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fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints
Open accessAbstract
In response to the need of establishing a high-resolution spatiotemporal neuroimaging technique, tremendous efforts have been focused on developing multimodal strategies that combine the complementary advantages of high-spatial-resolution functional magnetic resonance imaging (fMRI) and high-temporal-resolution electroencephalography (EEG) or magnetoencephalography (MEG). A critical challenge to the fMRI-EEG/MEG integration lies in the spatial mismatches between fMRI activations and instantaneous electrical source activities. Such mismatches are fundamentally due to the fact that fMRI and EEG/MEG signals are generated and collected in highly different time scales. In this paper, we propose a new theoretical framework to solve the problem of fMRI-EEG integrated cortical source imaging. The new framework has two principal technical advancements. First, by assuming a linear neurovascular coupling, a method is derived to quantify the fMRI signal in each voxel as proportional to the time integral of the power of local electrical current during the period of event-related potentials (ERP). Second, the EEG inverse problem is solved for every time instant using an adaptive Wiener filter, in which the prior time-variant source covariance matrix is estimated by combining the quantified fMRI responses and the segmented EEG signals before response averaging. A series of computer simulations were conducted to evaluate the proposed methods in terms of imaging the instantaneous cortical current density (CCD) distribution and estimating the source time courses with a millisecond temporal resolution. As shown in the simulation results, the instantaneous CCD reconstruction by using the proposed fMRI-EEG integration method was robust against both fMRI false positives and false negatives while retaining a spatial resolution nearly as high as that of fMRI. The proposed method could also reliably estimate the source waveforms when multiple sources were temporally correlated or uncorrelated, or were sustained or transient, or had some features in frequency or phase, or had even more complicated temporal dynamics. Moreover, applying the proposed method to real fMRI and EEG data acquired in a visual experiment yielded a time series of reconstructed CCD images, in agreement with the traditional view of hierarchical visual processing. In conclusion, the proposed method provides a reliable technique for the fMRI-EEG integration and represents a significant advancement over the conventional fMRI-weighted EEG (or MEG) source imaging techniques and is also applicable to the fMRI-MEG integrated source imaging.
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“such algorithms all share and utilize a common assumption that EEG/MEG sources are more likely to occur at where fMRI views as activated.”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [150]
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“This relationship yields a quantitative model to relate fMRI to EEG/MEG, enabling a more principled way for using fMRI to constrain the EEG/MEG inverse solution for stimulus-evoked responses (150–152).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [150]
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Cited by
Sorted by citations
Electrophysiological source imaging: A noninvasive window to brain dynamics
He, B., Sohrabpour, A., Brown, E., & Liu, Z. (2018) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-120853 · Cited by 297
“Although they may be implemented in different theoretical frameworks, such as WMN (97), Wiener filter (149, 150), and Bayesian inference (43), all such algorithms share and utilize a common assumption that EEG/MEG sources are more likely to occur in locations that fMRI views as activated.”
BackgroundSelf-citationThe Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging
Ritter, P., Schirner, M., McIntosh, A. R., & Jirsa, V. K. (2013) · Brain Connectivity · doi:10.1089/brain.2012.0120 · Cited by 268
“Further, priors derived from BOLD contrast fluctuation can be exploited to inform source imaging [cf. technical details and reviews: (Grech 132 RITTER ET AL. et al., 2008; He and Liu, 2008; Liu and He, 2008; Michel et al., 2004; Pascual-Marqui, 1999)].”
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“…reliable techniques for the fMRI-EEG integration have been made possible that offer greater spatio-temporal resolution of imaging dynamic brain activity as well as significant improvement over the conventional fMRI-weighted EEG source imaging techniques (Liu and He, 2008; Yang et al., 2010).”
MethodElectrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities
He, B., Yang, L., Wilke, C., & Yuan, H. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2139210 · Cited by 300
“been proposed toward overcoming this limitation, by means of a time-variant spatial constraint estimated from a combination of quantified fMRI and EEG responses [65] or estimating re-”
BackgroundMethodSelf-citationYuan, H., Liu, T., Szarkowski, R., Rios, C., Ashe, J., & He, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.028 · Cited by 274
“, 2004) and event-related potentials (ERP) through phase-locked averaging (Huettel et al., 2004; Mulert et al., 2004; Schulz et al., 2004; Babiloni et al., 2005; Liu and He, 2008) – all of which are generated by synchronized neuronal populations.”
BackgroundSelf-citation
Metadata
- Authors
- Liu, Z., & He, B.
- Affiliations
- University of Minnesota (US)
- Corresponding author
- He, Bin
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2008-02
- Volume
- 39
- Issue
- 3
- Pages
- 1198-1214
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2291020
- OpenAlex
- W2160450584
- Cited by
- 103
- References
- 69
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 1 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 3 · 2019: 1 · 2018: 7 · 2017: 5 · 2016: 3 · 2015: 6 · 2014: 8 · 2013: 9 · 2012: 8
- FWCI
- 4.17
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- University of Minnesota
- National Institutes of Health
- National Institute of Biomedical Imaging and Bioengineering (R01-EB-00178, R01EB000178, R01EB007920)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- EEG inverse problem, adaptive Wiener filter, neurovascular coupling, event-related potentials, visual processing
- MeSH terms
- Adult, Algorithms, Cerebral Cortex, Computer Simulation, Electroencephalography, Evoked Potentials, Visual, False Positive Reactions, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Oxygen, Photic Stimulation
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