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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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“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-”
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“, 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-citationeConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity
He, B., Dai, Y., Astolfi, L., Babiloni, F., Yuan, H., & Yang, L. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.11.015 · Cited by 229
Self-citationMultimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG
He, B., & Liu, Z. (2008) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2008.2008233 · Cited by 171
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“Other authors built upon these ideas to achieve increasingly more reliable reconstructions (Liu and He, 2008; Ou et al., 2010).”
Analysis of Multimodal Neuroimaging Data
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BackgroundGenerative Adversarial Networks Conditioned by Brain Signals
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“Additionally, to compensate for the low temporal resolution of the fMRI scanners, we are going to investigate methods to combine fMRI data with EEG data [12].”
MethodRoy, A. V., Baxter, B., & He, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2311071 · Cited by 131
“Although the BOLD signal is an indirect measure of neural activity, it is well established that the BOLD signal changes relative to large changes in synchronized neural activity which may be directly recorded using EEG [56]–[59].”
BackgroundSelf-citationAn EEG Finger-Print of fMRI deep regional activation
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“EEG dynamics can be extracted by selecting unique EEG features and comparing their time courses to fMRI data (He and Liu, 2008; Liu and He, 2008).”
BackgroundSelf-citationTakeuchi, M., Hori, E., Takamoto, K., Tran, A. H., Kohno, S., Ishikawa, A., Ono, T., Endo, S., & Nishijo, H. (2009) · Brain Topography · doi:10.1007/s10548-009-0109-2 · Cited by 88
“Consistent with the present results, previous studies reported that BOLD responses could be estimated from cortical current density distribution in simultaneous fMRI and EEG recording (Liu and He 2008; Minati et al. 2008).”
ResultLinear and nonlinear relationships between visual stimuli, EEG and BOLD fMRI signals
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“For the group-level analysis, individual subjects' fMRI data were re-aligned to a common Talairach space and analyzed using a multi-study GLM implemented in BrainVoyager QX (Goebel et al., 2006).”
BackgroundMethodInfluentialSelf-citationThree-dimensional brain current source reconstruction from intra-cranial ECoG recordings
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“…Pascual-Marqui, 1995, 1999; He et al.,1996,1999, 2001, 2002a, 2002b, 2002c; Babiloni et al., 1997, 2003, 2005; Phillips et al., 1997; Wang and He, 1998; Fuchs et al., 1999; Grave de Peralta Menendez et al., 2000; Liu et al., 2006; Liu and He, 2008; Astolfi et al., 2007a, 2007b; Ding and He, 2007).”
Self-citationMultimodal functional imaging using fMRI-informed regional EEG/MEG source estimation
Wanmei Ou, A., Nummenmaa, A. R., Ahveninen, J., Belliveau, J. W., Hämäläinen, M., & Golland, P. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.001 · Cited by 66
A weighted small world network measure for assessing functional connectivity
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Self-citationEEG Mapping and Source Imaging
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Self-citationAttentional Bias Predicts Increased Reward Salience and Risk Taking in Bipolar Disorder
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How to use fMRI functional localizers to improve EEG/MEG source estimation
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“…ddress this issue by imposing a partial fMRI constraint (Ahlfors nd Simpson, 2004; Dale et al., 2000; Liu et al., 1998, 2002) or by odeling more finely the relationship between the BOLD signals nd the EEG/MEG measurements (Sato et al., 2004; Yoshioka et al., 008; Liu and He, 2008; Ou et al., 2010).”
BackgroundSystems Neuroengineering: Understanding and Interacting with the Brain
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“fMRI analysis can also be used to constrain the inverse solution in ESI, usually referred to as fMRI-constrained E/MEG source imaging [33, 34], in which the spatial extent and location of fMRI activity is used to constrain the ESI solution.”
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Self-citationSurvey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective
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“This method was employed in cortical source imaging in (Liu and He 2008; Vulliemoz et al. 2009).”
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“The timing of each heartbeat artifact in this channel was determined using an R-peak detection algorithm adapted from Liu et al. (Liu et al., 2012; Liu and He, 2008).”
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“The fMRI response provided additional spatial constraints to further improve the spatial specificity (Liu and He, 2008).”
InfluentialSelf-citationExploring the extent of source imaging: Recent advances in noninvasive electromagnetic brain imaging
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Self-citationLateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI
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“It is commonly used in combination with simultaneously collected scalp EEG (Liu et al., 2006; Liu & He, 2008; He & Liu, 2008).”
MethodSelf-citationBayesian symmetrical EEG/fMRI fusion with spatially adaptive priors
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“Another method is based on an adaptive Wiener fi lter where it is assumed that the energy of the electrical activity at every location on the cortex is proportional to the magnitude of the BOLD response at the same location (Liu and He, 2008).”
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“, 2005), or employ an adaptiveWiener filter (Liu and He, 2008) that is updated by EEG and fMRI data.”
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“The performance of EEG source imaging could further be improved by incorporating a prior information such as anisotropic conductivities (Michel et al., 2004) and fMRI constrains (He and Liu, 2008; Liu and He, 2008; Yang et al., 2010).”
Self-citationEEG-fMRI reciprocal functional neuroimaging
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“EEG sources can be invisible in fMRI and fMRI sources can be invisible in EEG (Liu et al., 2006; He and Liu, 2008; Liu and He, 2008).”
InfluentialSelf-citationIntegration of Multivariate Data Streams With Bandpower Signals
Dähne, S., Bießmann, F., Meinecke, F. C., Mehnert, J., Fazli, S., & Müller, K. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2250267 · Cited by 33
“Inherently unimodal methods require trading of one modality against the other [21], [22] and different preprocessing parameters might lead to very different fusion results [45].”
BackgroundLiu, Y., Moser, J. S., & Aviyente, S. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2296778 · Cited by 32
“We first present the details of EEG data and the computation of effective connectivity for this data.”
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Wang, G., & Ren, D. (2013) · BioMed Research International · doi:10.1155/2013/459346 · Cited by 30
“In this study, around 8000 equivalent current dipole sources were employed, and these dipoles were evenly placed over the folded cortical surface reconstructed from the MR images, and the orientation of each dipole was assumed to be perpendicular to the local cortical patch [16, 17].”
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“E XPERIMENTAL evidence suggests that the estimation of the cortical activity performed with the use of neuroelectromagnetic recordings improves with the use of the hemodynamic information recording during the same task [1]–[9].”
Self-citationAnalysis of Time and Space Invariance of BOLD Responses in the Rat Visual System
Bailey, C. J., Sanganahalli, B. G., Hermán, P., Blumenfeld, H., GJEDDE, A. H., & Hyder, F. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs008 · Cited by 28
“…do not have measured neural activity) show subtle variations of neurovascular coupling across larger distances in nonhomologous cortical regions (Engel et al. 1997; Birn et al. 2001; Janz et al. 2001; Liu and He 2008; Zhang et al. 2008; Liu et al. 2010; Ostwald et al. 2010; Carlson et al. 2011).”
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“We utilized a 64channel MRI-compatible EEG system (BrainAmp MRplus, BrainProducts) to continuously record both EEG and ECG over the course of the experiment (Im et al., 2007; Im et al., 2006; Liu and He, 2008; Liu et al., 2009; He and Liu, 2008; He et al., 2011).”
Self-citationElectrophysiological Mapping and Neuroimaging
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Self-citationSource-Informed Segmentation: A Data-Driven Approach for the Temporal Segmentation of EEG
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SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI
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“While the hemodynamic response can be nonlinear,(44,45) in simulation studies, it is common to make the simplifying assumption that hemodynamics can be stimulated by convolving the neuronal activation with a fixed HRF.(2,30) In practice, while often a good assumption, linearization here can be a poor approximation in…”
BackgroundInfluentialMultimodal Imaging of Human Brain Activity: Rational, Biophysical Aspects and Modes of Integration
Blinowska, K. J., Müller-Putz, G., Kaiser, V., Astolfi, L., Vanderperren, K., Huffel, S. V., & Lemieux, L. (2009) · Computational Intelligence and Neuroscience · doi:10.1155/2009/813607 · Cited by 22
“[30] Z.”
MethodOptimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors
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EEG spatio-spectral mapping during video game play
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“As demonstrated by numerous studies, scalp EEG data can be fed into an inverse procedure estimating the source distribution within the brain which accounts for the scalp measurements [ 6-10 ].”
BackgroundSelf-citationInuggi, A., Filippi, M., Chieffo, R., Agosta, F., Rocca, M., González-Rosa, J., Cursi, M., Comi, G., & Leocani, L. (2010) · Brain Research · doi:10.1016/j.brainres.2009.10.042 · Cited by 19
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“Furthermore, EEG yields signal data in different frequency bands, and the energy of each frequency band has a different physiologic significance; therefore, methods based on EEG may offer richer information (Liu & He, 2008; Kanayama, Kimura, & Hiraki, 2015).”
MethodA New fMRI Informed Mixed-Norm Constrained Algorithm for EEG Source Localization
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“However, most of the previousmodels were developed for a single time-point, based on the adaptiveWiener filter, and estimate the source covariance matrix by combining the quantified fMRI responses with the segmented EEG signals before response averaging [43], [44].”
MethodAcharya, D., Ruesch, A., Schmitt, S. E., Yang, J., Smith, M. A., & Kainerstorfer, J. M. (2022) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x221076566 · Cited by 17
“It was noted in fMRI literature that the power of the local neural activity closely resembled the cellular metabolism of the local tissue and hence matched with the consequent blood oxygen level dependent (BOLD) response.(26,35) Multi-modal comparison studies further made it possible to replicate the BOLD signal using…”
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Improving human brain mapping via joint inversion of brain electrodynamics and the BOLD signal
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“Several methods have been proposed including Bayesian statistical methods (Phillips et al., 2005) and those based on linear time-invariant system theory (Liu and He, 2008).”
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“Finally, we examined the potential effect of the lag size on the alpha – DMN dynamic coupling to consider the sluggish-ness of fMRI relative to EEG indices for neural activity (Boynton et al., 1996; Liu and He, 2008).”
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“Several studies compared source localization of EEG or evoked potentials with fMRI (Klamer et al., 2015; Lascano et al., 2016; Liu and He, 2008; Plomp et al., 2010), electrocortical stimulation (Lascano et al.”
EEG/MEG source imaging using fMRI informed time‐variant constraints
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“Moreover, fMRI missing sources have greater negative impacts on the accuracy of fMRI constrained EEG or MEG source imaging (Liu et al., 1998; Liu and He, 2008).”
BackgroundElectrophysiological Mapping and Source Imaging
He, B., Ding, L., & Sohrabpour, A. (2020) · Springer eBooks · doi:10.1007/978-3-030-43395-6_13 · Cited by 11
Self-citationDiffuse optical tomography spatial prior for EEG source localization in human visual cortex
Cao, J., Bulger, E., Shinn‐Cunningham, B. G., Grover, P., & Kainerstorfer, J. M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120210 · Cited by 11
Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI
Mangalathu-Arumana, J., Liebenthal, E., & Beardsley, S. A. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00013 · Cited by 11
“…spatial scale (Bonmassar et al., 2001; Dale and Halgren, 2001; Horovitz et al., 2002; Liebenthal et al., 2003, 2010; Mulert et al., 2004; Debener et al., 2005, 2006; Bénar et al., 2007; Liu and He, 2008; Liu et al., 2010; Bridwell et al., 2013; Cottereau et al., 2015; Nguyen et al., 2016).”
BackgroundYun, C., Sohn, C., Yeon, J., Chung, K.-J., Min, B.-J., Yun, C., & Han, B. S. (2025) · Diagnostics · doi:10.3390/diagnostics15141798 · Cited by 11
“This approach allows for the integration of artifact-free EEG data recorded during the 90 s intervals between DTI scans, thereby maintaining the reliability and accuracy of the analysis [28].”
Minati, L., Rosazza, C., Zucca, I., D’Incerti, L., Scaioli, V., & Bruzzone, M. G. (2008) · Brain Topography · doi:10.1007/s10548-008-0064-3 · Cited by 10
“activity originates from the same areas for which a heamodynamic response is detected, and with the fact that the EEG and BOLD signals, although very different, are both representative of local synaptic activity (Liu and He 2008).”
InfluentialDecoding motor execution and motor imagery from EEG with deep learning and source localization
Kaviri, S. M., & Vinjamuri, R. K. (2025) · Biomedical Engineering Advances · doi:10.1016/j.bea.2025.100156 · Cited by 9
EEG and fMRI coupling and decoupling based on joint independent component analysis (jICA)
Heugel, N., Beardsley, S. A., & Liebenthal, E. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109477 · Cited by 8
Three-Dimensional Source Imaging From Simultaneously Recorded ERP and BOLD-fMRI
Bai, X., Liu, Z., Zhang, N., Chen, W., & He, B. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2015196 · Cited by 7
“multimodal integration in a principled way, such as in [37].”
Self-citationBondi, E., Ding, Y., Zhang, Y., Maggioni, E., & He, B. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121311 · Cited by 12
“…coupling across various brain activities, aimed at achieving high spatio-temporal resolution for human brain mapping (Case et al., 2016; He and Liu, 2008; Huster et al., 2012; Liu and He, 2008; Liu et al., 2009, 2010; Maggioni et al., 2016; Ritter and Villringer, 2006).”
Self-citationSimultaneous EEG-fMRI data acquisition during cognitive task
Ahmad, R. F., Malik, A. S., Kamel, N., Reza, F., & Karim, A. H. A. (2014) · International Conference on Intelligent and Advanced Systems · doi:10.1109/icias.2014.6869471 · Cited by 6
“However, it is well known that fMRI is not capable of measuring the fast temporal rapidly varying neuronal event because hemodynamic response evolves in seconds instead of milliseconds[2].”
BackgroundZheng, L., Sheng, J., Cen, Z., Teng, P., Wang, J., Wang, Q., Lee, R. R., Luan, G., Huang, M., & Gao, J. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3016468 · Cited by 6
“focal activations [14], [15], leading to low spatial resolution in reconstructed images and overestimation of the boundaries of activated areas.”
BackgroundBrain plasticity and phonetic training for English-as-a-second-language learners
Zhang, Y., & Cheng, B. (2011) · Cited by 5
Jung, Y., & Im, C. (2011) · Biomedical Engineering Letters · doi:10.1007/s13534-011-0002-2 · Cited by 5
Multimodal Neuroimaging with Simultaneous fMRI and EEG
Poudel, G. R., & Jones, R. D. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_81-1 · Cited by 5
Qin, Y., Wu, J., Bulger, E., Cao, J., Dehghani, H., Shinn‐Cunningham, B. G., & Kainerstorfer, J. M. (2024) · Biomedical Optics Express · doi:10.1364/boe.531576 · Cited by 5
“Previous research has shown that EEG source localization can be improved by incorporating spatial priors from modalities such as functional magnetic resonance imaging (fMRI) [8], and positron emission tomography (PET) [9].”
BackgroundExploring Functional and Causal Connectivity in the Brain
Ding, M., & He, B. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5227-0_13 · Cited by 5
Self-citationThe relationship between conductivity uncertainties and EEG source localization accuracy
Wang, G., Yang, L., Worrell, G. A., & He, B. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5332637 · Cited by 5
“Around 8000 current dipoles were evenly placed over the folded cortical surface reconstructed from the MRI images and the orientation of each dipole was assumed to be perpendicular to the local cortical patch [11], [12].”
Self-citationMultimodal functional imaging using fMRI-Informed regional EEG/MEG source estimation
Wanmei Ou, A., Nummenmaa, A. R., Golland, P., & Hämäläinen, M. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5333926 · Cited by 4
Tracking cognitive changes in new‐onset epilepsy: Functional imaging challenges
D’Arcy, R. C. N., Gawryluk, J. R., Beyea, S., Hajra, S. G., Feindel, K. W., & Clarke, D. B. (2011) · Epilepsia · doi:10.1111/j.1528-1167.2011.03152.x · Cited by 3
“Taken together, the emerging concept of integrating results across multiple imaging modalities will likely yield improved detection of subtle and/or complex changes in epilepsy (Liu & He, 2008).”
BackgroundIntegration of electrical neuroimaging with other functional imaging methods
Brandeis, D., Michel, C. M., Koenig, T. W., & Gianotti, L. R. R. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511596889.011 · Cited by 3
Inverse source imaging methods in recovering distributed brain sources
Ding, L., & Yuan, H. (2012) · Biomedical Engineering Letters · doi:10.1007/s13534-012-0047-x · Cited by 3
Hausinger, T., Reisinger, P., Weisz, N., Hansen, A., Harris, T., & Pletzer, B. A. (2025) · Psychophysiology · doi:10.1111/psyp.70032 · Cited by 2
“In contrast, EEG and MEG capture rapid fluctuations in electromagnetic fields generated by synchronized neural activity, providing fine-grained temporal resolution at the scale of milliseconds (Liu and He 2008).”
Using functional MRI alone for localization in focal epilepsy
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-citationEEG Source Imaging and Multimodal Neuroimaging
Zhang, Y. (2018) · Biological and medical physics, biomedical engineering · doi:10.1007/978-981-13-0908-3_5 · Cited by 2
Heugel, N., Liebenthal, E., & Beardsley, S. A. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108401 · Cited by 2
“Cortex, 9 (1999), pp. 815-823 Liu and He, 2008 Z. Liu, B.”
BackgroundMultimodal Neuroimaging with Simultaneous fMRI and EEG
Poudel, G. R., & Jones, R. D. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_81 · Cited by 2
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
“.”
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
“Progress has been made with the introduction of realistic head models using the boundary element method [27] or finite element method [28,29], which allows the addition of information such as anisotropic conductivities [30], but also anatomical constraints [29,31,32].”
MethodFunctional neuroimaging of dynamic brain activation
He, B. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649924 · Cited by 1
“We have recently developed a new theoretical framework to solve the problem of fMRI-EEG integrated source imaging using time-varying source covariance [ 3 ].”
MethodSelf-citationDynamic seizure imaging in patients with extratemporal lobe epilepsy
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
Self-citationMulti-area integrated E/MEG and fMRI modeling
Abbas Babajani-feremi, A., Bowyer, S. M., Moran, J., Elisevich, K. V., Podell, K., & Hamid Soltanian-zadeh, A. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.813801 · Cited by 1
“INTRODUCTION There are several efforts in the literature to integrate E/MEG and fMRI analysis using data-driven strategies [1-6] as well as model-based analysis [7-10].”
Joint Analysis of EEG and fMRI
Sanei, S. (2013) · Wiley eBooks · doi:10.1002/9781118622162.ch16
Functional neuroimaging of dynamic brain activity
He, B. (2008) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2008.4570499
Self-citationSurgery in Epilepsy – How Advances in Human Neurology can benefit Veterinary Patients
Charalambous, M. (2016) · Veterinary Evidence · doi:10.18849/ve.v1i1.19
“Its current use involves identification of the eloquent cortex affecting visual, oral and motor functions that need to remain intact during surgery (Thornton et al., 2009) and it is commonly combined with simultaneous scalp EEG (Liu and He, 2008, Liu et al., 2006, He and Liu, 2008).”
BackgroundNoninvasive Functional Neuroimaging of Electrophysiological Brain Activities in Epilepsy Patients
Lu, Y. (2014) · University of Minnesota Digital Conservancy (University of Minnesota)
Joint Analysis of EEG and Other Simultaneously Recorded Brain Functional Neuroimaging Modalities
Sanei, S., & Chambers, J. A. (2021) · Wiley eBooks · doi:10.1002/9781119386957.ch18
Current Awareness in NMR in Biomedicine
(2008) · NMR in Biomedicine · doi:10.1002/nbm.1313
Exploring Functional and Causal Connectivity in the Brain
Ding, M., & He, B. (2020) · Springer eBooks · doi:10.1007/978-3-030-43395-6_14
Self-citationKnösche, T. R., & Haueisen, J. (2022) · Springer eBooks · doi:10.1007/978-3-030-74918-7_6
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
“Given that the generation of E/MEG and fMRI signals are not identical [23], this strategy effectively balances prior knowledge with the evidence from data.”
Multifractal Cascade Modeling Reveals Fundamental Limits of Current Neuroimaging Strategies
Mangalam, M. (2025) · Sensors · doi:10.3390/s25237388
“Multiscale integration across modalities can leverage complementary spatial and temporal sensitivities via joint inverse modeling, cross-modal constraints, and harmonized preprocessing [51–53].”
Functional Neuroimaging of Electrophysiological Rhythms in Pathological and Normal Brains
Yang, L. (2012) · University of Minnesota Digital Conservancy (University of Minnesota)
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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