Subject-Specific Structural Parcellations Based on Randomized AB-divergences

  • Nicolas HonnoratEmail author
  • Drew Parker
  • Birkan Tunç
  • Christos Davatzikos
  • Ragini Verma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


Brain parcellation provides a means to approach the brain in smaller regions. It also affords an appropriate dimensionality reduction in the creation of connectomes. Most approaches to creating connectomes start with registering individual scans to a template, which is then parcellated. Data processing usually ends with the projection of individual scans onto the parcellation for extracting individual biomarkers, such as connectivity signatures. During this process, registration errors can significantly alter the quality of biomarkers. In this paper, we propose to mitigate this issue with a hybrid approach for brain parcellation. We use diffusion MRI (dMRI) based structural connectivity measures to drive the refinement of an anatomical prior parcellation. Our method generates highly coherent structural parcels in native subject space while maintaining interpretability and correspondences across the population. This goal is achieved by registering a population-wide anatomical prior to individual dMRI scan and generating connectivity signatures for each voxel. The anatomical prior is then deformed by re-parcellating the brain according to the similarity between voxel connectivity signatures while constraining the number of parcels. We investigate a broad family of signature similarities known as AB-divergences and explain how a divergence adapted to our segmentation task can be selected. This divergence is used for parcellating a high-resolution dataset using two graph-based methods. The promising results obtained suggest that our approach produces coherent parcels and stronger connectomes than the original anatomical priors.


  1. 1.
    Behrens, T., Berg, H., Jbabdi, S., Rushworth, M., Woolrich, M.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1), 144–155 (2007)CrossRefGoogle Scholar
  2. 2.
    Bullmore, S.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009)CrossRefGoogle Scholar
  3. 3.
    Cichocki, C.: Amari: Generalized alpha-beta divergences and their application to robust nonnegative matrix factorization. Entropy 13, 134–170 (2011)CrossRefGoogle Scholar
  4. 4.
    Clarkson, M.J., Malone, I.B., Modat, M., Leung, K.K., Ryan, N., Alexander, D.C., Fox, N.C., Ourselin, S.: A framework for using diffusion weighted imaging to improve cortical parcellation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 534–541. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15705-9_65CrossRefGoogle Scholar
  5. 5.
    Dale, A., Fischl, B., Sereno, M.: Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999)CrossRefGoogle Scholar
  6. 6.
    Desikan, R., Segonne, F., Fischl, B., Quinn, B., Dickerson, B., Blacker, D., Buckner, R., Dale, A., Maguire, R., Hyman, B., Albert, M., Killiany, R.: 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
  7. 7.
    Fredman, M., Tarjan, R.: Fibonacci heaps and their uses in improved network optimization algorithms. J. Assoc. Comput. Mach. 34(3), 596–615 (1987)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gallardo, G., Wells III., W., Deriche, R., Wassermann, D.: Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach. Neuroimage (2017, in press)Google Scholar
  9. 9.
    Gordon, E., Laumann, T., Adeyemo, B., Huckins, J., Kelley, W., Petersen, S.: Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2014)CrossRefGoogle Scholar
  10. 10.
    Halko, N., Martinsson, P., Tropp, J.A.: Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev. 53(2), 217–288 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Honnorat, N., Satterthwaite, T., Gur, R., Gur, R., Davatzikos, C.: sGraSP: a graph-based method for the derivation of subject-specific functional parcellations of the brain. J. Neurosci. Methods 227, 1–20 (2017)CrossRefGoogle Scholar
  12. 12.
    Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2, 193–218 (1985)CrossRefGoogle Scholar
  13. 13.
    Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T., Elliott, M., Ruparel, K., Hakonarson, H., Gur, R., Gur, R., Verma, R.: Sex differences in the structural connectome of the human brain. Proc. Natl. Acad. Sci. 111(2), 823–828 (2014)CrossRefGoogle Scholar
  14. 14.
    Mars, R., Jbabdi, S., Sallet, J., O’Reilly, J., Croxson, P., Olivier, E., Noonan, M., Bergmann, C., Mitchell, A.S., Baxter, M., Behrens, T., Johansen-Berg, H., Tomassini, V., Miller, K., Rushworth, M.: Diffusion-weighted imaging tractography-based parcellation of the human parietal cortex and comparison with human and macaque resting-state functional connectivity. J. Neurosci. 31(11), 4087–4100 (2011)CrossRefGoogle Scholar
  15. 15.
    Parisot, S., Arslan, S., Passerat-Palmbach, J., Wells, W., Rueckert, D.: Group-wise parcellation of the cortex through multi-scale spectral clustering. NeuroImage 136, 68–83 (2016)CrossRefGoogle Scholar
  16. 16.
    Tunç, B., Parker, W.A., Ingalhalikar, M., Verma, R.: Automated tract extraction via atlas based adaptive clustering. Neuroimage 102(2), 596–607 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nicolas Honnorat
    • 1
    Email author
  • Drew Parker
    • 1
  • Birkan Tunç
    • 1
  • Christos Davatzikos
    • 1
  • Ragini Verma
    • 1
  1. 1.University of PennsylvaniaPhiladelphiaUSA

Personalised recommendations