (Code and results are available at https://github.com/lodurality/35_methods_MICCAI_2017).

Keywords

Machine learning DWI Structural connectomes 
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References

  1. 1.
    Aganj, I., Lenglet, C., Sapiro, G., Yacoub, E., Ugurbil, K., Harel, N.: Reconstruction of the orientation distribution function in single-and multiple-shell q-ball imaging within constant solid angle. Magn. Reson. Med. 64(2), 554–566 (2010)Google Scholar
  2. 2.
    Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTS similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)CrossRefGoogle Scholar
  3. 3.
    Bassett, D.S., Brown, J.A., Deshpande, V., Carlson, J.M., Grafton, S.T.: Conserved and variable architecture of human white matter connectivity. Neuroimage 54(2), 1262–1279 (2011)CrossRefGoogle Scholar
  4. 4.
    Daducci, A., Canales-Rodrı, E.J., Descoteaux, M., Garyfallidis, E., Gur, Y., Lin, Y.C., Mani, M., Merlet, S., Paquette, M., Ramirez-Manzanares, A., et al.: Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI. IEEE Trans. Med. Imaging 33(2), 384–399 (2014)CrossRefGoogle Scholar
  5. 5.
    Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006)CrossRefGoogle Scholar
  6. 6.
    Destrieux, C., Fischl, B., Dale, A., Halgren, E.: Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53(1), 1–15 (2010)CrossRefGoogle Scholar
  7. 7.
    Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S., Descoteaux, M., Nimmo-Smith, I.: Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8, 8 (2014)CrossRefGoogle Scholar
  8. 8.
    Greve, D., Fischl, B.: A boundary-based cost function for within-subject, cross-modal registration. Neuroimage 47, S100 (2009)CrossRefGoogle Scholar
  9. 9.
    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 Biol. 6(7), e159 (2008)CrossRefGoogle Scholar
  10. 10.
    Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V.J., Meuli, R., Thiran, J.P.: Mapping human whole-brain structural networks with diffusion MRI. PLoS One 2(7), e597 (2007)CrossRefGoogle Scholar
  11. 11.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Stanford InfoLab (1999)Google Scholar
  12. 12.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  13. 13.
    Shrout, P.E., Fleiss, J.L.: Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86(2), 420 (1979)CrossRefGoogle Scholar
  14. 14.
    Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4), 1459–1472 (2007)CrossRefGoogle Scholar
  15. 15.
    Van Wijk, B.C., Stam, C.J., Daffertshofer, A.: Comparing brain networks of different size and connectivity density using graph theory. PLoS One 5(10), e13701 (2010)CrossRefGoogle Scholar
  16. 16.
    Zuo, X.N., Anderson, J.S., Bellec, P., Birn, R.M., Biswal, B.B., Blautzik, J., Breitner, J.C., Buckner, R.L., Calhoun, V.D., Castellanos, F.X., et al.: An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dmitry Petrov
    • 1
    • 2
    Email author
  • Alexander Ivanov
    • 2
    • 4
  • Joshua Faskowitz
    • 3
  • Boris Gutman
    • 1
  • Daniel Moyer
    • 1
  • Julio Villalon
    • 1
  • Neda Jahanshad
    • 1
  • Paul Thompson
    • 1
  1. 1.Imaging Genetics Center, Stevens Institute for Neuroimaging and InformaticsUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.The Institute for Information Transmission ProblemsMoscowRussia
  3. 3.Indiana UniversityBloomingtonUSA
  4. 4.Skoltech Institute of Science and TechnologyMoscowRussia

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