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Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery

Author

Listed:
  • Miao Cao

    (The University of Melbourne
    St Vincent’s Hospital Melbourne)

  • Daniel Galvis

    (University of Exeter
    University of Exeter
    University of Birmingham
    University of Birmingham)

  • Simon J. Vogrin

    (The University of Melbourne
    St Vincent’s Hospital Melbourne
    Swinburne University of Technology)

  • William P. Woods

    (Swinburne University of Technology)

  • Sara Vogrin

    (The University of Melbourne
    The University of Melbourne)

  • Fan Wang

    (Chinese Academy of Sciences
    CAS Centre for Excellence in Brain Science and Intelligence Technology
    University of Chinese Academy of Sciences)

  • Wessel Woldman

    (University of Exeter
    University of Exeter
    University of Birmingham
    University of Birmingham)

  • John R. Terry

    (University of Exeter
    University of Exeter
    University of Birmingham
    University of Birmingham)

  • Andre Peterson

    (The University of Melbourne
    St Vincent’s Hospital Melbourne
    The University of Melbourne)

  • Chris Plummer

    (The University of Melbourne
    St Vincent’s Hospital Melbourne
    Swinburne University of Technology)

  • Mark J. Cook

    (The University of Melbourne
    St Vincent’s Hospital Melbourne
    The University of Melbourne)

Abstract

Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose virtual intracranial electroencephalography (ViEEG), which combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. We show the non-invasive ViEEG approach may have some advantage over intracranial electroencephalography (iEEG). Future work may be designed to test the potential of the virtual iEEG approach for use in surgical management of epilepsy.

Suggested Citation

  • Miao Cao & Daniel Galvis & Simon J. Vogrin & William P. Woods & Sara Vogrin & Fan Wang & Wessel Woldman & John R. Terry & Andre Peterson & Chris Plummer & Mark J. Cook, 2022. "Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28640-x
    DOI: 10.1038/s41467-022-28640-x
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    References listed on IDEAS

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    1. Timothée Proix & Viktor K. Jirsa & Fabrice Bartolomei & Maxime Guye & Wilson Truccolo, 2018. "Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
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