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International Conference on Information Processing in Medical Imaging

IPMI 2019: Information Processing in Medical Imaging pp 743–754Cite as

Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11492))

Abstract

Magnetoencephalography (MEG) and electroencephalography (EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Inferring the location of the current sources that generated these magnetic fields is an ill-posed inverse problem known as source imaging. When considering a group study, a baseline approach consists in carrying out the estimation of these sources independently for each subject. The ill-posedness of each problem is typically addressed using sparsity promoting regularizations. A straightforward way to define a common pattern for these sources is then to average them. A more advanced alternative relies on a joint localization of sources for all subjects taken together, by enforcing some similarity across all estimated sources. An important advantage of this approach is that it consists in a single estimation in which all measurements are pooled together, making the inverse problem better posed. Such a joint estimation poses however a few challenges, notably the selection of a valid regularizer that can quantify such spatial similarities. We propose in this work a new procedure that can do so while taking into account the geometrical structure of the cortex. We call this procedure Minimum Wasserstein Estimates (MWE). The benefits of this model are twofold. First, joint inference allows to pool together the data of different brain geometries, accumulating more spatial information. Second, MWE are defined through Optimal Transport (OT) metrics which provide a tool to model spatial proximity between cortical sources of different subjects, hence not enforcing identical source location in the group. These benefits allow MWE to be more accurate than standard MEG source localization techniques. To support these claims, we perform source localization on realistic MEG simulations based on forward operators derived from MRI scans. On a visual task dataset, we demonstrate how MWE infer neural patterns similar to functional Magnetic Resonance Imaging (fMRI) maps.

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Author information

Authors and Affiliations

  1. Inria, CEA Neurospin, Saclay, France

    H. Janati, T. Bazeille, B. Thirion & A. Gramfort

  2. Google Brain, Paris, France

    M. Cuturi

  3. CREST/ENSAE, Palaiseau, France

    H. Janati & M. Cuturi

Authors
  1. H. Janati
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  2. T. Bazeille
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  3. B. Thirion
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  4. M. Cuturi
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  5. A. Gramfort
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Corresponding author

Correspondence to H. Janati .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Albert C. S. Chung

  2. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    James C. Gee

  3. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    Paul A. Yushkevich

  4. Department of Natural Language Processing, Baidu Inc., Shenzhen, China

    Siqi Bao

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Janati, H., Bazeille, T., Thirion, B., Cuturi, M., Gramfort, A. (2019). Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_58

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  • DOI: https://doi.org/10.1007/978-3-030-20351-1_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20350-4

  • Online ISBN: 978-3-030-20351-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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