Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas.

Humans Magnetoencephalography / methods Electrocorticography / methods Brain Brain Mapping / methods Electroencephalography / methods
Intracranial EEG Magnetoencephalography Resting state Source imaging Spectral analysis Validation

Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
01 07 2023
Historique:
received: 03 10 2022
revised: 27 03 2023
accepted: 04 05 2023
medline: 17 5 2023
pubmed: 7 5 2023
entrez: 6 5 2023
Statut: ppublish

Résumé

Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (iEEG) atlas (https://mni-open-ieegatlas. mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space by applying a forward model to the MEG-reconstructed source maps, and estimated virtual iEEG (ViEEG) potentials on each iEEG channel location; we finally quantitatively compared those with actual iEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the ViEEG than in the iEEG were more accurately recovered. In the deep regions, MEG-estimated amplitudes were largely underestimated and the spectra were poorly recovered. Overall, our wMEM results were similar to those obtained with minimum norm or beamformer source localization. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of iEEG but detected by MEG. Importantly, we found that MEG-estimated spectra were more comparable to spectra from the iEEG atlas after the aperiodic components were removed. This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies.

Sections du résumé

BACKGROUND
Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation.
METHOD
We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (iEEG) atlas (https://mni-open-ieegatlas.
RESEARCH
mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space by applying a forward model to the MEG-reconstructed source maps, and estimated virtual iEEG (ViEEG) potentials on each iEEG channel location; we finally quantitatively compared those with actual iEEG signals from the atlas for 38 regions of interest in the canonical frequency bands.
RESULTS
The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the ViEEG than in the iEEG were more accurately recovered. In the deep regions, MEG-estimated amplitudes were largely underestimated and the spectra were poorly recovered. Overall, our wMEM results were similar to those obtained with minimum norm or beamformer source localization. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of iEEG but detected by MEG. Importantly, we found that MEG-estimated spectra were more comparable to spectra from the iEEG atlas after the aperiodic components were removed.
CONCLUSION
This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies.

Identifiants

DOI: 10.1016/j.neuroimage.2023.120158 PMID: 37149236
pubmed: 37149236
pii: S1053-8119(23)00309-9
doi: 10.1016/j.neuroimage.2023.120158
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

120158

Subventions

Organisme : CIHR
ID : PJT-175056
Pays : Canada

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare no competing interests.

Auteurs

Jawata Afnan (J)

Integrated Program in Neuroscience, McGill University, Montréal, Québec H3A 1A1, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada. Electronic address: jawata.afnan@mail.mcgill.ca.

Nicolás von Ellenrieder (N)

Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada.

Jean-Marc Lina (JM)

Centre De Recherches Mathématiques, Montréal, Québec H3C 3J7, Canada; Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec H3C 1K3, Canada; Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital, Montréal, Québec, Canada.

Giovanni Pellegrino (G)

Epilepsy program, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5C1, Canada.

Giorgio Arcara (G)

Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy.

Zhengchen Cai (Z)

Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada.

Tanguy Hedrich (T)

Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada.

Chifaou Abdallah (C)

Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.

Hassan Khajehpour (H)

Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada.

Birgit Frauscher (B)

Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada; Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.

Jean Gotman (J)

Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada. Electronic address: jean.gotman@mcgill.ca.

Christophe Grova (C)

Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada; Centre De Recherches Mathématiques, Montréal, Québec H3C 3J7, Canada; Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada. Electronic address: christophe.grova@concordia.ca.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Validating MEG source imaging of resting state...
questionsmedicales.fr › Techniques d'investigation › Magnétométrie › Magnétoencéphalographie › Validating MEG source imaging of resting state...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Électrodiagnostic › Électrocorticographie › Validating MEG source imaging of resting state...
questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Validating MEG source imaging of resting state...
questionsmedicales.fr › Techniques d'investigation › Neuroimagerie › Neuroimagerie fonctionnelle › Cartographie cérébrale › Validating MEG source imaging of resting state...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Électrodiagnostic › Électroencéphalographie › Validating MEG source imaging of resting state...