Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function

Babiloni, Fabio; Cincotti, Febo; Babiloni, Claudio; Carducci, Filippo; Mattia, Donatella; Astolfi, Laura; Basilisco, A.; Rossini, Paolo Maria; Ding, Lei; Ni, Ying; Cheng, Jianjun; Christine, Kathleen S.; Sweeney, John A.; He, Bin

doi:10.1016/j.neuroimage.2004.09.036

Cited by 364 publications

(315 citation statements)

References 49 publications

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“…Consistent conclusion was recently obtained in independent simulation studies (Babiloni et al, 2003;Ahlfors and Simpson, 2004). The 90% fMRI-constrained Wiener filter approach was also successfully applied in a variety of experimental studies (Dale et al, 2000;Bonmassar et al, 2001;Jaaskelainen et al, 2004;Babiloni et al, 2005).…”

Section: % Fmri-constrained Wiener Filtersupporting

confidence: 70%

“…This hypothesis is reasonable in that neural activity, modulating neuronal firing and generating EEG signals, increases the demands for oxygen and induces larger cerebral blood flow, and consequently produces larger fMRI responses (Babiloni et al, 2005).…”

Section: % Fmri-constrained Wiener Filtermentioning

confidence: 97%

“…Typically, the fMRI "hotspots" (locations with significant hemodynamic change) are preferred in the fMRI-constrained cortical imaging, which can be accomplished, for example, by encoding the fMRI spatial information into the source covariance matrix and constructing an optimal linear estimator in the form of Wiener filter (Dale and Sereno, 1993;Liu et al, 1998). Previous simulation and experimental studies have discussed the efficacy of using fMRI constraints to enhance the spatial resolution of EEG-or MEG-based cortical imaging (Liu et al, 1998;Babiloni et al, 2003;Ahlfors and Simpson, 2004), and applications of this method have already advanced our understanding of the spatiotemporal pattern of brain activity and connectivity underlying perception, motion and cognition (Dale et al, 2000;Bonmassar et al, 2001;Jaaskelainen et al, 2004;Babiloni et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, a qualitative simulation study (Ahlfors and Simpson, 2004) also showed that the setting of 90% fMRI weighting gave smallest error of source reconstruction. Other than these computer simulation studies, the 90% fMRI weighting strategy has been successfully applied in a variety of experimental studies combining fMRI with EEG (or MEG) (Dale et al, 2000;Bonmassar et al, 2001;Jaaskelainen et al, 2004;Babiloni et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: % Fmri-constrained Wiener Filtersupporting

confidence: 70%

Section: % Fmri-constrained Wiener Filtermentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Since synchronously activated pyramidal cortical neurons, which are located perpendicularly on the cortical surface, are widely believed to be the main EEG and MEG generators, many recent studies have adopted this physiological phenomenon as a basic anatomical constraint in EEG or MEG source imaging (Dale and Sereno, 1993;Kincses et al, 1999;Dale et al, 2000;Babiloni et al, 2003Babiloni et al, , 2005. The source imaging with the anatomical constraint, which has been often called cortically distributed source modeling or cortical source imaging, resulted in the elimination of spurious sources (Baillet et al, 1998) as well as the reduction of crosstalk distribution (Liu et al, 1998), compared to conventional volume based imaging techniques.…”

Section: Eeg Cortical Source Imagingmentioning

confidence: 99%

Spatial resolution of EEG cortical source imaging revealed by localization of retinotopic organization in human primary visual cortex

Gururajan

Zhang

et al. 2007

Journal of Neuroscience Methods

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The aim of the present study is to investigate the spatial resolution of electroencephalography (EEG) cortical source imaging by localizing the retinotopic organization in the human primary visual cortex (V1). Retinotopic characteristics in V1 obtained from functional magnetic resonance imaging (fMRI) study were used as reference to assess the spatial resolution of EEG since fMRI can discriminate small changes in activation in visual field. It is well-known that the activation of the early C1 component in the visual evoked potential (VEP) elicited by pattern onset stimuli coincides well with the activation in the striate cortex localized by fMRI. In the present experiments, we moved small circular checkerboard stimuli along horizontal meridian and compared the activations localized by EEG cortical source imaging with those from fMRI. Both fMRI and EEG cortical source imaging identified spatially correlated activity within V1 in each subject studied. The mean location error, between the fMRI-determined activation centers in V1 and the EEG source imaging activation peak estimated at equivalent C1 components (peak latency: 74.8 ± 10.6 ms), was 7 mm (25% and 75% percentiles are 6.45 mm and 8.4 mm, respectively), which is less than the change in fMRI activation map by a 3° visual field change (7.8 mm). Moreover, the source estimates at the earliest major VEP component showed statistically good correlation with those obtained by fMRI. The present results suggest that the spatial resolution of the EEG cortical source imaging is can correctly discriminate cortical activation changes in V1 corresponding to less than 3° visual field changes.

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Electroencephalography (EEG): Inverse Problems

Hori

Babiloni³

2006

Wiley Encyclopedia of Biomedical Engineering

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Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function

Cited by 364 publications

References 49 publications

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

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

Spatial resolution of EEG cortical source imaging revealed by localization of retinotopic organization in human primary visual cortex

Electroencephalography (EEG): Inverse Problems

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