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Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

Dmitry Petrov, Alexander Ivanov, Joshua Faskowitz, Boris Gutman, Daniel Moyer, Julio Villalon, Neda Jahanshad, Paul Thompson
doi: https://doi.org/10.1101/151993
Dmitry Petrov
1Imaging Genetics Center, University of Southern California, Los Angeles, USA
2The Institute for Information Transmission Problems, Moscow, Russia
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  • For correspondence: to.dmitry.petrov@gmail.com
Alexander Ivanov
2The Institute for Information Transmission Problems, Moscow, Russia
4Skoltech Institute of Science and Technology, Moscow, Russia
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Joshua Faskowitz
3Indiana University, Bloomington, USA
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Boris Gutman
1Imaging Genetics Center, University of Southern California, Los Angeles, USA
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  • For correspondence: to.dmitry.petrov@gmail.com
Daniel Moyer
1Imaging Genetics Center, University of Southern California, Los Angeles, USA
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  • For correspondence: to.dmitry.petrov@gmail.com
Julio Villalon
1Imaging Genetics Center, University of Southern California, Los Angeles, USA
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  • For correspondence: to.dmitry.petrov@gmail.com
Neda Jahanshad
1Imaging Genetics Center, University of Southern California, Los Angeles, USA
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  • For correspondence: to.dmitry.petrov@gmail.com
Paul Thompson
1Imaging Genetics Center, University of Southern California, Los Angeles, USA
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  • For correspondence: to.dmitry.petrov@gmail.com
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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)‡.

Footnotes

  • ↵‡ Code and results are available at https://github.com/lodurality/35_methods_ MICCAI_2017

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 19, 2017.
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Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
Dmitry Petrov, Alexander Ivanov, Joshua Faskowitz, Boris Gutman, Daniel Moyer, Julio Villalon, Neda Jahanshad, Paul Thompson
bioRxiv 151993; doi: https://doi.org/10.1101/151993
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Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
Dmitry Petrov, Alexander Ivanov, Joshua Faskowitz, Boris Gutman, Daniel Moyer, Julio Villalon, Neda Jahanshad, Paul Thompson
bioRxiv 151993; doi: https://doi.org/10.1101/151993

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