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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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A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Neuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
Brain magnetic resonance imaging with contrast dependent on blood oxygenation.
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.24.9868 · Cited by 6,483
MR diffusion tensor spectroscopy and imaging
Basser, P. J., Mattiello, J., & LeBihan, D. (1994) · Biophysical Journal · doi:10.1016/s0006-3495(94)80775-1 · Cited by 5,970
Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993) · Reviews of Modern Physics · doi:10.1103/revmodphys.65.413 · Cited by 4,684
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- liu, 2008, Multimodal Functional Neuroimaging Integrating Functional Magnetic Resonance Imaging and Electroencephalography
- 2004, Modeling and Imaing of Bioelectrical Activity Principles and Applications
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Gargiulo, Á. J. M., Gargiulo, A. P. Í., Gargiulo, A. P. Í., Mesones, G., Martin, G., Gargiulo, P. Á., Mesones-Arroyo, H. L., & Gargiulo, P. Á. (2015) · Springer eBooks · doi:10.1007/978-3-319-17103-6_23 · Cited by 5
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Hiwaki, O., & Miyaguchi, H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192095 · Cited by 5
Ashjazadeh, N., Boostani, R., Ekhtiari, H., Emamghoreishi, M., Farrokhi, M. R., Ghanizadeh, A. J., Hatam, G., Hadianfard, H., Lotfi, M., Mortazavi, S. M. J., et al. (2014) · Europe PMC (PubMed Central) · Cited by 5
Schulz, S., Bolz, M., Bär, K., & Voss, A. (2018) · Methods of Information in Medicine · doi:10.3414/me17-02-0002 · Cited by 5
Neural brain activation imaging
Panchuelo, R. M. S., Stephenson, M. C., Francis, S., & Morris, P. G. (2014) · Elsevier eBooks · doi:10.1533/9780857097477.2.112 · Cited by 4
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Bolognini, N., & Miniussi, C. (2016) · Springer eBooks · doi:10.1007/978-3-319-33967-2_9 · Cited by 4
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Madkhali, Y., Aldehmi, N., & Pollick, F. E. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7589493 · Cited by 4
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Li, J., Bezerianos, A., & Thakor, N. V. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_77-2 · Cited by 4
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Meneghini, F., Vatta, F., Esposito, F., Mininel, S., & Salle, F. D. (2010) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt.2010.010 · Cited by 4
Oralhan, Z. (2019) · Düzce Üniversitesi Bilim ve Teknoloji Dergisi · doi:10.29130/dubited.562610 · Cited by 3
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Liu, Z., & Cao, J. (2020) · Springer eBooks · doi:10.1007/978-3-030-43395-6_11 · Cited by 3
Self-citationForbes, E., Hassien, A., Tan, R. J., Wang, D., & Lega, B. C. (2024) · Cortex · doi:10.1016/j.cortex.2024.03.010 · Cited by 3
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Ahlfors, S. P. (2019) · Springer eBooks · doi:10.1007/978-3-030-00087-5_7 · Cited by 3
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N.Mohammed, A., Taha, T. E., & Faragallah, O. S. (2014) · International Journal of Computer Applications · doi:10.5120/16623-6475 · Cited by 3
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Matić, Z., Catrambone, V., & Valenza, G. (2026) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00803.2025 · Cited by 3
A geometry aware framework enhances noninvasive mapping of whole human brain dynamics
Wang, S., Lou, K., Wei, C., Sheng, Z., Tang, J., Peng, K., Shen, X., Mei, S., Chen, L., Gu, D., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01664-0 · Cited by 2
Chen, G., Yu, C., Yang, Y., Ding, P., Zhao, R., Bai, R., Liu, S., Zhang, G., Li, S., Xu, X., et al. (2026) · Lupus Science & Medicine · doi:10.1136/lupus-2026-002009 · Cited by 2
Liu, W., Zhang, H., Yang, L., & Gu, Y. (2022) · PubMed Central · doi:10.7507/1001-5515.202108048 · Cited by 2
George, E., & Barkovich, M. J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-10944-7.00021-4 · Cited by 2
Autti, S., Hirvi, P., Keitaanniemi, M., Mustaniemi, H., Kotilahti, K. M., Renvall, H., & Nissila, I. T. (2025) · Human Brain Mapping · doi:10.1002/hbm.70293 · Cited by 2
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Ahlfors, S. P. (2014) · Springer eBooks · doi:10.1007/978-3-642-33045-2_7 · Cited by 2
CMAC-based Computational Model of Affects (CCMA) for profiling emotion from EEG signals
Yaacob, H. S., Abdul, W., Alshaikhli, I. F. T., & Kamaruddin, N. (2014) · International Conference on Information and Communication Technology for the Muslim World (ICT4M) · doi:10.1109/ict4m.2014.7020584 · Cited by 2
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Zhang, C. H., Lu, Y., Brinkmann, B. H., Welker, K. M., Worrell, G. A., & He, B. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943694 · Cited by 2
Self-citationKim, S., Shon, K., & Sung, K. R. (2019) · Korean Journal of Ophthalmology · doi:10.3341/kjo.2019.0004 · Cited by 1
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Jho, H. (2022) · Integrated science · doi:10.1007/978-3-030-96814-4_3 · Cited by 1
Glazebrook, A. J., Shakespeare‐Finch, J., Andrew, B., & van der Meer, J. N. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105926 · Cited by 2
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Yadav, D., Garg, N., Gautam, C., & Yadav, S. (2024) · Springer eBooks · doi:10.1007/978-981-97-8100-3_44 · Cited by 1
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Chandra, K. A., Upadhyay, P. K., & Kumar, P. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100855 · Cited by 1
Block-Champagne: A Novel Bayesian Framework for Imaging Extended E/MEG Source
Feng, Z., Guan, C.-T., & Sun, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3642620 · Cited by 3
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Hou, T., Dai, W., Huang, J., Kong, Y., & Ding, W. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.104096 · Cited by 2
Yuan, Z., Xu, W., Hu, Z., Guo, J., Tao, Y., Wang, L., Peng, Y., Houston, M. J., Zhang, Y., Qiao, J., et al. (2026) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-026-01940-9 · Cited by 1
High-density EEG and source analysis: Principles, recent progress and applications in children
Wallois, F., & Bourel‐Ponchel, E. (2015) · Journal of Pediatric Epilepsy · doi:10.3233/pep-13041 · Cited by 1
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Silva, M. V., Maestú, F., & Castro, A. F. (2011) · InTech eBooks · doi:10.5772/30929 · Cited by 1
Nasseroleslami, B., Lakany, H., & Conway, B. A. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6696046 · Cited by 1
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Lu, Y., Yang, L., Worrell, G. A., Brinkmann, B. H., Nelson, C., & He, B. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347417 · Cited by 1
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Michalopoulos, K., & Bourbakis, N. (2014) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2014.53 · Cited by 1
Estimation and detection based on correlated observations from a heterogeneous sensor network
Sobhiyeh, S., & Naraghi‐Pour, M. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7996429 · Cited by 1
Clinical Neurophysiology: An Overview
Mader, E. C., Miller, D., & Olejniczak, P. W. (2018) · Springer Publishing Company eBooks · doi:10.1891/9780826169358.0001 · Cited by 1
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Bolognini, N., & Diana, L. (2021) · Springer eBooks · doi:10.1007/978-3-030-76136-3_7 · Cited by 1
Multi-parameter analysis of electroencephalogram (EEG): A diagnostic measure for epilepsy
Tibdewal, M. N., Manjunatha, M., Ray, A., & Malokar, M. (2013) · 2013 IEEE Point-of-Care Healthcare Technologies (PHT) · doi:10.1109/pht.2013.6461287
Brain topographic mapping of emotions using computational cerebellum
Yaacob, H. S., Kamaruddin, N., & Abdul, W. (2013) · International Conference on Electronics, Computer and Computation · doi:10.1109/icecco.2013.6718220
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Charalambous, M. (2016) · Veterinary Evidence · doi:10.18849/ve.v1i1.19
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ek, D. B. V., & Fowler, C. J. (2010) · CRC Press eBooks · doi:10.3109/9781439807217-35
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Kumar, B. V. K. V., & Zhang, J. (2013)
Michalopoulos, K. (2014) · OhioLink ETD Center (Ohio Library and Information Network)
Noninvasive Functional Neuroimaging of Electrophysiological Brain Activities in Epilepsy Patients
Lu, Y. (2014) · University of Minnesota Digital Conservancy (University of Minnesota)
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Han, X., & Li, T. (2018) · Medical Research and Innovations · doi:10.15761/mri.1000134
MEG and Multimodal Integration
Ahlfors, S. P. (2019) · Springer eBooks · doi:10.1007/978-3-319-62657-4_7-1
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Xu, J., Nguyen, A. T., Zhao, W., Chen, W., & Yang, Z. (2023) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2023.3318699
Disruptive Metrology for Acoustical Innovations
Yadav, D., Garg, N., Gautam, C., & Yadav, S. (2024) · Springer eBooks · doi:10.1007/978-981-99-4638-9_44-1
Evaluating frontoparietal network topography for diagnostic markers of Alzheimer’s disease
Rogers, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-64699-w
Block-Champagne: A novel Bayesian framework for Imaging E/MEG SourcePreprint
Feng, Z., Guan, C.-T., & Sun, Y. (2025) · bioRxiv · doi:10.1101/2025.02.17.638774
Progress and Prospects in Functional MRI (fMRI) Studies of Neural Networks
Kumar, S. N., Fred, A. L., Kumar, H. A., Padmanabhan, P., & Jane, I. C. (2025) · Biological and medical physics, biomedical engineering · doi:10.1007/978-981-96-1124-9_13
Tătăranu, L. G. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14082689
Iranfar, S., Wallace, J., Selmaoui, B., & Yahia‐Cherif, L. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.11.007
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Reddy, N. K. S., Mani, D. S., Zaid, M., Kumar, N. S. S. S. G., & Jabbar, M. A. (2025) · World Conference on Communication & Computing (WCONF) · doi:10.1109/wconf64849.2025.11233393
Mincewicz, W. (2025) · International Review of Sociology · doi:10.1080/03906701.2025.2600412
Functional Neuroimaging of Electrophysiological Rhythms in Pathological and Normal Brains
Yang, L. (2012) · University of Minnesota Digital Conservancy (University of Minnesota)
Wearable Radio Frequency (RF) Sensing System for Neural Activity Detection of Human Brain Waves
Huo, K., Anwar, U., Arslan, T., & Lomax, P. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3645412
Graph Convolutional Network–Based Surrogate Modeling for MRI-EEG Connectivity Analysis
Rezaei, A., & Abbaszadeh, B. (2025) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme68496.2025.11392514
Traitement des configurations spatiales dans le cortex visuel chez le primate non-humain
Audurier, P. (2021) · theses.fr (ABES) · doi:10.70675/0f36a324zc03cz4df0z8d2bz85e8f8e9b1f5
Integrated Noninvasive Brain Imaging Advancements: A Multimodal Approach
Balasubramanian, S., Patel, P., Kathi, S., Joseph, R., & Annavarapu, R. N. (2026) · IEEE Journal of Selected Areas in Sensors · doi:10.1109/jsas.2026.3685662
Editorial: Multimodal brain data integration and computational modeling
Sheynin, J., Naseri, M., & Callara, A. L. (2026) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2026.1838147
Yuan, Z., Hu, Z., Xu, W., Guo, J., Tao, Y., Chen, M., Wang, L., Zhao, Y., Li, R., He, Y., et al. (2026) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2026.3728062
Zhong, R., Li, H., Zuo, M., Liu, Z., & Zhang, Q. (2026) · IEEE Access · doi:10.1109/access.2026.3732163
Self-citationPollard-Wright, H. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1966072
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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