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

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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jawata Afnan [verfasserIn] Nicolás von Ellenrieder [verfasserIn] Jean-Marc Lina [verfasserIn] Giovanni Pellegrino [verfasserIn] Giorgio Arcara [verfasserIn] Zhengchen Cai [verfasserIn] Tanguy Hedrich [verfasserIn] Chifaou Abdallah [verfasserIn] Hassan Khajehpour [verfasserIn] Birgit Frauscher [verfasserIn] Jean Gotman [verfasserIn] Christophe Grova [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Intracranial EEG Magnetoencephalography Source imaging Validation Resting state Spectral analysis

Übergeordnetes Werk:	In: NeuroImage - Elsevier, 2020, 274(2023), Seite 120158-
Übergeordnetes Werk:	volume:274 ; year:2023 ; pages:120158-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.neuroimage.2023.120158

Katalog-ID:	DOAJ090256697

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520			\|a 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.
650		4	\|a Intracranial EEG
650		4	\|a Magnetoencephalography
650		4	\|a Source imaging
650		4	\|a Validation
650		4	\|a Resting state
650		4	\|a Spectral analysis
653		0	\|a Neurosciences. Biological psychiatry. Neuropsychiatry
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spelling	10.1016/j.neuroimage.2023.120158 doi (DE-627)DOAJ090256697 (DE-599)DOAJee3b294f4d954c7b857f7b38f2fb2f1e DE-627 ger DE-627 rakwb eng RC321-571 Jawata Afnan verfasserin aut Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Intracranial EEG Magnetoencephalography Source imaging Validation Resting state Spectral analysis Neurosciences. Biological psychiatry. Neuropsychiatry Nicolás von Ellenrieder verfasserin aut Jean-Marc Lina verfasserin aut Giovanni Pellegrino verfasserin aut Giorgio Arcara verfasserin aut Zhengchen Cai verfasserin aut Tanguy Hedrich verfasserin aut Chifaou Abdallah verfasserin aut Hassan Khajehpour verfasserin aut Birgit Frauscher verfasserin aut Jean Gotman verfasserin aut Christophe Grova verfasserin aut In NeuroImage Elsevier, 2020 274(2023), Seite 120158- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:274 year:2023 pages:120158- https://doi.org/10.1016/j.neuroimage.2023.120158 kostenfrei https://doaj.org/article/ee3b294f4d954c7b857f7b38f2fb2f1e kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811923003099 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 274 2023 120158-
allfields_unstemmed	10.1016/j.neuroimage.2023.120158 doi (DE-627)DOAJ090256697 (DE-599)DOAJee3b294f4d954c7b857f7b38f2fb2f1e DE-627 ger DE-627 rakwb eng RC321-571 Jawata Afnan verfasserin aut Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Intracranial EEG Magnetoencephalography Source imaging Validation Resting state Spectral analysis Neurosciences. Biological psychiatry. Neuropsychiatry Nicolás von Ellenrieder verfasserin aut Jean-Marc Lina verfasserin aut Giovanni Pellegrino verfasserin aut Giorgio Arcara verfasserin aut Zhengchen Cai verfasserin aut Tanguy Hedrich verfasserin aut Chifaou Abdallah verfasserin aut Hassan Khajehpour verfasserin aut Birgit Frauscher verfasserin aut Jean Gotman verfasserin aut Christophe Grova verfasserin aut In NeuroImage Elsevier, 2020 274(2023), Seite 120158- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:274 year:2023 pages:120158- https://doi.org/10.1016/j.neuroimage.2023.120158 kostenfrei https://doaj.org/article/ee3b294f4d954c7b857f7b38f2fb2f1e kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811923003099 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 274 2023 120158-
allfieldsGer	10.1016/j.neuroimage.2023.120158 doi (DE-627)DOAJ090256697 (DE-599)DOAJee3b294f4d954c7b857f7b38f2fb2f1e DE-627 ger DE-627 rakwb eng RC321-571 Jawata Afnan verfasserin aut Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Intracranial EEG Magnetoencephalography Source imaging Validation Resting state Spectral analysis Neurosciences. Biological psychiatry. Neuropsychiatry Nicolás von Ellenrieder verfasserin aut Jean-Marc Lina verfasserin aut Giovanni Pellegrino verfasserin aut Giorgio Arcara verfasserin aut Zhengchen Cai verfasserin aut Tanguy Hedrich verfasserin aut Chifaou Abdallah verfasserin aut Hassan Khajehpour verfasserin aut Birgit Frauscher verfasserin aut Jean Gotman verfasserin aut Christophe Grova verfasserin aut In NeuroImage Elsevier, 2020 274(2023), Seite 120158- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:274 year:2023 pages:120158- https://doi.org/10.1016/j.neuroimage.2023.120158 kostenfrei https://doaj.org/article/ee3b294f4d954c7b857f7b38f2fb2f1e kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811923003099 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 274 2023 120158-
allfieldsSound	10.1016/j.neuroimage.2023.120158 doi (DE-627)DOAJ090256697 (DE-599)DOAJee3b294f4d954c7b857f7b38f2fb2f1e DE-627 ger DE-627 rakwb eng RC321-571 Jawata Afnan verfasserin aut Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Intracranial EEG Magnetoencephalography Source imaging Validation Resting state Spectral analysis Neurosciences. Biological psychiatry. Neuropsychiatry Nicolás von Ellenrieder verfasserin aut Jean-Marc Lina verfasserin aut Giovanni Pellegrino verfasserin aut Giorgio Arcara verfasserin aut Zhengchen Cai verfasserin aut Tanguy Hedrich verfasserin aut Chifaou Abdallah verfasserin aut Hassan Khajehpour verfasserin aut Birgit Frauscher verfasserin aut Jean Gotman verfasserin aut Christophe Grova verfasserin aut In NeuroImage Elsevier, 2020 274(2023), Seite 120158- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:274 year:2023 pages:120158- https://doi.org/10.1016/j.neuroimage.2023.120158 kostenfrei https://doaj.org/article/ee3b294f4d954c7b857f7b38f2fb2f1e kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811923003099 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 274 2023 120158-
language	English
source	In NeuroImage 274(2023), Seite 120158- volume:274 year:2023 pages:120158-
sourceStr	In NeuroImage 274(2023), Seite 120158- volume:274 year:2023 pages:120158-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Intracranial EEG Magnetoencephalography Source imaging Validation Resting state Spectral analysis Neurosciences. Biological psychiatry. Neuropsychiatry
isfreeaccess_bool	true
container_title	NeuroImage
authorswithroles_txt_mv	Jawata Afnan @@aut@@ Nicolás von Ellenrieder @@aut@@ Jean-Marc Lina @@aut@@ Giovanni Pellegrino @@aut@@ Giorgio Arcara @@aut@@ Zhengchen Cai @@aut@@ Tanguy Hedrich @@aut@@ Chifaou Abdallah @@aut@@ Hassan Khajehpour @@aut@@ Birgit Frauscher @@aut@@ Jean Gotman @@aut@@ Christophe Grova @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	268125503
id	DOAJ090256697
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ090256697</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230526110122.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230526s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neuroimage.2023.120158</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ090256697</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJee3b294f4d954c7b857f7b38f2fb2f1e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC321-571</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Jawata Afnan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intracranial EEG</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Magnetoencephalography</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Source imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Validation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Resting state</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spectral analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurosciences. Biological psychiatry. 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callnumber-first	R - Medicine
author	Jawata Afnan
spellingShingle	Jawata Afnan misc RC321-571 misc Intracranial EEG misc Magnetoencephalography misc Source imaging misc Validation misc Resting state misc Spectral analysis misc Neurosciences. Biological psychiatry. Neuropsychiatry Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas
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topic_title	RC321-571 Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas Intracranial EEG Magnetoencephalography Source imaging Validation Resting state Spectral analysis
topic	misc RC321-571 misc Intracranial EEG misc Magnetoencephalography misc Source imaging misc Validation misc Resting state misc Spectral analysis misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc Intracranial EEG misc Magnetoencephalography misc Source imaging misc Validation misc Resting state misc Spectral analysis misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_browse	misc RC321-571 misc Intracranial EEG misc Magnetoencephalography misc Source imaging misc Validation misc Resting state misc Spectral analysis misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas
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title_full	Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas
author_sort	Jawata Afnan
journal	NeuroImage
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author_browse	Jawata Afnan Nicolás von Ellenrieder Jean-Marc Lina Giovanni Pellegrino Giorgio Arcara Zhengchen Cai Tanguy Hedrich Chifaou Abdallah Hassan Khajehpour Birgit Frauscher Jean Gotman Christophe Grova
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doi_str_mv	10.1016/j.neuroimage.2023.120158
author2-role	verfasserin
title_sort	validating meg source imaging of resting state oscillatory patterns with an intracranial eeg atlas
callnumber	RC321-571
title_auth	Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas
url	https://doi.org/10.1016/j.neuroimage.2023.120158 https://doaj.org/article/ee3b294f4d954c7b857f7b38f2fb2f1e http://www.sciencedirect.com/science/article/pii/S1053811923003099 https://doaj.org/toc/1095-9572
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author2	Nicolás von Ellenrieder Jean-Marc Lina Giovanni Pellegrino Giorgio Arcara Zhengchen Cai Tanguy Hedrich Chifaou Abdallah Hassan Khajehpour Birgit Frauscher Jean Gotman Christophe Grova
author2Str	Nicolás von Ellenrieder Jean-Marc Lina Giovanni Pellegrino Giorgio Arcara Zhengchen Cai Tanguy Hedrich Chifaou Abdallah Hassan Khajehpour Birgit Frauscher Jean Gotman Christophe Grova
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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. 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Neuropsychiatry</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Nicolás von Ellenrieder</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jean-Marc Lina</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Giovanni Pellegrino</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Giorgio Arcara</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhengchen Cai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Tanguy Hedrich</subfield><subfield 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Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

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