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Machine Learning Application to Human Brain Network Studies: A Kernel Approach

  • Anvar Kurmukov
  • Yulia Dodonova
  • Leonid E. Zhukov
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 197)

Abstract

We consider a task of predicting normal and pathological phenotypes from macroscale human brain networks. These networks (connectomes) represent aggregated neural pathways between brain regions. We point to properties of connectomes that make them different from graphs arising in other application areas of network science. We discuss how machine learning can be organized on brain networks and focus on kernel classification methods. We describe different kernels on brain networks, including those that use information about similarity in spectral distributions of brain graphs and distances between optimal partitions of connectomes. We compare performance of the reviewed kernels in tasks of classifying autism spectrum disorder versus typical development and carriers versus noncarriers of an allele associated with an increased risk of Alzheimer’s disease.

Keywords

Machine learning Brain networks Classification Kernel SVM Graph spectra Clustering 

Notes

Acknowledgements

The study was supported within the framework of the Academic Fund Program at the National Research University Higher School of Economics (HSE) in 2016 (grant #16-05-0050) and by the Russian Academic Excellence Project “5–100”.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anvar Kurmukov
    • 1
  • Yulia Dodonova
    • 1
  • Leonid E. Zhukov
    • 1
  1. 1.National Research University Higher School of EconomicsMoscowRussia

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