Application of multi-source minimum variance beamformers for reconstruction of correlated neural activity

Linearly constrained minimum variance beamformers are highly effective for analysis of weakly correlated brain activity, but their performance degrades when correlations become significant. Multiple constrained minimum variance (MCMV) beamformers are insensitive to source correlations but require a priori information about the source locations. Besides the question whether unbiased estimates of source positions and orientations can be obtained remained unanswered. In this work, we derive MCMV-based source localizers that can be applied to both induced and evoked brain activity. They may be regarded as a generalization of scalar minimum-variance beamformers for the case of multiple correlated sources. We show that for arbitrary noise covariance these beamformers provide simultaneous unbiased estimates of multiple source positions and orientations and remain bounded at singular points. We also propose an iterative search algorithm that makes it possible to find sources approximately without a priori assumptions about their locations and orientations. Simulations and analyses of real MEG data demonstrate that presented approach is superior to traditional single-source beamformers in situations where correlations between the sources are significant.     

Low- and high-frequency evoked responses following pattern reversal stimuli: a MEG study supported by fMRI constraint

We investigated the neural generators of N1 and P1 components of visual magnetic responses through the concomitant study of low (1-15 Hz)- and high (15-30 Hz)-frequency brain activities phase-locked to stimulus and elicited by pattern reversal visual stimuli. Whole helmet magnetic recordings and dipole modeling technique with support of functional magnetic resonance imaging (fMRI) were used to characterize locations and orientations of N1 and P1 sources as a function of four stimulated visual field quadrants. A comparison between low- and high-frequency activities revealed fundamental differences among orientations of the quadrants dipoles thus suggesting partly distinct neural populations underlying low- and high-frequency responses to transient contrast visual stimuli. Moreover, for both low- and high-frequency bands the specific study of locations and orientations of N1 and P1 sources indicated V1/V2 cortex as the neural substrate generating the two components. In summary, we provided strong support for a cortical genesis of human oscillatory mass activity following transient contrast stimuli with specific neural districts active in the low- and high-frequency bands. The converging results obtained from the concomitant investigation of probably different brain activities provided new evidences for a striate genesis of N1 and P1 components of the broadband visual-evoked responses following pattern reversal.     

Investigations of dipole localization accuracy in MEG using the bootstrap

We describe the use of the nonparametric bootstrap to investigate the accuracy of current dipole localization from magnetoencephalography (MEG) studies of event-related neural activity. The bootstrap is well suited to the analysis of event-related MEG data since the experiments are repeated tens or even hundreds of times and averaged to achieve acceptable signal-to-noise ratios (SNRs). The set of repetitions or epochs can be viewed as a set of independent realizations of the brain's response to the experiment. Bootstrap resamples can be generated by sampling with replacement from these epochs and averaging. In this study, we applied the bootstrap resampling technique to MEG data from somatotopic experimental and simulated data. Four fingers of the right and left hand of a healthy subject were electrically stimulated, and about 400 trials per stimulation were recorded and averaged in order to measure the somatotopic mapping of the fingers in the S1 area of the brain. Based on single-trial recordings for each finger we performed 5000 bootstrap resamples. We reconstructed dipoles from these resampled averages using the Recursively Applied and Projected (RAP)-MUSIC source localization algorithm. We also performed a simulation for two dipolar sources with overlapping time courses embedded in realistic background brain activity generated using the prestimulus segments of the somatotopic data. To find correspondences between multiple sources in each bootstrap, sample dipoles with similar time series and forward fields were assumed to represent the same source. These dipoles were then clustered by a Gaussian Mixture Model (GMM) clustering algorithm using their combined normalized time series and topographies as feature vectors. The mean and standard deviation of the dipole position and the dipole time series in each cluster were computed to provide estimates of the accuracy of the reconstructed source locations and time series.     

EEG source analysis and fMRI reveal two electrical sources in the fronto-parietal operculum during subepidermal finger stimulation

Using functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) source dipole analysis in 10 normal subjects, two electrical source dipoles in the contralateral fronto-parietal operculum were identified during repetitive painful subepidermal stimulation of the right index finger. The anterior source dipole peaking at 79 +/- 8 ms (mean +/- SD) was located in the frontal operculum, and oriented tangentially toward the cortical surface. The posterior source dipole peaking at 118 +/- 12 ms was located in the upper bank of the Sylvian fissure corresponding to the second somatosensory cortex (S2). The orientations of the posterior source dipoles displayed large variability, but differed significantly (P < 0.05) from the orientations of the anterior source dipoles. Electrical sources and fMRI clusters were also observed in ipsilateral fronto-parietal operculum. However, due to low signal-to-noise ratio of ipsilateral EEG sources in individual recordings, separation of sources into anterior and posterior clusters was not performed. Combined fMRI and source dipole EEG analysis of individual data suggests the presence of two distinct electrical sources in the fronto-parietal operculum participating in processing of somatosensory stimuli. The anterior region of the fronto-parietal operculum shows earlier peak activation than the posterior region.     

Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data

We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. Both methods localize source activity using a linear mixture of temporal basis functions (TBFs) learned from the data. In contrast to existing methods that use predetermined TBFs, we compute TBFs from data using a graphical factor analysis based model [Nagarajan, S.S., Attias, H.T., Hild, K.E., Sekihara, K., 2007a. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26, 3886-3910], which separates evoked or event-related source activity from ongoing spontaneous background brain activity. Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each voxel. We explicitly model, with two different robust parameterizations, the contribution from signals outside a voxel of interest. The two models differ in a trade-off of computational speed versus accuracy of learning the unknown interference contributions. Performance in simulations and real data, both with large noise and interference and/or correlated sources, demonstrates significant improvement over existing source localization methods.     

Phantom Validation of Multichannel Magnetocardiography Source Localization

FENICI, R., et al .: Phantom Validation of Multichannel Magnetocardiography Source Localization. Multichannel magnetocardiography (MMCG) is used clinically for noninvasive localization of the site of origin of cardiac arrhythmias. However, its accuracy in unshielded environments is still unknown. The aim of this study was to test the accuracy of three-dimensional localization of intracardiac sources by means of MMCG in an unshielded catheterization laboratory using a saline-filled phantom, together with a nonmagnetic catheter designed for multiple monophasic action potential recordings in a clinical setting. A nine-channel direct current superconducting quantum interference device (DC-SQUID) system (sensitivity fT/Hz^0.5) was used for MMCG from 36 points in a measuring area of 20 × 20 cm. The artificial sources to be localized were dipoles embedded in the distal end of the catheter, placed 12 cm below the sensor's plane. Equivalent current dipoles, effective magnetic dipoles, and distributed currents models were used for the inverse solution. The localization error was estimated as the three-dimensional difference between the physical position of the tip of the catheter and the three-dimensional localization of the dipoles derived by means of the inverse solution calculated from MMCG data. The reproducibility was tested by repeating the MMCG after repositioning the phantom and the measurement system. The average location error of the catheter dipole was   9 ± 4 mm   and was due primarily to imprecise depth estimation. Localization was reproducible within 0.73 mm. The distributed currents model provided an accurate image of current distribution centered over the catheter tip. The authors conclude that MMCG estimation is accurate enough to guarantee proper localization of cardiac dipolar sources even in an unshielded clinical electrophysiological laboratory. (PACE 2003; 26[Pt. II]:426–430)     

Reconstruction of correlated brain activity with adaptive spatial filters in MEG

Adaptive spatial filters (beamformers) have gained popularity as an effective method for the localization of brain activity from magnetoencephalography (MEG) data. Among the attractive features of some beamforming methods are high spatial resolution and no localization bias even in the presence of random noise. A drawback common to all beamforming methods, however, is significant degradation in performance in the presence of sources with high temporal correlations. Using numerical simulations and examples of auditory and visual evoked field responses, we demonstrate that, at typical signal-to-noise levels, the complete attenuation of fully correlated brain activity is less likely to occur, although significant localization and amplitude biases may occur. We compared various methods for correcting these biases and found the coherent source suppression model (CSSM) (Dalal et al., 2006) to be the most effective, with small biases for widely separated sources (e.g., bilateral auditory areas), however, amplitude biases increased systematically as distance between the sources was decreased. We assessed the performance and systematic biases that may result from the use of this model, and confirmed our findings with real examples of correlated brain activity in bilateral occipital and inferior temporal areas evoked by visually presented faces in a group of 21 adults. We demonstrated the ability to localize source activity in both regions, including correlated sources that are in close proximity (～ 3 cm) in bilateral primary visual cortex when using a priori information regarding source location. We conclude that CSSM, when carefully applied, can significantly improve localization accuracy, although amplitude biases may remain.     

Measuring directional coupling between EEG sources

Directional connectivity in the brain has been typically computed between scalp electroencephalographic (EEG) signals, neglecting the fact that correlations between scalp measurements are partly caused by electrical conduction through the head volume. Although recently proposed techniques are able to identify causality relationships between EEG sources rather than between recording sites, most of them need a priori assumptions about the cerebral regions involved in the EEG generation. We present a novel methodology based on multivariate autoregressive (MVAR) modeling and Independent Component Analysis (ICA) able to determine the temporal activation of the intracerebral EEG sources as well as their approximate locations. The direction of synaptic flow between these EEG sources is then estimated using the directed transfer function (DTF), and the significance of directional coupling strength evaluated with surrogated data. The reliability of this approach was assessed with simulations manipulating the number of data samples, the depth and orientation of the equivalent source dipoles, the presence of different noise sources, and the violation of the non-Gaussianity assumption inherent to the proposed technique. The simulations showed the superior accuracy of the proposed approach over other traditional techniques in most tested scenarios. Its validity was also evaluated analyzing the generation mechanisms of the EEG-alpha rhythm recorded from 20 volunteers under resting conditions. Results suggested that the major generation mechanism underlying EEG-alpha oscillations consists of a strong bidirectional feedback between thalamus and cuneus. The precuneus also seemed to actively participate in the generation of the alpha rhythm although it did not exert a significant causal influence neither on the thalamus nor on the cuneus. All together, these results suggest that the proposed methodology is a promising non-invasive approach for studying directional coupling between mutually interconnected neural populations.     

Opercular to interhemispheric source distribution of benign rolandic spikes of childhood

We evaluated the source distribution of benign rolandic spikes of childhood along and across the central sulcus in 15 patients, aged between 7 and 15 years, who suffered from seizure disorders. Previous routine EEG showed centrotemporal spikes, but none of them had major abnormalities on brain magnetic resonance imaging or neurological deficits. The equivalent current dipoles (ECDs) of the spikes measured by whole-head magnetoencephalography (MEG) were compared to the spike distributions detected by simultaneous scalp EEG according to the international 10-20 system. Locations and orientations of the MEG spikes corresponded to the EEG spike distribution as follows: superiorly oriented spike MEG dipoles in the opercular area corresponded to T3/4 negative peaks (8 spike groups in 6 patients); anteriorly oriented spike dipoles in the rolandic area corresponded to C3/4 or P3/4 negative peaks (17 spike groups in 13 patients); laterally oriented spike dipoles in the interhemispheric area corresponded to Cz/Pz negative peaks (4 spike groups in 3 patients); and others (4 spike groups in 4 patients). Rolandic spikes include three main types according to the ECD location from the opercular to the interhemispheric areas. The functional anatomy of benign rolandic spikes was correlated with partial seizure semiology. All three rolandic spike types can be explained by a precentral origin, assuming that the surface negative potential is continuous from the gyral to fissural cortices.     

A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data

We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian methods and by exploiting knowledge about their timing and spatial covariance properties. Full posterior distributions are computed rather than just the MAP estimates. In simulation, the algorithm can accurately localize and estimate the time courses of several simultaneously active dipoles, with rotating or fixed orientation, at noise levels typical for averaged MEG data. The algorithm even performs reasonably at noise levels typical of an average of just a few trials. The algorithm is superior to beamforming techniques, which we show to be an approximation to our graphical model, in estimation of temporally correlated sources. Success of this algorithm using MEG data for localizing bilateral auditory cortex, low-SNR somatosensory activations, and for localizing an epileptic spike source are also demonstrated.     

Generators of movement-related cortical potentials: fMRI-constrained EEG dipole source analysis

To clarify the precise location and timing of the mo tor cortical activation in voluntary movement, dipole source analysis integrating multiple constraints wa conducted for the movement-related cortical potentia (MRCP). Six healthy subjects performed single self paced extensions of the right index finger at about 15-intervals during EEG and event-related fMRI acquisi tions. EEG was recorded from 58 scalp electrodes, and fMRI of the entire brain was obtained every 2.6 s. Coordinates of the two methods were coregistered us ing anatomical landmarks. During dipole source mod eling, a realistic three-layer head model was used as a volume conductor. To identify the number of uncorre lated source s in the MRCP, principal component (PC analysis was performed, which was consistent with the existence of six sources in the left (Lt SM1) and right (Rt SMI) sensorimotor and medial frontocentral (MFC) areas. After dipoles were seeded at the acti vated spots revealed by fMRI, dipole orientations were fixed based on the interpretation of the topography of distribution of the PC. The strength of the six dipoles (three dipoles in Lt SMI, two in Rt SMI, and one in MFC) was then computed over time. Within the bilat eral SM1, activation of the precentral gyrus occurs bilaterally with similar strength from -1.2 s, followed by that of the precentral bank from -0.5 s with con tralateral preponderance. Subsequently, the postcen tral bank becomes active only on the contralateral side at 0.1 s after movement. Activation of the MFC shows timing similar to that of the bilateral precentral gyri These deduced patterns of activation are consis tent with previous studies of electrocorticography in humans.     

Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer

Beamformer spatial filters are commonly used to explore the active neuronal sources underlying magnetoencephalography (MEG) recordings at low signal-to-noise ratio (SNR). Conventional beamformer techniques are successful in localizing uncorrelated neuronal sources under poor SNR conditions. However, the spatial and temporal features from conventional beamformer reconstructions suffer when sources are correlated, which is a common and important property of real neuronal networks. Dual-beamformer techniques, originally developed by Brookes et al. to deal with this limitation, successfully localize highly-correlated sources and determine their orientations and weightings, but their performance degrades at low correlations. They also lack the capability to produce individual time courses and therefore cannot quantify source correlation. In this paper, we present an enhanced formulation of our earlier dual-core beamformer (DCBF) approach that reconstructs individual source time courses and their correlations. Through computer simulations, we show that the enhanced DCBF (eDCBF) consistently and accurately models dual-source activity regardless of the correlation strength. Simulations also show that a multi-core extension of eDCBF effectively handles the presence of additional correlated sources. In a human auditory task, we further demonstrate that eDCBF accurately reconstructs left and right auditory temporal responses and their correlations. Spatial resolution and source localization strategies corresponding to different measures within the eDCBF framework are also discussed. In summary, eDCBF accurately reconstructs source spatio-temporal behavior, providing a means for characterizing complex neuronal networks and their communication.     

Relationship of brain glutamine and brain neutral amino acid concentrations after portacaval anastomosis in rats

Evidence from several sources suggest that blood-brain transport of the large neutral amino acids (NAA) is abnormal in animals with a portacaval anastomosis (PCA) and in patients with liver cirrhosis and portal-systemic shunting and encephalopathy, but the underlying mechanisms are unknown. After PCA, the concentration of glutamine (Gln) in brain is markedly increased as a by-product of cerebral ammonia detoxification, and the rate of efflux of Gln from brain is also increased. The following studies were undertaken to clarify the relationships among plasma and brain concentrations of NAA after PCA in rats and to examine the relationship of brain Gln concentration to plasma and brain NAA concentrations. After PCA plasma phenylalanine, tyrosine and histidine were elevated and leucine, isoleucine and valine were lowered. In brain, phenylalanine, tyrosine, histidine and methionine were markedly elevated after PCA and their concentrations in brain far exceeded the concentrations in plasma. Analyses of single, partial and multiple correlations of plasma NAA ratios expressed as plasma competitor function (PCF), brain NAA and brain Gln showed significant correlations between PCF nd brain NAA in shunted rats. A better correlation was found between brain NAA and brain Gln. Correlation coefficients obtained from multiple correlation analysis equalled or exceeded those obtained in the partial correlation or in the single correlation, suggesting that the effects of PCF and brain Gln on brain NAA were separate and additive. Gln was shown to compete with other NAA for blood brain transport by inhibiting brain 14C phenylalanine uptake.(ABSTRACT TRUNCATED AT 250 WORDS)     

An fMRI-constrained MEG source analysis with procedures for dividing and grouping activation

To analyze neural activity using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), we developed a method for fixing equivalent current dipoles of MEG in activation areas of fMRI. It includes a procedure for dividing large fMRI activation volumes into subvolumes in each of which a dipole is placed and another procedure for grouping neighboring dipoles whose temporal changes are inseparable based on MEG data. To optimize the procedures' parameters, we carried out simulations and found that (1) any single dipole within 10 mm from a true source can explain MEG data with a correlation of 94% on average for the low signal-to-noise ratio of 3 and (2) a neighboring dipole within a few tens of millimeters from the dipole nearest to the true source tends to be highly incorporated in explaining MEG data. We applied the method to data measured in a language experiment and detected 13 significant sources. The results show that the present method is promising for detecting neural activity originating from a number of separate neural sources.     

Dynamical causal modelling for M/EEG: spatial and temporal symmetry constraints

We describe the use of spatial and temporal constraints in dynamic causal modelling (DCM) of magneto- and electroencephalography (M/EEG) data. DCM for M/EEG is based on a spatiotemporal, generative model of electromagnetic brain activity. The temporal dynamics are described by neural-mass models of equivalent current dipole (ECD) sources and their spatial expression is modelled by parameterized lead-field functions. Often, in classical ECD models, symmetry constraints are used to model homologous pairs of dipoles in both hemispheres. These constraints are motivated by assumptions about symmetric activation of bilateral sensory sources. In classical approaches, these constraints are 'hard'; i.e. the parameters of homologous dipoles are shared. Here, in the context of DCM, we illustrate the use of informed Bayesian priors to implement 'soft' symmetry constraints that are expressed in the posterior estimates only when supported by the data. Critically, with DCM one can deploy symmetry constraints in either the temporal or spatial components of the model. This enables one to test for symmetry in temporal (neural-mass) parameters in the presence of non-symmetric spatial expressions of homologous sources (and vice versa). Furthermore, we demonstrate that Bayesian model comparison can be used to identify the best models among a range of symmetric and non-symmetric variants. Our main finding is that the use of 'soft' symmetry priors is recommended for evoked responses to bilateral sensory input. We illustrate the use of symmetry constraints in DCM on synthetic and real EEG data.     

Sources of interference in the use of 2,3-diaminonaphthalene for the fluorimetric determination of nitric oxide synthase activity in biological samples

The use of 2,3-diaminonaphthalene (DAN) for the fluorimetric determination of nitric oxide synthase (NOS) activity in rat brain extracts has been re-examined. Two types of interference were observed, due either to components of the reaction mixture or to the enzymatic sample itself. One of the substrates (NADPH) and some cofactors (FADH(2), FMNH(2)) required for the enzyme activity interfere in the assay by quenching the fluorescence produced. Interference was minimized by using lower FADH(2), FMNH(2) and NADPH concentrations (1 micromol/l) and a NADPH recycling system in the reaction mixture. The addition of bovine serum albumin or hemoglobin to the sample quenched fluorescence intensity, but these protein interferences could be reduced by filtering the samples after reaction. We conclude that the DAN fluorimetric assay as originally described is not suitable for the determination of NOS activity in crude extracts such as rat brain cytosolic fraction, due to the presence of interfering substances. Nevertheless, DAN could be used for the determination of enzyme activity after reducing protein interference by filtering, or in less complex samples such as cell cultures (e.g. activated macrophages), or in chromatographic fractions obtained during the purification of the enzyme. A careful use of the commercial kits based on the use of DAN for the determination of NOS activity is recommended.     

Common and unique components of response inhibition revealed by fMRI

The ability to inhibit inappropriate responses is central to cognitive control, but whether the same brain mechanisms mediate inhibition across different tasks is not known. We present evidence for a common set of frontal and parietal regions engaged in response inhibition across three tasks: a go/no-go task, a flanker task, and a stimulus-response compatibility task. Regions included bilateral anterior insula/frontal operculum and anterior prefrontal, right dorsolateral and premotor, and parietal cortices. Insula activity was positively correlated with interference costs in behavioral performance in each task. Principal components analysis showed a coherent pattern of individual differences in these regions that was also positively correlated with performance in all three tasks. However, correlations among tasks were low, for both brain activity and performance. We suggest that common interference detection and/or resolution mechanisms are engaged across tasks, and that inter-task correlations in behavioral performance are low because they conflate measurements of common mechanisms with measurements of individual biases unique to each task.     

EEG correlates of haptic feedback in a visuomotor tracking task

This study investigates the temporal brain dynamics associated with haptic feedback in a visuomotor tracking task. Haptic feedback with deviation-related forces was used throughout tracking experiments in which subjects' behavioral responses and electroencephalogram (EEG) data were simultaneously measured. Independent component analysis was employed to decompose the acquired EEG signals into temporally independent time courses arising from distinct brain sources. Clustering analysis was used to extract independent components that were comparable across participants. The resultant independent brain processes were further analyzed via time-frequency analysis (event-related spectral perturbation) and event-related coherence (ERCOH) to contrast brain activity during tracking experiments with or without haptic feedback. Across subjects, in epochs with haptic feedback, components with equivalent dipoles in or near the right motor region exhibited greater alpha band power suppression. Components with equivalent dipoles in or near the left frontal, central, left motor, right motor, and parietal regions exhibited greater beta-band power suppression, while components with equivalent dipoles in or near the left frontal, left motor, and right motor regions showed greater gamma-band power suppression relative to non-haptic conditions. In contrast, the right occipital component cluster exhibited less beta-band power suppression in epochs with haptic feedback compared to non-haptic conditions. The results of ERCOH analysis of the six component clusters showed that there were significant increases in coherence between different brain networks in response to haptic feedback relative to the coherence observed when haptic feedback was not present. The results of this study provide novel insight into the effects of haptic feedback on the brain and may aid the development of new tools to facilitate the learning of motor skills.     

Identifying true cortical interactions in MEG using the nulling beamformer

Modeling functional brain interaction networks using non-invasive EEG and MEG data is more challenging than using intracranial recording data. This is because most interaction measures are not robust to the cross-talk (interference) between cortical regions, which may arise due to the limited spatial resolution of EEG/MEG inverse procedures. In this article, we describe a modified beamforming approach to accurately measure cortical interactions from EEG/MEG data, designed to suppress cross-talk between cortical regions. We estimate interaction measures from the output of the modified beamformer and test for statistical significance using permutation tests. Since the underlying neuronal sources and their interactions are unknown in real MEG data, we demonstrate the performance of the proposed beamforming method in a novel simulation scheme, where intracranial recordings from a macaque monkey are used as neural sources to simulate realistic MEG signals. The advantage of this approach is that local field potentials are more realistic representations of true neuronal sources than simulation models and therefore are more suitable to indicate the performance of our nulling beamforming method.     

A method for spatiotemporal mapping of event-related modulation of cortical rhythmic activity

Cortical rhythmic activity can be systematically modulated by stimuli or tasks and may thus provide relevant information about brain function. Meaningful use of those phenomena requires characterization of both locations and time courses of event-related suppressions and increases of oscillatory activity. However, localization of the neural sources of cortical rhythms during intervals of very low levels of activity, and within short time intervals, is not a trivial matter. Hence, event-related modulation of rhythmic activity has typically been described at the level of magnetoencephalography (MEG) sensors or electroencephalography (EEG) electrodes, without reaching into the brain. Here, we introduce erDICS, an event-related version of Dynamic Imaging of Coherent Sources that allows spatial mapping of the level of oscillatory activity in the brain as a function of time, with respect to stimulus or task timing. By utilizing a time-resolved frequency-domain beamformer, erDICS yields the spatial distribution of both power suppressions and power increases. Permutation tests further reveal areas and time windows in which the modulations of oscillatory power are statistically significant, in individual subjects. We demonstrate the usability of erDICS on simulated and real MEG data. From the erDICS maps we identify areas showing salient event-related changes of rhythmic activity, represent them with equivalent current dipoles and calculate their contribution to the measured signal. Comparison of this multidipole model with the original signal yields a quantitative measure of goodness for the identified source areas and the analysis approach in general.