MEG-SIM: a web portal for testing MEG analysis methods using realistic simulated and empirical data.

MEG and EEG measure electrophysiological activity in the brain with exquisite temporal resolution. Because of this unique strength relative to noninvasive hemodynamic-based measures (fMRI, PET), the complementary nature of hemodynamic and electrophysiological techniques is becoming more widely recognized (e.g., Human Connectome Project). However, the available analysis methods for solving the inverse problem for MEG and EEG have not been compared and standardized to the extent that they have for fMRI/PET. A number of factors, including the non-uniqueness of the solution to the inverse problem for MEG/EEG, have led to multiple analysis techniques which have not been tested on consistent datasets, making direct comparisons of techniques challenging (or impossible). Since each of the methods is known to have their own set of strengths and weaknesses, it would be beneficial to quantify them. Toward this end, we are announcing the establishment of a website containing an extensive series of realistic simulated data for testing purposes ( http://cobre.mrn.org/megsim/ ). Here, we present: 1) a brief overview of the basic types of inverse procedures; 2) the rationale and description of the testbed created; and 3) cases emphasizing functional connectivity (e.g., oscillatory activity) suitable for a wide assortment of analyses including independent component analysis (ICA), Granger Causality/Directed transfer function, and single-trial analysis.

Different strategies for auditory word recognition in healthy versus normal aging.

To explore the effects of commonly encountered pathology on auditory recognition strategies in elderly participants, magnetoencephalographic (MEG) brain activation patterns and performance were examined in 30 elderly [18 controls and 12 elderly with mild cognitive impairment (MCI) or probable Alzheimer's disease (AD)]. It was predicted that participants with known pathology would reveal different networks of brain activation, compared to healthy elderly, which should correlate with poorer performance. Participants heard a list of words representing common objects, twice. After 20 minutes a list of new and old words was presented and participants judged whether each word was heard earlier. MEG responses were analyzed using a semiautomated source modeling procedure. A cluster analysis using all subjects' MEG sources revealed three dominant patterns of activity which correlated with IQ and task performance. The highest performing group revealed activity in premotor, anterior temporal, and superior parietal lobes with little contribution from prefrontal cortex. Performance and brain activation patterns were also compared for individuals with or without abnormalities such as white matter hyperintensities and/or volume reduction evidenced on their MRIs. Memory performance and activation patterns for individuals with white matter hyperintensities resembled the group of MCI/AD patients. These results emphasize the following: (1) general pathology correlates with cognitive decline and (2) full characterization of the health of elderly participants is important in studies of normal aging since random samples from the elderly population are apt to include individuals with subclinical pathology that can affect cognitive performance.

Modelling the magnetic signature of neuronal tissue.

Neuronal communication in the brain involves electrochemical currents, which produce magnetic fields. Stimulus-evoked brain responses lead to changes in these fields and can be studied using magneto- and electro-encephalography (MEG/EEG). In this paper we model the spatiotemporal distribution of the magnetic field of a physiologically idealized but anatomically realistic neuron to assess the possibility of using magnetic resonance imaging (MRI) for directly mapping the neuronal currents in the human brain. Our results show that the magnetic field several centimeters from the centre of the neuron is well approximated by a dipole source, but the field close to the neuron is not, a finding particularly important for understanding the possible contrast mechanism underlying the use of MRI to detect and locate these currents. We discuss the importance of the spatiotemporal characteristics of the magnetic field in cortical tissue for evaluating and optimizing an experiment based on this mechanism and establish an upper bound for the expected MRI signal change due to stimulus-induced cortical response. Our simulations show that the expected change of the signal magnitude is 1.6% and its phase shift is 1 degrees . An unexpected finding of this work is that the cortical orientation with respect to the external magnetic field has little effect on the predicted MRI contrast. This encouraging result shows that magnetic resonance contrast directly based on the neuronal currents present in the cortex is theoretically a feasible imaging technique. MRI contrast generation based on neuronal currents depends on the dendritic architecture and we obtained high-resolution optical images of cortical tissue to discuss the spatial structure of the magnetic field in grey matter.

Modeling spatiotemporal covariance for magnetoencephalography or electroencephalography source analysis.

We propose a new model to approximate spatiotemporal noise covariance for use in neural electromagnetic source analysis, which better captures temporal variability in background activity. As with other existing formalisms, our model employs a Kronecker product of matrices representing temporal and spatial covariance. In our model, spatial components are allowed to have differing temporal covariances. Variability is represented as a series of Kronecker products of spatial component covariances and corresponding temporal covariances. Unlike previous attempts to model covariance through a sum of Kronecker products, our model is designed to have a computationally manageable inverse. Despite increased descriptive power, inversion of the model is fast, making it useful in source analysis. We have explored two versions of the model. One is estimated based on the assumption that spatial components of background noise have uncorrelated time courses. Another version, which gives closer approximation, is based on the assumption that time courses are statistically independent. The accuracy of the structural approximation is compared to an existing model, based on a single Kronecker product, using both Frobenius norm of the difference between spatiotemporal sample covariance and a model, and scatter plots. Performance of ours and previous models is compared in source analysis of a large number of single dipole problems with simulated time courses and with background from authentic magnetoencephalography data.

A generalized spatiotemporal covariance model for stationary background in analysis of MEG data.

Using a noise covariance model based on a single Kronecker product of spatial and temporal covariance in the spatiotemporal analysis of MEG data was demonstrated to provide improvement in the results over that of the commonly used diagonal noise covariance model. In this paper we present a model that is a generalization of all of the above models. It describes models based on a single Kronecker product of spatial and temporal covariance as well as more complicated multi-pair models together with any intermediate form expressed as a sum of Kronecker products of spatial component matrices of reduced rank and their corresponding temporal covariance matrices. The model provides a framework for controlling the tradeoff between the described complexity of the background and computational demand for the analysis using this model. Ways to estimate the value of the parameter controlling this tradeoff are also discussed.

MUSIC seeded multi-dipole MEG modeling using the Constrained Start Spatio-Temporal modeling procedure.

The Constrained Start Spatio-Temporal modeling program (CSST) is an objective multi-dipole, multi-start MEG/EEG analysis procedure that randomly selects from 100 to 100,000 initial dipole configurations, and runs a nonlinear simplex search on each of these configurations employing a reduced Chi-square statistic as the minimization criterion, to obtain a set of dipole configurations that best fit the data [Ranken, 2002]. A parallel version of CSST is implemented in IDL and MPI, making CSST usable on a single computer, or on a Linux cluster. We have now developed a multi-resolution version of MUSIC [Mosher, 1992] [Mosher, 1998] that provides an 80% or more reduction in the number of forward calculations needed to obtain results comparable to a 160,000 point MUSIC scan, on a 2 mm grid that defines a brain volume. The multi-resolution MUSIC scan provides an improved set of initial dipole estimates for the CSST analysis. In preliminary tests on real and simulated MEG data, with model orders ranging between 5 and 7 dipoles, the best performance improvements were obtained by mixing in 1 to 3 dipole locations randomly drawn from the best MUSIC locations, with randomly selected locations from the brain volume to complete the selected model order. We have also developed an improved method for sampling the brain volume for initial configurations. These improvements have led to a 75% reduction in the number of starting configurations required to obtain 5-10 best solutions with equal or lower reduced Chi-square values, when compared to the best solutions from the previous version of CSST.

A model for frequency dependence of conductivities of the live human skull.

A mathematical model (sigma(omega) approximately equal to A omega alpha, where, sigma is identical with conductivity, omega = 2 pi f is identical with applied frequency (Hz), A (amplitude) and alpha (unit less) is identical with search parameters) was used to fit the frequency dependence of electrical conductivities of compact, spongiosum, and bulk layers of the live and, subsequently, dead human skull samples. The results indicate that the fit of this model to the experimental data is excellent. The ranges of values of A and alpha were, spongiform (12.0-36.5, 0.0083-0.0549), the top compact (5.02-7.76, -0.137-0.0144), the lower compact (2.31-10.6, 0.0267-0.0452), and the bulk (7.46-10.6, 0.0133-0.0239). The respective values A and alpha for the respective layers of the dead skull samples were (40.1-89.7, -0.0017-0.0287), (5.53-14.5, -0.0296 - -0.0061), (4.58-15.9, -0.0226-0.0268), and (12.7-25.3, -0.0158-0.0132).

Multidipole analysis of simulated epileptic spikes with real background activity.

This simulated magnetoencephalographic study was designed to determine the variability in source parameters with real subject background activity when applying multidipole spatial-temporal dipole analyses, for which the correct model was compared with undermodeled and overmodeled cases. The simulated sources were created from patches of the cortical surface of each subject's MRI. One- and two-source frontal lobe spikes were generated in two cortical regions seen commonly in frontal lobe epilepsy patients tested at our site (orbital frontal and premotor cortex). In general, the modeling results were adequate for the correct model order and the correct model order plus one. In addition, if the localization error was less than 10 mm from the simulated source, the peak latency of the spike and orientation were very reliable, but the peak amplitude was not. The additional source in the overmodeled condition, on the other hand, was not localized reliably across the different epochs within subjects. The results suggest that consistency of the spike localization and inconsistency of other sources will allow one to determine reliably the appropriate model order in real data, and therefore determine single and multifocal spike generators.

Conductivities of three-layer live human skull.

Electrical conductivities of compact, spongiosum, and bulk layers of the live human skull were determined at varying frequencies and electric fields at room temperature using the four-electrode method. Current, at higher densities that occur in human cranium, was applied and withdrawn over the top and bottom surfaces of each sample and potential drop across different layers was measured. We used a model that considers variations in skull thicknesses to determine the conductivity of the tri-layer skull and its individual anatomical structures. The results indicate that the conductivities of the spongiform (16.2-41.1 milliS/m), the top compact (5.4-7.2 milliS/m) and lower compact (2.8-10.2 milliS/m) layers of the skull have significantly different and inhomogeneous conductivities. The conductivities of the skull layers are frequency dependent in the 10-90 Hz region and are non-ohmic in the 0.45-2.07 A/m2 region. These current densities are much higher than those occurring in human brain.

Conductivities of three-layer human skull.

In this study, electrical conductivities of compact, spongiosum, and bulk layers of cadaver skull were determined at varying electric fields at room temperature. Current was applied and withdrawn over the top and bottom surfaces of each sample and potential drop across different layers was measured using the four-electrode method. We developed a model, which considers of variations in skull thicknesses, to determine the conductivity of the tri-layer skull and its individual anatomical structures. The results indicate that the spongiform and the two compact layers of the skull have significantly different and inhomogeneous conductivities ranging from 0.76 +/- .14 to 11.5 +/- 1.8 milliS/m.

Single vs. paired visual stimulation: superposition of early neuromagnetic responses and retinotopy in extrastriate cortex in humans.

Neuromagnetic techniques were used in conjunction with magnetic resonance imaging (MRI) techniques to: (1) localize and characterize cortical sources evoked by visual stimuli presented at different locations in the lower right visual field; (2) examine the superposition of cortical responses by comparing the summation of responses to the presentation of single stimuli with responses to paired stimuli; and (3) examine the spatial resolution of magnetoencephalographic (MEG) techniques by comparing the identified source locations evoked by the presentation of single vs. paired stimuli. Using multi-dipole, non-linear minimization analyses, three sources were localized for each stimulus condition during the initial 80-170 ms poststimulus interval for all subjects. In addition to an occipital source, two extrastriate sources were identified: occipital-parietal and occipital-temporal. Each source evidenced a systematic shift in location associated with changes in stimulus placement parallel to the vertical meridian. To our knowledge, this is the first demonstration of retinotopic organization of extrastriate areas, using non-invasive neuromagnetic techniques. The paired presentation of stimuli reflected superposition of the responses evoked by single stimuli but only for early activity up to 150 ms poststimulus. Undersummation was evident after 150 ms. All sources identified for single stimuli were also identified in the paired-stimulus responses; but at the expense of larger errors for some of the estimated parameters.

Multi-start downhill simplex method for spatio-temporal source localization in magnetoencephalography.

A multi-start downhill simplex method is examined as a global minimization technique for fitting multidipole, spatio-temporal magnetoencephalography (MEG) data. This procedure has been performed on both simulated and empirical human visual data, known to exhibit complex field patterns due to multiple sources. Unlike some other non-linear fitting techniques the multi-start downhill simplex method does not require users to provide initial guesses for the dipole parameters, hence the fitting procedure is less time-consuming, more objective, and user-friendly. In addition, this method offers more than one adequate solution thus providing a range of uncertainty for the estimated parameters. The Multi-start downhill simplex method is used to fit the non-linear dipole spatial parameters, while the linear temporal parameters are fit using a separate linear fitting procedure. Singular value decomposition (SVD) is also used in order to improve the procedure for determining the adequate number of modeled dipoles.

Nonlinear analysis of biological systems using short M-sequences and sparse-stimulation techniques.

The m-sequence pseudorandom signal has been shown to be a more effective probing signal than traditional Gaussian white noise for studying nonlinear biological systems using cross-correlation techniques. The effectiveness is evidenced by the high signal-to-noise (S/N) ratio and speed of data acquisition. However, the "anomalies" that occur in the estimations of the cross-correlations represent an obstacle that prevents m-sequences from being more widely used for studying nonlinear systems. The sparse-stimulation method for measuring system kernels can help alleviate estimation errors caused by anomalies. In this paper, a "padded sparse-stimulation" method is evaluated, a modification of the "inserted sparse-stimulation" technique introduced by Sutter, along with a short m-sequence as a probing signal. Computer simulations show that both the "padded" and "inserted" methods can effectively eliminate the anomalies in the calculation of the second-order kernel, even when short m-sequences were used (length of 1023 for a binary m-sequence, and 728 for a ternary m-sequence). Preliminary experimental data from neuromagnetic studies of the human visual system are also presented, demonstrating that the system kernels can be measured with high signal-to-noise (S/N) ratios using short m-sequences.

Retinotopic organization of human visual cortex: departures from the classical model.

Retinotopic mapping strategies similar to those used for invasive electrophysiological studies to identify multiple visual areas in monkeys have been adapted for noninvasive studies in humans, using magnetic recordings of brain activity in conjunction with anatomical magnetic resonance imaging. The retinotopic organization of the primary visual area (V1) in the left hemisphere of human subjects was examined by presenting a small patterned stimuli near the vertical and horizontal meridians in the lower right visual field. In contrast with the classical model of V1 retinotopy, our results suggest that the representation of the horizontal meridian does not necessarily correspond in a one-to-one manner with the base of the calcarine fissure and that some lower field stimuli can activate regions in the lower bank of the fissure. The results also indicate significant individual variability in the details of how V1 maps around the calcarine fissure.

Mapping function in the human brain with magnetoencephalography, anatomical magnetic resonance imaging, and functional magnetic resonance imaging.

Integrated analyses of human anatomical and functional measurements offer a powerful paradigm for human brain mapping. Magnetoencephalography (MEG) and EEG provide excellent temporal resolution of neural population dynamics as well as capabilities for source localization. Anatomical magnetic resonance imaging (MRI) provides excellent spatial resolution of head and brain anatomy, whereas functional MRI (fMRI) techniques provide an alternative measure of neural activation based on associated hemodynamic changes. These methodologies constrain and complement each other and can thereby improve our interpretation of functional neural organization. We have developed a number of computational tools and techniques for the visualization, comparison, and integrated analysis of multiple neuroimaging techniques. Construction of geometric anatomical models from volumetric MRI data allows improved models of the head volume conductor and can provide powerful constraints for neural electromagnetic source modeling. These approaches, coupled to enhanced algorithmic strategies for the inverse problem, can significantly enhance the accuracy of source-localization procedures. We have begun to apply these techniques for studies of the functional organization of the human visual system. Such studies have demonstrated multiple, functionally distinct visual areas that can be resolved on the basis of their locations, temporal dynamics, and differential sensitivity to stimulus parameters. Our studies have also produced evidence of internal retinotopic organization in both striate and extrastriate visual areas but have disclosed organizational departures from classical models. Comparative studies of MEG and fMRI suggest a reasonable but imperfect correlation between electrophysiological and hemodynamic responses. We have demonstrated a method for the integrated analysis of fMRI and MEG, and we outline strategies for improvement of these methods. By combining multiple measurement techniques, we can exploit the complementary strengths and transcend the limitations of the individual neuro-imaging methods.

Evaluation of boundary element methods for the EEG forward problem: effect of linear interpolation.

We implement the approach for solving the boundary integral equation for the electroencephalography (EEG) forward problem proposed by de Munck [1], in which the electric potential varies linearly across each plane triangle of the mesh. Previous solutions have assumed the potential is constant across an element. We calculate the electric potential and systematically investigate the effect of different mesh choices and dipole locations by using a three concentric sphere head model for which there is an analytic solution. Implementing the linear interpolation approximation results in errors that are approximately half those of the same mesh when the potential is assumed to be constant, and provides a reliable method for solving the problem.