Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations

Abstract

The goal of our research is to develop an experimental and analytical framework for spatiotemporal imaging of human brain function. Preliminary studies suggest that noninvasive spatiotemporal maps of cerebral activity can be produced by combining the high spatial resolution (millimeters) of functional MRI (fMRI) with the high temporal resolution (milliseconds) of electroencephalography (EEG) and magnetoencephalography (MEG). Although MEG and EEG are sensitive to millisecond changes in mental activity, the ability to resolve source localization and timing is limited by the ill-posed ''inverse'' problem. We conducted Monte Carlo simulations to evaluate the use of MRI constraints in a linear estimation inverse procedure, where fMRI weighting, cortical location and orientation, and sensor noise statistics were realistically incorporated. An error metric was computed to quantify the effects of fMRI invisible (''missing'') sources, ''extra'' fMRI sources, and cortical orientation errors. Our simulation results demonstrate that prior anatomical and functional information from MRI can be used to regularize the EEG͞MEG inverse problem, giving an improved solution with high spatial and temporal resolution. An fMRI weighting of approximately 90% was determined to provide the best compromise between separation of activity from correctly localized sources and minimization of error caused by missing sources. The accuracy of the estimate was relatively independent of the number and extent of the sources, allowing for incorporation of physiologically realistic multiple distributed sources. This linear estimation method provides an operatorindependent approach for combining information from fMRI, MEG, and EEG and represents a significant advance over traditional dipole modeling.The ultimate goal of our research is to characterize in both space and time areas of the brain participating in a given task or behavior. The integration of the spatial and temporal domains represents a unique challenge because of the existence of anatomically distinct processing regions that communicate across several time scales. Within the last decade,

Monte Carlo simulation studies of EEG and MEG localization accuracy

“…We began with a realistic sensor description of 122 MEG sensors [Knuutila et al, 1993], which is the same configuration that was used in our previous modeling study [Liu et al, 1998]. The 122 MEG sensors are placed at 61 discrete locations, with two orthogonal planar gradiometers at each location.…”

“…To quantify one aspect of the accuracy of the linear estimation technique, we used a crosstalk metric [Liu et al, 1998], which is similar to the averaging kernel of the Backus-Gilbert method [Backus and Gilbert, 1970]. The crosstalk metric describes the sensitivity of the estimate at a specified location to activity at other locations.…”

“…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.…”

“…The effect of sensor sampling density (i.e., number of sensors) was also examined. In addition, as it is becoming more common to utilize other spatial information, such as functional magnetic resonance imaging (fMRI), we also modeled the effect of fMRI spatial constraints on the relative accuracy of EEG and MEG source localization [Liu et al, 1998]. …”

“…It is more appropriate to define localization accuracy and resolution in terms of metrics that include the spatial aspect of the extended source distributions. Therefore, we quantified localization accuracy using a crosstalk metric [Liu et al, 1998] and a point spread metric that are specified by the resolution matrix [Grave de Peralta Menendez et al, 1996Grave de Peralta-Menendez and Gonzalez-Andino, 1998;Lutkenhoner and Grave de Peralta Menendez, 1997;Menke, 1989]. These two metrics provide complementary accuracy information.…”