Mapping human brain function with MEG and EEG: methods and validation

We survey the field of magnetoencephalography (MEG) and electroencephalography (EEG) source estimation. These modalities offer the potential for functional brain mapping with temporal resolution in the millisecond range. However, the limited number of spatial measurements and the ill-posedness of the inverse problem present significant limits to our ability to produce accurate spatial maps from these data without imposing major restrictions on the form of the inverse solution. Here we describe approaches to solving the forward problem of computing the mapping from putative inverse solutions into the data space. We then describe the inverse problem in terms of low dimensional solutions, based on the equivalent current dipole (ECD), and high dimensional solutions, in which images of neural activation are constrained to the cerebral cortex. We also address the issue of objective assessment of the relative performance of inverse procedures by the free-response receiver operating characteristic (FROC) curve. We conclude with a discussion of methods for assessing statistical significance of experimental results through use of the bootstrap for determining confidence regions in dipole-fitting methods, and random field (RF) and permutation methods for detecting significant activation in cortically constrained imaging studies.     

The influence of brain tumor treatment on pathological delta activity in MEG

The goal of the MEG study was to investigate the influence of tumor treatment on pathological delta activity (1-4 Hz). The treatment consisted of neurosurgery, and in some of the patients, additional radiotherapy. MEG and MR recordings were made both before and after the treatment in 17 patients. The signal power in the delta frequency band was determined for each recording. The malignant tumors were associated with large tumor volumes. Furthermore, both malignant tumors and tumor volume were associated with high signal powers in the delta band, indicating a correlation of delta power with the severity of the lesions. In all patients with high grade tumors, the delta power was lower after the treatment. The sources underlying the delta signals were estimated with an automatic single dipole analysis method. Estimated sources were projected onto MR scans. Preoperatively 14 clusters of equivalent sources describing focal activity were found in 12 out of 17 patients. Thirteen of these clusters were located near the tumor, and one cluster near an edema border. The locations near tumors are plausible and suggest that in general the source estimation was reliable. After the operation, 13 such clusters were found in 12 patients. Eleven clusters were located near the lesion border and one cluster near the edema border. Furthermore a cluster contralateral to the lesion in the other hemisphere indicated that brain lesions can affect the functioning of more distant brain areas than just the peritumoral brain tissue. Of the 12 patients who had preoperatively peritumoral clusters, 11 patients had postoperatively perilesional sources. In these cases the shift in source locations was in general considerably smaller than the dimension of the preoperative tumors. This finding indicates that similar areas generate the pre- and postoperative delta activity. Furthermore, focal delta sources were found in a case without tumor recurrence, and also in cases that most tumor tissue was removed. These findings suggest that the pathology underlying the slow waves is not the presence of the tumor bulk but the structural damage done by the tumors on the surrounding white/gray matter.     

Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC

A number of brain imaging techniques have been developed in order to investigate brain function and to develop diagnostic tools for various brain disorders. Each modality has strengths as well as weaknesses compared to the others. Recent work has explored how multiple modalities can be integrated effectively so that they complement one another while maintaining their individual strengths. Bayesian inference employing Markov Chain Monte Carlo (MCMC) techniques provides a straightforward way to combine disparate forms of information while dealing with the uncertainty in each. In this paper we introduce methods of Bayesian inference as a way to integrate different forms of brain imaging data in a probabilistic framework. We formulate Bayesian integration of magnetoencephalography (MEG) data and functional magnetic resonance imaging (fMRI) data by incorporating fMRI data into a spatial prior. The usefulness and feasibility of the method are verified through testing with both simulated and empirical data.     

Localization of extended brain sources from EEG/MEG: the ExSo-MUSIC approach

We propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q ≥ 1). This method is an extension of the 2q-MUSIC (q ≥ 1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q ≥ 2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms.     

Diffusion tensor MRI-based estimation of the influence of brain tissue anisotropy on the effects of transcranial magnetic stimulation

We evaluate and discuss the relevance of fiber anisotropy in estimating the effect of transcranial magnetic stimulation (TMS) on the human brain. Finite element simulations were carried out on a three-dimensional model of the head that included anisotropic conductivity information derived from diffusion tensor imaging (DTI). The results show that anisotropy has minor effects both on the position of the main locus of activation and on its intensity. It has considerably more effect on the spatial distribution of the induced electric field, yielding differences of the order of 10% of the maximum induced field. Moreover the area affected by magnetic stimulation is slightly larger when we include fiber anisotropy in the calculations than in an isotropic model. We also show that the induced field observed in the anisotropic model does not always align with the local fiber orientation but rather follows specific patterns of parallelity. These findings will help to improve the estimation of the areas involved in magnetic stimulation.     

Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: a simulation and visualization study using high-resolution finite element modeling

To achieve a deeper understanding of the brain, scientists, and clinicians use electroencephalography (EEG) and magnetoencephalography (MEG) inverse methods to reconstruct sources in the cortical sheet of the human brain. The influence of structural and electrical anisotropy in both the skull and the white matter on the EEG and MEG source reconstruction is not well understood. In this paper, we report on a study of the sensitivity to tissue anisotropy of the EEG/MEG forward problem for deep and superficial neocortical sources with differing orientation components in an anatomically accurate model of the human head. The goal of the study was to gain insight into the effect of anisotropy of skull and white matter conductivity through the visualization of field distributions, isopotential surfaces, and return current flow and through statistical error measures. One implicit premise of the study is that factors that affect the accuracy of the forward solution will have at least as strong an influence over solutions to the associated inverse problem. Major findings of the study include (1) anisotropic white matter conductivity causes return currents to flow in directions parallel to the white matter fiber tracts; (2) skull anisotropy has a smearing effect on the forward potential computation; and (3) the deeper a source lies and the more it is surrounded by anisotropic tissue, the larger the influence of this anisotropy on the resulting electric and magnetic fields. Therefore, for the EEG, the presence of tissue anisotropy both for the skull and white matter compartment substantially compromises the forward potential computation and as a consequence, the inverse source reconstruction. In contrast, for the MEG, only the anisotropy of the white matter compartment has a significant effect. Finally, return currents with high amplitudes were found in the highly conducting cerebrospinal fluid compartment, underscoring the need for accurate modeling of this space.     

Time-coherent expansion of MEG/EEG cortical sources

In this study we estimated the spatial extent of cortical areas of time-coherent activity using the inverse problem in magneto/electroencephalography (MEEG). The model discussed here uses classical regularization tools in order to force the inverse solution to be piecewise coherent. First, the cortex was seeded by focal dipolar sources. Then, a time-coherent expansion (TCE) onto the cortical surface was performed in order to obtain surface source models composed of patches with uniform current density. Patches represent extended cortical regions with one single time course per active area. Results obtained from synthetic data show that using the TCE method is relevant even with a low signal-to-noise ratio, although the final estimation is often slightly biased. We applied the TCE method to evoked magnetic fields obtained after electrical stimulation of fingers in order to estimate the somatotopic cortical maps of the primary somatosensory cortex.     

Simultaneous MEG and intracranial EEG recordings during attentive reading

The relationship between neural oscillations recorded at various spatial scales remains poorly understood partly due to an overall dearth of studies utilizing simultaneous measurements. In an effort to study quantitative markers of attention during reading, we performed simultaneous magnetoencephalography (MEG) and intracranial electroencephalography (iEEG) recordings in four epileptic patients. Patients were asked to attend to a specific color when presented with an intermixed series of red words and green words, with words of a given color forming a cohesive story. We analyzed alpha, beta, and gamma band oscillatory responses to the word presentation and compared the strength and spatial organization of those responses in both electrophysiological recordings. Time-frequency analysis of iEEG revealed a network of clear attention-modulated high gamma band (50–150 Hz) power increases and alpha/beta (9–25 Hz) suppressions in response to the words. In addition to analyses at the sensor level, MEG time-frequency analysis was performed at the source level using a sliding window beamformer technique. Strong alpha/beta suppressions were observed in MEG reconstructions, in tandem with iEEG effects. While the MEG counterpart of high gamma band enhancement was difficult to interpret at the sensor level in two patients, MEG time-frequency source reconstruction revealed additional activation patterns in accordance with iEEG results. Importantly, iEEG allowed us to confirm that several sources of gamma band modulation observed with MEG were indeed of cortical origin rather than EMG muscular or ocular artifact.     

The penetration of ceftriaxone into human brain tissue

The cerebral penetration of ceftriaxone in patients who underwent surgery for cerebral tumours was investigated. Seventeen patients received 2 g of ceftriaxone given intravenously 2 to 13 h before blood and brain samples were taken. Antibiotic levels were determined by an agar-well diffusion method. Cerebral ceftriaxone concentrations ranged from 0.3 to 12 micrograms/g, with a mean value of 1.63 micrograms/g. These values were less than 2% of corresponding serum concentrations, but enough to inhibit 75% of bacterial strains recently isolated from brain abscesses in our unit.     

Mechanisms of evoked and induced responses in MEG/EEG

Cortical responses, recorded by electroencephalography and magnetoencephalography, can be characterized in the time domain, to study event-related potentials/fields, or in the time-frequency domain, to study oscillatory activity. In the literature, there is a common conception that evoked, induced, and on-going oscillations reflect different neuronal processes and mechanisms. In this work, we consider the relationship between the mechanisms generating neuronal transients and how they are expressed in terms of evoked and induced power. This relationship is addressed using a neuronally realistic model of interacting neuronal subpopulations. Neuronal transients were generated by changing neuronal input (a dynamic mechanism) or by perturbing the systems coupling parameters (a structural mechanism) to produce induced responses. By applying conventional time-frequency analyses, we show that, in contradistinction to common conceptions, induced and evoked oscillations are perhaps more related than previously reported. Specifically, structural mechanisms normally associated with induced responses can be expressed in evoked power. Conversely, dynamic mechanisms posited for evoked responses can induce responses, if there is variation in neuronal input. We conclude, it may be better to consider evoked responses as the results of mixed dynamic and structural effects. We introduce adjusted power to complement induced power. Adjusted power is unaffected by trial-to-trial variations in input and can be attributed to structural perturbations without ambiguity.     

Decoding word and category-specific spatiotemporal representations from MEG and EEG

The organization and localization of lexico-semantic information in the brain has been debated for many years. Specifically, lesion and imaging studies have attempted to map the brain areas representing living versus nonliving objects, however, results remain variable. This may be due, in part, to the fact that the univariate statistical mapping analyses used to detect these brain areas are typically insensitive to subtle, but widespread, effects. Decoding techniques, on the other hand, allow for a powerful multivariate analysis of multichannel neural data. In this study, we utilize machine-learning algorithms to first demonstrate that semantic category, as well as individual words, can be decoded from EEG and MEG recordings of subjects performing a language task. Mean accuracies of 76% (chance=50%) and 83% (chance=20%) were obtained for the decoding of living vs. nonliving category or individual words respectively. Furthermore, we utilize this decoding analysis to demonstrate that the representations of words and semantic category are highly distributed both spatially and temporally. In particular, bilateral anterior temporal, bilateral inferior frontal, and left inferior temporal-occipital sensors are most important for discrimination. Successful intersubject and intermodality decoding shows that semantic representations between stimulus modalities and individuals are reasonably consistent. These results suggest that both word and category-specific information are present in extracranially recorded neural activity and that these representations may be more distributed, both spatially and temporally, than previous studies suggest.     

Dynamic causal modeling of evoked responses in EEG and MEG

Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged. In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework.     

Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space

The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specifically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a finger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data.     

Geometrical interpretation of fMRI-guided MEG/EEG inverse estimates

Magneto- and electroencephalography (MEG/EEG) and functional magnetic resonance imaging (fMRI) provide complementary information about the functional organization of the human brain. An important advantage of MEG/EEG is the millisecond time resolution in detecting electrical activity in the cerebral cortex. The interpretation of MEG/EEG signals, however, is limited by the difficulty of determining the spatial distribution of the neural activity. Functional MRI can help in the MEG/EEG source analysis by suggesting likely locations of activity. We present a geometric interpretation of fMRI-guided inverse solutions in which the MEG/EEG source estimate minimizes a distance to a subspace defined by the fMRI data. In this subspace regularization (SSR) approach, the fMRI bias does not assume preferred amplitudes for MEG/EEG sources, only locations. Characteristic dependence of the source estimates on the regularization parameters is illustrated with simulations. When the fMRI locations match the true MEG/EEG source locations, they serve to bias the underdetermined MEG/EEG inverse solution toward the fMRI loci. Importantly, when the fMRI loci do not match the true MEG/EEG loci, the solution is insensitive to those fMRI loci.     

Grouping of MEG gamma oscillations by EEG sleep spindles

Studies have revealed an association between EEG sleep spindles and processing of memories during sleep. Here we investigated whether there is a temporal relation between sleep spindles and MEG oscillatory activity in the gamma frequency band (> 30 Hz) which is considered to reflect local cortical processing of memory representations. MEG and simultaneous EEG (at Cz) were obtained in subjects during sleep together with standard polysomnography. As expected EEG spindles were correlated with power increases in MEG spindle (12.5-15.5 Hz) power mainly over prefrontal and occipital cortical areas. During EEG spindles we revealed both transient significant increases and decreases in MEG power, with decreases occurring significantly more often than increases. The modulations in gamma power occurred mainly at sites of increased MEG spindle power, and more often during peaks than troughs within the EEG spindle cycle. Cross-frequency coherence analyses confirmed a strong phase-coupling of gamma band activity with the spindle rhythm. The findings are consistent with the idea that spindles provide a fine-tuned temporal frame for integrated cortical memory processing during sleep.     

A Multivariate Analysis of Evoked Responses in EEG and MEG Data

This paper presents a multivariate analysis of evoked responses and their spatiotemporal dynamics as measured with electro- or magnetoencephalography. This analysis uses standard techniques (ManCova) to make possible statistical inference about differential responses, after the data have been transformed using singular value decomposition. The generality of this approach is limited only by the assumptions implicit in the general linear model and can range from simple analyses like Hotelling'sT²test (in comparing evoked responses among different conditions) to complex analyses of a multivariate regression type (e.g., characterizing the response components associated with a behavioral or psychophysical parameter). To illustrate the technique we have characterized time-dependent changes (both within and between trials) in magnetic fields, evoked by self-paced movements. Our illustrative analysis showed that movement-evoked components were less prone to adaptation than premovement components, suggesting that functionally distinct (preparatory and early executive) biomagnetic signals show differential adaptation.     

The effect of poliomyelitis virus on human brain cells in tissue culture

Poliomyelitis virus I, Mahoney strain, affected human brain cells grown in tissue cultures usually causing death of the cells in 3 days. The neurons reacted in different ways to the virus, some died with their neurites extended, others contracted one or more of their neurites. Terminal bulbs were frequently formed at the tips of the neurites when they were being drawn into the cell body. The final contraction of the cell body and the change into a mass of granules were often very sudden. Vacuoles often developed in the neuron. There was no recovery. Astrocytes, oligodendroglia, and macrophages were affected by the virus but not as quickly as the neurons. The age of the tissue culture was not a factor when the cells were in good condition. The age of the individual donor of the brain tissue was a factor; the fetal brain cells appeared to be more sensitive to the virus than the adult brain cells. The fetal neurons often reacted ½ hour after inoculation while the adult neurons reacted more slowly, 2 to 24 hours after inoculation. All these changes seemed to be caused by virus infection because they were prevented by specific antiserum or by preheating the virus.     

Magnetic susceptibility anisotropy of human brain in vivo and its molecular underpinnings

Frequency shift of gradient-echo MRI provides valuable information for assessing brain tissues. Recent studies suggest that the frequency and susceptibility contrast depend on white matter fiber orientation. However, the molecular underpinning of the orientation dependence is unclear. In this study, we investigated the orientation dependence of susceptibility of human brain in vivo and mouse brains ex vivo. The source of susceptibility anisotropy in white matter is likely to be myelin as evidenced by the loss of anisotropy in the dysmyelinating shiverer mouse brain. A biophysical model is developed to investigate the effect of the molecular susceptibility anisotropy of myelin components, especially myelin lipids, on the bulk anisotropy observed by MRI. This model provides a consistent interpretation of the orientation dependence of macroscopic magnetic susceptibility in normal mouse brain ex vivo and human brain in vivo and the microscopic origin of anisotropic susceptibility. It is predicted by the theoretical model and illustrated by the experimental data that the magnetic susceptibility of the white matter is least diamagnetic along the fiber direction. This relationship allows an efficient extraction of fiber orientation using susceptibility tensor imaging. These results suggest that anisotropy on the molecular level can be observed on the macroscopic level when the molecules are aligned in a highly ordered manner. Similar to the utilization of magnetic susceptibility anisotropy in elucidating molecular structures, imaging magnetic susceptibility anisotropy may also provide a useful tool for elucidating the microstructure of ordered biological tissues.