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

Abstract

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...

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

“…First, there is no generalized method to quantify the fMRI signals such that the resulting quantification has an explicit physical interpretation in the context of EEG source imaging (Liu et al, 2006b). It is perhaps such limitation that makes it difficult to develop a principled way of using the fMRI data in solving the EEG inverse problem.…”

“…Depending on different EEG source models, the fMRI map can be used to constrain the locations of multiple current dipoles, namely the fMRI-constrained dipole fitting (Ahlfors et al, 1999;Korvenoja et al, 1999;Fujimaki et al, 2002;Vanni et al, 2004), or to constrain the distributed source distribution over the folded cortical surface or in the 3-D brain volume, namely the fMRI-constrained current density imaging (George et al, 1995;Liu et al, 1998;Dale et al, 2000;Wagner et al, 2000;Babiloni et al, 2005;Ahlfors and Simpson, 2004;Sato et al, 2004;Phillips et al, 2005;Liu et al, 2006b;Mattout et al, 2006).…”

“…The fMRI-EEG mismatches can be further categorized into two types, namely fMRI extra sources and fMRI invisible sources (Liu et al, 1998;Wagner et al, 2000;Liu et al, 2006b). The fMRI extra sources represent the source regions deemed as active in fMRI but are not the EEG sources at certain time instants.…”

“…During a short period of interest following the event onset, the fMRI activations have to be thought of as "static" (or time-invariant) while the EEG signals are variable and the source imaging is carried out instant by instant. Electrical source activities at certain time instants may only involve a subset of the activated fMRI areas, whereas other areas may appear as false positives if including them all as time-invariant spatial constraints (Liu et al, 2006b). The fMRI invisible sources are the real EEG sources but not deemed as active by fMRI.…”

“…Existing methods for the fMRI-constrained current density imaging have been implemented under different frameworks such as Wiener estimation (Dale and Sereno, 1993;Liu et al, 1998;Dale et al, 2000), weighted minimum norm (Wagner et al, 2000;Babiloni et al, 2005;Ahlfors and Simpson et al, 2004), Bayesian estimation (Sato et al, 2004;Phillips et al, 2005;Mattout et al, 2006) and Twomey regularization (Liu et al, 2006b). …”