Improving source reconstructions by combining bioelectric and biomagnetic data

Fuchs, Manfred; Wagner, Michael; Wischmann, H.‐A.; Köhler, Thomas; Theißen, A.; Drenckhahn, R.; Büchner, Helmut

doi:10.1016/s0013-4694(98)00046-7

Cited by 219 publications

(249 citation statements)

References 32 publications

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“…Recently, other simulations examining the combination of EEG and MEG measurements were presented [Fuchs et al, 1998]. Fuchs et al [1998] used a single equivalent current dipole (ECD) inverse approach to localize test dipoles in a spherical three-shell head model.…”

Section: Discussionmentioning

confidence: 99%

“…As pointed out by numerous authors [e.g., Fuchs et al, 1998;Hari et al, 1988;Liu et al, 1998;Murro et al, 1995;Supek and Aine, 1993], localization accuracy is highly dependent on the location of the source. Therefore, to better approximate realistic data which can occur anywhere in the brain, our simulations use a large random sampling of source locations to provide an average estimate of localization accuracy.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

“…If the actual conductivities differ from the assumed conductivities, even though the conductivity ratios are correctly estimated, then the computed source activities using EEG will not be in the same units as the computed source activities using MEG. This effectively introduces a scaling factor between the EEG and MEG forward solutions [Fuchs et al, 1998]. We modeled the effect of mis-estimating the scaling factor between EEG and MEG forward solutions (corresponding to the misestimate of the actual conductivities) from 0.2-5.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

Monte Carlo simulation studies of EEG and MEG localization accuracy

Liu¹,

Dale²,

Belliveau³

2002

Hum. Brain Mapp.

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Both electroencephalography (EEG) and magnetoencephalography (MEG) are currently used to localize brain activity. The accuracy of source localization depends on numerous factors, including the specific inverse approach and source model, fundamental differences in EEG and MEG data, and the accuracy of the volume conductor model of the head (i.e., the forward model). Using Monte Carlo simulations, this study removes the effect of forward model errors and theoretically compares the use of EEG alone, MEG alone, and combined EEG/MEG data sets for source localization. Here, we use a linear estimation inverse approach with a distributed source model and a realistic forward head model. We evaluated its accuracy using the crosstalk and point spread metrics. The crosstalk metric for a specified location on the cortex describes the amount of activity incorrectly localized onto that location from other locations. The point spread metric provides the complementary measure: for that same location, the point spread describes the mis-localization of activity from that specified location to other locations in the brain. We also propose and examine the utility of a "noise sensitivity normalized" inverse operator. Given our particular forward and inverse models, our results show that 1) surprisingly, EEG localization is more accurate than MEG localization for the same number of sensors averaged over many source locations and orientations; 2) as expected, combining EEG with MEG produces the best accuracy for the same total number of sensors; 3) the noise sensitivity normalized inverse operator improves the spatial resolution relative to the standard linear estimation operator; and 4) use of an a priori fMRI constraint universally reduces both crosstalk and point spread. Hum. Brain Mapping 16:47-62, 2002.

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Section: Discussionmentioning

confidence: 99%

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Section: Monte Carlo Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

Monte Carlo simulation studies of EEG and MEG localization accuracy

Liu¹,

Dale²,

Belliveau³

2002

Hum. Brain Mapp.

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“…(18) (19) In practice, the noise covariance matrix, C b (t), can be estimated directly from the EEG data (Fuchs et al, 1998), whereas a priori knowledge of C s (t) is usually unavailable and hence it is typically assumed to be proportional to an identity matrix (Hämäläinen and Ilmoniemi, 1984) or a spatial Laplacian operator (Pascual-Marqui et al, 1994). It has also been demonstrated that the Wiener filter in Eq.…”

Section: Eeg Inverse Problem Of Cortical Source Imagingmentioning

confidence: 99%

fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints

2008

NeuroImage

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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 based on 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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“…Here, the weighting matrix C −1/2 is still introduced to compensate for different levels of noise contamination in the sensor space (Fuchs et al, 1998;Babiloni et al, 2003). We can see that the influence from fMRI spatial priors has been softened in the second step of Twomey algorithm, which yields the robustness of the estimated solution in the face of the possible fMRI-EEG misspecifications.…”

Section: Twomey Algorithmmentioning

confidence: 98%

Effects of fMRI–EEG mismatches in cortical current density estimation integrating fMRI and EEG: A simulation study

Li¹,

Kecman²,

He³

2006

Clinical Neurophysiology

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Objective-Multimodal functional neuroimaging by combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has been studied to achieve high-resolution reconstruction of the spatiotemporal cortical current density (CCD) distribution. However, mismatches between these two imaging modalities may occur due to their different underlying mechanisms. The aim of the present study is to investigate the effects of different types of fMRI-EEG mismatches, including fMRI invisible sources, fMRI extra regions and fMRI displacement, on the fMRI-constrained cortical imaging in a computer simulation based on realistic-geometry boundary-element-method (BEM) model. Methods-Two methods have been adopted to integrate the synthetic fMRI and EEG data for CCD imaging. In addition to the well-known 90% fMRI-constrained Wiener filter approach (Liu AK, Belliveau JW and Dale AM, PNAS, 95: 8945-8950, 1998), we propose a novel two-step algorithm (referred to as "Twomey algorithm") for fMRI-EEG integration. In the first step, a "hard" spatial prior derived from fMRI is imposed to solve the EEG inverse problem with a reduced source space; in the second step, the fMRI constraint is removed and the source estimate from the first step is reentered as the initial guess of the desired solution into an EEG least squares fitting procedure with Twomey regularization. Twomey regularization is a modified Tikhonov technique that attempts to simultaneously minimize the distance between the desired solution and the initial estimate, and the residual errors of fitness to EEG data. The performance of the proposed Twomey algorithm has been evaluated both qualitatively and quantitatively along with the lead-field normalized minimum norm (WMN) and the 90% fMRI-weighted Wiener filter approach, under repeated and randomized source configurations. Point spread function (PSF) and localization error (LE) are used to measure the performance of different imaging approaches with or without a variety of fMRI-EEG mismatches.Results-The results of the simulation show that the Twomey algorithm can successfully reduce the PSF of fMRI invisible sources compared to the Wiener estimation, without losing the merit of having much lower PSF of fMRI visible sources relative to the WMN solution. In addition, the existence of fMRI extra sources does not significantly affect the accuracy of the fMRI-EEG integrated CCD estimation for both the Wiener filter method and the proposed Twomey algorithm, while the Twomey algorithm may further reduce the chance of occurring spurious sources in the extra fMRI regions. The fMRI displacement away from the electrical source causes enlarged localization error in the imaging results of both the Twomey and Wiener approaches, while Twomey gives smaller LE than Wiener with the fMRI displacement ranging from 1-cm to 2-cm. With less than 2-cm fMRI displacement, the LEs for the Twomey and Wiener approaches are still smaller than in the WMN solution. Conclusions-The present study suggests that the presence of fMRI invi...

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Improving source reconstructions by combining bioelectric and biomagnetic data

Cited by 219 publications

References 32 publications

Monte Carlo simulation studies of EEG and MEG localization accuracy

Monte Carlo simulation studies of EEG and MEG localization accuracy

fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints

Effects of fMRI–EEG mismatches in cortical current density estimation integrating fMRI and EEG: A simulation study

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