Advertisement

Connectivity-Driven Brain Parcellation via Consensus Clustering

  • Anvar Kurmukov
  • Ayagoz Musabaeva
  • Yulia Denisova
  • Daniel Moyer
  • Boris Gutman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11083)

Abstract

We present two related methods for deriving connectivity-based brain atlases from individual connectomes. The proposed methods exploit a previously proposed dense connectivity representation, termed continuous connectivity, by first performing graph-based hierarchical clustering of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. We assess the quality of our parcellations using (1) Kullback-Liebler and Jensen-Shannon divergence with respect to the dense connectome representation, (2) inter-hemispheric symmetry, and (3) performance of the simplified connectome in a biological sex classification task. We find that the parcellation based-atlas computed using a greedy search at a hierarchical depth 3 outperforms all other parcellation-based atlases as well as the standard Dessikan-Killiany anatomical atlas in all three assessments.

Notes

Acknowledgements

This work was funded in part by the Russian Science Foundation grant 17-11-01390 at IITP RAS.

References

  1. 1.
    Blondel, V.D., et al.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  2. 2.
    Dimitriadou, E., Weingessel, A., Hornik, K.: A combination scheme for fuzzy clustering. Int. J. Pattern Recogn. Artif. Intell. 16(07), 901–912 (2002)CrossRefGoogle Scholar
  3. 3.
    Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)CrossRefGoogle Scholar
  4. 4.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Kurmukov, A., et al.: Classifying phenotypes based on the community structure of human brain networks. In: Cardoso, M.J. (ed.) GRAIL/MFCA/MICGen 2017. LNCS, vol. 10551, pp. 3–11. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67675-3_1CrossRefGoogle Scholar
  6. 6.
    Lin, J.: Divergence measures based on Shannon entropy. IEEE Trans. Inf. Theory 37(14), 145–151 (1991)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Meunier, D., Lambiotte, R., Bullmore, E.T.: Modular and hierarchically modular organization of brain networks. Front. Neurosci. 4, 200 (2010)CrossRefGoogle Scholar
  8. 8.
    Moyer, D., et al.: Continuous representations of brain connectivity using spatial point processes. Med. Image Anal. 41, 32–39 (2017)CrossRefGoogle Scholar
  9. 9.
    Nicolini, C., Bordier, C., Bifone, A.: Community detection in weighted brain connectivity networks beyond the resolution limit. Neuroimage 146, 28–39 (2017)CrossRefGoogle Scholar
  10. 10.
    Parisot, S., Glocker, B., Schirmer, M.D., Rueckert, D.: GraMPa: graph-based multi-modal parcellation of the cortex using fusion moves. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 148–156. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46720-7_18CrossRefGoogle Scholar
  11. 11.
    Strehl, A., Ghosh, J.: Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Taylor, P.N., Wang, Y., Kaiser, M.: Within brain area tractography suggests local modularity using high resolution connectomics. Sci. Rep. 7, 39859 (2017)CrossRefGoogle Scholar
  13. 13.
    Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)CrossRefGoogle Scholar
  14. 14.
    Vega-Pons, S., Ruiz-Shulcloper, J.: A survey of clustering ensemble algorithms. Int. J. Pattern Recogn. Artif. Intell. 25(03), 337–372 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Petrov, D., et al.: Evaluating 35 methods to generate structural connectomes using pairwise classification. arXiv e-prints, eprint = 1706.06031, June 2017Google Scholar
  17. 17.
    Arslan, S., Ktena, S.I., Makropoulos, A., Robinson, E.C., Rueckert, D., Parisot, S.: Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex. NeuroImage 170, 5–30 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Anvar Kurmukov
    • 1
    • 2
  • Ayagoz Musabaeva
    • 1
  • Yulia Denisova
    • 1
  • Daniel Moyer
    • 4
  • Boris Gutman
    • 1
    • 3
  1. 1.The Institute for Information Transmission ProblemsMoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia
  3. 3.Illinois Institute of TechnologyChicagoUSA
  4. 4.University of Southern CaliforniaLos AngelesUSA

Personalised recommendations