Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings

Lai, Yu‐Shu; Drongelen, Wim van; Ding, Lei; Hecox, Kurt; Towle, Vernon L.; Frim, David M.; He, Bin

doi:10.1016/j.clinph.2004.08.017

Cited by 187 publications

(159 citation statements)

References 42 publications

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“…The simulation setting was based on a realistically-shaped head volume conductor and cortical surface models that were constructed from high resolution T1-weighted MR images of a human subject (256 slices, matrix size: 256×256, voxel size: 1×1×1 mm 3 ). The electrical conductivities of the scalp, skull and brain were set to be 0.33, 0.0165, and 0.33 S/m, respectively (Lai et al, 2005;Oostendorp et al, 2000;Zhang et al, 2006). The cortical current density source model consisted of around 7,000 current dipoles evenly placed on the cortical surface.…”

Section: Computer Simulationsmentioning

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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Section: Computer Simulationsmentioning

confidence: 99%

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

2008

NeuroImage

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“…The simulation was conducted on a realistic-geometry BEM-model reconstructed from the T1-weighted MRI images of a human subject (128 slices, 256×256 pixels; 1.6 mm thickness, 1.17×1.17 mm in-plane resolution). The conductivities of the scalp, skull and brain were taken as 0.33, 0.0165 and 0.33 S/m, respectively (Oostendorp et al, 2000;Lai et al, 2005). The cortical surface was also segmented from the MRI images obtaining a triangulated mesh with about 20,199 surface triangles.…”

Section: Computer Simulationmentioning

confidence: 99%

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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“…The inter-node distance of for the surfaces of brain, skull, skin and cortex were 7, 9, 10 and 3 mm, respectively. The conductivities of the skin, skull and the brain were assumed to be 0.33, 0.0165 and 0.33 S/m, respectively (Oostendorp et al, 2000;Lai et al, 2005).…”

Section: 24mentioning

confidence: 99%

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Xiang

Towle

et al. 2007

NeuroImage

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Objective-EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI.Methods-Five source imaging algorithms, including the minimum norm least square (MNLS), LORETA with L p -norm (p equal to 1, 1.5 and 2), sLORETA, the minimum L p -norm (p equal to 1 and 1.5. When p = 2, the MNLS method is mathematically equivalent to the minimum L p -norm.), and L 1 -norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex.Results-The CCD distributions estimated by the L p -norm and LORETA-L p methods were smoother when the p values were high. The LORETA based on the L 1 norm performed better than the LORETA-L 2 method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimum L p -norm (with p equal to 1), L 1 -norm (the Linear programming) and LORETA-L p (with p equal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA with L p -norm (p equal to 1.5 and 2.0), sLORETA, and the minimum L p -norm (p equal to 1.5) methods, were over 11 mm and 6 mm, respectively.Conclusions-The present experimental study suggests that L 1 -norm based algorithms provide better performance than L 2 and L 1.5 norm based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist.

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Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings

Cited by 187 publications

References 42 publications

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

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

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

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