Connectivity-Driven Brain Parcellation via Consensus Clustering

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


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.



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


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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
    Email author
  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

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