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Multimodal Brain Subnetwork Extraction Using Provincial Hub Guided Random Walks

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Information Processing in Medical Imaging (IPMI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10265))

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Abstract

Community detection methods have been widely used for studying the modular structure of the brain. However, few of these methods exploit the intrinsic properties of brain networks other than modularity to tackle the pronounced noise in neuroimaging data. We propose a random walker (RW) based approach that reflects how regions of a brain subnetwork tend to be inter-linked by a provincial hub. By using provincial hubs to guide seed setting, RW provides the exact posterior probability of a brain region belonging to each given subnetwork, which mitigates forced hard assignments of brain regions to subnetworks as is the case in most existing methods. We further present an extension that enables multimodal integration for exploiting complementary information from functional Magnetic Resonance Imaging (fMRI) and diffusion MRI (dMRI) data. On synthetic data, our approach achieves higher accuracy in subnetwork extraction than unimodal and existing multimodal approaches. On real data from the Human Connectome Project (HCP), our estimated subnetworks match well with established brain systems and attain higher inter-subject reproducibility.

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Authors and Affiliations

  1. Biomedical Signal and Image Computing Lab, The University of British Columbia, Vancouver, Canada

    Chendi Wang & Rafeef Abugharbieh

  2. The Department of Statistics, The University of British Columbia, Vancouver, Canada

    Bernard Ng

Authors
  1. Chendi Wang
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  2. Bernard Ng
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  3. Rafeef Abugharbieh
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Corresponding author

Correspondence to Chendi Wang .

Editor information

Editors and Affiliations

  1. University of North Carolina, Chapel Hill, North Carolina, USA

    Marc Niethammer

  2. University of North Carolina, Chapel Hill, North Carolina, USA

    Martin Styner

  3. Kitware Inc., Carrboro, North Carolina, USA

    Stephen Aylward

  4. University of North Carolina, Chapel Hill, North Carolina, USA

    Hongtu Zhu

  5. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Ipek Oguz

  6. University of North Carolina, Chapel Hill, North Carolina, USA

    Pew-Thian Yap

  7. University of North Carolina, Chapel Hill, North Carolina, USA

    Dinggang Shen

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Wang, C., Ng, B., Abugharbieh, R. (2017). Multimodal Brain Subnetwork Extraction Using Provincial Hub Guided Random Walks. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_23

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  • DOI: https://doi.org/10.1007/978-3-319-59050-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59049-3

  • Online ISBN: 978-3-319-59050-9

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