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

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

doi:10.1038/s41467-022-28640-x

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

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

Metabolism and Systems Science

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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.

Original language	English
Article number	994
Number of pages	12
Journal	Nature Communications
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41467-022-28640-x
Publication status	Published - 22 Feb 2022

Bibliographical note

Funding Information:
The authors acknowledge the facilities, and the scientific and technical assistance of the National Imaging Facility at the Swinburne Node, Swinburne University of Technology with particular thanks to Dr. Rachel Batty, Ms. Mahla Cameron-Bradley, and Ms. Johanna Stephens for their technical support. We acknowledge the Australian National Imaging Facility for the support of W. Woods and the MEG system at Swinburne University of Technology. We acknowledge the neurologists Dr. Simon Harvey (Royal Children?s Hospital Melbourne), A/Prof. John Archer (Austin Hospital Melbourne), A/Prof. Wendyl D?Souza (St. Vincent?s Hospital Melbourne), A/Prof. Ross Carne (St. Vincent?s Hospital Melbourne), and the neurosurgeons A/Prof. Michael Murphy (St. Vincent?s Hospital Melbourne), Mr. Kristian Bulluss (St. Vincent?s Hospital Melbourne), and Ms. Wirginia Maixner (Royal Children?s Hospital Melbourne) whose patients were included in the study. The authors would also like to thank Prof. Thomas Knosche, Dr. Christian Rummel and Dr. Marinho Lopes for their insightful discussions on MEG source imaging and dynamical models. M.C. acknowledges the support from Australian Government Research Training Scholarship and St. Vincent?s Health Foundation, Australia. M.J.C., A.P. and J.R.T. acknowledge the financial support from the Royal Society International Exchanges Award (grant number IE170112). D.G. and J.R.T. acknowledge the generous support of the Wellcome Trust Institutional Strategic Support Award (grant no. 204909/Z/16/Z). W.W. acknowledges the financial support of the MRC via grant (MR/N01524X/1)?and Epilepsy Research UK via Grant (F2002). J.R.T. acknowledges the financial support of the EPSRC via grants (EP/N014391/2?and EP/T027703/1). F.W. acknowledges the support from Youth Innovation Promotion Association at the Chinese Academy of Sciences via grant (2019096).

Funding Information:
The authors acknowledge the facilities, and the scientific and technical assistance of the National Imaging Facility at the Swinburne Node, Swinburne University of Technology with particular thanks to Dr. Rachel Batty, Ms. Mahla Cameron-Bradley, and Ms. Johanna Stephens for their technical support. We acknowledge the Australian National Imaging Facility for the support of W. Woods and the MEG system at Swinburne University of Technology. We acknowledge the neurologists Dr. Simon Harvey (Royal Children’s Hospital Melbourne), A/Prof. John Archer (Austin Hospital Melbourne), A/Prof. Wendyl D’Souza (St. Vincent’s Hospital Melbourne), A/Prof. Ross Carne (St. Vincent’s Hospital Melbourne), and the neurosurgeons A/Prof. Michael Murphy (St. Vincent’s Hospital Melbourne), Mr. Kristian Bulluss (St. Vincent’s Hospital Melbourne), and Ms. Wirginia Maixner (Royal Children’s Hospital Melbourne) whose patients were included in the study. The authors would also like to thank Prof. Thomas Knosche, Dr. Christian Rummel and Dr. Marinho Lopes for their insightful discussions on MEG source imaging and dynamical models. M.C. acknowledges the support from Australian Government Research Training Scholarship and St. Vincent’s Health Foundation, Australia. M.J.C., A.P. and J.R.T. acknowledge the financial support from the Royal Society International Exchanges Award (grant number IE170112). D.G. and J.R.T. acknowledge the generous support of the Wellcome Trust Institutional Strategic Support Award (grant no. 204909/Z/16/Z). W.W. acknowledges the financial support of the MRC via grant (MR/N01524X/1) and Epilepsy Research UK via Grant (F2002). J.R.T. acknowledges the financial support of the EPSRC via grants (EP/N014391/2 and EP/T027703/1). F.W. acknowledges the support from Youth Innovation Promotion Association at the Chinese Academy of Sciences via grant (2019096).

Publisher Copyright:
© 2022, The Author(s).

Keywords

Electrocorticography/methods
Electroencephalography/methods
Epilepsy/surgery
Humans
Magnetoencephalography/methods
Seizures

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Cite this

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title = "Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery",

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.",

keywords = "Electrocorticography/methods, Electroencephalography/methods, Epilepsy/surgery, Humans, Magnetoencephalography/methods, Seizures",

author = "Miao Cao and Daniel Galvis and Vogrin, {Simon J.} and Woods, {William P.} and Sara Vogrin and Fan Wang and Wessel Woldman and Terry, {John R.} and Andre Peterson and Chris Plummer and Cook, {Mark J.}",

note = "Funding Information: The authors acknowledge the facilities, and the scientific and technical assistance of the National Imaging Facility at the Swinburne Node, Swinburne University of Technology with particular thanks to Dr. Rachel Batty, Ms. Mahla Cameron-Bradley, and Ms. Johanna Stephens for their technical support. We acknowledge the Australian National Imaging Facility for the support of W. Woods and the MEG system at Swinburne University of Technology. We acknowledge the neurologists Dr. Simon Harvey (Royal Children?s Hospital Melbourne), A/Prof. John Archer (Austin Hospital Melbourne), A/Prof. Wendyl D?Souza (St. Vincent?s Hospital Melbourne), A/Prof. Ross Carne (St. Vincent?s Hospital Melbourne), and the neurosurgeons A/Prof. Michael Murphy (St. Vincent?s Hospital Melbourne), Mr. Kristian Bulluss (St. Vincent?s Hospital Melbourne), and Ms. Wirginia Maixner (Royal Children?s Hospital Melbourne) whose patients were included in the study. The authors would also like to thank Prof. Thomas Knosche, Dr. Christian Rummel and Dr. Marinho Lopes for their insightful discussions on MEG source imaging and dynamical models. M.C. acknowledges the support from Australian Government Research Training Scholarship and St. Vincent?s Health Foundation, Australia. M.J.C., A.P. and J.R.T. acknowledge the financial support from the Royal Society International Exchanges Award (grant number IE170112). D.G. and J.R.T. acknowledge the generous support of the Wellcome Trust Institutional Strategic Support Award (grant no. 204909/Z/16/Z). W.W. acknowledges the financial support of the MRC via grant (MR/N01524X/1)?and Epilepsy Research UK via Grant (F2002). J.R.T. acknowledges the financial support of the EPSRC via grants (EP/N014391/2?and EP/T027703/1). F.W. acknowledges the support from Youth Innovation Promotion Association at the Chinese Academy of Sciences via grant (2019096). Funding Information: The authors acknowledge the facilities, and the scientific and technical assistance of the National Imaging Facility at the Swinburne Node, Swinburne University of Technology with particular thanks to Dr. Rachel Batty, Ms. Mahla Cameron-Bradley, and Ms. Johanna Stephens for their technical support. We acknowledge the Australian National Imaging Facility for the support of W. Woods and the MEG system at Swinburne University of Technology. We acknowledge the neurologists Dr. Simon Harvey (Royal Children{\textquoteright}s Hospital Melbourne), A/Prof. John Archer (Austin Hospital Melbourne), A/Prof. Wendyl D{\textquoteright}Souza (St. Vincent{\textquoteright}s Hospital Melbourne), A/Prof. Ross Carne (St. Vincent{\textquoteright}s Hospital Melbourne), and the neurosurgeons A/Prof. Michael Murphy (St. Vincent{\textquoteright}s Hospital Melbourne), Mr. Kristian Bulluss (St. Vincent{\textquoteright}s Hospital Melbourne), and Ms. Wirginia Maixner (Royal Children{\textquoteright}s Hospital Melbourne) whose patients were included in the study. The authors would also like to thank Prof. Thomas Knosche, Dr. Christian Rummel and Dr. Marinho Lopes for their insightful discussions on MEG source imaging and dynamical models. M.C. acknowledges the support from Australian Government Research Training Scholarship and St. Vincent{\textquoteright}s Health Foundation, Australia. M.J.C., A.P. and J.R.T. acknowledge the financial support from the Royal Society International Exchanges Award (grant number IE170112). D.G. and J.R.T. acknowledge the generous support of the Wellcome Trust Institutional Strategic Support Award (grant no. 204909/Z/16/Z). W.W. acknowledges the financial support of the MRC via grant (MR/N01524X/1) and Epilepsy Research UK via Grant (F2002). J.R.T. acknowledges the financial support of the EPSRC via grants (EP/N014391/2 and EP/T027703/1). F.W. acknowledges the support from Youth Innovation Promotion Association at the Chinese Academy of Sciences via grant (2019096). Publisher Copyright: {\textcopyright} 2022, The Author(s).",

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doi = "10.1038/s41467-022-28640-x",

language = "English",

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T1 - Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery

AU - Cao, Miao

AU - Galvis, Daniel

AU - Vogrin, Simon J.

AU - Woods, William P.

AU - Vogrin, Sara

AU - Wang, Fan

AU - Woldman, Wessel

AU - Terry, John R.

AU - Peterson, Andre

AU - Plummer, Chris

AU - Cook, Mark J.

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Y1 - 2022/2/22

N2 - 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.

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KW - Electroencephalography/methods

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KW - Magnetoencephalography/methods

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

Abstract

Bibliographical note

Keywords

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