Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI
Functional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2020.00323/full |
| _version_ | 1819040001763049472 |
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| author | Rodolfo Abreu Marco Simões Marco Simões Miguel Castelo-Branco |
| author_facet | Rodolfo Abreu Marco Simões Marco Simões Miguel Castelo-Branco |
| author_sort | Rodolfo Abreu |
| collection | DOAJ |
| description | Functional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such networks, particularly the resting-state networks (RSNs). However, a truly unbiased validation of such claims is still missing, which can only be accomplished by using simultaneously acquired EEG and fMRI data, due to the spontaneous nature of the activity underlying the RSNs. Additionally, EEG is still poorly explored for the purpose of mapping task-specific networks, and no studies so far have been focused on investigating networks’ dynamic functional connectivity (dFC) with EEG. Here, we started by validating RSNs derived from the continuous reconstruction of EEG sources by directly comparing them with those derived from simultaneous fMRI data of 10 healthy participants, and obtaining an average overlap (quantified by the Dice coefficient) of 0.4. We also showed the ability of EEG to map the facial expressions processing network (FEPN), highlighting regions near the posterior superior temporal sulcus, where the FEPN is anchored. Then, we measured the dFC using EEG for the first time in this context, estimated dFC brain states using dictionary learning, and compared such states with those obtained from the fMRI. We found a statistically significant match between fMRI and EEG dFC states, and determined the existence of two matched dFC states which contribution over time was associated with the brain activity at the FEPN, showing that the dynamics of FEPN can be captured by both fMRI and EEG. Our results push the limits of EEG toward being used as a brain imaging tool, while supporting the growing literature on EEG correlates of (dynamic) functional connectivity measured with fMRI, and providing novel insights into the coupling mechanisms underlying the two imaging techniques. |
| first_indexed | 2024-12-21T09:02:10Z |
| format | Article |
| id | doaj.art-e3dad0bd76d0408c99d9b3548fdddc2c |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-21T09:02:10Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-e3dad0bd76d0408c99d9b3548fdddc2c2022-12-21T19:09:27ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2020-04-011410.3389/fnins.2020.00323513164Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRIRodolfo Abreu0Marco Simões1Marco Simões2Miguel Castelo-Branco3Faculty of Medicine, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, PortugalFaculty of Medicine, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, PortugalCenter for Informatics and Systems (CISUC), University of Coimbra, Coimbra, PortugalFaculty of Medicine, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, PortugalFunctional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such networks, particularly the resting-state networks (RSNs). However, a truly unbiased validation of such claims is still missing, which can only be accomplished by using simultaneously acquired EEG and fMRI data, due to the spontaneous nature of the activity underlying the RSNs. Additionally, EEG is still poorly explored for the purpose of mapping task-specific networks, and no studies so far have been focused on investigating networks’ dynamic functional connectivity (dFC) with EEG. Here, we started by validating RSNs derived from the continuous reconstruction of EEG sources by directly comparing them with those derived from simultaneous fMRI data of 10 healthy participants, and obtaining an average overlap (quantified by the Dice coefficient) of 0.4. We also showed the ability of EEG to map the facial expressions processing network (FEPN), highlighting regions near the posterior superior temporal sulcus, where the FEPN is anchored. Then, we measured the dFC using EEG for the first time in this context, estimated dFC brain states using dictionary learning, and compared such states with those obtained from the fMRI. We found a statistically significant match between fMRI and EEG dFC states, and determined the existence of two matched dFC states which contribution over time was associated with the brain activity at the FEPN, showing that the dynamics of FEPN can be captured by both fMRI and EEG. Our results push the limits of EEG toward being used as a brain imaging tool, while supporting the growing literature on EEG correlates of (dynamic) functional connectivity measured with fMRI, and providing novel insights into the coupling mechanisms underlying the two imaging techniques.https://www.frontiersin.org/article/10.3389/fnins.2020.00323/fullsimultaneous EEG-fMRIlarge-scale functional brain networksdynamic functional connectivity (dFNC)electrical source imaging (ESI)task-based fMRIresting-state functional network connectivity (rs-FNC) |
| spellingShingle | Rodolfo Abreu Marco Simões Marco Simões Miguel Castelo-Branco Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI Frontiers in Neuroscience simultaneous EEG-fMRI large-scale functional brain networks dynamic functional connectivity (dFNC) electrical source imaging (ESI) task-based fMRI resting-state functional network connectivity (rs-FNC) |
| title | Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI |
| title_full | Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI |
| title_fullStr | Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI |
| title_full_unstemmed | Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI |
| title_short | Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI |
| title_sort | pushing the limits of eeg estimation of large scale functional brain networks and their dynamics validated by simultaneous fmri |
| topic | simultaneous EEG-fMRI large-scale functional brain networks dynamic functional connectivity (dFNC) electrical source imaging (ESI) task-based fMRI resting-state functional network connectivity (rs-FNC) |
| url | https://www.frontiersin.org/article/10.3389/fnins.2020.00323/full |
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