Comparing MEG and high-density EEG for intrinsic functional connectivity mapping
Magnetoencephalography (MEG) has been used in conjunction with resting-state functional connectivity (rsFC) based on band-limited power envelope correlation to study the intrinsic human brain network organization into resting-state networks (RSNs). However, the limited availability of current MEG sy...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-04-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920300434 |
| _version_ | 1818435816731443200 |
|---|---|
| author | N. Coquelet X. De Tiège F. Destoky L. Roshchupkina M. Bourguignon S. Goldman P. Peigneux V. Wens |
| author_facet | N. Coquelet X. De Tiège F. Destoky L. Roshchupkina M. Bourguignon S. Goldman P. Peigneux V. Wens |
| author_sort | N. Coquelet |
| collection | DOAJ |
| description | Magnetoencephalography (MEG) has been used in conjunction with resting-state functional connectivity (rsFC) based on band-limited power envelope correlation to study the intrinsic human brain network organization into resting-state networks (RSNs). However, the limited availability of current MEG systems hampers the clinical applications of electrophysiological rsFC. Here, we directly compared well-known RSNs as well as the whole-brain rsFC connectome together with its state dynamics, obtained from simultaneously-recorded MEG and high-density scalp electroencephalography (EEG) resting-state data. We also examined the impact of head model precision on EEG rsFC estimation, by comparing results obtained with boundary and finite element head models. Results showed that most RSN topographies obtained with MEG and EEG are similar, except for the fronto-parietal network. At the connectome level, sensitivity was lower to frontal rsFC and higher to parieto-occipital rsFC with MEG compared to EEG. This was mostly due to inhomogeneity of MEG sensor locations relative to the scalp and significant MEG-EEG differences disappeared when taking relative MEG-EEG sensor locations into account. The default-mode network was the only RSN requiring advanced head modeling in EEG, in which gray and white matter are distinguished. Importantly, comparison of rsFC state dynamics evidenced a poor correspondence between MEG and scalp EEG, suggesting sensitivity to different components of transient neural functional integration. This study therefore shows that the investigation of static rsFC based on the human brain connectome can be performed with scalp EEG in a similar way than with MEG, opening the avenue to widespread clinical applications of rsFC analyses. |
| first_indexed | 2024-12-14T16:58:54Z |
| format | Article |
| id | doaj.art-d5419f18469a4868b053617f055809a6 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-14T16:58:54Z |
| publishDate | 2020-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-d5419f18469a4868b053617f055809a62022-12-21T22:53:53ZengElsevierNeuroImage1095-95722020-04-01210116556Comparing MEG and high-density EEG for intrinsic functional connectivity mappingN. Coquelet0X. De Tiège1F. Destoky2L. Roshchupkina3M. Bourguignon4S. Goldman5P. Peigneux6V. Wens7Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Corresponding author. Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Lennik Street, 1070, Brussels, Belgium.Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Centre for Research in Cognition and Neurosciences (CRCN), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, BelgiumLaboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, BelgiumLaboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Centre for Research in Cognition and Neurosciences (CRCN), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, BelgiumLaboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Laboratoire Cognition Langage et Développement, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; BCBL, Basque Center on Cognition, Brain and Language, 20009, San Sebastian, SpainLaboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Brussels, BelgiumNeuropsychology and Functional Neuroimaging Research Unit (UR2NF), Centre for Research in Cognition and Neurosciences (CRCN), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, BelgiumLaboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Brussels, BelgiumMagnetoencephalography (MEG) has been used in conjunction with resting-state functional connectivity (rsFC) based on band-limited power envelope correlation to study the intrinsic human brain network organization into resting-state networks (RSNs). However, the limited availability of current MEG systems hampers the clinical applications of electrophysiological rsFC. Here, we directly compared well-known RSNs as well as the whole-brain rsFC connectome together with its state dynamics, obtained from simultaneously-recorded MEG and high-density scalp electroencephalography (EEG) resting-state data. We also examined the impact of head model precision on EEG rsFC estimation, by comparing results obtained with boundary and finite element head models. Results showed that most RSN topographies obtained with MEG and EEG are similar, except for the fronto-parietal network. At the connectome level, sensitivity was lower to frontal rsFC and higher to parieto-occipital rsFC with MEG compared to EEG. This was mostly due to inhomogeneity of MEG sensor locations relative to the scalp and significant MEG-EEG differences disappeared when taking relative MEG-EEG sensor locations into account. The default-mode network was the only RSN requiring advanced head modeling in EEG, in which gray and white matter are distinguished. Importantly, comparison of rsFC state dynamics evidenced a poor correspondence between MEG and scalp EEG, suggesting sensitivity to different components of transient neural functional integration. This study therefore shows that the investigation of static rsFC based on the human brain connectome can be performed with scalp EEG in a similar way than with MEG, opening the avenue to widespread clinical applications of rsFC analyses.http://www.sciencedirect.com/science/article/pii/S1053811920300434ConnectomeState dynamicsResting-state networksEnvelope correlationMagnetoencephalographyElectroencephalography |
| spellingShingle | N. Coquelet X. De Tiège F. Destoky L. Roshchupkina M. Bourguignon S. Goldman P. Peigneux V. Wens Comparing MEG and high-density EEG for intrinsic functional connectivity mapping NeuroImage Connectome State dynamics Resting-state networks Envelope correlation Magnetoencephalography Electroencephalography |
| title | Comparing MEG and high-density EEG for intrinsic functional connectivity mapping |
| title_full | Comparing MEG and high-density EEG for intrinsic functional connectivity mapping |
| title_fullStr | Comparing MEG and high-density EEG for intrinsic functional connectivity mapping |
| title_full_unstemmed | Comparing MEG and high-density EEG for intrinsic functional connectivity mapping |
| title_short | Comparing MEG and high-density EEG for intrinsic functional connectivity mapping |
| title_sort | comparing meg and high density eeg for intrinsic functional connectivity mapping |
| topic | Connectome State dynamics Resting-state networks Envelope correlation Magnetoencephalography Electroencephalography |
| url | http://www.sciencedirect.com/science/article/pii/S1053811920300434 |
| work_keys_str_mv | AT ncoquelet comparingmegandhighdensityeegforintrinsicfunctionalconnectivitymapping AT xdetiege comparingmegandhighdensityeegforintrinsicfunctionalconnectivitymapping AT fdestoky comparingmegandhighdensityeegforintrinsicfunctionalconnectivitymapping AT lroshchupkina comparingmegandhighdensityeegforintrinsicfunctionalconnectivitymapping AT mbourguignon comparingmegandhighdensityeegforintrinsicfunctionalconnectivitymapping AT sgoldman comparingmegandhighdensityeegforintrinsicfunctionalconnectivitymapping AT ppeigneux comparingmegandhighdensityeegforintrinsicfunctionalconnectivitymapping AT vwens comparingmegandhighdensityeegforintrinsicfunctionalconnectivitymapping |