Exploiting Machine Learning Principles for Assessing the Fingerprinting Potential of Connectivity Features

  • Silvia Obertino
  • Sofía Jiménez Hernández
  • Ilaria Boscolo Galazzo
  • Francesca Benedetta Pizzini
  • Mauro Zucchelli
  • Gloria Menegaz
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


To which extent connectivity measures are able to characterize subjective features? The pipeline leading from the signal acquisition to the connectivity matrix allows numerous degrees of freedom each having an impact on the final result. In this paper, we investigated the sensitivity and specificity of the connectivity models within a machine learning framework through the assessment of the detectability of repeated measures of the same subject versus other subjects. Two fiber Orientation Distribution Function (fODF) reconstruction methods, one of which firstly proposed in this paper, three tractography algorithms and four connectivity features were considered and performance was expressed in terms of Area Under the Curve of the test-retest recognition task. Results suggest that there is a trade-off between the selectivity of the fODF reconstruction methods and the conservativeness of the fiber tracking algorithms across all microstructural indices . The best solution was provided by using an high angular resolution fODF estimation method and the most restrictive deterministic tractography algorithm.


  1. 1.
    Kaden, E., Kruggel, F., Alexander, D.C.: Quantitative mapping of the per-axon diffusion coefficients in brain white matter. Magn. Reson. Med. 75(4), 1752–1763 (2016)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Vaessen, M., Hofman, P., Tijssen, H., Aldenkamp, A., Jansen, J.F., Backes, W.H.: The effect and reproducibility of different clinical DTI gradient sets on small world brain connectivity measures. NeuroImage 51(3), 1106–1116 (2010)Google Scholar
  4. 4.
    Zalesky, A., Fornito, A., Harding, I.H., Cocchi, L., Yücel, M., Pantelis, C., Bullmore, E.T.: Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 50(3), 970–983 (2010)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Cammoun, L., Gigandet, X., Meskaldji, D., Thiran, J.P., Sporns, O., Do, K.Q., Maeder, P., Meuli, R., Hagmann, P.: Mapping the human connectome at multiple scales with diffusion spectrum MRI. J. Neurosci. Methods 203(2), 386–397 (2012)Google Scholar
  7. 7.
    Cheng, H., Wang, Y., Sheng, J., Kronenberger, W.G., Mathews, V.P., Hummer, T.A., Saykin, A.J.: Characteristics and variability of structural networks derived from diffusion tensor imaging. NeuroImage 61(4), 1153–1164 (2012)Google Scholar
  8. 8.
    Buchanan, C.R., Pernet, C.R., Gorgolewski, K.J., Storkey, A.J., Bastin, M.E.: Test–retest reliability of structural brain networks from diffusion MRI. NeuroImage 86, 231–243 (2014)Google Scholar
  9. 9.
    Zucchelli, M., Descoteaux, M., Menegaz, G.: A generalized SMT-based framework for diffusion MRI microstructural model estimation. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), Workshop on Computational Diffusion MRI (CDMRI) (2017)Google Scholar
  10. 10.
    Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61(4), 1000–1016 (2012)CrossRefGoogle Scholar
  11. 11.
    Cheng, J., Deriche, R., Jiang, T., Shen, D., Yap, P.T.: Non-negative spherical deconvolution (NNSD) for estimation of fiber orientation distribution function in single-/multi-shell diffusion MRI. NeuroImage 101, 750–764 (2014)CrossRefGoogle Scholar
  12. 12.
    Brusini, L., Obertino, S., Galazzo, I.B., Zucchelli, M., Krueger, G., Granziera, C., Menegaz, G.: Ensemble average propagator-based detection of microstructural alterations after stroke. Int. J. Comput. Assist. Radiol. Surg. 11(9), 1585–1597 (2016)CrossRefGoogle Scholar
  13. 13.
    Kaden, E., Kelm, N.D., Carson, R.P., Does, M.D., Alexander, D.C.: Multi-compartment microscopic diffusion imaging. NeuroImage 139, 346–359 (2016)CrossRefGoogle Scholar
  14. 14.
    Zucchelli, M., Brusini, L., Méndez, C.A., Daducci, A., Granziera, C., Menegaz, G.: What lies beneath? diffusion eap-based study of brain tissue microstructure. Med. Image Anal. 32, 145–156 (2016)CrossRefGoogle Scholar
  15. 15.
    Jeurissen, B., Tournier, J.D., Dhollander, T., Connelly, A., Sijbers, J.: Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103, 411–426 (2014)CrossRefGoogle Scholar
  16. 16.
    Andersson, J.L., Skare, S., Ashburner, J.: How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage 20(2), 870–888 (2003)CrossRefGoogle Scholar
  17. 17.
    Andersson, J.L., Sotiropoulos, S.N.: Non-parametric representation and prediction of single-and multi-shell diffusion-weighted MRI data using Gaussian processes. NeuroImage 122, 166–176 (2015)CrossRefGoogle Scholar
  18. 18.
    Garyfallidis, E.: Towards an accurate brain tractography. PhD thesis, University of Cambridge (2013)Google Scholar
  19. 19.
    Tournier, J., Calamante, F., Connelly, A., et al.: MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22(1), 53–66 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Silvia Obertino
    • 1
  • Sofía Jiménez Hernández
    • 2
  • Ilaria Boscolo Galazzo
    • 1
  • Francesca Benedetta Pizzini
    • 3
  • Mauro Zucchelli
    • 1
  • Gloria Menegaz
    • 1
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly
  2. 2.Universidad Politécnica de ValenciaValènciaSpain
  3. 3.Department of Diagnostics and PathologyVerona University HospitalVeronaItaly

Personalised recommendations