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Classifying Phenotypes Based on the Community Structure of Human Brain Networks

  • Anvar Kurmukov
  • Marina Ananyeva
  • Yulia Dodonova
  • Boris Gutman
  • Joshua Faskowitz
  • Neda Jahanshad
  • Paul Thompson
  • Leonid Zhukov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10551)

Abstract

Human anatomical brain networks derived from the analysis of neuroimaging data are known to demonstrate modular organization. Modules, or communities, of cortical brain regions capture information about the structure of connections in the entire network. Hence, anatomical changes in network connectivity (e.g., caused by a certain disease) should translate into changes in the community structure of brain regions. This means that essential structural differences between phenotypes (e.g., healthy and diseased) should be reflected in how brain networks cluster into communities. To test this hypothesis, we propose a pipeline to classify brain networks based on their underlying community structure. We consider network partitionings into both non-overlapping and overlapping communities and introduce a distance between connectomes based on whether or not they cluster into modules similarly. We next construct a classifier that uses partitioning-based kernels to predict a phenotype from brain networks. We demonstrate the performance of the proposed approach in a task of classifying structural connectomes of healthy subjects and those with mild cognitive impairment and Alzheimer’s disease.

Notes

Acknowledgments

The data used in preparing this paper were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A complete listing of ADNI investigators and imaging protocols may be found at www.adni.loni.usc.edu.

The results of Sects. 25 are based on the scientific research conducted at IITP RAS and supported by the Russian Science Foundation under grant 17-11-01390.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anvar Kurmukov
    • 1
  • Marina Ananyeva
    • 2
  • Yulia Dodonova
    • 1
  • Boris Gutman
    • 3
  • Joshua Faskowitz
    • 3
  • Neda Jahanshad
    • 3
  • Paul Thompson
    • 3
  • Leonid Zhukov
    • 2
  1. 1.Kharkevich Institute for Information Transmission ProblemsMoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia
  3. 3.Imaging Genetics Center, Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaMarina del ReyUSA

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