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Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG
Open accessAbstract
Noninvasive functional neuroimaging, as an important tool for basic neuroscience research and clinical diagnosis, continues to face the need of improving the spatial and temporal resolution. While existing neuroimaging modalities might approach their limits in imaging capability mostly due to fundamental as well as technical reasons, it becomes increasingly attractive to integrate multiple complementary modalities in an attempt to significantly enhance the spatiotemporal resolution that cannot be achieved by any modality individually. Electrophysiological and hemodynamic/metabolic signals reflect distinct but closely coupled aspects of the underlying neural activity. Combining fMRI and EEG/MEG data allows us to study brain function from different perspectives. In this review, we start with an overview of the physiological origins of EEG/MEG and fMRI, as well as their fundamental biophysics and imaging principles, we proceed with a review of the major advances in the understanding and modeling of neurovascular coupling and in the methodologies for the fMRI-EEG/MEG simultaneous recording. Finally, we summarize important remaining issues and perspectives concerning multimodal functional neuroimaging, including brain connectivity imaging.
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“Their complementary strengths and limitations have motivated researchers to integrate EEG/MEG and fMRI towards a multimodal imaging tool that is more powerful than each of them alone (115–117).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [115]
In the citing paper
“When fMRI is utilized to help solve the EEG/MEG inverse problem (i.e. fMRI-constrained EEG/MEG ESI), the solution benefits from the spatial precision and resolution of fMRI while inheriting the intrinsic temporal resolution from EEG/MEG (115, 117).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [115]
In the citing paper
“It is feasible to model the relationship between fMRI and EEG/MEG given relatively simple stimuli that repeat as discrete events and/or in alternating blocks (115).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [115]
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Self-citationQu, Y., Cao, J., Chen, L., Guo, J., Tian, Z., Liu, T., Gong, Y., Xiong, J., Lin, Z., Yang, X., et al. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.1103872 · Cited by 7
Network disruption based on multi-modal EEG-MRI in α-synucleinopathies
Wang, C., Hu, J., Li, P., Zhang, M., Zhou, L., Luo, N., Zhu, X., Yin, Q., Zhong, M., Zhou, X., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1442851 · Cited by 6
Bondi, E., Ding, Y., Zhang, Y., Maggioni, E., & He, B. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121311 · Cited by 12
Self-citationElectroencephalography: A valuable tool for assessing motor impairment and recovery post-stroke
Feng, J., Jia, W., & Li, Z. (2025) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2025.110518 · Cited by 6
Electrophysiological insights into Alzheimer's disease: A review of human and animal studies
Calvin, K., McNeela, A. M., Osse, A. M. L., Bhasin, G., Ridenour, M., Kinney, J. W., & Hyman, J. M. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105987 · Cited by 8
Hypothesis Testing With Dependent Observations
Sobhiyeh, S., & Naraghi‐Pour, M. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2633243 · Cited by 6
Cognitive State Analysis, Understanding, and Decoding from the Perspective of Brain Connectivity
Li, J., Bezerianos, A., & Thakor, N. V. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_77 · Cited by 6
MEG-based brain functional connectivity analysis using eConnectome
Dai, Y., & He, B. (2011) · Joint Meeting of International Symposium on Noninvasive Functional Source Imaging of Brain and Heart and International Conference on Functional Biomedical Imaging · doi:10.1109/nfsi.2011.5936808 · Cited by 6
Self-citationSimultaneous mesoscopic Ca 2+ imaging and fMRI: Neuroimaging spanning spatiotemporal scalesPreprint
Lake, E. M. R., Ge, X., Shen, X., Hermán, P., Hyder, F., Cardin, J. A., Higley, M. J., Scheinost, D., Papademetris, X., Crair, M. C., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/464305 · Cited by 7
Decoding Brain States Based on Magnetoencephalography From Prespecified Cortical Regions
Zhang, J., Li, X., Foldes, S. T., Wang, W., Collinger, J. L., Weber, D. J., & Bagic, A. I. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2439216 · Cited by 5
Metadata
- Authors
- He, B., & Liu, Z.
- Affiliations
- University of Minnesota (US)
- Corresponding author
- He, Bin
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2008
- Volume
- 1
- Pages
- 23-40
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- PMC
- PMC2903760
- OpenAlex
- W2128390591
- Cited by
- 171
- References
- 217
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 15 · 2023: 7 · 2022: 12 · 2021: 5 · 2020: 6 · 2019: 10 · 2018: 15 · 2017: 4 · 2016: 7 · 2015: 13 · 2014: 19 · 2013: 6 · 2012: 5
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Reviews in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01-EB-00178, R01EB000178, R01EB007920)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- simultaneous recording, spatiotemporal resolution, neurovascular coupling, hemodynamic response, electrophysiological signals
- MeSH terms
- Animals, Brain, Brain Mapping, Electroencephalography, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging
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