Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

  • Nikita Mokrov
  • Maxim PanovEmail author
  • Boris A. Gutman
  • Joshua I. Faskowitz
  • Neda Jahanshad
  • Paul M. Thompson
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)


This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer’s disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification.



The research was supported by the Russian Science Foundation grant (project 14-50-00150). Some data used in preparing this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A complete listing of ADNI investigators and imaging protocols can be found at


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Nikita Mokrov
    • 1
  • Maxim Panov
    • 2
    Email author
  • Boris A. Gutman
    • 3
  • Joshua I. Faskowitz
    • 3
  • Neda Jahanshad
    • 3
  • Paul M. Thompson
    • 3
  1. 1.Moscow Institute of Physics and TechnologyInstitute for Information Transmission Problems of RASMoscowRussia
  2. 2.Skolkovo Institute of Science and Technology (Skoltech)Institute for Information Transmission Problems of RASMoscowRussia
  3. 3.Imaging Genetics Center, Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaMarina del ReyUSA

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