IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0059856.html
   My bibliography  Save this article

Assessment of Subcortical Source Localization Using Deep Brain Activity Imaging Model with Minimum Norm Operators: A MEG Study

Author

Listed:
  • Yohan Attal
  • Denis Schwartz

Abstract

Subcortical structures are involved in many healthy and pathological brain processes. It is crucial for many studies to use magnetoencephalography (MEG) to assess the ability to detect subcortical generators. This study aims to assess the source localization accuracy and to compare the characteristics of three inverse operators in the specific case of subcortical generators. MEG has a low sensitivity to subcortical sources mainly because of their distance from sensors and their complex cyto-architecture. However, we show that using a realistic anatomical and electrophysiological model of deep brain activity (DBA), the sources make measurable contributions to MEG sensors signals. Furthermore, we study the point-spread and cross-talk functions of the wMNE, sLORETA and dSPM inverse operators to characterize distortions in cortical and subcortical regions and to study how noise-normalization methods can improve or bias accuracy. We then run Monte Carlo simulations with neocortical and subcortical activations. In the case of single hippocampus patch activations, the results indicate that MEG can indeed localize the generators in the head and the body of the hippocampus with good accuracy. We then tackle the question of simultaneous cortical and subcortical activations. wMNE can detect hippocampal activations that are embedded in cortical activations that have less than double their amplitude, but it does not completely correct the bias to more superficial sources. dSPM and sLORETA can still detect hippocampal activity above this threshold, but such detection might include the creation of ghost deeper sources. Finally, using the DBA model, we showed that the detection of weak thalamic modulations of ongoing brain activity is possible.

Suggested Citation

  • Yohan Attal & Denis Schwartz, 2013. "Assessment of Subcortical Source Localization Using Deep Brain Activity Imaging Model with Minimum Norm Operators: A MEG Study," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-14, March.
  • Handle: RePEc:plo:pone00:0059856
    DOI: 10.1371/journal.pone.0059856
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0059856
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0059856&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0059856?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rey R. Ramírez & David Wipf & Sylvain Baillet, 2010. "Neuroelectromagnetic Source Imaging of Brain Dynamics," Springer Optimization and Its Applications, in: Wanpracha Chaovalitwongse & Panos M. Pardalos & Petros Xanthopoulos (ed.), Computational Neuroscience, chapter 0, pages 127-155, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stéphane Sockeel & Denis Schwartz & Mélanie Pélégrini-Issac & Habib Benali, 2016. "Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-18, January.
    2. Richard Gast & Daniel Rose & Christoph Salomon & Harald E Möller & Nikolaus Weiskopf & Thomas R Knösche, 2019. "PyRates—A Python framework for rate-based neural simulations," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-26, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0059856. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.