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Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

  • Dmitry PetrovEmail author
  • Alexander Ivanov
  • Joshua Faskowitz
  • Boris Gutman
  • Daniel Moyer
  • Julio Villalon
  • Neda Jahanshad
  • Paul Thompson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC) (Code and results are available at https://github.com/lodurality/35_methods_MICCAI_2017).

Keywords

Machine learning DWI Structural connectomes 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dmitry Petrov
    • 1
    • 2
    Email author
  • Alexander Ivanov
    • 2
    • 4
  • Joshua Faskowitz
    • 3
  • Boris Gutman
    • 1
  • Daniel Moyer
    • 1
  • Julio Villalon
    • 1
  • Neda Jahanshad
    • 1
  • Paul Thompson
    • 1
  1. 1.Imaging Genetics Center, Stevens Institute for Neuroimaging and InformaticsUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.The Institute for Information Transmission ProblemsMoscowRussia
  3. 3.Indiana UniversityBloomingtonUSA
  4. 4.Skoltech Institute of Science and TechnologyMoscowRussia

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